repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
zoeyuchao/onpolicy-release | [
"c2cb64e59c5b1f21cce022db76c378b396fd480e"
] | [
"onpolicy/envs/mpe/scenarios/simple_push.py"
] | [
"import numpy as np\nfrom onpolicy.envs.mpe.core import World, Agent, Landmark\nfrom onpolicy.envs.mpe.scenario import BaseScenario\nimport random\n\n#\n# # the non-ensemble version of <ensemble_push>\n#\n#\n\nclass Scenario(BaseScenario):\n def make_world(self, args):\n world = World()\n world... | [
[
"numpy.square",
"numpy.random.choice",
"numpy.concatenate",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ael-noblegas/pychron | [
"1a81e05d9fba43b797f335ceff6837c016633bcf",
"1a81e05d9fba43b797f335ceff6837c016633bcf"
] | [
"pychron/core/ui/qt/color_map_bar_editor.py",
"pychron/mv/focus/autofocus_manager.py"
] | [
"# ===============================================================================\n# Copyright 2012 Jake Ross\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.apa... | [
[
"numpy.array"
],
[
"numpy.random.random",
"scipy.ndimage.measurements.variance",
"numpy.linspace",
"numpy.asarray",
"scipy.ndimage.filters.generic_gradient_magnitude",
"numpy.argmax",
"numpy.argmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.10",
"1.3",
"0.19",
"1.5",
"0.18",
... |
awinawin1/prediksi | [
"b3d552555f775d7b6a1b22077146443fe09bbf5d"
] | [
"public/code/simpleCropPredictSpektogram.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 15 00:21:05 2021\n\n@author: marina\n\"\"\"\nimport os\nimport shutil\nimport pyedflib\nimport numpy as np\nimport pandas as pd\nimport sys\nimport mne \nfrom pywt import wavedec\nfrom sklearn.preprocessing import LabelEncoder\nimport matplotlib.pyplot as plt\nfr... | [
[
"matplotlib.pyplot.pcolormesh",
"numpy.arange",
"scipy.signal.spectrogram",
"pandas.DataFrame",
"numpy.argmax",
"numpy.load",
"numpy.array",
"tensorflow.keras.optimizers.SGD"
]
] | [
{
"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": [
"1.6",
"1.10",
"1.4",
"0.16",
"1.... |
Aympab/BigDataHadoopSparkDaskCourse | [
"42f9e0475cbd7c5db240ccc6dc00c19b9006012a"
] | [
"TPs/TP4/test_flower.py"
] | [
"import pyspark\nfrom pyspark import SparkContext\nfrom pyspark.sql import Row\nfrom pyspark.sql import SQLContext\nfrom pyspark import SparkFiles\nimport os\nimport pandas as pd\n\nsc =SparkContext()\nsqlContext = SQLContext(sc)\n\n\ndata_dir=\"/work/irlin355_1/gratienj/ParallelProgrammingCourse/BigDataHadoopSpark... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
owenshen24/acme | [
"71434dffd3449236f9b8aaf7a53ceab515e75a2a",
"71434dffd3449236f9b8aaf7a53ceab515e75a2a"
] | [
"acme/agents/actors_tf2_test.py",
"acme/agents/mpo/agent_test.py"
] | [
"# python3\n# Copyright 2018 DeepMind Technologies Limited. 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... | [
[
"tensorflow.argmax"
],
[
"numpy.prod"
]
] | [
{
"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... |
prasadph/ga-learner-dsmp-repo | [
"ac1cc9d96250718f2842592e643c885d54ab2903"
] | [
"NLP/code.py"
] | [
"# --------------\n# import packages\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\nimport re\nfrom nltk.corpus import stopwords\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nf... | [
[
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.model_selection.train_test_split",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
okdshin/onnx | [
"27b40225ea98f6412ae2879ed67211d49564af2a",
"27b40225ea98f6412ae2879ed67211d49564af2a",
"31ca96ca3331d05884a71c38975d34870eb9c81d"
] | [
"onnx/backend/test/case/node/xor.py",
"onnx/backend/test/case/node/flatten.py",
"onnx/backend/test/case/node/globalaveragepool.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport numpy as np\n\nimport onnx\nfrom ..base import Base\nfrom . import expect\n\n\nclass Xor(Base):\n\n @staticmethod\n def export():\n node = o... | [
[
"numpy.logical_xor",
"numpy.random.randn"
],
[
"numpy.reshape",
"numpy.random.random_sample",
"numpy.prod"
],
[
"numpy.ndim",
"numpy.array",
"numpy.expand_dims",
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GAA-UAM/scikit-fda | [
"a9953a3104195ce9796397d094b17b1b90fd090f"
] | [
"skfda/_utils/_utils.py"
] | [
"\"\"\"Module with generic methods.\"\"\"\n\nfrom __future__ import annotations\n\nimport functools\nimport numbers\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Iterable,\n List,\n Optional,\n Sequence,\n Tuple,\n TypeVar,\n Union,\n cast,\n overload,\n)\n\nimport ... | [
[
"numpy.array_equal",
"numpy.meshgrid",
"numpy.asarray",
"numpy.triu_indices",
"sklearn.utils.estimator_checks.check_set_params",
"sklearn.utils.multiclass.check_classification_targets",
"numpy.indices",
"numpy.concatenate",
"numpy.atleast_2d",
"sklearn.base.clone",
"pan... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.0",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
tombackstrom/mdct | [
"f59e708f9a7f65ee672dbf44e6f164e79c82d83a"
] | [
"tests/test_windows.py"
] | [
"import pytest\nimport numpy\nimport mdct.windows\n\n\ndef test_kbd():\n M = 100\n w = mdct.windows.kaiser_derived(M, beta=4.)\n\n assert numpy.allclose(w[:M//2] ** 2 + w[-M//2:] ** 2, 1.)\n\n with pytest.raises(ValueError):\n mdct.windows.kaiser_derived(M + 1, beta=4.)\n\n assert numpy.allclo... | [
[
"numpy.sqrt",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dpetrini/nova | [
"00b7637901420f68c7d805c13ccd4c39d514efb1"
] | [
"trainer.py"
] | [
"from matplotlib.pyplot import show\nimport torch\nfrom torch.autograd import Variable\nfrom torch.cuda.amp import GradScaler, autocast\nimport numpy as np\nfrom sklearn.metrics import roc_auc_score\n\nfrom callbacks.cb_handler import CallbackHandler\nfrom callbacks.cb_base import BaseCB\nfrom callbacks.cb_lr_patch... | [
[
"torch.softmax",
"torch.cat",
"torch.rot90",
"torch.cuda.amp.autocast",
"torch.cuda.amp.GradScaler",
"torch.no_grad",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
QDaria/pennylane | [
"5a28983fc7bd950cde8a4014e54261fef4b54293",
"5a28983fc7bd950cde8a4014e54261fef4b54293",
"5a28983fc7bd950cde8a4014e54261fef4b54293",
"5a28983fc7bd950cde8a4014e54261fef4b54293"
] | [
"tests/templates/test_subroutines/test_qmc.py",
"pennylane/ops/qubit/arithmetic_ops.py",
"tests/transforms/test_adjoint.py",
"tests/interfaces/test_batch_jax.py"
] | [
"# Copyright 2018-2021 Xanadu Quantum 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/LICENSE-2.0\n\n# Unless required by ap... | [
[
"numpy.allclose",
"numpy.linspace",
"numpy.sqrt",
"numpy.abs",
"numpy.eye",
"numpy.cos",
"numpy.ones",
"numpy.sin",
"scipy.stats.norm",
"numpy.array",
"numpy.sum"
],
[
"numpy.array"
],
[
"numpy.random.random",
"numpy.sqrt",
"numpy.allclose",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
kgizdov/hep_ml | [
"114ac9e896c3a601761092760a7b315f448d59c6"
] | [
"tests/test_nnet.py"
] | [
"from __future__ import division, print_function\n\nimport numpy\nfrom sklearn.linear_model.logistic import LogisticRegression\nfrom sklearn.metrics import roc_auc_score, mean_squared_error, log_loss\nfrom sklearn.base import clone\nfrom sklearn.datasets import make_blobs\n\nfrom hep_ml import nnet\nfrom hep_ml.com... | [
[
"sklearn.metrics.roc_auc_score",
"numpy.allclose",
"sklearn.metrics.mean_squared_error",
"sklearn.datasets.make_regression",
"sklearn.base.clone",
"sklearn.linear_model.logistic.LogisticRegression",
"sklearn.datasets.make_blobs",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jopetty/transd-dev | [
"0078dfd8a049f5b97a7b3be6e883821e4994d4c0"
] | [
"src/models/modules/rnn_decoder.py"
] | [
"import random\nfrom typing import Dict\n\nimport torch\nfrom torch import Tensor, nn\nfrom torch.nn import functional as F\n\n\nclass RNNDecoder(nn.Module):\n @property\n def max_gen_length(self) -> int:\n return self.hparams[\"dec_max_gen_length\"]\n\n @property\n def EOS_idx(self) -> int:\n ... | [
[
"torch.transpose",
"torch.zeros",
"torch.nn.RNN",
"torch.nn.Embedding",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
continue-nature/google-research | [
"7011fe008efc4f11592ace842dbd4c9dffd46c29"
] | [
"capsule_em/norb/norb_record.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Google Research 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 requ... | [
[
