repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
tzonglin66/r2c | [
"77813d9e335711759c25df79c348a7c2a8275d72"
] | [
"data/get_bert_embeddings/modeling.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unl... | [
[
"tensorflow.concat",
"tensorflow.nn.log_softmax",
"tensorflow.zeros",
"tensorflow.gfile.GFile",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.truncated_normal_initializer",
"tensorflow.squeeze",
"tensorflow.train.list_variables",
"tensorflow.nn.dropout",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
attre2vec/attre2vec | [
"f36a2581f3d17887d6201a76624d4ced93d6503f"
] | [
"experiments/notebooks/paper_graph_utils.py"
] | [
"\"\"\"Utils for paper graph.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom collections import defaultdict\nimport json\nimport os\nimport pickle\n\nimport networkx as nx\nimport pytorch_lightning as ptl\nimport torch\nfrom torch.utils import data as pt_data\n\nfrom attre2vec.datasets import ed... | [
[
"torch.tensor",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Heovang097/S-DCNet | [
"e340661dfbf04721f3451cd4961a9a414e3e5963"
] | [
"Val.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jul 20 20:06:33 2018\r\n\r\n@author: poppinace\r\n\"\"\"\r\n\r\nimport torch.nn as nn\r\nimport torch\r\nimport torch.optim as optim\r\nfrom torch.utils.data import DataLoader\r\nimport torch.nn.functional as F\r\n#from torchvision import models\r\n\r\nimport os... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WeiZhiHuang/SlowFast | [
"b9a8d876b34bc2f6498a0609ad2591f3e4e907ce"
] | [
"tools/demo_net.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\nimport numpy as np\nimport time\nimport torch\nimport tqdm\n\nfrom slowfast.utils import logging\nfrom slowfast.visualization.async_predictor import AsyncDemo, AsyncVis\nfrom slowfast.visualization.ava_demo_precomput... | [
[
"torch.manual_seed",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
limpins/Deep-Learning-for-Time-Series | [
"237598881f69a0637f16c6bb488db17cd5b23aa4"
] | [
"RNN_GC.py"
] | [
"\"\"\"\nEmail: autuanliu@163.com\nDate: 2018/10/11\n\"\"\"\n\nimport numpy as np\nimport torch\nfrom torch import nn, optim\nimport matplotlib.pyplot as plt\n\nfrom Models import Modeler, RNN_Net, AdaBoundW, AdaBound\nfrom core import (Timer, get_Granger_Causality, get_json_data, get_mat_data, make_loader, matshow... | [
[
"numpy.squeeze",
"torch.stack",
"numpy.array",
"torch.nn.MSELoss",
"numpy.savetxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Agnar22/NeuralNetwork | [
"bb01675d45596e5e22ac7da3b60cb5604c38e40f"
] | [
"NeuralNetwork.py"
] | [
"import numpy as np\nimport math\nimport os\n\n\nclass NeuralNetwork:\n def __init__(self):\n\n # Adjustable parameters\n self.learning_rate = 0.01\n self.lambda_value = 0.015\n\n # Weights- and weighted sum for each layer, number of input nodes\n self.weights = []\n sel... | [
[
"numpy.random.uniform",
"numpy.max",
"numpy.array",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
claycurry34/e3nn | [
"3cfbf679d10781a01d9c83b04a2e7d79d4914c23"
] | [
"e3nn/o3/_so3grid.py"
] | [
"import torch\nfrom e3nn.util.jit import compile_mode\n\nfrom ._wigner import wigner_D\nfrom ._s2grid import _quadrature_weights, s2_grid\n\n\ndef flat_wigner(lmax, alpha, beta, gamma):\n return torch.cat([(2 * l + 1)**0.5 * wigner_D(l, alpha, beta, gamma).flatten(-2) for l in range(lmax + 1)], dim=-1)\n\n\n@com... | [
[
"torch.einsum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hexiang-hu/answer_embedding | [
"154182974565de3fd24b669d7d298278e1e8a5d0"
] | [
"ansemb/dataset/base.py"
] | [
"import json\nimport os\nimport os.path as osp\nimport random\nimport nltk\nimport h5py\n\nfrom collections import Counter\nimport torch\nimport torch.utils.data as data\nfrom torch.utils.data.dataloader import default_collate\n\nfrom ansemb.config import cfg\nfrom ansemb.dataset.preprocess import invert_dict\n\ncl... | [
[
"torch.cat",
"torch.from_numpy",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
david8862/five-video-classification-methods | [
"818276dca1df1519155309dbceb8b139c637a814"
] | [
"data.py"
] | [
"\"\"\"\nClass for managing our data.\n\"\"\"\nimport csv\nimport numpy as np\nimport random\nimport glob\nimport os.path\nimport sys\nimport operator\nimport threading\nfrom processor import process_image\nfrom tensorflow.keras.utils import to_categorical\n\nclass threadsafe_iterator:\n def __init__(self, itera... | [
[
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zyxia1009/OadTR | [
"ec6e4dafcb465719e80cbf39dcd2099e51927d51"
] | [
"dataset.py"
] | [
"import os.path as osp\r\nimport pickle\r\nimport torch\r\nimport torch.utils.data as data\r\nimport numpy as np\r\n\r\n\r\n\r\nclass TRNTHUMOSDataLayer(data.Dataset):\r\n def __init__(self, args, phase='train'):\r\n args.data_root = './OadTR_THUMOS'\r\n self.pickle_root = './data/anno_thumos'\r\n ... | [
[
"numpy.random.randint",
"numpy.zeros",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vrvrv/PyTorch-SBM | [
"086b531e4318c5cf12d3df4f28e03bcd1e5a0dbd"
] | [
"src/models/ddpm.py"
] | [
"# https://github.com/w86763777/pytorch-ddpm/blob/master/main.py\nfrom functools import partial\nfrom typing import List, Optional\nimport os\nimport copy\nimport torch\nimport torch.nn.functional as F\n\nimport pytorch_lightning as pl\nfrom .modules import WideResnet\nfrom .sampler import DDPMSampler\nfrom torchvi... | [
[
"torch.randn_like",
"torch.linspace",
"torch.optim.lr_scheduler.LambdaLR",
"torch.randint",
"torch.sqrt",
"torch.randn",
"torch.no_grad",
"torch.cumprod",
"torch.gather"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hsk9767/recover_plane_copy | [
"c7c7ca0d9753a63aba028666b75950ac3abdf2ae"
] | [
"eval/generate3D.py"
] | [
"import os\nimport numpy as np\nimport scipy.misc\nimport PIL.Image as pil\nimport cv2\nimport argparse\n\n'''\nGenerate 3D model from the input depth\n@author -- Fengting Yang \n@created time -- Mar.24 2020\n\n@Usage:\n 1. Set TEST_LIST and all the DIR. Note for dir, a final '/' is required\n 2. Ensure a right... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eladb3/BoxeR | [
"995a38b67e3f84b5d6ea6fedbcb16896c4b1d020"
] | [
"tools/analyze.py"
] | [
"import os\nimport argparse\nfrom collections import Counter\n\nimport torch\nfrom fvcore.nn import flop_count_table\nfrom detectron2.utils.analysis import (\n FlopCountAnalysis,\n parameter_count_table,\n)\nfrom detectron2.utils.logger import setup_logger\nfrom e2edet.utils.configuration import load_yaml\nfr... | [
[
"torch.mean",
"torch.load",
"torch.std",
"torch.FloatTensor",
"torch.no_grad",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
guolinke/fairseq | [
"eac9fd70839cb94f300d8439be7ccd629777664b"
] | [
"fairseq/modules/multihead_attention.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 math\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn... | [
[
"torch.Tensor",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.init.xavier_normal_",
"torch.nn.Linear",
"torch.bmm",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
antelk/covid-19 | [
"6b1c4e7a414850de69b65ccd494a1a2410889c1e"
] | [
"coropy/growth_models.py"
