repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
yandex-research/shifts | [
"12c8ca805ff4d18bdc1300611c318b264d79fdec"
] | [
"translation/assessment/evaluate_remote.py"
] | [
"import json\nimport numpy as np\nimport sacrebleu\nfrom nltk.translate import gleu_score\nfrom sklearn.metrics import roc_auc_score, roc_curve, auc, f1_score, fbeta_score\nfrom sklearn.metrics import precision_recall_curve\nimport pandas as pd\n\nimport numpy as np\nfrom sklearn.metrics import auc\nfrom sklearn.ut... | [
[
"sklearn.metrics.roc_auc_score",
"sklearn.utils.check_consistent_length",
"numpy.asarray",
"numpy.cumsum",
"numpy.int",
"numpy.mean",
"sklearn.utils.assert_all_finite",
"numpy.unique",
"numpy.arange",
"sklearn.utils.column_or_1d",
"numpy.zeros",
"numpy.isnan",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YotamElor/sagemaker-scikit-learn-extension | [
"930e0ad5b2ef3caa1d7565850f63b3ce4a39b146"
] | [
"test/test_date_time.py"
] | [
"# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in the \"... | [
[
"numpy.isnan",
"numpy.arange",
"numpy.all",
"numpy.prod",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vyraun/TinyShakespeare-Speaks | [
"a412041f8c60a95004f3d4acebd3193db6ff3341"
] | [
"train.py"
] | [
"from __future__ import print_function\nimport numpy as np\nimport tensorflow as tf\n\nimport argparse\nimport time\nimport os\nfrom six.moves import cPickle\n\nfrom utils import TextLoader\nfrom model import Model\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--data_dir', type=st... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.all_variables",
"tensorflow.assign",
"tensorflow.initialize_all_variables",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
stellaraccident/iree-jax | [
"cc22664e5ea9d8120c080fd9417a636539105cd7"
] | [
"tests/module_api_test.py"
] | [
"# RUN: %PYTHON %s\n# Copyright 2021 Google LLC\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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applic... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xxxnhb/fewshot-egnn | [
"205fa80ec7cb12550f7b52a63f921171f92dac4c"
] | [
"data.py"
] | [
"from __future__ import print_function\nfrom torchtools import *\nimport torch.utils.data as data\nimport random\nimport os\nimport numpy as np\nfrom PIL import Image as pil_image\nimport pickle\nfrom itertools import islice\nfrom torchvision import transforms\n\n\nclass MiniImagenetLoader(data.Dataset):\n def _... | [
[
"numpy.asarray",
"numpy.uint8",
"numpy.transpose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BarryLiu97/SEEG_Scripts | [
"fd0a79cfedc7a18f9995d808ab608a64facd5fe6"
] | [
"example/electrodes_analysis.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jul 12 14:28:00 2021\n\n@author: barryliu\n\"\"\"\n\nimport os\nimport glob\nimport numpy as np\nimport pandas as pd\nfrom visbrain.objects import SourceObj, RoiObj\n\nroot = 'E:\\Projects\\Video\\data'\nload_path = os.path.join(root, 'Electrodes\\\\')\nsave_path = o... | [
[
"pandas.read_table",
"numpy.array",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
visinf/ppac_refinement | [
"04a8676f5eb96c41ec6b1125c6bcad430218ef30"
] | [
"bin/train_flow_refined.py"
] | [
"# Author: Anne Wannenwetsch, TU Darmstadt (anne.wannenwetsch@visinf.tu-darmstadt.de)\n# Parts of this code were adapted from https://github.com/ucbdrive/hd3\nimport argparse\nimport logging\nimport os\nimport shutil\n\nimport torch\nfrom tensorboardX import SummaryWriter\n\nfrom datasets import datasets_flow\nfrom... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.load",
"torch.utils.data.DataLoader",
"torch.cuda.empty_cache",
"torch.no_grad",
"torch.device",
"torch.nn.DataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lynncyrin/galaxySim | [
"e649d2fc560189fdda2be96d1c1df42a8182cdea"
] | [
"custom.py"
] | [
"#custom.py\n\nfrom __future__ import division\nimport math\nimport numpy\nimport sys\n\nclass partitionData (object):\n '''\n Partitions data into 2d or 3d boxes.\n \n [Input]\n data\n type: numpy ndarray\n a collection of the data to be partitioned\n \n [API]\n self.partition... | [
[
"numpy.ndenumerate",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nongroup-lanl/riv-processing | [
"2fce3a4741a61a9fa94c813322f5d4f51002a5fe"
] | [
"process_legacy_data.py"
] | [
"\"\"\"\nScript to compare results with Ucayali classifications and channel masks\n\"\"\"\n\nimport os\nimport rasterio\nimport numpy as np\nfrom scipy.io import loadmat\n\n\nIMAGERY_PATH = '/Users/rmsare/data/Ucayali/images/'\nMASK_PATH = '/Users/rmsare/data/Ucayali/masks/'\nSTRUCT_FIELD_NAME = 'cmap'\n\n\ndef get... | [
[
"numpy.array",
"scipy.io.loadmat"
]
] | [
{
"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"... |
geophysics-ubonn/REDA | [
"8f0399031121f5a937171231a25f9ab03a3c8873"
] | [
"lib/reda/utils/eit_fzj_utils.py"
] | [
"# various utility functions used in conjunction with the FZ EIT systems\nimport numpy as np\nimport pandas as pd\n\nimport pylab as plt\nimport scipy.io as sio\n\nimport reda\nimport reda.utils.geometric_factors as geometric_factors\nimport reda.utils.fix_sign_with_K as fixK\nimport reda.importers.eit_fzj as eit_f... | [
[
"pandas.read_csv",
"numpy.abs",
"numpy.logspace",
"numpy.arange",
"numpy.vstack",
"scipy.io.loadmat",
"numpy.sort",
"pandas.DataFrame",
"numpy.arctan2",
"numpy.array",
"numpy.where",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4"... |
yusin2it/SARoptical_fusion | [
"896da9f436b90b8eb6609e981ea0ba8f495be278"
] | [
"datasets/superpixels_seg.py"
] | [
"import os\r\nimport glob\r\nimport rasterio\r\nimport numpy as np\r\nfrom tqdm import tqdm\r\n\r\nimport torch.utils.data as data\r\nfrom skimage.segmentation import felzenszwalb, slic, mark_boundaries\r\n\r\ndef normalization(data):\r\n _range = np.max(data) - np.min(data)\r\n return (data - np.min(data)) /... | [
[
"numpy.rollaxis",
"numpy.min",
"numpy.clip",
"numpy.nan_to_num",
"numpy.max",
"numpy.load",
"torch.utils.data.read"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZuowenWang0000/GRUBERT-A-GRU-Based-Method-to-Fuse-BERT-Hidden-Layers | [
"992967fe102493eadf37423de5710761f007bcb1"
] | [
"train.py"
] | [
"import sys\nimport os\nimport random\nimport torch\nimport numpy as np\nif __name__ == \"__main__\":\n try:\n # Try to set the random seed, have to do this here instead of in main()\n seed = int(sys.argv[sys.argv.index(\"--seed\") + 1])\n print(\"Using seed: %d\" % seed)\n os.environ... | [
[
"torch.nn.CrossEntropyLoss",
"torch.norm",
"numpy.random.seed",
"torch.load",
"torch.eq",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JunjieHu/ReCo-RL | [
"4406f6eec2d6bee4aa12c8b22494f2d167c570c1"
] | [
"src/StackedRNN.py"
] | [
"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef dot_prod_attention(h_t, src_encoding, src_encoding_att_linear, mask=None):\n \"\"\"\n :param h_t: (batch_size, hidden_size)\n :param src_encoding: (batch_size, src_sent_len, hidden_size * 2)\n :param src_encoding_att_linear... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.LSTMCell",
