repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
danield137/deep_query_optimzation | [
"01a25c966338007f15d14dea1b37e388e47bcfe3"
] | [
"dqo/query_generator/guided.py"
] | [
"import logging\nimport os\nimport time\nfrom collections import defaultdict\nfrom queue import Queue\nfrom typing import Tuple, Callable, Dict, Optional\n\nimport numpy as np\n\nfrom dqo import log_utils\nfrom dqo.db.clients import DatabaseClient\nfrom dqo.db.models import Database\nfrom dqo.lab.query_executor imp... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StingrayMarineSolutions/amazeing-race | [
"0cb1653a3f3aeb22a81febfd4b32a2f50e5ae9ea"
] | [
"amaze/engine.py"
] | [
"import numpy as np\nimport cv2\n\n\nclass Sprite(object):\n def __init__(self, filenames, offset):\n self.current_frame = 0\n self.width = None\n self.height = None\n self.frames = self.load_frames(filenames)\n self.offset_x, self.offset_y = offset\n\n def load_frames(self,... | [
[
"numpy.abs",
"numpy.zeros_like",
"numpy.any",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
georgearun/ga-learner-dsmp-repo | [
"415a59c818a1dd460f8d7821bf5b68b7d339f49d"
] | [
"Superhero-Stistics/code.py"
] | [
"# --------------\n#Header files\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n#path of the data file- path\r\ndata =pd.read_csv(path,sep=',')\r\ndata['Gender'].replace('-','Agender',inplace=True)\r\ngender_count = data['Gender'].value_counts()\r\n\r\nplt.bar(gender_coun... | [
[
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.pie",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
pierfra-ro/VIP | [
"2b7c683de368d95f13d27a54797d1b15556f9dfc"
] | [
"vip_hci/preproc/derotation.py"
] | [
"#! /usr/bin/env python\n\n\"\"\"\nModule with frame de-rotation routine for ADI.\n\"\"\"\n\n\n__author__ = 'Carlos Alberto Gomez Gonzalez, Valentin Christiaens'\n__all__ = ['cube_derotate',\n 'frame_rotate',\n 'rotate_fft']\n\nfrom astropy.stats import sigma_clipped_stats\nimport numpy as np\nf... | [
[
"numpy.amax",
"numpy.arctan",
"numpy.nan_to_num",
"numpy.fft.fftshift",
"numpy.max",
"numpy.zeros_like",
"numpy.exp",
"numpy.where",
"numpy.real",
"numpy.zeros",
"numpy.rot90",
"numpy.min",
"numpy.isnan",
"numpy.amin",
"numpy.rint",
"numpy.fft.ifft",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nmasahiro/optuna | [
"0b334f1d6405ca59e7ed26fddd95b092750fe59b"
] | [
"examples/pytorch/catalyst_simple.py"
] | [
"\"\"\"\nOptuna example that optimizes multi-layer perceptrons using Catalyst.\n\nIn this example, we optimize the validation accuracy of hand-written digit recognition using\nCatalyst, and FashionMNIST. We optimize the neural network architecture.\n\nYou can run this example as follows, pruning can be turned on an... | [
[
"torch.nn.Dropout",
"torch.nn.Linear",
"torch.nn.CrossEntropyLoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jappa/PyFR | [
"d99120c1db245c7a2a35c72dae51ea72c49efef5",
"d99120c1db245c7a2a35c72dae51ea72c49efef5"
] | [
"pyfr/backends/openmp/types.py",
"pyfr/bases/tensorprod.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport collections\nimport itertools as it\nfrom ctypes import c_void_p\n\nfrom mpi4py import MPI\nimport numpy as np\n\nimport pyfr.backends.base as base\nfrom pyfr.nputil import npaligned\nfrom pyfr.util import ndrange\n\n\nclass OpenMPMatrixBase(base.MatrixBase):\n def __init__(sel... | [
[
"numpy.array",
"numpy.asanyarray",
"numpy.where",
"numpy.dtype"
],
[
"numpy.dot",
"numpy.ix_",
"numpy.prod",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mcassuranceiq/categorical-encoding-public-old | [
"0d62c77a645bba308cb1bfda3e3bd41665a37a5e"
] | [
"category_encoders/ordinal.py"
] | [
"\"\"\"Ordinal or label encoding\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.base import BaseEstimator, TransformerMixin\nimport category_encoders.utils as util\nimport warnings\n\n__author__ = 'willmcginnis'\n\n\nclass OrdinalEncoder(BaseEstimator, TransformerMixin):\n \"\"\"Encodes categoric... | [
[
"pandas.Series"
]
] | [
{
"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": []
}
] |
gerlero/imageio | [
"4b3550ddedf99ecdfa71768d20a4a6251adcbabf"
] | [
"imageio/plugins/_bsdf.py"
] | [
"#!/usr/bin/env python\n# This file is distributed under the terms of the 2-clause BSD License.\n# Copyright (c) 2017-2018, Almar Klein\n\n\"\"\"\nPython implementation of the Binary Structured Data Format (BSDF).\n\nBSDF is a binary format for serializing structured (scientific) data.\nSee http://bsdf.io for more ... | [
[
"numpy.frombuffer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leo-liuzy/neural_persona | [
"de117711dbbcddee1b170b93542e31a68268938e",
"de117711dbbcddee1b170b93542e31a68268938e"
] | [
"cluster/basic/cluster_toy.py",
"scripts/make_reference_corpus_toy.py"
] | [
"from neural_persona.models import BasicL\nfrom neural_persona.common import PROJ_DIR\nfrom neural_persona.common.util import partition_labeling, movies_ontology, variation_of_information, purity\nfrom allennlp.models.archival import load_archive\nfrom sklearn.manifold import TSNE\nfrom sklearn.decomposition import... | [
[
"matplotlib.pyplot.legend",
"matplotlib.cm.colors.get_named_colors_mapping",
"sklearn.manifold.TSNE",
"scipy.stats.describe",
"torch.from_numpy",
"numpy.argmax",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"numpy.isnan",
"matplotlib... | [
{
"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"
... |
shinny-yangyang/perspective | [
"91ade3c19bf9cdd39ce2d019cb92c6fa0d31d724"
] | [
"python/perspective/perspective/tests/table/test_table_datetime.py"
] | [
"################################################################################\n#\n# Copyright (c) 2019, the Perspective Authors.\n#\n# This file is part of the Perspective library, distributed under the terms of\n# the Apache License 2.0. The full license can be found in the LICENSE file.\n#\nimport os\nimport... | [
[
"pandas.Timestamp",
"pandas.DataFrame",
"numpy.datetime64"
]
] | [
{
"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": []
}
] |
cristina-liniuc/ML-model | [
"a53d0b44347eb04f022d0ca89a81717d222f6028"
] | [
"code/train/train.py"
] | [
"import os\nimport argparse\nimport itertools\nimport joblib\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score\nfrom sklearn.model_selection import train_test_split\n\nfrom azureml.core... | [
[
"pandas.read_csv",
"sklearn.metrics.mean_absolute_error",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"sklearn.preprocessing.normalize",
"sklearn.linear_model.LinearRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
AbangLZU/OpenPCDet | [
"c9d31d393acaae34d74cb03bd6ccff9976d3d1f3"
] | [
"pcdet/models/roi_heads/roi_head_template.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ...utils import box_coder_utils, common_utils, loss_utils\nfrom ..model_utils.model_nms_utils import class_agnostic_nms\nfrom .target_assigner.proposal_target_layer import ProposalTargetLayer\n\n\nclass RoIHeadTemplate... | [
[
"torch.nn.Sequential",
