repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
Sense-X/UniFormer | [
"e8024703bffb89cb7c7d09e0d774a0d2a9f96c25"
] | [
"image_classification/token_labeling/tlt/models/uniformer.py"
] | [
"# --------------------------------------------------------\n# UniFormer\n# Copyright (c) 2022 SenseTime X-Lab\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Kunchang Li\n# --------------------------------------------------------\n\nfrom collections import OrderedDict\nimport torch\nimpor... | [
[
"torch.nn.Dropout",
"torch.nn.GELU",
"numpy.random.beta",
"numpy.sqrt",
"torch.ones",
"numpy.clip",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.LayerNorm",
"torch.nn.Tanh",
"torch.nn.Linear",
"numpy.int",
"torch.nn.Identity",
"torch.nn.BatchNorm2... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leafvmaple/machine_learning-decision_tree | [
"925b68c0b56c3b7c3b06ae6ccd652ebc4fb88e45"
] | [
"decision_tree.py"
] | [
"import numpy as np\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split\n\nclass Leaf:\n def __init__(self, data, uncert):\n self.predict = np.sum(data[:,-1]) / float(data.shape[0])\n self.uncert = uncert\n\nclass NodeInfo:\n def __init__(self, ... | [
[
"numpy.log2",
"sklearn.datasets.load_breast_cancer",
"sklearn.model_selection.train_test_split",
"numpy.column_stack",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
izabelcavassim/Noncoding | [
"31b7a9524b7f7dda6d0b9f3c0c48e5445001cde3"
] | [
"scripts/infer_dfe_Bernard.py"
] | [
"#! /usr/bin/env python\n# script for 1000 genomes EUR DFE workflow\n\nimport dadi\nimport numpy\nimport scipy\nimport pickle\nimport sys\nimport Selection #make sure Selection.py is copied into working dir\n\ndef eur_demog(params, ns, pts):\n \"\"\"\n generic european/asian demographic model\n bottleneck ... | [
[
"numpy.log",
"numpy.frompyfunc",
"numpy.genfromtxt",
"numpy.append",
"numpy.log10",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JasonGUTU/NAA | [
"48a5a75aed9432c9dc83ccc9148333ada97ce844"
] | [
"naa/naa.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nAuthor : JasonGUTU\nEmail : hellojasongt@gmail.com\nPython : anaconda3\nDate : 2016/11/18\n\"\"\"\nimport random\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass NAA_base(object):\n\n def __init__(self, dimension, bound, iteration, parameters, verbose=None, a... | [
[
"numpy.argsort",
"numpy.array",
"numpy.random.rand",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ttslr/gtos | [
"87d313d092b67a1c1494bae4d5682102fb150985"
] | [
"generator/utils.py"
] | [
"import torch\nfrom torch import nn\nimport math\n\ndef move_to_cuda(maybe_tensor, device):\n if torch.is_tensor(maybe_tensor):\n return maybe_tensor.cuda(device)\n elif isinstance(maybe_tensor, dict):\n return {\n key: move_to_cuda(value, device)\n for key, value in maybe_... | [
[
"torch.pow",
"torch.is_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OpenSourceEconomics/estimagic | [
"85163b4cdc601d60d654c6ca1f42b9db17a130a3"
] | [
"tests/dashboard/test_monitoring_app.py"
] | [
"\"\"\"Test the functions of the monitoring app.\"\"\"\nimport estimagic.dashboard.monitoring_app as monitoring\nimport numpy as np\nimport pandas as pd\nimport pandas.testing as pdt\nimport pytest\nfrom bokeh.document import Document\nfrom bokeh.models import ColumnDataSource\nfrom estimagic.config import EXAMPLE_... | [
[
"pandas.testing.assert_series_equal",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
otakbeku/ALPR-Indonesia | [
"0b495673a4c84faf56db78da91201719ad0416eb"
] | [
"GenData.py"
] | [
"# GenData.py\nimport argparse\nimport os\nimport sys\n\nimport cv2\nimport numpy as np\n\n# module level variables ##########################################################################\nMIN_CONTOUR_AREA = 100\n\nRESIZED_IMAGE_WIDTH = 20\nRESIZED_IMAGE_HEIGHT = 30\n\n\n#########################################... | [
[
"numpy.empty",
"numpy.append",
"numpy.savetxt",
"numpy.array",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Iceland-Leo/StyleGAN2_PyTorch | [
"3621f5e4ba1c7fde7e2fae1f4700d050656a0b02"
] | [
"utils/libs.py"
] | [
"# -*- coding: utf-8 -*-\n\n\n\"\"\"\n Miscellaneous utility classes and functions For StyleGAN2 Network.\n\"\"\"\nimport torch\nimport numpy as np\n\n\n# TWO = {1: 0, 2: 1, 4: 2, 8: 3, 16: 4, 32: 5,\n# 64: 6, 128: 7, 256: 8, 512: 9, 1024: 10}\n\nTWO = [pow(2, _) for _ in range(11)]\n\ndef _setup_kernel(k... | [
[
"numpy.asarray",
"numpy.outer",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ClimBin/models | [
"10989b361732ee5b93f5595f672fd7d0c18e8f93"
] | [
"Transformer/odd_numbers/train_transformer_odd_numbers.py"
] | [
"# https://github.com/Kenneth111/TransformerDemo/blob/master/predict_odd_numbers.py\nimport sys\nimport argparse\nimport os\nimport shutil\nimport numpy as np\n\nimport oneflow as flow\nimport oneflow.nn as nn\n\nsys.path.append(\"../\")\nfrom model import TransformerModel\n\nTO_CUDA = True\n\nparser = argparse.Arg... | [
[
"numpy.arange",
"numpy.random.shuffle",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rgerum/ElViS | [
"27b8be0769eaf78cfa1be4e320e3dc6bff5973ed"
] | [
"springModule.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nElViS Simulator\r\n\r\nELastic-VIscous-System Simulator\r\n\r\n\"\"\"\r\nimport numpy as np\r\nfrom elements import Spring, Dashpot, Force\r\n\r\nPOINT_static = 0\r\nPOINT_dynamic = 1\r\n\r\n\r\nclass MySim:\r\n big_point_array = None\r\n\r\n def __init__(self):\r\n\r\n ... | [
[
"numpy.min",
"numpy.linalg.inv",
"numpy.arange",
"numpy.asarray",
"numpy.concatenate",
"numpy.all",
"numpy.delete",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Steap/tavolo | [
"4af5f645b5ef4399c3746a3d930c5c8fc0892fb1"
] | [
"tests/seq2vec/yang_attention_test.py"
] | [
"import tensorflow as tf\n\nfrom tavolo.seq2vec import YangAttention\n\n\ndef test_shapes():\n \"\"\" Test input-output shapes \"\"\"\n\n # Inputs shape\n input_shape_3d = (56, 10, 30)\n attention_units = 100\n\n inputs_3d = tf.random.normal(shape=input_shape_3d)\n\n yang_attention = YangAttention... | [
[
"tensorflow.zeros_like",
"tensorflow.random.normal",
"tensorflow.keras.layers.Masking",
"tensorflow.reduce_sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
ustyuzhaninky/OSAR-keras | [
"0eacf8d1e49d6e9a0f9ec82799169c4720e67ac2",
"0eacf8d1e49d6e9a0f9ec82799169c4720e67ac2"
] | [
"OSAR/helix_memory.py",
"OSAR/tfxl/rel_bias.py"
] | [
"# coding=utf-8\n# Copyright 2020 Konstantin Ustyuzhanin.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required... | [
[
"tensorflow.keras.backend.tile",
"tensorflow.python.ops.nn.conv1d",
"tensorflow.python.ops.nn.avg_pool1d",
"tensorflow.keras.constraints.get",
"tensorflow.keras.constraints.serialize",
"tensorflow.keras.regularizers.get",
"tensorflow.keras.initializers.serialize",
"tensorflow.keras... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"... |
kaituohuo/kaldo | [
"537bceea2c3206711a8899d68e1dbd23fb0c38b6"
] | [
"examples/silicon_bulk_LDA_ASE_QE_hiPhive/1_Si_hiPhive_generate_fcs.py"
] | [
