repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
Lagergren-Lab/tomexo | [
"cf3693b5feebaeea4a41fc83dd33bcb288349a62"
] | [
"gdac_preproc.py"
] | [
"#! /usr/bin/env python3\n\nimport numpy as np\nimport pandas as pd\nimport os\n\n\nfilter_silents = True\nhm_coeff = 0.02 # Threshold of mutation rate of genes to be considered\n\ninput_dir = 'gdac.broadinstitute.org_GBM.Mutation_Packager_Calls.Level_3.2016012800.0.0'\ndf_full_dir = 'gdac_firehose_gbm_full.csv' # ... | [
[
"numpy.intersect1d",
"numpy.array",
"pandas.read_csv",
"pandas.DataFrame"
]
] |
JuliusSchwartz/FlowMO | [
"e221d989914f906501e1ad19cd3629d88eac1785",
"e221d989914f906501e1ad19cd3629d88eac1785",
"e221d989914f906501e1ad19cd3629d88eac1785"
] | [
"Theano-master/theano/tensor/tests/test_raw_random.py",
"Theano-master/theano/tensor/tests/test_gc.py",
"smiles_enumeration/smiles_x_enum.py"
] | [
"from __future__ import absolute_import, print_function, division\nimport numpy\nimport pickle\n\nfrom theano.tests import unittest_tools as utt\n\nfrom theano.tensor.raw_random import *\nfrom theano.tensor import (raw_random, ivector, dvector, iscalar, dcol,\n dtensor3)\nfrom theano impor... | [
[
"numpy.allclose",
"numpy.version.short_version.split",
"numpy.asarray",
"numpy.arange",
"numpy.sort",
"numpy.all"
],
[
"numpy.ones"
],
[
"numpy.array",
"pandas.DataFrame"
]
] |
NogaBar/open_lth | [
"09bcea21e69708549ecff2659690162a6c45f9ca",
"09bcea21e69708549ecff2659690162a6c45f9ca"
] | [
"platforms/base.py",
"datasets/base.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport abc\nfrom dataclasses import dataclass\nimport os\nimport torch\n\nfrom foundations.hparams import Hparams\nimport platforms.p... | [
[
"torch.load",
"torch.cuda.is_available",
"torch.device",
"torch.cuda.device_count",
"torch.save"
],
[
"torch.randperm",
"torch.Generator",
"numpy.random.RandomState",
"numpy.random.randint"
]
] |
adriensas/flair | [
"f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21",
"f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21"
] | [
"flair/trainers/trainer.py",
"flair/models/sequence_tagger_utils/viterbi.py"
] | [
"import copy\nimport datetime\nimport inspect\nimport logging\nimport os\nimport sys\nimport time\nimport warnings\nfrom inspect import signature\nfrom pathlib import Path\nfrom typing import Any, Dict, Optional, Tuple, Type, Union, cast\n\nimport torch\nfrom torch.optim.sgd import SGD\nfrom torch.utils.data.datase... | [
[
"torch.optim.lr_scheduler.OneCycleLR",
"torch.utils.data.dataset.Subset",
"torch.isnan",
"torch.utils.tensorboard.SummaryWriter",
"torch.utils.data.dataset.ConcatDataset"
],
[
"torch.nn.functional.softmax",
"torch.ones",
"torch.max",
"torch.zeros",
"torch.nn.utils.rnn.p... |
tingidev/PyDev | [
"afd55187a4666b611bff49dbe63710fdc876ca7a"
] | [
"app.py"
] | [
"# Based on: https://www.statworx.com/at/blog/how-to-build-a-dashboard-in-python-plotly-dash-step-by-step-tutorial/\n# Padding: https://www.htmldog.com/guides/css/beginner/margins/\n\n# System\nimport base64\nimport datetime\nimport io\n\n# Dash\nimport dash\nfrom dash.dependencies import Input, Output, State\nimpo... | [
[
"pandas.read_json"
]
] |
IIGROUP/PUM | [
"f66f92bc92f95baf015f1e003c661c8a8b3bcb66"
] | [
"visualization/visualize_recall_diff.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# visualization code for categorical recall difference between two models\n\"\"\"\nExample:\n python visualization/visualize_recall_diff.py \\\n --baseline_result ../VCTree-Scene-Graph-Generation/caches/prd_recall.pkl \\\n --new_model_result checkpoints/predcls-... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.tight_layout",
"matplotlib.use",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.tick_params",
"matplotlib.pyplot.ylabel"
]
] |
szmmm/speechchain | [
"909724c6f305588a52958f64f584ad21696b5173",
"909724c6f305588a52958f64f584ad21696b5173"
] | [
"test/test_e2e_tts_fastspeech.py",
"espnet/lm/lm_utils.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Copyright 2019 Tomoki Hayashi\n# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)\n\nimport json\nimport os\nimport shutil\nimport tempfile\n\nfrom argparse import Namespace\n\nimport numpy as np\nimport pytest\nimport torch\n\nfrom espnet.nets.pytorch_... | [
[
"torch.LongTensor",
"torch.cuda.device_count",
"torch.randn",
"torch.from_numpy",
"torch.no_grad",
"numpy.random.randn",
"torch.cuda.is_available",
"torch.arange",
"torch.device",
"torch.nn.DataParallel",
"numpy.random.randint"
],
[
"numpy.array",
"numpy.exp... |
dhimmel/pubmedpy | [
"9d716768f5ab798ec448154588e4fd99afd7584a"
] | [
"pubmedpy/esummary.py"
] | [
"import collections\nimport contextlib\nimport datetime\nimport itertools\nimport locale\nimport logging\nimport re\nimport threading\nfrom typing import List, Optional\n\nimport pandas\nimport tqdm\nimport lxml.etree\n\nfrom .xml import iter_extract_elems\nfrom .utils import PathType\n\nlocale_lock = threading.Loc... | [
[
"pandas.DataFrame"
]
] |
GuiiFerrari/pyRANSAC-3D | [
"d9fa1371d972ccb50d80067a5719b3cd5be63cbc"
] | [
"pyransac3d/cuboid.py"
] | [
"import numpy as np\nimport random\n\nclass Cuboid:\n \"\"\" \n Implementation for box (Cuboid) RANSAC.\n\n A cuboid is defined as convex polyhedron bounded by six faces formed by three orthogonal normal vectors. Cats love to play with this kind of geometry.\n This method uses 6 points to find 3 best pl... | [
[
"numpy.sqrt",
"numpy.multiply",
"numpy.abs",
"numpy.asarray",
"numpy.amin",
"numpy.linalg.norm",
"numpy.cross"
]
] |
damianmatusik96/SoilIdentificationSystem | [
"2be32759c3381c8e985623147414659fd63714e4"
] | [
"app/main.py"
] | [
"from app.IndentificationSystem import data_cluster, data_handler_3, data_cluster_3\nfrom app.IndentificationSystem.data.ProfilePredictor import ProfilePredictor\nimport matplotlib.pyplot as plt\n\n\nif __name__ == \"__main__\":\n data_cluster.choose_best_cluster(2, 8)\n profile_predictor = ProfilePredictor(d... | [
[
"matplotlib.pyplot.show"
]
] |
nasoboleva/magenta | [
"38614f87e4670ecfcca171c7c7a59ae96ab886df"
] | [
"magenta/models/coconet/lib_tfsampling.py"
] | [
"# Copyright 2019 The Magenta Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law o... | [
[
"tensorflow.random_uniform",
"tensorflow.control_dependencies",
"tensorflow.equal",
"numpy.zeros_like",
"tensorflow.assert_greater",
"tensorflow.placeholder_with_default",
"tensorflow.to_float",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.zeros",
"tensorflow.... |
lauramsmith/fine-tuning-locomotion | [
"96d7c81458511c0a7a11b59cf8c2c3fb8df8a64b",
"96d7c81458511c0a7a11b59cf8c2c3fb8df8a64b"
] | [
"motion_imitation/envs/env_wrappers/reset_task.py",
"motion_imitation/envs/env_wrappers/logging_wrapper.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Google Research Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"numpy.abs",
"numpy.clip",
"numpy.cos",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.random.uniform",
"numpy.array",
"numpy.exp",
"numpy.zeros",
"numpy.sum",
"numpy.random.randint"
],
[
"numpy.savez"
]
] |
conica-cui/python-pcl | [
"1d83d2d7ce9ce2c22ff5855249459bfc22025000",
"b54e80e7da94ac9e2279b95fdac597f1de7145d7"
] | [
"tests/test_filters.py",
"examples/external/laspy/visualization_test_rgb.py"
] | [
