repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
auroraai-bot-platform/rasa | [
"92a79a9c0191fedffe045582095a21d1192646b3"
] | [
"tests/core/test_policies.py"
] | [
"from pathlib import Path\nfrom typing import Type, List, Text, Tuple, Optional, Any\nfrom unittest.mock import patch\n\nimport numpy as np\nimport pytest\n\nfrom rasa.core.channels import OutputChannel\nfrom rasa.core.exceptions import UnsupportedDialogueModelError\nfrom rasa.core.nlg import NaturalLanguageGenerat... | [
[
"numpy.random.randn",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
duguyue100/pytransyou | [
"54e024c85e43173c0ce201587f5a02f87b4832e2"
] | [
"transyou/transfun.py"
] | [
"\"\"\"Transform Functions.\n\nAuthor: Yuhuang Hu\nEmail : duguyue100@gmail.com\n\"\"\"\n\nfrom __future__ import print_function\nimport numpy as np\nfrom scipy.misc import imresize\nimport transyou\n\n\ndef trans_you(ori_image, img_db, target_size=(8, 8)):\n \"\"\"Transfer original image to composition of image... | [
[
"scipy.misc.imresize",
"numpy.array",
"numpy.zeros_like",
"numpy.random.choice"
]
] | [
{
"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": []
}
] |
devinplatt/ms_thesis | [
"5ed05023c9929d9f068a01b2b022785678215e18"
] | [
"train/sample-cnn/sample_cnn/data/mtt/annotation_processing.py"
] | [
"import pandas as pd\nimport numpy as np\n\nnp.random.seed(0)\n\n\ndef load_annotations(filename,\n n_top=50,\n n_audios_per_shard=100):\n \"\"\"Reads annotation file, takes top N tags, and splits data samples.\n\n Results 54 (top50_tags + [clip_id, mp3_path, split, shard])... | [
[
"numpy.random.permutation",
"numpy.arange",
"pandas.read_csv",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Schakirov/humanoid | [
"5b64165577aa77dc22d4f62716bdefdd96304d05"
] | [
"gym/envs/mujoco/swimmer.py"
] | [
"import numpy as np\nfrom gym import utils\nfrom gym.envs.mujoco import mujoco_env\n\nclass SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):\n def __init__(self):\n mujoco_env.MujocoEnv.__init__(self, 'swimmer_my.xml', 4)\n utils.EzPickle.__init__(self)\n\n def step(self, a):\n ctrl_cost... | [
[
"numpy.concatenate",
"numpy.square"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vandewinckel/SCARPA | [
"0e8adbfcf1c3a8f5936c577e7ffc2df2bc5ff24f"
] | [
"lib/search.py"
] | [
"import pandas as pd\n\nimport os, sys\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\nparentdir = os.path.dirname(currentdir)\nsys.path.append(parentdir)\n\nvendors = pd.read_csv('resources/macaddress-db.csv')\n\ndef vendor(df,mac):\n valid_mac = mac[:8].upper()\n company_name = str(list(df.loc[df... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
rohitdwivedula/pymoo | [
"a33dfda43bdd0ba99af606b42a9d33ba0c7aabd3"
] | [
"pymoo/algorithms/so_aco.py"
] | [
"from abc import abstractmethod\nfrom copy import deepcopy\n\nfrom pymoo.algorithms.so_genetic_algorithm import FitnessSurvival\nfrom pymoo.docs import parse_doc_string\nfrom pymoo.model.algorithm import Algorithm\nfrom pymoo.model.evaluator import set_cv, set_feasibility, Evaluator\nfrom pymoo.model.individual imp... | [
[
"numpy.random.random",
"numpy.full",
"numpy.concatenate",
"numpy.array",
"numpy.where",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pmayd/The-Data-Science-Workshop | [
"e16b96016314590ffe7294c7186923cfbec7b8b8"
] | [
"Chapter03/Exercise3.05/Test3_05.py"
] | [
"import unittest\r\nimport import_ipynb\r\nimport pandas as pd\r\nimport pandas.testing as pd_testing\r\nfrom sklearn import preprocessing\r\nfrom matplotlib import pyplot\r\n\r\nclass Test(unittest.TestCase):\r\n def setUp(self):\r\n import Exercise3_05\r\n self.exercises = Exercise3_05\r\n ... | [
[
"pandas.read_csv",
"pandas.testing.assert_frame_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ithallojunior/data_transmission | [
"eaf1ef81c018dc5a42f1756b39e19bfaa7f34ed5"
] | [
"test_code/python-plotter-serial-beta/spectrum.py"
] | [
"import serial\nimport matplotlib.pyplot as plt\n#import multiprocessing\nimport numpy as np\nimport time\n#import pdb\n#pdb.set_trace\n#pd = '/dev/cu.usbmodem1421'\npd ='/dev/cu.wchusbserial1410'\ndef worker():\n p = serial.Serial(port= pd, baudrate=115200,\n bytesize=serial.EIGHTBITS, pari... | [
[
"numpy.linspace",
"numpy.fft.fft",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ion",
"numpy.zeros",
"matplotlib.pyplot.pause"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
olivares-j/ChainConsumer | [
"748ea2dd90add6eb4ee53ee9f539e9a9c6f8ac50"
] | [
"tests/test_analysis.py"
] | [
"import os\nimport tempfile\n\nimport numpy as np\nfrom scipy.interpolate import interp1d\nfrom scipy.stats import skewnorm, norm\nimport pytest\n\nfrom chainconsumer import ChainConsumer\n\n\nclass TestChain(object):\n np.random.seed(1)\n n = 2000000\n data = np.random.normal(loc=5.0, scale=1.5, size=n)\n... | [
[
"scipy.stats.skewnorm.cdf",
"numpy.linspace",
"numpy.sqrt",
"numpy.random.multivariate_normal",
"numpy.all",
"numpy.concatenate",
"numpy.argmin",
"numpy.exp",
"numpy.allclose",
"numpy.save",
"scipy.interpolate.interp1d",
"numpy.zeros",
"numpy.isclose",
"scip... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vshipitsin/Ultrasound | [
"deb1e7f0edee023748e675300573656f81e8a5b7"
] | [
"data/dataset.py"
] | [
"from torch.utils.data.dataset import Dataset\nfrom torch.utils.data import DataLoader\nfrom PIL import Image\n\nimport sys\nimport os\nimport random\nimport numpy as np\nimport pandas as pd\n\n\nclass UltrasoundDataset(object):\n def __init__(self, data_path, val_size=0.2, random_seed=1):\n self.data_pat... | [
[
"pandas.read_csv",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
wszsycn/DarkPose-for-VIP2021 | [
"3658c74ed8bc76c497cb0269dbe10ed6898e07fb",
"3658c74ed8bc76c497cb0269dbe10ed6898e07fb"
] | [
"lib/dataset/JointsDataset.py",
"tools/draft.py"
] | [
"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Written by Bin Xiao (Bin.Xiao@microsoft.com)\n# Modified by Hanbin Dai (daihanbin.ac@gmail.com)\n# ----------------------------------------------------------------------... | [
[
"numpy.amax",
"numpy.multiply",
"numpy.amin",
"numpy.arange",
"torch.from_numpy",
"numpy.linalg.norm",
"numpy.ones",
"numpy.random.randn",
"numpy.random.rand",
"numpy.exp",
"numpy.array",
"numpy.zeros",
"numpy.sum"
],
[
"torch.optim.lr_scheduler.MultiSte... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
champon1020/ViViT-pytorch | [
"9bb3ea8746360e2031fed15fd7b0fcde132725ba"
] | [
"vivit.py"
] | [
"import numpy as np\nimport torch\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\nfrom torch import nn\n\nfrom module import Attention, FeedForward, PreNorm\n\n\nclass Transformer(nn.Module):\n def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.0):\n super... | [
[
"torch.nn.Dropout",
"torch.ones",
"torch.cat",
"torch.randn",
"torch.nn.ModuleList",
"torch.nn.LayerNorm",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
openeuler-mirror/A-Tune | [
"7479b30e4ab688cf2ca53235d9fd72fc22f332bd"
] | [
"analysis/optimizer/optimizer.py"
] | [
"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# Copyright (c) 2019 Huawei Technologies Co., Ltd.\n# A-Tune is licensed under the Mulan PSL v2.\n# You can use this software according to the terms and conditions of the Mulan PSL v2.\n# You may obtain a copy of Mulan PSL v2 at:\n# http://license.coscl.org.cn/Mulan... | [
