repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
VITA-Group/KD-ticket | [
"cc52a75bbf6eca6a083eec8cbc447eb4f8750ce5"
] | [
"CIFAR10/cifar_baseline.py"
] | [
"from __future__ import print_function\n\nimport argparse\nimport os\nimport random\nimport shutil\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\nimport torchvision.... | [
[
"torch.nn.CrossEntropyLoss",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.autograd.Variable",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all",
"torch.utils.data.Subset",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mikcnt/dlai-project | [
"56fa0d1e682d07cd89cb011400b0a4ef92ec9265"
] | [
"src/pl_data/vae_datamodule.py"
] | [
"import random\nfrom typing import Optional, Sequence\n\nimport hydra\nimport numpy as np\nimport omegaconf\nimport pytorch_lightning as pl\nimport torch\nfrom omegaconf import DictConfig\nfrom torch.utils.data import DataLoader, Dataset\n\nfrom src.common.utils import PROJECT_ROOT\n\n\ndef worker_init_fn(id: int):... | [
[
"torch.utils.data.DataLoader",
"torch.initial_seed",
"numpy.random.SeedSequence"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LolloCappo/TSApp | [
"91191c281d3cc60f8f7be1f981107cbfa2be21bf"
] | [
"click.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nx = np.arange(-10,10)\ny = x**2\n\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.plot(x,y)\n\ncoords = []\n\ndef onclick(event):\n \n global ix,iy\n\n ix, iy = event.xdata, event.ydata\n\n print('x = %d, y = %d'%(ix, iy))\n\n global coords\n... | [
[
"numpy.arange",
"matplotlib.pyplot.show",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
whitealex95/pt2pc | [
"599728c4b7c0df3e265ccfe0819b44ddd4bc5f36"
] | [
"trainer.py"
] | [
"import os\nimport sys\nimport torch\nimport time\nimport math\nimport numpy as np\nfrom torch.autograd import Variable\nfrom utils import render_part_pcs, export_part_pcs, render_pc, export_pc\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(os.path.join(BASE_DIR, 'metrics'))\nsys.path.appen... | [
[
"torch.mean",
"numpy.expand_dims",
"torch.Tensor",
"torch.cat",
"torch.load",
"torch.randn",
"numpy.concatenate",
"torch.no_grad",
"torch.rand",
"torch.arange",
"torch.autograd.grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jsjang93/joony | [
"62f7a325094c887212b894932263bf84500e0f03"
] | [
"9day/Mat02.py"
] | [
"# Mat02.py\n\nimport matplotlib.pyplot as plt\na = [10,20,30,40]\nb = range(0,100)\nc = [1,2,3,4,5,4,2,1,3]\nd = range(0,11)\ne = [i**2 for i in d]\n\nplt.plot(d,e,'gs') # (x축,y축,그래프표현형태)\nplt.show()"
] | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jdwhitfield/pyscf | [
"0984b648fee052cff77f76511f6a5ce25f11785c"
] | [
"pyscf/pbc/df/df_jk.py"
] | [
"#!/usr/bin/env python\n# Copyright 2014-2018 The PySCF Developers. 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/LIC... | [
[
"numpy.diag",
"numpy.dot",
"numpy.einsum",
"numpy.array_equal",
"numpy.asarray",
"numpy.reshape",
"numpy.ndarray",
"numpy.linalg.norm",
"numpy.shape",
"numpy.iscomplexobj",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dishenwx/tf-faster-rcnn | [
"956a75c8a4e8292e2322e565d6b7b5f1029c23a3"
] | [
"lib/datasets/pascal_voc.py"
] | [
"# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick and Xinlei Chen\n# --------------------------------------------------------\nfrom __future__ import absolute_import\nfrom ... | [
[
"numpy.zeros",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JakeWags/ray-optics | [
"c64baf0a77df249477ec5b54265a1cd16b3cbb7a"
] | [
"src/rayoptics/parax/paraxialdesign.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright © 2018 Michael J. Hayford\n\"\"\" First order paraxial design space\n\n.. Created on Sat Mar 31 21:14:42 2018\n\n.. codeauthor: Michael J. Hayford\n\"\"\"\n\nimport numpy as np\n\nimport rayoptics.optical.model_constants as mc\nimport rayoptics.parax.fir... | [
[
"numpy.min",
"numpy.matmul",
"numpy.linalg.norm",
"numpy.max",
"numpy.cross",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yuansky/SimCSE-Chinese-Pytorch | [
"011e4b2de8b6e057b76ae4047f1818696e10261d"
] | [
"simcse_sup.py"
] | [
"# -*- encoding: utf-8 -*-\n\nimport random\nimport time\nfrom typing import List\n\nimport jsonlines\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom loguru import logger\nfrom scipy.stats import spearmanr\nfrom torch.utils.data import DataLoader, Dataset\nfrom tqdm i... | [
[
"torch.mean",
"torch.load",
"torch.cat",
"torch.nn.functional.cross_entropy",
"torch.eye",
"torch.tensor",
"torch.avg_pool1d",
"torch.no_grad",
"torch.nn.functional.cosine_similarity",
"torch.cuda.is_available",
"torch.arange",
"torch.where",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AymericBerthoumieu/webScrapingForFinancialAnalysis | [
"239bd3f94e00f7a0d2b35ca26ba674380290399f"
] | [
"DataAnalysis/sentiment_analysis.py"
] | [
"from sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.model_selection import train_test_split\nfrom DataAnalysis.data_cleaner import DataCleaner\nfrom sklearn.naive_bayes import GaussianNB\nfrom Utils.decorators import time_elapsed\nfrom nltk.corpus import twitter_samples\nimport pandas as pd\n... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"pandas.read_csv",
"sklearn.naive_bayes.GaussianNB",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
zerojiang/mmocr | [
"ecfc1c7ebf452ddc290c884785b3c6a55e9e9906"
] | [
"mmocr/datasets/pipelines/transforms.py"
] | [
"import math\n\nimport cv2\nimport numpy as np\nimport Polygon as plg\nimport torchvision.transforms as transforms\nfrom PIL import Image\n\nimport mmocr.core.evaluation.utils as eval_utils\nfrom mmdet.core import BitmapMasks, PolygonMasks\nfrom mmdet.datasets.builder import PIPELINES\nfrom mmdet.datasets.pipelines... | [
[
"numpy.asarray",
"numpy.random.random_sample",
"numpy.round",
"numpy.max",
"numpy.zeros_like",
"numpy.where",
"numpy.random.randint",
"numpy.hstack",
"numpy.clip",
"numpy.stack",
"numpy.zeros",
"numpy.int0",
"numpy.random.choice",
"numpy.ascontiguousarray",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
psg2/great_expectations | [
"feacf843f6b9d439c77382e2cfd8c612cc4be53b"
] | [
"great_expectations/expectations/core/expect_column_kl_divergence_to_be_less_than.py"
] | [
"from typing import Any, Dict, Optional, Tuple, Union\n\nimport altair as alt\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats as stats\n\nfrom great_expectations.core import ExpectationConfiguration\nfrom great_expectations.execution_engine import ExecutionEngine\nfrom great_expectations.execution... | [
[
"pandas.concat",
"numpy.sum",
"pandas.Series",
"numpy.isnan",
"pandas.DataFrame",
"numpy.concatenate",
"scipy.stats.entropy",
"numpy.count_nonzero",
"numpy.array",
"numpy.isinf"
]
] | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
martinnj/mpi_experiments | [
"98aef571e2079ed4678be40bc19a6a22df0a094a",
"98aef571e2079ed4678be40bc19a6a22df0a094a"
] | [
"euler/0001/solution.py",
"test/comm_5.py"
] | [