"import os.path\nimport pickle\nimport shutil\nimport tempfile\nimport unittest\n\nimport pcl\nimport numpy as np\n\nfrom nose.plugins.attrib import attr\n\n\n_data = [(i, 2 * i, 3 * i + 0.2) for i in range(5)]\n_DATA = \"\"\"0.0, 0.0, 0.2;\n 1.0, 2.0, 3.2;\n 2.0, 4.0, 6.2;\n 3.0, 6.0,... | [
[
"numpy.array",
"numpy.zeros"
],
[
"numpy.right_shift",
"numpy.mean",
"numpy.left_shift",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] |
theodumont/client | [
"7402ac67ada5bc8078078a49fd3e0cb4b6172307"
] | [
"wandb/sklearn/utils.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom six.moves.collections_abc import Sequence, Iterable\nimport sklearn\nimport scipy\nimport wandb\n\n\ndef encode_labels(df):\n le = sklearn.preprocessing.LabelEncoder()\n # apply le on categorical feature columns\n categorical_cols = df.select_dtypes(\n ... | [
[
"sklearn.utils.validation.check_is_fitted",
"pandas.isnull",
"numpy.asarray",
"sklearn.base.is_classifier",
"sklearn.base.is_regressor",
"sklearn.preprocessing.LabelEncoder",
"numpy.zeros"
]
] |
mizterbas/hoc | [
"91f4875dc4546b80d40bbb4a422f0c6849491faf"
] | [
"squad_utils.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requi... | [
[
"tensorflow.compat.v1.data.TFRecordDataset",
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.zeros_initializer",
"tensorflow.compat.v1.train.Scaffold",
"tensorflow.compat.v1.shape",
"tensorflow.compat.v1.gfile.GFile",
"tensorflow.contrib.tpu.TPUEstimatorSpec",
"tensorflow.comp... |
TrazLander/Traz-Fork-MCEdit-Unified | [
"829a6807ec3f64a6e936c5b5b9a0ec8e03c75954",
"829a6807ec3f64a6e936c5b5b9a0ec8e03c75954"
] | [
"filters/topsoil.py",
"editortools/thumbview.py"
] | [
"from numpy import zeros\nimport itertools\nfrom pymclevel import alphaMaterials\nfrom pymclevel.level import extractHeights\n\nam = alphaMaterials\n\n# naturally occuring materials\nblocks = [\n am.Grass,\n am.Dirt,\n am.Stone,\n am.Bedrock,\n am.Sand,\n am.Gravel,\n am.GoldOre,\n am.IronOr... | [
[
"numpy.zeros"
],
[
"numpy.array"
]
] |
charles96322/conditional-image-generation | [
"8854612fbeb4ef485f1d1613b78d42f240692161",
"8854612fbeb4ef485f1d1613b78d42f240692161",
"8854612fbeb4ef485f1d1613b78d42f240692161"
] | [
"lib/inits.py",
"lib/theano_utils.py",
"lib/image.py"
] | [
"import numpy as np\nimport theano, theano.tensor as T\n\ndef He(shape, name, fan_in):\n \"\"\" He initialization of parameters \"\"\"\n rng = np.random.RandomState(1)\n W = rng.normal(0, np.sqrt(2. / fan_in), size=shape)\n return theano.shared(W, borrow=True, name=name).astype('float32')\n\ndef Constan... | [
[
"numpy.random.RandomState",
"numpy.zeros",
"numpy.sqrt"
],
[
"numpy.asarray",
"numpy.zeros",
"numpy.ones"
],
[
"numpy.transpose",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show",
"numpy.flip",
"matplotlib.pyplot.figure"
]
] |
FranzForstmayr/scipy | [
"9a12908f843aed87203b32e45d1001353d90c548"
] | [
"scipy/fft/_fftlog_multimethods.py"
] | [
"'''Multimethods for fast Hankel transforms.\n'''\n\nimport numpy as np\n\nfrom ._basic import _dispatch\nfrom ._fftlog import fht as _fht\nfrom ._fftlog import ifht as _ifht\nfrom scipy._lib.uarray import Dispatchable\n\n\n__all__ = ['fht', 'ifht']\n\n\n@_dispatch\ndef fht(a, dln, mu, offset=0.0, bias=0.0):\n \... | [
[
"scipy._lib.uarray.Dispatchable"
]
] |
Hoeze/kipoiseq | [
"f57493e90df12f3be6f9028bd9da8d478fbc3748"
] | [
"tests/extractors/test_protein.py"
] | [
"import pytest\nfrom pytest_mock import mocker\nimport pandas as pd\nfrom kipoiseq.transforms.functional import translate, rc_dna\nfrom kipoiseq.dataclasses import Interval, Variant\nfrom kipoiseq.extractors.protein import cut_transcript_seq, TranscriptSeqExtractor, ProteinSeqExtractor, \\\n ProteinVCFSeqExtract... | [
[
"pandas.Series"
]
] |
ShenDezhou/LSTM2 | [
"b29ab680260673f407cb566be4af38aaf7d9ce8f"
] | [
"msr_dic/loadEmbedding.py"
] | [
"import codecs\nimport bz2\nimport numpy\n\nchars = []\ninit='。'\nwith codecs.open('../msr_dic/msr_dict.utf8', 'r', encoding='utf8') as f:\n lines = f.readlines()\n for line in lines:\n for w in line:\n if w == '\\n':\n continue\n else:\n chars.append... | [
[
"numpy.fromstring",
"numpy.save"
]
] |
MariBax/Face-masking-with-CV | [
"e211afe8ebe82553ee4089e7dc288bc127c81107"
] | [
"utils.py"
] | [
"import cv2\r\nimport dlib\r\nimport numpy as np\r\nimport math\r\nfrom scipy.spatial import distance as dist\r\n\r\n\r\n# CONSTANTS\r\n\r\nMOUTH_THRESH = 0.9\r\nEYE_AR_THRESH = 0.3\r\nEYE_AR_CONSEC_FRAMES = 3\r\nMOUTH_THRESH = 0.9\r\n\r\nMASK_INFO = { 'mask1.jpg': {\r\n \"src_points\": np.float32(... | [
[
"numpy.append",
"numpy.float32",
"scipy.spatial.distance.euclidean"
]
] |
ItsTheSebbe/4vHelix_GUI | [
"6626d29bf9a2150b2ab49104918ab2faa5f45c30"
] | [
"supporting_scripts/tacoxDNA/src/libs/pdb.py"
] | [
"import numpy as np\nimport itertools\nfrom math import sqrt\nimport sys\nimport copy\n\nBASE_SHIFT = 1.13\nCOM_SHIFT = 0.5\nFROM_OXDNA_TO_ANGSTROM = 8.518\nFROM_ANGSTROM_TO_OXDNA = 1. / FROM_OXDNA_TO_ANGSTROM\n\nNAME_TO_BASE = {\n \"ADE\" : \"A\",\n \"CYT\" : \"C\",\n \"GUA\" : \"G\",\n ... | [
[
"numpy.dot",
"numpy.rint",
"numpy.array",
"numpy.cross"
]
] |
Microsoft/onnxruntime | [
"0869f4f4ea9abeb4edf2e5d5b570880f77f81bfa"
] | [
"onnxruntime/python/torch_cpp_extensions/setup.py"
] | [
"# -------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n# --------------------------------------------------------------------------\n\nimport os\n\nfrom setuptools import setup\nfrom torch.utils im... | [
[
"torch.utils.cpp_extension.CppExtension"
]
] |
mhauskn/hfo | [
"b8b2a1d462823c6732f4d5581aa7fe2e371d55cb"
] | [
"hfo/hfo.py"
] | [
"from ctypes import *\nimport numpy as np\nfrom numpy.ctypeslib import as_ctypes\nimport os\n\nhfo_lib = cdll.LoadLibrary(os.path.join(os.path.dirname(__file__),\n 'libhfo_c.so'))\n\n\"\"\"Possible feature sets\"\"\"\nNUM_FEATURE_SETS = 2\nLOW_LEVEL_FEATURE_SET, HIGH_LEVEL_FEA... | [
[
"numpy.asarray",
"numpy.ctypeslib.as_ctypes"
]
] |
lucidrains/openprotein | [
"c3a996a2fd233e465760888d2255ce35be050c5e"
] | [
"experiments/tmhmm3/tm_util.py"
] | [
"# This file is part of the TMHMM3 project.\n#\n# @author Jeppe Hallgren\n#\n# For license information, please see the LICENSE file in the root directory.\n\nimport torch\nfrom torch.utils.data.dataset import Dataset\nimport numpy as np\nimport math\nimport random\n\nfrom util import write_out\n\nclass TMDataset(Da... | [
[
"torch.LongTensor",
"torch.ones",
"torch.zeros",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.unique",
"torch.unique_consecutive",
"torch.utils.data.sampler.SequentialSampler",
"numpy.array"
]
] |
opeltre/geomstats | [
"135d5bb6f19e29dd453c68399e04100a9e2c76bf",
"135d5bb6f19e29dd453c68399e04100a9e2c76bf"
] | [
"tests/test_backend_tensorflow.py",
"examples/plot_square_h2_poincare_disk.py"
] | [
"\"\"\"\nUnit tests for tensorflow backend.\n\"\"\"\n\nimport geomstats.backend as gs\nimport geomstats.tests\n\n\n@geomstats.tests.tf_only\nclass TestBackendTensorFlow(geomstats.tests.TestCase):\n def test_vstack(self):\n import tensorflow as tf\n with self.test_session():\n tensor_1 = ... | [
[
"tensorflow.convert_to_tensor"
],
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.show",
"numpy.linspace"
]
] |
witnessai/GRAN | [
"952c2b08a58f3b0087f0f18fd48f8e385e45908b",
"952c2b08a58f3b0087f0f18fd48f8e385e45908b",
"952c2b08a58f3b0087f0f18fd48f8e385e45908b",
"952c2b08a58f3b0087f0f18fd48f8e385e45908b",
"952c2b08a58f3b0087f0f18fd48f8e385e45908b",
"952c2b08a58f3b0087f0f18fd48f8e385e45908b",
