repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
caixunshiren/pytorch-metal-oxide-memristor-crossbar | [
"ef48468910fba455ccc58709e336c58c862a3cb1"
] | [
"memristor/devices.py"
] | [
"import yaml\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\npath = Path(__file__).parent\nwith open(path / \"params.yaml\", 'r') as stream:\n CONFIG = yaml.safe_load(stream)\n PARAMS = CONFIG[\"StaticParameters\"]\n\nK_b = 1.38065e-23 # Boltzmann constant\n\nHEADER = [\"c0_set\", \"c1_... | [
[
"numpy.log",
"pandas.read_csv",
"numpy.tanh",
"numpy.random.normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
RaISy-Net/Intelligent_picking | [
"7168b9ccd1f66c26367a534bddac3c8b2f5dc192"
] | [
"src/utils/visualisation/plot.py"
] | [
"import warnings\nfrom datetime import datetime\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom src.utils.dataset_processing.grasp import detect_grasps\n\nwarnings.filterwarnings(\"ignore\")\n\n\ndef plot_results(\n fig,\n rgb_img,\n grasp_q_img,\n grasp_angle_img,\n ... | [
[
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.close",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
krantirk/Transformers | [
"4054335fc6910c8aaed3b947bd4e3bfc2ced6370"
] | [
"transformers/modeling_t5.py"
] | [
"# coding=utf-8\n# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. 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/lice... | [
[
"torch.abs",
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.Embedding",
"torch.where",
"torch.full_like",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.sqrt",
"torch.einsum",
"torch.tensor",
"torch.nn.functional.relu",
"torch.a... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
benfred/cudf | [
"3cd4c9f0602840dddb9a0e247d5a0bcf3d7266e1"
] | [
"python/cudf/cudf/tests/test_indexing.py"
] | [
"from itertools import combinations\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport cudf\nfrom cudf import DataFrame, Series\nfrom cudf.tests import utils\nfrom cudf.tests.utils import assert_eq\n\nindex_dtypes = [np.int64, np.int32, np.int16, np.int8]\n\n\n@pytest.fixture\ndef pdf_gdf():\n p... | [
[
"pandas.to_datetime",
"numpy.random.random",
"pandas.Series",
"numpy.random.seed",
"pandas.MultiIndex",
"pandas.date_range",
"numpy.arange",
"pandas.Categorical",
"pandas.DataFrame",
"numpy.datetime64",
"numpy.testing.assert_array_equal",
"pandas.testing.assert_fram... | [
{
"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": []
}
] |
buzbarstow/collectionmc | [
"0efd107d675f953d74259f2f9c396e51a1d9879c"
] | [
"kosudoku/montecarlo.py"
] | [
"# ------------------------------------------------------------------------------------------------ #\ndef ImportEssentialityData(fileName):\n# Not yet ready for prime time\n# Import a defined format essentiality data file\n# Assumes that data is in the format: locus tag, gene name, essentiality\t\n\n\tfrom .utils ... | [
[
"scipy.unique",
"numpy.random.choice",
"numpy.arange",
"scipy.intersect1d",
"numpy.std",
"numpy.mean",
"numpy.exp",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hazenai/cvat | [
"4ac44df577b79f3329b036e78e71c08bb384e7e2"
] | [
"cvat/apps/engine/media_extractors.py"
] | [
"# Copyright (C) 2019-2020 Intel Corporation\n#\n# SPDX-License-Identifier: MIT\n\nimport os\nimport tempfile\nimport shutil\nimport zipfile\nimport io\nfrom abc import ABC, abstractmethod\n\nimport av\nimport numpy as np\nfrom pyunpack import Archive\nfrom PIL import Image, ImageFile\n\n# fixes: \"OSError:broken d... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hndgzkn/alphacsc | [
"467cd4e6fad54aab23ea7eb6a11a8024c078d73b"
] | [
"alphacsc/update_d_multi.py"
] | [
"# Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>\n# Tom Dupre La Tour <tom.duprelatour@telecom-paristech.fr>\n# Umut Simsekli <umut.simsekli@telecom-paristech.fr>\n# Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Thomas Moreau <thomas.moreau@inria.fr>\nimport numpy a... | [
[
"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": [],
... |
zhandand/DogNet | [
"ee15f3e057a34adf9ed9cc09d049ec0eaf8df048"
] | [
"code/baseline/train_DMNC.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nfrom sklearn.metrics import jaccard_similarity_score, roc_auc_score, precision_score, f1_score, average_precision_score\r\nimport numpy as np\r\nimport dill\r\nimport time\r\nfrom torch.nn import CrossEntropyLoss\r\nfrom torch.optim import Adam\r\nimport os\r\nfrom collecti... | [
[
"torch.nn.CrossEntropyLoss",
"torch.LongTensor",
"torch.manual_seed",
"numpy.mean",
"torch.device",
"numpy.argsort",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liujuanLT/facenet | [
"58a6fdaa1b6e62dd8c781d376b83d13c86cdd76e"
] | [
"src/models/densenet.py"
] | [
"\"\"\"Contains the definition of the DenseNet architecture.\n\nAs described in https://arxiv.org/abs/1608.06993.\n\n Densely Connected Convolutional Networks\n Gao Huang, Zhuang Liu, Kilian Q. Weinberger, Laurens van der Maaten\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfro... | [
[
"tensorflow.nn.relu",
"tensorflow.transpose",
"tensorflow.concat",
"tensorflow.reduce_mean",
"tensorflow.zeros_initializer",
"tensorflow.squeeze",
"tensorflow.variable_scope",
"tensorflow.nn.dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Avin0323/Paddle | [
"a615002abdfe8cfdab78f1b7b344ef2939345548"
] | [
"python/paddle/fluid/tests/unittests/test_egr_python_api.py"
] | [
"# 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... | [
[
"numpy.random.random",
"numpy.ones",
"numpy.random.rand",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yurilavinas/failure_diversity_maximisation | [
"24d5b90455eb554bc91b6092df53d83f4b1023df"
] | [
"fdm/fdm_code/domain/wann_task_gym.py"
] | [
"import random\nimport numpy as np\nimport sys\nfrom domain.make_env import make_env\nfrom domain.task_gym import GymTask\nfrom neat_src import *\nimport math\n\n\nclass WannGymTask(GymTask):\n \"\"\"Problem domain to be solved by neural network. Uses OpenAI Gym patterns.\n \"\"\" \n def __init__(self, game, par... | [
[
"numpy.linspace",
"numpy.unique",
"numpy.reshape",
"numpy.isnan",
"numpy.asarray",
"numpy.copy",
"numpy.std",
"numpy.mean",
"numpy.shape",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vhosouza/xcoord | [
"9226a6f919b3edec933753ff17815092ab95df9a",
"9226a6f919b3edec933753ff17815092ab95df9a"
] | [
"visualization/visualize_tms_scene.py",
"tractography/vtk_inv_tracts.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# xcoord - Tools for cross-software spatial coordinate manipulation\n#\n# This file is part of xcoord package which is released under copyright.\n# See file LICENSE or go to website for full license details.\n# Copyright (C) 2018 Victor Hugo Souza - All Rights Rese... | [
[
"numpy.asarray",
"numpy.linalg.inv",
"numpy.linalg.norm",
"numpy.linalg.det",
"numpy.identity",
"numpy.cross",
"scipy.io.savemat"
],
[
"numpy.linalg.inv",
"numpy.asarray",
"numpy.linalg.norm",
