repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
robinvanschaik/flair | [
"82ba872d094620345de5f1d00315eaae6a2360ba"
] | [
"flair/__init__.py"
] | [
"import os\nimport torch\nfrom pathlib import Path\n\n# global variable: cache_root\ncache_root = os.getenv('FLAIR_CACHE_ROOT', Path(Path.home(), \".flair\"))\n\n# global variable: device\ndevice = None\nif torch.cuda.is_available():\n device = torch.device(\"cuda:0\")\nelse:\n device = torch.device(\"cpu\")\... | [
[
"torch.device",
"torch.cuda.is_available"
]
] |
MichaelGW123/NPCAlignmentResponse | [
"e4810a4be209c31b8eaea7c6a6634e73e5bf43b3"
] | [
"Extremes Classifier.py"
] | [
"# Juan Davalos and Michael Williamson\n# Computer Science 542 - Machine Learning\n# Good and Evil Classifier Code\n\n# Natural Language Processing\n# Sentiment Analysis\n\n# Importing the libraries\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\n# Importing the dataset\npath = Path(__file__).... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"pandas.read_csv",
"sklearn.svm.SVC"
]
] |
DaBaiTuuu/SynthCP | [
"42b6e6185f572dd1bf82bba2878014f5283dbe27",
"42b6e6185f572dd1bf82bba2878014f5283dbe27",
"42b6e6185f572dd1bf82bba2878014f5283dbe27",
"42b6e6185f572dd1bf82bba2878014f5283dbe27"
] | [
"options/deeplab_options.py",
"data/iou_dataset.py",
"tools/eval_ae.py",
"spade-cityscapes/models/resnet.py"
] | [
"\"\"\"\nCopyright (C) 2019 NVIDIA Corporation. All rights reserved.\nLicensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).\n\"\"\"\n\nimport sys\nimport argparse\nimport os\nfrom util import util\nimport torch\nimport models\nimport data\nimport pickle\n\n\nclas... | [
[
"torch.cuda.set_device"
],
[
"numpy.load",
"torch.from_numpy",
"torch.tensor"
],
[
"torch.no_grad"
],
[
"torch.nn.Sequential",
"torch.nn.Softmax",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.Sigmoid",
"torch.nn.MaxPool2d",
"t... |
NeolithEra/sphire-janni | [
"af2a95d58afdeb4d6ff77fb4efe29eac6b4bdc84"
] | [
"janni/jmain.py"
] | [
"\"\"\"\nMIT License\n\nCopyright (c) 2019 Max Planck Institute of Molecular Physiology\n\nAuthor: Thorsten Wagner (thorsten.wagner@mpi-dortmund.mpg.de)\nAuthor: Luca Lusnig (luca.lusnig@mpi-dortmund.mpg.de)\nAuthor: Fabian Schoenfeld (fabian.schoenfeld@mpi-dortmund.mpg.de)\n\nPermission is hereby granted, free of ... | [
[
"numpy.array"
]
] |
BigDataRepublic/bdr-analytics-py | [
"fda8f0b4fe303cc55a1a06ef64a52f81c0ccfae5"
] | [
"bdranalytics/sklearn/preprocessing/tests/test_scaling.py"
] | [
"import unittest\n\nimport numpy as np\nfrom sklearn.dummy import DummyRegressor\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom bdranalytics.sklearn.preprocessing import ScaledRegressor\n\n\nclass TestPreprocessing(unittest.TestCase):\n\n @staticmethod\n def c... | [
[
"sklearn.dummy.DummyRegressor",
"sklearn.pipeline.Pipeline",
"numpy.mean",
"numpy.random.rand",
"sklearn.preprocessing.StandardScaler"
]
] |
anmolkabra/avicaching-summ17 | [
"3b85c1b70adcbe5d5b2764195090b28093081b1f"
] | [
"test_lp_time.py"
] | [
"#!/usr/bin/env python\n\n# =============================================================================\n# test_lp_time.py\n# Author: Anmol Kabra -- github: @anmolkabra\n# Project: Solving the Avicaching Game Faster and Better (Summer 2017)\n# ----------------------------------------------------------------------... | [
[
"torch.mean",
"torch.nn.functional.softmax",
"torch.mv",
"numpy.random.seed",
"torch.cuda.manual_seed",
"torch.manual_seed",
"torch.randn",
"torch.nn.functional.relu",
"torch.rand",
"torch.cuda.is_available",
"torch.bmm",
"numpy.savetxt",
"torch.get_num_threads"... |
alexisduque/vlc-camcomsim | [
"e45f6b755fc0124259f0d0977798a90216061d32"
] | [
"tools/to_pickle.py"
] | [
"import json\nimport pandas as pd\nfrom subprocess import *\n\n\n# Load json simulation result\ndef loadSimuResults(files, concat=False):\n FILENAME = 'merged.json'\n simu = pd.DataFrame()\n for file_ in files:\n if file_ != FILENAME:\n with open(file_) as data_file:\n simu... | [
[
"pandas.io.json.json_normalize",
"pandas.concat",
"pandas.DataFrame"
]
] |
BDI-pathogens/seq_dbutils | [
"fc0ace07653388ba1907ee510f292d39225895f9"
] | [
"tests/test_table.py"
] | [
"import logging\nfrom os.path import abspath, dirname, join\nfrom unittest import TestCase\n\nimport pandas as pd\nfrom mock import patch, Mock\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.ext.declarative import declarative_base\n\nimport seq_dbutils\n\nlogging.basicConfig(level=logging.DEBUG, for... | [
[
"pandas.DataFrame"
]
] |
ypsoh/fairseq | [
"578ee3d456034f12af13e446bba9447844374356"
] | [
"fairseq/data/multilingual/sampled_multi_epoch_dataset.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 hashlib\nimport math\nimport logging\nimport time\n\nimport numpy as np\nimport torch\nfrom fairseq import distributed_utils\... | [
[
"torch.distributed.is_initialized",
"torch.cuda.is_available",
"numpy.argsort",
"numpy.array",
"torch.DoubleTensor"
]
] |
FLHonker/AMTML-KD-code | [
"926ab3c65e995cd97dc98bfcdb8c1bc62994329c"
] | [
"models/teacher/densenet.py"
] | [
"'''DenseNet in PyTorch.'''\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Bottleneck(nn.Module):\n def __init__(self, in_planes, growth_rate):\n super(Bottleneck, self).__init__()\n self.bn1 = nn.BatchNorm2d(in_planes)\n self.conv1 = nn.Conv2... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.randn",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.BatchNorm2d"
]
] |
mauritsunkel/mausic | [
"2197b68f87576080a3dc68971f3c92ef059a6abd"
] | [
"Mausic/gui.py"
] | [
"import tkinter as tk\nimport player\nimport queue\nimport sys\nimport os\nimport update_database as ud\nimport download_music as dm \nimport pandas as pd\nimport ctypes\nimport webbrowser\n\nfrom threading import Thread, Lock\nfrom tkinter import messagebox\nfrom tkinter import ttk \nfrom utils.top_level_locator i... | [
[
"pandas.DataFrame"
]
] |
MagnusWagner/rlcard | [
"1a3aaef76e78968ebc68eb5b92e57be4709f7e38"
] | [
"rlcard/games/wizard/utils.py"
] | [
"import os\nimport json\nimport numpy as np\nfrom collections import OrderedDict\n\nimport rlcard\n\nfrom rlcard.games.wizard.card import WizardCard as Card\n\n# Read required docs\nROOT_PATH = rlcard.__path__[0]\n\n# a map of abstract action to its index and a list of abstract action\nwith open(os.path.join(ROOT_P... | [
[
"numpy.zeros"
]
] |
DerThorsten/cnnef | [
"08c5ffa30c7c86ac5efe48c12f735a7680f6420d"
] | [
"sandbox/learn_edge_weights.py"
] | [
"from __future__ import print_function\nfrom __future__ import division\nimport vigra\nimport nifty\nimport h5py\nimport fastfilters as ffilt\nimport numpy\n\ndef watersheds(raw, sigma):\n edgeIndicator = ffilt.hessianOfGaussianEigenvalues(raw, sigma)[:,:,0]\n seg, nseg = vigra.analysis.watersheds(edgeIndicat... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.pad",
"numpy.zeros"
]
] |