"952c2b08a58f3b0087f0f18fd48f8e385e45908... | [
"mmdet/datasets/coco_seen65.py",
"mmdet/datasets/vg_unseen130.py",
"mmdet/core/evaluation/bbox_overlaps.py",
"mmdet/core/anchor/anchor_generator.py",
"mmdet/models/detectors/test_mixins.py",
"mmdet/models/bbox_heads/global_context_head_semantic.py",
"mmdet/datasets/loader/sampler.py"
] | [
"import numpy as np\r\nfrom pycocotools.coco import COCO\r\n\r\nfrom .custom import CustomDataset\r\nfrom .registry import DATASETS\r\n\r\n\r\n@DATASETS.register_module\r\nclass CocoDatasetSeen65(CustomDataset):\r\n\r\n CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'boat',\r\n ... | [
[
"numpy.array",
"numpy.zeros"
],
[
"numpy.array",
"numpy.zeros"
],
[
"numpy.maximum",
"numpy.zeros",
"numpy.minimum"
],
[
"torch.Tensor",
"torch.zeros",
"torch.sqrt",
"torch.arange",
"torch.stack"
],
[
"torch.Tensor"
],
[
"torch.nn.init.co... |
KungCheops/aoc2020 | [
"6e11ddf93fd99e86b58af19fc328cd54f78834bb"
] | [
"day22/day22.py"
] | [
"import sys\nfrom collections import deque\nimport numpy as np\n\nclass Player():\n def __init__(self, id, deck):\n self.id = id\n self.deck = deque(deck)\n\n def __str__(self):\n return f'<Player({self.id}, {self.deck})>'\n\n def __repr__(self):\n return str(self)\n\n def pl... | [
[
"numpy.argmax"
]
] |
brodderickrodriguez/EMAworkbench | [
"90031223a4b6feb49633d45816e20981dc9415a0",
"90031223a4b6feb49633d45816e20981dc9415a0",
"90031223a4b6feb49633d45816e20981dc9415a0"
] | [
"ema_workbench/examples/prim_constrained.py",
"ema_workbench/analysis/scenario_discovery_util.py",
"docs/source/pyplots/basicMultiplotScatter.py"
] | [
"'''\na short example on how to use the constrained prim function.\n\nfor more details see Kwakkel (2019) A generalized many‐objective optimization\napproach for scenario discovery, doi: https://doi.org/10.1002/ffo2.8\n\n'''\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom ema_workbench.analysis import... | [
[
"pandas.read_csv",
"matplotlib.pyplot.show"
],
[
"matplotlib.colors.BoundaryNorm",
"matplotlib.pyplot.tight_layout",
"pandas.api.types.is_float_dtype",
"numpy.allclose",
"scipy.stats.binom_test",
"matplotlib.patches.Rectangle",
"pandas.DataFrame",
"matplotlib.pyplot.gcf... |
i4oolish/mindspore | [
"4276050f2494cfbf8682560a1647576f859991e8",
"4276050f2494cfbf8682560a1647576f859991e8",
"4276050f2494cfbf8682560a1647576f859991e8",
"4276050f2494cfbf8682560a1647576f859991e8",
"4276050f2494cfbf8682560a1647576f859991e8"
] | [
"mindspore/common/initializer.py",
"tests/st/serving/generate_model.py",
"model_zoo/official/cv/maskrcnn/src/util.py",
"tests/ut/python/dataset/test_dataset_numpy_slices.py",
"model_zoo/official/gnn/gcn/src/dataset.py"
] | [
"# Copyright 2020 Huawei Technologies Co., Ltd\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 l... | [
[
"numpy.random.seed",
"numpy.ndarray",
"numpy.random.normal",
"numpy.random.uniform",
"scipy.stats.truncnorm.rvs"
],
[
"numpy.ones",
"numpy.zeros",
"numpy.random.seed",
"numpy.random.randint"
],
[
"numpy.round",
"numpy.array",
"numpy.zeros"
],
[
"pand... |
amaotone/caruta-contest-manager | [
"33bbbc8a8ff2903a2763a1270715f224c329e7a2"
] | [
"murasame/divider.py"
] | [
"import os\n\nimport pandas as pd\n\nfrom .utils import load_setting\n\n\nclass Divider(object):\n def __init__(self, df, files, base):\n self.data = df\n self.files = files\n self.base = base\n self.writers = dict()\n\n def _setup_writer(self, outdir):\n assert self.files\n... | [
[
"pandas.ExcelWriter"
]
] |
qhy5755/xalpha | [
"99b9aa30d494b02533f518f125d46443cd9f0dd5"
] | [
"xalpha/indicator.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nmodule for implementation of indicator class, which is designed as MinIn for systems with netvalues\n\"\"\"\n\nimport pandas as pd\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Kline, Line, Bar, Grid\nfrom pyecharts.commons.utils import JsCode\n\nfrom xalpha.... | [
[
"pandas.to_datetime",
"pandas.Series",
"pandas.DataFrame",
"pandas.date_range",
"pandas.merge_asof"
]
] |
JamesBarciz/project_flask | [
"d123312b03a6a780a94319ed125b3944615aa8d3"
] | [
"src/predict.py"
] | [
"from src.orm_model import Tweet, Author\nimport pandas as pd\nfrom sklearn.linear_model import LogisticRegression\n# import pickle\n\n\ndef get_most_likely_author(tweet_body, spacy_model):\n authors = Author.query.all()\n features = pd.DataFrame()\n target = pd.Series()\n for a in authors:\n for... | [
[
"pandas.Series",
"pandas.DataFrame",
"sklearn.linear_model.LogisticRegression"
]
] |
gaoxiao/tacotron2 | [
"0a58682c8025f892b29898088ae275b9086887b6"
] | [
"infer_single.py"
] | [
"import warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nimport sys\n\nimport matplotlib.pylab as plt\nimport scipy\n\nsys.path.append('waveglow/')\nimport numpy as np\nimport torch\n\nfrom hparams import create_hparams\nfrom train import load_model\nfrom text import text_to_sequence\n\n\ndef plot_data(data, figs... | [
[
"torch.no_grad",
"torch.from_numpy",
"torch.load"
]
] |
ScapeQin/PerceptualSimilarity | [
"9b88429a599fa2f08dd33713c52d98843333d242"
] | [
"models/dist_model.py"
] | [
"import numpy as np\nimport torch\nfrom torch import nn\nimport os\nfrom collections import OrderedDict\nfrom torch.autograd import Variable\nimport itertools\nimport util.util as util\nfrom .base_model import BaseModel\nfrom . import networks_basic as networks\nfrom scipy.ndimage import zoom\nimport fractions\nimp... | [
[
"torch.optim.Adam",
"torch.mean",
"torch.load",
"scipy.ndimage.zoom",
"numpy.cumsum",
"numpy.concatenate",
"numpy.mean",
"numpy.argsort",
"torch.clamp",
"numpy.array",
"numpy.sum",
"torch.autograd.Variable"
]
] |
tonysy/pointnet2_tf | [
"e878a8f3dce0d15745be2dc33aee6fc35002c0b8"
] | [
"tf_ops/build_operator.py"
] | [
"import os\nimport sys\n\nimport tensorflow as tf \ninclude_path = tf.sysconfig.get_include()\nlib_path = tf.sysconfig.get_lib()\n\nos.system('sh ./build.sh {} {}'.format(include_path, lib_path))"
] | [
[
"tensorflow.sysconfig.get_include",
"tensorflow.sysconfig.get_lib"
]
] |
sanchitintel/pytorch | [
"416f59308023b5d98f6ea4ecdd0bcd3829edb7a7",
"416f59308023b5d98f6ea4ecdd0bcd3829edb7a7"
] | [
"test/test_quantization.py",
"torch/onnx/symbolic_opset10.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom torch.testing._internal.common_utils import run_tests\n\n# Quantization core tests. These include tests for\n# - quantized kernels\n# - quantized functional operators\n# - quantized workflow modules\n# - quantized workflow operators\n# - quantized tensor\n\n# 1. Quantized Kernels\n#... | [
[
"torch.testing._internal.common_utils.run_tests"
],
[
"torch.onnx.symbolic_helper._interpolate_get_scales_and_mode",
"torch.onnx.symbolic_helper.parse_args",
"torch.onnx.symbolic_opset9.sub",
"torch.onnx.symbolic_helper._interpolate_warning",
"torch.onnx.symbolic_helper._reducesum_help... |
Shyonokaze/FCN_STEM | [
"5ffb4f4bcea12646694e48246b7c2b0566cc120a"
] | [
"FCN.py"
] | [
"from __future__ import print_function\nimport tensorflow as tf\nimport numpy as np\n\nimport TensorflowUtils as utils\nimport read_MITSceneParsingData as scene_parsing\nimport datetime\nimport BatchDatsetReader as dataset\nfrom six.moves import xrange\n\nimport os.path as osp\n\nFLAGS = tf.flags.FLAGS\ntf.flags.DE... | [
[
"tensorflow.stack",
"numpy.squeeze",
"tensorflow.cast",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"tensorflow.flags.DEFINE_float",
