repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
thetomcraig/HPI | [
"5eecd8721dc0cbfc68040106bb7b540b1567dff3"
] | [
"my/jawbone/__init__.py"
] | [
"#!/usr/bin/env python3\nfrom typing import Dict, Any, List\nimport json\nfrom functools import lru_cache\nfrom datetime import datetime, date, time, timedelta\nfrom pathlib import Path\nimport logging\nimport pytz\n\nfrom my.config import jawbone as config\n\n\nBDIR = config.export_dir\nPHASES_FILE = BDIR / 'phase... | [
[
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.tight_layout",
"pandas.DataFrame",
"matplotlib.dates.date2num",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
ericchen321/ros_x_habitat | [
"f256b62fe8dda059baaf9bad87cf53f7d769f2f9"
] | [
"src/test/test_habitat_ros/test_habitat_ros_env_node_discrete.py"
] | [
"import os\nimport shlex\nimport unittest\nfrom subprocess import Popen\n\nimport numpy as np\nimport rostest\n\nfrom mock_habitat_ros_evaluator import MockHabitatROSEvaluator\nfrom src.evaluators.habitat_sim_evaluator import HabitatSimEvaluator\nfrom src.constants.constants import NumericalMetrics, PACKAGE_NAME\nf... | [
[
"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": [],
... |
liuweilin17/cleverhans | [
"13b248e88c1955ef585e235db8e7ca65bae8b2d5"
] | [
"examples/imagenet_featadvs/model.py"
] | [
"import functools\nimport tensorflow as tf\nfrom cleverhans.model import Model\nfrom cleverhans_tutorials.tutorial_models import HeReLuNormalInitializer\n\n\nclass ModelImageNetCNN(Model):\n def __init__(self, scope, nb_classes=1000, **kwargs):\n del kwargs\n Model.__init__(self, scope, nb_classes, locals())... | [
[
"tensorflow.variable_scope",
"tensorflow.layers.flatten",
"tensorflow.nn.softmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
XiaojingGeorgeZhang/PythonRobotics | [
"2723a0a2eb3c8ccddf5810aa9380f1f9276caabd"
] | [
"PathTracking/lqr/lqr_tracking.py"
] | [
"#! /usr/bin/python\n\"\"\"\n\nPath tracking simulation with LQR steering control and PID speed control.\n\nauthor: Atsushi Sakai\n\n\"\"\"\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport unicycle_model\nfrom pycubicspline import pycubicspline\nfrom matplotrecorder import matplotrecorder\n... | [
[
"matplotlib.pyplot.legend",
"numpy.eye",
"matplotlib.pyplot.cla",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.close",
"matplotlib.pyplot.grid",
"scipy.linalg.inv",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.sh... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
zhangganlin/Improved-PSMNet-for-Deep-Stereo-Disparity-Estimation | [
"47fa95a52360ce1e2b507e385fc21ad2567b86ee"
] | [
"models/dilatedcostfiltering.py"
] | [
"from __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport math\nfrom .submodule import *\n\nclass dilated_sub(nn.Module):\n def __init__(self, inplanes):\n super(dilated_sub, self).__in... | [
[
"torch.nn.functional.softmax",
"torch.cat",
"torch.nn.ConvTranspose3d",
"torch.nn.Conv3d",
"torch.nn.functional.interpolate",
"torch.nn.ReLU",
"torch.nn.BatchNorm3d",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
VandinLab/MASTRO | [
"0197aaf49e497d6742a236fd47b1c9a6e4865145"
] | [
"MASTRO/plot_results.py"
] | [
"import pandas as pd\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-r\", help=\"input file with results\",default=\"edges_matrix_final.txt\")\nparser.add_argument(\"-minp\", help=\"path of minpvalues\",default=\"minpvals.csv\")\nparser.add_argument(\"-t\", help=\"plot title\",default=\... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xscale",
"matplotlib.rc",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ThoughtWorksInc/tf-image-intepreter | [
"113fc808a081984c8be4814bc3403b908bb6b2c6"
] | [
"image_interpreter/layers/rpn_data.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom image_interpreter.layers.common import AnchorTargetMixin\n\n\nclass RpnData(AnchorTargetMixin):\n def __init__(self, debug=False):\n super().__init__(debug=debug)\n\n def generate(self, image, scale, bboxes):\n shape = tf.shape(image)\n # TODO: NotImplem... | [