"numpy.identity",
"numpy.array"
]
] | [
{
"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"... |
bierschi/nclt2ros | [
"77b30ca6750d4b0cd82ccb6660f2fd0946581091"
] | [
"nclt2ros/visualizer/gt.py"
] | [
"import math\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom nclt2ros.visualizer.plotter import Plotter\r\nfrom nclt2ros.transformer.coordinate_frame import CoordinateFrame\r\n\r\n\r\nclass GroundTruth(Plotter):\r\n \"\"\"Class to visualize the ground truth data as a kml and png file\r\n\r\n USAGE:\r\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cyphylab/crazyflie_ros | [
"fa81ecfff16ea2e1e30369fa5dcfbad40acc3dba"
] | [
"crazyflie_demo/scripts/uav_trajectory.py"
] | [
"#!/usr/bin/env python\nimport numpy as np\n\ndef normalize(v):\n norm = np.linalg.norm(v)\n assert norm > 0\n return v / norm\n\n\nclass Polynomial:\n def __init__(self, p):\n self.p = p\n\n # evaluate a polynomial using horner's rule\n def eval(self, t):\n assert t >= 0\n x = 0.0\n for i in rang... | [
[
"numpy.dot",
"numpy.linalg.norm",
"numpy.cos",
"numpy.sin",
"numpy.cross",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hzfinfdu/DataLab | [
"0da0226866f59ed2e535c346833f0797499b5174"
] | [
"src/datalabs/operations/aggregate/general.py"
] | [
"from typing import Dict, List, Optional, Any\nfrom typing import Callable, Mapping, Iterator\n# nltk package for\nimport nltk\nimport numpy as np\n#sklearn is used for tfidf\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nfrom .aggregating import Aggregating, aggregating\n\n\n\n\n@aggregating(name=... | [
[
"numpy.average",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
georgetown-analytics/Baseball-Hall-of-Fame | [
"5a41bb2c1a1e7aa2b7621311a5d2baff40751515",
"5a41bb2c1a1e7aa2b7621311a5d2baff40751515"
] | [
"wrangle_538.py",
"clutchness_03.py"
] | [
"# File: wrangle_538.py\n# Date Created: 2018-11-10\n# Author(s): Mahkah Wu\n# Purpose: Extracts team elo rankings from 538 file\n\n\nimport pandas as pd\nimport psycopg2\nfrom ignore import db_cred\n\n\ndf = pd.read_csv('ignore\\\\large_data\\\\538\\\\mlb_elo.csv')\n\ndf = df[(df['season'] >= 1950) & (df['season']... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.read_sql"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
jesnyder/cell-source_momentum | [
"2a88a399e635f54fdc9aa67031521d1a0dbd2fd4"
] | [
"code/python/a0001_admin.py"
] | [
"from bs4 import BeautifulSoup\r\nfrom datetime import datetime\r\nimport json\r\nimport lxml\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport os\r\nimport pandas as pd\r\nfrom serpapi import GoogleSearch\r\nimport re\r\nimport requests\r\nimport time\r\n\r\n\r\ndef clean_dataframe(df):\r\n\r\n\r\... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
puririshi98/GNMT | [
"12099ff622c1d459fae9b0cda10b21615a1a5064"
] | [
"seq2seq/utils.py"
] | [
"import logging.config\nimport os\nimport random\nimport sys\nimport time\nfrom contextlib import contextmanager\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn.init as init\nimport torch.utils.collect_env\n\n\ndef init_lstm_(lstm, init_weight=0.1):\n \"\"\"\n Initialize... | [
[
"torch.nn.init.uniform_",
"torch.distributed.broadcast",
"torch.utils.collect_env.get_pretty_env_info",
"torch.FloatTensor",
"torch.device",
"torch.distributed.get_rank",
"torch.cuda.synchronize",
"torch.distributed.init_process_group",
"torch.LongTensor",
"numpy.isnan",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JeppeDruedahl/HighFreqInc | [
"a06883586fa00b208c5cbe731108bfa925fee09b"
] | [
"dstcode/moments.py"
] | [
"import numpy as np\nfrom numba import njit\n\n@njit\ndef mean_var_skew_kurt_ages(x,age,cond,ages,periods=12):\n \"\"\" calcualte mean, variance, skewness and kurtosis \"\"\"\n \n # a. allocate memory\n N = x.shape[0]\n T = x.shape[1]\n Nages = ages.size\n\n Nactive = np.zeros(ages.size,dtype=n... | [
[
"numpy.isnan",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
srinidhigoud/tvm | [
"3861fb8ee39746caa67f4577d17afc239be1dec5"
] | [
"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.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pureexe/point-cloud-projection | [
"10d492d8caa7de081a884fe4ae49b1d7efda4b62"
] | [
"projection1.py"
] | [
"from scipy.io import loadmat\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfocal = 30\nmat = loadmat('data/0001_mesh_rightfar.mat')\ncolor = mat['colors']\nvertex = mat['vertices']\ncamera_matrix = np.loadtxt('data/0001_camera_matrix_rightfar.txt')\n\n# Extensic\nextrinsic = camera_matrix\n\n#Intrinsic\n... | [
[
"matplotlib.pyplot.imsave",
"matplotlib.pyplot.imshow",
"scipy.io.loadmat",
"matplotlib.pyplot.show",
"numpy.ones",
"numpy.round",
"numpy.array",
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"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"... |
BodenmillerGroup/spherpro | [
"0bcbc76942c7bae82deddcdc4fa5239e04442553"
] | [
"spherpro/bromodules/filter_objectfilters.py"
] | [
"\"\"\"\nA class to generate and add filters to the filter table.\n\"\"\"\nimport pandas as pd\nimport sqlalchemy as sa\n\nimport spherpro.bromodules.filter_base as filter_base\nimport spherpro.db as db\n\n# TODO: move to default configuration?\nFILTERSTACKNAME = \"FilterStack\"\nFILTERTYPENAME = \"filter\"\n\n\ncl... | [
[
"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": []
}
] |
YanhuiJoe/SCAN-BGLL-community-detection | [
"699b8af9bc496a9afbfee57b4ce750a386896726"
] | [
"tst.py"
] | [
"import networkx as nx\nimport pandas as pd\nimport math\nfrom sklearn import metrics\nfrom BGLL import PyLouvain\nimport numpy as np\n\n#\n# G = nx.read_gml('data/lesmis.gml', label='label')\n# print(G)\n# G = nx.Graph()\n# f = pd.read_csv('t.txt', sep=',', header=None)\n# edge_list = []\n# for i, j in zip(f[0], f... | [
[
"numpy.array",
"sklearn.metrics.normalized_mutual_info_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lte2000/cwfx | [
"dc8daee44cea4b7c0286a7676e4a2829744fee64"
] | [
"result/each_hangye.py"
] | [
"# coding: utf-8\r\n\"\"\"\r\n\r\n\"\"\"\r\nimport pandas as pd\r\nimport numpy as np\r\nimport re\r\nimport csv\r\nimport io\r\nimport time\r\nimport traceback\r\nimport logging\r\n\r\n\r\nif __name__ == '__main__':\r\n filtered_df = pd.read_csv(r\"filtered.csv\", encoding='gbk', sep='\\t', index_col=None, dtyp... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
cruzdanilo/dask | [
"965c9e401801689a6a68cec5c0529f912a459960"
] | [
"dask/utils.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport codecs\nimport functools\nimport inspect\nimport io\nimport math\nimport os\nimport re\nimport shutil\nimport struct\nimport sys\nimport tempfile\nfrom errno import ENOENT\nfrom collections import Iterator\nfrom contextlib import contextman... | [
[
"numpy.allclose",
"numpy.cumsum",
"numpy.iinfo",
"numpy.random.RandomState",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
f-chenyi/Chlamydomonas_CCM | [
"3c7bf8ea178193b468217c55a770e1b83280c9a8"
