repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
bienkyo/gender-prediction | [
"087d0ca7fea5a06ff63a9794eae0fc56681fb59d"
] | [
"run_classifier.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom os.path import join\nfrom absl import flags\nimport os\nimport sys\nimport csv\nimport collections\nimport numpy as np\nimport time\nimport math\nimport json\nimport random\nimport pandas as pd\nf... | [
[
"tensorflow.logging.warning",
"tensorflow.metrics.accuracy",
"tensorflow.FixedLenFeature",
"tensorflow.gfile.Exists",
"tensorflow.cast",
"tensorflow.equal",
"tensorflow.gfile.MakeDirs",
"tensorflow.contrib.tpu.TPUEstimatorSpec",
"tensorflow.contrib.tpu.TPUEstimator",
"panda... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Amr-Mustafa/pygpmf | [
"04e394d4cee1f7d8b41e644a4100dbe0208e8ee5"
] | [
"gpmf/gps_plot.py"
] | [
"import matplotlib.pyplot as plt\nimport geopandas as gpd\nimport contextily as ctx\nimport numpy\nimport pandas\n\n\nfrom .gps import extract_gps_blocks, parse_gps_block\n\n\nLATLON = \"EPSG:4326\"\nLAMBERT93 = \"EPSG:2154\"\n\n\ndef to_dataframe(gps_data_blocks):\n \"\"\"Convert a sequence of GPSData into pand... | [
[
"matplotlib.pyplot.gca",
"pandas.concat",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.ylim",
"numpy.quantile",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlim",
"numpy.array",
"numpy.vstack",
"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": []
}
] |
AllenInstitute/render-modules | [
"c29b2126622e39d94957bcb178c97da993363f2b"
] | [
"integration_tests/test_rough_align.py"
] | [
"import os\nimport pytest\nimport renderapi\nimport random\nimport json\nimport glob\nimport copy\nimport re\nimport marshmallow as mm\nimport six\nfrom six.moves import urllib\nfrom test_data import (\n ROUGH_MONTAGE_TILESPECS_JSON,\n ROUGH_MONTAGE_TRANSFORM_JSON,\n ROUGH_POINT_MATCH_COLLECTIO... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Glovo/foodi-ml-dataset | [
"b01e1c4fd12e983fac835c756920f42529dadffb"
] | [
"benchmarks/gan/src/loader.py"
] | [
"# PyTorch StudioGAN: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN\n# The MIT License (MIT)\n# See license file or visit https://github.com/POSTECH-CVLab/PyTorch-StudioGAN for details\n\n# src/loader.py\n\n\nimport json\nimport glob\nimport os\nimport warnings\nimport random\nfrom os.path import dirname, absp... | [
[
"torch.utils.data.distributed.DistributedSampler",
"torch.cuda.set_device",
"torch.utils.data.DataLoader",
"torch.nn.SyncBatchNorm.convert_sync_batchnorm",
"torch.nn.DataParallel",
"torch.nn.parallel.DistributedDataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vkoriukina/distiller | [
"5463ddf2b0b0e6c53a475e412c048de9be411e50"
] | [
"distiller/model_summaries.py"
] | [
"#\n# Copyright (c) 2018 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# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ... | [
[
"torch.numel",
"torch.randn",
"pandas.set_option",
"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": []
}
] |
nazariyb/ASL-Real-time-Detection-and-Recognition | [
"c7ca8c444804c2c177296f8ef93c6d3cd2bd0305"
] | [
"detection/data/wider_voc.py"
] | [
"import os\nimport os.path\nimport sys\nimport torch\nimport torch.utils.data as data\nimport cv2\nimport numpy as np\nif sys.version_info[0] == 2:\n import xml.etree.cElementTree as ET\nelse:\n import xml.etree.ElementTree as ET\n\n\nWIDER_CLASSES = ('__background__', 'hand')\n\n\nclass AnnotationTransform(o... | [
[
"torch.is_tensor",
"torch.from_numpy",
"numpy.empty",
"torch.stack",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mlamo/pyjang | [
"7aa5c9ef8b14d672c4f231e25b809eaa4ca1015d"
] | [
"examples/superkamiokande.py"
] | [
"\"\"\"Example to handle Super-Kamiokande specific format.\n\nIn this case, the input from Super-K are simply the number of events (observed and expected),\nas well as the detector effective area in the format of 2D histograms with x=log10(energy [in GeV]) and y=zenith angle [in rad].\nThe provided values in this e... | [
[
"numpy.log10",
"numpy.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Niko-La/leafmap | [
"208b79a944c9d980f92ebab1cade6b59a5796f59"
] | [
"leafmap/toolbar.py"
] | [
"\"\"\"Module for dealing with the toolbar.\n\"\"\"\nimport math\nimport os\nimport ipyevents\nimport ipyleaflet\nimport ipywidgets as widgets\nfrom ipyleaflet import TileLayer, WidgetControl\nfrom IPython.core.display import display\nfrom ipyfilechooser import FileChooser\nfrom .common import *\n\n\ndef tool_templ... | [
[
"pandas.read_csv",
"matplotlib.pyplot.colormaps"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Olfactomics/IonVision-code-examples | [
"ac23ff357364e037815017bc920cbc64ce0ee4c3"
] | [
"01-Python-Live results history/visualiser.py"
] | [
"\"\"\"\nLicensed under the MIT license.\n\nCopyright © 2021 Olfactomics Oy\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and\nassociated documentation files (the “Software”), to deal in the Software without restriction,\nincluding without limitation the rights to ... | [
[
"matplotlib.use",
"numpy.empty_like",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.draw",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.pause"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
knived/ia-flood-risk-project | [
"de911f9a1b90d8a18af664be5af773f6dd0fc0bf"
] | [
"Task1F.py"
] | [
"from numpy import sort\nfrom floodsystem.stationdata import build_station_list\nfrom floodsystem.station import inconsistent_typical_range_stations\n\ndef run():\n \"\"\"Requirements for Task 1F\"\"\"\n\n # Build list of stations\n stations = build_station_list() \n\n # Build list of stations with inco... | [
[
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RandalJBarnes/OnekaPy | [
"ba082f76f3f7f4394a11864623981cc876e1b253"
] | [
"oneka/deterministic.py"
] | [
"\"\"\"\nDefines and implements a single well capture zone.\n\nClasses\n-------\nNone\n\nExceptions\n----------\nError\nDistributionError\n\nFunctions\n---------\ncreate_deterministic_capturezone(\n target, npaths, duration,\n base, c_dist, p_dist, t_dist,\n stochastic_wells, observations,\n spacing, um... | [
[
"numpy.reshape",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
schatzopoulos/VeTo-workloads | [
"1475c4d1638b9897c9e52c9192d3a6723bb1bdc4",
"1475c4d1638b9897c9e52c9192d3a6723bb1bdc4"
] | [
"rev-sim-recommender-veto/main_rrf.py",
"add_names.py"
] | [
"import sys\nimport numpy as np\nfrom VeTo import VeTo\n\nveto = VeTo()\n\nfor method in [ \"rrf\"]:\n for dataset in [\"data/VLDB/\", \"data/SIGMOD/\", \"data/CIKM/\"]:\n for k in [100, 500, 1000, 2000, 5000]:\n for alpha in np.linspace(0, 1.0, 21):\n for beta in np.linspace(0, ... | [
[
"numpy.mean",
"numpy.linspace"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Xianchao-Wu/megatron2 | [
"f793c37223b32051cb61d3b1d5661dddd57634bf",
"f793c37223b32051cb61d3b1d5661dddd57634bf"
] | [
"tasks/zeroshot_gpt2/evaluate.py",
"megatron/mpu/tests/commons.py"
] | [
"# coding=utf-8\n# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0... | [
[
"torch.no_grad",
"torch.argmax"
],
[