"tensorflow.summary.scalar",
"tensorflow.summary.image",
"tensorflow.squeeze",
"tensorflow.add",
"tensorflow.Session",
"tensorflo... |
anandmy/PyBaMM | [
"dd8e5ebf85dc4324e163adad274ccb56c88f3698"
] | [
"tests/unit/test_simulation.py"
] | [
"import pybamm\nimport numpy as np\nimport pandas as pd\nimport os\nimport unittest\n\n\nclass TestSimulation(unittest.TestCase):\n def test_basic_ops(self):\n\n model = pybamm.lithium_ion.SPM()\n sim = pybamm.Simulation(model)\n\n self.assertEqual(model.__class__, sim._model_class)\n\n ... | [
[
"numpy.linspace",
"numpy.ones",
"numpy.testing.assert_array_equal",
"numpy.array",
"numpy.testing.assert_array_almost_equal"
]
] |
GuyTevet/MotionCLIP | [
"c2b9f40b0e721e42981f3e8b58133a1c51fde715"
] | [
"src/datasets/dataset.py"
] | [
"import random\n\nimport numpy as np\nimport torch\nfrom src.utils.action_label_to_idx import action_label_to_idx\nfrom ..utils.tensors import collate\nfrom ..utils.misc import to_torch\nimport src.utils.rotation_conversions as geometry\nUNSUPERVISED_BABEL_ACTION_CAT_LABELS_IDXS = [48, 50, 28, 38, 52, 11, 29, 19, 5... | [
[
"torch.transpose",
"torch.cat",
"torch.zeros",
"numpy.arange",
"numpy.unique",
"numpy.random.choice",
"numpy.ones",
"numpy.argwhere",
"numpy.argmax",
"numpy.mean",
"numpy.array"
]
] |
akshitj1/mavsim_template_files | [
"25c646ed274385c13492ed64d9821c1b8033c8ef"
] | [
"mavsim_python/parameters/planner_parameters.py"
] | [
"import sys\nsys.path.append('..')\nimport numpy as np\nimport parameters.aerosonde_parameters as MAV\n\n# size of the waypoint array used for the path planner. This is the\n# maximum number of waypoints that might be transmitted to the path\n# manager.\nsize_waypoint_array = 100\n\n# airspeed commanded by planner... | [
[
"numpy.tan",
"numpy.radians"
]
] |
emiliano-f/time-series-forecasting-crypto | [
"918da08105cdf784fdd7ca76e9707b226b3ebfd3"
] | [
"code/lstm.py"
] | [
"from keras.models import Sequential\nfrom keras.layers import Activation, Dense, Dropout, LSTM\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n\ndef train_test_split(df, test_size=0.2):\... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"sklearn.metrics.r2_score",
"numpy.random.seed",
"pandas.Series",
"sklearn.metrics.mean_absolute_error",
"matplotlib.pyplot.subplots",
"sklearn.metrics.mean_squared_error",
"numpy.array"
]
] |
clementchadebec/benchmark_VAE | [
"943e231f9e5dfa40b4eec14d4536f1c229ad9be1"
] | [
"tests/test_VQVAE.py"
] | [
"import os\nfrom copy import deepcopy\n\nimport pytest\nimport torch\nfrom torch.optim import SGD, Adadelta, Adagrad, Adam, RMSprop\n\nfrom pythae.customexception import BadInheritanceError\nfrom pythae.models.base.base_utils import ModelOutput\nfrom pythae.models import VQVAE, VQVAEConfig\n\nfrom pythae.trainers i... | [
[
"torch.rand",
"torch.equal"
]
] |
tommyreddad/tommy2tommy | [
"c634bedbc8b498abd272eecb27ca8dd2d013cdc8",
"c634bedbc8b498abd272eecb27ca8dd2d013cdc8"
] | [
"tommy2tommy/optimizers/radam_test.py",
"tommy2tommy/layers/bias.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom tommy2tommy.optimizers import radam\n\n\nclass RAdamTest(tf.test.TestCase):\n pass\n\n\nif __name__ == \"__main__\":\n tf.test.main()\n",
"\"\"\"Bias layers for attention logits.\n\nThis module implements layers which compute bias to be applied to\nattentio... | [
[
"tensorflow.test.main"
],
[
"tensorflow.equal",
"tensorflow.ones",
"tensorflow.shape"
]
] |
njuaplusplus/AmI | [
"b5b93afdae135dd60df78cb7276b49ba82a924b4"
] | [
"src/utils.py"
] | [
"import cv2\nimport numpy as np\nimport re\n\n\ndef read_list(f):\n l = []\n for line in open(f, 'r'):\n l.append(line.strip())\n return l\n\n\ndef get_identity(img_name, names):\n indices = [i.start() for i in re.finditer('_', img_name)]\n name = img_name[:indices[len(indices)-5]]\n if nam... | [
[
"numpy.rollaxis",
"numpy.load",
"numpy.array"
]
] |
antonmattsson/diabetic_retinopathy | [
"6aaa0ab3631d1cb0075d3acf2778822d62d1b532"
] | [
"src/data_separator.py"
] | [
"import numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom numpy.core.defchararray import add, replace\n\n# Separate the data set into test and train data\n\n# Read filenames from a text file listing all the images\nfilenames = np.genfromtxt('../data/train_list.txt', dtype=... | [
[
"matplotlib.pyplot.title",
"numpy.unique",
"numpy.random.seed",
"matplotlib.use",
"numpy.asarray",
"matplotlib.pyplot.savefig",
"numpy.genfromtxt",
"numpy.random.shuffle",
"numpy.full",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.bar",
"numpy.savetxt",
"matpl... |
venushong667/rasa | [
"dc0af420818e263fb4ef97c0d7f1c65e1da83bd1"
] | [
"rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py"
] | [
"from __future__ import annotations\nimport numpy as np\nimport logging\n\nfrom typing import Any, Optional, Text, List, Dict, Tuple\n\nfrom rasa.engine.graph import ExecutionContext, GraphComponent\nfrom rasa.engine.storage.resource import Resource\nfrom rasa.engine.storage.storage import ModelStorage\nfrom rasa.n... | [
[
"numpy.reshape",
"numpy.array",
"numpy.zeros"
]
] |
RuibingS/cimcb | [
"382f7d8fff30d3d276f18ac8c7dc686e0e643fa9",
"382f7d8fff30d3d276f18ac8c7dc686e0e643fa9",
"382f7d8fff30d3d276f18ac8c7dc686e0e643fa9",
"382f7d8fff30d3d276f18ac8c7dc686e0e643fa9"
] | [
"cimcb_lite/bootstrap/BC.py",
"cimcb_lite/plot/distribution.py",
"cimcb_lite/utils/univariate_2class.py",
"cimcb_lite/utils/table_check.py"
] | [
"import numpy as np\nimport scipy\nfrom scipy.stats import norm\nfrom .BaseBootstrap import BaseBootstrap\nfrom ..utils import nested_getattr\n\n\nclass BC(BaseBootstrap):\n \"\"\" Returns bootstrap confidence intervals using the bias-corrected boostrap interval.\n\n Parameters\n ----------\n model : ob... | [
[
"scipy.stats.norm.ppf",
"numpy.array",
"scipy.stats.norm.cdf",
"numpy.percentile"
],
[
"scipy.stats.gaussian_kde",
"numpy.insert",
"numpy.linspace",
"numpy.unique"
],
[
"numpy.nanmedian",
"numpy.random.seed",
"numpy.isnan",
"pandas.DataFrame",
"numpy.per... |
f6v/pandas | [
"bc65fe6c12dc78679ba8584eee83c6e3e243b5b9"
] | [
"pandas/core/indexes/period.py"
] | [
"from datetime import datetime, timedelta\nimport warnings\nimport weakref\n\nimport numpy as np\n\nfrom pandas._libs import index as libindex\nfrom pandas._libs.tslibs import NaT, frequencies as libfrequencies, iNaT, resolution\nfrom pandas._libs.tslibs.period import DIFFERENT_FREQ, IncompatibleFrequency, Period\n... | [
[
"pandas.tseries.frequencies.to_offset",
"pandas.core.missing.isna",
"numpy.asarray",
"pandas.core.accessor.delegate_names",
"pandas._libs.tslibs.resolution.get_freq_group",
"numpy.searchsorted",
"pandas._libs.tslibs.period.IncompatibleFrequency",
"pandas.core.common.values_from_obj... |
johncollinsai/post-high-frequency-data | [
"88533b0e0afc7e7f82fee1d3ca4b68abc30aaeb4",
"88533b0e0afc7e7f82fee1d3ca4b68abc30aaeb4",
"88533b0e0afc7e7f82fee1d3ca4b68abc30aaeb4",
"88533b0e0afc7e7f82fee1d3ca4b68abc30aaeb4",
"88533b0e0afc7e7f82fee1d3ca4b68abc30aaeb4",
"88533b0e0afc7e7f82fee1d3ca4b68abc30aaeb4",
"88533b0e0afc7e7f82fee1d3ca4b68abc30aaeb... | [
"venv/lib/python3.8/site-packages/statsmodels/stats/rates.py",
"venv/lib/python3.8/site-packages/statsmodels/genmod/families/links.py",
"venv/lib/python3.8/site-packages/statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py",