[
"numpy.arange",
"sklearn.preprocessing.StandardScaler",
"numpy.abs",
"sklearn.linear_model.Lasso"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
A1chemyStars/mmdetection-intermediate | [
"dbcaa637f29c03aed7349ecad02f3e90305005b7"
] | [
"mmdet/models/roi_heads/standard_roi_head.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport torch\n\nfrom mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler\nfrom ..builder import HEADS, build_head, build_roi_extractor\nfrom .base_roi_head import BaseRoIHead\nfrom .test_mixins import BBoxTestMixin, MaskTestMixin\n\n\n@HEADS.regis... | [
[
"torch.zeros",
"torch.ones",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MarcoPerdomo/PDF-PPTX-Text-to-CSV | [
"460f2b1e63f571901b07daba23b48578ef79179d"
] | [
"PPTX and PDF to CSV.py"
] | [
"\"\"\"\r\nCreated on Sun May 3 21:01:25 2020\r\n\r\n\r\n\r\n@author: Marco Perdomo\r\n\"\"\"\r\n#!/usr/bin/env python\r\nfrom pptx import Presentation\r\nimport glob\r\nimport pandas as pd\r\nimport PyPDF2 \r\nimport textract \r\n\r\nfrom nltk.tokenize import word_tokenize\r\nfrom nltk.corpus import stopwords\r\n... | [
[
"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": []
}
] |
pechyonkin/model-analysis | [
"bb134b81c5c02a71b9dfe51d7aae8570038cd2e3"
] | [
"tensorflow_model_analysis/eval_saved_model/post_export_metrics/post_export_metrics.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 ... | [
[
"tensorflow.constant",
"tensorflow.range",
"tensorflow.shape",
"tensorflow.stack",
"tensorflow.sparse_tensor_to_dense",
"tensorflow.squeeze",
"tensorflow.identity",
"tensorflow.logging.info",
"tensorflow.no_op",
"tensorflow.group",
"tensorflow.metrics.auc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
magusikrak/NAMI-TERM-I-GroupWork | [
"f0a9a5f219ccbec024eb5316361db3fca46e171c"
] | [
"Python/100Excersises/.history/51 to 75/74/74_20201119130405.py"
] | [
"import pandas as p\nd1=p.read_csv(\"http://pythonhow.com/data/sampledata_x_2.txt\")\nd2=p.read_csv(\"http://www.pythonhow.com/data/sampledata.txt\")\nd3=p.concat([d1,d2])\nprint(d3)\n@staticmethod\ndef funcname(parameter_list):\n \"\"\"\n docstring\n \"\"\"\n pass\n"
] | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
khalilbalaree/EnhancedKGQA | [
"ffb36327b8515db87d079de2373b840212d7fdcc"
] | [
"train.py"
] | [
"\r\nfrom networkx.algorithms import matching\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch.nn.modules.activation import ReLU, Threshold\r\nfrom transformers import BertTokenizer, BertModel, AutoTokenizer, AutoModel\r\nfrom sklearn.model_selection import train_test_split\r\nimport pytorch_lightning as pl\r... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.KLDivLoss",
"torch.transpose",
"torch.nn.Dropout",
"torch.nn.functional.log_softmax",
"torch.eq",
"torch.nn.Conv2d",
"torch.sum",
"sklearn.model_selection.train_test_split",
"numpy.stack",
"torch.nn.Sigmoid",
"torch.tensor",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mahikazi/c111 | [
"6b7e5ec89855f21488f96e73426aeb839cd7f030"
] | [
"main.py"
] | [
"import plotly.figure_factory as ff\nimport plotly.graph_objects as go\nimport statistics\nimport random\nimport pandas as pd\nimport csv\n\ndf = pd.read_csv(\"studentMarks.csv\")\ndata = df[\"Math_score\"].tolist()\n\n#plotting the graph\nfig = ff.create_distplot([data],[\"Math Scores\"], show_hist= False)\nfig.sh... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
srcaddle/plastic-networks | [
"639bd71d299b409529b83e40cb613ef4c612a63b"
] | [
"Visualization/Greenland-input_data_errors.py"
] | [
"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nPlot retreat rate differences by glacier ID to identify regional features\nCreated on Tue Sep 29 16:08:30 2020\n\n@author: lizz\n\"\"\"\n\nimport numpy as np\nfrom scipy.stats import linregress\nimport pylab as plt\n\n\n# Input file for observations\nobs_dat... | [
[
"numpy.isnan",
"scipy.stats.linregress",
"numpy.array",
"numpy.loadtxt"
]
] | [
{
"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"
... |
fedario/holoviews | [
"1cf01c36f746cff17f48c700c1f67b1b6254be85"
] | [
"holoviews/tests/plotting/matplotlib/testlayoutplot.py"
] | [
"import numpy as np\n\nfrom holoviews.core import HoloMap, NdOverlay, DynamicMap\nfrom holoviews.element import Image, Curve\nfrom holoviews.streams import Stream\n\nfrom .testplot import TestMPLPlot, mpl_renderer\n\n\nclass TestLayoutPlot(TestMPLPlot):\n\n def test_layout_instantiate_subplots(self):\n la... | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deeplearninc/auger-cli | [
"afa52224043834e11f40d69d2042d53dfccc5ae5"
] | [
"auger_cli/utils.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport sys\nimport uuid\nimport os\nimport subprocess\nimport base64\nimport time\nimport re\n\n\ndef camelize(snake_cased_string, join_string=\" \"):\n parts = snake_cased_string.split('_')\n for idx, part in enumerate(parts):\n if idx == 0 and len(join_string) == 0:\n ... | [
[
"pandas.DataFrame.from_records",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
gerwang/onnx-tensorflow | [
"ac850cb29e916f61062efc73d5de16da0181a152"
] | [
"onnx_tf/handlers/backend/lstm.py"
] | [
"from functools import partial\n\nimport tensorflow as tf\n\nfrom onnx_tf.common import get_unique_suffix\nfrom onnx_tf.common import exception\nfrom onnx_tf.handlers.backend_handler import BackendHandler\nfrom onnx_tf.handlers.handler import onnx_op\nfrom onnx_tf.handlers.handler import partial_support\nfrom onnx_... | [
[
"tensorflow.concat",
"tensorflow.nn.rnn_cell.LSTMStateTuple",
"tensorflow.expand_dims",
"tensorflow.squeeze",
"tensorflow.zeros_like",
"tensorflow.add",
"tensorflow.split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
rngtna/openvino | [
"b320061ea379de2066a05d829e3455e594de3e43"
] | [
"runtime/bindings/python/tests/test_inference_engine/test_tensor.py"
] | [
"# Copyright (C) 2021 Intel Corporation\n# SPDX-License-Identifier: Apache-2.0\n\nimport numpy as np\nimport pytest\n\nfrom ..conftest import image_path\nfrom openvino import Tensor\nimport openvino as ov\n\n\ndef read_image():\n import cv2\n\n n, c, h, w = (1, 3, 32, 32)\n image = cv2.imread(image_path())... | [
[
"numpy.array_equal",
"numpy.ascontiguousarray",
"numpy.shares_memory",
"numpy.dtype",
"numpy.ones",
"numpy.all",
"numpy.random.normal",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhjpqq/Mask_RCNN_V2 | [
"d00e9bf395d318843a0517d0a94c376305aa93db"
] | [
"samples/coco/coco.py"
] | [
"import os\nimport sys\nimport time\nimport numpy as np\nimport imgaug # https://github.com/aleju/imgaug (pip3 install imgaug)\n\n# Download and install the Python COCO tools from https://github.com/waleedka/coco\n# That's a fork from the original https://github.com/pdollar/coco with a bug\n# fix for Python 3.\n# ... | [
[
"numpy.around",
"numpy.asfortranarray",
"numpy.stack",
"numpy.ones",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neer201/catboost_SE | [
"091d44aed29ebb40a4bf76ef67ee22cf9779263e"
] | [
"catboost/python-package/ut/medium/run_catboost.py"
] | [