"# Example: silicon bulk, LDA pseudo potential \n# Computes: force constant potential for silicon bulk (2 atoms per cell)\n# Uses: hiPhive, ASE, Quantum ESPRESSO (QE)\n# External files: Si.pz-n-kjpaw_psl.0.1.UPF\n\nfrom ase.build import bulk\nfrom ase.calculators.espresso import Espresso\nfrom ase.io import write, ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kyuhyoung/differentiable-point-clouds | [
"76f3baf54c18a4aff0e8a593952dda6e63459a60"
] | [
"dpc/nets/pose_net.py"
] | [
"import tensorflow as tf\nimport tensorflow.contrib.slim as slim\n\n\ndef pose_branch(inputs, cfg):\n num_layers = cfg.pose_candidates_num_layers\n f_dim = 32\n t = inputs\n for k in range(num_layers):\n if k == (num_layers - 1):\n out_dim = 4\n act_func = None\n else... | [
[
"tensorflow.concat",
"tensorflow.contrib.slim.arg_scope",
"tensorflow.contrib.layers.variance_scaling_initializer",
"tensorflow.reshape",
"tensorflow.truncated_normal_initializer",
"tensorflow.contrib.slim.fully_connected",
"tensorflow.tanh",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
nikibhatt/Groa | [
"31b3624bfe61e772b55f8175b4e95d63c9e67966"
] | [
"Flask/application.py"
] | [
"from flask import Flask, session, render_template, request, flash, redirect, send_file\nfrom flask_session import Session\nfrom time import sleep\nimport pandas as pd\nimport math\nimport numpy as np\nfrom zipfile import ZipFile\nimport json\nimport os, shutil, io\nimport psycopg2\n\n# self import\nfrom psycopg2_b... | [
[
"numpy.random.uniform",
"pandas.read_csv",
"pandas.read_json",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
terrorizer1980/torchrec | [
"824efb76e4a1c8500e5ce976ac01e6bae894e03a"
] | [
"torchrec/modules/tests/test_lazy_extension.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport inspect\nimport re\nimport unittest\nfrom typing import Tuple\n\nimpo... | [
[
"torch.ones",
"torch.zeros",
"torch.tensor",
"torch.nn.Identity",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
0todd0000/lmfree2d | [
"5c5d398b4dac6d99878068855ad21114d0bba31f"
] | [
"Python/fig_corresp.py"
] | [
"\n'''\nFigure: demonstration of a simple point correspondence algorithm.\n'''\n\n\nimport os\nimport numpy as np\nfrom matplotlib import pyplot as plt\nplt.ion()\nimport lmfree2d as lm\n\n\n\n#(0) Load data:\ndirREPO = lm.get_repository_path()\nnames = ['Bell', 'Comma', 'Device8', 'Face', 'Flatfish', 'Ham... | [
[
"numpy.random.seed",
"numpy.linspace",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.close",
"matplotlib.pyplot.ion",
"numpy.roll",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mmiki21/Viola-SV | [
"10fe2c326749024551d3e41f900b37e20dbde35c"
] | [
"tests/vcf/test_to_vcf_like_lumpy.py"
] | [
"import viola\nimport sys, os\nimport pandas as pd\nfrom io import StringIO\nHERE = os.path.abspath(os.path.dirname(__file__))\nHEADER = \"\"\"##fileformat=VCFv4.2\n##source=LUMPY\n##INFO=<ID=SVTYPE,Number=1,Type=String,Description=\"Type of structural variant\">\n##INFO=<ID=STRANDS,Number=.,Type=String,Description... | [
[
"pandas.testing.assert_frame_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Thanduriel/StableNN | [
"2457fbadee8491eaa3d52ff76d66dc45c27cb84a"
] | [
"evaluation/heat_diff_error.py"
] | [
"import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport math\n\nmatplotlib.style.use('seaborn')\n\ndata = np.genfromtxt(fname=\"../build/mse.txt\",\n dtype=np.float32,\n delimiter=',',\n skip_header=0)\n\nend = 4096\nenergy = ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.semilogy",
"numpy.linspace",
"matplotlib.style.use",
"numpy.genfromtxt",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zdaiot/NAIC-Person-Re-identification | [
"762be875b68e85fbaab8b7730b5a857bfcc9e218"
] | [
"models/backbones/resnet.py"
] | [
"import math\nimport torch\nfrom torch import nn\nfrom torchvision import models\n\n\nclass Bottleneck(nn.Module):\n expansion = 4\n\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(Bottleneck, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, b... | [
[
"torch.nn.Sequential",
"torch.load",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
4Subsea/evapy | [
"be4f37b73bc22af700b75019dd23f73826e47f39"
] | [
"tests/test_distributions.py"
] | [
"import unittest\n\nimport numpy as np\n\nimport evapy.distributions as dist\n\n\nclass Test_rayleigh_gen(unittest.TestCase):\n def setUp(self):\n self.dist = dist._distns.rayleigh_gen()\n\n def tearDown(self):\n pass\n\n def test_cdf(self):\n calculated = self.dist.cdf(2.5, loc=0.5, s... | [
[
"numpy.log",
"numpy.exp",
"numpy.sqrt",
"numpy.expm1"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aydogduali/DAPPER | [
"ce59d4cbdff07327be2ef66502eb4ca1d9e7027f"
] | [
"dapper/stats.py"
] | [
"\"\"\"Stats computation for the assessment of DA methods.\"\"\"\n\nimport warnings\n\nimport numpy as np\nimport scipy.linalg as sla\nimport struct_tools\nfrom matplotlib import pyplot as plt\nfrom patlib.std import do_once\nfrom tabulate import tabulate\n\nimport dapper.tools.liveplotting as liveplotting\nimport ... | [
[
"numpy.diag",
"numpy.seterrcall",
"numpy.sqrt",
"numpy.mean",
"numpy.nanmean",
"numpy.where",
"numpy.eye",
"scipy.linalg.eigh",
"matplotlib.pyplot.figure",
"numpy.log",
"matplotlib.pyplot.fignum_exists",
"numpy.full_like",
"numpy.errstate",
"numpy.isreal",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
SunskyF/EasyPR-python | [
"c35b34876d63dbdee56719ebf419f4e3af99004a"
] | [
"lib/easypr/chars_segment.py"
] | [
"import cv2\nimport numpy as np\n\nfrom lib.easypr.core_func import getPlateType, Color, ThresholdOtsu, clearLiuDingChar\n\n\nclass CharsSegment(object):\n def __init__(self):\n self.LiuDingSize = 7\n self.MatWidth = 136\n\n self.colorThreshold = 150\n self.BluePercent = 0.3\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isabella232/DBAP-simulation | [
"bdba0b58c4a01e0742e97299ce3bd1587ad2aa25"
] | [
"adept_envs/franka/franka_2element_newcode_withchanges.py"
] | [
"\"\"\"\nCopyright 2021 Google LLC\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 https://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in ... | [
[
"torch.nn.Softmax",
"numpy.abs",
"torch.Tensor",
"torch.load",
"numpy.clip",
"numpy.random.choice",
"numpy.linalg.norm",
"numpy.ones",
"numpy.concatenate",
"numpy.binary_repr",
"numpy.random.rand",
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.floa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
myotheone/cs231n | [
"09d3fb15a3ede03fad97c0dc254e67295a157467"
] | [
"assignment2/cs231n/classifiers/fc_net.py"
] | [
"from builtins import range\nfrom builtins import object\nimport numpy as np\n\nfrom cs231n.layers import *\nfrom cs231n.layer_utils import *\n\n\nclass TwoLayerNet(object):\n \"\"\"\n A two-layer fully-connected neural network with ReLU nonlinearity and\n softmax loss that uses a modular layer design. We ... | [
[
"numpy.random.randn",
"numpy.random.normal",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NuealYoon/deep-learning-from-scratch-master | [
"83ebba46ac599a61bb1d3b8c0c7ee1eb3d11f8af"
] | [
"ch05/train_neuralnet.py"
] | [
