repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
parmeet/text | [
"1fb2aedb48b5ecc5e81741e7c8504486b91655c6"
] | [
"test/legacy/test_vocab.py"
] | [
"# -*- coding: utf-8 -*-\nfrom collections import Counter\nimport os\nimport pickle\n\n\nimport numpy as np\nimport torch\nfrom torchtext.legacy import vocab\n\nfrom ..common.torchtext_test_case import TorchtextTestCase\n\n\ndef conditional_remove(f):\n if os.path.isfile(f):\n os.remove(f)\n\n\nclass Test... | [
[
"numpy.array",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
albert17/models | [
"bc2500a1872c6cfc4c7f2ab90d6583759c0ccb8a"
] | [
"merlin/models/tf/blocks/core/index.py"
] | [
"#\n# Copyright (c) 2021, NVIDIA CORPORATION.\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... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.matmul",
"tensorflow.TensorShape",
"tensorflow.concat",
"tensorflow.range",
"tensorflow.shape",
"pandas.RangeIndex",
"tensorflow.expand_dims",
"tensorflow.keras.utils.register_keras_serializable",
"tensorflow.squeeze",
"tensor... | [
{
"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": []
}
] |
microsoft/PySiamTracking | [
"a82dabeaa42a7816dbd8e823da7b7e92ebb622ce"
] | [
"siam_tracker/datasets/train_pair_dataset.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nimport time\nimport logging\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom . import transforms\nfrom .image_dataset import ImageDataset\nfrom ..utils.parallel import DataContainer\nfro... | [
[
"numpy.random.rand",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SungwookLE/ReND_Car_TensorLab_with_NeuralNet | [
"dc0cb320cb5b3adb2e3a9525ace3a8c9a445a63b"
] | [
"2.Convolutional_Neural_with_LeNet_Study/LeNet_Lab_mnist.py"
] | [
"############################################################################################################ DATA_SET IMPORT: LOAD MNIST\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nmnist = input_data.read_data_sets(\"MNIST_data/\", reshape=False)\nX_train, y_train = mnist.train.images... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.zeros",
"tensorflow.nn.max_pool",
"tensorflow.cast",
"tensorflow.contrib.layers.flatten",
"tensorflow.train.AdamOptimizer",
"tensorflow.nn.conv2d",
"numpy.pad",
"tensorflow.train.impo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
gkapatai/deepbgc | [
"977fa56972a38b9405725315f566a939d5d21759"
] | [
"deepbgc/output/cluster_tsv.py"
] | [
"from deepbgc import util\nfrom deepbgc.output.writer import OutputWriter\nimport logging\nimport pandas as pd\n\nclass ClusterTSVWriter(OutputWriter):\n @classmethod\n def get_description(cls):\n return 'Table of detected BGCs and their properties'\n\n @classmethod\n def get_name(cls):\n ... | [
[
"pandas.concat"
]
] | [
{
"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": []
}
] |
woblob/Crystal_Symmetry | [
"be2984b4487d6075986ef60822a347d0b0e6b885",
"be2984b4487d6075986ef60822a347d0b0e6b885"
] | [
"krysztalki/cif_parsing.py",
"krysztalki/workDir/tests/test matrices/test_matrices_like_m3_xxx.py"
] | [
"from crystals import Crystal\nimport numpy as np\n\ndef eqPoints(POINT):\n \"\"\"\n Get point and make all combinations of ones and zeros by addding numbers in binary \n \"\"\" \n zera = np.where(POINT == 0)[0]\n ilepow = 2**zera.size\n mylist = np.empty((ilepow-1,3))\n for n in range(1,ile... | [
[
"numpy.unique",
"numpy.empty",
"numpy.array",
"numpy.where",
"numpy.vstack"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eastonhou/generative-models | [
"b930d5fa9e2f69adfd4ea8ec759f38f6ce6da4c2",
"b930d5fa9e2f69adfd4ea8ec759f38f6ce6da4c2"
] | [
"GAN/boundary_seeking_gan/bgan_pytorch.py",
"GAN/ebgan/ebgan_tensorflow.py"
] | [
"import torch\nimport torch.nn\nimport torch.nn.functional as nn\nimport torch.autograd as autograd\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport os\nfrom torch.autograd import Variable\nfrom tensorflow.examples.tutorials.mnist impo... | [
[
"torch.randn",
"torch.from_numpy",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"matplotlib.pyplot.subplot",
"torch.log",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"torch.nn.ReLU",
"tensorflow.examples.tutorials.mnist.input_data.read... | [
{
"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... |
peasant98/pfrl | [
"26164d7784e9d3291f28411073f23ce34326df8c"
] | [
"pfrl/replay_buffer.py"
] | [
"from abc import ABCMeta\nfrom abc import abstractmethod\nfrom abc import abstractproperty\n\nimport numpy as np\nimport torch\n\nfrom pfrl.utils.batch_states import batch_states\nfrom pfrl.utils.recurrent import concatenate_recurrent_states\nfrom pfrl.utils.recurrent import flatten_sequences_time_first\nfrom pfrl.... | [
[
"torch.as_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
newstzpz/ClassyVision-1 | [
"e8704ecaa59a15dbb2f4b0724e85d6e5cb2f704e"
] | [
"test/generic/meter_test_utils.py"
] | [
"#!/usr/bin/env python3\n# 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 multiprocessing\nimport queue\nimport tempfile\nimport unittest\n\nimport torch\n\n\nUPDATE_SIGNAL = ... | [
[
"torch.eq",
"torch.is_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ashwinn226/graphwave | [
"34025a4d89e06e941c8b034e77e2f22988963959"
] | [
"graphwave/utils/utils.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n@author: cdonnat\n\"\"\"\n### Random tools useful for saveing stuff and manipulating pickle/numpy objects\nimport numpy as np\nimport pickle\nimport gzip\nimport re\nimport networkx as nx\n\n\ndef save_obj(obj, name, path, compress=False):\n # print path+name+ \".pkl\"\n if c... | [
[
"numpy.savetxt",
"numpy.argsort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elad-c/model_optimization | [
"b0ecf41c3f9434008d57d7fe724ff8585e19d4cc"
] | [
"model_compression_toolkit/common/mixed_precision/search_methods/linear_programming.py"
] | [
"# Copyright 2021 Sony Semiconductors Israel, Inc. 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# Un... | [
[
"numpy.diag",
"numpy.array",
"numpy.matmul",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Oliver-Tautz/T5-frame-identification | [
"85cb7b1d36f65264f14192aa5bb03a016557e546"
] | [
"evaluate.py"
] | [
"\"\"\"\nEvaluate finetuned T5 model on test dataset. Also plot learning curves from train history.\n\nUsed to evaluate without training and plot data. Not necessary for the task.\n\"\"\"\nfrom os.path import join\nfrom os import listdir\nimport pickle\nimport matplotlib.pyplot as plt\nimport defaults\nfrom train_e... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hellwig-Working-Group/PetFinder | [
"ece1667fb18bb1551a153181d35348ccddf59bee"
] | [
"evaluation_utils.py"
] | [
"\"\"\"\nThis module contains implementation of evaluation/loss function\nBased on: https://www.kaggle.com/aroraaman/quadratic-kappa-metric-explained-in-5-simple-steps\n\"\"\"\n\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix, cohen_kappa_score\n\n\ndef sklearn_quadratic_kappa(actuals, predictions... | [