[
"tensorflow.boolean_mask",
"tensorflow.constant",
"tensorflow.Print",
"tensorflow.arg_max",
"tensorflow.shape",
"tensorflow.cast",
"numpy.ones",
"tensorflow.gather",
"tensorflow.initialize_all_variables",
"tensorflow.where",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
gafusion/omas | [
"6740fd040d6af59e0aec54f977637b221733bd07"
] | [
"omas/examples/omas_resample.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nInterpolate whole ODS\n=====================\nSeamless handling of coordinates within OMAS makes it easy to reinterpolate a whole ODS on a new grid\n\"\"\"\n\nimport numpy\nfrom omas import *\n\n# original ODS\nods = ODS()\nods.sample_equilibrium()\n\n# inter... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kashif/neon | [
"d4d8ed498ee826b67f5fda1746d2d65c8ce613d2"
] | [
"neon/params/val_init.py"
] | [
"# ----------------------------------------------------------------------------\n# Copyright 2014 Nervana Systems Inc.\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:/... | [
[
"numpy.linalg.svd",
"numpy.absolute",
"numpy.linalg.eig",
"numpy.eye",
"numpy.random.normal",
"numpy.random.permutation",
"numpy.random.randn",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sutianjin/MachineLearn | [
"1597516f56f1320b7d024ed1ad41c3ad3ee9eb7c"
] | [
"tensorflow/day1/fetch-op.py"
] | [
"import tensorflow as tf\n\ninput1 = tf.constant(3.0)\ninput2 = tf.constant(2.0)\ninput3 = tf.constant(5.0)\nintermed = tf.add(input2, input3)\nmul = tf.multiply(input1, intermed)\n\nwith tf.Session() as sess:\n\tresult = sess.run([mul, intermed])\n\tprint(result)"
] | [
[
"tensorflow.add",
"tensorflow.multiply",
"tensorflow.constant",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"1.0",
"1.2"
]
}
] |
watronfire/monterey | [
"d1fea8e8a98dd39a6658fa7e5b2dd6a0fc3ec5e0"
] | [
"workflow/scripts/shuffle_tips.py"
] | [
"import pandas as pd\nfrom dendropy import Tree\nfrom epiweeks import Week\nfrom subprocess import run\n\ndef shuffle_tips( tree, metadata, id_col, date_col, map_file, output_loc ):\n \n # Get all the tips in tree\n gotree_output = run( f\"gotree labels -i {tree}\", capture_output=True, text=True, shell=Tr... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dolphin-zs/HierST | [
"34a5e2c6729b849ba7534408e3d04aa336621aa1"
] | [
"src/run_ensemble.py"
] | [
"import argparse\nimport os\nimport torch\nimport time\nimport numpy as np\nimport pandas as pd\nimport shutil\n\nfrom data_utils import g_node_col, g_date_col, process_cdc_truth_from_csse, process_cdc_loc, get_all_cdc_label, read_cdc_forecast\nfrom base_task import load_json_from\n\n\n# exp_dir_template = '../Exp_... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.merge",
"numpy.abs",
"pandas.Timedelta",
"numpy.expm1"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mohitsudhakar/visual-dialog-experiments | [
"77cc65938b0ce99fc52b839b7821f29c7a6b32a0"
] | [
"visdialch/decoders/decoder.py"
] | [
"import torch\nfrom torch import nn\nimport numpy as np\n# from gensim.test.utils import common_texts\n# from gensim.models import Word2Vec\nfrom visdialch.utils import DynamicRNN\nfrom visdialch.utils.util import get_pretrained_weights\n\nclass DiscriminativeDecoder(nn.Module):\n def __init__(self, config, voca... | [
[
"torch.sum",
"torch.nn.Embedding.from_pretrained",
"torch.nn.LSTM"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Worm4047/TVR | [
"2a8ce2edbdc0966aef3b84c28872267039f01700"
] | [
"baselines/excl/config.py"
] | [
"import os\nimport time\nimport torch\nimport argparse\n\nfrom utils.basic_utils import mkdirp, load_json, save_json, make_zipfile\nfrom baselines.clip_alignment_with_language.local_utils.proposal import ProposalConfigs\n\n\nclass BaseOptions(object):\n saved_option_filename = \"opt.json\"\n ckpt_filename = \... | [
