repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
kitchenprinzessin3880/pangaea-recsys | [
"bacdf27f7433f38e63565d62e602dfed264df736"
] | [
"usage_analysis/usage_scripts/extractlogs_downloads_parallel_skcosine.py"
] | [
"import configparser as ConfigParser\nimport os\nimport argparse\nimport re\nimport pandas as pd\nimport time\nimport datetime as dt\nfrom scipy import sparse\nimport numpy as np\nimport tables\nimport pickle\nimport json\nimport multiprocessing\nimport logging\nfrom sklearn.metrics.pairwise import cosine_similarit... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.read_csv",
"sklearn.metrics.pairwise.cosine_similarity",
"scipy.sparse.csr_matrix",
"numpy.fill_diagonal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"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"... |
XuJ/CPM-Pretrain | [
"fb29452f3c6ec3575c78c050f4108aae2dedd518"
] | [
"megatron/model/transformer.py"
] | [
"# coding=utf-8\n# 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... | [
[
"torch.nn.Dropout",
"torch._C._jit_set_profiling_executor",
"torch.enable_grad",
"torch.nn.functional.dropout",
"torch.cuda.current_device",
"torch._C._jit_set_profiling_mode",
"torch._C._jit_override_can_fuse_on_gpu",
"torch._C._jit_override_can_fuse_on_cpu",
"torch.no_grad",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
moritzkern/stable-baselines3 | [
"547259f49eff35710fd9e6c82bbb8708ff24890c"
] | [
"stable_baselines3/ppo/ppo.py"
] | [
"import warnings\nfrom typing import Any, Dict, Optional, Type, Union\n\nimport numpy as np\nimport torch as th\nfrom gym import spaces\nfrom torch.nn import functional as F\nfrom loguru import logger\n\nfrom stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm\nfrom stable_baselines3.common.polici... | [
[
"torch.mean",
"torch.abs",
"torch.min",
"torch.exp",
"torch.nn.functional.mse_loss",
"torch.no_grad",
"numpy.mean",
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DaraOrange/mlforhealthlabpub | [
"9db861c850c94c6cf1f8bf75ed2ad8dcbd648aa3"
] | [
"alg/pategan/pate_gan.py"
] | [
"\"\"\"PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees Codebase.\n\nReference: James Jordon, Jinsung Yoon, Mihaela van der Schaar, \n\"PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees,\" \nInternational Conference on Learning Representations (ICLR), 2019.\nPaper link... | [
[
"tensorflow.zeros",
"numpy.asarray",
"numpy.concatenate",
"numpy.random.laplace",
"numpy.exp",
"numpy.reshape",
"tensorflow.reset_default_graph",
"tensorflow.Session",
"numpy.zeros",
"tensorflow.matmul",
"numpy.log",
"tensorflow.train.RMSPropOptimizer",
"numpy.m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mdsmith-cim/ssd.pytorch | [
"c10b2310ee35fd320c7b34967ed62c3e4293e994"
] | [
"utils/augmentations.py"
] | [
"import torch\nfrom torchvision import transforms\nimport cv2\nimport numpy as np\nimport types\nfrom numpy import random\n\n\ndef intersect(box_a, box_b):\n max_xy = np.minimum(box_a[:, 2:], box_b[2:])\n min_xy = np.maximum(box_a[:, :2], box_b[:2])\n inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.in... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.clip",
"numpy.random.choice",
"numpy.random.uniform",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alafage/traffic | [
"bd5c5a5d504f83b36bf2b096b88fdf43fec0f893"
] | [
"traffic/data/adsb/opensky_impala.py"
] | [
"import hashlib\nimport logging\nimport re\nimport string\nimport time\nfrom datetime import timedelta\nfrom io import StringIO\nfrom pathlib import Path\nfrom tempfile import gettempdir\nfrom typing import Any, Dict, Iterable, List, Optional, Tuple, Union, cast\n\nimport paramiko\nfrom tqdm.autonotebook import tqd... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
wwdok/mask2json | [
"403c6b3df677185d5951239d13187b55bda6465a"
] | [
"convertmask/labelme_sub/cli/draw_label_png.py"
] | [
"'''\nlanhuage: python\nDescripttion: \nversion: beta\nAuthor: xiaoshuyui\nDate: 2020-10-16 10:08:47\nLastEditors: xiaoshuyui\nLastEditTime: 2020-10-16 11:01:04\n'''\nimport argparse\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport PIL.Image\n\nfrom convertmask.labelme_sub.logger import logger\nfrom c... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hummat/occupancy_networks | [
"c7b89d58f3839fb56df53c37288d22c33529aeac"
] | [
"im2mesh/dmc/utils/pred2mesh.py"
] | [
"import numpy as np\nimport torch\n\nfrom im2mesh.dmc.ops.cpp_modules import pred2mesh\n\n\ndef unique_rows(a):\n \"\"\" Return the matrix with unique rows \"\"\"\n rowtype = np.dtype((np.void, a.dtype.itemsize * a.shape[1]))\n b = np.ascontiguousarray(a).view(rowtype)\n _, idx, inverse = np.unique(b, r... | [
[
"torch.LongTensor",
"torch.max",
"numpy.unique",
"numpy.asarray",
"numpy.ascontiguousarray",
"numpy.dtype",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YangLi1221/CoRA-pytorch | [
"5f46fd6681bf99c082c4a26c08febd616a0aa84c"
] | [
"data_sampler/NYT10_sampler.py"
] | [
"import os\r\nimport sys\r\nimport numpy as np\r\nfrom random import sample\r\n\r\nclass NYT10_data_sampler(object):\r\n\r\n def __init__(self, args):\r\n self.data_path = args.data_path + \"/raw_data/\"\r\n self.processed_data_path = args.data_path + \"/preprocessed_nyt10/\"\r\n self.fixlen... | [
[
"numpy.max",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhang-informatics/active_learning | [
"17366bd27e61f31fe200020023025d80ddcfd14e"
] | [
"activelearning/tests/test_activelearning.py"
] | [
"import unittest\nimport numpy as np\nfrom sklearn.svm import SVC\n\nfrom activelearning.activelearning import ActiveLearningModel\nfrom activelearning.querystrategies import *\n\n\nclass ActiveLearningModelTest(unittest.TestCase):\n def setUp(self):\n np.random.seed(311)\n self.clf = SVC(probabili... | [
[
"sklearn.svm.SVC",
"numpy.array",
"numpy.random.seed",
"numpy.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pby04188/path_following | [
"791d12a4ddbb5d85e06ce53641901c5c00ec9c53"
] | [
"path_following/pose_test.py"
] | [
"from math import radians\nimport numpy as np\nfrom rclpy.qos import QoSDurabilityPolicy\nfrom rclpy.qos import QoSHistoryPolicy\nfrom rclpy.qos import QoSProfile\nfrom rclpy.qos import QoSReliabilityPolicy\nimport rclpy\nfrom rclpy.node import Node\nfrom geometry_msgs.msg import Twist, Vector3, PoseStamped\nfrom t... | [
[
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
awav/probability | [
"c833ee5cd9f60f3257366b25447b9e50210b0590"
] | [
"tensorflow_probability/python/distributions/negative_binomial_test.py"
] | [
"# Copyright 2018 The TensorFlow Probability Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by a... | [
[
"scipy.stats.nbinom.mean",
"scipy.stats.nbinom.var",
"tensorflow.compat.v2.TensorShape",
"numpy.exp",
"tensorflow.compat.v1.placeholder_with_default",
"numpy.ones_like",
"scipy.stats.nbinom.logpmf",
"numpy.float32",
"tensorflow.compat.v2.math.log",
"tensorflow.compat.v2.Var... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sourcery-ai-bot/eniric | [
"d0f9056a62ced45fee9017fd1f92dbb5760036fd"
] | [
"tests/test_broaden.py"
] | [
