repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
cameronmartino/BacDivePy | [
"b3433ded1328a1e7b5b1cc3858ffd5367a91df89"
] | [
"bacdive/DSMZClient.py"
] | [
"# ----------------------------------------------------------------------------\n# Copyright (c) 2016--, BacDivePy development team.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is in the file COPYING.txt, distributed with this software.\n#\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": []
}
] |
sinhngn/Context-Aware | [
"ba7fae6f713336451e43ed74917b8c15ae604517",
"ba7fae6f713336451e43ed74917b8c15ae604517"
] | [
"fairseq/modules/relative_multihead_attention.py",
"fairseq/models/rand_noise_transformer.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the LICENSE file in\n# the root directory of this source tree. An additional grant of patent rights\n# can be found in the PATENTS file in the same directory.\n\nimport torch\nfrom tor... | [
[
"torch.range",
"torch.Tensor",
"torch.nn.functional.dropout",
"torch.nn.init.constant_",
"torch.cat",
"torch.nn.init.xavier_normal_",
"torch.arange",
"torch.nn.Linear",
"torch.bmm",
"torch.nn.init.xavier_uniform_",
"torch.clamp",
"torch.nn.functional.linear"
],
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rbiessel/CovSAR | [
"27448b98d761a4fdf4ed88a4380c91b2aaadaee1"
] | [
"applications/least_norm.py"
] | [
"import numpy as np\nfrom matplotlib import pyplot as plt\n\n\ndef least_norm(A, closures, pinv=False):\n '''\n Solve: Ax = b\n Find the minimum norm vector 'x' of phases that can explain 'b' phase closures\n '''\n if pinv:\n return np.linalg.pinv(A) @ closures\n return A.T @ np.lin... | [
[
"matplotlib.pyplot.legend",
"numpy.linalg.inv",
"numpy.linalg.pinv",
"numpy.angle",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gglin001/poptorch | [
"61f38ed2d8c6b672e023862eb698865fa7f4724e",
"61f38ed2d8c6b672e023862eb698865fa7f4724e"
] | [
"docs/user_guide/autocast.py",
"tests/tensor_ops_test.py"
] | [
"# Copyright (c) 2020 Graphcore Ltd. All rights reserved.\n\nimport torch\nimport poptorch\n\n\n# decorator_autocast_start\nclass MyModel(torch.nn.Module):\n @poptorch.autocast()\n def forward(self, x, y):\n return torch.bmm(x, y)\n\n\n# decorator_autocast_end\n\n# model_autocast_start\nmodel = MyModel... | [
[
"torch.nn.Sequential",
"torch.randn",
"torch.nn.Conv2d",
"torch.bmm",
"torch.nn.ReLU"
],
[
"torch.transpose",
"torch.randint",
"torch.chunk",
"torch.cat",
"torch.zeros",
"torch.split",
"torch.full_like",
"torch.ones",
"torch.randn",
"torch.reshape",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vineeths96/QSGD | [
"e5b61b1ebfa4288c3c2a9109819b72c13a93b5f7"
] | [
"extensions/Extension CPU/demo.py"
] | [
"import sys\nimport torch\nfrom torch.utils.cpp_extension import load\n\n\nbitpacking = load(name=\"bitpacking\", sources=[\"bitpacking.cpp\"], verbose=True)\n\n# src = torch.randint(low=0, high=256, size=[64], dtype=torch.int32)\nsrc = (10 * torch.randn(size=[100000])).abs().to(dtype=torch.int32)\nsrc_copy = src.c... | [
[
"torch.randn",
"torch.utils.cpp_extension.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
demelin/nematode | [
"d475588c939986afcff0b7c10b5767b33cf0d4bb"
] | [
"codebase/transformer.py"
] | [
"import tensorflow as tf\n\nfrom layers import \\\n EmbeddingLayer, \\\n MaskedCrossEntropy, \\\n get_shape_list, \\\n get_right_context_mask, \\\n get_positional_signal\n\nfrom blocks import AttentionBlock, FFNBlock\nfrom inference import greedy_search, beam_search\n\n\nclass Transformer(object):\n ... | [
[
"tensorflow.device",
"tensorflow.get_variable",
"tensorflow.transpose",
"tensorflow.fill",
"tensorflow.zeros",
"tensorflow.shape",
"tensorflow.layers.dropout",
"tensorflow.placeholder_with_default",
"tensorflow.equal",
"tensorflow.ones_like",
"tensorflow.placeholder",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
robfairh/d3ploy | [
"5561a4640e765d8165535b672bf5e407afe47ea2"
] | [
"d3ploy/deterministic_inst.py"
] | [
"\"\"\"\nThis cyclus archetype uses time series methods to predict the demand and supply\nfor future time steps and manages the deployment of facilities to ensure\nsupply is greater than demand. Time series predicition methods can be used\nin this archetype.\n\"\"\"\n\nimport math\nfrom collections import defaultdi... | [
[
"numpy.linalg.solve"
]
] | [
{
"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": [],
... |
Improbable-AI/legged_gym | [
"fbde4cbc8df29dba5270d212e36f2d8bde39d6c7"
] | [
"legged_gym/utils/terrain.py"
] | [
"# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: BSD-3-Clause\n# \n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# 1. Redistributio... | [
[
"numpy.random.choice",
"numpy.max",
"numpy.random.uniform",
"numpy.unravel_index",
"numpy.sum",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
harshit158/sentiment-analysis-using-BERT | [
"09264281c0a6d1393d52610f67a5c54506821b32"
] | [
"src/model.py"
] | [
"import transformers\nimport torch.nn as nn\n\n\nclass BERTSentimentModel(nn.Module):\n def __init__(self):\n super(BERTSentimentModel, self).__init__()\n self.bert = transformers.BertModel.from_pretrained('bert-base-uncased')\n self.bert_drop = nn.Dropout(0.2)\n self.out = nn.Linear(... | [
[
"torch.nn.Linear",
"torch.nn.Dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KimNikita/open_model_zoo | [
"ae46cb83f046b5429331dec0f93805fb1f65450a",
"ae46cb83f046b5429331dec0f93805fb1f65450a"
] | [
"tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/text_to_speech_evaluator.py",
"tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/asr_encoder_decoder_evaluator.py"
] | [
"\"\"\"\nCopyright (c) 2018-2021 Intel Corporation\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law ... | [
[
"numpy.expand_dims",
"numpy.arange",
"numpy.cumsum",
"numpy.ndim",
"numpy.max",
"numpy.array",
"numpy.zeros",
"numpy.where"
],
[
"numpy.array",
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bwallace/evidence-inference-1 | [
"d997c38551b920d0494b787b7c7ddd60311ca288"
] | [
"scripts/paper_statistics.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 16 16:39:34 2018\n\n@author: Eric\n\"\"\"\nimport glob\nimport numpy as np\nimport pandas as pd\n\n\ndef load_data(l):\n \"\"\"\n Load some file.\n \n @param l is the location to load.\n \"\"\"\n df = pd.read_csv(l, engine = \"python\", encoding... | [
[
"numpy.asarray",
"pandas.read_csv",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ruhyadi/pseudo_lidar | [
"032c7a0d73c3fdf84e934af3f57f8eb489a52906"
] | [
"psmnet/models/stackhourglass.py"
] | [
"from __future__ import print_function\n\nfrom submodule import *\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport math\n\n\nclass hourglass(nn.Module):\n def __init__(self, inplanes):\n super(hourglass, self).__ini... | [
