repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
Manny27nyc/intel-extension-for-pytorch | [
"b40faedf6b00d520f6483d519d2e82bce0a6c0d1",
"b40faedf6b00d520f6483d519d2e82bce0a6c0d1"
] | [
"intel_extension_for_pytorch/optim/_functional.py",
"tests/cpu/test_layer_norm.py"
] | [
"r\"\"\"Functional interface, port from torch/optim/_function.py\"\"\"\nimport torch\nfrom torch import Tensor\nfrom typing import List, Optional\n\ndef is_master_weight(param, params_attr):\n return (\n param.dtype == torch.float and\n param in params_attr and\n 'bf16_param' in params_attr... | [
[
"torch.ops.torch_ipex.adagrad_fused_step",
"torch.empty_like",
"torch.ops.torch_ipex.sgd_fused_step",
"torch.enable_grad",
"torch.Tensor",
"torch.ops.torch_ipex.packed_add",
"torch.zeros",
"torch.clone",
"torch.ops.torch_ipex.lamb_fused_step",
"torch.sparse_coo_tensor",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sedurCode/seld-dcase2021 | [
"f8e09dbbbb5ac7d6ae0b82083f1a11b013c5dd51"
] | [
"seld.py"
] | [
"#\n# A wrapper script that trains the SELDnet. The training stops when the early stopping metric - SELD error stops improving.\n#\n\nimport os\nimport sys\nimport numpy as np\nimport cls_feature_class\nimport cls_data_generator\nfrom cls_compute_seld_results import ComputeSELDResults, reshape_3Dto2D\nimport keras_... | [
[
"numpy.zeros",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jongwookyi/Mask_RCNN | [
"9a26fa067a2087dbdf07f21a43dc2aa872ffe059"
] | [
"mrcnn/model.py"
] | [
"\"\"\"\nMask R-CNN\nThe main Mask R-CNN model implementation.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport os\nimport datetime\nimport re\nimport math\nfrom collections import OrderedDict\nimport multiprocessing\nimpor... | [
[
"tensorflow.control_dependencies",
"tensorflow.reduce_sum",
"tensorflow.image.non_max_suppression",
"tensorflow.minimum",
"tensorflow.keras.layers.Conv2DTranspose",
"numpy.where",
"tensorflow.keras.optimizers.SGD",
"tensorflow.keras.layers.Lambda",
"tensorflow.keras.backend.int... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
ictnlp/Dual-Path | [
"8c4577236908797ede1d971c11c2b3ef247e3469",
"8c4577236908797ede1d971c11c2b3ef247e3469"
] | [
"examples/simultaneous_translation/utils/functions.py",
"fairseq/modules/dual_path.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\n\n\ndef exclusive_cumprod(tensor, dim: int, eps: float = 1e-10):\n \"\"\"\n Implementing exclusive cumprod.\n ... | [
[
"torch.ones",
"torch.nn.functional.conv1d",
"torch.exp",
"torch.log",
"torch.arange",
"torch.cumsum"
],
[
"torch.full",
"torch.ones",
"torch.cummax",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LucasAlegre/stable-baselines3 | [
"6b598323ae070bb0a998d25230f6e11eca4cbe61",
"6b598323ae070bb0a998d25230f6e11eca4cbe61"
] | [
"stable_baselines3/dqn/policies.py",
"tests/test_save_load.py"
] | [
"from typing import Any, Dict, List, Optional, Type\n\nimport gym\nimport torch as th\nfrom torch import nn\n\nfrom stable_baselines3.common.policies import BasePolicy, register_policy\nfrom stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp\nfrom stable_base... | [
[
"torch.nn.Sequential"
],
[
"torch.allclose",
"numpy.allclose",
"torch.rand_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
srihari-humbarwadi/adain-tensorflow2.x | [
"c0da16e4d39d5316683ed0988787aedbb1c9768c",
"c0da16e4d39d5316683ed0988787aedbb1c9768c"
] | [
"adain/learning_rate_schedule.py",
"adain/dataset_utils/create_tfrecords.py"
] | [
"import tensorflow as tf\r\n\r\n\r\nclass InverseDecay(tf.optimizers.schedules.LearningRateSchedule):\r\n def __init__(self, initial_learning_rate, decay_rate):\r\n super(InverseDecay, self).__init__()\r\n\r\n self.initial_learning_rate = initial_learning_rate\r\n self.decay_rate = decay_rat... | [
[
"tensorflow.cast"
],
[
"tensorflow.io.gfile.GFile",
"tensorflow.image.decode_image"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
andrewgryan/bokeh-playground | [
"aeab70627a5ccd7f210c354098d30bdf92bb553f"
] | [
"grove/main.py"
] | [
"import argparse\nimport bokeh.plotting\nimport bokeh.models\nimport bokeh.palettes\nimport bokeh.colors\nimport cartopy\nimport numpy as np\nimport netCDF4\n\n\nGOOGLE = cartopy.crs.Mercator.GOOGLE\nPLATE_CARREE = cartopy.crs.PlateCarree()\n\n\ndef parse_args(argv=None):\n parser = argparse.ArgumentParser()\n ... | [
[
"numpy.ma.copy",
"numpy.asarray",
"numpy.ma.abs",
"numpy.argsort",
"numpy.ma.where"
]
] | [
{
"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": []
}
] |
PTPeraire/openprotein | [
"3f6ede8c63d18f14e938bd47935001a82c4d6897"
] | [
"experiments/tmhmm3/tm_models.py"
] | [
"\"\"\"\nThis file is part of the OpenProtein project.\n\nFor license information, please see the LICENSE file in the root directory.\n\"\"\"\n\nimport sys\nfrom enum import Enum\nimport glob\nimport pickle\nimport numpy as np\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport openprote... | [
[
"torch.nn.init.uniform",
"torch.LongTensor",
"torch.ones",
"torch.max",
"torch.nn.functional.log_softmax",
"torch.zeros",
"torch.eq",
"torch.manual_seed",
"torch.nn.utils.rnn.pad_sequence",
"torch.cuda.LongTensor",
"torch.from_numpy",
"torch.nn.Linear",
"numpy.f... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
THUYimingLi/Open-sourced_Dataset_Protection | [
"910962c57e7d132497443b26c8e5da1dcb5ba4eb",
"910962c57e7d132497443b26c8e5da1dcb5ba4eb",
"910962c57e7d132497443b26c8e5da1dcb5ba4eb"
] | [
"GTSRB/train_standard_vgg.py",
"CIFAR/tools.py",
"GTSRB/train_watermarked_vgg.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n'''\nThis is the implement of standard training on GTSRB dataset.\n\nCopyright (c) Yiming Li, 2020\n'''\n\n\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport shutil\nimport time\nimport random\nimport torch\nimport torch.nn as nn\nimport... | [
[
"torch.nn.CrossEntropyLoss",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all",
"torch.nn.DataParallel",
"torch.save"
],
[
"numpy.array"
],
[
"torch.nn.CrossEntropyLoss",
"torch.max",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iosurodri/Fancy_aggregations | [
"647019452a074767706893ecdd431a3ee503b554"
] | [
"Fancy_aggregations/moderate_deviations.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nFile containing different functions to aggregate data using Moderate Deviations. The expressions have been obtained from the following paper:\n\nA.H. Altalhi, J.I. Forcén, M. Pagola, E. Barrenechea, H. Bustince, Zdenko Takáč,\nModerate deviation and restricted equivalence functions... | [
[
"numpy.sqrt",
"numpy.power",
"numpy.sort",
"numpy.transpose",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jprzywoski/faster-python | [
"44252bf0a746dd862d752efbe2012a8a404ec7bf"
] | [
"ceuclid.py"
] | [
"import ctypes\nfrom numpy.ctypeslib import ndpointer\n\nlib = ctypes.cdll.LoadLibrary('./libdist.so')\nfn = lib.dist\nfn.restype = ctypes.c_double\nfn.argtypes = [\n ndpointer(ctypes.c_double),\n ndpointer(ctypes.c_double),\n ctypes.c_size_t\n]\n\n\ndef dist(x, y):\n return fn(x, y, len(x))\n"
] | [
[
"numpy.ctypeslib.ndpointer"
]
] | [
{
"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": [],
... |
jieyanzhu/codes-effective-computation-in-physics | [
"0c99f2da9d462229e6b174a010d7c7b08af4482b"
] | [
"chap_7/decay_plot.py"
] | [
"import numpy as np\n\n# as in the previous example, load decays.csv into a NumPy array\ndecaydata = np.loadtxt('decays.csv', delimiter=',', skiprows=1)\n\n# provide handles for the x and y columns\ntime = decaydata[:,0]\ndecays = decaydata[:,1]\n\n# import the matplotlib plotting functionality\nimport pylab as plt... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vuk119/RL | [
"2f5309bfff719b2965060492a19d008ed8382856"
] | [
"Easy21/plot_cuts.py"
] | [
"\"\"\"\n\nSome useful plot functions\n\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef matrix_surf(m, xlimits=None, ylimits=None, **kwargs):\n if xlimits is None:\n xlimits = [0, m.shape[0]]\n if ylimits is None:\n ylimits = [0, m.shape[1]]\n\n Y, X = np.meshgrid(np.a... | [
[
