repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
openvinotoolkit/nncf_pytorch | [
"13a483eac6ed891720ba90d7902142c4b3bfa599",
"13a483eac6ed891720ba90d7902142c4b3bfa599"
] | [
"tests/torch/nas/test_state.py",
"examples/experimental/onnx/run_ptq.py"
] | [
"\"\"\"\n Copyright (c) 2022 Intel Corporation\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n http://www.apache.org/licenses/LICENSE-2.0\n Unless required by applicable law or agr... | [
[
"torch.allclose",
"torch.ones"
],
[
"numpy.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
baba1587/jax | [
"cb77f2a22de49e85da93f43b7dc448aa238d5207"
] | [
"jax/lax/lax.py"
] | [
"# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.take",
"numpy.sqrt",
"numpy.asarray",
"numpy.issubdtype",
"numpy.cumsum",
"numpy.dtype",
"numpy.all",
"numpy.max",
"numpy.zeros_like",
"numpy.any",
"numpy.iinfo",
"numpy.negative",
"numpy.where",
"numpy.swapaxes",
"numpy.greater",
"numpy.arang... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mak213k/Servidor_automatizado_python | [
"75a111b9d3b2c50c6f2a9a36d21432053f02284d",
"34cfac4a889e2c973651c1c07740ea0908542d68",
"4403ef8027a2f814220baacc95856cf5fbf01d21",
"75a111b9d3b2c50c6f2a9a36d21432053f02284d",
"34cfac4a889e2c973651c1c07740ea0908542d68",
"4403ef8027a2f814220baacc95856cf5fbf01d21"
] | [
"ServidorPython/python32_web/Lib/site-packages/numpy/lib/tests/test_recfunctions.py",
"ServidorPython/python32_web/Lib/site-packages/sklearn/utils/estimator_checks.py",
"ServidorPython/python32_web/Lib/site-packages/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py",
"ServidorPython/pyt... | [
"from __future__ import division, absolute_import, print_function\n\nimport pytest\n\nimport numpy as np\nimport numpy.ma as ma\nfrom numpy.ma.mrecords import MaskedRecords\nfrom numpy.ma.testutils import assert_equal\nfrom numpy.testing import assert_, assert_raises\nfrom numpy.lib.recfunctions import (\n drop_... | [
[
"numpy.lib.recfunctions.rename_fields",
"numpy.lib.recfunctions.merge_arrays",
"numpy.dtype",
"numpy.lib.recfunctions.require_fields",
"numpy.lib.recfunctions.recursive_fill_fields",
"numpy.ma.array",
"numpy.lib.recfunctions.structured_to_unstructured",
"numpy.arange",
"numpy.l... | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.19",
"1.24",
"1.16",
"1.23",
"1.20",
"1.7",
"1.12",
"1.21",
"1.22",
"1.14",
"1.6",
"1.13",
"1.9",
"1.17",
"1.10",
"1.18",
"1.15",
"1.8"
],
"pand... |
sainjusajan/django-oscar | [
"466e8edc807be689b0a28c9e525c8323cc48b8e1"
] | [
"oscar/lib/python2.7/site-packages/IPython/core/pylabtools.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"Pylab (matplotlib) support utilities.\"\"\"\r\nfrom __future__ import print_function\r\n\r\n# Copyright (c) IPython Development Team.\r\n# Distributed under the terms of the Modified BSD License.\r\n\r\nfrom io import BytesIO\r\n\r\nfrom IPython.core.display import _pngxy\r\nfrom I... | [
[
"matplotlib._pylab_helpers.Gcf.get_all_fig_managers",
"matplotlib.pyplot.switch_backend",
"matplotlib.pyplot.draw",
"matplotlib.get_backend",
"matplotlib.rcParams.update",
"matplotlib.interactive",
"matplotlib._pylab_helpers.Gcf.figs.get"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sherrytp/TradingEvolved | [
"4bc9cc18244954bff37a80f67cce658bd0802b5d",
"4bc9cc18244954bff37a80f67cce658bd0802b5d",
"4bc9cc18244954bff37a80f67cce658bd0802b5d"
] | [
"examples/old/zipline_alpaca2.py",
"examples/old/zipline_model5.py",
"examples/quantinsti/svm_momentum.py"
] | [
"# https://github.com/RomanMichaelPaolucci/AI_Stock_Trading/blob/master/IBM.csv\nimport abc\nimport threading\nimport time\nimport pandas as pd\nimport numpy as np\nfrom keras.layers import Dense\nfrom keras.models import Sequential, model_from_json\nfrom sklearn.model_selection import train_test_split\nfrom sklear... | [
[
"numpy.around",
"pandas.read_csv",
"sklearn.metrics.classification_report",
"sklearn.model_selection.train_test_split"
],
[
"matplotlib.pyplot.show",
"numpy.where",
"pandas.DataFrame",
"matplotlib.pyplot.figure"
],
[
"numpy.sign",
"numpy.zeros",
"sklearn.svm.SVC... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
... |
amelieEmily/RobustDARTS | [
"b26e127c6e9c330258786f5eb77b17d367f546ff",
"b26e127c6e9c330258786f5eb77b17d367f546ff"
] | [
"src/utils.py",
"src/evaluation/eval_imagenet.py"
] | [
"import os\nimport yaml\nimport numpy as np\nimport torch\nimport shutil\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nfrom collections import namedtuple\n\nclass MyDumper(yaml.Dumper):\n\n def increase_indent(self, flow=False, indentless=False):\n return super(MyDumpe... | [
[
"torch.load",
"numpy.clip",
"torch.from_numpy",
"numpy.ones",
"numpy.ceil",
"torch.save",
"numpy.mean",
"numpy.floor",
"numpy.random.binomial",
"numpy.random.randint"
],
[
"torch.nn.CrossEntropyLoss",
"torch.cuda.set_device",
"torch.cuda.manual_seed",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gertjanvanzwieten/nutils | [
"ec04d66e4797398496453181f96b14ad2edae228"
] | [
"tests/test_types.py"
] | [
"from nutils.testing import *\nimport nutils.types\nimport inspect, pickle, itertools, ctypes, stringly, tempfile, io, os\nimport numpy\n\nclass apply_annotations(TestCase):\n\n def test_without_annotations(self):\n @nutils.types.apply_annotations\n def f(a, b):\n return a, b\n a, b = f(1, 2)\n se... | [
[
"numpy.int64",
"numpy.array",
"numpy.float64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
islasimpson/snowpaper_2022 | [
"d6ee677f696d7fd6e7cadef8168ce4fd8b184cac",
"d6ee677f696d7fd6e7cadef8168ce4fd8b184cac",
"d6ee677f696d7fd6e7cadef8168ce4fd8b184cac",
"d6ee677f696d7fd6e7cadef8168ce4fd8b184cac",
"d6ee677f696d7fd6e7cadef8168ce4fd8b184cac",
"d6ee677f696d7fd6e7cadef8168ce4fd8b184cac",
"d6ee677f696d7fd6e7cadef8168ce4fd8b184ca... | [
"DATA_SORT/3cities/SCAM/outputcesmscam_TREFHT_CLM5_CLM5F_001.py",
"DATA_SORT/3cities/SCAM_CLMINIT_60days_withclearsky/outputcesmscam_FLNSC_CLM5_CLM5F_001.py",
"DATA_SORT/3cities/SCAM/weakerrelaxation/outputcesmscam_TREFHT_SNOWDa_CLM5F_002.py",
"DATA_SORT/3cities/SCAM/weakerrelaxation/outputcesmscam_TBOT_CLM5_... | [
"import importlib\nimport xarray as xr\nimport numpy as np\nimport pandas as pd\nimport sys\n\nfrom CASutils import filter_utils as filt\nfrom CASutils import readdata_utils as read\nfrom CASutils import calendar_utils as cal\n\nimportlib.reload(filt)\nimportlib.reload(read)\nimportlib.reload(cal)\n\n\nexpname=['SA... | [
[
"numpy.arange",
"numpy.zeros"
],
[
"numpy.arange",
"numpy.zeros"
],
[
"numpy.arange",
"numpy.zeros"
],
[
"numpy.arange",
"numpy.zeros"
],
[
"numpy.arange",
"numpy.zeros"
],
[
"numpy.arange",
"numpy.zeros"
],
[
"numpy.arange",
"numpy.z... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
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"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
GuillaumeRochette/HumanViewSynthesis | [
"358d9cb55486ad0f81a31df8ab4159153765e7e5",
"d65ea8744e284ec956bbc04f294f05e47731360f",
"d65ea8744e284ec956bbc04f294f05e47731360f",
"d65ea8744e284ec956bbc04f294f05e47731360f"
] | [
"geometry/matrix.py",
"nn/blur.py",
"data/Panoptic/transform.py",
"operations/metrics/pose.py"
] | [
"from typing import Tuple\nimport torch\nfrom torch import Tensor\n\n\ndef homogeneous(A: Tensor, b: Tensor) -> Tensor:\n \"\"\"\n Converts heterogeneous matrix into homogeneous matrix.\n\n :param A: Heterogeneous matrix of shape [*, N, N].\n :param b: Heterogeneous vector of shape [*, N, 1].\n :retu... | [
[
"torch.eye",
"torch.ones",
"torch.cat",
"torch.zeros"
],
[
"torch.nn.functional.pad",
"torch.tensor"
],
[
"torch.tensor"
],
[
"torch.sum",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
tomcattigerkkk/traj_gen | [
"d01882c17d8e979860fb1f09defa968a86adb494"
] | [
"python/scripts/traj_gen/chomp_trajectory.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n\nfrom .traj_gen_base import TrajGen\nimport numpy as np\nimport casadi as ca\nfrom scipy.interpolate import interp1d\nclass CHOMPTrajGen(TrajGen):\n def __init__(self, knots_, dim_, pntDensity_):\n super().__init__(knots_, dim_)\n self.pntDensity = pntDensit... | [
[
"numpy.dot",
"numpy.linspace",
"numpy.min",
"numpy.ones",
"numpy.concatenate",
"scipy.interpolate.interp1d",
"numpy.floor",
"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"
... |
SmirnovKol/recurrent-visual-attention | [
"a38ac8958ebf1c61a10c4d5320f1e31d3d0b73dd"
] | [
"data_loader.py"
] | [
"import numpy as np\nfrom utils import plot_images\n\nimport torch\nfrom torchvision import datasets\nfrom torchvision import transforms\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\n\ndef get_train_valid_loader(\n data_dir,\n batch_size,\n random_seed,\n valid_size=0.1,\n shuffle=True... | [
