repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
DenisDPR/plantcv | [
"6922b4bcf57495214588d071ed1c7dce172b06ea"
] | [
"plantcv/plantcv/visualize/pseudocolor.py"
] | [
"# Pseudocolor any grayscale image\n\nimport os\nimport cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom plantcv.plantcv import params\nfrom plantcv.plantcv import plot_image\nfrom plantcv.plantcv import fatal_error\n\n\ndef pseudocolor(gray_img, obj=None, mask=None, cmap=None, background=\"image... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.title",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.colorbar",
"numpy.copy",
"numpy.shape",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jantunez14/merlin | [
"66d602919aa8d3ca20b77d4515b8214b5b1be508"
] | [
"src/frontend/acoustic_composition.py"
] | [
"################################################################################\n# The Neural Network (NN) based Speech Synthesis System\n# https://svn.ecdf.ed.ac.uk/repo/inf/dnn_tts/\n#\n# Centre for Speech Technology Research\n# University of Edinburgh... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arsenyinfo/idrnd_face_antispoofing | [
"8f438badbb3425dfd18cd39c558865f4b887b809"
] | [
"evaluate.py"
] | [
"import os\nfrom functools import lru_cache\nfrom glob import glob\nfrom time import time\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport yaml\nfrom fire import Fire\nfrom glog import logger\nfrom tensorboard.backend.event_processing.event_accumulator import EventAccumulator\nfrom torch.utils.data ... | [
[
"torch.jit.load",
"torch.softmax",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"torch.no_grad",
"numpy.mean",
"torch.cuda.is_available",
"numpy.float32"
]
] | [
{
"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": []
}
] |
iampartho/UniverseNet | [
"6aa7b1fb1134842da63fc291123ec370d52d63df"
] | [
"tools/convert_waymo_checkpoint_for_nightowls.py"
] | [
"import argparse\nfrom collections import OrderedDict\n\nimport torch\n\n\ndef convert(in_file, out_file):\n \"\"\"Convert values in checkpoints.\n \"\"\"\n # to ('pedestrian', 'bicycledriver', 'motorbikedriver')\n # from ('TYPE_VEHICLE', 'TYPE_PEDESTRIAN', 'TYPE_CYCLIST')\n indices = [1, 2, 2]\n\n ... | [
[
"torch.split",
"torch.save",
"torch.cat",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ajay051198/Facial-Feature-Detection | [
"f90bf4ee3ff3f3425a3aa47dcf96313c34c9431f"
] | [
"utils.py"
] | [
"import matplotlib.pyplot as plt\nimport load_data\nimport tensorflow as tf\nfrom random import randint\n\nX, y = load_data.prepare_data('validation')\nmodel = tf.keras.models.load_model('assets/CNN_facefeatures.h5')\nmodel.load_weights('assets/CNN_facefeatures_weights.h5')\nprint(model.summary())\nprint(X.shape)\n... | [
[
"tensorflow.keras.models.load_model",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
dirkzon/gesture-recognition | [
"f71658bb216f438f5c5ff3d9519b0f7625c989d6"
] | [
"gesture.py"
] | [
"import cv2\nimport time\nimport numpy as np\nfrom joblib import load\nfrom mediapipe.python.solutions import drawing_utils, hands\n\nprint('GESTURE RECOGNITION')\nprint(\"Press 'q' to quit\")\nprint(\"Press 'd' for debug\")\n\ndebug = False\nt = 0\ntxt_offset = (-60, 30)\n\n# load the models\ngesture_model = load(... | [
[
"numpy.asarray",
"numpy.stack",
"numpy.multiply"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jinxu06/gsubsampling | [
"2e0cace553cf43835709a34a11f9c15b08c15004"
] | [
"genesis/modules/blocks.py"
] | [
"# =========================== A2I Copyright Header ===========================\n#\n# Copyright (c) 2003-2020 University of Oxford. All rights reserved.\n# Authors: Applied AI Lab, Oxford Robotics Institute, University of Oxford\n# https://ori.ox.ac.uk/labs/a2i/\n#\n# This file is the property of the Unive... | [
[
"torch.sigmoid",
"torch.linspace",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.InstanceNorm2d",
"torch.nn.functional.interpolate",
"torch.nn.ReLU",
"torch.nn.functional.softplus"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
octaviancheng/question_generation | [
"cd870ee78fbfa14c2a95cdc8fbe4f033ac3688e2"
] | [
"prepare_data.py"
] | [
"import os\nimport logging\nfrom dataclasses import dataclass, field\nfrom typing import Dict, List, Optional\n\nimport torch\nimport nlp\nfrom transformers import T5Tokenizer, BartTokenizer, HfArgumentParser\n\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass DataTrainingArguments:\n \"\"\"\n Ar... | [
[
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IrisLi17/baselines | [
"3795f3275815d7d9e288f586fa76d682e7190ca2"
] | [
"baselines/deepq/expert.py"
] | [
"import pickle\nimport tensorflow as tf\nimport numpy as np\nfrom baselines.deepq.memory import Memory\n# from baselines.ddpg.ddpg import normalize, denormalize\nimport os\nimport math\nimport cv2\n# import baselines.deepq.agc as agc\nfrom baselines.deepq.agc import dataset,util\n\nclass Expert:\n def __init__(s... | [
[
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.random.random_integers"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
umarov90/DeepREFind | [
"c3b24b760f3829ea8ed6ba6ed7db76cf90c45a9e"
] | [
"validation/performance_species.py"
] | [
"import os\nimport numpy as np\nimport math\nimport common as cm\n\nos.chdir(open(\"../data_dir\").read().strip())\n\n\ndef get_refeafi(fpath):\n min_score = 100\n max_score = -100\n preds = {}\n with open(fpath) as file:\n for line in file:\n vals = line.split(\"\\t\")\n ch... | [
[
"numpy.asarray",
"numpy.searchsorted"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
a-slide/snakemake_nanopore_DNA_methylation | [
"67c23e01326aff6aef54a7a2a18ae95d74471709"
] | [
"pycoSnake/common.py"
] | [
"# -*- coding: utf-8 -*-\n\n#~~~~~~~~~~~~~~IMPORTS~~~~~~~~~~~~~~#\n# Standard library imports\nimport os\nimport sys\nfrom collections import *\nimport shutil\nimport yaml\nimport inspect\n\n# Third party lib\nimport pandas as pd\nfrom snakemake.logging import logger, setup_logger\n\n#~~~~~~~~~~~~~~CUSTOM EXCEPTION... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
HRLTY/google-research | [
"2303ff51647120cafa9448853abe433c41944e01"
] | [
"poem/core/keypoint_utils_test.py"
] | [
"# coding=utf-8\n# Copyright 2021 The Google Research Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.stack",
"tensorflow.constant",
"tensorflow.test.main"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LegendTianjin/Point-Then-Operate | [
"a6b0818343bc34c468738ab91ecea89dd03a9535",
"a6b0818343bc34c468738ab91ecea89dd03a9535"
] | [
"PTO-yelp/Modules/discriminator.py",
"PTO-yelp/config.py"
] | [
"import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom utils.utils import gpu_wrapper\n\n\nclass Discriminator(nn.Module):\n\n def __init__(self, kernels, conv_dim, D, dim_h, dropout):\n super(Discriminator, self).__i... | [
[
"torch.nn.Dropout",
"torch.max",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.LeakyReLU",
"torch.nn.Conv1d"
],
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carnot-shailesh/cr-sparse | [
"989ebead8a8ac37ade643093e1caa31ae2a3eda1"
] | [
"examples/cluster/kmeans.py"
] | [