"numpy.arange",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zmlabe/StratoVari | [
"c5549f54482a2b05e89bded3e3b0b3c9faa686f3",
"c5549f54482a2b05e89bded3e3b0b3c9faa686f3"
] | [
"Scripts/plot_ProfileVar_Monthly_FDR.py",
"Scripts/plot_ProfileVar_Monthly_100yrPeriods_STD.py"
] | [
"\"\"\"\nPlot vertical plots of PAMIP data for each month from November to April using\nthe ensemble mean (300)\n\nNotes\n-----\n Author : Zachary Labe\n Date : 26 June 2019\n\"\"\"\n\n### Import modules\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport datetime\nimport read_MonthlyData as MO\nim... | [
[
"matplotlib.pyplot.contourf",
"matplotlib.pyplot.minorticks_off",
"matplotlib.pyplot.rc",
"numpy.nanmean",
"numpy.where",
"matplotlib.pyplot.gca",
"numpy.reshape",
"numpy.arange",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.figur... | [
{
"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"
... |
DEVESHTARASIA/tensorflow | [
"d3edb8c60ed4fd831d62833ed22f5c23486c561c",
"d3edb8c60ed4fd831d62833ed22f5c23486c561c",
"d3edb8c60ed4fd831d62833ed22f5c23486c561c",
"d3edb8c60ed4fd831d62833ed22f5c23486c561c",
"5828e285209ff8c3d1bef2e4bd7c55ca611080d5",
"f9e536b4a9d96322d7e971073602c8969dbd9369",
"5828e285209ff8c3d1bef2e4bd7c55ca611080d... | [
"tensorflow/contrib/keras/python/keras/models_test.py",
"tensorflow/contrib/keras/python/keras/applications/inception_v3.py",
"tensorflow/python/ops/rnn_cell_impl.py",
"tensorflow/compiler/tests/ftrl_test.py",
"tensorflow/compiler/tests/xla_test.py",
"tensorflow/python/training/adam.py",
"tensorflow/pyt... | [
"# Copyright 2016 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.contrib.keras.python.keras.optimizers.RMSprop",
"numpy.random.random",
"tensorflow.contrib.keras.python.keras.models.Model",
"tensorflow.contrib.keras.python.keras.layers.RepeatVector",
"tensorflow.contrib.keras.python.keras.models.load_model",
"tensorflow.contrib.keras.python.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": []... |
sparks-baird/modnet | [
"2b4a88aa8a3323756b6daee52450569cddd0068b",
"2b4a88aa8a3323756b6daee52450569cddd0068b"
] | [
"modnet/matbench/benchmark.py",
"modnet/featurizers/featurizers.py"
] | [
"import os\nfrom collections import defaultdict\nfrom traceback import print_exc\nfrom typing import List, Dict, Any, Optional, Tuple, Type\n\nimport numpy as np\n\nfrom modnet.preprocessing import MODData\nfrom modnet.models import MODNetModel\nfrom modnet.utils import LOG\nfrom modnet.hyper_opt import FitGenetic\... | [
[
"sklearn.metrics.roc_auc_score",
"sklearn.preprocessing.OneHotEncoder",
"numpy.abs",
"sklearn.model_selection.KFold"
],
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"... |
lippman1125/pytorch_FAN | [
"ffc9c968478d55cb0c75c062bb8774923f961110"
] | [
"utils/evaluation.py"
] | [
"from __future__ import absolute_import, print_function\n\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom random import randint\n\nfrom .misc import *\nfrom .transforms import transform, transform_preds\n\n__all__ = ['accuracy', 'AverageMeter']\n\n\ndef get_preds(scores):\n ''' get predic... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"numpy.arange",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"numpy.zeros",
"numpy.sum",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StanfordASL/MATS | [
"b31a86eb56728fc6025c71c7202ab425b078e3e5"
] | [
"mats/model/components/gmm2d.py"
] | [
"import torch\nimport torch.distributions as td\n\n\nclass GMM2D(td.MixtureSameFamily):\n def __init__(self, mixture_distribution, component_distribution):\n super(GMM2D, self).__init__(mixture_distribution, component_distribution)\n\n def mode_mode(self):\n mode_k = torch.argmax(self.mixture_di... | [
[
"torch.distributions.MixtureSameFamily",
"torch.distributions.MultivariateNormal",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emacip/incubator-superset | [
"83ee9178328c5193808fe356ceb3090a299477f6"
] | [
"superset/db_engine_specs.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... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ppmdatix/rtdl | [
"a01ecd9ae6b673f4e82e51f804ffd7031c7350a0"
] | [
"lib/metrics.py"
] | [
"import typing as ty\n\nimport numpy as np\nimport scipy.special\nimport sklearn.metrics as skm\n\nfrom . import util\n\n\ndef calculate_metrics(\n task_type: str,\n y: np.ndarray,\n prediction: np.ndarray,\n classification_mode: str,\n y_info: ty.Optional[ty.Dict[str, ty.Any]],\n) -> ty.Dict[str, fl... | [
[
"numpy.round",
"sklearn.metrics.roc_auc_score",
"sklearn.metrics.classification_report",
"sklearn.metrics.mean_squared_error"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gonsp/LotterySampling | [
"92ff14f602c05d747708b522cf05b9f9066c43e0"
] | [
"test/streams.py"
] | [
"import itertools\nimport math\nimport numpy as np\nfrom abc import abstractmethod\nfrom io import TextIOWrapper\nfrom sorted_list import SortedList\n\n\nclass Stream():\n\n def __init__(self, length, save=True):\n self.length = length\n self.N = 0\n self.n = 0\n self.save = save\n ... | [
[
"numpy.random.seed",
"numpy.random.zipf",
"numpy.random.permutation",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CosmosHua/Deformable-ConvNets | [
"6aeda878a95bcb55eadffbe125804e730574de8d",
"6aeda878a95bcb55eadffbe125804e730574de8d"
] | [
"fpn/operator_py/fpn_roi_pooling.py",
"fpn/core/rcnn.py"
] | [
"# --------------------------------------------------------\n# Deformable Convolutional Networks\n# Copyright (c) 2017 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Modified by Haozhi Qi, Yuwen Xiong\n# --------------------------------------------------------\n\nimport mxnet as mx\nimport... | [
[
"numpy.hstack",
"numpy.sqrt",
"numpy.fromstring",
"numpy.where",
"numpy.zeros"
],
[
"numpy.hstack",
"numpy.minimum",
"numpy.random.choice",
"numpy.ones",
"numpy.round",
"numpy.append",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shaheen19/FAIR | [
"345c23b3d35918729e7aa49ecb39047494c48a6e",
"345c23b3d35918729e7aa49ecb39047494c48a6e"
] | [
"fair/forward.py",
"tests/reproduction/reproduction_test.py"
] | [
"from __future__ import division\n\nimport inspect\nimport numpy as np\nimport warnings\nfrom scipy.optimize import root\nfrom .ancil import natural, cmip6_volcanic, cmip6_solar, historical_scaling\nfrom .constants import molwt, lifetime, radeff\nfrom .constants.general import M_ATMOS, ppm_gtc\nfrom .defaults impor... | [
[
"numpy.min",
"numpy.asarray",
"numpy.squeeze",
"scipy.optimize.root",
"numpy.tile",
"numpy.ones",
"numpy.max",
"numpy.copy",
"numpy.isscalar",
"numpy.exp",
"numpy.array",
"numpy.zeros",
"numpy.sum"
],
[
"numpy.allclose",
"numpy.arange",
"numpy.si... | [
{
"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"
... |
douglasdaly/spot-robot | [
"7a4fdd7eb5fe5fc2d31180ed6b9f7ea21647bea2"
] | [
"src/squad/graphs.py"
] | [
"from typing import Any, Dict, List, Optional, Tuple, Type, Union, overload\n\nimport numpy as np\n\nfrom squad.exceptions import (\n EdgeAlreadyExists,\n EdgeNotFound,\n NodeAlreadyExists,\n NodeNotFound,\n)\n\n\nclass Node:\n \"\"\"\n Single node in a graph.\n \"\"\"\n\n def __init__(self,... | [
[
"numpy.delete",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xdjiangkai/ColossalAI | [
"4a3d3446b04065fa1c89b78cba673e96115c6325",
"4a3d3446b04065fa1c89b78cba673e96115c6325"
] | [
"colossalai/nn/optimizer/lamb.py",
"tests/test_trainer/test_pipeline/model/resnet.py"
] | [
"\"\"\"\nAdapted from the pytorch-lamb library at https://github.com/cybertronai/pytorch-lamb\n\"\"\"\n\nimport torch\nfrom torch.optim import Optimizer\n\nfrom colossalai.registry import OPTIMIZERS\n\n\n@OPTIMIZERS.register_module\nclass Lamb(Optimizer):\n r\"\"\"Implements Lamb algorithm.\n It has been prop... | [
[
"torch.zeros_like"
],
[
"torch.nn.init.constant_",
"torch.flatten",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JunCEEE/hummingbird | [
"0b1bdf5023b92090f31d9bc857e0854a805cf2cd",
"0b1bdf5023b92090f31d9bc857e0854a805cf2cd",
"0b1bdf5023b92090f31d9bc857e0854a805cf2cd"
] | [
"scripts/flash/plot_hitscores.py",
"src/utils/reader.py",