[
"numpy.random.seed",
"torch.utils.data.DataLoader",
"torch.utils.data.sampler.SubsetRandomSampler",
"numpy.random.shuffle",
"numpy.floor",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
badrutdinovrr/darts | [
"434708e63cbda8f710d3c1810d06ad31c11db923"
] | [
"cnn/model_search.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom operations import *\nfrom torch.autograd import Variable\nfrom genotypes import PRIMITIVES\nfrom genotypes import Genotype\n\n\nclass MixedOp(nn.Module):\n\n def __init__(self, C, stride):\n super(MixedOp, self).__init__()\n self._op... | [
[
"torch.nn.functional.softmax",
"torch.cat",
"torch.randn",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
915288938lx/Personae-master-01 | [
"0885c37956bd3f9157c66109e09755a51ad5d3a1"
] | [
"algorithm/RL/DDPG.py"
] | [
"# coding=utf-8\n\nimport tensorflow as tf\nimport numpy as np\n\nimport os\n\nfrom algorithm import config\nfrom base.env.market import Market\nfrom checkpoints import CHECKPOINTS_DIR\nfrom base.algorithm.model import BaseRLTFModel\nfrom helper.args_parser import model_launcher_parser\nfrom helper.data_logger impo... | [
[
"numpy.hstack",
"tensorflow.losses.mean_squared_error",
"tensorflow.multiply",
"tensorflow.nn.relu",
"tensorflow.train.RMSPropOptimizer",
"numpy.random.choice",
"tensorflow.get_collection",
"tensorflow.reduce_mean",
"tensorflow.assign",
"tensorflow.placeholder",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
fragrussu/qMRINet | [
"418cbe22cefa2974d8a97b359324ff4c35865d22"
] | [
"tools/trainpar_deepqmri.py"
] | [
"# Author: Francesco Grussu, University College London\n#\t\t <f.grussu@ucl.ac.uk> <francegrussu@gmail.com>\n#\n# Code released under BSD Two-Clause license\n#\n# Copyright (c) 2020 University College London. \n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without modific... | [
[
"numpy.random.seed",
"torch.Tensor",
"torch.manual_seed",
"numpy.nanmin",
"torch.Tensor.numpy",
"numpy.ones",
"numpy.concatenate",
"numpy.max",
"numpy.float32",
"numpy.zeros",
"torch.nn.MSELoss",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Morisset/Mexico-datos | [
"29d5ed1079732d5d809bc14eb5d3438662508728"
] | [
"codigo/process_datos_abiertos.py"
] | [
"import os\nimport csv\nimport pandas as pd\nimport geopandas as gpd\nfrom datetime import datetime, timedelta\n\n\n## PROCESSING FUNCTIONS ##\n\ndef confirmados_diarios_por_estado(datos, entidades):\n \"\"\"\n Calcula el número total de casos confirmados por fecha y por estado.\n\n Input:\n - datos: da... | [
[
"pandas.to_datetime",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
mariolpantunes/ml-deti | [
"a47fdb5df70e3f6fda5768be14f97462dfe057fb"
] | [
"code/Ex02.py"
] | [
"import matplotlib.pyplot as plt\nimport arff\nimport numpy as np\nfrom sklearn import linear_model\n\n# Load dataset\ndataset = arff.load(open('dataset/dataset01.arff', 'r'))\ndata = np.array(dataset['data'])\n\n# Reshape vector\nX1 = data[:, 0].reshape(-1, 1)\nX2 = np.multiply(X1, X1)\nX = np.concatenate((X1, X2)... | [
[
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.scatter",
"numpy.multiply",
"numpy.concatenate",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplot.xticks",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JacobEkedahl/detect-intros-from-video | [
"9b2bac1c7209558711072f967a3359d2ca698cd4"
] | [
"src/stats/intro_stats.py"
] | [
"import matplotlib.pyplot as plt\n\nimport utils.extractor as extractor\nimport utils.file_handler as file_handler\nimport utils.time_handler as time_handler\n\n\ndef plot_intros():\n intros = extractor.get_intros_from_data()\n only_valid_intros = [x for x in intros if not x[\"end\"] == \"00:00:00\"]\n x_d... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
idanmoradarthas/DataScienceUtils | [
"be4806ebcb9ab0e2cdd189842227bd242f0c8910"
] | [
"tests/test_strings.py"
] | [
"import pandas\n\nfrom ds_utils.strings import append_tags_to_frame, extract_significant_terms_from_subset\n\n\ndef test_append_tags_to_frame():\n x_train = pandas.DataFrame([{\"article_name\": \"1\", \"article_tags\": \"ds,ml,dl\"},\n {\"article_name\": \"2\", \"article_tags\": \"... | [
[
"pandas.testing.assert_series_equal",
"pandas.testing.assert_frame_equal",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
slomrafgrav/models | [
"91a59c78e8c48e8a1b2fec37143e52dae3f066c1",
"e498d28503fd4a12d1fa9ade41891f2f9601c674",
"e498d28503fd4a12d1fa9ade41891f2f9601c674",
"e498d28503fd4a12d1fa9ade41891f2f9601c674",
"e498d28503fd4a12d1fa9ade41891f2f9601c674",
"e498d28503fd4a12d1fa9ade41891f2f9601c674",
"91a59c78e8c48e8a1b2fec37143e52dae3f066c... | [
"research/object_detection/eval_util.py",
"research/object_detection/builders/image_resizer_builder_test.py",
"research/object_detection/models/ssd_inception_v2_feature_extractor.py",
"research/object_detection/core/region_similarity_calculator.py",
"official/mnist/mnist_eager.py",
"official/utils/misc/mo... | [
"# 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... | [
[
"numpy.squeeze",
"numpy.mean",
"tensorflow.get_default_graph",
"tensorflow.to_int32",
"tensorflow.to_int64",
"tensorflow.greater",
"tensorflow.squeeze",
"tensorflow.train.get_global_step",
"tensorflow.train.Saver",
"tensorflow.train.write_graph",
"tensorflow.shape",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
... |
DrAuxin/WestpaTools | [
"4e236e0a3d65504d1937260316a4a5c6f39aa610",
"4e236e0a3d65504d1937260316a4a5c6f39aa610"
] | [
"wtools/plotting.py",
"wtools/extrema.py"
] | [
"import h5py\nimport numpy\nimport matplotlib.pyplot as plt\n\ndef plotflux(h5file, state=1):\n \"\"\"\n A function that plots the dataset target_flux_evolution from a direct.h5 file.\n\n Parameters\n ----------\n h5file: dictionary\n The user's HDF5 file loaded with loadh5.\n state: intege... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
],
[
"numpy.max",
"numpy.where",
"numpy.char.zfill",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anonips/-MDP-Playground | [
"74431f98c210830a93a1bc83fcdcb95bf1644696",
"74431f98c210830a93a1bc83fcdcb95bf1644696"
] | [
"experiments/custom_agents_opt.py",
"experiments/custom_agents_rainbow_noises.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nfrom ray.rllib.agents.trainer import Trainer, with_common_config\nfrom ray.rllib.utils.annotations import override\n\n# yapf: disable\n# __sphinx_doc_begin__\nclass RandomAgent(Tr... | [
[
"numpy.absolute",
"numpy.zeros",
"numpy.mean"
],
[
"numpy.absolute",
"numpy.zeros",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhengzangw/RODNet | [
"eca5f2bd1f3051c2b823d279532ddafa71b009c1"
] | [
"rodnet/models/backbones/hgwi.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass RadarStackedHourglass(nn.Module):\n def __init__(self, n_class, stacked_num=1):\n super(RadarStackedHourglass, self).__init__()\n self.stacked_num = stacked_num\n self.conv1a = nn.Conv3d(\n in_channels=2,\n out_channels=32... | [
[
"torch.cat",
"torch.nn.ConvTranspose3d",
"torch.nn.PReLU",
"torch.nn.ModuleList",
"torch.nn.Sigmoid",
"torch.nn.Conv3d",
"torch.nn.ReLU",
"torch.nn.BatchNorm3d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bopopescu/fbserver | [
"e812dbc4dc0cbf2fda19473015a3d7e253718a19",
"e812dbc4dc0cbf2fda19473015a3d7e253718a19",
"e812dbc4dc0cbf2fda19473015a3d7e253718a19",
"e812dbc4dc0cbf2fda19473015a3d7e253718a19"
] | [
"venv/lib/python2.7/site-packages/sklearn/base.py",
"venv/lib/python2.7/site-packages/sklearn/utils/random.py",
"venv/lib/python2.7/site-packages/sklearn/neighbors/base.py",
"venv/lib/python2.7/site-packages/sklearn/ensemble/tests/test_weight_boosting.py"
] | [
"\"\"\"Base classes for all estimators.\"\"\"\n# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>\n# License: BSD 3 clause\n\nimport copy\nimport inspect\nimport warnings\n\nimport numpy as np\nfrom scipy import sparse\nfrom .externals import six\n\n\n##########################################################... | [
[
"numpy.all",
"numpy.set_printoptions",
"scipy.sparse.issparse",
"numpy.get_printoptions"
],
[
"numpy.sum",
"numpy.unique",
"numpy.cumsum",
"numpy.any",
"numpy.prod",
"numpy.array",
"numpy.zeros",
"sklearn.utils.check_random_state",
"numpy.empty"
],
[
... | [
{
"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"... |
jupiterman/Data-Transfer-Neural-Way | [
"d900a5552c78f81450c3918640aa3e9210a57488"
] | [
"script_helper/Script/Network.py"
] | [
"from __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nfrom scipy.misc import imread, imresize, imsave, fromimage, toimage\nfrom scipy.optimize import fmin_l_bfgs_b\nimport numpy as np\nimport time\nimport argparse\nimport warnings\n\nfrom keras.models imp... | [
[
"numpy.amax",
"numpy.expand_dims",