"#!/usr/bin/env python3\n\n# Standard imports\nfrom mpi4py import MPI as mpi\nimport numpy as np\nimport math\n\n\ndef run():\n comm = mpi.COMM_WORLD\n num_ranks = comm.Get_size()\n rank = comm.Get_rank()\n problem_size = 1000\n\n # Check if input is partionable with our cluster size.\n if not pro... | [
[
"numpy.array",
"numpy.zeros",
"numpy.empty"
],
[
"numpy.empty",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Codemonkey51/spooktober | [
"36158cd8c5d60ba06d84ca8fb980e2d0fe9c6b81"
] | [
"main.py"
] | [
"from PIL import Image\nimport numpy\nimport blend_modes\n\ninp = Image.open(\"in.png\").convert(\"L\").convert(\"RGBA\")\ncolor = Image.new('RGB', (inp.width, inp.height), (77, 25, 0)).convert(\"RGBA\")\n\nnew = numpy.uint8(blend_modes.hard_light(numpy.array(inp).astype(float), numpy.array(color).astype(float), 0.... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rmshaffer/pulse-sequence-simulator | [
"8a64d5192079c1169dec58338405a41f25491ea2"
] | [
"sequences/subsequences/bichro_excitation.py"
] | [
"from artiq.experiment import *\nfrom artiq.coredevice.ad9910 import RAM_MODE_RAMPUP, RAM_DEST_ASF\nfrom artiq.coredevice.ad9910 import PHASE_MODE_TRACKING, PHASE_MODE_ABSOLUTE\nimport numpy as np\n\n\nclass BichroExcitation:\n bichro_enable=\"MolmerSorensen.bichro_enable\"\n due_carrier_enable=\"MolmerSorens... | [
[
"numpy.int64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tim5go/SDV | [
"c58f174e156a5d36eec628726307af48a54b3f12"
] | [
"tests/unit/constraints/test_base.py"
] | [
"\"\"\"Tests for the sdv.constraints.base module.\"\"\"\nfrom unittest.mock import Mock, patch\n\nimport pandas as pd\nimport pytest\nfrom copulas.multivariate.gaussian import GaussianMultivariate\nfrom rdt.hyper_transformer import HyperTransformer\n\nfrom sdv.constraints.base import Constraint, _get_qualified_name... | [
[
"pandas.testing.assert_series_equal",
"pandas.testing.assert_frame_equal",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
expobrain/mstrio-py | [
"d389f3a1981eb7bb7a59df9fc6086e051c05ca74"
] | [
"tests/utils/test_formjson.py"
] | [
"import unittest\nimport pandas as pd\nfrom mstrio.utils.formjson import formjson\n\n\ndef make_df():\n raw_data = {'id_int': [1, 2, 3, 4, 5],\n 'id_str': ['1', '2', '3', '4', '5'],\n 'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],\n 'last_name': ['Miller', '... | [
[
"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": []
}
] |
pc2/n-body-ring-solver | [
"aa871851cd5c22511e4ade98aef67dff4584a980"
] | [
"final_results/csvplot_weak.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 13 14:39:12 2020\n\n@author: tnellius\n\n\n\"\"\"\nimport csv\nimport sys\nfrom matplotlib import pyplot as plt\nfrom matplotlib import rc\nimport numpy as np\nimport matplotlib.scale\n\n#Configuration:\nFONTSIZE = 12\nBOLD = True\n\n#rc('... | [
[
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.subplots",
"numpy.ceil",
"matplotlib.pyplot.grid",
"numpy.array",
"matplotlib.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pauldmccarthy/pyopengl | [
"bf11e7e59bff4a57dffbe79e1b27dac5a09166fc"
] | [
"accelerate/setup.py"
] | [
"#!/usr/bin/env python\n\"\"\"Builds accelleration functions for PyOpenGL\n\"\"\"\ntry:\n from setuptools import setup,Extension\nexcept ImportError:\n from distutils.core import setup,Extension\ntry:\n from Cython.Distutils import build_ext\nexcept ImportError:\n have_cython = False\nelse:\n have_cy... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CarlosGarciaMX/tensorflow | [
"de80b2f523630e9295a2b359a40938fac6cb3d1a"
] | [
"tensorflow/python/feature_column/feature_column_v2_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.feature_column.feature_column._categorical_column_with_hash_bucket",
"tensorflow.core.example.feature_pb2.BytesList",
"tensorflow.python.feature_column.feature_column_v2.numeric_column",
"tensorflow.python.framework.test_util.run_in_graph_and_eager_modes",
"tensorflow.python... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13"
]
}
] |
Conzel/cblearn | [
"4e75ba5533796607d7c182b37d2ab108324f2b54"
] | [
"cblearn/utils/_validate_data.py"
] | [
"from typing import Union, Optional, Tuple\n\nimport numpy as np\nimport scipy\nimport sparse\nfrom sklearn.utils import check_X_y, check_array\n\nfrom ._data_format import data_format, check_format\nfrom ._data_format import QueryFormat, ResponseFormat, Format\nfrom ._typing import Query\n\n\ndef _check_list_query... | [
[
"numpy.log",
"numpy.product",
"sklearn.utils.check_X_y",
"sklearn.utils.check_array",
"numpy.empty_like",
"numpy.full",
"numpy.all",
"numpy.argmin",
"numpy.any",
"numpy.not_equal",
"numpy.repeat",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yangqiufei/financial_data_pools | [
"7642d6f8719f373a0b4c442256bce6a3a7fe22bb",
"7642d6f8719f373a0b4c442256bce6a3a7fe22bb"
] | [
"strategy/beauty/main.py",
"stock_financial/instance_greatest.py"
] | [
"import pandas as pd\nimport sys\nimport os\nimport json\n\nsys.path.append(\"../..\")\n\nfrom api.data import StockApi\nfrom stock_financial.comm_funcs import get_rise_price\nfrom stock_financial.comm_funcs import get_db_engine_for_pandas\nfrom stock_financial.comm_funcs import get_mysql_client\nfrom stock_financi... | [
[
"pandas.read_sql",
"pandas.merge",
"pandas.read_csv",
"pandas.DataFrame"
],
[
"pandas.to_numeric",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
... |
Youwang-Kim/sds-dataset | [
"2adfe38d27fcd6cf9c32984a12bc48aa66e69636"
] | [
"networks/batch_smpl.py"
] | [
"\"\"\" \nTensorflow SMPL implementation as batch.\nSpecify joint types:\n'coco': Returns COCO+ 19 joints\n'lsp': Returns H3.6M-LSP 14 joints\nNote: To get original smpl joints, use self.J_transformed\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\nfrom utils.u... | [
[
"torch.nn.functional.pad",
"torch.div",
"torch.norm",
"torch.ones",
"torch.sin",
"torch.cat",
"torch.zeros",
"numpy.reshape",
"numpy.arange",
"torch.eye",
"torch.matmul",
"torch.FloatTensor",
"torch.stack",
"numpy.array",
"torch.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hs-cha/myo-python | [
"511e39411fde3171ead9b04853b7ca977a73e417"
] | [
"udp.py"
] | [
"#! /usr/bin/env python\n# Client and server for udp (datagram) echo.\n#\n# Usage: udpecho -s [port] (to start a server)\n# or: udpecho -c host [port] <file (client)\n# 해당 파일을 main.py -c 192.168.0.9 로 실행하고 원하는 명령어에 맞는 string을 보내는 식으로 사용중입니다\n# 코드 읽어보시면 -c 플래그는 클라이언트로써 아이피 주소에 udp 연결을 하는 것을 알 수 있는데\n# ... | [
[
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jasonbunk/Detectron | [
"f967d4553b034c0899936e0d292b6b0eaebe1ca6"
] | [
"detectron/datasets/voc_eval.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\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 applicabl... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.arange",
"numpy.cumsum",
"numpy.finfo",
"numpy.concatenate",
"numpy.max",
"numpy.argmax",
"numpy.where",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bdyetton/BayesModa | [
"cea6ede7028b5e5a06cba1dfcea12f88fc883a93"
] | [
"src/models_simple.py"
] | [