"src/plotting/image.py"
] | [
"#!/usr/bin/env python\nimport h5py\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport sys\n\nrunnr = int(sys.argv[1])\nfilename = '/asap3/flash/gpfs/bl1/2017/data/11001733/processed/hummingbird/r%04d_ol1.h5' %runnr\nwith h5py.File(filename, 'r') as f:\n hitsco... | [
[
"matplotlib.use",
"matplotlib.pyplot.figure"
],
[
"numpy.round"
],
[
"numpy.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheoPantaz/Control-of-robotic-vehicle-via-brain-activity | [
"4cae5a69503659581f510c748f59f045d1f2b145"
] | [
"real_time.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan 17 18:06:40 2020\n\n@author: Kokkinos\n\nlines for telnet communication: 31,32,136,139,149,152,201,204,212,215,296\n\"\"\"\nfrom threading import Thread\n\nimport numpy as np\nimport scipy.io as sio\nfrom pylsl import StreamInlet, resolve_stream\nfrom tkinter imp... | [
[
"numpy.delete",
"scipy.io.savemat",
"numpy.array",
"numpy.where",
"numpy.random.randint"
]
] | [
{
"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"... |
Katarina11/PreSumm | [
"616e72f038d512e9e9112af375d66a0b2e3db6cd"
] | [
"src/models/trainer_ext.py"
] | [
"import os\n\nimport numpy as np\nimport torch\nfrom tensorboardX import SummaryWriter\n\nimport distributed\nfrom models.reporter_ext import ReportMgr, Statistics\nfrom others.logging import logger\nfrom others.utils import test_rouge, rouge_results_to_str\n\n\ndef _tally_parameters(model):\n n_params = sum([p.... | [
[
"numpy.argsort",
"torch.save",
"torch.no_grad",
"torch.nn.BCELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
knowledgevis/large_image | [
"ab5c213d3a68de8a2144707fc0dc1115d1e4664f"
] | [
"test/test_source_gdal.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport glob\nimport json\nimport numpy\nimport os\nimport PIL.Image\nimport PIL.ImageChops\nimport pytest\nimport six\n\nfrom large_image import constants\nfrom large_image.exceptions import TileSourceException\n\nimport large_image_source_gdal\n\nfrom . import utilities\n\n\ndef _assert... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shikisawamura/nnabla-examples | [
"070d25078ad3d5458744dbfd390cdd926e20e573",
"baf4e4cc620dedbf4368683325c0fb868676850d",
"baf4e4cc620dedbf4368683325c0fb868676850d",
"baf4e4cc620dedbf4368683325c0fb868676850d"
] | [
"GANs/stargan/generate.py",
"mnist-collection/dcgan.py",
"shape-reconstruction/implicit-geometric-regularization/datasets.py",
"object-detection/yolov2/yolov2_detection.py"
] | [
"# Copyright (c) 2019 Sony Corporation. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requir... | [
[
"numpy.asarray",
"numpy.array"
],
[
"numpy.random.randn"
],
[
"numpy.asarray",
"numpy.arange",
"numpy.random.RandomState",
"scipy.spatial.cKDTree"
],
[
"numpy.clip",
"numpy.ones",
"numpy.genfromtxt",
"numpy.array",
"numpy.where",
"numpy.random.Random... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
OminiaVincit/qphase-trans | [
"40e0c078dcd74282e8d8f44690433bf670bff8cb"
] | [
"source/draw_ising_ph.py"
] | [
"import sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nimport os\nimport time\nimport argparse\nfrom visual_utils import generate_listcol\nimport seaborn as sns\n\ndef calculate_npent(death_scales):\n sd = np.sum(death_scales)\n npent = 0\n for d in death_scales:\n dr = d/sd\n... | [
[
"numpy.log",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.show",
"numpy.sum",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
blutooth/dgp | [
"bedbbc3595fbe124d7a06c3d6d64f9009304491e"
] | [
"GPflow/test_gplvm.py"
] | [
"from __future__ import print_function\nimport kernels\nimport numpy as np\nimport unittest\nimport gplvm\n\nclass TestBayesianGPLVM(unittest.TestCase):\n def setUp(self):\n N = 10 # number of data points\n D = 1 # latent dimensions\n M = 5 # inducings points\n R = 2 # data dimension... | [
[
"numpy.expand_dims",
"numpy.linspace",
"numpy.ones",
"numpy.random.RandomState",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
astromaddie/pywavelets-py3 | [
"9d434929cb748eb44be86a4b712d8f3009326693",
"9d434929cb748eb44be86a4b712d8f3009326693"
] | [
"demo/wp_scalogram.py",
"pywt/tests/test__pywt.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport pywt\n\n\nx = np.linspace(0, 1, num=512)\ndata = np.sin(250 * np.pi * x**2)\n\nwavelet = 'db2'\nlevel = 4\norder = \"freq\" # other option is \"normal\"\ninterpolation = 'nearest'\ncmap = plt.cm.cool\n\... | [
[
"numpy.linspace",
"numpy.sin",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"numpy.arange",
"numpy.testing.run_module_suite",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pronton2001/carla_pilotnet | [
"813ca14e04eccd405fde5fff350fe23c6ada5657"
] | [
"pilotnet/viz_gamepy.py"
] | [
"#!/usr/bin/env python\n\n# Copyright (c) 2019 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see <https://opensource.org/licenses/MIT>.\n\n# Allows controlling a vehicle with a keyboard. For a simpler an... | [
[
"numpy.reshape",
"tensorflow.expand_dims",
"numpy.dtype",
"tensorflow.image.resize",
"numpy.float32",
"numpy.array",
"numpy.zeros",
"numpy.fabs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hkennyv/pandas | [
"31875eb3d8a56f359c2f529f86b867572d5dfeb1",
"31875eb3d8a56f359c2f529f86b867572d5dfeb1"
] | [
"pandas/tests/indexes/timedeltas/test_timedelta.py",
"pandas/tests/indexes/datetimes/test_insert.py"
] | [
"from datetime import timedelta\n\nimport numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n Index,\n Int64Index,\n Series,\n Timedelta,\n TimedeltaIndex,\n array,\n date_range,\n timedelta_range,\n)\nimport pandas._testing as tm\n\nfrom ..datetimelike ... | [
[
"pandas.timedelta_range",
"pandas.offsets.Hour",
"pandas._testing.assert_numpy_array_equal",
"pandas.TimedeltaIndex",
"pandas.Series",
"pandas.Timestamp",
"pandas._testing.makeTimedeltaIndex",
"pandas.Index",
"pandas.DataFrame",
"numpy.timedelta64",
"pandas.date_range",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adipasquale/frontmatter-analysis | [
"068b8870ee35569a81600f637569ad589087e2a8"
] | [
"fma/analyze.py"
] | [
"import pandas as pd\nimport os\nfrom pathlib import Path\nimport frontmatter\nimport argparse\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"path\", help=\"path containing .md files\")\n args = parser.parse_args()\n data = [frontmatter.load(path).metadata... | [
[
"pandas.option_context",
"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": []
}
] |
kevinmtian/pytorchvideo | [
"168e16859a6029ef8ebeb476f9163bebb6c6b87d",
"168e16859a6029ef8ebeb476f9163bebb6c6b87d",
"168e16859a6029ef8ebeb476f9163bebb6c6b87d",
"168e16859a6029ef8ebeb476f9163bebb6c6b87d"
] | [
"pytorchvideo/models/memory_bank.py",
"tests/test_layers_convolutions.py",
"pytorchvideo/models/hub/efficient_x3d_mobile_cpu.py",
"tests/test_accelerator_efficient_blocks_mobile_cpu_head_layer.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\nimport math\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom pytorchvideo.layers.utils import set_attributes\n\n\nclass MemoryBank(nn.Module):\n \"\"\"\n Performs Non-Parametri... | [
[
"torch.nn.functional.normalize",
"torch.div",
"torch.randint",
"torch.zeros",
"torch.einsum",
"torch.nn.functional.cross_entropy",
"torch.mul",
"torch.no_grad",
"torch.rand"
],
[
"torch.manual_seed",
"torch.nn.Conv3d",
"torch.no_grad",
"torch.rand",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
fxdmhtt/airflow | [
"cf88f7bc7bbd3e9bf110e98f025759a96c130235"
] | [
"tests/contrib/operators/test_hive_to_dynamodb_operator.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# 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... | [
[
"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": []
}
] |
ziko1305/Hidden-Markov-Based-Mathematical-Model | [
"0ad906e6c4f99ad91d4047aed78df49399447633",
"0ad906e6c4f99ad91d4047aed78df49399447633",
"0ad906e6c4f99ad91d4047aed78df49399447633",
"0ad906e6c4f99ad91d4047aed78df49399447633",
"0ad906e6c4f99ad91d4047aed78df49399447633"
] | [
"Ingredient_Extractor/with_unknown_words_concideration/with_various_accuracies_on_first_layer/mean_values_1.py",