"venv/lib/python3.8/site-packages/pandas/tests/dtypes/test_inference.... | [
"'''Test for ratio of Poisson intensities in two independent samples\n\nAuthor: Josef Perktold\nLicense: BSD-3\n\n'''\n\n\nimport numpy as np\nfrom scipy import stats\n\nfrom statsmodels.stats.base import HolderTuple\nfrom statsmodels.stats.weightstats import _zstat_generic2\n\n\ndef test_poisson_2indep(count1, exp... | [
[
"numpy.sqrt",
"numpy.abs",
"numpy.arange",
"scipy.stats.binom.pmf",
"scipy.stats.poisson.pmf"
],
[
"numpy.log",
"numpy.ones_like",
"numpy.clip",
"numpy.asarray",
"numpy.power",
"numpy.cos",
"numpy.finfo",
"numpy.atleast_1d",
"numpy.sin",
"numpy.zeros... |
tommytanaka00/IML.HUJI | [
"156382fad84026ce069c6be70fa389cda32f3501"
] | [
"IMLearn/learners/regressors/linear_regression.py"
] | [
"from __future__ import annotations\nfrom typing import NoReturn\nfrom IMLearn.base import BaseEstimator\nimport numpy as np\nfrom numpy.linalg import pinv\nfrom IMLearn.metrics.loss_functions import mean_square_error\n\n\n\n\nclass LinearRegression(BaseEstimator):\n \"\"\"\n Linear Regression Estimator\n\n ... | [
[
"numpy.linalg.pinv",
"numpy.insert"
]
] |
rshilliday/sfm | [
"ca0eead387ba68582bf166c9a0780adcd99e3bf3"
] | [
"bundle_adjustment.py"
] | [
"import numpy as np\nimport cv2\nfrom scipy.optimize import least_squares\nfrom scipy.sparse import lil_matrix\n\ndef bundle_adjustment_sparsity(n_cameras, n_points, camera_indices, point_indices):\n \"\"\"\n\n :param n_cameras Integer. Number of cameras/images currently resected\n :param n_points: number ... | [
[
"numpy.expand_dims",
"scipy.optimize.least_squares",
"numpy.arange",
"numpy.squeeze",
"numpy.array",
"scipy.sparse.lil_matrix"
]
] |
ncoman32/promise12ProstateCancerUNETSegmentation | [
"20060a8c118a4bf4ae1f702d73782d86d8276e68"
] | [
"mhd_to_jpg.py"
] | [
"# code scris urat in cazul in care ne mai trebuie conversia\nimport os\nfrom os import listdir\n\nimport SimpleITK as sitk\nimport matplotlib.pylab as plt\n\n\ndef to_images(file, path):\n print(\"Processing file: \" + file)\n input_path = path + file\n output_path = path\n\n file_name = os.path.splite... | [
[
"matplotlib.pylab.savefig",
"matplotlib.pylab.imshow",
"matplotlib.pylab.axis"
]
] |
malshaV/sar_transformer | [
"b3ac845f96f2332aa4f1af94b455f71630978b17"
] | [
"test.py"
] | [
"# Code for testing on real SAR images \n# Author: Malsha Perera\nimport argparse\nimport torch\nimport torchvision\nfrom torch import nn\nfrom torchvision.transforms import functional as F\nimport os\nimport numpy as np\nimport torch\nfrom transform_main import TransSAR, TransSARV2, TransSARV3\nimport cv2\n\n\n\np... | [
[
"torch.load",
"torch.nn.DataParallel",
"numpy.float32",
"torch.device",
"torch.cuda.device_count"
]
] |
santinoacco/multiclass_cnn | [
"a5ce73c065f69b0025f2969a9ed2dc13789304a3"
] | [
"src/preprocess/load_data.py"
] | [
"#!/usr/bin/env python3\n\nfrom ..common.parser import set_parser\nimport tensorflow as tf\n\ndef load_data(data_dir, img_height=180, img_width=180, batch_size=32):\n train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n data_dir,\n validation_split=0.2,\n subset=\"training\",\n ... | [
[
"tensorflow.keras.preprocessing.image_dataset_from_directory"
]
] |
icedream2/DAVAR-Lab-OCR | [
"c8b82f45516850eeadcab2739fb2a4292f2fdca1",
"c8b82f45516850eeadcab2739fb2a4292f2fdca1",
"c8b82f45516850eeadcab2739fb2a4292f2fdca1",
"c8b82f45516850eeadcab2739fb2a4292f2fdca1",
"c8b82f45516850eeadcab2739fb2a4292f2fdca1",
"c8b82f45516850eeadcab2739fb2a4292f2fdca1"
] | [
"davarocr/davarocr/davar_common/apis/test.py",
"davarocr/davarocr/davar_videotext/apis/test.py",
"davarocr/davarocr/davar_rcg/models/transformations/tps_transformation.py",
"davarocr/davarocr/davar_rcg/models/sequence_heads/warpctc_head.py",
"demo/videotext/yoro/det/test.py",
"davarocr/davarocr/davar_comm... | [
"\"\"\"\n##################################################################################################\n# Copyright Info : Copyright (c) Davar Lab @ Hikvision Research Institute. All rights reserved.\n# Filename : test.py\n# Abstract : The common testing api for davarocr, used in online/of... | [
[
"torch.no_grad"
],
[
"torch.no_grad",
"numpy.stack"
],
[
"numpy.expand_dims",
"torch.zeros",
"numpy.concatenate",
"numpy.fill_diagonal",
"numpy.square",
"numpy.arange",
"torch.from_numpy",
"numpy.stack",
"torch.bmm",
"numpy.zeros",
"numpy.log",
"... |
samirgadkari/companies | [
"f683a3d077ec3d9b7241e9c91e6393b290f80b2e",
"f683a3d077ec3d9b7241e9c91e6393b290f80b2e"
] | [
"ml/tokens.py",
"ml/validation_test_split.py"
] | [
"import os\nimport re\nimport string\nimport numpy as np\nfrom ml.clean_tables import tag_actions\nfrom utils.environ import cleaned_tags_dir, generated_data_dir\nfrom utils.file import remove_files, get_json_from_file, \\\n write_json_to_file\n\ntokenize_attr_names = ['rowspan', 'colspan', 'style', 'align', 'wi... | [
[
"numpy.arange",
"numpy.random.choice",
"numpy.random.randint"
],
[
"numpy.array"
]
] |
aldro61/microbiome-summer-school-2017 | [
"5f7fa384b66ea776db0d6e9c397f3d143254389b"
] | [
"exercises/code/basics.model.complexity.py"
] | [
"\"\"\"\nUnderfitting vs overfitting interactive example\n\nAuthor: Alexandre Drouin\nInspired by http://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html\n\n\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom matplotlib.widgets import Slider\nfrom sklearn.line... | [
[
"numpy.hstack",
"numpy.linspace",
"numpy.random.RandomState",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.subplots",
"numpy.cos",
"sklearn.preprocessing.PolynomialFeatures",
"sklearn.linear_model.LinearRegression",
"matplotlib.widgets.Slider.__init__",
"m... |
acl21/deep-active-learning-pytorch | [
"637fd507235632903bcf84ed841ff524d847b94e",
"637fd507235632903bcf84ed841ff524d847b94e"
] | [
"pycls/models/alexnet.py",
"pycls/models/vaal_model.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nfrom torch.utils.model_zoo import load_url as load_state_dict_from_url\r\nfrom typing import Any\r\n\r\n\r\n__all__ = ['AlexNet', 'alexnet']\r\n\r\n\r\nmodel_urls = {\r\n 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',\r\n}\r\n\r\n\r\nclass Ale... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.flatten",
"torch.nn.ReLU",
"torch.utils.model_zoo.load_url"
],
[
"torch.nn.ConvTranspose2d",
"torch.nn.Conv2d",
"torch.nn.Sigmoid",
"torch.nn.Linear",
... |
anguswilliams91/jbc-turing-rss-nowcasting | [
"8c91e568dcf0dfcdf48e03cac86ad01bc47f8dcc"
] | [
"models/distributions.py"
] | [
"import numpy as np\nimport scipy.stats\nimport torch as t\nfrom torch.distributions import Normal, Poisson\nimport math\n\nfrom .utils import betaln, binomln, kappa_marginal_moments\n\n\n# flat gaussian mixture\nclass GaussianMixture:\n def __init__(self, mus, sigs):\n \"\"\"\n Args:\n ... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.random.choice",
"torch.arange",
"torch.tensor",
"torch.exp",
"numpy.mean",
"torch.distributions.Normal",
"torch.distributions.Poisson",
"torch.logsumexp",
"torch.lgamma",
"torch.stack"
]
] |
ka-ryo/M2Det | [
"d947f135e7aad996da43f5fe3a350eeead237fd0"
] | [
"layers/modules/multibox_loss.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom utils.box_utils import match, log_sum_exp\n\nclass MultiBoxLoss(nn.Module):\n \"\"\"SSD Weighted Loss Function\n Compute Targets:\n 1) Produce Confidence Target Indices by matching ground t... | [