"import pandas as pd\nimport pymc3 as pm\nimport matplotlib.pyplot as plt\nimport pickle\nimport numpy as np\nimport sys\nimport theano.tensor as tt\n\ndata = pd.read_csv('markers.csv')\ndata = data.loc[data['epoch_i']<1,:] #Just do a single epoch for simplicity\n\n# shapes and sizes\nn_epochs = data['epoch_i'].max... | [
[
"numpy.arange",
"numpy.array",
"pandas.read_csv",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
roberttk01/TensorFlowTutorial | [
"674f61fafc80dbbbcfff2d91b2a0bb2c83dcc88a",
"674f61fafc80dbbbcfff2d91b2a0bb2c83dcc88a"
] | [
"TensorFlowTutorial/pt16_creating_our_k_nearest_neighbors_algorithm.py",
"TensorFlowTutorial/pt28_completing_svm_from_scratch.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nimport warnings\nfrom math import sqrt\nfrom collections import Counter\nstyle.use('fivethirtyeight')\n\ndataset = {'k': [[1, 2], [2, 3], [3, 1]], 'r': [[6, 5], [7, 7], [8, 6]]}\nnew_features = [5, 7]\n\n[[plt.scatter(ii[0], ii[1], ... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.scatter",
"matplotlib.style.use"
],
[
"numpy.dot",
"matplotlib.style.use",
"numpy.arange",
"numpy.linalg.norm",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ekedziora/twinty | [
"5f0ba01e9e1b93f686569ebd907517a4f41d5cf7"
] | [
"sentiment/movie_reviews.py"
] | [
"from nltk.classify.scikitlearn import SklearnClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.svm import SVC\nfrom sklearn.svm import NuSVC\nfrom nltk.collocations import BigramCollocationFinder\nfrom nltk.collocations import TrigramCollocationFinder\nfrom nltk.corpus import movie_reviews\nimport nltk\n... | [
[
"sklearn.svm.NuSVC",
"sklearn.cross_validation.StratifiedKFold"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Szynal/Graph-Coloring-NIA | [
"b1d2c87e545b185d06e7bede8cd3be6cf6b0a420"
] | [
"src/tester_mut.py"
] | [
"from genetic_algorithm import GeneticAlgorithm\nfrom graph import Graph\nimport numpy as np\nfrom scipy.stats import ttest_rel\nfrom tabulate import tabulate\n\ncrossing = (0.7, 0.8, 0.85)\nmutation = (0.02, 0.05, 0.1)\ngraph = Graph(\"graphs/games120.graph\")\n# graph = Graph(\"graphs/miles750.graph\")\ncombinati... | [
[
"numpy.concatenate",
"scipy.stats.ttest_rel",
"numpy.array",
"numpy.mean"
]
] | [
{
"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"
... |
CorrelAid/compgen-ii-cgv | [
"810a044d6bbe1ce058a359115e3e5fc71a358549"
] | [
"src/compgen2/const_synthetic.py"
] | [
"import numpy as np\n\nP_SHUFFLE = 5 * 1e-02\nP_FRACTAL = 0.2 * 1e-02\nP_LINO = 0.2 * 1e-02\nP_DROP = 1.5 * 1e-02\nP_SHORTEN = 10 * 1e-02\n\nkey_matrix_linotype = np.array([\n ['e', 'r', 'u', 'b', '@', 'fl', '.', ',', '-', '1', '6', 'E', 'R', 'U', 'B',],\n ['n', 'd', 'm', 'f', '@', 'en', 'ä', '?', ':', ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GT-RAIL/assistance_arbitration | [
"84d7cfb6e08f0dd23de9fa106264726f19ef82ea"
] | [
"fetch_tasks/scripts/randomize_environments.py"
] | [
"#!/usr/bin/env python\n# Randomize the test environment and then launch\n\nfrom __future__ import print_function, division\n\nimport os\nimport sys\nimport shlex\nimport argparse\nimport subprocess\nimport collections\n\nimport numpy as np\n\nimport rospkg\n\n# Import isolation\ntry:\n import isolation\nexcept ... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qslim/epcb-gnns | [
"1b2c44884ac8c3ce8bc5a2485651526ff5d0ef54"
] | [
"ogbg/code/main.py"
] | [
"import random\nimport torch\nfrom torch_geometric.data import DataLoader\nfrom tensorboardX import SummaryWriter\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torchvision import transforms\n\nfrom tqdm import tqdm\nimport time\nimport numpy as np\nimport pandas as pd\nimport os\n\n### importi... | [
[
"torch.nn.CrossEntropyLoss",
"numpy.random.seed",
"torch.cat",
"torch.manual_seed",
"torch.argmax",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all",
"numpy.array",
"numpy.sum",
"torch.optim.lr_scheduler.StepLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tipevo/webstruct | [
"17c8254898368ead7448a9145e98a125f8891558"
] | [
"webstruct/gazetteers/geonames.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nimport os\nimport csv\nimport six\nimport zipfile\nimport numpy as np\n\nGAZETTEER_FORMAT = \"2s 1s 5s 2s 3s\"\nGAZETTEER_COLUMNS = ['country_code', 'feature_class', 'feature_code',\n 'admin1_code', 'admin2_code']\n\n_GEONAMES_COL... | [
[
"numpy.concatenate",
"numpy.repeat",
"pandas.read_csv",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
peacedudegregoryks/officialsocialbenefitcoin | [
"d8e9d95e3764bb65c5c305e012102024ab7cf577"
] | [
"database/community.py"
] | [
"import csv\nimport time\nimport pandas as pd\nimport pymongo\nimport configparser\n\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\n\ntry:\n client = pymongo.MongoClient(config[\"DEFAULT\"][\"MONGO_URL\"], ssl_ca_certs='./cert.pem', connectTimeoutMS=30000, socketTimeoutMS=None)\n db = clien... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
emdemor/porto-seguro-data-challenge | [
"0b22a184a796dad638d7c4a4e3f1b033f3ba80ff"
] | [
"experiments/hgradboost/base.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nfrom copy import deepcopy\nfrom scipy import stats\nfrom scipy.integrate import cumtrapz\nfrom scipy.special import erfinv, erf\nfrom sklearn.base import TransformerMixin, BaseEstimator\nfrom statsmodels.distributions.empirical_distributio... | [
[
"scipy.stats.ks_2samp",
"pandas.Series",
"numpy.linspace",
"numpy.sqrt",
"scipy.special.erfinv",
"pandas.DataFrame",
"scipy.integrate.cumtrapz",
"numpy.clip",
"numpy.unique",
"sklearn.impute.SimpleImputer",
"numpy.power",
"numpy.isnan",
"numpy.array",
"matpl... | [
{
"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": [
"0.13",
"0.14",
"0.15",
"1.4",
"0... |
0YuanZhang0/hapi | [
"f5e4e123fd1d72f232ae31fec2a6ddd9b69aa9ff"
] | [
"hapi/model.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... | [
[
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sreenik1972/ml-projects | [
"6a84689ee7a746ad0b46d9a11974d6bfbd9949bd"
] | [
"packages/house_price_predict_model/house_price_predict_model/train_pipeline.py"
] | [
"import numpy as np\nfrom sklearn.model_selection import train_test_split\n\n#from house_price_predict_model import pipeline\nfrom house_price_predict_model.processing.data_management import (\n load_dataset, save_pipeline)\nfrom house_price_predict_model.config import config\nfrom house_price_predict_model impo... | [
[
"numpy.log",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ameliamounts/MachineLearningBCI-Lau_Mounts | [
"f0d93ebdd314980bdb9c6ce4c743036eb0704039"
] | [
"main_csp.py"
] | [
"#!/usr/bin/env python3\n\n''' \nModel for common spatial pattern (CSP) feature calculation and classification for EEG data\n'''\n\nimport numpy as np\nimport time\nfrom sklearn.svm import LinearSVC, SVC\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.neighbors import KNeighborsC... | [
[
"sklearn.naive_bayes.GaussianNB",
"sklearn.model_selection.KFold",
"sklearn.neighbors.KNeighborsClassifier",
"sklearn.ensemble.GradientBoostingClassifier",
"sklearn.ensemble.AdaBoostClassifier",
"sklearn.svm.LinearSVC",
"sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
"s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meganhumhrey/pytorch | [