"2nd_order_HMM/with_unknown_words_concideration/6/code.py",
"Ingredient_Extractor/with_unknown_words_concideration/with_100%_accuracy_on_first_layer/mean_values.py",
"Ingredient_Extractor/with_unkn... | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 20 09:49:40 2020\r\n\r\n@author: Mehdi\r\n\"\"\"\r\n\r\nimport numpy as np\r\n\r\na1=np.nanmean([table_11.loc['A'].accuracy,table_12.loc['A'].accuracy,table_13.loc['A'].accuracy,table_14.loc['A'].accuracy,\r\n table_15.loc['A'].accuracy,table_16.loc... | [
[
"numpy.mean",
"numpy.nanmean"
],
[
"numpy.log",
"numpy.ones",
"numpy.concatenate",
"numpy.argmax",
"numpy.where",
"sklearn.metrics.f1_score",
"numpy.zeros",
"numpy.sum"
],
[
"numpy.mean"
],
[
"numpy.log",
"numpy.ones",
"numpy.concatenate",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
emarche/Value-based-DeepRL | [
"8b6458d4b82f293b401fc9e9c81cc482e0948830"
] | [
"DuelingDQN/agent.py"
] | [
"\"\"\"DuelingDQN agent script \n\nThis manages the training phase of the off-policy DuelingDQN.\n\"\"\"\n\nimport random\nfrom collections import deque\n\nimport yaml\nimport numpy as np\n\nwith open('config.yml', 'r') as ymlfile:\n cfg = yaml.load(ymlfile, Loader=yaml.FullLoader)\n seed = cfg['setup']['seed... | [
[
"tensorflow.math.subtract",
"tensorflow.math.argmax",
"tensorflow.gather_nd",
"numpy.random.seed",
"tensorflow.math.reduce_mean",
"tensorflow.GradientTape",
"tensorflow.keras.optimizers.Adam",
"numpy.argmax",
"numpy.mean",
"tensorflow.math.square",
"numpy.random.uniform... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JakobGM/polars | [
"fe10d4a180e59e5e34f4ab17303f12f1cd64e6c8"
] | [
"py-polars/polars/utils.py"
] | [
"import ctypes\nimport os\nimport sys\nfrom datetime import date, datetime, timedelta, timezone\nfrom pathlib import Path\nfrom typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type, Union\n\nimport numpy as np\n\nfrom polars.datatypes import DataType, Date, Datetime\n\nif sys.version_info >= (3,... | [
[
"numpy.ctypeslib.as_array"
]
] | [
{
"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": [],
... |
Dapid/scipy | [
"dde07a64407ffaa9442b3d8298c6c26ff91fb384"
] | [
"scipy/stats/stats.py"
] | [
"# Copyright (c) Gary Strangman. All rights reserved\n#\n# Disclaimer\n#\n# This software is provided \"as-is\". There are no expressed or implied\n# warranties of any kind, including, but not limited to, the warranties\n# of merchantability and fitness for a given application. In no event\n# shall Gary Strangma... | [
[
"numpy.deprecate",
"numpy.dot",
"numpy.nanmedian",
"numpy.expand_dims",
"numpy.sqrt",
"numpy.ma.masked_less",
"numpy.asarray",
"scipy.special.chdtrc",
"numpy.cumsum",
"numpy.all",
"numpy.seterr",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.any... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cs-gn/tpu | [
"4727594874e8587a60cb088627d46f73a1769823",
"4727594874e8587a60cb088627d46f73a1769823"
] | [
"models/experimental/mnist_keras_ds/mnist.py",
"models/experimental/distribution_strategy/imagenet_input_keras.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.contrib.cluster_resolver.TPUClusterResolver",
"numpy.random.random",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.datasets.mnist.load_data",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.logging.set_verbosity",
"numpy.z... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
... |
byu-dml/d3m-profiler | [
"9a3bc45061267091b0109f2159648785e370a18b"
] | [
"example.py"
] | [
"import numpy as np\nimport multiprocessing as mp\nimport pathlib as pl\nimport pandas as pd\nimport pickle\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import SVC as SupportVectorClassifier\nfrom sklearn.metrics import accuracy_score, f1_score\nfrom sklearn.model_selection import train_... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.metrics.f1_score",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
YunongPan/swin_gui | [
"52adc917d3413781e76609d021c6a2579fdf44d1"
] | [
"mmdet/datasets/coco.py"
] | [
"import itertools\nimport logging\nimport os.path as osp\nimport tempfile\nfrom collections import OrderedDict\n\nimport mmcv\nimport numpy as np\nimport pycocotools\nfrom mmcv.utils import print_log\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom terminaltables import AsciiTable... | [
[
"numpy.round",
"numpy.array",
"numpy.zeros",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SmirnovKol/Paddle | [
"a3730dc87bc61593514b830727e36e5d19e753cd",
"a3730dc87bc61593514b830727e36e5d19e753cd"
] | [
"python/paddle/nn/layer/conv.py",
"python/paddle/fluid/tests/unittests/test_fused_multi_transformer_op.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 ... | [
[
"numpy.prod"
],
[
"numpy.random.random",
"numpy.ones",
"numpy.concatenate",
"numpy.random.rand",
"numpy.testing.assert_allclose",
"numpy.zeros",
"numpy.tril"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
serazing/xscale | [
"a804866aa6f6a5a0f293a7f6765ea17403159134"
] | [
"xscale/signal/tests/test_fitting.py"
] | [
"# Python 2/3 compatibility\nfrom __future__ import absolute_import, division, print_function\nimport xarray as xr\nimport numpy as np\nimport pandas as pd\nimport xscale.signal.fitting as xfit\n\ndef test_polyfit():\n\tNt, Nx, Ny = 100, 128, 128\n\trand = xr.DataArray(np.random.rand(Nt, Nx, Ny), dims=['time', 'x',... | [
[
"numpy.array_equal",
"numpy.cos",
"numpy.sin",
"numpy.ones",
"numpy.random.rand",
"pandas.date_range",
"numpy.zeros"
]
] | [
{
"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": []
}
] |
OlegBezverhii/python-notebooks | [
"5d4b501173a2f3519bff9a085c3d2190ce6cf808"
] | [
"webcams/eye_status.py"
] | [
"import os\nfrom PIL import Image\nimport numpy as np\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D\nfrom tensorflow.keras.layers import AveragePooling2D\nfrom tensorflow.keras.layers import Flatten\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.mod... | [
[
"tensorflow.keras.layers.AveragePooling2D",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.models.model_from_json",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.layers.Flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
KustomApe/ksie | [
"d6f97d0298d04d06788563546c66ff50c6bb2d31",
"d6f97d0298d04d06788563546c66ff50c6bb2d31"
] | [
".history/spider/pokemon_spider_20201213130808.py",
".history/spider/pokemon_spider_20201213130913.py"
] | [
"from selenium import webdriver\nimport pandas as pd\nimport time\n\n\"\"\"[注意事項]\nrobot.txtを必ず読んで、ルールに沿った形でクローリングするように気をつけてください。\nあくまで自己責任でお願いできればと思います。\n\"\"\"\n\n\"\"\"[Initial Setting]\n初期設定\n\"\"\"\noptions = webdriver.ChromeOptions()\noptions.add_argument('--headeless')\noptions.add_argument('--disable-gpu')\... | [
[
"pandas.Series",
"pandas.DataFrame"
],
[
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
sumanmichael/lightning-flash | [
"4c69c1bf49fa74d0f2fdb9c4dbdcdfd5942352db",
"4c69c1bf49fa74d0f2fdb9c4dbdcdfd5942352db"
] | [
"flash/core/data/data_module.py",
"tests/image/segmentation/test_model.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.cuda.is_available",
"torch.utils.data.DataLoader",
"numpy.random.shuffle"
],
[
"torch.jit.save",
"torch.jit.load",
"torch.Size",
"torch.randint",
"numpy.ones",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Webbah/sec-for-reinforcement-learning | [
"19db622dce4963d25cb1b6e4ae12ddf98b6d27d2",
"19db622dce4963d25cb1b6e4ae12ddf98b6d27d2"
] | [
"OMG/env/random_load.py",
"OMG/plot_dessca_load.py"
] | [
"import numpy as np\nimport pandas as pd\n\nfrom openmodelica_microgrid_gym.util import RandProcess\n\n\n\nclass RandomLoad:\n def __init__(self, train_episode_length: int, ts: float, rand_process: RandProcess, loadstep_time: int = None,\n load_curve: pd.DataFrame = None, bounds=None, bounds_std=... | [
[
"numpy.random.normal",
"numpy.random.randint"
],
[
"matplotlib.pyplot.legend",
"pandas.read_pickle",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"numpy.ar... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
kurtamohler/numpy | [