[
"torch.nn.functional.cross_entropy",
"torch.nn.functional.smooth_l1_loss",
"torch.cuda.is_available",
"torch.zeros"
]
] |
jermwatt/blog | [
"3dd0d464d7a17c1c7a6508f714edc938dc3c03e9"
] | [
"posts/markov_chains/library/char_level_markov_model.py"
] | [
"# custom imports\nfrom . import text_parsing_utils as util\n\n# standard imporrts\nimport numpy as np\n\nclass Markov:\n def __init__(self,csvname):\n # preprocess input text (remove bad characters, make all lowercase, etc.,)\n self.text = util.load_preprocess(csvname)\n \n # parse i... | [
[
"numpy.unique"
]
] |
l-Imoon/jcvi | [
"db70bb98c7969bb0cc7b9941a2cc2dc8c5d1b783",
"db70bb98c7969bb0cc7b9941a2cc2dc8c5d1b783"
] | [
"jcvi/apps/ks.py",
"jcvi/graphics/base.py"
] | [
"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n\"\"\"\nCalculation of synonymous substitutions (Ks).\n\"\"\"\nfrom __future__ import print_function\n\nimport sys\nimport os\nimport os.path as op\nimport csv\nimport logging\n\nimport numpy as np\n\nfrom math import log, sqrt, pi, exp\nfrom itertools import produ... | [
[
"numpy.arange",
"numpy.log"
],
[
"numpy.linspace",
"numpy.max",
"numpy.mean",
"matplotlib.pyplot.gca",
"numpy.arange",
"matplotlib.colors.LinearSegmentedColormap",
"scipy.interpolate.interp1d",
"matplotlib.pyplot.rcdefaults",
"numpy.zeros",
"matplotlib.pyplot.fi... |
milyiyo/nlu | [
"d209ed11c6a84639c268f08435552248391c5573",
"d209ed11c6a84639c268f08435552248391c5573",
"d209ed11c6a84639c268f08435552248391c5573"
] | [
"tests/test_utils.py",
"tests/nlu_hc_tests/training_tests/chunk_resolution/chunk_resolver_tests.py",
"nlu/pipe/utils/pyarrow_conversion/pa_conversion.py"
] | [
"import nlu\nimport pandas as pd\nimport sparknlp\n\n\n\ndef get_sample_pdf():\n data = {\"text\": ['This day sucks but tomorrow will be better ! ', 'I love this day', 'I dont like Sami']}\n text_df = pd.DataFrame(data)\n return text_df\n\n\ndef get_sample_pdf_with_labels():\n data = {\"text\": ['This d... | [
[
"pandas.DataFrame"
],
[
"pandas.read_csv",
"pandas.DataFrame"
],
[
"pandas.DataFrame.from_records"
]
] |
jnez71/misc | [
"397ac0b8e3bccec4daa73db8963bb0510eebebfe"
] | [
"geometry/bezier_surface.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\nEfficient implementation of a Bezier surface and its differential geometry.\n\n\"\"\"\nfrom __future__ import division\nimport numpy as np\n\n################################################## CORE\n\nclass Bezier(object):\n \"\"\"\n Bezier manifold of dimension 2 embedded in ... | [
[
"numpy.cross",
"numpy.linspace",
"numpy.eye",
"numpy.linalg.norm",
"numpy.cos",
"numpy.sin",
"numpy.diff",
"numpy.float64",
"numpy.random.sample",
"numpy.column_stack",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
israelem/aceptaelreto.github.io | [
"91ac0586ef504cf4b1dd05eda32def6c39fbb34c"
] | [
"codes/2017-11-20-ardilla.py"
] | [
"import numpy as np\nfrom itertools import product\n\n\nclass Punto:\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n def __eq__(self, punto):\n return self.x == punto.x and self.y == punto.y\n\n\ndef hay_camino(mapa, salto, inicio=Punto(0, 0)):\n if inicio.x == mapa.shape[0] ... | [
[
"numpy.empty"
]
] |
Ureimu/weather-robot | [
"7634195af388538a566ccea9f8a8534c5fb0f4b6"
] | [
"python_code/botcode/plugins/fun_pic_plug/point_of_intersection.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nx1 = np.array([712,653,625,605,617,635,677,762,800,872,947,1025,1111,1218,1309, 500])\ny1 = np.array([2022,1876,1710,1544,1347,1309,1025,995,850,723,705,710,761,873,1050, 2000])\n\nx_start = np.min(x1)\nx_end = np.max(x1)+1\n\nx_line = x1.copy()\ny_line = x_li... | [
[
"numpy.dot",
"numpy.min",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] |
antoniomezzacapo/qiskit-acqua | [
"102743203266ccbb18fef6d337c160246195e313",
"102743203266ccbb18fef6d337c160246195e313"
] | [
"qiskit_acqua/utils/jsonutils.py",
"qiskit_acqua/qpe/qpe.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Copyright 2018 IBM.\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 appli... | [
[
"numpy.imag",
"numpy.real",
"numpy.array"
],
[
"numpy.all",
"numpy.zeros"
]
] |
lzb863/Structured-Knowledge-Distillation-for-Dense-Prediction | [
"56db01645a0925b53ad8e9d81816858d4b5b8d78",
"56db01645a0925b53ad8e9d81816858d4b5b8d78"
] | [
"libs/net/pytorchcvNets/bninception.py",
"libs/net/UperNet/mobilenet.py"
] | [
"# encoding: utf-8\n\"\"\"\n BN-Inception for ImageNet-1K, implemented in PyTorch.\n Original paper: 'Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,'\n https://arxiv.org/abs/1502.03167.\n\"\"\"\n\n__all__ = ['BNInception', 'bninception']\n\nimport os\nimport t... | [
[
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.nn.init.kaiming_uniform_",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d"
],
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.ReLU6",
"torch.load",
"torch.nn.Conv2d",
"torch.nn.Linear"... |
lucasdavid/tf-experiment | [
"0c6e6b52c91f498dd0cb5a13beafadfeab627429"
] | [
"experiments/evaluate.py"
] | [
"# Copyright 2021 Lucas Oliveira David\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 o... | [
[
"tensorflow.keras.models.load_model"
]
] |
milankarunarathne/performance-predictor | [
"b0df42631ebd81d4b3f177ebb2346a5a54c00ee9"
] | [
"svr_regression.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom sklearn import preprocessing\nfrom sklearn.svm import SVR\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\n\nsummary_data = 'resources/wso2apimanagerperformanceresults.csv'\nx_select_columns = [0, 1, 2, 3] # select columns to x (... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.show",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.plot",
"sklearn.svm.SVR",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.xlabel",
"sklearn.preprocessing.scale",
... |
SinaKhorami/visage | [
"8dbbe6397bc20ed33a041bd07fa2887351f0e099"
] | [
"src/model/models.py"
] | [
"import os\nimport numpy as np\n\nfrom keras.models import Sequential\nfrom keras.optimizers import Adadelta\nfrom keras.losses import categorical_crossentropy\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.layers import Flatten\nfrom keras.layers import Conv2D\nfrom keras.layers impo... | [
[
"numpy.argmax"
]
] |
freewym/lhotse | [
"66e9bbaf25b75011388ab00189baa162c3c1d435"
] | [
"test/cut/test_padding_cut.py"
] | [
"from tempfile import NamedTemporaryFile\n\nimport numpy as np\nimport pytest\n\nfrom lhotse.audio import AudioSource, Recording\nfrom lhotse.cut import Cut, CutSet, PaddingCut\nfrom lhotse.features import Features\nfrom lhotse.utils import EPSILON, LOG_EPSILON\n\nPADDING_ENERGY = EPSILON\nPADDING_LOG_ENERGY = LOG_... | [
[
"numpy.testing.assert_array_less",
"numpy.testing.assert_almost_equal",
"numpy.testing.assert_allclose",
"numpy.testing.assert_equal"
]
] |
abailoni/manual_annotation_spacem | [
"53a38bcb5107c9f9804f2c3a61251d9b01b5b1ef"
] | [
"traincellpose/core.py"
] | [
"import math\nimport os\nimport shutil\nfrom copy import deepcopy\nfrom shutil import copyfile\n\nimport numpy as np\nimport pandas\nimport tifffile\nimport yaml\nfrom pathlib import Path\n\nfrom speedrun import BaseExperiment, locate\nfrom speedrun.yaml_utils import recursive_update\nfrom .cellpose_training.start_... | [
[
"pandas.read_csv",
"numpy.allclose",
"pandas.DataFrame",
"numpy.stack",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.any",