[
"torch.nn.functional.softmax",
"torch.nn.ConvTranspose3d",
"torch.nn.Conv3d",
"torch.nn.functional.relu",
"torch.nn.ReLU",
"torch.nn.BatchNorm3d",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
usmanwardag/pytorch-CycleGAN-and-pix2pix | [
"72f2050600e7821476c9e19fcf8f1973f6a6f78c"
] | [
"models/networks.py"
] | [
"import torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport functools\nfrom torch.optim import lr_scheduler\n\n\n###############################################################################\n# Helper Functions\n###############################################################################\n\n\nclass... | [
[
"torch.nn.ConstantPad1d",
"torch.optim.lr_scheduler.LambdaLR",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"torch.sum",
"torch.nn.BCEWithLogitsLoss",
"torch.cuda.is_available",
"torch.nn.L1Loss",
"torch.nn.ReplicationPad2d",
"torch.nn.Dropout",
"torch.optim.lr_scheduler.R... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mvdebolskiy/CommunityFirnModel | [
"5380479cdf776132d549f48e9d71b674564ad9cc"
] | [
"CFM_main/firnbatch_generic.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n'''\nThis is what I use to batch CFM runs (in combination with GNU parallel).\n\nBut, it can also be used a standalone access to the CFM. Previously (and still)\n\nthe CFM is called using main.py and a .json file. \n\n\nThis file takes a different direction - its inputs ... | [
[
"numpy.fromstring",
"numpy.mean",
"numpy.floor",
"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": []
}
] |
mtreviso/lxmls-toolkit | [
"7b135d98c8bde592649fface8e6f24f112939937"
] | [
"lxmls/classifiers/perceptron.py"
] | [
"import sys\nimport numpy as np\nimport lxmls.classifiers.linear_classifier as lc\n\n\nclass Perceptron(lc.LinearClassifier):\n\n def __init__(self, nr_epochs=10, learning_rate=1, averaged=True):\n lc.LinearClassifier.__init__(self)\n self.trained = False\n self.nr_epochs = nr_epochs\n ... | [
[
"numpy.random.permutation",
"numpy.zeros",
"numpy.random.seed",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zphang/saliency_investigation | [
"cdd03e92c4b8b26c43a60cb50f9d97ea2c7f9400"
] | [
"casme/stats.py"
] | [
"import numpy as np\nimport time\n\nimport torch\n\n\nclass AverageMeter:\n def __init__(self):\n self.val = None\n self.avg = None\n self.sum = None\n self.count = None\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n ... | [
[
"numpy.log2",
"torch.no_grad",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
soy567/AR-Vision | [
"f6dd9d768f200b38c2d639e7df4efee5ab101e17"
] | [
"tools/test_lanenet_lidar.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Time : 18-5-23 上午11:33\n# @Author : MaybeShewill-CV\n# @Site : https://github.com/MaybeShewill-CV/lanenet-lane-detection\n# @File : test_lanenet.py\n# @IDE: PyCharm Community Edition\n\"\"\"\ntest LaneNet model on single image\n\"\"\"\nimport argparse\n... | [
[
"numpy.min",
"tensorflow.placeholder",
"tensorflow.ConfigProto",
"numpy.max",
"tensorflow.train.ExponentialMovingAverage",
"tensorflow.variable_scope",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
KarchinLab/probabilistic2020 | [
"8e0b1b9578bd8189b1690dd2f17476c3305b98dc"
] | [
"scripts/mut2bed.py"
] | [
"import pandas as pd\nimport argparse\n\n\ndef correct_chrom_names(chroms):\n chrom_list = []\n for chrom in chroms:\n # fix chrom numbering\n chrom = chrom.replace('23', 'X')\n chrom = chrom.replace('24', 'Y')\n chrom = chrom.replace('25', 'Mt')\n if not chrom.startswith('c... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yang-han/Self-Enhanced_GNN | [
"53b98e990f0d7d090de09fac32a9914a5a7e25a8"
] | [
"topology_update_train.py"
] | [
"import argparse\nimport os\nimport pickle\nimport sys\nfrom datetime import datetime\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch_geometric.transforms as T\nfrom torch_geometric.datasets import Planetoid\n\nfrom models import GAT, GCN, SGC, USGC\nfrom utils import (EPOCHS_CONF... | [
[
"torch.manual_seed",
"numpy.zeros",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cristianofontana/testeboticario | [
"f3e139fce38dbf0f364b53b28cff203a6ddf3e65"
] | [
"dags/ingestdata.py"
] | [
"\"\"\"\nCode that goes along with the Airflow located at:\nhttp://airflow.readthedocs.org/en/latest/tutorial.html\n\"\"\"\nfrom airflow import DAG\nfrom airflow.operators.bash_operator import BashOperator\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom airflow.operators.python_operator import Pyt... | [
[
"pandas.read_excel"
]
] | [
{
"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": []
}
] |
DavidBMcCoy/ZSFG-UCSF_Machine_Learning | [
"144c938fe52862e69c91078fdf1ffd85a506d930"
] | [
"Images_3D/Inception_Networks/inception_resnet_v1_noise_adapt.py"
] | [
"#!/usr/bin/env python2\n\n\"\"\"\nModel adapted from:\nImplementation of Inception-Residual Network v1 [Inception Network v4 Paper](http://arxiv.org/pdf/1602.07261v1.pdf) in Keras.\n\nSome additional details:\n\n Each of the A, B and C blocks have a 'scale_residual' parameter as mentioned in the paper\n Defa... | [
[
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
o-hx/CV-Proj | [
"46d517f56f589a10a9670502bb4762d0612bb7e8"
] | [
"run_data.py"
] | [
"import numpy as np\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\n\nfrom utils.data import rle_to_mask\n\n\ndef images_in_class(df):\n # get count for each class\n fish = df[df['label'] == 'Fish'].EncodedPixels.count()\n flower = df[df['label'] == 'Flower'].EncodedPixels.count()\n gr... | [
[
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
anujanegi/TrueSight | [
"4aabfb8d1d2bddbbd7a71bb1a4d12b764684842a"
] | [
"scripts/scene_classification.py"
] | [
"import torch\nfrom torch.autograd import Variable as V\nimport torchvision.models as models\nfrom torchvision import transforms as trn\nfrom torch.nn import functional as F\nimport os\nfrom PIL import Image\nimport shutil\n\n# load the pre-trained weights\narch = 'resnet50'\nmodel_file = os.path.abspath('./models/... | [
[
"torch.nn.functional.softmax",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HephaestusProject/pytorch-FCN | [
"3976d6539fe1dbd9aec81d96aec77676182df1cf"
] | [
"src/data.py"
] | [
"# coding: utf-8\nimport os\n\nimport numpy as np\nimport torch\nimport torchaudio\nfrom torch.utils import data\n\n\nclass MTATDataset(data.Dataset):\n \"\"\"MTATDataset class\"\"\"\n\n def __init__(self, root, split, input_length=80000):\n \"\"\"Instantiating MTATDataset class\n Args:\n ... | [
[
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jhuapl-boss/boss | [
"c2e26d272bd7b8d54abdc2948193163537e31291"
] | [
"django/bosstiles/test/tiles_view_uint8.py"
] | [
"# Copyright 2016 The Johns Hopkins University Applied Physics Laboratory\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# U... | [
