repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
erexhepa/MONAI | [
"98210a266db428a0769fde260dda0703b1c4ea95",
"98210a266db428a0769fde260dda0703b1c4ea95",
"98210a266db428a0769fde260dda0703b1c4ea95"
] | [
"examples/segmentation_3d/unet_training_dict.py",
"tests/test_integration_segmentation_3d.py",
"tests/test_png_saver.py"
] | [
"# Copyright 2020 MONAI Consortium\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# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to i... | [
[
"numpy.eye",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available",
"torch.device"
],
[
"torch.load",
"torch.manual_seed",
"numpy.eye",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.testing.assert... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tahesse/trickster | [
"d22072bebbebff319c724806d583bfa982d429be"
] | [
"trickster/rollout/trajectory.py"
] | [
"from typing import Union\n\nimport numpy as np\n\nfrom .abstract import RolloutBase\nfrom ..agent.abstract import RLAgentBase\nfrom ..utility import history, render_utils\nfrom .. import callbacks as _cbs\n\n\nclass Trajectory(RolloutBase):\n\n \"\"\"Generate complete trajectories for Monte Carlo learning or te... | [
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tszdanger/torch_grammartest | [
"ac199a9ddcfe4aaf8893fb5285f85efd725ad6d9"
] | [
"jd_fenci.py"
] | [
"#抓取网页内容用的程序包\nimport json\nimport requests\n\n#PyTorch用的包\nimport torch\nimport torch.nn as nn\nimport torch.optim\nfrom torch.autograd import Variable\n\n# 自然语言处理相关的包\nimport re #正则表达式的包\nimport jieba #结巴分词包\nfrom collections import Counter #搜集器，可以让统计词频更简单\n\n#绘图、计算用的程序包\nimport matplotlib.pyplot as plt\nimport n... | [
[
"numpy.array",
"matplotlib.pyplot.legend",
"torch.nn.NLLLoss",
"torch.nn.LogSoftmax",
"torch.max",
"matplotlib.pyplot.figure",
"torch.tensor",
"matplotlib.pyplot.plot",
"torch.nn.Linear",
"torch.FloatTensor",
"numpy.mean",
"matplotlib.pyplot.xlabel",
"torch.nn.R... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ElPapi42/MNIST-Classifier | [
"9bfdb970c10f6c07b4339ecd97b3436590cbe2a9"
] | [
"DigitsClassifier.py"
] | [
"import tensorflow as tf\nimport tensorflow.keras.layers as layers\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nclass ConvBlock(tf.keras.layers.Layer):\n \"\"\"Convolutional Block featuring Conv2D + Pooling\"\"\"\n\n def __init__(self, conv_deep=1, kernel... | [
[
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.MaxPool2D",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.Flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
DeepPSP/cpsc2020 | [
"47acb884ea1f2f819e564d8a17ad37001ed0df27"
] | [
"references/cpsc2019/CPSC0416_1st_qrs9214_hr9489/CPSC2019_challenge.py"
] | [
"import numpy as np\n\ndef CPSC2019_challenge(result):\n pos = np.argwhere(result>0.5).flatten()\n rpos = []\n pre = 0\n last = len(pos)\n # to intervals of qrs\n for j in np.where(np.diff(pos)>2)[0]:\n if j-pre>2: # duration > 2 * (8 * 2 ms)\n rpos.append((pos[pre]+pos[j])*4) ... | [
[
"numpy.delete",
"numpy.array",
"numpy.diff",
"numpy.argwhere"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
harika-24/Image-Processing-and-Machine-Learning-using-Parallel-Computing | [
"b13b8f20551a9d5960b146713182b167e35d65e7"
] | [
"knn_try.py"
] | [
"import math\nfrom sklearn import neighbors\nimport os\nimport os.path\nimport pickle\nfrom PIL import Image, ImageDraw\nimport face_recognition\nfrom face_recognition.face_recognition_cli import image_files_in_folder\n\nALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}\n\n\ndef train(train_dir, model_save_path=None, n_n... | [
[
"sklearn.neighbors.KNeighborsClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cbrnr/pybv | [
"0088662504098cd117e5b36c2f256c4580d1fe1f"
] | [
"pybv/io.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"BrainVision writer.\"\"\"\n\n# Authors: Phillip Alday <phillip.alday@unisa.edu.au>\n# Chris Holdgraf <choldgraf@berkeley.edu>\n# Stefan Appelhoff <stefan.appelhoff@mailbox.org>\n# Tristan Stenner <stenner@med-psych.uni-kiel.de>\n# Clemens Brunner <... | [
[
"numpy.format_float_positional",
"numpy.issubdtype",
"numpy.ones",
"numpy.atleast_1d",
"numpy.atleast_2d",
"numpy.max",
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shijing001/Text_classifier | [
"21402ba4ab46e55df40285546775507c6d6933c8"
] | [
"examples/run_20news_label.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may... | [
[
"torch.load",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.nn.Embedding.from_pretrained",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"torch.device",
"torch.distributed.get_rank",
"sklearn.datasets.fetch_20newsgroups",
"torch.save",
"torch.distrib... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deep1185/ga-learner-dsmp-repo | [
"74ebe2f5eac359076976db5f8788415d1c17ff8c"
] | [
"Titanic/code.py"
] | [
"# --------------\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nimport pandas as pd\nimport warnings\nwarnings.filterwarnings('ignore')\n# Code starts here\ndf=pd.read_csv(path)\ndf.head(5)\n# Dependent variable\ny = df['attr1089']\n# Independent variable\nX ... | [
[
"sklearn.metrics.roc_auc_score",
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.model_selection.train_test_split",
"sklearn.ensemble.VotingClassifier",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.metrics.classif... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
LongTailBio/pangea-django | [
"630551dded7f9e38f95eda8c36039e0de46961e7"
] | [
"client-programs/contrib/metagenscope/metagenscope/modules/volcano.py"
] | [
"\nimport pandas as pd\nimport math\nfrom scipy.stats import mannwhitneyu\nfrom pangea_api import (\n Sample,\n SampleAnalysisResultField,\n SampleGroupAnalysisResultField,\n SampleGroup,\n)\n\nfrom ..base_module import Module\nfrom ..data_utils import (\n group_samples_by_metadata,\n sample_modul... | [
[
"scipy.stats.mannwhitneyu"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
robertjkerr/Monte-Carlo-Math-Tools | [
"a78dbe9e2ce1832d187afd298cec8778d8c9c0bc"
] | [
"mctools/modules/integrate.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: Robert Kerr\r\n\r\nMonte Carlo Integration function \r\n\"\"\"\r\n\r\nimport numpy as _np\r\nfrom itertools import product as _product\r\nfrom functools import lru_cache as _lru_cache\r\n\r\n\"\"\"\r\nAllocation subroutines. Assists with parallelisation.\r\n\"\"\"\r\n\... | [
[
"numpy.array",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Xx-Ashutosh-xX/WellcomeML | [
"133089c072cb4e7bd8e841796ef7643846d5d7af"
] | [
"examples/voting_classifier_ensemble.py"
] | [
"from sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.naive_bayes import MultinomialNB\n\nfrom wellcomeml.ml.voting_classifier import WellcomeVotingClassifier\n\nX = [\n \"One two\",\n \"On... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.linear_model.SGDClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
skeydan/probability | [
"64107deba2aa0a5029f2288cf67f87f0237209fd"
] | [
"tensorflow_probability/python/layers/distribution_layer_test.py"
] | [
"# Copyright 2018 The TensorFlow Probability 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 a... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"numpy.linspace",