[
"numpy.outer",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaozj89/Physical-Commonsense-Learning-as-Knowledge-Graph-Completion | [
"1e5422799a3bdc026845b37f84c925f94622bec7"
] | [
"metrics.py"
] | [
"# this script is modified from Forbes et al., 2019\n\nimport numpy as np\n\ndef cms(y_hat, y, y_labels):\n y_hat = y_hat.squeeze()\n y = y.squeeze()\n\n res = {}\n for i in range(len(y_labels[0].split(\"/\"))):\n res[i] = {\"overall\": np.zeros((2, 2)), \"per-item\": {}}\n\n for i, y_label in... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MrRedbloX/SmartphoneToVirtuality | [
"287e0677a12cf1f13ae18a9d5f7354ec20681060"
] | [
"server/tracking.py"
] | [
"import cv2\nfrom VideoCaptureAsync import VideoCaptureAsync\n\nimport numpy as np\nfrom math import *\n\nfrom operator import and_,truth,add\nfrom functools import reduce\nfrom itertools import *\nfrom more_itertools import *\n\nimport argparse\nfrom client import ODUdp\n\nfrom time import time\nfrom reconstructio... | [
[
"numpy.hstack",
"numpy.sqrt",
"numpy.max",
"numpy.load",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Blowoffvalve/OpenCv | [
"ddc1eaa907ff0267b7f0382ee6f7423a574311de"
] | [
"Face_Detect_Caffe_resnet/detect_faces_video.py"
] | [
"#import the necessary packages\nfrom imutils.video import VideoStream\nimport numpy as np\nimport argparse\nimport imutils\nimport time\nimport cv2\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-p\", \"--prototxt\", required = True, help = \"path to Caffe 'deploy' prototxt file\")\nap.add_argument(\"-m\", \... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YogaLYJ/ReColorAdv | [
"b49aa59049e73f7ad02de1bef1add52e65b6f160"
] | [
"recoloradv/mister_ed/utils/checkpoints.py"
] | [
"\"\"\" Code for saving/loading pytorch models and batches of adversarial images\n\nCHECKPOINT NAMING CONVENTIONS:\n <unique_experiment_name>.<architecture_abbreviation>.<6 digits of epoch number>path.tar\ne.g.\n fgsm_def.resnet32.20180301.120000.path.tar\n\nAll checkpoints are stored in CHECKPOINT_DIR\n\nChe... | [
[
"numpy.concatenate",
"numpy.load",
"torch.Tensor",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Art-MC/SKX_NN | [
"02d5089ea9c4b3ca7c1878e1d9a5811f5da9f6bd"
] | [
"smallUnet.py"
] | [
"\"\"\"\r\nA fully convolutional U-net like neural network for image segmentation.\r\n\r\nArthur McCray\r\namccray@anl.gov\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\n\r\nclass smallUnet(nn.Module):\r\n \"\"\"\r\n Builds a fully convol... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.cuda.manual_seed",
"numpy.random.seed",
"torch.nn.ConvTranspose2d",
"torch.manual_seed",
"torch.nn.Conv2d",
"torch.cuda.empty_cache",
"torch.nn.LeakyReLU",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Suryavf/SelfDrivingCar | [
"97170e81d7c1a0f683a0fdaae4c42989350823fc"
] | [
"common/Carla100.py"
] | [
"import glob\nimport os\n\nimport json\nimport cv2 as cv\nimport numpy as np\n\nimport h5py\n\npath = \"/home/suryavf/raw\"\nout = \"/home/suryavf/CARLA100\"\nfolders = glob.glob(os.path.join(path,'episode_*'))\nfolders.sort()\n\n# Skip \nblacklist = ['episode_01936','episode_02594','episode_03476','episode_03590'... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kentaroy47/Swin-Transformer-Object-Detection | [
"80cb5892b874b334d9ef6cf7fe437ecd99cee995"
] | [
"mmdet/models/backbones/swin_transformer.py"
] | [
"# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ze Liu, Yutong Lin, Yixuan Wei\n# --------------------------------------------------------\n\nimport torch\nimport torch.nn as nn\... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.cat",
"torch.zeros",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.arange",
"torch.nn.Linear",
"torch.nn.Identity",
"numpy.ceil",
"torch.utils.checkpoint.checkpoint",
"torch.nn.function... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ndrwmlnk/PHYRE-Reasoning-about-Paths | [
"b26a80f23e8484ad53bd3eb1aa86b06441f69e6f"
] | [
"phyre_utils.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport torch as T\nimport torch.nn.functional as F\nfrom phyre_rolllout_collector import load_phyre_rollouts, collect_solving_observations, collect_solving_dataset\nfrom matplotlib import cm\nimport matplotlib.colors as colors\nimport cv2\nimport phyre\nimport o... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imshow",
"matplotlib.colors.NoNorm",
"torch.load",
"torch.cat",
"torch.sum",
"numpy.concatenate",
"numpy.max",
"numpy.zeros_like",
"numpy.any",
"numpy.random.randn",
"numpy.where",
"torch.save",
"numpy.pad",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Yuyanze/PaddleX | [
"c7ae49ff4d119577402614d520170bf8b124967a"
] | [
"paddlex/cv/models/slim/visualize.py"
] | [
"# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.\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... | [
[
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.title",
"matplotlib.use",
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
plaforgue/kae | [
"7ad968e988c7ad6efcb316f786b113095937bcfe"
] | [
"toy_example/toy_example.py"
] | [
"import torch\nfrom kae import KAE\nimport matplotlib.pyplot as plt\n\ntorch.manual_seed(1)\n\n\n# Global parameters\nn = 100 # number of samples\nd_in, d_hid = 10, 5 # layers dimensions\nlayer_dims = [d_in, d_hid, d_in]\nd_tot = d_in + d_hid\nL = len(layer_dims) - 1\n\n# Dat... | [
[
"torch.t",
"matplotlib.pyplot.legend",
"torch.norm",
"torch.manual_seed",
"torch.eye",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"torch.rand",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
niuguy/suanming | [
"acc6e5b46b80563cebc5eba43f2a0c0753c0ac0f"
] | [
"dtoc/.ipynb_checkpoints/deep_episode_dtoc-checkpoint.py"
] | [
"import os\r\nimport time\r\nimport argparse\r\nimport numpy as np \r\nfrom numpy import zeros\r\nimport pandas as pd \r\nimport math\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn import metrics\r\n\r\nfrom keras.layers import Dense, Input, CuDNNLSTM, Embedding, Dropout, Activation, CuDNNG... | [
[
"matplotlib.pyplot.legend",
"sklearn.metrics.roc_auc_score",
"sklearn.metrics.confusion_matrix",
"matplotlib.pyplot.plot",
"sklearn.metrics.f1_score",
"sklearn.metrics.classification_report",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
forkbabu/ncsnv2 | [
"a22db5573ca567f78552993e165fd19749f72aa5"
] | [
"models/__init__.py"
] | [
"import torch\nimport numpy as np\n\ndef get_sigmas(config):\n if config.model.sigma_dist == 'geometric':\n sigmas = torch.tensor(\n np.exp(np.linspace(np.log(config.model.sigma_begin), np.log(config.model.sigma_end),\n config.model.num_classes))).float().to(config... | [
[
"torch.randn_like",
"torch.linspace",
"numpy.log",
