repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
SimonsRoad/UnDeepVO | [
"956598958e0dba4729a8af70ee7a4cdcc10f09ec",
"956598958e0dba4729a8af70ee7a4cdcc10f09ec"
] | [
"demo_odometry.py",
"generators_test.py"
] | [
"\n\"\"\"Example of pykitti.odometry usage.\"\"\"\nimport itertools\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nimport pykitti\n\n__author__ = \"Lee Clement\"\n__email__ = \"lee.clement@robotics.utias.utoronto.ca\"\n\n# Change this to the directory where you stor... | [
[
"numpy.set_printoptions",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.figure"
],
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thouska/SALib | [
"5deeaf316ef58ea0a26295c8ad2ca57cdc739d45",
"fd64136192f00a9e3e65a8c5c05e30d93ed5e750"
] | [
"tests/test_test_functions.py",
"tests/test_regression.py"
] | [
"from nose.tools import assert_almost_equal, assert_equal, raises\n\nfrom numpy.testing import assert_allclose\nimport numpy as np\n\nfrom SALib.test_functions.Sobol_G import evaluate, total_variance, \\\n partial_first_order_variance, \\\n sen... | [
[
"numpy.array",
"numpy.zeros",
"numpy.testing.assert_allclose"
],
[
"numpy.random.seed",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dineshpinto/nft_analytics | [
"99fd4adbfe786f4de6fa2a6a0c5e8a58eaaf338a"
] | [
"src/nft_analytics.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nMIT License\n\nCopyright (c) 2021 Dinesh Pinto\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rig... | [
[
"numpy.array",
"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": []
}
] |
smsaladi/fixthejet | [
"b3089e6ee8cf2afbf24251de47702e0b1446eb73"
] | [
"colors_from_mpl.py"
] | [
"\"\"\"\nWrites out hex colors from color scales provided in matplotlib\ninto JS file\n\npython colors_from_mpl.py >> js/colorscales.js\n\"\"\"\n\nimport itertools\nimport json\n\nimport numpy as np\nimport matplotlib.colors\nimport matplotlib.cm\n\n# Have colormaps separated into categories:\n# http://matplotlib.o... | [
[
"numpy.apply_along_axis",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wesley-Tse/Road-Detection | [
"c3b444287d9b41ccc4234e737e4421b5d1b3c3da"
] | [
"train.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @author: Wesley\n# @time: 2020-12-11 10:47\n\nimport os\nimport time\nimport torch\nfrom torch import nn\nfrom models.dinknet34 import DinkNet34\nfrom loss import dice_bce_loss\nfrom models.unet import UNet\nfrom dataset import MyDataset\nfrom torch.utils.data imp... | [
[
"torch.no_grad",
"torch.cuda.device_count",
"torch.utils.data.DataLoader",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
p5a0u9l/clamm | [
"a41ce2526e9792ce08263bf27eb9c417608d1f5d"
] | [
"clamm/streams/plot_big_stft.py"
] | [
"\"\"\"\nConvert a large audio wav file (album length, i.e. > 30 minutes typically)\ninto a series of videos consisting of the audio synchronized with images of the\nspectrogram.\n\"\"\"\nimport os\nimport sys\nimport multiprocessing as mp\nimport subprocess\n\nimport tqdm\nimport numpy as np\nimport librosa.core\n... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.switch_backend",
"matplotlib.pyplot.savefig",
"numpy.save",
"numpy.max",
"matplotlib.pyplot.close",
"matplotlib.pyplot.vlines",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mbooali/introduction-to-machine | [
"3f75f9897f1f63f07bb6eace312fa35e16786623"
] | [
"mglearn/plot_animal_tree.py"
] | [
"from imageio import imread\nimport matplotlib.pyplot as plt\n\n\ndef plot_animal_tree(ax=None):\n import graphviz\n if ax is None:\n ax = plt.gca()\n mygraph = graphviz.Digraph(node_attr={'shape': 'box'},\n edge_attr={'labeldistance': \"10.5\"},\n ... | [
[
"matplotlib.pyplot.gca"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shihao-Feng-98/RRP_Hopper_simulation | [
"444dbcce90d5ffb6bf577ed03adc9717183e21ae"
] | [
"controller.py"
] | [
"'''\nonly for RRP Hopper\nShihao Feng\n2021.10.28\n'''\n\nimport numpy as np\nimport pybullet as p\nfrom leg_kinematics import LegKinematicsRRP\nimport pinocchio as pin\n\nclass JointPDController(object):\n def __init__ (self):\n self.kp = np.array([70, 70, 1500])\n self.kd = np.array([2, 2, 10])\... | [
[
"numpy.cos",
"numpy.sin",
"numpy.ones",
"numpy.tan",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XinlongSBU/pynucastro | [
"4f1547e99208ad03d8f79d748601219591a157b5",
"4f1547e99208ad03d8f79d748601219591a157b5"
] | [
"pynucastro/networks/rate_collection.py",
"pynucastro/templates/fortran-vode/weak.py"
] | [
"\"\"\"A collection of classes and methods to deal with collections of\nrates that together make up a network.\"\"\"\n\n# Common Imports\nfrom __future__ import print_function\n\nimport functools\nimport math\nfrom operator import mul\nimport os\nfrom collections import OrderedDict\n\nfrom ipywidgets import interac... | [
[
"matplotlib.collections.PatchCollection",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"numpy.amin",
"nump... | [
{
"matplotlib": [],
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"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MGIMM/dynamic_balancing | [
"74482a970996ec75f5fb3f433b8285420787ccd7"
] | [
"notebooks/static_simulation.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport torch\nimport numpy as np\nimport matplotlib.pyplot as plt\n# from MMDBalancing import MMDBalancing as MMDB\n# from OptimalTransportBalancing import OptimalTransportBalancing as OTB\n# from NeuralAdversarialBalancing import NeuralAdversarialBalancing a... | [
[
"sklearn.ensemble.RandomForestRegressor",
"torch.normal",
"torch.abs",
"torch.ones",
"sklearn.linear_model.LogisticRegression",
"torch.zeros",
"torch.sin",
"torch.from_numpy",
"sklearn.linear_model.LinearRegression",
"torch.rand",
"torch.arange",
"pandas.set_option"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Xuerui-Yang/xuerui-stat | [
"08b9dfedac810cbad5ee5969ca554212eb989db0"
] | [
"xuerui_stat/analysis/random_forest/plot_tree.py"
] | [
"import matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\n\r\n\r\nclass PlotTree():\r\n def __init__(self,tree_class):\r\n self._tree_class=tree_class\r\n self._decision_node = dict(boxstyle=\"sawtooth\", fc=\"0.8\")\r\n self._leaf_node = dict(boxstyle=\"round4\", fc=\"0.8\")\r\n ... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cvisb/cvisb_data | [
"81ebf22782f2c44f8aa8ab9437cc4fb54248c3ed",
"81ebf22782f2c44f8aa8ab9437cc4fb54248c3ed",
"81ebf22782f2c44f8aa8ab9437cc4fb54248c3ed",
"81ebf22782f2c44f8aa8ab9437cc4fb54248c3ed"
] | [
"sample-viewer-api/src/static/data/exploratory_scripts/2019-06-03_bonnie_plasmasamples.py",
"sample-viewer-api/src/static/data/exploratory_scripts/merge_public_ids_20190227.py",
"sample-viewer-api/src/static/data/compile_cvisb_data/clean_serology/clean_sero_covid_32783920.py",
"sample-viewer-api/src/static/da... | [
"# Goal: get ebola/Lassa for Bonnie's plasma samples.\n# Simple clean and merge\n\nimport pandas as pd\n\nimport os\nos.chdir(\"/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/\")\n\nimport helpers\n\ndf = pd.read_excel(\"/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/... | [
[
"pandas.merge",
"pandas.read_excel",
"pandas.read_json"
],
[
"pandas.read_csv",
"pandas.read_excel",
"pandas.melt",
"pandas.merge"
],
[
"pandas.read_excel",
"pandas.melt"
],
[
"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": []
},
{
"matplotlib": [],
"nump... |
plutasnyy/mgr | [
"4ca5686ba7d62d0e2b8c172f17eb90bd822fdc21"
] | [
"src/models/conv_block.py"
] | [
"from torch import nn\n\n\nclass ConvolutionalBlock(nn.Module):\n\n def __init__(self, in_channels=128, out_channels=256, kernel_size=3, padding=1, stride=1, padding_mode='zeros'):\n super().__init__()\n self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, padding=padding, str... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.ReLU",
"torch.nn.Conv1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
peterchanw/utils | [
"26133c52ba5b0407d38371100b7b56fe2cf68149"
] | [
"LinReg/utilsLinReg.py"
] | [
"import sklearn.metrics as metrics\nimport pandas as pd\nimport numpy as np\n\ndef repair_chrdata(df, tCol):\n ### Parameters:\n # df: input dataframe\n # tCol: targeted column label with NaN\n ### Output\n # df: repaired dataframe\n # word: string of related dataframe column with some records hav... | [
[
"sklearn.metrics.explained_variance_score",
"sklearn.metrics.r2_score",
"numpy.sqrt",
"numpy.isnan",
"sklearn.metrics.mean_absolute_error",
"sklearn.metrics.mean_squared_error",
"numpy.float64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hansonmcoombs/flopy | [
"49398983c36d381992621d5bf698ea7f78fc0014",
"49398983c36d381992621d5bf698ea7f78fc0014"
] | [
