repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
apurvak/tapas | [
"2987658c3b65c5ab6e698d6c57823dc30d3d0f96",
"7884280be78d2f58ad9c125504d710ef89f49f9a"
] | [
"tapas/experiments/table_retriever_experiment.py",
"tapas/utils/create_data_test.py"
] | [
"# coding=utf-8\n# Copyright 2019 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.compat.v1.io.gfile.makedirs",
"tensorflow.compat.v1.logging.info",
"tensorflow.compat.v1.io.gfile.GFile",
"tensorflow.compat.v1.disable_v2_behavior"
],
[
"tensorflow.compat.v1.python_io.tf_record_iterator",
"tensorflow.compat.v1.train.Example",
"tensorflow.compat.v1.gfi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zmxdream/Paddle | [
"04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c",
"04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c",
"04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c",
"04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c",
"04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c",
"04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c",
"04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45... | [
"python/paddle/fluid/tests/unittests/mkldnn/test_stack_mkldnn_op.py",
"python/paddle/fluid/tests/unittests/test_dataloader_unkeep_order.py",
"python/paddle/fluid/tests/unittests/ir/inference/test_fc_gru_fuse_pass.py",
"python/paddle/fluid/tests/unittests/test_modified_huber_loss_op.py",
"python/paddle/fluid... | [
"# Copyright (c) 2021 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... | [
[
"numpy.random.random",
"numpy.stack"
],
[
"numpy.array",
"numpy.ones"
],
[
"numpy.random.randint"
],
[
"numpy.ndenumerate",
"numpy.vectorize",
"numpy.random.uniform",
"numpy.random.choice"
],
[
"numpy.array"
],
[
"numpy.random.random",
"numpy.all... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
Polas/omim | [
"03558b418b338f506fbf3aa72ddf15187a2005ee"
] | [
"search/search_quality/scoring_model.py"
] | [
"#!/usr/bin/env python3\n\nfrom math import exp, log\nfrom scipy.stats import pearsonr, t\nfrom sklearn import svm\nfrom sklearn.model_selection import GridSearchCV, KFold\nfrom sklearn.utils import resample\nimport argparse\nimport collections\nimport itertools\nimport numpy as np\nimport pandas as pd\nimport rand... | [
[
"numpy.dot",
"sklearn.model_selection.GridSearchCV",
"pandas.read_csv",
"numpy.arange",
"scipy.stats.pearsonr",
"sklearn.model_selection.KFold",
"numpy.percentile",
"numpy.sign",
"numpy.std",
"numpy.mean",
"sklearn.svm.LinearSVC",
"sklearn.utils.resample",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2... |
meramossepu1/groundmotion-processing | [
"5cc19023b94e5b5b718590ce8cd05a22a4088a67",
"5cc19023b94e5b5b718590ce8cd05a22a4088a67",
"5cc19023b94e5b5b718590ce8cd05a22a4088a67",
"5cc19023b94e5b5b718590ce8cd05a22a4088a67"
] | [
"tests/gmprocess/metrics/imt/fas_arithmetic_mean_test.py",
"gmprocess/core/stationtrace.py",
"gmprocess/utils/base_utils.py",
"gmprocess/io/nsmn/turkey_fetcher.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# stdlib imports\nimport os.path\nimport re\n\n# third party imports\nimport numpy as np\nimport pandas as pd\nimport pkg_resources\n\n# Local imports\nfrom gmprocess.metrics.station_summary import StationSummary\nfrom gmprocess.core.stationstream import StationStr... | [
[
"pandas.read_pickle",
"numpy.testing.assert_allclose"
],
[
"pandas.Series",
"numpy.abs",
"numpy.isnan",
"numpy.ma.count_masked",
"numpy.dtype",
"pandas.DataFrame",
"numpy.array"
],
[
"pandas.read_csv",
"pandas.to_datetime"
],
[
"pandas.Timestamp",
"p... | [
{
"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... |
romybu22/chameleon-smart-sampling | [
"d0f0588ed9d38e9c133482a68e84379c21892080"
] | [
"acr_module/acr/preprocessing/doc2vec_adressa.py"
] | [
"import argparse\nimport pandas as pd\nimport numpy as np\nimport re\nimport nltk\nfrom sklearn.preprocessing import LabelEncoder\n\n\nfrom ..utils import serialize\nfrom .tokenization import tokenize_articles, nan_to_str, convert_tokens_to_int, get_words_freq\n\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDoc... | [
[
"pandas.read_csv",
"numpy.mean",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mabrahamdevops/python_notebooks | [
"6d5e7383b60cc7fd476f6e85ab93e239c9c32330",
"6d5e7383b60cc7fd476f6e85ab93e239c9c32330",
"6d5e7383b60cc7fd476f6e85ab93e239c9c32330",
"6d5e7383b60cc7fd476f6e85ab93e239c9c32330",
"6d5e7383b60cc7fd476f6e85ab93e239c9c32330",
"6d5e7383b60cc7fd476f6e85ab93e239c9c32330"
] | [
"notebooks/__code/radial_profile/event_handler.py",
"notebooks/__code/bragg_edge/get.py",
"notebooks/__code/panoramic_stitching/profile.py",
"notebooks/__code/metadata_overlapping_images/get.py",
"notebooks/__code/hfir_reactor_element_analysis/hfir_reactor_element_analysis.py",
"notebooks/__code/_utilitie... | [
"import numpy as np\nimport pyqtgraph as pg\nfrom qtpy import QtGui\n\nfrom __code._utilities.parent import Parent\nfrom __code.radial_profile.display import Display\n\n\nclass EventHandler(Parent):\n\n def file_index_changed(self):\n file_index = self.parent.ui.slider.value()\n live_image = self.p... | [
[
"numpy.abs",
"numpy.arctan",
"numpy.sqrt",
"numpy.rad2deg",
"numpy.tan",
"numpy.max",
"numpy.int",
"numpy.deg2rad",
"numpy.transpose",
"numpy.array"
],
[
"numpy.int",
"numpy.mean"
],
[
"numpy.min",
"numpy.arange",
"numpy.int",
"numpy.max",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
pything/draugr | [
"2fda662f2fa97236e4495a6af2b8237516fa428b"
] | [
"draugr/visualisation/matplotlib_utilities/styles/cyclers.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = \"Christian Heider Nielsen\"\n__doc__ = r\"\"\"\n\n Created on 18-02-2021\n \"\"\"\n\n__all__ = [\n \"monochrome_hatch_cycler\",\n \"simple_hatch_cycler\",\n \"monochrome_line_no_marker_cycler\",\n \"monochrome_line_cyc... | [
[
"matplotlib.cycler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emarkou/scikit-learn | [
"d73822f84f2832dcc25f0ff58769f60871a78025",
"d73822f84f2832dcc25f0ff58769f60871a78025",
"d73822f84f2832dcc25f0ff58769f60871a78025",
"d73822f84f2832dcc25f0ff58769f60871a78025",
"d73822f84f2832dcc25f0ff58769f60871a78025",
"d73822f84f2832dcc25f0ff58769f60871a78025",
"d73822f84f2832dcc25f0ff58769f60871a7802... | [
"examples/compose/plot_compare_reduction.py",
"sklearn/utils/random.py",
"benchmarks/bench_plot_randomized_svd.py",
"sklearn/inspection/tests/test_partial_dependence.py",
"examples/neural_networks/plot_rbm_logistic_classification.py",
"sklearn/datasets/_kddcup99.py",
"examples/ensemble/plot_feature_tran... | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n=================================================================\nSelecting dimensionality reduction with Pipeline and GridSearchCV\n=================================================================\n\nThis example constructs a pipeline that does dimensional... | [
[
"matplotlib.pyplot.legend",
"sklearn.model_selection.GridSearchCV",
"sklearn.decomposition.NMF",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"sklearn.feature_selection.SelectKBest",
"matplotlib.pyplot.ylabel",
"sklearn.datasets.load_digits",
"matplotlib.pyplot.bar",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
junhyeokahn/RLBasicAlgorithm | [
"25e3e471336cb7855e28c0f9905e2214afdeb2e4"
] | [
"DP/PolicyEvaluation.py"
] | [
"import numpy as np\nimport sys\n\nif \"../\" not in sys.path:\n sys.path.append(\"../\")\n\nfrom lib.envs.gridworld import GridworldEnv\n\nenv = GridworldEnv()\n\ndef policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\n \"\"\"\n Evaluate a policy given an environment and a full description of t... | [
[
"numpy.abs",
"numpy.ones",
"numpy.array",
"numpy.zeros",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pmartincalvo/osmnx | [
"15eddc0672f0ec951ada1b89eb417df44d35636e",
"15eddc0672f0ec951ada1b89eb417df44d35636e"
] | [
"osmnx/utils.py",
"osmnx/footprints.py"
] | [
"import sys\nimport os\nimport datetime as dt\nimport unicodedata\nimport networkx as nx\nimport numpy as np\nimport logging as lg\nfrom . import settings\n\n\ndef citation():\n \"\"\"\n Print the OSMnx package's citation information.\n\n Boeing, G. 2017. OSMnx: New Methods for Acquiring, Constructing, Ana... | [
[
"numpy.minimum",
"numpy.sqrt",
"numpy.unique",
"numpy.cos",
"numpy.sin",
"numpy.deg2rad"
],
[