"import catboost\nfrom catboost import CatBoost, CatBoostClassifier, CatBoostRegressor, cv, Pool, train, utils\nfrom catboost.datasets import titanic, amazon\nfrom catboost.utils import create_cd\nimport numpy as np\n\ndef test():\n train_df = titanic()[0].fillna(-999)\n X, y = train_df.drop('Survived', axis=... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KhalilJDamouni/RMSGD_Pretest | [
"dcc68fb03f9968b3338c8b8c2c3c400331a0c7d2"
] | [
"src/adas/optim/sgd.py"
] | [
"\"\"\"\nFrom PyTorch:\n\nCopyright (c) 2016- Facebook, Inc (Adam Paszke)\nCopyright (c) 2014- Facebook, Inc (Soumith Chintala)\nCopyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\nCopyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)\nCopyright (c) 2011-... | [
[
"torch.clone",
"torch.no_grad",
"torch.enable_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dthain/coffea | [
"fa5e0bcb2310cbea668c96013d4792124d2fbe3f"
] | [
"coffea/processor/test_items/NanoTestProcessorPandas.py"
] | [
"from coffea import hist, processor\nfrom coffea.analysis_objects import JaggedCandidateArray as CandArray\nfrom coffea.util import awkward as akd\nfrom coffea.util import numpy as np\nimport pandas as pd\n\n\nclass NanoTestProcessorPandas(processor.ProcessorABC):\n def __init__(self, columns=[]):\n self.... | [
[
"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": []
}
] |
bliptrip/h3d-cf | [
"45cae3981491aed57b05e618f6075f33a80ca52f"
] | [
"cf_3D_H.py"
] | [
"#!/usr/bin/env python\n\nimport argparse\nfrom cvfit import cvfit\nimport cv2\nimport cv2.ximgproc\nimport numpy as np\nimport os\nimport skimage\nimport skimage.color\nimport sys\n\n# construct the argument parser and parse the arguments\ndef parse_args():\n parser = argparse.ArgumentParser(prog=sys.argv[0], d... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.min",
"numpy.linalg.inv",
"numpy.eye",
"numpy.ones",
"numpy.savez_compressed",
"numpy.mean",
"numpy.floor",
"numpy.load",
"numpy.repeat",
"numpy.diagonal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
inventor71/larq-zoo | [
"a9b2ae9c7416477736545ba87f7592e2edcd2f6b",
"a9b2ae9c7416477736545ba87f7592e2edcd2f6b"
] | [
"tests/models_test.py",
"larq_zoo/sota/quicknet.py"
] | [
"import os\n\nimport numpy as np\nimport pytest\nfrom tensorflow import keras\n\nimport larq_zoo as lqz\n\n\n@pytest.fixture(autouse=True)\ndef cleanup(request):\n request.addfinalizer(keras.backend.clear_session)\n\n\n@pytest.fixture(scope=\"module\")\ndef test_image(request):\n file = os.path.join(os.path.d... | [
[
"numpy.expand_dims",
"tensorflow.keras.preprocessing.image.load_img",
"numpy.testing.assert_allclose",
"tensorflow.keras.preprocessing.image.img_to_array",
"tensorflow.keras.layers.Input"
],
[
"tensorflow.keras.layers.DepthwiseConv2D",
"tensorflow.keras.layers.Activation",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.3",
"2.4",
"2.5",
"2.6"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
... |
perlman/cloud-volume | [
"e3c01f69e6519f0c53d13d39e7a42bb38d63a936"
] | [
"test/test_lib.py"
] | [
"import pytest\n\nimport numpy as np\n\nimport cloudvolume.lib as lib\nfrom cloudvolume.lib import Bbox, Vec\n\ndef test_divisors():\n\n divisors = (\n (1, (1,)), \n (2, (1,2)),\n (3, (1,3)),\n (4, (1,2,4)),\n (6, (1,2,3,6)),\n (35, (1,5,7,35)),\n (128, (1,2,4,8,16,32,64,128)),\n (258, (1,2... | [
[
"numpy.abs",
"numpy.array_equal",
"numpy.int32",
"numpy.all",
"numpy.float32",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qiuwch/lt | [
"3a36f325a70a37f8152e8f62387628f6dabcabeb"
] | [
"mvn/utils/vis.py"
] | [
"import numpy as np\nimport scipy.ndimage\nimport skimage\nimport cv2\n\nimport torch\n\nimport matplotlib\nfrom matplotlib import pylab as plt\nfrom mpl_toolkits.mplot3d import axes3d, Axes3D\nmatplotlib.use('Agg')\n\n\nfrom mvn.utils.img import image_batch_to_numpy, to_numpy, denormalize_image, resize_image\nfrom... | [
[
"matplotlib.pylab.get_cmap",
"torch.max",
"matplotlib.use",
"matplotlib.colors.to_hex",
"numpy.ones",
"matplotlib.pylab.figure",
"matplotlib.pylab.subplots",
"numpy.mean",
"numpy.array",
"numpy.zeros",
"matplotlib.pylab.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hunt-genes/ncls | [
"75f1e970822a90ef31a313d1124629c6f6919a42"
] | [
"examples/test_all_overlaps_both.py"
] | [
"\n\n\nfrom ncls import NCLS\n\nimport pickle\nimport pandas as pd\nimport numpy as np\n\n\nstarts = np.array(list(reversed([3, 5, 8])), dtype=np.int)\nends = np.array(list(reversed([6, 7, 9])), dtype=np.int)\nindexes = np.array(list(reversed([0, 1, 2])), dtype=np.int)\n\n# starts = np.array([3, 5, 8], dtype=np.int... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ucrscholar/HandWashNet | [
"2a01099b9d054956fd335ec4de6aff2064d5ef4e"
] | [
"segmentalAnalysis.py"
] | [
"# %%\n\nimport os\n\nfrom matplotlib import pyplot\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n# % matplotlib inline\nimport glob\nimport os\nimport sys\nfrom PIL import Image\n\nfrom keras.models import Model\n\n# %% md\n\n## Load data\n\n# %%\n\nmasks =... | [
[
"numpy.asarray",
"matplotlib.pyplot.show",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dfm/SurPyval | [
"014fba8f1d4a0f43218a3713ce80a78191ad8be9"
] | [
"surpyval/parametric/logistic.py"
] | [
"import autograd.numpy as np\nfrom scipy.stats import uniform\nfrom scipy.special import ndtri as z\n\nfrom surpyval import xcn_handler\nfrom surpyval.parametric.parametric_fitter import ParametricFitter\n\nclass Logistic_(ParametricFitter):\n def __init__(self, name):\n self.name = name\n self.k =... | [
[
"scipy.stats.uniform.rvs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
accasio/3DPyraNet | [
"19f8d453ac5d089b5c5514ea744d895e6f8aee14"
] | [
"pyranet/datasets/input_data.py"
] | [
"from __future__ import division\n\nimport numpy as np\nimport time\n\n\ndef generate_batch(x, y, batch_size=100, shuffle=True):\n total_iteration = x.shape[0] // batch_size\n while True:\n if shuffle:\n idx_perm = np.random.permutation(x.shape[0])\n else:\n idx_perm = rang... | [
[
"numpy.isfinite",
"numpy.arange",
"numpy.std",
"numpy.random.permutation",
"numpy.mean",
"numpy.zeros_like",
"numpy.load",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hassberg/active_learning_ts | [
"7ebdabd3349d3ac4ea2761a8aa869b8d222a2d83",
"7ebdabd3349d3ac4ea2761a8aa869b8d222a2d83"
] | [
"active_learning_ts/pools/retrievement_strategy.py",
"tests/pools/test_discrete_vector_pool.py"
] | [
"from typing import Protocol\n\nimport tensorflow as tf\n\nfrom active_learning_ts.pool import Pool\n\n\nclass RetrievementStrategy(Protocol):\n def __init__(self):\n self.pool: Pool = None\n\n def post_init(self, pool: Pool):\n self.pool = pool\n\n def find(self, points: tf.Tensor) -> tf.Ten... | [
[
"tensorflow.reshape"
],
[
"tensorflow.constant"
]
] | [
{
"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... |
cclauss/semanticflowgraph | [
"55e23e489cbbfcd165a09b598529c6fbea3d6fda"
] | [
"test/data/sklearn_clustering_kmeans.py"
] | [
"import pandas as pd\nfrom sklearn.cluster import KMeans\n\niris = pd.read_csv('datasets/iris.csv')\niris = iris.drop('Species', 1)\n\nkmeans = KMeans(n_clusters=3).fit(iris.values)\ncentroids = kmeans.cluster_centers_\nclusters = kmeans.labels_\n"
] | [
[
"pandas.read_csv",
"sklearn.cluster.KMeans"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
admariner/yellowbrick | [
"cc8017647545e1312359b8fbb5b77c013d25c483"
] | [
"tests/test_model_selection/test_cross_validation.py"
] | [