"import numpy as np\nimport pytest\nfrom hypothesis import given, settings, strategies as st\nfrom joblib import Parallel\n\nfrom eniric.broaden import (\n convolution,\n resolution_convolution,\n rotation_kernel,\n rotational_convolution,\n unitary_gaussian,\n)\n\n\n@settings(max_examples=100)\n@giv... | [
[
"numpy.linspace",
"numpy.asarray",
"numpy.arange",
"numpy.flipud",
"numpy.all",
"numpy.max",
"numpy.int",
"numpy.random.randn",
"numpy.insert",
"numpy.float64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gonanc13/Cylinder2DFlowControlDRL | [
"0a2ec37f15971d90bdbf4d64274799ac47803be3"
] | [
"Rectangle2DFlowControlWithRL/baseline_200s_sq_Xd26_H25_dxcyl05_dxmed3_dt004_Xu3/env.py"
] | [
"\"\"\"Resume and use the environment in the configuration version number 2.\n Here you set the environment parameters.\n You can modify, for example, the position of the probes, reward function used, etc.\n\n Define:\n -geometry parameters\n -mesh parameters\n\n -profile: Inflow profile\n -mu, rho: f... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eric-ycw/solomon | [
"9cdc44edea5d35df76cffa8e7266a94976dce50b"
] | [
"src/examples/amzn_regression.py"
] | [
"from datetime import datetime\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.linear_model import LinearRegression as skl_LR\nfrom sklearn.metrics import r2_score\nplt.style.use('seaborn')\n\nimport sys\nsys.path.insert(0, '..')\nimport os\nfrom src.re... | [
[
"matplotlib.pyplot.legend",
"pandas.to_datetime",
"sklearn.metrics.r2_score",
"matplotlib.pyplot.title",
"numpy.arange",
"numpy.squeeze",
"numpy.ones",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.styl... | [
{
"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": []
}
] |
claudiojung/iwpod-net | [
"8cb667e54baef7ccb14bee33e293624627b2838a"
] | [
"src/detection_utils.py"
] | [
"import numpy as np\r\nimport cv2\r\nfrom src.keras_utils \t\t\timport detect_lp\r\nfrom src.utils \t\t\t\t\timport im2single, nms_darkflow, nms_darkflow_target, adjust_pts, print_digits\r\n#from src.label \t\t\t\t\timport Shape, writeShapes\r\nfrom src.drawing_utils\t\t\timport draw_losangle\r\n\r\n\r\n#\r\n# Run... | [
[
"numpy.median"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aghinsa/dopamine | [
"e7d780d7c80954b7c396d984325002d60557f7d1"
] | [
"dopamine/discrete_domains/atari_lib.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Dopamine Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required b... | [
[
"numpy.expand_dims",
"numpy.sqrt",
"tensorflow.compat.v1.keras.activations.softmax",
"numpy.asarray",
"tensorflow.contrib.slim.flatten",
"tensorflow.compat.v1.keras.layers.Dense",
"tensorflow.compat.v1.constant",
"tensorflow.compat.v1.cos",
"tensorflow.compat.v1.reshape",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sasile/openface | [
"5946f37d6045820dfa4e81ba2baac719563abc87"
] | [
"openface/align_dlib.py"
] | [
"# Copyright 2015-2016 Carnegie Mellon University\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.max",
"numpy.array",
"numpy.float32",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DavidV17/ReAgent | [
"866f91785ca86db32fb67744aa063fe77791ff21"
] | [
"reagent/preprocessing/transforms.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nimport logging\nfrom typing import Callable, Dict, List, Optional, Tuple\n\nimport numpy as np\nimport reagent.types as rlt\nimport torch\nimport torch.nn.functional as F\nfrom reagent.parameters import Normalization... | [
[
"torch.cat",
"torch.nn.functional.one_hot",
"torch.arange",
"torch.device",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Asbetos/fast-bert | [
"5d2bdc152d96ca016e1e442256c121f606529609"
] | [
"run_classifier.py"
] | [
"from fast_bert.data_cls import BertDataBunch\n\nfrom fast_bert.learner_cls import BertLearner\nfrom fast_bert.metrics import accuracy_multilabel\n \nimport torch\n\nimport argparse\n\nimport pandas as pd\n\nimport os\n\ndef main():\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--data_dir\",\n ... | [
[
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LachlanCourt/VisualMesh | [
"2af80474973cbbf848cf3b27e241ed30e060a773"
] | [
"training/metrics/seeker_stddev.py"
] | [
"# Copyright (C) 2017-2020 Trent Houliston <trent@houliston.me>\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the\n# rights to use... | [
[
"tensorflow.clip_by_value",
"tensorflow.shape",
"tensorflow.math.squared_difference",
"tensorflow.cast",
"tensorflow.expand_dims",
"tensorflow.gather",
"tensorflow.zeros_like",
"tensorflow.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
AlexanderKalistratov/numba | [
"f5c5ba339b980830e73f1dc76efb6b043adcddbb",
"f5c5ba339b980830e73f1dc76efb6b043adcddbb"
] | [
"numba/dppl/tests/dppl/test_caching.py",
"numba/core/ir_utils.py"
] | [
"from __future__ import print_function\nfrom timeit import default_timer as time\n\nimport sys\nimport numpy as np\nfrom numba import dppl\nimport dppl.ocldrv as ocldrv\nfrom numba.dppl.testing import unittest\nfrom numba.dppl.testing import DPPLTestCase\n\n\ndef data_parallel_sum(a, b, c):\n i = dppl.get_global... | [
[
"numpy.random.random",
"numpy.ones_like"
],
[
"numpy.issubdtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KwatME/ccal | [
"d96dfa811482eee067f346386a2181ec514625f4",
"d96dfa811482eee067f346386a2181ec514625f4",
"d96dfa811482eee067f346386a2181ec514625f4"
] | [
"kwat/plot/plot_bubble_map.py",
"kwat/significance/get_p_value_and_q_value.py",
"kwat/geo/get.py"
] | [
"from numpy import arange, isnan, meshgrid\n\nfrom ..array import apply, normalize\nfrom ..dictionary import merge\nfrom .COLORBAR import COLORBAR\nfrom .NAME_COLORSCALE import NAME_COLORSCALE\nfrom .plot_plotly import plot_plotly\n\n\ndef plot_bubble_map(\n das, mac=None, si=24, colorscale=NAME_COLORSCALE[\"con... | [
[
"numpy.isnan",
"numpy.arange",
"numpy.meshgrid"
],
[
"numpy.where"
],
[
"pandas.Index",
"pandas.DataFrame",
"pandas.api.types.is_numeric_dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4... |
jond01/imageio | [
"e467b7a7b03d8d8843fe0c2cdbdb37fd6a48b331"
] | [
"tests/test_tifffile.py"
] | [
"\"\"\" Test tifffile plugin functionality.\n\"\"\"\n\nimport os\nimport datetime\n\nimport numpy as np\n\nfrom pytest import raises\nfrom imageio.testing import run_tests_if_main, get_test_dir, need_internet\nfrom imageio.core import get_remote_file\n\nimport imageio\n\ntest_dir = get_test_dir()\n\n\ndef test_tiff... | [
[
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kcarnold/sentiment-slant-gi18 | [
"6028b42627e3eec14a1f27986f8925d8b1e6ad9c"
] | [
"scripts/preprocess_airbnb.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nPreprocess Airbnb data.\n\"\"\"\n\n__author__ = \"Kenneth C. Arnold <kcarnold@alum.mit.edu>\"\n\n\nimport os\nimport glob\nimport argparse\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport tqdm\nimport cytoolz\nimport operator\nimport nltk\nfrom suggestion import toke... | [
[
"numpy.split",
"pandas.read_csv",
"numpy.cumsum",
"numpy.random.permutation",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nArrow4/AutoAim | [
"74c492a55ac68167618e616aaa37ec70c23bb5b5"
] | [