"41054f2ab5bb39d28a3eb8497f1a65b42385a996"
] | [
"test/test_fx.py"
] | [
"import builtins\nimport contextlib\nimport copy\nimport functools\nimport inspect\nimport math\nimport numbers\nimport operator\nimport os\nimport pickle\nimport sys\nimport torch\nimport traceback\nimport warnings\nimport unittest\nfrom math import sqrt\nfrom pathlib import Path\nfrom torch.multiprocessing import... | [
[
"torch.fx.immutable_collections.immutable_dict",
"torch.max",
"torch.fx.wrap",
"torch.sin",
"torch.zeros",
"torch.typename",
"torch.nn.RNN",
"torch.sum",
"torch.neg",
"torch.nn.Embedding",
"torch.fx.Interpreter",
"torch.tanh",
"torch.cat",
"torch.testing.Fil... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cbishop76/openpilot | [
"a67e2bf12e9fb4a5e8826fae47bfae3cdba26421"
] | [
"selfdrive/controls/lib/driver_monitor.py"
] | [
"import numpy as np\nfrom common.realtime import sec_since_boot, DT_CTRL, DT_DMON\nfrom selfdrive.controls.lib.drive_helpers import create_event, EventTypes as ET\nfrom common.filter_simple import FirstOrderFilter\n\n_AWARENESS_TIME = 180 # 3 minutes limit without user touching steering wheels make the car e... | [
[
"numpy.arctan2",
"numpy.array",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cryotools/ct-dataanalysis | [
"04a5b45a948f709c8291300ac1bf9da2a69985b1"
] | [
"ct-dataanalysis/CT_statistics.py"
] | [
"import numpy as np\nimport scipy.stats as sp_stats\nimport matplotlib.pyplot as plt\n\n\ndef histogram(x, allow_emptybins=False):\n \"\"\"\n Determine a suitable number of bins and create a histogram.\n\n Input:\n x -> 1D numpy array\n allow_emptybins -> Boolean, set to true to allow the result cont... | [
[
"numpy.nanmax",
"matplotlib.pyplot.legend",
"numpy.sqrt",
"numpy.nanmin",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.var",
"numpy.exp",
"numpy.histogram",
"numpy.arange",
"numpy.std",
"scipy.stats.skew",
"matplotlib.pyplot.figure",
"scipy.stats.chi2.ppf",... | [
{
"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"
... |
VietDunghacker/CenterNet2 | [
"6e99ca1bb74582c115891deaf7db5f3bcc4b0518"
] | [
"projects/CenterNet2/centernet/modeling/backbone/res2net.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# This file is modified from https://github.com/Res2Net/Res2Net-detectron2/blob/master/detectron2/modeling/backbone/resnet.py\n# The original file is under Apache-2.0 License\nimport numpy as np\nimport fvcore.nn.weight_init as weight_init\nim... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.flatten",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.functional.relu",
"torch.utils.checkpoint.checkpoint",
"torch.nn.AdaptiveAvgPool2d",
"torch.n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
benpm/PrisonersDilemmaTournament | [
"856be183a6fe36be80f0b56f5248625c60278d39"
] | [
"code/prisonersDilemma.py"
] | [
"import os\r\nimport itertools\r\nimport importlib\r\nimport numpy as np\r\nimport random\r\nimport sys\r\n\r\nSTRATEGY_FOLDERS = [\"strats\", \"strats/extras\"]\r\nRESULTS_FILE = \"results.txt\"\r\nME = sys.argv[1] if len(sys.argv) > 1 else ''\r\n\r\nLENGTH_MULT = 1\r\n\r\npointsArray = [[1,5],[0,3]] # The i-j-th ... | [
[
"numpy.argsort",
"numpy.flip",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kazstat/sdg-data-kazstat | [
"fd1725c39b2a9df491eb3a1cf3df9f2d0a94b9bd"
] | [
"scripts/import-smdx-mapping-cleanup.py"
] | [
"import pandas as pd\nimport numpy as np\nimport os\nimport yaml\nimport sdg\n\nimport_filename = 'Kazakhstan SDG SDMX mappings - Cleanup.xls'\ndisaggregations = {}\ncodes = None\ncomposite_breakdowns = {}\nnew_translations = {}\ndebug = False\n\nlanguages = ['ru', 'kk', 'en']\n\ndef parse_code_sheet(df):\n rena... | [
[
"pandas.isna",
"pandas.read_excel",
"pandas.isnull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ArtesiaWater/pastastore | [
"27a70b667bbb5a6dc657004430f419a45e2a0ed1"
] | [
"pastastore/base.py"
] | [
"import functools\nimport json\nimport warnings\nfrom abc import ABC, abstractmethod, abstractproperty\nfrom collections.abc import Iterable\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport pandas as pd\nimport pastas as ps\nfrom numpy import isin\nfrom pastas.io.pas import PastasEncoder\nfrom tqdm ... | [
[
"pandas.to_datetime",
"pandas.read_json",
"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": []
}
] |
VasSider/project-euler | [
"33f9eab500d12cc49f0ffa3cde97d2cb83d74a05"
] | [
"problem 99/problem 99.py"
] | [
"import pandas as pd\nimport math\n\n#p099_base_exp.txt\ndata = pd.read_csv(\"p099_base_exp.txt\", sep=\",\", header=None) \n\nmax_data = data[1].loc[0]*math.log10(data[0].loc[0])\nls=[0]\nfor i in range(0,len(data)):\n if max_data < data[1].loc[i]*math.log10(data[0].loc[i]):\n max_data = data[1].loc[i]*m... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
gokceneraslan/scvelo | [
"95d90de3d0935ce58a01218c9f179c9494ff593e"
] | [
"scvelo/tools/dynamical_model_utils_deprecated.py"
] | [
"# DEPRECATED\n\nfrom .. import settings\nfrom .. import logging as logg\nfrom ..preprocessing.moments import get_connectivities\nfrom .utils import make_dense, make_unique_list, test_bimodality\n\nimport warnings\nimport matplotlib.pyplot as pl\nfrom matplotlib import rcParams\n\nimport numpy as np\n\nexp = np.exp... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"numpy.linspace",
"numpy.nan_to_num",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.concatenate",
"numpy.round",
"numpy.argmin",
"numpy.any",
"numpy.mean",
"numpy.where",
"numpy.clip",
"numpy.intersect1d",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShahedSabab/Video-Analytics | [
"6e1c65fc5f4235ae4f9bd08765b2298ee3cdeb67"
] | [
"app/tritonclient.py"
] | [
"#!/usr/bin/env python\n# Copyright (c) 2018-2020, NVIDIA CORPORATION. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n# * Redistributions of source code must retain the above copyright\... | [
[
"numpy.reshape",
"numpy.asarray",
"numpy.dtype",
"numpy.transpose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jakehyvonen/QLEDLife | [
"d7e798c79f843b27072194b7a126387d3e0d4a67"
] | [
"emulator.py"
] | [
"import numpy as np\nimport serial\nimport sys\nimport time\nfrom threading import *\n\nclass Emulator:\n def __init__(self,ser):\n self.LedMeas = np.zeros((16,4))\n self.PdMeas = np.zeros((16,4))\n self.DataValid = np.zeros(16)\n self.DevId = np.zeros(16)\n self.DacSet = np.ze... | [
[
"numpy.zeros",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deeptrouble2021/Binance-volatility-trading-bot | [
"b65d5fd267c9e2eb62a1796c3e6799e1e055c0a6"
] | [
"VolScan.py"
] | [
"# VolScan is a Binance Volatility Bot(BVT Bot)\n# compatible module that generates crypto buying signals based upon negative price change & volatility.\n# It does this in two different ways,\n# the main one being by calculating the aggregate price change within a user defined period,\n# the second way being by use... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Forty-lock/cGAN_with_conditional_convolution_layer-pytorch | [
"099b121869802799245b21c449c916ee7bef3bc1"
] | [
"fid.py"
] | [