captainnova/dmri_segmenter | [
"df496189cbba07ca89945079985000c852390175"
] | [
"dmri_segmenter/tests/test_train.py"
] | [
"import dmri_segmenter.train as train\nimport nibabel as nib\nimport numpy as np\nimport os\n\n\ndef test_make_fvecs(fvecfn, fakedata):\n assert os.path.isfile(fvecfn)\n fvnii = nib.load(fvecfn)\n assert (fvnii.affine == fakedata.aff).all()\n assert np.linalg.norm(fvnii.dataobj[40, 45, 35] -\n ... | [
[
"numpy.linalg.norm"
]
] |
upmem/scikit-dpu | [
"1ddeb5d195b9b119e379eb473b28c82a12a2b5fd"
] | [
"benchmarks/scripts/weak_scaling.py"
] | [
"from skdpu.tree._classes import DecisionTreeClassifierDpu\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.tree import export_graphviz\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import accuracy_score\nfrom time import perf_counter\nfrom sklearn.tree import _perfcounter as _perfcou... | [
[
"sklearn.tree.DecisionTreeClassifier",
"pandas.DataFrame",
"sklearn.datasets.make_blobs",
"sklearn.metrics.accuracy_score"
]
] |
wnma3mz/Face-Transformer | [
"87e67471a9e0f6ab21f6749d57eaa202e9b3e724"
] | [
"image_iter.py"
] | [
"#!/usr/bin/env python\r\n# encoding: utf-8\r\n'''\r\n@author: yaoyaozhong\r\n@contact: zhongyaoyao@bupt.edu.cn\r\n@file: image_iter_yy.py\r\n@time: 2020/06/03\r\n@desc: training dataset loader for .rec\r\n'''\r\n\r\nimport torchvision.transforms as transforms\r\nimport torch.utils.data as data\r\nimport numpy as n... | [
[
"torch.utils.data.DataLoader",
"torch.from_numpy"
]
] |
TravisCzechorskiUMaine/Catalysis | [
"af7868053278f65feeb2603f29c6e17ea3f41b87"
] | [
"micro/HW 2 Clean.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Sep 8 13:17:30 2021\r\n\r\n@author: travis.czechorski\r\n\r\nEmail: travis.czechorski@maine.edu\r\n tjczec01@gmail.com\r\n \r\nAdvisor: thomas.schwartz@maine.edu\r\n \r\nGithub: https://github.com/tjczec01 \r\n\r\n\"\"\"\r\n\r\nimpo... | [
[
"matplotlib.pylab.ylim",
"matplotlib.pylab.show",
"numpy.sqrt",
"matplotlib.pylab.grid",
"matplotlib.pylab.xlim",
"numpy.arange",
"matplotlib.pylab.title",
"numpy.exp",
"matplotlib.pylab.figure",
"matplotlib.pylab.ylabel",
"matplotlib.pylab.plot",
"matplotlib.pylab.... |
wangzy22/SemAffiNet | [
"a5e6d9f289713ec185a93b6d3c2a269cfdee603b"
] | [
"models/unet_2d.py"
] | [
"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nimport models.resnet_d as models\n\n\nclass ResUnet(nn.Module):\n def __init__(self, layers=18, classes=2, BatchNorm=nn.BatchNorm2d, pretrained=True, out_channels=128):\n super(ResUnet, self).__init__()\n assert classes > 1\n ... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] |
norlandrhagen/forest-risks | [
"2cbc87064ac05299dba952c9f0cb8022ffd8909a"
] | [
"scripts/biomass.py"
] | [
"import sys\n\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\nfrom carbonplan_forest_risks import fit, load\n\nargs = sys.argv\n\nif len(args) < 2:\n store = 'local'\nelse:\n store = args[1]\n\nvariables = ['tmean', 'ppt']\n\nprint('[biomass] loading data')\ndf = load.fia(store=store, state... | [
[
"numpy.arange",
"numpy.maximum",
"pandas.DataFrame"
]
] |
jennan/iblrig | [
"46591429951f5cec6deb307318c7c63616c240f3",
"46591429951f5cec6deb307318c7c63616c240f3",
"46591429951f5cec6deb307318c7c63616c240f3"
] | [
"tasks/_iblrig_tasks_trainingChoiceWorld/online_plots.py",
"iblrig/blocks.py",
"iblrig/adaptive.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author: Niccolò Bonacchi\n# @Date: 2018-02-20 14:46:10\n# matplotlib.use('Qt5Agg')\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef make_fig(sph):\n plt.ion()\n f = plt.figure() # figsize=(19.2, 10.8), dpi=100)\n ... | [
[
"numpy.isnan",
"numpy.median",
"numpy.sort",
"numpy.round",
"matplotlib.pyplot.subplot2grid",
"numpy.append",
"numpy.array",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"numpy.sign",
"numpy.su... |
AICommunityInno/NeuralRatingRegression | [
"9380d247ef6bfa70bc17772ce4f1d13a60b470ce"
] | [
"src/data_reader.py"
] | [
"import numpy as np\nimport torch\nfrom torchtext import data\n\nfrom src.constants import MAX_LENGTH\n\nMIN_FREQ = 11\n\n\ndef __review_postprocessing(review, vocab, _):\n def freqs_sent(sentence):\n length = np.where(sentence == eos_ind)[0][0] + 1\n freqs = np.bincount(sentence, minlength=len(voc... | [
[
"numpy.array",
"numpy.apply_along_axis",
"numpy.where"
]
] |
wangsiqinudt/MVAD_Bayesian | [
"b0e5fe333709d0e986aff4372ef733bb00a9bf55"
] | [
"utility/funpack.py"
] | [
"\r\nimport numpy as np\r\n\r\nfrom scipy.linalg import eigh, inv\r\n\r\nfrom sklearn.metrics.pairwise import pairwise_kernels,paired_cosine_distances\r\n\r\n\r\n# -------------------------------------------------\r\n# linear co-regularized amomaly detection technique\r\n# ------------------------------------------... | [
[
"sklearn.metrics.pairwise.paired_cosine_distances",
"numpy.linspace",
"numpy.median",
"scipy.linalg.eigh",
"numpy.delete",
"sklearn.metrics.pairwise.pairwise_kernels",
"numpy.identity",
"numpy.transpose",
"scipy.linalg.inv",
"numpy.argsort",
"numpy.logical_and",
"nu... |
SgtChrome/THOR | [
"398ddb03a57be3ce5e3a01f602b18b02c3d1bb85"
] | [
"benchmark/vot.py"
] | [
"import cv2\nfrom os import makedirs\nfrom os.path import join, realpath, dirname, isdir, isfile\nimport numpy as np\nimport itertools\nfrom shutil import rmtree\n\nfrom .bench_utils.pysot.datasets import VOTDataset\nfrom .bench_utils.pysot.evaluation import AccuracyRobustnessBenchmark, EAOBenchmark\nfrom .bench_ut... | [
[
"numpy.int0",
"numpy.array",
"numpy.nanmean"
]
] |
920911love/DINK | [
"d760ea99f21c4d6334ff5f517b49c4fdfca32399"
] | [
"dink/src/tf_server/src/detector.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nDetector class to use TensorFlow detection API\n\nThe codes are from TensorFlow/Models Repo. I just transferred the code to\nROS.\n\nCagatay Odabasi\n\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\nimport time\n\nimport cv2\n\nfrom object_detection.utils import label_map_uti... | [
[
"tensorflow.Graph",
"tensorflow.import_graph_def",
"numpy.expand_dims",
"tensorflow.greater",
"tensorflow.slice",
"tensorflow.gfile.GFile",
"tensorflow.cast",
"tensorflow.squeeze",
"tensorflow.expand_dims",
"tensorflow.ConfigProto",
"tensorflow.Session",
"tensorflow... |
grasskin/SimCLR | [
"fb0e3de71337d958ceee8c28f642651085fe1907",
"fb0e3de71337d958ceee8c28f642651085fe1907"
] | [
"split.py",
"data_aug/contrastive_learning_dataset.py"
] | [
"import argparse\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nfrom torchvision import models\nfrom models.resnet_simclr import ResNetBertSimCLR, ResNetSimCLR\nfrom simclr import BertSimCLR, SimCLR\n\n\nmodel_names = sorted(name for name in models.__dict__\n if name... | [
[
"torch.load"
],
[
"torch.stack",
"numpy.array",
"torch.from_numpy"
]
] |
tianjianhe/Paddle | [
"2b11c710b3dddf07873fefaaa3758349d2396e88"
] | [
"python/paddle/fluid/tests/unittests/test_adam_op.py"
] | [