"torch.nn.Linear",
"torch.stack",
"torch.nn.GRUCell",
"torch.nn.functional.tanh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
csukuangfj/transducer-loss-benchmarking | [
"8373897fd86a64425baa198247cde439f10b1bd2"
] | [
"generate_shape_info.py"
] | [
"#!/usr/bin/env python3\n#\n# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)\n#\n# See ../LICENSE for clarification regarding multiple authors\n#\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 ob... | [
[
"torch.save",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sanskar107/NeRD-Neural-Reflectance-Decomposition | [
"f4408645cdc49033acb9eaf0f6a58bacc549a774"
] | [
"train_nerd.py"
] | [
"import os\nfrom typing import Callable, List, Dict\n\nimport imageio\nimport numpy as np\nimport tensorflow as tf\nfrom tqdm import tqdm\n\nimport dataflow.nerd as data\nimport nn_utils.math_utils as math_utils\nimport utils.training_setup_utils as train_utils\nfrom models.nerd_net import NerdModel\nfrom nn_utils.... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.stack",
"numpy.concatenate",
"numpy.max",
"tensorflow.summary.scalar",
"tensorflow.Variable",
"tensorflow.summary.image",
"numpy.stack",
"numpy.load",
"tensorflow.data.Options",
"tensorflow.zeros_like",
"tensorflow.summary... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
X-CCS/mandarin_tacotron2_world | [
"46065c4b6735ed66cdb6b66433b85cd74e838df2"
] | [
"datasets/audio.py"
] | [
"import librosa\nimport numpy as np\nimport pysptk\nimport pyworld\nimport soundfile as sf\nimport tensorflow as tf\n\n\ndef load_wav(path, hparams):\n\twav, _ = sf.read(path)\n\treturn wav\n\ndef save_wav(wav, path, hparams):\n\tsf.write(path, wav, hparams.sample_rate)\n\ndef trim_silence(wav, hparams):\n\treturn ... | [
[
"numpy.hstack",
"numpy.log",
"numpy.zeros",
"numpy.nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pabloi09/physionet-challenge-2020 | [
"8e18e326bbad70bdcac11b27c65951dde698e2f3"
] | [
"webtool/apiserver/get_12ECG_features.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\nfrom scipy.signal import butter, lfilter, resample\nfrom scipy import stats\nlead_names = np.array([\"I\",\"II\", \"III\", \"aVR\", \"aVL\", \"aVF\", \"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\"])\n\ndef get_slices(signal):\n signals = []\n while len(signal) > 2... | [
[
"numpy.amax",
"numpy.amin",
"numpy.asarray",
"numpy.reshape",
"scipy.signal.resample",
"scipy.signal.butter",
"numpy.mean",
"scipy.signal.lfilter",
"numpy.array",
"numpy.where",
"numpy.zeros"
]
] | [
{
"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"
... |
younes-h/keras-video-classifier | [
"f4eeec2bc8e19b695a72ff00b9c8a556b54d505c"
] | [
"demo/vgg16_lstm_train.py"
] | [
"import numpy as np\nfrom keras import backend as K\nimport sys\nimport os\n\n\ndef main():\n #K.set_image_dim_ordering('tf')\n sys.path.append(os.path.join(os.path.dirname(__file__), '..'))\n\n from keras_video_classifier.library.utility.plot_utils import plot_and_save_history\n from keras_video_classi... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pjsier/national-voter-file | [
"f8bae42418c9307150d10c9e71174defaefa4e60"
] | [
"src/python/utils/censusreporter/censusreporter_api.py"
] | [
"import requests\r\nimport pandas as pd\r\nimport csv\r\nimport sys\r\nimport re\r\nimport collections\r\nfrom jsonmerge import merge\r\n\r\nAPI_URL=\"http://api.censusreporter.org/1.0/data/show/{release}?table_ids={table_ids}&geo_ids={geoids}\"\r\n\r\n\r\ndef _clean_list_arg(arg,default):\r\n if arg is None:\r\... | [
[
"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": []
}
] |
CarlosMR91/formation-evaluation | [
"185597e8ea8069bbd40aa967ff6667b6e4de328c"
] | [
"well_log_display.py"
] | [
"def well_log_display(df, column_depth, column_list, \n column_semilog=None, min_depth=None, max_depth=None, \n column_min=None, column_max=None, colors=None, \n fm_tops=None, fm_depths=None, \n tight_layout=1, title_size=10):\n \"\"\"... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dingdanhao110/Conch | [
"befa022dd08590062213ef2a17d0cf697fa26ec4",
"5c209865429cc711a40d6b529c7f3ab26083633b"
] | [
"problem.py",
"preprocess/cora.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\n problem.py\n\"\"\"\n\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nfrom scipy import sparse\nfrom sklearn import metrics\n\nimport torch\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\n\nfrom helpers i... | [
[
"torch.LongTensor",
"torch.nn.functional.multilabel_soft_margin_loss",
"torch.nn.functional.l1_loss",
"numpy.abs",
"numpy.arange",
"numpy.eye",
"torch.nn.functional.cross_entropy",
"torch.from_numpy",
"numpy.argmax",
"torch.FloatTensor",
"numpy.random.permutation",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
badeaadi/Computer_Vision | [
"843bdd6b6b4ea5b2332a16962195fee4cf7577f2"
] | [
"build_mosaic.py"
] | [
"\"\"\"\r\n PROIECT MOZAIC\r\n \r\n Badea Adrian Catalin, grupa 334, anul III, FMI\r\n\"\"\"\r\n\r\nimport os\r\nimport cv2 as cv\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pdb\r\nimport glob\r\n\r\nfrom add_pieces_mosaic import *\r\nfrom parameters import *\r\n\r\n\r\ndef load_piec... | [
[
"matplotlib.pyplot.imshow",
"numpy.asarray",
"numpy.stack",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
roy881020/pytorch-yolo2 | [
"6b2538b0ec314486479173920e6cacafbf5cd9e6"
] | [
"dataset.py"
] | [
"#!/usr/bin/python\n# encoding: utf-8\n\nimport os\nimport random\nimport torch\nimport numpy as np\nfrom torch.utils.data import Dataset\nfrom PIL import Image\nfrom utils import read_truths_args, read_truths\nfrom image import *\n\n\nclass listDataset(Dataset):\n\n def __init__(self, root, shape=None, shuffle=... | [
[
"torch.from_numpy",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fereshteh1992/sciann-applications | [
"8b2c173d226d769d7fb800359723025ec3ba91e6"
] | [
"SciANN-Vibrations/PlateVibration/membrane_inv.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport sciann as sn\nfrom sciann.utils.math import diff, sign, sin\nfrom gen_dataset import gen_grid\n\n\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom matplotlib.ticker import LinearLocator, FormatStrF... | [
[
"matplotlib.pyplot.figaspect",
"numpy.sqrt",
"matplotlib.pyplot.title",
"numpy.linspace",
"numpy.abs",
"matplotlib.pyplot.figure",
"numpy.cos",
"matplotlib.pyplot.savefig",
"numpy.sin",
"numpy.concatenate",
"matplotlib.gridspec.GridSpec",
"numpy.random.rand",
"m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wakananai/MBLLEN | [
"f3ce1663235d060e48e36b5bdf62e793dd75e37d"
] | [
"main/test.py"
] | [
"from glob import glob\r\nimport numpy as np\r\nimport scipy\r\nimport keras\r\nimport os\r\nimport Network\r\nimport utls\r\nimport time\r\nimport cv2\r\nimport argparse\r\nfrom tqdm import tqdm\r\n\r\nfrom keras.backend.tensorflow_backend import set_session\r\nimport tensorflow as tf\r\nconfig = tf.ConfigProto()\... | [