"torch.sigmoid",
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.max",
"torch.nn.ReLU",
"torch.nn.functional.cross_entropy",
"torch.no_grad",
"torch.nn.Conv1d",
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rubensmau/ktrain | [
"de32b87b9d3996f35882e7ca18273356c72d342c"
] | [
"ktrain/text/ner/anago/layers.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom ....imports import *\nfrom .... import utils as U\n\n#from keras_contrib.losses import crf_loss\n#from keras_contrib.metrics import crf_marginal_accuracy\n#from keras_contrib.metrics import crf_viterbi_accuracy\n#from keras_contrib.utils... | [
[
"tensorflow.TensorShape",
"tensorflow.gather_nd",
"tensorflow.range",
"tensorflow.slice",
"tensorflow.reduce_logsumexp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
anishgollakota/ArticleBiasChromeExtension | [
"5cc085e02f915c85c3a8e095fb62fb2c4d58e066"
] | [
"reliability/reliable_rnn.py"
] | [
"import pandas as pd\nimport pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom keras.models import Model\nfrom keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\nfrom keras.opti... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
duttashi/valet | [
"25b57db860d5c1abce9f1d8b45b73bc8e8743025"
] | [
"python_3/visuals/test_plots_function.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 7 11:50:39 2021\n\n@author: Ashish\n\"\"\"\n\nfrom matplotlib import pyplot as plt\nimport math\nimport seaborn as sns\nimport pandas as pd\n# create sample\ndf = pd.read_csv('uciml_adult.csv')\n\n# replace all occurence of ? with NA\ndf1 = df.replace(to_replace... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jn1818/pandas-zh-cn-translate | [
"22f0c7e8a6e5ff9e9ae5b424985b2455186a478a"
] | [
"pandas/io/tests/test_sas.py"
] | [
"import pandas as pd\nimport pandas.util.testing as tm\nfrom pandas import compat\nfrom pandas.io.sas import XportReader, read_sas\nimport numpy as np\nimport os\n\n# CSV versions of test XPT files were obtained using the R foreign library\n\n# Numbers in a SAS xport file are always float64, so need to convert\n# b... | [
[
"pandas.io.sas.XportReader",
"pandas.concat",
"pandas.util.testing.get_data_path",
"numpy.dtype",
"pandas.util.testing.assert_frame_equal",
"pandas.io.sas.read_sas"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.20"
],
"scipy": [],
"tensorflow": []
}
] |
usaito/zr-obp | [
"57b4bc79d21301703a2b7e6e0c284a308194a795"
] | [
"obp/dataset/real.py"
] | [
"# Copyright (c) Yuta Saito, Yusuke Narita, and ZOZO Technologies, Inc. All rights reserved.\n# Licensed under the Apache 2.0 License.\n\n\"\"\"Dataset Class for Real-World Logged Bandit Feedback.\"\"\"\nfrom dataclasses import dataclass\nfrom logging import getLogger, basicConfig, INFO\nfrom pathlib import Path\nf... | [
[
"pandas.concat",
"pandas.read_csv",
"scipy.stats.rankdata",
"numpy.arange",
"numpy.int",
"sklearn.preprocessing.LabelEncoder",
"sklearn.utils.check_random_state",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
... |
epfl-dlab/GenIE | [
"62ae6af936c9375c36d3d5ad60401bf579875bd9"
] | [
"genie/constrained_generation/ie_prefix_constraints.py"
] | [
"from collections import deque\nfrom typing import Dict, List\n\nimport numpy as np\nimport torch\n\nfrom .trie import Trie\n\n\ndef get_information_extraction_prefix_allowed_tokens_fn_hf(\n model,\n sentences: List[str],\n subject_token=\"sub\",\n relation_token=\"rel\",\n object_token=\"obj\",\n ... | [
[
"numpy.array",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
a5116638/PaddleNLP | [
"9eb9e23b01d044706c789158ac6cf0d365aea848"
] | [
"examples/machine_translation/transformer/train.py"
] | [
"import os\nimport time\n\nimport yaml\nimport argparse\nimport numpy as np\nfrom pprint import pprint\nfrom attrdict import AttrDict\n\nimport paddle\nimport paddle.distributed as dist\n\nimport reader\nfrom paddlenlp.transformers import TransformerModel, CrossEntropyCriterion\nfrom paddlenlp.utils.log import logg... | [
[
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
minhhaiphys/pymacrospin | [
"af66d68532b71b28a50c8321ea9172267f1f1cf2"
] | [
"pymacrospin/numba/torque.py"
] | [
"#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n# Functions for calculating field-induced and spin-transfer torque\n#\n# pymacrospin Python package\n# Authors: Colin Jermain, Minh-Hai Nguyen\n# Copyright: 2014-2020\n#\n#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>... | [
[
"numpy.cross"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SpidLab/GWAS-Verification | [
"fc949b449f92190a78166bd5a257b8c85f5c324b"
] | [
"Source Code/verifier_computations.py"
] | [
"import csv\nimport pandas as pd\nimport argparse\nimport statsmodels.api as sm\nimport numpy as np\nimport math\nimport warnings\nfrom pandas.core.common import SettingWithCopyWarning\nwarnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n\nparser = argparse.ArgumentParser(description=\"Parame... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"numpy.random.uniform",
"numpy.array",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
asle85/qe-tools | [
"359579ee4a88fb6920270de39f49524e9365fda2"
] | [
"qe_tools/converters/_structure.py"
] | [
"# -*- coding: utf-8 -*-\n\n__all__ = ('get_parameters_from_cell', )\n\nfrom typing import Iterable, Union, Dict, Optional\n\nimport numpy as np\nimport scipy.linalg as la\n\nfrom .. import CONSTANTS\nfrom ..parsers._input_base import _get_cell_from_parameters\n\nCellT = Iterable[Iterable[float]]\nParametersT = Dic... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.allclose",
"scipy.linalg.norm",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
gamaievsky/DescripteursHarmoniques | [
"7a1ec257e47a3d8442c28454d0a436c0f3af8c9a"
] | [
"Estrada.py"
] | [
"import numpy as np\n# import matplotlib.pyplot as plt\nimport pickle\nimport sys\nimport os\nfrom p5 import *\nfrom tkinter import *\nimport tkinter as tk\nfrom tkinter.ttk import *\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\n... | [
[
"numpy.log",
"matplotlib.use",
"matplotlib.pyplot.get_cmap",
"matplotlib.colors.Normalize",
"numpy.cos",
"numpy.tan",
"numpy.sin",
"numpy.mean",
"matplotlib.cm.ScalarMappable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
noonespecial009/resnet-variations | [
"11ee33d1855c292b15930a2a2c1d757d1ac85699"
] | [
"networks/pyramid_deep_expand.py"
] | [
"# https://github.com/dyhan0920/PyramidNet-PyTorch/blob/master/PyramidNet.py\n# https://github.com/drimpossible/Deep-Expander-Networks/blob/master/code/models/densenetexpander_cifar.py\n\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.nn.init as init\nfrom torch.autograd import... | [
[
"torch.nn.Sequential",
"numpy.sqrt",
"torch.Tensor",
"torch.zeros",
"torch.cat",
"torch.randn",
"torch.nn.functional.conv2d",
"torch.nn.Conv2d",
"torch.randperm",
"torch.cuda.FloatTensor",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.BatchNorm2d",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheAustinator/scGSEA | [