] | [
"import warnings\n\nimport numpy as np \nfrom scipy.optimize import curve_fit \n\nfrom .utils import Scaler, normalize, restore\n\n\n__all__ = ['GrowthCOVIDModel', '_exp_func', '_logistic_func']\n\n\ndef _exp_func(x, a, b, c):\n \"\"\"Exponential function of a single variable, x.\n \n Parameters\n -----... | [
[
"numpy.diag",
"numpy.ones_like",
"numpy.linspace",
"numpy.arange",
"numpy.max",
"numpy.diff",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rbturnbull/dcodex | [
"85ac729aa9d2cfd0540738efc7d2ce5913c97351"
] | [
"dcodex/similarity.py"
] | [
"from .models import *\n\nLIKELY__UNLIKELY = 0\nHIGHLY_LIKELY__ELSE = 1\nHIGHLY_LIKELY__LIKELY__ELSE = 2\nSOLID = 3\n\n\ndef plot_rolling_average(\n manuscript,\n mss_sigla,\n range=None,\n csv_filename=None,\n force_compute=False,\n gotoh_param=[\n 6.6995597099885345,\n -0.920987505... | [
[
"pandas.read_csv",
"matplotlib.ticker.PercentFormatter",
"matplotlib.ticker.FixedLocator",
"matplotlib.pyplot.axvline",
"matplotlib.pyplot.ylim",
"numpy.arange",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"matplotlib.rcParams.update",
"matplotlib.pyplot.xticks"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Grayson-liuz/hotspotmonitoring | [
"7362f2c91067ec6b045633f22f0c8908459062b2"
] | [
"application/common/command/wordCloud/wordcloud_cn.py"
] | [
"\"\"\"\ncreate wordcloud with chinese\n=============================\n\nWordcloud is a very good tool, but if you want to create\nChinese wordcloud only wordcloud is not enough. The file\nshows how to use wordcloud with Chinese. First, you need a\nChinese word segmentation library jieba, jieba is now the\nmost ele... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MPIB/qsiprep | [
"76c306305538a1d439838683c923f114e0cf3e89"
] | [
"setup.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\" fmriprep setup script \"\"\"\nimport sys\nfrom setuptools import setup\nfrom setuptools.extension import Extension\nimport versioneer\n\n\n# Give setuptools a hint to complain if it's too old a version\n# 30.3.0 allows us to put most metadata in setup.cfg\n# S... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meghanaravikumar/sigopt-examples | [
"e2d938928384f340d77efb52b226f678b6008fb9",
"e2d938928384f340d77efb52b226f678b6008fb9"
] | [
"optimizing-memn2n/end2end_mem_nn_tensorflow/sigopt_memn2n_setup/random_search_parameters_config.py",
"tensorflow-cnn/python/cnn_example_cpu.py"
] | [
"from sigopt_memn2n_setup.sigopt_hyperparameters_enum import ParametersList\nfrom sigopt_memn2n_setup.sigopt_hyperparameters_enum import SGDOptimizer\nimport numpy as np\n\nparameters_list = [dict(name=ParametersList.LEARNING_RATE.value, bounds=dict(min=np.log(10e-7), max=np.log(1)), type=\"double\"),\n ... | [
[
"numpy.log"
],
[
"sklearn.cross_validation.train_test_split",
"tensorflow.nn.max_pool",
"tensorflow.cast",
"numpy.concatenate",
"tensorflow.nn.conv2d",
"tensorflow.Variable",
"numpy.reshape",
"scipy.io.loadmat",
"tensorflow.initialize_all_variables",
"tensorflow.arg... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
... |
mauicv/genrl | [
"daf904a2171720ab2680549800ed12ab311ca6b0"
] | [
"src/gerel/algorithms/NEAT/mutator.py"
] | [
"import numpy as np\nfrom numpy.random import choice\nfrom gerel.genome.factories import copy\nfrom random import random\nfrom gerel.mutators.mutator import Mutator\nfrom gerel.algorithms.NEAT.functions import curry_weight_mutator, curry_crossover, add_node, add_edge\n\n\nclass NEATMutator(Mutator):\n def __init... | [
[
"numpy.random.uniform",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cvignac/SMP | [
"95b55a880d0fc9149ddf32e8c2fdf5eac5b474b3",
"95b55a880d0fc9149ddf32e8c2fdf5eac5b474b3"
] | [
"models/gin.py",
"models/smp_layers.py"
] | [
"import torch\nimport torch.nn as nn\nfrom torch.nn import Sequential, Linear, ReLU, ModuleList\nimport torch.nn.functional as F\nfrom torch_geometric.nn import GINConv\nfrom models.utils.layers import XtoGlobal\n\n\nclass FeatureExtractor(nn.Module):\n def __init__(self, in_features: int, out_features: int):\n ... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.functional.log_softmax",
"torch.nn.functional.dropout",
"torch.nn.ModuleList",
"torch.zeros_like",
"torch.nn.Linear",
"torch.unique",
"torch.nn.functional.relu"
],
[
"torch.nn.Linear",
"torch.relu",
"torch.zeros",
"torch.cat"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangkao/IIP_TwoS_Saliency | [
"4d72cd6c21c98f8d52427dafb30329fdd19132f8"
] | [
"Tools/Visualization/visualization_img_demo.py"
] | [
"import os, cv2\nimport numpy as np\nimport hdf5storage as h5io\n\nfrom heatmap_overlay import *\n\n\n\ndef visual_color_img(RootDir, MethodNames, with_fix=0):\n\n\timgsDir = RootDir + 'images/'\n\tfixsDir = RootDir + 'fixations/maps/'\n\tsalsDir = RootDir + 'Results/Saliency/'\n\n\timg_ext = '.jpg'\n\tsal_ext = '.... | [
[
"numpy.max",
"numpy.expand_dims",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamreidsmith/PICO60-image-generation | [
"934fdb36b770eeb4014886fdcc9638134df773f5"
] | [
"raytracerfiles/generate_event.py"
] | [
"import matlab.engine\nimport pickle\nimport os\nimport numpy as np\nimport cv2\nimport time\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--n_events\", type=int, default=1, help=\"Number of events to generate. Default: 1\")\nparser.add_argument(\"--mult\", type=int, default=1, help=... | [
[
"numpy.dot",
"numpy.rot90",
"numpy.sqrt",
"numpy.abs",
"numpy.random.choice",
"numpy.median",
"numpy.linalg.norm",
"numpy.random.rand",
"numpy.cross",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jim-Song/tacotron_multi_style | [
"e68a8a8e2485aa7033024ed96b4d6a97b58af42a"
] | [
"datasets/datafeeder.py"
] | [
"import numpy as np\nimport os\nimport random\nimport tensorflow as tf\nimport threading\nimport time\nimport traceback\nfrom text import cmudict, text_to_sequence\nfrom util.infolog import log\n\n\n_batches_per_group = 32\n_p_cmudict = 0.5\n_pad = 0\n\n\nclass DataFeeder(threading.Thread):\n '''Feeds batches of d... | [
[
"numpy.load",
"tensorflow.FIFOQueue",
"numpy.pad",
"tensorflow.placeholder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
atomicai/modin | [
"ecaab1baafbf7e94aeb59aab8dd7fb48b687b4a3"
] | [
"modin/engines/base/io/text/csv_glob_dispatcher.py"
] | [
"# Licensed to Modin Development Team under one or more contributor license agreements.\n# See the NOTICE file distributed with this work for additional information regarding\n# copyright ownership. The Modin Development Team licenses this file to you under the\n# Apache License, Version 2.0 (the \"License\"); you... | [
[
"pandas.read_csv",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
kastnerkyle/tfbldr | [
"58ad1437d500924acd15d1c6eec4a864f57e9c7c",
"58ad1437d500924acd15d1c6eec4a864f57e9c7c",
"58ad1437d500924acd15d1c6eec4a864f57e9c7c"
] | [
"examples/vq_vae_music/generate_vq_pixel_cnn_music_bxe_2d_multichannel.py",
"examples/unaligned_ljspeech_chars/reconstruct_from_mels.py",
"examples/vq_vae_music/vq_vae_music_dml.py"
] | [