"# Copyright (c) 2018 PaddlePaddle 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 ... | [
[
"numpy.square",
"numpy.random.random",
"numpy.sqrt",
"numpy.full",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros"
]
] |
oracle/accelerated-data-science | [
"d594ed0c8c1365daf4cf9e860daebc760fa9a24b",
"d594ed0c8c1365daf4cf9e860daebc760fa9a24b",
"d594ed0c8c1365daf4cf9e860daebc760fa9a24b"
] | [
"ads/dataset/correlation.py",
"ads/dataset/dataset.py",
"ads/feature_engineering/accessor/dataframe_accessor.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8; -*-\n\n# Copyright (c) 2020, 2022 Oracle and/or its affiliates.\n# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/\n\nfrom __future__ import print_function, absolute_import\n\nimport numpy as np\nimport pandas as pd\... | [
[
"numpy.square",
"numpy.sqrt",
"pandas.isnull",
"pandas.DataFrame",
"numpy.nanmean",
"numpy.bincount",
"pandas.DataFrame.from_dict",
"numpy.array",
"numpy.zeros",
"numpy.sum"
],
[
"pandas.concat",
"sklearn.preprocessing.FunctionTransformer",
"pandas.DataFrame... |
hsiangheleellnl/e3sm_diags | [
"02e2c3ac1f533865ba7a21320c9004cede9db4a2"
] | [
"acme_diags/parameter/zonal_mean_2d_parameter.py"
] | [
"import numpy\n\nfrom acme_diags.driver.utils.general import monotonic\n\nfrom .core_parameter import CoreParameter\n\n\nclass ZonalMean2dParameter(CoreParameter):\n def __init__(self):\n super(ZonalMean2dParameter, self).__init__()\n self.plevs = numpy.logspace(2.0, 3.0, num=17).tolist()\n ... | [
[
"numpy.logspace"
]
] |
wslc1314/atec_nlp_sim_update | [
"f9067f9a60917a224fb1c9c7ce2e45e2238c98d3"
] | [
"utils/wv_utils.py"
] | [
"# coding: utf-8\n\nimport numpy as np\nimport os\nfrom gensim import models\nfrom data.data_utils import loadDict\n\n\ndef load_global_embedding_matrix(wv_path_w,wv_path_c,global_dict_path=\"data/atec/training-2-2.json\"):\n wv_path_list=[wv_path_w,wv_path_c]\n try:\n wv_type_w = wv_path_w.split(\"/\"... | [
[
"numpy.asarray",
"numpy.load",
"numpy.save",
"numpy.random.uniform"
]
] |
DropFL/ML | [
"1e58589f444af78259150929d5cf48fea31a2fa4"
] | [
"models/Decision_Tree_Answer.py"
] | [
"from utils.utils_Answer import impurity_func, Finding_split_point\nimport numpy as np\nimport warnings\n\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nHeart_Category_feature_idx = [1, 2, 5, 6, 8, 10, 11, 12]\nCarseats_Category_feature_idx = [6, 9]\nTennis_Category_feature_idx = [0, 1, 2, 3]\n\... | [
[
"numpy.unique",
"numpy.round",
"numpy.logical_or",
"numpy.argmin",
"numpy.where"
]
] |
gddcx/pytorch-yolov1 | [
"c1fc1efc6f5345b96592258af8f63bbdbb793666"
] | [
"yolov1/resnet/train.py"
] | [
"# -*- coding: utf-8 -*-\r\n# Author: Changxing DENG\r\n# @Time: 2021/10/8 11:27\r\n\r\nimport glob\r\nimport os\r\nimport random\r\nimport time\r\nimport xml.dom.minidom as xdm\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nfrom sacred import Experiment\r\nfrom sacred.observers import MongoO... | [
[
"numpy.random.seed",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.stack",
"torch.nn.DataParallel",
"numpy.array"
]
] |
Oktosha/OpenSfM | [
"308ff4267ccc6fcad77eef07e21fa006cc2cdd5f"
] | [
"opensfm/types.py"
] | [
"\"\"\"Basic types for building a reconstruction.\"\"\"\n\nimport numpy as np\nimport cv2\n\n\nclass Pose(object):\n \"\"\"Defines the pose parameters of a camera.\n\n The extrinsic parameters are defined by a 3x1 rotation vector which\n maps the camera rotation respect to the origin frame (rotation) and\n... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.asarray",
"numpy.linalg.inv",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.linalg.det",
"numpy.column_stack",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] |
Fortune-Adekogbe/prophet | [
"90230e7ba57ce36fe68d78195b12886369e4a4ac"
] | [
"python/fbprophet/diagnostics.py"
] | [
"# -*- coding: utf-8 -*-\n# 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\nfrom __future__ import absolute_import, division, print_function\n\nimport logging\nfrom tqdm.auto import tq... | [
[
"pandas.concat",
"numpy.abs",
"numpy.array_equal",
"numpy.sqrt",
"numpy.median",
"pandas.Timedelta",
"pandas.DataFrame",
"numpy.array",
"numpy.sum"
]
] |
garciart/FullStackML | [
"0f3c7be759cbce38b2e08a920da68d68f04dd0f0"
] | [
"demo5.py"
] | [
"#!/usr/bin/python3\n''' Summary: Script to identify and count vehicles in zones '''\nimport datetime\nimport os\nfrom pathlib import Path\n\nimport cv2\nimport numpy as np\nfrom numpy import array\nfrom shapely.geometry import Polygon, asPoint\n\nimport mrcnn.config\nfrom mrcnn.model import MaskRCNN\n\n# Configura... | [
[
"numpy.delete",
"numpy.array"
]
] |
IORDS2021/happiness_project | [
"6ae4dad7dc08b35dc48e121f96fcac33c9ae769b"
] | [
"happypred/happyio.py"
] | [
"#Python function skeleton\nimport os\nimport pandas as pd\nfrom typing import List\n\ndef whdata_to_csv(whdata_excel_file_path: str) -> List[str]:\n \"\"\" Read the World Happiness data at the provided path and extract training\n data (years 2006 to 2019) and testing data (year 2020 only). \n \n ... | [
[
"pandas.read_excel",
"pandas.read_csv"
]
] |
zaynahjaved/AWAC | [
"e225eeb8c0cd3498ab55ce15a9de60cb4e957c50"
] | [
"rsa/utils/pytorch_utils.py"
] | [
"import torch\nimport numpy as np\n\n\nTORCH_DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\n\ndef setup(idx=0):\n global TORCH_DEVICE\n if idx < 0:\n TORCH_DEVICE = torch.device('cpu')\n else:\n TORCH_DEVICE = torch.device('cuda', idx) if torch.cuda.is_... | [
[
"torch.device",
"numpy.array",
"torch.no_grad",
"torch.cuda.is_available"
]
] |
JinayJain/timidity | [
"c3c369b3b0e7d31cfb46510611b562caa486e264"
] | [
"timidity/__main__.py"
] | [
"from timidity.parser import parse_midi\nfrom timidity.player import play_notes\nfrom glob import glob\nfrom scipy.io import wavfile\nimport os\n# from scipy.signal import sawtooth, square\nimport numpy as np\n\n\ndef run():\n midi_list = glob(\"songs/*.mid\")\n\n i = 1\n for filename in midi_list:\n ... | [
[
"scipy.io.wavfile.write"
]
] |
scroix/MUNIT-runway | [
"04ed5d48dd6345a193bfb63fd1a505251644c26b"
] | [
"tests/test_data_types.py"
] | [
"# Ensure that the local version of the runway module is used, not a pip\n# installed version\nimport sys\nsys.path.insert(0, '..')\nsys.path.insert(0, '.')\n\nimport os\nfrom io import BytesIO as IO\nimport base64\nimport pytest\nimport numpy as np\nfrom PIL import Image\nfrom runway.data_types import *\nfrom runw... | [
[
"numpy.array_equal",
"numpy.asarray",
"numpy.array",
"numpy.zeros",
"numpy.float"
]
] |
SeptumCapital/FAR-HO | [
"33817738c01ef7011475dbf6d986f3a1bf9f69c6",
"33817738c01ef7011475dbf6d986f3a1bf9f69c6"
] | [
"far_ho/optimizer.py",
"far_ho/examples/utils.py"
] | [
"from __future__ import print_function, absolute_import, division\n\n# import numpy as np\n\nimport tensorflow as tf\nfrom collections import OrderedDict\n\nfrom far_ho import utils\n\nGRADIENT_NONE_MESSAGE = 'WARNING: the gradient w.r.t.the tf.Variable\\n {}\\n is None;\\n ' \\\n 'Check the ... | [