"torch.ones",
"numpy.sqrt",
"numpy.linspace",
"torch.sin",
"torch.randn",
"torch.no_grad",
"torch.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EmmaStensby/poet-morphology | [
"da2d19b554d37520661d670c347d6d1157f24d58"
] | [
"ga_test.py"
] | [
"from individuals.neural_network import Individual\nfrom evaluators.bipedal_walker_fitness_behaviour_evaluator import Evaluator\nfrom optimisers.ga import GAOptimiser\nfrom box2D.bipedal_walker_hardcore_custom import BipedalWalkerCustom, Env_config\nimport numpy as np\n\nif __name__ == \"__main__\":\n evaluator ... | [
[
"numpy.random.SeedSequence"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maltetoelle/mfvi-dip | [
"cc67baa8abf38174763943a2618d8a14070746a4"
] | [
"utils/common_utils.py"
] | [
"from typing import List, Union, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torchvision\n\nimport numpy as np\nfrom PIL import Image\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\n\ndef get_fname(img_name: str) -> str:\n \"\"\"Convenience fn. for getting image by ... | [
[
"matplotlib.pyplot.imshow",
"torch.zeros",
"numpy.clip",
"numpy.arange",
"torch.from_numpy",
"numpy.concatenate",
"torch.nn.init.normal_",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jdurbin/sandbox | [
"ee982f7386ae02c5937dbaee867710b5cd2cc71b"
] | [
"python/panda/dfmath.py"
] | [
"#!/usr/bin/env python \n\nimport pandas as pd\n\n\ndf = pd.DataFrame(columns=['authorName', 'journal', 'date'])\n\nprint(df)\n\ndf = df.append({'authorName':'Bob Smith','journal':'Journal of Witchcraft','date':2018},ignore_index=True)\ndf = df.append({'authorName':'Mary Elizabeth','journal':'Duck Duck Goose Transa... | [
[
"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": []
}
] |
hanjin-jung-93/C3D-R-2-1-D-R3D | [
"acfceb14c5f99ba0892f0d3b5cf1fb46d47bcd0c"
] | [
"dataloaders/dataset.py"
] | [
"import os\nfrom sklearn.model_selection import train_test_split\nimport sys\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\n\nimport torch\nimport cv2\nimport numpy as np\nfrom torch.utils.data import Dataset\nfrom mypath import Path\n\n\nclass VideoDataset(Dataset):\n r\"\"\"A Da... | [
[
"numpy.random.random",
"torch.utils.data.DataLoader",
"sklearn.model_selection.train_test_split",
"torch.from_numpy",
"numpy.dtype",
"numpy.shape",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wrwetzel/Birdland | [
"3b6a68fc753c69810690076ab311daaaa4f6563b"
] | [
"src/Index-Sources/StompBox/do-stompbox.py"
] | [
"#!/usr/bin/python\n# ---------------------------------------------------------------------------\n# WRW 20 Sept 2020 - Found a bunch of csv fakebook indexes\n\n# wget --recursive --no-parent http://diystompboxes.com/unrealbook/csvindexes/\n\n# Cleaned up resultant directories by hand. Removed USER stuff, Libr... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
KnightHacks20/KnightHacksAPI | [
"a347be4efc995f097a4706a64b85b75822da2f3b"
] | [
"FasterRCNNDetection/data/dataset.py"
] | [
"################\n#\n# dataset.py\n#\n# Base classes and utilities for loading training/test data. The only data set relevant to the\n# species classification model is defined in coco_camera_traps_dataset.py; others are legacy code from the \n# framework or other data sets we load for other projects.\n#\n########... | [
[
"numpy.asarray",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RichardoMrMu/cnn_model | [
"42b0a999bbec78f371785a74e2894865853aa4aa"
] | [
"fcn_project/net/UNET.py"
] | [
"# -*- coding: utf-8 -*-\n# @Time : 2020-12-02 19:15\n# @Author : RichardoMu\n# @File : UNET.py\n# @Software: PyCharm\nfrom tensorflow_examples.models.pix2pix import pix2pix\nimport tensorflow as tf\n\n\nbase_model = tf.keras.applications.MobileNetV2(input_shape=[128,128,3],include_top=False)\n# 使用这些层的featur... | [
[
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.layers.Conv2DTranspose",
"tensorflow.keras.Model",
"tensorflow.keras.applications.MobileNetV2",
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
paulxiong/pytorch-lightning | [
"ee35907170977bf9cba43c2c921520a2cb8a7901",
"ee35907170977bf9cba43c2c921520a2cb8a7901"
] | [
"pytorch_lightning/accelerators/cpu_accelerator.py",
"tests/trainer/logging/test_logger_connector.py"
] | [
"# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law... | [
[
"torch.cuda.amp.autocast"
],
[
"torch.randn",
"torch.tensor",
"torch.nn.Linear",
"torch.rand",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexkyllo/fake-faces | [
"95d7467598bc1275e6c6c0bea331e036da4e625e"
] | [
"fake_faces/predicting.py"
] | [
"\"\"\"predicting.py\nMake a prediction (inference) on a single image file.\"\"\"\nimport numpy as np\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array, array_to_img\nfrom tensorflow.keras.models import load_model\nfrom fake_faces.training import SHAPE\nimport click\n\ndef predict_image(model... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.preprocessing.image.img_to_array",
"tensorflow.keras.preprocessing.image.load_img"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.4",
"2.3",
"2.5",
"2.6"
]
}
] |
ashimneu/ee245 | [
"8ef1bd28cd2b95845d25bbd1364bc9e853791215"
] | [
"scripts/phase3.py"
] | [
"import numpy as np\nfrom numpy import block\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits import mplot3d\n\n\n\ntfinal = 60\nnumPoint = 200\n\nt = np.linspace(0,tfinal, numPoint)\n\n\n# 2d circular trajectory\nomega0 = np.pi/30 # angular velocity [rad/s]\nr = 0.5 # radius of trajecoty [m]\nt... | [
[
"numpy.linspace",
"numpy.cos",
"numpy.sin",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SebastianoF/calie | [
"187318fa340b6d2fbf8c5dbc643304b66e9d1c44",
"187318fa340b6d2fbf8c5dbc643304b66e9d1c44"
] | [
"tests/test_fields_composition.py",
"calie/visualisations/fields/triptych.py"
] | [
"\"\"\"\nTest to perform the composition between vector fields.\n\nHere can be found some hints to compare the error of the composition provided by the resampling.\nThis is actually very high, as soon as the field gets complicated.\n\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy.testing imp... | [
[
"numpy.cos",
"numpy.sin",
"numpy.copy",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.testing.assert_array_almost_equal"
],
[
"matplotlib.pyplot.cla",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zmcddn/dshelper | [
"99a876e0926b6af123207969c6f4ca1fce5369da"
] | [
"dshelper/data/utils.py"
] | [
"import numpy as np\n\n\ndef reduce_mem_usage(df):\n \"\"\"\n Iterate through all the columns of a dataframe and modify the data type\n to reduce memory usage.\n \"\"\"\n\n for col in df.columns:\n col_type = df[col].dtype\n\n if col_type != object:\n c_min = df[col].min()\n ... | [
[
"numpy.iinfo",
"numpy.finfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BreadFeet/Django_pjt01 | [
"0d79a522d35c74ebf52f36677626202ef1fa5bb3"