"src/utils/libs/numpy/random/setup.py"
] | [
"import os\nimport platform\nimport sys\nfrom os.path import join\n\nfrom numpy.distutils.system_info import platform_bits\n\nis_msvc = (platform.platform().startswith('Windows') and\n platform.python_compiler().startswith('MS'))\n\n\ndef configuration(parent_package='', top_path=None):\n from numpy.di... | [
[
"numpy.distutils.misc_util.get_mathlibs",
"numpy.distutils.misc_util.Configuration",
"numpy.distutils.core.setup"
]
] | [
{
"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... |
Soumi7/fury | [
"1c369a8590d5fa5eaa8948431a6d378bb6a678a6"
] | [
"fury/actor.py"
] | [
"\"\"\"Module that provide actors to render.\"\"\"\n\nimport warnings\nimport os.path as op\nimport numpy as np\nimport vtk\nfrom vtk.util import numpy_support\n\nfrom fury.shaders import (load, shader_to_actor, attribute_to_actor,\n add_shader_callback, replace_shader_in_actor)\nfrom fury ... | [
[
"numpy.dot",
"numpy.minimum",
"numpy.asarray",
"numpy.concatenate",
"numpy.swapaxes",
"numpy.unique",
"numpy.reshape",
"numpy.eye",
"numpy.repeat",
"numpy.zeros",
"numpy.isin",
"numpy.nonzero",
"numpy.ascontiguousarray",
"numpy.random.rand",
"numpy.floor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adrianop01/hover_net | [
"bc1802349902f330e42ab122d85248264805e780"
] | [
"models/hovernet/run_desc.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn.functional as F\n\nfrom misc.utils import center_pad_to_shape, cropping_center\nfrom .utils import crop_to_shape, dice_loss, mse_loss, msge_loss, xentropy_loss\n\nfrom collections import OrderedDict\n\n####\ndef train_step(batch_dat... | [
[
"torch.nn.functional.softmax",
"torch.randint",
"matplotlib.pyplot.get_cmap",
"numpy.concatenate",
"numpy.size",
"torch.no_grad",
"torch.nn.functional.one_hot",
"numpy.array",
"torch.squeeze",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joshloyal/reduce-learn | [
"243bde236d8c615f9563279a0a2095e2fa2f4650"
] | [
"examples/plot_banknote.py"
] | [
"\"\"\"\n====================\nClustering with SAVE\n====================\n\nSliced Average Variance Estimation is able to find three distinct clusters\nin a dataset used to classify counterfeit swiss banknotes.\n\"\"\"\nimport matplotlib.pyplot as plt\n\nfrom sliced.datasets import load_banknote\nfrom sliced impor... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nansencenter/DAPPER | [
"77b218f5a9a8607c99ef78a8a4141f8afe7da4e3"
] | [
"dapper/stats.py"
] | [
"\"\"\"Statistics for the assessment of DA methods.\n\n`Stats` is a data container for ([mostly] time series of) statistics.\nIt comes with a battery of methods to compute the default statistics.\n\n`Avrgs` is a data container *for the same statistics*,\nbut after they have been averaged in time (after the assimila... | [
[
"numpy.diag",
"numpy.seterrcall",
"numpy.sqrt",
"numpy.mean",
"numpy.nanmean",
"numpy.where",
"numpy.eye",
"scipy.linalg.eigh",
"matplotlib.pyplot.figure",
"numpy.log",
"matplotlib.pyplot.fignum_exists",
"numpy.full_like",
"numpy.errstate",
"numpy.isreal",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
fossatiA/PCprophet | [
"6a91936da3bb7918150cf647a9b55e9b6025ca88"
] | [
"PCprophet/collapse.py"
] | [
"import os\nfrom functools import reduce\nimport numpy as np\nfrom scipy import interpolate\nimport pandas as pd\nimport networkx as nx\n\nimport PCprophet.io_ as io\nimport PCprophet.go_fdr as go_fdr\nfrom PCprophet.exceptions import NotImplementedError\n\n\nclass ProphetExperiment(object):\n \"\"\"\n docstr... | [
[
"pandas.merge",
"pandas.read_csv",
"numpy.poly1d",
"numpy.polyfit",
"pandas.concat",
"pandas.DataFrame",
"numpy.argmax",
"numpy.log10",
"sklearn.linear_model.LinearRegression",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
junweima/MCMC | [
"42966dba7f59d6edbb748e7bcc58deebead7dbcf"
] | [
"algo/sgld.py"
] | [
"\"\"\"\nStochastic Gradient Langevin Dynamics (Welling, Teh, ICML 2011)\n---------------------------------------------------------------\nExample of simple SGLD sampling the posterior of param of 1D linear regression.\n\"\"\"\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\nN = 100\... | [
[
"numpy.sqrt",
"matplotlib.pyplot.scatter",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.mean",
"numpy.random.randn",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.sum",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
553269487/ConvLSTM-on-TIANCHI-CIKM-2017 | [
"c1082a839ffb29533c8cfd7d75122caead0e8b9c"
] | [
"encoder.py"
] | [
"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@File : encoder.py\n@Time : 2020/03/09 18:47:50\n@Author : jhhuang96\n@Mail : hjh096@126.com\n@Version : 1.0\n@Description: encoder\n'''\n\nfrom torch import nn\nfrom utils import make_layers\nimport torch\nimport logging\n\n\nclass Encod... | [
[
"torch.device",
"torch.cuda.device_count",
"torch.reshape",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
robbie-nichols/vmaf | [
"d5978d15f7c413e8aa78c891ce43291ead3fa287",
"d5978d15f7c413e8aa78c891ce43291ead3fa287"
] | [
"python/test/perf_metric_test.py",
"python/test/bootstrap_train_test_model_test.py"
] | [
"import sys\nimport unittest\n\nimport numpy as np\nimport scipy.io\n\nfrom vmaf.config import VmafConfig\nfrom vmaf.core.perf_metric import RmsePerfMetric, SrccPerfMetric, PccPerfMetric, \\\n KendallPerfMetric, AucPerfMetric, ResolvingPowerPerfMetric\n\n__copyright__ = \"Copyright 2016-2019, Netflix, Inc.\"\n__... | [
[
"numpy.random.seed",
"numpy.arange",
"numpy.random.normal",
"numpy.mean",
"numpy.array"
],
[
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LvHang/waldo | [
"e4031270cf89be264f3d80fc9545f08898999658"
] | [
"egs/dsb2018/v1/local/segment.py"
] | [
"#!/usr/bin/env python3\n\nimport torch\nimport csv\nimport argparse\nimport os\nimport random\nimport numpy as np\nimport scipy.misc\nfrom models.Unet import UNet\nfrom waldo.segmenter import ObjectSegmenter, SegmenterOptions\nfrom skimage.transform import resize\nfrom waldo.core_config import CoreConfig\nfrom wal... | [
[
"torch.no_grad",
"torch.utils.data.DataLoader",
"numpy.random.seed",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frances-ha/egm722 | [
"358996cc064cf856ca0831b2fd6ea1df0d7f1688"
] | [
"Week3/exercise_script.py"
] | [
"import geopandas as gpd\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom cartopy.feature import ShapelyFeature\nimport cartopy.crs as ccrs\nimport matplotlib.patches as mpatches\nimport pandas as pd\n\n\n# generate matplotlib handles to create a legend of the features... | [
[
"pandas.concat",
"matplotlib.pyplot.ion",
"matplotlib.patches.Rectangle"
]
] | [
{
"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": []
}
] |
poldracklab/nondefaced-detector | [
"260f0cac97da632018a57771e1b90be89dd0c859"
] | [
"nondefaced_detector/cli/main.py"
] | [
"\"\"\"Main command-line interface for nondefaced-detector.\"\"\"\n\nimport click\nimport csv\nimport datetime\nimport errno\nimport logging\nimport os\nimport platform\nimport sys\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n\nimport nobrainer\n\nimport nibabel as nib\nimport numpy as np\nimport tensorflow as ... | [