"d96b5e60a228c1dc83c36d6a4921f9b68dda2ec5"
] | [
"scgsea/GSEAGroup.py"
] | [
"import logging\nfrom typing import Union\n\nimport pandas as pd\n\nfrom cellforest.CellORM import CellForest\nfrom cellforest.ProcessMethodsSC import ProcessMethodsSC\n\n\nclass GSEAGroup:\n \"\"\"\n\n Args:\n orm: the base orm which is not subset for each constituent group. Includes `partition`\n ... | [
[
"pandas.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
OceanParcels/parcels_efficiency | [
"69fc2ab3282f544ff40207aa13e671c2b424c0d2"
] | [
"stommel_mpi/stommel_mpi_long_1e6.py"
] | [
"from parcels import (FieldSet, ParticleSet, JITParticle,\n Variable, AdvectionRK4)\nimport numpy as np\nimport math\nfrom datetime import timedelta as delta\nimport time as clock\nimport os\nfrom argparse import ArgumentParser\nfrom mpi4py import MPI\n\n\ncomm = MPI.COMM_WORLD\nrank = comm.Get_... | [
[
"numpy.zeros",
"numpy.cos",
"numpy.linspace",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CoMind-Technologies/deepinterpolation | [
"2f583c4fdde4ed92139e40eb8076dd5b129d29d9"
] | [
"examples/paper_generation_code/multi_ophys/multi-network/done/2020-06-15-local_ophys_training_pre_5_post_5_depth_3_feature_32_unet_true.py"
] | [
"import deepinterpolation as de\nimport sys\nfrom shutil import copyfile\nimport os\nfrom deepinterpolation.generic import JsonSaver, ClassLoader\nimport datetime\nfrom typing import Any, Dict\nimport tensorflow\n\ntensorflow.compat.v1.disable_eager_execution()\n\nnow = datetime.datetime.now()\nrun_uid = now.strfti... | [
[
"tensorflow.compat.v1.disable_eager_execution"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mburakbozbey/models | [
"db6aca44d8970606899439d5ff89fc05bcec3cda"
] | [
"official/benchmark/shakespeare_benchmark.py"
] | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.test.main"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sirimullalab/biasNet | [
"e6c0ba8bd272a9d81d26d7b0a0f63f1169168e79"
] | [
"features.py"
] | [
"# ********************************* #\n# Govinda KC #\n# UTEP, Computational Science #\n# Last modified: 8/19/2020 #\n# ********************************* #\n\nimport sys,os,glob\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom rdkit import Chem\nfrom rd... | [
[
"numpy.asarray",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
QuIIL/Noninvasive_Histopathology_MRI | [
"b418ca5cbd507bb813ac9db43b708823a03a957e"
] | [
"labelled_data_preparation/s01_gen_2D_data.py"
] | [
"import re\nfrom glob import glob\nimport os\nimport numpy as np\n\nDENSITY_CHANNELS_MAPPING = {\n 'EPI': 2,\n 'ENUC': 3,\n 'STR': 4,\n 'LUM': 5\n}\n\n\nclass Generate2D:\n def __init__(self, density_type=None, dataset_dir='data/preproc_EESL', dataset_file='', train_ratio=.6):\n \"\"\"\n\n ... | [
[
"numpy.concatenate",
"numpy.load",
"numpy.save",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
allixender/random_raster_gdal_processing_scripts | [
"d180acc19277585e6d99f81a64f2ac1be219a217"
] | [
"first_stats_amz_prode_yearly.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 8 18:21:38 2018\n\n@author: Alexander\n\"\"\"\n\n# Import base libraries\nfrom lcmodel import LC_Initialize, compute_simple_statistics\nimport pandas as pd\nimport numpy as np\n\nimport timeit\n\n# initialise the calculator\n\n\n# raster = \"extract_utm.tif\"\nr... | [
[
"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": []
}
] |
theim101/LandfillEmissionModelling | [
"fb14233cca1c1f98e7a8414c6579ebd7f4ef6462"
] | [
"BB11N_noExchange/make_plotsBB.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 23 13:59:18 2020\n\n@author: theimovaara\n\"\"\"\n\n#Plot measurements in plots\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom Eval_SL_BB11N_DREAM02_longterm import *\nloc_name = 'Braambergen 11Z'\n\n\n\nloc_name = 'Braamber... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bifs/bifs | [
"3d56208b48f9583926ff40c6f832820232d4e958"
] | [
"examples/bifs_cl_2D.py"
] | [
"# A command line type example for using BIFS example\n\nimport numpy as np\nimport imageio\nimport random\nfrom pylab import *\nimport matplotlib.pyplot as plt\nimport bifs\nimport bifs_util.util as bu\n\n# 2D image\n# Load image - standard Lena for now\nim = imageio.imread('../../images/lena512.bmp')\nim = np.asa... | [
[
"matplotlib.pyplot.imshow",
"numpy.log",
"matplotlib.pyplot.title",
"numpy.min",
"numpy.asarray",
"numpy.max",
"matplotlib.pyplot.subplot",
"numpy.random.rand",
"numpy.isscalar",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show",
"numpy.roll"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
papkov/CSBDeep | [
"5624919fa71007bb2258592927e267967c62e25e",
"5624919fa71007bb2258592927e267967c62e25e"
] | [
"csbdeep/utils/plot_utils.py",
"csbdeep/internals/blocks.py"
] | [
"from __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\n\nfrom .utils import normalize\n\n\n\ndef plot_history(history,*keys,**kwargs):\n \"\"\"Plot (Keras) training history ret... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.title",
"numpy.clip",
"numpy.asarray",
"numpy.squeeze",
"numpy.percentile",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.plot",
"numpy.max",
"matplotlib.pyplot.subplot",
"numpy.argmin",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NijatZeynalov/Neural-Machine-Translation-Model | [
"a97d66328bcb936ca5c27c3cfb7ffe27667927ee"
] | [
"data_cleaning.py"
] | [
"#import modules\r\nimport string\r\nimport re\r\nfrom pickle import dump\r\nfrom unicodedata import normalize\r\nfrom numpy import array\r\n\r\n#load doc into memory\r\ndef load_doc(fn):\r\n file = open(fn, mode = 'r', encoding = 'utf-8')\r\n txt = file.read()\r\n file.close()\r\n return txt\r\n\r\n#sp... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goldpiggy/fsdl-text-recognizer-project | [
"573b8a06cf8375aa4e46c2243347b56f451308d5"
] | [
"lab2/text_recognizer/networks/line_cnn_sliding_window.py"
] | [
"import pathlib\nfrom typing import Tuple\n\nfrom boltons.cacheutils import cachedproperty\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Activation, Conv2D, Dense, Dropout, Flatten, Input, MaxPooling2D, Permute, Reshape, TimeDistributed, Lambda, ZeroPadding2D\nfrom tensorflow.ker... | [
[
"tensorflow.keras.layers.Lambda",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.TimeDistributed",
"tensorflow.keras.layers.Conv2D",
"tensorflow.expand_dims",
"tensorflow.squeeze",
"tensorflow.keras.layers.Reshape",
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
verkaik/modflow6-parallel | [
"fac2892612d044634b9527fc7a417530ed43ca8f"
] | [
"autotest/test_gwf_csub_sub03.py"
] | [
"import os\nimport numpy as np\n\ntry:\n import pymake\nexcept:\n msg = 'Error. Pymake package is not available.\\n'\n msg += 'Try installing using the following command:\\n'\n msg += ' pip install https://github.com/modflowpy/pymake/zipball/master'\n raise Exception(msg)\n\ntry:\n import flopy\ne... | [
[
"numpy.abs",
"numpy.ones",
"numpy.genfromtxt",
"numpy.recarray",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amlarraz/deep_sort_yolov3 | [