"# coding: utf-8\nimport sys, os\nsys.path.append(os.pardir)\n\nimport numpy as np\nfrom dataset.mnist import load_mnist\nfrom two_layer_net import TwoLayerNet\n\n# 데이터 읽기\n(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)\n# train_data, test_Data = load_mnist(normalize=True, one... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Parall-UD/sallfus | [
"ffe5ba9224c914d1da2e396b4993f59778d16216"
] | [
"sallfus/measures.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\n\ndef check_images(fusioned, original):\n assert len(fusioned) == len(original), \"Supplied images have different sizes \" + \\\n str(fusioned.shape) + \" and \" + str(original.shape)\n if(len(fusioned.shape) == len(original.shape)):\n estado = 'mtom'\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NicolasHug/hmmkay | [
"24f5c1b25ef2e4ee2bc401c1a7c6cdbf87de59c6"
] | [
"benchmark.py"
] | [
"from time import time\nfrom warnings import simplefilter\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom hmmkay import HMM\nfrom hmmkay.utils import (\n _get_hmm_learn_model,\n _to_weird_format,\n make_proba_matrices,\n make_observation_sequences,\n)\n\n\nn_hidden_states, n_observable_st... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sys-bio/network-modeling-summer-school-2021 | [
"9215861074466c045bdbbe06046c13a388f34c79"
] | [
"src/util.py"
] | [
"import pandas as pd\nimport urllib.request\n\n# Linear pathway data\nBASE_URL = \"https://github.com/sys-bio/network-modeling-summer-school-2021/raw/main/\"\nBASE_DATA_URL = \"%sdata/\" % BASE_URL\nBASE_MODULE_URL = \"%ssrc/\" % BASE_URL\nBASE_MODEL_URL = \"%smodels/\" % BASE_URL\nLOCAL_FILE = \"local_file.txt\"\n... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
alexmlamb/relational-rnn-pytorch | [
"1b16ae32988625b16b95f920b0f6fe55ed4e45e7"
] | [
"train_rmc.py"
] | [
"# copypasta from main.py of pytorch word_language_model code\n# coding: utf-8\nimport argparse\nimport time\nimport math\nimport os\nimport torch\nimport torch.nn as nn\nimport torch.onnx\nimport datetime\nimport shutil\nimport pickle\nimport data\nfrom relational_rnn_models import RelationalMemory\n\n# is it fast... | [
[
"torch.mean",
"torch.onnx.export",
"torch.cuda.synchronize",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.LongTensor",
"torch.load",
"torch.manual_seed",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.nn.DataParallel",
"torch.t"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daevem/tensorflow-research | [
"ebb8e8243889f55affa354c49eb54db4fbcd2c87"
] | [
"src/models/segmentation/satellite_unet_model.py"
] | [
"# https://deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/\nimport tensorflow as tf\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras.layers import (\n BatchNormalization,\n Conv2D,\n Conv2DTranspose,\n Input,\n MaxPooling2D,\n UpSampling2D,\n concatenat... | [
[
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Conv2DTranspose",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.concatenate",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
beibeiJ/deep-reinforcement-learning | [
"ab1b0f4ada8da69af2e38d3e2e82e3ae55837c60"
] | [
"p1_navigation/prioritized_memory.py"
] | [
"import random\nimport numpy as np\nimport torch\nfrom collections import namedtuple\nfrom SumTree import SumTree\n\n\n\"\"\"\n Prioritized experience reply buffer\n Get from https://github.com/austinsilveria/Banana-Collection-DQN/blob/master/Banana_DoubleDQN_PER.py which was adjusted from original source: ht... | [
[
"numpy.random.uniform",
"numpy.vstack",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
masteropen/machine-learning-flask-api-skeloton | [
"cb8405771450e12079dc9b362504ac3fdf93e92f"
] | [
"src/app/api/main.py"
] | [
"import pandas as pd\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom joblib import dump\n\n_data_path = '../data/salary_data.csv'\n_dumps_path = '../dumps/salary_model.joblib'\n\ndata = pd.read_csv(_data_path)\nfeatures = data.iloc[:, :-1]\ntarget = dat... | [
[
"sklearn.linear_model.LinearRegression",
"pandas.read_csv",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
PiterPentester/luminoth | [
"da0186515586291fbb9544c98240979480355f7a"
] | [
"luminoth/datasets/base_dataset.py"
] | [
"import os\nimport tensorflow as tf\nimport sonnet as snt\n\nfrom luminoth.datasets.exceptions import InvalidDataDirectory\n\n\nclass BaseDataset(snt.AbstractModule):\n def __init__(self, config, **kwargs):\n super(BaseDataset, self).__init__(**kwargs)\n self._dataset_dir = config.dataset.dir\n ... | [
[
"tensorflow.FIFOQueue",
"tensorflow.gfile.Exists",
"tensorflow.train.QueueRunner",
"tensorflow.train.add_queue_runner",
"tensorflow.RandomShuffleQueue",
"tensorflow.train.string_input_producer",
"tensorflow.TFRecordReader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
vileme/diploma | [
"ae4f01aa5025345d37504b7a993e0bcdbfd33d57"
] | [
"validation.py"
] | [
"import numpy as np\nimport utils\n\nfrom torch import nn\nimport torch\nimport torch.nn.functional as F\nfrom metrics import AllInOneMeter\nimport time\nimport torchvision.transforms as transforms\n\n\ndef validation_binary(model: nn.Module, criterion, valid_loader, device, device_id, num_classes=None):\n with ... | [
[
"torch.nn.functional.binary_cross_entropy_with_logits",
"numpy.histogramdd",
"torch.nn.functional.sigmoid",
"torch.no_grad",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gwanglee/VisDA2020 | [
"23ecc1bb2ce3ce4bece9159ca4ecc420e3e8f34c"
] | [
"devkit/data/collate_batch.py"
] | [
"# encoding: utf-8\n\"\"\"\n@author: liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\n\n\ndef train_collate_fn(batch):\n imgs, pids, _, _, = zip(*batch)\n pids = torch.tensor(pids, dtype=torch.int64)\n return torch.stack(imgs, dim=0), pids\n\n\ndef val_collate_fn(batch):\n imgs, ... | [
[
"torch.stack",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nollety/joseki | [
"dffc837cff185b0a1c931de7076bbefda7742405"
] | [
"src/joseki/afgl_1986.py"
] | [
"\"\"\"Module to read AFGL 1986 data files.\"\"\"\nimport enum\nimport importlib.resources as pkg_resources\n\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\n\nfrom .data import afgl_1986\nfrom .units import ureg\nfrom .util import make_data_set\n\n\nclass Identifier(enum.Enum):\n \"\"\"AFGL 1986 ... | [
[
"pandas.concat",
"numpy.array",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Nexus01/zheye-crawler | [
"42a86109c73225866234b91d8d0fd067d270f54e"
] | [
"Slave.py"
] | [
"from Error import NoFolloweeError\nimport random\nimport datetime\nimport urllib\nimport requests\nimport json\nimport re\nimport time\nfrom bs4 import BeautifulSoup\nimport pymongo\nimport os\nimport json\n#from fake_useragent import UserAgent\nimport sys\nimport base64\nfrom PIL import Image\nimport cnn_test_en\... | [
[
"tensorflow.Graph"
]
] | [
{
"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... |
redoclag/plaidml | [
"46d9e8b3f1e1093aab2a0dfa40b2e15e3cc7d314",
"46d9e8b3f1e1093aab2a0dfa40b2e15e3cc7d314"
] | [
"networks/keras/examples/reuters_mlp.py",
"networks/keras/examples/addition_rnn.py"
] | [