"numpy.array",
"numpy.empty"
]
] |
ZziTaiLeo/encoder4editing | [
"9c72444a2c813bb46f6bab76caddbb566f32b5f6"
] | [
"inv_npy.py"
] | [
"import os\n\nimport cv2\nimport numpy\nimport numpy as np\nfrom skimage import transform\nimport PIL.Image\nimport torch\n\npath_to_images = r'./result/inference_inversions/'\npath_to_inv_params = r'result/npy/integrated_affine.npy'\ninv_M = np.load(path_to_inv_params)\nfiles = os.listdir(path_to_images)\nfiles.so... | [
[
"numpy.load"
]
] |
hz-ants/KerasPersonLab | [
"32d44dd1f33377128a87d6e074cf8214224f0174"
] | [
"train.py"
] | [
"import os\n\nfrom model import get_personlab\nfrom tf_data_generator import *\nfrom config import config\nfrom keras.models import load_model\nfrom keras.utils import multi_gpu_model\nfrom keras.optimizers import Adam\nfrom keras.callbacks import LambdaCallback\nfrom keras import backend as KB\n\nfrom polyak_callb... | [
[
"tensorflow.ConfigProto",
"tensorflow.device",
"tensorflow.Session"
]
] |
stuarteberg/quilted | [
"11c6e38cbce95133ab4e9cfe0dd2afb14ae088f7"
] | [
"bin/export-to-hdf5.py"
] | [
"import sys\nimport logging\nimport argparse\nimport numpy as np\nfrom quilted.h5blockstore import H5BlockStore\n\ndef main():\n logger = logging.getLogger('quilted.h5blockstore')\n logger.setLevel(logging.INFO)\n logger.addHandler(logging.StreamHandler(sys.stdout))\n\n parser = argparse.ArgumentParser(... | [
[
"numpy.array"
]
] |
JeffreyThiessen/staramr | [
"8550f231b7dc528b91a2c3665a5f99f0fa3d350b"
] | [
"staramr/blast/pointfinder/PointfinderDatabaseInfo.py"
] | [
"import logging\nfrom os import path\n\nimport pandas as pd\n\n\"\"\"\nA Class storing information about the specific PointFinder database.\n\"\"\"\n\nlogger = logging.getLogger('PointfinderDatabaseInfo')\n\n\nclass PointfinderDatabaseInfo:\n\n def __init__(self, database_info_dataframe, file=None):\n \"\... | [
[
"pandas.read_csv"
]
] |
ccp5UK/dlpoly-py | [
"a7f2f83dd97b963248d706894dc1d12f7fec16d8"
] | [
"configbuilder/builder.py"
] | [
"'''\nCode to build CONFIG/FIELD from layound files\n'''\n\nimport copy\nimport random\nimport numpy as np\nfrom dlpoly.field import Field\nfrom dlpoly.config import Config\nfrom dlpoly.utility import parse_line, read_line\nfrom .cfgLoader import CFG\n\nclass System:\n keywords = ('cell', 'include', 'potential')... | [
[
"numpy.asarray",
"numpy.dot",
"numpy.zeros"
]
] |
SkanderGar/QuantMacro | [
"329eae290a34ca8cb794d4bbf05ecc2ae4ede8cd",
"329eae290a34ca8cb794d4bbf05ecc2ae4ede8cd"
] | [
"Pset5_hand_in/Agent1_bis1.py",
"Pset3_submit/Ex2/Q2_Tvio.py"
] | [
"import numpy as np\r\nfrom scipy.stats import norm\r\n\r\nfrom numpy import vectorize\r\n\r\n@vectorize\r\ndef U1(C, C_):\r\n if C <= 0:\r\n U = -np.inf\r\n else:\r\n U = -(1/2)*(C-C_)**2\r\n return U\r\n\r\n@vectorize\r\ndef U2(C, S):\r\n if C <= 0:\r\n U = -np.inf\r\n else:\r\... | [
[
"scipy.stats.norm.cdf",
"numpy.zeros",
"numpy.abs",
"numpy.linspace"
],
[
"numpy.hstack",
"numpy.abs",
"numpy.linspace",
"numpy.tile",
"numpy.ones",
"numpy.savetxt",
"numpy.array",
"numpy.vstack"
]
] |
charlesblakemore/opt_lev_analysis | [
"704f174e9860907de349688ed82b5812bbb07c2d",
"704f174e9860907de349688ed82b5812bbb07c2d",
"704f174e9860907de349688ed82b5812bbb07c2d",
"704f174e9860907de349688ed82b5812bbb07c2d",
"704f174e9860907de349688ed82b5812bbb07c2d"
] | [
"scripts/mod_grav/process_to_aggdat_copy.py",
"scripts/mod_grav/old/alpha_lambda_from_manifold.py",
"scripts/mod_grav/alpha_vs_separation.py",
"casimir/scuffCode/CubeSphere/compare.py",
"scripts/cant_force/force_v_pos_cantV_comparison.py"
] | [
"import sys, re, os\n\nimport dill as pickle\n\nimport numpy as np\nimport pandas as pd\n\nimport scipy.interpolate as interpolate\n\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nplt.rcParams.update({'font.size': 14})\n\nimport grav_util_3 as gu\nimport bead_util as bu\nimport configura... | [
[
"numpy.log",
"numpy.abs",
"numpy.fft.rfftfreq",
"numpy.mean",
"matplotlib.pyplot.rcParams.update",
"numpy.array"
],
[
"numpy.sqrt",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"scipy.optimize.curve_fit",
"matplotlib.pyplot.tight_... |
Danbinabo/Mask_Rcnn | [
"a06b1d3c35fbc63e269b735729ecc6b2b84bf13e"
] | [
"pb_model_test.py"
] | [
"import tensorflow as tf\nimport cv2\nimport glob\nimport numpy as np\npb_path = 'landmark.pb' # pb模型\n\nsess = tf.Session()\nwith sess.as_default():\n with tf.gfile.FastGFile(pb_path, 'rb') as f:\n graph_def = sess.graph_def\n graph_def.ParseFromString(f.read())\n tf.import_graph_def(graph_... | [
[
"numpy.expand_dims",
"tensorflow.import_graph_def",
"tensorflow.gfile.FastGFile",
"tensorflow.Session"
]
] |
houzeyu2683/VariationalAutoEncoder | [
"6774824d14a5b49de46fb7f8a6f9441eca6c77d5"
] | [
"script/initialization.py"
] | [
"\n##\n## The packages.\nimport pandas, os\nimport sklearn.model_selection\n\n\"\"\"\n影像資料儲存於資料夾，根據影像名稱以及路徑，建立資料表。\n\"\"\"\n\n##\n## Handle the link of image.\nroot = \"/media/houzeyu2683/120C3F2F0C3F0D6B/DataSetGroup/celebfacesattribute/\"\nfolder = os.path.join(root, \"jpg\")\ngroup = os.listdir(folder)\nlink =... | [
[
"pandas.concat",
"pandas.DataFrame"
]
] |
larsmaaloee/BIVA | [
"e47201113d779c6ea1245875714101b2bbfcbdae"
] | [
"layers/_neural.py"
] | [
"\nimport tensorflow as tf\nimport warnings\n\n\ndef conv2d(x, dim=(32, [3, 3], [1, 1]), pad='SAME', scope=\"conv2d\", training=True, ema=None, init=False, bias_initializer=tf.constant_initializer(0.)):\n num_filters, filter_size, stride = dim\n with tf.variable_scope(scope):\n V = tf.get_variable('V',... | [
[
"tensorflow.get_variable",
"tensorflow.reduce_sum",
"tensorflow.layers.dropout",
"tensorflow.nn.moments",
"tensorflow.square",
"tensorflow.random_normal_initializer",
"tensorflow.nn.l2_normalize",
"tensorflow.matmul",
"tensorflow.shape",
"tensorflow.exp",
"tensorflow.nn... |
HDFGroup/aiohstools | [
"394aae78375616b9084e6ee36106e0853344e746"
] | [
"aiohsload/utillib.py"
] | [
"##############################################################################\n# Copyright by The HDF Group. #\n# All rights reserved. #\n# ... | [
[
"numpy.array",
"numpy.product",
"numpy.zeros",
"numpy.dtype"
]
] |
HiDiHlabs/pygorich | [
"64b5a44b7c4040f3f5b91109494d0c8b290e89f8"
] | [
"pygorich/gsea.py"
] | [
"import json\nimport requests\nimport pandas as pnd\nfrom scipy.stats import hypergeom, fisher_exact, binom_test\nfrom statsmodels.stats.multitest import multipletests\nimport sys\nimport geanno\n\nclass Enricher():\n '''\n Class for managing gene sets and performing GSEA.\n\n ...\n\n Attributes\n --... | [
[
"scipy.stats.binom_test",
"scipy.stats.hypergeom.cdf",
"pandas.DataFrame",
"scipy.stats.fisher_exact"
]
] |
ast0815/likelihood-machine | [
"4b0ebd193253775c31539c4a0046b79cbec8fa2b"
] | [
"docs/examples/simple_experiment/vary_detector.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"Script to generate toy detector variations.\n\nThis modifies the output data of a MC simulation.\n\"\"\"\n\nimport argparse\nimport csv\n\nimport experiment\nimport numpy as np\nimport numpy.lib.recfunctions as rfn\n\nparser = argparse.ArgumentParser(\n description=\"Modify the re... | [
[
"numpy.random.seed",
"numpy.lib.recfunctions.merge_arrays",
"numpy.genfromtxt",
"numpy.random.randn",
"numpy.lib.recfunctions.drop_fields",
"numpy.array"
]
] |
deep-spin/S7 | [
"c987906b032eaa727c8bcbec53f48befb467e515",