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.image.per_image_standardization",
"tensorflow.compat.v1.TFRecordReader",
"tensorflow.compat.v1.image.random_brightness",
"tensorflow.compat.v1.image.random_contrast",
"tensorflow.compat.v1.decode_raw",
"tensorflow.compat.v1.image.res... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haowen-xu/ml-essentials | [
"ca44186be37887461205227c32995f1485b4ff41"
] | [
"mltk/data/loaders.py"
] | [
"\"\"\"\nSimple dataset loaders.\n\nFor more datasets and more comprehensive loaders, you may turn to dedicated\nlibraries like `fuel`.\n\"\"\"\n\nimport gzip\nimport hashlib\nimport os\nimport pickle\nfrom typing import *\n\nimport idx2numpy\nimport numpy as np\n\nfrom ..typing_ import *\nfrom ..utils import Cache... | [
[
"numpy.asarray",
"numpy.zeros",
"numpy.prod",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fxia22/gibson_demos | [
"5f8d253694b23b41c53959774203ba5787578b74",
"5f8d253694b23b41c53959774203ba5787578b74"
] | [
"igibson/test/test_motion_planning.py",
"igibson/test/benchmark/benchmark_interactive_scene_rendering.py"
] | [
"import igibson\nfrom igibson.envs.igibson_env import iGibsonEnv\nfrom time import time\nimport os\nfrom igibson.utils.assets_utils import download_assets, download_demo_data\nfrom igibson.utils.motion_planning_wrapper import MotionPlanningWrapper\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef test_occ... | [
[
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.imshow",
"numpy.sum",
"matplotlib.pyplot.savefig"
],
[
"matplotlib.pyplot.tight_layout",
"numpy.min",
"matplotlib.pyplot.savefig",
"numpy.max",
"numpy.mean",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.figure"
]
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adityabasu1/Event-Extraction-NLP | [
"98faa88d36f09330ebce6fc180ab2f087776f2e1"
] | [
"Joint_Event_Extraction.py"
] | [
"import sys\nimport os\nimport numpy as np\nimport random\n\nfrom collections import OrderedDict\nimport pickle\nimport datetime\nfrom tqdm import tqdm\nfrom recordclass import recordclass\nimport math\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport t... | [
[
"torch.nn.functional.softmax",
"torch.cat",
"torch.load",
"torch.zeros",
"torch.sum",
"torch.nn.Embedding",
"torch.tanh",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all",
"torch.autograd.Variable",
"torch.nn.Dropout",
"torch.from_numpy",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
samellem/autodp | [
"fd14fed07e0bb67fca5f7e82bbdab6cf60b339d3"
] | [
"test/unit_test_fdp_to_approxdp_conversion.py"
] | [
"from autodp.mechanism_zoo import GaussianMechanism\nfrom autodp.dp_bank import get_eps_ana_gaussian\n\nimport numpy as np\n\nfrom absl.testing import absltest\nfrom absl.testing import parameterized\n\nparams = [0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]\n\n\ndef _fdp_conversion(sigma):\n\n delta_list = [0,1e-8,... | [
[
"numpy.zeros_like",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GautamV234/pyro | [
"d5474ebc6101b330bf9060a3731830d4b6a585d5",
"d5474ebc6101b330bf9060a3731830d4b6a585d5"
] | [
"pyro/contrib/gp/models/gpr.py",
"pyro/contrib/gp/models/sgpr.py"
] | [
"# Copyright (c) 2017-2019 Uber Technologies, Inc.\n# SPDX-License-Identifier: Apache-2.0\n\nimport torch\nimport torch.distributions as torchdist\nfrom torch.distributions import constraints\n\nimport pyro\nimport pyro.distributions as dist\nfrom pyro.contrib.gp.models.model import GPModel\nfrom pyro.contrib.gp.ut... | [
[
"torch.linalg.cholesky",
"torch.cat"
],
[
"torch.linalg.cholesky",
"torch.nn.Parameter",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aphearin/c3dev | [
"d36d083c9eb688640670dbe066bf299777a78ba7",
"d36d083c9eb688640670dbe066bf299777a78ba7"
] | [
"c3dev/galmocks/data_loaders/load_tng_data.py",
"c3dev/galmocks/galhalo_models/galsampler_phase_space.py"
] | [
"\"\"\"\n\"\"\"\nfrom collections import OrderedDict\nimport numpy as np\nfrom halotools.utils import sliding_conditional_percentile\nfrom astropy.table import Table\nfrom ..utils.galprops import compute_lg_ssfr\n\n\nSANDY_SCRATCH_PATH = \"/global/cscratch1/sd/sihany/TNG300-1/output\"\nBEBOP = \"/lcrc/project/halot... | [
[
"numpy.log10"
],
[
"numpy.copy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yurivict/incubator-mxnet | [
"3d38dbde744954854015919d4faf56ac1aea16de"
] | [
"python/mxnet/model.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.zeros",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
feiwu77777/Face-detection-and-tracking | [
"1135d2d93d5b667110551dc7e4b985b5861eb380"
] | [
"eval_tiny_one_image.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Dec 10 15:49:15 2018\r\n\r\n@author: fei.wu\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\nfrom __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\nimport tensorflow as tf\r\nimport tiny_face_model\r\n... | [
[
"tensorflow.convert_to_tensor",
"numpy.hstack",
"matplotlib.pyplot.imshow",
"tensorflow.Graph",
"scipy.special.expit",
"numpy.power",
"numpy.vstack",
"tensorflow.placeholder",
"numpy.ceil",
"tensorflow.global_variables_initializer",
"numpy.max",
"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"
]
}
] |
valanm22/pytorch-lightning | [
"5d190eabd28671a6222741f5dd9ee3f214e519b1",
"5d190eabd28671a6222741f5dd9ee3f214e519b1"
] | [
"pytorch_lightning/trainer/trainer.py",
"tests/deprecated_api/test_remove_1-8.py"
] | [
"# Copyright The PyTorch Lightning team.\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... | [
[
"torch.autograd.set_detect_anomaly",
"torch._C._log_api_usage_once",
"torch.set_grad_enabled",
"torch.no_grad",
"torch.cuda.is_available"
],
[
"torch.device",
"torch.tensor",
"numpy.sum",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saranyakrish14/glow | [
"3562fba6a77d2bb4aacf98a5bff5a737a93f6adc",
"3562fba6a77d2bb4aacf98a5bff5a737a93f6adc"
] | [
"torch_glow/tests/nodes/add_test.py",
"torch_glow/tests/nodes/sigmoid_test.py"
] | [
"from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport torch\nfrom tests import utils\n\n\nclass SimpleAddModule(torch.nn.Module):\n def __init__(self, inplace=False):\n super(SimpleAddModule, self).__init__()\n self.inplace = inplace\n\n def forward(self, ... | [
[
"torch.torch.randint",
"torch.randn",
"torch.Size",
"torch.tensor"
],
[
"torch.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuangliu/pytorch-ssd | [
"02ed1cbe6962e791895ab1c455dc5ddfb87291b9"
] | [
"encoder.py"
] | [
"'''Encode target locations and labels.'''\nimport torch\n\nimport math\nimport itertools\n\nclass DataEncoder:\n def __init__(self):\n '''Compute default box sizes with scale and aspect transform.'''\n scale = 300.\n steps = [s / scale for s in (8, 16, 32, 64, 100, 300)]\n sizes = [s... | [
[
"torch.LongTensor",
"torch.Tensor",
"torch.cat",
"torch.exp",
"torch.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johnmgregoire/JCAPGeneratePrintCode | [
"afc1dbe6125d0024a46889011ab653ed24016fe4"
] | [
"platemapgenerator_calccompsforsingleplate.py"
] | [
"import time, copy, pickle\nimport os, os.path\nimport sys\nimport numpy, pylab\n\nsys.path.append('C:/Users/Gregoire/Documents/PythonCode/JCAP')\nfrom readplatemap import *\n\nmodelpath='C:/Users/Gregoire/Documents/CaltechWork/platemaps/Quaternarysingleplate/plate333_1map_full.txt'\nnewpath='C:/Users/Gregoire/Docu... | [
[
"numpy.arange",
"numpy.array",
"numpy.zeros",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hfurkanbozkurt/syne-tune | [
"05ee2668f0155b40c3ee3b61e4b3d58f3f9f3c4f",
"05ee2668f0155b40c3ee3b61e4b3d58f3f9f3c4f",
"05ee2668f0155b40c3ee3b61e4b3d58f3f9f3c4f"
] | [
"syne_tune/optimizer/schedulers/searchers/bayesopt/utils/test_objects.py",
"syne_tune/optimizer/schedulers/searchers/bayesopt/models/meanstd_acqfunc_impl.py",
"syne_tune/optimizer/schedulers/searchers/bayesopt/utils/comparison_gpy.py"
] | [
"# Copyright 2021 Amazon.com, Inc. or its affiliates. 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# A copy of the License is located at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# or ... | [
[
"numpy.array",
"numpy.log10",
"numpy.sum"
],
[
"numpy.maximum",
"scipy.stats.norm.cdf",
"scipy.stats.norm.pdf",
"numpy.power",
"numpy.ones_like",
"numpy.isnan",
"numpy.nanmin",
"numpy.mean",