"autotest/t024_test.py",
"flopy/modpath/mp6sim.py"
] | [
"import os\n\nimport numpy as np\nimport pytest\nfrom ci_framework import FlopyTestSetup, base_test_dir\n\nimport flopy\n\nbase_dir = base_test_dir(__file__, rel_path=\"temp\", verbose=True)\n\nex_pth = os.path.join(\"..\", \"examples\", \"data\", \"mf2005_test\")\ntestmodels = [\n os.path.join(ex_pth, f) for f ... | [
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
],
[
"numpy.arange",
"numpy.zeros",
"numpy.dtype",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
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"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ggzhang0071/nni | [
"f4145e62d89c3ca383cf00f2de5dfd2d1025ad92",
"eaad98528c7aa714c9848800d607d6aa3bdd531d",
"eaad98528c7aa714c9848800d607d6aa3bdd531d"
] | [
"nni/retiarii/nn/pytorch/api.py",
"nni/compression/pytorch/utils/mask_conflict.py",
"nni/retiarii/oneshot/pytorch/random.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport math\nimport operator\nimport warnings\nfrom typing import Any, List, Union, Dict, Optional, Callable, Iterable, NoReturn, TypeVar\n\nimport torch\nimport torch.nn as nn\n\nfrom nni.common.serializer import Translatable\nfrom nni.r... | [
[
"torch.cat"
],
[
"torch.ones",
"torch.jit.trace",
"torch.load",
"torch.zeros_like",
"numpy.prod",
"torch.nonzero",
"torch.save"
],
[
"torch.no_grad",
"torch.utils.data.DataLoader",
"torch.cuda.is_available",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neochristou/tensorflow | [
"1fb338b1c42930c0eef4d0b4d8d5fdf24a678654",
"50b55bfc5c9132c3bd82505181380bffbb47a5ff",
"1fb338b1c42930c0eef4d0b4d8d5fdf24a678654"
] | [
"tensorflow/python/distribute/collective_all_reduce_strategy.py",
"tensorflow/python/data/experimental/ops/data_service_ops.py",
"tensorflow/tools/docs/generate2_test.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.distribute.distribute_lib.StrategyExtendedV1.__init__",
"tensorflow.python.platform.tf_logging.error",
"tensorflow.python.eager.context.async_wait",
"tensorflow.python.distribute.numpy_dataset.SingleDevice",
"tensorflow.python.framework.ops.executing_eagerly_outside_function... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"2.7",
"2.4",
"2.9",
"2.5",
"2.6",
"2.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"... |
SparkJiao/MERIt | [
"e887dd11bd2969345a5fb07c47d49bd0245e41e6"
] | [
"reclor_trainer_base_v2.py"
] | [
"# coding=utf-8\n#\n# Copyright 2020 Heinrich Heine University Duesseldorf\n#\n# Part of this code is based on the source code of BERT-DST\n# (arXiv:1907.03040)\n# Part of this code is based on the source code of Transformers\n# (arXiv:1910.03771)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\... | [
[
"torch.load",
"torch.utils.data.DataLoader",
"torch.cuda.amp.grad_scaler.GradScaler",
"torch.cuda.amp.autocast",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available",
"torch.device",
"torch.distributed.init_process_group",
"torch.utils.data.di... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors | [
"4198bd2d325a32ffc4e714c486540e63440ab110"
] | [
"deblurring_celeba_algorithm_1.py"
] | [
"import tensorflow as tf\nimport keras.backend as K\nimport numpy as np\nfrom Utils import *\nfrom generators.MotionBlurGenerator import *\nfrom generators.CelebAGenerator import *\nK.set_learning_phase(0)\nfrom glob import glob\nimport os\n\n\n# paths\nOrig_Path = './results/CelebA/Original Images/*.png'\nRa... | [
[
"numpy.exp",
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dmontemayor/CRPM | [
"e896831fad7bed42d17574b137e600fc5adbf6b0",
"e896831fad7bed42d17574b137e600fc5adbf6b0"
] | [
"crpm/pvalue.py",
"crpm/gradientdecent.py"
] | [
"\"\"\" Calcualte p-values, ROC, AUC, and proportion of significant observations for\na set of observations given the null hypothesis distribution\n\n Args:\n variable: array of observed values\n hypothesis: optional null hypotheis distribution (beta distribution by default)\n alpha: optiona... | [
[
"numpy.random.beta",
"numpy.linspace",
"numpy.array",
"numpy.sum",
"numpy.divide"
],
[
"numpy.copy",
"numpy.sum",
"numpy.multiply"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tywang89/pyprobml | [
"82cfdcb8daea653cda8f77e8737e585418476ca7"
] | [
"book/linreg_poly_vs_degree.py"
] | [
"# Plot polynomial regression on 1d problem\n# Based on https://github.com/probml/pmtk3/blob/master/demos/linregPolyVsDegree.m\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pyprobml_utils import save_fig\n\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import Linea... | [
[
"numpy.square",
"numpy.sqrt",
"numpy.random.seed",
"numpy.linspace",
"numpy.arange",
"matplotlib.pyplot.subplots",
"sklearn.preprocessing.PolynomialFeatures",
"sklearn.metrics.mean_squared_error",
"numpy.max",
"numpy.random.normal",
"sklearn.linear_model.LinearRegressio... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haymrpig/Pytorch_template | [
"9a0eda43b2da27807461b305ed42e1bd7c1341dd"
] | [
"baseline/utils/mainFunctions.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\nfrom tqdm import tqdm\n\nclass _BaseWrapper():\n def __init__(self, model):\n super().__init__()\n self.model = model\n self.handlers = []\n\n def forward(self, images):\n self.image_sha... | [
[
"torch.nn.functional.softmax",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.nn.functional.relu",
"torch.mul",
"torch.nn.functional.interpolate",
"torch.nn.functional.one_hot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JamesBrofos/Thresholds-in-Hamiltonian-Monte-Carlo | [
"7ee1b530db0eb536666dbc872fbf8200e53dd49b",
"7ee1b530db0eb536666dbc872fbf8200e53dd49b",
"7ee1b530db0eb536666dbc872fbf8200e53dd49b"
] | [
"hmc/tests/test_cox_poisson.py",
"hmc/applications/cox_poisson/prior.py",
"hmc/averaging.py"
] | [
"import unittest\n\nimport numpy as np\n\nfrom hmc.applications.cox_poisson import forward_transform, inverse_transform, generate_data, gaussian_posterior_factory, hyperparameter_posterior_factory\nfrom hmc.applications.cox_poisson.prior import log_prior, grad_log_prior, hess_log_prior, grad_hess_log_prior\n\n\ncla... | [
[
"numpy.log",
"numpy.allclose",
"numpy.random.normal",
"numpy.random.uniform",
"numpy.array"
],
[
"numpy.square",
"numpy.log",
"numpy.power",
"numpy.exp",
"numpy.reciprocal",
"numpy.array",
"numpy.zeros"
],
[
"numpy.sqrt",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UF-f1tenth/F1tenth-UFL | [
"93b0a822c67b2b425664642955342138e65974f4"
] | [
"Object detection and depth estimation/catkin_ws/src/f110-fall2018-skeltons/labs/wall_following/scripts/utils/other.py"
] | [
"\"\"\"\nCreated on Fri Oct 29 18:54:18 2021\n\n@author: Krishna Nuthalapati\n\"\"\"\n\nimport numpy as np\n\ndef iou(boxA, boxB):\n\t# determine the (x, y)-coordinates of the intersection rectangle\n\txA = max(boxA[0], boxB[0])\n\tyA = max(boxA[1], boxB[1])\n\txB = min(boxA[2], boxB[2])\n\tyB = min(boxA[3], boxB[3... | [
[
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alstonlo/fgh-gnn | [
"099aee925a3c5077070803d31b6e45793972239c"
] | [
"fgh_gnn/data/graph_builder.py"
] | [
"import itertools\n\nimport dgl\nimport torch\nfrom rdkit import Chem\nfrom scipy.sparse import csr_matrix\nfrom scipy.sparse.csgraph import minimum_spanning_tree\n\nfrom fgh_gnn.utils import FGROUP_MOLS, get_ring_fragments, ogb_graph_to_mol\n\n\nclass FGroupHetGraphBuilder:\n\n def __init__(self, vocab):\n ... | [
[
"torch.min",
"torch.from_numpy",
"scipy.sparse.csr_matrix",
"torch.tensor",
"scipy.sparse.csgraph.minimum_spanning_tree",
"torch.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wangjiangtao-NJPI/MachineLearning | [
"78124b56a26ec68efb3c517a4a2420860b6e4a75",
"78124b56a26ec68efb3c517a4a2420860b6e4a75",
"78124b56a26ec68efb3c517a4a2420860b6e4a75"
] | [
"g_CNN/Optimizers.py",
"Notebooks/SVM/zh-cn/Util.py",
"NN/PyTorch/__Dev/Networks.py"
] | [
"import os\nimport sys\nroot_path = os.path.abspath(\"../\")\nif root_path not in sys.path:\n sys.path.append(root_path)\n\nimport tensorflow as tf\n\n\nclass Optimizer:\n def __init__(self, lr=1e-3):\n self._lr = lr\n self._opt = None\n\n @property\n def name(self):\n return str(se... | [
[
"tensorflow.train.AdagradOptimizer",
"tensorflow.train.RMSPropOptimizer",
"tensorflow.train.AdadeltaOptimizer",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.train.MomentumOptimizer",
"tensorflow.train.AdamOptimizer"
],
[
"numpy.linspace",
"numpy.max",
"numpy.ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
fshp971/mcmc-unlearning | [
"3113dedca6de33bcaf316b804cb9c1e636db7fd5"
] | [
"BNN/forget.py"
] | [