"c987906b032eaa727c8bcbec53f48befb467e515",
"c987906b032eaa727c8bcbec53f48befb467e515"
] | [
"joeynmt/training.py",
"joeynmt/sample_decoding.py",
"test/unit/test_transformer_decoder.py"
] | [
"# coding: utf-8\n\n\"\"\"\nTraining module\n\"\"\"\n\nfrom itertools import count\nimport argparse\nimport time\nimport shutil\nfrom typing import List, Dict\nimport os\nfrom os.path import join\nimport queue\nfrom functools import partial\nimport random\n\nimport numpy as np\n\nimport torch\nimport torch.nn as nn... | [
[
"torch.FloatTensor",
"torch.save"
],
[
"torch.distributions.categorical.Categorical",
"torch.full",
"torch.cat",
"torch.zeros",
"torch.stack"
],
[
"torch.nn.init.uniform_",
"torch.ones",
"torch.Tensor",
"torch.manual_seed",
"torch.rand"
]
] |
1jinwoo/YHack2018 | [
"2cdb7961917daa7d6f592ac8bad81421d063638e"
] | [
"Models/multiple_layer_model.py"
] | [
"# libraries import\nfrom keras.models import Sequential\nfrom keras import layers\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.model_selection import StratifiedKFold\nimport numpy as np\nimport pandas as pd\n\n# file import\nimport data_cleaner as dc\nimport model_helper as mh\n\ndf =... | [
[
"pandas.concat",
"numpy.random.seed",
"sklearn.model_selection.StratifiedKFold",
"sklearn.feature_extraction.text.CountVectorizer",
"numpy.std",
"numpy.mean"
]
] |
shubhscode/onePerceptron | [
"4c9bc540de2e2ff6a6f792b5e5d4cab184ea336e"
] | [
"and.py"
] | [
"from utils.model import Perceptron\nfrom utils.all_utils import prepare_data, save_model, save_plot\nimport pandas as pd\n\nimport logging\n\nlogging_str = \"[%(asctime)s: %(levelname)s: %(module)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=logging_str)\n\n\ndef main(data, eta, epochs, filename... | [
[
"pandas.DataFrame"
]
] |
triskadecaepyon/pyworkout-toolkit | [
"8a93f9a086666508435a631774bbd6f97f3f7a52"
] | [
"pyworkout/parsers/tcxtools.py"
] | [
"\"\"\"\nTools to process TCX files,\nspecifically for parsing and\nconverting to other formats.\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom lxml import objectify\nimport dateutil.parser\nimport logging\n\nTPXNS = \"{http://www.garmin.com/xmlschemas/ActivityExtension/v2}TPX\"\nLXNS = \"{http://www.garm... | [
[
"numpy.float"
]
] |
eltoto1219/vltk | [
"e84c0efe9062eb864604d96345f71483816340aa",
"e84c0efe9062eb864604d96345f71483816340aa"
] | [
"vltk/abc/processor.py",
"build/lib/vltk/modeling/frcnn.py"
] | [
"import torch\nfrom vltk.inspection import collect_args_to_func\n\n\nclass Processor:\n _type = None\n _keys = ()\n\n @property\n def keys(self):\n if isinstance(self._keys, str):\n return set([self._keys])\n return set(self._keys)\n\n def __init__(self, **kwargs):\n f... | [
[
"torch.no_grad"
],
[
"torch.all",
"torch.abs",
"torch.nn.functional.softmax",
"torch.ge",
"torch.cat",
"torch.zeros",
"torch.nn.Embedding",
"torch.le",
"torch.no_grad",
"torch.flatten",
"torch.device",
"torch.log2",
"torch.isfinite",
"torch.nn.functi... |
retamia/tvm | [
"5d25dc54d874bf2ddf0e8cf34c4748e9e2656fd8",
"5d25dc54d874bf2ddf0e8cf34c4748e9e2656fd8",
"5d25dc54d874bf2ddf0e8cf34c4748e9e2656fd8",
"5d25dc54d874bf2ddf0e8cf34c4748e9e2656fd8"
] | [
"web/tests/python/websock_rpc_test.py",
"topi/python/topi/cuda/conv2d_nhwc_tensorcore.py",
"tests/python/unittest/test_target_codegen_llvm.py",
"tutorials/autotvm/tune_relay_arm.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.random.uniform",
"numpy.zeros"
],
[
"numpy.prod"
],
[
"numpy.arange",
"numpy.full",
"numpy.random.uniform",
"numpy.zeros",
"numpy.random.randint"
],
[
"numpy.random.uniform",
"numpy.std",
"numpy.mean"
]
] |
tyohei/examples | [
"38652f48aca2b668bcc116ba401795d4be2f8f18",
"38652f48aca2b668bcc116ba401795d4be2f8f18"
] | [
"mpi/python/bcast.py",
"mpi/python/isend.py"
] | [
"#!/usr/bin/env python\nfrom __future__ import print_function\nfrom mpi4py import MPI\nimport numpy\n\nimport common\n\n\ndef bcast(comm):\n n = 8192\n\n print_mpi = common.create_print_mpi(comm)\n\n # Allocate buffer and set value\n if comm.rank == 0:\n buf = numpy.arange(n).astype(numpy.float32... | [
[
"numpy.arange",
"numpy.array",
"numpy.empty"
],
[
"numpy.array"
]
] |
aclong/transx2gtfs | [
"36d5b87d425c5dd299a3fbc7e973aff91876c2ca"
] | [
"transx2gtfs/tests/test_calendar.py"
] | [
"from transx2gtfs.data import get_path\nimport pytest\n\n@pytest.fixture\ndef test_tfl_data():\n return get_path('test_tfl_format')\n\n\n@pytest.fixture\ndef test_txc21_data():\n return get_path('test_txc21_format')\n\n\n@pytest.fixture\ndef test_naptan_data():\n return get_path('naptan_stops')\n\n\ndef te... | [
[
"numpy.int64",
"pandas.testing.assert_frame_equal",
"pandas.DataFrame"
]
] |
texasmichelle/open_spiel | [
"d9a9b8f9f1f44143867217fc3f6ff2db71b174b0",
"d9a9b8f9f1f44143867217fc3f6ff2db71b174b0"
] | [
"open_spiel/python/egt/visualization_test.py",
"open_spiel/python/bots/is_mcts_test.py"
] | [
"# Copyright 2019 DeepMind Technologies Ltd. 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 r... | [
[
"matplotlib.figure.Figure",
"numpy.zeros",
"numpy.testing.assert_allclose",
"numpy.ones"
],
[
"numpy.random.seed"
]
] |
ivivan/DualHeadSSIM | [
"6c4157873bfbbb7d16d2fa89c947eaf816c18653"
] | [
"utils/prepare_QLD.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nimport random, math, os, time\n\nfrom utils.VLSW import pad_all_cases\n# from VLSW import pad_all_cases\n# set the random seeds for reproducability\nSEED = ... | [
[
"numpy.split",
"numpy.expand_dims",
"pandas.read_csv",
"numpy.isnan",
"numpy.delete",
"numpy.zeros",
"sklearn.preprocessing.MinMaxScaler"
]
] |
bonejay/mdetr | [
"38c6d7c26d6d493f7bf6772ba65a72b493573d90"
] | [
"scripts/pre-training/vg_preprocessing.py"
] | [
"# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved\nimport argparse\nimport json\nimport math\nimport os\nimport pickle\nfrom collections import Counter, defaultdict\nfrom copy import deepcopy\nfrom functools import partial\nfrom multiprocessing import Poo... | [
[
"torch.stack",
"torch.as_tensor"
]
] |
Vsevolod-pl/hivemind | [
"0300cfd91adeb14d91d9659a98221628f9b775b9",
"0300cfd91adeb14d91d9659a98221628f9b775b9"
] | [
"benchmarks/benchmark_tensor_compression.py",
"hivemind/client/averaging/load_balancing.py"
] | [
"import argparse\nimport time\n\nimport torch\n\nfrom hivemind.proto.runtime_pb2 import CompressionType\nfrom hivemind.utils.compression import serialize_torch_tensor, deserialize_torch_tensor\n\n\ndef benchmark_compression(tensor: torch.Tensor, compression_type: CompressionType) -> float:\n t = time.time()\n ... | [
[
"torch.manual_seed",
"torch.randn"
],
[
"numpy.diag",
"numpy.maximum",
"numpy.asarray",
"numpy.eye",
"numpy.ones",
"numpy.all",
"numpy.round",
"numpy.max",
"numpy.mean",
"numpy.any",
"numpy.argsort",
"numpy.array",
"numpy.zeros"
]
] |
montigno/mri_works | [
"8ec6ff1500aa34d3540e44e4b0148023cf821f61",
"8ec6ff1500aa34d3540e44e4b0148023cf821f61"
] | [
"mri_works/NodeEditor/modules/Skimage/Morphology.py",
"mri_works/NodeEditor/modules/C/Maths.py"
] | [
"from h5py.h5t import np\nclass remove_small_holes:\n def __init__(self, image=[[0.0]], area_threshold=64, **options):\n from skimage import morphology\n import numpy as np\n self.a = np.array(image, dtype=bool)\n for sl1 in range(self.a.shape[2]):\n self.a[:, :, sl1] =... | [
[
"numpy.array"
],
[
"numpy.ctypeslib.load_library"
]
] |
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