] | [
"ThylakoidStacks/EffectiveDiffusion_mesh.py"
] | [
"# =============================================== #\n# ============ Import head packages ============ #\nfrom fenics import *\nimport pygmsh as pg\nimport os\nimport meshio\nimport numpy as np\nimport csv\n# =============================================== #\n# =============================================== #\n\n... | [
[
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kevinbro96/perceptual-advex | [
"e40ee996ab8c4ae4575004bf7b6b8ab5757ed6bb"
] | [
"perceptual_advex/vae.py"
] | [
"from __future__ import print_function\nimport abc\nimport os\nimport math\n\nimport numpy as np\nimport logging\nimport torch\nimport torch.utils.data\nfrom torch import nn\nfrom torch.nn import init\nfrom torch.nn import functional as F\nfrom torch.autograd import Variable\nimport pdb\n\nCIFAR_MEAN = [0.4914, 0.4... | [
[
"torch.nn.Sequential",
"numpy.sqrt",
"torch.full",
"torch.nn.ConvTranspose2d",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.Sigmoid",
"torch.tanh",
"torch.nn.Linear",
"torch.no_grad",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d",
"torch.stack",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danholdaway/eva | [
"a5a784953479132080fa3a2ea5b9e9d3dc08cd68"
] | [
"src/eva/data/data_collections.py"
] | [
"# (C) Copyright 2021-2022 NOAA/NWS/EMC\n#\n# (C) Copyright 2021-2022 United States Government as represented by the Administrator of the\n# National Aeronautics and Space Administration. All Rights Reserved.\n#\n# This software is licensed under the terms of the Apache Licence Version 2.0\n# which can be obtained ... | [
[
"numpy.nanmax",
"numpy.abs",
"numpy.squeeze",
"numpy.nanmin",
"numpy.nanmean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kchen0x/emotion-detection | [
"08402e724d218feb5d3df1fa31f9631cf97d3629"
] | [
"src/camera.py"
] | [
"from statistics import mode\n\nimport cv2\nfrom keras.models import load_model\nimport numpy as np\n\nfrom utils.datasets import get_labels\nfrom utils.inference import detect_faces\nfrom utils.inference import draw_text\nfrom utils.inference import draw_bounding_box\nfrom utils.inference import apply_offsets\nfro... | [
[
"numpy.asarray",
"numpy.max",
"numpy.expand_dims",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GuoQiang-Fu/UQpy | [
"3a4ddb152c4b04f82dbd515c1677a92a92e6ba4f"
] | [
"src/UQpy/Surrogates.py"
] | [
"# UQpy is distributed under the MIT license.\n#\n# Copyright (C) 2018 -- Michael D. Shields\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without l... | [
[
"numpy.minimum",
"numpy.linalg.matrix_rank",
"numpy.einsum",
"numpy.sqrt",
"numpy.squeeze",
"numpy.cumsum",
"numpy.round",
"numpy.concatenate",
"numpy.argmin",
"numpy.mean",
"scipy.special.hermitenorm",
"numpy.linalg.qr",
"numpy.negative",
"numpy.roll",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": []
}
] |
fraimondo/mne-python | [
"2fe126debc27d14e5f1d92762757915bb86fcaf5"
] | [
"mne/io/base.py"
] | [
"# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n# Matti Hamalainen <msh@nmr.mgh.harvard.edu>\n# Martin Luessi <mluessi@nmr.mgh.harvard.edu>\n# Denis Engemann <denis.engemann@gmail.com>\n# Teon Brooks <teon.brooks@gmail.com>\n# Marijn van Vliet <... | [
[
"numpy.dot",
"numpy.asarray",
"numpy.cumsum",
"numpy.round",
"numpy.concatenate",
"numpy.any",
"numpy.iscomplexobj",
"numpy.where",
"scipy.signal.hilbert",
"numpy.hstack",
"numpy.unique",
"numpy.less",
"numpy.arange",
"numpy.atleast_1d",
"numpy.greater_e... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
mcognetta/federated | [
"fa0c1a00b5d77768bc2f38f503f3ef1a65693945",
"fa0c1a00b5d77768bc2f38f503f3ef1a65693945"
] | [
"tensorflow_federated/python/core/impl/tensorflow_serialization.py",
"tensorflow_federated/python/examples/mnist/models.py"
] | [
"# Lint as: python3\n# Copyright 2018, The TensorFlow Federated 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# ... | [
[
"tensorflow.Graph",
"tensorflow.compat.v1.local_variables",
"tensorflow.control_dependencies",
"tensorflow.io.gfile.walk",
"tensorflow.train.Checkpoint",
"tensorflow.compat.v1.global_variables",
"tensorflow.saved_model.save",
"tensorflow.Module",
"tensorflow.compat.v2.saved_mod... | [
{
"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... |
imadmali/data-manipulation | [
"9888e78b535768c5a38b8a4ffb5a4f48309f4831"
] | [
"data-manipulation/src/dm_pyspark.py"
] | [
"from pyspark.sql import SparkSession\nfrom pyspark.sql.functions import col, lit, when, sum, max, lag, DataFrame, udf\nfrom pyspark.sql.window import Window\nfrom pyspark.sql.types import StringType\nfrom pyspark.sql.types import *\nfrom pyspark.sql.functions import pandas_udf\nimport numpy as np\nimport pandas as... | [
[
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isseikz/StarTracker | [
"01c1dfcf8c9a6886acfa18c038acc723f56cc94d"
] | [
"main.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport http\n\nimport time\nimport io\nimport cv2\n\nfrom PIL import Image\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\nimport json\nimport urllib.parse\nfrom time import sleep\n\nimport PD\nimport PID\n\nimport serial\n\nimport binmom\nimport controlVi... | [
[
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eliask/georinex | [
"f2d9c03cecbe1ecd27a17eb0fc7957ac69c1a34c"
] | [
"georinex/nav2.py"
] | [
"#!/usr/bin/env python\nfrom pathlib import Path\nfrom datetime import datetime\nfrom typing import Dict, Union, Any, Sequence\nfrom typing.io import TextIO\nimport xarray\nimport numpy as np\nimport logging\n\nfrom .io import opener, rinexinfo\nfrom .common import rinex_string_to_float\n#\nSTARTCOL2 = 3 # column ... | [
[
"numpy.asarray",
"numpy.hstack",
"numpy.nonzero",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ndevenish/dxtbx | [
"2e3fff616dd99e5e7557e9774e4357bacae59f1b"
] | [
"command_line/plot_detector_models.py"
] | [
"# LIBTBX_PRE_DISPATCHER_INCLUDE_SH export PHENIX_GUI_ENVIRONMENT=1\nfrom __future__ import absolute_import, division, print_function\n\nimport os\nimport sys\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom matplotlib.patches import FancyArrowPatch\... | [
[
"matplotlib.backends.backend_pdf.PdfPages",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.axes",
"matplotlib.patches.FancyArrowPatch.draw",
"matplotlib.pyplot.get_fignums",
"matplotlib.patches.FancyArrowPatch.__init__",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"ma... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Spico197/DocEE | [
"d6b585e29e5908b891e765066b96ff7642587e5a"
] | [
"dee/tasks/dee_task.py"
] | [
"import copy\nimport glob\nimport logging\nimport os\nfrom itertools import combinations, product\n\nimport torch\nimport torch.distributed as dist\nimport torch.optim as optim\nfrom loguru import logger\nfrom tqdm import tqdm\nfrom transformers.models.bert.modeling_bert import BertConfig\n\nimport dee.models\nfrom... | [
[
"torch.cat",