] | [
"myanalysis/part03.py"
] | [
"# Day34\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom config.settings import DATA_DIR\n\ndf = pd.read_csv(DATA_DIR[0] + '/auto-mpg.csv', header=None)\ndf.columns = ['mpg', 'cylinders', 'displacement', 'hp', 'weight', 'acceleration', 'model_year', 'origin', 'name']\n\nddf = pd.read_excel(DATA_DIR[0] +... | [
[
"pandas.read_excel",
"pandas.read_csv",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
narahahn/continuous_measurement | [
"2391f712880ec18957dc5491af6cd3665b97e086"
] | [
"example/miso.py"
] | [
"\"\"\"\nContinuous measurement of room impulse responses using a moving microphone.\n\n* point source in a free-field\n* omnidirectional microphone moving on a circle at a constant speed\n* captured signal computed by using fractional delay filters + oversampling\n* system identification based on spatial interpola... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.clim",
"numpy.rad2deg",
"matplotlib.pyplot.plot",
"numpy.zeros_like",
"numpy.random.randint",
"numpy.arange",
"numpy.sin",
"numpy.ceil",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AnushaPB/geonomics-1 | [
"deee0c377e81f509463eaf6f9d0b2f0809f2ddc3"
] | [
"scratch/tmp_mess_with_tskit_LD.py"
] | [
"import tskit\nimport matplotlib.pyplot as pyplot\n\ndef ld_matrix_example(ts):\n ld_calc = tskit.LdCalculator(ts)\n A = ld_calc.r2_matrix()\n # Now plot this matrix.\n x = A.shape[0] / pyplot.rcParams[\"figure.dpi\"]\n x = max(x, pyplot.rcParams[\"figure.figsize\"][0])\n fig, ax = pyplot.subplots... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.gcf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wunaicheng/Crime-Prediction-Fairness-Calibrator | [
"e0df49585b066a271f7bbc9d0fe3bcc25b8629fc"
] | [
"scr/examples_prob.py"
] | [
"from .uncertainty_estimator import empirical_percentile_estimator\nfrom .uncertainty_estimator import MPCE2_test_estimator, error_witness_function, construct_credible_error_vector\nimport matplotlib.pylab as plt\nimport numpy as np\nimport os\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn impor... | [
[
"sklearn.datasets.make_classification",
"numpy.linspace",
"numpy.nan_to_num",
"pandas.DataFrame",
"numpy.max",
"numpy.mean",
"sklearn.svm.LinearSVC",
"numpy.exp",
"matplotlib.pylab.tight_layout",
"pandas.read_csv",
"matplotlib.pylab.yticks",
"sklearn.ensemble.Random... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
dvsseed/tensorflow_cnn | [
"b1fef45ef5c7dee8196a8dfd449564574f12b359"
] | [
"tf2_cnn_battery.py"
] | [
"from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport tensorflow as tf\nprint('TensorFlow:', tf.__version__)\nprint('Keras:', tf.keras.__version__)\nfrom tensorflow.keras import backend, utils, callbacks\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau # , EarlyStop... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.cast",
"tensorflow.keras.backend.clear_session",
"tensorflow.keras.callbacks.TensorBoard",
"tensorflow.data.Dataset.zip",
"matplotlib.pyplot.subplot",
"tensorflow.keras.backend.set_learning_phase",
"matplotlib.pyplot.figure",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
clinicalml/ml_mmrf | [
"fa5f41965f2fe3939272205caafa63c9eb67b1d8"
] | [
"ml_mmrf/core/splitter.py"
] | [
"import os, sys, glob\nimport pickle\nimport pandas as pd\nimport numpy as np\nimport warnings\nimport matplotlib.pylab as plt\nimport seaborn as sns\nplt.rc('font', family='serif')\nplt.rc('xtick', labelsize='x-large')\nplt.rc('ytick', labelsize='x-large')\nplt.rc('axes', labelsize='x-large')\nplt.rc('font', size=... | [
[
"numpy.random.seed",
"numpy.isnan",
"numpy.arange",
"pandas.DataFrame",
"numpy.argwhere",
"numpy.concatenate",
"numpy.max",
"numpy.copy",
"numpy.random.permutation",
"matplotlib.pylab.rc",
"numpy.array",
"numpy.where"
]
] | [
{
"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": []
}
] |
RookieDay/object_detection | [
"8ad733706298ae7a0749b520184c3370b836db7a"
] | [
"visualization.py"
] | [
"# Copyright 2017 Paul Balanca. 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 required by ap... | [
[
"matplotlib.pyplot.Rectangle",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
VolundrAI/pokemon-nuzlocke-ai | [
"648f39cbe3b024d8ce0f5d83990c645d7215ebbb"
] | [
"nuzlockeai/nuzlocke_utils/encounter_pool.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom poke_env.teambuilder.teambuilder_pokemon import TeambuilderPokemon\nfrom pydantic import BaseModel\nfrom typing import List, Tuple\nimport requests\nfrom typing import Optional\nfrom ..utils import PokeCache, get_encounter_basic_info\n\nclass Encounter(BaseModel):\n ... | [
[
"numpy.array",
"numpy.random.randint",
"numpy.random.rand",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Islam-Torikul/models | [
"96ab7384f21782582f3fbe374b0b4be638742904"
] | [
"research/deeplab/utils/train_utils.py"
] | [
"# Copyright 2018 The TensorFlow Authors All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requi... | [
[
"tensorflow.reduce_sum",
"tensorflow.equal",
"tensorflow.minimum",
"tensorflow.where",
"tensorflow.to_int32",
"tensorflow.train.get_or_create_global_step",
"tensorflow.model_variables",
"tensorflow.train.exponential_decay",
"tensorflow.stop_gradient",
"tensorflow.nn.top_k",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
js1294/ECM-2434-Group-Software-Engineering-Project | [
"c7edb7d9006920712341d780e3941a99f729630b"
] | [
"mysite/polls/ml_ai_image_classification.py"
] | [
"\"\"\"Classify images and find faces in them\"\"\"\nfrom keras.applications.xception import Xception\nfrom keras.preprocessing import image\nfrom keras.applications.xception import preprocess_input, decode_predictions\nfrom pathlib import Path\nimport numpy as np\nimport cv2 as cv\n# load the model\nmodel = Xcepti... | [
[
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gisce/pandapower | [
"12e68eff41c1bb71436901d96867ce9571102600"
] | [
"pandapower/plotting/plotly/pf_res_plotly.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Copyright (c) 2016-2019 by University of Kassel and Fraunhofer Institute for Energy Economics\n# and Energy System Technology (IEE), Kassel. All rights reserved.\n\n\nimport pandas as pd\n\nfrom pandapower.plotting.generic_geodata import create_generic_coordinates\nfrom pandapower.plot... | [
[
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ufpa-organization-repositories/evolutionary-computing | [
"e16786f9619e2b357b94ab91ff3a7b352e6a0d92"
] | [
"homework_08/teste.py"
] | [
"import numpy as np\nimport csv\nw = csv.writer(open(\"dicionario_teste.csv\", \"w\"))\n\nimport pickle\nfrom time import sleep\nfile = open('dicionario_teste.pkl', 'wb')\n\nlista = []\ndicionario = {}\n\nprecisao = 0.00001\nvalor = 201\nn_elementos = round(valor / precisao)\n\nprint(n_elementos)\n# n_elementos = i... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gpdsec/yolact-onnx | [