"import argparse\nimport tensorflow as tf\nimport numpy as np\nfrom tfbldr.datasets import piano_roll_imlike_to_image_array\nfrom tfbldr.datasets import save_image_array\nfrom tfbldr.datasets import notes_to_midi\nfrom tfbldr.datasets import midi_to_notes\nfrom collections import namedtuple\nimport sys\nimport matp... | [
[
"numpy.log",
"tensorflow.get_collection",
"matplotlib.use",
"tensorflow.train.import_meta_graph",
"tensorflow.ConfigProto",
"tensorflow.reset_default_graph",
"numpy.shape",
"tensorflow.Session",
"numpy.load",
"numpy.random.RandomState",
"numpy.zeros",
"numpy.where"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
... |
kstpr/Thesis | [
"3d4c0d70c284db553b59011c1b55ed03aaf0d16b"
] | [
"GANs/DCGAN/util/visualization.py"
] | [
"import matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom numpy.core.numeric import full\nimport wandb\nimport numpy as np\nimport torchvision.utils as vutils\n\n\ndef save_current_fakes_snapshot(fake_batch, epoch, isFinal, device, full_output_path):\n fig = plt.figure(figsize=(16, 16))\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
klarh/symmys | [
"13b232a817975a15fa471879fdd8d21de783ecd6"
] | [
"symmys/layers/QuaternionRotation.py"
] | [
"import math\n\nimport tensorflow as tf\nimport tensorflow.keras as keras\nimport tensorflow.keras.backend as K\n\ndef projected_quaternion_initializer(shape, dtype=None):\n \"\"\"Initialize a set of quaternions (to be summed over the last dimension) randomly.\n\n This method produces a uniform distribution o... | [
[
"tensorflow.keras.backend.tile",
"tensorflow.sin",
"tensorflow.cos",
"tensorflow.concat",
"tensorflow.constant",
"tensorflow.keras.backend.int_shape",
"tensorflow.keras.utils.get_custom_objects",
"tensorflow.random.uniform",
"tensorflow.keras.backend.sum",
"tensorflow.expan... | [
{
"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... |
johndamen/plotutils | [
"60bc043314c823bbcb27740b22393f0fd2b58476"
] | [
"examples/example01.py"
] | [
"from matplotlib import pyplot as plt\nimport plotutils\n\nfig = plt.figure()\nax = fig.add_axes([.1, .1, .8, .8])\np = plotutils.PositioningAxes.from_axes(fig, ax, anchor='C')\n\np.set_anchor('C')\nprint(p.x, p.y)\n\np.set_anchor('SW')\nprint(p.x, p.y)\n\np.set_anchor('NE')\nprint(p.x, p.y)"
] | [
[
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pagopa/cstar-cli | [
"8bbac4977a256a09eada1bfa649dfc5b383757cb"
] | [
"src/cstar-cli/cstar/cli/bpd/paymentinstrument.py"
] | [
"import psycopg2\nimport psycopg2.extras\nimport pandas as pd\nimport uuid\n\n\nclass Paymentinstrument():\n\n def __init__(self, args):\n self.args = args\n self.db_connection = psycopg2.connect(args.connection_string)\n \n def enroll(self):\n \n payment_instruments_df = pd.read_csv(self.args.file, ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
GianC0/IST-SOE | [
"250786d12507298c0a7909ed2cc6be0e3249df02"
] | [
"Application/tests/unit/test_evaluator.py"
] | [
"import unittest\nfrom pandas import DataFrame\nimport os, sys\nimport pandas as pd\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\nparentdir = os.path.dirname(os.path.dirname(currentdir))\nsys.path.append(parentdir)\nfrom evaluator import Evaluator\nfrom utilities import resource_path\n\nclass TestEvalu... | [
[
"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": []
}
] |
seungjaeryanlee/optuna | [
"28c063b8787405e4b978ff2e961f8717de048a9e"
] | [
"optuna/integration/cma.py"
] | [
"import math\nimport random\n\nimport numpy\n\nimport optuna\nfrom optuna import distributions\nfrom optuna.distributions import CategoricalDistribution\nfrom optuna.distributions import DiscreteUniformDistribution\nfrom optuna.distributions import IntUniformDistribution\nfrom optuna.distributions import LogUniform... | [
[
"numpy.round",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AppliedAcousticsChalmers/Spherical-Helmholtz-Translation-and-Rotation | [
"84c5b51dab5d7ee26886ece44945a5d887bff369"
] | [
"shetar/translations.py"
] | [
"import numpy as np\nfrom . import coordinates, rotations, expansions\n\n\nclass CoaxialTranslation(coordinates.OwnerMixin):\n _default_output_type = expansions.Expansion\n\n def __init__(self, input_order, output_order, position=None, radius=None, wavenumber=None, defer_evaluation=False):\n self._inpu... | [
[
"numpy.asarray",
"numpy.shape",
"numpy.allclose",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mpenza19/LatentDirichletAlloc | [
"1223034b1746c1a5ead1e6e051fa10a62a239b28"
] | [
"src/find_similar_docs.py"
] | [
"# Adapted from:\n# https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/\n\nimport read_bibtex\nimport os, shutil, sys\nimport numpy as np\nfrom nltk.corpus import stopwords \nfrom nltk.stem.wordnet import WordNetLemmatizer\nfrom nltk.stem.porter import PorterStemmer\nimport str... | [
[
"numpy.load",
"numpy.zeros",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kevinleestone/pyrender | [
"4a289a6205c5baa623cd0e7da1be3d898bcbc4da"
] | [
"tests/unit/test_lights.py"
] | [
"import numpy as np\nimport pytest\n\nfrom pyrender import (DirectionalLight, SpotLight, PointLight, Texture,\n PerspectiveCamera, OrthographicCamera)\nfrom pyrender.constants import SHADOW_TEX_SZ\n\n\ndef test_directional_light():\n\n d = DirectionalLight()\n assert d.name is None\n a... | [
[
"numpy.all",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aravinho/frankmocap | [
"6a150a9cb96e9b32a60d8047eaa84d0c37e471f5"
] | [
"demo/demo_handmocap.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport os, sys, shutil\nimport rospy\nimport ipdb\nimport os.path as osp\nimport numpy as np\nimport cv2\nimport json\nimport torch\nfrom torchvision.transforms import Normalize\n\nfrom demo.demo_options import DemoOptions\nimport mocap_utils.general_utils as g... | [
[
"torch.device",
"numpy.array",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miararoy/bliz | [
"0d197b6790ccfa0c71682abf551aa4df83a9b589"
] | [
"tests/evaluation/test_monte_carlo.py"
] | [
"import numpy as np\nfrom sklearn.metrics import mean_squared_error\n\n\nfrom roulette.evaluation.monte_carlo import MonteCarloSimulation\n\nreal = np.asarray([1, 2, 3])\nmodel = np.asarray([1.1, 2.2, 3.3])\nrand = np.asarray([4, 5, 6])\n\nMC = MonteCarloSimulation(exp_type=\"reg\")\nMC.load_experiment(\n real,\... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
agalbachicar/swing_for_the_fences | [
"3871e88884a90e5c9dd80d71b20b811485007273"
] | [
"src/labelling.py"
] | [
"import numpy as np\nimport pandas as pd\n\nfrom mpfin import mpPandasObj\n\n\ndef getDailyVol(close, span0=100):\n '''\n Computes the daily volatility of price returns.\n It takes a closing price series, applies a diff sample to sample\n (assumes each sample is the closing price), computes an EWM with ... | [
[
"pandas.concat",
"pandas.Series",
"pandas.Timedelta",
"pandas.DataFrame",
"numpy.sign"
]
] | [
{
"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": []
}
] |
saymonp/pets-tf | [
"91736c7db50e105f44165d7532988345592dcbca"
] | [
"data_normalization.py"
] | [
"import os\nimport pickle\nimport random\nfrom typing import List\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nclass DataManager(object):\n\n def __init__(self, IMG_SIZE: int, DATADIR: str, CATEGORIES: List[str]):\n self.IMG_SIZE = IMG_SIZE\n self.DATADIR = DATADIR\n ... | [