[
"numpy.minimum",
"numpy.maximum",
"numpy.power",
"numpy.percentile",
"tensorflow.ConfigProto",
"numpy.concatenate",
"tensorflow.Session",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
rheiland/pc4covid19_v6_test | [
"1f0dc29a787b008436da0299bf93168af9520925"
] | [
"bin/substrates-v4-new.py"
] | [
"# substrates Out:Plots\n\nimport os, math\nfrom pathlib import Path\nfrom ipywidgets import Layout, Label, Text, Checkbox, Button, BoundedIntText, HBox, VBox, Box, \\\n FloatText, Dropdown, SelectMultiple, RadioButtons, interactive\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import BoundaryNorm\nf... | [
[
"matplotlib.collections.PatchCollection",
"matplotlib.colors.to_rgb",
"numpy.fabs",
"matplotlib.patches.Circle",
"matplotlib.pyplot.subplots",
"numpy.broadcast",
"numpy.broadcast_to",
"numpy.isscalar",
"matplotlib.ticker.MaxNLocator",
"numpy.count_nonzero",
"numpy.array... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhijieW94/ETNet | [
"dd6676f38ec446703c3b71289457a53188701a43"
] | [
"data_processing/data_processing.py"
] | [
"import tensorflow as tf\nfrom scipy import misc\nimport numpy as np\nimport random\n\nclass ImageData:\n def __init__(self, img_h, img_w, channels):\n self.img_h = img_h\n self.img_w = img_w\n self.channels = channels\n\n def image_processing(self, filename):\n x = tf.read_file(fi... | [
[
"numpy.expand_dims",
"tensorflow.read_file",
"numpy.clip",
"tensorflow.image.resize_images",
"tensorflow.shape",
"tensorflow.cast",
"tensorflow.random_crop",
"scipy.misc.imread",
"numpy.zeros",
"tensorflow.image.decode_jpeg"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": [
"1.10"
]
}
] |
vksysd/P4_INT | [
"ddf51610cbdcc1e79f493df42f87a37f320360b6"
] | [
"INT_headerstack/results/graphs.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nx = pd.read_csv('s3_qlength.txt',index_col=None,sep=' ')\n\nprint(x.columns)\nx.columns = ['Time','Queue Length']\n# x.columns = ['Time','Switch Latency']\nx.plot(x='Time',y='Queue Length',kind='line')\n# x.plot(x='Time',y='Switch Latency'... | [
[
"matplotlib.pyplot.xlabel",
"pandas.read_csv",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jklymak/yt | [
"c1e23d74f5288846bd14c6aea3e2b78c70f7af62"
] | [
"yt/visualization/volume_rendering/render_source.py"
] | [
"import abc\nfrom functools import wraps\n\nimport numpy as np\n\nfrom yt.config import ytcfg\nfrom yt.data_objects.image_array import ImageArray\nfrom yt.funcs import ensure_numpy_array, is_sequence, mylog\nfrom yt.geometry.grid_geometry_handler import GridIndex\nfrom yt.geometry.oct_geometry_handler import Octree... | [
[
"numpy.expand_dims",
"numpy.isnan",
"numpy.dstack",
"numpy.full",
"numpy.ones",
"numpy.prod",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LCAV/lippmann-photography | [
"d2f1c0bb2817d762f813ca5b4de12a4f02ee3069",
"d2f1c0bb2817d762f813ca5b4de12a4f02ee3069"
] | [
"test_cosine_transforms.py",
"test_perspective.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 20 15:18:17 2018\n\n@author: gbaechle\n\"\"\"\nimport numpy as np\nimport scipy as sp\n\nimport matplotlib.pyplot as plt\n\nc = 299792458\n#c = 2\n\ndef cosine_transform(x, y, w, inv=False, theta=0):\n \n cosines = np.cos(x[None, :] ... | [
[
"matplotlib.pyplot.gca",
"scipy.special.hyp1f1",
"numpy.imag",
"scipy.special.gamma",
"numpy.conj",
"numpy.linspace",
"numpy.gradient",
"numpy.cos",
"numpy.sin",
"matplotlib.pyplot.plot",
"scipy.stats.norm",
"numpy.real",
"matplotlib.pyplot.close",
"numpy.mo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luizgfalqueto/algNum1 | [
"e8c28656bf2abdd18b4abe42a6668dbf188f3f7d"
] | [
"ajusteCurvas.py"
] | [
"from sympy import Symbol\nimport matplotlib.pyplot as plt\n\n\ndef geraFuncao(a, b):\n func = 0\n var = Symbol('x') # Simbolo para montar o polinomio interpolador\n\n return a + b * var\n\n\ndef calculaReta(a, b, x):\n Y = a + b * x\n\n return Y\n\n\ndef tabelaQuadradosMinimos(x, y):\n x2 = 0\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.text",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Alanthink/banditpylib | [
"d455424ed74be1850ee3969b7b31f08d49339005",
"d455424ed74be1850ee3969b7b31f08d49339005"
] | [
"banditpylib/learners/mab_learner/softmax.py",
"banditpylib/learners/mab_learner/ucb_test.py"
] | [
"from typing import Optional\n\nimport math\n\nimport numpy as np\n\nfrom banditpylib.arms import PseudoArm\nfrom banditpylib.data_pb2 import Context, Actions, Feedback\nfrom .utils import MABLearner\n\n\nclass Softmax(MABLearner):\n r\"\"\"Softmax policy\n\n At time :math:`t`, sample arm :math:`i` to play with s... | [
[
"numpy.array"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nespinoza/mirage | [
"b5ab8f8c6a1e02bb8402aff6f4aedc62f1dabbbc",
"b5ab8f8c6a1e02bb8402aff6f4aedc62f1dabbbc"
] | [
"mirage/catalogs/utils.py",
"mirage/utils/siaf_interface.py"
] | [
"#! /usr/bin/env python\n\n\"\"\"This module contains utility functions related to source catalogs\n\"\"\"\nimport os\n\nfrom astropy.io import ascii\nimport numpy as np\nfrom mirage.utils.constants import IMAGING_ALLOWED_CATALOGS, WFSS_ALLOWED_CATALOGS, \\\n TS_IMAGING_ALLOWED_CAT... | [
[
"numpy.min",
"numpy.all",
"numpy.max",
"numpy.argsort",
"numpy.array"
],
[
"numpy.ceil",
"numpy.array",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iesl/s-diora | [
"e93b4d3b0a8f52b629161769622bbcbae4d35d87"
] | [
"eval_parsing.py"
] | [
"import collections\nimport json\nimport os\n\nimport nltk\nfrom nltk.treeprettyprinter import TreePrettyPrinter\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\n\nfrom cky import ParsePredictor as CKY\nfrom experiment_logger import get_logger\nfrom evaluation_utils import BaseEvalFunc\n\n\ndef convert_to_... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JoeyOhman/alps | [
"77945f414304ec749110bd4dd2b8236d9a4868b3"
] | [
"src/train.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.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.utils.data.TensorDataset",
"torch.utils.data.SequentialSampler",
"torch.utils.data.DataLoader",
"torch.utils.data.RandomSampler",
"torch.distributed.barrier",
"torch.tensor",
"numpy.squeeze",
"numpy.set... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hsnemlekar/irl-maxent | [
"6ff99dae5571bb17e771bc2f38d7b47ffb107fa0"
] | [
"src/experiments.py"
] | [
"# import python libraries\nimport numpy as np\nfrom copy import deepcopy\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# import functions\nimport optimizer as O # stochastic gradient descent optimizer\nfrom vi import value_iteration\nfrom maxent_irl import *\nfrom assembly_tasks ... | [
[
"numpy.savetxt",
"pandas.read_csv",
"numpy.mean",
"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": [
... |
cristicmf/tpu | [
"05f7b15cdf0ae36bac84beb4aef0a09983ce8f66"
] | [
"models/experimental/detection/export_saved_model.py"
] | [
"# Copyright 2019 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.contrib.tpu.python.tpu.tpu_config.TPUConfig",