"torch.optim.AdamW",
"torch.no_grad",
"torch.distributed.get_rank",
"torch.distributed.get_world_size"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
valeriupredoi/cmip5datafinder | [
"ac411f4194bac1f56decabec0a59ee349ed39f5e"
] | [
"cmip5datafinder_v2.py"
] | [
"#!/home/valeriu/sdt/bin/python\n\n\"\"\"\ncmip5datafinder.py\nPython 2.7.13\nScript that searches for data locally and on valid ESGF nodes. It builds \ncache files using the results of the search.\n\n\"\"\"\n\n# -------------------------------------------------------------------------\n# Setup.\n# -----------... | [
[
"matplotlib.pyplot.title",
"numpy.unique",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.genfromtxt",
"numpy.mean",
"numpy.savetxt",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jmaslek/etf_scraper | [
"e4eb4eda035541340e0abd18cb267cd715b76727"
] | [
"scrape_data.py"
] | [
"import requests\nimport pandas as pd\nimport json\nfrom bs4 import BeautifulSoup as bs\nfrom bs4 import BeautifulSoup\nimport numpy as np\n\n\ndef assets_to_num(x):\n x = x.strip(\"$\")\n if x.endswith(\"M\"):\n return float(x.strip(\"M\"))\n elif x.endswith(\"B\"):\n return float(x.strip(\"... | [
[
"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": []
}
] |
peytondmurray/mx3tools | [
"0f367c0c1c0ebb1887bf105555bd2a1edb9fc654"
] | [
"test_vis.py"
] | [
"\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nmatplotlib.use('Qt5Agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.collections as collections\nimport matplotlib.patches as patches\nimport matplotlib.animation as animation\nimport mx3tools.ovftools as ovftools\nimport mx3tools.statutil as st... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.use",
"numpy.set_printoptions",
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MDAnalysis/pytng | [
"65b4c0c915fe60525d9b2dacb83c2c5d73b3f13f"
] | [
"tests/conftest.py"
] | [
"from collections import namedtuple\nimport numpy as np\nimport os\nimport pytest\n\nHERE = os.path.dirname(__file__)\n\n\n@pytest.fixture()\ndef CORRUPT_FILEPATH():\n # actually just an ascii file\n return os.path.join(HERE, \"reference_files\", \"badtngfile.tng\")\n\n\n@pytest.fixture()\ndef MISSING_FILEPAT... | [
[
"numpy.eye",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vluzko/652-project | [
"e8129a7e451998676ecdd4c2a7283bbd22074e72"
] | [
"gail_and_bc/behavior_clone.py"
] | [
"\"\"\"This code is based heavily on OpenAI's TRPO implementation.\nSee here for the original code: https://github.com/openai/baselines/tree/master/baselines/trpo_mpi/.\n\"\"\"\n\nimport argparse\nimport tempfile\nimport os.path as osp\nimport gym\nimport logging\nfrom tqdm import tqdm\n\nimport tensorflow as tf\n\... | [
[
"tensorflow.square"
]
] | [
{
"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... |
itzsimpl/NeMo | [
"c03f87d47fc57abc89c0ebf859fccba397dd0f8e"
] | [
"nemo/collections/nlp/data/language_modeling/megatron/retro_dataset.py"
] | [
"# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"torch.manual_seed",
"torch.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
microsoft/HuRL | [
"c9d0710ff6fd67b3cdbd46fc031cdbc3b3738cd2"
] | [
"hurl/rl_utils.py"
] | [
"# Some helper functions for using garage\n\n\nimport numpy as np\nimport torch\n\nfrom garage.torch.policies import GaussianMLPPolicy, TanhGaussianMLPPolicy, DeterministicMLPPolicy\nfrom garage.torch.q_functions import ContinuousMLPQFunction\nfrom garage.torch.value_functions import GaussianMLPValueFunction\nfrom ... | [
[
"numpy.cumsum",
"numpy.exp",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WildbookOrg/wbia-plugin-deepsense | [
"d452da23673e4bf97eb5e8e9b4844e729e6c2706"
] | [
"wbia_deepsense/_plugin.py"
] | [
"# -*- coding: utf-8 -*-\nimport logging\nfrom os.path import abspath, exists, join, dirname, split, splitext\nimport wbia\nfrom wbia.control import controller_inject, docker_control\nfrom wbia.constants import ANNOTATION_TABLE\nfrom wbia.web.apis_engine import ensure_uuid_list\nimport wbia.constants as const\nimpo... | [
[
"numpy.rot90",
"numpy.all",
"numpy.concatenate",
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.sum",
"numpy.hypot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sunutf/TSM | [
"fe42b612e0d39a61dbebf8aa6a0b93e62ec4a858"
] | [
"ops/channel_non_local.py"
] | [
"# Non-local block using embedded gaussian\n# Code from\n# https://github.com/AlexHex7/Non-local_pytorch/blob/master/Non-Local_pytorch_0.3.1/lib/non_local_embedded_gaussian.py\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass _NonLocalBlockND(nn.Module):\n def __init__(self, in... | [
[
"torch.nn.Sequential",
"torch.nn.functional.softmax",
"torch.zeros",
"torch.nn.init.constant_",
"torch.randn",
"torch.matmul",
"torch.nn.MaxPool3d",
"torch.nn.MaxPool2d",
"torch.nn.MaxPool1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CVanchieri/forty-jekyll-theme | [
"454941578bb3e0d0f5d4db75907ba7df036b925a"
] | [
"posts/DecisionTreeFromScratchPost/dt.py"
] | [
"# necessary imports\r\nimport numpy as np\r\n\r\n\"\"\"\r\n### Decision Tree Class ###\r\nDecision trees are one way to display an algorithm that only contains conditional control statements,\r\ncommonly used in operations research, specifically in decision analysis, to help identify a strategy\r\nmost likely to r... | [
[
"numpy.unique",
"numpy.argmax",
"numpy.column_stack",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Healthedata1/pubsub-endpoint | [
"89169850b0f05c92498061a7efe2c696cbd35029"
] | [
"app.py"
] | [
"# A very simple Flask app to get started with using\n# FHIR Subscriptions\n# This is a reciever for the FHIR R4 Server URL (https://subscriptions.argo.run/)\n# with an ednpoint = \"http://healthedatainc2.pythonanywhere.com/webhook\"\n# It just saves the subscription notification data to a flat csv file \"data.csv\... | [
[
"pandas.read_csv",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dbddqy/robot_kinematics | [
"55cb956869ac805b0caf629c40216e3149b3fd1a"
] | [
"examples/trajectory.py"
] | [
"#!/usr/bin/env python3\n\nfrom visual_kinematics.RobotSerial import *\nfrom visual_kinematics.RobotTrajectory import *\nimport numpy as np\nfrom math import pi\n\n\ndef main():\n np.set_printoptions(precision=3, suppress=True)\n\n dh_params = np.array([[0.163, 0., 0.5 * pi, 0.],\n [0... | [
[
"numpy.set_printoptions",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elbeejay/pyvista | [
"ad469624e19cfa7c94a551475708e547d1341fa1"
] | [
"tests/test_utilities.py"
] | [
"\"\"\" test pyvista.utilities \"\"\"\nimport os\n\nimport numpy as np\nimport pytest\nimport vtk\n\nimport pyvista\nfrom pyvista import examples as ex\nfrom pyvista.utilities import errors\nfrom pyvista.utilities import fileio\nfrom pyvista.utilities import helpers\n\n# Only set this here just the once.\npyvista.s... | [
[
"numpy.random.random",
"numpy.allclose",
"numpy.linspace",
"numpy.meshgrid",
"numpy.arange",
"numpy.save",