"e941d57bec977624aafe12825836d66257f9ada9"
] | [
"yolo.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nRun a YOLO_v3 style detection model on test images.\n\"\"\"\n\nimport colorsys\nimport os\nimport random\nfrom timeit import time\nfrom timeit import default_timer as timer ### to calculate FPS\n\nimport numpy as np\nfrom keras import backend as K\nfrom ker... | [
[
"numpy.array",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Liuhongzhi2018/Person_ReID | [
"39151e2f8493221138404e2942afbf03e3afbf08"
] | [
"NAIC_Challenge/NAIC_Person_ReID_DMT/loss/metric_learning.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.autograd\nfrom torch.nn import Parameter\nimport math\n\n\nclass ContrastiveLoss(nn.Module):\n def __init__(self, margin=0.3, **kwargs):\n super(ContrastiveLoss, self).__init__()\n self.margin = margin\n\n def fo... | [
[
"torch.nn.functional.normalize",
"torch.div",
"torch.nn.CrossEntropyLoss",
"torch.mm",
"torch.norm",
"torch.Tensor",
"torch.randn",
"torch.nn.init.xavier_normal_",
"torch.sum",
"torch.FloatTensor",
"torch.sort",
"torch.where",
"torch.nn.init.xavier_uniform_",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caiyueliang/MyAgeGenderEstimate | [
"3f44dde36efe6cc0850b7656fa3e1ac3df7937f0"
] | [
"saved_model.py"
] | [
"import tensorflow as tf\nimport inception_resnet_v1\n\n\nsess = tf.Session()\n\nimages_pl = tf.placeholder(tf.float32, shape=[None, 160, 160, 3], name='input_image')\nimages_norm = tf.map_fn(lambda frame: tf.image.per_image_standardization(frame), images_pl)\ntrain_mode = tf.placeholder(tf.bool)\nage_logits, gende... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.nn.softmax",
"tensorflow.local_variables_initializer",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.image.per_image_standardization",
"tensorflow.Session",
"tensorflow.train.Saver"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mdheller/SemanticDataDictionary | [
"42ed36885b30619af6a5c5ac6d3667d3e10f74d0"
] | [
"sdd2rdf/sdd2setl.py"
] | [
"\nfrom jinja2 import Environment, PackageLoader, select_autoescape\n\nimport pandas as pd\nimport argparse\nimport numpy as np\nimport re\nfrom setlr import isempty\nfrom slugify import slugify\nimport io\nimport magic\n\nbase_context = {\n \"void\" : \"http://rdfs.org/ns/void#\",\n \"csvw\" : \"http://www.w... | [
[
"pandas.read_excel",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
brightway-lca/bw_processing | [
"c765698928700a19979b382b1d8dfa0d5685b8df"
] | [
"tests/test_utils.py"
] | [
"import os\nfrom io import BytesIO\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\n\nfrom bw_processing.errors import InvalidName\nfrom bw_processing.utils import (\n check_name,\n check_suffix,\n dictionary_formatter,\n load_bytes,\n)\n\n\ndef test_load_bytes():\n obj = BytesIO()\n ... | [
[
"numpy.isnan",
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xiaoyequ/models | [
"82bef28a2f5e5201c7ab8002b19500a43cfccf70"
] | [
"official/nlp/xlnet/run_classifier.py"
] | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.distribute.experimental.TPUStrategy",
"numpy.argmax",
"numpy.shape",
"numpy.equal",
"tensorflow.keras.metrics.SparseCategoricalAccuracy",
"tensorflow.version.VERSION.startswith",
"numpy.array",
"tensorflow.distribute.MirroredStrategy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rwgn23/TOBIAS | [
"61ad7418483b2ba1c67ca4f49490da93081fd712"
] | [
"tobias/utils/motifs.py"
] | [
"#!/usr/bin/env python\r\n\r\n\"\"\"\r\nClasses for working with motifs and scanning with moods\r\n\r\n@author: Mette Bentsen\r\n@contact: mette.bentsen (at) mpi-bn.mpg.de\r\n@license: MIT\r\n\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport copy\r\nimport re\r\nimport os\r\n#import matplotlib as mpl\r\n#mpl.use('Agg'... | [
[
"numpy.log",
"numpy.log2",
"numpy.isnan",
"pandas.DataFrame",
"numpy.copy",
"numpy.mean",
"scipy.cluster.hierarchy.linkage",
"numpy.array",
"numpy.sum",
"scipy.cluster.hierarchy.fcluster"
]
] | [
{
"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... |
swdev1202/Pseudo_Lidar_V2 | [
"9ffdba260ce7c3915c71d9057aa2cad96776178d"
] | [
"gdc/gdc.py"
] | [
"'''\nCorrect predicted depthmaps with sparse LiDAR ground-truths\nby Graph-based Depth Correction (GDC)\n\nAuthor: Yurong You\nDate: Feb 2020\n'''\n\nfrom pykdtree.kdtree import KDTree\nfrom scipy.sparse.linalg import LinearOperator\nfrom scipy.sparse.linalg import gmres, cg\nfrom scipy.sparse import eye as seye\n... | [
[
"numpy.logical_not",
"numpy.linalg.solve",
"numpy.radians",
"numpy.sqrt",
"numpy.abs",
"numpy.arcsin",
"scipy.sparse.eye",
"numpy.arange",
"numpy.eye",
"scipy.sparse.csr_matrix",
"numpy.stack",
"numpy.concatenate",
"numpy.full_like",
"numpy.random.permutatio... | [
{
"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"... |
minhlucvan/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 | [
"36b2ddfb2c1467a3c6014e44c05dc4c4eff381ae"
] | [
"env/EnvMultipleStock_validation.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom gym.utils import seeding\nimport gym\nfrom gym import spaces\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport pickle\n\n# shares normalization factor\n# 100 shares per trade\nHMAX_NORMALIZE = 100\n# initial amount of money we have in o... | [
[
"matplotlib.use",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.close",
"numpy.argsort",
"numpy.array",
"numpy.where"
]
] | [
{
"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": []
}
] |
Anychnn/unif | [
"7b25ba0aac28c4e60dc4f9973d4338ad09c94177"
] | [
"uf/application/retro_reader.py"
] | [
"# coding:=utf-8\n# Copyright 2020 Tencent. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the 'License');\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
georgegu1997/DeepIM-PyTorch | [
"cb58761b6e2a64633141949dca576f9f5afd013f"
] | [
"lib/datasets/ycb_video.py"
] | [
"# Copyright (c) 2020 NVIDIA Corporation. All rights reserved.\n# This work is licensed under the NVIDIA Source Code License - Non-commercial. Full\n# text can be found in LICENSE.md\n\nimport torch\nimport torch.utils.data as data\n\nimport os, math, sys\nimport os.path as osp\nfrom os.path import *\nimport numpy ... | [
[
"numpy.matrix",
"torch.sum",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.round",
"numpy.mean",
"numpy.random.randn",
"numpy.where",
"numpy.divide",
"numpy.random.randint",
"numpy.clip",
"numpy.reshape",
"numpy.matmul",
"torch.from_numpy",
"numpy.copy",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sroet/openpathsampling | [
"72fedad9ba8bc60d17c7cc73c641129898d5d530"
] | [
"openpathsampling/experimental/simstore/storable_functions.py"
] | [
"import collections\nimport warnings\nimport inspect\nimport types\n\nimport numpy as np\n\nfrom .tools import none_to_default\nfrom .callable_codec import CallableCodec\nfrom .serialization_helpers import get_uuid, has_uuid, default_find_uuids\nfrom .class_info import ClassInfo\nfrom .serialization_helpers import ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hakanyi/hmr | [