[
"numpy.testing.assert_equal",
"numpy.squeeze",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ytkj/apm | [
"40077d099c6a42e5bba459239bfafbab33e4cc5e"
] | [
"pandas_utils.py"
] | [
"from typing import Union, List\n\nimport numpy as np\nimport pandas as pd\n\n\nDF = type(pd.DataFrame)\nND = type(np.ndarray)\nS = type(pd.Series)\n\n\ndef missing_rate(df: pd.DataFrame) -> pd.Series:\n return df.isnull().sum() / len(df)\n\n\ndef is_pd(x: Union[DF, ND]) -> bool:\n return type(x) is pd.DataFr... | [
[
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ovb197310/opencv_2.4.13.2 | [
"940159dab8ea8f5ee019d2038b59e1daf4119d1c",
"940159dab8ea8f5ee019d2038b59e1daf4119d1c"
] | [
"sources/modules/python/test/tests_common.py",
"sources/modules/python/test/test_texture_flow.py"
] | [
"#!/usr/bin/env python\r\n\r\nfrom __future__ import print_function\r\n\r\nimport unittest\r\nimport sys\r\nimport hashlib\r\nimport os\r\nimport numpy as np\r\nimport cv2\r\nimport cv2.cv as cv\r\n\r\n# Python 3 moved urlopen to urllib.requests\r\ntry:\r\n from urllib.request import urlopen\r\nexcept ImportErro... | [
[
"numpy.array",
"numpy.fromstring"
],
[
"numpy.dstack",
"numpy.int32",
"numpy.uint32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
halostorm/lingvo | [
"0919a77e4fb899e1adb65658523d49098aa9ae3b"
] | [
"lingvo/tasks/car/calibration_processing.py"
] | [
"# Lint as: python2, python3\n# 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/... | [
[
"numpy.abs",
"numpy.linspace",
"numpy.clip",
"numpy.isnan",
"numpy.arange",
"numpy.mean",
"numpy.digitize",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
airmachine/EM-MIL-WeaklyActionDetection | [
"5c8fb591890d0a7bdf8b77e92c1e33198243a663"
] | [
"main_test.py"
] | [
"# --------------------------------------------------------\n# EM-MIL\n# Copyright (c) 2021 University of California, Berkeley\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Zhekun Luo\n# --------------------------------------------------------\n\nimport os\nimport torch\nfrom model impor... | [
[
"torch.utils.data.DataLoader",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nttpc/anymotion-python-sdk | [
"72afa34a08a21231a361a6f02368e6cbec090a55"
] | [
"anymotion_sdk/extras.py"
] | [
"from pathlib import Path\n\nimport cv2\nimport numpy as np\n\n\ndef read_image(path: Path) -> np.ndarray:\n \"\"\"Read the image from the file in numpy format.\n\n Args:\n path: The path to the file to be read.\n\n Returns:\n Image data in numpy format, where the shape is (Height, Width, Cha... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johndpope/text2image_manipulation | [
"ce32ed0e3055441002155f605bbd3d3f3421326d"
] | [
"mapper/training/ranger.py"
] | [
"import math\nimport torch\nfrom torch.optim.optimizer import Optimizer\n\n\nclass Ranger(Optimizer):\n\tdef __init__(self, params, lr=1e-3, # lr\n\t\t\t\t alpha=0.5, k=6, N_sma_threshhold=5, # Ranger options\n\t\t\t\t betas=(.95, 0.999), eps=1e-5, weight_decay=0, # Adam options\n\t\t\t\t use_gc=True, gc_conv_on... | [
[
"torch.empty_like",
"torch.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CISiPLab/cisip-GreenCap | [
"74c3f8461772016eec49840822f1407db4abe2b2"
] | [
"coca/models/relation_transformer_prune.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 09 Oct 2020 17:27:20\n@author: jiahuei\n\"\"\"\nimport logging\nimport torch.nn as nn\nfrom copy import deepcopy\nfrom argparse import ArgumentParser, _ArgumentGroup\nfrom typing import Union\nfrom . import register_model\nfrom . import relation_transformer as rtrans\nfr... | [
[
"torch.nn.ReLU",
"torch.nn.Dropout",
"torch.nn.Module.__init__",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
diogenesjusto/flask_leaderboard | [
"86dac90785e01747ffbde99e6ba65cf42e4c016e"
] | [
"main.py"
] | [
"import numpy as np \nimport pandas as pd\nimport os\nimport datetime as dt\nimport time\n\nfrom flask import Flask,render_template,url_for,request,g, flash, redirect,jsonify\nfrom werkzeug.utils import secure_filename\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_login imp... | [
[
"pandas.read_sql"
]
] | [
{
"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": []
}
] |
lhq1/legal-predicetion | [
"c9186b132e79830fd4e855777b06a601d76bf0a2"
] | [
"train.py"
] | [
"import argparse\nimport os\nimport pdb\nimport torch\n\nfrom net.model import get_model\nfrom net.file_reader import init_dataset, init_transformer\nfrom net.work import train_file\nfrom net.utils import print_info\nfrom net.parser import ConfigParser\nfrom net.loader import init\nfrom net.utils import init_thulac... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hieuthehungry/OCR_en_vn | [
"40c69a948107dfd59441563c78b6a070a83b6b64"
] | [
"new_train.py"
] | [
"from __future__ import print_function\nfrom __future__ import division\n\nimport argparse\nimport random\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nimport torch.utils.data\nfrom torch.autograd import Variable\nimport numpy as np\nfrom warpctc_pytorch import CTCLoss\nimport os... | [
[
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.FloatTensor",
"torch.cuda.is_available",
"torch.IntTensor",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
robertholding/medical_image_segmentation | [
"0b4be1710e01ef9271bf295e9830747dd9232d96"
] | [
"code/baseline_aug.py"
] | [
"# part of this script was taken from https://github.com/jocicmarko/ultrasound-nerve-segmentation\nimport argparse\nfrom glob import glob\n\nimport numpy as np\nfrom PIL import Image\nfrom keras import backend as K\nfrom keras import losses\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlat... | [
[
"tensorflow.zeros_like",
"numpy.array",
"tensorflow.equal",
"tensorflow.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
hujieqing/needle-in-hashbrown | [
"2715c92f0131b1e1e31581d6b8c10c4d28294417"
] | [
"model.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch_geometric as tg\nimport torch.nn.functional as F\nfrom torch.nn import init\n\n####################### Basic Ops #############################\n# PGNN layer, only pick closest node for message passing\n\n\nclass PGNN_layer(nn.Module):\n def __init__(self, input_... | [
[
"torch.nn.functional.normalize",
"torch.mean",
"torch.nn.init.calculate_gain",
"torch.cat",
"torch.nn.functional.dropout",
"torch.nn.init.constant_",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ElieKadoche/gym_missile_command | [
"e729721024005d2adcabc759768756516c70bc06"
] | [
"gym_missile_command/game/batteries.py"
] | [
"\"\"\"Anti-missiles batteries.\"\"\"\n\nimport cv2\nimport numpy as np\n\nfrom gym_missile_command.config import CONFIG\nfrom gym_missile_command.utils import get_cv2_xy\n\n\nclass Batteries():\n \"\"\"Anti-missiles batteries class.\n\n Attributes:\n NB_BATTERY (int): the number of batteries. It only ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