"torch.distributed.init_process_group",
"torch.cuda.set_device",
"numpy.random.seed",
"torch.manual_seed",
"torch.randn",
"torch.distributed.barrier",
"torch.distributed.get_rank",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andersonjwan/tltk-mtl-ext | [
"a4f0314df6c55d532b7f9d169b6d08bff3811ef3"
] | [
"examples/example1b.py"
] | [
"import sys\nimport tltk_mtl as MTL\nimport tltk_mtl_ext as MTLE\nimport numpy as np\n\nAr3 = -1\nbr3 = -160\n\nAr4 = 1\nbr4 = 4500\n\npreds = {}\npreds['speed'] = MTL.Predicate('speed', Ar3, br3)\npreds['rpm'] = MTL.Predicate('rpm', Ar4, br4)\n\nroot = MTLE.parse_mtl('!(F_ts:(0, 100) (speed && rpm))', preds)\n\n... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
darraghdog/convolutional-handwriting-gan | [
"3999ef74610d0e79b0884b881f4575f917b48f5e"
] | [
"data/create_text_data_dread_lines.py"
] | [
"# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n# SPDX-License-Identifier: MIT\n\nimport os\nimport platform\nimport sys\nPATH = '/Users/dhanley/Documents/scrgan/' \\\n if platform.system() == 'Darwin' else '/mount/scrgan'\nos.chdir(PATH)\nsys.path.append(PATH)\nimport lmdb\nimport cv2\nim... | [
[
"pandas.read_csv",
"numpy.random.choice",
"pandas.DataFrame",
"numpy.max",
"numpy.fromstring",
"numpy.where",
"pandas.set_option",
"numpy.array",
"numpy.zeros",
"sklearn.decomposition.PCA"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lorischl-otter/lambdata-1 | [
"e4aec397950da717d1a7a25c1db9119150a0f0e0"
] | [
"lambdata_gptix/__init__.py"
] | [
"\"\"\"\nlambdata - A collection fof data science helper functions.\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\n\n# sample code\n \nONES = pd.DataFrame(np.ones(10))\nZEROES = pd.DataFrame(np.zeros(50))\n"
] | [
[
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pmorerio/ddn | [
"68e44e3ccfbeed285a78bf75cc778802dd15890e"
] | [
"apps/classification/image/main.py"
] | [
"# IMAGENET CLASSIFICATION WITH DECLARATIVE PROJECTION NODES\n# Dylan Campbell <dylan.campbell@anu.edu.au>\n# Stephen Gould <stephen.gould@anu.edu.au>\n#\n# Modified from PyTorch ImageNet example:\n# https://github.com/pytorch/examples/blob/ee964a2eeb41e1712fe719b83645c79bcbd0ba1a/imagenet/main.py\n# with mean aver... | [
[
"torch.multiprocessing.spawn",
"torch.load",
"torch.utils.data.DataLoader",
"numpy.cumsum",
"numpy.concatenate",
"numpy.max",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"numpy.mean",
"numpy.where",
"torch.save",
"torch.nn.CrossEntropyLoss",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
demsarjure/NeMo | [
"d43b3d0af3aea88ec5ee3904200866abd320f39b"
] | [
"nemo/collections/nlp/models/language_modeling/megatron_ptune_gpt_model.py"
] | [
"# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"torch.nn.functional.log_softmax",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.full_like",
"torch.autocast"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jumperkables/trying_blp | [
"4987e4d00b4ed8caa38b3e606b98feff1f88cd5d"
] | [
"tvqa/model/tvqa_abc_bert_nofc_bert-glove.py"
] | [
"__author__ = \"Jie Lei\"\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom .rnn import RNNEncoder, max_along_time\nfrom .bidaf import BidafAttn\nfrom .mlp import MLP\n\n# For BERT\nfrom transformers import *\n\nclass ABC(nn.Module):\n def __init__(self, opt):\n super(ABC, self)... | [
[
"torch.nn.Dropout",
"torch.ones",
"torch.cat",
"torch.from_numpy",
"torch.nn.Embedding",
"torch.nn.Tanh",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
philtap/Yelp_reviews_analysis | [
"8f7ad08106dae154fea908c63f479010e9be30d4"
] | [
"NLP_code/NLP.py"
] | [
"import pandas as pd\nimport numpy as np\nimport tensorflow as tf\n\nfrom keras.layers import Dense, Input, LSTM, Embedding, Dropout, SpatialDropout1D, Bidirectional, GRU, GlobalAveragePooling1D, GlobalMaxPooling1D, concatenate, Conv1D\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequen... | [
[
"tensorflow.keras.models.load_model",
"pandas.read_csv",
"numpy.asarray",
"sklearn.metrics.accuracy_score",
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.classification_report",
"numpy.argmax",
"numpy.zeros",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
stfnwong/lernomatic | [
"2cd7506c04c4e2afca22b7c05ebb0cc94f8048d9"
] | [
"test/test_lr_finder.py"
] | [
"\"\"\"\nTEST_FIND_LR\nUnit test for learning rate finder\n\nStefan Wong 2019\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\n# unit(s) under test\nfrom lernomatic.param import lr_common\nfrom lernomatic.train import cifar_trainer\nfrom lernomatic.models import cifar\nfrom lernomatic.vis... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kmckiern/scripts | [
"acc8326ca653d804ee06752af9e7f5b011fc6e0e",
"acc8326ca653d804ee06752af9e7f5b011fc6e0e"
] | [
"projects/clc/build/align_monomer.py",
"projects/lipid/misc/scd.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nbecause the monomer is hard to align in the membrane given it's symmetry\n\"\"\"\n\nimport argparse\nimport mdtraj\nimport numpy as np\n\nparser = argparse.ArgumentParser(description='align 3nmo')\nparser.add_argument('--pdb', type=str, help='system pdb')\nparser.add_argument('--re... | [
[
"numpy.arange"
],
[
"numpy.cross",
"numpy.dot",
"numpy.sqrt",
"numpy.cos",
"numpy.average",
"numpy.sin",
"numpy.transpose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fuzzy-stonk/Elegant-FinRL | [
"8a710c17d101bb3c14fc5462a2595065bdd9a34c"
] | [
"elegant_finrl/env.py"
] | [
"import os\nimport numpy as np\nimport numpy.random as rd\nimport pandas as pd\nimport yfinance as yf\nimport matplotlib.pyplot as plt\nfrom stockstats import StockDataFrame as Sdf # for Sdf.retype\nfrom pyfolio import timeseries\nimport pyfolio\nfrom copy import deepcopy\n\nclass StockTradingEnv:\n def __init_... | [
[
"numpy.hstack",
"pandas.to_datetime",
"pandas.read_pickle",
"pandas.Series",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.random.uniform",
"numpy.linalg.pinv",
"numpy.mean",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.random.randint"... | [
{
"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": []
}
] |
Pipazoul/ganspace | [
"85c3fec4028391d1c9317eb620ba72f6d6afa275"
] | [
"decomposition.py"
] | [
"# Copyright 2020 Erik Härkönen. All rights reserved.\n# This file is licensed to you under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License. You may obtain a copy\n# of the License at http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless require... | [
[
"torch.cuda.get_device_properties",
"numpy.dot",
"numpy.dtype",
"numpy.all",
"numpy.mean",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.iinfo",
"torch.device",
"numpy.ones_like",
"torch.from_numpy",
"numpy.linalg.det",
"numpy.zeros",
"numpy.floor_divid... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Preston4tw/snowflake-connector-python | [
"7bcbca2acf1e0407a029e4502cd407172b41f684"
] | [
"setup.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2012-2019 Snowflake Computing Inc. All right reserved.\n#\nfrom codecs import open\nfrom os import path\nimport os\nimport sys\nfrom sys import platform\nfrom shutil import copy\nimport glob\n\nfrom setuptools import setup, Extension\n\nTHIS_DIR = ... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nrsander/covid-data-model | [