[
"numpy.array",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
42-AI/DemocracyWatcher | [
"14028eb210259e07347f4ee05546255be69356cc"
] | [
"src/data/make_dataset/make_dataset_allocine.py"
] | [
"import pandas as pd\nimport os\nimport sys\nfrom datasets import load_dataset\n\n\ndef make_dataset_allocine(args):\n # Check for parsing errors\n if args.split not in [\"test\", \"train\"]:\n print(\"--split argument is required for allocine: [train, test]\")\n sys.exit()\n if args.split ==... | [
[
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
ho4040/ml-agents | [
"60c284f4972f73b6122bfdd3e809ce88706c5597"
] | [
"ml-agents/mlagents/trainers/ppo/optimizer.py"
] | [
"from typing import Optional, Any, Dict\nimport numpy as np\nfrom mlagents.tf_utils import tf\nfrom mlagents_envs.timers import timed\nfrom mlagents.trainers.models import ModelUtils, EncoderType, LearningRateSchedule\nfrom mlagents.trainers.policy.tf_policy import TFPolicy\nfrom mlagents.trainers.optimizer.tf_opti... | [
[
"numpy.cumsum",
"numpy.tile",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
proximacent/variational_autoencoder | [
"3c967e273e08954418d4141d3e39847a2b433a60"
] | [
"mlp_vae.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom helpers import dense\n# See paper: https://arxiv.org/abs/1312.6114\n\nclass VAE():\n def __init__(self):\n # Some hyperparams.\n self.input_dim = 784\n self.h_dim = 256 # hidden layer dimension\n self.latent_dim = 2 # size of latent state hidden layer\... | [
[
"tensorflow.shape",
"tensorflow.reduce_mean",
"tensorflow.placeholder",
"tensorflow.exp",
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"tensorflow.train.AdamOptimizer",
"tensorflow.square",
"tensorflow.random_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
kristhaj/pyxcel | [
"ed954ade80c28fdb9aa7fb21f4596d75d26065d2"
] | [
"src/Rapporter/Load.py"
] | [
"import pandas as pd\n\nclass Load:\n\n def Members(self, path, columns):\n print(f'Loading member data from {path}')\n\n df = pd.read_excel(path, usecols=columns, parse_dates=True)\n data = df.to_dict()\n\n ('Formatting birthdates...')\n for key in list(data['BirthDate'].keys(... | [
[
"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": []
}
] |
jdabel123/UdacityProject2-DisasterResponsePipeline | [
"4d9bef6fecaaf2f0e88e8d1f760afad9d8730cca"
] | [
"models/train_classifier.py"
] | [
"import sys\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\nimport re\n\nimport nltk\n\nnltk.download('punkt')\nnltk.download('wordnet')\nnltk.download('averaged_perceptron_tagger')\n\nfrom nltk.corpus import stopwords\nfrom nltk.stem.wordnet import WordNetLemmatizer\nfrom nltk.tokenize import word_to... | [
[
"sklearn.model_selection.GridSearchCV",
"pandas.Series",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.svm.LinearSVC",
"sklearn.feature_extraction.text.TfidfTransformer",
"sklearn.metrics.classification_r... | [
{
"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": []
}
] |
yunus-kalkan/ivy | [
"d574a9f6afd042b6e45f24e38ee028a85a8e55bf"
] | [
"ivy/functional/backends/tensorflow/array_api/elementwise_functions.py"
] | [
"# global\nimport tensorflow as tf\nfrom tensorflow.python.types.core import Tensor\n\n\n# local\nimport ivy\n\n\ndef isfinite(x: Tensor)\\\n -> Tensor:\n if ivy.is_int_dtype(x):\n return tf.ones_like(x, tf.bool)\n return tf.math.is_finite(x)\n\n\ndef atanh(x: Tensor)\\\n -> Tensor:\n ... | [
[
"tensorflow.math.is_finite",
"tensorflow.ones_like",
"tensorflow.math.atanh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
zeffii/sverchok-nodes | [
"213f462fca78f3b4115c9f9270fdf591906982f0"
] | [
"attractor3d.py"
] | [
"\"\"\"Attractor Field\n\nThis Sverchok scripted node applies an \"attractor field\" to another\ncollection of vertices. This means that the source vertices are going\nto be displaced along the Z axis, as if being attracted to the nearby\nattractor vertices.\n\nUnder the hood this node uses sklearn's kth nearest ne... | [
[
"sklearn.neighbors.NearestNeighbors",
"numpy.array",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
tiepvupsu/tabml | [
"81ae3c6c4376b23095a0d2675fd2dcc0cce8aac7"
] | [
"tabml/tests/utils/test_embedding.py"
] | [
"import numpy as np\nfrom qcore.asserts import assert_eq\n\nfrom tabml.utils import embedding\n\n\nclass TestNearestNeighbor:\n def test_1(self):\n query = np.array([1, 2])\n items = np.array(\n [\n [1, 2.2], # neareast in l2\n [10, 21], # neareast in dot ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Carlos-UR/Info-HCVAE | [
"6d869c8be2ed123c3e8eb9970c4a0b60b0b3c6d5",
"6d869c8be2ed123c3e8eb9970c4a0b60b0b3c6d5",
"6d869c8be2ed123c3e8eb9970c4a0b60b0b3c6d5"
] | [
"qa-eval/utils.py",
"qa-eval/distributed_run.py",
"qa-eval/squad_utils.py"
] | [
"import time\nimport math\nimport torch\n\ndef time_since(t):\n \"\"\" Function for time. \"\"\"\n return time.time() - t\n\n\ndef progress_bar(completed, total, step=5):\n \"\"\" Function returning a string progress bar. \"\"\"\n percent = int((completed / total) * 100)\n bar = '[='\n arrow_reach... | [
[
"torch.exp"
],
[
"torch.cuda.device_count",
"torch.multiprocessing.spawn"
],
[
"numpy.asarray",
"numpy.sign"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brunston/bpdb | [
"735bb64643babfd2a614fefcf400721768cbf828"
] | [
"db.py"
] | [
"## BPDB ##\n# brunston (c) 2016\n\nimport numpy as np\n\nclass BPDB:\n \"\"\"\n A new BPDB object, which contains both structured and unstructured data.\n Access this data with clean syntax.\n >>> b = BPDB()\n >>> print(b)\n BPDB(0,0)\n >>> foo = [100]\n >>> b.add_data(foo, 's')\n >>> pr... | [
[
"numpy.savetxt",
"numpy.append",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fabbo-repo/AWS-Samples | [
"d8c71b8977567ae2b12dae40f08be42f867f7e76"
] | [
"python3/soundCode/soundRecording.py"
] | [
"from numpy import int16\nimport sounddevice as sd\nfrom scipy.io.wavfile import write\nimport pickle\n\ndef recordPCM(seconds = 3, fs = 16000) :\n # fs Sample rate\n # seconds = 3 # Duration of recording\n myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=1, dtype=int16)\n sd.wait() # W... | [
[
"scipy.io.wavfile.write"
]
] | [
{
"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"... |
h4nyu/TransferDet | [
"d4cc9b53a24a98630851695d48e6cb18374c4173"
] | [
"train_baseline.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\nimport sys\nsys.path.insert(0, \"./timm-efficientdet-pytorch-small-anchor\")\n\nimport torch\nimport os\nfrom datetime import datetime\nimport time\nimport random\nimport cv2\nimport pandas as pd\nimport numpy as np\nimport albumentations as A\nimport matplotlib.pyplot as pl... | [
[
"torch.load",
"numpy.concatenate",
"torch.no_grad",
"torch.device",
"numpy.where",
"pandas.read_csv",
"torch.ones",
"numpy.clip",
"sklearn.model_selection.StratifiedKFold",
"torch.tensor",
"numpy.full",
"torch.utils.data.sampler.RandomSampler",
"torch.stack",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Paulocracy/PyNetAnalyzer | [
"36a46714540861f796e9bdd7aadf80ece17ccc42"
] | [
"cnapy/gui_elements/yield_optimization_dialog.py"
] | [