clnrp/logicqubit-gpu | [
"5de45887e95d3bd5d9316d594ab7049604f52962"
] | [
"logicqubit/logic.py"
] | [
"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n# Author Cleoner S. Pietralonga\n# e-mail: cleonerp@gmail.com\n# Apache License\n\nimport sympy as sp\nimport numpy as np\nfrom sympy.physics.quantum import TensorProduct\nfrom cmath import *\nimport random\n\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.... | [
[
"matplotlib.pyplot.suptitle",
"numpy.meshgrid",
"numpy.arange",
"matplotlib.pyplot.bar",
"matplotlib.cm.rainbow",
"matplotlib.pyplot.xticks",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
llondon6/koalas | [
"bc778ba492027b3d40f9c92ef44da5949d0e43c7"
] | [
"positive/physics.py"
] | [
"\n#\nfrom __future__ import print_function\nfrom . import *\nfrom positive.api import *\nfrom positive.plotting import *\nfrom positive.learning import *\n\n# >>>>>>>>>>>> #\n# misc.py\n# >>>>>>>>>>>> #\n\n\n# High level function for calculating remant mass and spin\ndef remnant(m1,m2,chi1,chi2,arxiv=None,verbose=... | [
[
"numpy.dot",
"numpy.complex128",
"matplotlib.pyplot.legend",
"numpy.amax",
"numpy.sqrt",
"numpy.linspace",
"numpy.vstack",
"scipy.optimize.root",
"scipy.fftpack.fft",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"scipy.fftpack.fft... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
bansan85/diptych | [
"297e6b291893a6e7abaab16025dc04d7d397a493"
] | [
"diptych/cv2ext.py"
] | [
"from typing import Any, Dict, List, Optional, Tuple\n\nimport cv2\nimport numpy as np\n\nfrom . import compute\nfrom .angle import Angle, is_between\nfrom .debug_image import DebugImage\nfrom .parameters import HoughLinesParameters\n\n\ndef charge_image(fichier: str) -> np.ndarray:\n return cv2.imread(fichier, ... | [
[
"numpy.minimum",
"numpy.abs",
"numpy.maximum",
"numpy.clip",
"numpy.asarray",
"numpy.linalg.norm",
"numpy.ones",
"numpy.concatenate",
"numpy.arctan2",
"numpy.sign",
"numpy.mean",
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
omars-lab/demo-py | [
"08d656968ee330e607b100e58727b4503a5cde33"
] | [
"discovery/download-csv-from-google-sheets.py"
] | [
"# This is based off of the following links ...\n# -\n\n# For it to work ... a service account must be created ... and the google sheet file must be shared with that service account as well\n\n# Sample Usage:\n# python test.py '/Users/oeid/Downloads/project-id-0905112707509830376-028dc8bad02b.json' '12lCPaKogTJx9d5... | [
[
"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": []
}
] |
TristanBilot/phishGNN | [
"69293337640cf87cf4363167b3342dd3ca3fd1b0",
"69293337640cf87cf4363167b3342dd3ca3fd1b0"
] | [
"phishGNN/dataset_v1.py",
"phishGNN/utils/fix_https.py"
] | [
"import glob\nimport os\n\nimport pandas as pd\nimport torch\nimport torch_geometric\nfrom torch_geometric.data import Data, Dataset\nfrom tqdm import tqdm\n\nimport dataprep\nfrom utils.utils import normalize_www_prefix\n\nprint(f\"Torch version: {torch.__version__}\")\nprint(f\"Cuda available: {torch.cuda.is_avai... | [
[
"pandas.Series",
"torch.cuda.is_available",
"torch.tensor"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
Geoffrey-Paris/Deep-SVDD-PyTorch | [
"49cf5aa10452c5cd4aa1f8c2cda81aedd18bd00b"
] | [
"src/datasets/criann.py"
] | [
"from numpy.random.mtrand import normal\nfrom torch.utils.data import TensorDataset\nimport torch\nfrom PIL import Image\nfrom base.torchvision_dataset import TorchvisionDataset\nfrom .preprocessing import get_target_label_idx, global_contrast_normalization\n\nimport numpy as np\n\n\nimport torchvision.transforms a... | [
[
"numpy.random.random",
"torch.Tensor",
"numpy.random.choice",
"numpy.arange",
"numpy.concatenate",
"numpy.random.rand",
"numpy.load",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexeybokhovkin/CAD-Deform | [
"462fc6c97d91cc579f9e5551ed983cc10ecb9976"
] | [
"shapefit/deform/src/deformation/preproc.py"
] | [
"import numpy as np\nimport trimesh\nfrom trimesh.grouping import clusters\n\nfrom .utils import filter_edges_by_parts\n\n\ndef compute_bitriangles(mesh_unique_faces, mesh_unique_edges):\n bitriangles = {}\n for face in mesh_unique_faces:\n edge_1 = tuple(sorted([face[0], face[1]]))\n if edge_1 ... | [
[
"numpy.linalg.inv",
"numpy.eye",
"numpy.sort",
"numpy.argmax",
"numpy.argmin",
"numpy.array",
"numpy.sum",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johnh865/election_sim | [
"b73b7e65f1bb22abb82cbe8442fcf02b0c20894e"
] | [
"archive/examples/electionNd/post.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport pandas as pd\nfrom itertools import cycle\n\n\nimport votesim\nfrom votesim.models import spatial\nfrom votesim.utilities.write import StringTable\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\n###################################################... | [
[
"numpy.arange",
"numpy.percentile",
"pandas.DataFrame",
"numpy.mean",
"pandas.read_pickle"
]
] | [
{
"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": []
}
] |
giangdip2410/Framingham-Heart-Study | [
"8f18abab2a71bc5fab4d982adffe7e96d720d7e2"
] | [
"datasets.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, LabelEncoder\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.p... | [
[
"sklearn.metrics.roc_auc_score",
"pandas.concat",
"pandas.read_csv",
"numpy.log",
"sklearn.linear_model.LogisticRegression",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.impute.SimpleImputer",
"sklearn.model_selection.train_test_spl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
aiboxlab/TSPNet | [
"359402151afd262857cde6fae3fc13445d73c9a7"
] | [
"fairseq/modules/logsumexp_moe.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\r\n#\r\n# This source code is licensed under the MIT license found in the\r\n# LICENSE file in the root directory of this source tree.\r\n\r\nimport torch\r\n\r\n\r\nclass LogSumExpMoE(torch.autograd.Function):\r\n \"\"\"Standard LogSumExp forward pass, but use... | [
[
"torch.logsumexp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tingying2020/incubator-mxnet | [
"287e3b52524ef791a0429a741084fe2db28b0c44"
] | [
"python/mxnet/symbol/numpy/_symbol.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.isscalar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cjekel/rbfopt_py | [
"1f0e443a6e40b0441c1a3fa6751dc3cecc4955bb"
] | [
"examples/himmelblau.py"
] | [
"import numpy as np\nimport sbopt\n\n\ndef my_fun(x):\n # define Himmelblau's function to minmize\n A = (x[0]**2 + x[1] - 11.0)**2\n B = (x[0] + x[1]**2 - 7.0)**2\n return A + B\n\n\nbounds = np.zeros((2, 2))\nbounds[:, 0] = -5.0\nbounds[:, 1] = 5.0\n\n# set random seed for reproducibility\nnp.random.se... | [