[
"tensorflow.while_loop",
"tensorflow.control_dependencies",
"tensorflow.cast",
"tensorflow.variables_initializer",
"tensorflow.identity",
"tensorflow.add",
"tensorflow.name_scope",
"tensorflow.trainable_variables",
"tensorflow.sqrt"
],
[
"numpy.concatenate",
"numpy.... |
codernayeem/python-data-science-cheat-sheet | [
"539078bf9ac8295987d8e93086fbb3c3977792c9"
] | [
"3.4_matplotlib_live.py"
] | [
"from matplotlib import pyplot as plt\nimport pandas as pd\nimport random\nfrom itertools import count\nfrom matplotlib.animation import FuncAnimation\nplt.style.use('bmh')\n\n# index = count()\n# x = []\n# y = []\n\n# def animate(i):\n# x.append(next(index))\n# y.append(random.randint(1, 10))\n\n# plt.... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.cla",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.fill_between",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
... |
Cinbarker/OpenTurret | [
"347044da9282c0c3afeb5a3e6cc52ee20e88e2fb"
] | [
"Raspberry_Pi/turret_camera.py"
] | [
"import cv2\nfrom numba import jit\nimport numpy as np\nimport imutils\n\n\n# import RPi.GPIO as GPIO\n\n#def img_process():\n\n\ndef get_laser_point(video_capture, lower, upper, threshold, show_video=False):\n # Capture a frame from camera\n ret, frame = video_capture.read()\n\n frame_hsv = cv2.cvtColor(f... | [
[
"numpy.array"
]
] |
gwsystems/scum | [
"006c071abf23dc6b70b44475e702135180829150"
] | [
"fastai/confusionMatrix.py"
] | [
"import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nwaste_types = ['cardboard','glass','metal','paper','plastic','trash']\ny_true = [\"cardboard\", \"cardboard\", \"cardboard\", \"plastic\", \"plastic\", \"trash\"]\ny_pred = [\"glass\", \"cardb... | [
[
"matplotlib.pyplot.show",
"sklearn.metrics.confusion_matrix",
"pandas.DataFrame",
"matplotlib.pyplot.figure"
]
] |
parthe/scikit-rmt | [
"fd1d48dd027e3ba5ca3fb3b355ca3de012407286"
] | [
"skrmt/ensemble/tridiagonal_utils.py"
] | [
"\"\"\"Tridiagonal utils module\n\nThis module contains utilities for tridiagolization or to handle\ntridiagonal matrices.\n\n\"\"\"\n\nimport numpy as np\n\n\ndef tridiag_eigval_neg(tridiag_mtx):\n \"\"\"Calculates number of negative eigenvalues.\n\n Given a tridiagonal matrix, this function calculates the\n... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.arange",
"numpy.matmul",
"numpy.identity",
"numpy.diff",
"numpy.array",
"numpy.zeros"
]
] |
indutny/haggling_rl | [
"7ae70b180b33f3d87303dc2ec38b18f40299e6c2"
] | [
"src/transform-save.py"
] | [
"import os\nimport time\nimport sys\nimport json\nimport tensorflow as tf\n\nfrom args import parse_args\nfrom env import Environment\nfrom model import Model\n\n_, CONFIG, args = parse_args('transform-save')\n\nenv = Environment()\n\nimport_path = args.source\nexport_path = args.target\n\nwith tf.Session() as sess... | [
[
"tensorflow.saved_model.signature_def_utils.build_signature_def",
"tensorflow.saved_model.builder.SavedModelBuilder",
"tensorflow.trainable_variables",
"tensorflow.Session",
"tensorflow.train.Saver",
"tensorflow.saved_model.utils.build_tensor_info"
]
] |
huxian0402/ConvLSTM-Moving-mnist- | [
"a917831b11f8662826b0c4c1af3e27484128f5a8"
] | [
"rnn.py"
] | [
"import tensorflow as tf\nfrom tensorflow.python.util import nest\n\nclass DropoutWrapper(tf.contrib.rnn.RNNCell):\n \"\"\"Operator adding dropout to inputs and outputs of the given cell.\"\"\"\n\n def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,\n seed=None):\n\n if ... | [
[
"tensorflow.variable_scope",
"tensorflow.python.util.nest.is_sequence",
"tensorflow.nn.dropout"
]
] |
NCAR/musica-tutorial | [
"87e8adbfd57cd27f9b31552f480fc251c988fa91"
] | [
"functions/Plot_2D.py"
] | [
"'''\nPlot_2D.py\nthis code is designed for plotting CESM output \ncan be used for either finite volume or spectral element (+ regional refinement)\n(1) 2D map plotting (class Plot_2D)\n\nMODIFICATION HISTORY:\n Duseong Jo, 19, JAN, 2021: VERSION 1.00\n - Initial version\n Duseong Jo, 20, JAN, 2021: VERSIO... | [
[
"numpy.nanmax",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.colors.SymLogNorm",
"numpy.around",
"numpy.nanmin",
"numpy.concatenate",
"numpy.max",
"numpy.int",
"numpy.mean",
"numpy.where",
"numpy.arange",
"numpy.ceil",
"numpy.copy",
"matplotlib.pyplot.fig... |
autonomousvision/data_aggregation | [
"76777156a465cbb77d6d5ab88da8f1812e7ff043",
"76777156a465cbb77d6d5ab88da8f1812e7ff043"
] | [
"plotter/metrics.py",
"modules/noiser.py"
] | [
"import numpy as np\nimport sys\nimport math\n\ndef compute_and_aggregate(metric_func, data, param):\n # metric_func = locals()['compute_' + metric_name]\n metric_results = []\n for step, data_item in data['values'].items():\n # print(metric_func)\n metric_raw = metric_func(data_item,... | [
[
"numpy.absolute",
"numpy.abs",
"numpy.multiply",
"numpy.isinf",
"numpy.isnan",
"numpy.set_printoptions",
"numpy.percentile",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.isscalar",
"numpy.digitize",
"numpy.array",
"numpy.where",
"numpy.sum"
],
[
... |
leggedrobotics/anomaly_navigation | [
"e4d87a1b67904e0537de3fa4b3e53bc4932d1681"
] | [
"anomaly_detection/optim/anomalyDetection_trainer.py"
] | [
"from anomaly_detection.base.base_trainer import BaseTrainer\nfrom anomaly_detection.base.base_dataset import BaseADDataset\nfrom anomaly_detection.base.base_net import BaseNet\nfrom anomaly_detection.utils.eval_functions import computeMaxYoudensIndex, computeTprAt5Fpr\nfrom torch.utils.data.dataloader import DataL... | [
[
"torch.optim.Adam",
"torch.optim.lr_scheduler.MultiStepLR",
"sklearn.metrics.roc_auc_score",
"torch.mean",
"torch.zeros",
"matplotlib.use",
"torch.eye",
"matplotlib.pyplot.subplots",
"sklearn.metrics.roc_curve",
"torch.tensor",
"torch.sum",
"torch.zeros_like",
"... |
serjtroshin/PLBART | [
"58e5de3041a2fc8b98e54648c6489fb3c23db9cb"
] | [
"interprete/src/models/bert/model.py"
] | [
"from pathlib import Path\nfrom typing import List\n\nimport torch\nfrom transformers import AutoModel, AutoTokenizer\n\nfrom interprete.src.models.model import Model, ModelOutput\nfrom interprete.src.models.utils import to_cpu\n\ndef repl_special_char(x):\n if x.startswith(\"##\"):\n return x.replace(\"#... | [
[
"torch.no_grad",
"torch.cuda.is_available",
"torch.cat"
]
] |
anujaagarwal/pysal | [
"8a6681744d73e3307f0b76013d906523f412a94e",
"8a6681744d73e3307f0b76013d906523f412a94e"
] | [
"pysal/model/spreg/error_sp_het_regimes.py",
"pysal/model/spreg/diagnostics_tsls.py"
] | [
"'''\nSpatial Error with Heteroskedasticity and Regimes family of models\n'''\n__author__ = \"Luc Anselin luc.anselin@asu.edu, Pedro V. Amaral pedro.amaral@asu.edu\"\n\nimport numpy as np\nimport multiprocessing as mp\nfrom . import user_output as USER\nfrom . import summary_output as SUMMARY\nfrom . import utils a... | [