[
"tensorflow.autograph.set_verbosity",
"tensorflow.config.list_physical_devices",
"tensorflow.test.is_built_with_gpu_support",
"tensorflow.get_logger"
]
] | [
{
"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"
]
}
] |
adinissim-160495/bci4als | [
"99443fd944f7fce6cdb7c1b22fb75f6b3fec654b"
] | [
"examples/onlinereport.py"
] | [
"import json\nimport typing\nimport matplotlib.pyplot as plt\nimport numpy\nimport numpy as np\n\n\ndef trial_accuracy(trial: typing.List[typing.List[int]]):\n target = trial[0][0]\n n_attempts = len(trial)\n n_success = len([1 for t, p in trial if t == p])\n accuracy = n_success / n_attempts\n retur... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"numpy.mean",
"numpy.var",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caetera/RawMetaData | [
"dd9c7ce44183a3502c45673167937858a8720182"
] | [
"FileRecord.py"
] | [
"# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Integer, Float, Text, DateTime, Boolean\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom pandas import read_csv\nfrom os import path\nfrom datetime import datetime\nimport clr\nclr.AddReference(path.abspath(\"RawFileReader/ThermoFisher.C... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nimily/cv-impute | [
"f43a179c0872cd020a3ac9844cc17c41d43882f0"
] | [
"gen_plot.py"
] | [
"from argparse import ArgumentParser\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\nfrom utils import plot_data\nfrom utils import moments_to_confidence_band\n\n\ndef load_data(data_file):\n stats = {}\n\n with open(data_file, 'r') as f:\n # skip the header\n f.readline()\n\n ... | [
[
"numpy.array",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hmatty/11 | [
"ee6ee054bd6af7ec4f30056e5d372d75ce464de6"
] | [
"painter.py"
] | [
"import os\r\nimport cv2\r\nimport random\r\n\r\nimport utils\r\nimport loss\r\nfrom networks import *\r\nimport morphology\r\n\r\nimport renderer\r\n\r\nimport torch\r\n\r\n# Decide which device we want to run on\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\n\r\nclass Painte... | [
[
"torch.abs",
"torch.zeros",
"torch.cat",
"torch.reshape",
"torch.tensor",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
georgetown-analytics/Used-Cars-Analyzers | [
"440cfd8453a9b6d3ebc66a381e639105da7a1610"
] | [
"scripts/marketcheck_api.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n@author: Daniel Navarrete\n\nThis script allows for the MarketCheck Inventory Search API to be queried.\nSlight wrangling allows for data contained in nested lists to be retrieved,\ndata to be arrayed in dataframes, merged, and ultimately exported as CSV(s).\n\"\"\"\n\nimport reque... | [
[
"pandas.merge",
"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": []
}
] |
94JuHo/study_for_deeplearning | [
"ababf482b6a24d94b5f860ea9a68e34fe324d182"
] | [
"pytorch_myself/fashion_mnist.py"
] | [
"#%%\nfrom torchvision import datasets, transforms, utils\nfrom torch.utils import data\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ntransform = transforms.Compose([\n transforms.ToTensor()\n])\n\ntrainset = datasets.FashionMNIST(\n root='./data/',\n train=True,\n download=True,\n transf... | [
[
"matplotlib.pyplot.imshow",
"torch.utils.data.DataLoader",
"numpy.transpose",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
therealspring/nci-ndr-analysis | [
"568ed607fedc331c423aa8e49f6e73fadd21e406"
] | [
"nci_mask_generator_for_peter.py"
] | [
"\"\"\"NCI Special Scenario Generator for Peter's masks.\"\"\"\nimport logging\nimport glob\nimport os\nimport multiprocessing\nimport shutil\nimport subprocess\nimport sys\n\nfrom osgeo import gdal\nfrom osgeo import ogr\nfrom osgeo import osr\nimport numpy\nimport pandas\nimport pygeoprocessing\nimport taskgraph\... | [
[
"pandas.read_csv",
"numpy.sqrt",
"numpy.linspace",
"numpy.cos",
"numpy.empty",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
NirantK/span_cat_demo | [
"a9e95b2d5968da266e7ba17dbb2a2a8d3a46297f"
] | [
"conll2003/test_functions.py"
] | [
"import numpy as np\nimport spacy\nfrom numpy.testing import assert_equal\nfrom spacy import registry\nfrom spacy.tokens import Doc, DocBin, Span\nfrom spacy.training import Example\nfrom spacy.util import fix_random_seed, registry\nfrom thinc.api import (Config, Model, Ops, get_current_ops, set_dropout_rate,\n ... | [
[
"numpy.testing.assert_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
syllogismos/wandb_baselines | [
"140011d40570f6f213650ea720cfe9374191786f"
] | [
"baselines/ppo2/ppo2.py"
] | [
"import os\nimport time\nimport joblib\nimport numpy as np\nimport os.path as osp\nimport tensorflow as tf\nfrom baselines import logger\nfrom collections import deque\nfrom baselines.common import explained_variance\nfrom baselines.common.runners import AbstractEnvRunner\n\nclass Model(object):\n def __init__(s... | [
[
"tensorflow.get_default_session",
"tensorflow.clip_by_value",
"numpy.asarray",
"tensorflow.maximum",
"numpy.arange",
"tensorflow.gradients",
"tensorflow.exp",
"tensorflow.placeholder",
"tensorflow.trainable_variables",
"numpy.random.shuffle",
"tensorflow.global_variable... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
xuweigogogo/DSACA | [
"7b7de22bd770c3cc2856fcb6dc58ff07cd2a3571"
] | [
"make_npydata/RSOC_make_npydata.py"
] | [
"import os\r\nimport numpy as np\r\n\r\nif not os.path.exists('../npydata'):\r\n os.makedirs('../npydata')\r\n\r\n'''please set your dataset path'''\r\ntry:\r\n DOTA_train_path='../dataset/RSOC/train_data/images/'\r\n DOTA_test_path='../dataset/RSOC/test_data/images/'\r\n\r\n train_list = []\r\n for ... | [
[
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fish258/MonoRec | [
"c0612d2710802004cdd83205e63d0582de543c41"
] | [
"model/monorec/monorec_model.py"
] | [
"import time\n\nimport kornia.augmentation as K\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nfrom torch import nn\n\nfrom model.layers import point_projection, PadSameConv2d, ConvReLU2, ConvReLU, Upconv, Refine, SSIM, Backprojection\nfrom utils import conditional_flip, fil... | [
[
"torch.nn.functional.upsample",
"torch.abs",
"torch.all",
"torch.max",
"torch.cat",
"torch.nn.functional.dropout",
"torch.sum",
"torch.no_grad",
"torch.device",
"torch.nn.functional.tanh",
"torch.logical_not",
"torch.ones",
"torch.inverse",
"torch.nn.Sigmoid... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RosterMouch/Elins-Auto-Judfement-System | [
"7f64d749399907a8f3b93174f90234372b025db5"
] | [
"src/main.py"
] | [
"# Author:Elin\n# Create Time:2021-10-29 23:55:01\n# Last Modified By:Elin\n# Update Time:2021-10-29 23:55:01\n# -*- coding: utf-8 -*-\nimport os\nimport pandas as pd\nfrom datetime import date, datetime\nimport subprocess\nfrom docxtpl import DocxTemplate, InlineImage\nfrom docx.shared import Cm, Inches, Mm, Emu\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": []
}
] |
kukas/framewise_2016 | [
"7f897845eb88f37a0a572ac07d9ada5aa4666229"
] | [
"models.py"
] | [