"tensorflow.zeros",
"numpy.asarray",
"tensorflow.keras.layers.InputLayer",
"numpy.random.randn",
"tensorflow.compat.v1.initializers.constant",
"tensorflow.pad",
"tensorflow.rank",
"numpy.exp",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
jon-young/ParallelTest | [
"eb24e2634c402a8cc74794ae89b4b8a7c5dc2209"
] | [
"parallel_test.py"
] | [
"__author__ = 'jyoung'\n\nimport multiprocessing as mp\nimport numpy\nimport time\n\ndef sum_range_serial(start, end):\n return numpy.sum(numpy.arange(start, end+1))\n\ndef sum_range_par(start, end, output):\n output.put(numpy.sum(numpy.arange(start, end+1)))\n\nsumLimit = int(input('Enter a number to sum to:... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CommanderPho/pyPhoPlaceCellAnalysis | [
"6e6a5cd9c0f2abbe6a367d4c87299fcd01c750a6"
] | [
"src/pyphoplacecellanalysis/PhoPositionalData/import_data.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 9 12:33:48 2021\n\n@author: Pho\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nfrom pyphoplacecellanalysis.PhoPositionalData.load_exported import import_mat_file\n# from pyphoplacecellanalysis.PhoPositionalData.process_data impor... | [
[
"numpy.arange",
"numpy.shape",
"numpy.interp",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
axelmagn/vertex-mlops-template | [
"0dbc939e8454ca7d0b7e146efae3d7bed3b90383"
] | [
"mlops_manager/templates-old/app/examples/fashion-mnist/__APP_NAME__/__APP_NAME__/trainers/fashion_mnist/task.py"
] | [
"\"\"\"\nFashion MNIST trainer function.\n\nNOTE: this is a monolithic script for the purpose of prototyping.\nTODO: break down into modular components\n\"\"\"\n\nimport logging\nimport os\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom ...cli import task, arg\n\n\n@task([\n arg('--width', type=int, defau... | [
[
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.io.gfile.GFile",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.callbacks.TensorBoard",
"numpy.load",
"tensorflow.keras.layers.Flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
kmckiern/pyquil | [
"e8189cadeafd5b1a234c25ed5eeebab61dd268c8",
"e8189cadeafd5b1a234c25ed5eeebab61dd268c8"
] | [
"pyquil/api/_qpu.py",
"pyquil/api/_qvm.py"
] | [
"##############################################################################\n# Copyright 2016-2018 Rigetti Computing\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#\... | [
[
"numpy.frombuffer",
"numpy.zeros"
],
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NCI-DOE-Cababilities-Transfer/DOE-NCI-Pilot1-Learning | [
"152a6e256304d7e4adfe73e7c8ad8e05d2b160e0"
] | [
"LearningCurves/lrn_crv.py"
] | [
"\"\"\"\nFunctions to generate learning curves.\nRecords performance (error or score) vs training set size.\n\nTODO: move utils.calc_scores to a more local function.\n\"\"\"\nimport os\nimport sys\nfrom pathlib import Path\nfrom collections import OrderedDict\n\nimport sklearn\nimport numpy as np\nimport pandas as ... | [
[
"sklearn.metrics.roc_auc_score",
"sklearn.externals.joblib.dump",
"sklearn.metrics.r2_score",
"numpy.linspace",
"numpy.squeeze",
"sklearn.metrics.mean_absolute_error",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"sklearn.model_selection.KFold",
"numpy.mean",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.1... |
popgengent/gnomix | [
"6caf793761a3a40b315e0e93d301434d3f249a31"
] | [
"src/Smooth/cnn.py"
] | [
"import numpy as np\nimport torch\nfrom torch import nn\n\nclass LAIDataset(torch.utils.data.Dataset):\n \n def __init__(self, data, labels):\n self.labels = labels\n self.data = data\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, index):\n\n # selec... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"numpy.swapaxes",
"torch.nn.Softmax",
"torch.nn.NLLLoss",
"torch.mean",
"numpy.asarray",
"torch.utils.data.DataLoader",
"torch.tensor",
"numpy.copy",
"torch.nn.Conv1d",
"torch.log",
"numpy.transpose",
"numpy.array",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zmk5/multi_robot_trainer | [
"b85f668c1302040717d0129f092558279bec5237"
] | [
"mrt_server/mrt_server/policy/actor_critic.py"
] | [
"\"\"\"Actor-Critic model class for RL experiments with neural net function approx.\n\nWritten by: Zahi Kakish (zmk5)\n\"\"\"\nfrom typing import List\n\nimport numpy as np\n\nfrom tensorflow.keras.optimizers import Adam\n\nfrom mrt_msgs.srv import Gradients\nfrom mrt_msgs.srv import Weights\n\nfrom mrt_server.poli... | [
[
"numpy.array",
"tensorflow.keras.optimizers.Adam"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
rdarie/elephant | [
"9f327d042a54531117ddf36c732262d4e78a5d85"
] | [
"elephant/test/test_cubic.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nUnit tests for the CUBIC analysis.\n\n:copyright: Copyright 2016 by the Elephant team, see `doc/authors.rst`.\n:license: Modified BSD, see LICENSE.txt for details.\n\"\"\"\n\nimport sys\nimport unittest\n\nimport neo\nimport numpy\nimport quantities as pq\n\nimport elephant.cubic a... | [
[
"numpy.array",
"numpy.tile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gopalm-ai/Machine-Learning-For-Finance | [
"2a07f9190cc58c1e540899ad97597a5bd9172b1c"
] | [
"Regression Based Machine Learning for Algorithmic Trading/Pairs Trading - Lasso Regression.py"
] | [
"'''\r\nLasso Regression\r\n\r\n'''\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom zipline.utils import tradingcalendar\r\nimport pytz\r\nfrom sklearn import linear_model\r\nreg = linear_model.Lasso(alpha = .1)\r\n\r\ndef initialize(context):\r\n # Quantopian backtester specific variables\r\n set_sl... | [
[
"numpy.hstack",
"numpy.ndarray",
"sklearn.linear_model.Lasso"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
matham/nsniff | [
"9751f67f0144e549207bf84f10c65b4f6543c95d"
] | [
"nsniff/widget.py"
] | [
"import numpy as np\nfrom typing import List, Dict, Optional, Tuple\nfrom matplotlib import cm\nfrom kivy_trio.to_trio import kivy_run_in_async, mark, KivyEventCancelled\nfrom pymoa_remote.threading import ThreadExecutor\nfrom base_kivy_app.app import app_error\nfrom kivy_garden.graph import Graph, ContourPlot, Lin... | [
[
"numpy.linspace",
"numpy.min",
"numpy.asarray",
"numpy.clip",
"numpy.logspace",
"numpy.power",
"numpy.logical_or",
"numpy.max",
"numpy.log10",
"numpy.any",
"matplotlib.cm.get_cmap"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Cher-B/ivy | [
"95273172201071ebf7b83d56bb314450ebe41071"
] | [
"ivy/functional/backends/torch/core/math.py"
] | [
"\"\"\"\nCollection of PyTorch math functions, wrapped to fit Ivy syntax and signature.\n\"\"\"\n\n# global\nimport math\nimport torch as _torch\n\n\ndef sin(x):\n if isinstance(x, float):\n return math.sin(x)\n return _torch.sin(x)\n\n\ndef cos(x):\n if isinstance(x, float):\n return math.co... | [
[
"torch.tan",
"torch.sin",
"torch.asinh",
"torch.atan2",
"torch.acosh",
"torch.tanh",
"torch.cosh",
"torch.asin",
"torch.acos",
"torch.sinh",
"torch.log",
"torch.exp",
"torch.atanh",
"torch.erf",
"torch.atan",
"torch.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mauricemager/multiagent_robot | [
"b8d8158fb7ffbb29116aaab03d8a47a60aac6482",
"b8d8158fb7ffbb29116aaab03d8a47a60aac6482"
] | [
"robot/robot_scenarios/simple_robot.py",
"robot/robot_scenarios/pick_and_drop.py"
] | [