"911000097547986e40907193c8dbc3119bbeaa40"
] | [
"libs/datasets/sources/cds_dataset.py"
] | [
"import logging\nimport numpy\nimport pandas as pd\nfrom libs.datasets import data_source\nfrom libs.datasets import dataset_utils\nfrom libs.us_state_abbrev import US_STATE_ABBREV\nfrom libs.datasets.common_fields import CommonIndexFields\nfrom libs.datasets.common_fields import CommonFields\n\n_logger = logging.g... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.where",
"pandas.isnull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Will3577/improved_wgan_training | [
"66f091a00fac23814ff371f6df9e8b1fbe7cd395"
] | [
"gan_cifar.py"
] | [
"import os, sys\nsys.path.append(os.getcwd())\n\nimport time\n\nimport numpy as np\n# import tensorflow as tf\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\n\ntry:\n # Python 2\n xrange\nexcept NameError:\n # Python 3, xrange is now named range\n xrange = range\n\nimport tflib as lib\nimp... | [
[
"tensorflow.compat.v1.random_normal",
"tensorflow.compat.v1.initialize_all_variables",
"tensorflow.compat.v1.group",
"numpy.mean",
"tensorflow.compat.v1.train.AdamOptimizer",
"tensorflow.compat.v1.reshape",
"tensorflow.compat.v1.maximum",
"tensorflow.compat.v1.random_uniform",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lishengfeng/Compute-Vision | [
"34e5cee00166e73a8c597826fed54b8f4c863d90",
"34e5cee00166e73a8c597826fed54b8f4c863d90"
] | [
"2-3-Histogram-Equalization-ROI/histEql_ROI.py",
"2-4-edge-detection/edge_detection.py"
] | [
"import cv2\nimport numpy as np\n\nstart_x, start_y = -1, -1\nend_x, end_y = -1, -1\npaste_x, paste_y = -1, -1\n\ndrawing = False # True if mouse is pressed\nrows = None\ncolumns = None\nroi = None\n\n\ndef myHEQ_YCRCB():\n global clone_image, start_x, start_y, end_x, end_y, roi\n img_ori = clone_image.copy(... | [
[
"numpy.hstack"
],
[
"numpy.absolute"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abreu4/jina | [
"d1d045e9e0933dffb3bd668cb9cfebab6cd52202"
] | [
"tests/unit/flow/test_flow_yaml_parser.py"
] | [
"from pathlib import Path\n\nimport numpy as np\nimport pytest\n\nfrom jina import Flow, AsyncFlow\nfrom jina.enums import FlowOptimizeLevel\nfrom jina.excepts import BadFlowYAMLVersion\nfrom jina.executors.encoders import BaseEncoder\nfrom jina.flow import BaseFlow\nfrom jina.jaml import JAML\nfrom jina.jaml.parse... | [
[
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aditya30394/PredictForestFires | [
"9a9a5996af36305b2ce85090b648a3be0c3e7a96"
] | [
"ML_HW1-LinearRegression.py"
] | [
"\n# coding: utf-8\n\n# In[2]:\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom scipy import stats\n\n# Import the dataset\ndataset = pd.read_csv('train.csv')\nfull_dataset = ... | [
[
"sklearn.preprocessing.PolynomialFeatures",
"sklearn.metrics.mean_squared_error",
"pandas.read_csv",
"numpy.matmul",
"numpy.log",
"matplotlib.pyplot.title",
"numpy.linalg.inv",
"scipy.stats.pearsonr",
"numpy.cov",
"numpy.corrcoef",
"matplotlib.pyplot.show",
"matplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"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... |
juzb/DeeProtein | [
"487694a24abdb4656499111c8a8904dfcb1d98ab"
] | [
"DeeProtein/scripts/calculate_sensitivity.py"
] | [
"import sys, os\nimport pandas as pd\nimport numpy as np\n\n\ndef one_protein(infile, outfile):\n \"\"\" calc the sensitivities\"\"\"\n # read data\n md_df = pd.read_csv(infile, sep='\\t', engine='python')\n\n # make sure to get every GO only once\n for go in [c.strip('_-') for c in md_df.columns if ... | [
[
"numpy.concatenate",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mcarla/Max-O-Matic | [
"a832a3ec72923108a974c65ae6e02337b7ef1d15"
] | [
"ai.py"
] | [
"import string\nimport nltk\nfrom nltk.stem import WordNetLemmatizer\nimport numpy as np\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\n\ndef chew_chat(path, max_id):\n challenge = []\n response = []\n last_who = None\n\n with open(path,'r') as f:\n w... | [
[
"numpy.max",
"numpy.array",
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DukeEnglish/transformers | [
"38a555a83c8aceae77895d325174af5bd576cec7"
] | [
"src/transformers/modeling_tf_utils.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"tensorflow.nn.log_softmax",
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.keras.utils.register_keras_serializable",
"tensorflow.python.keras.saving.hdf5_format.load_attributes_from_hdf5_group",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
birajaghoshal/BDL-Evaluate | [
"7126d9ab170ef2e4d5098913a59ea7c706d66f81"
] | [
"bdlb/diabetic_retinopathy_diagnosis/benchmark.py"
] | [
"# Copyright 2019 BDL Benchmarks 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... | [
[
"numpy.asarray",
"numpy.empty_like",
"tensorflow.cast",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.argsort",
"numpy.zeros"
]
] | [
{
"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... |
prakhar82/docker-airflow-master | [
"7c1d950c332c5cb2a22c35749641f776c5a39193"
] | [
"dags/subdag_factory.py"
] | [
"import requests,csv,json\nimport pandas as pd\nfrom itertools import groupby \nfrom collections import OrderedDict\nfrom datetime import datetime,date\n\nfrom airflow.models import DAG\nfrom airflow.operators.python_operator import PythonOperator,BranchPythonOperator\nfrom airflow.operators.dummy_operator import D... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ChoKyuWon/SchoolProjects | [
"71a5decefc85ae941ba2d537c4507ba8e615cc34"
] | [
"MachineLearning/hw1/models/LogisticRegression.py"
] | [
"import numpy as np\r\n\r\nclass LogisticRegression:\r\n def __init__(self, num_features):\r\n self.num_features = num_features\r\n self.W = np.zeros((self.num_features, 1))\r\n\r\n def train(self, x, y, epochs, batch_size, lr, optim):\r\n final_loss = None # loss of final epoch\r\n\r\n... | [
[
"numpy.exp",
"numpy.array",
"numpy.zeros",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eric-vader/wordle | [
"c3f4ad3c8b6c5551b16f3bedd9e0ba29668525db"
] | [
"wordle.py"
] | [
"#!/bin/python3\n# Author: Han Liang Wee, Eric\n# MIT License\n\nimport copy\nimport pickle\nimport numpy as np\nimport re\n\nfrom collections import Counter, defaultdict\nfrom itertools import product\nfrom tqdm import tqdm\n\ndef is_wordpos_correct(ws, cs):\n for w, c in zip(ws, cs):\n if c == set():\n ... | [
[
"numpy.log2",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
generalova-kate/openvino | [
"322c87411399f83ab9c9b99c941c1ba2ed623214"
] | [
"model-optimizer/extensions/back/ReverseInputChannels.py"
] | [
"# Copyright (C) 2018-2021 Intel Corporation\n# SPDX-License-Identifier: Apache-2.0\n\nimport logging as log\n\nimport numpy as np\n\nfrom extensions.ops.gather import Gather\nfrom extensions.ops.split import Split\nfrom mo.back.replacement import BackReplacementPattern\nfrom mo.front.common.partial_infer.utils imp... | [
[
"numpy.arange",
"numpy.array",
"numpy.where",