"'''Trains and evaluate a simple MLP\non the Reuters newswire topic classification task.\n'''\nfrom __future__ import print_function\n\nimport numpy as np\nimport keras\nfrom keras.datasets import reuters\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.preproces... | [
[
"numpy.max",
"numpy.array"
],
[
"numpy.array",
"numpy.random.shuffle",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thomasarmstrong/sstcam-simulation | [
"1da5a1dd0ce23b2a2299db72b7f3a03114689ce5"
] | [
"sstcam_simulation/event/source.py"
] | [
"import numpy as np\nfrom ..camera import Camera\nfrom .photoelectrons import Photoelectrons\nfrom .cherenkov import get_cherenkov_shower_image\n\n__all__ = [\"PhotoelectronSource\"]\n\n\nclass PhotoelectronSource:\n def __init__(self, camera, seed=None):\n \"\"\"\n Collection of methods which simu... | [
[
"numpy.arange",
"numpy.repeat",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DulinkLu/Action-Verification | [
"3f603d0e05c866ce3b62286de511bf3eb7868cad"
] | [
"utils/visualization.py"
] | [
"import torch\nimport torchvision.transforms as tf\nimport os\nimport numpy as np\n\nimport pdb\nfrom PIL import Image\n\nfrom utils.input import frames_preprocess\nfrom data.dataset import action_ids_bank\nfrom tensorboardX import SummaryWriter\nfrom tqdm import tqdm\n\nimport cv2\n\n\n\ndef sample_frames(data_pat... | [
[
"numpy.random.random",
"torch.cat",
"torch.cuda.device_count",
"torch.tensor",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.DataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jingxianwen/e3sm_diags | [
"4409d0da2cb724b44e19dbfb1f132d0ccfbbed1e"
] | [
"acme_diags/plot/cartopy/cosp_histogram_plot.py"
] | [
"from __future__ import print_function\n\nimport os\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nfrom acme_diags.driver.utils.general import get_output_dir\nfrom acme_diags.plot import get_colormap\n\nplotTitle = {'fontsize': 11.... | [
[
"matplotlib.pyplot.axvline",
"matplotlib.colors.BoundaryNorm",
"matplotlib.pyplot.axhline",
"numpy.linspace",
"matplotlib.use",
"matplotlib.transforms.Bbox.from_extents",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.pcolormesh",
"matplotlib.pyp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChristophReich1996/ToeffiPy | [
"34ca9cd97a488cdc58d2b909ba963edb80ae2b76"
] | [
"autograd/nn/optim.py"
] | [
"from typing import Iterator, Callable\n\nimport numpy as np\n\nfrom .parameter import Parameter\n\n\nclass Optimizer(object):\n \"\"\"\n Super class of optimizer\n \"\"\"\n\n def __init__(self, parameters: Callable[[], Iterator[Parameter]]) -> None:\n \"\"\"\n Constructor method\n ... | [
[
"numpy.zeros_like",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Saransh-cpp/liionpack | [
"82ab00ad257ccb2bc8dbcb71bc08baa30fa9ed43",
"82ab00ad257ccb2bc8dbcb71bc08baa30fa9ed43"
] | [
"tests/unit/test_utils.py",
"tests/integration/test_all_solvers.py"
] | [
"import liionpack as lp\nimport pandas as pd\nimport pybamm\nimport unittest\n\n\nclass utilsTest(unittest.TestCase):\n def test_interp_current(self):\n d = {\"Time\": [0, 10], \"Cells Total Current\": [2.0, 4.0]}\n df = pd.DataFrame(data=d)\n f = lp.interp_current(df)\n assert f(5) =... | [
[
"pandas.DataFrame"
],
[
"numpy.allclose"
]
] | [
{
"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... |
joppichristian/tirg | [
"fba0afec562149148bf25099561fe358e8389e74"
] | [
"torch_functions.py"
] | [
"\n# TODO(lujiang): put it into the third-party\n# MIT License\n\n# Copyright (c) 2018 Nam Vo\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without... | [
[
"torch.mm",
"torch.transpose",
"torch.norm",
"torch.from_numpy",
"torch.FloatTensor",
"torch.clamp",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
openseg-group/detr | [
"d947cf39ab716aedf7502103fc51b85b9d82822b",
"d947cf39ab716aedf7502103fc51b85b9d82822b"
] | [
"util/box_ops.py",
"hrnet/hrnet.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport pdb\nimport torch\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n x_c, y_c, w, h = x.unbind(-1)\n b = [(x_c - 0.5 * w), (y_c - 0.5 * ... | [
[
"torch.max",
"torch.zeros",
"torch.min",
"torch.arange",
"torch.stack",
"torch.meshgrid"
],
[
"torch.nn.Sequential",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.ModuleList"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sambit-giri/21cmtools | [
"5a5977f918abdc80e8fa9470a58667b58441c20b"
] | [
"src/tools21cm/segmentation.py"
] | [
"\"\"\"\nCreated by Michele Bianco, 9 July 2021\n\"\"\"\n\nimport numpy as np\nimport pkg_resources\nfrom tqdm import tqdm\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport tensorflow as tf\ntry:\n from tensorflow.keras.models import load_model\n from tensorflow.keras import backend as K\nexce... | [
[
"tensorflow.python.ops.nn_ops._ensure_xent_args",
"numpy.flipud",
"tensorflow.python.ops.math_ops.exp",
"numpy.mean",
"tensorflow.python.keras.backend.log",
"tensorflow.python.keras.backend.square",
"numpy.fliplr",
"tensorflow.python.ops.array_ops.where",
"tensorflow.python.ker... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mick-tz/PolynomialPDEs | [
"ecb1b11c1d61f493a66163ef1624efdd3cfbf6ff"
] | [
"PoliticalPricing.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 29 19:38:34 2018\n\n@author: Chino\n\"\"\"\n\nfrom scipy.misc import derivative\nfrom scipy.integrate import quad\n\n\ndef P(t, y, alpha, beta):\n \"\"\"\n penalty function associated with costumer's tendancy to\n wait for the pro... | [
[
"scipy.integrate.quad",
"scipy.misc.derivative"
]
] | [
{
"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"
... |
XAVILLA/nbdt | [
"8016a8a57259cfa9f2cb7f872b44bcfde3eed614"
] | [
"nbdt/data/transforms.py"
] | [
"import torch\n\n\nclass InverseNormalize:\n def __init__(self, mean, std):\n self.mean = torch.Tensor(mean)[None, :, None, None]\n self.std = torch.Tensor(std)[None, :, None, None]\n\n def __call__(self, sample):\n return (sample * self.std) + self.mean\n\n def to(self, device):\n ... | [
[
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
00sapo/ASMD | [
"48e021f98d5fbecd09bed1cdd58024d9b471fad4"
] | [
"asmd/conversion_tool.py"
] | [
"import gzip\nimport json\nimport multiprocessing as mp\nimport os\nimport random\nimport sys\nimport tarfile\nfrom copy import deepcopy\nfrom difflib import SequenceMatcher\nfrom os.path import join as joinpath\nfrom typing import Callable, List, Optional\n\nimport numpy as np\nfrom pretty_midi.constants import IN... | [
[
"numpy.zeros",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ProfessorHuang/2D-UNet-Pytorch | [
"b3941e8dc0ac3e76b6eedb656f943f1bd66fa799"
] | [
"models/unet.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass DoubleConv(nn.Module):\n \"\"\"(convolution => [BN] => ReLU) * 2\"\"\"\n\n def __init__(self, in_channels, out_channels, mid_channels=None):\n super().__init__()\n if not mid_channels:\n mid_channels = out_... | [
[
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChristianSteger/HORAYZON | [
"ba82834d6884909ce4964197eedfb5661ad24188"
] | [
"src/geoid.py"
] | [