"\"\"\"\nK-means Clustering\n============================\n\nCR-Sparse includes a K-means implementation as\npart of its sparse subspace clustering module.\n\n\"\"\"\n\n\n# %% \n# Configure JAX to work with 64-bit floating point precision. \nfrom jax.config import config\nconfig.update(\"jax_enable_x64\", True)\n\n... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.scatter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
allawn/tensorflow | [
"35ec2e9d2b8261df1df3a61c4c286e3cf2cdd3b4"
] | [
"tensorflow/python/training/monitored_session_test.py"
] | [
"# pylint: disable=g-bad-file-header\n# 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/l... | [
[
"tensorflow.core.protobuf.config_pb2.RunMetadata",
"tensorflow.python.training.monitored_session.Scaffold",
"tensorflow.python.training.monitored_session.MonitoredSession",
"tensorflow.python.training.monitored_session._HookedSession",
"tensorflow.python.ops.state_ops.assign_add",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
SaeedTaghavi/itng_toolbox | [
"baae7abbea78cbeec3b5b8b29fad7d748dff3c20"
] | [
"itng/networks.py"
] | [
"import os\nimport bct\nimport igraph\nimport numpy as np\nimport pylab as pl\nfrom sys import exit\nfrom copy import copy\nimport networkx as nx\nfrom random import shuffle\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import bisect, newton, brenth\nfrom itng.drawing import Drawing\nimport itng.graphUtilit... | [
[
"numpy.abs",
"numpy.random.seed",
"numpy.asarray",
"numpy.rint",
"numpy.cumsum",
"numpy.random.rand",
"scipy.optimize.bisect",
"numpy.zeros",
"numpy.sum",
"numpy.random.randint"
]
] | [
{
"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"... |
UWSEDS/homework-2-python-functions-and-modules-Prattbuw | [
"fdd7bf0d02f146c126b5b1afb53878c9b680c673"
] | [
"create_dataframeV2.py"
] | [
"# Read the data in a csv file from a URL into a pandas DataFrame\n# Brandon Pratt\n# 10/14/2019\n\n# Data description:\n# Hospital charges for hospitals that receive Medicare Inpatient Prospective Payment System\n# See https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-... | [
[
"pandas.read_csv",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
starling021/negroTIGMI- | [
"e8d9e632299a106e60a2e6724d85f5c25dd4d5f6",
"e8d9e632299a106e60a2e6724d85f5c25dd4d5f6"
] | [
"Python_Scripts/twitter/similar_hashtags/similar_hashtags.py",
"Python_Scripts/geolocation_analysis/top_mentions_hashtags_geo.py"
] | [
"import twint\nimport heapq\nimport matplotlib.pyplot as plt\nimport os\nimport sys\n\ndef get_similar_hashtags(seed_hashtag, limit=500):\n os.chdir(\"Python_Scripts\")\n currentDir = os.getcwd() + \"/result/twitter/\"\n\n try:\n os.mkdir(currentDir)\n except:\n pass\n os.chdir(currentDir)\n c = twint.C... | [
[
"matplotlib.pyplot.close",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.title"
],
[
"matplotlib.pyplot.close",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.title"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tobias-fyi/deft | [
"8dabc80dd681f7cd901cec8b182f607d7425ed83"
] | [
"deft/deftools.py"
] | [
"\"\"\"\ndeftools :: Deft Data Wrangling\n\"\"\"\n\n# ====== Imports ====== #\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n\n\n# ====== Code ====== #\nclass DeftString:\n \"\"\"\n A general class for string manipulation and functions.\n \"\"\"\n\n def _... | [
[
"pandas.to_datetime",
"sklearn.model_selection.train_test_split",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
Elvisambition/bert_seq2seq | [
"643ac537c16872f0d13200de06001d8201a54fbb"
] | [
"bert_seq2seq/paddle_model/transformers/nezha/modeling.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n# Copyright 2020 Huawei Technologies Co., Ltd.\n# Copyright 2018 The Google AI Language Team Authors, The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (th... | [
[
"numpy.zeros",
"numpy.power"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isabella232/model_optimization | [
"074d1dfd8b4d18e57c6186c0ec5e49eb17a0fc7a"
] | [
"tests/keras_tests/function_tests/test_symmetric_threshold_selection_weights.py"
] | [
"# Copyright 2022 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.round",
"numpy.log2",
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ElucidataInc/cellxgene | [
"2c2761d9ddf80d69cbe0e4d5ea2c772a5bd002ba"
] | [
"server/test/unit/common/utils/test_matrix_utils.py"
] | [
"import unittest\n\nimport numpy as np\n\nfrom server.common.utils.matrix_utils import is_matrix_sparse, get_column_shift_encode_for_matrix\n\n\nclass TestMatrixUtils(unittest.TestCase):\n\n def test__is_matrix_sparse__zero_and_one_hundred_percent_threshold(self):\n matrix = np.array([1, 2, 3])\n\n ... | [
[
"numpy.array",
"numpy.zeros",
"numpy.random.rand",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
th2l/E2EMAL | [
"5f4f4a58dfb00f7062994b852a73f8e222a9bf62",
"5f4f4a58dfb00f7062994b852a73f8e222a9bf62"
] | [
"src/MERC2020/utils.py",
"src/MERC2020/main.py"
] | [
"import tensorflow as tf\nimport math\nimport numpy as np\nimport random, time\nimport tabulate\nimport copy\n\ndef set_gpu_growth_or_cpu(use_cpu=False, write_info=False):\n\n gpus = tf.config.list_physical_devices('GPU')\n if gpus:\n if use_cpu:\n if write_info:\n print(\"Use... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.cast",
"tensorflow.keras.backend.clear_session",
"tensorflow.config.list_physical_devices",
"tensorflow.random.set_seed",
"tensorflow.config.experimental.set_memory_growth",
"tensorflow.name_scope",
"tensorflow.config.set_visible_devices"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1... |
pedrodiamel/pytorch-classification | [
"0fa9f597ba1e7cf8fcde322e2e9ee924970593aa"
] | [
"train.py"
] | [
"\n# STD MODULE\nimport os\nimport numpy as np\nimport cv2\nimport random\n\n# TORCH MODULE\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.data.sampler import SubsetRandomSampler, WeightedRandomSampler\nfrom torchvision import transforms, utils\nimport torch.backends.cudnn as cudn... | [
[
"numpy.unique",
"numpy.arange",
"torch.utils.data.DataLoader",
"numpy.random.permutation",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lobek123/fuzzy-octo-tribble | [
"b6800b246442e3b026517416d949d107063aa90a"
] | [
"test.py"
] | [
"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\n\nx = [1, 2, 3, 4, 55]\ny = [1, 4, 9, 16, 30]\n\ndef xy_scatter(x, y, x_lab, y_lab, title):\n plt.scatter(x, y)\n plt.title(title)\n plt.xlabel(x_lab)\n plt.ylabel(y_lab)\n plt.legend()\n fig = plt.show()\n return(fig)... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BigFishMaster/tnt | [
"8b80bb3b194eb87ac18924428ef0924c2fb263c5"
] | [
"tnt/comb_converter.py"
] | [
"import os\nimport numpy as np\nimport onnx\nfrom onnx_tf.backend import prepare\nimport torch\nimport tensorflow as tf\nfrom tnt.impls import ModelImpl\nimport onnxruntime as ort\n\nimport argparse\nimport tnt.opts as opts\nfrom tnt.utils.logging import init_logger, logger, beautify_info\nfrom collections import O... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"torch.load",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.compat.v1.wrap_function",
"torch.no_grad",
"torch.onnx.export",