"73157efcd17da95ce984d1595ac4907233b9dbf5",
"73157efcd17da95ce984d1595ac4907233b9dbf5"
] | [
"numpy/core/tests/test_datetime.py",
"numpy/core/tests/test_array_coercion.py"
] | [
"\nimport numpy\nimport numpy as np\nimport datetime\nimport pytest\nfrom numpy.testing import (\n assert_, assert_equal, assert_raises, assert_warns, suppress_warnings,\n assert_raises_regex,\n )\nfrom numpy.compat import pickle\n\n# Use pytz to test out various time zones if available\ntry:\n from pyt... | [
[
"numpy.true_divide",
"numpy.can_cast",
"numpy.compat.pickle.loads",
"numpy.minimum",
"numpy.datetime_as_string",
"numpy.isnat",
"numpy.dtype",
"numpy.zeros_like",
"numpy.iinfo",
"numpy.busday_offset",
"numpy.busday_count",
"numpy.negative",
"numpy.bool_",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Andruxin52rus/openvino | [
"769bb7709597c14debdaa356dd60c5a78bdfa97e",
"769bb7709597c14debdaa356dd60c5a78bdfa97e",
"769bb7709597c14debdaa356dd60c5a78bdfa97e",
"769bb7709597c14debdaa356dd60c5a78bdfa97e",
"769bb7709597c14debdaa356dd60c5a78bdfa97e",
"769bb7709597c14debdaa356dd60c5a78bdfa97e",
"769bb7709597c14debdaa356dd60c5a78bdfa97... | [
"model-optimizer/mo/front/kaldi/extractors/normalize_component_ext.py",
"model-optimizer/extensions/ops/cumsum.py",
"ngraph/python/tests/test_ngraph/test_convolution.py",
"model-optimizer/mo/front/kaldi/loader/loader_test.py",
"model-optimizer/extensions/ops/gathernd.py",
"model-optimizer/extensions/front... | [
"\"\"\"\n Copyright (C) 2018-2020 Intel 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... | [
[
"numpy.sqrt"
],
[
"numpy.flip",
"numpy.cumsum"
],
[
"numpy.arange",
"numpy.array",
"numpy.allclose",
"numpy.ones"
],
[
"numpy.array"
],
[
"numpy.array",
"numpy.zeros",
"numpy.prod"
],
[
"numpy.float64"
],
[
"numpy.array",
"numpy.array... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
cindychu/LargeScaleComputing_S20 | [
"913b0155f47914c258b503df677067a510dd23f5"
] | [
"Labs/Lab 1 Midway RCC and mpi4py/mpi_rand_walk.py"
] | [
"\nfrom mpi4py import MPI\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport time\n\ndef sim_rand_walks_parallel(n_runs):\n # Get rank of process and overall size of communicator:\n comm = MPI.COMM_WORLD\n rank = comm.Get_rank()\n size = comm.Get_size()\n\n # Start time:\n t0 = time.time... | [
[
"numpy.cumsum",
"matplotlib.pyplot.savefig",
"numpy.std",
"numpy.random.normal",
"numpy.mean",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahernandez1801/donkey_rl_mqtt | [
"02bbfc3d036220a4061b95e50780984e657aff43"
] | [
"src/train.py"
] | [
"'''\nTrain\nTrain your nerual network\nAuthor: Tawn Kramer\n'''\nfrom __future__ import print_function\nimport os\nimport sys\nimport glob\nimport time\nimport fnmatch\nimport argparse\nimport numpy as np\nfrom PIL import Image\nimport keras\nimport conf\nimport random\nimport augment\nimport models\n\n\n'''\nmatp... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HybridRobotics/cbf | [
"d8a1b376e7e910de71df60cdf3619f68c40ab3ed"
] | [
"planning/path_generator/search_path_generator.py"
] | [
"import sys\n\nimport numpy as np\n\nfrom planning.path_generator.astar import *\n\n\ndef plot_global_map(path, obstacles):\n fig, ax = plt.subplots()\n for o in obstacles:\n patch = o.get_plot_patch()\n ax.add_patch(patch)\n ax.plot(path[:, 0], path[:, 1])\n plt.xlim([-1 * 0.15, 11 * 0.15... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HyperGAN/imgclsmob | [
"88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3",
"88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3"
] | [
"pytorch/pytorchcv/models/common.py",
"tensorflow_/tensorflowcv/models/mobilenetv2.py"
] | [
"\"\"\"\n Common routines for models in PyTorch.\n\"\"\"\n\n__all__ = ['HSwish', 'get_activation_layer', 'conv1x1', 'conv3x3', 'depthwise_conv3x3', 'ConvBlock', 'conv1x1_block',\n 'conv3x3_block', 'conv7x7_block', 'dwconv3x3_block', 'dwconv5x5_block', 'PreConvBlock', 'pre_conv1x1_block',\n 'p... | [
[
"torch.sigmoid",
"torch.transpose",
"torch.nn.ReLU6",
"torch.cat",
"torch.nn.functional.relu6",
"torch.nn.Conv2d",
"torch.nn.Sigmoid",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.InstanceNorm2d",
"torch.nn.BatchNorm2d",
"torch.split",
"torch.nn.ReLU"
],
[
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
DoranLyong/dynamic-images-for-action-recognition | [
"06a68c2337b45c44a8c7ec50e94585a9b9615ad0"
] | [
"dynamic_image_networks/hmdb51/training_scripts/train_resnext50_hmdb51.py"
] | [
"# import apex - !!!! INCLUDE THIS IMPORT IF YOU WANT TO USE MIXED PRECISION TRAINING !!!!\nimport torch\nimport os\nimport sys\nimport torch.optim as optim\nimport torch.nn as nn\nfrom datetime import datetime\nfrom tqdm import tqdm\nfrom pathlib import Path\n\n# Make sure that the project root is in your PATH (i.... | [
[
"torch.nn.CrossEntropyLoss",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.manual_seed",
"torch.set_grad_enabled",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cwkeam/pytorch-metric-learning | [
"63e4ecb781c5735ad714f61a3eecc55f72496905",
"63e4ecb781c5735ad714f61a3eecc55f72496905",
"63e4ecb781c5735ad714f61a3eecc55f72496905",
"63e4ecb781c5735ad714f61a3eecc55f72496905",
"63e4ecb781c5735ad714f61a3eecc55f72496905"
] | [
"tests/losses/test_fastap_loss.py",
"tests/miners/test_batch_hard_miner.py",
"src/pytorch_metric_learning/losses/large_margin_softmax_loss.py",
"tests/samplers/test_fixed_set_of_triplets.py",
"src/pytorch_metric_learning/reducers/class_weighted_reducer.py"
] | [
"######################################\n#######ORIGINAL IMPLEMENTATION########\n######################################\n# FROM https://github.com/kunhe/FastAP-metric-learning/blob/master/pytorch/FastAP_loss.py\n# This code is copied directly from the official implementation\n# so that we can make sure our implemen... | [
[
"torch.abs",
"torch.mean",
"torch.randint",
"torch.zeros",
"torch.sum",
"torch.pow",
"torch.isclose",
"torch.autograd.Variable",
"torch.mm",
"torch.ones",
"torch.randn",
"torch.eye",
"torch.tensor",
"torch.arange",
"torch.linspace",
"torch.isinf",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
dennismalmgren/marl | [
"baa846dc4144cf6f53e51d8cf1e2fcf5800c9f95"
] | [
"src/runners/episode_runner.py"
] | [
"from envs import REGISTRY as env_REGISTRY\nfrom functools import partial\nfrom components.episode_buffer import EpisodeBatch\nimport numpy as np\n\n\nclass EpisodeRunner:\n\n def __init__(self, args, logger):\n self.args = args\n self.logger = logger\n self.batch_size = self.args.batch_size... | [
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chanhee0222/feed2resp | [
"16dc7071f17af56cbf019eeabcd12a5dbd0693e7"
] | [
"main.py"
] | [
"import argparse\nimport datetime\nimport glob\nimport logging\nimport os\nimport time\n\nimport torch\n\nfrom logging_helper import init_logger\nfrom models import Discriminator, BartSystem\nfrom train import train\nfrom transformer_base import add_generic_args, generic_train\n\n\nclass Config():\n # data_path ... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tsalo/PyMARE | [
"7eb950fb137b6221f2ea5d381ca91d16eb4b8a35"
] | [
"pymare/stats.py"
] | [
"\"\"\"Miscellaneous statistical functions.\"\"\"\n\nimport numpy as np\nimport scipy.stats as ss\nfrom scipy.optimize import Bounds, minimize\n\n\ndef weighted_least_squares(y, v, X, tau2=0.0, return_cov=False):\n \"\"\"2-D weighted least squares.\n\n Args:\n y (NDArray): 2-d array of estimates (studi... | [
[
"scipy.stats.chi2.ppf",
"numpy.einsum",
"numpy.linalg.pinv",
"scipy.optimize.Bounds",
"numpy.any",
"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": []
}
] |
Yashgh7076/CU-Thesis | [
"59a7c6e8009395b5773b1ee47c38ca287ed6c189"
] | [
"tseries_crossval.py"
] | [
"import numpy as np \r\nimport sys\r\nimport os\r\nimport math\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TF info\r\nimport tensorflow as tf\r\n#import matplotlib.pyplot as plt\r\n\r\n# Define constants\r\nstride = 15 #1 second @ 15 Hz sampling\r\nwindow = 30*15 #30 seconds window considered\r\n\r\nfold... | [
[
"numpy.amax",