"numpy.zeros_like",
"numpy.any",
"numpy.where"
],
[
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
... |
uhrwecker/GRDonuts | [
"3087aeb5c169251bdb711b425dcc3040ff962da7",
"3087aeb5c169251bdb711b425dcc3040ff962da7"
] | [
"util/utility.py",
"vis/simple.py"
] | [
"import numpy as np\n\nclass UtilInverse():\n def __init__(self, verbose=True):\n self.verbose = verbose\n\n def find_nearest_ind(self, array, value):\n index = []\n for ind in range(len(array)-1):\n if array[ind] < value and array[ind+1] > value:\n index.append(... | [
[
"numpy.delete",
"numpy.where",
"numpy.trapz"
],
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"numpy.where",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
blokhinnv/dgl | [
"bcf92f6c21afd4ad48a86d2ee543386099190791"
] | [
"python/dgl/distributed/dist_graph.py"
] | [
"\"\"\"Define distributed graph.\"\"\"\n\nfrom collections.abc import MutableMapping\nfrom collections import namedtuple\n\nimport os\nimport numpy as np\n\nfrom ..heterograph import DGLHeteroGraph\nfrom ..convert import heterograph as dgl_heterograph\nfrom ..convert import graph as dgl_graph\nfrom ..transform impo... | [
[
"numpy.nonzero",
"numpy.random.seed",
"numpy.cumsum",
"numpy.concatenate",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
minrk/discretisedfield | [
"251584f8d976a7fafdff5402d16327489407c4dd"
] | [
"discretisedfield/field.py"
] | [
"import pyvtk\nimport struct\nimport matplotlib\nimport numpy as np\nimport mpl_toolkits.axes_grid1\nimport discretisedfield as df\nimport ubermagutil.typesystem as ts\nimport discretisedfield.util as dfu\nimport matplotlib.pyplot as plt\n\n\n@ts.typesystem(mesh=ts.Typed(expected_type=df.Mesh),\n dim=... | [
[
"numpy.swapaxes",
"numpy.array_equal",
"numpy.linspace",
"numpy.isnan",
"numpy.squeeze",
"numpy.linalg.norm",
"numpy.all",
"matplotlib.pyplot.colorbar",
"numpy.copy",
"numpy.equal",
"matplotlib.cm.get_cmap",
"numpy.array",
"numpy.divide",
"matplotlib.pyplot.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Fei-Wang/dl-pytorch | [
"a7672603e2de7824d0ff7e97b69dedad3fd9d476"
] | [
"test/test_models/test_palm.py"
] | [
"import torch\n\nfrom luffy.models.palm import *\n\n\ndef test_palm_tony():\n model = PaLMTony(num_tokens=20000)\n\n tokens = torch.randint(0, 20000, (1, 2048))\n feat = model(tokens)\n assert feat.shape == (1, 2048, 20000)\n"
] | [
[
"torch.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bestetc/batchflow | [
"d2a843640383fbe860654236881483f755227e06",
"d2a843640383fbe860654236881483f755227e06",
"d2a843640383fbe860654236881483f755227e06",
"d2a843640383fbe860654236881483f755227e06",
"d2a843640383fbe860654236881483f755227e06",
"d2a843640383fbe860654236881483f755227e06"
] | [
"batchflow/models/tf/nn/train.py",
"batchflow/models/metrics/loss.py",
"batchflow/batch_image.py",
"batchflow/models/tf/utils.py",
"batchflow/models/torch/losses/lovasz.py",
"batchflow/models/tf/layers/drop_block.py"
] | [
"\"\"\" Helpers for training \"\"\"\nfrom math import pi\n\nimport tensorflow as tf\n\ndef piecewise_constant(global_step, *args, **kwargs):\n \"\"\" Constant learning rate decay (uses global_step param instead of x) \"\"\"\n return tf.train.piecewise_constant(global_step, *args, **kwargs)\n\ndef cyclic_learn... | [
[
"tensorflow.sin",
"tensorflow.cast",
"tensorflow.mod",
"tensorflow.train.piecewise_constant",
"tensorflow.name_scope",
"tensorflow.abs"
],
[
"numpy.asarray",
"numpy.sum"
],
[
"numpy.unique",
"numpy.asarray",
"numpy.clip",
"numpy.stack",
"numpy.concatenat... | [
{
"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... |
sayanmondal2098/pandas | [
"a1fee9199eba7ebf423880243936b9f1501d3d3a",
"a1fee9199eba7ebf423880243936b9f1501d3d3a"
] | [
"pandas/tests/io/parser/test_parse_dates.py",
"pandas/tests/io/test_pytables.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nTests date parsing functionality for all of the\nparsers defined in parsers.py\n\"\"\"\n\nfrom datetime import date, datetime\nfrom io import StringIO\n\nimport numpy as np\nimport pytest\nimport pytz\n\nfrom pandas._libs.tslib import Timestamp\nfrom pandas._libs.tslibs import pa... | [
[
"pandas.util.testing.ensure_clean",
"pandas.Timestamp",
"pandas.MultiIndex.from_tuples",
"pandas.compat.numpy.np_array_datetime64_compat",
"pandas.DataFrame",
"pandas.util.testing.assert_frame_equal",
"pandas.compat.parse_date",
"pandas.DatetimeIndex",
"pandas.core.indexes.date... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sambuddinc/DLTK | [
"9511b0b9860118a9285c2fe730ea49dfe247cab6"
] | [
"data/IXI_HH/download_IXI_HH.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Download and extract the IXI Hammersmith Hospital 3T dataset\n\nurl: http://brain-development.org/ixi-dataset/\nref: IXI – Information eXtraction from Images (EPSRC GR/S21533/02)\n\n\"\"\"\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\nfrom __future_... | [
[
"numpy.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
klarman-cell-observatory/scCloud.py | [
"5a04a2f22574db044d018656ac4705ec83840226",
"5a04a2f22574db044d018656ac4705ec83840226",
"5a04a2f22574db044d018656ac4705ec83840226"
] | [
"sccloud/misc/misc.py",
"sccloud/__init__.py",
"sccloud/tools/visualization.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom typing import List\nfrom anndata import AnnData\n\nfrom sccloud.io import read_input\n\n\ndef search_genes(\n data: AnnData,\n gene_list: List[str],\n rec_key: str = \"de_res\",\n measure: str = \"percentage\",\n) -> pd.DataFrame:\n \"\"\"Extract and dis... | [
[
"scipy.stats.f_oneway",
"numpy.unique",
"numpy.isnan",
"pandas.DataFrame",
"numpy.ones",
"numpy.dtype",
"numpy.warnings.filterwarnings",
"numpy.exp",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.isin"
],
[
"matplotlib.use"
],
[
"numpy.random... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
waldo2590/thunder | [
"967ff8f3e7c2fabe1705743d95eb2746d4329786",
"967ff8f3e7c2fabe1705743d95eb2746d4329786"
] | [
"test/test_series_io.py",
"thunder/series/series.py"
] | [
"import pytest\nimport os\nimport glob\nimport json\nfrom numpy import arange, array, allclose, save, savetxt\n\nfrom bolt import array as barray\nfrom thunder.series.readers import fromarray, fromtext, frombinary, fromexample\n\npytestmark = pytest.mark.usefixtures(\"eng\")\n\n\ndef test_from_array(eng):\n a = ... | [
[
"numpy.savetxt",
"numpy.arange",
"numpy.allclose"
],
[
"numpy.dot",
"numpy.polyfit",
"numpy.expand_dims",
"numpy.asarray",
"numpy.max",
"numpy.mean",
"numpy.fix",
"numpy.polyval",
"numpy.roll",
"numpy.where",
"numpy.random.randint",
"numpy.unique",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
hx-Tang/GANet | [
"8935c9d3d82189fa6f940c2a877534a398a041e4",
"8935c9d3d82189fa6f940c2a877534a398a041e4"
] | [
"libs/sync_bn/src/__init__.py",
"view.py"
] | [
"import os\nimport torch\nfrom torch.utils.cpp_extension import load\n\ncwd = os.path.dirname(os.path.realpath(__file__))\ncpu_path = os.path.join(cwd, 'cpu')\ngpu_path = os.path.join(cwd, 'gpu')\n\ncpu = load('sync_bn_cpu', [\n os.path.join(cpu_path, 'operator.cpp'),\n os.path.join(cpu_path, 'sync_bn.cpp'),\... | [
[
"torch.cuda.is_available"
],
[
"torch.randn",
"torch.nn.DataParallel",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
scottfredericks/PyXtal_Old | [
"3fa39b2f188197b42576087c6f4c3bca14b2e8f3"
] | [
"examples/LJ_38_Oh.py"
] | [
"from pyxtal.crystal import random_cluster\nfrom copy import deepcopy\nfrom optparse import OptionParser\nfrom random import randint, choice\nfrom scipy.optimize import minimize\nfrom scipy.spatial.distance import pdist, cdist\nfrom pyxtal.molecule import PointGroupAnalyzer\nfrom pymatgen import Molecule\nfrom pyxt... | [
[
"matplotlib.pyplot.legend",
"numpy.dot",
"numpy.linspace",
"numpy.reshape",
"matplotlib.pyplot.close",
"numpy.zeros",
"matplotlib.pyplot.style.use",
"numpy.multiply",
"numpy.power",
"scipy.spatial.distance.cdist",
"numpy.delete",
"scipy.optimize.minimize",
"nump... | [
{
"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"
... |
quantapix/qnarre.com | [
"f51d5945c20ef8182c4aa11f1b407d064c190c70",
"f51d5945c20ef8182c4aa11f1b407d064c190c70",
"f51d5945c20ef8182c4aa11f1b407d064c190c70",
"f51d5945c20ef8182c4aa11f1b407d064c190c70",