"from datetime import datetime\nimport os\nimport pickle\nimport argparse\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom mcmc_unlearner import sgmcmcUnlearner\nimport utils\nimport models\n\n\nclass myUnlearner(sgmcmcUnlearner):\n def _apply_sample(self, z):\n x, y = z\n ... | [
[
"torch.cuda.synchronize",
"torch.load",
"torch.cat",
"torch.nn.functional.cross_entropy",
"torch.tensor",
"torch.no_grad",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nina-pinheiro/Data-Science-Python | [
"b6b2bc28f2f8f925e1b43408330641bd72388232"
] | [
"files/regressao_linear/regressaolinear1.py"
] | [
"# Importar as bibliotecas necessárias\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\nimport seaborn as sns\nfrom sklearn.linear_model import LinearRegression\n\n# Leit... | [
[
"pandas.read_csv",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.ylabel",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
alancsouza/chip_clas | [
"e6df8713ae7dd70a5719af83b3b6cb5686f87e29"
] | [
"Experimental setup/Window size test/data6.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\ndata6 = Breast cancer\n\n\"\"\"\nfrom chip_clas_new import chip_clas_new\nimport statistics\nfrom functions import remove_noise\nfrom sklearn.model_selection import train_test_split, KFold\nfrom sklearn.preprocessing import MinMaxScaler\nimport numpy as np\n... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lhcezx/Deteciton_3D | [
"e98b9bb0dd96dfa112e196ec93129caf1ffef39e",
"e98b9bb0dd96dfa112e196ec93129caf1ffef39e"
] | [
"sfa/data_process/transformation.py",
"sfa/data_process/kitti_dataloader.py"
] | [
"import os\nimport sys\nimport math\n\nimport numpy as np\nimport torch\n\nsrc_dir = os.path.dirname(os.path.realpath(__file__))\nwhile not src_dir.endswith(\"sfa\"):\n src_dir = os.path.dirname(src_dir)\nif src_dir not in sys.path:\n sys.path.append(src_dir)\n\nfrom config import kitti_config as cnf\n\n\ndef... | [
[
"numpy.dot",
"numpy.random.random",
"numpy.clip",
"numpy.linalg.inv",
"numpy.eye",
"numpy.matmul",
"numpy.cos",
"numpy.tile",
"numpy.sin",
"numpy.ones",
"numpy.zeros_like",
"numpy.transpose",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
art-vish/neuro-comma | [
"148ff7150e92d734d926a576c50bcabf1ae0ec0a"
] | [
"src/neuro_comma/dataset.py"
] | [
"from typing import Dict, List, Optional, Tuple, Union\nfrom typing_extensions import TypedDict\n\nimport numpy as np\nimport torch\nfrom torch import Tensor\nfrom tqdm import tqdm\nfrom transformers import PreTrainedTokenizer\n\nfrom neuro_comma.augmentation import AUGMENTATIONS\nfrom neuro_comma.pretrained import... | [
[
"numpy.random.rand",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ajayiagbebaku/NFL-Model | [
"afcc67a85ca7138c58c3334d45988ada2da158ed",
"afcc67a85ca7138c58c3334d45988ada2da158ed",
"afcc67a85ca7138c58c3334d45988ada2da158ed",
"afcc67a85ca7138c58c3334d45988ada2da158ed",
"afcc67a85ca7138c58c3334d45988ada2da158ed",
"afcc67a85ca7138c58c3334d45988ada2da158ed",
"afcc67a85ca7138c58c3334d45988ada2da158e... | [
"venv/Lib/site-packages/streamlit/caching/hashing.py",
"venv/Lib/site-packages/pandas/tests/test_optional_dependency.py",
"venv/Lib/site-packages/pandas/tests/indexing/test_coercion.py",
"venv/Lib/site-packages/pandas/core/indexes/period.py",
"venv/Lib/site-packages/pandas/tests/groupby/aggregate/test_other... | [
"# Copyright 2018-2021 Streamlit 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 ... | [
[
"pandas.util.hash_pandas_object",
"numpy.random.RandomState"
],
[
"pandas._testing.assert_produces_warning",
"pandas.compat._optional.import_optional_dependency"
],
[
"pandas.Series",
"pandas.PeriodIndex",
"numpy.asarray",
"pandas.Index",
"pandas.DatetimeIndex",
"pa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"p... |
DeVriesMatt/pointMLP-pytorch | [
"e9c09a2038551e83b072353f3fd7e3294463e892",
"e9c09a2038551e83b072353f3fd7e3294463e892",
"e9c09a2038551e83b072353f3fd7e3294463e892"
] | [
"classification_ModelNet40/test.py",
"autoencoder_elite_tearing_rotation_tokenclassifier.py",
"create_autoencoder_tearing.py"
] | [
"\"\"\"\npython test.py --model pointMLP --msg 20220209053148-404\n\"\"\"\nimport argparse\nimport os\nimport datetime\nimport torch\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.optim\nimport torch.utils.data\nimport torch.utils.data.distributed\nfrom torch.utils.data import DataLoa... | [
[
"sklearn.metrics.balanced_accuracy_score",
"numpy.concatenate",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.nn.DataParallel",
"sklearn.metrics.accuracy_score"
],
[
"torch.nn.Parameter",
"torch.Tensor",
"torch.load",
"numpy.asarray",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
roy881020/VSGNet | [
"a9ba741871d1d7ff401cecf23659f0b75576e7c3",
"a9ba741871d1d7ff401cecf23659f0b75576e7c3"
] | [
"scripts_hico/HICO_eval/bbox_utils.py",
"scripts/prior_vcoco.py"
] | [
"import numpy as np\n#import skimage.draw as skdraw\n\n\ndef add_bbox(img,bbox,color=[0,0,0],fill=False,alpha=1):\n x1,y1,x2,y2 = bbox\n \n # Clockwise starting from top left\n r = [y1,y1,y2,y2]\n c = [x1,x2,x2,x1]\n \n if fill:\n coords = skdraw.polygon(r,c,shape=img.shape[0:2])\n ... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.stack",
"numpy.copy",
"numpy.logical_and",
"numpy.zeros"
],
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PacktPublishing/Python-Machine-Learning-Solutions-V- | [
"130c9881757fa90bbb124d48ddd0c6c1136fa20c"
] | [
"Section_07_code/speech_recognizer.py"
] | [
"import os\nimport argparse\nimport warnings\nimport numpy as np\nfrom scipy.io import wavfile\nfrom hmmlearn import hmm\nfrom python_speech_features import mfcc\n\n# Function to parse input arguments\ndef build_arg_parser():\n parser = argparse.ArgumentParser(description='Trains the HMM classifier')\n parser... | [
[
"numpy.seterr",
"numpy.append",
"numpy.array",
"scipy.io.wavfile.read"
]
] | [
{
"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"... |
baklanovp/pystella | [
"f6f44ed12d9648585a52a09e15d494daa4c70c59",
"f6f44ed12d9648585a52a09e15d494daa4c70c59"
] | [
"tests/test_reader_table.py",
"tests/test_velocity.py"
] | [
"# coding=utf-8\nimport numpy as np\nimport unittest\n\nimport pystella as ps\n# from pystella.rf import band\n# from pystella.rf.lc import LightCurve\n# from pystella.util.reader_table import read_table_header_float, table2curves, read_obs_table_header, curves2table\n\n__author__ = 'bakl'\n\n\ndef lc_create(b, m=-... | [
[
"numpy.linspace",
"numpy.ones"
],
[
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.show",
"matplotlib.pyplot.matplotlib.rcParams.update",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jreback/ibis | [
"fdcca59b085416b1311eb268be3886abad1db230",
"fdcca59b085416b1311eb268be3886abad1db230"
] | [
"ibis/backends/clickhouse/tests/test_functions.py",
"ibis/backends/pandas/tests/test_udf.py"
] | [
"import math\nimport operator\nfrom datetime import date, datetime\nfrom operator import methodcaller\n\nimport pandas as pd\nimport pandas.testing as tm\nimport pytest\nfrom pytest import param\n\nimport ibis\nimport ibis.expr.datatypes as dt\nimport ibis.expr.types as ir\nfrom ibis import literal as L\n\nclickhou... | [
[
"pandas.Timestamp",
"pandas.testing.assert_series_equal",
"pandas.Series",
"pandas.isnull"
],
[
"pandas.testing.assert_series_equal",
"numpy.arange",
"pandas.testing.assert_frame_equal",
"numpy.random.rand",
"pandas.date_range",
"numpy.repeat",
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"p... |
HugoPfister/Pyrats | [
"fc2cab0d1e14b8dd19b3eba361d47f053187ab47",
"fc2cab0d1e14b8dd19b3eba361d47f053187ab47"
] | [
"pyrats/halos.py",
"pyrats/scripts/Struc_To_hdf5.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"Module to deal with halos, to be used with HaloMaker.\n\nThis module is heavily inspired by the set of IDL routines originally\nfound in the Ramses Analysis ToolSuite (RATS).\n\nTODO: Some more documentation\n\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport yt\nfrom yt.uti... | [
[
"numpy.loadtxt",
"numpy.array",
"pandas.DataFrame"
],
[
"numpy.log",
"numpy.sqrt",
"pandas.DataFrame",
"numpy.logical_and",
"numpy.maximum.reduce"
]
] | [
{
"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... |
gr33n-made/catalyst | [
"bd413abc908ef7cbdeab42b0e805277a791e3ddb",
"bd413abc908ef7cbdeab42b0e805277a791e3ddb",
"bd413abc908ef7cbdeab42b0e805277a791e3ddb",
"bd413abc908ef7cbdeab42b0e805277a791e3ddb",
"bd413abc908ef7cbdeab42b0e805277a791e3ddb",
"bd413abc908ef7cbdeab42b0e805277a791e3ddb"
] | [
"tests/pipelines/test_distillation.py",
"tests/pipelines/test_multilabel_classification.py",
"tests/catalyst/metrics/functional/test_cmc_metric.py",
"tests/pipelines/test_optuna.py",
"tests/catalyst/metrics/functional/test_average_precision.py",
"tests/catalyst/engines/test_parallel_amp.py"