"import numpy as np\nimport math\nfrom robot.robot_core import Robot, Robotworld, Landmark\nfrom multiagent.scenario import BaseScenario\n\nnp.random.seed(2)\n\nclass Scenario(BaseScenario):\n def make_world(self):\n # define scenario properties\n num_agents = 1\n num_objects = 1\n nu... | [
[
"numpy.random.seed",
"numpy.linalg.norm",
"numpy.ones",
"numpy.random.rand",
"numpy.ndarray.tolist",
"numpy.array"
],
[
"numpy.linalg.norm",
"numpy.ones",
"numpy.random.rand",
"numpy.ndarray.tolist",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UT-Austin-RPL/GIGA | [
"4c51610cbbf3556941f8b2ac1c3d4d7850cca44b",
"4c51610cbbf3556941f8b2ac1c3d4d7850cca44b"
] | [
"scripts/sim_grasp_multiple.py",
"src/vgn/ConvONets/eval.py"
] | [
"import argparse\nimport numpy as np\nimport json\nfrom pathlib import Path\n\nfrom vgn.detection import VGN\nfrom vgn.detection_implicit import VGNImplicit\nfrom vgn.experiments import clutter_removal\nfrom vgn.utils.misc import set_random_seed\n\n\ndef main(args):\n\n if args.type in ['giga', 'giga_aff']:\n ... | [
[
"numpy.std",
"numpy.mean"
],
[
"numpy.abs",
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"numpy.logical_and",
"numpy.linalg.norm",
"numpy.array",
"numpy.sum",
"numpy.empty",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
givenone/MichiGAN | [
"67c48da53e0a038273150f26baf9127e906a7f3f"
] | [
"models/networks/base_network.py"
] | [
"\"\"\"\r\nCopyright (C) University of Science and Technology of China.\r\nLicensed under the MIT License.\r\n\"\"\"\r\n\r\nimport torch.nn as nn\r\nfrom torch.nn import init\r\n\r\n\r\nclass BaseNetwork(nn.Module):\r\n def __init__(self):\r\n super(BaseNetwork, self).__init__()\r\n\r\n @staticmethod\r... | [
[
"torch.nn.init.constant_",
"torch.nn.init.xavier_normal_",
"torch.nn.init.normal_",
"torch.nn.init.orthogonal_",
"torch.nn.init.xavier_uniform_",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bioidiap/bob.io.video | [
"4aaf0b2197200d7e31ef1d5d2be341ea52f37c5e"
] | [
"bob/io/video/script/video_test.py"
] | [
"#!/usr/bin/env python\n# vim: set fileencoding=utf-8 :\n# Andre Anjos <andre.dos.anjos@gmail.com>\n# Thu 14 Mar 17:53:16 2013\n\n\"\"\"This program can run manual tests using any video codec available in Bob. It\ncan report standard distortion figures and build video test sequences for\nmanual inspection. It tries... | [
[
"numpy.ndarray",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
learningsimulator/learningsimulator | [
"79b00bb0155537a4219637e68d5092fd10a1017f"
] | [
"tests/test_run.py"
] | [
"import matplotlib.pyplot as plt\n\nfrom .testutil import LsTestCase, run\n\n\nclass TestBasic(LsTestCase):\n @classmethod\n def setUpClass(cls):\n pass\n\n def setUp(self):\n pass\n\n def tearDown(self):\n plt.close('all')\n\n def test_simple(self):\n text = '''\n ... | [
[
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sanjaykkukreja/AIML | [
"3d17ecbf5d59db47fece39f9180916ea4d44aa65"
] | [
"HackathonOne/appModel/Order.py"
] | [
"from . import Record_audio\nimport numpy as np\nimport warnings\nimport os\nimport joblib\nwarnings.filterwarnings(\"ignore\")\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nn_mfcc = 30\nn_mels = 128\nframes = 15\n\nlist_task = [['Idly', ' Dosa', ' Wada', 'Puri ', 'Chapathi'],[0, 1, 2... | [
[
"numpy.reshape",
"numpy.amax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kefirski/pytorch_TDNN | [
"a4e50c110d1b4f52a465b2a1d13b30a7ff567d0c"
] | [
"TDNN/tdnn.py"
] | [
"import torch as t\nfrom torch.nn import Parameter\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass TDNN(nn.Module):\n def __init__(self, kernels, input_embed_size, bias=False):\n \"\"\"\n :param kernels: array of pairs (width, out_dim)\n :param input_embed_size: size of in... | [
[
"torch.nn.functional.conv1d",
"torch.Tensor",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yuliangguo/bop_toolkit_cosypose | [
"046728dcf1a01a739900ec77dc4754ba3f6eb56e"
] | [
"scripts/eval_calc_errors.py"
] | [
"# Author: Tomas Hodan (hodantom@cmp.felk.cvut.cz)\n# Center for Machine Perception, Czech Technical University in Prague\n\n\"\"\"Calculates error of 6D object pose estimates.\"\"\"\n\nimport os\nimport time\nimport argparse\nimport copy\nimport numpy as np\n\nfrom bop_toolkit_lib import config\nfrom bop_toolkit_l... | [
[
"numpy.arange",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
hanson-young/hdface | [
"527b9ccadf3d44d59f547f81ed2b563b033099de"
] | [
"hdface/models.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass PNet(nn.Module):\n ''' PNet '''\n\n def __init__(self, is_train=False, use_cuda=True):\n super(PNet, self).__init__()\n self.is_train = is_train\n self.use_cuda = use_cuda\n\n # backend\n self.pre... | [
[
"torch.nn.PReLU",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShapRL/ShapRL | [
"11327bb5e3b2f2e6ac3a631097bd712f13b9d275",
"11327bb5e3b2f2e6ac3a631097bd712f13b9d275",
"11327bb5e3b2f2e6ac3a631097bd712f13b9d275"
] | [
"batch_rl/fixed_replay/run_experiment_test_buffer.py",
"batch_rl/fixed_replay/replay_memory/circular_replay_buffer.py",
"batch_rl/fixed_replay/run_experiment_test_one.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Dopamine 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 b... | [
[
"tensorflow.compat.v1.ConfigProto",
"tensorflow.compat.v1.disable_v2_behavior",
"numpy.clip",
"tensorflow.compat.v1.global_variables_initializer",
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1.get_collection",
"tensorflow.name_scope",
"tensorflow.compat.v1.train.Saver",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexander-g/vkJAX | [
"8ef0105891ee41734293b2f1a8c247957018d7c2"
] | [
"tests/test_function.py"
] | [
"import vkjax\nimport numpy as np\nimport jax\n\nGLOBAL_VAR = 0\n\ndef func0(x):\n global GLOBAL_VAR\n GLOBAL_VAR = x\n return x+1\n\n\ndef test_function_basic():\n vk_func = vkjax.Function(func0)\n assert vk_func(65)==66\n assert vk_func(-5)==-4\n\n\ndef test_shape_checking():\n global GLOBAL_... | [
[
"numpy.arange",
"numpy.zeros",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gurkengewuerz/OpenSfM | [
"bb0be52a22313572a6dc4f0b2701bf918af1ecef"
] | [
"opensfm/pairs_selection.py"
] | [
"import copy\nimport logging\nimport math\nfrom collections import defaultdict\nfrom itertools import combinations\nfrom typing import Optional, Tuple, List, Set, Dict, Iterable, Any\n\nimport numpy as np\nimport scipy.spatial as spatial\nfrom opensfm import bow, context, feature_loader, vlad, geo, geometry\nfrom o... | [
[
"numpy.polyfit",
"numpy.min",
"numpy.median",
"scipy.spatial.Delaunay",
"numpy.max",
"numpy.random.rand",
"numpy.argsort",
"numpy.array",
"scipy.spatial.cKDTree",
"numpy.fabs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jthielen/xarray | [
"10f0227a1667c5ab3c88465ff1572065322cde77"
] | [
"xarray/plot/utils.py"
] | [
"import itertools\nimport textwrap\nimport warnings\nfrom datetime import datetime\nfrom inspect import getfullargspec\nfrom typing import Any, Iterable, Mapping, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom ..core.options import OPTIONS\nfrom ..core.utils import is_scalar\n\ntry:\n import nc_... | [
[
"numpy.take",
"numpy.linspace",
"numpy.asarray",
"numpy.issubdtype",
"matplotlib.pyplot.get_cmap",