[
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
20centcroak/pyDocxReport | [
"5184357241e765c12082c56e2ef1805e36e78f37"
] | [
"tests/unit/test_databridge.py"
] | [
"import unittest\nfrom pandas import DataFrame\nfrom yaml import safe_load\n\nfrom pyDocxReport import DataBridge\n\n\nclass TestDataBridge(unittest.TestCase):\n\n def test_bridge(self):\n\n d = {'col1': [1, 2], 'col2': [3, 4]}\n df1 = DataFrame(data=d)\n\n bridge = DataBridge('tests/unit/re... | [
[
"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": []
}
] |
wangleon/stella | [
"3942f8e687065bb96760140596747cbbe6dad04b"
] | [
"stellarlab/kinetics/orbit.py"
] | [
"import math\nimport numpy as np\nfrom astropy.coordinates import SkyCoord\n\nfrom ..constant import ALPHA_NGP, DELTA_NGP, L_NCP, AU, tropical_year\n\ndef parse_pairwise(arg):\n \"\"\"Parse value with error\"\"\"\n if (isinstance(arg, list) or isinstance(arg, tuple)) and \\\n len(arg)==2:\n retu... | [
[
"numpy.array",
"numpy.mat",
"numpy.sqrt",
"scipy.integrate.odeint"
]
] | [
{
"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"
... |
rhanschristiansen/association_algo_performance | [
"1a03875098eb4f45f7b9cd2dfbd80ae4d87c758d"
] | [
"src/association/association.py"
] | [
"import numpy as np\nfrom src.association import munkres\n\n\nclass Association:\n def __init__(self):\n pass\n\n # the evaluate cost function receives to arguments:\n # 1 - a dictionary called cost functions with the function method name as the key and the weight as the value\n #\n # cost... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AnshMittal1811/Mask_RCNN | [
"d4ed6a2065bd3cc2b506ab4dbdfc18efab2f53ea"
] | [
"mrcnn/parallel_model.py"
] | [
"\"\"\"\nMask R-CNN\nMulti-GPU Support for Keras.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\nIdeas and a small code snippets from these sources:\nhttps://github.com/fchollet/keras/issues/2436\nhttps://medium.com/@kuza55/transparent-... | [
[
"tensorflow.device",
"numpy.expand_dims",
"tensorflow.reset_default_graph",
"tensorflow.name_scope",
"tensorflow.split",
"tensorflow.add_n"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
smericks/lenstronomy | [
"3f095595e36888ac7b4ee5345b6abc5aaa8275f1",
"3f095595e36888ac7b4ee5345b6abc5aaa8275f1"
] | [
"lenstronomy/LensModel/Profiles/gauss_decomposition.py",
"lenstronomy/Plots/plot_util.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nThis module contains the class to compute lensing properties of any\nelliptical profile using Shajib (2019)'s Gauss decomposition.\n\"\"\"\n\n__author__ = 'ajshajib'\n\nimport numpy as np\nimport abc\nfrom scipy.special import comb\n\nfrom lenstronomy.LensModel.Profiles.gaussian_el... | [
[
"numpy.logical_not",
"numpy.log",
"numpy.sqrt",
"numpy.arange",
"numpy.arccosh",
"numpy.arccos",
"scipy.special.comb",
"numpy.log10",
"numpy.zeros_like",
"numpy.any",
"numpy.exp",
"numpy.zeros",
"numpy.empty"
],
[
"matplotlib.pyplot.get_cmap",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
... |
cerisara/weibull-knowledge-informed-ml | [
"bdcb838807ee6bbb5655b275ba0169b76e3f5acc"
] | [
"src/visualization/visualize_data.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy import signal, fftpack\nfrom matplotlib import gridspec\nimport pandas as pd\nimport logging\nfrom pathlib import Path\nimport os\nimport torch\nfrom src.data.data_utils import load_train_test_ims, load_train_test_femto\nfrom sr... | [
[
"scipy.signal.find_peaks",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.exp",
"numpy.where",
"pandas.read_csv",
"numpy.arange",
"torch.reshape",
"matplotlib.pyplot.subplot",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
liusy182/jina | [
"c272bbddef733167804c5a68d5f41ec789fa1732"
] | [
"tests/unit/flow-construct/test_flow.py"
] | [