] | [
"# flake8: noqa\n\nimport os\nfrom tempfile import TemporaryDirectory\n\nfrom pytest import mark\nimport torch\nfrom torch import nn, optim\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\n\nfrom catalyst import dl\nfrom catalyst.contrib.datasets import MNIST\nfrom catalyst.data impor... | [
[
"torch.nn.functional.softmax",
"torch.nn.CrossEntropyLoss",
"torch.nn.KLDivLoss",
"torch.nn.functional.log_softmax",
"torch.nn.Flatten",
"torch.nn.Linear",
"torch.no_grad"
],
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.utils.data.TensorDataset",
"torch.utils.data.D... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
mcasanova1445/models | [
"7214e17eb425963ec3d0295be215d5d26deaeb32",
"7214e17eb425963ec3d0295be215d5d26deaeb32",
"7214e17eb425963ec3d0295be215d5d26deaeb32",
"7214e17eb425963ec3d0295be215d5d26deaeb32",
"7214e17eb425963ec3d0295be215d5d26deaeb32",
"7214e17eb425963ec3d0295be215d5d26deaeb32",
"37be0fdb4abccca633bb3199a4e6f3f71cd174d... | [
"official/nlp/modeling/networks/albert_encoder_test.py",
"official/projects/edgetpu/vision/serving/tflite_imagenet_evaluator_run.py",
"official/nlp/tasks/masked_lm.py",
"official/nlp/data/classifier_data_lib_test.py",
"official/recommendation/ranking/common.py",
"official/legacy/bert/model_training_utils.... | [
"# Copyright 2022 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.keras.mixed_precision.set_global_policy",
"tensorflow.keras.Input",
"tensorflow.test.main",
"tensorflow.keras.Model",
"tensorflow.keras.activations.get",
"tensorflow.keras.initializers.get",
"numpy.random.randint"
],
[
"tensorflow.io.gfile.GFile"
],
[
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
kouroshHakha/circuit-fewshot-code | [
"32007e119da30632736868a3f643027624bf08d2",
"32007e119da30632736868a3f643027624bf08d2",
"32007e119da30632736868a3f643027624bf08d2"
] | [
"configs/opamp/biased_pmos_gain/15-layer-ft-all-pool-0.5/config.py",
"configs/opamp/biased_pmos/15-layer-scratch-5/config.py",
"scripts/test_rdiv_topology_generalization.py"
] | [
"import time\n\nimport hashlib\nimport torch\nfrom torch_geometric.data import DataLoader\n\nfrom cgl.utils.params import ParamDict\nfrom cgl.data.graph_data import CircuitInMemDataset, CircuitGraphDataset\n\n# from cgl.models.gnn import DeepGENNet\n\ns = time.time()\nprint('Loading the dataset ...')\nroot = '/stor... | [
[
"torch.where"
],
[
"torch.where"
],
[
"torch.device",
"numpy.mean",
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zamwell/pandapower | [
"ce51946342109e969b87b60c8883d7eec02d3060",
"630e3278ca012535f78282ae73f1b86f3fe932fc",
"630e3278ca012535f78282ae73f1b86f3fe932fc"
] | [
"pandapower/plotting/plotly/traces.py",
"pandapower/pypower/pqcost.py",
"pandapower/pypower/polycost.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Copyright (c) 2016-2019 by University of Kassel and Fraunhofer Institute for Energy Economics\n# and Energy System Technology (IEE), Kassel. All rights reserved.\n\n\nimport math\n\nimport numpy as np\nimport pandas as pd\nfrom packaging import version\nfrom collections.abc import Iter... | [
[
"numpy.asarray",
"numpy.array",
"pandas.Series"
],
[
"numpy.arange",
"numpy.array"
],
[
"numpy.arange",
"numpy.zeros",
"numpy.flatnonzero"
]
] | [
{
"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... |
stefan-de/lifelines | [
"519bd3abe6051bd9fb5da0dfffce24ab86171f3f"
] | [
"lifelines/tests/utils/test_utils.py"
] | [
"# -*- coding: utf-8 -*-\n\n\nimport pytest\nimport os\nimport numpy as np\nimport pandas as pd\nfrom pandas.testing import assert_frame_equal, assert_series_equal\nimport numpy.testing as npt\nfrom numpy.linalg import norm, lstsq\nfrom numpy.random import randn\nfrom flaky import flaky\n\nfrom lifelines import Cox... | [
[
"pandas.testing.assert_series_equal",
"pandas.Series",
"numpy.linspace",
"pandas.DataFrame",
"numpy.random.randn",
"pandas.testing.assert_frame_equal",
"numpy.zeros_like",
"pandas.DataFrame.from_records",
"numpy.exp",
"numpy.random.randint",
"numpy.ones_like",
"nump... | [
{
"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": []
}
] |
roozhou/botty | [
"a67a87845687cdf6900af10a13dc7170684faa9a",
"a67a87845687cdf6900af10a13dc7170684faa9a"
] | [
"src/char/trapsin.py",
"src/char/bone_necro.py"
] | [
"import keyboard\nfrom utils.custom_mouse import mouse\nfrom char import IChar\nfrom pather import Pather\nfrom logger import Logger\nfrom screen import convert_abs_to_monitor, convert_screen_to_abs, grab\nfrom config import Config\nfrom utils.misc import wait, rotate_vec, unit_vector\nimport random\nfrom pather im... | [
[
"numpy.array"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TD21forever/QoS-Predcition-Algorithm-library | [
"f4503462887d719a39c9ccddd6cc55546e783fd5"
] | [
"models/IPCC/model.py"
] | [
"import copy\nimport math\nimport numpy as np\nfrom tqdm import tqdm\nfrom utils.model_util import triad_to_matrix, nonzero_user_mean, nonzero_item_mean\n\n# 相似度计算库\nfrom scipy.stats import pearsonr\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n\nclass IPCCModel(object):\n def __init__(self) -> None... | [
[
"numpy.nonzero",
"numpy.asarray",
"scipy.stats.pearsonr",
"sklearn.metrics.pairwise.cosine_similarity",
"numpy.intersect1d",
"numpy.array",
"numpy.zeros"
]
] | [
{
"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"
... |
XiaoSong9905/tvm | [
"48940f697e15d5b50fa1f032003e6c700ae1e423",
"48940f697e15d5b50fa1f032003e6c700ae1e423",
"48940f697e15d5b50fa1f032003e6c700ae1e423",
"48940f697e15d5b50fa1f032003e6c700ae1e423",
"48940f697e15d5b50fa1f032003e6c700ae1e423",
"48940f697e15d5b50fa1f032003e6c700ae1e423",
"48940f697e15d5b50fa1f032003e6c700ae1e42... | [
"tests/python/relay/test_op_qnn_subtract.py",
"python/tvm/autotvm/feature.py",
"gallery/tutorial/autotvm_relay_x86.py",
"tests/python/contrib/test_ethosn/test_conv2d.py",
"python/tvm/relay/backend/contrib/ethosu/te/inline.py",
"tests/python/contrib/test_rpc_server_device.py",
"python/tvm/relay/backend/c... | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.array"
],
[
"numpy.concatenate"
],
[
"numpy.expand_dims",
"numpy.asarray",
"numpy.squeeze",
"numpy.median",
"numpy.std",
"numpy.mean",
"numpy.transpose",
"numpy.argsort",
"numpy.array",
"scipy.special.softmax"
],
[
"numpy.random.uniform",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"... |
astokely/seekr2 | [
"2fd8496dc885339437678a729b1f97a4b0bf9cfd",
"2fd8496dc885339437678a729b1f97a4b0bf9cfd",
"2fd8496dc885339437678a729b1f97a4b0bf9cfd"
] | [
"seekr2/tests/test_analyze.py",
"seekr2/analyze.py",
"seekr2/modules/elber_base.py"
] | [
"\"\"\"\ntest_analyze.py\n\nTesting analyze.py\n\"\"\"\n\nimport os\nfrom collections import defaultdict\n\nimport numpy as np\n\nimport seekr2.modules.common_analyze as common_analyze\nimport seekr2.modules.mmvt_analyze as mmvt_analyze\nimport seekr2.analyze as analyze\nimport seekr2.modules.common_base as base\ni... | [
[
"numpy.linalg.solve",
"numpy.linalg.norm",
"numpy.ones",
"numpy.array",
"numpy.zeros",
"numpy.isclose"
],
[
"numpy.sqrt",
"matplotlib.pyplot.subplots",
"numpy.std",
"numpy.random.normal",
"matplotlib.pyplot.errorbar",
"matplotlib.pyplot.xlabel",
"numpy.zeros... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",... |
marlene09/skan | [
"97a217d36ec1393b380d4a797b5b7ceb68e824ec"
] | [
"skan/pipe.py"
] | [
"import os\nfrom . import pre, csr\nimport imageio\nfrom tqdm import tqdm\nimport numpy as np\nfrom skimage import morphology\nimport pandas as pd\nfrom .image_stats import image_summary\nfrom skimage.feature import shape_index\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\nimport multiprocessing... | [
[
"numpy.ceil",
"pandas.concat",
"numpy.log2"
]
] | [
{
"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": []
}
] |
kohjingyu/prob-stats | [
"df396686b641079b5db93118b1b2373d79de7d7a"
] | [
"linear_regression.py"
] | [
"import matplotlib\nimport matplotlib.pyplot as plt\n\nx = [20, 23, 29, 27, 30, 34, 35, 37, 40, 43]\ny = [1.32, 1.67, 2.17, 2.70, 2.75, 2.87, 3.65, 2.86, 3.61, 4.25]\nn = len(x)\nassert(n == len(y))\n\n# Means\nbar_x = sum(x) / n\nbar_y = sum(y) / n\n\n# Sum of squares\nsxy = sum([(x[i] - bar_x) * (y[i] - bar_y) fo... | [
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JamesSample/icpw | [
"47562f601fc8fe23720267d083dabc540889565e"
] | [
"toc_trends_analysis.py"
] | [
"#------------------------------------------------------------------------------\n# Name: toc_trends_analysis.py\n# Purpose: Analyse RESA2 data for trends.\n#\n# Author: James Sample\n#\n# Created: Fri Jul 15 11:35:12 2016\n# Copyright: (c) James Sample and NIVA\n# Licence: \n#------------... | [
[
"scipy.stats.norm.ppf",