"# Copyright (c) 2022 ETH Zurich, Christian R. Steger\n# MIT License\n\n# Load modules\nimport os\nimport numpy as np\nfrom scipy import interpolate\n\n\n###############################################################################\n\ndef geoid_undulation(lon_ip, lat_ip, geoid=\"EGM96\", path=None):\n \"\"\"Co... | [
[
"numpy.hstack",
"numpy.fromfile",
"scipy.interpolate.RectBivariateSpline",
"numpy.linspace",
"numpy.flipud",
"numpy.append",
"numpy.diff"
]
] | [
{
"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"... |
leanhkhoi/AE_BERT_CROSS_SENTENCES | [
"e8ef8d6482db2e087ef452d2b49527e30a96e44c"
] | [
"src/common.py"
] | [
"import json\nimport os\nimport _locale\n\nfrom collections import namedtuple, deque\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import RandomSampler, TensorDataset, DataLoader, SequentialSampler\n\nFILE_ENCODING = \"utf-8\" #_locale._getdefaultlocale()[1]\n\nSentences = namedtuple('Sentences', [\n ... | [
[
"numpy.unique",
"torch.utils.data.TensorDataset",
"torch.utils.data.DataLoader",
"numpy.stack",
"torch.tensor",
"numpy.argmax",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexcu/argus-bib-detect | [
"883ffcf99230ff83ca88eb8f9361ccc3e7f09bd2"
] | [
"preprocess.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"\n Script to preprocess OCR output for Tesseract\n\n Usage:\n python3 preprocess.py /path/to/input/dir \\\n /path/to/output/dir\n\"\"\"\n\nfrom glob import glob\nimport os\nimport shutil\nimport sys\nimport cv2\nimport numpy as np\n\ndef preprocess(img):\n \... | [
[
"numpy.median"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fyangneil/DSFPN | [
"95ba534d451598db7af05b009aec9b40ac675182"
] | [
"detectron/roi_data/cascade_rcnn_deep_sup.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicabl... | [
[
"numpy.concatenate",
"numpy.where",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
santanaangel/shap | [
"1c1c4a45440f3475b8544251f9d9e5b43977cd0e"
] | [
"shap/explainers/other/random.py"
] | [
"from ..explainer import Explainer\nimport numpy as np\n\nclass RandomExplainer(Explainer):\n \"\"\" Simply returns random (normally distributed) feature attributions.\n\n This is only for benchmark comparisons. It supports both fully random attributions and random\n attributions that are constant across a... | [
[
"numpy.random.randn",
"numpy.tile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
acdh-oeaw/histogis | [
"cbe90e17a7e4f26849c7b97f453833e094d445b2"
] | [
"analyze/views.py"
] | [
"from django.http import JsonResponse\nfrom collections import Counter\nimport pandas as pd\nimport json\nfrom datetime import date, timedelta\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\nfrom django.urls import reverse\nfrom django.db.models impo... | [
[
"pandas.to_timedelta",
"pandas.set_option",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
NithinRama/DeepSpeed | [
"b4e5826a60b275600673a77478ecb749519caaf0"
] | [
"deepspeed/runtime/engine.py"
] | [
"'''\nCopyright 2019 The Microsoft DeepSpeed Team\n'''\nimport os\nimport re\nimport stat\nimport math\nimport torch\nimport warnings\nimport hashlib\nimport torch.distributed as dist\nfrom collections import defaultdict, OrderedDict\nfrom shutil import copyfile\n\nfrom torch.nn.modules import Module\nfrom torch.nn... | [
[
"torch.optim.Adam",
"torch.distributed.broadcast",
"torch.cuda.set_device",
"torch.cat",
"torch.load",
"torch.utils.data.SequentialSampler",
"torch.distributed.all_gather",
"torch.distributed.is_initialized",
"torch.is_tensor",
"torch.distributed.barrier",
"torch.optim.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KishManani/feature_engine | [
"6474b56aa2e2e61b579f118f656e752d48b169c9"
] | [
"feature_engine/estimator_checks.py"
] | [
"from typing import Tuple\n\nimport pandas as pd\nimport pytest\nfrom sklearn.base import clone\nfrom sklearn.datasets import make_classification\nfrom sklearn.exceptions import NotFittedError\n\n\ndef test_df(\n categorical: bool = False, datetime: bool = False\n) -> Tuple[pd.DataFrame, pd.Series]:\n \"\"\"\... | [
[
"sklearn.datasets.make_classification",
"pandas.Series",
"pandas.DataFrame",
"sklearn.model_selection.KFold",
"sklearn.model_selection.StratifiedKFold",
"sklearn.base.clone",
"pandas.date_range"
]
] | [
{
"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": []
}
] |
aveek22/tutorialspoint | [
"2a478bb91b38bcee62a857b75620741ea77a47c6"
] | [
"apache-airflow/materials/data-pipelines-with-apache-airflow-master/chapters/chapter15/dags/nyc_dag.py"
] | [
"import io\nimport json\n\nimport airflow.utils.dates\nimport geopandas\nimport pandas as pd\nimport requests\nfrom airflow.hooks.S3_hook import S3Hook\nfrom airflow.hooks.base_hook import BaseHook\nfrom airflow.models import DAG\nfrom airflow.operators.python_operator import PythonOperator\nfrom minio import Minio... | [
[
"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": []
}
] |
Chaitya62/NeuralNetworks | [
"db531226a58fc2292c98df9c304b8a54b7bbceea"
] | [
"CNN.py"
] | [
"import numpy as np \nimport scipy\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import load_digits\n\n\n\"\"\"\nCNN with 2 CNN layer \nand one Fully connected layer\n\"\"\"\n\n\n\n\n\ndf = load_digits()\n\nfilter1 = np.random.random(9)\nfilter1 = filter1.reshape(9,1)\nbias1 = np.random.random(1)[0]\n\n... | [
[
"numpy.random.random",
"numpy.zeros_like",
"sklearn.datasets.load_digits",
"numpy.exp",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wiekern/GenderPerformance | [
"d69d90a3f0284fb9f547f716eb8a8707d0a18e03",
"d69d90a3f0284fb9f547f716eb8a8707d0a18e03"
] | [
"models/GRU/inference.py",
"models/GRU/preprocess.py"
] | [
"from pathlib import Path\nimport sys\nsys.path.insert(0, str(Path.cwd().parents[1]))\n\nfrom torch.utils import data\nfrom torch.nn.utils import rnn\nfrom sklearn.metrics import accuracy_score\nfrom nltk.tokenize import word_tokenize\nimport torch\nimport torch.nn as nn\nfrom torch import optim\nimport torch.nn.fu... | [
[
"torch.nn.functional.softmax",
"torch.LongTensor",
"torch.max",
"torch.load",
"torch.utils.data.DataLoader",
"torch.tensor",
"torch.no_grad",
"torch.sort",
"torch.cuda.is_available"
],
[
"numpy.random.uniform",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DaraDadachanji/wordle | [
"aa14f8099b3fa251c050cf54e1b8d89d0df22873"
] | [
"src/guesser.py"
] | [
"from tkinter import W\nimport pandas as pd\nimport numpy as np\nimport copy\nimport game\n\ndef assist_guesses():\n guesser = Guesser()\n while True:\n hint = get_hint()\n guesser.give_hint(hint)\n guesser.print_remaining_answers()\n\n\nclass Guesser:\n def __init__(self) -> None:\n ... | [
[
"pandas.read_csv",
"numpy.vectorize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
amazon-research/gnn-tail-generalization | [
"1ff49e62b8a2e2a7273c50dce59167ea9d9161fb",
"1ff49e62b8a2e2a7273c50dce59167ea9d9161fb"
] | [
"Link_prediction_baseline/models/pretrain_masking_gin.py",
"GNN_model/drop_tricks.py"
] | [
"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom dgl.nn.pytorch import GraphConv, SAGEConv, GINConv\nfrom dgl.nn.pytorch.glob import SumPooling, AvgPooling, MaxPooling\n\nfrom src.models.MLP import MLP as feat_MLP\n\n\nclass ApplyNodeFunc(nn.Module):\n \"\"\"Update the node feature hv w... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.nn.functional.nll_loss",