"# Modified from: https://github.com/bioinf-jku/TTUR/blob/master/fid.py\r\nimport torch\r\nfrom torch import nn\r\nimport torch.nn.functional as F\r\nfrom torchvision.models import inception_v3\r\nimport numpy as np\r\nfrom scipy import linalg\r\n\r\n\r\nclass PartialInceptionNetwork(nn.Module):\r\n\r\n def __in... | [
[
"numpy.abs",
"numpy.diagonal",
"torch.from_numpy",
"torch.nn.functional.adaptive_avg_pool2d",
"numpy.atleast_1d",
"numpy.atleast_2d",
"numpy.ceil",
"numpy.cov",
"numpy.mean",
"torch.nn.functional.interpolate",
"numpy.iscomplexobj",
"numpy.zeros",
"numpy.trace"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ElsevierSoftwareX/SOFTX-D-16-00032 | [
"50b3d0bd1ceea236d8b4e95578cebf7c78a5035e"
] | [
"test/test_distance.py"
] | [
"\"\"\" test_distance.py\n\nTests the isi- and spike-distance computation\n\nCopyright 2014, Mario Mulansky <mario.mulansky@gmx.net>\n\nDistributed under the BSD License\n\n\"\"\"\n\nfrom __future__ import print_function\nimport numpy as np\nfrom copy import copy\nfrom numpy.testing import assert_equal, assert_almo... | [
[
"numpy.testing.assert_equal",
"numpy.ones_like",
"numpy.arange",
"numpy.sort",
"numpy.testing.assert_almost_equal",
"numpy.append",
"numpy.array",
"numpy.sum",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shallyan/EmbeddingMachine | [
"dcf4e8bf3f255f6b507f8156159d9c42bb9cdab7"
] | [
"embeddingmachine/models/weightedsum.py"
] | [
"# -*- coding=utf-8 -*-\n\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nimport math\nimport numpy as np\nfrom tqdm import tqdm\nimport json\n\ndef myLog2(x):\n return math.log(x) / math.log(2)\n\nclass TFIDFSum(object):\n def __init__(self, word_embedding_path):\n self.name = ... | [
[
"numpy.array",
"numpy.zeros",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
antmicro/edge-ai-tester | [
"6b145145ed1cec206ae0229c846fb33d272f3ffa"
] | [
"kenning/core/dataset.py"
] | [
"\"\"\"\nProvides an API for dataset loading, creation and configuration.\n\"\"\"\n\nfrom typing import Tuple, List, Any\nimport argparse\nfrom pathlib import Path\n\nfrom .measurements import Measurements\n\n\nclass Dataset(object):\n \"\"\"\n Wraps the datasets for training, evaluation and optimization.\n\n... | [
[
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
J-XZ/YCSB-cpp | [
"3bc6d9e8dca0b6dc09e2720b821018f03e8764bc"
] | [
"propose.py"
] | [
"import os\nimport re\nimport numpy as np\n\nf = open(\"ans.txt\")\nline = f.readline()\nload_through_put = []\nrun_through_put = []\nwhile line:\n if(line[0] != \"\\n\"):\n if(line[0:6] == \"Load t\"):\n load_through_put.append(float(re.findall(r\"\\d+\\.?\\d*\",line)[0]))\n elif(line[0... | [
[
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaaack-wang/ling_feature_extractor | [
"0b97e719d734794f36275df98936a594b45d5057"
] | [
"LFE/extractor.py"
] | [
"try:\n from LFE import features_set as fs\nexcept ImportError:\n import features_set as fs\ntry:\n from LFE import text_processor as tp\nexcept ImportError:\n import text_processor as tp\n \nimport pandas as pd\nimport os\n\n\ndef show_feature_names():\n return '\\n'.join([k for k in fs.FEATURE_D... | [
[
"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": []
}
] |
hugochan/Graph2Seq-for-KGQG | [
"0cfdec3a32d421f60aa019f1b54726715821cf85"
] | [
"src/core/model_handler.py"
] | [
"'''\nCreated on Oct, 2019\n\n@author: hugo\n\n'''\nimport os\nimport time\nimport json\n\nimport torch\nfrom torch.utils.data import DataLoader\nimport torch.backends.cudnn as cudnn\n\nfrom .model import Model, evaluate_predictions\nfrom .utils.data_utils import prepare_datasets, DataStream, vectorize_input\nfrom ... | [
[
"torch.device",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jeffery-Song/MXNet-RDMA | [
"16ed95ef93786c349c1d2ab1f34483783214b71f"
] | [
"python/mxnet/gluon/parameter.py"
] | [
"# coding: utf-8\n# pylint: disable=\n\"\"\"Neural network parameter.\"\"\"\n\nfrom collections import OrderedDict\nimport numpy as np\n\nfrom ..base import mx_real_t, MXNetError\nfrom .. import symbol, ndarray, initializer, context\nfrom ..context import Context\nfrom .. import autograd\n\n# pylint: disable= inval... | [
[
"numpy.dtype",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdpmas/Scotch | [
"c531803be5044f5b04617280421c9700801f2c0e"
] | [
"src/make_model.py"
] | [
"from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,\n RobertaConfig, RobertaModel, RobertaTokenizer)\nfrom model import Model\nimport torch\nimport scann\nimport numpy as np\nimport torch.nn.functional as f\nimport sys\nsys.path.append('.')\n\nif __name__=='__ma... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arika0093/or-series | [
"16d777e7402bbb59b6b7fc4fc405106f457e618f"
] | [
"sample/assignment_sat.py"
] | [
"# This sample is a simple rewrite of one of the OR-Tools samples.\n# The original file can be found at here.\n# https://github.com/google/or-tools/blob/stable/ortools/sat/samples/assignment_sat.py\n\nfrom ortools.sat.python import cp_model\nfrom orseries.array import CpArray\nimport pandas as pd\n\ndef data_initia... | [
[
"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": []
}
] |
jg1uaa/LPCNet.mozilla | [
"b002e6e0969128b93225e2e3ec276a7794f91e35"
] | [
"training_tf2/dump_lpcnet.py"
] | [
"#!/usr/bin/python3\n'''Copyright (c) 2017-2018 Mozilla\n\n Redistribution and use in source and binary forms, with or without\n modification, are permitted provided that the following conditions\n are met:\n\n - Redistributions of source code must retain the above copyright\n notice, this list of conditi... | [
[
"numpy.diag",
"numpy.dot",
"numpy.abs",
"numpy.reshape",
"numpy.concatenate",
"numpy.round",
"numpy.append",
"numpy.transpose",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jouve/coveragepy | [
"6aa3ae906a4d1fab8bbf5ef84e13ad7068ec361a"
] | [
"perf/solve_poly.py"
] | [
"# Licensed under the Apache License: http://www.apache.org/licenses/LICENSE-2.0\n# For details: https://github.com/nedbat/coveragepy/blob/master/NOTICE.txt\n\n# Given empirical data from perf_measure.py, calculate the coefficients of the\n# polynomials for file, call, and line operation counts.\n#\n# Written by Ky... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nkuhzx/VSG-IA | [
"075b58c2bf89562cc197e721f050396589861c6a",
"075b58c2bf89562cc197e721f050396589861c6a",
"075b58c2bf89562cc197e721f050396589861c6a",
"075b58c2bf89562cc197e721f050396589861c6a"
] | [
"vsgia_model/utils/model_utils.py",
"vsgia_model/models/utils/misc.py",
"vsgia_model/models/gazenet.py",
"vsgia_model/models/utils/transformer.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport os\nimport math\n\nimport vsgia_model.models.gazenet as gazenet\nfrom vsgia_model.models.utils.transformer import build_transformer\nfrom vsgia_model.models.vsgat import GraphDealModule\n\ndef weights_init(m):\n classname=m.__class__.__nam... | [
[
"torch.nn.NLLLoss",
"torch.load",
"torch.nn.init.constant_",
"torch.nn.init.xavier_normal_",
"torch.nn.BCEWithLogitsLoss",
"torch.nn.MSELoss",
"torch.nn.init.kaiming_normal_"
],
[
"torch.ones",
"torch.zeros",