"f51d5945c20ef8182c4aa11f1b407d064c190c70",
"f51d5945c20ef8182c4aa11f1b407d064c190c70",
"f51d5945c20ef8182c4aa11f1b407d064c190c7... | [
"qnarre/models/ibert_quant_modules.py",
"qnarre/prep/convert/roberta.py",
"qnarre/prep/convert/gpt_neo.py",
"std/pytorch/01-low/76.py",
"qnarre/prep/convert/rag.py",
"qnarre/prep/convert/segformer.py",
"std/pytorch/02-mid/05.py",
"std/huggingface/xlate.py",
"qnarre/old/bert/run_squad.py"
] | [
"import decimal\n\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.autograd import Function\n\nfrom ...utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass QuantEmbedding(qc.Module):\n def __init__(\n self,\n num_embeddings,\n embedding_dim,\n ... | [
[
"torch.abs",
"torch.nn.GELU",
"torch.mean",
"torch.max",
"torch.floor",
"torch.sign",
"torch.zeros",
"torch.sqrt",
"torch.round",
"torch.min",
"torch.sum",
"torch.zeros_like",
"torch.from_numpy",
"torch.tensor",
"torch.no_grad",
"torch.clamp",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"... |
frezaeix/evaluating_bdl | [
"bd0a464981c18de8479b6be2d91867527016c8d3"
] | [
"toyClassification/MC-Dropout-MAP-01-Adam/eval.py"
] | [
"# code-checked\n# server-checked\n\nfrom model import ToyNet\n\nimport torch\nimport torch.utils.data\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nimport numpy as np\nimport pickle\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport ma... | [
[
"torch.nn.functional.softmax",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.title",
"torch.load",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
drunkpig/rlcard | [
"db8a410bbfefb7f9fd958239aae8d79a8bfb29d3",
"db8a410bbfefb7f9fd958239aae8d79a8bfb29d3"
] | [
"examples/uno_single.py",
"rlcard/envs/blackjack.py"
] | [
"''' A toy example of training single-agent algorithm on Leduc Hold'em\n The environment can be treated as normal OpenAI gym style single-agent environment\n'''\n\nimport tensorflow as tf\nimport os\nimport numpy as np\n\nimport rlcard\nfrom rlcard.agents.dqn_agent import DQNAgent\nfrom rlcard.agents.random_agen... | [
[
"tensorflow.Variable",
"tensorflow.global_variables_initializer",
"numpy.mean",
"tensorflow.Session",
"tensorflow.train.Saver"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
noashin/local_global_attention_model | [
"531e6a4cc1dc364a6a4168de1b9f972727a8aeb1"
] | [
"src/LocalChoiceModel/vel_param.py"
] | [
"import sys\n\nimport numpy as np\nfrom scipy.stats import multivariate_normal\n\nsys.path.append('./../../')\nfrom src.HMC.hmcparameter import HMCParameter\n\nclass VelParam(HMCParameter):\n def __init__(self, init_val):\n super().__init__(np.array(init_val))\n dim = np.array(init_val).shape\n ... | [
[
"scipy.stats.multivariate_normal.rvs",
"numpy.dot",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ruriboshi/propnet | [
"770703fb4fc344f785f89c02f26b31ea5733d2bd",
"770703fb4fc344f785f89c02f26b31ea5733d2bd"
] | [
"propnet/models/python/electromechanical_coupling.py",
"propnet/web/layouts_correlate.py"
] | [
"import numpy as np\n\n\ndef plug_in(symbol_values):\n\n req_symbols = [\"S\", \"e\", \"d\"]\n data = {}\n if all(s in symbol_values for s in req_symbols):\n e = symbol_values[\"e\"]\n S = symbol_values[\"S\"]\n d = symbol_values[\"d\"]\n\n data[\"k\"] = np.abs(d[2][2] / np.sqrt... | [
[
"numpy.sqrt"
],
[
"numpy.isfinite"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eduardojdiniz/Buzznauts | [
"8ac242a8d5309b4090a0f0b148ec275cac762bc0"
] | [
"analysis/baseline/s02_perform_encoding.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n\nimport numpy as np\nimport os\nimport os.path as op\nimport argparse\nimport torch\nfrom Buzznauts.utils import load_dict, saveasnii, get_fmri, set_device\nfrom Buzznauts.analysis.baseline import get_activations, predict_fmri_fast\nfrom tqdm import tqdm\n\n\ndef main():\n ... | [
[
"numpy.zeros_like",
"numpy.zeros",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Anders-Holst/Bonsai | [
"841aa4e12c8bea8945396bd232c2006260127507"
] | [
"datapreparation/analyze.py"
] | [
"#! /usr/bin/env python3\n\n\n\"\"\" -------------------------------\n\n analyse.py\n\n Copyright (C) 2018 RISE\n This code was produced by RISE\n The 2013-04-10 version\n\n bonsai/src_v02/analyze.py\n\n simple analysis of pandas dataframes data\n such as \n\n 1. find duplicated rows\n\n ... | [
[
"numpy.datetime64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vinayak1998/Data_Driven_Astronomy | [
"1d0dd82b2e9066759c442807c30c70bef096d719"
] | [
"Week1/brightest_pixel_position_fits.py"
] | [
"import numpy as np\nimport time\nfrom astropy.io import fits\nimport matplotlib.pyplot as plt\n\ndef load_fits(filename):\n start = time.perf_counter()\n hdulist = fits.open(filename)\n data = hdulist[0].data\n result = np.where(data == np.amax(data))\n coornidates = list(zip(result[0],result[1]))\n end = ti... | [
[
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"numpy.amax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anaikawadi/svbrdf-estimation | [
"6c169b12210d2a92495c1ab1218dd3e4da0314a5"
] | [
"development/multiImage_pytorch/persistence.py"
] | [
"import gc\nimport json\nimport pathlib\nimport torch\n\nclass Checkpoint:\n def __init__(self, checkpoint=None):\n self.checkpoint = checkpoint\n\n @staticmethod\n def get_checkpoint_path(checkpoint_dir):\n return checkpoint_dir.joinpath(\"checkpoint.tar\")\n\n @staticmethod\n def load... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kobakobashu/posenet-python | [
"52290733504fd0a130cc2301bad5db761c14a4e9"
] | [
"models/helper.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Models helper\n\nThese are helper functions for models.\n\n\"\"\"\n\nimport torch.optim as optim\nimport torch.nn as nn\n\nfrom configs.supported_info import SUPPORTED_OPTIMIZER, SUPPORTED_CRITERION\n\n\ndef get_optimizer(cfg: object, network: object) -> object:\n \"\"\"Get optimi... | [
[
"torch.nn.NLLLoss",
"torch.nn.CrossEntropyLoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangziyezzy/DeepLearningMugenKnock | [
"26830fe049c7da8001977ca0df12e946c0f030eb",
"26830fe049c7da8001977ca0df12e946c0f030eb"
] | [
"Scripts_Model/scripts_pytorch/VGG19_pytorch.py",
"Scripts_Model/scripts_pytorch/DenseNet169_pytorch.py"
] | [
"import torch\nimport torch.nn.functional as F\nimport numpy as np\nfrom collections import OrderedDict\nfrom easydict import EasyDict\nfrom _main_base import main\nimport os\n\n#---\n# config\n#---\ncfg = EasyDict()\n\n# class\ncfg.CLASS_LABEL = ['akahara', 'madara']\ncfg.CLASS_NUM = len(cfg.CLASS_LABEL)\n\n# mode... | [
[
"torch.nn.NLLLoss",
"torch.nn.functional.softmax",
"torch.nn.Dropout",
"torch.manual_seed",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.cuda.is_available",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.functional.max_pool2d"
],
[
"torch.nn.NLLLoss",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DionysisChristopoulos/google-research | [
"7f59ef421beef32ca16c2a7215be74f7eba01a0f",
"eb2b142f26e39aac1dcbb768417465ae9d4e5af6",
"7cee4b22b925581d912e8d993625c180da2a5a4f",
"7cee4b22b925581d912e8d993625c180da2a5a4f",
"7cee4b22b925581d912e8d993625c180da2a5a4f",
"7cee4b22b925581d912e8d993625c180da2a5a4f",
"7f59ef421beef32ca16c2a7215be74f7eba01a0... | [
"blur/synapse_util.py",
"scann/scann/scann_ops/py/scann_ops.py",
"social_rl/multiagent_tfagents/multiagent_metrics.py",
"pse/dm_control/run_train_eval.py",
"pse/jumping_task/evaluation_helpers.py",
"dp_multiq/csmooth.py",
"meta_pseudo_labels/training_utils.py",
"es_enas/util.py",
"non_semantic_speec... | [
"# coding=utf-8\n# Copyright 2021 The Google Research 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 requ... | [
[
"numpy.random.RandomState",
"tensorflow.compat.v1.random.uniform"
],
[
"tensorflow.function",
"tensorflow.Variable",
"tensorflow.compat.v1.VariableScope"
],
[
"tensorflow.reduce_mean",
"tensorflow.zeros_like",
"tensorflow.compat.v2.summary.scalar",
"numpy.mean",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
ishine/malaya-speech | [
"fd34afc7107af1656dff4b3201fa51dda54fde18",
"fd34afc7107af1656dff4b3201fa51dda54fde18",
"fd34afc7107af1656dff4b3201fa51dda54fde18",
"fd34afc7107af1656dff4b3201fa51dda54fde18",
"fd34afc7107af1656dff4b3201fa51dda54fde18",
"fd34afc7107af1656dff4b3201fa51dda54fde18",