"2e113fba7c295904adc5967c29ceba9904f94a1e"
] | [
"src/util/renderer.py"
] | [
"\"\"\"\nRenders mesh using OpenDr for visualization.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport cv2\n\nfrom opendr.camera import ProjectPoints\nfrom opendr.renderer import ColoredRenderer\nfrom opendr.light... | [
[
"numpy.dot",
"numpy.hstack",
"numpy.radians",
"numpy.ones_like",
"numpy.min",
"numpy.issubdtype",
"numpy.cos",
"numpy.ones",
"numpy.round",
"numpy.sin",
"numpy.all",
"numpy.max",
"numpy.mean",
"numpy.load",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
usc-isi-i2/maa-analysis | [
"8d9351d9b5a438c0c8824e273f4fb71615484a81"
] | [
"analysis/convert_analysis_files_to_kgtk_edge.py"
] | [
"import pandas as pd\nimport json\nimport csv\n\n\n# p_ath = '/Users/amandeep/Github/maa-analysis/MAA_Datasets/v3.2.0'\n# edge_file_labels_descriptions = 'wikidata-20200803-all-edges-for-V3.2.0_KB-nodes-property-counts-with-labels-and-descriptions.tsv.gz'\n# subgraph_sorted = 'wikidata_maa_subgraph_sorted_2.tsv'\n#... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
driedler/tflite-micro-rpi0 | [
"54aa26681a3e38f43a9898060d43d45e87d43407"
] | [
"tensorflow/lite/micro/python/test_interpreter.py"
] | [
"import os \nimport sys \nimport numpy as np\n\ncurdir = os.path.dirname(os.path.abspath(__file__))\n\nbuild_dir = os.path.normpath(f'{curdir}/../../../../build/tensorflow/lite/micro/')\n\nsys.path.append(curdir)\nsys.path.append(build_dir)\n\ntry:\n from .interpreter import Interpreter\nexcept:\n from interp... | [
[
"numpy.copyto",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
heartbeatmaker/real-dog-breed-classifier | [
"1797ec0fba2411ae12975d72dcb06beea3ed2c6c"
] | [
"model/yolo/yolo.py"
] | [
"# (c) https://github.com/qqwweee/keras-yolo3\n\nimport colorsys\nimport os\n\nfrom keras import backend as K\nfrom keras.layers import Input\nfrom keras.models import load_model\nfrom keras.utils import multi_gpu_model\nimport numpy as np\nfrom PIL import Image, ImageFont, ImageDraw\n\nfrom .yolo_model import yolo... | [
[
"numpy.expand_dims",
"numpy.random.seed",
"numpy.random.shuffle",
"numpy.floor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wknoben/pysumma | [
"e118139c64f7c4c4d00e7ec80da869a935637987"
] | [
"pysumma/simulation.py"
] | [
"import os\nimport copy\nimport shutil\nimport subprocess\nimport numpy as np\nimport xarray as xr\nfrom pathlib import Path\nfrom typing import List\n\nfrom .decisions import Decisions\nfrom .file_manager import FileManager\nfrom .output_control import OutputControl\nfrom .global_params import GlobalParams\nfrom .... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pjpieper/RPI3-HandRecog | [
"d9dc2abb2077a02275475b4c174f04ed6ae13076"
] | [
"handrecog.py"
] | [
"#######################################################################\n# Program: Handwriting Recognition CNN\n# Date: 4/20/21\n# Author: Paul Pieper\n# Dependencies: OpenCV, Tensorflow, Numpy, Sensehat, Picamera\n# Model: Provided via: https://keras.io/examples/vision/mnist_convnet/\n#\n# Desc: After startup,... | [
[
"tensorflow.keras.models.load_model",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
jjhong922/scvi-tools | [
"0f860bf10d039a05a28484e02989d39c20173f30"
] | [
"scvi/model/base/_utils.py"
] | [
"import logging\nimport os\nimport pickle\nimport warnings\nfrom collections.abc import Iterable as IterableClass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom anndata import AnnData, read\n\nfrom scvi._compat import Literal\nfrom scvi.utils import Differen... | [
[
"pandas.concat",
"numpy.array_equal",
"torch.load",
"numpy.asarray",
"pandas.DataFrame",
"numpy.genfromtxt",
"numpy.zeros_like",
"numpy.argsort",
"numpy.array"
]
] | [
{
"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": []
}
] |
rfleiro/gati-lab | [
"13f514acc81623667308eeecd0ed4f399dcae576"
] | [
"static/pdf/methods/linear_fourier_filter_exercise.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Linear_fourier_filter_exercise.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/1KQlihNHOyF1_p6he0IWGrdA23wIfoUvA\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.misc import ascent... | [
[
"numpy.fft.fft2",
"matplotlib.pyplot.imshow",
"numpy.ones_like",
"numpy.linspace",
"scipy.misc.face",
"numpy.fft.fftshift",
"numpy.arctan2",
"matplotlib.pyplot.show"
]
] | [
{
"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"
... |
LuanaPereira/ComputacaoGrafica | [
"617f45d43199b9f68952e4a0d19f238df38b4bd3",
"617f45d43199b9f68952e4a0d19f238df38b4bd3"
] | [
"SuavizacaoMediana.py",
"ConvolucaoAtv.py"
] | [
"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\npath = \"arcoiris.jpg\"\nimg = cv2.imread(path)\nh, w = img.shape[:2]\n\ncv2.imshow(\"IMG Original \", img)\nimgNova = np.zeros((h, w, 3), np.uint8)\n#dimensao = 7 --> matriz 7x7\ndimensao = 11\nraio = int(dimensao - (dimensao/2)+1)\n\nfor i i... | [
[
"numpy.median",
"numpy.zeros"
],
[
"numpy.array",
"numpy.sum",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
willyh101/caiman_online | [
"c0718ad768b4737ff8988613896910066bee9bda"
] | [
"caiman_online/plot.py"
] | [
"\"\"\"\nCode for basic plotting. Uses matplotlib and seaborn.\n\"\"\"\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib.gridspec import GridSpec\nimport numpy as np\nimport seaborn as sns\nfrom .statistics import traces_ci\n\nmpl.rcParams['figure.constrained_layout.use'] = True\nmpl.rcPar... | [
[
"matplotlib.pyplot.gca",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.close",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thomasccp/eagle | [
"b50570ebaa60a9c6a02a3b9b1d32d9308a5809bd",
"b50570ebaa60a9c6a02a3b9b1d32d9308a5809bd"
] | [
"eagle/predictors/infer.py",
"eagle/predictors/__main__.py"
] | [
"# Copyright 2020 Samsung Electronics 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 applicabl... | [
[
"torch.load",
"torch.DoubleTensor",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.exp"
],
[
"torch.nn.KLDivLoss",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"torch.load",
"torch.utils.data.DataLoader",
"torch.nn.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jyuv/scipy | [
"8fa4dcae4553fa873c5553e018f80a8a6f5e9948"
] | [
"scipy/stats/tests/test_axis_nan_policy.py"
] | [
"# Many scipy.stats functions support `axis` and `nan_policy` parameters.\n# When the two are combined, it can be tricky to get all the behavior just\n# right. This file contains a suite of common tests for scipy.stats functions\n# that support `axis` and `nan_policy` and additional tests for some associated\n# fun... | [
[
"numpy.squeeze",
"scipy.stats._stats_py._broadcast_concatenate",
"numpy.mean",
"numpy.moveaxis",
"numpy.random.default_rng",
"numpy.testing.assert_equal",
"scipy.stats.kruskal",
"numpy.reshape",
"numpy.testing.suppress_warnings",
"numpy.finfo",
"numpy.full",
"scipy.... | [
{
"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": []
}
] |