"import datetime\nimport inspect\nimport json\nimport os\nfrom typing import Union\n\nimport numpy as np\nimport pytest\n\nfrom jina import Flow, Document, DocumentArray, Executor, requests, __windows__\nfrom jina.enums import FlowBuildLevel, PollingType\nfrom jina.excepts import RuntimeFailToStart\nfrom jina.execu... | [
[
"numpy.array",
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
petrapoklukar/disentanglement_sample_efficiency | [
"27b4bac6ee61973a728e48230c6eb449b167c46e"
] | [
"disentanglement_lib/preprocessing/methods.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 5 16:41:20 2020\n\n@author: petrapoklukar\n\"\"\"\n\nimport numpy as np\nimport h5py\nimport os\nimport gin.tf\n\n@gin.configurable(\"split_train_and_validation_per_model\", \n blacklist=[\"dataset_name\", \"model_name\"]... | [
[
"numpy.max",
"numpy.array",
"numpy.random.RandomState",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jakegrigsby/cc-afbc | [
"3fc587aff7d33fde89da9b4bfa39e2c5aa3866fc"
] | [
"estimator.py"
] | [
"from torch import nn\nimport torch.nn.functional as F\nimport torch\nimport random\n\n\nclass AdvantageEstimator(nn.Module):\n def __init__(self, actor, critics, popart=False, method=\"mean\", ensembling=\"min\"):\n super().__init__()\n assert method in [\"mean\", \"max\", \"expectation\"]\n ... | [
[
"torch.stack",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abhinav042/COMP3297 | [
"3c37a4330d94a45cdabaa26bc17147ae73118cfb"
] | [
"tutor_app/.~c9_invoke_ZPZ8Qk.py"
] | [
"from django.db import models\nfrom django.contrib.auth.models import User\nfrom course_app.models import Course\nimport numpy as np\nfrom datetime import datetime\n\n# Create your models here.\nclass Timeslot(models.Model):\n \n date_of_slot = models.DateTimeField(null=True);\n #time_of_slot = models.Time... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rucka/NeuralNetworkPlayground | [
"b1c9398ee3b0831de4982fdfef34892faa04440d"
] | [
"notebook/slim_people_classification/eval_image_classifier.py"
] | [
"\"\"\"Generic evaluation script that evaluates a model using a given dataset.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport tensorflow as tf\n\nfrom datasets import dataset_factory\nfrom nets import nets_factory\nfrom ... | [
[
"tensorflow.Graph",
"tensorflow.Print",
"tensorflow.train.latest_checkpoint",
"tensorflow.app.flags.DEFINE_integer",
"tensorflow.squeeze",
"tensorflow.train.ExponentialMovingAverage",
"tensorflow.gfile.IsDirectory",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.logging.set_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Ali-Sahili/Background-Subtraction-Unsupervised-Learning | [
"445b2cf8736a4a28cff2b074a32afe8fe6986d53",
"445b2cf8736a4a28cff2b074a32afe8fe6986d53",
"445b2cf8736a4a28cff2b074a32afe8fe6986d53"
] | [
"AE/train_complex.py",
"WDCGAN/models.py",
"VAE/train.py"
] | [
"import torch\nfrom torch import nn\nimport torchvision.utils as vutils\nimport pytorch_ssim\n\nimport numpy as np\n\nfrom AE.Complex_Attention import *\n\nfrom Param import *\nfrom utils import weights_init\n\nfrom Losses import *\n\nfrom PIL import ImageFile\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\n\n\n\ndef tr... | [
[
"torch.no_grad",
"torch.cuda.empty_cache",
"torch.nn.MSELoss"
],
[
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.nn.Sigmoid",
"torch.nn.MaxPool2d",
"torch.nn.Upsample",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiagengliu/small-caps | [
"2c112da11f6dded5e56568a60cc44ba96a3c9a38"
] | [
"K-line pattern/backtest.py"
] | [
"\"\"\"\nBacktest on K-line patterns\n\"\"\"\n# coding: utf-8\n\n# # Initialization\n\nimport talib as tl\nimport tushare as ts\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib import gridspec\nimport os\ncodes = pd.read_csv('index/code_50')\nexample = str(codes['code'... | [
[
"pandas.read_csv",
"numpy.multiply",