"tensorflow.app.run"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
dHannasch/datashader | [
"207e13dc372e03967aaee71ffb21bf6fc9a59fc4"
] | [
"datashader/reductions.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport numpy as np\nfrom datashape import dshape, isnumeric, Record, Option\nfrom datashape import coretypes as ct\nfrom toolz import concat, unique\nimport xarray as xr\n\nfrom datashader.glyphs.glyph import isnull\nfrom numba import cuda as nb_c... | [
[
"numpy.nanmax",
"numpy.sqrt",
"numpy.nanmin",
"numpy.stack",
"numpy.nansum",
"numpy.float64",
"numpy.errstate",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bcrafton/speed_read | [
"3e9c0c873e49e4948a216aae14ec0d4654d1a62c"
] | [
"src/resnet.py"
] | [
"\nimport numpy as np\nimport tensorflow as tf\n\nfrom layers import *\nfrom conv import *\nfrom block import *\nfrom model import *\n\n################\n\ndef quantize_np(x):\n scale = 127 / np.max(np.absolute(x))\n x = x * scale\n x = np.round(x)\n x = np.clip(x, -127, 127)\n return x, scale\n\ndef load_inpu... | [
[
"numpy.absolute",
"numpy.clip",
"numpy.round",
"numpy.shape",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lazyKindMan/BioBERT-MCNN | [
"d971e25fd1fe7b3ecbf46e1225b07eb15a759d78"
] | [
"ner_util/create_ner_data_bake.py"
] | [
"import codecs\nimport collections\nimport json\nimport os\nimport pickle\nfrom functools import partial\n\nimport tensorflow as tf\n\nfrom bert_base import tokenization\n\nfrom ner_util.logutil import set_logger\nfrom prepro_utils import preprocess_text, encode_ids\nimport sentencepiece as spm\n\nlogger = set_logg... | [
[
"tensorflow.logging.info",
"tensorflow.logging.set_verbosity",
"tensorflow.train.Features",
"tensorflow.python_io.TFRecordWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
coder-cell/Color-Detection | [
"4aa8e5ade20d1ef5143edd2ffef599ef4198278c"
] | [
"main.py"
] | [
"import cv2\nimport pandas as pd\n\n# #Creating argument parser to take image path from command line\n# ap = argparse.ArgumentParser()\n# ap.add_argument('-i', '--image', required=True, help=\"Image Path\")\n# args = vars(ap.parse_args())\n# img_path = args['image']\n\n# Reading the image with opencv\nimg = cv2.imr... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
akhilpm/gradCAM | [
"1a3b62cdfa81bb6584a0aee7fdda88608663a12b"
] | [
"script/hyp_test.py"
] | [
"import os\nimport sys\nimport time\nimport cv2 as cv\nimport pickle\nimport torch\nimport numpy as np\nimport torch.nn.functional as F\nimport dataset.dataset_factory as dataset_factory\nfrom colorama import Back, Fore\nfrom config import cfg, update_config_from_file\nfrom torch.utils.data import DataLoader\nfrom ... | [
[
"numpy.hstack",
"numpy.maximum",
"numpy.minimum",
"torch.load",
"numpy.around",
"torch.utils.data.DataLoader",
"matplotlib.pyplot.get_cmap",
"numpy.round",
"numpy.max",
"matplotlib.cm.ScalarMappable",
"torch.device",
"numpy.argsort",
"numpy.array",
"numpy.su... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
junj2ejj/ptranking.github.io | [
"06fa9751dd2eca89749ba4bb9641e4272cfc30a1"
] | [
"ptranking/metric/metric_utils.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\n\"\"\"Description\n\n\"\"\"\n\nimport torch\n\nfrom ptranking.metric.adhoc_metric import torch_ideal_dcg\nfrom ptranking.ltr_global import global_gpu as gpu, tensor\n\n#######\n# For Delta Metrics\n#######\n\ndef get_delta_ndcg(batch_stds, batch_stds_sorted_via_p... | [
[
"torch.abs",
"torch.transpose",
"torch.unsqueeze",
"torch.log2",
"torch.pow",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marcevrard/pyannote-audio | [
"1744715041c7393ddc8739698839abc6141fecaf"
] | [
"pyannote/audio/applications/speaker_embedding.py"
] | [
"#!/usr/bin/env python\n# encoding: utf-8\n\n# The MIT License (MIT)\n\n# Copyright (c) 2017-2020 CNRS\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, includin... | [
[
"numpy.min",
"numpy.vstack",
"numpy.max",
"numpy.mean",
"numpy.array",
"scipy.cluster.hierarchy.fcluster"
]
] | [
{
"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"
... |
mathialm/GPUTaskScheduler | [
"c65fb95950239cac792dd28a5807e239defb6727"
] | [
"gpu_task_scheduler/start_gpu_task.py"
] | [
"try:\n import matplotlib\nexcept ImportError:\n pass\nelse:\n matplotlib.use(\"Agg\")\n\nimport sys\nimport imp\ntry:\n import cPickle as pickle\nexcept ImportError:\n import _pickle as pickle\n\n\ndef main():\n cwd = sys.argv[4]\n sys.path.append(cwd)\n pkl_file = sys.argv[1]\n imp.load... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
makdaddy604/Tensorflow | [
"61793e67bd0dc3aedd5d395c281993261b597af8"
] | [
"official/resnet/resnet_run_loop.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.cond",
"tensorflow.cast",
"tensorflow.map_fn",
"tensorflow.where",
"tensorflow.estimator.RunConfig",
"tensorflow.group",
"tensorflow.estimator.train_and_evaluate",
"tensorflow.estimator.export.PredictOutput",
"tensorflow.contrib.opt.LARSOptimizer",
"tensorflow.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SiavashMT/OCT-MPS | [
"1fe8a8c25063ef3cee8b96128f20d040ac613ba7"
] | [
"util/visualization_BScan.py"
] | [
"import matplotlib\nmatplotlib.use('Qt5Agg')\nfrom matplotlib import pyplot as plt\nfrom src.python.octmps_output import parse_OCTMPS_output_file\n\nfont = {'family': 'serif',\n 'weight': 'normal',\n 'size': 18}\n\n\ndef force_aspect(ax, aspect=1):\n im = ax.get_images()\n extent = im[0].get_ext... | [
[
"matplotlib.use",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.subplots_adjust"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RyanWangZf/NRE-IF | [
"738126d3ea06b396c67417e684400f510405f319"
] | [
"main_findif.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom config import opt\nimport models\nimport dataset\nimport torch\nimport numpy as np\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom utils import save_pr, now, eval_metric\nfrom torch.autograd import g... | [
[
"torch.mean",
"torch.nn.functional.softmax",
"torch.max",
"torch.cat",
"torch.utils.data.DataLoader",
"numpy.concatenate",
"numpy.argmin",
"torch.cuda.manual_seed_all",
"numpy.exp",
"torch.nn.CrossEntropyLoss",
"torch.mm",
"numpy.arange",
"torch.eye",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
popsa-hq/keras-vggface | [
"ed40b687c1669a2aed2f216dd5670520c0a9a08f"
] | [
"keras_vggface/android_model_creation.py"
] | [
"import time\n\nimport tensorflow as tf\nfrom tflite_support import flatbuffers\nfrom tflite_support import metadata as _metadata\nfrom tflite_support import metadata_schema_py_generated as _metadata_fb\n\nfrom keras_vggface import VGGFace\nfrom keras_vggface.preprocessing import create_preprocessing_model\nfrom ke... | [
[
"numpy.expand_dims",
"tensorflow.lite.TFLiteConverter.from_keras_model",
"tensorflow.keras.preprocessing.image.load_img",
"tensorflow.lite.Interpreter",
"tensorflow.keras.preprocessing.image.img_to_array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
tamaswells/Nanocut_redistribute | [