"matplotlib.collections.PatchCollection",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bert9bert/statsmodels | [
"898ddfc483c45bb0f8e5156dd8506abda84c9b63",
"898ddfc483c45bb0f8e5156dd8506abda84c9b63",
"898ddfc483c45bb0f8e5156dd8506abda84c9b63"
] | [
"statsmodels/discrete/discrete_model.py",
"statsmodels/genmod/generalized_linear_model.py",
"statsmodels/robust/robust_linear_model.py"
] | [
"\"\"\"\nLimited dependent variable and qualitative variables.\n\nIncludes binary outcomes, count data, (ordered) ordinal data and limited\ndependent variables.\n\nGeneral References\n--------------------\n\nA.C. Cameron and P.K. Trivedi. `Regression Analysis of Count Data`.\n Cambridge, 1998\n\nG.S. Madalla. `... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"pandas.core.api.get_dummies",
"numpy.concatenate",
"numpy.all",
"numpy.max",
"numpy.exp",
"scipy.stats.norm._cdf",
"numpy.allclose",
"scipy.stats.chisqprob",
"numpy.eye",
"scipy.special.digamma",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20",
"0.25"
],
"scipy": [
"0.18",
"0.19"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow"... |
volpatto/UQpy | [
"acbe1d6e655e98917f56b324f019881ea9ccca82"
] | [
"example/Bayesian/More advanced examples with FE models - Sfepy/material_homogenization.py"
] | [
"#!/usr/bin/env python\n\n# This code was adapted from http://sfepy.org/doc-devel/mat_optim.html.\n\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nimport sys\nsys.path.append('.')\n\nimport matplotlib as mlp\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import PolyCo... | [
[
"numpy.dot",
"numpy.cos",
"numpy.sin",
"numpy.deg2rad",
"matplotlib.collections.PolyCollection",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
robotsorcerer/LevelSetPy | [
"54064ee7fd0144e0d658dd4f6121cbc1fda664b9",
"54064ee7fd0144e0d658dd4f6121cbc1fda664b9",
"54064ee7fd0144e0d658dd4f6121cbc1fda664b9"
] | [
"ExplicitIntegration/Integration/ode_cfl_1.py",
"Tensors/tensor_utils.py",
"Grids/cells_grid.py"
] | [
"__all__ = [\"odeCFL1\"]\n\nimport cupy as cp\nimport numpy as np\nfrom LevelSetPy.Utilities import *\nfrom .ode_cfl_set import odeCFLset\nfrom .ode_cfl_call import odeCFLcallPostTimestep\n\ndef odeCFL1(schemeFunc, tspan, y0, options=None, schemeData=None):\n \"\"\"\n odeCFL1: integrate a CFL constrained ODE... | [
[
"numpy.sign",
"numpy.hstack",
"numpy.zeros",
"numpy.abs"
],
[
"numpy.arange",
"numpy.sort",
"numpy.max",
"numpy.argsort",
"numpy.array"
],
[
"numpy.vstack",
"numpy.array",
"numpy.zeros",
"numpy.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
siqim/Machine-Learning-with-Graphs | [
"697d83bb206be0825ebaf0dad128b9eb24908705"
] | [
"examples/dataset.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nCreated on December 30, 2020\n\n@author: Siqi Miao\n\"\"\"\n\nimport torch\nfrom torch_sparse import SparseTensor\nimport torch_geometric.transforms as T\n\nfrom pathlib2 import Path\nimport scipy.io as sio\nfrom sklearn.metrics import f1_score, accuracy_score\nfrom sklearn.model... | [
[
"torch.nn.CrossEntropyLoss",
"torch.LongTensor",
"torch.sigmoid",
"torch.max",
"torch.zeros",
"scipy.io.loadmat",
"sklearn.model_selection.train_test_split",
"torch.nn.BCEWithLogitsLoss",
"torch.arange",
"torch.stack",
"sklearn.metrics.f1_score",
"sklearn.metrics.ac... | [
{
"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"... |
yinonbaron/biomass_distribution | [
"783a8d2f59754bde9b0ea802512b131abbe7d8a0",
"783a8d2f59754bde9b0ea802512b131abbe7d8a0"
] | [
"plants/non_wood_biomass/non_wood_biomass.py",
"bacteria_archaea/marine/marine_prok_biomass_estimate.py"
] | [
"\n# coding: utf-8\n\n# # Estimating the fraction of plant biomass which is not woody\n# To estimate the total non-woody plant biomass, we rely on two methods. The first is to estimate the global average leaf and root mass fractions, and the second is by estimating the total biomass of roots and leaves.\n# \n# ## M... | [
[
"scipy.stats.gmean",
"numpy.array",
"pandas.read_excel",
"numpy.average"
],
[
"numpy.array",
"pandas.read_excel"
]
] | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
Davidyz/AutoStacker | [
"9f637891b9379b166e41597bcd44a8011561beea"
] | [
"modules/algo.py"
] | [
"import numpy as np\n\nfrom modules.imageRW import Image\nfrom typing import Iterator, Optional, List\nfrom __future__ import annotations\n\nclass InputException(Exception):\n pass\n\ndef mean(images: Iterator[Image], group_size: int) -> Iterator[Image|None]:\n stackImage: Image|None = None\n while True:\n... | [
[
"numpy.maximum",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zoi-mibtp/pyDNase | [
"047d2f89af6109a530505b370782c4841d710cbf"
] | [
"pyDNase/scripts/dnase_average_profile.py"
] | [
"#!/usr/bin/env python\nimport argparse\nimport pyDNase\nimport numpy as np\nimport matplotlib as mpl\nfrom clint.textui import progress, puts\n#Required for headless operation\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib import rcParams\n\nparser = argparse.ArgumentParser(description='Plots av... | [
[
"matplotlib.pyplot.gca",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"numpy.mean",
"matplotlib.pyplot.xticks",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
remichartier/014_selfDrivingCarND_BehavioralCloningProject | [
"1dcaa7c5a937929d4481e5efbf7ccc856c04c4ff",
"1dcaa7c5a937929d4481e5efbf7ccc856c04c4ff"
] | [
"archiveOldVersions/generator_v02.py",
"archiveOldVersions/model_v10.py"
] | [
"#!/usr/bin/env python\n\n# History\n# v01 : adaptation from the one given by Udacity to work\n# v02 : adapt to commonFunctions_v10.py to use generator.\n# Start adding again everything from model_v12.py (image augmentation)\n\nimport os\nimport csv\nimport cv2\nimport numpy as np\nimport sklearn\n\nfrom math... | [
[
"sklearn.utils.shuffle",
"numpy.array",
"sklearn.model_selection.train_test_split"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DanPorter/babelscan | [
"71fa43f13a8318efbcdb412c4fca533d4b6f9ec9",
"71fa43f13a8318efbcdb412c4fca533d4b6f9ec9"
] | [
"babelscan_unit_test.py",
"babelscan/fitting.py"
] | [
"\"\"\"\nUnit test for babelscan\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport babelscan\n\n\nprint('####################################################')\nprint('############## babelscan unit tests ################')\nprint('####################################################')\nprint('\... | [
[
"matplotlib.pyplot.figure",
"numpy.max",
"matplotlib.pyplot.errorbar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"numpy.square",
"numpy.log",
"numpy.abs",
"numpy.linspace",
"numpy.min",
"numpy.asarray",
"numpy.nan_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
li-phone/DetectionCompetition | [
"a917f16790ec30358e3cfe1aa6e327a2070a1235",
"a917f16790ec30358e3cfe1aa6e327a2070a1235"
] | [
"mmdet-v2/tools/third_party/useless/cocoutils/coco_check.py",
"review_code/modelarts_deploy/mmdet/mmdet/models/detectors/base.py"
] | [
"import os\nimport json\nimport cv2 as cv\nimport numpy as np\nfrom tqdm import tqdm\n\ntry:\n from pandas import json_normalize\nexcept:\n from pandas.io.json import json_normalize\n\n\ndef load_dict(fname):\n with open(fname, \"r\") as fp:\n o = json.load(fp, )\n return o\n\n\ndef save_dict... | [
[
"pandas.io.json.json_normalize",
"numpy.array"
],
[
"numpy.full",
"numpy.concatenate",
"numpy.where",
"numpy.vstack",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20",
"0.25"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BaiduXLab/apollo | [
"2764e934b6d0da1342be781447348288ac84c5e9"
] | [
"modules/tools/create_map/create_map.py"
] | [
"#!/usr/bin/env python\n\n###############################################################################\n# Copyright 2017 The Apollo 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 ... | [
[
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
beiyuouo/fedhf | [
"0caa873a5db7494b0f9197848c34243fcb8c49f6",
"0caa873a5db7494b0f9197848c34243fcb8c49f6"
] | [
"fedhf/api/dpm/laplace.py",
"tests/test_api/test_dpm.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @File : fedhf\\api\\dpm\\laplace_noise.py\n# @Time : 2022-05-02 22:39:42\n# @Author : Bingjie Yan\n# @Email : bj.yan.pa@qq.com\n# @License : Apache License 2.0\n\nimport numpy as np\nimport torch\n\n\ndef laplace_noise(sensitivity, size, epsilon,... | [
[
"numpy.random.laplace"
],
[