"scipy.misc.imresize",
"scipy.misc.toimage",
"numpy.clip",
"scipy.misc.imsave",
"numpy.copy",
"scipy.misc.imread",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
Hazboun6/pta_sim | [
"cf8676e23056586ecb35a030dbaad45a1f985764",
"cf8676e23056586ecb35a030dbaad45a1f985764"
] | [
"pta_sim/pint_sim.py",
"pta_sim/scripts/ng12p5yr_nm_b1937_r4a.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\nimport numpy as np\nimport astropy.units as u\nfrom astropy.time import Time, TimeDelta\n\nfrom pint.residuals import resids\nimport pint.toa as toa\nfrom pint import models\n\n__all__ = ['make_ideal',\n 'createfourierdesignmatrix_red',\n 'add_rednoise',\... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.random.seed",
"numpy.linspace",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"numpy.ones",
"numpy.random.uniform",
"numpy.log10",
"numpy.random.randn",
"numpy.mean",
"numpy.isscalar",
"numpy.argsort",
"numpy.repeat",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Keesiu/meta-kaggle | [
"87de739aba2399fd31072ee81b391f9b7a63f540",
"87de739aba2399fd31072ee81b391f9b7a63f540",
"87de739aba2399fd31072ee81b391f9b7a63f540",
"87de739aba2399fd31072ee81b391f9b7a63f540",
"87de739aba2399fd31072ee81b391f9b7a63f540",
"87de739aba2399fd31072ee81b391f9b7a63f540",
"87de739aba2399fd31072ee81b391f9b7a63f54... | [
"data/external/repositories_2to3/137656/blundercheck-master/combine/data_prep/prepare_pgmodel.py",
"data/external/repositories_2to3/132160/kaggle-ndsb-master/average_predictions.py",
"data/external/repositories_2to3/197978/Grasp-and-lift-EEG-challenge-master/lvl3/genYOLO.py",
"data/external/repositories_2to3/... | [
"#!/usr/bin/env python\r\n\r\nfrom pandas import *\r\nfrom numpy import *\r\nfrom djeval import *\r\nimport csv, code\r\nimport pickle as pickle\r\nfrom sklearn.externals import joblib\r\n\r\nNUM_GAMES=50000\r\n\r\n\r\ndef shell():\r\n vars = globals()\r\n vars.update(locals())\r\n shell = code.Interacti... | [
[
"sklearn.externals.joblib.load"
],
[
"numpy.load",
"numpy.mean",
"numpy.save"
],
[
"sklearn.metrics.roc_auc_score",
"pandas.read_csv",
"numpy.save",
"pandas.DataFrame",
"numpy.std",
"numpy.mean",
"numpy.load"
],
[
"pandas.read_csv",
"numpy.random.see... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",... |
tacox5/elastic_pendulum | [
"c2058444ca161a420466b531b008fe247a87db60"
] | [
"pyelastic/pendulum.py"
] | [
"import os\nimport glob\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import solve_ivp\nfrom scipy.interpolate import interp1d\nfrom .settings import *\n\n\nclass ElasticPendulum:\n \"\"\"Class that handles the simulation of springy, double pendulums. This class\n handles a number... | [
[
"numpy.arange",
"scipy.integrate.solve_ivp",
"numpy.cos",
"numpy.sin",
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
Mark-Kinyua/python_public | [
"25c4eff3a6f93c35a949f94a2f9c3df3202a3113",
"25c4eff3a6f93c35a949f94a2f9c3df3202a3113"
] | [
"motion_detector/main.py",
"ai_class_work/nairobi_roadNetwork/NairobiRoadNetwork.py"
] | [
"import numpy as np\nimport cv2\n\n# A motion detecetor, yup... lol.\n# Remember to use an old python version < 3.6\n\n\nimage_path = 'room_people.jpg' # Photo\n\n# The model was already formulated, just need to loaad it into the system.\n\nprototxt_path = 'models/MobileNetSSD_deploy.prototxt' # Load Model\nmodel_p... | [
[
"numpy.random.seed"
],
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.axis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
demetoir/MLtools | [
"8c42fcd4cc71728333d9c116ade639fe57d50d37",
"8c42fcd4cc71728333d9c116ade639fe57d50d37",
"8c42fcd4cc71728333d9c116ade639fe57d50d37",
"8c42fcd4cc71728333d9c116ade639fe57d50d37"
] | [
"script/sklearn_like_toolkit/warpper/skClf_wrapper/skMultinomial_NBClf.py",
"script/sklearn_like_toolkit/warpper/skReg_wrapper/skARDReg.py",
"script/workbench/experiment_code.py",
"script/sklearn_like_toolkit/warpper/skReg_wrapper/skTheilSenReg.py"
] | [
"from hyperopt import hp\r\nfrom sklearn.naive_bayes import MultinomialNB as _skMultinomialNB\r\n\r\nfrom script.sklearn_like_toolkit.warpper.base.BaseWrapperClf import BaseWrapperClf\r\nfrom script.sklearn_like_toolkit.warpper.base.MixIn import MetaBaseWrapperClfWithABC\r\n\r\n\r\nclass skMultinomial_NBClf(BaseWra... | [
[
"sklearn.naive_bayes.MultinomialNB.__init__"
],
[
"sklearn.linear_model.ARDRegression.__init__"
],
[
"pandas.DataFrame.from_csv",
"numpy.sqrt",
"tensorflow.cast",
"pandas.DataFrame",
"numpy.concatenate",
"sklearn.neural_network.multilayer_perceptron.MLPRegressor",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4... |
umangino/pandas | [
"c492672699110fe711b7f76ded5828ff24bce5ab",
"c492672699110fe711b7f76ded5828ff24bce5ab",
"c492672699110fe711b7f76ded5828ff24bce5ab",
"c492672699110fe711b7f76ded5828ff24bce5ab",
"c492672699110fe711b7f76ded5828ff24bce5ab",
"c492672699110fe711b7f76ded5828ff24bce5ab",
"c492672699110fe711b7f76ded5828ff24bce5a... | [
"pandas/tests/indexes/multi/test_formats.py",
"pandas/io/parsers/base_parser.py",
"pandas/core/util/hashing.py",
"pandas/tests/window/test_win_type.py",
"pandas/tests/indexes/datetimelike_/test_is_monotonic.py",
"pandas/tests/io/formats/test_to_excel.py",
"pandas/tests/indexes/numeric/test_indexing.py"
... | [
"import warnings\n\nimport numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import (\n Index,\n MultiIndex,\n)\n\n\ndef test_format(idx):\n idx.format()\n idx[:0].format()\n\n\ndef test_format_integer_names():\n index = MultiIndex(\n levels=[[0, 1], [0, 1]], codes=[[0, 0, 1, 1], ... | [
[
"pandas.MultiIndex",
"numpy.arange",
"pandas.Index",
"pandas.option_context",
"pandas.DataFrame"
],
[
"pandas._libs.parsers.sanitize_objects",
"numpy.asarray",
"pandas.core.dtypes.common.is_extension_array_dtype",
"pandas._libs.lib.maybe_convert_numeric",
"pandas.core.d... | [
{
"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... |
jackd/graphics | [
"736b99a3306e302674a9b7599e3e2857b85fdb74",
"736b99a3306e302674a9b7599e3e2857b85fdb74",
"736b99a3306e302674a9b7599e3e2857b85fdb74"
] | [
"tensorflow_graphics/nn/metric/tests/fscore_test.py",
"tensorflow_graphics/notebooks/mesh_viewer.py",
"tensorflow_graphics/nn/layer/tests/graph_convolution_test.py"
] | [
"# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed t... | [
[
"numpy.random.uniform",
"numpy.tile",
"numpy.random.randint"
],
[
"numpy.asarray",
"numpy.array"
],
[
"tensorflow.compat.v1.initialize_all_variables",
"tensorflow.nn.l2_loss",
"numpy.zeros_like",
"numpy.where",
"numpy.random.randint",
"numpy.reshape",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaodi-Wen/Child_skin_disease_detect | [
"e95045341e8c27161eebb2c9c3b68026a4ea247b",
"e95045341e8c27161eebb2c9c3b68026a4ea247b"
] | [
"src/src/create_tf_record.py",
"src/src/slim/train_image_classifier.py"
] | [
"# -*-coding: utf-8 -*-\n\"\"\"\n @Project: create_tfrecord\n @File : create_tfrecord.py\n @Author : panjq\n @E-mail : pan_jinquan@163.com\n @Date : 2018-07-27 17:19:54\n @desc : 将图片数据保存为单个tfrecord文件\n\"\"\"\n\n##########################################################################\n\nimp... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.FixedLenFeature",
"tensorflow.cast",
"tensorflow.TFRecordReader",
"tensorflow.train.batch",
"tensorflow.train.Int64List",
"tensorflow.decode_raw",
"tensorflow.python_io.TFRecordWriter",
"tensorflow.initialize_all_variables",
"numpy.as... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CoAxLab/infomercial | [
"fa5d1c1e5c1351735dda2961a2a94f71cd17e270"
] | [
"infomercial/exp/softmeta_bandit.py"
] | [
"import os\nimport fire\nimport gym\n\nimport numpy as np\nfrom scipy.special import softmax\n\nfrom noboard.csv import SummaryWriter\n\nfrom copy import deepcopy\nfrom scipy.stats import entropy\nfrom collections import OrderedDict\n\nfrom infomercial.distance import kl\nfrom infomercial.memory import DiscreteDist... | [
[
"numpy.abs",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dnicholson/gasex-python | [
"53b8c3ff4e64e724d8883bdef299d465621b124f"
] | [
"gasex/diff.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n% Diffusion coeff and Schmidt number for gases in fresh/sea water\n%=========================================================================\n% Modified by D. Nicholson from MATLAB gas_diffusion Version 2.0 16 July 2013\n% Author: Roberta C. Hamme ... | [
[
"numpy.polynomial.polynomial.polyval",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alixhami/training-data-analyst | [
"3eb60cb6c8b55fd7f38414c1082da36b8e62558e"
] | [
"courses/machine_learning/deepdive/05_artandscience/simplernn/trainer/model.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright 2017 Google Inc. 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#... | [