[
"numpy.get_printoptions",
"numpy.set_printoptions",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
],
[
"scipy.stats.pearsonr"
]
] |
spack/spack-user-survey | [
"8c59aa7d877e93046090b2636c9b88e76cb3a2d6"
] | [
"plot.py"
] | [
"#!/usr/bin/env python3\n\nimport os\nimport sys\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport pandas as pd\nimport seaborn as sn\n\nimport column_names as cols\n\n\nfile_formats = [\"pdf\", \"svg\"]\n\ndef save(name):\n if not os.path.isdir(\"figs\"):\n os.mkdir(\"figs\")\n fo... | [
[
"pandas.concat",
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"pandas.Series",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.cm.get_cmap",
"matplotlib.pyplot.xlabel",
... |
GCZhang/Draco | [
"2884b1cad47b6be2face803f57494721d3009fb8",
"2884b1cad47b6be2face803f57494721d3009fb8"
] | [
"src/cdi_ipcress/python/ipcress_reader.py",
"src/cdi_ipcress/python/plot_ipcress_opacity.py"
] | [
"#!/usr/bin/env python\n#-----------------------------*-python-*----------------------------------------#\n# file src/cdi_ipcress/python/ipcress_reader.py\n# author Alex Long <along@lanl.gov>\n# date Monday, December 15, 2014, 5:44 pm\n# brief This script has fucntions that parse an IPCRESS file and returns a\... | [
[
"numpy.max",
"numpy.min"
],
[
"numpy.min",
"matplotlib.figure.Figure",
"matplotlib.use",
"matplotlib.backends.backend_tkagg.NavigationToolbar2TkAgg",
"numpy.max",
"matplotlib.backends.backend_tkagg.FigureCanvasTkAgg"
]
] |
zengjie617789/FairMOT-TRT | [
"06d2a9a7da92f6da6b8c8081b41cca58c1950eda",
"06d2a9a7da92f6da6b8c8081b41cca58c1950eda"
] | [
"src/lib/datasets/dataset/jde_yolov5.py",
"src/lib/ModelPrune/demo01.py"
] | [
"import glob\nimport math\nimport os\nimport os.path as osp\nimport random\nimport time\nfrom collections import OrderedDict\n\nimport cv2\nimport json\nimport numpy as np\nimport torch\nimport copy\n\nfrom torch.utils.data import Dataset\nfrom torchvision.transforms import transforms as T\nfrom cython_bbox import ... | [
[
"numpy.maximum",
"numpy.random.beta",
"numpy.clip",
"numpy.ascontiguousarray",
"numpy.arange",
"numpy.eye",
"numpy.fliplr",
"numpy.ones",
"numpy.concatenate",
"numpy.full",
"numpy.max",
"numpy.mod",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
... |
gfacciol/IS18 | [
"e4cab9e985ffe04b6d806e6bd2dd8c60c71ba48a"
] | [
"utils.py"
] | [
"\"\"\"\n* Geotiff Read and Write\n* extract metadata: datetime, RPC, \n* miscelaneous function for crop\n* wrappers for gdaltransform and gdalwarp\n\nCopyright (C) 2018, Gabriele Facciolo <facciolo@cmla.ens-cachan.fr>\nCopyright (C) 2018, Carlo de Franchis <carlo.de-franchis@ens-cachan.fr>\n\"\"\"\n\n\nimport os\n... | [
[
"numpy.hstack",
"numpy.dot",
"numpy.maximum",
"numpy.isfinite",
"numpy.asarray",
"numpy.tile",
"numpy.mean",
"numpy.array",
"numpy.meshgrid"
]
] |
zzzace2000/mimic-preprocess | [
"4897d597d5ecc71d2dd2ce1ced76cae48dd50fb5"
] | [
"scripts/create_length_of_stay.py"
] | [
"from __future__ import print_function\n\nimport os\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport random\nrandom.seed(49297)\n\n\nparser = argparse.ArgumentParser(description=\"Create data for length of stay prediction task.\")\nparser.add_argument('root_path', type=str, help=\"Path to root fold... | [
[
"numpy.arange",
"pandas.isnull"
]
] |
kitiv/DeepFaceLive | [
"ca3a005917ae067576b795d8b9fef5a8b3483010"
] | [
"xlib/player/VideoFilePlayer.py"
] | [
"from pathlib import Path\nfrom typing import Tuple\n\nimport numpy as np\nfrom .. import ffmpeg as lib_ffmpeg\nfrom .. import io as lib_io \n\nfrom .FramePlayer import FramePlayer\n\nclass VideoFilePlayer(FramePlayer):\n \"\"\"\n Play video track from the video file using subprocess ffmpeg.\n\n arguments\... | [
[
"numpy.ndarray"
]
] |
z-ryan1/cusignal-dev | [
"707656d6d96cd2a3ce5e48f455b906c25aa8aa1d"
] | [
"python/cusignal/convolution/convolve.py"
] | [
"# Copyright (c) 2019-2020, NVIDIA CORPORATION.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable... | [
[
"numpy.maximum",
"numpy.arange",
"numpy.issubdtype",
"numpy.all",
"numpy.array"
]
] |
dk-liang/TransCrowd | [
"1615f99af2bb37b6cfc225b19c39cb8827a23f4a"
] | [
"image.py"
] | [
"import scipy.spatial\nfrom PIL import Image\nimport scipy.io as io\nimport scipy\nimport numpy as np\nimport h5py\nimport cv2\nimport random\n\ndef load_data(img_path, args, train=True):\n\n gt_path = img_path.replace('.jpg', '.h5').replace('images', 'gt_density_map')\n img = Image.open(img_path).convert('RG... | [
[
"numpy.asarray"
]
] |
sgbaird/LobsterAutomation | [
"2d987baa9f453c481f1478180f066510959fe4ad"
] | [
"Analysis_Scripts/analyse_oxynitrides.py"
] | [
"import os\n\nimport pandas as pd\nfrom lobsterpy.cohp.analyze import Analysis\nfrom lobsterpy.cohp.describe import Description\nfrom pymatgen.core.structure import Structure\nfrom pymatgen.io.lobster import Lobsterout\nfrom pymatgen.io.vasp.outputs import Vasprun\nfrom pymatgen.symmetry.analyzer import SpacegroupA... | [
[
"matplotlib.pyplot.legend",
"matplotlib.style.use",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"scipy.stats.linregress",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] |
RobinQrtz/egglayingwoolmilksow | [
"fea7d6a58f9387c4139c4cc2c96b353c9dcd0fca"
] | [
"src/awd/weight_drop.py"
] | [
"import torch\nimport warnings\nfrom torch.nn import Parameter\n\n\nclass WeightDrop(torch.nn.Module):\n def __init__(self, module, weights, dropout=0):\n super(WeightDrop, self).__init__()\n self.module = module\n self.weights = weights\n if hasattr(module, \"bidirectional\") and mod... | [
[
"torch.nn.Parameter",
"torch.nn.functional.dropout",
"torch.nn.LSTM",
"torch.randn",
"torch.nn.Linear"
]
] |
xuzhezhaozhao/tensorflow | [
"282828af67de29d13dd2c69d96413c030b02543c"
] | [
"tensorflow/python/keras/mixed_precision/experimental/policy_test.py"
] | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.eager.context.eager_mode",
"tensorflow.python.framework.config.list_physical_devices",
"tensorflow.python.keras.mixed_precision.experimental.policy.serialize",
"tensorflow.python.keras.optimizer_v2.gradient_descent.SGD",
"tensorflow.python.keras.mixed_precision.experimental.... |
amansrivastava17/xlnet | [
"87904c1b244a7e65b787c9a99a6c1ceca75e3bbd"
] | [
"xlnet/xlnet.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport json\nimport os\nimport tensorflow as tf\nfrom xlnet import modeling\n\n\ndef _get_initializer(FLAGS):\n \"\"\"Get variable intializer.\"\"\"\n if FLAGS.init == \"uniform\":\n initializer =... | [
[
"tensorflow.gfile.Open",
"tensorflow.gfile.Exists",
"tensorflow.initializers.random_normal",
"tensorflow.gfile.MakeDirs",
"tensorflow.variable_scope",
"tensorflow.initializers.random_uniform"
]
] |
chenwang/QuantEcon.lectures.code | [
"d61ac7bc54529dd5c77470c17539eb2418b047c9",
"8832a74acd219a71cb0a99dc63c5e976598ac999"
] | [
"perm_income/perm_inc_figs.py",