"3103eaa3c015ab1c04fb254d51c263a00df90cae"
] | [
"src/nanocut/periodic_1D_prism.py"
] | [
"import numpy as np\nfrom nanocut.common import EPSILON, PERIODIC_TOLERANCE\nfrom nanocut.polyhedron import Polyhedron\nfrom nanocut.output import error\n\nclass Periodic1DPrism(Polyhedron):\n \"\"\"Class for periodic bodies bounded by a group of planes.\"\"\"\n \n\n def __init__(self, geometry, period, **... | [
[
"numpy.array",
"numpy.linalg.norm",
"numpy.any",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rmarx/quic_iot | [
"56e3c184bdaa20c065150c33851a5b6608987b8b"
] | [
"proxy/fiat_module/feature_selection.py"
] | [
"import json\nimport time\n\nfrom scapy.all import PcapReader\nfrom scapy.layers.inet import IP, TCP, UDP, ICMP, Ether\nimport scapy.all as sc\nsc.load_layer(\"tls\")\nimport sys\nimport os\nimport struct\nimport copy\nfrom collections import defaultdict\nimport hashlib\nimport hmac\nimport socket\n\nfrom string im... | [
[
"numpy.std",
"numpy.average",
"numpy.percentile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raymondyeh07/tv_layers_for_cv | [
"623379b249acc115ccb3ac0873fbf6d64ea9768f"
] | [
"tv_opt_layers/layers/l1_tv_1d_layer.py"
] | [
"\"\"\"Implements an 1D TVL1 layer.\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom tv_opt_layers.ops.proximity_tv_cuda import ProxTV_l1_cuda\n\n\nclass L1TV1DLayer(nn.Module):\n def __init__(self, lmbd_mode='learned', lmbd_init=-1, lmbd_zero=-1, num_channels=1,\n direction='row', dtype=torch.f... | [
[
"torch.tensor",
"torch.ones",
"torch.rand",
"torch.nn.functional.softplus"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IBM/yaso-tsa | [
"148d2ba14c8213d9d946305bc558066028c43468"
] | [
"tests/test_CategoricalLabel.py"
] | [
"# © Copyright IBM Corporation 2021.\n#\n# LICENSE: Apache License 2.0 (Apache-2.0)\n# http://www.apache.org/licenses/LICENSE-2.0\n\nfrom unittest import TestCase\nfrom collections import Counter\n\nfrom yaso_tsa.infra.CategoricalLabel import CategoricalLabel\n\n\nclass TestCategoricalLabel(TestCase):\n def test... | [
[
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dmavridis/catz | [
"5c5d2c451dc8128889c65e57f0465787d1b7f6c9"
] | [
"train.py"
] | [
"from keras.layers import Conv2D, UpSampling2D, MaxPooling2D\nfrom keras.models import Sequential\nfrom keras.callbacks import Callback\nimport random\nimport glob\nimport wandb\nfrom wandb.keras import WandbCallback\nimport subprocess\nimport os\nfrom PIL import Image\nimport numpy as np\nfrom keras import backend... | [
[
"numpy.concatenate",
"numpy.split",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joshmurr/style-based-gan-pytorch | [
"d65e5f03805ffa56a38f3d187d8a8734a56b17d7"
] | [
"generate.py"
] | [
"import argparse\r\nimport math\r\n\r\nimport torch\r\nfrom torchvision import utils, transforms, io\r\nimport numpy as np\r\nfrom PIL import Image\r\n\r\nfrom model import StyledGenerator\r\n\r\n\r\n@torch.no_grad()\r\ndef get_mean_style(generator, device):\r\n\tmean_style = None\r\n\r\n\tfor i in range(10):\r\n\t... | [
[
"torch.ones",
"torch.load",
"torch.cat",
"numpy.arange",
"torch.randn",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rsese/dedupe | [
"cfba7c08f03a0949c5d72f0f4d8ebfb8e48e4507"
] | [
"dedupe/clustering.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport itertools\nfrom collections import defaultdict\nimport array\nimport logging\n\nimport numpy\nimport fastcluster\nimport hcluster\n\nlogger = logging.getLogger(__name__)\n\n\ndef connected_components(edgelist, max_components):\n\n if len(edgelist) == 0:\n ... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.nditer",
"numpy.min",
"numpy.unique",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NannyML/NannyML | [
"a6fc73f627d1e0c23c4ef7cc43bd653684c48e1a"
] | [
"tests/test_chunk.py"
] | [
"# Author: Niels Nuyttens <niels@nannyml.com>\n#\n# License: Apache Software License 2.0\n\n\"\"\"Tests for the chunking functionality.\"\"\"\nimport datetime\nimport math\nfrom typing import List\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas import Timestamp\n\nfrom nannyml.chunk import... | [
[
"numpy.sqrt",
"numpy.random.seed",
"pandas.DataFrame",
"numpy.random.randn",
"numpy.random.rand",
"pandas.date_range",
"pandas.Timestamp",
"numpy.random.default_rng",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HCGB-IGTP/BacterialTyper | [
"215e29a0381d4ae616cf0a6462a04117dc30e293"
] | [
"BacterialTyper/report/Staphylococcus/agr_typing.py"
] | [
"#!/usr/bin/env python3\n##########################################################\n## Jose F. Sanchez ##\n## Copyright (C) 2019-2021 Lauro Sumoy Lab, IGTP, Spain ##\n##########################################################\n\"\"\"\nCreates agr typing\n\"\"\"\n## useful impor... | [
[
"pandas.concat",
"pandas.DataFrame",
"pandas.ExcelWriter"
]
] | [
{
"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": []
}
] |
anuj-harisinghani/canary-nlp | [
"5225fa028f0f744cd6582f927f3990c1a50b1f9b"
] | [
"classes/handlers/ResultsHandler.py"
] | [
"import sys\nimport os\nimport pandas as pd\n\nMETRICS = 'metric'\nMODEL = 'model'\nACCURACY = 'acc'\nROC = 'roc'\nF1_SCORE = 'f1'\nPRECISION = 'precision'\nRECALL = 'recall'\nSETTINGS = 'settings'\nSPECIFICITY = 'specificity'\n\nACCURACY_SD = 'acc_sd'\nROC_SD = 'roc_sd'\nF1_SD = 'f1_sd'\nPREC_SD = 'prec_sd'\nREC_S... | [
[
"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": []
}
] |
lmillard79/DFUDA_2019_Adapter | [
"933410d197a87e7defed20a084bf8a250f4c1dca"
] | [
"03_Post_Adapter.py"
] | [
"\nfrom xml.etree.ElementTree import * # Import everything \n\nimport time\nfrom datetime import datetime\nimport csv\nimport os\n\n## from xlsxwriter.workbook import Workbook\n\nimport numpy as np\nimport pandas as pd ## Abbreviate it using the convention\npd.options.display.date_dayfirst=True # Important to so... | [
[
"pandas.read_table",
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
shiwj16/SSINet | [
"7f285878a7798b7a8eeb3c64f99e2787ffa0b32e"
] | [
"pretrain_ppo_atari.py"
] | [
"import os\nimport numpy as np\n\nfrom src.ppo_atari import PPO\nfrom src.args import get_ppo_args\nfrom src.utils import get_dirs, write_arguments, seed\nfrom envs_utils.create_env import create_multiple_envs, create_single_env\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n\n\nif __name__ == '__main__':\n # se... | [
[
"numpy.savez",
"numpy.asarray",
"numpy.ones",
"numpy.copy",
"numpy.zeros_like",
"numpy.array",
"numpy.zeros",
"numpy.float"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lermert/scipy | [
"b7dce8db4bb9833d7f3faee9d783c483295711cb"
] | [
"scipy/optimize/optimize.py"
] | [
"#__docformat__ = \"restructuredtext en\"\n# ******NOTICE***************\n# optimize.py module by Travis E. Oliphant\n#\n# You may copy and use this module as you see fit with no\n# guarantee implied provided you keep this notice in all copies.\n# *****END NOTICE************\n\n# A collection of optimization algori... | [