[
"tensorflow.stack",
"tensorflow.data.TextLineDataset",
"tensorflow.keras.estimator.model_to_estimator",
"tensorflow.estimator.train_and_evaluate",
"tensorflow.decode_csv",
"tensorflow.estimator.export.PredictOutput",
"tensorflow.squeeze",
"tensorflow.train.get_global_step",
"te... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
NiccoloSacchi/rlcard | [
"046129e8616b12e25652957869a94ab5fd838ae1"
] | [
"rlcard/games/leducholdem/game.py"
] | [
"import numpy as np\nfrom copy import copy\n\nfrom rlcard.games.leducholdem.dealer import LeducholdemDealer as Dealer\nfrom rlcard.games.leducholdem.player import LeducholdemPlayer as Player\nfrom rlcard.games.leducholdem.judger import LeducholdemJudger as Judger\nfrom rlcard.games.leducholdem.round import Leduchol... | [
[
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DanielHuji-RB/RB-article | [
"e5a9ba30edfb030db1cd3bcf562c6abff3f9d48e",
"e5a9ba30edfb030db1cd3bcf562c6abff3f9d48e"
] | [
"Python/Functions_base/Functions/replace_ElecNaming.py",
"Python/Functions_base/Functions/DL_functions.py"
] | [
"#Daniel Sand\n\nimport pandas as pd\nimport numpy as np\n\n\nfileName='/Tscores.csv'\n\nnewFileName='/Tscores_v3.csv'\ndf=pd.read_csv(fileName, sep=',')\n\n#6 differnt electordes\noldFormat=['0-1','0-2','0-3','2-Jan','3-Jan','3-Feb']\nnewFormat=['0_1','0_2','0_3','2_1','3_1','3_2']\n\nfor iCont in range(0, len(old... | [
[
"pandas.read_csv"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ThinkBigAnalytics/ludwig | [
"0a3159af4cc91f57251f3dec0cdb863c7003cf00"
] | [
"ludwig/features/image_feature.py"
] | [
"#! /usr/bin/env python\n# coding=utf-8\n# Copyright (c) 2019 Uber Technologies, 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/LICENS... | [
[
"numpy.arange",
"tensorflow.placeholder",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
devinlife/tensorflow | [
"1445444c15a396410f25ae91b7d1c19d724e2afc",
"1445444c15a396410f25ae91b7d1c19d724e2afc",
"1445444c15a396410f25ae91b7d1c19d724e2afc",
"1445444c15a396410f25ae91b7d1c19d724e2afc",
"1445444c15a396410f25ae91b7d1c19d724e2afc"
] | [
"tensorflow/python/keras/layers/convolutional.py",
"tensorflow/python/kernel_tests/lu_op_test.py",
"tensorflow/python/ops/nn_impl.py",
"tensorflow/compiler/tests/ternary_ops_test.py",
"tensorflow/python/keras/layers/pooling.py"
] | [
"# Copyright 2015 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.framework.tensor_shape.TensorShape",
"tensorflow.python.ops.array_ops.shape",
"tensorflow.python.keras.backend.spatial_3d_padding",
"tensorflow.python.keras.utils.conv_utils.convert_data_format",
"tensorflow.python.keras.regularizers.get",
"tensorflow.python.ops.array_op... | [
{
"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"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
... |
ChidanandKumarKS/mxnet | [
"1ed8b19849046bce92fd3d4a390b2adc405b584a"
] | [
"python/mxnet/base.py"
] | [
"# coding: utf-8\n# pylint: disable=invalid-name, no-member\n\"\"\"ctypes library of mxnet and helper functions.\"\"\"\nfrom __future__ import absolute_import\n\nimport sys\nimport ctypes\nimport atexit\nimport warnings\nimport inspect\nimport numpy as np\nfrom . import libinfo\nwarnings.filterwarnings('default', c... | [
[
"numpy.frombuffer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpivarski/awkward-1.0 | [
"49a3ff13ef90b8778a80573211d58c544729eaa5",
"49a3ff13ef90b8778a80573211d58c544729eaa5",
"49a3ff13ef90b8778a80573211d58c544729eaa5",
"49a3ff13ef90b8778a80573211d58c544729eaa5",
"49a3ff13ef90b8778a80573211d58c544729eaa5",
"49a3ff13ef90b8778a80573211d58c544729eaa5",
"49a3ff13ef90b8778a80573211d58c544729eaa... | [
"tests/v2/test_0879-non-primitive-with-field.py",
"tests/v2/test_0224-arrow-to-awkward.py",
"tests/v2/test_0410-fix-argminmax-positions-for-missing-values.py",
"tests/v2/test_0198-tutorial-documentation-1.py",
"tests/v2/test_1999-bug1406.py",
"tests/v2/test_0028-add-dressed-types.py",
"tests/v2/test_107... | [
"# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE\n\nimport pytest # noqa: F401\nimport numpy as np # noqa: F401\nimport awkward as ak # noqa: F401\n\n\ndef test_unknown_type():\n array = ak._v2.Array({\"x\": np.arange(10)})\n array = ak._v2.operations.with_field(base... | [
[
"numpy.arange"
],
[
"numpy.frombuffer",
"numpy.array"
],
[
"numpy.array"
],
[
"numpy.array"
],
[
"numpy.array"
],
[
"numpy.arange",
"numpy.array"
],
[
"numpy.argsort",
"numpy.arange",
"numpy.array",
"numpy.asarray"
],
[
"numpy.arange"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
VACUMM/xoa | [
"c6a0d860528cf33ae15c77fa111f95daab0321c0",
"c6a0d860528cf33ae15c77fa111f95daab0321c0",
"c6a0d860528cf33ae15c77fa111f95daab0321c0",
"c6a0d860528cf33ae15c77fa111f95daab0321c0"
] | [
"xoa/__init__.py",
"xoa/tests/test_coords.py",
"xoa/tests/test_regrid.py",
"xoa/geo.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nxarray-based ocean analysis library\n\nThe successor of Vacumm.\n\"\"\"\n# Copyright 2020-2021 Shom\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 ... | [
[
"pandas.read_csv"
],
[
"numpy.ones",
"numpy.testing.assert_allclose"
],
[
"numpy.resize",
"numpy.random.seed",
"numpy.linspace",
"numpy.isnan",
"numpy.arange",
"numpy.ones",
"numpy.datetime64",
"numpy.testing.assert_almost_equal",
"numpy.testing.assert_allcl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
fabianp/nipy | [
"40e89f3ca7f34df05631623807993026134e6de3"
] | [
"nipy/labs/spatial_models/hroi.py"
] | [
"# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nThis module contains the specification of 'hierarchical ROI' object,\nWhich is used in spatial models of the library such as structural analysis\n\nThe connection with other classes is not co... | [
[
"numpy.sum",
"numpy.nonzero",
"numpy.min",
"numpy.isnan",
"numpy.arange",
"numpy.median",
"numpy.ones",
"numpy.max",
"numpy.size",
"numpy.mean",
"numpy.ravel",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lgraesser/MCER | [
"250aa6965064dbc73462eb5edb559bf9ce949b70"
] | [
"utils.py"
] | [
"import json\nimport logging\nimport matplotlib.pyplot as plt\nimport os\nimport tensorflow as tf\nfrom sklearn.utils import shuffle\n\nimport model\nimport train\n\nlogger = logging.getLogger('utils')\nlogger.setLevel(logging.INFO)\n\n\ndef get_data_path():\n '''Returns the path to the image and annotation data... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"sklearn.utils.shuffle",
"tensorflow.keras.preprocessing.text.tokenizer_from_json",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"tensorflow.image.resize",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
imdone/tensorflow | [
"bb4d1ef3861c83627ee9586b85ac3070a7d38335",
"bb4d1ef3861c83627ee9586b85ac3070a7d38335",
"bb4d1ef3861c83627ee9586b85ac3070a7d38335",
"bb4d1ef3861c83627ee9586b85ac3070a7d38335",
"bb4d1ef3861c83627ee9586b85ac3070a7d38335",
"bb4d1ef3861c83627ee9586b85ac3070a7d38335",
"bb4d1ef3861c83627ee9586b85ac3070a7d3833... | [
"tensorflow/python/training/checkpointable_utils.py",
"tensorflow/contrib/batching/python/ops/batch_ops_test.py",
"tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py",
"tensorflow/python/client/timeline.py",
"tensorflow/contrib/autograph/converters/call_trees.py",
"tensorflow/python/ops... | [
"\"\"\"Utilities for saving/loading Checkpointable objects.\"\"\"\n# 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... | [
[
"tensorflow.python.training.saver.BulkSaverBuilder",
"tensorflow.python.ops.variable_scope._get_default_variable_store",
"tensorflow.core.protobuf.checkpointable_object_graph_pb2.CheckpointableObjectGraph",
"tensorflow.python.framework.ops.device",
"tensorflow.python.ops.control_flow_ops.no_op... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"1.4",
"2.7",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"2.6",
"2.10"
]
},
{
"ma... |
jakgel/clusterbuster | [
"d79400a0faf43dece457d99b024b955aef544fc2"
] | [
"surveysim/music2/interpolate.py"
] | [
"import numpy as np\nimport scipy.interpolate as interpolate\nimport matplotlib.pyplot as plt\nimport clusterbuster.mathut as math\n\"\"\"\nStart with e.g. InterpolateRadio2D(psiFile = '../Analysis_MUSIC2/Hoeft_radio/mach_psi_tablefine(10,3).txt', inter=(10,6)) \n\"\"\"\n\n\n\n# from ... | [
[
"matplotlib.pyplot.yticks",
"numpy.ones_like",
"scipy.interpolate.RectBivariateSpline",
"numpy.linspace",
"numpy.abs",
"numpy.empty",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.ylabel",
"numpy.log10",
"matplotlib.pyplot.subplot",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
ammar-khan/raspberry-pi-opencv-dnn-face-detection | [
"04ea998ee9e4d7bf71da022b0d8613940e8e7cfb"
] | [
"main.py"
] | [