"# tests.test_model_selection.test_cross_validation\n# Tests for the CVScores visualizer\n#\n# Author: Rebecca Bilbro\n# Created: Fri Aug 10 13:45:11 2018 -0400\n#\n# Copyright (C) 2018 The scikit-yb developers\n# For license information, see LICENSE.txt\n#\n# ID: test_cross_validation.py [962c8bb] rebeccabilbro@u... | [
[
"sklearn.tree.DecisionTreeRegressor",
"sklearn.model_selection.ShuffleSplit",
"numpy.testing.assert_array_almost_equal",
"sklearn.linear_model.LogisticRegressionCV",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.model_selection.StratifiedKFold",
"sklearn.neighbors.KNeighborsClassifie... | [
{
"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": []
}
] |
jinchihe/kfserving | [
"b3d88963a2d85bb4e5aa4992d421ed27550cd125"
] | [
"python/sklearnserver/sklearnserver/test_model.py"
] | [
"# Copyright 2019 kubeflow.org.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed... | [
[
"sklearn.datasets.load_iris",
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
senadkurtisi/pytorch-NER | [
"12e348b3b9501ec647dbf4845d3d0b05bc67fb71"
] | [
"transformer.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass PositionalEncodings(nn.Module):\n \"\"\"Attention is All You Need positional encoding layer\"\"\"\n\n def __init__(self, seq_len, d_model, p_dropout):\n \"\"\"Initializes the layer.\"\"\"\n super(PositionalEncodings... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.norm",
"torch.Tensor",
"torch.sin",
"torch.zeros",
"torch.tensor",
"torch.nn.Linear",
"torch.bmm",
"torch.arange",
"torch.nn.ReLU",
"torch.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
s171156/Web_Crawler | [
"3f727bd60514f34d5c8218330920441d2013e301"
] | [
"Amazon/base_crawler.py"
] | [
"from bs4 import BeautifulSoup\nimport re\nfrom pathlib import Path\nfrom my_module.path_manager import get_abs_path\nimport pandas as pd\nimport datetime as dt\nfrom abc import ABCMeta, abstractmethod\nfrom my_module import text_formatter as tf\nimport os\nimport urllib\n\n\nclass URLValidator:\n\n def __init__... | [
[
"pandas.read_csv",
"pandas.DataFrame.from_dict"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Yu-Group/veridical-flow | [
"2c26f3eae23fa85a43fa3c3b3aa13c7aaedc8c71"
] | [
"tests/test_pipelines.py"
] | [
"import time\nimport os\nfrom functools import partial\nfrom shutil import rmtree\n\nimport numpy as np\nimport pandas as pd\nimport ray\nimport sklearn\nfrom numpy.testing import assert_equal\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.inspect... | [
[
"sklearn.datasets.load_boston",
"numpy.testing.assert_equal",
"sklearn.ensemble.RandomForestRegressor",
"sklearn.datasets.make_classification",
"sklearn.linear_model.LogisticRegression",
"numpy.random.seed",
"sklearn.tree.DecisionTreeRegressor",
"numpy.arange",
"sklearn.model_s... | [
{
"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": []
}
] |
akey7/moldynquick | [
"1cfe78f331a47ff6a31226dc8f3cf8f30bc694c4"
] | [
"moldynquick/__main__.py"
] | [
"from time import time\nfrom argparse import ArgumentParser\nimport pandas as pd\n\nfrom moldynquick.namd import NAMDLog, NAMDTrajectory\n\n\nclass App:\n def __init__(self):\n \"\"\"\n This initializes the instance attributes that will hold filenames for\n input and output, as well as the t... | [
[
"pandas.ExcelWriter"
]
] | [
{
"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": []
}
] |
vishalbelsare/seq2tens | [
"e5d1160dbd228e4a284ba38460aa0a8c716340e0"
] | [
"tsc/models/.ipynb_checkpoints/init_ls2t_model-checkpoint.py"
] | [
"import sys\n\nsys.path.append('..')\n\n# from .layers import Time, Difference, LS2T\n\nimport seq2tens\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import InputLayer, Conv1D, Activation, Reshape, Dense, LayerNormalization, BatchNormalizatio... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.Masking",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv1D",
"tensorflow.keras.Sequential",
"tensorflow.keras.layers.InputLayer",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.Re... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
jg719/test_documentation | [
"0b466ada5b7de7b22dcc1135a15ed72aa68f7966"
] | [
"docs/source/CMAES_Analysis.py"
] | [
"import time \nimport string\nimport math\nimport random\nimport csv \nfrom functools import reduce\nfrom openpyxl import load_workbook\n\nimport pandas as pd\nimport numpy as np\n\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport seaborn as sns\nimport itertools\n\nimport seleni... | [
[
"matplotlib.pyplot.legend",
"pandas.read_excel",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"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": []
}
] |
jberkow713/Twittoff | [
"9849c6a584f55d5cc2f6ce4bce7ce6269ada383b"
] | [
"web_app/services/basilica_test.py"
] | [
"import basilica\nsentences = [\n \"This is a sentence!\",\n \"This is a similar sentence!\",\n \"I don't think this sentence is very similar at all...\",\n]\nwith basilica.Connection('73afeca4-b8fd-28d0-1771-75318c6889f1') as c:\n embeddings = list(c.embed_sentences(sentences))\nprint(embeddings)\n\nfr... | [
[
"scipy.spatial.distance.cosine"
]
] | [
{
"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"
... |
jeremyfix/ML-Recipes | [
"0b2079071611f949332c9d0acbe834b87ce1b4c2"
] | [
"recipes/ANN/voted-perceptron.py"
] | [
"# -----------------------------------------------------------------------------\n# Copyright 2019 (C) Jeremy Fix\n# Released under a BSD two-clauses license\n#\n# References: Y. Freund, R. E. Schapire. \"Large margin classification using\n# the perceptron algorithm\". In: 11th Annual Conference on\n# ... | [
[
"numpy.dot",
"matplotlib.pyplot.contourf",
"numpy.linspace",
"matplotlib.pyplot.ylim",
"numpy.ones",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"numpy.random.normal",
"matplotlib.pyplot.contour",
"numpy.zeros_like",
"numpy.random.randint",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ps789/multivariate-deep-learning | [
"aa09b1419dffc34cf2ee8b6ee00d7ecbe4d0d6e6"
] | [
"DeepAR-quantile/evaluate_quantile.py"
] | [
"import argparse\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.utils.data.sampler import RandomSampler\nfrom torch.utils.data.sampler import SequentialSampler\nfrom tqdm import tqdm\n\nimport utils\nimport model.net as net\nfrom dataloader import *\n\nimport matplotlib\nmatplotlib.use('... | [
[
"torch.zeros",
"numpy.random.choice",
"matplotlib.use",
"numpy.arange",
"torch.sum",
"numpy.transpose",
"numpy.concatenate",
"torch.no_grad",
"torch.cuda.is_available",
"matplotlib.pyplot.close",
"torch.device",
"torch.utils.data.sampler.SequentialSampler",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neale/A4C | [
"acbbb3cf14e31a19c12f27306971b4db4feafe09",
"acbbb3cf14e31a19c12f27306971b4db4feafe09"
] | [
"multilstm_tensorpack/tensorpack/models/regularize.py",
"multilstm_tensorpack/tensorpack/models/common.py"
] | [
"# -*- coding: UTF-8 -*-\n# File: regularize.py\n# Author: Yuxin Wu <ppwwyyxx@gmail.com>\n\nimport tensorflow as tf\nimport re\n\nfrom ..utils import logger\nfrom ..utils.argtools import memoized\nfrom ..tfutils.tower import get_current_tower_context\nfrom .common import layer_register\n\n__all__ = ['regularize_cos... | [
[
"tensorflow.constant",
"tensorflow.get_default_graph",
"tensorflow.add_n",