"import torch\nfrom torch import nn\nfrom torch.nn import init\nimport torch.nn.functional as F\n\n\nfrom math import log2\n\nclass HarmonicStacking(nn.Module):\n def __init__(self, octave_bins, undertones, overtones):\n super().__init__()\n\n self.octave_bins = octave_bins\n self.overtones ... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.nn.init.calculate_gain",
"torch.nn.Dropout2d",
"torch.empty",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.Identity",
"torch.nn.init.xavier_uniform_",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fjordonez/mcfly | [
"128561590e015be0877587939bec24a60441c4fa"
] | [
"mcfly/modelgen.py"
] | [
"#\n# mcfly\n#\n# Copyright 2020 Netherlands eScience Center\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 requir... | [
[
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MicheleOliva/Contest-Vision-2020 | [
"3c295892be3334b929f142dd83c5b256292581f2"
] | [
"other_models/train_light.py"
] | [
"#!/usr/bin/env python3\n\nimport os\nimport pickle\nimport sys\nfrom datetime import datetime\nfrom tensorflow.keras.applications import MobileNetV3Large\nfrom keras import Model, Sequential\nfrom keras.utils import plot_model\nfrom keras.models import load_model\nfrom keras.losses import MeanSquaredError\nfrom ke... | [
[
"tensorflow.keras.applications.MobileNetV3Large"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.4",
"2.5",
"2.6",
"2.7"
]
}
] |
ktingyew/scrobble-cloud-func | [
"1ee86cccb4cf5aae8841987f4b69c9121edda471"
] | [
"src/my_scrobble/lastfm.py"
] | [
"\"\"\"Module to read scrobbles to DataFrame using last.fm API.\"\"\"\nfrom collections import namedtuple\nfrom datetime import datetime, timedelta\nimport logging\nimport math\nfrom typing import Dict, List\n\nimport pandas as pd\nimport requests\n\nlogger = logging.getLogger(\"main.lastfm\")\n\n\ndef _get_raw_las... | [
[
"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": []
}
] |
STsanakas/trajectory-position | [
"67ce047ac095aa6f78585cc2a4c262fddf941c01"
] | [
"Conv2Ang.py"
] | [
"import sys\r\nimport math \r\nimport pandas as pd\r\nimport numpy as np\r\ndef calculate_initial_compass_bearing(pointA, pointB):\r\n if (type(pointA) != tuple) or (type(pointB) != tuple):\r\n raise TypeError(\"Only tuples are supported as arguments\")\r\n lat1 = math.radians(pointA[0])\r\n lat2 = ... | [
[
"numpy.delete",
"pandas.read_csv",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
passive-radio/peak_fitter_auto | [
"640cca6f70baccd0cdeb2c14c28239cae8b38221"
] | [
"core/detection.py"
] | [
"import csv\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.optimize import curve_fit\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\n\nclass find_peaks(object):\n def __init__(self, data, peaks=None) -> None:\n if type(peaks) == int:\n self.peaks = peaks\n else:\n... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.plot",
"numpy.zeros_like",
"numpy.array",
"numpy.exp",
"scipy.optimize.curve_fit"
]
] | [
{
"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"... |
qudsiramiz/PlasmaPy | [
"d3e46056d26926d488408b357523c90ac5445e47"
] | [
"plasmapy/dispersion/two_fluid_dispersion_solver.py"
] | [
"__all__ = [\"two_fluid_dispersion_solution\"]\n\nimport astropy.units as u\nimport numpy as np\n\nfrom astropy.constants.si import c, e, k_B, m_e, m_p, mu0\n\nimport plasmapy.formulary.parameters as pfp\n\nfrom plasmapy.utils.decorators import validate_quantities\n\n\n@validate_quantities(\n B={\"can_be_negativ... | [
[
"numpy.meshgrid",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nehapjoshi/halotools | [
"ad9e183ee7471f7876201ce83fdc36a76e653902"
] | [
"halotools/mock_observables/pair_counters/test_pair_counters/test_positional_marked_npairs_3d.py"
] | [
"\"\"\"\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport numpy as np\nimport pytest\n\nfrom .pure_python_positonal_marked_npairs import cos2theta_pairs\nfrom ..positional_marked_npairs_3d import positional_marked_npairs_3d\nfrom ..npairs_3d import npairs_3d\n\nfr... | [
[
"numpy.random.random",
"numpy.sqrt",
"numpy.linspace",
"numpy.allclose",
"numpy.cos",
"numpy.tile",
"numpy.ones",
"numpy.sin",
"numpy.max",
"numpy.mean",
"numpy.random.randint",
"numpy.cross",
"numpy.array",
"numpy.zeros",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
csullivan/ffi-navigator | [
"ed47678f9cb8c6d3637bf3219d3cf7b2754b84bb"
] | [
"tests/dummy_repo/pytorch/torch/backends/quantized/__init__.py"
] | [
"import sys\nimport torch\nimport types\n\nclass _QEngineProp(object):\n def __get__(self, obj, objtype):\n return _get_qengine_str(torch._C._get_qengine())\n\n def __set__(self, obj, val):\n torch._C._set_qengine(_get_qengine_id(val))\n\nclass _SupportedQEnginesProp(object):\n def __get__(se... | [
[
"torch._C._get_qengine",
"torch._C._supported_qengines"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
srijithbalakrishnan/dreaminsg-integrated-model | [
"e568ce37c0ffb40b8453ac084329e6e478a3db96"
] | [
"infrarisk/src/physical/integrated_network.py"
] | [
"from io import StringIO\nimport pandas as pd\nimport networkx as nx\nimport wntr\nimport math\nimport os\nimport numpy as np\nimport geopandas as gpd\n\nimport infrarisk.src.physical.interdependencies as interdependencies\nimport infrarisk.src.physical.water.water_network_model as water\nimport infrarisk.src.physi... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jklewis99/furiosa | [
"2e0feb83c1e198dd2e806464f431fd7c5f669d05"
] | [
"naive_bayes.py"
] | [
"'''\r\na naive bayes approach to the overview of a film\r\n'''\r\nimport pickle\r\nimport string\r\nfrom nltk import classify\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom collections import defaultdict\r\n## required with first time use: import nltk\r\n## required with first time use: nltk.download('stopwo... | [
[
"numpy.dot",
"numpy.log",
"pandas.read_csv",
"sklearn.naive_bayes.GaussianNB",
"sklearn.naive_bayes.ComplementNB",
"numpy.abs",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.confusion_matrix",
"sklearn.naive_bayes.Bernoull... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lazyCodes7/SmartScrapes | [
"72b03253f22ef29aaaf2aee484525a8f4e9ea1a1"
] | [
"utils/ReportGenerator.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom tweets.graphs.plot_graph import GraphRenderer\nfrom tweets.user.user_context import getUserContext,getUserInfo\nfrom collections import Counter\nfrom tweets.user.user_details import *\nfrom tweets.user.user_stats import *\n# Util class to generate reports\nclass Report... | [
[
"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": []
}
] |
indigoYoshimaru/3d-brain-thesis | [
"bc6fd5e85e7e8e88c5a3cccafad098c7f3d7586a"
] | [
"app/model_controller/model_loader.py"
] | [
"import sys\nimport torch\nimport torch.nn.functional as F\nimport os\nimport argparse\nimport copy\nimport nibabel as nib\nimport numpy as np\nimport math\n\nfrom models.segtran_modified.code.networks.segtran3d import Segtran3d, set_segtran3d_config, CONFIG\nfrom models.gan_based_unet.multiscale_generator import M... | [
[
"torch.device",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CallumHoughton18/Mushroom-Classification | [
"ab8a08498c93aa0d307dbcfb37b00f27a63055df"
] | [