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ruoqi-liu/DSW | [
"74a718e81bad7a294ce774a433aecf91ed33f12c"
] | [
"simulation/simulate_mimic.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nimport os\n\ntreatment_option = 'vaso'\nobservation_window = 30\nstep = 3\n\n# icustay ids for sepsis patient in MIMIC-III\ntreatment_ids = pd.read_csv('../data/icustay_ids.txt')\n\n# hadm ids for sepsis patient in MIMIC-III\nicu2hadm = pd.read_json('... | [
[
"numpy.expand_dims",
"numpy.nan_to_num",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.mean",
"numpy.where",
"pandas.read_csv",
"numpy.save",
"numpy.std",
"numpy.load",
"numpy.zeros",
"numpy.multiply",
"pandas.read_json",
"numpy.array",
"numpy.sum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
RDeRenzi/mujpy | [
"f7aa0eb97c3db668a1b099d00aba8e1bd41d4444"
] | [
"mujpy/musr2py/musr2py.py"
] | [
"import ctypes\nimport numpy as np\nfrom numpy.ctypeslib import ndpointer\nfrom ctypes import cdll\nfrom ctypes import c_int, c_char_p, c_void_p, c_double\nfrom numpy.ctypeslib import ndpointer\n\n#######################################\n# see MuSR_td_PSI_bin.cpp by A. Amato #\n# returns 0 for successful reading ... | [
[
"numpy.ctypeslib.ndpointer",
"numpy.set_printoptions",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
4the1appdevs/face_recognition | [
"d69d387f6e7bca45eeb15107f67b9c305c6082d0"
] | [
"train.py"
] | [
"import math\nfrom sklearn import neighbors\nimport os\nimport os.path\nimport pickle\nfrom PIL import Image, ImageDraw\nimport face_recognition\nfrom face_recognition.face_recognition_cli import image_files_in_folder\n\nALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}\n\n\ndef train(train_dir, model_save_path=None, n_n... | [
[
"sklearn.neighbors.KNeighborsClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kaysera/fuzzy-lore | [
"128131e0f41f480d509b63c5e75d0ce58f07bae4"
] | [
"teacher/explanation/FDT_explainer.py"
] | [
"from ._factual_local_explainer import FactualLocalExplainer\nfrom sklearn.utils import check_array\nfrom teacher.tree import FDT\nfrom teacher.explanation import m_factual, mr_factual, c_factual, i_counterfactual, f_counterfactual\n\nFACTUAL_METHODS = {\n 'm_factual': m_factual,\n 'mr_factual': mr_factual,\n... | [
[
"sklearn.utils.check_array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aaron8tang/trading-bitcoin-with-reinforcement-learning | [
"4066567b3003cdea66ea47ba446906aced20fb2d"
] | [
"env.py"
] | [
"import numpy as np\nimport torch\n\n\nclass Env(object):\n\n def __init__(self, train_df, train_label, init_act):\n self.init_act = init_act\n\n self.i_step = 0\n self.i_act = init_act\n self.OHLCV = None\n self.reward = None\n\n self.train_df = torch.from_numpy(np.expa... | [
[
"numpy.expand_dims",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mit-gfx/diff_stokes_flow | [
"55eb7c0f3a9d58a50c1a09c2231177b81e0da84e"
] | [
"python/example/shape_composition_2d.py"
] | [
"import sys\nsys.path.append('../')\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\n\nfrom py_diff_stokes_flow.core.py_diff_stokes_flow_core import ShapeComposition2d, StdIntArray2d\nfrom py_diff_stokes_flow.common.common import ndarray, create_folder, print_error\n\ndef visualize_... | [
[
"numpy.random.seed",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.concatenate",
"numpy.random.normal",
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IvoOVerhoeven/meta-learning-emotion-detection | [
"fb076d7644173b13eb62d6301544af9e98352512",
"fb076d7644173b13eb62d6301544af9e98352512"
] | [
"baseline/models/custombert.py",
"modules/encoders.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom modules.encoders import TransformerEncoder\nfrom modules.mlp_clf import MLP\n\nclass CustomBERT(nn.Module):\n def __init__(self, num_classes):\n \"\"\"Custom BERT Transformer based sequence model.\n \"\"\"\n super().__init__()\n\n # BERT en... | [
[
"torch.nn.Linear",
"torch.nn.CrossEntropyLoss"
],
[
"torch.nn.Embedding"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DanielDo98/curiosity-grounding | [
"c7615a65d90d86f23aa715cb8ffcf6115bb4b1b8"
] | [
"curious_env.py"
] | [
"from __future__ import print_function\nfrom time import sleep\n\nimport numpy as np\nfrom skimage.transform import rescale\nimport collections\nimport codecs\nimport random\nimport json\n\nfrom utils.doom import *\nfrom utils.points import *\nfrom constants import *\n\nactions = [[True, False, False], [False, True... | [
[
"numpy.zeros",
"numpy.random.choice",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
VCG/alignment_and_padding | [
"5bed6f38356335b621d267dc5577aacb831efe07"
] | [
"padding.py"
] | [
"import os\nimport cv2\nimport numpy as np\n\ndef compute_padding(imgs, transforms):\n\tfor i in range(len(transforms)):\n\t\t#compute transofrms for bnd pts\n\t\t(h,w) = imgs[i].shape\n\t\tbnd_pts_src = np.array([[0,0,1], [w,0,1], [0,h,1], [w,h,1]])\n\t\tif i == 0:\t\n\t\t\tbnd_pts_dest = np.dot(transforms[i], bnd... | [
[
"numpy.asarray",
"numpy.dot",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
juho-lee/bnp | [
"4cf4b92b38b3ac2aa4bfbbd81de596c9bf7e9645"
] | [
"regression/data/celeba.py"
] | [
"import torch\nimport os.path as osp\nimport argparse\n\nfrom utils.paths import datasets_path\nfrom utils.misc import gen_load_func\n\nclass CelebA(object):\n def __init__(self, train=True):\n self.data, self.targets = torch.load(\n osp.join(datasets_path, 'celeba',\n 't... | [
[
"torch.stack",
"torch.LongTensor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
christiankuhl/VortexDynamics | [
"97b96a6231aacb3d0f1e74fda52457bafb833964"
] | [
"utils.py"
] | [
"import numpy as np\nimport math\nfrom functools import wraps\nfrom itertools import chain, combinations, tee\n\ndef star_configuration(r, Gamma, axes, centralGamma=None):\n \"\"\"\n Returns a \"star-shaped\" configuration of vortices in the following way:\n The vortices with strength Gamma_i are placed on... | [
[
"numpy.append",
"numpy.vectorize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kwinkunks/xarray-spatial | [
"59984d859820e6e1cd9f11f1bf7696c04d1924fb"
] | [
"xrspatial/tests/conftest.py"
] | [
"import numpy as np\nimport pytest\n\n\n@pytest.fixture\ndef random_data(size, dtype):\n rng = np.random.default_rng(2841)\n data = rng.integers(-100, 100, size=size)\n data = data.astype(dtype)\n return data\n"
] | [
[
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MillionIntegrals/image-data-encode | [
"fd11e9fbc6f71a6351e407b96bd7030ea3c34e68"
] | [
"encoder/algorithms/xor_encoding.py"
] | [
"__author__ = 'jrx'\n\nimport numpy as np\n\nfrom encoder.bit_density import pad_bit_array, convert_to_bit_density, convert_from_bit_density\nfrom encoder.constants import BITS_PER_BYTE, BYTES_PER_UINT64\nfrom encoder.utilities import add_length_info, strip_length_info\n\n\nclass XorEncoding:\n\n def __init__(se... | [
[
"numpy.arange",
"numpy.bitwise_xor.reduce",
"numpy.bitwise_xor",
"numpy.packbits",
"numpy.unpackbits"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
avanetten/apls | [