"tensorflow.nn.dropout"
],
[
"tensorflow.variable_scope"
]
] | [
{
"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",
... |
darkrsw/fuzzingbook | [
"3ed5a4ba14dd2837dff2c4b8c6d222c102dea338"
] | [
"docs/code/MutationFuzzer.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# \"Mutation-Based Fuzzing\" - a chapter of \"The Fuzzing Book\"\n# Web site: https://www.fuzzingbook.org/html/MutationFuzzer.html\n# Last change: 2021-11-03 13:00:56+01:00\n#\n# Copyright (c) 2021 CISPA Helmholtz Center for Information Security\n# Copyright (c) 2... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sue128/RNN-based-hyper | [
"ee777b8df0b25618a7f99384d6393876ebbaae60"
] | [
"tensorflow_compression/python/layers/entropy_models_test.py"
] | [
"# Copyright 2018 Google LLC. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appl... | [
[
"numpy.log",
"numpy.allclose",
"numpy.linspace",
"tensorflow.compat.v1.test.main",
"numpy.around",
"numpy.arange",
"tensorflow.compat.v1.global_variables_initializer",
"numpy.full_like",
"numpy.random.uniform",
"tensorflow.compat.v1.placeholder",
"numpy.random.normal",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
franloza/Glycemic-Patterns | [
"fe8072286acbf51e4a949530019517152f6f09ef"
] | [
"glycemic_patterns/preprocessor.py"
] | [
"import datetime\nimport logging\nimport matplotlib\n# Force matplotlib to not use any Xwindows backend.\nmatplotlib.use('Agg')\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.preprocessing import LabelBinarizer, Imputer\nfrom .lib import peakdetect\n\n# Get logger\nlogger = logging.getLogger(__name__)\n\n... | [
[
"pandas.merge",
"pandas.concat",
"numpy.abs",
"pandas.isnull",
"matplotlib.use",
"pandas.DataFrame",
"sklearn.preprocessing.Imputer",
"numpy.std",
"numpy.diff",
"sklearn.preprocessing.LabelBinarizer",
"numpy.array",
"numpy.sum"
]
] | [
{
"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": []
}
] |
casus/mala | [
"4980a7a6f6399f564782d6ecd461b1f3b855c540",
"4980a7a6f6399f564782d6ecd461b1f3b855c540"
] | [
"mala/targets/target_base.py",
"mala/targets/ldos.py"
] | [
"\"\"\"Base class for all target calculators.\"\"\"\nfrom abc import ABC, abstractmethod\n\nfrom ase.units import Rydberg, Bohr, kB\nimport ase.io\nimport numpy as np\n\nfrom mala.common.parameters import Parameters, ParametersTargets\nfrom mala.targets.calculation_helpers import fermi_function\n\n\nclass TargetBas... | [
[
"numpy.zeros",
"numpy.sum"
],
[
"numpy.linspace",
"numpy.reshape",
"numpy.shape",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ebucheli/FreeSound_GPAT | [
"6737732276cb664e682fdc631d429ffcd5875990"
] | [
"networks/RNNetworks.py"
] | [
"from tensorflow.keras.layers import Input, Dense, Flatten, Dropout, Activation, CuDNNGRU\nfrom tensorflow.keras.layers import GRU, LSTM, TimeDistributed, Lambda, Dot, Softmax\nfrom tensorflow.keras.layers import Conv2D, Conv1D, Reshape, Permute, GRUCell, LSTMCell\nfrom tensorflow.keras.layers import Bidirectional,... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.MaxPool1D",
"tensorflow.keras.layers.Lambda",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.CuDNNLSTM",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv1D",
"tensorflow.keras.layers.Conv2D",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
rwoodsrobinson/propnet | [
"05643d400add6c8354ec95e1c3cbe1191f580faa"
] | [
"propnet/core/graph.py"
] | [
"\"\"\"\nModule containing classes and methods for graph functionality in Propnet code.\n\"\"\"\n\nimport logging\nfrom collections import defaultdict\nfrom itertools import product\nfrom chronic import Timer, timings, clear\nfrom pandas import DataFrame\n\nimport networkx as nx\n\nfrom propnet.core.materials impor... | [
[
"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": []
}
] |
vnbot2/pggan-pytorch | [
"530579beed6dd0b3feebb768d4285c998177adff"
] | [
"trainer.py"
] | [
"import dataloader as DL\nfrom config import config\nimport network as net\nfrom math import floor, ceil\nimport os, sys\nimport torch\nimport torch.nn as nn\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nfrom torch.optim import Adam\nfrom tqdm import tqdm\nimport tf_recorder as ... | [
[
"torch.set_default_tensor_type",
"torch.mean",
"torch.cuda.manual_seed",
"torch.load",
"torch.from_numpy",
"torch.FloatTensor",
"torch.no_grad",
"torch.cuda.is_available",
"torch.save",
"torch.nn.MSELoss",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
masasin/latexipy | [
"1f888a44f2077a5c0ef63216616cd24c279e44d0"
] | [
"tests/test_latexipy.py"
] | [
"#!/usr/bin/env python\n'''\nTests for `latexipy` package.\n\n'''\nfrom functools import partial\nimport inspect\nimport math\nfrom unittest.mock import patch\n\nimport matplotlib as mpl\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nimport pytest\n\nimport latexipy as lp\nfrom latexipy._latexipy import INCH_PER... | [
[
"matplotlib.use",
"matplotlib.pyplot.get_backend"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RubenFr/ICARUS-framework | [
"e57a1f50c3bb9522b2a279fee6b625628afd056f"
] | [
"sat_plotter/geo_plot_builder.py"
] | [
"# 2020 Tommaso Ciussani and Giacomo Giuliari\n\"\"\"\nThis Builder object builds a geographic plot step by step. The order in which methods are called influences the plot.\nThe class is self-explanatory, method names add what they promise on the globe map.\n\"\"\"\nimport numpy as np\nimport plotly.graph_objects ... | [
[
"numpy.array",
"numpy.linalg.norm",
"matplotlib.cm.get_cmap"
]
] | [
{
"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": [],
... |
kinghows/bokeh | [
"aeb7abc1dbe2b67ce0f4422838a96fb8362c52c7"
] | [
"examples/reference/models/Annulus.py"
] | [
"import numpy as np\n\nfrom bokeh.models import ColumnDataSource, Plot, LinearAxis, Grid\nfrom bokeh.models.glyphs import Annulus\nfrom bokeh.io import curdoc, show\n\nN = 9\nx = np.linspace(-2, 2, N)\ny = x**2\n\nsource = ColumnDataSource(dict(x=x, y=y))\n\nplot = Plot(\n title=None, plot_width=300, plot_height... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
randomguy4214/poor-almanac-6 | [
"8134c584217bd23e9125d16849ed7edf9681277d"
] | [
"bundle_main/process_prices.py"
] | [
"#!/usr/bin/python\n\nprint('prices_process - initiating.')\n\nimport os\nimport pandas as pd\n\npd.options.mode.chained_assignment = None\n\n# set directories and files\ncwd = os.getcwd()\ninput_folder = \"0_input\"\nprices_folder = \"data\"\noutput_folder = \"0_output\"\ntemp_folder = \"temp\"\nprices_temp = \"pr... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
NatalieBarbosa/hida-datathon-ufz | [
"6c3272e07696993523fd97e61c38b981852248f4"
] | [
"netcdf_helpers/reader.py"
] | [
"# -*- coding: utf-8 -*-\nimport netCDF4\nimport numpy as np\nimport os.path as osp\n\n\ndef say_hello():\n print(\"hello\")\n\n\ndef get_time_series_from_location(data, var_name, lat_target, lon_target):\n \"\"\"\n Get a time series for a variable at a specific location with the\n corresponding time.\n... | [
[
"numpy.argmin",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HPI-MachineIntelligence-MetaLearning/chainercv_ssd | [
"304098bafed1dc709ec40193ac77fd70297b88a0"
] | [
"examples/ssd/train_pastyear.py"
] | [