[
"numpy.diag",
"numpy.dot",
"numpy.take",
"numpy.sqrt",
"numpy.asarray",
"numpy.squeeze",
"numpy.all",
"numpy.zeros_like",
"numpy.reshape",
"numpy.eye",
"numpy.add.reduce",
"numpy.finfo",
"numpy.atleast_1d",
"numpy.asfarray",
"numpy.size",
"scipy._lib... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
slowbull/a3c | [
"d146ff10fc06d9278957872d882f6eb06751f41b"
] | [
"train.py"
] | [
"import math\nimport os\nimport sys\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom envs import create_atari_env\nfrom model import ActorCritic\nfrom torch.autograd import Variable\nfrom torchvision import datasets, transforms\n\n\ndef ensure_shared_grads(model, shared_model):\n ... | [
[
"torch.nn.functional.softmax",
"torch.nn.functional.log_softmax",
"torch.zeros",
"torch.manual_seed",
"torch.from_numpy",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pevisscher/camelot | [
"d93423e0dc78b64e3f2881714c91709d9241d163"
] | [
"camelot/image_processing.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport cv2\nimport numpy as np\n\n\ndef adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2):\n \"\"\"Thresholds an image using OpenCV's adaptiveThreshold.\n\n Parameters\n ----------\n imagename : string\n Path to image file.\n process_backgr... | [
[
"numpy.invert",
"numpy.zeros",
"numpy.multiply"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ishaan-narula/coursera-deep-learning-specialisation | [
"ee33cd3cb5b0b72beadd73d9ec51acd97f1398ff"
] | [
"4 Coursera - Convolutional Neural Networks/Programming Assignments/Week 1 - Foundations of Convolutional Neural Networks/W1A2/test_utils.py"
] | [
"import numpy as np\nfrom termcolor import colored\n\nfrom tensorflow.keras.layers import Input\nfrom tensorflow.keras.layers import Conv2D\nfrom tensorflow.keras.layers import MaxPooling2D\nfrom tensorflow.keras.layers import Dropout \nfrom tensorflow.keras.layers import Conv2DTranspose\nfrom tensorflow.keras.laye... | [
[
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
airflow-plugins/spreadsheet_plugin | [
"bcb75666db14272dc31032920bb17263e6a6def0"
] | [
"operators/s3_to_spreadsheet_operator.py"
] | [
"from airflow.utils.decorators import apply_defaults\nfrom airflow.hooks.S3_hook import S3Hook\nfrom airflow.models import BaseOperator\nimport pandas as pd\nfrom BoxPlugin.hooks.box_hook import BoxHook\n\n\nclass S3ToSpreadsheetOperator(BaseOperator):\n \"\"\"\n S3 to Spreadsheet Operator\n :param input_s... | [
[
"pandas.read_csv",
"pandas.ExcelWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
nitrawf/recommender | [
"66f30665a3bfae9fd719d0947fa3306ecaf5b63e"
] | [
"scraper.py"
] | [
"import requests\nfrom bs4 import BeautifulSoup\nimport json\nimport os\nimport pandas as pd\nimport time\n\ndef cls():\n os.system('cls' if os.name=='nt' else 'clear')\n\ndef get_details(player):\n\tuser_id = player.find(class_ = \"ranking-page-table__user-link-text js-usercard\").get('data-user-id')\n\trank = ... | [
[
"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": []
}
] |
mitmedialab/color_tag_tracker | [
"9eddbb76d41ca6ea531a8b7c7c76792312be1ab4"
] | [
"color_tag_tracker_webcam_test.py"
] | [
"import numpy as np\nimport cv2\nfrom color_tag_tracker import find_tags\n\nframes = 5000\n\ncam_mtx = np.load('mtx.npy')\ncam_dist = np.load('dist.npy')\n\ncamera = cv2.VideoCapture(0)\n\nprint(\"Starting test\")\n\nfor i in range(frames):\n if i % 50 is 0:\n print(i)\n\n # Read image from camera\n ... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
worldveil/tensorflow | [
"f5de234d7f601214443f371e90fbadc8f128bb9a"
] | [
"tensorflow/contrib/py2tf/conversion.py"
] | [
"# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.contrib.py2tf.pyct.parser.parse_object",
"tensorflow.contrib.py2tf.convert.control_flow.transform",
"tensorflow.contrib.py2tf.convert.logical_expressions.transform",
"tensorflow.contrib.py2tf.convert.side_effect_guards.transform",
"tensorflow.contrib.py2tf.naming.Namer",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
klauscc/tfcx | [
"df96fd433f13f277d831da6ffc91163566bb4a56",
"df96fd433f13f277d831da6ffc91163566bb4a56"
] | [
"network/unet.py",
"callbacks/ckpt_callbacks.py"
] | [
"# -*- coding: utf-8 -*-\n#================================================================\n# God Bless You.\n#\n# author: klaus\n# email: chengfeng2333@gmail.com\n# created date: 2019/09/26\n# description:\n#\n#================================================================\n\nimport numpy as np\nimpor... | [
[
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.layers.ReLU",
"numpy.sqrt",
"tensorflow.keras.Sequential",
"tensorflow.keras.layers.MaxPool2D",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.backend.random_uniform",
"tensorflow.keras.layers.Dropout"
],
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
... |
WhuEven/multi_hyp_cc | [
"53a6bc438b865d606f5e6a53a442efbd8a04fe5b"
] | [
"loss/ffcc.py"
] | [
"# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT,... | [
[
"torch.inverse",
"torch.matmul",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Katsumata420/generic-pretrained-GEC | [
"c7c6391e8d033ac784f72490d1aceabacb27ad43"
] | [
"BART-GEC/translate.py"
] | [
"import torch\nfrom fairseq.models.bart import BARTModel\n\nimport sys\nimport os\n\nassert len(sys.argv) == 4, \"translate.py model_dir input_text output_dir\"\nmodel_dir = sys.argv[1]\ninput_text = sys.argv[2]\noutput_dir = sys.argv[3]\noutput_path = os.path.join(output_dir, \"hyp.txt\")\n\n\nbart = BARTModel.fro... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LukasMosser/MPyS | [
"e1be4ab9ca77fb4fbcd8481cb67605586161a05e"
] | [
"mpys/ds/nearest_neighbor_search.py"
] | [
"from numba import jit\nimport numpy as np\nimport bottleneck as btn\n\n\n@jit(nopython=True, nogil=True)\ndef compute_distances_brute_force_2d(current_node, traced_path, distances):\n for i in range(len(traced_path)):\n distances[i] = (traced_path[i][0] - current_node[0]) * (traced_path[i][0] - current_n... | [
[
"scipy.spatial.cKDTree",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
namabilly/iLOCuS | [
"761fe4162a9fb551f43d887c3ae9d448c3cc8c14"
] | [
"RL/driver_func_test.py"
] | [
"import numpy as np\r\nclass DriverSim:\r\n def __init__(self):\r\n pass\r\n\r\n def react(self, pricing):\r\n return np.random.rand(4,15,15), False\r\n \r\n def reset(self):\r\n return np.random.rand(4,15,15)\r\n"
] | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cmbi/Benchmarking_splice_prediction_tools | [
"d0b50220091c031c46656c7feac120e3f972d890",
"d0b50220091c031c46656c7feac120e3f972d890"
] | [
"DSSP/DSSP_DI_input.py",
"analysis_variants.py"
] | [
"#import\nimport pandas as pd\nimport numpy as np\nimport pybedtools\nfrom Bio import SeqIO\nimport hgvs\nfrom hgvs.easy import parser\nimport sys\nsys.path.insert(1, '../')\nfrom functions import reverse_sequence\n\ngene = 'ABCA4'\nlength = 140\n\n# define variables\nexcel_file = '../data/variant_scores.xlsx'\ngen... | [