"torch.zeros_like",
"torch.stack",
"torch.nn.function... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
pmrv/pyiron | [
"3631053231761510a2fd4b1416a93adee44fd6f7"
] | [
"pyiron/lammps/control.py"
] | [
"# coding: utf-8\n# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department\n# Distributed under the terms of \"New BSD License\", see the LICENSE file.\n\nfrom __future__ import print_function\nfrom collections import OrderedDict\nimport hashlib\nimport numpy as n... | [
[
"numpy.array",
"numpy.any",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ClassroomSuite/SubmissionsViewer | [
"1f8dd931e1a6c68b289d23deb1c25a2c438137e6"
] | [
"submissions_viewer/tests_results/figures.py"
] | [
"import itertools\n\nimport matplotlib.pyplot as plt\n\n\nclass Figures:\n def __init__(self):\n self.counter = itertools.count()\n\n def _plot_barh(self, title, y, width):\n plt.ion()\n plt.clf()\n plt.style.use('ggplot')\n fig = plt.figure()\n plt.barh(y=y, width=wi... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.barh",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luyangny/Cat-detection | [
"6bdf989520ca6aba4cde30e48a6ea869db6eeee6"
] | [
"tensorflow/python/kernel_tests/control_flow_ops_py_test.py"
] | [
"# Copyright 2015 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.core.protobuf.config_pb2.RunMetadata",
"tensorflow.python.ops.math_ops.subtract",
"tensorflow.python.ops.gen_array_ops.ref_identity",
"tensorflow.python.ops.variables.Variable",
"tensorflow.python.ops.init_ops.constant_initializer",
"tensorflow.python.ops.gradients_impl.gradien... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.13",
"2.3",
"2.4",
"2.9",
"1.7",
"2.5",
"2.2",
"2.10"
]
}
] |
lkora/DS3 | [
"653115657d8c42f501dcb5dbf25c892a4e0ed3d1"
] | [
"02. Revised Simplex method/old/Revised_simplex_old.py"
] | [
"import os.path\nfrom os import path\nimport sys\nimport numpy as np\nfrom random import seed\nfrom random import randrange\n\nprecision = 0.0001\n\n# Prints current vectors A, b, c\ndef print_current(A, b, c):\n print(\"c:\\n\", c)\n #print(c)\n print(\"A:\\n\", A)\n #print(A)\n print(\"b:\\n\", b)\... | [
[
"numpy.abs",
"numpy.linalg.inv",
"numpy.set_printoptions",
"numpy.matmul",
"numpy.stack",
"numpy.all",
"numpy.round",
"numpy.append",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rheacarmel/pytorch | [
"88a1e8eb0107dfa9bd0e63f5f7008459ac5d41a2"
] | [
"torch/testing/_internal/common_methods_invocations.py"
] | [
"from functools import reduce, wraps, partial\nfrom itertools import product\nfrom operator import mul\nimport collections\nimport operator\nimport random\n\nimport torch\nimport numpy as np\nfrom torch._six import inf\nfrom torch.autograd import Variable\nimport collections.abc\n\nfrom typing import List, Sequence... | [
[
"torch.testing._internal.common_utils.random_symmetric_pd_matrix",
"torch.testing._internal.common_utils.random_symmetric_matrix",
"numpy.sqrt",
"torch.zeros",
"torch.randperm",
"torch.testing._internal.common_utils.set_rng_seed",
"torch.testing.all_types_and",
"torch.diag_embed",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
klarh/agglomerative_attention_flowws | [
"aae3bfff780ca5f62511e3f9686377c549a25386"
] | [
"agglom_attention_flowws/Text8.py"
] | [
"import functools\nimport os\n\nimport flowws\nfrom flowws import Argument as Arg\nimport numpy as np\n\nTEXT8_LOCATION = os.path.join(os.path.dirname(__file__), 'text8.npz')\n\nclass Text8DataWrapper:\n def __init__(self):\n self.text8_data = np.load(TEXT8_LOCATION)['text8']\n\n # remove spaces in... | [
[
"numpy.cumsum",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rapidsai/dask-sql | [
"964e193bf259095a43b162e5d0052d059b8cdfe3"
] | [
"dask_sql/utils.py"
] | [
"import importlib\nimport logging\nimport re\nfrom collections import defaultdict\nfrom datetime import datetime\nfrom typing import Any, Dict\nfrom unittest.mock import patch\nfrom uuid import uuid4\n\nimport cloudpickle\nimport dask.dataframe as dd\nimport numpy as np\nimport pandas as pd\nfrom nvtx import annota... | [
[
"pandas.to_datetime",
"pandas.api.types.is_datetime64_any_dtype",
"numpy.isscalar"
]
] | [
{
"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": []
}
] |
axelbr/berkeley-cs285-deep-reinforcement-learning | [
"d5d0487e612a0704082951f7b283ca4584430017",
"d5d0487e612a0704082951f7b283ca4584430017",
"d5d0487e612a0704082951f7b283ca4584430017"
] | [
"hw4/cs285/infrastructure/utils.py",
"hw5/cs285/policies/MLP_policy.py",
"hw3/cs285/infrastructure/rl_trainer.py"
] | [
"import numpy as np\nimport time\nimport copy\n\n############################################\n############################################\n\ndef calculate_mean_prediction_error(env, action_sequence, models, data_statistics):\n\n model = models[0]\n\n # true\n true_states = perform_actions(env, action_seq... | [
[
"numpy.expand_dims",
"numpy.absolute",
"numpy.squeeze",
"numpy.stack",
"numpy.concatenate",
"numpy.mean",
"numpy.array"
],
[
"torch.exp",
"torch.distributions.Categorical",
"torch.distributions.MultivariateNormal",
"torch.zeros"
],
[
"numpy.random.seed",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XtraAstronomy/Pumpkin | [
"b001c4689dc4a5521e5cd5d358c618090367a942"
] | [
"StN150_Single.py"
] | [
"'''\nPython script that uses XSPEC to create synthetic ICM X-ray Spectra\n'''\n\n#--------------------------------IMPORTS---------------------------------------#\nimport numpy as np\nimport random\nimport os\nfrom tqdm import tqdm\nfrom sherpa.astro.ui import *\n#-------------------------------VARIABLES-----------... | [
[
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liuyuns/models | [
"16788c580641b3e525ad615336b416d9f224be1b"
] | [
"fluid/neural_machine_translation/transformer/model.py"
] | [
"from functools import partial\nimport numpy as np\n\nimport paddle.fluid as fluid\nimport paddle.fluid.layers as layers\n\nfrom config import *\n\n\ndef position_encoding_init(n_position, d_pos_vec):\n \"\"\"\n Generate the initial values for the sinusoid position encoding table.\n \"\"\"\n position_en... | [
[
"numpy.zeros",
"numpy.cos",
"numpy.power",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
natsukium/gps_log_connector | [
"d6008151425e0aac4cf7b35f423e92dd19f96304"
] | [
"gps.py"
] | [
"#!/usr/bin/env python3\nfrom os import listdir\nfrom os.path import join\nimport matplotlib.pyplot as plt\nimport gpxpy\n\n\ndef draw():\n # Initialization\n data_path = 'log'\n data = [i for i in listdir(data_path)]\n\n fig = plt.figure(facecolor='white')\n ax = plt.Axes(fig, [0., 0., 1., 1.], )\n ... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.Axes",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lazarohcm/opencv_exercises | [
"9e55593abe147cc4d0363f331b3edeb72fa42c52"
] | [
"diabetic_retinopathy_svm/svm/svm.py"
] | [
"import cv2\nimport sys\nimport numpy as np\nimport csv\nfrom helpers.class_model import ClassModel\nfrom typing import List\n\n\n# Usar Keras\n# Keras -> deep features\n# VGG 16/19 e RESNET\n\n# Testar autosklearn -> Estimador de classificador\n\nclass SVM:\n Classes = List[ClassModel]\n\n def __init__(self,... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.random.permutation",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yostaka/advanced-lane-detection | [