"pandas.merge",
"numpy.sqrt",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.tight_layout",
"numpy.unique",
"matplotlib.pyplot.close",
"numpy.zeros",
"pandas.read_sql",
"matplotlib.pyplot.figure",
"pandas.concat",
"panda... | [
{
"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": [
"1.6",
"1.10",
"0.15",
"1.4",
"0.... |
zfisher/trax | [
"c8187944fc036418a5c3b0491fc53c223e73faa6",
"bbabf6cc8a0682218927080bce33a4f90591aa0b",
"bbabf6cc8a0682218927080bce33a4f90591aa0b"
] | [
"trax/layers/normalization_test.py",
"trax/rl/envs/async_trajectory_collector_lib.py",
"trax/tf_numpy/numpy/tests/array_manipulation_test.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Trax 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 app... | [
[
"numpy.testing.assert_allclose"
],
[
"tensorflow.compat.v1.io.gfile.makedirs"
],
[
"tensorflow.compat.v2.test.main",
"tensorflow.compat.v2.compat.v1.enable_eager_execution",
"numpy.broadcast_to"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
watanka/CRAFTS-implementation | [
"bc514638755fe798a0d5eb583d6d477e8eb55bff"
] | [
"file_utils.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nimport numpy as np\nimport cv2\nimport imgproc\nfrom PIL import Image, ImageDraw\n\n\n\n# borrowed from https://github.com/lengstrom/fast-style-transfer/blob/master/src/utils.py\ndef get_files(img_dir):\n imgs, masks, xmls = list_files(img_dir)\n return imgs, masks, xmls\n... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SaraR-1/model-patching | [
"97b30bad4bb4575a5f3a4cc23fbd333b10a057a8"
] | [
"augmentation/methods/cyclegan/utils.py"
] | [
"import datetime\n\nimport tensorflow as tf\nimport random\nimport wandb\nfrom tensorflow_examples.models.pix2pix import pix2pix\n\nfrom augmentation.dataflows.utils import create_paired_direct_dataflow, \\\n create_paired_parallel_dataflow_via_numpy\nfrom augmentation.methods.cyclegan.models import mnist_unet_g... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.constant",
"tensorflow.reduce_mean",
"tensorflow.shape",
"tensorflow.keras.losses.MeanSquaredError",
"tensorflow.random.uniform",
"tensorflow.ones_like",
"tensorflow.keras.losses.BinaryCrossentropy",
"tensorflow.math.reduce_std",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13",
"1.10",
"1.12"
]
}
] |
globusgenomics/galaxy | [
"7caf74d9700057587b3e3434c64e82c5b16540f1",
"7caf74d9700057587b3e3434c64e82c5b16540f1",
"7caf74d9700057587b3e3434c64e82c5b16540f1",
"7caf74d9700057587b3e3434c64e82c5b16540f1",
"7caf74d9700057587b3e3434c64e82c5b16540f1",
"7caf74d9700057587b3e3434c64e82c5b16540f1",
"7caf74d9700057587b3e3434c64e82c5b16540f... | [
"tools/intogen/runtime/pyenv/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/lsmr.py",
"tools/intogen/runtime/pyenv/lib/python2.7/site-packages/scipy/spatial/__init__.py",
"tools/intogen/runtime/pyenv/lib/python2.7/site-packages/scipy/signal/tests/test_filter_design.py",
"tools/intogen/runtime/pyenv/li... | [
"\"\"\"\nCopyright (C) 2010 David Fong and Michael Saunders\n\nLSMR uses an iterative method.\n\n07 Jun 2010: Documentation updated\n03 Jun 2010: First release version in Python\n\nDavid Chin-lung Fong clfong@stanford.edu\nInstitute for Computational and Mathematical Engineering\nStanford University\n\nM... | [
[
"scipy.sparse.linalg.interface.aslinearoperator",
"numpy.linalg.norm",
"numpy.zeros"
],
[
"numpy.testing.Tester"
],
[
"numpy.testing.assert_equal",
"numpy.testing.run_module_suite",
"numpy.sqrt",
"scipy.signal.freqz",
"numpy.arange",
"scipy.signal.tf2zpk",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
sisl/mechamodlearn | [
"ed514b5d1193ce546b0221ba9222b0228d6c319a"
] | [
"mechamodlearn/rigidbody.py"
] | [
"# File: rigidbody.py\n\nimport abc\nimport torch\n\nfrom mechamodlearn import nn, utils\nfrom mechamodlearn.models import CholeskyMMNet, PotentialNet, GeneralizedForceNet\n\n\nclass AbstractRigidBody:\n\n @property\n @abc.abstractmethod\n def thetamask(self):\n \"\"\"Returns theta mask for configur... | [
[
"torch.stack",
"torch.sum",
"torch.zeros_like",
"torch.enable_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ucx-code/ucXception | [
"6b1f4fe4aa53a28e87584d07f540095c20ee50e9"
] | [
"framework/parsers/ucXception_fi_parser.py"
] | [
"import numpy as np\n\ndef map_reg_to_text(reg_code):\n\treg_dict = (\"rip\", \"rsp\", \"rax\", \"rbx\", \"rcx\", \"rdx\", \"cs\", \"ss\", \"eflags\", \"rbp\", \"r8\", \"r9\",\n\t\t\t\t\"r10\", \"r11\", \"r12\", \"r13\", \"r14\", \"r15\", \"rsi\", \"rdi\", \"orig_rax\", \"fs_base\", \"gs_base\",\n\t\t\t\t\"ds\", \"... | [
[
"numpy.int",
"numpy.int32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lyuyangh/Cross-Attention-VizWiz-VQA | [
"853bfe480dac5bd1363f60c6b17e25134acdc2fa"
] | [
"demo/predict.py"
] | [
"import datetime\nimport json\nimport os\nimport sys\nimport time\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nfrom utils.flags import FLAGS\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom model.vqa_model ... | [
[
"torch.autograd.Variable",
"torch.nn.DataParallel",
"torch.utils.data.DataLoader",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Janetteeeeeeee/nnUNet | [
"db654c445aa5ced436dbf842d432dbbcdc01f4b5",
"db654c445aa5ced436dbf842d432dbbcdc01f4b5"
] | [
"nnunet/experiment_planning/experiment_planner_baseline_3DUNet.py",
"nnunet/training/network_training/nnUNetTrainer.py"
] | [
"# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany\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# ... | [
[
"numpy.median",
"numpy.round",
"numpy.argmax",
"numpy.any",
"numpy.prod",
"numpy.argsort",
"numpy.array",
"numpy.vstack"
],
[
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.zeros",
"numpy.isnan",
"matplotlib.use",
"torch.cuda.empty_cache",
"numpy.r... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
echasnovski/randomvars | [
"15417b0e3ecd27f185b70471102c158f60d51c28",
"15417b0e3ecd27f185b70471102c158f60d51c28"
] | [
"randomvars/tests/test__continuous.py",
"experiments/from_sample.py"
] | [
"# pylint: disable=missing-function-docstring\n\"\"\"Tests for '_continuous.py' file\"\"\"\nimport numpy as np\nfrom numpy.testing import assert_array_equal, assert_array_almost_equal\nimport scipy.stats.distributions as distrs\nfrom scipy.stats.kde import gaussian_kde\nfrom scipy.integrate import quad\nimport pyte... | [
[
"numpy.sqrt",
"numpy.linspace",
"scipy.stats.distributions.weibull_min",
"numpy.asarray",
"scipy.stats.distributions.f",
"numpy.cumsum",
"numpy.concatenate",
"scipy.stats.distributions.norm",
"numpy.zeros_like",
"scipy.stats.distributions.t",
"scipy.stats.distributions.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChenShawn/Grad-Paper-Experiments | [
"00fe1142dae4077b197e99253cc5a4ab759db2ff"
] | [
"TD3/artest.py"
] | [
"import gym\nimport pybullet_envs\nfrom PIL import Image\nimport argparse\nimport numpy as np\nimport torch\nimport copy\nimport os\nfrom sklearn.preprocessing import normalize as Normalize\n\nfrom models import TD3, TD3_adv2\n\n\ndef parse_arguments():\n parser = argparse.ArgumentParser(\"TESTING\")\n parser... | [
[
"numpy.random.seed",
"numpy.clip",
"torch.manual_seed",
"numpy.random.normal",
"numpy.random.rand",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cyanide1x/covid19-dashboard | [
"7da01c2477c0691caf869d7401587dc85cacef29"
] | [
"_notebooks/canada_voc.py"
] | [
"import pandas as pd\nimport plotly.express as px\n\nurl = 'https://health-infobase.canada.ca/src/data/covidLive/covid19-epiSummary-voc.csv' \n\nprov_dict = {\n\t\"AB\" : \"Alberta\",\n\t\"BC\" : \"British Columbia\",\n\t\"CA\" : \"Canada\",\n\t\"MB\" : \"Manitoba\",\t\n\t\"NB\" : \"New Brunswick\",\n\t\"NL\" : \"... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yufei1900/cs231n-homework | [
"b7f5a03d5a2b650603074a7c43f203b465b74333"
] | [
"assignment1/cs231n/classifiers/neural_net.py"
] | [
"from __future__ import print_function\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass TwoLayerNet(object):\n \"\"\"\n A two-layer fully-connected neural network. The net has an input dimension of\n N, a hidden layer dimension of H, and performs classification over C classes.\n We train the netw... | [
[
"numpy.log",
"numpy.random.choice",
"numpy.arange",
"numpy.random.randn",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MarcoFavorito/breakout-env | [
"b41f9ed1da693874d7d34f83e7200fd51a59c97e"
] | [
"breakout_env/wrappers/wrappers.py"
] | [
"import copy\nfrom gym import Wrapper\nfrom pythogic.base.Symbol import Symbol\nfrom pythogic.base.Alphabet import Alphabet\nfrom pythogic.base.Formula import AtomicFormula, PathExpressionEventually, PathExpressionSequence, And, Not, \\\n LogicalTrue, PathExpressionStar\nfrom pythogic.base.utils import _to_pytho... | [