"torch.nn.CrossEntropyLoss",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiahfong/alr | [
"ee561c545bd98ec17c4f9c3040ef23b0222ef71a",
"ee561c545bd98ec17c4f9c3040ef23b0222ef71a",
"ee561c545bd98ec17c4f9c3040ef23b0222ef71a",
"ee561c545bd98ec17c4f9c3040ef23b0222ef71a",
"ee561c545bd98ec17c4f9c3040ef23b0222ef71a",
"ee561c545bd98ec17c4f9c3040ef23b0222ef71a",
"ee561c545bd98ec17c4f9c3040ef23b0222ef71... | [
"docs/source/experiments/legacy/ssl_vs_bald_vs_ssal_basic/mnist/recycle/det_SSL/pseudo_label.py",
"docs/source/experiments/warm_start/mnist/restart/train.py",
"docs/source/experiments/custom/cifar10/temporal_batch_bald/train.py",
"docs/source/experiments/old/model_selection/cifar/train.py",
"experiments/the... | [
"r\"\"\"\nvanilla pseudo-labeling implementation\n\"\"\"\nfrom collections import defaultdict\n\nfrom alr.utils import timeop, manual_seed\nfrom alr.data.datasets import Dataset\nfrom alr.data import UnlabelledDataset\nfrom alr.training import VanillaPLTrainer\nfrom alr.training.samplers import RandomFixedLengthSam... | [
[
"numpy.arange",
"torch.utils.data.DataLoader",
"torch.cuda.is_available"
],
[
"torch.utils.data.Subset",
"torch.utils.data.ConcatDataset",
"torch.utils.data.DataLoader",
"torch.cuda.is_available"
],
[
"numpy.isfinite",
"numpy.array",
"torch.utils.data.DataLoader",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
texifter/trust-defender | [
"08747df28adc3d2431a73087e06cb0647e8397d2"
] | [
"test_nnet.py"
] | [
"import argparse\nimport numpy\nimport pandas as pd\nimport os\nfrom keras import backend as K\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.models import model_from_json\nfrom ngram_classifier import NGramClassifier\nfrom sklearn.metrics import precision_recall_fscore_support\n\n... | [
[
"numpy.array",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
liuzuxin/metadrive | [
"850c207536531bc85179084acd7c30ab14a66111",
"850c207536531bc85179084acd7c30ab14a66111",
"850c207536531bc85179084acd7c30ab14a66111",
"850c207536531bc85179084acd7c30ab14a66111"
] | [
"metadrive/examples/profile_metadrive.py",
"metadrive/policy/idm_policy.py",
"metadrive/tests/test_functionality/test_bicycle_model.py",
"metadrive/component/vehicle_module/lidar.py"
] | [
"import time\n\nimport numpy as np\n\nfrom metadrive import MetaDriveEnv\nfrom metadrive.utils import setup_logger\n\nif __name__ == '__main__':\n print(\"Start to profile the efficiency of MetaDrive with 1000 maps and ~8 vehicles!\")\n setup_logger(debug=False)\n env = MetaDriveEnv(dict(\n environm... | [
[
"numpy.mean"
],
[
"numpy.dot",
"numpy.sqrt"
],
[
"numpy.arctan2"
],
[
"numpy.zeros",
"numpy.rad2deg"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
archiviral/machine-learning-assignments | [
"198d5a713344ac33fe479eed01c534a3ab12d78c"
] | [
"assignment_4/dtd.py"
] | [
"import argparse\nimport os\nimport sys\nimport time\nimport datetime\nfrom copy import deepcopy\n\nimport numpy as np\n\nCONTINOUS_COLUMNS = [0, 2, 3, 9, 10, 11]\nTTL = 30\n\n\nclass Node:\n def __init__(self, prediction, continuous=None, unqs=None, column=None, median=None):\n self.children = []\n ... | [
[
"numpy.log2",
"numpy.unique",
"numpy.median",
"numpy.argwhere",
"numpy.delete",
"numpy.argmax",
"numpy.savetxt",
"numpy.array",
"numpy.sum",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pentagram-lang/pentagram | [
"5c4dc2fc516ec2844dc71ddb778ddadec036ce55"
] | [
"bootstrap/pentagram/interpret/block_test.py"
] | [
"from __future__ import annotations\n\nfrom numpy import int32\nfrom pentagram.interpret.block import interpret_block\nfrom pentagram.interpret.test import init_test_frame_stack\nfrom pentagram.machine import MachineExpressionStack\nfrom pentagram.machine import MachineFrameStack\nfrom pentagram.machine import Mach... | [
[
"numpy.int32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cnll0075/Merlion | [
"37fb75ccb204d128fde8ad4230f7893da724cf7c"
] | [
"ts_datasets/ts_datasets/anomaly/smd.py"
] | [
"#\n# Copyright (c) 2021 salesforce.com, inc.\n# All rights reserved.\n# SPDX-License-Identifier: BSD-3-Clause\n# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n#\nimport os\nimport sys\nimport logging\nimport requests\nimport tarfile\nimport numpy as n... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.DataFrame",
"numpy.genfromtxt",
"numpy.zeros"
]
] | [
{
"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": []
}
] |
asa008/nhyai | [
"33be2078cf2835d85fedc901d343568e79a5941f"
] | [
"backend/api/ocr/text/keras_detect.py"
] | [
"\"\"\"\nYOLO_v3 Model Defined in Keras.\nReference: https://github.com/qqwweee/keras-yolo3.git\n\"\"\"\nfrom config import kerasTextModel,IMGSIZE,keras_anchors,class_names,GPU,GPUID\nfrom .keras_yolo3 import yolo_text,box_layer,K\n\nfrom apphelper.image import resize_im,letterbox_image\nfrom PIL import Image\nimpo... | [
[
"tensorflow.get_default_graph",
"numpy.array",
"numpy.expand_dims",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
eyalnaor/DeepTemporalSR | [
"7d8c821431dec3a4c480550c61a6033fcac5e640"
] | [
"torch_resizer.py"
] | [
"'''\nCode courtesy of Ben Feinstein & Assaf Shocher\nPlease see their work:\nhttps://github.com/assafshocher/PyTorch-Resizer\nhttps://github.com/feinsteinben\n'''\nimport numpy as np\nimport torch\nfrom math import pi\nfrom torch import nn\n\n\nclass Resizer(nn.Module):\n def __init__(self, in_shape, scale_fact... | [
[
"torch.transpose",
"numpy.expand_dims",
"numpy.abs",
"numpy.mod",
"numpy.arange",
"numpy.squeeze",
"torch.sum",
"torch.tensor",
"numpy.sin",
"numpy.ceil",
"numpy.finfo",
"torch.nn.ParameterList",
"numpy.floor",
"numpy.any",
"numpy.isscalar",
"numpy.a... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
robfalck/AoC2017 | [
"fa19f3fb42d979b60888a1954bea571c9d4ee735"
] | [
"day24/day24.py"
] | [
"from __future__ import print_function, division, absolute_import\n\nimport copy\nimport time\nimport numpy as np\nimport sys\n\n\nclass Bridge(object):\n\n def __init__(self, initial_components, available_components):\n self.components = list(initial_components)\n self.score = sum([sum(tup) for tu... | [
[
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
verypluming/transitivity | [
"46808ff20a2aed55a54be58c35427b630711d014"
] | [
"scripts/format_veridicality.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# ... | [
[
"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": []
}
] |
jvparidon/sub2vec | [
"adb9e72b64dc6dbde3c2060ee0d3964ab623a149"
] | [
"subs2vec/norms.py"
] | [
"\"\"\"Predict lexical norms, either to evaluate word vectors, or to get norms for unnormed words.\"\"\"\nimport numpy as np\nimport pandas as pd\nimport sklearn.linear_model\nimport sklearn.model_selection\nimport sklearn.preprocessing\nimport sklearn.utils\nimport argparse\nimport os\nfrom .vecs import Vectors\nf... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.sqrt",
"numpy.median",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
IntelligentSensor/PHMRepository | [
"8684c7851970293d607d18c580cec7edbf72ad17"
] | [
"Prognostics/dl-models.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nimport random\nimport numpy as np\nimport seaborn as sns\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nfrom preprocess import preprocess\n\n\nimport keras as K\nimport tensorflow as tf\nfrom keras.regularizers import l2\nfrom keras.util... | [
[
"tensorflow.local_variables_initializer",
"numpy.sqrt",
"numpy.argmax",
"tensorflow.metrics.auc",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
xccheng/mars | [
"8146d1b7d3f3bc2a652c414a336a2f884a06a108",
"8146d1b7d3f3bc2a652c414a336a2f884a06a108",
"8146d1b7d3f3bc2a652c414a336a2f884a06a108"
] | [
"mars/dataframe/groupby/transform.py",
"mars/learn/metrics/tests/integrated/test_ranking.py",
"mars/learn/cluster/tests/integrated/test_distributed_kmeans.py"
] | [
"# Copyright 1999-2020 Alibaba Group Holding 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 appl... | [
[
"numpy.errstate"
],
[
"sklearn.metrics.auc",
"numpy.random.RandomState"
],
[
"sklearn.cluster.KMeans",
"numpy.testing.assert_array_equal",
"numpy.testing.assert_allclose",
"numpy.random.RandomState",
"sklearn.datasets.make_blobs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AyrtonB/ElexonDataPortal | [
"939c811f85dff15d0f7eb164fd1982ba0307192e"
] | [