"numpy.random.rand",
"numpy.any",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sahar2/qiskit-terra | [
"19fbaeb68f2b279c9748384e919e1d1b006860f2"
] | [
"qiskit/visualization/state_visualization.py"
] | [
"# -*- coding: utf-8 -*-\n\n# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2017, 2018.\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... | [
[
"numpy.imag",
"numpy.sqrt",
"numpy.linspace",
"numpy.max",
"numpy.any",
"numpy.cross",
"matplotlib.patches.FancyArrowPatch.__init__",
"numpy.exp",
"matplotlib.pyplot.tight_layout",
"numpy.ones_like",
"numpy.arange",
"numpy.sin",
"scipy.linalg.eigh",
"numpy.r... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
Huynhanh883/twitterBot | [
"6067d89abf65c6540254e7636d8f818f7f4ef08c"
] | [
"analyze_stat.py"
] | [
"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jul 8 14:52:08 2019\r\n\r\n@author: edoardottt\r\n\r\nThis file contains code for analyze the database.\r\nIt uses matplotlib library to displays the result.\r\nIt shows a chart with likes, retweets, followers per day.\r\n\r\nThis file ... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhenzonglei/dnnbrain | [
"1808f08d2497df59ea695a1a0cd16c0129fbebbf"
] | [
"dnnbrain/dnn/base.py"
] | [
"import os\nimport cv2\nimport torch\nimport numpy as np\n\nfrom PIL import Image\nfrom os.path import join as pjoin\nfrom copy import deepcopy\nfrom sklearn.decomposition import PCA\nfrom sklearn.linear_model import LinearRegression, LogisticRegression, Lasso\nfrom sklearn.svm import SVC\nfrom sklearn.model_select... | [
[
"sklearn.model_selection.cross_val_score",
"sklearn.linear_model.LogisticRegression",
"numpy.histogram",
"torch.zeros",
"torch.cat",
"numpy.median",
"numpy.linalg.norm",
"torch.unsqueeze",
"sklearn.linear_model.Lasso",
"numpy.max",
"numpy.argmax",
"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"
... |
crioso/PICwriter | [
"24b4ca37361899cba9d23c057b14429055a3da0f"
] | [
"picwriter/components/disk.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport numpy as np\nimport gdspy\nimport uuid\nimport picwriter.toolkit as tk\n\nclass Disk(gdspy.Cell):\n \"\"\" Disk Resonator Cell class (subclass of gdspy.Cell).\n\n Args:\n * **wg... | [
[
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ducky-hong/pytorch-dcase-task3 | [
"9272ce7b5ac2f4838908704f38d0392d519262c2"
] | [
"data_loader/data_loaders.py"
] | [
"import os\nimport glob\nimport itertools\nimport numpy as np\nimport torch\nfrom base import BaseDataLoader\nfrom torch.utils.data import Dataset, DataLoader\n \nclass FeatureNpyDataset(Dataset):\n def __init__(self, root_dir, datasets, transform=None):\n self.data = list(itertools.chain(*[glob.gl... | [
[
"numpy.load",
"numpy.expand_dims",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dustindall/idi-model | [
"5d026f4756f03f9cb797de5a8f0c3c6d2b349ccb"
] | [
"tests/models/policy_models/disabled_deterministic_res/test_disabled_deterministic_res.py"
] | [
"import os\n\nimport pandas as pd\nimport pytest\nfrom footings.audit import AuditConfig, AuditStepConfig\nfrom footings.testing import assert_footings_files_equal\n\nfrom footings_idi_model.models import DValResRPMD\n\nCASES = [\n (\n \"test_1\",\n {\n \"valuation_dt\": pd.Timestamp(\"2... | [
[
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
albertovilla/transformers | [
"47a98fc4cb6a561576309a57b315b042977d194c"
] | [
"src/transformers/models/deberta/modeling_deberta.py"
] | [
"# coding=utf-8\n# Copyright 2020 Microsoft and the Hugging Face Inc. 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... | [
[
"torch.softmax",
"torch.nn.Dropout",
"torch.ones",
"torch.nn.CrossEntropyLoss",
"torch.nn.LogSoftmax",
"torch.zeros",
"torch.sqrt",
"torch.empty_like",
"torch.zeros_like",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch._softmax_backward_data",
"torch.nn.Line... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hanna-hansen/MuseGAN | [
"77937b2506003037ea3989a7e4cf9980817339ad"
] | [
"model.py"
] | [
"import torch\nfrom torch import nn\n\n######################################\n#### Helper functions ######\n######################################\ndef initialize_weights(layer, mean=0.0, std=0.02):\n if isinstance(layer, nn.Conv3d) or isinstance(layer, nn.ConvTranspose2d):\n torch.nn.init.norm... | [
[
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.ModuleDict",
"torch.nn.Flatten",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.nn.Conv3d",
"torch.nn.init.normal_",
"torch.nn.LeakyReLU",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pulsarise/SCAMP-I | [
"a814d9851075b4a89a09bd772e9dcf5dfb26788a"
] | [
"parameter_extraction.py"
] | [
"#! /usr/bin/env python3\n\nimport numpy as np\nimport emcee\nimport argparse\nimport pandas\n\nfrom SCAMP_I.dmcorrcalc import get_old_new_DM\n\ndef get_bestfit_params(flat_burned_samples, P0, nbins):\n # Get the best fit params out from the chains.\n pc = np.percentile(flat_burned_samples[:, 0], [16, 50, 84]... | [
[
"pandas.read_csv",
"numpy.percentile",
"numpy.diff",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lks1248/SPH-EXA | [
"0cac399d43118bda1ed2a5e42b593eb18ca55c30"
] | [
"scripts/slice.py"
] | [
"#!/usr/bin/env python3\n\nimport h5py\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport sys\n\n\ndef printSteps(fname):\n \"\"\" Display contents of HDF5 file: step, iteration and time \"\"\"\n ifile = h5py.File(fname, \"r\")\n print(fname, \"contains the following steps:\")\n print(\"hdf5... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.cm.get_cmap",
"matplotlib.pyplot.subplots",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
q1135718080/yolov4-tiny | [
"d5248327da3ff56563e42b3786ed6a40ab9310df"
] | [
"nets/yolo_training.py"
] | [
"import math\nfrom random import shuffle\n\nimport cv2\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom matplotlib.colors import hsv_to_rgb, rgb_to_hsv\nfrom PIL import Image\nfrom utils.utils import bbox_iou, merge_bboxes\n\n\ndef jaccard(_box_a, _box_b):\n b1_x1, ... | [
[
"numpy.minimum",
"torch.max",
"torch.zeros",
"torch.cat",
"torch.sum",
"numpy.concatenate",
"torch.FloatTensor",
"torch.pow",
"numpy.zeros",
"torch.ones_like",
"torch.sigmoid",
"torch.linspace",
"torch.floor",
"torch.min",
"torch.zeros_like",
"torch.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
koko1996/EECS-4415-NYC-Taxi-Uber | [
"0e45a57f58a3fa2bd2513ce6994c552017b7a708"
] | [
"src/analysis_html/sparkplot-residential-analysis.py"
] | [
"from pyspark import SparkConf,SparkContext\nfrom pyspark.sql import SQLContext\nfrom pyspark.sql.types import *\nfrom operator import add\nimport pyspark\nimport sys\nimport requests\nfrom pprint import pprint\nimport pandas as pd\nimport numpy as np\nfrom timescaleplot import graph\nfrom dateutil.parser import pa... | [
[
"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": []
}
] |
tejalkotkar/web-scraping-challenge | [
"583f03218498e4d0546ff4e7a92d4a26b85dcdf1"