"40cdd13093eb04152af3c1c001d1fbad9b3cbf19"
] | [
"CarND/perstransform.py"
] | [
"import numpy as np\nimport cv2\n\n# Apply a perspective transform to rectify binary image (\"birds-eye view\")\ndef warp(img):\n img_size = (img.shape[1], img.shape[0])\n\n # Four source coordinates\n src = np.float32(\n [[761, 499],\n [1034, 673],\n [274, 673],\n [526, 499]... | [
[
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hongyehu/Sim-Clifford | [
"00be6a479361fb9ee9b6e240f631a1030b881b2f"
] | [
"base/.ipynb_checkpoints/paulialg-checkpoint.py"
] | [
"import numpy\nfrom .utils import (\n ipow, pauli_tokenize, \n clifford_rotate, pauli_transform,\n batch_dot, aggregate)\n\nclass Pauli(object):\n '''Represents a Pauli operator.\n\n Parameters:\n g: int (2*N) - a Pauli string in binary repr.\n p: int - phase indicator (i power).'''\n def __... | [
[
"numpy.expand_dims",
"numpy.abs",
"numpy.unique",
"numpy.stack",
"numpy.ones",
"numpy.concatenate",
"numpy.repeat",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joan726/BigDL | [
"3c89ff7e8bbdc713110536c18099506811cd2b3a"
] | [
"python/orca/test/bigdl/orca/learn/ray/tf/test_tf_spark_estimator.py"
] | [
"#\n# Copyright 2016 The BigDL 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 ... | [
[
"tensorflow.keras.layers.Dense",
"numpy.random.randint",
"numpy.random.randn",
"tensorflow.keras.optimizers.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
Harris-2020-Grad-Side-Projects/US-schoolday-temperatures | [
"023963c3c557f701436679a6501aec9c84b07d8e"
] | [
"build/check_plot.py"
] | [
"# Check plot:\n# simple point plot to verify that the controuform is not misleading\n\n\nimport os\nimport geopandas\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n#change this only\nos.chdir('/Users/Sarah/Documents/GitHub/US-schoolday-temperatures')\ndate = 'Winter 2020-21'\ntitle = 'Average Daily Temp ... | [
[
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jetd1/Normal-Assisted-Stereo | [
"93e0c8403d3f636fd5f128a2c29275cc022287ba"
] | [
"custom_transforms.py"
] | [
"from __future__ import division\nimport torch\nimport random\nimport numpy as np\nfrom scipy.misc import imresize\nfrom scipy.ndimage.interpolation import zoom\nimport skimage\nfrom skimage import transform\nimport time\n'''Set of tranform random routines that takes list of inputs as arguments,\nin order to have r... | [
[
"torch.from_numpy",
"numpy.copy",
"numpy.transpose",
"numpy.random.uniform",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
matejker/Kassiopea | [
"49b92f3b158dd64a4f777ab592ce21826625b6f7"
] | [
"voter_model.py"
] | [
"import copy\nimport numpy as np\nnext_value = 1\n\n\ndef myrand():\n global next_value\n next_value = (((next_value * 1103515245) % 18446744073709551616) + 12345) % 18446744073709551616\n return (next_value / 65536) % 4294967296\n\n\ndef asign_label(G, edge, p=0.5):\n # Labels are the same, nothing hap... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamheins/collision-detection-pybullet | [
"a33a5a63f88dfa44678de9d5c6142f0b646014ed"
] | [
"pyb_utils/camera.py"
] | [
"\"\"\"Provides a utility Camera class for PyBullet.\"\"\"\nimport numpy as np\nimport pybullet as pyb\nfrom PIL import Image\nimport cv2\n\n\nclass Camera:\n \"\"\"A PyBullet camera.\"\"\"\n\n def __init__(\n self,\n view_matrix,\n near=0.1,\n far=1000.0,\n fov=60.0,\n ... | [
[
"numpy.linalg.inv",
"numpy.array",
"numpy.zeros",
"numpy.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gyy8426/TF_concaption | [
"7b3face47c96c885b2715605122328b7b6bef609",
"7b3face47c96c885b2715605122328b7b6bef609"
] | [
"seq2seq/data/sequence_split_tokens_decoder.py",
"seq2seq/metrics/test.py"
] | [
"# Copyright 2017 Google Inc.\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/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicab... | [
[
"tensorflow.string_split",
"tensorflow.parse_single_sequence_example",
"tensorflow.reshape",
"tensorflow.concat"
],
[
"tensorflow.assert_type",
"tensorflow.concat",
"tensorflow.Variable",
"tensorflow.identity",
"tensorflow.ConfigProto",
"tensorflow.GPUOptions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
prajwalppv/Active-Learning-Uncertainity-sampling | [
"4f8c604ac10c06cea0f10f41bd1b544cc9e8b2c3"
] | [
"Code/active.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.naive_bayes import GaussianNB\nimport matplotlib.patches as mpatches\nfrom passiveRandomLearners import compute_loss\n\n\ndef calc_entropy(row):\n ''... | [
[
"numpy.hstack",
"numpy.log",
"pandas.read_csv",
"sklearn.naive_bayes.GaussianNB",
"sklearn.ensemble.RandomForestClassifier",
"numpy.delete",
"numpy.argmax",
"numpy.apply_along_axis",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
tylerjrichards/streamlit-judiciary-app | [
"17fb2575cf383c47cebec15123140caffc90ef6e"
] | [
"utils.py"
] | [
"\"\"\"\nCommon code and config for all the data app prototypes.\n\"\"\"\n\nimport pandas as pd\nimport convoys.utils as cutl\nfrom convoys.multi import KaplanMeier\n\ntry:\n import plotly.express as px\nexcept:\n pass\n\n\nPOTUS_TERMS = {\n \"Clinton\": (pd.Timestamp(1993, 1, 20), pd.Timestamp(2001, 1, 20... | [
[
"pandas.Timestamp",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
SysBioChalmers/Pan_Model_For_All_Existing_Yeast_Species | [
"06763a47e36e41781c25591f93a5b6bf65eef588"
] | [
"model_script_py/code/print_rxn_mets_of_Yeast8.py"
] | [
"# -*- coding: utf-8 -*-\n# -*- python 3 -*-\n# -*- hongzhong Lu -*-\n\n'''\nUsing the genome annotation from eggnog,kegg web service and bi-directional blast analysis,\nFirstly we need find the best hit for new pan proteins from s288c which connect the reaction in yeastGEM\nThen these new pan proteins could be mer... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jli0117/ehrMGAN | [
"be6afe6526f68f7ccf69db8474662123857dacab"
] | [
"main_train.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport pickle\nimport os\nfrom networks import C_VAE_NET, D_VAE_NET, C_GAN_NET, D_GAN_NET\nfrom m3gan import m3gan\n\nimport timeit\nstart = timeit.default_timer()\n\ndata_path = 'data/real/mimic/'\n\nwith open(os.path.join(data_path, 'vital_sign_24hrs.pkl'), 'rb') as f... | [
[
"tensorflow.ConfigProto",
"tensorflow.reset_default_graph",
"numpy.asarray",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
Mschikay/FCIS-babycam | [
"603dab002fb0e665435acd119b02cf3f18c4eb02"
] | [
"babycam/config/config.py"
] | [
"import yaml\nimport numpy as np\nfrom easydict import EasyDict as edict\n\nconfig = edict()\n\n#test image path\nconfig.TEST_YAML = '/../experiments/fcis/cfgs/fcis_coco_demo.yaml'\nconfig.FG = 'baby1.jpg'\nconfig.BG = 'bg.jpg'\nconfig.EX_MXNET = '../external/mxnet'\nconfig.TEST_CLASS_NAME = ['person', 'bicycle', '... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sonachitchyan/tardis | [
"a8bad890d8ccd906993012e954ea7bcd683a96b7"
] | [
"tardis/plasma/tests/test_plasma_contiuum.py"
] | [