"tensorflow.saved_model.save",
"tensorflow.compat.v1.GraphDef",
"tensorflow.nn.l2_n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MinxZ/D3Q | [
"eb9cb05ffc3c22fcd4972371a987dbacab3e4ff6"
] | [
"D3Q/src/deep_dialog/dialog_system/state_tracker.py"
] | [
"\"\"\"\nCreated on May 20, 2016\n\nstate tracker\n\n@author: xiul, t-zalipt\n\"\"\"\n\nfrom . import KBHelper\nimport numpy as np\nimport copy\n\n\nclass StateTracker:\n \"\"\" The state tracker maintains a record of which request slots are filled and which inform slots are filled \"\"\"\n\n def __init__(sel... | [
[
"numpy.zeros",
"numpy.vstack",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ksaur/onnx-tensorflow | [
"3665b64a0ad62f41a0565dd5b0816047ec8464fc",
"3665b64a0ad62f41a0565dd5b0816047ec8464fc"
] | [
"test/backend/test_model.py",
"onnx_tf/handlers/backend/q_linear_conv.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport unittest\n\nimport numpy as np\nimport onnx\nfrom onnx_tf.backend import prepare\nfrom onnx import helper\nfrom onnx import TensorProto\n\nfrom onnx_tf.c... | [
[
"numpy.clip",
"numpy.int32",
"numpy.testing.assert_almost_equal",
"numpy.random.randn",
"numpy.prod",
"numpy.exp",
"numpy.tanh",
"numpy.array"
],
[
"tensorflow.fill",
"tensorflow.concat",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.... |
yuta1125tp/birds-eye-view | [
"48d6b8429d82a5d08e39b8ab7ffa7592ac1ed802"
] | [
"nets/vgg.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 required... | [
[
"tensorflow.variable_scope",
"tensorflow.zeros_initializer",
"tensorflow.squeeze",
"tensorflow.reduce_mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
turepeken/Deep-Learning-for-SVD-and-Hybrid-Beamforming | [
"481dfcd45689632780b0c6c7130f3b330ffd8025"
] | [
"DL_Hybrid_BF/4-by-4/2Layers/Geometric Channel/Quantization Approach 1/hybrid_bf_for_dnn_for_rank_k_approximation.py"
] | [
"# Hybrid BF based on DNN for rank-k approximation for 4-by-4 mmW system with geometric channel model\n#Quantization approach 1 in which combination of step and piecec-wise linear functions are used. \n# Author: Ture Peken\n\nimport numpy as np\nimport scipy.io\nfrom keras.layers import Input, Lambda,Dense, concate... | [
[
"tensorflow.concat",
"tensorflow.stack",
"numpy.squeeze",
"tensorflow.cast",
"tensorflow.matrix_determinant",
"numpy.concatenate",
"tensorflow.linalg.tensor_diag",
"numpy.arange",
"tensorflow.real",
"tensorflow.floor",
"tensorflow.Session",
"tensorflow.complex",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
snumrl/CrowdSimulation_jaedong | [
"d317e9fca08617f8dddf27d8e5a80f337417482f",
"d317e9fca08617f8dddf27d8e5a80f337417482f"
] | [
"srcs/src/scenarios/mix4.py",
"srcs/src/scenarios/mix1.py"
] | [
"import sys\nsys.path.append('../base')\n\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom OpenGL.GLUT import *\n\nfrom agent import Agent, Obstacle, Wall\nfrom constants import Constants as cst\n\nimport numpy as np\nimport queue\nimport copy\nimport math\n\nclass Mix4:\n\tdef __init__(self, obs):\n\t\tsel... | [
[
"numpy.array"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sabihxh/AppliedDataScience | [
"861289f7d9df9ea66fcedbafc3fc9f764b4aa5fd"
] | [
"datascience_env/lib/python3.6/site-packages/matplotlib/colorbar.py"
] | [
"'''\nColorbar toolkit with two classes and a function:\n\n :class:`ColorbarBase`\n the base class with full colorbar drawing functionality.\n It can be used as-is to make a colorbar for a given colormap;\n a mappable object (e.g., image) is not needed.\n\n :class:`Colorbar`\n the ... | [
[
"matplotlib.transforms.Bbox",
"matplotlib.ticker.FixedFormatter",
"numpy.linspace",
"numpy.take",
"numpy.asarray",
"matplotlib.transforms.nonsingular",
"numpy.searchsorted",
"matplotlib.ticker.FixedLocator",
"matplotlib.patches.Polygon",
"numpy.hstack",
"matplotlib.cboo... | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JokerHB/mealpy | [
"4bd00f47ed575d01f246d5fd0ef306d7c1fa5a5f",
"4bd00f47ed575d01f246d5fd0ef306d7c1fa5a5f"
] | [
"mealpy/evolutionary_based/CRO.py",
"mealpy/human_based/SARO.py"
] | [
"#!/usr/bin/env python\n# ------------------------------------------------------------------------------------------------------%\n# Created by \"Thieu\" at 14:56, 19/11/2021 %\n# ... | [
[
"numpy.random.random",
"numpy.concatenate",
"numpy.delete",
"numpy.random.normal",
"numpy.append",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
],
[
"numpy.random.uniform",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sockeye-Project/decl-power-seq | [
"b3eed4b7abfdac647b8ff7653806665436e3e54d"
] | [
"sequence_generation.py"
] | [
"import operator\nimport functools\nimport numpy as np\nimport copy\nimport sys\nimport random\nimport z3\nimport subprocess\nimport time\nfrom enum import IntEnum, Enum\n#pylint: disable = E0602\n\n#enums used to make indexing into structures more readable/maintainable\nclass SET(Enum):\n Explicit = 0\n Imp... | [
[
"numpy.ndindex",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishalbelsare/jubakit | [
"f6252ba627ce4e2e42eb9aafaaf05c882bc1c678",
"f6252ba627ce4e2e42eb9aafaaf05c882bc1c678"
] | [
"jubakit/test/loader/test_sparse.py",
"example/regression_csv.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom unittest import TestCase\n\ntry:\n import numpy as np\n from scipy.sparse import csr_matrix\nexcept ImportError:\n pass\n\nfrom jubakit.loader.sparse import SparseMatrixLoader\n\nfrom .. import ... | [
[
"numpy.array",
"scipy.sparse.csr_matrix"
],
[
"numpy.sqrt",
"numpy.abs"
]
] | [
{
"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"... |
Aroksak/binance_tg_bot | [
"49007af48dea1975a0913f4bf097267d8ee2af9a"
] | [
"binance_api/history.py"
] | [
"import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.dates import DateFormatter\nfrom matplotlib.ticker import FormatStrFormatter\n\n\ndef history_to_png(history_file, output_file, period=None):\n history = pd.read_csv(history_file, parse_dates=True, index_col=0)\n if period is None:\n ... | [
[
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"pandas.to_datetime",
"matplotlib.pyplot.MaxNLocator",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"pandas.to_timedelta",
"matplotlib.ticker.FormatStrFormatter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ABeastofPrey/AmazingGym | [
"d92697f3aa1025550140d3318f045fa1f497a2ea"
] | [
"custom_envs/maze_env.py"
] | [
"import gym\nimport random\nimport numpy as np\n\nclass MazeEnv(gym.Env):\n # 定义窗口的宽和高。\n __window_width, __window_height = 550, 600\n\n def __init__(self):\n self.viewer = None # 窗口。\n self.state = None # 初始状态。\n\n # 描述马尔可夫决策过程, 元祖(S, P, A, R, gamma)\n self.__MDP()\n\n de... | [
[
"numpy.arange",
"numpy.argwhere"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KomalTauqeer/ParticleNet | [
"a35f6ca6ac5d70cce35b51dad0d5000baa9d2ecc",
"a35f6ca6ac5d70cce35b51dad0d5000baa9d2ecc"
] | [
"tf-keras/tf_keras_model.py",
"tf-keras/keras_train.py"
] | [
"import tensorflow as tf\nfrom tensorflow import keras\n\n\n# A shape is (N, P_A, C), B shape is (N, P_B, C)\n# D shape is (N, P_A, P_B)\ndef batch_distance_matrix_general(A, B):\n with tf.name_scope('dmat'):\n r_A = tf.reduce_sum(A * A, axis=2, keepdims=True)\n r_B = tf.reduce_sum(B * B, axis=2, k... | [