"a234427e9c24b3946fd65402b0b657de09eb36dd"
] | [
"eval.py"
] | [
"from data import COCODetection, get_label_map, MEANS, COLORS\nfrom yolact import Yolact\nfrom utils.augmentations import BaseTransform, FastBaseTransform, Resize\nfrom utils.functions import MovingAverage, ProgressBar\nfrom layers.box_utils import jaccard, center_size\nfrom utils import timer\nfrom utils.functions... | [
[
"torch.set_default_tensor_type",
"torch.cuda.synchronize",
"matplotlib.pyplot.imshow",
"torch.Tensor",
"matplotlib.pyplot.title",
"torch.sum",
"torch.from_numpy",
"numpy.save",
"torch.no_grad",
"numpy.searchsorted",
"torch.stack",
"numpy.array",
"matplotlib.pypl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Bfox132/AudioFingerprinting | [
"985294bf93c7396e2a940123111f295bd793da42"
] | [
"audalign/filehandler.py"
] | [
"import fnmatch\nimport math\nimport multiprocessing\nimport os\nimport typing\nfrom functools import partial\n\nimport noisereduce\nimport numpy as np\nfrom numpy.core.defchararray import array\nfrom pydub import AudioSegment, effects\nfrom pydub.exceptions import CouldntDecodeError\n\nfrom audalign.config import ... | [
[
"numpy.frombuffer",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arangatang/gluon-ts | [
"b333a2b90d4d47d95675d828d37f21cb99f145e5"
] | [
"test/dataset/test_multiprocessing_loader.py"
] | [
"# Copyright 2018 Amazon.com, Inc. or its affiliates. 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# A copy of the License is located at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# or ... | [
[
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
francesclluis/source-separation-wavenet | [
"f818a50d86e131fed5493cab98a33f33fb76b726"
] | [
"datasets.py"
] | [
"# A Wavenet For Source Separation - Francesc Lluis - 25.10.2018\n# Datasets.py\n\nimport util\nimport os\nimport numpy as np\nimport musdb\nimport logging\n\n\nclass SingingVoiceMUSDB18Dataset():\n\n def __init__(self, config, model):\n self.model = model\n self.path = config['dataset']['path']\n ... | [
[
"numpy.floor",
"numpy.array",
"numpy.random.uniform",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
paypal/Iguanas | [
"166ea81b7d370eb4281a27aa449719ed1d38a74a"
] | [
"iguanas/rule_selection/bayes_search_cv.py"
] | [
"\"\"\"Optimises the parameters of a pipeline using Bayesian Optimisation.\"\"\"\nfrom typing import Callable, Tuple, Dict, List, Union\nfrom sklearn.model_selection import StratifiedKFold\nfrom joblib import Parallel, delayed\nfrom hyperopt import tpe, fmin\nimport numpy as np\nfrom copy import deepcopy\nimport pa... | [
[
"numpy.random.RandomState",
"numpy.array",
"sklearn.model_selection.StratifiedKFold",
"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": []
}
] |
bhumikapahariapuresoftware/visions | [
"8838d89b4f02e401112378b4662a779227ead9f8"
] | [
"src/visions/backends/pandas/types/boolean.py"
] | [
"from typing import Dict, List\n\nimport pandas as pd\nimport pandas.api.types as pdt\n\nfrom visions.backends.pandas.series_utils import (\n series_handle_nulls,\n series_not_empty,\n series_not_sparse,\n)\nfrom visions.backends.pandas.test_utils import (\n coercion_map,\n coercion_map_test,\n pa... | [
[
"pandas.api.types.is_categorical_dtype",
"pandas.api.types.is_bool_dtype"
]
] | [
{
"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": []
}
] |
tkanng/spark | [
"4431d54c42d64e7272227ef35f67d809867437c5"
] | [
"python/pyspark/sql/tests.py"
] | [
"# -*- encoding: utf-8 -*-\n#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2... | [
[
"pandas.Timestamp.now"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IBM/oct-glaucoma-detection | [
"76ecb276700cd394d00c921db3b09a0a8675f674"
] | [
"ogd/view_data.py"
] | [
"\"\"\"\nView bscans in cubes\n\"\"\"\nimport numpy as np\nimport nutsml.imageutil as ni\n\nfrom nutsflow import *\nfrom nutsml import *\nfrom common import *\nfrom preprocessing import flatten_layers\n\n\n@nut_processor\ndef ReadCubeScans(samples):\n for sample in samples:\n filepath, label, uid = sample... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lens-biophotonics/dcimg | [
"d09af219441da72a0bed4a0db867526c7c311001"
] | [
"dcimg.py"
] | [
"# Based on information gathered from:\n# https://github.com/StuartLittlefair/dcimg/blob/master/dcimg/Raw.py\n# hamamatsuOrcaTools: https://github.com/orlandi/hamamatsuOrcaTools\n# Python Microscopy: http://www.python-microscopy.org\n# https://bitbucket.org/david_baddeley/python-microscopy\n\n# A... | [
[
"numpy.memmap",
"numpy.ndarray",
"numpy.dtype",
"numpy.datetime64",
"numpy.copy",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chilampoon/APAeval | [
"ed9f86bf76d760bb20b1484e5853de4d4a30d240"
] | [
"docs/templates/nextflow_summary_workflow/benchmarking_dockers/apaeval_validation/validation.py"
] | [
"from __future__ import division, print_function\nimport pandas\nimport os, json\nimport sys\nfrom argparse import ArgumentParser\nfrom JSON_templates import JSON_templates\n\nparser = ArgumentParser()\nparser.add_argument(\"-i\", \"--participant_data\", help=\"list of cancer genes prediction\", required=True)\npar... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
DavideRuzza/moderngl-window | [
"e9debc6ed4a1899aa83c0da2320e03b0c2922b80"
] | [
"examples/advanced/tetrahedral_mesh.py"
] | [
"from pathlib import Path\r\nimport numpy as np\r\nfrom pyrr import Matrix44\r\n\r\nimport moderngl\r\nimport moderngl_window\r\nfrom moderngl_window.opengl.vao import VAO\r\nfrom moderngl_window import geometry\r\nfrom base import CameraWindow\r\n\r\n\r\nclass VolumetricTetrahedralMesh(CameraWindow):\r\n \"\"\"... | [
[
"numpy.radians",
"numpy.random.random_sample",
"numpy.concatenate",
"numpy.ceil",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
autoliuweijie/FastPLM | [
"fc92a0a5c757b8f3bc7f1af32ade5137d705e031"
] | [
"examples/imdb/test.py"
] | [
"# coding: utf-8\n\"\"\"\nAn example of using fastbert model for single sentence classificaion\n\n@author: weijie liu\n\"\"\"\nimport os, sys\nfastplm_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), \"../../\"))\nsys.path.append(fastplm_dir)\nimport torch\nimport numpy as np\nfrom fastplm import FastP... | [
[
"numpy.mean",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rbfricks/PhantomClassifier_SPIE2020 | [
"0bb4b30082ed0e4947f772cbf68a46db9cb6c528"
] | [
"genAndTrain.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 21 14:24:42 2020\n\n@author: Rafael Fricks\n\nDescription: Top level demonstration script to train & evaluate the phantom \nclassifier described in SPIE 2020 publication by R. Fricks et al. titled\n\"Automatic phantom test pattern classification through transfer ... | [
[
"sklearn.utils.shuffle",
"sklearn.preprocessing.LabelBinarizer",
"numpy.random.seed",