"numpy.concatenate",
"matplotlib.colors.from_levels_and_colors",
"numpy.all",
"matplotlib.pyplot.gca",
"numpy.arange",
"matplotlib.ticker.LinearLocator",
"numpy.diff",... | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chrissullivanecon/pyRVtest | [
"ac9a5ca408e64695c3c452ef7584eaf43a5e8d9a"
] | [
"pyRVtest/economies/economy.py"
] | [
"\"\"\"Economy underlying the BLP model.\"\"\"\r\n\r\nimport abc\r\nimport collections.abc\r\nfrom typing import Any, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union\r\n\r\nimport numpy as np\r\n\r\nfrom .. import exceptions, options\r\nfrom ..configurations.formulation import Formulation, Absorb, M... | [
[
"numpy.asarray",
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
katosh/pymc3 | [
"38fa415c15b0c0469fbd9cad3a3b9ba974fc8733"
] | [
"pymc3/tests/test_mixture.py"
] | [
"import pytest\nimport numpy as np\nfrom numpy.testing import assert_allclose\n\nfrom .helpers import SeededTest\nimport pymc3 as pm\nfrom pymc3 import Dirichlet, Gamma, Normal, Lognormal, Poisson, Exponential, \\\n Mixture, NormalMixture, MvNormal, sample, Metropolis, Model\nimport scipy.stats as st\nfrom scipy... | [
[
"numpy.diag",
"numpy.sqrt",
"numpy.asarray",
"numpy.concatenate",
"numpy.random.randn",
"numpy.exp",
"numpy.ones_like",
"numpy.reshape",
"numpy.arange",
"numpy.atleast_1d",
"numpy.random.poisson",
"numpy.random.multinomial",
"numpy.diff",
"scipy.stats.expon.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jbell1991/reddit-scraping | [
"73d88501ed0205e78000b9c30780a33186154fda"
] | [
"submissions_comments.py"
] | [
"# imports\nfrom decouple import config\nimport pandas as pd\nimport praw\nimport psycopg2\nimport schedule\nfrom sqlalchemy import create_engine\nimport time\n\n\ndef job():\n current_day = time.strftime(\"%m/%d/%Y\")\n print(f\"Performing job on {current_day}\")\n startTime = time.time()\n\n # connect... | [
[
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Raabia-Asif/luke | [
"9323b216dd5f72b61545bc4133f7709fd19bfa95"
] | [
"examples/entity_typing/main.py"
] | [
"import json\nimport logging\nimport os\nfrom argparse import Namespace\n\nimport click\nimport torch\nfrom torch.utils.data import DataLoader, RandomSampler\nfrom torch.utils.data.distributed import DistributedSampler\nfrom tqdm import tqdm\nfrom transformers import WEIGHTS_NAME\nfrom luke.utils.entity_vocab impor... | [
[
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.cat",
"torch.nn.utils.rnn.pad_sequence",
"torch.utils.data.DataLoader",
"torch.cuda.empty_cache",
"torch.distributed.barrier",
"torch.utils.data.RandomSampler",
"torch.tensor",
"torch.no_grad",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
da03/OpenNMT-py | [
"234e1ef16f388ddd5bbd86578cdc2a6fc9efe181"
] | [
"onmt/model_builder.py"
] | [
"\"\"\"\nThis file is for models creation, which consults options\nand creates each encoder and decoder accordingly.\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.init import xavier_uniform_\n\nimport onmt.inputters as inputters\nimport onmt.modules\nfrom onmt.encoders.rnn_encoder import RNNEncoder\... | [
[
"torch.device",
"torch.nn.init.xavier_uniform_",
"torch.nn.LogSoftmax",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fatiando/harmonica | [
"c4e832b09da2d25c847552cc0ba701cf801724ed"
] | [
"harmonica/gravity_corrections.py"
] | [
"# Copyright (c) 2018 The Harmonica Developers.\n# Distributed under the terms of the BSD 3-Clause License.\n# SPDX-License-Identifier: BSD-3-Clause\n#\n# This code is part of the Fatiando a Terra project (https://www.fatiando.org)\n#\n\"\"\"\nGravity corrections like Normal Gravity and Bouguer corrections.\n\"\"\"... | [
[
"numpy.logical_not",
"numpy.array",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tarsoly/NaoTH | [
"dcd2b67ef6bf9953c81d3e1b26e543b5922b7d52"
] | [
"Utils/py/ActionSelection/tools/potential_field.py"
] | [
"from __future__ import division\n\nimport numpy as np\nimport pickle\nimport copy\n\n# from evaluation.potentialfield_generated_plot import cleanup_nan_values\n\nfrom . import field_info as field\nfrom .action import Category\nfrom .action import ActionResults\n\n# BUG: this is a cyclic inclusion\n# NOTE: without ... | [
[
"numpy.arctan2",
"numpy.exp",
"numpy.abs",
"numpy.hypot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cadop/seg1d | [
"f5de3949da1fee71110435c5700c4774a03fedb1"
] | [
"seg1d/examples/ex_ecg.py"
] | [
"'''\nIn this example we use the ECG data included with scipy signal module. \nThe references roughly includes the Q-T interval (https://en.wikipedia.org/wiki/Electrocardiography).\nIn the first portion, two sample segments are used. While the segments are not aligned, they are able to find some segments correctly.... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.asarray",
"numpy.around",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"scipy.misc.electrocardiogram",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
fumish/LearningModels | [
"d4b46082625d52122b046369d872864e64b2e489"
] | [
"hypsecant_related/HyperbolicSecantMixtureModelVB.py"
] | [
"## standard libraries\nimport sys\nsys.path.append(\"../lib\")\nimport math\nimport itertools\nfrom abc import ABCMeta, abstractmethod\n\n## 3rd party libraries\nimport numpy as np\nfrom scipy.special import gammaln, psi, multigammaln\nfrom scipy.stats import multivariate_normal\nfrom sklearn.base import BaseEstim... | [
[
"numpy.diag",
"numpy.log",
"numpy.sqrt",
"numpy.random.seed",
"numpy.abs",
"numpy.ones",
"numpy.random.normal",
"numpy.floor",
"scipy.special.gammaln",
"numpy.repeat",
"numpy.exp",
"scipy.special.psi"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jparkerholder/ES | [
"cee031f601986b8f0c2f634d7e7c9f4b69e3f912"
] | [
"es/shared_noise.py"
] | [
"\n\nimport ray\nimport numpy as np\n\n@ray.remote\ndef create_shared_noise():\n \"\"\"\n Create a large array of noise to be shared by all workers. Used \n for avoiding the communication of the random perturbations delta.\n \"\"\"\n\n seed = 12345\n count = 25000000\n noise = np.random.RandomS... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
s-matrix/smpr3d | [
"f11c36c37bba749fe8aeb43f6cfbf303ab817064"
] | [
"examples/reconstruct_simul_data_big.py"
] | [
"from smpr3d.util import *\nfrom smpr3d.algorithm import *\nfrom smpr3d.setup import *\nimport torch as th\nimport os\nimport numpy as np\n\n# salloc -C gpu -N 2 -t 30 -c 10 --gres=gpu:8 -A m1759 --ntasks-per-node=8\n# srun -N 2 python ./admm_smatrix_dist_pytorch.py\n# module purge\n# module load pytorch/v1.4.0-gpu... | [
[
"numpy.fft.ifft2",
"torch.zeros",
"torch.sqrt",
"torch.zeros_like",
"numpy.linalg.norm",
"torch.fft.ifft2",
"torch.tensor",
"torch.arange",
"numpy.array",
"torch.ones_like",
"torch.as_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sands-lab/grace | [
"cb411f7364b519700e9c1ffa3cae48dc76885e4c"
] | [
"patch_files/horovod/torch/optimizer.py"
] | [
"# Copyright 2019 Uber Technologies, Inc. All Rights Reserved.\n# Modifications copyright Microsoft\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/l... | [
[
"torch.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CristianCrispini/TesiLaurea | [
"7c0db4da1d48c6739877cfce2037f3b2929bad67"
] | [