"\"\"\"The cnapy yield optimization dialog\"\"\"\nfrom random import randint\nfrom numpy import isnan, isinf\nimport re\nfrom qtpy.QtCore import Qt, Signal, Slot\nfrom qtpy.QtWidgets import (QDialog, QHBoxLayout, QLabel, QComboBox,\n QMessageBox, QPushButton, QVBoxLayout, QFrame)\n\nfrom ... | [
[
"numpy.isnan",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
j-luo93/lingvo | [
"7398974078391362f0c1b027164a8f33f88cf86b",
"7398974078391362f0c1b027164a8f33f88cf86b",
"7398974078391362f0c1b027164a8f33f88cf86b"
] | [
"lingvo/core/attention_test.py",
"lingvo/core/inference_graph_exporter.py",
"lingvo/tasks/punctuator/input_generator_test.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... | [
[
"numpy.dot",
"numpy.expand_dims",
"tensorflow.concat",
"tensorflow.zeros",
"numpy.asarray",
"tensorflow.reduce_sum",
"numpy.random.randn",
"numpy.exp",
"tensorflow.Graph",
"tensorflow.all_variables",
"tensorflow.get_collection",
"numpy.array_repr",
"tensorflow.g... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": ... |
microsoft/SpeedyRec | [
"7ae2b49729b44b370c1054702afca3d87ed98414"
] | [
"speedy_mind/models/tnlrv3/convert_state_dict.py"
] | [
"import torch\nimport logging\n\nfrom transformers.modeling_utils import cached_path, WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_checkpoint_from_transformer_cache(\n archive_file, pretrained_model_name_or_path, pretrained_model_archive_map,\n c... | [
[
"torch.split",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ParanoiaSYT/Qulab-backup | [
"09ec5457145b3789d4c1ac02c43dd3e6dfafc96f",
"09ec5457145b3789d4c1ac02c43dd3e6dfafc96f"
] | [
"qulab/drivers/PG_AWG/Waveform.py",
"tests/test_protocol.py"
] | [
"import csv\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.pyplot import savefig\r\nimport matplotlib as mpl\r\n\r\nplt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）\r\nplt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）\r\n\r\nmode = (\r\n ... | [
[
"matplotlib.pyplot.title",
"numpy.asarray",
"numpy.arange",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.xticks"
],
[
"numpy.all",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lrwb-aou/curation | [
"e80447e56d269dc2c9c8bc79e78218d4b0dc504c"
] | [
"data_steward/utils/participant_summary_requests.py"
] | [
"\"\"\"\nUtility to fetch and store deactivated participants information.\n\nOriginal Issues: DC-1213, DC-1042, DC-971, DC-972\n\nThe intent of the get_deactivated_participants function is to call and store deactivated participants information by\n leveraging the RDR Participant Summary API. Deactivated particip... | [
[
"pandas.DataFrame.from_records",
"pandas.to_datetime"
]
] | [
{
"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": []
}
] |
keunhong/scanner | [
"e14153c18078f707fe1b487c0cffb95d6997d737"
] | [
"python/scannerpy/stdlib/montage.py"
] | [
"import cv2\nimport math\nimport numpy as np\n\n\ndef make_montage(n, frames, frame_width=64, frames_per_row=16):\n frame = next(frames)\n (frame_h, frame_w, _) = frame.shape\n target_w = frame_width\n target_h = int(target_w / float(frame_w) * frame_h)\n img_w = frames_per_row * target_w\n img_h ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
spro66/MLinPractice | [
"c5af00f67da597ec85f57d4e893197e1b184d324"
] | [
"code/classification/run_classifier.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nTrain or evaluate a single classifier with its given set of hyperparameters.\n\nCreated on Wed Sep 29 14:23:48 2021\n\n@author: lbechberger\n\"\"\"\n\nimport argparse, pickle\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.metrics import accuracy_sc... | [
[
"sklearn.dummy.DummyClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EricCacciavillani/Video-Game-Analysis | [
"ca19069fb8ca625d650bc7d3ec90a518b4d4e615"
] | [
"Toolbox/ClusterMaster.py"
] | [
"# Getting Sklearn Models\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import KMeans\nfrom scipy.cluster.hierarchy import linkage, dendrogram\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.neighbors import kneighbors_graph\n\n# Vi... | [
[
"pandas.Series",
"sklearn.cluster.KMeans",
"numpy.asarray",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"numpy.mean",
"sklearn.cluster.AgglomerativeClustering",
"numpy.where",
"matplotlib.pyplot.tight_layout",
"numpy.unique",
"numpy.std",
"matplotlib.pyplot.close"... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
dlens/dlpy | [
"f4a59f26c7b48a86609efc572245145574f1f1f6"
] | [
"tests/test_percentile.py"
] | [
"from unittest import TestCase\nimport numpy.testing as npt\nimport dlpy.percentile as dlpr\n\n\nclass Test(TestCase):\n def test_std_perc(self):\n X = (10, 20, 30, 40, 50)\n npt.assert_almost_equal(dlpr.std_perc(X, 0), 0)\n npt.assert_almost_equal(dlpr.std_perc(X, 10), 0.10)\n npt.as... | [
[
"numpy.testing.assert_array_equal",
"numpy.testing.assert_allclose",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anjiang2016/rcnn-xilie | [
"d68ead1504732bd92cde59f8246f9bb2ebc6fe5c"
] | [
"2imageclassify/ml_svm.py"
] | [
"# 课程可以围绕这个文档展开\n# https://scikit-learn.org/stable/modules/svm.html\n# 内部计算是依赖libsvm和liblinear库的\n# libsvm:https://www.csie.ntu.edu.tw/~cjlin/libsvm/\n# liblinear:https://www.csie.ntu.edu.tw/~cjlin/liblinear/\nfrom sklearn import svm\nX = [[0,0],[1,1]]\ny = [0,1]\n# 二分类\nclf = svm.SVC()\nclf.fit(X,y)\nclf.predict([... | [
[
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YoogottamK/os-course-a5 | [
"7eefca6fc028601a7a71982ec4b3fdf530792372"
] | [
"graph.py"
] | [
"#!/usr/bin/env python3\n# [ticks] Action {pid} =srcq= _dstq_\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nimport re\n\nLOGS = \"\"\n\nwith open(\"logs\") as f:\n LOGS = f.read()\n\ninp = LOGS.strip().split(\"\\n\")\n\nGRAPH_DATA = {}\n\n#ignore = [ \"0\", \"1\", \"2\", \"3\" ]\nignore = [ \"0\" ]\n\... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
920232796/MedicalSeg | [
"385ed000c04d828133faeaa9a56ba14488adc8c5",
"385ed000c04d828133faeaa9a56ba14488adc8c5"
] | [
"test/test_spleen_data.py",
"medical_seg/networks/nets/kiunet/model.py"
] | [
"from functools import total_ordering\nimport matplotlib.pyplot as plt \n\nimport glob \nfrom tqdm import tqdm\nimport numpy as np \nimport torch \nimport SimpleITK as sitk \nfrom medical_seg.dataset import collate_fn\nfrom medical_seg.utils import resample_image_array_size\nfrom medical_seg.transformer import Rand... | [
[
"matplotlib.pyplot.imshow",
"numpy.expand_dims",
"torch.utils.data.DataLoader",
"matplotlib.pyplot.subplot",
"numpy.random.RandomState",
"matplotlib.pyplot.show"
],
[
"torch.nn.Softmax",
"torch.add",
"torch.nn.init.constant_",
"torch.nn.MaxPool3d",
"torch.nn.Conv3d"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
waldoPHD/scanpy | [
"b3dc34a57ccaa6ac9a4ac8718fe9f128c967e3dc",
"b3dc34a57ccaa6ac9a4ac8718fe9f128c967e3dc"
] | [
"scanpy/tests/test_preprocessing.py",
"scanpy/preprocessing/_simple.py"
] | [
"from pathlib import Path\n\nimport numpy as np\nfrom scipy import sparse as sp\nimport scanpy as sc\nimport pytest\nfrom anndata import AnnData\n\n\nHERE = Path(__file__).parent\n\n\ndef test_log1p_chunked():\n A = np.random.rand(200, 10)\n ad = AnnData(A)\n ad2 = AnnData(A)\n ad3 = AnnData(A)\n ad3... | [