[
"numpy.zeros",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
silky/fatiando | [
"5041c6b29758a5e73e9d7b2b906fa5e493fd9aba"
] | [
"test/test_gravmag_sphere.py"
] | [
"import numpy as np\n\nfrom fatiando.mesher import Sphere\nfrom fatiando.gravmag import _sphere_numpy, sphere\nfrom fatiando import utils, gridder\n\nmodel = None\nxp, yp, zp = None, None, None\ninc, dec = None, None\nprecision = 10 ** (-15)\nlower_precision = 10 ** (-12)\n\n\ndef setup():\n global model, xp, yp... | [
[
"numpy.all",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
josjsjen/cifar10_pytorch_feature_extractions | [
"771753b2a50cf574dab14742307ebb91f7cdba59"
] | [
"models/resnet.py"
] | [
"'''ResNet in PyTorch.\n\nFor Pre-activation ResNet, see 'preact_resnet.py'.\n\nReference:\n[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n Deep Residual Learning for Image Recognition. arXiv:1512.03385\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass BasicBlock(nn.Mo... | [
[
"torch.nn.Sequential",
"torch.randn",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tuzhucheng/sent-sim | [
"ebda09322be1dca3e967b80ffcf6437adb789132"
] | [
"metrics/retrieval_metrics.py"
] | [
"import os\nimport subprocess\nimport uuid\n\nfrom ignite.exceptions import NotComputableError\nfrom ignite.metrics.metric import Metric\nimport torch\n\n\nclass RetrievalMetrics(Metric):\n \"\"\"\n Calculates retrieval metrics using trec_eval\n `update` must receive output of the form (ids, y_pred, y).\n ... | [
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GELIELEO/vision_for_paper | [
"e01c6f59dcbcc0e6b7c9a8bdc7a22f936bbea0ce"
] | [
"test.py"
] | [
"import os \nfrom gym_robothor.envs.robothor_env import RoboThorEnv, env_generator\nimport torch\nfrom algorithms.ppo.core import ActorCritic\n\nhidden_state = 512\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\nall_files = os.walk('./pt')\n\n\nfor root, dirs, files in all_fi... | [
[
"torch.device",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wysockipiotr/deep-scanner | [
"d80799a3790d51a90374b8904aebc8e12a1e783e"
] | [
"research/scripts/scanner/transformation.py"
] | [
"import typing\n\nimport cv2\nimport numpy as np\n\nimport contour\nimport utils\n\n\ndef resize_image(image: np.ndarray,\n width: typing.Optional[int] = None,\n height: typing.Optional[int] = None,\n interpolation=cv2.INTER_AREA):\n \"\"\"\n Resize image using ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kokimishev/TensorflowTTS | [
"234db13ccf0eb53bd7373c409514fb092588a755"
] | [
"tensorflow_tts/models/fastspeech.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright 2020 The FastSpeech Authors, The HuggingFace Inc. team and Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# ... | [
[
"tensorflow.keras.layers.LayerNormalization",
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"numpy.sqrt",
"tensorflow.zeros",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.keras.Sequential",
"tensorflow.pad",
"numpy.sin",
"tensorflow.gather",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
JohnleeHIT/SEX-Net | [
"7153c5fe0f89ce07f7dc243e00a6231c44d5979a"
] | [
"src/config.py"
] | [
"import torch\nimport torch.backends.cudnn as cudnn\n\n\nclass Config(object):\n drop_rate = 0\n lamda_sem = 0.2\n alpha = [0.1, 1]\n gamma = 2\n data_path = \"/home/lixiangyu/Dataset/new_hemorrhage_cut_slices_count\"\n # Network setting\n pre_trained = True\n load_old_model = False\n aug... | [
[
"torch.cuda.device_count",
"torch.cuda.current_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bsamseth/project-euler | [
"60d70b117960f37411935bc18eab5bb2fca220e2"
] | [
"083/83.py"
] | [
"\"\"\"\nThe minimal path sum in the 5 by 5 matrix below, by starting in any cell in the\nleft column and finishing in any cell in the right column, and only moving up,\ndown, and right, is indicated in red and bold; the sum is equal to 994.\n\n A = np.array([ [131, 201, 630, 537, 805],\n [673, 96... | [
[
"numpy.array",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alesgenova/cclib | [
"a337bfd8f57df2f791c30abaa74c6942e8879607"
] | [
"cclib/parser/psi4parser.py"
] | [
"# -*- coding: utf-8 -*-\r\n#\r\n# Copyright (c) 2017, the cclib development team\r\n#\r\n# This file is part of cclib (http://cclib.github.io) and is distributed under\r\n# the terms of the BSD 3-Clause License.\r\n\r\n\"\"\"Parser for Psi4 output files.\"\"\"\r\n\r\nfrom collections import namedtuple\r\n\r\nimpor... | [
[
"numpy.all",
"numpy.array",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
simonchu47/TFSegmentation | [
"52f268523daed3f650dc21538e97f159f10c019c"
] | [
"train/train.py"
] | [
"\"\"\"\nTrainer class to train Segmentation models\n\"\"\"\n\nfrom train.basic_train import BasicTrain\nfrom metrics.metrics import Metrics\nfrom utils.reporter import Reporter\nfrom utils.misc import timeit\nfrom utils.average_meter import FPSMeter\n\nfrom tqdm import tqdm\nimport numpy as np\nimport tensorflow a... | [
[
"tensorflow.device",
"numpy.expand_dims",
"numpy.asarray",
"numpy.concatenate",
"numpy.mean",
"tensorflow.summary.scalar",
"tensorflow.get_collection",
"tensorflow.summary.image",
"numpy.arange",
"tensorflow.Session",
"numpy.load",
"numpy.zeros",
"tensorflow.tra... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.... |
sbreuers/BiternionNets-ROS | [
"954d6a2fbd97a01231f3411b366f3a3cccae5cf9"
] | [
"scripts/common.py"
] | [
"import numpy as np\n\n\ndef normalized(x, axis=-1):\n return x / (np.linalg.norm(x, axis=axis, keepdims=True) + 1e-12)\n\n\ndef sigmoid(x):\n return np.exp(-np.logaddexp(0, -x))\n\n\ndef deg2bit(deg):\n rad = np.deg2rad(deg)\n return np.stack([np.cos(rad), np.sin(rad)], axis=-1)\n\n\ndef bit2deg(bit):\n r... | [
[
"numpy.nanpercentile",
"numpy.isnan",
"numpy.linalg.norm",
"numpy.cos",
"numpy.sin",
"numpy.all",
"numpy.percentile",
"numpy.deg2rad",
"numpy.arctan2",
"numpy.mean",
"numpy.logaddexp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jimregan/spaCy | [
"7d50804644eccb3dabd421ce413d1d2f748814a5"
] | [
"spacy/training/initialize.py"
] | [
"from typing import Union, Dict, Optional, Any, IO, TYPE_CHECKING\nfrom thinc.api import Config, fix_random_seed, set_gpu_allocator\nfrom thinc.api import ConfigValidationError\nfrom pathlib import Path\nimport srsly\nimport numpy\nimport tarfile\nimport gzip\nimport zipfile\nimport tqdm\nfrom itertools import isli... | [
[
"numpy.asarray",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gilbertfrancois/mxnet-cookbook | [
"95add0415d7c1ddfc4bcdd3d3a41c2e44162f888"
] | [
"src/dcgan/dcgan_gluon.py"
] | [
"# Copyright 2019 Gilbert Francois Duivesteijn\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.axis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
edeustua/wepy | [