"src/test_mushroom_classifier/test_mlutils.py"
] | [
"\"\"\"\nContains mlutils tests\n\"\"\"\nimport unittest\n\nimport numpy as np\n\nfrom mushroom_classifier.mlutils import add_intercept\nfrom test_mushroom_classifier.base_test import TestBase\n\nclass AddInterceptTests(TestBase):\n \"\"\"\n Tests add_intercept mlutils method\n \"\"\"\n def test_add_int... | [
[
"numpy.testing.assert_array_equal",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiangdi1998/PointNet.pytorch | [
"997f32c660a9fe4ecf85575b24807727b4613e99"
] | [
"utils/train_segmentation.py"
] | [
"from __future__ import print_function\nimport argparse\nimport os\nimport random\nimport torch\nimport torch.nn.parallel\nimport torch.optim as optim\nimport torch.utils.data\nfrom pointnet.dataset import ShapeNetDataset\nfrom pointnet.model import PointNetDenseCls, feature_transform_regularizer\nimport torch.nn.f... | [
[
"torch.nn.functional.nll_loss",
"torch.load",
"torch.manual_seed",
"numpy.logical_or",
"numpy.mean",
"numpy.logical_and",
"torch.optim.lr_scheduler.StepLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Takuya1031/CSBDeep | [
"75877938b329173fb33cc19b2d96a773ed000b62"
] | [
"csbdeep/io/__init__.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import print_function, unicode_literals, absolute_import, division\nfrom six.moves import range, zip, map, reduce, filter\nfrom six import string_types\n\nimport numpy as np\nfrom tifffile import imsave\nimport warnings\n\nfrom ..utils import _raise, axes_check_and_normaliz... | [
[
"numpy.load",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
corneliusroemer/pyro-cov | [
"54e89d128293f9ff9e995c442f72fa73f5f99b76"
] | [
"mutrans.py"
] | [
"# Copyright Contributors to the Pyro-Cov project.\n# SPDX-License-Identifier: Apache-2.0\n\nimport argparse\nimport functools\nimport gc\nimport logging\nimport os\nimport re\nfrom typing import Callable, Union\n\nimport pyro\nimport torch\n\nfrom pyrocov import mutrans, pangolin, sarscov2\nfrom pyrocov.util impor... | [
[
"torch.set_default_tensor_type",
"torch.empty",
"torch.autograd.set_detect_anomaly",
"torch.set_default_dtype",
"torch.cuda.is_available",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
spotify-research/RIPS_KDD202 | [
"84f9cde9528a176b36cad2417f363a7890e92486"
] | [
"rips/eval/offpolicy/drips.py"
] | [
"#\n# Copyright 2020 Spotify AB\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed t... | [
[
"numpy.log",
"numpy.multiply",
"numpy.asarray",
"numpy.ones",
"numpy.max",
"numpy.zeros",
"scipy.special.logsumexp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
mathisrichter/dft_loihi | [
"a72da06c92db0b24dd5be4b4ab9bf04c7a68c50a"
] | [
"dft_loihi/examples/1d/1d_selection_sustained_tracking.py"
] | [
"import numpy as np\nimport nxsdk.net.net\n\nfrom dft_loihi.visualization.plotting import Plotter\nfrom dft_loihi.dft.field import Field\nfrom dft_loihi.dft.kernel import SelectiveKernel\nfrom dft_loihi.inputs.simulated_input import GaussPiecewiseStaticInput\nfrom dft_loihi.dft.util import connect\n\n\n# set up the... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gifford-lab/DeepLigand | [
"581eb10f3611bc8e8455b239f06999f6b432ca32"
] | [
"resnet.py"
] | [
"import torch.nn as nn\nimport math\nimport torch.utils.model_zoo as model_zoo\n\n\ndef conv1x3(in_planes, out_planes, stride=1):\n \"\"\"1x3 convolution with padding\"\"\"\n return nn.Conv1d(in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\n\n\nclass BasicBloc... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.nn.Conv1d",
"torch.nn.ReLU",
"torch.nn.AvgPool1d",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rgraz/PIFFZTF | [
"5f47a7fbdb9040d871f40a5ce7de08740fdfbdb1"
] | [
"piff/basis_interp.py"
] | [
"# Copyright (c) 2016 by Mike Jarvis and the other collaborators on GitHub at\n# https://github.com/rmjarvis/Piff All rights reserved.\n#\n# Piff is free software: Redistribution and use in source and binary forms\n# with or without modification, are permitted provided that the following\n# conditions are met:\n#\... | [
[
"numpy.diag",
"numpy.dot",
"numpy.hstack",
"numpy.ix_",
"numpy.abs",
"numpy.min",
"numpy.arange",
"numpy.empty",
"numpy.ones",
"numpy.concatenate",
"numpy.max",
"numpy.cumprod",
"numpy.mean",
"numpy.prod",
"numpy.count_nonzero",
"numpy.array",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
initialneil/PatchmatchNet | [
"ec9819c4a6504753522adcafb70dc412cdb1d04d"
] | [
"models/net.py"
] | [
"\r\nfrom typing import Dict, List, Tuple\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\nfrom .module import ConvBnReLU, depth_regression\r\nfrom .patchmatch import PatchMatch\r\n\r\n\r\nclass FeatureNet(nn.Module):\r\n \"\"\"Feature Extraction Network: to extract features... | [
[
"torch.empty",
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.nn.Conv2d",
"torch.gather",
"torch.arange",
"torch.nn.BatchNorm2d",
"torch.unbind",
"torch.nn.functional.interpolate",
"torch.nn.functional.smooth_l1_loss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ErikEkstedt/TurnGPT | [
"162d4604174df35e59aae25a58595408cec1722f"
] | [
"turngpt/models/gpt_sparse.py"
] | [
"import math\nimport logging\n\nfrom argparse import ArgumentParser\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\nfrom nupic.torch.modules import KWinners, SparseWeights\n\nfrom pl_modules import TurnGPT\n\nlogger = logging.getLogger(__name__)\n\n\"\"\"\ndef kwinners(x, duty_cycles, ... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.nn.GELU",
"torch.randint",
"torch.ones",
"torch.zeros",
"matplotlib.pyplot.subplots",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"matplotlib.pyplot.pause"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dwanev/gpt-neo-fine-tuning-example2 | [
"f14aa3686c0404a9ad410ba98d2d55ab53f7ab76"
] | [
"gpt_neo_xl_deepspeed.py"
] | [
"import os\nos.environ['MASTER_ADDR'] = 'localhost'\nos.environ['MASTER_PORT'] = '9994'\nos.environ['RANK'] = \"0\"\nos.environ['LOCAL_RANK'] = \"0\"\nos.environ['WORLD_SIZE'] = \"1\" # parallalism\nimport pandas as pd\nimport torch\nfrom torch.utils.data import Dataset, random_split\nfrom transformers import GPT... | [
[
"torch.stack",
"torch.manual_seed",
"pandas.read_csv",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
syys96/digital_rf | [
"74829b7d9065c49378681d2688976aee6bad0aca"
] | [
"python/digital_rf/digital_rf_hdf5.py"
] | [
"# ----------------------------------------------------------------------------\n# Copyright (c) 2017 Massachusetts Institute of Technology (MIT)\n# All rights reserved.\n#\n# Distributed under the terms of the BSD 3-clause license.\n#\n# The full license is in the LICENSE file, distributed with this software.\n# -... | [
[
"numpy.allclose",
"numpy.logical_and",
"numpy.ascontiguousarray",
"numpy.issubdtype",
"numpy.promote_types",
"numpy.dtype",
"numpy.compress",
"numpy.concatenate",
"numpy.ceil",
"numpy.longdouble",
"numpy.uint64",
"numpy.any",
"numpy.diff",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liuyenting/RANSAC-Flow | [
"2f348a4e31b6087ad2d9c0e5e35d5ad382f2a7b3"
] | [