[
"tensorflow.reduce_sum",
"tensorflow.equal",
"tensorflow.keras.Input",
"tensorflow.keras.layers.Conv2D",
"tensorflow.squeeze",
"tensorflow.subtract",
"tensorflow.nn.top_k",
"tensorflow.add",
"tensorflow.name_scope",
"tensorflow.gather_nd",
"tensorflow.shape",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
... |
AaronDon2018/SSL4MIS | [
"ed919cf7fb4aa912fa510cd5a8d7c1810f4e2379"
] | [
"code/networks/neural_network.py"
] | [
"# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany\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# ... | [
[
"numpy.min",
"torch.cuda.empty_cache",
"torch.from_numpy",
"numpy.ones",
"numpy.concatenate",
"numpy.max",
"numpy.ceil",
"numpy.round",
"torch.no_grad",
"scipy.ndimage.filters.gaussian_filter",
"torch.flip",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
eduagarcia/meli-challenge-2019 | [
"76ac9a10ff832d3dc7f1d06b571fbc634959dcb1"
] | [
"models/bi_lstm_gru_balanced.py"
] | [
"from keras import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam\nimport keras_metrics\nimport util\nimport keras\nfrom parameters import DATA_PATH, padding_len\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping, CSVLogger, TensorBoard\nfrom imblearn.over_... | [
[
"pandas.concat",
"numpy.unique",
"numpy.asarray",
"numpy.cumsum",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.load"
]
] | [
{
"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": []
}
] |
harbarsem/streamlit-demo-2021 | [
"eb34307dd040dfecfffd5308f959bc02778e0915"
] | [
"demo_app.py"
] | [
"import streamlit as st\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nst.title(\"Hello, World!\")\n\"\"\"\nThis is a test.\n\"\"\"\nx = np.linspace(0, 10, 500)\nfig = plt.figure()\nplt.plot(x, np.sin(x))\nplt.ylim(-2, 3)\nst.pyplot(fig)\n"
] | [
[
"matplotlib.pyplot.ylim",
"numpy.sin",
"numpy.linspace",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UmbertoGostoli/ABM-for-social-care | [
"9488c6040bc748c9d4365babd2290d3c1c795360"
] | [
"charts.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Sep 06 12:05:05 2018\r\n\r\n@author: Umberto Gostoli\r\n\"\"\"\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.ticker import MaxNLocator\r\nfrom matplotlib.backends.backend_pdf import PdfPages\r\nimport os\r\nimport pandas as pd\r\n\r\... | [
[
"matplotlib.backends.backend_pdf.PdfPages",
"pandas.read_csv",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.ylim",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.xlim",
"numpy.mean",
"matplotlib.ticker.MaxNLocator",
"matplotlib.pyplot.xticks",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
noahzhy/temporal-shift-module | [
"41fbcca37e0148849d55ec581980cd12ad1386ee"
] | [
"online_demo/main.py"
] | [
"import numpy as np\nimport os\nfrom typing import Tuple\nimport io\nimport tvm\nimport tvm.relay\nimport time\nimport cv2\nimport torch\nimport torchvision\nimport torch.onnx\nfrom PIL import Image, ImageOps\nimport onnx\nimport tvm.contrib.graph_runtime as graph_runtime\nfrom mobilenet_v2_tsm import MobileNetV2\n... | [
[
"numpy.expand_dims",
"torch.load",
"torch.zeros",
"torch.from_numpy",
"numpy.concatenate",
"numpy.argmax",
"torch.no_grad",
"torch.rand",
"numpy.transpose",
"numpy.array",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
olyasps/tensorflow | [
"7d7dce16b8e7aef53467d8eb08d4249ef6cd71fb"
] | [
"tensorflow/python/profiler/tfprof_logger.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.framework.tensor_shape.TensorShape",
"tensorflow.python.eager.context.in_graph_mode",
"tensorflow.python.framework.ops.get_default_graph",
"tensorflow.core.profiler.tfprof_log_pb2.OpLogProto",
"tensorflow.core.profiler.tfprof_log_pb2.OpLogEntry",
"tensorflow.python.frame... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
ajlee21/LINCS_latent_space-1 | [
"7c6b8cbc5cc777cd72bb0a72c1337c83e2e66bec"
] | [
"scripts/functions/helper_ae.py"
] | [
"#----------------------------------------------------------------------------------------------------------------\n# Helper functions for running autoencoder\n#----------------------------------------------------------------------------------------------------------------\n\nimport os\nimport argparse\nimport pand... | [
[
"numpy.random.seed",
"tensorflow.shape",
"tensorflow.ConfigProto",
"tensorflow.set_random_seed",
"tensorflow.get_default_graph"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
James-T1/openpilot | [
"08a7bc0dca31cdb130cf649bc2a2650e1f1f44b5"
] | [
"selfdrive/car/hyundai/carstate.py"
] | [
"from selfdrive.car.hyundai.values import DBC\nfrom selfdrive.can.parser import CANParser\nfrom selfdrive.config import Conversions as CV\nfrom common.kalman.simple_kalman import KF1D\nimport numpy as np\n\n\ndef get_can_parser(CP):\n\n signals = [\n # sig_name, sig_address, default\n (\"WHL_SPD_FL\", \"WHL_... | [
[
"numpy.matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
volodymyrlut/masters-project | [
"d725f5d0f4d623e024bd0835f1c611b719fef93f"
] | [
"neural-architecture-search/query_nasbench.py"
] | [
"from nasbench import api\nimport numpy as np\nimport pandas as pd\nfrom utils import calculate_hash, timestamp\n# =======================\n# ===== Not the hyperparameters! Those are the constants from nasbench\n\nINPUT = 'input'\nOUTPUT = 'output'\nCONV1X1 = 'conv1x1-bn-relu'\nCONV3X3 = 'conv3x3-bn-relu'\nMAXPOOL3... | [
[
"pandas.concat",
"numpy.random.seed",
"numpy.random.choice",
"pandas.DataFrame.from_records",
"numpy.triu"
]
] | [
{
"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": []
}
] |
SYAN83/machine-learning-practice | [
"10a9562e1c3e3b5611af9997b971bb2efce4e5de"
] | [
"mlpractice/regression.py"
] | [
"import numpy\nfrom .base import Algorithm, Model\nfrom collections import OrderedDict\n\n\nclass LinearRegression(Algorithm):\n\n def __init__(self, features=[], label='label', prediction='prediction', fit_intercept=True):\n super().__init__(features=features, label=label, prediction=prediction, fit_inte... | [
[
"numpy.dot",
"numpy.insert",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deep050692git/Tensorflow_Lite | [
"8c01781c51a56ee2e78fbbdc696581c6942f92c3"
] | [
"tensorflow_examples/lite/model_maker/third_party/recommendation/ml/model/losses.py"
] | [
"# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n#... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.transpose",
"tensorflow.shape",
"tensorflow.reduce_mean",
"tensorflow.keras.backend.cast"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
AmpelProject/Ampel-ZTF | [
"7f9736a7be3aa526571004716160cae2a800e410"
] | [
"ampel/ztf/dev/DevSkyPortalClient.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# File: Ampel-ZTF/ampel/ztf/dev/DevSkyPortalClient.py\n# Author: Jakob van Santen <jakob.van.santen@desy.de>\n# Date: 16.09.2020\n# Last Modified Date: 16.09.2020\n# Last Modified By: Jakob van Santen <jakob.van.santen@d... | [
[
"numpy.percentile",
"numpy.flipud",
"numpy.isfinite",
"matplotlib.figure.Figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neer201/Multi-Object-Tracking-for-Automotive-Systems-in-python | [