"352d4c1316b20e839aae8824eedd66f0f2d0b456"
] | [
"src/wepy/resampling/resamplers/clone_merge.py"
] | [
"import numpy as np\n\nfrom wepy.resampling.resamplers.resampler import Resampler, ResamplerError\nfrom wepy.resampling.decisions.clone_merge import MultiCloneMergeDecision\n\n\nclass CloneMergeResampler(Resampler):\n \"\"\"Abstract base class for resamplers using the clone-merge decision\n class.\n\n Prov... | [
[
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Agnostic12/keras-rl | [
"183c1b3bc26ef251d7ecac6e3060efc70c9c436c"
] | [
"examples/torch_codes/ddqn_atari_200202_1.py"
] | [
"\"\"\"\nThis is a torch implementation of DDQN BreakoutDeterministic-v4\n\nThis is based on dqn_atari_200131_4.py. I will revise it to DDQN\n\n1. change batch size\n2. revise to DDQN\n\nWRONG REVISION: OOM Error!\n\"\"\"\nimport os\nfrom time import time\nfrom copy import deepcopy\n\nimport numpy as np\nimport gym... | [
[
"torch.mean",
"torch.max",
"numpy.arange",
"torch.nn.Conv2d",
"torch.from_numpy",
"numpy.ones",
"numpy.all",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.gather",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Edwolt/Jogo-de-Tabuleiro | [
"7e7680cd5ccdf0b2d0005b5160a73b5bf9bda964"
] | [
"Class/Recursos.py"
] | [
"\"\"\"\nContém as seguintes classes para utilizar os recursos (Imagens e Configd)\n * Recurso -> Guarda todos os recursos em seus atributos\n * GeradorRecursos -> Serve para mudar as imagens\n\"\"\"\n\nimport cv2\nimport numpy\nimport pygame\n\nfrom Class.PecasXadrez import nome_pecas\n\n\ndef all_configs(jo... | [
[
"numpy.uint8",
"numpy.dstack",
"numpy.zeros",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
beckerrh/simfempy | [
"10bf30f330d921bffaa8572bbd73286da5971905"
] | [
"simfempy/fems/p1sys.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 4 18:14:29 2016\n\n@author: becker\n\"\"\"\n\nimport numpy as np\nimport scipy.linalg as linalg\nimport scipy.sparse as sparse\nfrom simfempy.fems import femsys, p1\n\n#=================================================================#\nclass P1sys(femsys.Femsys... | [
[
"numpy.add.at",
"numpy.einsum",
"scipy.sparse.dok_matrix",
"numpy.ones",
"scipy.sparse.dia_matrix",
"numpy.zeros_like",
"matplotlib.gridspec.GridSpec",
"scipy.linalg.norm",
"numpy.mean",
"numpy.repeat",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.sum",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
WarlockUnicorn/numerical_ode | [
"54ca00f15175a7768212fb6a2bc9dc61ed768d68"
] | [
"radioactive_decay.py"
] | [
"# =============================================================================\n# ***** radioactive_decay.py *****\n# Python script to solve first order ODE for radioactive decay.\n#\n# A = -dN/dt = lambda*N\n#\n# Author: Ryan Clement\n# Created: July 2021\n#\n# Change Log:\n# Who:\n# Date: M... | [
[
"numpy.log",
"numpy.exp",
"matplotlib.pyplot.subplots",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CLARIN-PL/multiemo | [
"016081141cfdd7e7d1eb631661f466cce41c5a2e"
] | [
"src/custom_labse.py"
] | [
"from mosestokenizer import MosesSentenceSplitter\nimport torch\nfrom transformers import AutoTokenizer, AutoModel\n\nfrom src.language_identification import LanguageIdentification\n\n\nclass CustomLabse:\n def __init__(self):\n self.language_identification = LanguageIdentification()\n self.labse_t... | [
[
"torch.no_grad",
"torch.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mueller-franzes/ReliableRadiomics | [
"66500b26baeb0a019b3eafb631b36a23405748be"
] | [
"reliableradiomics/utils/radiomics.py"
] | [
"import pandas as pd\nimport numpy as np \nimport h5py \nimport json\n\n\ndef load_features(path_feat_file, anno_names=['GT', 'Pred'], \n exclude_features=[]):\n \"\"\"Load feature values from .csv and group them by annotators and feature name\"\"\"\n # Example: exclude_features=['Flatness', ... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
cgq45120/HDACAPS | [
"5d5b416de893c65ef31f864dee04631cd3914ae1"
] | [
"cnn_model.py"
] | [
"import tensorflow as tf\nimport datetime\nfrom tensorflow.examples.tutorials.mnist import input_data\nimport numpy as np\nimport import_data\nimport math\nfrom tqdm import tqdm\n\ndef weight_variable(shape):#权重正态分布初始化\n initial=tf.truncated_normal(shape,stddev=0.1)\n return tf.Variable(initial)\n \n# tf.t... | [
[
"numpy.sqrt",
"tensorflow.nn.max_pool",
"tensorflow.reduce_sum",
"tensorflow.train.AdamOptimizer",
"numpy.exp",
"tensorflow.nn.conv2d",
"numpy.hstack",
"tensorflow.Variable",
"tensorflow.gradients",
"tensorflow.train.exponential_decay",
"tensorflow.ConfigProto",
"te... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
lee-man/pixel-cnn-pp | [
"5f8d466aa860d911c832a5bf12a7ad664f07d462"
] | [
"histogram.py"
] | [
"import time\nimport os\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torchvision import datasets, transforms, utils\nfrom tensorboardX import SummaryWriter\nfrom utils import * \nfrom model import * \n... | [
[
"torch.cuda.synchronize",
"matplotlib.pyplot.title",
"matplotlib.pyplot.switch_backend",
"torch.manual_seed",
"matplotlib.pyplot.savefig",
"torch.no_grad",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexhnguyen/MoreMori | [
"1921618a088faf076a3e9319add79015f0585bd1"
] | [
"augment/cyclegan_augment/augment_background.py"
] | [
"import os\nimport cv2\nimport numpy as np\nfrom PIL.Image import fromarray\nfrom random import randint\n\n\nclass AugmentBackground(object):\n\n def __init__(self, dataroot, isTrain, fineSize):\n self.image_size = fineSize\n folder = 'test'\n if isTrain:\n folder = 'train'\n ... | [
[
"numpy.array",
"numpy.multiply"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bpopeters/NNowledge | [
"3d9cf25d9df2e381d104acb471e9ff2243ea4ccb"
] | [
"nnow/IO.py"
] | [
"from collections import Counter\nfrom itertools import count, chain, takewhile\nimport pandas as pd\nimport torch\nfrom torch.nn.utils.rnn import pad_sequence\n\nBOS = '<s>'\nEOS = '</s>'\nUNK = '<unk>'\nPAD = '<blank>'\n\n\ndef tokenize_line(line, lowercase=True):\n if lowercase:\n line = line.lower()\n... | [
[
"torch.LongTensor",
"torch.nn.utils.rnn.pad_sequence",
"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": []
}
] |
fanszoro/tfx | [
"b1acab7bf89ec1364c96b9b4e2cc41594407b86c"
] | [
"tfx/dsl/placeholder/placeholder_test.py"
] | [
"# Lint as: python3\n# Copyright 2020 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# Unl... | [
[
"tensorflow.test.main"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kodalli/ChemStructureRecognition | [
"2d4e6b3421b5313873f134e65870583c9ea7b172"
] | [
"src/lines.py"
] | [
"import cv2 as cv\nimport numpy as np\n\n\ndef draw_lines(img, line_color, line_thickness, lines_array):\n line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n\n if lines_array is not None:\n for line in lines_array:\n for points in line:\n x1, y1, x2, y2 = p... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lhztop/zipline-extension-cn-broker | [