"tensorflow.random.set_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HCA97/Master-Thesis | [
"21471e6d774f46e29bf1a9993a815ae46b931804"
] | [
"scripts/dataloader.py"
] | [
"from typing import Tuple, List, Union\nimport os\nimport random\n\nfrom PIL import Image\nimport torchvision.transforms as transforms\nfrom torch.utils.data import DataLoader, Dataset\nfrom pytorch_lightning.core.lightning import LightningModule\n\n\nclass ImageFolder(Dataset):\n \"\"\"Image folder dataset\n\n ... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nrhodes/tensorforce | [
"f41c89cda596ca56f26fb42a498cd17a2545579b"
] | [
"tensorforce/core/memories/queue.py"
] | [
"# Copyright 2017 reinforce.io. 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 required by ap... | [
[
"tensorflow.scatter_update",
"tensorflow.boolean_mask",
"tensorflow.assign_add",
"tensorflow.range",
"tensorflow.shape",
"tensorflow.control_dependencies",
"tensorflow.minimum",
"tensorflow.assign",
"tensorflow.gather",
"tensorflow.no_op",
"tensorflow.make_template",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
kevin-ssy/ViP | [
"dedc27616ab24e78685e21291d1e6ef9b56de889"
] | [
"utils/flop_count/jit_analysis.py"
] | [
"import logging\nimport typing\nimport warnings\nfrom collections import Counter\nfrom copy import copy\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Set, Tuple, Union, Iterable\n\nimport torch\nimport torch.nn as nn\n\nfrom torch import Tensor\nfrom torch.jit import TracerWarnin... | [
[
"torch._C._get_tracing_state",
"torch.jit._get_trace_graph",
"torch.jit.get_trace_graph"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goodgravy/srome.github.io | [
"cb6b8ae0f3f6b7e1d4bc39296a76bb6f8cc97031"
] | [
"files/nfl_optimizer/nfl_optimizer.py"
] | [
"from gurobipy import *\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\npoints = \"Points\"\r\nsalary = \"Salary\"\r\nposition = \"Position\"\r\n\r\ndef optimize(player_names, player_data):\r\n varbs = {} # key: player name, value : var\r\n\r\n m = Model()\r\n\r\n for p in player_names:\r\n var... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
danforthcenter/plantcv-setaria-molplant | [
"7619e14e7b69243ba8730e3de6f45c05ce39c2ce"
] | [
"image_analysis_scripts/nir_sv/nir_sv_z3500.py"
] | [
"#!/usr/bin/python\n\n# This is a computational pipeline which uses the plantcv module to sharpen, filter and analyze NIR images\n# Pipeline designed for use with Setaria plants at zoom X3500\n# The strategy/methodology is adopted from the textbook \"Digital Image Processing\" by Gonzalez and Woods\n# Version 0.9 M... | [
[
"matplotlib.use",
"numpy.copy",
"numpy.zeros",
"scipy.ndimage.rotate"
]
] | [
{
"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"... |
Sokhna1109/visual_kinematics | [
"87916c115177709d4626079dc41b32a2a2c2f7bc"
] | [
"examples/inverse.py"
] | [
"#!/usr/bin/env python3\n\nfrom visual_kinematics.RobotSerial 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., 0.632, pi, 0.5 * pi],\n ... | [
[
"numpy.set_printoptions",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gabeoh/FCND-P02-Motiong-Planning | [
"e7333314e2654816ab8fa68932b542dcc8049072"
] | [
"planner/sampler.py"
] | [
"import numpy as np\nfrom shapely.geometry import Polygon, Point, LineString\nimport networkx as nx\nfrom sklearn.neighbors import KDTree\nfrom planner.datamap import DataMap\nfrom planner.packet import Packet3d\nfrom planner.graph import PlannerGraph\nfrom matplotlib.axes._subplots import Axes\nfrom planner.util ... | [
[
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gato-X/sPyGlass | [
"acf71010c97a545728cabc280289773aa0b6d5b1"
] | [
"sPyGlass/camera.py"
] | [
"\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2015 Guillermo Romero Franco (AKA Gato)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation ... | [
[
"numpy.dot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SauravMaheshkar/gnn-lspe | [
"6011cd176799139803f6a2734ccc46f9cecb0f2e"
] | [
"nets/OGBMOL_graph_classification/san_net.py"
] | [
"import dgl\nimport torch\nimport torch.nn as nn\nfrom ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder\n\nfrom layers.mlp_readout_layer import MLPReadout\nfrom layers.san_gt_layer import SAN_GT_Layer\nfrom layers.san_gt_lspe_layer import SAN_GT_LSPE_Layer\n\n\nclass SANNet(nn.Module):\n def __init_... | [
[
"torch.nn.Dropout",
"torch.cat",
"torch.sqrt",
"torch.nn.Linear",
"torch.nn.BCEWithLogitsLoss",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dimitrios-Ar/decnet2022 | [
"812644570fb40852fd08f55e7a9b41e75757fe49"
] | [
"src/utils/dataset_checker.py"
] | [
"import glob\nimport numpy as np\nimport os\nfrom PIL import Image\n#import mean_std_custom\nfrom pathlib import Path\n#from dataloader import DecnetDataset\nfrom torch.utils.data import Dataset, DataLoader, sampler\nfrom imutils import paths\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport torch\nfr... | [
[
"torch.mean",
"matplotlib.pyplot.imshow",
"torch.transpose",
"numpy.delete",
"matplotlib.pyplot.subplot",
"numpy.append",
"numpy.mean",
"numpy.std",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chanceeakin/opera-dataset | [
"09d35acf305735c66563713f9de55a2917f0d889"
] | [
"utils/arias.py"
] | [
"import pandas as pd\n\ndf = pd.read_csv(\"./../data/raw-data.csv\")\n\nprint(df.head())\narias = df[[\"Aria\", \"Composer\", \"Artist Category\", \"Frequency\", \"Voice Type\"]].copy()\narias = arias.groupby(['Aria', 'Composer', 'Artist Category', \"Voice Type\"]).sum()\nprint(arias.head())\nout = arias.to_json(or... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
IRMVLab/ASTA3DConv | [
"7f0bf7191fdd21918a844beb5ed01985b2d49ce7"
] | [
"action/action_cls/model_cls_direct.py"
] | [
"\"\"\"\n Compared with model_baseline, do not use correlation output for skip link\n Compared to model_baseline_fixed, added return values to test whether nsample is set reasonably.\n\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\nimport math\nimport sys\nimport os\nBASE_DIR = os.path.dirname(os.path.... | [
[
"tensorflow.Graph",
"tensorflow.concat",
"tensorflow.constant",
"numpy.linspace",
"tensorflow.reduce_mean",
"tensorflow.zeros",
"tensorflow.reshape",
"tensorflow.placeholder",
"tensorflow.ones",
"tensorflow.add_to_collection",
"tensorflow.nn.sparse_softmax_cross_entropy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Steigner/VAI | [
"9be757fa2001442f32e71698f1cd3ed7ec1143de"
] | [
"path_planning/src/heuristic.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\n\n# calculate euclidian distance \nclass Heuristic(object):\n @classmethod\n def euclidan_dist(self, arr1, arr2):\n arr1 = np.array(arr1)\n arr2 = np.array(arr2)\n return np.linalg.norm(arr1 - arr2)\n"
] | [
[
"numpy.array",
"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": [],
... |