[
"numpy.all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
salomonw/mixed-traffic-amod-route-rebalance | [
"7f1edeb195a7bfab835e596ad84deead2957943e",
"7f1edeb195a7bfab835e596ad84deead2957943e",
"7f1edeb195a7bfab835e596ad84deead2957943e"
] | [
"experiments/plots_journal_CARS.py",
"src/CARS.py",
"experiments/run_penRate_CARS.py"
] | [
"import src.tnet as tnet\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport pandas as pd\nimport math \n\nplt.style.use(['science','ieee', 'high-vis'])\n\n\ndef txt2list(fname):\n\treturn [line for line in open(fname)]\n\ndef read_result(fname):\n\tdf = pd.read_csv(fname)\n\tresults = df.T.values... | [
[
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.savefig"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"numpy.linspace",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.grid",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
FedePeralta/ASVs_Deep_Reinforcement_Learning_with_CNNs | [
"23b9b181499a4b06f2ca2951c002359c1959e727"
] | [
"utils/Paralell_Experience_Generator.py"
] | [
"import random\nimport torch\nimport sys\nfrom contextlib import closing\nfrom torch.multiprocessing import Pool\nfrom random import randint\nfrom exploration_strategies.OUNoise import OrnsteinUhlenbeckActionNoise\n\n\nclass Parallel_Experience_Generator(object):\n \"\"\" Plays n episode in parallel using a fixe... | [
[
"torch.manual_seed",
"torch.abs",
"torch.from_numpy",
"torch.multiprocessing.Pool"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AbdulHoffmann/carla_carissma | [
"9444dce96954c546333d5aecc92a06c3bfd19aa5",
"9444dce96954c546333d5aecc92a06c3bfd19aa5",
"9444dce96954c546333d5aecc92a06c3bfd19aa5",
"8d382769ffa02a6c61a22c57160285505f5ff0a4",
"8d382769ffa02a6c61a22c57160285505f5ff0a4",
"8d382769ffa02a6c61a22c57160285505f5ff0a4",
"8d382769ffa02a6c61a22c57160285505f5ff0a... | [
"PythonAPI/carissma_project/lib/python3.5/site-packages/mpl_toolkits/axes_grid/__init__.py",
"PythonAPI/carissma_project/lib/python3.5/site-packages/matplotlib/tri/tripcolor.py",
"PythonAPI/carissma_project/lib/python3.5/site-packages/matplotlib/tests/test_style.py",
"PythonAPI/carissma_project/lib/python3.5/... | [
"from . import axes_size as Size\nfrom .axes_divider import Divider, SubplotDivider, LocatableAxes, \\\n make_axes_locatable\nfrom .axes_grid import Grid, ImageGrid, AxesGrid\n#from axes_divider import make_axes_locatable\nfrom matplotlib.cbook import warn_deprecated\nwarn_deprecated(since='2.1',\n ... | [
[
"matplotlib.cbook.warn_deprecated"
],
[
"matplotlib.tri.triangulation.Triangulation.get_from_args_and_kwargs",
"numpy.asarray",
"matplotlib.collections.TriMesh",
"numpy.stack",
"matplotlib.collections.PolyCollection"
],
[
"matplotlib.style.reload_library",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
DexiongYung/robustnav_AE | [
"f2b1b5bb8780e4e6ae5f81c127b7589cfc949801",
"f2b1b5bb8780e4e6ae5f81c127b7589cfc949801"
] | [
"domain_adaptation/corruptions/corruptions.py",
"projects/robustnav_baselines/experiments/robustnav_eval/pointnav_robothor_vanilla_rgb_resnet_rot_pred_ddppo.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np\nfrom PIL import Image\n\n# /////////////// Corruption Helpers ///////////////\n\nimport skimage as sk\nfrom torchvision import transforms\nimport torchvision.transforms.functional as F\nfrom skimage.filters import gaussian\nfrom io import BytesIO\nfrom wand.image impo... | [
[
"scipy.ndimage.interpolation.map_coordinates",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"numpy.where",
"numpy.roll",
"numpy.random.randint",
"numpy.ones_like",
"numpy.clip",
"numpy.reshape",
"numpy.arange",
"scipy.ndimage.zoom",
"numpy.random.poisson",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.10",
"1.3",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16"
],
"tensorflow": [... |
simondlevy/TinyNEF | [
"2e42754cf22996c86f1e35780d77591ec2bbb658"
] | [
"gym/pendulum_test.py"
] | [
"#!/usr/bin/env python3\n'''\nUse the Neural Engineering framework to solve Pendulum via an elitist GA\n\nCopyright (C) 2020 Simon D. Levy\n\nMIT License\n'''\n\nfrom lib import NefGym\nfrom sys import argv\nimport pickle\nimport numpy as np\n\nfrom sueap.algorithms.elitist import Elitist\n\nclass NefPendulum(NefGy... | [
[
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
astrax/astro2019 | [
"c1f5309415c80fbd986d6760bcb8bc095898beda"
] | [
"docs/.src/programs/skyplot_proj/skyplotv1.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\n\nimport astropy\nfrom scipy.spatial import cKDTree\n\nimport numpy as np\nimport matplotlib.pyplot as plt\ndata=np.genfromtxt('ybs.degbv',names=True)\nmessier=np.genfromtxt('Messierdec.txt',names=True)\n\nvlim=4.5\nmagsc... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.annotate",
"matplotlib.pyplot.savefig",
"numpy.genfromtxt",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.clf",
"numpy.array",
"matplotlib... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ClementRolinat/stable-baselines | [
"333c59379f23e1f5c5c9e8bf93cbfa56ac52d13b"
] | [
"stable_baselines/a2c/a2c.py"
] | [
"import time\nfrom collections import deque\n\nimport gym\nimport numpy as np\nimport tensorflow as tf\n\nfrom stable_baselines import logger\nfrom stable_baselines.common import explained_variance, tf_util, ActorCriticRLModel, SetVerbosity, TensorboardWriter\nfrom stable_baselines.common.policies import ActorCriti... | [
[
"numpy.asarray",
"tensorflow.RunMetadata",
"tensorflow.summary.scalar",
"tensorflow.Graph",
"numpy.clip",
"tensorflow.summary.image",
"tensorflow.gradients",
"tensorflow.squeeze",
"numpy.copy",
"numpy.zeros",
"tensorflow.train.RMSPropOptimizer",
"tensorflow.RunOptio... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
lsst-sqre/qa-dashboard | [
"57d40a33f1d6fdc04fb8f5e6e7e4fcfaee25340c"
] | [
"squash/dashboard/viz/api_helper.py"
] | [
"import os\nimport pandas as pd\nimport requests\nfrom datetime import datetime\nfrom furl import furl\n\nSQUASH_API_URL = os.environ.get('SQUASH_API_URL',\n 'http://localhost:8000/dashboard/api/')\n\n\ndef get_endpoint_urls():\n \"\"\"\n Lookup API endpoint URLs\n \"\"\"\n\n... | [
[
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
srubenacker/DeepDog | [
"ce6613e01c04a14f62a2d6f6cd1c60f97efa790a"
] | [
"src/ddog.py"
] | [
"import util\nimport json\nimport numpy as np\nimport random\nimport tensorflow as tf\n\nclass DeepDog:\n \"\"\"\n The DeepDog class loads the training and test set images from\n disk into RAM, and provides functions to get the test set\n and mini batches of the training set. \n \"\"\"\n\n def __i... | [
[
"numpy.fliplr",
"numpy.array",
"tensorflow.map_fn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
decisionforce/HACO | [
"ebd1dc49598e6ae2704e58c053cc35f2d9e28429",
"ebd1dc49598e6ae2704e58c053cc35f2d9e28429",
"ebd1dc49598e6ae2704e58c053cc35f2d9e28429",
"ebd1dc49598e6ae2704e58c053cc35f2d9e28429",
"ebd1dc49598e6ae2704e58c053cc35f2d9e28429"
] | [
"haco/DIDrive_core/demo/cilrs/cilrs_collect_data.py",
"haco/DIDrive_core/utils/simulator_utils/sensor_utils.py",
"haco/DIDrive_core/simulators/srunner/scenarios/signalized_junction_left_turn.py",
"haco/DIDrive_core/demo/implicit/collect_data.py",
"haco/DIDrive_core/demo/lbc/lbc_bev_test.py"
] | [
"import os\nfrom functools import partial\n\nimport PIL\nimport lmdb\nimport numpy as np\nfrom ding.envs import SyncSubprocessEnvManager\nfrom ding.utils.default_helper import deep_merge_dicts\nfrom easydict import EasyDict\nfrom tqdm import tqdm\n\nfrom haco.DIDrive_core.data import CarlaBenchmarkCollector, Benchm... | [
[
"numpy.asarray",
"numpy.array"
],
[
"numpy.random.random",
"numpy.reshape",
"numpy.arange",
"numpy.dtype",
"numpy.array"
],
[
"numpy.array",
"numpy.cross"
],
[
"numpy.ascontiguousarray"
],
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
yuguiyang/python_demo | [
"1be2406bfc920e22a0f92bf10d9a3665984067ba",
"1be2406bfc920e22a0f92bf10d9a3665984067ba",
"1be2406bfc920e22a0f92bf10d9a3665984067ba",
"1be2406bfc920e22a0f92bf10d9a3665984067ba"
] | [
"old_code/pandas_order.py",
"old_code/plt_bar.py",
"old_code/plt_histogram.py",
"code/pandas/pd_duplicated.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 28 13:42:30 2017\n\n@author: hexo\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\n#读取第一个sheet页\ndf = pd.read_excel('D:\\Tableau_data\\示例 - 超市.xls',sheetname=0)\n\nprint(type(df))\n\n#每一列的数据类型\nprint(df.dtypes)\n#每种类型的数量\nprint(df.get_dtype_counts())\n\n#还不知... | [
[
"pandas.read_excel"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.font_manager.FontProperties",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"matplotlib.pyplot.gca",