"harrison_kreps/hk_price_singlebeliefs.py"
] | [
"import random\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nr = 0.05\nbeta = 1 / (1 + r)\nT = 60\nsigma = 0.15\nmu = 1\n\n\ndef time_path():\n w = np.random.randn(T+1) # w_0, w_1, ..., w_T\n w[0] = 0\n b = np.zeros(T+1)\n for t in range(1, T+1):\n b[t] = w[1:t].sum()\... | [
[
"matplotlib.pyplot.show",
"numpy.random.randn",
"matplotlib.pyplot.subplots",
"numpy.zeros"
],
[
"numpy.dot",
"numpy.eye"
]
] |
c-randall/p2d_pemfc_2phase | [
"39eb690b4ff2aaa46ef877add3e92347236636cd"
] | [
"Shared_Funcs/pem_pak.py"
] | [
"\"\"\"\r\nThis tool is to be used with pemfc models. It generalizes the use of building\r\nphases, pointers, geometry, and transport.\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom math import pi, exp, tanh\r\nfrom Shared_Funcs.pemfc_property_funcs import * \r\n\r\n# generic class to define electrodes\r\nclass elect... | [
[
"numpy.zeros"
]
] |
buriedms/-3D-ResNets-Paddle | [
"2b8e51faa03e86be7308cb0706b4d41d24570ebb"
] | [
"spatial_transforms.py"
] | [
"import random\nimport math\nimport numbers\nimport collections\nimport numpy as np\nimport paddle\nfrom PIL import Image, ImageOps\n\n\nclass Compose(object):\n \"\"\"Composes several transforms together.\n Args:\n transforms (list of ``Transform`` objects): list of transforms to compose.\n Example... | [
[
"numpy.array"
]
] |
lvapeab/interactive-keras-captioning | [
"9791f0e96aeb89c9b4d97f2f3352b50f18f14df9"
] | [
"preprocessing/feature_extraction/keras/simple_extractor.py"
] | [
"from config import load_parameters\nimport numpy as np\nimport time\nfrom keras_wrapper.extra.read_write import *\nimport ast\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG, format='[%(asctime)s] %(message)s', datefmt='%d/%m/%Y %H:%M:%S')\nlogger = logging.getLogger(__name__)\n\nfrom keras.preprocessin... | [
[
"numpy.expand_dims"
]
] |
yuxuanwu17/kaggle | [
"ea29ec067f8009d2eceb5df48424bbad01b2b9be"
] | [
"code_parts_withtests/learning_curve.py"
] | [
"import pandas as pd\nfrom sklearn.model_selection import LeaveOneOut, cross_val_score, train_test_split, learning_curve\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import confusion_matrix, plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve\ni... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"sklearn.model_selection.LeaveOneOut",
"sklearn.model_selection.learning_curve",
"sklearn.model_selection.train_test_split",
"sklearn.svm.SVC",
"sklearn.preprocessing.StandardScaler",
"matplotlib.pyplot.show",
"matplotlib.pyplot.yla... |
gagb/lime-1 | [
"6f9472eaa03fad1226e32a5d377704054c3c4251"
] | [
"lime/lime_base.py"
] | [
"\"\"\"\nContains abstract functionality for learning locally linear sparse model.\n\"\"\"\nfrom __future__ import print_function\nimport sys\n\nsys.path.append('../../pyGAM')\n\nimport numpy as np\nfrom sklearn.linear_model import Ridge, lars_path\nfrom sklearn.utils import check_random_state\n\nimport matplotlib.... | [
[
"sklearn.metrics.explained_variance_score",
"sklearn.tree.DecisionTreeRegressor",
"numpy.abs",
"numpy.sqrt",
"sklearn.model_selection.train_test_split",
"sklearn.linear_model.lars_path",
"sklearn.linear_model.Ridge",
"numpy.average",
"numpy.array",
"sklearn.utils.check_rand... |
migueldvb/pyspeckit | [
"fa7d875da7c684c8f6aaa3ba206ef3ff2e196652"
] | [
"examples/voigt.py"
] | [
"import pyspeckit\nimport numpy as np\nfrom pyspeckit.spectrum.models import inherited_voigtfitter\n\n# technically, the voigt fitter works as a singlefitter (i.e., you can fit the\n# background level and the peak simultaneously) \n# in practice, however, you need to fit the background independently except for\n# g... | [
[
"numpy.random.randn",
"numpy.linspace",
"numpy.ones"
]
] |
yamasampo/fsim | [
"30100789b03981dd9ea11c5c2e17a3c53910f724"
] | [
"fsim/drift.py"
] | [
"import numpy as np\n\nimport os\nfrom typing import Union\nfrom fsim.utils import read_control_file, write_settings, write_info_to_file\n\ndef main(control_file):\n kw_args = read_control_file(control_file)\n fsim_genetic_drift(**kw_args)\n\ndef fsim_genetic_drift(\n pop_size:int, \n ns:Union[f... | [
[
"numpy.random.binomial",
"numpy.random.rand"
]
] |
PetteriPulkkinen/SMPyBandits | [
"b44917e331eef0378304f1d9e856a177a4bd8989",
"b44917e331eef0378304f1d9e856a177a4bd8989"
] | [
"SMPyBandits/PoliciesCombinatorial/ExhaustiveSearch.py",
"SMPyBandits/Policies/Experimentals/UnsupervisedLearning.py"
] | [
"import numpy as np\nfrom typing import Callable\n\n__author__ = \"Petteri Pulkkinen\"\n__version__ = \"0.6\"\n\n\nclass SelectionAlgorithm(object):\n \"\"\"Algorithm for combinatorial problems\"\"\"\n\n def __init__(self, actions: np.array, objective: Callable[[np.array, np.array], float]):\n self.n_s... | [
[
"numpy.max"
],
[
"numpy.sqrt",
"scipy.stats.bernoulli.pmf",
"numpy.min",
"numpy.asarray",
"numpy.random.random_sample",
"numpy.max",
"numpy.random.normal",
"numpy.std",
"numpy.mean",
"numpy.nanmean",
"numpy.argmax"
]
] |
Saethlin/python-astronomy-workshops | [
"8911805e5979d13cf78139b771c347a7a4119e2a"
] | [
"examples/do_not/effective_field.py"
] | [
"\"\"\"\r\nCompute the three-component effective H-field at each lattice point\r\nExpects lmbda as an array with same shape as spin\r\n\"\"\"\r\nimport numpy as np\r\nfrom parameters import *\r\nfrom magnet import magnet\r\nfrom neighborsum import neighborsum\r\n\r\ndef effective_field(spin):\r\n #Components of ... | [
[
"numpy.any",
"numpy.cross"
]
] |
weixu927/weixu_deepsort | [
"3af9af77f5eb51e1ab4a58db64777e63d24773b9"
] | [
"deep_sort/kalman_filter.py"
] | [
"# vim: expandtab:ts=4:sw=4\nimport numpy as np\nimport scipy.linalg\n\n\n\"\"\"\nTable for the 0.95 quantile of the chi-square distribution with N degrees of\nfreedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv\nfunction and used as Mahalanobis gating threshold.\n\"\"\"\nchi2inv95 = {\n ... | [
[
"numpy.square",
"numpy.dot",
"numpy.eye",
"numpy.linalg.multi_dot",
"numpy.zeros_like",
"numpy.linalg.cholesky",
"numpy.sum"
]
] |
k-zen/SigmaProject | [
"b844766d28d142ed1fb4d2e20f4e9dbad0ad90a6",
"b844766d28d142ed1fb4d2e20f4e9dbad0ad90a6",
"b844766d28d142ed1fb4d2e20f4e9dbad0ad90a6"
] | [
"sigmaproject/computer_vision/utilities.py",
"sigmaproject/computer_vision/dummy_network.py",
"sigmaproject/computer_vision/convolution.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nCopyright (c) 2019, Andreas Koenzen <akoenzen | uvic.ca>\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the ab... | [
[
"numpy.zeros"
],
[
"matplotlib.pyplot.figure"
],
[
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] |
Tanvikapoor14/zr-obp | [
"51eba00f0dda5c26c1fa6826f544c60de485da52",
"51eba00f0dda5c26c1fa6826f544c60de485da52",
"51eba00f0dda5c26c1fa6826f544c60de485da52"
] | [
"tests/dataset/test_synthetic.py",
"tests/ope/test_ipw_estimators.py",
"tests/ope/test_meta_slate.py"