] | [
"Missions_to_Mars/scrape_mars.py"
] | [
"from splinter import Browser\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nfrom webdriver_manager.chrome import ChromeDriverManager\n\ndef init_browser():\n # @NOTE: Replace the path with your actual path to the chromedriver\n # executable_path = {\"executable_path\": \"chromedriver.exe\"}\n execut... | [
[
"pandas.read_html"
]
] | [
{
"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": []
}
] |
Giyn/DoubanMovieRecommendationSystem | [
"f5a4b017a97f72ddbc8657e7d25a7093ac9e6fa2"
] | [
"GUI/movie_detailed.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 14 09:26:03 2020\n\n@author: 许继元\n\"\"\"\n\nimport sys\n\nimport pandas as pd\nfrom PyQt5.QtGui import QIcon, QPixmap\nfrom PyQt5.QtWidgets import QApplication\nfrom PyQt5.QtWidgets import QHBoxLayout\nfrom PyQt5.QtWidgets import QLabel\nfrom PyQt5.QtWidgets impo... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
arvinsahni/ml4 | [
"5f9f4c5ed9f60abc842e0696ddbf41305919df5f"
] | [
"flask/app/viz_1.py"
] | [
"from __future__ import division\n\nfrom flask import render_template, request, Response, jsonify, send_from_directory\nfrom app import app\n\nimport json\nimport psycopg2\nimport os\nimport sys\nimport psycopg2.extras\nimport pandas as pd\n\nmodule_path = os.path.abspath(os.path.join('../'))\nif module_path not in... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
DariusTorabian/lyrics-classifier | [
"f15e6d48c80ed7101080565d8061cda1766b57a3"
] | [
"src/lyrics_scraper.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n'''\nThis module scrapes lyrics and songtitles of a given artist and saves them\nin a JSON file.\n'''\n\nimport random\nfrom time import sleep\nimport re\nimport argparse\nimport warnings\nimport requests\nfrom bs4 import BeautifulSoup\nfrom requests.adapters import HTTPAd... | [
[
"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": []
}
] |
QUAPNH/NucDetSeg | [
"5b0400ca5dc98d09beca36d46cc55bfabb9ce4e0"
] | [
"seg_utils/seg_transforms.py"
] | [
"import numpy as np\nfrom numpy import random\nimport cv2\nimport torch\n\n\nclass Compose(object):\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, img, bboxes=None, labels=None, masks=None):\n for t in self.transforms:\n img, bboxes, labels, ma... | [
[
"numpy.maximum",
"numpy.minimum",
"torch.Tensor",
"numpy.clip",
"numpy.random.choice",
"numpy.random.uniform",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HyperbolicTangent/HAR | [
"2fd03ff7b1fb138159079b991698184da2affd22"
] | [
"input_pipeline/S2S.py"
] | [
"import gin\r\nimport logging\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport zipfile\r\nimport os\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom sklearn import preprocessing\r\n\r\nAUTOTUNE = tf.data.experimental.AUTOTUNE\r\n\r\nfirst_time_run = False\r\n\r\ndef one_hot_coding(x):... | [
[
"tensorflow.io.TFRecordWriter",
"tensorflow.train.Example",
"tensorflow.data.TFRecordDataset",
"tensorflow.data.Dataset.from_tensor_slices",
"numpy.set_printoptions",
"numpy.eye",
"tensorflow.io.parse_single_example",
"tensorflow.io.decode_raw",
"tensorflow.reshape",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andremoeller/distributed | [
"9622b8f9bef1855412e9b23265378e2da1f47f2f"
] | [
"distributed/protocol/tests/test_numpy.py"
] | [
"import sys\nfrom zlib import crc32\n\nimport numpy as np\nimport pytest\n\nfrom distributed.protocol import (\n serialize,\n deserialize,\n decompress,\n dumps,\n loads,\n to_serialize,\n msgpack,\n)\nfrom distributed.protocol.utils import BIG_BYTES_SHARD_SIZE\nfrom distributed.protocol.numpy ... | [
[
"numpy.testing.assert_equal",
"numpy.random.random",
"numpy.arange",
"numpy.memmap",
"numpy.dtype",
"numpy.ones",
"numpy.broadcast_to",
"numpy.ma.masked_array",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miltonbd/panoptic_segmentation | [
"beb6211c2cc92cdbb7ae891d48d30996a1ec8150"
] | [
"loader/isic_skin_lesion_loader.py"
] | [
"import os\nimport collections\nimport torch\nimport torchvision\nimport numpy as np\nimport scipy.misc as m\nimport matplotlib.pyplot as plt\n\nfrom torch.utils import data\nfrom ptsemseg.augmentations import *\n\nclass IsicSkinLoader(data.Dataset):\n def __init__(self, root, split=\"train\", \n ... | [
[
"scipy.misc.imresize",
"torch.utils.data.DataLoader",
"matplotlib.pyplot.subplots",
"torch.from_numpy",
"scipy.misc.imread",
"matplotlib.pyplot.close",
"numpy.transpose",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
mattking-smith/gpmp2 | [
"48c41c142df062832ffbe9792f0cbecdb36485cc"
] | [
"gpmp2_python/gpmp2_python/utils/signedDistanceField2D.py"
] | [
"import numpy as np\nfrom gtsam import *\nfrom gpmp2 import *\nimport numpy as np\nfrom gtsam import *\nfrom gpmp2 import *\nimport math\nfrom scipy import ndimage\n\n\ndef signedDistanceField2D(ground_truth_map, cell_size):\n # SIGNEDDISTANCEFIELD2D 2D signed distance field\n # Given a ground truth 2D map ... | [
[
"numpy.amax",
"scipy.ndimage.distance_transform_edt",
"numpy.ones"
]
] | [
{
"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"... |
vandermeerlab/nept | [
"fcb0b83d30f4be2783f3e8a9b3c842e4eef4426b"
] | [
"nept/loaders_neuralynx.py"
] | [
"# -*- coding: utf-8 -*-\n# Adapted from nlxio written by Bernard Willards <https://github.com/bwillers/nlxio>\n\nimport numpy as np\nimport nept\n\n\ndef load_events(filename, labels):\n \"\"\"Loads neuralynx events\n\n Parameters\n ----------\n filename: str\n labels: dict\n With event name ... | [
[
"numpy.fromfile",
"numpy.unique",
"numpy.arange",
"numpy.dtype",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PanyiDong/AutoML | [
"510727bd797e4f6fa213939c62d1d7601952e491"
] | [
"My_AutoML/_imputation/_multiple.py"
] | [
"\"\"\"\nFile: _multiple.py\nAuthor: Panyi Dong\nGitHub: https://github.com/PanyiDong/\nMathematics Department, University of Illinois at Urbana-Champaign (UIUC)\n\nProject: My_AutoML\nLatest Version: 0.2.0\nRelative Path: /My_AutoML/_imputation/_multiple.py\nFile Created: Tuesday, 5th April 2022 11:50:03 pm\nAutho... | [
[
"sklearn.ensemble.RandomForestRegressor",
"numpy.abs",
"numpy.random.seed",
"sklearn.linear_model.LogisticRegression",
"pandas.DataFrame",
"sklearn.neighbors.KNeighborsRegressor",
"numpy.random.normal",
"numpy.nanmean",
"sklearn.linear_model.LinearRegression",
"sklearn.line... | [
{
"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": []
}
] |
meipham/transformers | [
"6d873bb7c72495c594791b037d774552353ad95e"
] | [
"src/transformers/models/roberta/modeling_roberta.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.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.zeros",
"torch.cat",
"torch.einsum",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.matmul",
"torch.tanh",
"torch.nn.BCEWithLogi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TranHuyHoang18/GCP_TF- | [
"486545591dfdcfe628532c2f6640d1b9a9652522"
] | [