"torch.nn.ModuleList",
"torch.nn.Linear",
"torch.nn.functional.relu"
],
[
"torch.ones",
"torch.zeros",
"torch.zeros_like",
"torch.bernoulli",
"torch.arange",
"torch.full_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
open-risk/correlationMatrix | [
"2ce4ef55e234bf5ff5e4e6a8d6bb373a48147db9"
] | [
"tests/test_utils.py"
] | [
"# encoding: utf-8\n\n# (c) 2019 Open Risk, all rights reserved\n#\n# correlationMatrix is licensed under the Apache 2.0 license a copy of which is included\n# in the source distribution of correlationMatrix. This is notwithstanding any licenses of\n# third-party software included in this distribution. You may not ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ayush237/Business-news-scrapper | [
"82bb2c06ab5459e69333bf924c19072267344ba3"
] | [
"code/quick_scraper/quick_scraper/spiders/quick_scraper.py"
] | [
"import scrapy\nfrom bs4 import BeautifulSoup\nimport sys\nimport os\nimport _pickle as pickle\nimport pandas as pd\nfrom .scrape_with_bs4 import *\nimport datetime\nimport os\n\nDATA_DIR = os.path.join(os.getcwd(),'..','..','data')\nprint(DATA_DIR,\"$4$\"*10)\nclass ContentSpider(scrapy.Spider):\n name = \"yolo... | [
[
"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": []
}
] |
adamrankin/IPCAI.CNN-US-Needle-Segmentation | [
"c2600917f8b9f13627473776d037506e58cc97c4",
"c2600917f8b9f13627473776d037506e58cc97c4"
] | [
"segmentation.py",
"manual_seg.py"
] | [
"import numpy as np\nfrom tensorflow.keras.models import load_model\nimport cv2\n\n\n'''\nReturns the (x, y) coordinate of the centroid of the needle in an ultrasound image\nPARAMS:\n- x: A grayscale image of shape (w, h, 1), with pixel intensities normalized to [0.0, 1.0]\nRETURNS: The predicted centroid coordinat... | [
[
"tensorflow.keras.models.load_model",
"numpy.squeeze",
"numpy.expand_dims"
],
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.scatter",
"numpy.squeeze",
"matplotlib.pyplot.subplots",
"numpy.save",
"numpy.delete",
"matplotlib.pyplot.clf",
"numpy.load",
"matplotl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tchin-divergent/tacs | [
"34743b370da4ab6ea16d24de7c574c3fec9d333a"
] | [
"tests/constitutive_tests/test_solid_constitutive.py"
] | [
"from tacs import TACS, constitutive\nimport numpy as np\nimport unittest\n\n\nclass ConstitutiveTest(unittest.TestCase):\n def setUp(self):\n # fd/cs step size\n if TACS.dtype is complex:\n self.dh = 1e-50\n self.rtol = 1e-11\n else:\n self.dh = 1e-6\n ... | [
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nikbaya/risk_gradients | [
"8a70a13ee19eef812382066c9ebccae425ad7af9"
] | [
"python/sim_tree_sequences.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 29 07:39:01 2020\n\nRuns large-scale tree sequence simulations using:\n - Flat recombination map\n - Modeling chromosomes as separate tree sequences\n - Hybrid simulations (discrete-time Wright-Fisher for recent past, coalescent f... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
shreejitverma/Data-Scientist | [
"f82939a411484311171465591455880c8e354750",
"f82939a411484311171465591455880c8e354750"
] | [
"Cluster Analysis in Python/Hierarchical_Clustering.py",
"Cluster Analysis in Python/K-Means_Clustering.py"
] | [
"# Hierarchical Clustering\n# This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The ch... | [
[
"scipy.cluster.hierarchy.linkage",
"scipy.cluster.hierarchy.dendrogram",
"matplotlib.pyplot.show",
"scipy.cluster.hierarchy.fcluster"
],
[
"numpy.random.seed",
"scipy.cluster.vq.vq",
"scipy.cluster.vq.kmeans"
]
] | [
{
"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"
... |
jinlafan/mmt-dropnet | [
"3c14dd3c38274aedf8b463d0c73daa9f50096e51"
] | [
"nmtpytorch/models/amnmtfeats.py"
] | [
"# -*- coding: utf-8 -*-\nimport logging\n\nimport torch\n\nfrom ..datasets import MultimodalDataset\nfrom ..layers import ConditionalMMDecoder, TextEncoder\nfrom .nmt import NMT\n\nlogger = logging.getLogger('nmtpytorch')\n\n\nclass AttentiveMNMTFeatures(NMT):\n \"\"\"An end-to-end sequence-to-sequence NMT mode... | [
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
William-Zhanng/Protein_affinity | [
"8abd12073b182274bf464ff23fd3be406c4e39ac"
] | [
"evaluation.py"
] | [
"import os\r\nimport time\r\nimport random\r\nimport argparse\r\nimport numpy as np\r\nfrom tqdm import tqdm\r\nimport torch\r\nimport torch.nn as nn\r\nimport esm\r\n# For DDP\r\nimport torch.distributed as dist\r\nfrom torch.nn.parallel import DistributedDataParallel as DDP\r\nfrom transformers import AdamW, get_... | [
[
"torch.abs",
"torch.nn.CrossEntropyLoss",
"torch.distributed.init_process_group",
"torch.utils.data.distributed.DistributedSampler",
"torch.cuda.set_device",
"numpy.random.seed",
"torch.manual_seed",
"torch.load",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sbrisard/gollum | [
"25d5b9aea63a8f2812c4b41850450fcbead64da7"
] | [
"python/tests/test_hooke.py"
] | [
"import numpy as np\nimport pytest\n\nfrom scapin.hooke import HookeFloat64_2D, HookeFloat64_3D\n\nSQRT2 = np.sqrt(2.0)\n\n_ij2i = {2: np.array([0, 1, 0]), 3: np.array([0, 1, 2, 1, 2, 0])}\n\n_ij2j = {2: np.array([0, 1, 1]), 3: np.array([0, 1, 2, 2, 0, 1])}\n\n\ndef directions_2D(num_theta):\n out = np.empty((nu... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.cos",
"numpy.sin",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maniteja123/numpy | [
"1147490663d36b05fad8dcce1e104601c2724560",
"77ce5f48506c6305cd8987683291275726cde623"
] | [
"numpy/polynomial/laguerre.py",
"numpy/distutils/fcompiler/gnu.py"
] | [
"\"\"\"\nObjects for dealing with Laguerre series.\n\nThis module provides a number of objects (mostly functions) useful for\ndealing with Laguerre series, including a `Laguerre` class that\nencapsulates the usual arithmetic operations. (General information\non how this module represents and works with such polyno... | [
[
"numpy.rollaxis",
"numpy.square",
"numpy.linalg.eigvals",
"numpy.iterable",
"numpy.abs",
"numpy.asarray",
"numpy.arange",
"numpy.sort",
"numpy.ones",
"numpy.all",
"numpy.linalg.lstsq",
"numpy.finfo",
"numpy.exp",
"numpy.linalg.eigvalsh",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.6",
"1.10",
"1.11",
"1.12",
"1.13",
"1.16",
"1.9",
"1.18",
"1.7",
"1.15",
"1.14",
"1.17",
"1... |
sqrhussain/homophily-community-gnn | [
"1d980828c13c577b06c7755a1245eeba31ed699f"
] | [
"src/data/inject_edges_experiment.py"
] | [
"\n\nfrom src.data.create_stochastic_block_model import create_graph_and_node_mappings_from_file, build_stochastic_block_matrix, load_communities\nfrom src.data.create_stochastic_block_model import create_community_id_to_node_id, create_sbm_graph\nimport pandas as pd\nimport numpy as np\nimport networkx as nx\nfrom... | [
[
"numpy.amin",
"numpy.ones",
"numpy.array",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
suyashbire1/oocgcm | [
"c9616872077494b14b41915d1b6202aeea545c82"
] | [