] | [
"import numpy as np\nimport pytest\n\nfrom obp.dataset import SyntheticBanditDataset\nfrom obp.dataset.synthetic import linear_behavior_policy\nfrom obp.dataset.synthetic import linear_reward_function\nfrom obp.dataset.synthetic import logistic_polynomial_reward_function\nfrom obp.dataset.synthetic import logistic_... | [
[
"numpy.ones_like",
"numpy.allclose",
"numpy.eye",
"numpy.ones",
"numpy.all",
"numpy.array"
],
[
"numpy.arange",
"numpy.zeros",
"numpy.random.choice",
"numpy.ones"
],
[
"numpy.abs",
"numpy.unique",
"pandas.DataFrame",
"numpy.ones",
"pandas.testing... |
idekany/lcfit | [
"4a0080fca981afe2b8974db8f5d3484c663b6c13"
] | [
"lcfit.py"
] | [
"import joblib\nimport lcfit_io as io\nimport os\nimport sys\nimport fourier as ff\nimport multiprocessing\nimport numpy as np\nimport lcfit_utils as ut\nfrom joblib import Parallel, delayed, load\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import mean_squared_error\nfrom time import ... | [
[
"numpy.sqrt",
"numpy.random.seed",
"numpy.linspace",
"numpy.min",
"numpy.arange",
"numpy.invert",
"sklearn.model_selection.StratifiedKFold",
"numpy.ones",
"sklearn.metrics.mean_squared_error",
"numpy.max",
"numpy.copy",
"numpy.std",
"numpy.zeros_like",
"nump... |
raunak-agarwal/antarctic-captions | [
"0862d9b36444071c405af5b5c8912888d3fefb05"
] | [
"dataset.py"
] | [
"import collections\nimport nltk\nimport torch\nimport PIL\nimport pytorch_lightning as pl\nimport numpy as np\nimport torchvision.transforms.functional as F\n\nfrom pathlib import Path\nfrom torchvision import transforms as T\nfrom random import randint, choice\nfrom torch.utils.data import DataLoader\n\nimport ut... | [
[
"torch.stack",
"torch.utils.data.DataLoader"
]
] |
mecthew/CasRelFork | [
"48304ae951dbba6bbe80aa73edec4739da3934c7"
] | [
"data/NYT/raw_NYT/generate.py"
] | [
"import json\nimport numpy as np\n\ndef is_normal_triple(triples, is_relation_first=False):\n entities = set()\n for i, e in enumerate(triples):\n key = 0 if is_relation_first else 2\n if i % 3 != key:\n entities.add(e)\n return len(entities) == 2 * int(len(triples) / 3)\n\ndef is_... | [
[
"numpy.reshape"
]
] |
declanmillar/qiskit-machine-learning | [
"3bf98b67618885bd09f4abbff7e381537a646fac"
] | [
"qiskit_machine_learning/neural_networks/opflow_qnn.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020, 2022.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modificatio... | [
[
"numpy.asarray",
"numpy.full",
"numpy.all",
"numpy.real",
"numpy.array"
]
] |
He-Ze/Final-Project-of-SYSU-Intelligent-Algorithms-and-Applications | [
"d6b8277a6b124ef8f0a1898e12b975591197e86c"
] | [
"1/src/LS-3.py"
] | [
"import matplotlib.pyplot as plt\r\nimport random\r\nimport math\nimport re\r\n\nclass city:\r\n def __init__(self, index, x, y):\r\n self.cityIndex = index\r\n self.x = x\r\n self.y = y\n\ndef initCities(filePath):\r\n with open(filePath) as file:\r\n cities = []\r\n begin ... | [
[
"matplotlib.pyplot.axes",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause"
]
] |
tamaskenez/artificial-intelligence | [
"b4d588bceb09dd9e48becc9d1b2a592d217c1745"
] | [
"Projects/4_HMM Tagger/helpers.py"
] | [
"\nimport os\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mplimg\nimport networkx as nx\nimport random\n\nfrom io import BytesIO\nfrom itertools import chain\nfrom collections import namedtuple, OrderedDict\n\n\nSentence = namedtuple(\"Sentence\", \"words tags\")\n\ndef read_data(filename):\n \"\... | [
[
"matplotlib.image.imread",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] |
debbiemarkslab/edward2 | [
"d071268c0439b434b508fe23abd938a5effb7a70",
"d071268c0439b434b508fe23abd938a5effb7a70"
] | [
"edward2/tensorflow/layers/normalization.py",
"edward2/trace_test.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Edward2 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 ... | [
[
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.reduce_sum",
"tensorflow.cast",
"numpy.arange",
"tensorflow.square",
"tensorflow.tile",
"tensorflow.TensorShape",
"tensorflow.shape",
"tensorflow.exp",
"tensorflow.math.rsqrt",
"tensorflow.split",
... |
SAngeliuk/neoml_python | [
"09e24dd726426ec880ff1793c287f03c3f1d362e"
] | [
"NeoML/Python/neoml/KMeans.py"
] | [
"\"\"\" Copyright © 2017-2021 ABBYY Production LLC\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n\thttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or a... | [
[
"numpy.array",
"scipy.sparse.csr_matrix",
"numpy.ones"
]
] |
ctao5660/oppia-ml | [
"3234d6f6c854c5d9cdbf332eb0e743d1013354e8",
"3234d6f6c854c5d9cdbf332eb0e743d1013354e8"
] | [
"core/classifiers/CodeClassifier/CodeClassifier.py",
"core/classifiers/classifier_utils_test.py"
] | [
"# coding: utf-8\n#\n# Copyright 2017 The Oppia 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... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"numpy.squeeze",
"numpy.array",
"sklearn.svm.SVC"
],
[
"numpy.array",
"sklearn.svm.SVC"
]
] |
tarunh9/Fashion-Class-Classification | [
"1699c9cc0a4f9ec2f5b6680b274b13020f87ec30"
] | [
"fashion.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\nfashion_train_df = pd.read_csv('fashion-mnist_train.csv', sep = ',')\nfashion_test_df = pd.read_csv('fashion-mnist_test.csv', sep = ',')\n\ntraining = np.array(fashion_train_df, dtype = 'float32')\ntesting = np.array... | [
[
"pandas.read_csv",
"sklearn.cross_validation.train_test_split",
"numpy.arange",
"matplotlib.pyplot.subplots",
"sklearn.metrics.confusion_matrix",
"numpy.random.randint",
"matplotlib.pyplot.subplots_adjust",
"numpy.array",
"sklearn.metrics.classification_report",
"matplotlib... |
bayramcicek/algoritma-analizi | [
"928891d04f570a45346fa14af887a4a99e70358b"
] | [
"013-odev-3-linear-empiric.py"
] | [
"#!/usr/bin/python3.6\n# created by cicek on Apr 16, 2020 18:03\n\nimport random\nimport matplotlib.pyplot as plt\n\n'''\nBayram Çiçek - 160401002 - linear search empiric study\n\nDue April 17 at 11:59 PM\nkonu: week_3_deneysel analiz uygulaması linear/binary search\n\nHafta 3 için yüklenen video lardaki kodu kendi... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] |
gferraro2019/python-meegkit | [
"aed858dc3603a3b71e620df3f29da6ae1a8f68da"
] | [
"examples/example_ress.py"
] | [
"\"\"\"\nRhythmic Entrainment Source Separation (RESS) example\n=====================================================\n\nFind the linear combinations of multichannel data that maximize the\nsignal-to-noise ratio of the narrow-band steady-state response in the frequency\ndomain.\n\nUses `meegkit.RESS()`.\n\"\"\"\n\n... | [
[
"numpy.random.seed",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.tile",
"numpy.random.randn",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] |
YoPatapon/allennlp | [
"d50964b1f5b6887c74855a4fbbd1e948d406d206",
"d50964b1f5b6887c74855a4fbbd1e948d406d206",
"4c99f8e82f7fd70c86652109bfca5282d470e981"
] | [