"import argparse\nimport copy\nimport numpy as np\nimport json\n\nimport chainer\nfrom chainer.datasets import TransformDataset\nfrom chainer.optimizer import WeightDecay\nfrom chainer import serializers\nfrom chainer import training\nfrom chainer.training import extensions\nfrom chainer.training import triggers\n\... | [
[
"numpy.stack",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaykang-heo/poseAnalysis | [
"34cfac4a889e2c973651c1c07740ea0908542d68"
] | [
"mysite/pose/pose_3d/src/networks.py"
] | [
"import os\n\nimport tensorflow as tf\nfrom .network_mobilenet import MobilenetNetwork\nfrom .network_mobilenet_thin import MobilenetNetworkThin\n\nfrom .network_cmu import CmuNetwork\n\n\ndef _get_base_path():\n if not os.environ.get('OPENPOSE_MODEL', ''):\n return './models'\n return os.environ.get('... | [
[
"tensorflow.train.Saver"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
medetovyerma/MODNet | [
"1ba8578ca21ecdc2c727135757e7617de185be3d"
] | [
"src/trainer.py"
] | [
"import math\nimport scipy\nimport numpy as np\nfrom scipy.ndimage import grey_dilation, grey_erosion\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom segmentation_models_pytorch.utils.functional import iou\n\n__all__ = [\n 'supervised_training_iter',\n 'soc_adaptation_iter',\n]... | [
[
"torch.mean",
"scipy.ndimage.gaussian_filter",
"torch.nn.functional.l1_loss",
"scipy.ndimage.grey_dilation",
"torch.nn.Conv2d",
"torch.sum",
"torch.from_numpy",
"torch.tensor",
"torch.nn.functional.mse_loss",
"torch.no_grad",
"torch.nn.functional.interpolate",
"torc... | [
{
"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"... |
sunway1999/deep_omics | [
"5ceb61aa1555ceed49c85a1b49c99ca9ca48e6b5"
] | [
"hyper_parameter_tuning/v2_separate_dense_template_HLA_I.py"
] | [
"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport random\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\nimport argparse\n\nfrom random import shuffle, sample\n\nfrom collections import Counter\nfrom collections import defaultdict\n\nfrom tensorflow.keras.callbacks import ModelCheckpoint... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.keras.metrics.BinaryAccuracy",
"numpy.random.seed",
"tensorflow.keras.metrics.AUC",
"tensorflow.keras.losses.BinaryCrossentropy",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.backend.clear_session",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SHENGYUKing/playMNIST | [
"3d3afafa5e78c8e75ee6bde883a2e3fcbd100dca"
] | [
"model.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport os\nimport struct\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef load_mnist(path, kind='train'):\n \"\"\"load_mnist\n Load MNIST data from path and save as numpy.ndarray(label, data...)\n Args:\n path (filepath): where the dataset file is\n ki... | [
[
"numpy.fromfile",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TransPositionAust/synthpop | [
"4073f2bba7d90c71344a2cf7bba88b407279ddc8"
] | [
"synthpop/recipes/starter.py"
] | [
"from .. import categorizer as cat\nfrom ..census_helpers import Census\nimport pandas as pd\nimport numpy as np\n\n\n# TODO DOCSTRINGS!!\nclass Starter:\n \"\"\"\n This is a recipe for getting the marginals and joint distributions to use\n to pass to the synthesizer using simple categories - population, a... | [
[
"numpy.isnan",
"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": []
}
] |
WesGtoX/denavit-hartenberg-api | [
"424116ca38613ff56aac49e98efc6789af96f045"
] | [
"app/utils.py"
] | [
"import numpy as np\n\nfrom typing import List, Tuple\nfrom app.model import DenavitHartenberg\n\n\ndef linalg_multi_dot(dh: List[np.ndarray]) -> np.ndarray:\n if len(dh) > 1:\n return np.linalg.multi_dot(dh)\n\n return dh[0]\n\n\ndef get_coord(dh: np.ndarray) -> Tuple:\n vector = np.array([[0], [0]... | [
[
"numpy.array",
"numpy.linalg.multi_dot",
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EvilScott/nlp-flask | [
"99d68751fa34c4c788467b6de195d679d41f4227"
] | [
"nlp/textrank.py"
] | [
"import networkx as nx\nimport numpy as np\nimport re\nfrom nlp.stopwords import SEO_STOP_WORDS\n# TODO from nltk.stem.porter import PorterStemmer\nfrom nltk.tokenize import word_tokenize\n\nNUM_RETURNED = 10\nRADIUS = 10\n\n\ndef combine_keywords(keywords, doc):\n for word in keywords:\n others = keyword... | [
[
"numpy.random.seed",
"numpy.matmul",
"numpy.linalg.norm",
"numpy.ones",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
twever/state-of-the-data | [
"3074a09b801c9189f9394c237c54c12e7a0dc0f7"
] | [
"state-of-the-data-for-desktop/src/distance_tools/distance/_hausdorff.py"
] | [
"import numpy as np\nfrom numpy.core.umath_tests import inner1d\nimport math\n\ndef HausdorffDist(A,B):\n # Hausdorf Distance: Compute the Hausdorff distance between two point\n # clouds.\n # Let A and B be subsets of metric space (Z,dZ),\n # The Hausdorff distance between A and B, denoted by dH(A,B),\n... | [
[
"numpy.dot",
"numpy.array",
"numpy.core.umath_tests.inner1d",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zwang132/espnet | [
"55d07e1be26204850937f4db875de7f7fd8fbe6d"
] | [
"src/bin/asr_recog.py"
] | [
"#!/usr/bin/env python\n# encoding: utf-8\n\n# Copyright 2017 Johns Hopkins University (Shinji Watanabe)\n# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)\n\n\nimport argparse\nimport logging\nimport os\nimport random\nimport sys\n\nimport numpy as np\n\n\ndef main():\n parser = argparse.ArgumentParse... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ryan-Rhys/Mrk_335 | [
"bb573c1d5353091ce280e66aec7d00a3ee9d4378"
] | [
"simulations/fit_xray_sims.py"
] | [
"# Copyright Ryan-Rhys Griffiths 2019\n# Author: Ryan-Rhys Griffiths\n\"\"\"\nThis script fits a Gaussian Process to x-ray simulations.\n\"\"\"\n\nimport logging\nimport time as real_time # avoid aliasing with time variable in code.\n\nimport gpflow\nimport numpy as np\nimport tensorflow as tf\nfrom gpflow.mean_fu... | [
[
"matplotlib.pyplot.legend",
"numpy.squeeze",
"tensorflow.cast",
"matplotlib.pyplot.plot",
"numpy.mean",
"tensorflow.random.set_seed",
"matplotlib.pyplot.tight_layout",
"numpy.reshape",
"matplotlib.pyplot.close",
"numpy.log",
"matplotlib.pyplot.ylim",
"sklearn.prepro... | [
{
"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... |
anorak-k/tensorflow | [
"b737d47b533ec0abfc12977022f8fb8e550e7f44"
] | [
"tensorflow/python/training/tracking/util.py"
] | [
"\"\"\"Utilities for saving/loading Trackable objects.\"\"\"\n# 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# ... | [
[
"tensorflow.python.training.tracking.base._SlotVariableRestoration",
"tensorflow.python.ops.variable_scope.variable_creator_scope",
"tensorflow.python.ops.variable_scope._get_default_variable_store",
"tensorflow.python.training.saving.saveable_object_util.saveable_objects_for_op",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"1.4",
"2.7",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"2.6",
"2.10"
]
}
] |
annahedstrom/gym-minigrid | [
"346bf47c4a5861f8b32042023dba49876ff2d372"
] | [
"inverse_agent.py"
] | [
"#!/usr/bin/env python3\n\nimport sys\nimport numpy as np\nimport gym\nimport time\nfrom optparse import OptionParser\nimport ipdb\nimport random\n\nimport matplotlib.pyplot as plt\n\n# -----------------------------\n# ---MaxEnt Inverse RL agent---\n# -----------------------------\nclass InverseAgentClass():\n \... | [
[