"##\n# Copyright 2018, Ammar Ali Khan\n# Licensed under MIT.\n# Since: v1.0.0\n##\n\nimport time\nimport cv2\nimport numpy as np\nfrom src.common.package.config import application\nfrom src.opencv.package.config import application as _application\nfrom src.common.package.http import server as _server\nfrom src.comm... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ashleylqx/AIB | [
"77e418cac52f0ca5f2a7c54927468a7bd75a8fc9"
] | [
"CUB-experiments/nearest_embed.py"
] | [
"# adapted from https://github.com/nadavbh12/VQ-VAE\n\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.autograd import Function, Variable\nimport torch.nn.functional as F\n\nfrom config import *\n\nimport pdb\n\n\nclass NearestEmbedFunc(Function):\n \"\"\"\n Input:\n ------\n x - (bat... | [
[
"torch.linspace",
"torch.zeros",
"torch.rand",
"torch.arange",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hartmanwilliam/federated | [
"ecf51cdf8b86cbd000f6edc5715dc904bce07540",
"ecf51cdf8b86cbd000f6edc5715dc904bce07540",
"ecf51cdf8b86cbd000f6edc5715dc904bce07540",
"ecf51cdf8b86cbd000f6edc5715dc904bce07540",
"ecf51cdf8b86cbd000f6edc5715dc904bce07540",
"ecf51cdf8b86cbd000f6edc5715dc904bce07540",
"ecf51cdf8b86cbd000f6edc5715dc904bce0754... | [
"tensorflow_federated/python/research/optimization/stackoverflow/dataset.py",
"tensorflow_federated/python/core/backends/mapreduce/canonical_form_test.py",
"tensorflow_federated/python/research/triehh/triehh_tff_test.py",
"tensorflow_federated/python/research/simple_fedavg/simple_fedavg_tff.py",
"tensorflow... | [
"# Lint as: python3\n# Copyright 2019, The TensorFlow Federated 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# ... | [
[
"tensorflow.concat",
"tensorflow.reshape",
"tensorflow.strings.split",
"tensorflow.map_fn",
"tensorflow.lookup.KeyValueTensorInitializer",
"tensorflow.size"
],
[
"tensorflow.compat.v1.enable_v2_behavior",
"tensorflow.constant"
],
[
"tensorflow.data.Dataset.from_tensor_s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
aliavni/statsmodels | [
"ef5d57a8d45de76a895e9401705280d558d688ad",
"ef5d57a8d45de76a895e9401705280d558d688ad",
"ef5d57a8d45de76a895e9401705280d558d688ad",
"ef5d57a8d45de76a895e9401705280d558d688ad",
"ef5d57a8d45de76a895e9401705280d558d688ad",
"ef5d57a8d45de76a895e9401705280d558d688ad",
"ef5d57a8d45de76a895e9401705280d558d688a... | [
"statsmodels/multivariate/cancorr.py",
"statsmodels/graphics/tests/test_factorplots.py",
"statsmodels/tsa/statespace/tests/test_varmax.py",
"statsmodels/distributions/empirical_distribution.py",
"tools/validate_docstrings.py",
"statsmodels/sandbox/nonparametric/densityorthopoly.py",
"statsmodels/tsa/sta... | [
"# -*- coding: utf-8 -*-\n\n\"\"\"Canonical correlation analysis\n\nauthor: Yichuan Liu\n\"\"\"\nimport numpy as np\nfrom numpy.linalg import svd\nimport scipy\nimport pandas as pd\n\nfrom statsmodels.base.model import Model\nfrom statsmodels.iolib import summary2\nfrom .multivariate_ols import multivariate_stats\n... | [
[
"numpy.linalg.svd",
"numpy.sqrt",
"numpy.power",
"numpy.min",
"numpy.array",
"scipy.stats.f.sf"
],
[
"numpy.testing.assert_equal",
"pandas.Series",
"numpy.random.seed",
"numpy.arange",
"numpy.testing.assert_raises",
"numpy.random.randint"
],
[
"numpy.dia... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
rongtianjie/dcraw_py | [
"fd45d819a67d2f52d7ca61abbe145ab1b172bee9"
] | [
"demosaic_pack/amaze_demosaic.py"
] | [
"import numpy as np\n \ndef amaze_demosaic(src, raw):\n\n cfarray = raw.raw_colors\n cfarray[cfarray == 3] = 1\n\n rgb = amaze_demosaic_libraw(src, cfarray, raw.daylight_whitebalance)\n\n return rgb\n\ndef amaze_demosaic_libraw(src, cfarray, daylight_wb):\n\n TS = 512\n winx = winy = 0\n wid... | [
[
"numpy.median",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daxpryce/graspologic | [
"b076f58ca03a41eb2e1462d20a61ff09abfd6045",
"b076f58ca03a41eb2e1462d20a61ff09abfd6045"
] | [
"tests/test_plot.py",
"graspologic/pipeline/embed/omnibus_embedding.py"
] | [
"# Copyright (c) Microsoft Corporation and contributors.\n# Licensed under the MIT License.\n\nimport unittest\n\nimport numpy as np\nfrom sklearn.mixture import GaussianMixture\n\nfrom graspologic.plot.plot import (\n _sort_inds,\n gridplot,\n heatmap,\n pairplot,\n pairplot_with_gmm,\n)\nfrom grasp... | [
[
"sklearn.mixture.GaussianMixture",
"numpy.all",
"numpy.diff",
"numpy.random.rand",
"numpy.argsort",
"numpy.array"
],
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TUDelftHao/models | [
"faf0c2dc442ceaa8425aff73abd00f92f3137b7b",
"faf0c2dc442ceaa8425aff73abd00f92f3137b7b",
"faf0c2dc442ceaa8425aff73abd00f92f3137b7b",
"faf0c2dc442ceaa8425aff73abd00f92f3137b7b",
"faf0c2dc442ceaa8425aff73abd00f92f3137b7b",
"faf0c2dc442ceaa8425aff73abd00f92f3137b7b",
"faf0c2dc442ceaa8425aff73abd00f92f3137b7... | [
"research/slim/nets/mobilenet_v1.py",
"research/object_detection/core/box_list_ops.py",
"official/vision/beta/modeling/backbones/spinenet.py",
"research/object_detection/meta_architectures/center_net_meta_arch.py",
"official/core/train_utils.py",
"official/nlp/modeling/networks/mobile_bert_encoder.py",
... | [
"# 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 required... | [
[
"tensorflow.compat.v1.truncated_normal_initializer",
"tensorflow.compat.v1.reduce_mean",
"tensorflow.compat.v1.squeeze",
"tensorflow.compat.v1.variable_scope",
"tensorflow.compat.v1.pad"
],
[
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.equal",
"tensorflow.compat.v1.ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",... |
HapKoM/pyhowfar | [
"b12c248f696dc9bc2b50455b63a2b6ca7a440ba7"
] | [
"datasets/W300.py"
] | [
"from __future__ import print_function\n\nimport os\nimport numpy as np\nimport random\nimport math\nfrom skimage import io\n\nimport torch\nimport torch.utils.data as data\nimport torchfile\n\n# from utils.utils import *\nfrom utils.imutils import *\nfrom utils.transforms import *\n\n\nclass W300(data.Dataset):\n\... | [
[
"torch.Tensor",
"torch.load",
"torch.zeros",
"torch.randn",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vijaysharmapc/Python-End-to-end-Data-Analysis | [
"a00f2d5d1547993e000b2551ec6a1360240885ba"
] | [
"Module1/Getting_Started_with_Data_Analysis_Code/4/annotate.py"
] | [
"#!/usr/bin/env python\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.linspace(-2.4, 0.4, 20)\ny = x * x + 2 * x + 1\nplt.plot(x, y, 'c', linewidth=2.0)\nplt.text(-1.5, 1.8, 'y=x^2 + 2*x + 1',\n fontsize=14, style='italic')\nplt.annotate('minima point', xy=(-1, 0),\n xytext=(-1, 0.3), horizont... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.text",
"matplotlib.pyplot.savefig",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AYCHAIN/PracticalAI | [
"1657e31dfc60645f4f999475803f57c0ab9f1a2d",
"1657e31dfc60645f4f999475803f57c0ab9f1a2d"
] | [
"Section 4/04.02_omniscient_agent_webapp.py",
"Section 5/05.02_random_agent.py"
] | [
"from flask import Flask, redirect, render_template, url_for\nimport numpy as np\n\napp = Flask( __name__ )\n\n@app.route( '/home' )\ndef index():\n # retrieve the agent\n agent = app.config['AGENT']\n\n print( 'Episode: {}/{}'.format( agent.get_episode(), agent.get_episodes() ) )\n print( 'Trial: {}/{}... | [
[
"numpy.mean"
],
[
"numpy.round",
"numpy.random.random",
"numpy.mean",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andrei-assa/pandas | [
"ded76dbbfdff3211cfff0ec7039611b50d531efb"
] | [
"pandas/core/indexes/extension.py"
] | [
"\"\"\"\nShared methods for Index subclasses backed by ExtensionArray.\n\"\"\"\nfrom typing import (\n Hashable,\n List,\n Type,\n TypeVar,\n Union,\n)\n\nimport numpy as np\n\nfrom pandas.compat.numpy import function as nv\nfrom pandas.errors import AbstractMethodError\nfrom pandas.util._decorators ... | [
[
"pandas.core.indexers.deprecate_ndim_indexing",
"pandas.core.dtypes.cast.infer_dtype_from",
"numpy.asarray",
"pandas.errors.AbstractMethodError",
"pandas.core.dtypes.common.pandas_dtype",
"pandas.core.dtypes.common.is_dtype_equal",
"pandas.core.dtypes.cast.find_common_type",
"panda... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lanl/SEPIA | [
"0a1e606e1d1072f49e4f3f358962bd8918a5d3a3",
"0a1e606e1d1072f49e4f3f358962bd8918a5d3a3"
] | [
"examples/Ball_Drop/GenDataBallDrop1.py",
"sepia/SepiaLogLik.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 24 07:52:25 2020\nGenerate, Plot, and write all data needed for ball drop example 1\n@author: granthutchings\n\"\"\"\n#%% Imports\nimport numpy as np\n#import pyDOE # Latin Hypercube\nimport matplotlib.pyplot as plt\nfrom matplotlib.gridsp... | [
[
"numpy.abs",
"numpy.linspace",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.savefig",
"numpy.max",
"matplotlib.pyplot.ylabel",