"fd34afc7107af1656dff4b3201fa51dda54fde1... | [
"pretrained-model/stt/hubert/conformer-tiny-ctc.py",
"session/speaker-change/finetune-vggvox-v2.py",
"pretrained-model/stt/jasper/medium-jasper-ctc.py",
"malaya_speech/train/model/fastsplit/model.py",
"malaya_speech/train/model/fastsplit/loss.py",
"malaya_speech/train/model/pix2pix/discriminator.py",
"p... | [
"import os\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '3'\n\nimport pyroomacoustics as pra\nimport numpy as np\nfrom pydub import AudioSegment\nfrom sklearn.utils import shuffle\nfrom glob import glob\nimport random\nimport json\nfrom malaya_speech.train.model.conformer.model import Model as ConformerModel\nfrom malay... | [
[
"tensorflow.train.LoggingTensorHook",
"numpy.amax",
"tensorflow.TensorShape",
"numpy.abs",
"tensorflow.constant",
"numpy.clip",
"tensorflow.get_collection",
"sklearn.utils.shuffle",
"tensorflow.cast",
"tensorflow.identity",
"tensorflow.train.init_from_checkpoint",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
fab464654/SSD_on_ActiveVisionDataset | [
"1bc6f0745241d0b45c3f257c6fb09ea0435c993e"
] | [
"train.py"
] | [
"import time\nimport torch.backends.cudnn as cudnn\nimport torch.optim\nimport torch.utils.data\nfrom model import SSD300, MultiBoxLoss\nfrom datasets import PascalVOCDataset\nfrom utils import *\n\n# Data parameters\ndata_folder = 'google_drive/MyDrive/ColabNotebooks/Project/GT' # folder with data files\nkeep_diff... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
1nadequacy/dm_control | [
"a55474768cf0a6d570fe4a376802630027ad5f01"
] | [
"dm_control/rl/specs_test.py"
] | [
"# Copyright 2017 The dm_control 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... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JoeTao-097/Multi-REZ-Evalution-for-Breast-Ultrasound-Images | [
"344d64ad2fe9d790c49e8005b3abee219d362278",
"344d64ad2fe9d790c49e8005b3abee219d362278"
] | [
"Model_test.py",
"Model Perfomance Comparasion.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Aug 2 17:32:52 2021\r\n\r\n@author: jiangyt\r\n\"\"\"\r\n\r\nfrom Tools import *\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras.layers import Dense, Activation, Flatten, Dropout, Input, BatchNormalization\r\nfrom tensorflow.keras.layers import Conv2D, ... | [
[
"tensorflow.keras.applications.resnet50.ResNet50",
"tensorflow.keras.layers.GlobalAveragePooling2D",
"tensorflow.keras.applications.efficientnet.EfficientNetB0",
"tensorflow.keras.applications.xception.Xception",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.applications.densenet.Dens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.4",
"2.3",
"2.5",
"2.6"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
... |
arnoyu-hub/COMP0016miemie | [
"59af664dcf190eab4f93cefb8471908717415fea",
"59af664dcf190eab4f93cefb8471908717415fea",
"59af664dcf190eab4f93cefb8471908717415fea",
"59af664dcf190eab4f93cefb8471908717415fea",
"59af664dcf190eab4f93cefb8471908717415fea",
"59af664dcf190eab4f93cefb8471908717415fea",
"59af664dcf190eab4f93cefb8471908717415fe... | [
"venv/Lib/site-packages/pandas/tests/series/indexing/test_get.py",
"venv/Lib/site-packages/pandas/tests/frame/methods/test_at_time.py",
"venv/Lib/site-packages/pandas/tests/arrays/categorical/test_operators.py",
"venv/Lib/site-packages/pandas/tests/frame/methods/test_filter.py",
"venv/Lib/site-packages/skle... | [
"import numpy as np\r\nimport pytest\r\n\r\nimport pandas as pd\r\nfrom pandas import Series\r\nimport pandas._testing as tm\r\n\r\n\r\ndef test_get():\r\n # GH 6383\r\n s = Series(\r\n np.array(\r\n [\r\n 43,\r\n 48,\r\n 60,\r\n 48... | [
[
"pandas.Series",
"numpy.arange",
"pandas.Float64Index",
"pandas.DataFrame",
"numpy.random.randn",
"pandas._testing.makeDateIndex",
"pandas._testing.assert_series_equal",
"numpy.array"
],
[
"pandas._testing.assert_equal",
"pandas._libs.tslibs.timezones.maybe_get_tz",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
MountainRange/mobius_score | [
"fc900ab456b3e3431cfa6d9684b97ec6321d0a23"
] | [
"audiospec.py"
] | [
"\nimport numpy as np\nimport librosa\nfrom tqdm import tqdm\nfrom audiomisc import ks_key\n\nfrom constants import VERTICALCUTOFF, FFT_SIZE, FFT_HOP\n\ndef stft(x, fft_size, hopsamp):\n window = np.hanning(fft_size)\n return np.array([np.fft.rfft(window*x[i:i+fft_size])\n for i in range(0... | [
[
"numpy.fft.rfft",
"numpy.min",
"numpy.concatenate",
"numpy.max",
"numpy.std",
"numpy.argmax",
"numpy.mean",
"numpy.hanning",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Prasad9/Detect-Flags-SSD | [
"c0d662bde99ed8df33d72bd06d61d5eb869d31a5"
] | [
"detect/image_detector.py"
] | [
"from __future__ import print_function\nimport mxnet as mx\nimport numpy as np\nfrom timeit import default_timer as timer\nfrom dataset.iterator import DetTestImageIter\nimport cv2\n\nclass ImageDetector(object):\n\t\"\"\"\n\tSSD detector which hold a detection network and wraps detection API\n\n\tParameters:\n\t--... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
richardtjornhammar/graphtastic | [
"1e64d408ffb3e09d5ad068986c847032d5cfdcbd"
] | [
"src/graphtastic/clustering.py"
] | [
"\"\"\"\nCopyright 2022 RICHARD TJÖRNHAMMAR\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed... | [
[
"numpy.array",
"numpy.shape",
"numpy.sum",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TochkaAI/Paddle | [
"f249a5f05f0f5832279244d88c8cb4eaaad1fbd4",
"f249a5f05f0f5832279244d88c8cb4eaaad1fbd4",
"f249a5f05f0f5832279244d88c8cb4eaaad1fbd4"
] | [
"python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py",
"python/paddle/fluid/tests/unittests/npu/test_truncated_gaussian_random_op_npu.py",
"python/paddle/fluid/tests/unittests/test_imperative_basic.py"
] | [
"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.random.random"
],
[
"numpy.allclose",
"numpy.random.seed"
],
[
"numpy.random.random",
"numpy.allclose",
"numpy.array_equal",
"numpy.ones",
"numpy.random.rand",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
naveenkambham/big_five_personality_machine_learning | [
"a4d673e7e72287f2448b6a7b2729e5231b4f7ab2",
"a4d673e7e72287f2448b6a7b2729e5231b4f7ab2"
] | [
"UnitTests/test_battery_sensor_features_extractor.py",
"FeatureExtraction/wifi_sensor_features_extractor.py"
] | [
"\"\"\"\nDeveloper : Naveen Kambham\nDescription: Unit testing for battery sensor feature extractor code. Majority of the data extraction code has to be tested visually by looking at the plots distributions.\n\"\"\"\n#Importing the required libraries.\nimport unittest\nimport numpy as np\nfrom FeatureExtraction im... | [
[
"numpy.max",
"numpy.min"
],
[
"pandas.read_csv",
"pandas.DataFrame",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
KOLANICH/qiskit-terra | [
"3947f258ddb31a2b8dd17aff5d2d041d29d74601"
] | [
"qiskit/quantum_info/operators/measures.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2017.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modifications or ... | [
[
"scipy.sparse.eye",
"numpy.real",
"numpy.trace",
"numpy.reshape"
]
] | [
{
"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"... |
noenfugler/jesse | [
"217a3168620a755c1a9576d9deb27105db7dccf8",
"217a3168620a755c1a9576d9deb27105db7dccf8",
"217a3168620a755c1a9576d9deb27105db7dccf8",
"217a3168620a755c1a9576d9deb27105db7dccf8"
] | [
"jesse/indicators/supersmoother.py",
"jesse/indicators/sinwma.py",
"jesse/indicators/damiani_volatmeter.py",
"jesse/indicators/alligator.py"
] | [
"from typing import Union\n\nimport numpy as np\nfrom numba import njit\n\nfrom jesse.helpers import get_candle_source, slice_candles\n\n\ndef supersmoother(candles: np.ndarray, period: int = 14, source_type: str = \"close\", sequential: bool = False) -> Union[\n float, np.ndarray]:\n \"\"\"\n Super Smooth... | [
[
"numpy.copy",
"numpy.exp",
"numpy.cos"
],
[
"numpy.lib.stride_tricks.sliding_window_view",
"numpy.average",
"numpy.sin"
],
[
"numpy.full_like",
"numpy.std"
],
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
WeilerP/cellrank | [
"c8c2b9f6bd2448861fb414435aee7620ca5a0bad",
"c8c2b9f6bd2448861fb414435aee7620ca5a0bad",
"c8c2b9f6bd2448861fb414435aee7620ca5a0bad",
"c8c2b9f6bd2448861fb414435aee7620ca5a0bad"
] | [
"cellrank/pl/_circular_projection.py",