"oocgcm/plot/plot1d.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\noocgcm.plot.plot1d\nDefine nice plotting function for unidimensional data series using matplotlib\n\"\"\"\n\nimport numpy as np\nimport pylab as plt\nimport matplotlib.mlab as mlab\nfrom matplotlib.ticker import MultipleLocator\nimport matplotlib\n\ndef spectrum_plot(ax, x, y, **kw... | [
[
"numpy.arange",
"numpy.max",
"numpy.abs",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ashwhall/tvl | [
"78fa8d2908d8eac8a032273d3142ab530cee1a33"
] | [
"benchmarks/read_frames.py"
] | [
"import os\nimport time\n\nimport numpy as np\nimport torch\n\nimport tvl\nfrom tvl_backends.fffr import FffrBackendFactory\nfrom tvl_backends.nvdec import NvdecBackendFactory\nfrom tvl_backends.pyav import PyAvBackendFactory\n\nvideo_file = os.path.join(os.path.dirname(__file__), '../data/board_game-h264.mkv')\n\n... | [
[
"torch.device",
"numpy.arange",
"torch.cuda.device_count",
"torch.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
albacg5/TOML-Project1 | [
"4c1a8caa785e1a1c09165f8b870d02db5dc472f9",
"4c1a8caa785e1a1c09165f8b870d02db5dc472f9"
] | [
"Exercise_5.py",
"Exercise_2.py"
] | [
"from cvxpy import *\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Create two scalar optimization variables.\nx = Variable(2, name='x')\n\n# Constraints\nc1 = cvxpy.square((x[0] - 1)) + cvxpy.square((x[1] - 1))\nc2 = cvxpy.square((x[0] - 1)) + cvxpy.square((x[1] + 1))\nconstraints = [c1 <= 1., c2 <= 1.]... | [
[
"matplotlib.pyplot.legend",
"numpy.arange",
"numpy.meshgrid",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"matplotlib.pyplot.legend",
"numpy.asarray",
"numpy.arange",
"numpy.array",
"numpy.meshgrid",
"matplotlib.pyplot.show",
"matplotlib.pyplot.fig... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ab-e/nerc-importer | [
"c8b4935173e995dfde3add676a1759c62e22f89b"
] | [
"harvester.py"
] | [
"import argparse\n\nimport requests\nimport configparser\nfrom xml.etree import ElementTree as ET\nimport pandas as pd\nimport numpy as np\nimport logging.config\nimport datetime\nimport json\nimport os\nimport sql_nerc\nimport configparser as ConfigParser\n#from requests.adapters import HTTPAdapter\n\ndef read_xml... | [
[
"pandas.concat",
"pandas.to_datetime",
"numpy.where",
"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": []
}
] |
shongi-yd/FEniCSopt | [
"44c7e6a70400b5f0cc84599e20391e1e4032931d"
] | [
"ind_cross_direction_sdfem.py"
] | [
"from dolfin import *\nfrom scipy.optimize import minimize\nimport numpy as np\nimport time as pyt\nimport pprint\ncoth = lambda x: 1./np.tanh(x)\n\nfrom fenicsopt.core.convdif import *\nfrom fenicsopt.examples.sc_examples import sc_setup\nimport fenicsopt.exports.results as rs\n\n##################################... | [
[
"numpy.subtract",
"scipy.optimize.minimize",
"numpy.transpose",
"numpy.add",
"numpy.tanh",
"numpy.array"
]
] | [
{
"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"
... |
MrMaik/platformer-ml-game | [
"bbcabe3ddea1e3cfddb01b4cd60c8dd1bd79acac"
] | [
"PlatformerGame/malmopy/summaries.py"
] | [
"# --------------------------------------------------------------------------------------------------\n# Copyright (c) 2018 Microsoft Corporation\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and\n# associated documentation files (the \"Software\"), to deal i... | [
[
"numpy.amax",
"numpy.histogram",
"numpy.amin",
"numpy.median",
"numpy.dtype",
"numpy.copy",
"numpy.std",
"numpy.mean",
"numpy.isscalar",
"numpy.var",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
washreve/hyp3-lib | [
"5e7f11f1de9576a519b25fb56ccdb40e72ca9982"
] | [
"hyp3lib/rasterMask.py"
] | [
"\"\"\"Generate an AOI mask and apply it\"\"\"\n\nfrom __future__ import print_function, absolute_import, division, unicode_literals\n\nimport argparse\nimport os\nimport numpy as np\nfrom osgeo import gdal\nfrom hyp3lib.asf_geometry import geotiff2data, data2geotiff\nfrom hyp3lib.asf_time_series import vector_meta... | [
[
"numpy.rint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lol-cubes/cygraph | [
"b8dbfdcfdb81579181a382311649d166b04c768e"
] | [
"examples/cython/setup.py"
] | [
"from setuptools import setup\n\nfrom Cython.Build import cythonize\nimport numpy as np\n\n\nsetup(\n ext_modules=cythonize(['bayesian_network.pyx']),\n include_dirs=[np.get_include()]\n)"
] | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GrumpyZhou/pytorch_geometric | [
"a7143b8d9ace60cf2ec1bd14ecc20ff7c3141151"
] | [
"torch_geometric/transforms/sample_points.py"
] | [
"import torch\n\n\nclass SamplePoints(object):\n r\"\"\"Uniformly samples :obj:`num` points on the mesh faces according to\n their face area.\n\n Args:\n num (int): The number of points to sample.\n remove_faces (bool, optional): If set to :obj:`False`, the face tensor\n will not b... | [
[
"torch.rand",
"torch.multinomial"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mickare/Deformation-Transfer-for-Triangle-Meshes | [
"fbb3b73c78cf3df529759f7497cc1894be5754bb"
] | [
"meshlib/sparsesolver.py"
] | [
"\"\"\"\nMultithreading sparse solver, that is not needed anymore!\n\"\"\"\nimport multiprocessing\nimport os\nfrom abc import abstractmethod, ABC\nfrom typing import Union, Optional, Dict, Any, Sequence, Callable\n\nimport numpy as np\n\nfrom scipy import sparse\nimport scipy.sparse.linalg\n\n\nclass ComponentSolv... | [
[
"scipy.sparse.linalg.lsqr",
"scipy.sparse.linalg.lsmr"
]
] | [
{
"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"
... |
iremnasir/Lyrics_Classifier | [
"e071077b925893abb6b84ac2e1bdbda2bbb957fc"
] | [
"Lyrics_Classifier/Model.py"
] | [
"import pandas as pd\nimport numpy as np\nimport spacy\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sys import argv\nimport warnings\nwarnings.filterwarnings('ignore')\n\ndef train_test(datafr, size_test)... | [
[
"pandas.concat",
"sklearn.model_selection.GridSearchCV",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.model_selection.train_test_split",
"numpy.max"
]
] | [
{
"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": []
}
] |
xdr940/xdr940_Bian2019 | [
"2ab982f531b4bf61593015ceeea3241d33d66963"
] | [
"loss_functions.py"
] | [
"from __future__ import division\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom inverse_warp import inverse_warp2\nimport math\n\ndevice = torch.device(\n \"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n\n\n# compute photometric loss (with ssim) and geometry consist... | [
[
"torch.mean",
"torch.abs",
"torch.max",
"torch.cat",
"torch.nn.functional.conv2d",
"torch.median",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.functional.interpolate",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dmike16/study-notes | [
"9c12393f3bb4bacfe7bfa2489500c5129bd375ae"
] | [
"machine-learning/common/plot/discrete_scattered.py"
] | [
"import numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\n\ndef plot(x1, x2, y=None, ax=None):\n if ax is None:\n ax = plt.gca()\n if y is None:\n y = np.zeros(len(x1))\n\n uy = np.unique(y)\n\n markers = ['o', '^', 'v', 'D', 's', '*', 'p', 'h', 'H', '8', '<', '>'] ... | [
[
"matplotlib.pyplot.gca",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SoftwareDevEngResearch/AutoFunc | [