"eec537db51cb72283633c11fc47d9ba5ae2a0c04"
] | [
"apls/apls_utils.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 6 14:05:30 2019\n\n@author: avanetten\n\"\"\"\n\nimport numpy as np\nfrom osgeo import gdal, ogr, osr\nimport scipy.spatial\nimport rasterio as rio\nimport affine as af\nimport shapely\nimport time\nimport os\nimport sys\nimport subproces... | [
[
"numpy.min",
"numpy.asarray",
"numpy.rint",
"numpy.round",
"numpy.max",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dddraxxx/tensorflow | [
"681f450656229f72e31c34de7fdf891ba24baf62"
] | [
"tensorflow/python/distribute/values.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.distribute.distribution_strategy_context.has_strategy",
"tensorflow.python.framework.ops.executing_eagerly_outside_functions",
"tensorflow.python.framework.type_spec.type_spec_from_value",
"tensorflow.python.framework.ops.register_dense_tensor_like_type",
"tensorflow.python.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
luckyp71/ptda | [
"e1c51c0a0724de9ed00f2e30e11cc8bba866c331"
] | [
"ptda/general.py"
] | [
"import pandas as pd\nimport numpy as np\nimport itertools\n\n\"\"\"\npmr is a function to calulcate permutation with repetition and form the possible arrangement as a result.\nWhere x is a set of objects and r is number of objects selected\n\"\"\"\ndef pmr(x,r):\n output = [p for p in itertools.product(x, repea... | [
[
"numpy.vstack",
"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": []
}
] |
lw-2018/edvr-idea2 | [
"a9681e899bf81d0f22650d60dacd1c246f1e2d8f"
] | [
"basicsr/data/video_test_dataset160-hr.py"
] | [
"import glob\nimport mmcv\nimport torch\nfrom os import path as osp\nfrom torch.utils import data as data\n\nfrom basicsr.data import util as util\nfrom basicsr.data.util import duf_downsample\nfrom basicsr.utils import get_root_logger\n\n\nclass VideoTestDataset(data.Dataset):\n \"\"\"Video test dataset.\n\n ... | [
[
"torch.LongTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiazeWang/SP-GAN | [
"455003f78b1160ebe0a2056005b069808c0df35b"
] | [
"Common/data_utils.py"
] | [
"import torch\nimport numpy as np\n\nimport sys,os\nimport torch.nn.functional as F\n#from Common.Const import GPU\n#GPU = 50\nsys.path.append(os.path.join(os.getcwd(),\"metrics\"))\n\nfrom CD_EMD.emd_ import emd_module\nfrom CD_EMD.cd.chamferdist import ChamferDistance as CD\n\nclass PointcloudMixup(object):\n\n ... | [
[
"numpy.random.random",
"numpy.eye",
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin",
"torch.from_numpy",
"torch.matmul",
"torch.tensor",
"numpy.random.randn",
"numpy.random.uniform",
"numpy.array",
"numpy.outer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamblvck/deep-astro-wheel | [
"a096753f42d627b882dd416839b8d6850217b50d"
] | [
"hexbind.py"
] | [
"from skyfield.api import load\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom skyfield.api import utc\nfrom scipy.optimize import brentq # machine learning\n\nfrom datetime import timedelta, datetime\nimport pytz\n\n# Custom helper functions\nfrom... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"numpy.floor",
"numpy.array",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
yusufsarikaya/Image_Comparison | [
"4c52ab537cad26177a6130f6ab8707e22171d866"
] | [
"differenceChecker.py"
] | [
"###############################\n#\n# (c) Vlad Zat, Emmet Doyle, Trent Nguyen, Cristian Anton 2017\n# Student No: C14714071\n# Course: DT228\n# Date: 13-10-2017\n#\n# Title - Difference Checker\n#\n# Introduction:\n# - The difference checker is an application created for the purpose of difference tha... | [
[
"numpy.hstack",
"numpy.rad2deg",
"numpy.shape",
"numpy.float32",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
markf-gc/tensorflow | [
"09b2d2ab376a39bfcfe887fa57d03c0ae5bb6d1d"
] | [
"tensorflow/python/data/experimental/kernel_tests/data_service_ops_test.py"
] | [
"# Copyright 2020 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.data.ops.dataset_ops.Dataset.from_tensors",
"tensorflow.python.data.kernel_tests.test_base.default_test_combinations",
"tensorflow.python.ops.string_ops.string_length_v2",
"tensorflow.python.data.kernel_tests.test_base.v1_only_combinations",
"tensorflow.python.data.ops.datas... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.4",
"2.5",
"2.6"
]
}
] |
JanaGauss/Connectome | [
"9b59aabfb4040201c72d7ff239b50bb47f092ad1"
] | [
"connectome/preprocessing/data_preparation.py"
] | [
"\"\"\"\nhelper function to transform the processed data into the right format before modelling\n\"\"\"\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\n\ndef prepare_data(data: pd.DataFrame, classification: bool = Tr... | [
[
"sklearn.preprocessing.StandardScaler",
"sklearn.model_selection.train_test_split",
"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": []
}
] |
coltekin/germeval2020task1 | [
"8f045b8ddba17e7071c1f94f78f9667fb00e1850"
] | [
"average-output.py"
] | [
"#!/usr/bin/env python3\n\nimport sys\nimport numpy as np\nfrom collections import Counter\n\nlabels = list()\nfor f in sys.argv[1:]:\n with open(f, 'rt') as fp:\n a = None\n d = list()\n lab = list()\n for line in fp:\n if 'Label names:' in line: continue\n l, n... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
azhou42/acer-baselines | [
"524ad123de83aee01036d3809926bbdc98f84763"
] | [
"train.py"
] | [
"# -*- coding: utf-8 -*-\nimport math\nimport random\nimport gym\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom memory import EpisodicReplayMemory\nfrom model import ActorCritic\nfrom utils import state_to_tensor\n\nfrom rllab.misc import logger\n\n\n# Knuth's algorithm for genera... | [
[
"torch.LongTensor",
"torch.ones",
"torch.Tensor",
"torch.zeros",
"torch.manual_seed",
"torch.multinomial"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
asaran/cgnl-network.pytorch | [
"33e348bce7ba0f6141ec23ade7f4216851e1e1d6"
] | [
"maskrcnn_benchmark/modeling/backbone/resnet.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\"\"\"\nVariant of the resnet module that takes cfg as an argument.\nExample usage. Strings may be specified in the config file.\n model = ResNet(\n \"StemWithFixedBatchNorm\",\n \"BottleneckWithFixedBatchNorm\",\n \"R... | [
[
"torch.nn.Sequential",
"torch.nn.Softmax",
"torch.Tensor",
"torch.cat",
"torch.split",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.exp",
"torch.nn.init.normal_",
"torch.bmm",
"torch.nn.functional.relu_",
"torch.nn.BatchNorm2d",
"torch.nn.GroupNorm",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mfkiwl/gnss-ins-sim | [
"143cef152927cee36c6a9023c15736a88eb56e1a"
] | [
"gnss_ins_sim/sim/imu_model.py"
] | [
"# -*- coding: utf-8 -*-\n# Fielname = imu_model.py\n\n\"\"\"\nIMU class.\nCreated on 2017-12-19\n@author: dongxiaoguang\n\"\"\"\n\nimport math\nimport numpy as np\n\nD2R = math.pi/180\n\n## default IMU, magnetometer and GPS error profiles.\n# low accuracy, from AHRS380\n#http://www.memsic.cn/userfiles/files/Datash... | [
[
"numpy.eye",
"numpy.array",
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maheriya/disprcnn | [
"a5a563ea72bca0f46215bcab50544dfba2032030",