"import pytest\nimport numpy as np\nfrom numpy.testing import assert_allclose\nfrom tardis.plasma.properties import YgData\n\n\ndef test_exp1_times_exp():\n x = np.array([499.0, 501.0, 710.0])\n desired = np.array([0.00200000797, 0.0019920397, 0.0014064725])\n actual = YgData.exp1_times_exp(x)\n assert_... | [
[
"numpy.array",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anishgoyal24/statsmodels | [
"3caea9258dc5986178c1ea255db2b14256443c84"
] | [
"statsmodels/tsa/statespace/varmax.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nVector Autoregressive Moving Average with eXogenous regressors model\n\nAuthor: Chad Fulton\nLicense: Simplified-BSD\n\"\"\"\nfrom __future__ import division, absolute_import, print_function\n\nfrom warnings import warn\nfrom collections import OrderedDict\n\nimport pandas as pd\ni... | [
[
"numpy.diag",
"numpy.dot",
"numpy.isnan",
"numpy.tril_indices",
"numpy.eye",
"numpy.arange",
"numpy.concatenate",
"numpy.linalg.pinv",
"numpy.asanyarray",
"numpy.linalg.cholesky",
"numpy.diag_indices",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
minhpvwh/psenet-text-detection | [
"d8c829c08c6197b980520e0e2644fa0a239b9c41"
] | [
"psenet_text_detector/pypse.py"
] | [
"import numpy as np\r\nimport cv2\r\nimport queue\r\n\r\n\r\ndef pse(kernals, min_area):\r\n kernal_num = len(kernals)\r\n pred = np.zeros(kernals[0].shape, dtype='int32')\r\n\r\n label_num, label = cv2.connectedComponents(kernals[kernal_num - 1], connectivity=4)\r\n\r\n for label_idx in range(1, label_... | [
[
"numpy.where",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rhaps0dy/gpytorch | [
"278284e23e08340eb648389f205c159c2dd9461c"
] | [
"gpytorch/functions/_inv_quad.py"
] | [
"#!/usr/bin/env python3\n\nimport torch\nfrom torch.autograd import Function\n\nfrom .. import settings\n\n\ndef _solve(lazy_tsr, rhs):\n if (\n settings.fast_computations.solves.off()\n or settings.fast_computations.log_prob.off()\n or lazy_tsr.size(-1) <= settings.max_cholesky_size.value()... | [
[
"torch.zeros_like",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KirstieJane/bocpdms | [
"ed58d27da2dbafba8a4af9c6cc5b0b603892df5a"
] | [
"paper_pictures_AirPollution_NIPS.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 16 14:25:06 2018\n\n@author: jeremiasknoblauch\n\nDescription: Plot the Air Pollution Data for NIPS submission\n\"\"\"\n\n\nimport csv\nimport numpy as np\nimport scipy\nfrom Evaluation_tool import EvaluationTool\nfrom matplotlib import py... | [
[
"numpy.abs",
"numpy.linspace",
"numpy.power",
"matplotlib.pyplot.subplots",
"numpy.mean",
"matplotlib.pyplot.subplots_adjust",
"numpy.var",
"scipy.stats.sem",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"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"
... |
nmarchio/pyrosm | [
"7cad4439ae83c10fd40b9b296fa6d115d0febf6b"
] | [
"tests/test_building_parsing.py"
] | [
"import pytest\nfrom pyrosm import get_data\n\n\n@pytest.fixture\ndef test_pbf():\n pbf_path = get_data(\"test_pbf\")\n return pbf_path\n\n\n@pytest.fixture\ndef helsinki_pbf():\n pbf_path = get_data(\"helsinki_pbf\")\n return pbf_path\n\n\n@pytest.fixture\ndef helsinki_history_pbf():\n pbf_path = ge... | [
[
"pandas.to_datetime"
]
] | [
{
"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": []
}
] |
MACIEK1JAREMA/Gravitational-Lensing-python | [
"e7c9e8518055b5108a8ef56a0cc9c734465bdb73",
"e7c9e8518055b5108a8ef56a0cc9c734465bdb73"
] | [
"attempts/stage3/lensing a generated galaxy cluster.py",
"Project_completed/additional codes/lensed 2 body/(star + planet) extracting ratio of radii.py"
] | [
"# generating a random galaxy cluster and lensing it \n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport timeit\nimport attempts.stage3.function_lens as lensing\n\n# %%\n\nnp.random.seed(1234)\n\n# start the timer\nstart = timeit.default_timer()\n\n# set up some initial parameters\nrc = 0.7\neps = 0\ndo... | [
[
"matplotlib.pyplot.imshow",
"numpy.random.seed",
"matplotlib.pyplot.figure",
"numpy.cos",
"numpy.sin",
"numpy.random.rand",
"numpy.zeros",
"numpy.random.randint"
],
[
"numpy.linspace",
"numpy.min",
"scipy.integrate.odeint",
"numpy.floor",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
... |
yqnhjy/Image-Recognition-Of-Birds | [
"eaf0a125274e572cc5a12e8a2264af80e70a1a5d"
] | [
"main1.py"
] | [
"# -*- coding = utf-8 -*-\r\n# @Time : 2022/4/26 0026 16:31\r\n# @Author : 小白\r\n# @File : main.py\r\n# @Software : PyCharm\r\nimport torch.nn as nn\r\nimport argparse\r\nimport config\r\nfrom BCNN_fc import BCNN_fc\r\nfrom BCNN_all import BCNN_all\r\n# import data.data as data\r\nimport torch.backends.cudnn as cud... | [
[
"torch.nn.CrossEntropyLoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mogoweb/PhotoMathChecker | [
"6455f5262b6b65fd8e0e3ff16e1a09201b4790a4"
] | [
"scripts/inference_mnist.py"
] | [
"import numpy as np\nimport os\nimport six.moves.urllib as urllib\nimport sys\nimport tarfile\nimport tensorflow as tf\nimport zipfile\n\nfrom distutils.version import StrictVersion\nfrom collections import defaultdict\nfrom io import StringIO\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\n\nfrom obj... | [
[
"tensorflow.Graph",
"numpy.expand_dims",
"matplotlib.pyplot.imshow",
"tensorflow.import_graph_def",
"tensorflow.greater",
"tensorflow.slice",
"tensorflow.gfile.GFile",
"tensorflow.cast",
"tensorflow.squeeze",
"tensorflow.expand_dims",
"tensorflow.Session",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Saichandarreddy/common_python_programs | [
"6dba13fad055a20b9796d9747918c6278eedd705"
] | [
"common_python_programs/common_python_programs/__init__.py"
] | [
"from flask import Flask, render_template, request\r\nimport os\r\nimport pandas as pd\r\n\r\n\r\ndef joke():\r\n return (u'Wenn ist das Nunst\\u00fcck git und Slotermeyer? Ja! ... '\r\n u'Beiherhund das Oder die Flipperwaldt gersput.')\r\n\r\n\r\ndef introduction():\r\n return \"this package was c... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ankitbaluni123/Data_Science_Mini_project | [
"0b7902eb01d405176f40f6ad8d54d116b8adfc11"
] | [
"Batch07/PearsonCorrelation.py"
] | [
"from scipy.stats import pearsonr\r\nimport pandas as pd\r\n\r\ndef load_dataset(path):\r\n return pd.read_csv(path)\r\n\r\ndef PEARSONR(data):\r\n values = []\r\n columns = data.columns\r\n for i in range(len(columns)):\r\n l = []\r\n for j in range(len(columns)):\r\n l.append(... | [
[
"scipy.stats.pearsonr",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"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... |
Toby28/mask_rcnn_dataargumentation | [
"622521796958e5ae63c23ea6acf8311688275098"
] | [
"test.py"
] | [
"import cv2\r\nimport numpy as np\r\nfrom collections import Counter\r\nfrom multiprocessing import Pool\r\nimport os\r\nimport dataReader as dr\r\nimport matplotlib.pyplot as plt\r\ndef calcEntropy(img):\r\n entropy = []\r\n\r\n hist = cv2.calcHist([img], [0], None, [256], [0, 255])\r\n total_pixel = img.... | [
[
"numpy.hstack",
"numpy.log",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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