"886cd9e87283982381713dbf2e4ef695030f81de"
] | [
"src/mot/trackers/multiple_object_trackers/PMBM/common/multi_bernoulli_mixture.py"
] | [
"import logging\nfrom collections import defaultdict\nfrom typing import List\n\nimport numpy as np\n\n# TODO make it ok\nfrom .....common import normalize_log_weights\nfrom .....measurement_models import MeasurementModel\nfrom .....motion_models import MotionModel\nfrom .global_hypothesis import GlobalHypothesis\n... | [
[
"numpy.logical_or"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
data61/landshark | [
"01d28c47be4b688f4705acbf5dd64f7d790574fd"
] | [
"landshark/config.py"
] | [
"\"\"\"Useful functions for simplifying model (config) files.\"\"\"\n\n# Copyright 2019 CSIRO (Data61)\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... | [
[
"tensorflow.feature_column.input_layer",
"numpy.product",
"tensorflow.shape",
"tensorflow.cast",
"tensorflow.reshape",
"tensorflow.feature_column.categorical_column_with_identity",
"tensorflow.feature_column.numeric_column",
"tensorflow.logical_not"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ErrorOverflow/STALTA-earthquake | [
"96bfc0252c4c0e3fb8c0de7d606397c9b9f32f24"
] | [
"trigger_by_sta_lta.py"
] | [
"import argparse\nimport pandas as pd\nimport os\nimport math\nimport shutil\nimport obspy\nimport fnmatch\nfrom obspy.core import Stream, read, UTCDateTime\nfrom obspy.clients.arclink import Client\nfrom obspy.signal.trigger import recursive_sta_lta, classic_sta_lta, trigger_onset, ar_pick\n\n\ndef preprocess_stre... | [
[
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
Owl-AI/Reversing | [
"a4321642ccad6e7c2f4c71bcb960ee9c5edf5ed8"
] | [
"stereo/utils/tensor.py"
] | [
"import os\nimport numpy as np\nimport torch\n\ndef to_regular_format(tensor):\n '''\n Pytorch uses the format:\n [Batch] x Channels x Height x Width \n Outside Pytorch the format is instead:\n [Batch] x Height x Width x Channels\n This function rearrange the tensor in the latter way\n ... | [
[
"numpy.asarray",
"torch.from_numpy",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sorrowyn/C-Tran | [
"236d785952c59210e6812b4ad5ee12bab585ce4c"
] | [
"load_data.py"
] | [
"import torch\nfrom skimage import io, transform\nimport numpy as np\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\nfrom pdb import set_trace as stop\nimport os, random\nfrom dataloaders.voc2007_20 import Voc07Dataset\nfrom dataloaders.vg500_dataset import VGDataset\nfrom dat... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mathewdgardner/sklearn-porter | [
"d8927a6af06e96dd416be759321e93691c39cf73"
] | [
"tests/estimator/classifier/SVC/SVCCTest.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom unittest import TestCase\n\nimport numpy as np\n\nfrom sklearn.svm.classes import SVC\n\nfrom tests.estimator.classifier.Classifier import Classifier\nfrom tests.language.C import C\n\n\nclass SVCCTest(C, Classifier, TestCase):\n\n def setUp(self):\n super(SVCCTest, self).... | [
[
"numpy.amin",
"numpy.random.uniform",
"numpy.amax",
"sklearn.svm.classes.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Alkane0050/SimulateCovid19Spread | [
"5099c47b55f8f5007a758cde6b37bb19fc17175e"
] | [
"simulate/clear.py"
] | [
"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nx = np.linspace(1,10000)\r\ny = np.sin(np.pi*10**2/(np.sqrt(x**2+10**2)+x))\r\nplt.plot(x,y)\r\nplt.show()"
] | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"numpy.sqrt",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aravindhnivas/FELion-Spectrum-Analyser | [
"430f16884482089b2f717ea7dd50625078971e48"
] | [
"modules/FELion_baseline.py"
] | [
"#!/usr/bin/python3\n\n# FELion Module\nfrom FELion_definitions import move, FELion_Toplevel, ShowInfo, ErrorInfo\nfrom FELion_power import PowerCalibrator\nfrom FELion_sa import SpectrumAnalyserCalibrator\n\n# Built-In Module\nimport os\nfrom os.path import dirname, isdir, isfile, join\n\n# DATA analysis modules\n... | [
[
"numpy.sqrt",
"matplotlib.figure.Figure",
"numpy.asarray",
"matplotlib.use",
"numpy.arange",
"matplotlib.lines.Line2D",
"numpy.amin",
"numpy.genfromtxt",
"numpy.append",
"scipy.interpolate.interp1d",
"numpy.delete",
"matplotlib.gridspec.GridSpec",
"numpy.insert"... | [
{
"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"
... |
libing125/tatk | [
"fafabc45d02ad889f59354acac4e3b1367e7d4bf"
] | [
"tatk/dst/trade/utils/utils_multiWOZ_DST.py"
] | [
"import json\nimport torch\nimport torch.utils.data as data\nimport unicodedata\nimport string\nimport re\nimport random\nimport time\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom utils.config import *\nimport ast\nfrom collections import Counter\nfrom collections import O... | [
[
"torch.Tensor",
"torch.utils.data.DataLoader",
"torch.multinomial",
"torch.tensor",
"torch.utils.data.append",
"torch.DoubleTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheConstant3/crnn.pytorch | [
"8811ccdf1e10d1c979ee3a81ce5ec4e0195c1b1d"
] | [
"dataset.py"
] | [
"#!/usr/bin/python\n# encoding: utf-8\n\nimport random\nimport torch\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import sampler\nimport torchvision.transforms as transforms\nfrom src.generator.from_random import GeneratorFromRandom\nimport cv2\nimport lmdb\nimport six\nimport sys\nimport os\nfrom P... | [
[
"torch.range",
"numpy.array",
"numpy.zeros",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aquillen/phobos_deimos | [
"8767d17a0c7229c0603c8b16f6090efeed70614e"
] | [
"myexamples/phobos_pylab/orbsubs_ur.py"
] | [
"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage.filters import median_filter\nfrom numpy import linalg as LA\n#from matplotlib import rc\n#rc('text', usetex=True)\nfrom numpy import polyfit\nfrom matplotlib.lines import Line2D\nfrom matplotlib.ticker import MaxNLocator\nfrom math import f... | [
[
"numpy.matrix",
"numpy.sqrt",
"matplotlib.pyplot.autoscale",
"numpy.asarray",
"numpy.squeeze",
"numpy.arctan2",
"numpy.int",
"numpy.max",
"numpy.mean",
"scipy.signal.medfilt",
"numpy.linalg.eig",
"numpy.size",
"numpy.diff",
"matplotlib.pyplot.subplots_adjust... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.10",
"1.3",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16"
],
"tensorflow": [... |
hypercubestart/jax | [
"8d54a53b538a3e14a8425b5ec4273b2eb17a8667"
] | [
"jax/_src/random.py"
] | [
"# Copyright 2018 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 applicable law or agreed ... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.uint32",
"numpy.issubdtype",
"numpy.ndim",
"numpy.ceil",
"numpy.delete",
"numpy.int64",
"numpy.shape",
"numpy.iinfo",
"numpy.float32",
"numpy.binary_repr",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lblonk/chaospy | [
"5a415ece07e6535488174bac69a6c0fcc2ca272d"
] | [
"chaospy/distributions/collection/burr.py"
] | [
"\"\"\"Burr Type III distribution.\"\"\"\nimport numpy\nfrom scipy import special\n\nfrom ..baseclass import Dist\nfrom ..operators.addition import Add\n\n\nclass burr(Dist):\n \"\"\"Stadard Burr distribution.\"\"\"\n\n def __init__(self, alpha=1., kappa=1.):\n Dist.__init__(self, alpha=alpha, kappa=ka... | [