"tensorflow.keras.models.save_model",
"tensorflow.keras.layers.Conv2DTranspose",
"numpy.concatenate",
"tensorflow.keras.layers.ZeroPadding2D",
"tensorflow.keras.layers.Concatenate",
"numpy.reshape",
"tensorflow.keras.layers.Conv2D",
"numpy.zeros",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
gafaua/PolyTrack | [
"5a4b409732b9396be8271f5cba4ad426808d5af5"
] | [
"src/tools/vis_tracking_kittimots.py"
] | [
"import numpy as np\nimport cv2\nimport os\nimport glob\nimport sys\nfrom collections import defaultdict\nfrom pathlib import Path\nimport pycocotools.mask as rletools\nfrom PIL import Image, ImageDraw\nimport matplotlib.pyplot as plt\n\nDATA_PATH = '../../data/KITTIMOTS/'\nIMG_PATH = DATA_PATH + 'train/'\nSAVE_VID... | [
[
"numpy.logical_or",
"numpy.uint8",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
condereis/mean-variance-portfolio | [
"526b1e86d1e92f08ceca9a7c204b043089272744"
] | [
"tests/test_stock.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Tests for `mvport` package.\"\"\"\n\n\nimport unittest\nimport numpy as np\n\nfrom mvport.stock import Stock\n\n\nclass TestStock(unittest.TestCase):\n \"\"\"Tests for `mvport` package.\"\"\"\n\n def setUp(self):\n \"\"\"SetUp.\"\"\"\n sel... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LLcat1217/Cirq | [
"b88069f7b01457e592ad69d6b413642ef11a56b8",
"610b0d4ea3a7862169610797266734c844ddcc1f",
"b88069f7b01457e592ad69d6b413642ef11a56b8",
"b88069f7b01457e592ad69d6b413642ef11a56b8",
"b88069f7b01457e592ad69d6b413642ef11a56b8",
"610b0d4ea3a7862169610797266734c844ddcc1f",
"610b0d4ea3a7862169610797266734c844ddcc1... | [
"cirq-core/cirq/ops/two_qubit_diagonal_gate_test.py",
"cirq-core/cirq/protocols/equal_up_to_global_phase_protocol.py",
"cirq-core/cirq/optimizers/merge_interactions.py",
"cirq-ionq/cirq_ionq/sampler_test.py",
"cirq-core/cirq/interop/quirk/cells/arithmetic_cells_test.py",
"cirq-core/cirq/ops/state_preparat... | [
"# Copyright 2020 The Cirq Developers\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law o... | [
[
"numpy.exp"
],
[
"numpy.asarray"
],
[
"numpy.dot",
"numpy.eye",
"numpy.array"
],
[
"pandas.DataFrame"
],
[
"numpy.array"
],
[
"numpy.sqrt",
"numpy.eye",
"numpy.linalg.norm",
"numpy.ones",
"numpy.array",
"numpy.zeros"
],
[
"numpy.testi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
multirotorsociety/SAFMC-19-D2-Autonomous-Drone | [
"fd9f0fae5d7cbf618b327224e06a7f459612b4ca"
] | [
"Old/hoop_detection_angle.py"
] | [
"from __future__ import print_function\nimport time\nimport math\nimport thread\n\n# Dk imports\nfrom pymavlink import mavutil\nfrom dronekit import connect, VehicleMode, LocationGlobal, LocationGlobalRelative\n\n# Mux and TOF imports\nimport I2CMultiplexer\nimport VL53L1X\n\n# CV imports\nimport cv2\nimport numpy ... | [
[
"numpy.subtract",
"numpy.cos",
"numpy.sin",
"numpy.argwhere",
"numpy.arctan2",
"numpy.add",
"numpy.array",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ondrejba/hmm | [
"1e9fe47a6057d93e7c77614016a89d5d46959e97"
] | [
"hmm/scripts/easy_casino_learn.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom ..easy_casino import Casino\nfrom ..hmm_multinoulli import HMMMultinoulli\n\n\nhmm = HMMMultinoulli(Casino.A, Casino.PX, Casino.INIT)\n\n# generate sequence\nseq_length = 300\nbatch_size = 500\n\nxs_batch = []\nzs_batch = []\n\nfor j in range(batch_size):\n... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.unique",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ustbjdl1021/improved_snl_unet | [
"7f7bf092153e1a535337b80bd1b673eff3ddec52"
] | [
"model/snl_block.py"
] | [
"import torch\r\nimport torch.nn as nn\r\n\r\n\r\nclass ImprovedSNL(nn.Module):\r\n def __init__(self, in_channels, transfer_channels, stage_num=2):\r\n super(ImprovedSNL, self).__init__()\r\n self.in_channels = in_channels\r\n self.transfer_channels = transfer_channels\r\n self.stage... | [
[
"torch.sqrt",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.sum",
"torch.relu",
"torch.nn.init.normal_",
"torch.bmm",
"torch.nn.BatchNorm2d",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KnwSondess/Regym | [
"825c7dacf955a3e2f6c658c0ecb879a0ca036c1a",
"825c7dacf955a3e2f6c658c0ecb879a0ca036c1a",
"825c7dacf955a3e2f6c658c0ecb879a0ca036c1a",
"825c7dacf955a3e2f6c658c0ecb879a0ca036c1a",
"825c7dacf955a3e2f6c658c0ecb879a0ca036c1a"
] | [
"regym/rl_algorithms/algorithms/PPO/rnd_loss.py",
"regym/tests/integration/train_ppo_rnd_mz.py",
"regym/tests/rl_algorithms/agent_hook_test.py",
"regym/tests/rl_algorithms/replayBuffers_test.py",
"tests/environments/test_ppo_rnd_multiactor.py"
] | [
"from typing import Dict, List\nimport torch\nimport torch.nn.functional as F \n\n\ndef compute_loss(states: torch.Tensor, \n actions: torch.Tensor,\n next_states: torch.Tensor,\n log_probs_old: torch.Tensor, \n ext_returns: torch.Tensor,\n ... | [
[
"torch.exp",
"torch.nn.functional.mse_loss",
"torch.clamp",
"torch.min"
],
[
"torch.multiprocessing.set_start_method",
"matplotlib.pyplot.imshow",
"matplotlib.animation.ArtistAnimation",
"torch.load",
"numpy.asarray",
"numpy.arange",
"matplotlib.pyplot.subplot",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
glasgowcompbio/vimms-gym | [
"95cb6fa84ee6e3a64618b7a2a54c3835ad0d7867"
] | [
"vimms_gym/viewer_helper.py"
] | [
"import os\nimport sys\n\nimport numpy as np\nimport streamlit as st\nfrom stable_baselines3 import PPO\nfrom vimms.ChemicalSamplers import UniformRTAndIntensitySampler, GaussianChromatogramSampler, \\\n UniformMZFormulaSampler\nfrom vimms.Common import POSITIVE\n\nfrom vimms_gym.common import METHOD_PPO, METHOD... | [
[
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BrunoBertti/Scikit_Learning | [
"4b9e10ff7909f3728ac1e8bba19f5fd779340bc4",
"4b9e10ff7909f3728ac1e8bba19f5fd779340bc4",
"4b9e10ff7909f3728ac1e8bba19f5fd779340bc4",
"4b9e10ff7909f3728ac1e8bba19f5fd779340bc4"
] | [
"06_Transformacoes_do_Conjunto_de_Dados/6.6_Projecao_Aleatoria/6.6.1._O_Lema_de_Johnson-Lindenstrauss.py",
"06_Transformacoes_do_Conjunto_de_Dados/6.1_Pipelines_e_Estimadores_Compostos/6.1.2_Transformando_Alvo_em_Regressao.py",
"04_Inspecao/4.2_Importancia_do_Recurso_de_Permutacao/4.2_Importancia_do_Recurso_de_... | [
"########## 6.6.1. O lema de Johnson-Lindenstrauss ##########\n\n\n\n # O principal resultado teórico por trás da eficiência da projeção aleatória é o lema de Johnson-Lindenstrauss (citando a Wikipedia):\n\n # Em matemática, o lema de Johnson-Lindenstrauss é um resultado sobre embeddings de baixa distorção de... | [
[
"sklearn.random_projection.johnson_lindenstrauss_min_dim"
],
[
"sklearn.compose.TransformedTargetRegressor",
"numpy.log",
"sklearn.preprocessing.QuantileTransformer",
"sklearn.model_selection.train_test_split",
"sklearn.datasets.fetch_california_housing",
"sklearn.linear_model.Line... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
0xTDF/Quant-Trading-Strategy-Backtesting-Framework | [
"d77089bab3513013d456819e9790e67e44adec8e"
] | [
"MA cross.py"
] | [
"import backtrader as bt\r\nimport backtrader.analyzers as bta\r\nfrom datetime import datetime\r\nimport matplotlib.pyplot as plt\r\nimport yfinance\r\n\r\n\r\nclass MaCrossStrategy(bt.Strategy):\r\n\r\n # signal generator\r\n def __init__(self):\r\n\r\n ma_fast = bt.ind.SMA(period = 10)\r\n ma... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JensRL/PPaNM | [
"a28d9826d24c821cbc35a2e5fb5c478118f1e693"
] | [
"Lectures/9 - Matlib/test.py"
] | [
"import math\rimport scipy.integrate as integrate\r\rncalls = 0\rdef f(x):\r global ncalls\r ncalls +=1\r return math.log(x)/math.sqrt(x)\r\rresult = integrate.quad(f,0,1)\rprint(\"result=\", result, \"ncalls =\",ncalls)"
] | [
[
"scipy.integrate.quad"
]
] | [
{
"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"... |
d3m0n-r00t/BentoML | [
"e5c53b821369f5391de9ab3a20ecad5db9e77202"
] | [
"bentoml/adapters/dataframe_output.py"
] | [
"import json\nfrom typing import Sequence\n\nfrom bentoml.adapters.json_output import JsonOutput\nfrom bentoml.types import InferenceError, InferenceResult, InferenceTask\nfrom bentoml.utils.dataframe_util import PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS\n\n\ndef df_to_json(result, pandas_dataframe_orient=\"records\"... | [