"examples/other/plot_model.py",
"cellrank/tl/kernels/_cytotrace_kernel.py",
"cellrank/tl/estimators/_base_estimator.py"
] | [
"from typing import Any, Tuple, Union, Mapping, Callable, Optional, Sequence\nfrom typing_extensions import Literal\n\nfrom enum import auto\nfrom types import MappingProxyType\nfrom pathlib import Path\n\nimport scvelo as scv\nfrom anndata import AnnData\nfrom cellrank import logging as logg\nfrom cellrank.tl impo... | [
[
"sklearn.metrics.pairwise_distances",
"numpy.ones_like",
"matplotlib.colors.LogNorm",
"numpy.linspace",
"numpy.asarray",
"numpy.arange",
"matplotlib.collections.LineCollection",
"numpy.cos",
"matplotlib.pyplot.subplots",
"numpy.sin",
"numpy.nan_to_num",
"numpy.conca... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"... |
blnm/RSE | [
"6a3f0dd858ea4b6dafcfb1d97bb979e101d9911c"
] | [
"RAdam.py"
] | [
"import tensorflow as tf\r\nfrom tensorflow.python.eager import context\r\nfrom tensorflow.python.framework import ops\r\nfrom tensorflow.python.ops import clip_ops\r\nfrom tensorflow.python.ops import control_flow_ops\r\nfrom tensorflow.python.ops import math_ops\r\nfrom tensorflow.python.ops import resource_varia... | [
[
"tensorflow.control_dependencies",
"tensorflow.reduce_sum",
"tensorflow.python.ops.state_ops.assign_sub",
"tensorflow.python.ops.math_ops.sqrt",
"tensorflow.where",
"tensorflow.python.ops.state_ops.assign",
"tensorflow.python.eager.context.executing_eagerly",
"tensorflow.python.ops... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.7",
"1.10",
"1.12"
]
}
] |
rhshadrach/pandas | [
"777c0f90c6067c636fcd76ce003a8fbfcc311d7b"
] | [
"pandas/core/generic.py"
] | [
"import collections\nfrom datetime import timedelta\nimport functools\nimport gc\nimport json\nimport operator\nimport pickle\nimport re\nfrom textwrap import dedent\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Dict,\n FrozenSet,\n Hashable,\n List,\n Mapping,\n Optional,\n... | [
[
"pandas.tseries.frequencies.to_offset",
"pandas.util._validators.validate_bool_kwarg",
"pandas.core.dtypes.inference.is_hashable",
"numpy.unique",
"numpy.asanyarray",
"pandas.core.dtypes.common.is_re_compilable",
"pandas.concat",
"pandas.core.dtypes.common.is_list_like",
"panda... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
dajtmullaj/example_conda_pkg | [
"7c2bf657d14c714608e653d7218fa3cd658a6297"
] | [
"example_conda_pkg/descriptors.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 3 21:21:19 2020\n\nProject: chemplot (Chemical Space Visualization)\nContent: Descriptor operation methods\n\n@author: murat cihan sorkun\n\"\"\"\n\nfrom rdkit import Chem\nfrom rdkit.Chem import AllChem\nimport pandas as pd\nimport math\nimport mordred\nfrom mo... | [
[
"sklearn.linear_model.LogisticRegression",
"pandas.DataFrame",
"sklearn.linear_model.Lasso",
"sklearn.preprocessing.StandardScaler",
"sklearn.feature_selection.SelectFromModel"
]
] | [
{
"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": []
}
] |
pmarshwx/matplotlib | [
"12be528dbf2114f7c25abf60de8100cb2d4494af",
"12be528dbf2114f7c25abf60de8100cb2d4494af",
"12be528dbf2114f7c25abf60de8100cb2d4494af",
"12be528dbf2114f7c25abf60de8100cb2d4494af"
] | [
"lib/matplotlib/backends/qt_compat.py",
"lib/matplotlib/transforms.py",
"lib/matplotlib/tests/test_tightlayout.py",
"lib/matplotlib/tests/test_offsetbox.py"
] | [
"\"\"\" A Qt API selector that can be used to switch between PyQt and PySide.\n\"\"\"\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nimport six\n\nimport os\nfrom matplotlib import rcParams, verbose\n\n# Available APIs.\nQT_API_PYQT = 'PyQt4' ... | [
[
"matplotlib.verbose.report"
],
[
"numpy.dot",
"numpy.asarray",
"numpy.arctan2",
"numpy.concatenate",
"numpy.max",
"numpy.any",
"numpy.ma.getmask",
"numpy.ma.is_masked",
"numpy.sin",
"numpy.asanyarray",
"matplotlib._path.update_path_extents",
"numpy.ma.isMask... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
DaulPavid/pyturbo | [
"878e0b1b514c043f1b4ea5cd5268b23c0df5192e"
] | [
"turbo/turbo_encoder.py"
] | [
"#\n# Turbo Encoder\n#\n\nimport numpy as np\n\nfrom .rsc import RSC\n\n\nclass TurboEncoder:\n def __init__(self, interleaver):\n self.interleaver = interleaver\n self.block_size = len(self.interleaver)\n self.encoders = 2 * [RSC()]\n\n def reset(self):\n for e in self.encoders:\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wmcnally/evopose2d | [
"ea05b818044d8d84e9cbbee778bc465be59ebd59"
] | [
"inference_speed.py"
] | [
"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nimport tensorflow as tf\nfrom dataset.dataloader import load_tfds\nfrom time import time\nimport argparse\nfrom nets.simple_basline import SimpleBaseline\nfrom nets.evopose2d import EvoPose\nfrom nets.hrnet import HRNet\nfrom utils import detect_hardware\n\n\nde... | [
[
"tensorflow.distribute.OneDeviceStrategy",
"tensorflow.config.optimizer.set_experimental_options"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ourDirection/ourDirection | [
"b99ed67a8cc0fe5016e03fe3b5ad083b7f8bbdc0"
] | [
"momus/VHRED/split-examples-by-token.py"
] | [
"\"\"\"\nTakes as input a binarized dialogue corpus, splits the examples by a certain token and shuffles it\n\nExample run:\n\n python split-examples-by-token.py Training.dialogues.pkl 2 Training_SplitByDialogues.dialogues --join_last_two_examples\n\n@author Iulian Vlad Serban\n\"\"\"\n\nimport collections\nimpor... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YosefLab/SingleCellLineageTracing | [
"010072b307f7eadbf10dc4af8b2165e48f1736a7",
"010072b307f7eadbf10dc4af8b2165e48f1736a7",
"010072b307f7eadbf10dc4af8b2165e48f1736a7"
] | [
"test/simulator_tests/birth_death_simulator_test.py",
"test/preprocess_tests/align_sequence_test.py",
"test/solver_tests/neighborjoining_solver_test.py"
] | [
"import unittest\n\nimport networkx as nx\nimport numpy as np\n\nfrom typing import List, Tuple\n\n\nfrom cassiopeia.data.CassiopeiaTree import CassiopeiaTree\nfrom cassiopeia.mixins import TreeSimulatorError\nfrom cassiopeia.simulator.BirthDeathFitnessSimulator import (\n BirthDeathFitnessSimulator,\n)\n\nimpor... | [
[
"numpy.random.exponential",
"numpy.random.uniform",
"numpy.isclose"
],
[
"pandas.DataFrame.from_dict"
],
[
"numpy.allclose",
"pandas.DataFrame.from_dict"
]
] | [
{
"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",
"... |
ssccutyy/KWS-Transformer | [
"7ae6d2e8fce1a293d88eedc0dbfacae726151a08"
] | [
"kws_streaming/train/train.py"
] | [
"# coding=utf-8\n# Copyright (c) 2021, Arm Limited and Contributors.\n# SPDX-License-Identifier: Apache-2.0\n# Copyright 2021 The Google Research 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 c... | [
[
"tensorflow.compat.v1.keras.backend.set_learning_phase",
"tensorflow.compat.v1.keras.utils.to_categorical",
"tensorflow.compat.v1.keras.losses.CategoricalCrossentropy",
"tensorflow.compat.v1.keras.backend.set_value",
"numpy.exp",
"tensorflow.compat.v1.math.argmax",
"tensorflow.compat.v... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
paudetseis/OBStools | [
"c6c02d8864c25a14f22d1fae17ff5ad911b9ff00"
] | [
"obstools/scripts/atacr_clean_spectra.py"
] | [
"#!/usr/bin/env python\n\n# Copyright 2019 Pascal Audet & Helen Janiszewski\n#\n# This file is part of OBStools.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restrictio... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tempoCollaboration/OQuPy | [
"a389a161991a59259e5df47d8e0f405fcac75fe5"
] | [
"oqupy/backends/tempo_backend.py"
] | [
"# Copyright 2020 The TEMPO Collaboration\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.dot",
"numpy.moveaxis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
j-chan-hkust/deep_testing_of_advanced_learning_systems | [
"ec535e2b4dc489d407b664a138d3f5262b71d21e",
"ec535e2b4dc489d407b664a138d3f5262b71d21e"
] | [
"2_data_collection/CIFAR_10/vgg16_CIFAR10.py",
"4_Coverage_Evaluation/MNIST/utils.py"
] | [
"from __future__ import print_function\nimport keras\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D, BatchNormalization\nfr... | [
[
"numpy.std",
"numpy.argmax",
"numpy.mean"
],
[
"numpy.ones_like",
"numpy.clip",
"numpy.linalg.inv",
"numpy.asarray",
"numpy.zeros_like",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kul-group/MAZE-sim | [