"numpy.loadtxt",
"matplotlib.gridspec.GridSpec",
"numpy.transpose",
"numpy.savetxt",
"mat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kmohrman/coffea | [
"2baae94028c38b59f0eb52127d8fb92840dbf23d"
] | [
"coffea/lookup_tools/dense_lookup.py"
] | [
"from coffea.lookup_tools.lookup_base import lookup_base\n\nimport numpy\nfrom copy import deepcopy\n\n\nclass dense_lookup(lookup_base):\n def __init__(self, values, dims, feval_dim=None):\n super(dense_lookup, self).__init__()\n self._dimension = 0\n whattype = type(dims)\n if whatt... | [
[
"numpy.searchsorted"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dataiku/dss-plugin-timeseries-preparation | [
"bdb662c909a0ad6d7845325a70e3dac2bdcc6b28",
"bdb662c909a0ad6d7845325a70e3dac2bdcc6b28"
] | [
"tests/python/unit/dku_timeseries/resampling/test_resampler_helpers.py",
"tests/python/unit/dku_timeseries/windowing/test_windowing_long_format.py"
] | [
"import numpy as np\nimport pandas as pd\nimport pytest\n\nfrom dku_timeseries.timeseries_helpers import generate_date_range, get_date_offset\nfrom recipe_config_loading import get_resampling_params\n\n\n@pytest.fixture\ndef config():\n config = {u'clip_end': 0, u'constant_value': 0, u'extrapolation_method': u'n... | [
[
"numpy.testing.assert_array_equal",
"pandas.Timestamp",
"pandas.DatetimeIndex"
],
[
"pandas.date_range",
"pandas.DatetimeIndex",
"numpy.testing.assert_array_equal",
"numpy.round",
"pandas.DataFrame.from_dict",
"numpy.array"
]
] | [
{
"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... |
nata1y/fltk-testbed-group-3 | [
"e23b59fa2a5e638d3804a39fe5012983e2988ca6"
] | [
"fltk/nets/fashion_mnist_ls_gan.py"
] | [
"import torch.nn as nn\n\n\nclass Generator(nn.Module):\n def __init__(self, img_size=32):\n super(Generator, self).__init__()\n\n # TODO: update to proper image size\n self.init_size = img_size // 4\n self.l1 = nn.Sequential(nn.Linear(10, 128 * self.init_size ** 2))\n\n self.c... | [
[
"torch.nn.Dropout2d",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.nn.Upsample",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MRCIEU/ewascatalog | [
"a37dfeb207537831b4c5e313e0edecbad8a7c1a2"
] | [
"database/zenodo.py"
] | [
"# script to upload a file to zenodo sandbox via api\n# seperate sandbox- and real-zenodo accounts and ACCESS_TOKENs each need to be created\n\n# to adapt this script to real-zenodo (from sandbox implementation):\n # update urls to zenodo.org from sandbox.zenodo.org\n # update SANDBOX_TOKEN to a ACCESS_TOKEN ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lzhmarkk/pytorch-deeplab-xception | [
"63f699214e4095a4edda21173012cc29e53125b3"
] | [
"utils/summaries.py"
] | [
"import os\nimport torch\nfrom torchvision.utils import make_grid\nfrom tensorboardX import SummaryWriter\nfrom dataloaders.utils import decode_seg_map_sequence\n\nclass TensorboardSummary(object):\n def __init__(self, directory):\n self.directory = directory\n\n def create_summary(self):\n writ... | [
[
"torch.max",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
samsledje/D-SCRIPT | [
"3fa7ea685f7fcdc63468380267d1672f63bb8772"
] | [
"dscript/commands/train.py"
] | [
"\"\"\"\nTrain a new model.\n\"\"\"\n\nimport sys\nimport argparse\nimport h5py\nimport datetime\nimport subprocess as sp\nimport numpy as np\nimport pandas as pd\nimport gzip as gz\nfrom tqdm import tqdm\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torc... | [
[
"torch.mean",
"torch.cat",
"torch.load",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.no_grad",
"torch.cuda.is_available",
"torch.save",
"torch.autograd.Variable",
"pandas.read_csv",
"torch.ones",
"torch.from_numpy",
"torch.optim.Adam",
"pandas.concat"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
spk921/RTFNet | [
"4dad2a63e13e9c302da45ad5a3af4d85cf474694"
] | [
"test.py"
] | [
"# coding:utf-8\n# modified from: https://github.com/haqishen/MFNet-pytorch\n# By Yuxiang Sun, Aug. 2, 2019\n# Email: sun.yuxiang@outlook.com\n\nimport os\nimport argparse\nimport time\nimport datetime\nimport numpy as np\nimport sys\nimport torch \nfrom torch.autograd import Variable\nfrom torch.utils.data import ... | [
[
"torch.cuda.set_device",
"torch.cuda.current_device",
"torch.utils.data.DataLoader",
"sklearn.metrics.confusion_matrix",
"numpy.nan_to_num",
"torch.no_grad",
"torch.cuda.get_device_name",
"torch.cuda.device_count",
"numpy.zeros",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wxw-matt/xalpha | [
"b142a5daebac5f1129ead0553efcd40cd471190c"
] | [
"xalpha/multiple.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nmodule for mul and mulfix class: fund combination management\n\"\"\"\n\nimport logging\nimport pandas as pd\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Pie, ThemeRiver\n\nfrom xalpha.cons import convert_date, myround, yesterdaydash, yesterdayobj\nfrom xalph... | [
[
"pandas.Timedelta",
"pandas.DataFrame",
"pandas.date_range"
]
] | [
{
"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": []
}
] |
smrutiranjans/tensorflow | [
"d8e8b872eae63188c75046d5bb068e03a81b3f85",
"d8e8b872eae63188c75046d5bb068e03a81b3f85",
"7d9ab3eb485e6eb1778bad4ef01a1cd95b2d22d9",
"d8e8b872eae63188c75046d5bb068e03a81b3f85"
] | [
"tensorflow/python/ops/op_def_library.py",
"tensorflow/python/summary/event_multiplexer.py",
"tensorflow/python/kernel_tests/concat_op_test.py",
"tensorflow/tensorboard/backend/server_test.py"
] | [
"# Copyright 2015 Google Inc. 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 appl... | [
[
"tensorflow.python.util.compat.as_text",
"tensorflow.python.framework.ops.colocate_with",
"tensorflow.python.util.compat.as_bytes",
"tensorflow.python.framework.dtypes.as_dtype",
"tensorflow.python.framework.ops.name_scope",
"tensorflow.python.framework.ops.convert_n_to_tensor",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
Del9fina/robel | [
"63dfac65932757134e5766f1e20a339efe281bc7",
"63dfac65932757134e5766f1e20a339efe281bc7"
] | [
"robel/components/tracking/group_config.py",
"robel/dclaw/turn.py"
] | [
"# Copyright 2019 The ROBEL Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or ... | [
[
"numpy.array"
],
[
"numpy.abs",
"numpy.asarray",
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin",
"numpy.atleast_1d",
"numpy.mod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raphacosta27/geopandas | [
"2c22a26bd40ec48536026b160c54c6fe523d22d7"
] | [
"geopandas/tests/test_geom_methods.py"
] | [
"import string\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal\nfrom pandas import DataFrame, MultiIndex, Series\n\nfrom shapely.geometry import LinearRing, LineString, MultiPoint, Point, Polygon\nfrom shapely.geometry.collection import GeometryCollection\nfrom shapely.ops import unary_union\n\n... | [
[
"pandas.Series",
"numpy.sqrt",
"pandas.MultiIndex",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.testing.assert_array_equal",
"numpy.asanyarray",
"pandas.testing.assert_frame_equal",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
moesio-f/agents | [
"53ce87c9203222585fdcd833e052fcdce1b6fa37"
] | [
"tf_agents/policies/tf_policy.py"
] | [
"# coding=utf-8\n# Copyright 2020 The TF-Agents 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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required ... | [
[
"tensorflow.constant",
"tensorflow.shape",
"tensorflow.compat.dimension_value",
"tensorflow.no_op",
"tensorflow.nest.flatten",
"tensorflow.nest.map_structure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangyuwangumass/General-Data-Driven-Adaptive-Learning | [
"63c4ddef36b2b7bd7078cd9b431e3502c358915a"
] | [
"trajectoryPlugin/collate.py"
] | [
"r\"\"\"\"Contains definitions of the methods used by the _DataLoaderIter workers to\ncollate samples fetched from dataset into Tensor(s).\n\nThese **needs** to be in global scope since Py2 doesn't support serializing\nstatic methods.\n\"\"\"\n\nimport torch\nimport re\nfrom torch._six import container_abcs, string... | [
[
"torch.stack",
"torch.from_numpy",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
coco-robotics/rllab-curriculum | [
"f55b50224fcf5a9a5c064542eb0850a966cab223"
] | [
"curriculum/envs/maze/maze_swim/swimmer_env.py"
] | [
"from rllab.envs.base import Step\nfrom rllab.misc.overrides import overrides\nfrom rllab.envs.mujoco.mujoco_env import MujocoEnv\nimport numpy as np\nfrom rllab.core.serializable import Serializable\nfrom rllab.misc import logger\nfrom rllab.misc import autoargs\nfrom contextlib import contextmanager\n\nclass Swim... | [
[
"numpy.square",
"numpy.min",
"numpy.max",
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stragu/pandas | [
"b8890eb33b40993da00656f16c65070c42429f0d"
] | [
"pandas/io/common.py"
] | [