[
"numpy.zeros",
"scipy.special.beta"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
araffin/optuna | [
"3087c1dcbd624bb6344506541c62a5b2d6f491ef"
] | [
"examples/pytorch_lightning_simple.py"
] | [
"\"\"\"\nOptuna example that optimizes multi-layer perceptrons using PyTorch Lightning.\n\nIn this example, we optimize the validation accuracy of hand-written digit recognition using\nPyTorch Lightning, and MNIST. We optimize the neural network architecture. As it is too time\nconsuming to use the whole MNIST data... | [
[
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.cuda.is_available",
"torch.nn.functional.nll_loss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zeracesharon/reactorch | [
"cc7b34ee2a19d5cbc717e6a29e0cd18257abb914",
"b394fb6c322b2dc6f0f5dbcb93baf52f94032772"
] | [
"test/Solution_test.py",
"reactorch/import_kinetics.py"
] | [
"# from multiprocessing import Pool\nfrom time import perf_counter\n\nimport cantera as ct\nimport torch\nimport numpy as np\nimport reactorch as rt\n\ncpu = torch.device('cpu')\n\ncuda = torch.device('cuda:0')\n\ndevice = cpu\n\nmech_yaml = '../data/IC8H18_reduced.yaml'\ncomposition = 'IC8H18:0.8,O2:11.0,N2:39.0'\... | [
[
"numpy.isfinite",
"torch.Tensor",
"torch.cat",
"torch.device",
"numpy.where"
],
[
"torch.ones",
"torch.from_numpy",
"torch.Tensor",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dykuang/Deep----Koopman | [
"b6e9def39e0f619bd9b50f46aa1e927ddb95b317"
] | [
"Architecture.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 23 17:27:38 2019\n\n@author: dykua\n\nNetwork architecture\n\"\"\"\n\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras.layers import Dense, LSTM, Input, Lambda, TimeDistributed, Conv1D, Flatten,Concatenate, Reshape, Add\n#from keras i... | [
[
"tensorflow.sin",
"tensorflow.concat",
"tensorflow.cos",
"tensorflow.multiply",
"tensorflow.stack",
"tensorflow.reshape",
"tensorflow.exp",
"tensorflow.einsum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"1.4",
"2.6",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"1.0",
"2.2",
"1.2",
"2.... |
yl-1993/mmcv | [
"7fac7d41c6130df0b15b455c1e8b6e1b1c2d8569"
] | [
"mmcv/op/non_local.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom ..cnn import ConvModule, constant_init, normal_init\n\n\nclass NonLocal2D(nn.Module):\n \"\"\"Non-local module.\n\n See https://arxiv.org/abs/1711.07971 for details.\n\n Args:\n in_channels (int): Channels of the input feature map.\n reduction (int... | [
[
"torch.matmul"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TITC/PCANet | [
"4566ac4cc87ca80f14c7c0a0c373c7e3fddcfdb5"
] | [
"utils.py"
] | [
"import numpy as np\nimport pickle\n\ntry:\n import importlib\nexcept ImportError:\n import imp as importlib\n\nfrom keras.datasets import mnist\n\n\nGPU_ENABLED = False\n\n\ndef set_device(device_id):\n \"\"\"\n Set the device (CPU or GPU) to be used.\n if device_id >= 0 the corresponding GPU is use... | [
[
"numpy.swapaxes"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gyan42/rl_flappy_bird | [
"372418274090b8cb3a18dbaffe630a1e72bdd06a"
] | [
"fb/test.py"
] | [
"import torch\nfrom fb.nn.conv_net import NeuralNetwork\nfrom fb.utils import resize_and_bgr2gray, image_to_tensor\n\ndef test(model: NeuralNetwork,\n game_state):\n\n # initial action is do nothing\n action = torch.zeros([model.number_of_actions], dtype=torch.float32)\n action[0] = 1\n image_da... | [
[
"torch.argmax",
"torch.cat",
"torch.cuda.is_available",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
azarafrooz/corgail | [
"a1e6084054084fab93c84ed7c4377699a843664d"
] | [
"gym-added-environments/gym-synthetic2Dplane/gym_synthetic2Dplane/envs/synthetic2Dplane_env_backup.py"
] | [
"import random, math\nfrom collections import deque, namedtuple\nimport itertools\n\nimport numpy as np\n\nimport gym\nfrom gym import error, spaces, utils\nfrom gym.utils import seeding\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nmatplotlib.use('Agg')\n\n\ndef generate_random_point_on_circle(radius=0.1)... | [
[
"matplotlib.pyplot.gca",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ioff",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Geonhee-LEE/visual-pushing-grasping | [
"8dce1b4a4f55bf9cffef4480155489b471cdddd3"
] | [
"models.py"
] | [
"#!/usr/bin/env python\n\nfrom collections import OrderedDict\nimport numpy as np\nfrom scipy import ndimage\nimport torch \nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport torchvision\nimport matplotlib.pyplot as plt\nimport time\n\n\nclass reactive_net(nn.Modul... | [
[
"numpy.radians",
"torch.cat",
"torch.nn.Conv2d",
"numpy.cos",
"torch.from_numpy",
"numpy.sin",
"torch.set_grad_enabled",
"torch.nn.Upsample",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.init.kaiming_normal_",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meng-zha/bat-3d | [
"ba6fa5f3e1ecb7aecda3d4909c8b9fe72f7da77a"
] | [
"scripts/nuscenes_devkit/python-sdk/scripts/calculate_projection_matrix_torch.py"
] | [
"import torch\n\nintrinsic_matrix = torch.tensor([[2.82046004e+03, 0.00000000e+00, 9.60667182e+02],\n [0.00000000e+00, 2.81622151e+03, 5.25863289e+02],\n [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n\nrotation_matrix = torch.tensor([[-6.67942916... | [
[
"torch.mm",
"torch.zeros",
"torch.mv",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
spyysalo/crf-test | [
"7f5364e7ec7ae55a67b4721293c91d1f9a8cc28a"
] | [
"utils.py"
] | [
"#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport numpy as np\nimport collections\n\nfrom keras import backend as K\n\n\ndef ones(shape, dtype=K.floatx()):\n \"\"\"Return all-ones tensor of given shape and type.\"\"\"\n # As of Keras version 1.1.0, Keras ones() requires integer values\... | [
[
"tensorflow.gather_nd",
"tensorflow.range",
"tensorflow.zeros",
"numpy.asarray",
"numpy.eye",
"tensorflow.ones",
"tensorflow.gather",
"numpy.zeros_like",
"tensorflow.meshgrid",
"numpy.outer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
FHomewood/ScientificComputing | [
"bc3477b4607b25a700f2d89ca4f01cb3ea0998c4",
"bc3477b4607b25a700f2d89ca4f01cb3ea0998c4"
] | [
"ExamPrep/Shit Comp/SciComp Exam 2016/Modules from SyD/cubicSpline.py",
"ExamPrep/Shit Comp/ShitComp/1415/2.py"
] | [
"## module cubicSpline\n''' k = curvatures(xData,yData).\n Returns the curvatures of cubic spline at its knots.\n\n y = evalSpline(xData,yData,k,x).\n Evaluates cubic spline at x. The curvatures k can be\n computed with the function 'curvatures'.\n''' \nfrom numpy import zeros,ones\nfrom LUdecomp3 imp... | [
[
"numpy.zeros",
"numpy.ones"
],
[
"numpy.sqrt",
"numpy.linspace",
"numpy.cos",
"scipy.integrate.odeint",
"matplotlib.pyplot.plot",
"numpy.sin",
"numpy.deg2rad",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
... |
lehuyhunghedspi/demo_anh | [
"8b95f3f366c5cd531bbc4dd1f57aeea0ec42c8e1"
] | [
"datasetGenerator/documentsNumpyFileGenerator.py"
] | [
"import numpy as np\nimport cv2\nimport tensorflow as tf\nimport utils.utils as utils\nimport os\n\ndef argsProcessor():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-o\", \"--outputDir\", help=\"output Directory of Data\")\n parser.add_argument(\"-i\", \"--inputDir\", ... | [