SeventeenChen/Python_Speech_SZY | [
"0074ad1d519387a75d5eca42c77f4d6966eb0a0e",
"0074ad1d519387a75d5eca42c77f4d6966eb0a0e"
] | [
"Chapter2_TimeFrequency_ShortTime/pr2_4_0.py",
"Chapter6_VoiceActivityDetection/VAD.py"
] | [
"# 短时傅里叶变换\n\nfrom Universal import *\nfrom enframe import enframe\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef STFT(x, win, nfft, inc):\n\t\"\"\"\n\t计算语音信号的短时傅里叶变换\n\t:param x:\n\t:param win:\n\t:param nfft:\n\t:param inc:\n\t:return D : complex matrix\n\t\"\"\"\n\tD = librosa.stft(x, n_fft=nfft, ho... | [
[
"numpy.abs",
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.subplot",
"numpy.hanning",
"matplotlib.pyplot.xlabel",
"numpy.angle",
"matplotlib.pyplot.show",
"matplotlib.pyplot.xticks",
... | [
{
"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",
... |
fletp/COVID_Electric_Grid_Impacts | [
"4785e68d5d0d2e346af83143d257175ed853cdd1"
] | [
"src/data/initial_parsing.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 13 21:09:03 2020\n\n Initial cleaning/parsing of dataset\n\n@author: joshuageiser\n\"\"\"\n\nimport os\nimport pandas as pd\nimport numpy as np\n\n\ndef get_io_filepaths():\n '''\n Returns a list containing tuples of input filepat... | [
[
"pandas.read_csv",
"numpy.nan_to_num",
"pandas.DataFrame",
"numpy.full",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Kapil19-dev/MultiRespDL | [
"79aa114c4a0de063222f77155c702221008a843c"
] | [
"Dayi_Bian/rr_extration.py"
] | [
"import numpy as np\nimport scipy\ndef extremas_extraction(signal):\n '''\n Input -- Respiratory signal\n Output -- Average breathing duration and relevent extremas.\n\n Description -- This function takes the respiratory signal as an argument\n and then by using count advance algorithm... | [
[
"scipy.signal.find_peaks",
"numpy.percentile",
"numpy.sort",
"numpy.concatenate",
"numpy.append",
"numpy.diff",
"numpy.argmin",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
ivannz/TTmodule | [
"e894d9a0e6d22c847d485b8072d4baec90601fbf"
] | [
"ttmodule/layers.py"
] | [
"import math\n\nimport torch\nimport torch.nn.functional as F\n\nfrom numpy import prod\nfrom torch.nn import init\n\nfrom .matrix import tt_to_matrix\n\n\nclass TTLinear(torch.nn.Module):\n \"\"\"Tensor-Train linear layer, proposed in [1]_.\n\n References\n ----------\n .. [1] Novikov, A., Podoprikhin,... | [
[
"torch.tensordot",
"torch.nn.init.uniform_",
"torch.Tensor",
"torch.nn.init.normal_",
"numpy.prod",
"torch.nn.functional.linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Panlichen/pg_travel | [
"41ec6666a945e34dc974f1206c0e620704e75a06"
] | [
"pg_travel/deeprm/agent/A2C.py"
] | [
"import numpy as np\r\nimport torch\r\nfrom pg_travel.deeprm.hparams import HyperParams as Hp\r\n\r\n\r\nhp = Hp()\r\n\r\n\r\ndef get_returns(all_ret):\r\n all_ret = torch.Tensor(all_ret)\r\n norm_rets = (all_ret - all_ret.mean()) / all_ret.std()\r\n return norm_rets\r\n\r\n\r\ndef train_critic(critic, all... | [
[
"torch.LongTensor",
"torch.Tensor",
"numpy.arange",
"numpy.random.shuffle",
"torch.distributions.Categorical",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ankostis/wltp | [
"c95462cadbcab32d4fc94f8ea8bf9d85a0a3763e"
] | [
"Notebooks/RunVehicle.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.3.4\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n#... | [
[
"pandas.set_option",
"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": []
}
] |
METASPACE2020/sm-standalone | [
"14b8ec9edb45254322cb3445df8d07a3f1ea0565"
] | [
"colourmaps.py"
] | [
"def viridis_colormap():\n import numpy as np\n colors = np.array([(68, 1, 84), (68, 2, 85), (68, 3, 87), (69, 5, 88), (69, 6, 90), (69, 8, 91), (70, 9, 92), (70, 11, 94), (70, 12, 95), (70, 14, 97), (71, 15, 98), (71, 17, 99), (71, 18, 101), (71, 20, 102), (71, 21, 103), (71, 22, 105), (71, 24, 106), (72, 25... | [
[
"matplotlib.colors.LinearSegmentedColormap",
"numpy.array",
"matplotlib.cm.register_cmap"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cdeldon/cartesio | [
"a07d378b4752f7050d9194778863e27089220a67"
] | [
"cartesio/bbox/_iou_helper.py"
] | [
"\"\"\"Module containing helper functions for IOU computations\n\"\"\"\nimport numpy as np\n\nfrom ..core import jitted\n\n__all__ = [\n \"_intersection_bb_size\",\n]\n\n\n@jitted\ndef _intersection_bb_size(\n bb_0: np.ndarray,\n bb_1: np.ndarray,\n) -> np.ndarray:\n \"\"\"Computes the size of the inter... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
waheedrafiq/waheedrafiq.github.io | [
"f36b9eef2bc392fc4ceecc7be44c3685919523b2"
] | [
"dproj/OpenCV/bRm/DistanceTest1.py"
] | [
"\n# distance check\n\n\nimport numpy as np\nimport cv2\nimport glob\n\n# termination criteria\ncriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)\n\n# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)\nobjp = np.zeros((6*7,3), np.float32)\nobjp[:,:2] = np.mgrid[0:7,0:6].T.r... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NMZ0429/sample_pip | [
"2e03b6101bc90f53f0c3d5c5c45f89621fc77e65"
] | [
"namazu/acc.py"
] | [
"import torch\nfrom torchmetrics import Metric\n\n\nclass ACC(Metric):\n def __init__(self, dist_sync_on_step=False):\n # call `self.add_state`for every internal state that is needed for the metrics computations\n # dist_reduce_fx indicates the function that should be used to reduce\n # stat... | [
[
"torch.tensor",
"torch.sum",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ajaymota/vu-thesis-nn-eigendecomposition | [
"25bea073cf2e3b8b9764ae8a76dba3e752fc55e6"
] | [
"mnist/mnist_classifier_sgmd.py"
] | [
"# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.random.RandomState",
"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": []
}
] |
Albert-Ma/bert-fine-tuned-gain | [
"f752c1182f1c800f5f56998e13fd6115929df655"
] | [
"contextual-repr-analysis/tests/models/tagger/conllx_pos_tagging_test.py"
] | [
"import numpy\nimport pytest\nfrom allennlp.common.checks import ConfigurationError\n\nfrom contexteval.common.model_test_case import ModelTestCase\nfrom contexteval.models import PairwiseTagger # noqa: F401\n\n\nclass TestConllXPosTagging(ModelTestCase):\n def setUp(self):\n super(TestConllXPosTagging, ... | [
[
"numpy.sum",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pablocarb/appoptdes | [
"7c95f53683e9caf1709ee829f83a4e95445ae8a0"
] | [
"synbioCollections.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nsynbioParts (c) University of Manchester 2019\n\nsynbioParts is licensed under the MIT License.\n\nTo view a copy of this license, visit <http://opensource.org/licenses/MIT/>.\n\nCreated on Fri Oct 18 13:38:19 2019\n\n@author: Pablo Carbonell, SYNBIOCHEM\n@... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lidawei0124/ISD_yolo_dual | [
"a4617a6ad20b3988f3b422df7a1b8533e32e241b"
] | [
"utils/metrics.py"
] | [
"# Model validation metrics\n\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\n\nfrom . import general\n\n\ndef fitness(x):\n # Model fitness as a weighted combination of metrics\n w = [0.0, 0.0, 0.6, 0.4] # weights for [P, R, mAP@0.5, mAP@0.5:0.95][0.0, 0.0, 0.... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"numpy.unique",