[
"scipy.sparse.issparse",
"numpy.allclose",
"numpy.floor_divide",
"scipy.sparse.csr_matrix",
"numpy.ones",
"numpy.testing.assert_array_equal",
"scipy.sparse.random",
"numpy.all",
"numpy.random.rand",
"numpy.random.binomial",
"numpy.array",
"numpy.random.randint"
],... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"0.16",
"1.8"
],
"tensorflow": []
},
{
"matplotlib": [... |
normarivano/aiida-wannier90 | [
"9c672178195bb40dafcd3eca3e4b5004b49526d7"
] | [
"aiida_wannier90/parsers.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nimport io\nimport os\nimport six\nfrom six.moves import range\nfrom aiida.parsers import Parser\nfrom aiida.common import exceptions as exc\n\n__all__ = (\n 'Wannier90Parser',\n 'band_parser',\n 'raw_wout_parser',\n)\n\n\nclass Wannier90Pars... | [
[
"numpy.isclose",
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wycers/Codelib | [
"86d83787aa577b8f2d66b5410e73102411c45e46",
"86d83787aa577b8f2d66b5410e73102411c45e46"
] | [
"CS303/lab4-6/work/algorithm_ncs/ncs_c.py",
"CS303/lab1-3/work/code_check.py"
] | [
"import time\nfrom collections import namedtuple\n\nimport numpy as np\n\nimport algorithm_ncs.problem as ncs_problem\nimport algorithm_ncs.benchmark as benchmark\n\nNCS_CParameter = namedtuple(\n \"NCS_CParameter\", [\"tmax\", \"lambda_exp\", \"r\", \"epoch\", \"N\"])\n\n\nclass NCS_C(object):\n\n def __init... | [
[
"numpy.dot",
"numpy.log",
"numpy.random.random",
"numpy.random.seed",
"numpy.min",
"numpy.tile",
"numpy.full",
"numpy.random.normal",
"numpy.where",
"numpy.zeros"
],
[
"numpy.copy",
"numpy.zeros",
"numpy.random.choice",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
imaus10/thumbor_extras | [
"f58180c20b158944c428287bdc36715454ac88ea"
] | [
"thumbor_extras/filters/draw_focal_points.py"
] | [
"# -*- coding: utf-8 -*-\nimport cv2\nimport numpy as np\nfrom PIL import Image\nimport re\nfrom thumbor.filters import BaseFilter, filter_method\nfrom thumbor_extras.filters import rainbow\n\nclass Filter(BaseFilter):\n @filter_method(\n BaseFilter.PositiveNonZeroNumber,\n BaseFilter.DecimalNumber... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ukjinChoe/Open-OCR-Engine | [
"54a11e8baec505c9eb1ad5496a279a429fd9c5a2"
] | [
"generate_data/merge_lines.py"
] | [
"import numpy as np\nimport sys\nimport random\nimport pickle\nimport argparse\nfrom tqdm import tqdm\n\nfrom PIL import Image, ImageDraw\nfrom pathlib import Path\n\n\ndef cutList(li, mini=2, maxi=6):\n chunk_size = []\n while sum(chunk_size) <= len(li):\n if len(li)-sum(chunk_size) <= maxi:\n ... | [
[
"numpy.rollaxis",
"numpy.hstack",
"numpy.multiply",
"numpy.asarray",
"numpy.dstack",
"numpy.add",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
morrisalp/kraken | [
"9721a2493eb9fba2d539557c35388f04f2788a92"
] | [
"kraken/lib/util.py"
] | [
"\"\"\"\nOcropus's magic PIL-numpy array conversion routines. They express slightly\ndifferent behavior from PIL.Image.toarray().\n\"\"\"\nimport unicodedata\nimport numpy as np\n\nfrom PIL import Image\n\n__all__ = ['pil2array', 'array2pil', 'is_bitonal', 'make_printable', 'get_im_str']\n\n\ndef pil2array(im: Imag... | [
[
"numpy.array",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hyk95/yolov3_with_mobilenet | [
"de2a49ac7c6faf2881a42f8c41873421daa77377"
] | [
"data/convert_tfrecord.py"
] | [
"import sys\nimport argparse\nimport numpy as np\nimport tensorflow as tf\nimport os\n\ndef main(argv):\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_txt\", default='./2012_train.txt')\n parser.add_argument(\"--tfrecord_path_prefix\", default='./train_data/train')\n parser.add_a... | [
[
"numpy.zeros",
"tensorflow.train.BytesList",
"tensorflow.gfile.FastGFile",
"tensorflow.python_io.TFRecordWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
lidotcircle/PyTorch-GAN | [
"dcb95a05701f28a3b73ada35da4b8e7e72975642"
] | [
"implementations/cinfosgan/models.py"
] | [
"from typing import List, Tuple\nimport torch.nn as nn\nimport torch\nimport utils\n\n\ndef weights_init_normal(m):\n classname = m.__class__.__name__\n if classname.find(\"Conv\") != -1:\n torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n if hasattr(m, \"bias\") and m.bias is not None:\n ... | [
[
"torch.nn.ZeroPad2d",
"torch.nn.Sequential",
"torch.nn.ReflectionPad2d",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.nn.init.normal_",
"torch.nn.InstanceNorm2d",
"torch.nn.LeakyReLU",
"torch.nn.Upsample",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ricedatasci/workshop3-aws-floydhub | [
"3509cac88cc3d55b3a2207d89a10441308a4c4e7"
] | [
"plotter_callback.py"
] | [
"import keras\nimport numpy as np\ntry:\n import matplotlib.pyplot as plt\nexcept ImportError:\n plt = None\n\n\nclass Plotter(keras.callbacks.Callback):\n\n def __init__(self, monitor, scale='linear', plot_during_train=True, save_to_file=None):\n super().__init__()\n if plt is None:\n ... | [
[
"matplotlib.pyplot.ion",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jrbourbeau/cr-composition | [
"e9efb4b713492aaf544b5dd8bb67280d4f108056"
] | [
"plotting/plot_feature_importance.py"
] | [
"#!/usr/bin/env python\n\nfrom __future__ import division, print_function\nimport os\nimport argparse\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport comptools as comp\nimport comptools.analysis.plotting as plotting\n\ncolor_dict = comp.analysis.get_color_dict()\n\n\nif __name__ == '__main__':\n\n ... | [
[
"numpy.argsort",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ymori206226/test | [
"001639831ea28814e071ba6a23e8c35da85b6908",
"001639831ea28814e071ba6a23e8c35da85b6908"
] | [
"src/icmrucc.py",
"src/qite/qite_anti.py"
] | [
"import time\nimport copy\nimport itertools\n\nimport numpy as np\nfrom scipy.special import comb\nfrom qulacs import QuantumState, QuantumCircuit\n\nfrom . import config as cf\nfrom .utils import root_inv\nfrom .expope import Gdouble_ope\nfrom .ucclib import single_ope_Pauli\nfrom .fileio import prints, print_stat... | [
[
"numpy.argsort",
"numpy.linalg.eig",
"numpy.arange",
"numpy.sum"
],
[
"numpy.arange",
"numpy.linalg.norm",
"numpy.real",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bsaisudh/aStar_Dijxtra_PointRobot | [
"d961cb857919078c9bab019700176fe69933e2a1"
] | [
"Dijxtra Algo.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Mar 23 14:28:56 2019\r\n\r\n@author: balam\r\n\"\"\"\r\n\r\nfrom queue import PriorityQueue\r\nimport numpy as np\r\nfrom ObstacleSpace import genObstacleSpace\r\nimport MapDiaplay as md\r\n\r\ndef actions(currentNode, currentCost):\r\n newNodes = []\r\n ne... | [
[
"numpy.subtract",
"numpy.zeros",
"numpy.ravel_multi_index"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zer0-00/voxelmorph | [
"ed2e0384cf22d19f7e57bea5887fc197d55f60bc"
] | [
"train_template.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nExample script to train (unconditional) template creation.\n\nIf you use this code, please cite the following:\n\n Learning Conditional Deformable Templates with Convolutional Networks\n Adrian V. Dalca, Marianne Rakic, John Guttag, Mert R. Sabuncu\n NeurIPS 2019. https://... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.device",