"84911b42ef953ef061762b2f36ca12bc5529cb63"
] | [
"zipline_cn_extension/trade/citic.py"
] | [
"# coding=utf-8\r\n\r\nfrom __future__ import print_function\r\nfrom __future__ import absolute_import\r\nfrom dbfread import DBF\r\nimport os\r\nimport random\r\nimport time\r\nimport datetime\r\nimport pandas as pd\r\nfrom collections import namedtuple\r\ntry:\r\n from kuanke.user_space_api import *\r\nexcept:... | [
[
"pandas.to_datetime"
]
] | [
{
"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": []
}
] |
maxnvdm/ControlLabEquipment | [
"60c371bbcd1ca0023d1c13f20adbf2a9914bdc24"
] | [
"noiseReadings.py"
] | [
"import pyvisa\nimport time\nimport numpy as np\nimport pandas as pd\n\n# inital params\ndata = np.array(\n [\n (\n \"time[s]\",\n \"Voltage CH1\",\n \"Voltage CH2\",\n \"Current CH1\",\n \"Current CH2\",\n \"Power CH1\",\n \"Pow... | [
[
"numpy.append",
"numpy.array",
"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": []
}
] |
bombrun/GaiaLab | [
"186b39621cdc5d8fb165907107b1aaed933f0c8f"
] | [
"GaiaLab/scan/geometric_scanner/frame_transformations.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\nfrom .quaternion import Quaternion\n\n\ndef to_quaternion(vector):\n \"\"\"\n converts vector to quaternion with first component set to zero.\n :param vector: 3D np.array\n :return: Quaternion (0, vector*)\n \"\"\"\n return Quaternion(0, vector[0], vec... | [
[
"numpy.sqrt",
"numpy.arcsin",
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin",
"numpy.arctan2",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CuiRuikai/crf-as-ddn | [
"01d9bc0a14a376e8c3094597f306be414b74562e",
"01d9bc0a14a376e8c3094597f306be414b74562e"
] | [
"ddn/pytorch/suppix_pooling.py",
"utils/save_prediction.py"
] | [
"import torch\n\n# modify the hessian matrix's shape\n# it should be a summation along each channel\n\nclass Quadratic():\n is_convex = True\n\n @staticmethod\n def phi(z, alpha = 1.0):\n # Quadratic penalty function\n phi_at_z = 0.5 * torch.pow(z, 2)\n return phi_at_z\n\n @staticme... | [
[
"torch.enable_grad",
"torch.zeros",
"torch.zeros_like",
"torch.tensor",
"torch.exp",
"torch.optim.LBFGS",
"torch.pow",
"torch.clamp",
"torch.ones_like"
],
[
"torch.max"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
acmlia/brain | [
"53d90958d9996eb05a099b4cae52aa3d484a84cd"
] | [
"src/meteoro_skills.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 10 19:48:36 2019\n\n@author: dvdgmf\n\"\"\"\nimport numpy as np\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\n\n\nclass CategoricalScores:\n\n @staticmethod\n ... | [
[
"numpy.corrcoef",
"sklearn.metrics.mean_absolute_error",
"numpy.nanmean",
"sklearn.metrics.mean_squared_error"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
niemasd/VirStrain | [
"d6d6f882891b0fe273bc943e81861fa1a8bec469"
] | [
"bin/S4_Plot_strain_cov.py"
] | [
"import re\nimport os\nimport pandas as pd\nimport numpy as np\n#import matplotlib.pyplot as plt\nfrom _plotly_future_ import v4_subplots\nimport plotly.plotly as py\nimport plotly.graph_objs as go\nfrom plotly.subplots import make_subplots\n#from plotly import tools\n\nsfl=pd.read_csv('Mps_ps_depth.csv')\nsf2=pd.... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
beamlab-hsph/coverage_quantification | [
"901b4177395b220f97ae073fe174d185aca7f8bc"
] | [
"src/models/coverage_calculation_svi_methods.py"
] | [
"import csv\nimport h5py\nimport os\nimport numpy as np \n\nfrom data_utils import * \n\nDATASETS = choices=['bostonHousing', 'concerete', 'energy', 'kin8nm', \n 'naval-propulsion-plant', 'power-plant', 'protein-tertiary-structure', 'wine-quality-red', 'yacht']\n\nwith open('./data/processed/model_output_summa... | [
[
"numpy.concatenate",
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DimensionX004/greyatom-python-for-data-science | [
"d9355ef4f0318af3c05d47d02796d0d32f5d6a97"
] | [
"Loan-Approval-Analysis/code.py"
] | [
"# --------------\n# Importing header files\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom scipy.stats import mode \r\n \r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\n\r\n#Reading file\r\nbank_data = pd.read_csv(path)\r\n\r\n#Code starts here\r\nbank = pd.read_csv(path)\r\ncategorical_var = b... | [
[
"pandas.read_csv",
"pandas.pivot_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
powerscv1/openpilot | [
"7290f5886db367a68c18ff64fad660b3a593bd96"
] | [
"selfdrive/controls/lib/latcontrol_lqr.py"
] | [
"import math\nimport numpy as np\n\nfrom common.numpy_fast import clip\nfrom common.realtime import DT_CTRL\nfrom cereal import log\nfrom selfdrive.controls.lib.latcontrol import LatControl, MIN_STEER_SPEED\n\nfrom common.params import Params\nfrom decimal import Decimal\n\nclass LatControlLQR(LatControl):\n def _... | [
[
"numpy.sign",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hackiey/QAnet-pytorch | [
"aa9225bf27498bf43f4c9321fba6d00dfd312490"
] | [
"qanet/highway.py"
] | [
"from torch import nn\nimport torch.nn.functional as F\n\n\nclass Highway(nn.Module):\n \"\"\" Version 1 : carry gate is (1 - transform gate)\"\"\"\n\n def __init__(self, input_size=500, n_layers=2):\n super(Highway, self).__init__()\n\n self.n_layers = n_layers\n\n self.transform = nn.Mo... | [
[
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.Conv1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frodre/LMR | [
"4c00d3f9db96447e69bd3f426d59524f7b5f3ef5"
] | [
"verification/LMR_verify_proxy_plot.py"
] | [
"\"\"\"\nModule: LMR_verify_proxy_plot.py\n\nPurpose: Plotting of summary statistics from proxy-based verification. Both proxy \n chronologies that were assimilated to create reconstructions and those witheld for \n independent verification are considered.\n \nInput: Reads .pckl files containing ver... | [
[
"matplotlib.pyplot.legend",
"scipy.stats.ks_2samp",
"numpy.linspace",
"numpy.asarray",
"numpy.in1d",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.plot",
"matplotlib.colors.from_levels_and_colors",
"numpy.max",
"numpy.mean",
"numpy.histogram",
"matplotlib.pyplot.table"... | [
{
"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"
... |
davisyoshida/vqgan-haiku | [
"8495227ae3ec4a771bfbd8bfa4766726440f68be"
] | [
"vqgan/vqgan.py"
] | [
"import haiku as hk\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\n\nfrom .config import ConfigModule\nfrom .utils import interpolate_nearest, make_conv, maybe_hk_dropout, swish\n\ndef group_norm(x):\n return hk.GroupNorm(groups=32, eps=1e-6, data_format='channels_first')(x[None])[0]\n\nclass ResnetBl... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yiwen26/NLP_banking_chatbot | [