"\"\"\"Common IO api utilities\"\"\"\nfrom __future__ import annotations\n\nimport bz2\nimport codecs\nfrom collections import abc\nimport dataclasses\nimport gzip\nfrom io import BufferedIOBase, BytesIO, RawIOBase, StringIO, TextIOWrapper\nimport mmap\nimport os\nfrom typing import IO, Any, AnyStr, Dict, List, Map... | [
[
"pandas.compat.get_lzma_file",
"pandas.core.dtypes.common.is_file_like",
"pandas.compat.import_lzma",
"pandas.compat._optional.import_optional_dependency"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ravihammond/hanabi-convention-adaptation | [
"5dafa91742de8e8d5810e8213e0e2771818b2f54",
"5dafa91742de8e8d5810e8213e0e2771818b2f54"
] | [
"pyhanabi/common_utils/helper.py",
"pyhanabi/supervised_model.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\nimport os\nimport random\n\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom typing import Dict\n\n... | [
[
"numpy.convolve",
"torch.cuda.manual_seed",
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"numpy.ones",
"torch.FloatTensor",
"torch.nn.init.orthogonal_"
],
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.zeros",
"torch.nn.LSTM",
"torch.nn.Linea... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gboehl/pymetalog | [
"bcc1bfbf658f44f48d63a594d2b9de8b700a11a7"
] | [
"pymetalog/pdf_quantile_functions.py"
] | [
"import numpy as np\nfrom .support import pdfMetalog, quantileMetalog\n\n\ndef pdf_quantile_builder(temp, y, term_limit, bounds, boundedness):\n \"\"\"Builds the metalog pdf and quantile arrays based on the a coefficients found by fitting metalog distribution.\n\n Args:\n temp (:obj: `numpy.ndarray` of... | [
[
"numpy.append",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishalbelsare/DESlib | [
"64260ae7c6dd745ef0003cc6322c9f829c807708"
] | [
"deslib/dcs/a_posteriori.py"
] | [
"# coding=utf-8\n\n# Author: Rafael Menelau Oliveira e Cruz <rafaelmenelau@gmail.com>\n#\n# License: BSD 3 clause\n\nimport numpy as np\n\nfrom deslib.dcs.base import BaseDCS\n\n\nclass APosteriori(BaseDCS):\n \"\"\"A Posteriori Dynamic classifier selection.\n\n The A Posteriori method uses the probability of... | [
[
"numpy.ma.sum",
"numpy.expand_dims",
"numpy.ma.filled",
"numpy.atleast_2d",
"numpy.ma.MaskedArray"
]
] | [
{
"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": []
}
] |
Masao-Someki/CycleVAE_VC | [
"be4a27637a3f8b6272d96105f9b3c9327f6c16f7"
] | [
"src/decode/decoder.py"
] | [
"# Copyright 2020 Masao Someki\n# MIT License (https://opensource.org/licenses/MIT)\nimport os\nimport glob\nimport h5py\nimport logging\n\nimport librosa\nimport numpy as np\nfrom scipy.io import wavfile\n\nfrom speech import Synthesizer\n\nIRLEN = 1024\nINTERVALS = 10\nSEED = 1\nLP_CUTOFF = 20\n\n\nclass Decoder... | [
[
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
srinivasans/DeepSepsis | [
"8647a2ec93ad5a937638acfc279a756bbfa04f7f",
"8647a2ec93ad5a937638acfc279a756bbfa04f7f"
] | [
"deprecated/Imputation/GRUI/Run_GAN_imputed.py",
"evaluateModel.py"
] | [
"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 26 10:47:41 2018\n\n@author: yonghong\n\"\"\"\n\nfrom __future__ import print_function\nimport sys\nsys.path.append(\"..\")\nimport argparse\nimport os\nimport tensorflow as tf\nfrom Physionet2019ImputedSepsisData import readImputed\nimpor... | [
[
"tensorflow.ConfigProto",
"tensorflow.reset_default_graph",
"tensorflow.Session"
],
[
"tensorflow.ConfigProto",
"tensorflow.reset_default_graph",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
middleprince/fashionAi | [
"c512936b4983c2fb093008f06e04753180af0a90",
"c512936b4983c2fb093008f06e04753180af0a90"
] | [
"run_local_mertric.py",
"train_senet_cpn_onebyone.py"
] | [
"import os\nimport sys\nimport time\nimport numpy as np\nimport pandas as pd\nimport argparse\nimport math\n\nimport config as cfg\n\ndef str2bool(v):\n return v.lower() in (\"yes\", \"true\", \"t\", \"1\")\n\nparser = argparse.ArgumentParser(\n description='The Normarlized Error Mertric Calculation For Fashi... | [
[
"pandas.read_csv"
],
[
"tensorflow.floordiv",
"tensorflow.control_dependencies",
"tensorflow.cast",
"tensorflow.nn.l2_loss",
"tensorflow.gfile.MakeDirs",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.GPUOptions",
"tensorflow.app.flags.DEFINE_boolean",
"tensorflow.es... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
... |
aasensio/Lightweaver | [
"9a261e72235f05df548148da140012f40dbd1e4b",
"9a261e72235f05df548148da140012f40dbd1e4b"
] | [
"examples/plot_SimpleLineTest.py",
"lightweaver/zeeman.py"
] | [
"\"\"\"\n===============================================================\nComputing a simple NLTE 8542 line profile in a FAL C atmosphere\n===============================================================\n\"\"\"\n#%%\n# First, we import everything we need. Lightweaver is typically imported as\n# `lw`, but things lik... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"numpy.linspace"
],
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
satishjasthi/convnet-study | [
"ccd20c90e449fc8db694abf706db178e9413e57b"
] | [
"rme/datasets/mnist.py"
] | [
"from __future__ import absolute_import\n\nimport os\nimport numpy as np\nimport gzip\nimport struct\n\nfrom .preprocessing import one_hotify\n\ndef load(data_dir, valid_ratio=0.0, one_hot=True, shuffle=False, dtype='float32'):\n\n train_set, valid_set, test_set = {}, {}, {}\n # Get data from binary files\n for ... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Giorgiobientinesi/Workshop2 | [
"f454499d4befdb705b4672be25d8698ef2b37116"
] | [
"Model.py"
] | [
"import pandas as pd\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_absolute_error\n\n\ndf = pd.read_csv(\"Airbnb-cleaned.csv\")\ndf.columns\ndel df[\"Unnamed: 0\"]\n\ndf1 = ... | [
[
"sklearn.ensemble.RandomForestRegressor",
"pandas.read_csv",
"sklearn.metrics.mean_absolute_error",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
DiamondLightSource/SuRVoS2 | [
"42bacfb6a5cc267f38ca1337e51a443eae1a9d2b"
] | [
"survos2/improc/regions/ccl.py"
] | [
"import logging\r\n\r\nimport os.path as op\r\n\r\nimport numpy as np\r\n\r\nimport pycuda.driver as cuda\r\nimport pycuda.gpuarray as gpuarray\r\nimport pycuda.autoinit\r\nfrom pycuda.compiler import SourceModule\r\n\r\nfrom ..improc_types import int3\r\nfrom ..utils import gpuregion, cpuregion\r\nfrom ..cuda impo... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SamuelWiqvist/snpla | [
"9d586c5d09de3eecd2536485af6fc28a915443e4"
] | [
"mv_gaussian/low_dim_w_summary_stats/run_script_snpla.py"
] | [
"# Imports\nimport sys\nimport torch\nimport os\nimport time\nimport numpy as np\nfrom torch.distributions.multivariate_normal import MultivariateNormal\n\n# Initial set up\nlunarc = int(sys.argv[1])\ndim = int(sys.argv[2])\nseed = int(sys.argv[3])\nseed_data = int(sys.argv[4])\nhp_tuning = int(sys.argv[5]) # if h... | [
[
"torch.manual_seed",
"numpy.random.seed",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
victor-estrade/SystGradDescent | [
"822e7094290301ec47a99433381a8d6406798aff",
"822e7094290301ec47a99433381a8d6406798aff",
"822e7094290301ec47a99433381a8d6406798aff",
"822e7094290301ec47a99433381a8d6406798aff"
] | [
"model/summaries.py",
"benchmark/VAR/HIGGSTES/DA.py",
"benchmark/HARDGG/TP-Calib.py",
"benchmark/HIGGS/TP-Prior.py"
] | [
"# coding: utf-8\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nimport numpy as np\n\nDEFAULT_N_BINS = 10\n\ndef compute_summaries(clf, X, W, n_bins=DEFAULT_N_BINS):\n proba = clf.predict_proba(X)\n co... | [
[
"numpy.max",
"numpy.array",
"numpy.histogram",
"numpy.min"
],
[
"numpy.concatenate",
"pandas.concat"
],
[
"pandas.concat",
"pandas.DataFrame"
],
[
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
Aieener/SUS_3D | [
"8fc5a768a2339238939522baf96bce98bf61902e"
] | [
"DATA/10_64_64_64_1E7/analy.py"
] | [
"# analy.py\n# A python program to analyze the SUS weighting function in order to reach the following goals:\n# 1. plot the weight function\n# 2. generate the normalized distribution for Z=1\n# 3. extrapolate the N distribution for different Zs given by the user.\n# Author: Yuding Ai\n# Date: 2015 Oct 23\n\nimport ... | [
[
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.xlabel",
"matplotlib.rc",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
golmschenk/ramjet | [
"77fb4481a15088923308fda09804d80455d1a9cf"
] | [
"ramjet/data_interface/tess_eclipsing_binary_metadata_manager.py"
] | [
"\"\"\"\nCode for managing the TESS eclipsing binary metadata.\n\"\"\"\nimport pandas as pd\nfrom pathlib import Path\nfrom peewee import IntegerField, SchemaManager\n\nfrom ramjet.data_interface.metadatabase import MetadatabaseModel, metadatabase\n\n\nbrian_powell_eclipsing_binary_csv_path = Path('data/tess_eclips... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