[
"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": []
}
] |
linusg/Pyto | [
"901ac307b68486d8289105c159ca702318bea5b0",
"901ac307b68486d8289105c159ca702318bea5b0",
"901ac307b68486d8289105c159ca702318bea5b0"
] | [
"site-packages/sklearn/linear_model/_stochastic_gradient.py",
"site-packages/sklearn/linear_model/_logistic.py",
"site-packages/sklearn/model_selection/_split.py"
] | [
"# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)\n# Mathieu Blondel (partial_fit support)\n#\n# License: BSD 3 clause\n\"\"\"Classification and regression using Stochastic Gradient Descent (SGD).\"\"\"\n\nimport numpy as np\nimport warnings\n\nfrom abc import ABCMeta, abstractmet... | [
[
"numpy.unique",
"numpy.asarray",
"numpy.clip",
"numpy.ones",
"numpy.atleast_1d",
"numpy.iinfo",
"numpy.any",
"numpy.array",
"numpy.zeros"
],
[
"numpy.dot",
"numpy.expand_dims",
"numpy.asarray",
"numpy.mean",
"scipy.sparse.dia_matrix",
"numpy.exp",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
choudhury722k/English-to-French-translator | [
"e792ce92adbdd3100d73d9d8aebc109cc7c560d7"
] | [
"Model prediction/app.py"
] | [
"from re import X\nfrom flask import Flask,render_template,url_for,request\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras import models \nimport numpy as np\nimport pickle\n\nfrench_tokenizer = pickle.load(open('fr... | [
[
"tensorflow.keras.models.load_model",
"numpy.array",
"numpy.argmax",
"tensorflow.keras.preprocessing.sequence.pad_sequences"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
alxmamaev/catalyst | [
"d05120c68fbc5174ff74297d29c0fc00d7e94924"
] | [
"catalyst/engines/tests/test_parallel.py"
] | [
"# flake8: noqa\n\nfrom typing import Any, Dict, List\nimport logging\nfrom tempfile import TemporaryDirectory\n\nfrom pytest import mark\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom catalyst.callbacks import CheckpointCallback, CriterionCallback, OptimizerCallback\nfrom catalyst.core.runner impor... | [
[
"torch.utils.data.DataLoader",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LvHang/aps | [
"3e9c8b247e0526481970c28e8af1a6a93cc7f2cc"
] | [
"aps/loader/simu.py"
] | [
"#!/usr/bin/env python\n\n# Copyright 2020 Jian Wu\n# License: Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)\n\"\"\"\nAdopt from my another project: https://github.com/funcwj/setk\nSee https://github.com/funcwj/setk/tree/master/doc/data_simu for command line usage\n\"\"\"\nimport argparse\nimport numpy as... | [
[
"numpy.abs",
"numpy.zeros",
"numpy.mean",
"numpy.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JingweiZuo/SE2TeC | [
"f2aab845aa648e366d0f6917a5d8abfd4d556d13"
] | [
"SE4TeC_demo/GUI_function.py"
] | [
"import time\nimport tkinter as tk\nfrom tkinter import *\nimport tkinter.filedialog as filedialog\nfrom tkinter.filedialog import askopenfilename\nimport utils.utils as util\nimport utils.similarity_measures as sm\nimport SMAP.MatrixProfile as mp\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nfrom matplotlib.backe... | [
[
"matplotlib.use",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
NNHieu/loss-landscape | [
"dfe517f23993ffbafea99272026d09e074e50b4f"
] | [
"cifar10/models/repnet.py"
] | [
"\nimport torch \nimport torch.nn as nn \nimport torch.nn.functional as F\n\nimport matplotlib.pyplot as plt \n\nimport torch.autograd as autograd \n\nfrom torchvision import datasets, transforms \nfrom torch.utils.data import DataLoader \n\nimport torch.optim as optim\n\nimport os\nimport argparse\n\n\nclass ResNe... | [
[
"torch.nn.Conv2d",
"torch.nn.Flatten",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.GroupNorm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ayulockin/mmdetection | [
"70f6d9cfade4a2f0b198e4f64776521d181b28be"
] | [
"mmdet/models/detectors/maskformer.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport mmcv\nimport numpy as np\n\nfrom mmdet.core import INSTANCE_OFFSET\nfrom mmdet.core.visualization import imshow_det_bboxes\nfrom ..builder import DETECTORS, build_backbone, build_head, build_neck\nfrom .single_stage import SingleStageDetector\n\n\n@DETECTORS.... | [
[
"numpy.array",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Code37/zipline | [
"8beba055aa4211dc2debc5c3083077cbd19d0bbc",
"8beba055aa4211dc2debc5c3083077cbd19d0bbc",
"8beba055aa4211dc2debc5c3083077cbd19d0bbc",
"8beba055aa4211dc2debc5c3083077cbd19d0bbc"
] | [
"zipline/data/resample.py",
"tests/pipeline/test_adjusted_array.py",
"tests/risk/test_risk_period.py",
"tests/finance/test_commissions.py"
] | [
"# Copyright 2016 Quantopian, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or ag... | [
[
"numpy.nanmax",
"pandas.isnull",
"pandas.Timestamp",
"numpy.isnan",
"numpy.nanmin",
"pandas.Timedelta",
"numpy.full",
"numpy.append",
"numpy.nansum",
"numpy.array",
"numpy.zeros"
],
[
"numpy.asarray",
"numpy.arange",
"numpy.dtype",
"numpy.full",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
niopeng/elpips | [
"385012a2ee614c75a1631546c391039af85744f4"
] | [
"elpips/util.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\n\t\ndef switch_case_cond(cases, default_case):\n\tif cases:\n\t\tcondition, effect = cases[0]\n\t\treturn tf.cond(condition, effect, lambda: switch_case_cond(cases[1:], default_case))\n\treturn default_case()\n\ndef switch_case_where(cases, default_case):\n\tif cases:... | [
[
"tensorflow.cast"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
PaulBehler/estimagic | [
"c14f743986262d508e55738c90737cb504fe987b",
"c14f743986262d508e55738c90737cb504fe987b"
] | [
"src/estimagic/benchmarking/run_benchmark.py",
"src/estimagic/visualization/estimation_table.py"
] | [
"\"\"\"Functions to create, run and visualize optimization benchmarks.\n\nTO-DO:\n- Add other benchmark sets:\n - finish medium scale problems from https://arxiv.org/pdf/1710.11005.pdf, Page 34.\n - add scalar problems from https://github.com/AxelThevenot\n- Add option for deterministic noise or wiggle.\n\n\"... | [
[
"numpy.random.seed"
],
[
"pandas.concat",
"pandas.MultiIndex.from_frame",
"numpy.sqrt",
"pandas.Series",
"pandas.MultiIndex.from_arrays",
"numpy.digitize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorfl... |
KristofferLM96/TsetlinMachine-GO | [
"926091fc70042abe5a67230932398bdab2c46328"
] | [
"Data_Handling/Data_analyze.py"
] | [
"# -----------------------------------------------\n# ................. LIBRARIES ...................\n# -----------------------------------------------\nimport glob\nimport os\nimport time\nimport numpy as np\n\n\n# -----------------------------------------------\n# ............. GLOBAL VARIABLES ................\... | [
[
"numpy.nditer",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Khizar-Ali/Lip2Wav | [
"07f056b3468ca660823830680bf25bdd42034f9e"
] | [
"synthesizer/models/custom_decoder.py"
] | [
"from __future__ import absolute_import, division, print_function\nimport collections\nimport tensorflow as tf\nfrom synthesizer.models.helpers import TacoTestHelper, TacoTrainingHelper\nfrom tensorflow.contrib.seq2seq.python.ops import decoder\nfrom tensorflow.contrib.seq2seq.python.ops import helper as helper_py\... | [
[
"tensorflow.python.framework.ops.name_scope",
"tensorflow.python.util.nest.map_structure",
"tensorflow.python.framework.tensor_shape.TensorShape",
"tensorflow.python.util.nest.flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"2.7",
"1.4",
"2.6",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"1.0",
"2.2",
"1.2",
"2.10"
]
}
] |
wgurecky/StarVine | [
"b952a88eeaff476484ba6a26420cfe4ef575d162"
] | [
"starvine/bvcopula/tests/test_freeze_params.py"
] | [
"##\n# \\brief Test ability to determine best fit copula via AIC\nfrom __future__ import print_function, division\nfrom starvine.bvcopula.pc_base import PairCopula\nimport unittest\nimport numpy as np\nimport os\npwd_ = os.getcwd()\ndataDir = pwd_ + \"/tests/data/\"\nnp.random.seed(123)\n\n\nclass TestGaussFrozen(u... | [