"numpy.random.normal",
"matplotl... | [
{
"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... |
tuanho27/torchstat | [
"46d653795a1262f0e58a2069276a69d6bd43078c"
] | [
"torchstat/compute_flops.py"
] | [
"import torch.nn as nn\nimport torch\nimport numpy as np\n\n\ndef compute_flops(module, inp, out):\n if isinstance(module, nn.Conv2d):\n return compute_Conv2d_flops(module, inp, out)\n elif isinstance(module, nn.BatchNorm2d):\n return compute_BatchNorm2d_flops(module, inp, out)\n elif isinsta... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pshustov/DateTimeTools | [
"e542fd3f0e3c5290faad09b7cf8a2751132d4dd3"
] | [
"build/lib/DateTimeTools/WithinTimeRange.py"
] | [
"import numpy as np\nfrom ._CFunctions import _CWithinTimeRange\nfrom ._CTConv import _CTConv\n\ndef WithinTimeRange(Timet,Time0,Time1,BoolOut=False):\n\t'''\n\tPerforms a simple check on a test time (Timet) to see if it exists\n\tbetween Time0 and time1.\n\t\n\tInputs\n\t======\n\tTimet : tuple | float \n\t\tTest ... | [
[
"numpy.size",
"numpy.array",
"numpy.shape",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liute62/NumCpp | [
"d6922b2b5e1f575021b0577aea1445e041ec7180"
] | [
"unitTests/testScripts/TestIntegrate.py"
] | [
"import numpy as np\nimport scipy.special as sp\nfrom termcolor import colored\nimport sys\nif sys.platform == 'linux':\n sys.path.append(r'../lib')\nelse:\n sys.path.append(r'../build/x64/Release')\nimport NumCpp\n\n\n####################################################################################\nNUM_D... | [
[
"numpy.flipud",
"numpy.random.rand",
"numpy.sort",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vsegurar/DeepMSPeptide | [
"ab73f125b2297a7be01da3fa19a1c0b35c29d493"
] | [
"DeepMSPeptide/DeepMSPeptide.py"
] | [
"import warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport argparse\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\n\n\nparser = argparse.ArgumentParser(description='''Predicts the detectability of input peptides using a single dimension\n ... | [
[
"tensorflow.keras.models.load_model",
"numpy.array",
"tensorflow.keras.preprocessing.sequence.pad_sequences"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
markhliu99/CAI | [
"b97f0831ee8ea6e2352fe4f25032fdd1019c1ba8"
] | [
"PythonControllerTemplates/src/py-ctrl.py"
] | [
"\"\"\"\npy-ctrl script\n1. generate problem PD file\n 1.1 save PD file in /inputfiles\n2. solve convex hull\n 2.1 save hull information in /output\n 2.2 show figure for 10 sec\n 2.3 save figure in /output\n\"\"\"\n\nimport os\nimport subprocess\nimport argparse\nimport matplotlib.pyplot as plt\n\n\npar... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tcavazos2/hw2-Clustering | [
"60b536729ba03dcb28384be99e1575c3c3c0fe7e"
] | [
"hw2skeleton/cluster.py"
] | [
"from .utils import Atom, Residue, ActiveSite\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom .helpers import *\nfrom Bio import pairwise2\nimport rmsd\nfrom sklearn.decomposition import PCA\nimport networkx as nx\nfrom networkx.drawing.nx_agraph import graphviz_layout\n\ndef compute_similarity(site_a, s... | [
[
"numpy.nanmax",
"numpy.isnan",
"numpy.append",
"numpy.column_stack",
"numpy.ndenumerate",
"numpy.array",
"numpy.isinf",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ferenctorok/potential_field_planner | [
"7a9f7ae70a91523cc6d42029f869f9020cc1ea35",
"7a9f7ae70a91523cc6d42029f869f9020cc1ea35"
] | [
"gradplanner/controller/low_level_controller.py",
"tests/attractor_field_tests.py"
] | [
"import numpy as np\n\n\nclass LowLevelController:\n \"\"\"Low level controller of a point mass robot with dynamics:\n\n x_{k+1} = x_k + v_k * Ts * cos(psi_k)\n y_{k+1} = y_k + v_k * Ts * sin(psi_k)\n v_{k+1} = v_k + Ts * a_k\n psi_{k+1} = psi_k + Ts * omega_k\n omega_{k+1} = omega_k + Ts * epsilo... | [
[
"numpy.array"
],
[
"numpy.array",
"numpy.zeros",
"numpy.sqrt",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DPaletti/mida_acv | [
"9b492adaf75ce24c94dfa6993c5757e1bb96c700"
] | [
"src/mida_acv/utilities.py"
] | [
"from pathlib import Path\nfrom typing import Tuple, List, Dict\n\nimport pandas as pd\nimport numpy as np\nfrom tsfresh.utilities.dataframe_functions import roll_time_series\n\n\ndef get_path(df: pd.DataFrame) -> np.array:\n out = []\n for index, row in df.iterrows():\n out.append((row[\"Latitude\"], ... | [
[
"numpy.abs",
"pandas.Series",
"pandas.DataFrame",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.correlate"
]
] | [
{
"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": []
}
] |
by-liu/calibration-framework | [
"7b306e4bbe6361d411b209759b7ba3d016bd0d17"
] | [
"netcal/scaling/LogisticCalibration.py"
] | [
"# Copyright (C) 2019-2021 Ruhr West University of Applied Sciences, Bottrop, Germany\n# AND Elektronische Fahrwerksysteme GmbH, Gaimersheim Germany\n#\n# This Source Code Form is subject to the terms of the Apache License 2.0\n# If a copy of the APL2 was not distributed with this\n# file, You can obtain one at htt... | [
[
"torch.softmax",
"torch.ones",
"torch.Tensor",
"torch.zeros",
"numpy.reshape",
"torch.reshape",
"torch.matmul",
"torch.mul"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manzt/bioimage-latency-benchmark | [
"134a368f90cdf38532723f621e1766f31e2d3214"
] | [
"notebooks/chunks.py"
] | [
"#!/usr/bin/env python\nimport argparse\nimport math\n\nimport matplotlib.pyplot as plt\n\n\ndef file_count(shape, chunkXY, chunkZ=1, chunkT=1, chunkC=1):\n t, c, z, y, x = shape\n return (\n math.ceil(x / chunkXY)\n * math.ceil(y / chunkXY)\n * math.ceil(z / chunkZ)\n * math.ceil(... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
H43RO/examples | [
"54acd5f38d6368a29208b231e5028f16d18c954b"
] | [
"tensorflow_examples/lite/model_maker/core/task/model_spec/audio_spec.py"
] | [
"# Copyright 2020 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.signal.frame",
"tensorflow.keras.models.load_model",
"tensorflow.keras.Input",
"tensorflow.reduce_mean",
"tensorflow.keras.layers.Dense",
"tensorflow.reshape",
"tensorflow.keras.Sequential",
"tensorflow.squeeze",
"tensorflow.expand_dims",
"tensorflow.keras.Model... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
Jmion/SwisscomMIP | [
"d29b0de222be44f85a84bc7dc3f4521741fdeda1"
] | [
"dataFetcher.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# # Loading data\n\n\n\nimport pandas as pd\nimport plotly.express as px\nfrom tqdm import tqdm\nimport functools\nimport numpy as np\nfrom difflib import SequenceMatcher\nfrom oauthlib.oauth2 import BackendApplicationClient\nfrom requests_oauthlib import OAuth2Session\nfr... | [
[
"numpy.array",
"pandas.read_pickle",
"pandas.DataFrame",
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
acmlia/ann_training | [
"8cb39123203445cf79c4bd65350fa4063705a518",
"8cb39123203445cf79c4bd65350fa4063705a518"
] | [
"security/training_ann_3.py",
"src/training_ann_6.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport os\nimport time\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nfrom collections import Counter\nimport matplotlib.pyplot as plt\nfrom sklearn.externals import joblib\nfrom sklearn.preprocessing import Normalizer\nfrom skle... | [
[
"matplotlib.pyplot.legend",
"numpy.cumsum",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"numpy.where",
"matplotlib.pyplot.gca",
"numpy.asanyarray",
"matplotlib.pyplot.axis",
"numpy.ravel",
"tensorflow.keras.callbacks.EarlyStopping",
"matplotlib.pyplot.figure",
"ma... | [
{
"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": [
"1.10",
"2.7",
"2.2",
... |
vballoli/flower | [
"e8c58c09a8fd4d29186b2f590b0cbb44bb022e9a",
"e8c58c09a8fd4d29186b2f590b0cbb44bb022e9a"
] | [
"src/py/flwr_experimental/baseline/tf_fashion_mnist/gen_plots.py",
"src/py/flwr_example/pytorch/server.py"
] | [
"# Copyright 2020 Adap GmbH. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appli... | [
[
"numpy.array"
],
[
"torch.utils.data.DataLoader",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
strawlab/flyvr | [
"335892cae740e53e82e07b526e1ba53fbd34b0ce",
"335892cae740e53e82e07b526e1ba53fbd34b0ce",
"335892cae740e53e82e07b526e1ba53fbd34b0ce"
] | [
"src/freemovr_engine/cvnumpy.py",
"src/freemovr_engine/plot_utils.py",
"src/freemovr_engine/simple_geom.py"
] | [
"import numpy as np\nimport cv2\n\ndef rodrigues2matrix_cv(params):\n rvec = np.array(params,dtype=np.float64)\n rvec.shape = (1,3)\n Rmat, jacobian = cv2.Rodrigues(rvec)\n return Rmat\n\ndef rodrigues2matrix(params):\n # Written after the docs at\n # http://opencv.itseez.com/modules/calib3d/doc/c... | [
[
"numpy.eye",
"numpy.cos",
"numpy.sin",
"numpy.array",
"numpy.sum"
],
[