"official/nlp/modeling/layers/text_layers.py"
] | [
"# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.ensure_shape",
"tensorflow.strings.regex_replace",
"tensorflow.lookup.TextFileInitializer",
"tensorflow.nn.relu",
"tensorflow.executing_eagerly",
"tensorflow.Graph",
"tensorflow.shape",
"tensorflow.io.gfile.GFile",
"tensorflow.cast",
"tensorflow.reshape",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AaronDJohnson/enterprise | [
"d964464bba699a9455897d890a97c40f25c5b004"
] | [
"enterprise/signals/gp_signals.py"
] | [
"# gp_signals.py\n\"\"\"Contains class factories for Gaussian Process (GP) signals.\nGP signals are defined as the class of signals that have a basis\nfunction matrix and basis prior vector..\n\"\"\"\n\nimport functools\nimport itertools\nimport logging\n\nimport numpy as np\nimport scipy.sparse as sps\nfrom skspar... | [
[
"numpy.dot",
"numpy.log",
"numpy.ones",
"numpy.zeros_like",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lrq3000/vaex | [
"923911f47e7324335dfd84bc58a14d4cd6eb7ee6"
] | [
"tests/dataset_test.py"
] | [
"from io import BytesIO\nimport pickle\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nimport pyarrow.parquet\n\nimport vaex\nimport vaex.dataset as dataset\n\nHERE = Path(__file__).parent\n\n\ndef rebuild(ds):\n # pick and unpickle\n f = BytesIO()\n picked = pickle.dump(ds, f)\n f.seek(... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
beringresearch/label-studio | [
"ab8b9b5605ec9eab76c4f90967874898239ed94e"
] | [
"label_studio/ml/examples/pytorch_transfer_learning.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport time\nimport os\nimport numpy as np\nimport requests\nimport io\nimport hashlib\n\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import models, transforms\n\nfrom label_studio.ml import LabelStudioM... | [
[
"torch.nn.CrossEntropyLoss",
"torch.max",
"torch.load",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.nn.Linear",
"numpy.argmax",
"torch.no_grad",
"torch.cuda.is_available",
"torch.optim.lr_scheduler.StepLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JingChunzhen/Paddle | [
"1bce7caabc1c5e55b1fa13edb19719c397803c43"
] | [
"python/paddle/fluid/tests/unittests/test_inplace.py"
] | [
"# Copyright (c) 2020 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 ... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cbi-bioinfo/AGRAP | [
"f2ff7817e095109a351c71cf3d92f503a76fbeeb"
] | [
"workspace/pythonScripts/feature_selection_terminal.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\nimport os\nimport sys\nimport pandas as pd\nimport numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.linear_model import LogisticRegression\nf... | [
[
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.ensemble.RandomForestClassifier",
"pandas.Series",
"sklearn.svm.SVR",
"sklearn.feature_selection.RFE",
"sklearn.svm.LinearSVC",
"sklearn.feature_selection.SelectFromModel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
khurrammaqbool/SVDB | [
"a62a9308c308d1410e68fa9c16d0a4044aacee8b"
] | [
"tests/test_dbscan.py"
] | [
"import unittest\nimport numpy\n\nfrom svdb.DBSCAN import main\n\n\nclass TestDBSCAN(unittest.TestCase):\n\n #test that distant points are not merged\n def test_distant_points(self):\n data = numpy.array([[1,1],[1,101]])\n epsilon=100\n m=2\n result=main(data,epsilon,m)\n as... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marcio081010/TensorFlowProject | [
"ea0b6be8d63c3b8d86ef7135613170275cff017d"
] | [
"opencv_group_detection.py"
] | [
"import os\r\nimport cv2\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom picamera.array import PiRGBArray\r\nfrom picamera import PiCamera\r\nimport tensorflow as tf\r\nimport argparse\r\nimport sys\r\nimport time\r\nimport csv\r\n\r\n######## BOILERPLATE CODE #######\r\n# Set up camera constants\r\n#IM_WIDTH ... | [
[
"tensorflow.Graph",
"tensorflow.import_graph_def",
"numpy.expand_dims",
"tensorflow.gfile.GFile",
"numpy.squeeze",
"tensorflow.compat.v1.Session",
"numpy.copy",
"tensorflow.compat.v1.GraphDef"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GabrielSCabrera/ComputationalPhysics2 | [
"a840b97b651085090f99bf6a11abab57100c2e85"
] | [
"doc/src/MCsummary/src/mpqdot.py"
] | [
"# 2-electron VMC code for 2dim quantum dot with importance sampling\n# No Coulomb interaction\n# Using gaussian rng for new positions and Metropolis- Hastings \n# Energy minimization using standard gradient descent \n\n# Common imports\nimport os\n\n# Where to save the figures and data files\nPROJECT_ROOT_DIR = \"... | [
[
"matplotlib.pyplot.title",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.zeros",
"matplotlib.pyplot.ylabel"
]
] | [
{
"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": []
}
] |
dulex123/dqn-atari | [
"f5c8b6c0fd32becd026b83addc7b4b4f3004c0fc"
] | [
"env_loop.py"
] | [
"import gym\nimport numpy as np\nfrom utils import preprocess_frame\nfrom DeepQAgent import DeepQAgent\n\nBATCH_SIZE = 32\nBUFFER_START_SIZE = 40\nBUFFER_SIZE = 100\nTARGET_UPDATE = 50\nNUM_EPISODES = int(2e5)\nMAX_STEPS = int(1e6)\nGAMMA = 0.99\nEPSILON_DECAY_STEPS = int(1e6)\nMIN_EPS = 0.1\n\n\nenv = gym.make(\"B... | [
[
"numpy.max",
"numpy.random.random",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wjsutton/life_expectancy_in_chess | [
"58327bd04c4602faf939fc062a559293170a137d"
] | [
"chess_death_positions.py"
] | [
"from itertools import islice, cycle\nimport chess.pgn\nimport pandas as pd\npd.options.mode.chained_assignment = None # default='warn'\nimport numpy as np\nimport glob\nimport datetime\n\nwith open('data/standard_matches/lichess_db_standard_rated_2013-01.pgn') as pgn:\n for file in range(10000):\n\n sta... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.concat",
"pandas.DataFrame",
"numpy.select",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
bocklab/CloudVolumeServer | [
"9fda49e72d338612ce336fb6fce719489974e170"
] | [
"process.py"
] | [
"import multiprocessing as mp\nimport numpy as np\nimport pandas as pd\n\n\ndef _get_ids(vol, bl, co):\n \"\"\"Fetch block and extract IDs.\n\n Parameters\n ----------\n vol : CloudVolume\n Volume to query.\n bl : list-like\n Coordinates defining the block:\... | [
[
"numpy.hstack",
"pandas.DataFrame",
"numpy.max",
"pandas.cut",
"numpy.array",
"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": []
}
] |
mwillsey/incubator-tvm | [
"e02dc69fef294eb73dd65d18949ed9e108f60cda"
] | [
"tests/python/relay/test_backend_interpreter.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.array",
"numpy.random.rand",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
grainpowder/gru-forward-numpy-app | [
"efd24f9f397d51e7e18bdad5cba12451ad69d3de"
] | [
"src/npgru/predictor/tensorflow_predictor.py"
] | [
"from typing import List, Tuple\n\nimport sentencepiece as spm\nimport tensorflow as tf\nimport tensorflow.keras as keras\n\nfrom npgru.predictor.category_predictor import CategoryPredictor\nfrom npgru.preprocessor.model_file import get_model_dir\n\n\nclass TensorflowPredictor(CategoryPredictor):\n\n def __init_... | [