"tensorflow.keras.utils.multi_gpu_model",
"tensorflow.keras.optimizers.Adam",
"tensorflow.compat.v1.disable_eager_execution",
"tensorflow.keras.callbacks.TensorBoard",
"tensorflow.compat.v1.experimental.output_all_inter... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rodrigoAMF/DRLND-Udacity | [
"a640a1910e53c2c86fd6cfbc6734038c53e58404"
] | [
"p3_collab-compet-maddpg/agent.py"
] | [
"import numpy as np\nimport random\nimport copy\n\nfrom model import Actor, Critic\n\nimport torch\nimport torch.optim as optim\n\nimport hyperparameters\nfrom ou_noise import OUNoise\n\nclass Agent():\n \"\"\"Interacts with and learns from the environment.\"\"\"\n \n def __init__(self, state_size, action_... | [
[
"torch.no_grad",
"torch.from_numpy",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leliel12/sc_drv | [
"26e37e6801c9cd80fdfec736e23ef2cbf5fdbdd5"
] | [
"sc_drv/normtests.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Copyright (c) 2018, Cabral, Juan; Luczywo, Nadia; Zanazi Jose Luis\n# All rights reserved.\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# * Redistribution... | [
[
"numpy.rollaxis",
"scipy.stats.kstest",
"numpy.shape",
"scipy.stats.shapiro",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
jdj2261/pykin | [
"da952b8ec023382b8a324d1095b0cfd675c7452b"
] | [
"pykin/collision/collision_manager.py"
] | [
"import numpy as np\nimport collections \nimport itertools\nimport copy\n\ntry:\n import fcl\n import trimesh\nexcept BaseException:\n fcl = None\n trimesh = None\n\nfrom pykin.utils.error_utils import CollisionError\nfrom pykin.utils.transform_utils import get_h_mat\nfrom pykin.utils.log_utils import c... | [
[
"numpy.dot",
"numpy.eye",
"numpy.asanyarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AnBesp/crazyflie-dataset | [
"67dfd127ca1ab93f36b62a0af162be4becfaa892"
] | [
"src/decrypt_data/decrypt_controller_data.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nplotting controller data log\n\"\"\"\nimport cfusdlog\nimport matplotlib.pyplot as plt\nimport re\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"filename\")\nargs = parser.parse_args()\n\n# decode binary log data\nlogData = cfusdlog.decode(args.filena... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dvalp/coding-practice | [
"bc22de6dfc7590616fd38d029648ebf1ff102feb"
] | [
"EindhovenDS/hack9/main_ffnn2.py"
] | [
"from util.dataloader import read_data_sets\nfrom util.util_func import minimize\nimport tensorflow as tf\nfrom time import gmtime, strftime\n\n\n#Start a Tensorflow session\nsess = tf.Session()\n\n#Load the data and run once to see if it works\nMNIST = read_data_sets('../data/', norm=True)\nX_batch, y_batch = MNIS... | [
[
"tensorflow.nn.xw_plus_b",
"tensorflow.get_variable",
"tensorflow.nn.softmax",
"tensorflow.reduce_mean",
"tensorflow.reshape",
"tensorflow.equal",
"tensorflow.placeholder",
"tensorflow.nn.tanh",
"tensorflow.global_variables_initializer",
"tensorflow.nn.sparse_softmax_cross_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
nickzhuang0613/pyECG | [
"803f39088a504796357346dc3fd526c4b641bb5c"
] | [
"tests/test_ecg_signal.py"
] | [
"import numpy as np\nimport pytest\nfrom scipy.misc import electrocardiogram\n\nfrom pyecg import Signal\n\n\n@pytest.mark.parametrize(\"signal\", [\"a\"])\ndef test_bad_type(signal):\n with pytest.raises(TypeError):\n Signal(signal, \"II\")\n\n\ndef test_length():\n signal = Signal(electrocardiogram()... | [
[
"numpy.array",
"scipy.misc.electrocardiogram"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
mar-muel/local-geocode | [
"161b551f10cdb716cd4923f775f2ea48b9d7f3e6"
] | [
"geocode/geocode.py"
] | [
"import pandas as pd\nimport os\nimport pickle\nimport logging\nfrom tqdm import tqdm\nimport sys\nfrom flashtext import KeywordProcessor\nimport joblib\nimport multiprocessing\nimport numpy as np\nimport urllib.request\nimport zipfile\nimport numpy as np\nimport hashlib\nimport json\nfrom .flags import flags\n\n\n... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.array_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
StackOfMusic/StackOfMusic | [
"29d0457006840ec843948b4e868bdbc498b09732"
] | [
"reconstruct_drum/detect_beat.py"
] | [
"import os\nimport wave\n\nimport boto3\nimport librosa\nimport numpy as np\nimport pyaudio\nfrom celery import Celery\nfrom django.shortcuts import get_object_or_404\nfrom pydub import AudioSegment\n\nfrom StackOfMusic import settings\nfrom music.models import SubMusic\nfrom reconstruct_piano.detect_frequency.edit... | [
[
"numpy.log",
"numpy.blackman",
"numpy.nditer",
"numpy.fft.rfft"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.21",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dekkerlab/ALV-repo | [
"4194a1f6494dbfd3cf8cd831fe32d23f57c0f85d"
] | [
"supp_lib.py"
] | [
"########################################\n# Some notes on the installation and stuff ...\n########################################\n#!conda install -y -c bioconda bedtools\n#!conda install -y -c bioconda ucsc-bedgraphtobigwig -> that was bad ... just a binary from UCSC - was a much better solution\n\n#############... | [
[
"sklearn.cluster.KMeans",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.all",
"numpy.mean",
"numpy.nanmean",
"numpy.where",
"numpy.random.randint",
"numpy.hstack",
"numpy.arange",
"numpy.diff",
"matplotlib.pyplot.figure",
"pandas.concat",
"numpy.log",
... | [
{
"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": []
}
] |
scottkleinman/Lexos-Bootstrap | [
"1d2d07d75da5cfecad001abaee56bcd1f563941a"
] | [
"processors/analyze/topword.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import division\n\n# this program detects word anomaly using z-test for proportion and kruskal wallis test\n# assume the possibility of a particular word appear in a text follows normal distribution\n\nfrom math import sqrt\nfrom operator import itemgetter\nfrom helpers.gen... | [
[
"scipy.stats.mstats.kruskalwallis"
]
] | [
{
"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"... |
clementfoltran/projectHone | [
"55ae61eb3e0ca935ebf6dd9dccd3cc915e5a0c17"
] | [
"server/myapp/StatisticsByRegion.py"
] | [
"import os\nimport json\nimport pandas\nimport numpy as np\nfrom TweetByRegion import TweetByRegion\nfrom SentimentAnalyze import Sentiment\n\nREGIONS = [\"Ile-de-France\", \"Auvergne-Rhone-Alpes\", \"Bourgogne-Franche-Comte\", \"Bretagne\", \"Centre-Val_de_Loire\", \"Corse\", \"Grand_Est\", \"Hauts-de-France\", \"... | [
[
"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": []
}
] |
mengwangk/tensorflow | [
"7aad97c27b803a0d3524a715ed08f4db7a71de63"
] | [
"tensorflow/examples/speech_commands/freeze.py"
] | [
"# Copyright 2017 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.lite.experimental.microfrontend.python.ops.audio_microfrontend_op.audio_microfrontend",
"tensorflow.nn.softmax",
"tensorflow.multiply",
"tensorflow.reshape",
"tensorflow.expand_dims",
"tensorflow.python.ops.gen_audio_ops.mfcc",
"tensorflow.compat.v1.placeholder",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cquark7/sunpy | [
"f1fa5f721ee0ef2ebb0b8ac04e95d457e116406a"
] | [