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.title",
"numpy.reshape",
"matplotlib.pyplot.draw",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.ioff",
"numpy.random.rand",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.ion",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kaclark/DHS_intergenic_analysis | [
"5ae1dc1c257ae9dc0e001e07402bebd8e31f0f60",
"5ae1dc1c257ae9dc0e001e07402bebd8e31f0f60"
] | [
"get_group_from_DHS_pickling.py",
"basic_model.py"
] | [
"import numpy as np\nimport pandas as pd\nimport sys\nfrom pathlib import Path\nimport pickle\n\ndirectory = sys.argv[1]\npathlist = Path(directory).glob('*.csv')\ngroups = {}\nfor pre_path in pathlist:\n DHSs = []\n path = str(pre_path)\n csv_data = pd.read_csv(path, header=None, index_col=False)\n pre... | [
[
"pandas.read_csv"
],
[
"numpy.array",
"pandas.read_csv",
"tensorflow.keras.layers.Softmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
samuelgarcia/spikewidgets | [
"b7bce4a5e5da38f1e91b522ff12a9c97e7b5b9f8"
] | [
"spikewidgets/widgets/mapswidget/activitymapwidget.py"
] | [
"import numpy as np\nimport spiketoolkit as st\nimport matplotlib.pylab as plt\nfrom ..utils import LabeledRectangle\nfrom spikewidgets.widgets.basewidget import BaseWidget\n\n\ndef plot_activity_map(recording, channel_ids=None, trange=None, cmap='viridis', background='on', label_color='r',\n t... | [
[
"matplotlib.pylab.get_cmap",
"numpy.unique",
"numpy.min",
"numpy.ptp",
"numpy.max",
"numpy.diff",
"numpy.roll",
"matplotlib.pylab.Rectangle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luyuliu/lanenet-lane-detection | [
"ac0efb7e44a31347d5c1e1450c68ee024b6c41df"
] | [
"data_provider/tf_io_pipline_tools.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Time : 19-4-23 下午3:53\n# @Author : MaybeShewill-CV\n# @Site : https://github.com/MaybeShewill-CV/lanenet-lane-detection\n# @File : tf_io_pipline_tools.py\n# @IDE: PyCharm\n\"\"\"\ntensorflow io pip line tools\n\"\"\"\nimport os\nimport os.path as ops\n\... | [
[
"tensorflow.concat",
"tensorflow.constant",
"tensorflow.FixedLenFeature",
"tensorflow.shape",
"tensorflow.slice",
"tensorflow.stack",
"tensorflow.decode_raw",
"tensorflow.cast",
"tensorflow.reshape",
"tensorflow.python_io.TFRecordWriter",
"tensorflow.train.BytesList",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adasegroup/neural_prophet | [
"9647f6666d6c66f1c8a18613e5f66212df5fa09a"
] | [
"neuralprophet/tools/configure.py"
] | [
"from collections import OrderedDict\nfrom dataclasses import dataclass, field\nimport numpy as np\nimport pandas as pd\nimport logging\nimport inspect\nimport torch\nimport math\n\nfrom neuralprophet.utils import utils_torch\n\nlog = logging.getLogger(\"NP.config\")\n\n\ndef from_kwargs(cls, kwargs):\n return c... | [
[
"torch.optim.lr_scheduler.OneCycleLR",
"torch.nn.SmoothL1Loss",
"torch.nn.MSELoss",
"pandas.to_datetime",
"numpy.sqrt",
"numpy.cos",
"numpy.log10",
"torch.nn.L1Loss"
]
] | [
{
"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": []
}
] |
beverlyru/PyAbel | [
"8f8d7e864475ae823837b0925a88eaac7b3ffdc7"
] | [
"examples/example_onion_bordas.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport numpy as np\nimport abel\nimport matplotlib.pyplot as plt\n\n# Dribinski sample image\nIM = abel.tools.analytical.SampleImage(n=501).image \n\n# split into quadrants\no... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brandontrabucco/best_first | [
"504d0090d2b8f9099991fbda177a5324a8361136"
] | [
"best_first/orderings/sequential/backward_sequential_ordering.py"
] | [
"\"\"\"Author: Brandon Trabucco, Copyright 2019\"\"\"\n\n\nfrom best_first.orderings.ordering import Ordering\nimport numpy as np\n\n\nclass BackwardSequentialOrdering(Ordering):\n\n def score(\n self,\n words,\n tags,\n ):\n return np.arange(words.size)\n"
] | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
naoya0930/pytorch3d | [
"b2ca69ad3473374d6864e5c9756b36f27b2f233a"
] | [
"pytorch3d/io/ply_io.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\n\"\"\"\nThis module implements utility functions for loading and saving\nmeshes and point clouds as PLY files.\n\"\"... | [
[
"numpy.fromfile",
"torch.empty",
"torch.zeros",
"torch.cat",
"numpy.vstack",
"torch.tensor",
"numpy.frombuffer",
"torch.FloatTensor",
"numpy.savetxt",
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dabreegster/ua-aspics | [
"3214f3c2daef8d5f465bbc66cae2b01911e337de"
] | [
"aspics/snapshot.py"
] | [
"import numpy as np\n\nfrom aspics.buffers import Buffers\nfrom aspics.params import Params\n\n\nclass Snapshot:\n \"\"\"\n Thin wrapper around the .npz file format for saving/loading snapshots.\n This enables loading existing snapshots from file, or generating new snapshots full of random data or zeros.\n... | [
[
"numpy.load",
"numpy.uint32",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shemka/SteamTags | [
"a35463b7f30e112d8c18a73cd4abe54b102c0b4b"
] | [
"create_gs_tables.py"
] | [
"import pandas as pd \nimport numpy as np\nimport os\nimport sys\nimport json\nimport pymysql as mysql\nimport time\n\nhost, login, password, db, path = input('Enter host, login, password, database name and path to dist folder:\\n').split()\n\nstart_time = time.time()\nif not os.path.exists(path):\n os.makedirs(... | [
[
"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": []
}
] |
guillep/DPPy | [
"6938b5da5602cc4c0fab2d8c1081bc650f22a0d4"
] | [
"dppy/exotic_dpps.py"
] | [
"# coding: utf8\n\"\"\" Implementation of exotic DPP objects:\n\n- Uniform spanning trees :class:`UST`\n- Descent procresses :class:`Descent`:\n\n * :class:`CarriesProcess`\n * :class:`DescentProcess`\n * :class:`VirtualDescentProcess`\n\n- :class:`PoissonizedPlancherel` measure\n\n.. seealso:\n\n `Docu... | [
[
"matplotlib.pyplot.legend",
"scipy.linalg.qr",
"numpy.sqrt",
"matplotlib.pyplot.title",
"numpy.linspace",
"numpy.abs",
"numpy.arange",
"numpy.ones_like",
"matplotlib.pyplot.subplots",
"numpy.cumsum",
"matplotlib.pyplot.colorbar",
"numpy.zeros_like",
"matplotlib.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
Marcin-Szadkowski/B.A.S.I.O.R | [
"5b90ab6a05fdf2a3db8e5b9ba80a858a6628ab8c"
] | [
"tests/test_graphmodifier.py"
] | [
"import unittest\nimport networkx as nx\nimport osmnx as ox\nfrom matplotlib import pyplot as plt\nfrom graphmodifier import GraphModifier\nfrom dataloader import DataLoader\nfrom unittest import TestCase\n\n\nclass TestGraphModifier(TestCase):\n def test_simplify_for_tram_traffic(self):\n \"\"\"\n ... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
faradaymahe/surfaxe | [
"6cc8c386e7fba7ddbf1d63818b2b5affffd246e3",
"6cc8c386e7fba7ddbf1d63818b2b5affffd246e3"
] | [
"surfaxe/vasp_data.py",
"surfaxe/io.py"
] | [
"# Pymatgen \nfrom pymatgen.core import Structure\nfrom pymatgen.io.vasp.outputs import Locpot, Outcar, Vasprun\nfrom pymatgen.analysis.local_env import CrystalNN\n\n# Misc\nimport os\nimport pandas as pd \nimport numpy as np \nimport warnings \n\n# surfaxe \nfrom surfaxe.generation import oxidation_states\nfrom s... | [
[
"numpy.max",
"pandas.read_csv",
"pandas.DataFrame"
],
[
"pandas.read_csv",
"numpy.eye",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
arisp99/MIPTools | [
"072e5e4312e8f44294abc1a1fcc2b347e0b09567"
] | [
"src/mip_functions.py"
] | [