"4661778b4260ed56e301f4405dac79696846b0a6"
] | [
"general chatbot/evaluate.py"
] | [
"import torch\nimport random\nfrom train import indexesFromSentence\nfrom load import SOS_token, EOS_token\nfrom load import MAX_LENGTH, loadPrepareData, Voc\nfrom model import *\nfrom pairlist import fixedpair\nimport emoji\n#from audio import *\n\nUSE_CUDA = torch.cuda.is_available()\ndevice = torch.device(\"cuda... | [
[
"torch.LongTensor",
"torch.zeros",
"torch.load",
"torch.set_grad_enabled",
"torch.log",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EddieMG/LateTemporalModeling3DCNN | [
"94c87dc1d31d09bc310d0e735a2e55453976cb0d"
] | [
"eval/spatial_demo_bert.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 25 13:52:03 2019\n\n@author: esat\n\"\"\"\n\nimport os, sys\nimport numpy as np\nimport pandas as pd\n\nimport time\nimport argparse\n\nfrom ptflops import get_model_complexity_info\n\nimport torch\nimport torch.nn.parallel\nimport torch.o... | [
[
"torch.load",
"sklearn.metrics.confusion_matrix",
"numpy.mean",
"pandas.DataFrame.from_dict",
"torch.nn.DataParallel"
]
] | [
{
"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": []
}
] |
STARInformatics/biomed-workbench | [
"510f533d9ec1296f69b0a5e979da5831d0aeb2dd"
] | [
"backend/workflow/__init__.py"
] | [
"from cachetools import cached, LRUCache\nfrom threading import RLock\n\nfrom .biolink_client import BioLinkWrapper\nimport pandas as pd\nfrom os import makedirs\nfrom html3.html3 import XHTML\n\nfrom .Mod0_lookups import LookUp\nfrom .Mod1A_functional_sim import FunctionalSimilarity\nfrom .Mod1B1_phenotype_similar... | [
[
"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": []
}
] |
cyysc1998/face_painter | [
"70941443a5c738babddde488bfc41f1fae4a38aa"
] | [
"PaintTransformer_backup/train/util/morphology.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Erosion2d(nn.Module):\n\n def __init__(self, m=1):\n super(Erosion2d, self).__init__()\n self.m = m\n self.pad = [m, m, m, m]\n self.unfold = nn.Unfold(2 * m + 1, padding=0, stride=1)\n\n def forward(s... | [
[
"torch.nn.Unfold",
"torch.min",
"torch.max",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZJU-Robotics-Lab/CICT | [
"ff873a03ab03d9113b8db96d26246939bb5da0d4",
"ff873a03ab03d9113b8db96d26246939bb5da0d4"
] | [
"utils/__init__.py",
"scripts/real/draw_path.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport time\nimport numpy as np\nimport cv2\nfrom PIL import Image\n\nclass Singleton(object):\n _instance = None\n\n def __new__(cls, *args, **kw):\n if not cls._instance:\n cls._instance = super(Singleton, cls).__new__(cls) \n retur... | [
[
"numpy.asarray",
"numpy.roll",
"numpy.ones"
],
[
"numpy.dot",
"numpy.linspace",
"torch.load",
"torch.cat",
"torch.manual_seed",
"numpy.cos",
"numpy.stack",
"numpy.sin",
"numpy.ones",
"torch.no_grad",
"torch.FloatTensor",
"torch.cuda.is_available",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
s456951753/Customized_QUANTAXIS_With_Crius | [
"13b3e965bee07108bd48965ef9621dbedd4331df"
] | [
"AY/data_analysis/backtest_highestROA_LowestPE.py"
] | [
"from collections import OrderedDict\nfrom operator import itemgetter\n\nimport QUANTAXIS.QAFetch.QAQuery as dbQuery\nfrom QUANTAXIS.QAUtil import DATABASE\nimport pandas as pd\nfrom pandas.io.json import json_normalize\n\nimport AY.Crius.Utils.configuration_file_service as config_service\nimport AY.Crius.Utils.num... | [
[
"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": []
}
] |
pmav99/pyschism | [
"b533d29b881da6cd4f408e91a4749f86451c042a"
] | [
"pyschism/forcing/atmosphere/gfs.py"
] | [
"from datetime import datetime, timedelta\nfrom enum import Enum\nimport pathlib\nimport tempfile\nfrom typing import Union\nimport logging\n\n\nimport pytz\nfrom matplotlib.transforms import Bbox\nfrom netCDF4 import Dataset\nimport numpy as np\n\nfrom pyschism.enums import GFSProduct\nfrom pyschism.forcing.atmosp... | [
[
"numpy.min",
"numpy.arange",
"matplotlib.transforms.Bbox.from_extents",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
easyScience/easyCore | [
"5d16d5b27803277d0c44886f94dab599f764ae0b"
] | [
"easyCore/Symmetry/SymOp.py"
] | [
"__author__ = \"Shyue Ping Ong, Shyam Dwaraknath, Matthew Horton\"\n__copyright__ = \"Copyright 2011, The Materials Project\"\n__version__ = \"1.0\"\n__maintainer__ = \"Shyue Ping Ong\"\n__email__ = \"shyuep@gmail.com\"\n__status__ = \"Production\"\n__date__ = \"Sep 23, 2011\"\n\n# SPDX-FileCopyrightText: 2021 eas... | [
[
"numpy.dot",
"numpy.allclose",
"numpy.einsum",
"numpy.inner",
"numpy.linalg.inv",
"numpy.eye",
"numpy.linalg.norm",
"numpy.ones",
"numpy.round",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
onyd/kivyplot | [
"ce5e2b212eaf2ca33d2744272ab19af344112d12"
] | [
"tests/plot2d_filled_curve_exemple.py"
] | [
"from kivy.app import App\nimport numpy as np\nfrom kivyplot import Plot2D\n\n\nclass MainApp(App):\n\n def build(self):\n N = 20\n points = [np.array([\n i,\n np.sin(2*np.pi*i/(N-1) + np.pi/2)])\n for i in range(N)]\n\n self.root = Plot2D(xmin=0, xmax=N, ste... | [
[
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rediminds/avod | [
"87e4597ba1f301d35f568f6abf577d19e805d6a4"
] | [
"avod/datasets/kitti/kitti_dataset.py"
] | [
"\"\"\"Dataset utils for preparing data for the network.\"\"\"\n\nimport itertools\nimport fnmatch\nimport os\n\nimport numpy as np\nimport cv2\n\nfrom wavedata.tools.core import calib_utils\nfrom wavedata.tools.obj_detection import obj_utils\n\nfrom avod.core import box_3d_encoder\nfrom avod.core import constants\... | [
[
"numpy.asarray",
"numpy.arange",
"numpy.dstack",
"numpy.random.shuffle",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nw2190/CppGPs | [
"eb707e54dff274596238310a654a715930d62214"
] | [
"misc/TEST_CODE/Cholesky_Time_Comparison/SciPy_Cholesky.py"
] | [
"import numpy as np\nimport csv\nimport time\n\nfrom scipy.linalg import cholesky, cho_solve, solve_triangular\n\n# Function for converting time to formatted string\ndef convert_time(t):\n minutes = np.floor((t/3600.0) * 60)\n seconds = np.ceil(((t/3600.0) * 60 - minutes) * 60)\n if (minutes >= 1):\n ... | [
[
"numpy.einsum",
"numpy.reshape",
"numpy.eye",
"numpy.linalg.norm",
"numpy.ceil",
"scipy.linalg.cholesky",
"numpy.floor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
Surfndez/awesome-hand-pose-estimation | [
"70a6c8910264c1adc82810b89897a4534b768759"
] | [
"evaluation/src/util.py"
] | [
"'''\nThis file is modified on https://github.com/guohengkai/region-ensemble-network/blob/master/evaluation/util.py\n'''\n\nimport numpy as np\n\n\ndef get_positions(in_file):\n with open(in_file) as f:\n positions = [list(map(float, line.strip().split())) for line in f]\n return np.reshape(np.array(po... | [
[
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.