"a5a563ea72bca0f46215bcab50544dfba2032030"
] | [
"disprcnn/layers/misc.py",
"disprcnn/modeling/psmnet/submodule.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\"\"\"\nhelper class that supports empty tensors on some nn functions.\n\nIdeally, add support directly in PyTorch to empty tensors in\nthose functions.\n\nThis can be removed once https://github.com/pytorch/pytorch/issues/12013\nis implement... | [
[
"torch.nn.modules.utils._ntuple",
"torch.nn.functional.interpolate"
],
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.Conv2d",
"torch.sum",
"torch.nn.Conv3d",
"torch.nn.AvgPool2d",
"torch.arange",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
naoto0804/deepsvg | [
"e9c2cc55fc4d6202da6984fea25e36e0ede51c1c"
] | [
"deepsvg/model/layers/transformer.py"
] | [
"import torch\nimport copy\n\nfrom torch.nn import functional as F\nfrom torch.nn.modules.module import Module\nfrom torch.nn.modules.container import ModuleList\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.modules.dropout import Dropout\nfrom torch.nn.modules.linear import Linear\nfrom torch.nn.module... | [
[
"torch.nn.modules.linear.Linear",
"torch.ones",
"torch.nn.modules.dropout.Dropout",
"torch.nn.modules.normalization.LayerNorm",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
helloiamivan/quantbt | [
"c1cc801379c4da288f8e662300cadac811b66835"
] | [
"quantbt/analytics.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef performanceSummary(\n historicalNAV,\n historicalWeights,\n historicalPositions,\n historicalTCosts,\n historicalSlippageCosts):\n\n # Get price levels as list\n nav = list(historicalNAV.values())\n\n # Get date... | [
[
"numpy.sqrt",
"numpy.finfo",
"numpy.argmax",
"numpy.diff",
"numpy.maximum.accumulate",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
slateci/XCache | [
"59fa01728256dcd2550b56cd1974cd77e0c31135"
] | [
"analytics/SchedulingWithCache/site_info.py"
] | [
"import requests\nimport pandas as pd\nimport sys\n\n# get AGIS info\nsites = []\nr = requests.get('http://atlas-agis-api.cern.ch/request/site/query/list/?json&vo_name=atlas&state=ACTIVE')\nres = r.json()\nfor s in res:\n sites.append([s['name'], s['tier_level'], s['cloud']])\nprint('Sites loaded.')\n\nsites = p... | [
[
"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": []
}
] |
adam-dziedzic/manifold-defense | [
"23a73cfaf8425d3691a85bc994f164807df70dce",
"23a73cfaf8425d3691a85bc994f164807df70dce"
] | [
"adv-mnist/adv_train.py",
"adv-mnist/models/encoders.py"
] | [
"import torch as ch\nimport torch.nn.functional as F\nimport torch.optim as optim # Optimizers\nimport sys\nfrom torchvision import transforms\nfrom .attacks import pgd_l2, pgd_linf, opmaxmin, ce\nfrom argparse import ArgumentParser\nfrom .models import resnet\nimport numpy as np\nfrom .YellowFin_Pytorch.tuner_uti... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.optim.Adam",
"torch.nn.CrossEntropyLoss",
"torch.mean",
"torch.load",
"torch.sum",
"torch.no_grad",
"torch.optim.SGD"
],
[
"torch.randn_like",
"torch.exp",
"torch.nn.Linear",
"torch.nn.LeakyReLU",
"torch.nn.ReLU... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danielmk/pyDentate | [
"df8f67d4523ce463701c5e5675e74e309dd151e7",
"df8f67d4523ce463701c5e5675e74e309dd151e7"
] | [
"pydentate/input_generator.py",
"synaptic_fitting/Heatmap_3000-4000.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 13 15:37:20 2018\n\n@author: DanielM\n\"\"\"\n\nimport numpy as np\nfrom elephant import spike_train_generation as stg\nfrom neo.core import AnalogSignal\nimport quantities as pq\n\n\ndef inhom_poiss_30Hz():\n \"\"\"Generate an inhomogeneous poisson spike trai... | [
[
"numpy.random.seed",
"numpy.arange",
"numpy.sin",
"numpy.diff",
"numpy.array"
],
[
"numpy.square",
"numpy.split",
"numpy.savez",
"numpy.amax",
"numpy.arange",
"numpy.sort",
"numpy.argwhere",
"numpy.concatenate",
"numpy.append",
"numpy.transpose",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cookie1986/csa-chat-predict | [
"76fb07160930b0b824306b410b42c13e24983c19"
] | [
"python/baselines.py"
] | [
"'''\nBaseline estimator that uses PAN12 word frequency counts to predict chat type\n'''\n\nimport os\n\nimport numpy as np\nimport pandas as pd\n\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n\ndef baseline_bow(dataframe)... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"numpy.where",
"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": []
}
] |
adowaconan/Spindle_by_Graphical_Features | [
"660cad3ba9ca399997a12ebdddaf441445b10f64"
] | [
"test resampling.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 18 15:31:50 2017\n\n@author: ning\n\n\"\"\"\n\n#import pandas as pd\nimport os\nimport numpy as np\n#import matplotlib.pyplot as plt\nimport pickle\nfrom sklearn.model_selection import KFold\nfrom collections import Counter\nfrom imblearn.over_sampling import SMO... | [
[
"pandas.concat",
"sklearn.model_selection.GridSearchCV",
"sklearn.model_selection.cross_val_score",
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"numpy.arange",
"sklearn.metrics.matthews_corrcoef",
"sklearn.metrics.confusion_matrix",
"sklearn.model_selection.KF... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
guiwitz/Python_image_processing | [
"70f67808eb54555688e5d8a4095c3ec0b96f4eeb"
] | [
"deeplearning.py"
] | [
"from keras.models import Model\nfrom keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose, Reshape, Flatten\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint\nfrom keras import backend as K\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas ... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lizhifeng1998/schnetpack | [
"7a665f19b785addcc8f3e0abf9b1fd8fa6fe2c3d"
] | [
"src/schnetpack/md/integrators.py"
] | [
"\"\"\"\nIntegrators are used to propagate the simulated system in time. SchNetPack\nprovides two basic types of integrators. The Velocity Verlet integrator is a standard\nintegrator for a purely classical simulations of the nuclei. The ring polymer molecular dynamics\nintegrator simulates multiple replicas of the ... | [
[
"torch.zeros",
"torch.arange",
"torch.cos",
"torch.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chart21/Verification-of-Outsourced-Object-Detection | [
"ed1c27e5de6e6c98098969262aa8bd41da140c8b"
] | [
"contractor_with_multithreading.py"
] | [
"# Main class of a contractor or verifier using a regular CPU or GPU with the use of threading\n# Paramters associated with this class including if this device should act as a contractor or verifier can be set in parameters.py\nfrom parameters import ParticipantData\nfrom parameters import Parameters\nfrom paramete... | [
[
"tensorflow.compat.v1.ConfigProto",
"tensorflow.constant",
"tensorflow.saved_model.load",
"tensorflow.config.experimental.set_memory_growth",
"tensorflow.shape",
"tensorflow.lite.Interpreter",
"tensorflow.config.experimental.list_physical_devices",
"tensorflow.compat.v1.Interactive... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
noobermin/lspreader | [
"b4989ba32507fdcf87cc226ba93422639ed5c5fb"
] | [
"bin/filter-pext.py"