"# -*- coding: utf-8 -*-\nimport subprocess\nimport json\nimport os\nimport io\nfrom multiprocessing import Pool\nimport multiprocessing\nimport multiprocessing.pool\nfrom operator import itemgetter\nimport random\nimport string\nimport pickle\nimport copy\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom ... | [
[
"matplotlib.colors.BoundaryNorm",
"pandas.Series",
"matplotlib.colors.SymLogNorm",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.mean",
"numpy.nanmean",
"pandas.read_csv",
"matplotlib.pyplot.close",
"pandas.concat",
"numpy.log",
"numpy.isnan",
"pan... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
xiaolinpan/mordred | [
"2848b088fd7b6735590242b5e22573babc724f10"
] | [
"mordred/_atomic_property.py"
] | [
"from __future__ import division\n\nimport os\n\nimport six\nimport numpy as np\nfrom rdkit import Chem\nfrom rdkit.Chem.rdPartialCharges import ComputeGasteigerCharges\n\nfrom ._base import Descriptor\nfrom ._util import atoms_to_numpy\n\nhalogen = {9, 17, 35, 53, 85, 117}\n\n\ngetter_list = []\ngetters = {}\n\n\n... | [
[
"numpy.isnan",
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YudongYao/AutoPhaseNN | [
"58b83313d27c695d0601098f3e49d3ba13fadced"
] | [
"TF2/keras_helper.py"
] | [
"#Keras modules\nfrom tensorflow.keras.layers import Conv3D, MaxPool3D, UpSampling3D, ZeroPadding3D\nfrom tensorflow.keras.layers import LeakyReLU,BatchNormalization\n\nimport numpy as np\n\n\n# encoder layers\ndef Conv_Pool_block(x0,nfilters,w1=3,w2=3,w3=3,p1=2,p2=2,p3=2,Lalpha = 0.05, padding='same', data_format=... | [
[
"tensorflow.keras.layers.LeakyReLU",
"tensorflow.keras.layers.ZeroPadding3D",
"tensorflow.keras.layers.UpSampling3D",
"tensorflow.keras.layers.Conv3D",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.MaxPool3D"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
whoopnip/dero | [
"62e081b341cc711ea8e1578e7c65b581eb74fa3f"
] | [
"dero/ext_math.py"
] | [
"\nimport numpy as np\nimport pandas as pd\nfrom collections import Counter\n\n\ndef transition_matrix(states):\n \"\"\"\n Creates a numpy array containing the probability of transition from one state to another. Must \n pass states as a list of lists, where each inner list is an observation of state chang... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mykter/lichess-puzzle-stats | [
"e73ed8dceabb0d9194a6e022bdd006abdef9bece"
] | [
"puzzle.py"
] | [
"#!/usr/bin/env python3\n\nimport sys\nimport os\nimport logging\nimport json\nimport time\nimport argparse\nimport datetime\nimport random\n\nimport numpy\nimport matplotlib.pyplot as plt\nfrom boltons.fileutils import atomic_save\nimport berserk\nfrom berserk import exceptions\n\nclient = berserk.Client()\nlogger... | [
[
"numpy.ceil",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dbautista98/turbo_seti | [
"a094d56db0066b0ed537ecbea5171d8e9c0429e0"
] | [
"test/test_find_event.py"
] | [
"from tempfile import gettempdir\nfrom shutil import rmtree\nfrom os import mkdir\nfrom numpy import isclose\nfrom turbo_seti.find_event.find_event import make_table, calc_freq_range, find_events\n\nTESTDIR = gettempdir() + '/test_find_event/'\nRTOL_DIFF = 0.001 # isclose(), 0.1%\n\n# Hits 1-3 in table 1 are in all... | [
[
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DrStephenLynch/dynamical-systems-with-applications-using-python | [
"4c6e9e5b8107fc71440f6c6bdcfc9e0402ce21e1",
"4c6e9e5b8107fc71440f6c6bdcfc9e0402ce21e1"
] | [
"Anaconda-files/Program_14c.py",
"Anaconda-files/Program_09d.py"
] | [
"# Program 14c: Lyapunov exponents of the logistic map.\n# See Figure 14.17.\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnum_points = 16000\nresult = []\nlambdas = []\nmaps = []\nxmin, xmax = 3, 4\nmult = (xmax - xmin) * num_points\n\nmu_values = np.arange(xmin, xmax, 20/num_points)\n\nfor r in mu_val... | [
[
"numpy.linspace",
"numpy.arange",
"numpy.mean",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.subplots",
"numpy.cos",
"scipy.integrate.odeint",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
... | [
{
"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",
... |
mcooper12590/CaveXC | [
"1174c3d5e2896ace87829e349eaa976cf9fb7196",
"1174c3d5e2896ace87829e349eaa976cf9fb7196"
] | [
"CrossSection.py",
"sediment.py"
] | [
"from numpy import sin, cos, pi, fabs, sign, roll, arctan2, diff, cumsum, hypot, logical_and, where, linspace\nfrom scipy import interpolate\nimport matplotlib.pyplot as plt\n\n# Cross-section class that stores x, y points for the cross-section\n# and calculates various geometry data\n\nd = 1000\n\nclass CrossSecti... | [
[
"scipy.interpolate.splprep",
"numpy.cumsum",
"numpy.cos",
"scipy.interpolate.splev",
"numpy.hypot",
"numpy.arctan2",
"numpy.sin",
"numpy.sign",
"numpy.logical_and",
"numpy.where",
"numpy.roll",
"numpy.fabs"
],
[
"numpy.sqrt",
"numpy.radians",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
1596113436/oneflow | [
"501876dfee44d965cabb0937cbfda757e7be4774"
] | [
"python/oneflow/framework/tensor_str.py"
] | [
"\"\"\"\nCopyright 2020 The OneFlow Authors. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by ap... | [
[
"numpy.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xiangpeng95/my_pytorch_pruning_on_cifar10 | [
"a4d74221c423d9b689c4422b9c23e515a152fb8b"
] | [
"models/lenet.py"
] | [
"import torch\nimport torch.nn as nn\n\nimport torch.nn.functional as F\n\nfrom .layers import bn\n\nclass LeNet(nn.Module):\n def __init__(self, alpha=0.001):\n super(LeNet, self).__init__()\n self.alpha = alpha\n self.prune = False # change this to true when you want to remove channels\n ... | [
[
"torch.nn.Linear",
"torch.nn.Conv2d",
"torch.nn.functional.max_pool2d",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yyaodong/kerasMore | [
"79775599fa2ea434c3e6208b76ef0f8f0dbec1b6"
] | [
"build/lib/keras/models.py"
] | [
"#-*- coding:utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport warnings, time, copy\n\nfrom . import optimizers # 为了调用optimizers.get\nfrom . import objectives # 为了调用objective.get\nfrom . import callbacks as ... | [
[
"numpy.asarray",
"numpy.zeros",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
orionr/captum | [
"50899d70a5ad19c8db24d1159e085263fd4f9f5b"
] | [
"tests/attr/models/test_base.py"
] | [
"#!/usr/bin/env python3\n\nfrom __future__ import print_function\n\nimport torch\nimport unittest\n\nfrom torch.nn import Embedding\n\nfrom ..helpers.utils import assertArraysAlmostEqual\n\nfrom captum.attr._models.base import (\n configure_interpretable_embedding_layer,\n remove_interpretable_embedding_layer... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zjumml/multilingual-kd-pytorch | [
"a369a3edb08e255ba024cf76b00cc5a8d057bd2c"
] | [
"fairseq/distributed_utils.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the LICENSE file in\n# the root directory of this source tree. An additional grant of patent rights\n# can be found in the PATENTS file in the same directory.\n\nfrom collections impor... | [
[
"torch.distributed.c10d.get_rank",
"torch.distributed.c10d.get_world_size",
"torch.distributed.c10d.all_reduce",
"torch.cuda.ByteTensor",
"torch.distributed.get_rank",
"torch.distributed.get_world_size",
"torch.distributed.all_reduce"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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