[
"pandas.read_excel"
],
[
"pandas.read_excel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
Akssi/fps-rl | [
"3437ba6d1289350865bc4c65d6cc0e884897dd27"
] | [
"models/DQN.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass DQN(nn.Module):\n def __init__(self, dims, n_actions, conv_net):\n super(DQN, self).__init__()\n self.conv_net = conv_net\n self.n_actions = n_actions\n self.layers = nn.ModuleList()\n prev_dim = dims[0]\n for dim in dims[1... | [
[
"torch.nn.LSTM",
"torch.nn.ModuleList",
"torch.nn.Linear",
"torch.stack",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HackintoshMan/IndigoMQTTBridge | [
"d0d610fac45486167bcc8c9ab7e5d83ba3528e70"
] | [
"GreenSkyMQTTBridge.indigoPlugin/Contents/Server Plugin/jsonpickle/ext/numpy.py"
] | [
"from __future__ import absolute_import\nimport ast\nimport sys\nimport zlib\nimport warnings\n\nimport numpy as np\n\nfrom ..handlers import BaseHandler, register, unregister\nfrom ..compat import numeric_types\nfrom ..util import b64decode, b64encode\nfrom .. import compat\n\n__all__ = ['register_handlers', 'unre... | [
[
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
w-rfrsh/leads-recommender | [
"38706f1e9eb080a294c455394f7d09e8b200702f"
] | [
"src/models/recommender.py"
] | [
"import os\nimport pandas as pd\nfrom src.evaluation.evaluate import similarity_metric\n\nusefull_cols = ['sg_uf', 'nm_meso_regiao', 'nm_micro_regiao', 'fl_rm', 'setor', 'nm_segmento',\n 'de_natureza_juridica', 'de_nivel_atividade', 'idade_empresa_anos', 'vl_faturamento_estimado_aux']\n\n\ndef genera... | [
[
"pandas.RangeIndex"
]
] | [
{
"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": []
}
] |
ak710/Stocks-Dash | [
"c916dce1327a4c9d328da8437daff78acbdde169"
] | [
"dash.py"
] | [
"from altair.vegalite.v4.schema.core import Step\nimport yfinance as yf\nimport streamlit as st\nimport wealthsimple\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport ta\nimport backtrader as bt\n\ndef format_number(number):\n return f\"{number:,}\"\n\n\nst.sidebar.write(\"Navigat... | [
[
"matplotlib.pyplot.pie",
"matplotlib.pyplot.subplots",
"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": []
}
] |
NijatZeynalov/Building-Neural-Network-architectures-from-scratch | [
"c64ff9599f92d5ae1b7a3426dda2de8fc195d93e"
] | [
"vgg16.py"
] | [
"import tensorflow as tf\nimport tensorflow_datasets as tfds\n\n\nclass Block(tf.keras.Model):\n def __init__(self, filters, kernel_size, repetitions, pool_size=2, strides=2):\n super(Block, self).__init__()\n self.filters = filters\n self.kernel_size = kernel_size\n self.repetitions ... | [
[
"tensorflow.keras.layers.Dense",
"tensorflow.cast",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.keras.layers.Flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
yutiansut/ray | [
"4157bcb80b169779f43a549dd503911d95507cc2"
] | [
"python/ray/worker.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport atexit\nimport cloudpickle as pickle\nimport collections\nimport colorama\nimport copy\nimport hashlib\nimport inspect\nimport json\nimport numpy as np\nimport os\nimport redis\nimport signal\ni... | [
[
"numpy.random.get_state",
"numpy.random.seed",
"pandas.DataFrame",
"numpy.dtype",
"numpy.random.bytes",
"numpy.random.set_state"
]
] | [
{
"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": []
}
] |
stefanbschneider/keras-rl | [
"216c3145f3dc4d17877be26ca2185ce7db462bad"
] | [
"examples/ddpg_mujoco.py"
] | [
"import numpy as np\n\nimport gym\nfrom gym import wrappers\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Activation, Flatten, Input, Concatenate\nfrom keras.optimizers import Adam\n\nfrom rl.processors import WhiteningNormalizerProcessor\nfrom rl.agents import DDPGAgent\nfrom rl.me... | [
[
"numpy.random.seed",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yangyangxusheng/Sarcasm-style-tranfer | [
"5619a226e952bde0b6964a75b6e4d35ec42ca937"
] | [
"Predict_the_level_of_sarcasm.py"
] | [
"import torch\r\nimport json\r\nfrom run_pplm_discrim_train import predict,Discriminator\r\nfrom pplm_classification_head import ClassificationHead\r\n\r\nEPSILON = 1e-10\r\npretrained_model = 'gpt2-medium'\r\nidx2class = ['0', '1']\r\n\r\ndef load_classifier_head(weights_path, meta_path, device):\r\n with open(... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saist1993/parseq | [
"dce90d06d14ffbb0a471849f04c373a173475d3a"
] | [
"parseq/scripts/lcquad_vib.py"
] | [
"import math\nimport os\nimport random\nimport re\nimport sys\nfrom abc import ABC\nfrom functools import partial\nfrom typing import *\n\nimport dill as dill\nimport torch\nimport numpy as np\nimport ujson\n\nimport qelos as q\nfrom allennlp.modules.seq2seq_encoders import PytorchSeq2SeqWrapper\nfrom nltk import P... | [
[
"torch.nn.init.uniform_",
"torch.nn.init.uniform",
"torch.cat",
"numpy.asarray",
"torch.nn.init.constant_",
"torch.tensor",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.device",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SaiNikhileshReddy/Top_5_Projects | [
"1e754c80af8f5402fa828aad747d79d4edc62fb4"
] | [
"1) ASL Static Hand Gesture Recognition - Deep Learning/HandGesturev2.py"
] | [
"print(\"================================\")\n\nimport tensorflow as tf \nprint(f\"tensorflow : {tf.__version__}\")\nimport numpy as np\nprint(f\"numpy : {np.__version__}\")\nimport cv2\nprint(f\"cv2 : {cv2.__version__}\")\nimport time\n\ncam = cv2.VideoCapture(0)\nprint(f\"Web Camera Initialized\")\nmodel = tf.ker... | [
[
"tensorflow.keras.models.load_model",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
Totorino02/Numerical-algorithms | [
"2898386f52541612a6a21ceb413d0e55498152f1"
] | [
"eqDiff/runge-kutta-4.py"
] | [
"\"\"\"\n name: runge-kutta-4.py\n goal: numeric solve of differential equations\n author: Dr HOUNSI Madouvi antoine-sebastien\n date: 28/03/2022\n\"\"\"\n\nfrom math import exp, pow\nimport numpy as np\nimport matplotlib.pyplot as pt\nfrom interpolation.lagrange import Lagrange\n\n\nclass RungeKutta4:\... | [
[
"numpy.arange",
"matplotlib.pyplot.show",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.legend"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kage18/Split_Learning | [
"757e45e18797aa790ff7fd3438f2f69fcdc1c599"
] | [
"plotting.py"
] | [
"\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom typing import List\n\n\ndef generate_simple_plot(x: List, y: List, title: str=\"\", x_label: str=\"\",\n y_label: str=\"\", y_lim: List[float]=[0.0, 1.0], save: bool=True,\n fname: str=\"\"):\n fig, ax = plt.subplots()\... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AI4SIM/model-collection | [
"4e69558300e78d134d97d5a9665c5d0b717391eb"
] | [
"combustion/unets/utils.py"
] | [
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distri... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haakonrob/AI-Feynman | [
"445b68e9a260dcea67a94eed6e0aeb267f25d2ef"
] | [
"aifeynman/S_NN_eval.py"
] | [