"0f85e74bf93f9242a73bcfaa20a593ae966f38fa",
"0f85e74bf93f9242a73bcfaa20a593ae966f38fa"
] | [
"scraps/forcefield_v2.py",
"demos/double_defect_maker.py"
] | [
"from maze.extra_framework_maker import ExtraFrameworkMaker, ExtraFrameworkAnalyzer\nfrom maze.io_zeolite import read_vasp\nfrom maze.zeolite import PerfectZeolite, Zeolite\nfrom ase.neighborlist import natural_cutoffs, NeighborList\nimport os\nfrom pathlib import Path\nfrom ase.io import write, read, gromacs, prot... | [
[
"matplotlib.pyplot.title",
"numpy.reshape",
"matplotlib.pyplot.subplots",
"numpy.linalg.norm",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"scipy.optimize.minimize",
"numpy.mean",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplo... | [
{
"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"
... |
Duncanswilson/keras | [
"32aa192548b6b59bf407e583fbd246ba9f5f5676"
] | [
"keras/layers/recurrent.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Recurrent layers and their base classes.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport warnings\n\nfrom .. import backend as K\nfrom .. import activations\nfrom .. import initializ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Msegade/pyNastran | [
"ae36548579c6bb2ee3a4fff207f7211c1986a5ab",
"ae36548579c6bb2ee3a4fff207f7211c1986a5ab",
"ae36548579c6bb2ee3a4fff207f7211c1986a5ab",
"ae36548579c6bb2ee3a4fff207f7211c1986a5ab",
"ae36548579c6bb2ee3a4fff207f7211c1986a5ab",
"ae36548579c6bb2ee3a4fff207f7211c1986a5ab"
] | [
"pyNastran/dev/bdf_vectorized/cards/dynamic.py",
"pyNastran/bdf/cards/dmig.py",
"pyNastran/dev/bdf_vectorized/cards/elements/shell/pcomp_helper.py",
"pyNastran/op2/tables/oes_stressStrain/real/oes_bush1d.py",
"pyNastran/op2/tables/oes_stressStrain/random/oes_bars.py",
"pyNastran/op2/tables/oef_forces/oef_... | [
"# pylint: disable=C0103,R0902,R0904,R0914\n\"\"\"\nAll dynamic control cards are defined in this file. This includes:\n\n * FREQ\n * FREQ1\n * FREQ2 (not implemented)\n * FREQ3\n * FREQ4\n * FREQ5 (not implemented)\n * NLPCI\n * NLPARM\n * TSTEP\n * TSTEPNL\n\nAll cards are BaseCard objects.\n\n\"\"\"\nfrom math ... | [
[
"numpy.hstack",
"numpy.unique"
],
[
"scipy.sparse.coo_matrix",
"numpy.unique",
"numpy.asarray",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.where"
],
[
"numpy.array"
],
[
"numpy.allclose",
"numpy.array_equal",
"numpy.isfinite",
"pandas.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
... |
hengwei-chan/molecular_attention_transformer | [
"29193d4155df528e3a6a0c1e0da39111d0b8db93"
] | [
"soltrannet/__init__.py"
] | [
"from .predict import predict \nimport argparse\nimport sys, multiprocessing\nimport torch\n\ndef _parse_args():\n parser=argparse.ArgumentParser(description=\"Run SolTranNet aqueous solubility predictor\")\n parser.add_argument('input',nargs='?',type=argparse.FileType('r'),default=sys.stdin,help='PATH to the... | [
[
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adibellathur/garage | [
"482a26a07d46091f878c41b582f1478588e397ff"
] | [
"src/garage/torch/algos/_utils.py"
] | [
"\"\"\"Utility functions used by PyTorch algorithms.\"\"\"\nimport torch\nimport torch.nn.functional as F\n\n\nclass _Default: # pylint: disable=too-few-public-methods\n \"\"\"A wrapper class to represent default arguments.\n\n Args:\n val (object): Argument value.\n\n \"\"\"\n\n def __init__(se... | [
[
"torch.nn.functional.pad",
"torch.Tensor",
"torch.nn.functional.conv2d",
"torch.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amuamushu/wavedata | [
"1745c646ff3a76b38a81c439a0edd900c986c9f7"
] | [
"wavedata/tools/core/voxel_grid_2d.py"
] | [
"import numpy as np\n\nfrom wavedata.wavedata.tools.core import geometry_utils\n\n\nclass VoxelGrid2D(object):\n \"\"\"\n Voxel grids represent occupancy info. The voxelize_2d method projects a point cloud\n onto a plane, while saving height and point density information for each voxel.\n \"\"\"\n\n ... | [
[
"numpy.amax",
"numpy.unique",
"numpy.clip",
"numpy.amin",
"numpy.ascontiguousarray",
"numpy.int32",
"numpy.lexsort",
"numpy.dtype",
"numpy.ceil",
"numpy.append",
"numpy.diff",
"numpy.floor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zmcx16/ReclassifyAnimeCG | [
"f5f95b229447564502564d9ffc7edf6215fec83d"
] | [
"src/data/dataset.py"
] | [
"import torch\nfrom torch.utils.data import Dataset, DataLoader\nimport numpy as np\nfrom PIL import Image\nImage.MAX_IMAGE_PIXELS = None\n\nfrom data import get_train_transform, get_test_transform\n\n\nclass CustomDataset(Dataset):\n img_aug = True\n imgs = []\n transform = None\n\n def __init__(self, ... | [
[
"numpy.array",
"torch.utils.data.DataLoader",
"torch.from_numpy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stormymcstorm/condensa | [
"c7321e0a362f73eca9349769b341a7dd688ee1b9"
] | [
"test/schemes/test_qz.py"
] | [
"# Copyright 2019 NVIDIA Corporation\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 ... | [
[
"torch.nn.Linear",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mathischeap/mifem | [
"3242e253fb01ca205a76568eaac7bbdb99e3f059",
"3242e253fb01ca205a76568eaac7bbdb99e3f059",
"3242e253fb01ca205a76568eaac7bbdb99e3f059",
"3242e253fb01ca205a76568eaac7bbdb99e3f059",
"3242e253fb01ca205a76568eaac7bbdb99e3f059",
"3242e253fb01ca205a76568eaac7bbdb99e3f059"
] | [
"objects/CSCG/_3d/forms/standard/base/export/field.py",
"objects/CSCG/_3d/forms/trace/_2tr/discretize/scalar/boundary_wise.py",
"tools/deprecated/serial_runners/COMPONENTS/data/COMPONENTS/MODULES/m_tir_visualize.py",
"objects/CSCG/_3d/forms/standard/base/dofs/dof/basis_function.py",
"objects/CSCG/_3d/forms/... | [
"\"\"\"We want to export the field to some data files.\n\"\"\"\n\nfrom root.config.main import *\nfrom screws.freeze.main import FrozenOnly\nfrom screws.miscellaneous.timer import check_filename, check_no_splcharacter\nfrom scipy.io import savemat\n\n\n\nclass _3dCSC_SF_Export_Field(FrozenOnly):\n \"\"\"\"\"\"\n... | [
[
"scipy.io.savemat"
],
[
"numpy.array",
"numpy.kron",
"numpy.sqrt",
"numpy.einsum"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.rc",
"numpy.max",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.close",
"matplotlib.pyplot.f... | [
{
"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"... |
LongKt7/Face_Recognize_Pytorch | [
"baa02e633d379abe1001c8b8acb942617177329c",
"baa02e633d379abe1001c8b8acb942617177329c"
] | [
"config.py",
"Face_Alignt/predict_m.py"
] | [
"from easydict import EasyDict as edict\n# from pathlib import Path\nimport torch\nimport os\nfrom torchvision import transforms as trans\nfrom utils.constants import *\nlist_model = ['wget https://www.dropbox.com/s/akktsgxp0n8cwn2/model_mobilefacenet.pth?dl=0 -O model_mobilefacenet.pth',\n'wget https://www.dropbox... | [
[
"torch.cuda.is_available"
],
[
"torch.LongTensor",
"torch.cuda.synchronize",
"numpy.pad",
"numpy.abs",
"torch.cat",
"torch.Tensor",
"torch.load",
"torch.cuda.set_device",
"torch.unsqueeze",
"numpy.ones",
"torch.exp",
"torch.tensor",
"torch.set_num_thread... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Splendon/examples | [
"ed4a8a01857b6ddca49559141acf5d0986eb01e1",
"ed4a8a01857b6ddca49559141acf5d0986eb01e1",
"ed4a8a01857b6ddca49559141acf5d0986eb01e1",
"ed4a8a01857b6ddca49559141acf5d0986eb01e1",
"ed4a8a01857b6ddca49559141acf5d0986eb01e1"
] | [
"utils/tests/test_util.py",
"code_examples/tensorflow/kernel_benchmarks/dense.py",
"applications/tensorflow/cnns/inference/data.py",
"applications/tensorflow/cnns/training/Models/squeezenet.py",
"applications/popart/resnext_inference/get_model.py"
] | [
"# Copyright 2019 Graphcore Ltd.\nfrom statistics import mean\nimport numpy as np\nimport os\nimport re\nimport subprocess\nimport sys\nimport time\n\n\n\"\"\"Library of utility functions common between frameworks\"\"\"\n\n\ndef parse_results_for_speed(output, iter_tolerance, speed_tolerance):\n \"\"\"Look for <... | [
[
"numpy.array_repr"
],
[
"tensorflow.get_variable",
"tensorflow.control_dependencies",
"tensorflow.reduce_mean",
"tensorflow.broadcast_to",
"tensorflow.cast",
"tensorflow.identity",
"tensorflow.layers.dense",
"tensorflow.trainable_variables",
"tensorflow.global_variables... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"... |
MasterScott/Formasaurus | [