"ElexonDataPortal/dev/orchestrator.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/05-orchestrator.ipynb (unless otherwise specified).\n\n__all__ = ['retry_request', 'if_possible_parse_local_datetime', 'SP_and_date_request', 'handle_capping',\n 'date_range_request', 'year_request', 'construct_year_month_pairs', 'year_and_month_request',\n... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.Timedelta",
"pandas.DataFrame",
"pandas.date_range"
]
] | [
{
"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": []
}
] |
sx14/hierarchical-relationship | [
"d9ed2f0c3394e435374cf3ab5afeb47a6a56ed9a",
"d9ed2f0c3394e435374cf3ab5afeb47a6a56ed9a",
"d9ed2f0c3394e435374cf3ab5afeb47a6a56ed9a",
"d9ed2f0c3394e435374cf3ab5afeb47a6a56ed9a"
] | [
"open_relation/infer/tree_infer2.py",
"lib/roi_data_layer/roidb.py",
"open_relation/eval/proc_ext_fc7.py",
"open_relation/eval/show_det.py"
] | [
"# -*- coding: utf-8 -*-\nimport sys\nimport numpy as np\n\n\n\n\n\ndef cal_rank_scores(label_num):\n # rank scores [1 - 10]\n # s = a(x - b)^2 + c\n # if rank is 0, score is 10\n # b = num-1\n s_min = 1.0\n s_max = 10.0\n b = label_num - 1\n c = s_min\n a = (s_max - c) / b ** 2\n rank... | [
[
"numpy.argsort",
"numpy.arange",
"numpy.cos"
],
[
"numpy.log",
"numpy.where",
"numpy.zeros",
"numpy.sqrt"
],
[
"numpy.load",
"numpy.array",
"numpy.save"
],
[
"numpy.copy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
honpui/RFCN | [
"c3e24ea9a143e6ba31698dc6031f6681517eaaff"
] | [
"main.py"
] | [
"\"\"\"\nRFCN\n\"\"\"\nimport torch\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nimport torchvision\nimport torch.nn.functional as functional\n\nfrom dataset import SBDClassSeg, MyTestData\nfrom transform import Colorize\nfrom criterion import CrossEntropyLoss2d\nfrom model import ... | [
[
"torch.load",
"torch.zeros",
"torch.unsqueeze",
"torch.nn.functional.sigmoid",
"torch.FloatTensor",
"numpy.array",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ricklupton/cued_datalogger | [
"dde38d04819782922e757f1eed8e5eb44cbe4f84"
] | [
"cued_datalogger/analysis/sonogram.py"
] | [
"import sys,traceback\n\nfrom cued_datalogger.api.numpy_extensions import to_dB\nfrom cued_datalogger.api.pyqt_extensions import BaseNControl, MatplotlibCanvas\nfrom cued_datalogger.api.pyqtgraph_extensions import ColorMapPlotWidget\nfrom cued_datalogger.api.toolbox import Toolbox\n\nfrom PyQt5.QtCore import Qt, py... | [
[
"numpy.arange",
"numpy.angle",
"numpy.exp",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaojing1995/One-shot_ReID | [
"a109a1aee5ad1036b20ba0779af565c09506469a"
] | [
"tools.py"
] | [
"import numpy as np\n\n\n\nif __name__==\"__main__\":\n b = np.load(\"logs/l_feas/test1.npy\")\n print(b)"
] | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RyanJDick/halite_rl | [
"e6309a24d3d613171ceb6522ddf07fece3815e62"
] | [
"halite_rl/ppo/sample.py"
] | [
"import numpy as np\n\nimport torch\n\nfrom halite_rl.utils import SubProcessWrapper\n\n\nclass EpisodeData():\n def __init__(self):\n self.observations = [] # Observations (states).\n self.actions = [] # Selected actions.\n self.act_log_probs = [] # Log probability of selected action... | [
[
"numpy.array",
"torch.no_grad",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sethaxen/arviz | [
"422c00b3cc24f3983bea283396bff0195374dcc3",
"422c00b3cc24f3983bea283396bff0195374dcc3"
] | [
"arviz/plots/compareplot.py",
"arviz/plots/khatplot.py"
] | [
"\"\"\"Summary plot for model comparison.\"\"\"\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom .plot_utils import _scale_fig_size\r\n\r\n\r\ndef plot_compare(\r\n comp_df,\r\n insample_dev=True,\r\n plot_standard_error=True,\r\n plot_ic_diff=True,\r\n order_by_rank=True,\r\n figs... | [
[
"matplotlib.pyplot.subplots",
"numpy.linspace"
],
[
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.full",
"matplotlib.get_backend",
"numpy.mean",
"numpy.array",
"matplotlib.colors.to_rgba_array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rox38431/EyeJaundice | [
"ee5939d203013cd522fbacdfcb75970bd696c962"
] | [
"interpretability/guided_back_propagation.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on 2019/8/4 上午9:45\r\n\r\n@author: mick.yi\r\n\r\n\"\"\"\r\nimport torch\r\nfrom torch import nn\r\nimport numpy as np\r\n\r\n\r\nclass GuidedBackPropagation(object):\r\n\r\n def __init__(self, net):\r\n self.net = net\r\n for (name, module) in self.net... | [
[
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
angiemsu/netharn | [
"728cb40aad299baf62c689430d07b29c67d8cf21",
"728cb40aad299baf62c689430d07b29c67d8cf21"
] | [
"netharn/util/nms/torch_nms.py",
"netharn/util/nms/nms_core.py"
] | [
"import torch\nimport numpy as np\n\n\ndef torch_nms(tlbr, scores, classes=None, thresh=.5, bias=0, fast=False):\n \"\"\"\n Non maximum suppression implemented with pytorch tensors\n\n CURRENTLY NOT WORKING\n\n Args:\n tlbr (Tensor): Bounding boxes of one image in the format (tlbr)\n score... | [
[
"torch.ByteTensor",
"torch.cuda.synchronize",
"torch.cuda.is_available",
"torch.device",
"numpy.tril",
"numpy.isclose"
],
[
"torch.Tensor",
"torch.is_tensor",
"torch.cuda.is_available",
"torch.cuda.current_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eino/pyvista | [
"b9c4e67d43491958f70b04cd2664965b938910ba",
"b9c4e67d43491958f70b04cd2664965b938910ba",
"b9c4e67d43491958f70b04cd2664965b938910ba"
] | [
"examples/00-load/create-explicit-structured-grid.py",
"examples/00-load/create-tri-surface.py",
"examples/02-plot/gif.py"
] | [
"\"\"\"\n.. _ref_create_explicit_structured_grid:\n\nCreating an Explicit Structured Grid\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nCreate an explicit structured grid from NumPy arrays.\n\nNote this feature is only available for ``vtk>=9``.\n\n\"\"\"\n\nimport numpy as np\n\nimport pyvista as pv\n\nni, nj, nk = 4, 5... | [
[
"numpy.asarray",
"numpy.arange",
"numpy.tile",
"numpy.stack",
"numpy.transpose",
"numpy.repeat"
],
[
"numpy.meshgrid",
"numpy.linspace",
"numpy.arange",
"numpy.random.uniform",
"numpy.exp"
],
[
"numpy.sqrt",
"numpy.linspace",
"numpy.arange",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TiKeil/Trust-region-TSRBLOD-code | [
"70fb396aa07b57028771e3e6e424ab3d1ace10f0"
] | [
"scripts/plot_mu_d.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n#\n# ~~~\n# This file is part of the paper:\n#\n# \"A relaxed localized trust-region reduced basis approach for\n# optimization of multiscale problems\"\n#\n# by: Tim Keil and Mario Ohlberger\n#\n# https://github.com/TiKeil/Trust-region-TS... | [
[
"numpy.sqrt",
"numpy.linalg.norm",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liang324/wrs | [
"46eadec355c61a9c7bac1fa0f3cf419b2aac19aa",
"46eadec355c61a9c7bac1fa0f3cf419b2aac19aa",
"46eadec355c61a9c7bac1fa0f3cf419b2aac19aa",
"46eadec355c61a9c7bac1fa0f3cf419b2aac19aa",
"46eadec355c61a9c7bac1fa0f3cf419b2aac19aa"
] | [
"basis/trimesh_new/resources/helpers/id_helper.py",
"motion/trajectory/polynomial_wrsold.py",
"robot_sim/_kinematics/jlchain_mesh.py",
"0000_students_work/2021tro/gaussian_surface_bug/video_utils.py",
"modeling/_ode_cdhelper.py"
] | [
"\"\"\"\nfeatures.py\n---------------\n\nIn trimesh.comparison, we arbitrarily threshold identifier values\nat a certain number of significant figures.\n\nThis file permutates meshes around and observes how their identifier,\nwhich is supposed to be pretty invariant to translation and tessellation\nchanges. We use ... | [
[
"numpy.ones_like",
"numpy.abs",
"numpy.linspace",
"numpy.min",
"numpy.random.choice",
"numpy.random.random",
"numpy.percentile",
"numpy.asanyarray",
"numpy.mean",
"numpy.array"
],
[
"numpy.dot",
"numpy.ones_like",
"numpy.vstack",
"matplotlib.pyplot.subpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
mingcv/Bread | [
"20dedfe2105b08ce8499b216c3c2bfd3699af17f"
] | [
"train_NFM.py"
] | [
"import argparse\nimport datetime\nimport os\nimport traceback\n\nimport kornia\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom tqdm.autonotebook import tqdm\n\nimport models\nfrom datasets import LowLightDataset, LowLightFDatas... | [
[
"torch.clamp",
"torch.cuda.manual_seed",
"torch.load",
"torch.cat",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.exp",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.functional.interpolate",
"torch.clamp_min",