[
"tensorflow.ConfigProto",
"numpy.copy",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChiragKr/python-algorithms | [
"93b36ad689c3b4432897c960ceb81ba3d365a225"
] | [
"breadth_first_search/bfs.py"
] | [
"import sys\nimport math\nfrom collections import deque\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\nsys.stdin = open('in.txt','r')\nsys.stdout = open('out.txt', 'w')\n\nWHITE = 0\nGREY = 1\nBLACK = 2\n\ndef breadth_first_search(adj, n, source=0):\n\t# breadth_first_search() requires queue\n\tfrontier... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sindresf/TensorFlow | [
"a1b8b485e3f4ceac8dce511e5a37aa3c501b210a"
] | [
"contrib_learn_tuts/census_income/wide_n_deep.py"
] | [
"#TUTORIAL CODE\n\n\"\"\"Example code for TensorFlow Wide & Deep Tutorial using TF.Learn API.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tempfile\nfrom six.moves import urllib\n\nimport pandas as pd\nimport tensorflow as tf\n\nflag... | [
[
"tensorflow.contrib.layers.bucketized_column",
"tensorflow.constant",
"tensorflow.gfile.Open",
"tensorflow.contrib.layers.sparse_column_with_keys",
"tensorflow.contrib.learn.LinearClassifier",
"tensorflow.contrib.learn.DNNClassifier",
"tensorflow.contrib.layers.real_valued_column",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
enritoomey/python-sgp4 | [
"95b08fdfc8131bf6b7c81cd1211ba3eaa032349a"
] | [
"sgp4/propagation.py"
] | [
"\"\"\"The sgp4 procedures for analytical propagation of a satellite.\n\nI have made the rather unorthodox decision to leave as much of this C++\ncode alone as possible: if a line of code would run without change in\nPython, then I refused to re-indent it or remove its terminal semicolon,\nso that in the future it ... | [
[
"numpy.maximum",
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Abimbola-ai/Stock-price-prediction | [
"4e0e564791bb624c4000a20f27b6c6cbec516a09"
] | [
"src/predict.py"
] | [
"import numpy as np \nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom stocks.scraper import get_data\n\n\ndef predict_future_price(ticker, years):\n \"\"\"Predicts future one day price of stock,\n inputs: ticker:str, \n years: int\n o... | [
[
"sklearn.linear_model.LinearRegression",
"numpy.array",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neurophysics/DrumsAndBrains | [
"26c42c31f8e07c4f5a918f1d312790632fb33593"
] | [
"PlotEvokedComponent.py"
] | [
"import argparse\nimport numpy as np\nimport scipy\nimport scipy.linalg\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nmpl.rcParams['text.latex.preamble'] = [\n r'\\usepackage{amssymb}',\n r'\\usepackage{amsmath}',\n r'\\usepackage{xcolor}']\nimport sys\nimport os.path\nimport helper_functions... | [
[
"numpy.hstack",
"matplotlib.gridspec.GridSpecFromSubplotSpec",
"numpy.abs",
"numpy.arange",
"numpy.all",
"matplotlib.pyplot.colorbar",
"numpy.argmax",
"matplotlib.gridspec.GridSpec",
"numpy.array",
"matplotlib.transforms.blended_transform_factory",
"matplotlib.pyplot.fi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
peteos123/linearmodels | [
"2850e0debf69dfb6d42bcfb2cf33e6eb52231673"
] | [
"linearmodels/shared/hypotheses.py"
] | [
"from __future__ import annotations\n\nfrom typing import Dict, List, Optional, Union\n\nimport numpy as np\nfrom pandas.core.series import Series\nfrom patsy.design_info import DesignInfo\nfrom scipy.stats import chi2, f\n\nfrom linearmodels.typing import ArrayLike, OptionalArrayLike\n\n\nclass WaldTestStatistic(o... | [
[
"scipy.stats.chi2",
"numpy.asarray",
"numpy.linalg.inv",
"scipy.stats.f",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xyq7/Time-Series | [
"9cffa41626a7b84ccbba6c89bc7b66a9e6c590fa"
] | [
"arima_server1.py"
] | [
"import warnings\nwarnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n FutureWarning)\nwarnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n FutureWarning)\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import mean... | [
[
"numpy.log",
"numpy.savez",
"pandas.to_datetime",
"numpy.exp",
"numpy.array",
"pandas.read_pickle",
"numpy.vstack"
]
] | [
{
"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": []
}
] |
BlueBrain/atlas-annotation | [
"118af9b95518a19b64a9d8008aabed557eb0f646"
] | [
"src/atlannot/merge/fine.py"
] | [
"# Copyright 2021, Blue Brain Project, EPFL\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 ... | [
[
"numpy.ma.masked_array",
"numpy.where",
"numpy.isin",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lucarebuffi/wofrysrw | [
"174bedd7f9fc9711af371743f1b13577dbf838bb"
] | [
"wofrysrw/util/srw_hdf5_test.py"
] | [
"# coding: utf-8\n# /*##########################################################################\n#\n# Copyright (c) 2018 European Synchrotron Radiation Facility\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\... | [
[
"numpy.testing.assert_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lldelisle/bx-python | [
"19ab41e0905221e3fcaaed4b74faf2d7cda0d15a"
] | [
"lib/bx/align/score.py"
] | [
"\"\"\"\nSupport for scoring alignments using arbitrary scoring matrices, arbitrary\nalphabets, and affine gap penalties.\n\"\"\"\n\nfrom numpy import (\n float32,\n int32,\n ones,\n zeros\n)\n\n\nclass ScoringScheme:\n # note that gap_open and gap_extend are penalties, which means you should make th... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IsidoreCarinae/qiskit-terra | [
"d98540b0868f8b0a2989c0bfd4e184d770c5a01d"
] | [
"test/python/transpiler/test_consolidate_blocks.py"
] | [
"# -*- coding: utf-8 -*-\n\n# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2017, 2019.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EverLookNeverSee/scipy | [
"5ffd20ab831b3bc46bc5692c8624c01f8df09a9b"
] | [
"scipy/sparse/tests/test_spfuncs.py"
] | [
"from numpy import array, kron, diag\nfrom numpy.testing import assert_, assert_equal\n\nfrom scipy.sparse import spfuncs\nfrom scipy.sparse import csr_matrix, csc_matrix, bsr_matrix\nfrom scipy.sparse._sparsetools import (csr_scale_rows, csr_scale_columns,\n bsr_scale_rows, bs... | [
[
"numpy.diag",
"scipy.sparse.csc_matrix",
"scipy.sparse.sputils.matrix",
"scipy.sparse.bsr_matrix",
"scipy.sparse._sparsetools.bsr_scale_columns",
"numpy.kron",
"scipy.sparse.csr_matrix",
"scipy.sparse._sparsetools.csr_scale_rows",
"scipy.sparse.spfuncs.count_blocks",
"scipy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
franneck94/UdemyCppExt_Template | [
"90f847cf59f23e57222aa7debceb1644ee58b426"
] | [
"11_GUI/AdasGui3/scripts/data.py"
] | [
"import os\nimport json\nimport enum\nfrom typing import Any\nfrom typing import Dict\n\nimport numpy as np\n\n\nclass LaneAssociation(enum.Enum):\n UNKNOWN = 0\n RIGHT = 1\n CENTER = 2\n LEFT = 3\n\n\nNUM_VEHICLES = 6\nNUM_ITERATIONS = 1_000\nFPS = 25\n\nVEHICLE_FILENAME = \"vehicle_data.json\"\nVEHICL... | [
[
"numpy.random.uniform",
"numpy.random.normal",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
babbatem/motor_skills | [
"d5e9bfba7c8025a755027d13bbec7ad7d03de492"
] | [
"motor_skills/envs/mj_jaco/old_MjJacoDoorImpedance.py"
] | [