"allennlp/tests/training/trainer_test.py",
"allennlp/tests/data/fields/text_field_test.py",
"allennlp/modules/pruner.py"
] | [
"# pylint: disable=invalid-name,too-many-public-methods\nimport glob\nimport json\nimport os\nimport re\nimport time\nfrom typing import Dict\n\nimport torch\nimport pytest\nfrom allennlp.common.checks import ConfigurationError\n\nfrom allennlp.common.testing import AllenNlpTestCase\nfrom allennlp.training.trainer ... | [
[
"torch.cuda.device_count",
"torch.cuda.is_available",
"torch.rand",
"torch.nn.Embedding"
],
[
"numpy.array"
],
[
"torch.sort"
]
] |
joeyhu/math | [
"34efde801221e77824ea16e4e325d5c30de49c8c"
] | [
"ml/Cosine.py"
] | [
"'''\n几何中夹角余弦可用来衡量两个向量方向的差异，机器学习中借用这一概念来衡量样本向量之间的差异。\n\n1.二维空间向量的夹角余弦相似度\n'''\nimport numpy as np\n\n\ndef cosn(a, b):\n \"\"\"\n n维夹角余弦\n \"\"\"\n sum1 = sum2 = sum3 = 0\n for i in range(len(a)):\n sum1 += a[i] * b[i]\n sum2 += a[i] ** 2\n sum3 += b[i] ** 2\n cos = sum1 / (np... | [
[
"numpy.sqrt",
"numpy.array",
"numpy.sum",
"numpy.sin"
]
] |
malcodeman/python-recipes | [
"0a6a8aef0ba6a10a9f98c494555eb0ca143b5cb0"
] | [
"fake-data-to-excel/fake_data_to_excel.py"
] | [
"from faker import Faker\nimport pandas as pd\n\nfake = Faker()\ncount = 0\n\nfirstName = []\nlastName = []\ncountry = []\njob = []\n\nwhile count < 1000:\n firstName.append(fake.first_name())\n lastName.append(fake.last_name())\n country.append(fake.country())\n job.append(fake.job())\n\n count += 1... | [
[
"pandas.DataFrame"
]
] |
UMich-CURLY/3DMapping | [
"0c129f6fe2ab692e0f83dcb9d811420a2c98628e"
] | [
"Data/Visualization/VisualizeScene.py"
] | [
"import open3d as o3d\nimport time\nimport numpy as np\nimport os\nimport json\nimport pdb\nfrom PIL import Image\nimport psutil\n\nLABEL_COLORS = np.array([\n (255, 255, 255), # None\n (70, 70, 70), # Building\n (100, 40, 40), # Fences\n (55, 90, 80), # Other\n (255, 255, 0), # Pedestrian\... | [
[
"numpy.hstack",
"numpy.meshgrid",
"numpy.linspace",
"numpy.cos",
"numpy.stack",
"numpy.sin",
"numpy.transpose",
"numpy.array",
"numpy.zeros"
]
] |
nju-websoft/SR3 | [
"bb602926f8a260887326a812e19e23f50e7eb273"
] | [
"SEQA/trainer.py"
] | [
"import torch\nfrom tensorboardX import SummaryWriter\nfrom others.logging import logger\nimport others.distributed as distributed\nimport os\nimport numpy as np\nfrom evaluate import *\nfrom others.util import *\nfrom model.reporter import Statistics,ReportMgr\n\nfrom sklearn.metrics import precision_score, recall... | [
[
"sklearn.metrics.precision_score",
"torch.no_grad",
"numpy.argsort",
"sklearn.metrics.f1_score",
"sklearn.metrics.recall_score",
"torch.save"
]
] |
Ryou0634/pytorch_models | [
"cd48f9b3797839df5dbf4e51bed81de44e7b962e"
] | [
"torch_models/models/classifiers.py"
] | [
"import torch\nfrom ._classifier import ClassifierBase\n\nclass SingleClassifier(ClassifierBase):\n '''\n Classifier for single input.\n (e.g. sentiment analysis, document classification, image classification)\n '''\n def __init__(self, encoder, output_size, hidden_size=None,\n activa... | [
[
"torch.cat"
]
] |
uyeongkim/moca | [
"8a5870898b6d59258ce1064bab440b7e8107e9b4"
] | [
"models/model/seq2seq.py"
] | [
"import os\nimport random\nimport json\nimport torch\nimport pprint\nimport collections\nimport numpy as np\nfrom torch import nn\nfrom tensorboardX import SummaryWriter\nfrom tqdm import trange\n\n\nnot_perfect_list = [\n 'pick_and_place_simple-SprayBottle-None-Toilet-422/trial_T20190909_124852_071149',\n 'p... | [
[
"numpy.full_like",
"torch.load"
]
] |
dave3d/stlconvert | [
"cda54e96ac21caa176211fd0f551b0fe0b063245"
] | [
"glbthumb.py"
] | [
"\nimport sys\nimport argparse\nimport numpy as np\nimport trimesh\nimport pyrender\nimport glbutils\nimport matplotlib.pyplot as plt\n\ndef bound_corners(bounds):\n corners = []\n for z in range(2):\n for y in range(2):\n for x in range(2):\n c = [bounds[x][0], bounds[y][1], ... | [
[
"numpy.asarray",
"matplotlib.pyplot.imsave"
]
] |
swifmaneum/cellpylib | [
"4f7be652f2bb49b58ea482b2929617d813111f77",
"4f7be652f2bb49b58ea482b2929617d813111f77"
] | [
"tests/test_cellpylib2d.py",
"demos/hopfield_net_demo.py"
] | [
"import unittest\n\nimport numpy as np\nimport os\n\nimport cellpylib as cpl\n\nTHIS_DIR = os.path.dirname(os.path.abspath(__file__))\n\n\nclass TestCellularAutomataFunctions2D(unittest.TestCase):\n\n def test_init_simple2d_1x1(self):\n arr = cpl.init_simple2d(rows=1, cols=1)\n self.assertEqual(len... | [
[
"numpy.array"
],
[
"numpy.reshape",
"numpy.array"
]
] |
vis-opt-group/BDA | [
"0350187b12cb1f36d87ac4f6bc2f15a282e8fac4"
] | [
"boml/setup_model/meta_repr_v1.py"
] | [
"from collections import OrderedDict\nfrom functools import reduce\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.contrib.layers.python import layers\n\nfrom boml import extension\nfrom boml.setup_model import network_utils\nfrom boml.setup_model.network import BOMLNet\n\n\nclass BOMLNetMetaReprV1(... | [
[
"tensorflow.reduce_mean",
"tensorflow.zeros_initializer",
"tensorflow.reshape",
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.contrib.layers.xavier_initializer_conv2d",
"tensorflow.add_to_collections",
"tensorflow.svd"
]
] |
shalin-s/02-512_F20_Final_Project | [
"13e5ff287129edeab719dc8fc68c9fc134882b68"
] | [
"CTMM/02-512_Final_Project_CTMM.py"
] | [
"# Code for 02-512 Final Project, Fall 2020, by Shalin Shah.\n# Tested in Python 3.6.5\n\nimport sys\nimport numpy as np\nimport numpy.random\nfrom datetime import datetime\nfrom matplotlib import pyplot as plt\nnp.seterr(divide='ignore')\n\ndef log_print(text):\n print(\"{}: {}\".format(datetime.now(), text))\n... | [
[
"matplotlib.pyplot.legend",
"numpy.random.exponential",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.figtext",
"matplotlib.pyplot.gcf",
"numpy.seterr",
"matplotlib.pyplot.plot",
"numpy.argmin",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"numpy.array",
... |
develooper1994/Best-Deep-Learning-Optimizers | [
"9a14c83111d24e639096068985d24d13d1a0e32a"
] | [
"diffmod/diffmod.py"
] | [
"import math\nimport torch\nfrom torch import Tensor\nfrom torch.optim import Optimizer\n\n# source and paper link - https://github.com/lancopku/AdaMod/blob/master/adamod/adamod.py\n\n# modifications @lessw2020 - blend diffGrad + AdaMod = diffmod.\n# 1/1/20 = instead of b3, change to 'len_memory' and compute b3 (.9... | [
[
"torch.full_like",
"torch.min",
"torch.exp",
"torch.zeros_like"
]
] |
frichter/ore | [
"293e6959045213b2341e5549822c25c9c2c47e44"
] | [
"ore/genes.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"Gene processing.\n\n:Author: Felix Richter <Felix.Richter@icahn.mssm.edu>\n:Date: 2018-01-22\n:Copyright: 2018, Felix Richter\n:License: CC BY-SA\n\"\"\"\n\nimport os\nimport re\nimport subprocess\n\nimport pandas as pd\n\nimport pysam\n\n\nclass Error(Excep... | [
[
"pandas.read_table"
]
] |
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