"torch.nn.DataParallel",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NunoEdgarGFlowHub/agents-1 | [
"c62215debda5bf5d89723f4112f1e3e2f063cd52"
] | [
"tf_agents/bandits/policies/policy_utilities.py"
] | [
"# coding=utf-8\n# Copyright 2018 The TF-Agents 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# Unless required b... | [
[
"tensorflow.reduce_max",
"tensorflow.constant",
"tensorflow.shape",
"tensorflow.cast",
"tensorflow.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
hchyun6086/auto-editor | [
"beef008763bcaad00b83d5b506f436e6edc8963e"
] | [
"auto_editor/audiotsm2/base/analysis_synthesis.py"
] | [
"'''audiotsm2/base/analysis_synthesis.py'''\n\nimport numpy as np\n\nfrom auto_editor.audiotsm2.utils import (windows, CBuffer, NormalizeBuffer)\nfrom .tsm import TSM\n\nEPSILON = 0.0001\n\n\nclass AnalysisSynthesisTSM(TSM):\n def __init__(self, converter, channels, frame_length, analysis_hop, synthesis_hop,\n ... | [
[
"numpy.zeros",
"numpy.empty",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mastratton3/great_expectations | [
"151970d776c942bfc23cdd90c7ed00b57a34559d"
] | [
"great_expectations/dataset/pandas_dataset.py"
] | [
"from __future__ import division\n\nimport inspect\nimport json\nimport re\nfrom datetime import datetime\nfrom functools import wraps\nimport jsonschema\nimport sys\nimport numpy as np\nimport pandas as pd\nfrom dateutil.parser import parse\nfrom scipy import stats\nfrom six import PY3, integer_types, string_types... | [
[
"scipy.stats.kstest",
"pandas.concat",
"pandas.Series",
"pandas.isnull",
"numpy.random.choice",
"numpy.min",
"numpy.cumsum",
"pandas.DataFrame",
"pandas.Timedelta",
"numpy.concatenate",
"numpy.max",
"numpy.where",
"numpy.interp",
"numpy.count_nonzero",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
victimsnino/ReactivePlusPlus | [
"bb187cc52936bce7c1ef4899d7dbb9c970cef291"
] | [
"ci/create_graphs_for_benchmark_data.py"
] | [
"import plotly.offline as pyo\nimport plotly.express as px\nfrom plotly.subplots import make_subplots\nimport pandas as pd\nimport plotly.graph_objects as go\n\ndef rindex(lst, value):\n return len(lst) - lst[::-1].index(value) - 1\n \ndashboard = open(\"./gh-pages/benchmark.html\", 'w')\ndashboard.write(\"<h... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
broadinstitute/tissue_purifier | [
"989ce9d58bba99a3f1c49743eed22dcc64e5f159",
"989ce9d58bba99a3f1c49743eed22dcc64e5f159",
"989ce9d58bba99a3f1c49743eed22dcc64e5f159"
] | [
"src/tissue_purifier/utils/nms_util.py",
"src/tissue_purifier/data/dataset.py",
"src/tissue_purifier/models/_optim_scheduler.py"
] | [
"import torch\nimport numpy\nfrom typing import Union, List, Any\n\n\nclass NonMaxSuppression:\n \"\"\"\n Given a set of bounding box defined over possibly different tissue\n Use Intersection_over_Minimum criteria to filter out overlapping proposals.\n \"\"\"\n\n @staticmethod\n @torch.no_grad()\n... | [
[
"torch.max",
"torch.sum",
"torch.zeros_like",
"torch.from_numpy",
"torch.no_grad",
"torch.arange",
"torch.clamp",
"numpy.array",
"torch.ones_like"
],
[
"torch.randint",
"torch.randperm",
"torch.narrow",
"torch.tensor",
"torch.no_grad",
"torch.stack"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sergio0694/sepconv-gan | [
"82d908ed5c3dd55d7b2f8603450dac5108751a3b"
] | [
"training/networks/discriminators/vgg19.py"
] | [
"import tensorflow as tf\n\ndef get_network(x):\n '''Gets a discriminator network with the shared base of the VGG19 network.\n\n x(tf.Tensor) -- the VGG19 base network\n '''\n\n with tf.variable_scope('VGG19_top', None, [x], reuse=tf.AUTO_REUSE):\n conv1 = tf.layers.conv2d(x, 512, 3, activation=t... | [
[
"tensorflow.layers.conv2d",
"tensorflow.layers.dropout",
"tensorflow.reshape",
"tensorflow.layers.max_pooling2d",
"tensorflow.layers.dense",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
juanjosegarciaripoll/seeq | [
"3554550c3348fbaae398737cf4ae5510a34d6665"
] | [
"seeq/test/test_parametric_control.py"
] | [
"\nfrom seeq.control import *\n\nimport unittest\n\nclass TestQControl(unittest.TestCase):\n π = np.pi\n σz = np.array([[1., 0.],[0., -1.]])\n σx = np.array([[0., 1.],[1., 0.]])\n σy = np.array([[0., -1.j],[1.j, 0.]])\n ψ0 = np.eye(2)\n\n def test_nothing(self):\n \"\"\"For a qubit to remai... | [
[
"numpy.eye",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wangdingyan/hybridUQ | [
"c141a4bec0e716a12444f7e9ab0d7c975df93184",
"c141a4bec0e716a12444f7e9ab0d7c975df93184"
] | [
"chemprop/utils/uclass.py",
"chemprop/conformal/conformal.py"
] | [
"import numpy as np\n\n\nclass uncertainty:\n def __init__(self):\n pass\n\n\nclass uncertainties:\n\n def __init__(self):\n self.uncertainty_collection = {}\n self.uncertainty_count = {}\n self.norm_func = {'MinMax' : lambda x: (x-np.min(x)) / (np.max(x)-np.min(x)),\n ... | [
[
"numpy.min",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.argsort",
"numpy.sum"
],
[
"numpy.hstack",
"numpy.abs",
"numpy.ones",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HSU-S21-CS232/final-th150 | [
"cf0004c7a9e72b08a0c1c9985c8c43e83a0fb650"
] | [
"EZ Queue/screen.py"
] | [
"import os\nimport time\n\nimport cv2\nimport numpy as np\n\nfrom PIL import ImageGrab\n\n\nclass Screen(object):\n\n WINDOW_NAME = 'data'\n\n def __init__(self):\n self.image = None\n self.data = None\n self.event = None\n\n @property\n def inverted_image_size(self):\n retur... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HeyLey/catboost | [
"f472aed90604ebe727537d9d4a37147985e10ec2",
"f472aed90604ebe727537d9d4a37147985e10ec2",
"f472aed90604ebe727537d9d4a37147985e10ec2",
"f472aed90604ebe727537d9d4a37147985e10ec2",
"f472aed90604ebe727537d9d4a37147985e10ec2",
"f472aed90604ebe727537d9d4a37147985e10ec2",
"f472aed90604ebe727537d9d4a37147985e10ec... | [
"contrib/python/numpy/numpy/lib/tests/test_type_check.py",
"contrib/libs/onnx/onnx/mapping.py",
"contrib/python/numpy/numpy/lib/shape_base.py",
"contrib/python/numpy/numpy/matrixlib/tests/test_regression.py",
"contrib/python/numpy/numpy/polynomial/hermite_e.py",
"contrib/python/numpy/numpy/core/tests/test... | [
"from __future__ import division, absolute_import, print_function\n\nimport numpy as np\nfrom numpy.compat import long\nfrom numpy.testing import (\n TestCase, assert_, assert_equal, assert_array_equal, run_module_suite\n )\nfrom numpy.lib.type_check import (\n common_type, mintypecode, isreal, iscomplex, ... | [
[
"numpy.lib.type_check.iscomplexobj",
"numpy.imag",
"numpy.lib.type_check.isreal",
"numpy.issubdtype",
"numpy.all",
"numpy.testing.assert_equal",
"numpy.lib.type_check.nan_to_num",
"numpy.lib.type_check.common_type",
"numpy.compat.long",
"numpy.lib.type_check.isrealobj",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.11",
"1.12",
"1.19",
"1.13",... |
yusonghust/gcn | [
"4cacba4bd3d889a2139b19385774b2ee1cde80d4"
] | [
"graph.py"
] | [
"# -*- coding: utf-8 -*-\nimport networkx as nx\nimport numpy as np\nfrom utils import sparse_to_tuple\nimport scipy.sparse as sp\n\nclass Graph():\n def __init__(self,edgelist,weighted,directed,labelfile,featurefile):\n self.edgelist = edgelist\n self.weighted = weighted\n self.directed = d... | [
[
"scipy.sparse.coo_matrix"
]
] | [
{
"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"... |
joesider9/forecasting_library | [
"db07ff8f0f2693983058d49004f2fc6f8849d197",
"db07ff8f0f2693983058d49004f2fc6f8849d197",
"db07ff8f0f2693983058d49004f2fc6f8849d197",
"db07ff8f0f2693983058d49004f2fc6f8849d197",
"db07ff8f0f2693983058d49004f2fc6f8849d197",
"db07ff8f0f2693983058d49004f2fc6f8849d197",
"db07ff8f0f2693983058d49004f2fc6f8849d19... | [
"Fuzzy_clustering/ver_tf2/Models_predict_manager.py",
"Fuzzy_clustering/version3/project_manager/PredictModelManager/CombineModelPredict.py",
"Fuzzy_clustering/version2/sklearn_models/sklearn_models_skopt.py",
"Fuzzy_clustering/version3/FeatureSelectionManager/Feature_selection_linearsearch.py",
"Fuzzy_clus... | [
"import os\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport logging, shutil, glob\nimport pymongo, joblib\nfrom Fuzzy_clustering.ver_tf2.Clusterer import clusterer\nfrom Fuzzy_clustering.ver_tf2.Cluster_predict_regressors import cluster_predict\nfrom Fuzzy_clustering.ver_tf2.Global_predict_regressor ... | [
[
"numpy.square",
"numpy.arange",
"pandas.DataFrame",
"numpy.mean",
"numpy.array",
"numpy.where"
],
[
"numpy.isnan",
"numpy.array",