[
"numpy.loadtxt",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gavinlive/scd-data-manager | [
"570b67bacb4bf17f0b49c5875933f233ddd76e6c"
] | [
"scd_manager.py"
] | [
"import os, sys\n\nimport pydicom as pyd\nimport matplotlib.pyplot as plt\nimport collections\nimport pandas as pd\n\nPatientRecord = collections.namedtuple('PatientRecord', ['patient_id', 'image_folder', 'original_id', 'gender', 'age', 'pathology', 'all_scans', 'scans', 'scans_list', 'scans_total'])\nPatientScans ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
harinipsamy/Tensorflow-2-Reinforcement-Learning-Cookbook | [
"b8858554e4c819c96de10c100f8213ab41561c69"
] | [
"Chapter05/stock_trading_visual_continuous_env.py"
] | [
"#!/usr/bin/env python\n# Visual stock/share trading RL environment with continuous trade actions\n# Chapter 5, TensorFlow 2 Reinforcement Learning Cookbook | Praveen Palanisamy\n\nimport os\nimport random\nfrom typing import Dict\n\nimport cv2\nimport gym\nimport numpy as np\nimport pandas as pd\nfrom gym import s... | [
[
"numpy.array",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Marcel-Rodekamp/MLP | [
"349ac8e10679e2ec53980908c580902996a493e7"
] | [
"src/LossFunctions/ActionCrossEntropy.py"
] | [
"import torch\n\nclass ActionCrossEntropyFunction(torch.autograd.Function):\n @staticmethod\n def forward(self,input,target,action,force = None):\n self.mb_size,self.dim = input.size()\n\n # save the force for backward\n self.force = force\n\n\n # get action difference\n act... | [
[
"torch.sqrt",
"torch.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ArzelaAscoIi/haystack | [
"be8f50c9e3de4e264b3f345f5f4b9c9ec518ed08",
"be8f50c9e3de4e264b3f345f5f4b9c9ec518ed08"
] | [
"haystack/utils/squad_data.py",
"haystack/document_stores/elasticsearch.py"
] | [
"from typing import List\n\nimport logging\nimport json\nimport random\nimport pandas as pd\nfrom tqdm import tqdm\n\nfrom haystack.schema import Document, Label\nfrom haystack.modeling.data_handler.processor import _read_squad_file\n\n\nlogging.basicConfig()\nlogger = logging.getLogger(__name__)\nlogger.setLevel(l... | [
[
"pandas.DataFrame.from_records",
"pandas.concat",
"pandas.merge",
"pandas.DataFrame"
],
[
"numpy.asarray",
"numpy.median",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
LimJiaJing/Cam2BEV | [
"f788a9f58b464bc7b114e5a0dd1afcd6683f10e3",
"8177e13f7a3662daee28cce62f35b85f500941c0"
] | [
"preprocessing/homography_converter/uNetXST_homographies/2_F.py",
"model/architecture/uNetXST.py"
] | [
"# ==============================================================================\n# MIT License\n#\n# Copyright 2020 Institute for Automotive Engineering of RWTH Aachen University.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation fi... | [
[
"numpy.array"
],
[
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.layers.Activation",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Conv2DTranspose",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.D... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
phoenix1712/deep-person-reid | [
"70365320f5319e180d7fce4993003382b06906b0"
] | [
"torchreid/data_manager/cuhk03.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport glob\nimport re\nimport sys\nimport urllib\nimport tarfile\nimport zipfile\nimport os.path as osp\nfrom scipy.io import loadmat\nimport numpy as np\nimport h5py\nfrom scipy.misc impor... | [
[
"scipy.misc.imsave",
"scipy.io.loadmat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
selasley/pandas | [
"5b5574520dba1e79ac95e5079724a41151c20b9a"
] | [
"pandas/core/groupby/generic.py"
] | [
"\"\"\"\nDefine the SeriesGroupBy and DataFrameGroupBy\nclasses that hold the groupby interfaces (and some implementations).\n\nThese are user facing as the result of the ``df.groupby(...)`` operations,\nwhich here returns a DataFrameGroupBy object.\n\"\"\"\nfrom __future__ import annotations\n\nfrom collections im... | [
[
"pandas.core.common.get_callable_name",
"pandas.core.base.SpecificationError",
"numpy.asarray",
"pandas.core.dtypes.missing.notna",
"pandas.core.indexes.api.all_indexes_same",
"pandas._libs.reduction.check_result_array",
"pandas.core.frame.DataFrame",
"numpy.where",
"pandas.cor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"1.5",
"2.0",
"1.4"
],
"scipy": [],
"tensorflow": []
}
] |
sandeepsinghsengar/MPUNet2Plus | [
"fd97800cd349ee47d2c9cce1851a332dcbcb047c",
"fd97800cd349ee47d2c9cce1851a332dcbcb047c"
] | [
"mpunet/callbacks/callbacks.py",
"mpunet/models/unet.py"
] | [
"import matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport psutil\nimport numpy as np\nimport os\nfrom tensorflow.keras.callbacks import Callback\nfrom datetime import datetime\nfrom mpunet.logging import ScreenLogger\nfrom mpunet.utils.plotting import (imshow_with_l... | [
[
"numpy.testing.suppress_warnings",
"matplotlib.use",
"tensorflow.keras.backend.get_session",
"matplotlib.pyplot.subplots",
"numpy.mean",
"matplotlib.pyplot.close",
"numpy.moveaxis",
"matplotlib.pyplot.figure"
],
[
"tensorflow.keras.layers.Concatenate",
"numpy.sqrt",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.2",
"1.10"
]
}
] |
yux1991/PyRHEED | [
"b39ad03651c92e3649069919ae48b1e5158cd3dd"
] | [
"source/graph_3D_surface.py"
] | [
"from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar\nfrom PyQt5 import QtCore, QtGui, QtWidgets, QtDataVisualization\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\ni... | [
[
"numpy.abs",
"numpy.linspace",
"numpy.amin",
"numpy.cos",
"pandas.DataFrame",
"numpy.empty",
"numpy.sin",
"matplotlib.backends.backend_qt5agg.NavigationToolbar2QT",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg",
"numpy.where",
"numpy.loadtxt",
"matplotlib.p... | [
{
"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": []
}
] |
Turoad/CLRNet | [
"51e082db12973943bddefd76fd0d431fcb3350ff",
"51e082db12973943bddefd76fd0d431fcb3350ff"
] | [
"clrnet/models/necks/fpn.py",
"clrnet/datasets/llamas.py"
] | [
"import warnings\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmcv.cnn import ConvModule\nfrom ..registry import NECKS\n\n\n@NECKS.register_module\nclass FPN(nn.Module):\n def __init__(self,\n in_channels,\n out_channels,\n num_outs... | [
[
"torch.nn.ModuleList",
"torch.nn.functional.relu",
"torch.nn.functional.max_pool2d",
"torch.nn.functional.interpolate"
],
[
"numpy.arange",
"numpy.zeros",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DarioMarzella/pdb2sql | [
"64d5d4f27fb67a0ff41221bcd27667237048bb3f"
] | [
"pdb2sql/StructureSimilarity.py"
] | [
"import warnings\nimport numpy as np\nfrom .pdb2sqlcore import pdb2sql\nfrom .interface import interface\nfrom .superpose import get_trans_vect, get_rotation_matrix, superpose_selection\n\nfrom . import transform\nimport os\nimport pickle\n\n\nclass StructureSimilarity(object):\n\n def __init__(self, decoy, ref,... | [
[
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
romiosarkar6991/tfx-romio | [
"0703c1dd037c676e1d438c2e5ce831decfc9eed9",
"0703c1dd037c676e1d438c2e5ce831decfc9eed9",
"0703c1dd037c676e1d438c2e5ce831decfc9eed9",
"0703c1dd037c676e1d438c2e5ce831decfc9eed9",
"0703c1dd037c676e1d438c2e5ce831decfc9eed9",
"0703c1dd037c676e1d438c2e5ce831decfc9eed9",
"0703c1dd037c676e1d438c2e5ce831decfc9eed... | [
"tfx/components/schema_gen/executor_test.py",
"tfx/examples/iris/iris_pipeline_beam.py",
"tfx/orchestration/airflow/airflow_dag_runner_test.py",
"tfx/orchestration/pipeline_test.py",
"tfx/orchestration/launcher/base_component_launcher.py",
"tfx/tools/cli/e2e/cli_common_e2e_test.py",
"tfx/examples/iris/i... | [
"# Copyright 2019 Google LLC. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appl... | [
[
"tensorflow.gfile.ListDirectory",
"tensorflow.test.main"
],
[
"tensorflow.logging.set_verbosity"
],
[
"tensorflow.test.main"
],
[
"tensorflow.io.gfile.exists",
"tensorflow.test.main"
],
[
"tensorflow.logging.info"
],
[
"tensorflow.test.main"
],
[
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
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