BreezeDawn/numpy-pandas-matplotlib- | [
"e55dccb2442e57c2fccb2081966a7c19e731083a"
] | [
"pandas_study/PandasTest.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef base():\n index = pd.date_range('20181023', periods=9) # 生成9个行索引\n column = ['a', 'b', 'c', 'd'] # 生成4个列索引\n a = np.random.randn(9, 4) # 随便生成的9行4列的数据\n df = pd.DataFrame(a, index=index, columns=column)\n print(df)\n... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.merge",
"pandas.Series",
"numpy.arange",
"pandas.DataFrame",
"numpy.ones",
"numpy.random.randn",
"pandas.date_range",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
maximskorik/matchms | [
"922f5afaef123a793194bdd74391027477cbb844",
"922f5afaef123a793194bdd74391027477cbb844",
"922f5afaef123a793194bdd74391027477cbb844",
"922f5afaef123a793194bdd74391027477cbb844"
] | [
"matchms/exporting/save_as_json.py",
"matchms/Fragments.py",
"matchms/importing/load_from_usi.py",
"matchms/similarity/ModifiedCosine.py"
] | [
"import json\nfrom typing import List\nimport numpy\nfrom ..Spectrum import Spectrum\n\n\ndef save_as_json(spectrums: List[Spectrum], filename: str):\n \"\"\"Save spectrum(s) as json file.\n\n :py:attr:`~matchms.Spectrum.losses` of spectrum will not be saved.\n\n Example:\n\n .. code-block:: python\n\n ... | [
[
"numpy.vstack"
],
[
"numpy.asarray",
"numpy.all",
"numpy.allclose",
"numpy.vstack"
],
[
"numpy.array"
],
[
"numpy.asarray",
"numpy.argsort",
"numpy.zeros",
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
LenKerr/Colorization-1 | [
"bcfcdb24fc8ab107d34644d5a63b018f86784e21"
] | [
"transfer_subnet/xiaoketransfer2.py"
] | [
"\"\"\"\nCopyright (c) 2019 NAVER Corp.\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 rights\nto use, copy, modify, merge, publish... | [
[
"torch.device",
"torch.no_grad",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mukulbhave/YAD2K | [
"a6174285e036f95df83783b7b4d951094cbb08c8"
] | [
"retrain_yolo.py"
] | [
"\"\"\"\nThis is a script that can be used to retrain the YOLOv2 model for your own dataset.\n\"\"\"\nimport argparse\n\nimport os\nfrom PIL import ImageOps\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport PIL\nimport tensorflow as tf\nfrom keras import backend as K\nfrom keras.layers import Input, Lamb... | [
[
"tensorflow.device",
"numpy.expand_dims",
"matplotlib.pyplot.imshow",
"numpy.vstack",
"numpy.concatenate",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
traffic-ai/EvalDeT | [
"3b52698e1b03fb9066e3203c2f36aebfa0030aba",
"3b52698e1b03fb9066e3203c2f36aebfa0030aba"
] | [
"tests/unit/test_clearmot.py",
"src/evaldet/tracks.py"
] | [
"import numpy as np\nimport pytest\n\nfrom evaldet import Tracks\nfrom evaldet.mot_metrics.clearmot import calculate_clearmot_metrics\n\n\ndef test_missing_frame_hyp():\n gt = Tracks()\n gt.add_frame(0, [0], np.array([[0, 0, 1, 1]]))\n gt.add_frame(1, [0], np.array([[0, 0, 1, 1]]))\n\n hyp = Tracks()\n ... | [
[
"numpy.isnan",
"numpy.array"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
robustmetalearning/robust-meta-learning | [
"08fc3e9302c9fbd1fcfc3e001e0b080a3c783c81"
] | [
"MAML-ADML/meta.py"
] | [
"import torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.nn import functional as F\nfrom torch.utils.data import TensorDataset, DataLoader\nfrom torch import optim\nimport numpy as np\n\nfrom learner import Learner\nfrom copy import deepcopy\n\ndef zero_nontrainable_grads(grad... | [
[
"torch.nn.functional.softmax",
"torch.enable_grad",
"torch.max",
"torch.eq",
"torch.nn.functional.cross_entropy",
"torch.zeros_like",
"torch.no_grad",
"torch.clamp",
"numpy.array",
"torch.autograd.grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ondrejklejch/learning_to_adapt | [
"6de0b98370769596da16a1688582925ea2e1fa29",
"6de0b98370769596da16a1688582925ea2e1fa29"
] | [
"steps/nnet3/train.py",
"tests/model/meta_test.py"
] | [
"import sys\nimport numpy as np\n\nfrom keras.callbacks import ModelCheckpoint, CSVLogger, LearningRateScheduler\nfrom keras.models import Model\nfrom keras.layers import Input, Activation, Conv1D, BatchNormalization\nfrom keras.optimizers import Adam\n\nfrom learning_to_adapt.model import LHUC, Renorm\nfrom learni... | [
[
"tensorflow.ConfigProto",
"tensorflow.Session"
],
[
"numpy.array",
"numpy.expand_dims",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
madhukarkm/NeMo | [
"648c97f076147684bee6aaada209f2f20adcaf5d",
"648c97f076147684bee6aaada209f2f20adcaf5d",
"648c97f076147684bee6aaada209f2f20adcaf5d",
"648c97f076147684bee6aaada209f2f20adcaf5d",
"648c97f076147684bee6aaada209f2f20adcaf5d",
"648c97f076147684bee6aaada209f2f20adcaf5d",
"648c97f076147684bee6aaada209f2f20adcaf5... | [
"nemo/collections/nlp/data/data_utils/data_preprocessing.py",
"tests/core/test_fileio.py",
"scripts/dataset_processing/ljspeech/calculate_durs.py",
"scripts/voice_activity_detection/vad_overlap_posterior.py",
"nemo/collections/nlp/modules/common/megatron/megatron_utils.py",
"nemo/collections/tts/helpers/h... | [
"# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.min",
"numpy.asarray",
"numpy.median",
"torch.distributed.is_initialized",
"numpy.percentile",
"numpy.max",
"numpy.mean"
],
[
"numpy.array_equal",
"torch.load"
],
[
"numpy.round",
"torch.LongTensor",
"numpy.array",
"torch.save"
],
[
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
Kenneth-Wong/tf-faster-rcnn | [
"a6bd798df1b9075ebdfeb7744fffc13226c3a65e",
"a6bd798df1b9075ebdfeb7744fffc13226c3a65e"
] | [
"lib/model/config.py",
"lib/roi_data_layer/layer.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport os.path as osp\nimport numpy as np\n# `pip install easydict` if you don't have it\nfrom easydict import EasyDict as edict\n\n__C = edict()\n# Consumers can get config by:\n# from fa... | [
[
"numpy.array"
],
[
"numpy.logical_not",
"numpy.random.get_state",
"numpy.random.seed",
"numpy.reshape",
"numpy.arange",
"numpy.random.set_state",
"numpy.random.permutation",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Crazy-Jack/RL4GRN | [
"e683e17758eb468bd42e0ea0020e2246051c258c"
] | [
"RL_TD3/src/pe_model.py"
] | [
"'''\n The probabilistic ensemble dynamics model\n'''\n# pylint: disable=C0103, R0902, R0913, W0201, E0401, E1120\nimport time\nimport itertools\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom collections import defaultdict\n\nimport os\nos.environ['KMP_DUPLICATE_LIB_OK']='True'\... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"numpy.mean",
"numpy.random.randint",
"tensorflow.math.softplus",
"numpy.ceil",
"tensorflow.math.reduce_sum",
"tensorflow.gather",
"numpy.zeros",
"tensorflow.gather_nd",
"numpy.random.choice",
"tensorflow.keras.la... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
162/catalyst | [
"b4ba36be52c51160e0fabecdcb084a8d5cd96cb7",
"b4ba36be52c51160e0fabecdcb084a8d5cd96cb7"
] | [
"catalyst/dl/utils/trace.py",
"catalyst/data/reader.py"
] | [
"from typing import Type\n\nimport torch\nfrom torch import nn\nfrom torch.jit import ScriptModule\n\nfrom catalyst.dl.core import Experiment, Runner\n\n\nclass _ForwardOverrideModel(nn.Module):\n \"\"\"\n Model that calls specified method instead of forward\n\n (Workaround, single method tracing is not su... | [
[
"torch.device",
"torch.jit.trace"
],
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Diva-Pant/tensorflow | [
"f926d8c10efb07176ae559d0e098cdfdb4d03219",
"f926d8c10efb07176ae559d0e098cdfdb4d03219",
"f926d8c10efb07176ae559d0e098cdfdb4d03219"
] | [
"tensorflow/python/distribute/multi_process_runner_test.py",
"tensorflow/python/ops/special_math_ops.py",
"tensorflow/python/keras/layers/preprocessing/category_encoding_test.py"
] | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.distribute.multi_worker_test_base.get_task_type",
"tensorflow.python.distribute.multi_process_runner.barrier",
"tensorflow.python.distribute.multi_process_runner.run",
"tensorflow.python.distribute.multi_process_runner.test_main",
"tensorflow.python.distribute.multi_worker_t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.3"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"2.7",
"2.6",
"2.4",
"2.3",
"2.9",
"2.5",
"2... |
sano307/lambda-container-demo | [
"6c27c56819c9a3defb63bf26b4fd53bf6cdb71d3"
] | [
"lambda/index.py"
] | [
"import json\n\nimport pandas as pd\n\n\ndef handler(event, context):\n df = pd.DataFrame({\"id\": [1, 2], \"value\": [\"foo\", \"boo\"]})\n print(df)\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": \"This is a container lambda.\"\n })\n }\n"
] | [
[
"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": []
}
] |
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