"src/ransacflow/model/coarse_alignment.py"
] | [
"import kornia as K\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom ransacflow.util import get_model_root\nfrom torchvision import models, transforms\n\n\ndef get_resnet50_moco_state_dict() -> dict:\n \"\"\"\n Get weight of ResNet50 trained with MoCo.\n\n Returns:\n (dict): Parameters... | [
[
"torch.nn.functional.normalize",
"torch.nn.Sequential",
"numpy.linspace",
"torch.load",
"torch.flatten",
"torch.vstack",
"torch.hstack",
"torch.nn.Linear",
"torch.arange",
"torch.stack",
"torch.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nmandery/rasterio | [
"ba5e90c487bd1930f52e57dba999e889b4df9ade"
] | [
"tests/test_indexing.py"
] | [
"\"\"\"Test windows and indexing\"\"\"\n\n# TODO: break up multi-assertion tests.\n\nimport numpy as np\nimport pytest\n\nimport rasterio\nfrom rasterio.errors import WindowError\nfrom rasterio import windows\n\n\nDATA_WINDOW = ((3, 5), (2, 6))\n\n\ndef assert_window_almost_equals(a, b, precision=3):\n for attr ... | [
[
"numpy.ones_like",
"numpy.zeros",
"numpy.ma.masked_array"
]
] | [
{
"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": []
}
] |
samtx/pass-predictor | [
"6577f75cd7d64bd3c12a9512880d4b29c2682b4c"
] | [
"benchmarks/benchmark_solar.py"
] | [
"# Write the benchmarking functions here.\n# See \"Writing benchmarks\" in the asv docs for more information.\n\nimport numpy as np\n\nfrom passpredict import solar\nfrom passpredict import _solar\n\n\nclass SunPosition:\n def setup(self):\n self.jd = 2450540.547222222\n self.rmod = np.empty(3, dty... | [
[
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
killosky/Kernel-Flow-applying-in-dynamic-system | [
"a5eea04a99a94ba907584821f4b6a23ecada4082"
] | [
"train.py"
] | [
"\r\nimport numpy as np\r\nimport torch\r\nimport random\r\nimport scipy\r\nimport matplotlib.pyplot as plt\r\nfrom data import get_dataset_slide\r\nfrom args import get_args\r\n\r\n\r\n\r\ndef sample(N_f, N_c, i_Nf, args):\r\n '''\r\n :param N_f:\r\n :param N_c:\r\n :return: the sampling matrix of dime... | [
[
"numpy.random.seed",
"torch.manual_seed",
"numpy.arange",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"scipy.linalg.norm",
"scipy.linalg.inv",
"numpy.array",
"numpy.zeros",
"numpy.where",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
mingruimingrui/Transformer2 | [
"2b44289ee7c7312d699f2261c1e4ebccce0f21e2"
] | [
"do_train.py"
] | [
"#!/usr/bin/env python\n\nimport os\nimport sys\nimport shutil\nimport logging\nimport argparse\nimport warnings\nfrom time import time\nfrom tqdm import tqdm\n\nimport tensorflow as tf\n\nfrom transformer_2.models import Transformer\nfrom transformer_2.data.datasets import TranslationDataset\nfrom transformer_2.lo... | [
[
"tensorflow.constant",
"tensorflow.range",
"tensorflow.config.experimental.set_memory_growth",
"tensorflow.shape",
"tensorflow.config.experimental.list_physical_devices",
"tensorflow.GradientTape",
"tensorflow.keras.optimizers.Adam",
"tensorflow.summary.scalar",
"tensorflow.Ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Croydon-Brixton/qthought | [
"14060ee444c88b848ca196f00a4fdce45cb2d570"
] | [
"qthought/agents.py"
] | [
"# Basic library for implementing Agent systems in ProjectQ\n# Date: 8 Dec 2018\n# Author: Simon Mathis\n# Contact: mathissi@student.ethz.ch\nfrom math import ceil\nfrom numpy import log2\nfrom warnings import warn\n\nfrom projectq.meta import Control\nfrom projectq.ops import *\n\nfrom .utils.arithmeticg... | [
[
"numpy.log2"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
florianwolz/prime | [
"5ac29c523c5730e1d50c5d4703658427fdddfac3"
] | [
"prime/equations/C19.py"
] | [
"# Copyright 2019 The Prime Authors\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless req... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
astro-ck/Road-Extraction | [
"e509ddce9ced558e2e97d3510eb1e4a053113c97"
] | [
"initial_seg/road.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.utils.data as data\nfrom torch.autograd import Variable as V\nimport cv2\nimport os\nimport numpy as np\nimport argparse\nimport json\nfrom time import time\nfrom datetime import datetime\n\nfrom dlinknet import DLinkNet34, MyFrame, dice_bce_loss\nfrom datainput im... | [
[
"numpy.rot90",
"torch.Tensor",
"torch.load",
"torch.utils.data.DataLoader",
"numpy.concatenate",
"torch.cuda.device_count",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LaurynasMiksys/scipy | [
"bfc49e336c102269521eed6a6c3c48963ac33897"
] | [
"scipy/stats/tests/test_stats.py"
] | [
"\"\"\" Test functions for stats module\n\n WRITTEN BY LOUIS LUANGKESORN <lluang@yahoo.com> FOR THE STATS MODULE\n BASED ON WILKINSON'S STATISTICS QUIZ\n https://www.stanford.edu/~clint/bench/wilk.txt\n\n Additional tests by a host of SciPy developers.\n\"\"\"\nimport os\nimport warnings\nfrom collectio... | [
[
"numpy.testing.assert_approx_equal",
"numpy.sqrt",
"scipy.stats.mstats_basic.kendalltau",
"scipy.stats.zscore",
"numpy.all",
"numpy.broadcast",
"scipy.stats.energy_distance",
"numpy.ma.masked_where",
"numpy.exp",
"scipy.stats.ttest_rel",
"scipy.sparse.sputils.matrix",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.5"
],
"tensorflow": []
}
] |
RyanSaxe/mtg | [
"a444d528ad4e3eb4daae9691d720ba1f9cdda3aa"
] | [
"mtg/ml/layers.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\n\nclass Dense(tf.Module):\n def __init__(\n self,\n in_dim,\n out_dim,\n name=None,\n initializer=tf.initializers.GlorotNormal(),\n activation=tf.nn.relu,\n use_bias=True,\n ):\n super().__init__(name=name)... | [
[
"tensorflow.nn.bias_add",
"tensorflow.initializers.GlorotNormal",
"tensorflow.nn.batch_normalization",
"tensorflow.transpose",
"tensorflow.matmul",
"tensorflow.nn.softmax",
"tensorflow.shape",
"tensorflow.math.sqrt",
"tensorflow.zeros",
"tensorflow.nn.moments",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
shiraez/DataHack2018 | [
"7e546caa84b27ddf990f9e14b84d6965e4b72bd4"
] | [
"utilities/data_utils.py"
] | [
"# Copyright (C) 2018 Innoviz Technologies\n# All rights reserved.\n#\n# This software may be modified and distributed under the terms\n# of the BSD 3-Clause license. See the LICENSE file for details.\n\nimport os.path as osp\nimport glob\nimport pandas as pd\nimport numpy as np\nimport re\n\n\ndef find_sorted_file... | [
[
"numpy.array",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
UmutAlpaydin/OpenCV | [
"6bad3ed73714759b7f897ef751c60b1a33d93305"
] | [
"code/contours.py"
] | [
"import cv2 as cv\r\nimport numpy as np\r\n\r\nimg = cv.imread('Photos/cats.jpg')\r\ncv.imshow('Cats', img)\r\n\r\nblank = np.zeros(img.shape, dtype='uint8')\r\ncv.imshow('Blank', blank)\r\n\r\ngray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\r\ncv.imshow('Gray', gray)\r\n\r\nblur = cv.GaussianBlur(gray, (5, 5), cv.BORDE... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
irenedutta23/hepaccelerate | [
"c18fac25a0b88414836bc0a84a333f30ba02ac47"
] | [
"hepaccelerate/utils.py"
] | [