[
"tensorflow.constant"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.4",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1.2",
"2.... |
tanlin2013/TNpy | [
"bc450825f79b6a95ad724ed05c61fda8e0545975"
] | [
"tnpy/finite_dmrg.py"
] | [
"import time\nimport logging\nimport numpy as np\nfrom tensornetwork import Node\nfrom itertools import count\nfrom tnpy.finite_algorithm_base import FiniteAlgorithmBase\nfrom tnpy.linalg import svd, eigshmv\nfrom tnpy.operators import MPO\nfrom typing import Iterable, Union, Tuple\n\n\nclass FiniteDMRG(FiniteAlgor... | [
[
"numpy.diagflat",
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlexImb/automl-streams | [
"f730918f2a006def405b4c8c96b7849adc23eb2a"
] | [
"demos/tpot/results/batch_pipeline_covtype.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import BernoulliNB\nfrom sklearn.pipeline import make_pipeline, make_union\nfrom tpot.builtins import StackingEstimator\n\n# NOTE: Make sure tha... | [
[
"sklearn.ensemble.ExtraTreesClassifier",
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"sklearn.naive_bayes.BernoulliNB"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
cypherics/ttAugment | [
"149759f9c1ef86b7d0f5e634dd4ed16f868f56e5"
] | [
"tt_augment/tt_custom/tt_fwd_bkd.py"
] | [
"import cv2\nimport numpy as np\nfrom imgaug import imresize_single_image\n\nfrom imgaug.augmenters import sm, meta\nfrom imgaug.augmenters.flip import fliplr\n\n\nclass MirrorFWD(meta.Augmenter):\n \"\"\"\n Mirror the pixel to get to network_dimension\n \"\"\"\n\n def __init__(self, network_dimension: ... | [
[
"numpy.rot90",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dazzid/Deep_Learning_Techniques_Applied_to_Estimate_Music_Gestural_Patterns | [
"4a61a3d85429a978cb520a9efacee537747f813d"
] | [
"L3_Conv_LSTM_Model.py"
] | [
"# convlstm model\nimport numpy as np\nimport csv\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n# load a single file as a numpy array\ndef load_file(filepath):\n data = []\n with open(filepath) as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n f... | [
[
"matplotlib.pyplot.legend",
"tensorflow.keras.Sequential",
"numpy.mean",
"tensorflow.keras.layers.ConvLSTM2D",
"matplotlib.pyplot.gca",
"numpy.std",
"matplotlib.pyplot.axis",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.utils.plot_mo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
gasperpodobnik/nnUNet | [
"f11906b13344db9f54e303378748a0defdea8331"
] | [
"nnunet/utilities/overlay_plots.py"
] | [
"# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany\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\... | [
[
"matplotlib.pyplot.imsave",
"numpy.unique",
"numpy.tile",
"numpy.copy",
"numpy.argmax",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wannabeOG/NLP-Fall-2021-Course-Project | [
"4a4e46733915c09ecf1389e6aea50f93f8fd34f1"
] | [
"fairseq/options.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport argparse\n\nimport torch\nimport sys\n\nfrom fairseq import utils\nfrom fairseq.data.indexed_dataset import get_available_dat... | [
[
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
L30bigdick/Nashpy | [
"2033b4ffe6804efb50a324116e7c3776932546f3"
] | [
"src/nashpy/utils/is_best_response.py"
] | [
"\"\"\"Functions for testing of best responses\"\"\"\nimport numpy as np\n\n\ndef is_best_response(A: np.ndarray, sigma_c: np.ndarray, sigma_r: np.ndarray) -> bool:\n \"\"\"\n Checks if sigma_r is a best response to sigma_c when A is the payoff matrix\n for the player playing sigma_r.\n\n Parameters\n ... | [
[
"numpy.max"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ParikhKadam/zenml | [
"867e4d4c982a50447bd182b30af37f2141dac5a4",
"867e4d4c982a50447bd182b30af37f2141dac5a4"
] | [
"legacy/steps/split/utils_test.py",
"legacy/steps/split/split_step_test.py"
] | [
"# Copyright (c) ZenML GmbH 2020. 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.train.BytesList",
"tensorflow.train.FloatList",
"tensorflow.train.Features",
"tensorflow.train.Int64List"
],
[
"tensorflow.train.Features",
"tensorflow.train.BytesList"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yhl48/transformers | [
"b320d87eceb369ea22d5cd73866499851cb2cca3"
] | [
"src/transformers/models/wav2vec2/modeling_wav2vec2.py"
] | [
"# coding=utf-8\n# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. 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... | [
[
"torch.nn.functional.softmax",
"torch.nn.init.uniform_",
"torch.nn.functional.glu",
"torch.nn.functional.dropout",
"torch.zeros",
"torch.cat",
"torch.FloatTensor",
"numpy.random.randint",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.mm",
"torch.ones",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AI4medicine-Berlin/explainable-predictive-models | [
"994134700eae6291cfe0cbd547e941018b00548b"
] | [
"code/utils/models.py"
] | [
"\"\"\"\nFile name: models.py\nAuthor: Esra Zihni\nDate created: 21.05.2018\n\nThis file contains the Model metaclass object that is used for implementing \nthe given models. It contains a class object for each individual model type.\n\"\"\"\n\nimport os\nimport pickle\nfrom abc import ABCMeta, abstractmethod\nfrom... | [
[
"sklearn.naive_bayes.GaussianNB",
"sklearn.linear_model.LogisticRegression",
"numpy.random.seed",
"sklearn.model_selection.ParameterGrid",
"numpy.mean",
"sklearn.svm.SVC",
"tensorflow.set_random_seed",
"numpy.ravel",
"sklearn.linear_model.SGDClassifier",
"pandas.get_dummies... | [
{
"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": [
"1.10",
"1.12",
"1.4",
... |
l-bat/nncf | [
"6258916cd5fa7fc010ad09da63113354358bffd8",
"6258916cd5fa7fc010ad09da63113354358bffd8"
] | [
"tests/test_nncf_network.py",
"tests/quantization/test_hw_config.py"
] | [
"\"\"\"\n Copyright (c) 2019-2020 Intel Corporation\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 http://www.apache.org/licenses/LICENSE-2.0\n Unless required by applicable law o... | [
[
"torch.ones",
"torch.nn.ConvTranspose2d",
"torch.zeros",
"torch.eq",
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.FloatTensor",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d",
"torch.chunk",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Depersonalizc/nerf-pytorch | [
"dbc0211a059834ae078eb3a99ad8e20bd1171d2f"
] | [
"nerf/volume_rendering_utils.py"
] | [
"import torch\n\nfrom .nerf_helpers import cumprod_exclusive\n\n\ndef volume_render_radiance_field(\n radiance_field,\n depth_values,\n ray_directions,\n radiance_field_noise_std=0.0,\n white_background=False,\n\n render_rgb=True,\n render_disp=True,\n render_acc=True,\n render_depth=True... | [
[
"torch.sigmoid",
"torch.randn",
"torch.tensor",
"torch.exp",
"torch.nn.functional.relu",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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