misoA/DeepCalendar | [
"50cafc1e70f125f3b6b42cd88e1e9dd071676b49"
] | [
"3_classification/weather_classification/train.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 17 15:05:46 2018\n\n@author: hyunb\n\"\"\"\nimport os\nimport json\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import transforms\n\nfrom data_loader import get_loader\nfrom model import res_fashion_CNN\n\nimp... | [
[
"torch.optim.Adam",
"torch.nn.CrossEntropyLoss",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlinaWithAFace/3D-ResNets-PyTorch | [
"758ad2f7b15581cd420f6e525151c1587a92b753"
] | [
"datasets/videodataset.py"
] | [
"import json\nfrom pathlib import Path\n\nimport torch\nimport torch.utils.data as data\n\nfrom .loader import VideoLoader\n\n\ndef get_class_labels(data):\n class_labels_map = {}\n index = 0\n for class_label in data['labels']:\n class_labels_map[class_label] = index\n index += 1\n return... | [
[
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hyperiongeo/pyqtgraph | [
"7f7c6217cc2fae5fdfad6d3c27739461abd32c84"
] | [
"tests/graphicsItems/test_PlotDataItem.py"
] | [
"import warnings\n\nimport numpy as np\n\nimport pyqtgraph as pg\nfrom pyqtgraph.Qt import QtGui\n\npg.mkQApp()\n\n\ndef test_bool():\n truths = np.random.randint(0, 2, size=(100,)).astype(bool)\n pdi = pg.PlotDataItem(truths)\n bounds = pdi.dataBounds(1)\n assert isinstance(bounds[0], np.uint8)\n as... | [
[
"numpy.random.random",
"numpy.linspace",
"numpy.isfinite",
"numpy.isnan",
"numpy.sin",
"numpy.random.normal",
"numpy.log10",
"numpy.zeros_like",
"numpy.argmax",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sanelisiwentini/ann-benchmarks | [
"e121b309a9d56130a8b016ea925573c16dc04ef4"
] | [
"ann_benchmarks/algorithms/diskann.py"
] | [
"import sys\nimport os\nimport vamanapy as vp\nimport numpy as np\nimport struct\nimport time\nfrom ann_benchmarks.algorithms.base import BaseANN\n\n\nclass Vamana(BaseANN):\n def __init__(self, metric, param):\n self.metric = {'angular': 'cosine', 'euclidean': 'l2'}[metric]\n self.l_build = int(pa... | [
[
"numpy.insert"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kweird/githubintro | [
"70de6d48b2989d04430893ebc09428f2219be259"
] | [
"assessment1.py"
] | [
"import random\nimport operator\nimport tkinter\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot\nimport matplotlib.animation \nimport agentframework\nimport csv\nimport requests\nimport bs4\nimport time\n\nstart_time = time.time()\n\n# We set the random seed so we can see repeatable patterns\n... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.scatter",
"matplotlib.use",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.xlim",
"matplotlib.animation.FuncAnimation",
"matplotlib.backends.backend_tkagg.FigureCanvasTkAgg"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhupengyang/Paddle-Lite | [
"c8d77d38c5586a98a3ffaa0a87d4a3cd0aa42755"
] | [
"lite/tests/unittest_py/arm/test_conv_active_fuse_pass.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 re... | [
[
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GuanXiangHuang/FaceRecognition | [
"925e83d911ad2cbbbcad9e9c89c97f434070854d"
] | [
"faces-train.py"
] | [
"import cv2\r\nimport os\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport pickle\r\n\r\n\r\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\r\n\r\nimage_dir = os.path.join(BASE_DIR, \"images\")\r\n\r\nface_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')\r\nrecognizer ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jonas1711/football | [
"6a20dcb832da71d4e97e094e4afa060533aa7dcc"
] | [
"gfootball/env/players/bot.py"
] | [
"# coding=utf-8\n# Copyright 2019 Google LLC\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 la... | [
[
"numpy.dot",
"numpy.array",
"numpy.linalg.norm",
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jeongukjae/pytorch | [
"90e532f3ef17a9611e9e7a9f1f6189d4168bf084"
] | [
"test/onnx/test_pytorch_onnx_onnxruntime.py"
] | [
"import unittest\nimport onnxruntime\nimport torch\n\nimport numpy as np\nimport io\nimport itertools\nimport copy\nimport os\nimport random\n\nfrom torch.nn.utils import rnn as rnn_utils\nfrom model_defs.lstm_flattening_result import (LstmFlatteningResultWithSeqLength,\n ... | [
[
"torch.fmod",
"torch.randint",
"torch.max",
"torch.zeros",
"torch.nn.GRU",
"torch.narrow",
"torch.multinomial",
"torch.numel",
"torch.where",
"torch.topk",
"torch.device",
"numpy.where",
"torch.pow",
"torch.nn.EmbeddingBag",
"torch.outer",
"torch.sqr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nathcibirka/stackoverflow | [
"604fc270aa75e25ad1eaf6bd524d7cc7ce0db63f"
] | [
"Salary_ML.py"
] | [
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import r2_score, mean_squared_error\nimport seaborn as sns\n\nprint('teste change')\n\ndef clean_data(df):\n '''... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"sklearn.metrics.r2_score",
"matplotlib.pyplot.title",
"pandas.get_dummies",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.plot",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplot.xlabel",
"matplotlib... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
topazape/StressCheck_mhlw_ja | [
"cd783d1690184be5e45db2cbeb52966d9696d6c8"
] | [
"stresscheckMHLWja/methods.py"
] | [
"import functools\nimport re\nfrom typing import Any\n\nimport pandas as pd\n\n\nclass ScoreTable:\n def __repr__(self) -> str:\n return \"table\"\n\n def Male_areaA(self, D: Any, prefix: str) -> Any:\n wquant = 15 - D[[f\"{prefix}1\", f\"{prefix}2\", f\"{prefix}3\"]].sum(axis=1)\n wquant... | [
[
"pandas.concat"
]
] | [
{
"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": []
}
] |
EMBEDDIA/hackashop2021_comment_filtering | [
"54b984f36014044b353062ecfd55eb7105a9c5cd"
] | [
"inference.py"
] | [
"import torch\nfrom torch.utils.data import TensorDataset, SequentialSampler, DataLoader\nfrom tqdm import tqdm\nimport numpy as np\n\nfrom keras.preprocessing.sequence import pad_sequences\nimport argparse\nimport pandas as pd\nfrom transformers import BertTokenizer, BertConfig, BertForSequenceClassification\n\n\n... | [
[
"torch.nn.Softmax",
"pandas.read_csv",
"torch.utils.data.TensorDataset",
"torch.utils.data.SequentialSampler",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"torch.tensor",
"numpy.argmax",
"torch.no_grad",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
tarment10/CoolProp | [
"de465e1cf6755d23231f289c6f7c24fd58eca465"
] | [
"dev/scripts/fit_ancillary_ODRPACK.py"
] | [
"from __future__ import division\nimport numpy as np\nfrom scipy.odr import *\nimport scipy.optimize, random\nimport matplotlib.pyplot as plt\nimport textwrap\nimport random\nfrom CoolProp.CoolProp import Props, get_REFPROPname\n\n\ndef rsquared(x, y):\n \"\"\"\n Return R^2 where x and y are array-like.\n\n ... | [
[
"numpy.log",
"numpy.abs",