"matplotlib.pyplot.subplots",
"numpy.stack",
"numpy.concatenate",
"numpy.where",
"numpy.interp",
"torch.where",
"torch.stack",
"numpy.argsort",
"numpy.flip",
"numpy.zeros",
"numpy.sum",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pick56/DL-Experiments | [
"3375b0c1121d5e14255f08a5160aa0c1554bea44",
"3375b0c1121d5e14255f08a5160aa0c1554bea44"
] | [
"Experiment_8(NNLM)/ptb/reader.py",
"Experiment_1/helloworld.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.strided_slice",
"tensorflow.control_dependencies",
"tensorflow.gfile.GFile",
"tensorflow.train.range_input_producer",
"tensorflow.reshape",
"tensorflow.identity",
"tensorflow.name_scope",
"tensorflow.assert_positive",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
erez-aharonov/DeepBugs | [
"edf3d1eb3d52b9c60629ed060273dbcf2066d9a3"
] | [
"python/Util.py"
] | [
"'''\nCreated on Oct 26, 2017\n\n@author: Michael Pradel\n'''\n\nfrom scipy.spatial.distance import cosine\nimport random\nimport json\n\n\ndef in_group_similarity(vector_group):\n vector_group = list(vector_group)\n in_group_simil = 0.0\n in_group_ctr = 0\n for i in range(0, len(vector_group)):\n ... | [
[
"scipy.spatial.distance.cosine"
]
] | [
{
"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"
... |
lifebit-ai/nf-core-clinvap | [
"640a626b013d4d2396fcf9e295ece1e1b68a3451"
] | [
"bin/process_metadata.py"
] | [
"#!/usr/bin/env python3\n\nimport json\nimport sys\nimport pandas as pd\nimport copy\nimport os\nimport math\nfrom operator import itemgetter\nimport re\n\n\n# PROCESS METADATA\n\ndef get_field(key, metadata):\n \"\"\"Process metadata function.\"\"\"\n val = metadata.get(key, \"null\")\n if not val:\n ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
kennedyCzar/UNSUPERVISED-ML | [
"4e1464fed220075f7bce2f7c3130b52b1f9fabd5"
] | [
"LOGISTIC REGRESSION/logisticregression.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jun 30 18:25:46 2019\n\n@author: kenneth\n\"\"\"\nimport numpy as np\n\nclass Logistic():\n def __init__(self):\n return\n \n #classification metrics\n '''\n Actual\n +ve ... | [
[
"numpy.square",
"numpy.dot",
"numpy.random.permutation",
"numpy.average",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Andrew-DungLe/cornac | [
"199ab9181f8b6387cc8748ccf8ee3e5c9df087fb",
"199ab9181f8b6387cc8748ccf8ee3e5c9df087fb"
] | [
"cornac/models/pmf/recom_pmf.py",
"cornac/models/vbpr/vbpr.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\n@author: Aghiles Salah\n\"\"\"\n\nimport numpy as np\nimport scipy.sparse as sp\nimport pmf\nfrom ..recommender import Recommender\nfrom ...utils.generic_utils import sigmoid\nfrom ...utils.generic_utils import map_to\nfrom ...utils.generic_utils import intersects\nfrom ...except... | [
[
"scipy.sparse.find",
"numpy.asarray",
"numpy.ones",
"numpy.full",
"numpy.concatenate",
"numpy.array"
],
[
"torch.optim.Adam",
"torch.sigmoid",
"torch.randn",
"torch.sum",
"torch.from_numpy",
"torch.tensor",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
FragLegs/mrnet | [
"a05eb8902463cbcf88126c616911bd9f69d019df"
] | [
"scripts/plot_model_metrics.py"
] | [
"# -*- coding: utf-8 -*-\nimport argparse\nimport logging\nimport os\n\nimport pandas as pd\nimport seaborn as sns\n\n\nlog = logging.getLogger(__name__)\n\n\ndef load_data(model):\n dfs = []\n for series in ['axial', 'coronal', 'sagittal']:\n for diagnosis in ['abnormal', 'acl', 'meniscus']:\n ... | [
[
"pandas.DataFrame.from_records",
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
AliOsamaHassan/NLP-Cube | [
"c759633eccbeb46b4390d9b7584db2548d4a9a20"
] | [
"_cube/generic_networks/crf.py"
] | [
"#\n# Author: Tiberiu Boros\n#\n# Copyright (c) 2019 Adobe Systems Incorporated. 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/lic... | [
[
"numpy.ones",
"numpy.argmax",
"numpy.random.randn",
"numpy.equal",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdwalker62/Log-Diagnostics-Archive | [
"50f898435901e130d9f78059a6fc243a51ad8701"
] | [
"preprocessing/Word2Vec.py"
] | [
"import joblib\nimport tensorflow as tf\nimport os\nimport re\nimport tqdm\nimport io\nimport matplotlib.pyplot as plt\n\nfrom tensorflow.random import log_uniform_candidate_sampler as negative_skipgrams\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dot, Embedding, Flatten\nfrom te... | [
[
"tensorflow.keras.losses.CategoricalCrossentropy",
"tensorflow.concat",
"tensorflow.constant",
"matplotlib.pyplot.scatter",
"tensorflow.keras.layers.Embedding",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.expand_dims",
"matplotlib.pyplot.savefig",
"tensorflow.kera... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Joxis/VKD | [
"803388ac92fcf8c58c11d93b7a65a2d85ba78fca"
] | [
"data/misc.py"
] | [
"import functools\nimport os\nfrom argparse import Namespace\nimport numpy as np\nfrom PIL import Image\nfrom torchvision import transforms as T\n\nfrom data import temporal_transforms as TT\n\n\ndef pil_loader(path):\n # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/... | [
[
"numpy.asarray",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NegatioN/lightfm | [
"b7fd48d13d3e9f1b3672320056773fc8395470f4"
] | [
"lightfm/lightfm.py"
] | [
"# coding=utf-8\nfrom __future__ import print_function\n\nimport numpy as np\n\nimport scipy.sparse as sp\n\nfrom ._lightfm_fast import (\n CSRMatrix,\n FastLightFM,\n fit_bpr,\n fit_logistic,\n fit_warp,\n fit_warp_kos,\n predict_lightfm,\n predict_ranks,\n)\n\n__all__ = [\"LightFM\"]\n\nCY... | [
[
"numpy.ones_like",
"numpy.array_equal",
"numpy.array_equiv",
"numpy.int32",
"scipy.sparse.csr_matrix",
"numpy.savez_compressed",
"scipy.sparse.identity",
"numpy.zeros_like",
"numpy.load",
"numpy.array",
"numpy.random.RandomState",
"numpy.zeros",
"numpy.sum"
]
... | [
{
"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"... |
tianbingsz/SVRG | [
"c0ce832f05e02565969d2db12413ffb6e7a325fa"
] | [
"sandbox/vime/envs/double_pendulum_env_x1.py"
] | [
"import numpy as np\nfrom rllab.envs.box2d.parser import find_body\n\nfrom rllab.core.serializable import Serializable\nfrom rllab.envs.box2d.box2d_env import Box2DEnv\nfrom rllab.misc import autoargs\nfrom rllab.misc.overrides import overrides\nimport pdb\n\n\n# http://mlg.eng.cam.ac.uk/pilco/\nclass DoublePendulu... | [
[
"numpy.asarray",
"numpy.linalg.norm",
"numpy.cos",
"numpy.sin",
"numpy.random.randn",
"numpy.random.rand",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eyalbd2/LanguageModel-UsingRL | [
"b3467430be102ba23b1ab7e7a9515496c140f524"
] | [
"test_all_modells.py"
] | [
"\nimport os\nimport logging\nimport numpy as np\nimport torch\nimport argparse\n\nfrom libbots import data, model\nfrom model_test import run_test, run_test_mutual, run_test_preplexity, run_test_cosine\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nif __name__ == '__main__':\n p... | [
[
"torch.LongTensor",
"numpy.random.RandomState",