"numpy.linspace",
"numpy.ones",
"numpy.concatenate",
"numpy.array",
"numpy.vstack"
],
[
"numpy.dot",
"numpy.sqrt",
"numpy.nanmin",
"numpy.all",
"numpy.max",
"numpy.arctan2... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kjordahl/pandas | [
"e660c058a662426afc4d8855aabf4677f01b4a4c"
] | [
"pandas/tests/test_common.py"
] | [
"# -*- coding: utf-8 -*-\nimport collections\nfrom datetime import datetime\nimport re\n\nimport nose\nfrom nose.tools import assert_equal\nimport numpy as np\nfrom pandas.tslib import iNaT, NaT\nfrom pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp, Float64Index\nfrom pandas import compat\nfro... | [
[
"pandas.core.common.is_list_like",
"pandas.core.common._all_not_none",
"pandas.core.common.adjoin",
"pandas.Series",
"numpy.asarray",
"pandas.util.testing.makeObjectSeries",
"pandas.core.common.intersection",
"pandas.core.index.Int64Index",
"pandas.core.common.indent",
"pan... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ieliz/openvino | [
"403339f8f470c90dee6f6d94ed58644b2787f66b",
"403339f8f470c90dee6f6d94ed58644b2787f66b"
] | [
"tools/mo/openvino/tools/mo/ops/Reverse.py",
"tools/mo/openvino/tools/mo/ops/slice_like.py"
] | [
"# Copyright (C) 2018-2022 Intel Corporation\n# SPDX-License-Identifier: Apache-2.0\n\nimport numpy as np\n\nfrom openvino.tools.mo.front.common.partial_infer.utils import int64_array\nfrom openvino.tools.mo.graph.graph import Graph\nfrom openvino.tools.mo.ops.op import Op\n\n\nclass Reverse(Op):\n op = 'Reverse... | [
[
"numpy.flip"
],
[
"numpy.copy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
intel/neural-compressor | [
"16a4a12045fcb468da4d33769aff2c1a5e2ba6ba"
] | [
"examples/baremetal/nlp/sst2/bert_mini/bert_mini_export.py"
] | [
"import argparse\n\nimport torch\nfrom transformers import BertForSequenceClassification\n\ndef export_onnx_model(args, model, onnx_model_path):\n with torch.no_grad():\n inputs = {'input_ids': torch.ones(1,args.max_len, dtype=torch.int32),\n 'attention_mask': torch.ones(1,args.max... | [
[
"torch.onnx.export",
"torch.no_grad",
"torch.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
knightvishal/tensorflow | [
"5d3dd19b7146d954fc1b4e9e44e9881e75d363c1",
"5d3dd19b7146d954fc1b4e9e44e9881e75d363c1",
"5d3dd19b7146d954fc1b4e9e44e9881e75d363c1",
"5d3dd19b7146d954fc1b4e9e44e9881e75d363c1",
"5d3dd19b7146d954fc1b4e9e44e9881e75d363c1",
"07da23bfa2a9ca10cd7c1dd6bea0f85d981c013e"
] | [
"tensorflow/python/estimator/canned/dnn_linear_combined.py",
"tensorflow/python/kernel_tests/manip_ops_test.py",
"tensorflow/python/data/experimental/kernel_tests/prefetch_to_device_test.py",
"tensorflow/python/training/proximal_gradient_descent_test.py",
"tensorflow/contrib/hadoop/python/kernel_tests/hadoo... | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.estimator.canned.head._binary_logistic_head_with_sigmoid_cross_entropy_loss",
"tensorflow.python.util.tf_export.estimator_export",
"tensorflow.python.training.training_util.get_global_step",
"tensorflow.python.ops.control_flow_ops.group",
"tensorflow.python.ops.state_ops.ass... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.13",
"2.3",
"2.4",
"2.... |
imperial-genomics-facility/data-management-python | [
"7b867d8d4562a49173d0b823bdc4bf374a3688f0",
"7b867d8d4562a49173d0b823bdc4bf374a3688f0"
] | [
"igf_data/igfdb/useradaptor.py",
"test/igf_airflow/calculate_seqrun_file_size_test.py"
] | [
"import pandas as pd\r\nimport json, hashlib, os, codecs, base64\r\nfrom igf_data.igfdb.baseadaptor import BaseAdaptor\r\nfrom igf_data.igfdb.igfTables import User\r\n\r\nclass UserAdaptor(BaseAdaptor):\r\n '''\r\n An adaptor class for table User\r\n '''\r\n def _email_check(self, email):\r\n '''\r\n An i... | [
[
"pandas.isnull",
"pandas.DataFrame"
],
[
"pandas.read_json"
]
] | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
TheGupta2012/qiskit-terra | [
"5ea6e9557655b144228c29d7099375f5d2c91120",
"5ea6e9557655b144228c29d7099375f5d2c91120",
"5ea6e9557655b144228c29d7099375f5d2c91120",
"5ea6e9557655b144228c29d7099375f5d2c91120",
"5ea6e9557655b144228c29d7099375f5d2c91120",
"5ea6e9557655b144228c29d7099375f5d2c91120"
] | [
"qiskit/pulse/library/continuous.py",
"test/python/quantum_info/states/test_statevector.py",
"qiskit/quantum_info/synthesis/xx_decompose/circuits.py",
"test/python/quantum_info/operators/test_operator.py",
"test/python/quantum_info/operators/channel/test_choi.py",
"test/python/opflow/test_gradients.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2017, 2019.\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.cosh",
"numpy.asarray",
"numpy.cos",
"numpy.sin",
"numpy.floor",
"numpy.exp",
"numpy.array",
"numpy.tanh"
],
[
"numpy.dot",
"numpy.sqrt",
"numpy.conj",
"numpy.allclose",
"numpy.arange",
"numpy.eye",
"numpy.kron",
"numpy.linalg.norm",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
JunjieChen-2020/ColossalAI | [
"0e121a256ac4f628f5d26a16dc553cd0024ca2d5",
"0e121a256ac4f628f5d26a16dc553cd0024ca2d5"
] | [
"colossalai/context/moe_context.py",
"tests/test_moe/test_grad_handler.py"
] | [
"import torch\r\nimport torch.distributed as dist\r\nfrom .parallel_mode import ParallelMode\r\nfrom typing import Tuple\r\n\r\n\r\ndef _check_sanity():\r\n from colossalai.core import global_context as gpc\r\n if gpc.tensor_parallel_size > 1 or gpc.pipeline_parallel_size > 1:\r\n raise NotImplementedE... | [
[
"torch.distributed.get_rank",
"torch.distributed.get_world_size",
"torch.distributed.new_group",
"torch.cuda.is_available"
],
[
"torch.randn_like",
"torch.nn.Sequential",
"torch.multiprocessing.spawn",
"torch.cuda.manual_seed",
"torch.distributed.get_rank"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dprada/molsysmt | [
"83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d",
"83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d",
"83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d"
] | [
"molsysmt/structure/get_sasa.py",
"molsysmt/forms/api_nglview_NGLWidget.py",
"molsysmt/structure/fit.py"
] | [
"from molsysmt import puw\nfrom molsysmt.basic import convert, select, get\nfrom molsysmt._private_tools._digestion import digest_engine, digest_target\nimport numpy as np\n\ndef get_sasa (molecular_system, target='atom', selection='all', frame_indices='all', syntaxis='MolSysMT',\n engine='MDTraj'):\n\n ... | [
[
"numpy.empty"
],
[
"numpy.arange",
"numpy.array"
],
[
"numpy.ascontiguousarray",
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
agrinh/nih_prediction | [
"e94ae81935452e7928cda6b101ef58163525d81c"
] | [
"nih_prediction/data.py"
] | [
"\"\"\"Produce metadata and datasets of NIH Chest Xray images\n\"\"\"\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\n\ndef get_metadata(path):\n \"\"\"Produce metadata with relevant columns from NIH Chest Xray images\n\n Args:\n path: Path to NIH dataset\n\n Return... | [
[
"tensorflow.expand_dims",
"tensorflow.image.decode_image",
"tensorflow.image.convert_image_dtype",
"tensorflow.read_file"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Matrix-King-Studio/MaskDetection | [
"5fed65833a8c08380299d606f66e14df814b022f",
"5fed65833a8c08380299d606f66e14df814b022f"
] | [
"yolov5/detect.py",
"yolov5/utils/activations.py"
] | [
"import argparse\r\nimport time\r\nfrom pathlib import Path\r\n\r\nimport cv2\r\nimport torch\r\nimport torch.backends.cudnn as cudnn\r\nfrom numpy import random\r\n\r\nfrom models.experimental import attempt_load\r\nfrom utils.datasets import LoadStreams, LoadImages\r\nfrom utils.general import check_img_size, che... | [
[
"torch.zeros",
"torch.load",
"torch.from_numpy",
"torch.tensor",
"torch.no_grad",
"numpy.random.randint"
],
[
"torch.sigmoid",
"torch.nn.Conv2d",
"torch.nn.functional.hardtanh",
"torch.nn.BatchNorm2d",
"torch.nn.functional.softplus"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rolandbernard/adventofcode-2021 | [
"9249815af62d0fcf79b71357330a1456ea3be1ed",
"9249815af62d0fcf79b71357330a1456ea3be1ed",
"9249815af62d0fcf79b71357330a1456ea3be1ed"
] | [
"20.trench-map/py/part1.py",
"20.trench-map/py/part2.py",
"09.smoke-basin/py/part1.py"
] | [
"\nimport sys\nimport numpy as np\n\nrawalgo, rawimg = sys.stdin.read().strip().split('\\n\\n')\n\nalgo = np.array([1 if c == '#' else 0 for c in rawalgo], dtype=np.int8)\nimg = np.array([[1 if c == '#' else 0 for c in line] for line in rawimg.split('\\n')], dtype=np.int8)\n\ndef enhance(img, algo):\n img = np.p... | [
[
"numpy.arange",
"numpy.copy",
"numpy.array",
"numpy.pad"
],
[
"numpy.arange",
"numpy.array",
"numpy.pad",
"scipy.ndimage.convolve"
],
[
"numpy.roll",
"numpy.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
... |
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