"pyFusion/models/vgg16.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.models.vgg import vgg16\n\nclass VGG16(torch.nn.Module):\n def __init__(self, device='cpu'):\n super(VGG16, self).__init__()\n\n # Livelli Convoluzionali non si fa uso del classificatore\n features = list... | [
[
"torch.nn.ModuleList"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joannachuang/2019_summer_joanna | [
"1773cbad88cc2b7ebf20cebbd97d2988919372e6"
] | [
"pylily/0409/0409/0708.py"
] | [
"import json\nimport sqlite3\nfrom datetime import datetime\nimport os\n\nimport Lily.ctao.database as cdb\nimport Lily.crawler.url_string as url\nimport requests\nimport pandas as pd\nimport numpy\nimport io\n\n\n\ndf = pd.read_html('''https://taipeicity.github.io/traffic_realtime/''', header = 0)\n\n#for i in ran... | [
[
"pandas.read_html"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dimitrios-ebi/gene_symbol_classifier | [
"fe415f719fda4619041b9fe0639996c92e0f12a8"
] | [
"notebooks/real_life_assignments_statistics_plotly.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.11.3\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n... | [
[
"pandas.read_csv",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
alan-turing-institute/QUIPP-workflow | [
"db97911f2330902a2fc9156b9380a56199adf3f0"
] | [
"synth-methods/SDV/SDV_pipeline.py"
] | [
"# # SDV pipeline\n# \n# SDV (Synthetic Data Vault) is a Python library (https://github.com/HDI-Project/SDV) that allows users to statistically model an entire multi-table, relational dataset. Users can then use the statistical model to generate a synthetic dataset. \n# \n# Underneath the hood it uses a unique hier... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
kylebystrom/pawpyseed | [
"75af563cff82ed7c4fad272a36b24d4e14d83ae6"
] | [
"pawpyseed/core/tests/test_core.py"
] | [
"import os\nimport subprocess\nimport sys\n\nimport numpy as np\nfrom nose import SkipTest\nfrom nose.tools import nottest\nfrom numpy.testing import assert_almost_equal, assert_equal, assert_raises\nfrom scipy.interpolate import CubicSpline\nfrom scipy.special import factorial2 as fac2\nfrom scipy.special import l... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.cumsum",
"scipy.special.spherical_jn",
"numpy.max",
"numpy.mean",
"numpy.exp",
"numpy.trapz",
"numpy.testing.assert_equal",
"scipy.special.sph_harm",
"numpy.sin",
"numpy.testing.assert_almost_equal",
"numpy.copy",
"scip... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
onso19/AVmod | [
"93e2404560a194a3376b15c04f5bc9653c864e85"
] | [
"converter/face_transformer.py"
] | [
"from .color_correction import *\nimport cv2\nimport numpy as np\n\n\nclass FaceTransformer(object):\n \"\"\"\n Attributes:\n path_func: string, direction for the transformation: either AtoB or BtoA.\n model: the generator of the faceswap-GAN model\n \"\"\"\n def __init__(self): \n ... | [
[
"numpy.array",
"numpy.zeros_like",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dome272/Vector-Quantization | [
"c78808e5187930a978afc221be97ac0705293eca"
] | [
"VQ-VAE/functions.py"
] | [
"import torch\r\nfrom torch.autograd import Function\r\n\r\n\r\nclass VQ(Function):\r\n @staticmethod\r\n def forward(ctx, inputs, codebook):\r\n with torch.no_grad():\r\n inputs_size = inputs.size()\r\n inputs_flatten = inputs.view(-1, codebook.size(1))\r\n\r\n codeboo... | [
[
"torch.min",
"torch.sum",
"torch.zeros_like",
"torch.no_grad",
"torch.index_select"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carnby/corex_topic | [
"f5b46f88f1efdb5d21180575a339f6a6e1cc2b02"
] | [
"corextopic/vis_topic.py"
] | [
"\"\"\" This module implements some visualizations based on CorEx representations.\n\"\"\"\n\nimport os\nfrom shutil import copyfile\nimport codecs\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg') # to create visualizations on a display-less server\nimport pylab\nimport networkx as nx\nimport textwrap\... | [
[
"numpy.matrix",
"numpy.dot",
"numpy.sqrt",
"numpy.asarray",
"numpy.seterr",
"numpy.mean",
"scipy.sparse.vstack",
"numpy.where",
"numpy.unique",
"numpy.clip",
"sklearn.feature_extraction.text.CountVectorizer",
"numpy.std",
"matplotlib.pyplot.close",
"numpy.co... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
xian0gang/rknn-toolkit | [
"fc2a3f1813940061c79c2f11ea17d836c93475e9"
] | [
"examples/common_function_demos/load_quantized_model/onnx/test.py"
] | [
"import os\nimport time\nimport sys\nimport numpy as np\nimport cv2\nfrom rknn.api import RKNN\n\n\nONNX_MODEL = 'shufflenet-v2_quant.onnx'\nIMG_PATH = 'dog_224x224.jpg'\nRKNN_MODEL = './shufflenet-v2_quant.rknn'\n\n\ndef show_outputs(outputs):\n output = outputs[0][0]\n output_sorted = sorted(output, reverse... | [
[
"numpy.exp",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pauldhein/bank-telemarketing-classifier | [
"c2603e0d3905229851106a1720ed55856f329aef"
] | [
"source/utils.py"
] | [
"from typing import List, Dict, Tuple\n\nimport pandas as pd\n\nfrom sklearn.metrics import (\n accuracy_score,\n precision_score,\n recall_score,\n f1_score,\n roc_auc_score,\n)\n\n\ndef load_Xy_dataset(dataset_path: str) -> Tuple[pd.DataFrame, pd.DataFrame]:\n \"\"\"Load a dataset into a pandas ... | [
[
"sklearn.metrics.roc_auc_score",
"pandas.read_csv",
"sklearn.metrics.precision_score",
"sklearn.metrics.f1_score",
"sklearn.metrics.recall_score",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
saper0/scikit-hubness | [
"a50507fe791d8712db46347056f159f18aeb31fa"
] | [
"skhubness/neighbors/lsh.py"
] | [
"# -*- coding: utf-8 -*-\n# SPDX-License-Identifier: BSD-3-Clause\n\n# PEP 563: Postponed Evaluation of Annotations\nfrom __future__ import annotations\n\nfrom functools import partial\nimport logging\nimport sys\nfrom typing import Tuple, Union\nimport warnings\nimport numpy as np\nfrom sklearn.base import BaseEst... | [
[
"numpy.take_along_axis",
"sklearn.utils.validation.check_is_fitted",
"sklearn.utils.validation.check_array",
"numpy.empty_like",
"numpy.ones",
"sklearn.utils.validation.check_X_y",
"numpy.argsort",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mathcolo/t-performance-dash | [
"497fcadceda15d62d1fd6b39817306d48c2c4be5"
] | [
"server/bus/bus2train.py"
] | [
"import argparse\nimport pathlib\nimport pandas as pd\nfrom datetime import datetime\n\n\ndef load_data(input_csv, routes):\n \"\"\"\n Loads in the below format and makes some adjustments for processing.\n - Filter only points with actual trip data\n - Trim leading 0s from route_id\n - Select only ro... | [
[
"pandas.Grouper",
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Eaw7/RunestoneServer | [
"c301f4f9adc64edd3fa0cec9dd5a74b2b5a8e540",
"c301f4f9adc64edd3fa0cec9dd5a74b2b5a8e540"
] | [
"controllers/exams.py",
"controllers/admin.py"
] | [
"# *********************************************\n# |docname| - Endpoints relating to assignments\n# *********************************************\n#\n# Imports\n# =======\n# These are listed in the order prescribed by `PEP 8\n# <http://www.python.org/dev/peps/pep-0008/#imports>`_.\n#\n# Standard library\n# -------... | [
[
"pandas.read_sql_query"
],
[
"pandas.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
tw-yshuang/NTU_2021S_ML_HW4 | [
"b9560460a4531ea9c3086b608c5d357f39b09d47"