] | [
"#!/usr/bin/env python\n'''\nSimple script for basic filtering of particle extraction files, using\nlspreader.dump_pext on the backend, but offered as convienience for simple\ncases, for example, removing particles after a certain time for restarting\ngracefully.\n\nUsage:\n ./filter-pext.py [options] <input> <o... | [
[
"numpy.array",
"numpy.logical_and"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dscohen75/lambdata_dscohen75 | [
"4ef596727bbcc073876cd2d3faf31d24fd29f6c2"
] | [
"lambdata_dscohen75/chi2_test.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom scipy import stats\n\n\nclass Chi2:\n\n def __init__(self, x1, x2):\n \"\"\"\n Constructor for Chi2 reporting module\n Takes two categorical variable Series\n Returns Chi2 test info and contingency table\n \"\"\"\n # Attributes\n se... | [
[
"pandas.crosstab",
"scipy.stats.chi2_contingency"
]
] | [
{
"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": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.1... |
justin-oxford/dee-dee-reddit | [
"9e7b7f15327984ac65bbf93c8e3154de3dc8699b"
] | [
"deedee_ML_testModel.py"
] | [
"import os\r\nimport random\r\nimport deedee_ML_dataPull\r\nimport databaseConn\r\nfrom numpy import asarray\r\nfrom pandas import read_csv\r\nimport tensorflow as tf\r\n\r\nn_steps = 48\r\n\r\n# split sequence into samples\r\ndef split_sequence(sequence_in, n_steps):\r\n # define X (input)\r\n X = list()\r\n... | [
[
"numpy.asarray",
"tensorflow.keras.models.load_model",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
formatcom/aXeleRate | [
"40dfaf9edfd6a65753e9d60ba5a5f7397619141c"
] | [
"axelerate/networks/classifier/frontend_classifier.py"
] | [
"import time\nimport os\nimport numpy as np\n\nfrom axelerate.networks.common_utils.feature import create_feature_extractor\nfrom axelerate.networks.classifier.batch_gen import create_datagen\nfrom axelerate.networks.common_utils.fit import train\nfrom tensorflow.keras.models import Model, load_model\nfrom tensorfl... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.layers.GlobalAveragePooling2D",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Dense",
"numpy.argmax",
"tensorflow.keras.layers.Dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
youshyee/Greatape-Detection | [
"333b63d8f76538659bcd2bc6022128830a7a435b",
"333b63d8f76538659bcd2bc6022128830a7a435b"
] | [
"tools/vid_vis.py",
"mmdet/datasets/chimp.py"
] | [
"import argparse\nimport os.path as osp\nimport shutil\nimport tempfile\nimport numpy as np\nimport mmcv\nimport torch\nimport torch.distributed as dist\nfrom mmcv.runner import load_checkpoint, get_dist_info\nfrom mmcv.parallel import MMDataParallel, MMDistributedDataParallel\n\nfrom mmdet.apis import init_dist\nf... | [
[
"numpy.concatenate",
"torch.no_grad",
"numpy.vstack",
"numpy.full"
],
[
"numpy.hstack",
"numpy.random.seed",
"numpy.random.choice",
"numpy.random.shuffle",
"numpy.random.rand",
"torch.utils.data.dataloader.default_collate",
"numpy.array",
"numpy.where",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hrovatin/scib | [
"173d282bf17d7ff36e072c7cf8b1d6a40f811447"
] | [
"scIB/metrics.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n#import networkx as nx\nfrom scIB.utils import *\nfrom scIB.preprocessing import score_cell_cycle, hvg_batch\nfrom scIB.clustering import opt_louvain\nfrom scipy import sparse\nfrom scipy.sparse.csgraph import connect... | [
[
"numpy.dot",
"numpy.nanmedian",
"pandas.Series",
"sklearn.metrics.silhouette_samples",
"sklearn.metrics.silhouette_score",
"numpy.in1d",
"numpy.nanmin",
"pandas.api.types.is_numeric_dtype",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"skl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
NeoChenCCY/ITRIdenoiseCompetition | [
"7a989c169217632576a0f219e7a8a443f99560d7",
"7a989c169217632576a0f219e7a8a443f99560d7"
] | [
"python-pesq-master/tests/test_pesq.py",
"python-pesq-master/tests/filerIOread_pesq.py"
] | [
"import pytest\nimport numpy as np\nimport scipy.io.wavfile\n\nfrom pathlib import Path\n\nfrom pesq import pesq, NoUtterancesError, PesqError\n\ndef test():\n data_dir = Path(__file__).parent.parent / 'audio'\n ref_path = data_dir / 'speech.wav'\n deg_path = data_dir / 'speech_bab_0dB.wav'\n\n sample_r... | [
[
"numpy.random.randn",
"numpy.zeros"
],
[
"numpy.sqrt",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"scipy.signal.stft",
"numpy.arange",
"numpy.abs",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.yl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
... |
xuanyuanXIV/reframed | [
"77db74201d2f0720784a87b803f1e812b0c3f2e5",
"77db74201d2f0720784a87b803f1e812b0c3f2e5"
] | [
"reframed/alpha/plotting.py",
"reframed/cobra/thermodynamics.py"
] | [
"from math import isinf\nimport numpy as np\n\n\ndef plot_flux_bounds(range1, range2=None, values1=None, values2=None, reactions=None,\n threshold=100, ax=None, figsize=None):\n \"\"\" Plot and compare flux ranges (although other types of ranges also supported).\n\n Args:\n range1 (... | [
[
"numpy.array",
"matplotlib.pyplot.subplots"
],
[
"numpy.log",
"scipy.linalg.svd",
"numpy.ones",
"scipy.compress",
"numpy.exp",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RQuispeC/FairMOT | [
"cf0f4d9cb8e5979bd8871940bf93f389fa898487"
] | [
"src/lib/models/networks/pose_dla_dcn_split_8.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport math\nimport logging\nimport numpy as np\nfrom os.path import join\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\n\... | [
[
"torch.nn.Sequential",
"numpy.log2",
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.load",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.init.normal_",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tanyuqian/texar-pytorch | [
"4463ab6d50504606e627fd7589cbcfb0308cf09b"
] | [
"texar/torch/data/embedding.py"
] | [
"# Copyright 2018 The Texar 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... | [
[
"numpy.arange",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LanguegeEngineering/UW_basicNN | [
"6edfb1232018d45bc393525c0c70aba4260cb5e5"
] | [
"numpy.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"numpy.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/1GEdAiYS6cqOIaKMJVy4ca4A0Z1q-T-bk\n\"\"\"\n\nimport numpy as np\n\n\"\"\"Numpy to biblioteka służąca do operacji na macierzach \"\"\"\n\na = np.arange(1... | [
[
"numpy.arange",
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jakublala/LatticeDNAOrigamiJakub | [
"efd1147deea534f1c9cd0ab22bc3c5dec89c3c52"
] | [
"scripts/plotting/plot_ops_series.py"
] | [
"#!/usr/bin/python\n\n\"\"\"Plot order parameter series for all runs and reps.\n\nPlots number of bound staples, number of bound domains, number of misbound\ndomains, and number of stacked pairs.\n\"\"\"\n\nimport argparse\nimport sys\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimp... | [
[
"matplotlib.use",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhanglei1172/XBBO | [
"9bf9b778b29735f108457d5e491680785212d580",
"9bf9b778b29735f108457d5e491680785212d580",
"9bf9b778b29735f108457d5e491680785212d580"