"import time\nimport copy\nimport pathlib\nimport pickle\nimport hjson\nimport gym\nimport numpy as np\nfrom mujoco_py import load_model_from_path, MjSim, MjViewer\nfrom motor_skills.envs.mj_jaco import MjJacoEnv\n\nfrom mujoco_py import cymj\nfrom scipy.spatial.transform import Rotation as R\n\nfrom motor_skills.c... | [
[
"numpy.concatenate",
"numpy.random.rand",
"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": [],
... |
MichaelHopwood/PhilosopherNetwork | [
"e87fba49fb068ef3b84e3edc6972c6dcd389d128"
] | [
"wiki_scraper.py"
] | [
"\"\"\"\r\nWritten by Alexander Valarus\r\nAdapted by Michael Hopwood\r\nFall 2021\r\n\"\"\"\r\n\r\nimport requests\r\nimport re\r\nimport random\r\nfrom bs4 import BeautifulSoup\r\nfrom node import Node\r\nfrom tqdm import tqdm\r\nimport pandas as pd\r\n\r\nWIKI = \"https://en.wikipedia.org\"\r\nnodes = {}\r\nnode... | [
[
"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": []
}
] |
matthewghgriffiths/rowing | [
"d682e66ca8d8a811e033ae32c2a1948376659123"
] | [
"world_rowing/dashboard.py"
] | [
"\nimport datetime\nimport time\nimport logging\n\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n\nfrom .api import get_last_race_started\nfrom .utils import (\n format_yaxis_splits, update_table, format_totalseconds, CURRENT_TIMEZONE, ignore_... | [
[
"pandas.to_datetime",
"pandas.DataFrame",
"numpy.full",
"matplotlib.ticker.AutoLocator",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ion",
"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": []
}
] |
neilschark/ba-website | [
"a34cb74baf408943acb7e9d9868cfce32aa562af"
] | [
"performance_tests/pyplot_files/database_int_125_rps.py"
] | [
"import matplotlib.pyplot as plt\nimport sys\nfrom ExtractDataCsv import extract_data_csv\nfrom TableDataGenerator import write_table_data\nimport copy\n\ntimestamp = sys.argv[1]\ndata_type = sys.argv[2]\ntest_type = \"database_int_125\"\ndeployments = [\"classic\", \"docker\", \"orchestration\"]\nsave_filename = f... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Yard1/scikit-learn-intelex | [
"ecd3a04979e0d39656824647ad668a6ada1e5223",
"ecd3a04979e0d39656824647ad668a6ada1e5223"
] | [
"daal4py/sklearn/metrics/_ranking.py",
"sklearnex/tests/test_run_to_run_stability_tests.py"
] | [
"#===============================================================================\n# Copyright 2020-2021 Intel 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# ... | [
[
"numpy.array_equal",
"numpy.unique",
"numpy.asarray",
"sklearn.utils.check_array",
"sklearn.preprocessing.label_binarize",
"sklearn.metrics.ranking.roc_auc_score",
"sklearn.utils.multiclass.is_multilabel"
],
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.metrics.roc_a... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
... |
dtak/local-independence-public | [
"665578580e38b031ac0a3d6aacedf2f83c05eb48"
] | [
"dsprites.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\n\"\"\"\nCode for loading the dsprites dataset (https://github.com/deepmind/dsprites-dataset).\nCopied from https://github.com/miyosuda/disentangled_vae.\n... | [
[
"numpy.load",
"numpy.dot",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zkneupper/Audio-Classification-CNN | [
"e6d31bac1d5b102959bb95cf67ef1d7eeaa0f515"
] | [
"src/models/train_model_spec_depr.py"
] | [
"\nimport os\nimport sys\nimport h5py\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Convolution2D, MaxPooling2D\nfrom keras.layers import BatchNormalization\nfro... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.random.seed",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
zhuhu00/MOTSFusion_modify | [
"190224a7c3fbded69fedf9645a0ebbf08227fb6d",
"190224a7c3fbded69fedf9645a0ebbf08227fb6d",
"190224a7c3fbded69fedf9645a0ebbf08227fb6d"
] | [
"external/netdef_models/netdef_slim/tensorflow/core/ops/compute.py",
"external/BB2SegNet/refinement_net/datasets/COCO/COCO_detection.py",
"external/BB2SegNet/refinement_net/datasets/Augmentors.py"
] | [
"from netdef_slim.core.register import register_op\nimport tensorflow as tf\nfrom .blob import _slice\nimport numpy as np\nimport netdef_slim as nd\n\nnothing = None\n\n# ----------------------------------------------------------------------\ndef _threshold(tensor, thresh):\n condition = tf.less(tensor, thresh)\... | [
[
"tensorflow.zeros",
"tensorflow.pad",
"tensorflow.nn.depthwise_conv2d",
"tensorflow.subtract",
"tensorflow.to_float",
"tensorflow.square",
"tensorflow.argmax",
"tensorflow.fill",
"tensorflow.scalar_mul",
"tensorflow.less",
"tensorflow.pow",
"tensorflow.zeros_like",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
... |
diggee/twitter_disaster_tweets | [
"1b5f8c6b181039a872e51150be37d3bd4634316d"
] | [
"trial.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Mar 16 22:29:08 2020\r\n\r\n@author: admin\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Mar 16 14:02:52 2020\r\n\r\n@author: diggee\r\n\r\nscript created and compiled in spyder\r\n\"\"\"\r\n\r\n#%% importing libraries\r\n\r\nimport numpy a... | [
[
"pandas.read_csv",
"sklearn.model_selection.cross_val_score",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.neighbors.KNeighborsClassifier",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.svm.SVC",
"sklearn.preprocessing.StandardScaler",
"sklearn.linear_model.Rid... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
marklescroart/bvp | [
"4b973bacb2e6ed96538a1b81964b4bc13ffa1ccb"
] | [
"bvp/utils/math.py"
] | [
"\"\"\"BVP Math utilities\"\"\"\n\nfrom __future__ import division\n\nimport numpy as np\n\nfrom .basics import make_blender_safe\n\n\ndef vector_fn(a):\n b = np.array(a)\n b.shape = (len(a), 1)\n return b\n\n\ndef lst(a):\n a = np.array(a)\n return list(a.flatten())\n\n\ndef circ_avg(a, b):\n \"\... | [
[
"numpy.matrix",
"numpy.dot",
"numpy.radians",
"numpy.arctan",
"numpy.linspace",
"numpy.cumsum",
"numpy.arctan2",
"numpy.all",
"numpy.max",
"numpy.random.randn",
"numpy.arcsin",
"numpy.arange",
"numpy.nonzero",
"numpy.min",
"numpy.random.rand",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nesen2019/everyday | [
"8439d99b12a938cb8325cdaceef8861af062abc5"
] | [
"clecode/script/get_problem_py.py"
] | [
"import ast\nimport os\nimport json\nimport numpy as np\nimport pandas as pd\nfrom pandas.core.series import Series\nimport pickle\nfrom io import TextIOWrapper\n\nfrom bs4 import BeautifulSoup\nfrom markdown import markdown\n\nclass OneSeriesPy(object):\n def __init__(self, ds):\n \"\"\"\n status ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
MasaKat0/adaptive_OPE | [
"5609cba8c30a1bebbee0247d8835fb2f6f295a8a"
] | [
"bandit_experiment.py"
] | [
"import numpy as np\nimport argparse\nimport sys\nimport os\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import svm\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.datasets import load_svmlight_file\nfrom sklearn.decompositio... | [
[
"numpy.log",
"sklearn.linear_model.LogisticRegression",
"numpy.sqrt",
"numpy.unique",
"numpy.random.choice",
"numpy.random.multivariate_normal",
"sklearn.decomposition.PCA",
"numpy.argmax",
"numpy.identity",
"numpy.savetxt",
"numpy.random.uniform",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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