] | [
"src/data_process.py"
] | [
"import os\nimport json\nimport torch\nimport random\nfrom pathlib import Path\nfrom torch.utils.data import Dataset, DataLoader, random_split\nfrom torch.nn.utils.rnn import pad_sequence\n\n\nclass PhonemeDataset(Dataset):\n def __init__(self, data_dir, segment_len=128):\n self.data_dir = data_dir\n ... | [
[
"torch.FloatTensor",
"torch.utils.data.random_split",
"torch.nn.utils.rnn.pad_sequence",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
momacs/localedb-py | [
"8215653acbff7bfcd6336d2a8138fba66fd7e12e"
] | [
"src/localedb/util.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Miscellaneous utilities.\"\"\"\n\nimport itertools\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef plot_init(nrows=1, ncol... | [
[
"matplotlib.pyplot.title",
"numpy.min",
"matplotlib.patches.Rectangle",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.subplots",
"numpy.max",
"matplotlib.colors._colors_full_map.values",
"matplotlib.colors.cnames.values",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jychoi118/fsdl-text-recognizer-project | [
"27e02b5319fa59ac4ac840a7851c390d08c3f232"
] | [
"lab5/text_recognizer/networks/lenet.py"
] | [
"from typing import Optional, Tuple\n\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, Input, Lambda, MaxPooling2D, BatchNormalization\nfrom tensorflow.keras.models import Sequential, Model\n\n\ndef lenet(input_shape: Tuple[int, ...], output_shape: Tuple[int, ...]) -> M... | [
[
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.expand_dims",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.layers.Flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
llucid-97/dfa-scales-to-modern-deep-learning | [
"66efb4b4ef8a378bf01ea0e5e6794d6bb4380c97"
] | [
"paper-experiments/geometric-learning/geometric.py"
] | [
"import argparse\nimport os.path as path\nimport torch\nimport torch.nn.functional as F\nimport torch_geometric.transforms as T\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom torch_geometric.datasets import Planetoid\n\nfrom models import ChebNet, DFAChebNet, GraphNet, DFAGraphNet, SplineNet, DFASplin... | [
[
"torch.cat",
"torch.manual_seed",
"torch.from_numpy",
"sklearn.model_selection.StratifiedKFold",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Project-Ellie/capsnet-fashion | [
"1dae4ee000911cf82ace19c667ad095fd447e8ae"
] | [
"src/cifar10_distr.py"
] | [
"import argparse\nimport sys\n\nimport os\nimport tensorflow as tf\nimport time\n\nfrom models.particular_mnist import dense_4_layers, MODEL_FILE\nfrom models.trainer import Params\nfrom mnist import train_batcher, test_batcher\n\ntrain_batcher.set_preprocessor(lambda x: x.reshape(-1, 784))\ntest_batcher.set_prepro... | [
[
"tensorflow.train.Server",
"tensorflow.train.StopAtStepHook",
"tensorflow.train.ClusterSpec",
"tensorflow.train.get_or_create_global_step",
"tensorflow.train.replica_device_setter",
"tensorflow.global_variables_initializer",
"tensorflow.train.AdamOptimizer",
"tensorflow.train.Monit... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
DayGitH/Python-Challenges | [
"bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf"
] | [
"DailyProgrammer/20120507A.py"
] | [
"\"\"\"\nThe Monty Hall Problem [http://en.wikipedia.org/wiki/Monty_Hall_problem] is a probability brain teaser that has a\nrather unintuitive solution.\n\nThe gist of it, taken from Wikipedia:\nSuppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others,\n... | [
[
"numpy.delete",
"numpy.random.permutation",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Cristina-cxq/models-1 | [
"56f39f775273d57de6844cca7aa68c8223b2f607"
] | [
"resnet50/eager/train.py"
] | [
"import argparse\nimport numpy as np\nimport os\nimport time\nimport datetime\nimport shutil\nfrom tqdm import tqdm\nimport oneflow as flow\n\nimport sys\n\nsys.path.append(\".\")\nfrom models.resnet50 import resnet50\nfrom utils.ofrecord_data_utils import OFRecordDataLoader\n\n\nclass AverageMeter:\n \"\"\"Comp... | [
[
"numpy.append",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahnHeejune/Clothing-Matching | [
"257c679d396b397e6ba7a81ce1ae29706e1ddd06"
] | [
"1.Clothing-Texture-Alignment/2.cloth_texture_alignment_dilation.py"
] | [
"### Author: Matiur Rahman Minar ###\n### EMCOM Lab, SeoulTech, 2021 ###\n### Task: Extrapolating clothing boundary for aligning/matching texture to extended silhouette/mask ###\n### helpful for reducing artifacts in 3D clothing reconstruction ###\n### Focused method: Gray dilation ###\n\n\nimport os\nimport cv2\ni... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.title",
"numpy.reshape",
"numpy.ones",
"matplotlib.pyplot.subplot",
"numpy.float32",
"matplotlib.pyplot.xticks",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andreasstegmueller/ModelCalibration | [
"945623c3a4d46ec62632273f7ec77b1f353eeea2"
] | [
"usecases/linearFunction/Correlation.py"
] | [
"import logging\nimport numpy as np\nfrom pathlib import Path\nimport os\nimport unittest\n#from bayes.vb import *\nimport matplotlib.pyplot as plt\nfrom theano_operation import LogLikeWithGrad\nimport pymc3 as pm\nimport theano.tensor as tt\nfrom collections import OrderedDict\nimport copy\nimport matplotlib.cm as... | [
[
"numpy.diag",
"matplotlib.pyplot.legend",
"scipy.stats.invgamma",
"numpy.sqrt",
"numpy.linspace",
"numpy.max",
"numpy.exp",
"numpy.square",
"numpy.random.set_state",
"numpy.interp",
"numpy.repeat",
"numpy.log",
"numpy.min",
"scipy.stats.invgamma.pdf",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joeduris/openPMD-beamphysics | [
"c03c5cda7e8597af5b8781ecce1e360117b06dfb"
] | [
"pmd_beamphysics/units.py"
] | [
"\n\"\"\"\nSimple units functionality for the openPMD beamphysics records.\n\nFor more advanced units, use a package like Pint:\n https://pint.readthedocs.io/\n\n\n\"\"\"\nimport scipy.constants\n\nmec2 = scipy.constants.value('electron mass energy equivalent in MeV')*1e6\nmpc2 = scipy.constants.value('proton ma... | [
[
"numpy.array",
"numpy.sqrt",
"numpy.isscalar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
valtronforever/plconv | [
"ffc0aeea932d9dfc6494a75c733325b867916432"
] | [
"plconv/convert/sokul.py"
] | [
"from pathlib import Path\nimport pandas as pd\nimport numpy as np\nfrom typing import List\nimport xlrd\n\n\ndef sokul(in_path_list: List[Path], out_path: Path):\n result_df = None\n\n for in_path in in_path_list:\n try:\n xl = pd.ExcelFile(in_path)\n except UnicodeDecodeError:\n ... | [
[
"pandas.isna",
"pandas.Series",
"pandas.ExcelFile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
bekirufuk/pointer_summarizer | [
"8fc9726f9337b26339848d896a09e7e8f9456bcc"
] | [
"data_util/batcher.py"
] | [
"#Most of this file is copied form https://github.com/abisee/pointer-generator/blob/master/batcher.py\n\nimport Queue\nimport time\nfrom random import shuffle\nfrom threading import Thread\n\nimport numpy as np\nimport tensorflow as tf\n\nimport config\nimport data\n\nimport random\nrandom.seed(1234)\n\n\nclass Exa... | [
[
"tensorflow.logging.error",
"numpy.zeros",
"tensorflow.logging.info"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
chunfengh/seq2seq | [
"cc6e1a15f523c2ead809d48b1f6eebbeb94e3f0b"
] | [
"seq2seq/decoders/conv_decoder.py"