] | [
"xbbo/surrogate/transfer/weight_stategy.py",
"xbbo/search_space/fast_example_problem.py",
"xbbo/search_algorithm/de_optimizer.py"
] | [
"import abc\nimport typing\nimport numpy as np\nfrom xbbo.core.trials import Trials\n\n# from xbbo.surrogate.base import Surrogate\nfrom xbbo.surrogate.gaussian_process import GPR_sklearn\nfrom xbbo.surrogate.transfer.tst import BaseModel\nfrom xbbo.utils.constants import VERY_SMALL_NUMBER\n\nclass ABCWeightStategy... | [
[
"numpy.expand_dims",
"numpy.triu_indices",
"numpy.asarray",
"numpy.eye",
"numpy.median",
"numpy.squeeze",
"numpy.linalg.norm",
"numpy.stack",
"numpy.percentile",
"numpy.full",
"numpy.argwhere",
"numpy.append",
"numpy.delete",
"numpy.bincount",
"numpy.arr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ksoky/jointlytrained | [
"69a3f211a52c98c51750e154072578c5dc9019ec"
] | [
"espnet/bin/asr_train.py"
] | [
"#!/usr/bin/env python3\n# encoding: utf-8\n\n# Copyright 2017 Tomoki Hayashi (Nagoya University)\n# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)\n\n\"\"\"Automatic speech recognition model training script.\"\"\"\n\nimport logging\nimport os\nimport random\nimport subprocess\nimport sys\n\nfrom distuti... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wanxinjin/Safe-PDP | [
"1bdfe63ea56e91dd363948a60133a96e623b327d"
] | [
"Examples/MPC/CIOC/CIOC_Cartpole.py"
] | [
"import numpy as np\n\nfrom SafePDP import SafePDP\nfrom SafePDP import PDP\nfrom JinEnv import JinEnv\nfrom casadi import *\nimport scipy.io as sio\nimport matplotlib.pyplot as plt\nimport time\nimport random\n\n# --------------------------- load demonstration data ----------------------------------------\nload = ... | [
[
"numpy.random.seed",
"numpy.set_printoptions",
"numpy.save",
"numpy.load",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MTC-ETH/Federated-Learning-source | [
"cdfd69c39a5d5e2a3fb89e724259c3a80570197e"
] | [
"nodeserver/worker/node.py"
] | [
"# Copyright 2021, ETH Zurich, Media Technology Center\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... | [
[
"tensorflow.optimizers.get",
"numpy.set_printoptions",
"numpy.random.shuffle",
"tensorflow.keras.backend.clear_session",
"numpy.exp",
"tensorflow.losses.get"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Axel-Jacobsen/pyjoulescope_ui | [
"7d296b1ead0d36c6524dc399372f7888a340e9fa"
] | [
"joulescope_ui/usb_inrush.py"
] | [
"# Copyright 2018 Jetperch 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# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed... | [
[
"numpy.savetxt",
"numpy.hstack",
"numpy.mean",
"numpy.isfinite"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hobaez/trading_calendars | [
"8fb3e094f88f921f17529bb82bce8fbfd087ad24"
] | [
"tests/test_xlis_calendar.py"
] | [
"from unittest import TestCase\nimport pandas as pd\nfrom pytz import UTC\n\nfrom .test_trading_calendar import EuronextCalendarTestBase\nfrom trading_calendars.exchange_calendar_xlis import XLISExchangeCalendar\n\n\nclass XLISCalendarTestCase(EuronextCalendarTestBase, TestCase):\n\n answer_key_filename = 'xlis'... | [
[
"pandas.Timestamp",
"pandas.Timedelta"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kala855/3Dgan | [
"8322b0dbb28d5089993bc08c964dbfb7dfe5c61c"
] | [
"keras/paramScan/vegan_param3.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*- \n\n# This file has a function that will take a list of params as input and run training using that. Finally it will run analysis to get a single metric that we will need to optimize\n\n\nfrom __future__ import print_function\nfrom collections import defaultdict\ntr... | [
[
"numpy.logical_not",
"numpy.expand_dims",
"numpy.multiply",
"numpy.squeeze",
"numpy.ones",
"tensorflow.ConfigProto",
"numpy.random.normal",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zheng-da/GPT-GNN | [
"3ae707da5e22a363e55283a57e39113804913928"
] | [
"example_reddit/GPT_GNN/model.py"
] | [
"from .conv import *\nimport numpy as np\nfrom gensim.parsing.preprocessing import *\n\n\nclass GPT_GNN(nn.Module):\n def __init__(self, gnn, rem_edge_list, attr_decoder, types, neg_samp_num, device, neg_queue_size = 0):\n super(GPT_GNN, self).__init__()\n self.types = types\n self.gnn = gnn... | [
[
"numpy.concatenate",
"numpy.random.shuffle",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MatthewAbugeja/lmbn | [
"6e18e752f95614ef37d5a767672cb81e82708d94",
"6e18e752f95614ef37d5a767672cb81e82708d94"
] | [
"model/g_c.py",
"engine_v1.py"
] | [
"import copy\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nimport random\nimport math\nfrom .osnet import osnet_x1_0, OSBlock\nfrom .attention import BatchDrop, BatchRandomErasing, PAM_Module, CAM_Module, SE_Module, Dual_Module\nfrom .bnneck import BNNeck, BNNeck3\n\nfrom torch.autograd im... | [
[
"torch.nn.Sequential",
"torch.nn.AdaptiveMaxPool2d",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.FloatTensor",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d",
"torch.stack",
"torch.nn.ReLU",
"torch.nn.init.kaiming_normal_"
],
[
"torch.norm",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eldonb/tskit | [
"9614343eaa77e049b69c299ac90a9afba3ee4456"
] | [
"python/tests/test_combinatorics.py"
] | [
"#\n# MIT License\n#\n# Copyright (c) 2020 Tskit Developers\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use,... | [
[
"numpy.all",
"numpy.array",
"numpy.zeros",
"numpy.math.factorial"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rathea0286/routiiens-app | [
"46e9f7f14ddbf5d15743673064591aae27b26a22"
] | [
"src/get_data.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 13 17:57:01 2021\n\n@author: romainloirs\n\"\"\"\n\n\ndef get_adj_matrix(road_id,stop_data):\n import pandas as pd\n # Tri de stop_data\n ordered_stops = list(stop_data.columns)\n ordered_stops.sort()\n stop_data_bis = stop_... | [
[
"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": []
}
] |
Kota28/autogluon | [
"bdbaac2d13d14d075b7aa751561f0bbd39927789"
] | [
"tabular/src/autogluon/tabular/models/vowpalwabbit/vowpalwabbit_utils.py"
] | [
"import pandas as pd\nfrom autogluon.common.features.types import S_TEXT, R_INT, R_FLOAT, R_CATEGORY\n\n\nclass VWFeaturesConverter:\n \"\"\"\n Converts features in PandasDataFrame to VW format\n Ref: https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Input-format\n \"\"\"\n\n PIPE = '|'\n SPACE =... | [
[
"pandas.isnull"
]
] | [
{
"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": []
}
] |
housemecn/HanLP | [
"d10e40d28ac0c745ceb426ec3496189ccb10555e"
] | [
"hanlp/components/parsers/second_order/tree_crf_dependency_parser.py"
] | [
"# -*- coding:utf-8 -*-\n# Author: hankcs\n# Date: 2020-05-08 20:51\nimport functools\nimport os\nfrom typing import Union, Any, List\n\nimport torch\nfrom alnlp.modules.util import lengths_to_mask\nfrom torch import nn\nfrom torch.optim import Adam\nfrom torch.optim.lr_scheduler import ExponentialLR\nfrom torch.ut... | [
[
"torch.no_grad",
"torch.optim.lr_scheduler.ExponentialLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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