"d7d916237a6d2ca4c80c4c8ae5d66999c8beebed"
] | [
"tests/test_fieldtype_model.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division\nimport itertools\n\nimport numpy as np\nfrom sklearn_crfsuite.metrics import flat_accuracy_score\n\nfrom formasaurus.fieldtype_model import (\n train,\n _PRECISE_C1_C2,\n _REALISTIC_C1_C2,\n get_Xy,\n)\n\n\ndef test_training(sto... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wentaobi/Udacity | [
"00af9c36b42d6bca5f2d42d2744efed2ddb51587",
"00af9c36b42d6bca5f2d42d2744efed2ddb51587"
] | [
"Self_Driving_Car/P1/LaneLines-P1/P1.py",
"Self_Driving_Car/P4/project04.py"
] | [
"#importing some useful packages\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport numpy as np\nimport cv2\n\n\n#reading in an image\nimage = mpimg.imread('test_images/solidWhiteRight.jpg');\n#printing out some stats and plotting\nprint('This image is:', type(image), 'with dimesions:', imag... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.image.imread",
"numpy.zeros_like",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.figure"
],
[
"numpy.polyfit",
"matplotlib.pyplot.imshow",
"numpy.linspace",
"matplotlib.pyplot.hold",
"matplotlib.pyplot.plot",
"numpy.max... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
... |
adelavega/pliers | [
"dee21102689c77a56b7da48bf9a0ac10c90be0eb"
] | [
"pliers/tests/extractors/api/test_clarifai_extractors.py"
] | [
"from os.path import join\nfrom ...utils import get_test_data_path\nfrom pliers.extractors import ClarifaiAPIExtractor\nfrom pliers.stimuli import ImageStim\nfrom pliers.extractors.base import merge_results\nimport numpy as np\nimport pytest\n\n\n@pytest.mark.skipif(\"'CLARIFAI_API_KEY' not in os.environ\")\ndef te... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
makistsantekidis/opendr | [
"07dee3b59d3487b9c5a93d6946317178a02c9890",
"07dee3b59d3487b9c5a93d6946317178a02c9890",
"07dee3b59d3487b9c5a93d6946317178a02c9890",
"07dee3b59d3487b9c5a93d6946317178a02c9890",
"07dee3b59d3487b9c5a93d6946317178a02c9890",
"07dee3b59d3487b9c5a93d6946317178a02c9890"
] | [
"src/opendr/perception/object_tracking_2d/fair_mot/object_tracking_2d_fair_mot_learner.py",
"src/opendr/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/algorithm/datasets/gen_facial_muscles_data.py",
"projects/control/single_demo_grasp/simulation_ws/src/single_demo_grasping... | [
"# Copyright 2020-2021 OpenDR European Project\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 applicab... | [
[
"torch.onnx.export",
"torch.load",
"numpy.ascontiguousarray",
"torch.randn",
"torch.from_numpy"
],
[
"numpy.load",
"numpy.lib.format.open_memmap",
"scipy.spatial.Delaunay",
"numpy.transpose"
],
[
"numpy.asarray"
],
[
"torch.nn.BatchNorm1d",
"torch.nn.ini... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
pedrob37/Phys_Seg | [
"7adc65d7b228b3a5702acfa9e6d0494d6b4c2dee"
] | [
"Phys_Seg/run.py"
] | [
"import torch\nimport numpy as np\nimport SimpleITK as sitk\nfrom Phys_Seg.data_loading import load_and_preprocess, save_segmentation_nifti, read_file, save_img\nfrom Phys_Seg.predict_case import predict_phys_seg, physics_preprocessing, image_preprocessing\nimport importlib\nfrom Phys_Seg.utils import postprocess_p... | [
[
"numpy.array",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
teristam/openephys-fileIO | [
"8089e7c4aff829c13a79656b8812a3d3e68eb1eb"
] | [
"test/test_binary.py"
] | [
"import numpy as np \nfrom openephys_fileIO.fileIO import *\nfrom openephys_fileIO.Binary import *\n\ndef test_write_binary_data():\n # Test writing of binary data\n \n dataFolder = 'test/data'\n\n # Read the data in original int16 format\n data,headers = load_OpenEphysRecording4BinaryFile(dataFolder... | [
[
"numpy.random.randn",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FlowerForAlgernon/ai_tetris | [
"7ac0d3875ad9b31fb260f7567a218e0de340c4e4"
] | [
"QLearning.py"
] | [
"\"\"\"\n这份代码使用 Q learning 算法训练并运行俄罗斯方块游戏 ai。其中简化状态空间的方法可参考论文 Adapting Reinforcement Learning to Tetris\n\"\"\"\n\nimport numpy as np\nfrom game import *\n\n\n\nsub_well = 4\nbase = 7\n\n\ndef getStateIndex(field_width, field_height, field_map):\n \"\"\"\n 因为每一列有 7 种不同的情况，所以采用七进制数来作为状态索引\n \"\"\"\n temp... | [
[
"numpy.var",
"numpy.load",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
starkworld/Python-Course-work | [
"28715f079939129b442aedcd7edb2e0838886ba0"
] | [
"source code/Data Visualization.py"
] | [
"\"\"\"\nAuthor : nkalyan🤠\nimplementing Python Scripts on reading and returning the name no of mails that sent each day in week\n and plot/display them in bar graph\n\n I wrote code In counting to count the number of emails sent by each distinct user. That code may be helpful for this assignment.\n\"\"\"\n\n\n... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jasonrute/puzzle_cube | [
"7e05a21acd26cb30e729ba6a95e14e16c76c1780"
] | [
"analysis/stats.py"
] | [
"\"\"\"\nTraining Statics Tools\n\nA class for loading statistics related to a particular rutraiining session.\n\"\"\"\n\nimport numpy as np\n#from scipy import stats\nimport pandas as pd\nimport os\n\ndef str_between(s, start, end):\n return (s.split(start))[1].split(end)[0]\n\ndef is_stat_file_version(file_nam... | [
[
"pandas.read_hdf",
"pandas.concat",
"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": []
}
] |
kufusha/cabot | [
"52a40a39a29f0bd79b6fdd8f961708e09fda9a51",
"52a40a39a29f0bd79b6fdd8f961708e09fda9a51"
] | [
"cabot_ui/src/cabot_ui/geojson.py",
"mf_localization/src/altitude_manager.py"
] | [
"# Copyright (c) 2020 Carnegie Mellon University\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, mod... | [
[
"numpy.array",
"scipy.spatial.KDTree",
"numpy.linalg.norm"
],
[
"numpy.std"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
Corentin-LF/pyGPs | [
"b9d36777584cd53756bd4311c3c20ea52e945451"
] | [
"pyGPs/Core/gp.py"
] | [
"from __future__ import division\nfrom __future__ import absolute_import\nfrom builtins import str\nfrom builtins import range\nfrom builtins import object\nfrom past.utils import old_div\n#================================================================================\n# Marion Neumann [marion dot neumann at u... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.mean",
"numpy.exp",
"numpy.tril",
"numpy.ones_like",
"numpy.reshape",
"matplotlib.pyplot.axis",
"numpy.zeros",
"matplotlib.pyplot.figure",
"numpy.min",
"matplotlib... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Algomorph/NeuralTracking | [
"6312be8e18828344c65e25a423c239efcd3428dd",
"6312be8e18828344c65e25a423c239efcd3428dd",
"6312be8e18828344c65e25a423c239efcd3428dd"
] | [
"tests/data_generation/animate_berlin_y_stretch.py",
"tests/data_generation/animate_berlin_x_offset.py",
"tests/test_alignment_holistic.py"
] | [
"import sys\nimport os\nimport shutil\n\nimport cv2\nimport open3d as o3d\nimport open3d.core as o3c\nimport numpy as np\n\nfrom rendering.pytorch3d_renderer import PyTorch3DRenderer\nfrom data import StandaloneFrameDataset\nimport data.presets as presets\nimport tsdf.default_voxel_grid\nimport data.camera\nfrom se... | [
[
"numpy.array"
],
[
"numpy.array"
],
[
"torch.equal",
"torch.manual_seed",
"torch.no_grad",
"torch.cuda.manual_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aliabid2243/deepgaze | [
"8c602db89a1d1d8a644b44a381ddb8a693375e08"
] | [
"new_model/test_big.py"
] | [
"import os\nfrom load_data import load_batch, load_data_names, load_batch_from_names, load_batch_from_names_random\nfrom my_model import get_eye_tracker_model\nimport numpy as np\nfrom keras.models import load_model\nfrom keras.optimizers import SGD, adam\n\ndef generator(data, batch_size, img_cols, img_rows, img_c... | [
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
edpolanco/air_cargo | [
"20ddf6c72dafed85b87486ca46a9c09656f31d90"
] | [
"analysis.py"
] | [
"\"\"\"Module for summarizing cargo planning testing results.\n\n Ed Polanco\n ed.polanco@outlook.com\n\"\"\"\nimport pandas as pd\nfrom collections import OrderedDict\nimport datetime\nimport time \nfrom aimacode.search import Problem, Node\nfrom timeit import default_timer as timer\nfrom run_search import P... | [
[
"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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