"numpy.where",
"numpy.sum"
],
[
"sklearn.ensemble.RandomForestRegressor",
"numpy.hstack",
"numpy.square",
"sklearn.svm.N... | [
{
"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... |
Johnson-Lsx/espnet | [
"01214cff08cdd737bcab93dd62e127169394d073",
"01214cff08cdd737bcab93dd62e127169394d073"
] | [
"espnet/nets/pytorch_backend/transducer/transformer_decoder.py",
"test/test_e2e_asr_sa_transducer.py"
] | [
"\"\"\"Decoder definition for transformer-transducer models.\"\"\"\n\nimport torch\n\nfrom espnet.nets.pytorch_backend.transducer.blocks import build_blocks\nfrom espnet.nets.pytorch_backend.transducer.joint_network import JointNetwork\nfrom espnet.nets.pytorch_backend.transducer.utils import check_state\nfrom espn... | [
[
"torch.stack",
"torch.LongTensor",
"torch.nn.Module.__init__",
"torch.tensor"
],
[
"torch.no_grad",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rgap/storm | [
"5f477d6fa58c6c1ec8d8e2b57c3b21844cae17ac"
] | [
"storm_kit/mpc/control/control_utils.py"
] | [
"#\n# MIT License\n#\n# Copyright (c) 2020-2021 NVIDIA CORPORATION.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights... | [
[
"numpy.diag",
"torch.abs",
"torch.max",
"torch.zeros",
"numpy.cumsum",
"torch.tanh",
"torch.device",
"numpy.trace",
"torch.quasirandom.SobolEngine",
"torch.erfinv",
"torch.fliplr",
"torch.sqrt",
"torch.inverse",
"torch.tensor",
"numpy.linalg.det",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SamuelCahyawijaya/fast-transformers | [
"6ae8ed4cc50bd037968db4f5062e4d328aae73fe",
"6ae8ed4cc50bd037968db4f5062e4d328aae73fe",
"6ae8ed4cc50bd037968db4f5062e4d328aae73fe",
"6ae8ed4cc50bd037968db4f5062e4d328aae73fe"
] | [
"tests/sparse_product/test_clustered_sparse_product_backward_cpu.py",
"tests/sparse_product/test_sparse_product_backward_gpu.py",
"tests/recurrent/test_transformer_encoder.py",
"tests/aggregate/test_clustered_aggregate_cpu.py"
] | [
"#\n# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/\n# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,\n# Apoorv Vyas <avyas@idiap.ch>\n#\n\nimport os\nfrom os import getenv\nimport time\nimport unittest\n\nimport torch\nfrom torch.nn.init import normal_\n\nfrom fast_transform... | [
[
"torch.abs",
"torch.full",
"torch.zeros",
"torch.einsum",
"torch.clone",
"torch.randn",
"torch.nn.init.normal_",
"torch.arange",
"torch.topk",
"torch.ones_like"
],
[
"torch.abs",
"torch.cuda.synchronize",
"torch.einsum",
"torch.clone",
"torch.cuda.Ev... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
caitsithx/dogs-vs-cats-redux | [
"3ff588cac9048a3c9f5a76de842a9cd2a4140218"
] | [
"cscreendataset.py"
] | [
"import os\nimport random\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.utils.data as data\nfrom PIL import Image\nfrom torchvision import transforms\n\nimport settings\n\n# import transforms\n\nDATA_DIR = settings.DATA_DIR\nTRAIN_DIR = DATA_DIR + '/train-640'\nTEST_DIR = DATA_DIR + '/test-... | [
[
"numpy.random.permutation",
"numpy.array",
"pandas.read_csv",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
akashAD98/detectron2 | [
"295fbb8b96eda271869fc6955280d16596781766"
] | [
"detectron2/layers/batch_norm.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\nimport torch\nimport torch.distributed as dist\nfrom fvcore.nn.distributed import differentiable_all_reduce\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom ..utils import comm, env\n\nfrom .wrappers import BatchNorm2d\n\n\nclass FrozenBatchNor... | [
[
"torch.nn.functional.batch_norm",
"torch.mean",
"torch.ones",
"torch.zeros",
"torch.cat",
"torch.zeros_like",
"torch.rsqrt",
"torch.split",
"torch.nn.GroupNorm",
"torch.distributed.get_world_size",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DeuroIO/Deuro-tensorflow | [
"7d0fa4948a6232976c4828ef9041f92993503fd5",
"7d0fa4948a6232976c4828ef9041f92993503fd5",
"7d0fa4948a6232976c4828ef9041f92993503fd5",
"7d0fa4948a6232976c4828ef9041f92993503fd5",
"7d0fa4948a6232976c4828ef9041f92993503fd5",
"7d0fa4948a6232976c4828ef9041f92993503fd5"
] | [
"tensorflow/contrib/distribute/python/mirrored_strategy.py",
"tensorflow/python/ops/ragged/ragged_conversion_ops.py",
"tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py",
"tensorflow/python/ops/losses/losses_impl.py",
"tensorflow/python/kernel_tests/scatter_ops_test.py",
"tensorflow/python/keras/... | [
"# Copyright 2018 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... | [
[
"tensorflow.python.ops.variable_scope.variable_creator_scope",
"tensorflow.python.distribute.cross_device_ops.MultiWorkerAllReduce",
"tensorflow.python.ops.control_flow_ops.while_loop",
"tensorflow.python.distribute.cross_device_ops.choose_the_best",
"tensorflow.python.framework.device.DeviceS... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
joeybose/Adversarial-Example-Games | [
"4219137e5263cd7de86687ed74cc1cef7497bb78",
"4219137e5263cd7de86687ed74cc1cef7497bb78",
"4219137e5263cd7de86687ed74cc1cef7497bb78",
"4219137e5263cd7de86687ed74cc1cef7497bb78",
"4219137e5263cd7de86687ed74cc1cef7497bb78"
] | [
"attacks/wilcoxon.py",
"flows/flows.py",
"attacks/momentum_iterative_attack.py",
"cnn_models/lenet.py",
"attacks/diverse_input_attack.py"
] | [
"import numpy as np\nimport ipdb\nfrom scipy.stats import wilcoxon, ttest_rel\n\n# MNIST\nmi_attack = [90.000000, 87.575768, 81.515160, 90.909088, 84.848480, 88.787872,\n 89.090904]\ndi_attack = [90.606056, 90.000000, 85.454552, 91.818176, 88.484856, 89.696968,\n 0.606071]\ntid_attack = [90.... | [
[
"scipy.stats.ttest_rel",
"scipy.stats.wilcoxon"
],
[
"torch.nn.Parameter",
"torch.ones",
"torch.Tensor",
"torch.zeros",
"torch.nn.init.constant_",
"torch.cat",
"torch.zeros_like",
"torch.arange",
"torch.exp",
"torch.cuda.is_available",
"torch.nn.init.xavier_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
patozavala/spectrareader | [
"ebd77ca568726936832e909c2f38c7b35fb35134"
] | [
"readers/readers.py"
] | [
"import os\nimport glob\nimport pandas as pd\n\nclass BaseReader():\n \"\"\"\n Implements several verifications and utilities for handling spectral files.\n \"\"\"\n def __init__(self):\n pass\n\n def check_file_if_exists(self,filepath):\n \"\"\"\n Verifies that a required file e... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mandubian/codenets | [
"63be72b706d57dbfb2ecec94adc203fc7bdfa3cf"
] | [
"codenets/codesearchnet/query_code_ast/dataset.py"
] | [
"import os\nimport sys\nfrom typing import Iterable, Union, Dict, Tuple, List, Callable, TypeVar, Optional, Any, cast\nimport numpy as np\nfrom pathlib import Path\nfrom loguru import logger\nfrom pathos.pools import ProcessPool\nimport itertools\nimport pickle\nimport random\nfrom dpu_utils.codeutils import split_... | [
[
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
will-duncan/ramp_systems | [
"7db1964af6bdb26ee4fed25131a12f9294c4cc1d"
] | [
"src/ramp_to_hill/hill_system.py"
] | [
"import numpy as np\nfrom scipy.integrate import solve_ivp\n\n\ndef HS_ode(t,y,HS):\n rhs = -HS.gamma*y + HS.lambda_value(y)\n return rhs\n\ndef at_HS_equilibrium(t,y,HS,tol = 1e-3):\n val = np.linalg.norm(HS_ode(t,y,HS)) - tol\n if val < 0:\n return 0\n else:\n return val\n\ndef simula... | [
[
"numpy.diag",
"numpy.allclose",
"numpy.linalg.matrix_rank",
"numpy.array_equal",
"scipy.integrate.solve_ivp",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
taureandyernv/cuml | [
"c92b594d3bda342c64d88a9c44b5d6e507b13f6c",
"c92b594d3bda342c64d88a9c44b5d6e507b13f6c"
] | [
"python/cuml/test/test_tsne.py",
"python/cuml/test/test_metrics.py"
] | [
"\nfrom cuml.manifold import TSNE\n\nfrom sklearn.manifold.t_sne import trustworthiness\nfrom sklearn import datasets\nimport pandas as pd\nimport numpy as np\nimport cudf\nimport pytest\n\ndataset_names = ['digits', 'boston', 'iris', 'breast_cancer',\n 'diabetes']\n\n\n@pytest.mark.parametrize('nam... | [
[
"numpy.isnan",
"sklearn.manifold.t_sne.trustworthiness",
"pandas.DataFrame"
],
[
"sklearn.model_selection.GridSearchCV",
"sklearn.datasets.make_classification",
"numpy.logspace",
"numpy.asarray",
"numpy.int32",
"sklearn.model_selection.train_test_split",
"sklearn.datase... | [
{
"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... |
KiriLev/albumentations | [
"c91b67c710d20755d04166b7b5e41d430aef9662"
] | [
"tests/test_serialization.py"
] | [