] | [
"# Copyright 2017 Google Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed... | [
[
"tensorflow.random_uniform_initializer",
"tensorflow.shape",
"tensorflow.python.ops.math_ops.argmax",
"tensorflow.reshape",
"tensorflow.identity",
"tensorflow.contrib.layers.fully_connected",
"tensorflow.contrib.layers.stack",
"tensorflow.contrib.layers.flatten",
"tensorflow.py... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
danielvh8/RP-granzyme-B | [
"fcb29321f8ad55bfaa56e31f45eeab907e1ed1af"
] | [
"Statistics.py"
] | [
"from pathlib import Path\nfrom collections import Counter\nfrom tqdm import tqdm\nimport math\nfrom scipy.stats import norm\nimport re\nimport gzip\nfrom configparser import ConfigParser\n\nparser = ConfigParser()\nparser.read(Path('In/parameters.ini'))\n\ndef GetAcidFrequency():\n \"\"\"\n Read the ... | [
[
"scipy.stats.norm.cdf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cbrisboi/hawc_hal | [
"4126b48fbfd8b6b99aaba3b26570cf33f5feab94"
] | [
"hawc_hal/response/response_bin.py"
] | [
"from builtins import range\nfrom builtins import object\nimport numpy as np\nimport pandas as pd\n\nfrom threeML.exceptions.custom_exceptions import custom_warnings\n\nfrom ..psf_fast import PSFWrapper, InvalidPSF, InvalidPSFError\n\n\nclass ResponseBin(object):\n \"\"\"\n Stores detector response for one de... | [
[
"numpy.zeros_like",
"numpy.allclose",
"pandas.Series",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
LaurinFischer/qiskit-experiments | [
"44d249ff2ec2e90cd630431f66e560528572815e"
] | [
"qiskit_experiments/library/calibration/rough_amplitude_cal.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2021.\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 modifications or ... | [
[
"numpy.round",
"numpy.angle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Acforest/LogPrompt | [
"199766cea9988bc6e8b1c71352b090da68bbb71d"
] | [
"log_sample.py"
] | [
"import os\nimport argparse\nimport pandas as pd\n\nclass LogSampler:\n def __init__(self,\n log_structured_file: str,\n output_dir: str,\n log_type: str,\n sample_num: int,\n random: bool,\n random_state: int) -> Non... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
congzlwag/memetic_thin_film_py | [
"11bd29f6c2bd4880feb9e81ab08d531f98dc45cd"
] | [
"algorithm/disp_interp.py"
] | [
"from numpy import pi, sqrt, array, zeros, sin, cos, tan, linspace, \\\n concatenate, flip, interp \n \n#%%\ndef disp_interp(wvlen_in, data):\n N1 = len(wvlen_in)\n N2 = len(data[:, 0])\n \n wvlen_data = data[:, 0]\n n0 = data[:, 1]\n k0 = data[:, 2]... | [
[
"numpy.interp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
philomenec/reco-gym | [
"f8553d197f42ec2f415aefce48525d0e9b10ddaa"
] | [
"my_entries/test_agent.py"
] | [
"import numpy as np\n\nfrom recogym import Configuration, build_agent_init, to_categorical\nfrom recogym.agents import Agent\n\ntest_agent_args = {\n 'num_products': 10,\n 'with_ps_all': False,\n}\n\n\nclass TestAgent(Agent):\n \"\"\"Organic counter agent\"\"\"\n\n def __init__(self, config = Configurat... | [
[
"numpy.zeros",
"numpy.matmul"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hhuang2018/HLAWholeGeneAnalysis | [
"9cdd2e062a6cc2eed2ebfa84e1888687b2b98cf3"
] | [
"scripts/ARS_M_nonARS_mm_SG41_52.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 6 10:38:30 2017\n\n@author: hhuang2\n\"\"\"\n\nimport glob\nimport csv\n\nfrom utils import IMGTdbIO, CompareSeq\n\n\ngroupType = 'fiveLoci_paired' # groupType = 'ClassI_paired' # groupType = 'All_paired' ; 'fiveLoci_paired'\n\nAll_loci =... | [
[
"matplotlib.backends.backend_pdf.PdfPages",
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.rc",
"numpy.sin",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.close",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xticks",
"matplotlib.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SuhongMoon/uncertainty-baselines | [
"1a7b24f86994c7b69d9263bf47be7169736f0da9"
] | [
"baselines/jft/deterministic.py"
] | [
"# coding=utf-8\n# Copyright 2021 The Uncertainty Baselines 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# Unles... | [
[
"numpy.ones_like",
"tensorflow.random.set_seed",
"tensorflow.io.gfile.exists",
"numpy.min",
"numpy.vstack",
"tensorflow.io.gfile.makedirs",
"numpy.ceil",
"numpy.max",
"numpy.argmax",
"numpy.zeros_like",
"numpy.array",
"tensorflow.keras.metrics.Mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
nmud19/PropertyValuation | [
"9285029bd1f29c51b71321a46a98f97e564786ec"
] | [
"code/generate_output.py"
] | [
"from torch.utils.data import DataLoader\nfrom pytorch_lightning import Trainer\nfrom model import QuantModel\nimport pandas as pd\nfrom typing import List\nimport matplotlib.pyplot as plt\nimport shap\nimport sklearn\nimport numpy as np\n\n\nclass GenerateOutput:\n \"\"\"Calls to generate predictions and shap v... | [
[
"matplotlib.pyplot.legend",
"numpy.abs",
"sklearn.metrics.mean_absolute_error",
"matplotlib.pyplot.savefig",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.plot",
"sklearn.metrics.mean_absolute_percentage_error",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.figure"
]... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Glaciohound/VCML | [
"5a0f01a0baba238cef2f63131fccd412e3d7822b"
] | [
"models/visualize/visualize_utils.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# File : visualize_utils.py\n# Author : Chi Han, Jiayuan Mao\n# Email : haanchi@gmail.com, maojiayuan@gmail.com\n# Date : 01.08.2019\n# Last Modified Date: 26.08.2019\n# Last Modified By : Chi Han, Jiayuan Mao\n#\n# ... | [
[
"numpy.concatenate",
"sklearn.manifold.TSNE",
"numpy.ndenumerate",
"sklearn.decomposition.PCA"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiaxu825/VyPy | [
"47100bad9dea46f12cb8bfa1ba86886e06f5c85d",
"5acb40e8d19ea76f3cd45f9cf98f252ca15e23f6"
] | [
"trunk/VyPy/optimize/drivers/scipy/SLSQP.py",
"trunk/VyPy/regression/gpr/training/Training.py"
] | [
"\r\n# ----------------------------------------------------------------------\r\n# Imports\r\n# ----------------------------------------------------------------------\r\n\r\nimport VyPy\r\nfrom VyPy.data import ibunch\r\nfrom VyPy.optimize.drivers import Driver\r\nimport numpy as np\r\nfrom time import time\r\n\r... | [
[
"numpy.empty",
"numpy.squeeze",
"numpy.array",
"numpy.vstack"
],
[
"numpy.empty",
"numpy.argmin",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LiubovSobolevskaya/hpa-single-cell | [
"ebe6d046b651a1c45095f26e99cfb13adefb63d9"
] | [
"src/preprocessing/unify_predictions_from_image_level_densenet.py"
] | [
"import sys\n\nfrom src.models.encodings_pretrained import BestfittingEncodingsModel\n\nsys.path.insert(0, '..')\nimport argparse\nimport pickle\nimport pandas as pd\nimport torch\nimport torch.optim\nfrom torch.backends import cudnn\nimport torch.nn.functional as F\nfrom albumentations import Compose, VerticalFlip... | [
[
"pandas.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
munakoiso/alpha-zero | [
"49d0dee93955fa4fb3c91e1291b889bea24cb30f"
] | [
"train.py"
] | [
"# MIT License\n#\n# Copyright (c) 2018 Blanyal D'Souza\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, cop... | [
[
"numpy.reshape",