"from __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport pandas as pd\nimport numpy as np\nimport torch\nfrom torch.utils import data\nimport pickle\nfrom torch.optim.lr_scheduler import CosineAnnealingLR\nfrom matplotlib impor... | [
[
"torch.load",
"torch.nn.Linear",
"torch.nn.functional.mse_loss",
"torch.cuda.is_available",
"numpy.column_stack",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
orashi/PaintsPytorch | [
"41cf321722a035101758c0717f082d71c12c6cf4",
"41cf321722a035101758c0717f082d71c12c6cf4"
] | [
"models/cp_model2.py",
"old_train.py"
] | [
"import numpy as np\nimport torch\nimport os\nimport sys\nimport functools\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.nn import init\nimport torch.nn.functional as F\nimport torchvision.models as M\n\n\nclass ResNeXtBottleneck(nn.Module):\n def __init__(self, in_channels=256, out_cha... | [
[
"torch.nn.Sequential",
"torch.load",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.PixelShuffle",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.InstanceNorm2d",
"torch.nn.LeakyReLU",
"torch.nn.functional.leaky_relu"
],
[
"scipy.stats.truncnorm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Yustira/Zelibe-Ugwuanyi-And-Different-Standard-Deviation-Algorithm | [
"a50dff4fa413fc23bc507a2206cdf8468efac379"
] | [
"Zelibe Ugwuanyi.py"
] | [
"import pandas as pd\nimport numpy as np\n\ndef is_balanced(data):\n sum_a = data['Supply'].sum()\n sum_b = data.loc['Demand'].sum()\n if sum_a == sum_b: \n print('Balanced data: ', sum_a)\n pass\n elif sum_a < sum_b:\n print('Unbalanced data: total supply < total demand ({:d} < {:d... | [
[
"pandas.read_excel",
"numpy.zeros"
]
] | [
{
"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": []
}
] |
JasonChu1313/KagglePipeline | [
"c4e2ab1150276dc2f1bffa30e0be8d7f5314b53a"
] | [
"preprocess/PIMP.py"
] | [
"from preprocess.FeatureSelection import FeatureSelection\n\nimport lightgbm as lgb\nimport pandas as pd\nfrom sklearn.metrics import roc_auc_score, mean_squared_error\nfrom preprocess.Dataset import Dataset\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport seaborn... | [
[
"pandas.concat",
"matplotlib.pyplot.tight_layout",
"numpy.random.seed",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"numpy.percentile",
"matplotlib.pyplot.subplot",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.suptitle",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
loedata/OC-DS-P5-Segmentez_Clients_site_e-commerce | [
"fa3ccf6e284ed510867e90a25677bd1fc5c3b572"
] | [
"POLIST_07_outils_test.py"
] | [
"\"\"\" Librairie personnelle pour exécuter des tests de normalité,\n homostéradiscité, ANOVA, Kruskall-Wallis\n\"\"\"\n\n#! /usr/bin/env python3\n# coding: utf-8\n\n# ====================================================================\n# Outil visualisation - projet 3 Openclassrooms\n# Version : 0.0.0 - CRE L... | [
[
"scipy.stats.chi2.ppf",
"pandas.crosstab",
"scipy.stats.chi2_contingency",
"scipy.stats.anderson",
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
SR42-dev/color-detection-against-white-background | [
"f3db16fc615259dfe3f3a189ad99488944ad0fe3"
] | [
"source/HSVLimitFinder.py"
] | [
"# HSV limit finding from webcam feed\r\n\r\nimport cv2\r\nimport numpy as np\r\n\r\n\r\ndef empty(a): # argument required\r\n pass\r\n\r\ndef stackImages(scale,imgArray):\r\n rows = len(imgArray)\r\n cols = len(imgArray[0])\r\n rowsAvailable = isinstance(imgArray[0], list)\r\n width = imgArray[0][0... | [
[
"numpy.hstack",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nlvargas/capstone | [
"a74a073468fb1060e4f472722af620c17f887faf"
] | [
"simulation.py"
] | [
"import random\nimport copy\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom schedueling import cargar_calendario\n\ndef sigma_dispersion(tabla):\n sigma = np.mean([(tabla[x].puntaje - tabla[x+1].puntaje) for x in range(1,len(tabla)-2)])\n #maxs = np.mean([((tabla[x].puntaje - tabla[x+1].puntaje)-me... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"numpy.int",
"matplotlib.pyplot.show",
"numpy.float",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShichenLiu/CondensedNet | [
"833a91d5f859df25579f70a2439dfd62f7fefb29"
] | [
"main.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport argparse\nimport os\nimport shutil\nimport time\nimport math\nimport warnings\nimport models\nfrom utils import convert_model, measure_model\n\nparser = ... | [
[
"torch.nn.CrossEntropyLoss",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.autograd.Variable",
"torch.cuda.manual_seed_all",
"torch.nn.DataParallel",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
s-andrews/LipidFinder | [
"c91d6caa8008e0a67188914e48f30913deff888d"
] | [
"LipidFinder/update_params.py"
] | [
"#!/usr/bin/env python\n\n# Copyright (c) 2019 J. Alvarez-Jarreta and C.J. Brasher\n#\n# This file is part of the LipidFinder software tool and governed by the\n# 'MIT License'. Please see the LICENSE file that should have been\n# included as part of this software.\n\"\"\"Transform the old parameters CSV file for P... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
LiuFG/UpdatingHDmapByMonoCamera | [
"68e549661f6e583d09448bd0a0b122a6dc2e9fc9"
] | [
"detection/mmdetection/mmdet/apis/inference.py"
] | [
"import warnings\n\nimport mmcv\nimport numpy as np\nimport pycocotools.mask as maskUtils\nimport torch\nfrom mmcv.runner import load_checkpoint\n\nfrom mmdet.core import get_classes\nfrom mmdet.datasets import to_tensor\nfrom mmdet.datasets.transforms import ImageTransform\nfrom mmdet.models import build_detector\... | [
[
"numpy.full",
"numpy.concatenate",
"torch.no_grad",
"numpy.array",
"numpy.where",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheChief/charly25lc_test | [
"ee64e9c7568eda5d2f030eba24c17954c345d52f"
] | [
"scripts/setup.py"
] | [
"from setuptools import setup\nimport os\nimport py2exe\nimport matplotlib\n\nincludes = [\n 'sip',\n 'PyQt5',\n 'PyQt5.uic',\n 'PyQt5.QtCore',\n 'PyQt5.QtDesigner',\n 'PyQt5.QtGui',\n 'PyQt5.QtNetwork',\n 'PyQt5.QtMultimedia',\n 'PyQt5.QtPrintSupport',\n 'PyQt5.QtWebSockets',\n 'PyQt5.QtWidgets',\n 'Py... | [
[
"matplotlib.get_py2exe_datafiles"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rizwandel/finetuner | [
"7fef9df6b5101d19a4fd710084d54b5be45dc5d5"
] | [
"tests/unit/tailor/test_keras.py"
] | [
"import pytest\nimport tensorflow as tf\nimport numpy as np\n\nfrom finetuner.tailor.keras import KerasTailor\n\n\n@pytest.fixture\ndef dense_model():\n model = tf.keras.models.Sequential()\n model.add(tf.keras.layers.InputLayer(input_shape=(128,))) # (None, 128)\n model.add(tf.keras.layers.Dense(128, act... | [
[
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.applications.vgg16.VGG16",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.InputLayer",
"tensorflow.keras.layers.MaxPool2D",
"numpy.testing.assert_array_equal",
"tensorflow.keras.laye... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
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