"numpy.linspace",
"numpy.argmax",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tomekrut/DeepSpeed | [
"141f7bd5b3a5ac0b89e8c5a6e2b0764f28ad8dbe"
] | [
"deepspeed/pt/deepspeed_dataloader.py"
] | [
"'''\nCopyright 2019 The Microsoft DeepSpeed Team\n'''\n\nimport torch\nfrom torch.utils.data import DataLoader, RandomSampler\nfrom torch.utils.data.distributed import DistributedSampler\n\n\nclass DeepSpeedDataLoader(object):\n def __init__(self,\n dataset,\n batch_size,\n ... | [
[
"torch.cuda.device_count",
"torch.utils.data.DataLoader",
"torch.utils.data.distributed.DistributedSampler",
"torch.utils.data.RandomSampler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SooluThomas/qiskit-aqua | [
"4c3332845cf3853e8e676bb234288529e6aa9e78"
] | [
"test/aqua/test_mcr.py"
] | [
"# -*- coding: utf-8 -*-\n\n# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2019, 2020.\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.allclose",
"numpy.eye",
"numpy.cos",
"numpy.sin",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
panlm/karbon-platform-services | [
"80a0cffb81bcfaedb9546e806e0f461b0c7ac1fd"
] | [
"projects/IoT_Starter/facefeed/functions/raw_to_jpeg.py"
] | [
"# Copyright (c) 2018 Nutanix, Inc.\n# Use of this source code is governed by an MIT-style license \n# found in the LICENSE file at https://github.com/nutanix/xi-iot.\n\nimport numpy\nimport sys\nimport cv2\nimport msgpack\nimport base64\nimport json\n\ndef main(ctx,msg):\n unpacked_dict = msgpack.unpackb(msg, r... | [
[
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdat2/sof-agu | [
"b30b2f3d3a595147d1146637e75b6e123fc17dcb"
] | [
"src/plot/preprocessing_profiles.py"
] | [
"\"\"\"Make graphs to show how the preprocessing step has worked.\n\nThese are graphs B1 and B2 in appendix B of the paper.\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pyxpcm\nimport src.plot_utils.gen_panels as gp\nimport src.plot_utils.latex_style as lsty\nfrom src.constants import ZS, LZ\n... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.sqrt",
"matplotlib.pyplot.subplots",
"numpy.transpose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qkaTlehdrnf/FlowPy | [
"2a7924960afd3c39c42d8a500afba157d69f01fc"
] | [
"FlowPy_Visualizer.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jul 18 22:33:14 2018\r\n\r\n@author: Gaurav\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport sys\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.animation as animation\r\n\r\n#### Simulation inputs\r\nrowpts=201\r\ncolpts=51\r\nlength=5\r\nbread... | [
[
"numpy.amax",
"numpy.linspace",
"numpy.amin",
"numpy.sort",
"matplotlib.pyplot.axes",
"matplotlib.animation.FuncAnimation",
"numpy.meshgrid",
"numpy.zeros",
"numpy.loadtxt",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
imdoroshenko/kedro | [
"4054277f80eea4012ff7b4d07ed7ac36627f9e9a"
] | [
"tests/io/test_data_catalog.py"
] | [
"# Copyright 2021 QuantumBlack Visual Analytics Limited\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# THE SOFTWARE IS PR... | [
[
"pandas.util.testing.assert_frame_equal",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
WowCZ/strac | [
"a55195a04ea4f1a4051fdcf11e83777b47ca83e1"
] | [
"policy/PolicyCommittee.py"
] | [
"###############################################################################\n# PyDial: Multi-domain Statistical Spoken Dialogue System Software\n###############################################################################\n#\n# Copyright 2015 - 2017\n# Cambridge University Engineering Department Dialogue Sy... | [
[
"numpy.reciprocal",
"numpy.sqrt",
"numpy.sum",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
timgates42/colour | [
"cea486394e3925718a0f3dd427edc9dd5b674f0c",
"cea486394e3925718a0f3dd427edc9dd5b674f0c"
] | [
"colour/plotting/tests/test_common.py",
"colour/__init__.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nDefines the unit tests for the :mod:`colour.plotting.common` module.\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport shutil\nimport tempfile\nimport unittest\nfrom functools import partial\nfrom matplotlib.pyplot import Axes, Figure\n\nimport colou... | [
[
"numpy.array",
"numpy.random.random",
"numpy.linspace"
],
[
"numpy.set_printoptions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mengli11235/Bootstrapped-DQN-with-NP | [
"5dd7da17dd1930e81a248b3e2dc30431905434ae"
] | [
"replay.py"
] | [
"import numpy as np\nimport time\n\n# This function was mostly pulled from\n# https://github.com/fg91/Deep-Q-Learning/blob/master/DQN.ipynb\nclass ReplayMemory:\n \"\"\"Replay Memory that stores the last size=1,000,000 transitions\"\"\"\n def __init__(self, size=1000000, frame_height=84, frame_width=84,\n ... | [
[
"numpy.savez",
"numpy.load",
"numpy.random.RandomState",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hulkis/homeserv-inter | [
"b4a0e930fa31423ac28dfceb95129b057ddbf790"
] | [
"src/homeserv_inter/raw.py"
] | [
"import pandas as pd\n\nfrom homeserv_inter.constants import DATA_DIR, NUMERIC_COLS, RAW_DATA_DIR, STR_COLS\nfrom wax_toolbox import Timer\n\n\ndef to_datetimes(dt_cols, df):\n for col in dt_cols:\n df[col] = pd.to_datetime(df[col])\n return df\n\n\ndef convert_csv_to_parquet(engine=\"pyarrow\"):\n ... | [
[
"pandas.to_datetime",
"pandas.to_numeric",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
EchEdward/RadioTowersElevation | [
"ceae15d0154f0c2823a080024e9dc2243f6a2443"
] | [
"RadioTowersElevation.pyw"
] | [
"# pylint: disable=E0611\n# pylint: disable=E1101\n#pyi-makespec --onefile --icon=icon.ico --noconsole VEZRead.py\nfrom PyQt5.QtWidgets import QApplication,QMainWindow,QWidget,QVBoxLayout,QHBoxLayout,QLabel,QListView,QPushButton,\\\n QScrollArea,QSizePolicy, QTableWidgetItem,QSplitter, QFrame, QSizePolicy, QList... | [
[
"numpy.arange",
"matplotlib.font_manager.FontProperties",
"numpy.max",
"numpy.shape",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg",
"matplotlib.ticker.FormatStrFormatter",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
molecular-neuroimaging/RAGE | [
"60f01738780f58c1d729763f4a4966f21f69fe50"
] | [
"RAGE_train.py"
] | [
"import numpy as np\r\nfrom sklearn.decomposition import PCA\r\nimport os\r\nfrom sklearn.linear_model import LogisticRegression\r\nimport pickle\r\nfrom utils import load_config_file\r\nimport sys\r\nfrom sklearn.gaussian_process import GaussianProcessRegressor\r\nfrom sklearn.gaussian_process.kernels import DotPr... | [
[
"numpy.histogram",
"sklearn.linear_model.LogisticRegression",
"sklearn.gaussian_process.kernels.DotProduct",
"numpy.mean",
"sklearn.gaussian_process.GaussianProcessRegressor",
"numpy.shape",
"numpy.load",
"numpy.digitize",
"sklearn.gaussian_process.kernels.WhiteKernel",
"sk... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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