"numpy.rot90"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ccolas/sentence-transformers | [
"d7235076a663114c5267b093d5c28e1fc0272f76"
] | [
"sentence_transformers/losses/ContrastiveTensionLoss.py"
] | [
"import torch\nfrom torch import nn, Tensor\nfrom typing import Iterable, Dict\nfrom ..SentenceTransformer import SentenceTransformer\nfrom .. import util\nimport copy\nimport random\nimport math\nfrom .. import InputExample\nimport numpy as np\n\nclass ContrastiveTensionLoss(nn.Module):\n \"\"\"\n This l... | [
[
"torch.nn.CrossEntropyLoss",
"numpy.log",
"torch.ones",
"torch.matmul",
"torch.nn.BCEWithLogitsLoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tomasfernandez1212/Copulas | [
"1163a11a090149fb8e6a2cd4a0d00356f044536b"
] | [
"copulas/univariate/gaussian_kde.py"
] | [
"\nimport numpy as np\nfrom scipy.special import ndtr\nfrom scipy.stats import gaussian_kde\n\nfrom copulas import EPSILON, store_args\nfrom copulas.optimize import bisect, chandrupatla\nfrom copulas.univariate.base import BoundedType, ParametricType, ScipyModel\n\n\nclass GaussianKDE(ScipyModel):\n \"\"\"A wrap... | [
[
"numpy.sqrt",
"scipy.special.ndtr",
"numpy.min",
"numpy.unique",
"numpy.full",
"numpy.max",
"scipy.stats.gaussian_kde",
"numpy.std",
"numpy.any",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
merantix/mxlabs-daain | [
"0e87df5dd6e678939374dfadf44fc360d34425bb",
"0e87df5dd6e678939374dfadf44fc360d34425bb"
] | [
"src/daain/trainer/data.py",
"src/daain/backbones/esp_dropout_net/trainer/iou_eval.py"
] | [
"import torch\nimport torchvision\nfrom torch.utils.data import DataLoader, random_split\n\nfrom daain.data import ActivationDataset\nfrom daain.data.datasets import get_split_sizes\n\n\ndef _get_zarr_root(dataset_data, attack_name, mask, submask):\n return dataset_data[f\"/{mask}/{submask}/{attack_name}\"]\n\n\... | [
[
"torch.eye",
"torch.utils.data.DataLoader",
"torch.Tensor"
],
[
"torch.isnan",
"torch.zeros",
"torch.tensor",
"torch.flatten",
"torch.diag",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joseph-d-p/altair | [
"661cf9ff556ab8df3ea4371734a554237d2f6e7f"
] | [
"altair/utils/core.py"
] | [
"\"\"\"\nUtility routines\n\"\"\"\nimport collections\nfrom copy import deepcopy\nimport json\nimport itertools\nimport re\nimport sys\nimport traceback\nimport warnings\n\nimport jsonschema\nimport six\nimport pandas as pd\nimport numpy as np\n\nfrom .schemapi import SchemaBase, Undefined\n\ntry:\n from pandas.... | [
[
"pandas.lib.infer_dtype",
"numpy.issubdtype",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
grovina/deep101 | [
"3ea7b93e91b0cceed17fa9535c5477084eac8064"
] | [
"mnist_min.py"
] | [
"from tensorflow.examples.tutorials.mnist import input_data\n\nimport tensorflow as tf\n\n\n# Geral\ntf.set_random_seed(1)\nxavier = tf.contrib.layers.xavier_initializer()\n\n# Dados\nmnist = input_data.read_data_sets('.')\n\n# Modelo\nx = tf.placeholder(tf.float32, [None, 784])\n\nwith tf.name_scope('single'):\n ... | [
[
"tensorflow.matmul",
"tensorflow.losses.sparse_softmax_cross_entropy",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.contrib.layers.xavier_initializer",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
cfe316/atomic | [
"e9e264301bb8b4ccfe5b23a32c3e361cc140cdba"
] | [
"atomic/tests/test_collisional_radiative.py"
] | [
"import unittest\nimport numpy as np\nimport atomic\n\nclass TestCollRadEquilibrium(unittest.TestCase):\n def setUp(self):\n \"\"\"This is more of an Integration Test than a unit test.\"\"\"\n self.ad = atomic.element('Li')\n self.eq = atomic.CollRadEquilibrium(self.ad)\n\n def test___ini... | [
[
"numpy.array",
"numpy.sum",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ramarty/GOST_PublicGoods | [
"de9cf36e37208eaf69253e784833990ceeb1058a"
] | [
"Urbanization/GOST_Urban/rasterMisc.py"
] | [
"################################################################################\n# Miscellaneous Raster functions\n# Benjamin Stewart, September 2018\n# Purpose: Collect a number of useful raster functions in one place\n################################################################################\n\nimport sys... | [
[
"numpy.ma.masked_where",
"numpy.empty",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mistobaan/tpu | [
"cbbe780d157e8ee3b1b481f3963daa881dfcdf5d"
] | [
"models/official/detection/serving/detection.py"
] | [
"# Lint as: python2, python3\n# 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/... | [
[
"tensorflow.compat.v1.estimator.export.ServingInputReceiver",
"tensorflow.compat.v1.estimator.EstimatorSpec",
"tensorflow.compat.v1.expand_dims",
"tensorflow.compat.v1.estimator.tpu.TPUEstimatorSpec",
"tensorflow.compat.v1.cast",
"tensorflow.compat.v1.identity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
magwenelab/mini-term-2016 | [
"b1e92901729dfb7ac484ceb5c89cd99e554f23bd"
] | [
"hill-fxn-wlogic.py"
] | [
"import platform\n\nimport matplotlib\nif 'Darwin' in platform.platform(): # on OSX\n matplotlib.use('TkAgg')\nimport numpy as np\n\nimport pylab\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import Slider, Button, RadioButtons, MultiCursor\n\n\ndef hillfxn(x, B, K, n):\n xn = float(x**n)\n re... | [
[
"matplotlib.widgets.MultiCursor",
"numpy.linspace",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.axes",
"matplotlib.widgets.Slider",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.widgets.RadioButtons"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kzhangnd/dataset_curation | [
"1edcf07ed32d7b5b191a72dbd37d3433f201c7de"
] | [
"stage_2_efficient.py"
] | [
"import numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport argparse\nfrom os import path, makedirs\nfrom datetime import datetime\nfrom tqdm import tqdm\n\n\nPROBE_FILE = None\nPROBE = None\nPROBE_O = None\nMETRIC = None\n\n\ndef skip_diag(A): #remove the diagonal element of a matrix\n ... | [
[
"numpy.arange",
"sklearn.metrics.pairwise.cosine_similarity",
"numpy.argmax",
"numpy.diff",
"numpy.savetxt",
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dok529/smart-social-distancing | [
"fd054c92cf478cefd5326c7beaa288b24dd5110f",
"2b71c4330420758a3ff6833923cf2ef81cdebdb1"
] | [
"libs/metrics/base.py",
"libs/metrics/social_distancing.py"
] | [
"import os\nimport csv\nimport numpy as np\nimport pandas as pd\nimport logging\n\nfrom collections import deque\nfrom datetime import date, datetime, timedelta, time\nfrom typing import Dict, List, Iterator\n\nfrom libs.utils.loggers import get_source_log_directory, get_area_log_directory, get_source_logging_inter... | [
[
"numpy.polyfit",
"pandas.read_csv",
"pandas.to_datetime",
"pandas.date_range",
"numpy.array",
"numpy.zeros"
],
[
"numpy.floor",
"numpy.zeros",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wsdea/MKWii-bot | [
"a981bd58ae41770451cdc326a21447367bc923f3"
] | [
"utils.py"
] | [
"# Done by Frannecklp\n\nimport cv2\nimport numpy as np\nimport win32gui, win32ui, win32con, win32api\nimport matplotlib.pyplot as plt\nimport pickle\nimport os,shutil\n\nbox = (45,37,912,511)\ndef grab_screen(region=box):\n\n hwin = win32gui.GetDesktopWindow()\n\n if region:\n left,top,x2,y2 = reg... | [
[
"matplotlib.pyplot.imshow",
"numpy.fromstring",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.