"f6ee58f00472345029891ac6f6cea585abf15cd9"
] | [
"autofunc/tests/test_get_match_factor.py"
] | [
"from autofunc.get_match_factor import match\nfrom autofunc.get_top_results import get_top_results\nfrom autofunc.find_associations import find_associations\nfrom autofunc.get_data import get_data\nimport os.path\nimport numpy as np\n\n\ndef test_1():\n\n \"\"\"\n Tests that the match factor for a known learn... | [
[
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thunderhoser/GewitterGefahr | [
"41d207d8201dfb7d7e34a7e42d862e7fe6e1ae00"
] | [
"gewittergefahr/dissertation/myrorss/make_sanity_check_figure.py"
] | [
"\"\"\"Makes figure with sanity checks for MYRORSS saliency maps.\"\"\"\n\nimport os\nimport pickle\nimport argparse\nimport numpy\nfrom PIL import Image\nimport matplotlib\nmatplotlib.use('agg')\nfrom matplotlib import pyplot\nfrom gewittergefahr.gg_utils import general_utils\nfrom gewittergefahr.gg_utils import m... | [
[
"numpy.expand_dims",
"numpy.sqrt",
"matplotlib.pyplot.cm.get_cmap",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"numpy.full",
"matplotlib.pyplot.close",
"numpy.ravel",
"numpy.array",
"numpy.flip",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mikehagerty/mth-inst-resp | [
"69e95bc89f01242ea0d75fe6fedee9f2714630f0"
] | [
"mth_inst_resp/plotResp.py"
] | [
"\nfrom libInst import read_sacpz_file, getResponse\nfrom libPlotResp import plotResponse\nfrom libNominals import getPoleZero\nfrom mth_utils.liblog import getLogger\n\nfrom sys import exit\nimport sys\nimport getopt\nimport numpy as np\n\nlogger = getLogger()\n\nfname = 'plotResp.py'\n\n# Most polezero files - e.... | [
[
"numpy.logspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raaperrotta/PySyft | [
"65a4aa120d6f162d6afb08c42ee0c1d29ed38124"
] | [
"tests/syft/core/pointer/pointer_test.py"
] | [
"# third party\nimport pytest\nimport torch as th\n\n# syft absolute\nimport syft as sy\n\n\n@pytest.mark.slow\n@pytest.mark.parametrize(\"with_verify_key\", [True, False])\ndef test_make_pointable(with_verify_key: bool) -> None:\n bob = sy.VirtualMachine(name=\"Bob\")\n root_client = bob.get_root_client()\n ... | [
[
"torch.Tensor",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sudohainguyen/mlflow | [
"5cd6dd575941e48313e0fd686d48805e170d57c1",
"1ce3b5eadf6543878a62b070fd06735d471d75d5"
] | [
"tests/statsmodels/test_statsmodels_autolog.py",
"tests/pyfunc/test_spark.py"
] | [
"import pytest\nfrom unittest import mock\nimport numpy as np\nfrom statsmodels.tsa.base.tsa_model import TimeSeriesModel\nimport mlflow\nimport mlflow.statsmodels\nfrom tests.statsmodels.model_fixtures import (\n arma_model,\n ols_model,\n failing_logit_model,\n glsar_model,\n gee_model,\n glm_mo... | [
[
"numpy.testing.assert_array_almost_equal"
],
[
"pandas.DataFrame",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"... |
gitter-badger/py-bbn | [
"f1e296ea4bb9d1392ec585fbafe34011ea2f85fd"
] | [
"docs/source/code/generate-singly.py"
] | [
"import numpy as np\n\nfrom pybbn.generator.bbngenerator import generate_singly_bbn, convert_for_exact_inference, convert_for_drawing\n\n# very important to set the seed for reproducible results\nnp.random.seed(37)\n\n# this method generates the graph, g, and probabilities, p\n# note we are generating a singly-conn... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AI-Huang/Real-NVP-TF1 | [
"8f44a6415197c25ba656b5d5827e0cc6b849b2c9"
] | [
"test/test_coupling_layer.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Date : Jan-06-22 07:43\n# @Author : Kan HUANG (kan.huang@connect.ust.hk)\n\n\nimport tensorflow as tf\nfrom models.real_nvp.coupling_layer import CouplingLayerCompression, MaskType\n\n\ndef main():\n in_channels = 4\n mid_channels = 8\n num_blocks = ... | [
[
"tensorflow.global_variables_initializer",
"tensorflow.random.uniform",
"tensorflow.placeholder",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ashwinvaswani/lesion_detection | [
"26246e3954209075c56649dfb2ef565290e6dcb3"
] | [
"alignshift/models/truncated_densenet3d_acs.py"
] | [
"\"\"\"\nDenseNet121\nDifference from densenet in torchvision for higher resolution:\n1. Modify the stride of first convolution layer (7x7 with stride 2) into 1 \n2. Remove the first max-pooling layer\n\"\"\"\n\nimport re\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.mod... | [
[
"torch.nn.AvgPool3d",
"torch.cat",
"torch.nn.functional.dropout",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.init.kaiming_uniform_",
"torch.nn.MaxPool3d",
"torch.nn.Conv3d",
"torch.utils.checkpoint.checkpoint",
"torch.nn.BatchNorm3d",
"torch.nn.functional.i... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jjgoings/McMurchie-Davidson | [
"e4da94df6845ecf9876130d11fc4d62a17544e1d"
] | [
"tests/test004.py"
] | [
"import numpy as np\nfrom numpy.testing import assert_allclose \nfrom mmd.molecule import Molecule \n\n\nwater = \"\"\"\n0 1\nO 0.000000 -0.075791844 0.000000\nH 0.866811829 0.601435779 0.000000\nH -0.866811829 0.601435779 0.000000\n\"\"\"\n\ndef test_water_DZ():\n mol = Molecule(geom... | [
[
"numpy.array",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ppeddada97/SDMetrics | [
"3b83f25577292b24099668d0273f3282b61d7542",
"3b83f25577292b24099668d0273f3282b61d7542"
] | [
"sdmetrics/timeseries/ml_scorers.py",
"sdmetrics/multi_table/detection/parent_child.py"
] | [
"\"\"\"Machine Learning Detection based metrics for Time Series.\"\"\"\n\nimport rdt\nimport torch\nfrom sklearn.pipeline import Pipeline\nfrom sktime.classification.compose import TimeSeriesForestClassifier\n\ntry:\n from sktime.transformers.series_as_features.compose import ColumnConcatenator\nexcept (ImportEr... | [
[
"torch.nn.LSTM",
"torch.nn.functional.cross_entropy",
"sklearn.pipeline.Pipeline",
"torch.nn.Linear",
"torch.FloatTensor",
"torch.cuda.is_available",
"torch.nn.utils.rnn.pack_sequence",
"torch.argmax"
],
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mlcommons/mobile_open | [
"d0c62d5d633cbc6b62aa39fe33a901cc6d555b1a"
] | [
"vision/deeplab/models_and_code/model.py"
] | [
"# Lint as: python2, python3\n# 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/L... | [
[
"tensorflow.TensorShape",
"tensorflow.nn.softmax",
"tensorflow.concat",
"tensorflow.shape",
"tensorflow.reduce_mean",
"tensorflow.identity",
"tensorflow.squeeze",
"tensorflow.expand_dims",
"tensorflow.truncated_normal_initializer",
"tensorflow.logging.info",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
kasmith/rrt | [
"729cf0363136f75bb3eb9fd24857931c2907a462"
] | [
"rrt/rrt_base.py"
] | [
"\"\"\"Contains the basic RRT algorithm\n\nThis is only the most basic RRT algorithm (no star) but contains many of the\nimportant methods that are used for RRT planning\n\n\"\"\"\n\nfrom __future__ import division, print_function\nfrom .tree import *\nfrom .space import EmptySpace\nimport numpy as np\n\n__all__ = ... | [
[
"numpy.isinf",
"numpy.array",
"numpy.argmin",
"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": [],
... |
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