"import sys\nimport os\nimport time\nimport json\nimport numpy as np\nfrom collections import OrderedDict\nimport json\n\nimport uproot\n\nimport numba\nfrom numba import types\nfrom numba.typed import Dict\n\nimport awkward\nimport copy\nimport requests\n\n\ndef load_arrays_attempts(\n tt, arrays_to_load, execu... | [
[
"numpy.all",
"numpy.array",
"numpy.zeros",
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Eacaen/CLT-material-properties | [
"6d6238f6114e0fd749f3ffa729c9fa0cf360f856"
] | [
"code/laminate_Tools.py"
] | [
"# coding:utf-8\n# ////////////////////////////////////////////////////////////////////\n# // _ooOoo_ //\n# // o8888888o //\n# // 88\" . \"88 //\n# // ... | [
[
"numpy.linspace",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.annotate",
"numpy.linalg.norm",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"numpy.shape",
"matplotlib.pyplot.fill_betweenx",
"matplotlib.pyplot.grid",
"numpy.mean",
"numpy.array",
"matplotlib.pyplot... | [
{
"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": []
}
] |
hschilling/data-collection-and-prep | [
"b70ab54fd887592bad05d5748f492fc2f9ef0f6f"
] | [
"testing_ideas/try_doi_only_workflow/try_getting_paper_info_with_get_doi.py"
] | [
"# See if we can get paper info using just a two step process and only simple scraping:\n# 1. Use Jerry Qian's function to look for DOI links in a Web page\n# 2. If successful getting DOI, use the Semantic Search API to get the rest of the\n# paper info that we want\n#\n# Print out the results... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
FaustinCarter/scraps | [
"2398058b62b43a741bb88fdbb02564917a039872"
] | [
"scraps/plot_tools.py"
] | [
"import warnings\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gs\nimport matplotlib as mpl\nimport mpl_toolkits.mplot3d\nimport numpy as np\nimport scipy.signal as sps\nfrom scraps.resonator import indexResList\n\nAxes3D = mpl_toolkits.mplot3d.Axes3D\n\ndef plotResListData(resList, plot_types=['I... | [
[
"numpy.logical_and",
"numpy.unique",
"numpy.min",
"matplotlib.pyplot.colormaps",
"matplotlib.pyplot.get_cmap",
"matplotlib.colorbar.ColorbarBase",
"numpy.ceil",
"numpy.max",
"numpy.shape",
"matplotlib.gridspec.GridSpec",
"numpy.unwrap",
"numpy.log10",
"scipy.sig... | [
{
"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"... |
KI-cker/Ki-cker | [
"b48ae75bfeea970940ad657c73d71438531259c6"
] | [
"kicker/neural_net.py"
] | [
"import os\nimport tensorflow as tf\n\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.layers import Conv2D\nimport keras.backend.tensorflow_backend as KTF\n\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\nsess = tf.Se... | [
[
"tensorflow.ConfigProto",
"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"
]
}
] |
MitchellTesla/datasets | [
"bf08ea3f95e8209a7afd2b50410ad5db51409d11"
] | [
"metrics/f1/f1.py"
] | [
"# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.\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/licens... | [
[
"sklearn.metrics.f1_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
itsMao/2.5D-Visual-Sound | [
"a4be91157819258feeef62a4196a1837601329c2"
] | [
"models/criterion.py"
] | [
"#!/usr/bin/env python\n\n# 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 torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass BaseLos... | [
[
"torch.abs",
"torch.nn.functional.binary_cross_entropy_with_logits",
"torch.nn.functional.mse_loss",
"torch.nn.functional.binary_cross_entropy",
"torch.stack",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dreamsxin/mindspore | [
"b6b254f6e4d07c4eecd8cb4e0a1060da771d6441",
"b6b254f6e4d07c4eecd8cb4e0a1060da771d6441"
] | [
"tests/ut/python/parallel/test_auto_parallel_arithmetic.py",
"tests/ut/python/parallel/test_auto_parallel_cast.py"
] | [
"# Copyright 2019 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable l... | [
[
"numpy.ones"
],
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eldad-a/BioCRNPyler | [
"c165e885b6f5efe59d03e09015f297ad82e2c5ab"
] | [
"biocrnpyler/chemical_reaction_network.py"
] | [
"# Copyright (c) 2019, Build-A-Cell. All rights reserved.\n# See LICENSE file in the project root directory for details.\n\nfrom warnings import warn\nfrom .sbmlutil import *\nimport warnings\nimport numpy as np\nfrom typing import List, Union, Dict\n\n\n\nclass Species(object):\n \"\"\" A formal species objec... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rafaelsdellama/nnga | [
"9c04fd0c5f68644345d197db44b1bc6f6c780a30"
] | [
"nnga/architectures/segmentation/backbones/unet.py"
] | [
"from tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import (\n Input,\n Conv2D,\n MaxPooling2D,\n Dropout,\n UpSampling2D,\n concatenate,\n)\n\n\ndef unet(input_size=(256, 256, 1)):\n inputs = Input(input_size)\n conv1 = Conv2D(64, 3, activation='relu', padding='same', k... | [
[
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.UpSampling2D",
"tensorflow.keras.layers.concatenate",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
yanpei18345156216/FDTD | [
"fb598eeb7e84604b32588783e6461f7a1e9f3018"
] | [
"fdtd/backend.py"
] | [
"\"\"\" Backend for FDTD\n\nThree backends are available:\n\n - numpy [default]\n - torch\n - torch.cuda\n\nA backend can be set with the `set_backend` function:\n\n```\n import fdtd\n fdtd.set_backend(\"torch\")\n```\n\n\"\"\"\n\n## Imports\n\n# Numpy Backend\nimport numpy # numpy has to be present\n\n# Tor... | [
[
"torch.ones",
"torch.zeros",
"torch.set_default_dtype",
"torch.is_tensor",
"torch.tensor",
"torch.cuda.is_available",
"torch.get_default_dtype",
"torch._C.set_grad_enabled"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
a3ahmad/Cirq-cupy | [
"2d7641b8baf3ff7e506f02202b46ee0203dca190"
] | [
"cirq-core/cirq/sim/clifford/act_on_clifford_tableau_args_test.py"
] | [
"# Copyright 2018 The Cirq Developers\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 o... | [
[
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Dung-Han-Lee/Pointcloud-based-Row-Detection-using-ShellNet-and-PyTorch | [
"9b57969cf05818839a7ecce37163752e3fddec5e",
"9b57969cf05818839a7ecce37163752e3fddec5e"
] | [
"train/config.py",
"utils/viz_pointcloud.py"
] | [
"import torch\n\n'''\nTraining\n'''\n\n# Sanity Check\nsanity = False\nsubset = 10\n\n# Learning parameters\nweights = torch.tensor([0.1, 0.9]) # background, road\nlr = 3e-4\nsummary_prefix = \"shellnet\"\nnum_epoch = 100\nbatch_size = 32\nfc_scale = 2\nconv_scale = 2\n\n# Loading dicts\nload = False\nmodel =\"/42.... | [
[
"torch.tensor"
],
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
n-kats/pytorch_geometric | [
"9ef6ad5501d4f2439ae608ad0d197500a8acc2d8",
"f841484f45a3f9e86d44afef4b1643a25f8156ae"
] | [
"torch_geometric/nn/glob/sort.py",
"torch_geometric/nn/conv/point_conv.py"
] | [
"import torch\nfrom torch_geometric.utils import to_dense_batch\n\n\ndef global_sort_pool(x, batch, k):\n r\"\"\"The global pooling operator from the `\"An End-to-End Deep Learning\n Architecture for Graph Classification\"\n <https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf>`_ paper,\n where ... | [
[
"torch.full",
"torch.arange"
],
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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