"sunpy/visualization/animator/tests/test_basefuncanimator.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom functools import partial\nimport pytest\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as mplanim\n\nfrom sunpy.visualization.animator import base, BaseFuncAnimator\n\n\nclass FuncAnimatorTest(BaseFuncAnimator):\n def plot_start_image(self, ax... | [
[
"numpy.random.random",
"numpy.array_equal",
"numpy.arange",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
skimmy/lib-bio | [
"221d97f3aa37a3d276c21ade7c83d1aba4558c89"
] | [
"src/simulator/util/eccentricity_postprocess.py"
] | [
"import sys\nimport numpy as np\nimport pandas as pd\n\nn = 9\ncols = [str(x) for x in range(n+1)]\nheader = [\"x\"] + cols + [\"Ecc\", \"Alpha\"]\ndf = pd.read_csv(sys.argv[1], header=None, names=header)\ntmp = df[cols]*np.arange(n+1)\ndf['Ecc'] = tmp.sum(axis=1) / np.power(4,n)\ndf['Alpha'] = df['Ecc'] / n\ndf_so... | [
[
"numpy.arange",
"pandas.read_csv",
"numpy.power"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
archibate/taichi | [
"a5d6e04a3725203dbccd2a8ee86bd0349bd46235"
] | [
"examples/mpm128.py"
] | [
"import taichi as ti\nimport numpy as np\nti.init(arch=ti.cuda) # Try to run on GPU\nquality = 1 # Use a larger value for higher-res simulations\nn_particles, n_grid = 9000 * quality ** 2, 128 * quality\ndx, inv_dx = 1 / n_grid, float(n_grid)\ndt = 1e-4 / quality\np_vol, p_rho = (dx * 0.5)**2, 1\np_mass = p_vol * p... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MichiganCOG/devil | [
"296115cd5f4952c7dc65bbcaaf2d1d5c55ef5d35",
"296115cd5f4952c7dc65bbcaaf2d1d5c55ef5d35"
] | [
"src/devil/fg_displacement_scoring/__init__.py",
"src/fvi_masks/utils/util.py"
] | [
"import numpy as np\n\n\nclass ForegroundDisplacementScorer(object):\n \"\"\"Scores the foreground displacement by computing the union among BG masks by a percentage of the frame area.\n\n This algorithm works by taking the union of all BG masks, and then dividing by the total area of the frame (height\n t... | [
[
"numpy.bitwise_xor"
],
[
"numpy.subtract",
"numpy.ones",
"numpy.ceil",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JoshMayberry/Numerical_Methods | [
"8da9fad9b7751f10e9a57ea1b7638ee768119ea6"
] | [
"CurveFitting/Curve_Fit.py"
] | [
"#Curve Fit Program\n#This program creates trendlines from data points\n#By: Joshua Mayberry\n#\n#########################################\n# #\n# Table of Contents #\n# #\n# Main Program............26 - 70 #\n# ... | [
[
"matplotlib.pyplot.legend",
"numpy.log",
"numpy.linalg.solve",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"numpy.exp",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.sum",
"numpy.loadtxt",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
imatiach-msft/fairlearn | [
"0e458fed0ec4c9eb3e8e17609da4bbd9563cbc96"
] | [
"fairlearn/widget/_fairlearn_dashboard.py"
] | [
"# Copyright (c) Microsoft Corporation and contributors.\n# Licensed under the MIT License.\n\n\"\"\"Defines the fairlearn dashboard class.\"\"\"\n\nfrom ._fairlearn_widget import FairlearnWidget\nfrom fairlearn.metrics import (\n true_negative_rate_group_summary,\n false_positive_rate_group_summary,\n fal... | [
[
"numpy.shape",
"scipy.sparse.issparse"
]
] | [
{
"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"... |
haantran96/advanced-audio | [
"dea109f6c207d4635266aee4e430aa8204d7a25f"
] | [
"duy_script/resnet_d.py"
] | [
"# Source: https://github.com/MihawkHu/DCASE2020_task1/blob/2384da9d357bc6be9648701a0ae0c4b4be285e36/task1a/10class/resnet/resnet.py\nfrom torch import Tensor, cat, rand\nfrom torch.nn import Module, Sequential, Conv2d, BatchNorm2d, ReLU, AdaptiveAvgPool2d, Linear\nfrom torchvision.models import resnet18\n\n__autho... | [
[
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.AdaptiveAvgPool2d",
"torch.rand",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zeynep-aslan/opencv-tutorials | [
"fd7306935fc80255c640b47f95c82c6bab6446ea"
] | [
"goruntuDuzeltme1/duzeltme1.py"
] | [
"import cv2\nimport numpy as np\nimport random\n# benim blurla düzeltme\nimg_org = cv2.imread(\"ram2.jpg\",0)\n\ndef addNoise(img):\n yukseklik, genislik = img.shape\n new_image = np.zeros(img.shape, img.dtype)\n\n p = 0.08\n for i in range(yukseklik):\n for j in range(genislik):\n r =... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cmichal2/numpy | [
"12c6000601c7665e52502ab4a67a54d290498266"
] | [
"numpy/core/tests/test_deprecations.py"
] | [
"\"\"\"\nTests related to deprecation warnings. Also a convenient place\nto document how deprecations should eventually be turned into errors.\n\n\"\"\"\nimport datetime\nimport operator\nimport warnings\nimport pytest\nimport tempfile\nimport re\nimport sys\n\nimport numpy as np\nfrom numpy.testing import (\n a... | [
[
"numpy.testing.dec.skipif",
"numpy.long",
"numpy.testing.dec.deprecated",
"numpy.linspace",
"numpy.testing.KnownFailureException",
"numpy.asarray",
"numpy.bool",
"numpy.promote_types",
"numpy.dtype",
"numpy.concatenate",
"numpy.int",
"numpy.searchsorted",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emulhall/episodic-memory | [
"27bafec6e09c108f0efe5ac899eabde9d1ac40cc",
"51a3fd59343820aed82879266df3be0147231cab",
"27bafec6e09c108f0efe5ac899eabde9d1ac40cc"
] | [
"VQ3D/camera_pose_estimation/SuperGlueMatching/match_pairs_api.py",
"MQ/Models/Loss.py",
"VQ2D/vq2d/tracking/kys.py"
] | [
"#! /usr/bin/env python3\n#\n# %BANNER_BEGIN%\n# ---------------------------------------------------------------------\n# %COPYRIGHT_BEGIN%\n#\n# Magic Leap, Inc. (\"COMPANY\") CONFIDENTIAL\n#\n# Unpublished Copyright (c) 2020\n# Magic Leap, Inc., All Rights Reserved.\n#\n# NOTICE: All information contained her... | [
[
"numpy.expand_dims",
"matplotlib.cm.jet",
"numpy.maximum",
"numpy.isinf",
"numpy.clip",
"numpy.linalg.inv",
"numpy.eye",
"torch.tensor",
"torch.set_grad_enabled",
"numpy.mean",
"torch.cuda.is_available",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tku-iarc/collision_avoidance_without_img | [
"981e32e9f2b366fbf5eaa6d006fbb75f29c34028"
] | [
"train/src/test_obstacle_env_move.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"classic Acrobot task\"\"\"\nimport sys\nsys.path.insert(1, \"/home/yue/.local/lib/python3.5/site-packages/\")\nsys.path.insert(2, \"/home/yue/yuetin/collision_surrouding/catkin_workspace/install/lib/python3/dist-packages\")\n# sys.path.insert(1, \"/home/yue/.local/lib/python3.5/site... | [
[
"numpy.dot",
"numpy.isnan",
"numpy.subtract",
"numpy.linalg.norm",
"numpy.append",
"numpy.add",
"numpy.array",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Neronjust2017/BayesianCNN | [
"33fc227b3ec2ff9cde816403db6aa4ae68d586e2"
] | [
"TCN/mnist_pixel/utils.py"
] | [
"import torch\nfrom torchvision import datasets, transforms\n\nimport os\nimport torch\nimport numpy as np\n\n\ndef data_generator(root, batch_size):\n train_set = datasets.MNIST(root=root, train=True, download=True,\n transform=transforms.Compose([\n ... | [
[
"torch.max",
"torch.utils.data.DataLoader",
"torch.exp",
"numpy.fromstring",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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