"import random\nfrom unittest.mock import patch\n\nimport cv2\nimport pytest\nimport numpy as np\nimport imgaug as ia\n\nimport albumentations as A\nimport albumentations.augmentations.functional as F\nfrom .utils import OpenMock\n\nTEST_SEEDS = (0, 1, 42, 111, 9999)\n\n\ndef set_seed(seed):\n random.seed(seed)\... | [
[
"numpy.array_equal",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SarahGuo1999/SiamR-CNN | [
"df9b428aeb90da0c8b2c8076f54f632efb07366c"
] | [
"train.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# File: train.py\n\nimport argparse\nimport itertools\nimport numpy as np\nimport os\nimport cv2\nimport six\nimport shutil\n\nassert six.PY3, \"FasterRCNN requires Python 3!\"\nimport tensorflow as tf\nimport tqdm\n\nimport tensorpack.utils.viz as tpviz\nfrom tensor... | [
[
"tensorflow.get_variable",
"tensorflow.concat",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.where",
"tensorflow.add_n",
"tensorflow.summary.scalar",
"tensorflow.boolean_mask",
"tensorflow.squeeze",
"tensorflow.train.MomentumOptimizer",
"tensorflow.tile",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
yxfish13/plan_enumerator | [
"e081b4c6eb3b373c4b8d97fdb88c5c4de9c77ba3"
] | [
"TreeLSTM.py"
] | [
"# Copyright 2018-2021 Xiang Yu(x-yu17(at)mails.tsinghua.edu.cn)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"): you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"torch.nn.AdaptiveMaxPool2d",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.Sigmoid",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChristianOrr/Real-time-self-adaptive-deep-stereo | [
"29bbfb212ff7a62769d39f0fe15ecb2f408ac535",
"29bbfb212ff7a62769d39f0fe15ecb2f408ac535"
] | [
"custom_models_functional.py",
"losses_and_metrics.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom keras.engine import data_adapter\nfrom matplotlib import cm\n\n\ndef colorize_img(value, vmin=None, vmax=None, cmap='jet'):\n \"\"\"\n A utility function for TensorFlow that maps a grayscale image to a matplotlib colormap for use with TensorBoard image summar... | [
[
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.equal",
"tensorflow.pad",
"tensorflow.add_n",
"tensorflow.keras.layers.Concatenate",
"numpy.arange",
"tensorflow.keras.layers.Conv2D",
"tensorflow.floor",
"tensorflow.gather",
"tensorflow.keras.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
... |
antonevenepoel/open_spiel | [
"f2f0c786410018675fc40e9a5b82c40814555fa8",
"f2f0c786410018675fc40e9a5b82c40814555fa8"
] | [
".nox/tests/lib/python3.7/site-packages/nashpy/polytope/polytope.py",
"open_spiel/python/Project/part_2/project_plots_code/cfr+_adjustments_to_cfr_plot/cfr+_adjustments_to_cfr_plot_kuhn.py"
] | [
"\"\"\"A class for a normal form game\"\"\"\nfrom itertools import product\n\nimport numpy as np\nfrom scipy.optimize import linprog\nfrom scipy.spatial import HalfspaceIntersection\n\n\ndef build_halfspaces(M):\n \"\"\"\n Build a matrix representation for a halfspace corresponding to:\n\n Mx <= 1 and ... | [
[
"numpy.hstack",
"numpy.dot",
"scipy.spatial.HalfspaceIntersection",
"numpy.eye",
"numpy.linalg.norm",
"scipy.optimize.linprog",
"numpy.ones",
"numpy.zeros"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.loglog",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
},
{
"... |
amspector100/knockpy | [
"c4980ebd506c110473babd85836dbd8ae1d548b7"
] | [
"knockpy/kpytorch/deeppink.py"
] | [
"import warnings\nimport numpy as np\nimport scipy as sp\nfrom scipy import stats\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom .. import utilities\n\n\ndef create_batches(features, y, batchsize):\n\n # Create random indices to reorder datapoints\n n = features.shape[0]\n p ... | [
[
"torch.nn.Sequential",
"torch.nn.CrossEntropyLoss",
"torch.abs",
"torch.ones",
"numpy.dot",
"numpy.log",
"torch.cat",
"torch.randperm",
"numpy.ones",
"numpy.concatenate",
"torch.nn.Linear",
"torch.tensor",
"torch.no_grad",
"torch.nn.ReLU",
"torch.nn.MSEL... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
netoaraujjo/hal | [
"0cd66d5548659c4dde70381ad21ba5b9d8213365"
] | [
"clustering/agglomerative_clustering.py"
] | [
"#-*- coding: utf-8 -*-\nimport numpy as np\nfrom sklearn.cluster import AgglomerativeClustering as sk_AgglomerativeClustering\nfrom sklearn.externals.joblib import Memory\nfrom .clustering import Clustering\n\nclass AgglomerativeClustering(Clustering):\n \"\"\"docstring for AgglomerativeClustering.\"\"\"\n d... | [
[
"sklearn.cluster.AgglomerativeClustering",
"sklearn.externals.joblib.Memory"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
renier/qiskit-terra | [
"1f5e4c8f6768dfac5d68f39e9d38fdd783ba1346"
] | [
"qiskit/quantum_info/states/statevector.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2017, 2019.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modificatio... | [
[
"numpy.diag",
"numpy.dot",
"numpy.product",
"numpy.allclose",
"numpy.conj",
"numpy.sqrt",
"numpy.asarray",
"numpy.reshape",
"numpy.abs",
"numpy.ravel",
"numpy.kron",
"numpy.linalg.norm",
"numpy.transpose",
"numpy.argsort",
"numpy.array2string",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amorehead/metrics | [
"2e4cb70c46bd775629ceb9d710bc581af8bf92c5"
] | [
"torchmetrics/classification/f_beta.py"
] | [
"# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law... | [
[
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stewue/masterthesis-evaluation | [
"0fb825e196f386c628f95524aa9c80af2126617e"
] | [
"RQ1_Python/execution_time_per_benchmark.py"
] | [
"import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nfrom matplotlib.ticker import PercentFormatter\n\ndata = pd.read_csv('C:\\\\Users\\\\stewue\\\\OneDrive - Wuersten\\\\Uni\\\\19_HS\\\\Masterarbeit\\\\Repo\\\\Evaluation\\\\RQ1_Results\\\\aggregated\\\\executiontime.csv')\ntotalTime = data['e... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"matplotlib.pyplot.figure",
"numpy.arange",
"numpy.median",
"numpy.cumsum",
"numpy.max",
"matplotlib.ticker.PercentFormatter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
VITA-Group/BERT-Tickets | [
"4d8e0356939e7045e2f5ee908412a5026051d162",
"4d8e0356939e7045e2f5ee908412a5026051d162"
] | [
"squad_trans.py",
"transformers-master/examples/prun_utils.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team 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 file except in compliance with the License.\n# You may... | [
[
"torch.load",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"torch.device",
"torch.nn.utils.prune.CustomFromMask.apply",
"torch.save",
"torch.ones",
"torch.distributed.init_process_group",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ayushkumar63123/seqio | [
"23bcb59df59798074d7d5896a131980137c69ec8",
"23bcb59df59798074d7d5896a131980137c69ec8",
"23bcb59df59798074d7d5896a131980137c69ec8",
"23bcb59df59798074d7d5896a131980137c69ec8"
] | [
"seqio/loggers.py",
"seqio/feature_converters_test.py",
"seqio/vocabularies.py",
"seqio/test_utils.py"
] | [
"# Copyright 2021 The SeqIO 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# Unless required by applicable law or ... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.constant",
"tensorflow.summary.histogram",
"tensorflow.io.gfile.exists",
"tensorflow.io.gfile.GFile",
"tensorflow.summary.image",
"tensorflow.cast",
"tensorflow.summary.audio",
"tensorflow.summary.write",
"tensorflow.compat.v1.Gra... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
TTrapper/tensorflow | [
"64f0ebd33a7c868da3c8f1ea15adf358c578f227"
] | [
"tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py"
] | [
"# Copyright 2017 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... | [
[
"tensorflow.python.ops.array_ops.sparse_placeholder",
"tensorflow.python.ops.array_ops.placeholder",
"tensorflow.python.framework.ops.device",
"tensorflow.contrib.data.python.ops.dataset_ops.Dataset.from_tensors",
"tensorflow.contrib.data.python.ops.dataset_ops.Dataset.range",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YWJae/CS234-Reinforcement-Learning-Winter-2019 | [
"aa95a42b847a0e752b8caaa7b0bfeffb514ab7d3"
] | [
"assignment 3/pg.py"
] | [
"# -*- coding: UTF-8 -*-\n\nimport os\nimport argparse\nimport sys\nimport logging\nimport time\nimport numpy as np\nimport tensorflow as tf\nimport gym\nimport scipy.signal\nimport os\nimport time\nimport inspect\nfrom utils.general import get_logger, Progbar, export_plot\nfrom config import get_config\n\nparser =... | [