"torch.cuda.is_available",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GitHub30/weakly_supervised_control | [
"998034581710d148f7576a720383633e39b84a09"
] | [
"scripts/train_wsc.py"
] | [
"# Copyright 2020 The Weakly-Supervised 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 b... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rdno/pyasdf | [
"9748f0538075a8f9f884ff247e198e0689d10faa"
] | [
"doc/examples/process_observed.py"
] | [
"import obspy\nfrom obspy.core.util.geodetics import gps2DistAzimuth\nimport numpy as np\n\nfrom pyasdf import ASDFDataSet\n\nds = ASDFDataSet(\"./observed.h5\")\n\nevent = ds.events[0]\n\norigin = event.preferred_origin() or event.origins[0]\nevent_latitude = origin.latitude\nevent_longitude = origin.longitude\n\n... | [
[
"numpy.require"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yarikoptic/hdmf | [
"bab66f33d8fb8e715fccd105a27c28f4097034ba"
] | [
"tests/unit/utils_test/test_core_DataIO.py"
] | [
"import unittest2 as unittest\n\nfrom hdmf.data_utils import DataIO\nfrom hdmf.container import Data\nimport numpy as np\nfrom copy import copy, deepcopy\n\n\nclass DataIOTests(unittest.TestCase):\n\n def setUp(self):\n pass\n\n def tearDown(self):\n pass\n\n def test_copy(self):\n obj... | [
[
"numpy.all",
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MORE-EU/matrixprofile | [
"7c598385f7723f337d7bf7d3f90cffb690c6b0df"
] | [
"tests/test_statistics.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nrange = getattr(__builtins__, 'xrange', range)\n# end of py2 compatability boilerplate\n\nimport os\n\nimport py... | [
[
"numpy.arange",
"numpy.array",
"numpy.testing.assert_almost_equal",
"numpy.testing.assert_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dagisky/CBM_NEWSQA | [
"c8c0c132b096e83d9774570daa606fb2d0ea9d06"
] | [
"Utils/utils.py"
] | [
"import sys\nimport torch\nimport numpy as np\nimport time\nimport tensorflow as tf\nfrom io import BytesIO\nimport scipy.misc\n\n\nclass TimeMeter: \n\n def __init__(self):\n \"\"\"Counts time duaration\"\"\"\n self.start_time, self.duration, self.counter=0. ,0. ,0.\n\n def start(self):\n ... | [
[
"tensorflow.summary.FileWriter",
"torch.ones",
"tensorflow.compat.v1.Summary.Value",
"numpy.min",
"torch.zeros",
"torch.sqrt",
"torch.reshape",
"numpy.max",
"tensorflow.Summary.Value",
"numpy.prod",
"tensorflow.HistogramProto",
"tensorflow.Summary",
"numpy.histo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Muyun99/dydetection | [
"473880db339a1b141cce07eeca754a49eaaa2c42"
] | [
"tools/torch_utils.py"
] | [
"import cv2\nimport math\nimport torch\nimport random\nimport numpy as np\nimport os\nimport mmcv\nimport time\nimport pandas as pd\n\nimport torch.nn.functional as F\n\nfrom torch.optim.lr_scheduler import LambdaLR\n\ndef set_seed(cfg):\n seed = cfg.random_seed\n random.seed(seed)\n np.random.seed(seed)\n... | [
[
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.no_grad",
"numpy.mean",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pinshuai/pyvista | [
"131d780a6eadcbe051e2af2e1637433424319792"
] | [
"examples/01-filter/glyphs.py"
] | [
"\"\"\"\n.. _glyph_example:\n\nPlotting Glyphs (Vectors)\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nUse vectors in a dataset to plot and orient glyphs/geometric objects.\n\"\"\"\n\n# sphinx_gallery_thumbnail_number = 4\nimport pyvista as pv\nfrom pyvista import examples\nimport numpy as np\n\n###################################... | [
[
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amygreen/hw5 | [
"63ea7090ac2130a78ec00caedbc1bf8bde1b369d"
] | [
"visual_stim_data.py"
] | [
"import numpy as np\r\nimport random\r\nimport xarray as xr\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport seaborn as sns\r\n\r\n\r\nclass VisualStimData:\r\n \"\"\"\r\n Data and methods for the visual stimulus ePhys experiment.\r\n The data table itself is held in self.data, an `xarra... | [
[
"numpy.random.random",
"numpy.linspace",
"numpy.arange",
"pandas.DataFrame",
"numpy.random.uniform",
"matplotlib.pyplot.show"
]
] | [
{
"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": []
}
] |
mmtrebuchet/tfmodisco | [
"94882792d9a381efa9b1977a02cfb61e2790f270"
] | [
"modisco/tfmodisco_workflow/workflow.py"
] | [
"from __future__ import division, print_function, absolute_import\nfrom collections import defaultdict, OrderedDict, Counter\nimport numpy as np\nimport itertools\nimport time\nimport sys\nimport h5py\nimport json\nfrom . import seqlets_to_patterns\nfrom .. import core\nfrom .. import coordproducers\nfrom .. import... | [
[
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hazemalsaied/manzur | [
"747219e9d7b1f99d891e575300edef14f30b297a"
] | [
"manzur_utils/manzur_utils/wordpress_scrapper.py"
] | [
"import pandas as pd\nimport requests\n\n\ndef scrap_posts(site_name):\n api_url = f'https://{site_name}/wp-json/wp/v2/posts?page='\n for i in range(1, 40000):\n response = requests.get(api_url + str(i))\n if not posts.empty and len(posts) % 200 == 0:\n posts.to_pickle('posts.pkl')\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": []
}
] |
tcaram/astroquery | [
"f6f397c5638c081e9a9cadad42827f8a961ca338"
] | [
"astroquery/alma/tests/test_alma.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport numpy as np\nimport os\nimport pytest\nfrom ...utils.testing_tools import MockResponse\nfrom ...exceptions import (InvalidQueryError)\n\nfrom .. import Alma\n\nDATA_DIR = os.path.join(os.path.dirname(__file__), 'data')\n\n\ndef data_path(filen... | [
[
"numpy.testing.assert_approx_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ykawashima/exojax | [
"67d1b6c868d69892d4bbf9e620ed05e432cfe61f"
] | [
"src/exojax/spec/moldb.py"
] | [
"\"\"\"Molecular database (MDB) class\n\n * MdbExomol is the MDB for ExoMol\n * MdbHit is the MDB for HITRAN or HITEMP \n \n\"\"\"\nimport numpy as np\nimport jax.numpy as jnp\nimport pathlib\nfrom exojax.spec import hapi, exomolapi, exomol\nfrom exojax.spec.hitran import gamma_natural as gn\nimport pandas a... | [
[
"numpy.hstack",
"numpy.log",
"numpy.ones_like",
"numpy.min",
"numpy.unique",
"numpy.concatenate",
"numpy.max",
"numpy.searchsorted",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KonstantinosNikopoulos/Undergraduate-Projects | [
"f2525357da8e6d0717bb170076f1a95aa191d397"
] | [
"Computational_Intelligence_path/RBF_on_MNIST_with_sklearn/RBF.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n#Author: Konstantinos Nikopoulos\n\nfrom __future__ import print_function\nimport keras\nimport datetime as dt\nfrom sklearn.gaussian_process.kernels import PairwiseKernel\nfrom sklearn.cluster import KMeans\nfrom sklearn.decomposition import PCA\nfrom sklearn.gaussian_pro... | [
[
"sklearn.gaussian_process.kernels.PairwiseKernel",
"sklearn.gaussian_process.GaussianProcessRegressor",
"sklearn.decomposition.PCA",
"sklearn.cluster.KMeans"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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