[
"tensorflow.get_variable",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.zeros_like",
"tensorflow.train.AdamOptimizer",
"numpy.var",
"tensorflow.summary.scalar",
"tensorflow.contrib.distributions.MultivariateNormalDiag",
"tensorflow.squeeze",
"tensorflow.Sessio... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
abhijeetdhupia/WCE-Classification | [
"043805fe54d14ef3d24735375df1f387c62e7896"
] | [
"utils.py"
] | [
"import torch \n\ndef calculate_topk_accuracy(y_pred, y, k = 4):\n with torch.no_grad():\n batch_size = y.shape[0]\n _, top_pred = y_pred.topk(k, 1)\n top_pred = top_pred.t()\n correct = top_pred.eq(y.view(1, -1).expand_as(top_pred))\n correct_1 = correct[:1].reshape(-1).float(... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Anonymous633671/A-Comparison-on-Communication-and-Code-Dependency-Effects-on-Software-Code-Quality | [
"5a88f62513f9879178af3c5f763631b93e4f3054",
"5a88f62513f9879178af3c5f763631b93e4f3054"
] | [
"src/main/git_log/buggy_commit.py",
"src/RQ1_RQ2_data_extraction.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 31 12:49:38 2018\n\n@author: suvod\n\"\"\"\n\nfrom main.git_log import git2repo\nfrom main.api import api_access\nimport pygit2\nimport re\nimport pandas as pd\nfrom datetime import datetime\nimport re, unicodedata\nfrom pygit2 import GIT_SORT_TOPOLOGICAL, GIT_SO... | [
[
"pandas.concat",
"pandas.Series",
"pandas.DataFrame",
"numpy.array_split",
"pandas.read_pickle"
],
[
"pandas.read_csv"
]
] | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
pjain310/scRNAseq_Cell_Classification | [
"46d73ff257eef9974e1e425a52b30b61e96e3ca4"
] | [
"Scripts/run_parallel_VC.py"
] | [
"import os\r\nimport numpy as np\r\nimport pandas as pd\r\nimport time as tm\r\nfrom joblib import Parallel, delayed\r\nfrom sklearn.svm import LinearSVC\r\nfrom sklearn.ensemble import AdaBoostClassifier\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.discriminant_analysis import LinearDiscr... | [
[
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"numpy.squeeze",
"sklearn.ensemble.VotingClassifier",
"pandas.DataFrame",
"sklearn.svm.LinearSVC",
"sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
"numpy.log1p",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jdfekete/progressivis | [
"3bc79ce229cd628ef0aa4663136a674743697b47"
] | [
"tests/test_03_csv_crash.py"
] | [
"from . import ProgressiveTest, skip, skipIf\nfrom progressivis.io import CSVLoader\nfrom progressivis.table.constant import Constant\nfrom progressivis.table.table import Table\nfrom progressivis.datasets import (get_dataset, get_dataset_bz2,\n get_dataset_gz,\n ... | [
[
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EstelleHuang666/OpenNMT-py | [
"f7a239086d0db156535f3f5db9ed7060291485e8"
] | [
"onmt/inputters/inputter.py"
] | [
"# -*- coding: utf-8 -*-\nimport glob\nimport os\nimport codecs\nimport math\n\nfrom collections import Counter, defaultdict\nfrom itertools import chain, cycle\n\nimport torch\nimport torchtext.data\nfrom torchtext.data import Field, RawField\nfrom torchtext.vocab import Vocab\nfrom torchtext.data.utils import Ran... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lbouma/Cyclopath | [
"d09d927a1e6f9e07924007fd39e8e807cd9c0f8c"
] | [
"pyserver/bin/rpy2/robjects/tests/testNumpyConversions.py"
] | [
"import unittest\nimport rpy2.robjects as robjects\nr = robjects.r\n\ntry:\n import numpy\n has_numpy = True\n import rpy2.robjects.numpy2ri as rpyn\nexcept:\n has_numpy = False\n\n\nclass MissingNumpyDummyTestCase(unittest.TestCase):\n def testMissingNumpy(self):\n self.assertTrue(False) # nu... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sivaprakasaman/Python_Coding_Toolbox | [
"8bbcfb43eed49f49de7321e330f4b3943586038a"
] | [
"signal_processing/timbral_inspection/resynthesize.py"
] | [
"#Andrew Sivaprakasam\n#Purdue University\n#Email: asivapr@purdue.edu\n\n#DESCRIPTION: Code written to isolate the magnitudes of harmonics of a\n#given f_0 for a given audiofile/stimulus.\n\n#Additional Dependencies: scipy, numpy, matplotlib\n# pip3 install scipy\n# pip3 install numpy\n# pip3 install matplotlib\n\n... | [
[
"scipy.io.wavfile.read",
"numpy.multiply",
"numpy.asarray",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"numpy.asmatrix",
"numpy.max",
"matplotlib.pyplot.plot",
"numpy.concatenate",
"numpy.ones",
"numpy.exp",
"numpy.sum",
"numpy.divide",
"matplotlib.pyplot... | [
{
"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"... |
britt0508/ExplainedKinshipCorrect | [
"e0e255ff9531af1436bb9a9fe07256e72a0061f7"
] | [
"stylegan/pretrained_example.py"
] | [
"# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.\n#\n# This work is licensed under the Creative Commons Attribution-NonCommercial\n# 4.0 International License. To view a copy of this license, visit\n# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to\n# Creative Commons, PO Box 1866,... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zommiommy/cache_decorator | [
"e7d71dd48890247838612533481d0b5a808c03ec"
] | [
"tests/test_npz.py"
] | [
"import numpy as np\nfrom time import sleep\nfrom shutil import rmtree\nfrom cache_decorator import Cache\nfrom .utils import standard_test_arrays\n\n@Cache(\n cache_path=\"{cache_dir}/{_hash}.npz\",\n cache_dir=\"./test_cache\",\n backup=False,\n)\ndef cached_function_single(a):\n sleep(2)\n return ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vios-s/RA_FA_Cardiac | [
"8af4b82b62b53e29e96084113a5d379774c11b12"
] | [
"dice_loss.py"
] | [
"import torch\r\nfrom torch.autograd import Function\r\n\r\n\r\nclass DiceCoeff(Function):\r\n \"\"\"Dice coeff for individual examples\"\"\"\r\n\r\n def forward(self, input, target):\r\n self.save_for_backward(input, target)\r\n eps = 0.0001\r\n self.inter = torch.dot(input.view(-1), tar... | [
[
"torch.FloatTensor",
"torch.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
openforcefield/openff-recharge | [
"0ea3ef986e33c3ecf05924e64fb2e1872913b093"
] | [
"openff/recharge/esp/qcarchive.py"
] | [
"import json\nimport logging\nimport re\nfrom typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple\n\nimport numpy\nfrom openff.utilities import requires_package\nfrom pydantic import ValidationError\n\nfrom openff.recharge.esp import ESPSettings, PCMSettings\nfrom openff.recharge.esp.storage import M... | [
[
"numpy.dot",
"numpy.isclose",
"numpy.hstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Xuyuanjia2014/tvm | [
"892f8305e77ad506660b851f9ce4c81be0f95d9d",
"892f8305e77ad506660b851f9ce4c81be0f95d9d"
] | [
"tests/python/frontend/caffe/test_forward.py",
"python/tvm/relay/op/contrib/tensorrt.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.asarray",
"numpy.reshape",
"numpy.tile",
"numpy.random.shuffle",
"numpy.random.rand",
"numpy.array",
"numpy.random.randint"
],
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MasterMedo/typetest | [
"7d573c6bbf0d07ffd3b2fb4a8ee9ce783df2ac26"
] | [
"typetest/analyse/typing_speed_per_char.py"
] | [
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom io import StringIO\nfrom collections import deque\n\nfrom typetest.utils import validate_input_file_path\n\n\n@validate_input_file_path\ndef plot(input_file, size=10000, filter_func=lambda c: True):\n \"\"\"Reads last `size` lines ... | [
[
"numpy.arange",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
benjeffery/tsdate | [
"93c3dabdeb857a351bf994fc56bf5b8d18bb830d"
] | [
"tests/utility_functions.py"
] | [
"# MIT License\n#\n# Copyright (C) 2020 University of Oxford\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.random.seed",
"numpy.cumsum",
"numpy.ones",
"numpy.random.poisson",
"numpy.random.uniform",
"numpy.logical_and"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xjarvik/onnxmltools | [
"e4fbdc09814ceedc7655d85b6c4203ca21d8433a"
] | [
"tests/sparkml/test_decision_tree_classifier.py"
] | [
"# SPDX-License-Identifier: Apache-2.0\n\nimport sys\nimport inspect\nimport unittest\nfrom distutils.version import StrictVersion\n\nimport onnx\nimport pandas\nimport numpy\nfrom pyspark.ml import Pipeline\nfrom pyspark.ml.classification import DecisionTreeClassifier\nfrom pyspark.ml.linalg import VectorUDT, Spar... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.