"numpy.random.choice",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.gridspec.GridSpec",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
joaomh/Coding-Challenge-Py | [
"615eab0309fbefddebfcf6216dcccbafcf986543"
] | [
"CC_001_PiValueLeibniz.py"
] | [
"# Coding Challenge #001\n# Pi Approximation with Leibniz Series\n# Joao Pinheiro\n# YouTube Channel - ExataMenteS\n\n# Import some packages\nimport numpy as np \nimport matplotlib.pyplot as plt \n\n# User input ot 'n'\nInteraction = input('Insert the number of interactions: ')\nprint('The number of interactions is... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishalbelsare/TFNumPy | [
"577838cb6781207d561e389e9cd02aba02c25a1b"
] | [
"test/test_khatrirao_product.py"
] | [
"\nimport unittest\nimport numpy as np\nimport tensorflow.compat.v1 as tf\n\nfrom tfnumpy.tensor import khatrirao_product\n\n\nclass test_KJ(unittest.TestCase):\n def setUp(self):\n x1 = np.array([[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]], dtype=np.float32)\n x2 = np.array([[13, 16, 19, 22], [14... | [
[
"tensorflow.compat.v1.Session",
"numpy.array",
"numpy.zeros",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kyugorithm/pytorch-CycleGAN-Unet-D | [
"bca69aa1bd6329a0186188c53d501522d9f24fef"
] | [
"util/image_pool.py"
] | [
"import random\nimport torch\n\n\nclass ImagePool():\n \"\"\"This class implements an image buffer that stores previously generated images.\n\n This buffer enables us to update discriminators using a history of generated images\n rather than the ones produced by the latest generators.\n \"\"\"\n\n de... | [
[
"torch.unsqueeze",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Se-Hun/TIKITAKA | [
"e795945af71f9a66b9756bbe4062f6840e3ce70e"
] | [
"onmt/decoders/transformer.py"
] | [
"\"\"\"\nImplementation of \"Attention is All You Need\"\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom onmt.decoders.decoder import DecoderBase\nfrom onmt.modules import MultiHeadedAttention, AverageAttention\nfrom onmt.modules.position_ffn import PositionwiseFeedForward\nfrom onmt.utils.misc import sequen... | [
[
"torch.nn.Dropout",
"torch.ones",
"torch.zeros",
"torch.nn.LayerNorm",
"torch.gt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IdahoLabResearch/SFWDSubsurface | [
"6b926e90963cd24805c3eed12a2fddbb0a259835"
] | [
"GeoprocessingServiceScriptResources/StressFieldsRoseDiagram.py"
] | [
"\r\nimport arcpy\r\nfrom arcpy import env\r\nfrom arcpy.sa import *\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n\r\nimport os\r\nimport time\r\nimport sys\r\n\r\narcpy.env.parallelProcessingFactor = \"100%\"\r\n\r\nt0 = time.clock() \r\n\r\n# Set the environment:\r\narcpy.env.overwriteOutput = True\r\n\r\n... | [
[
"numpy.histogram",
"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": []
}
] |
goyal07nidhi/Stock-Market-Analysis-Pipeline-AWS | [
"24bbf629dbc296b002fb443741c8102bf5200e45"
] | [
"LSTM_model/main.py"
] | [
"import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense\r\nfrom tensorflow.keras.layers import LSTM\r\nimport tensorflow as tf\r\nimport boto3\r\nfrom io import StringIO\r\n\r\nACCESS_... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"tensorflow.keras.layers.Dense",
"pandas.DataFrame",
"tensorflow.keras.layers.LSTM",
"numpy.array",
"tensorflow.keras.models.Sequential",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
floatingstarZ/loc_cls_exp | [
"8b971db671753d3571914aaa760cc13ac47018e8"
] | [
"commonlibs/math_tools/coco_match_bbox.py"
] | [
"import torch\nfrom commonlibs.math_tools.IOU import IOU\n\ndef _match_DT_GT(dts, dt_scores, dt_labels,\n gts, gt_labels,\n cat_label, threshold):\n \"\"\"\n \n :param dts: M x 4, det bboxes, left top right down\n :param dt_labels: M, det bbox labels\n :param dt_scores: ... | [
[
"torch.sort",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RockawayIBMHackathonPrague2017/bumblebees | [
"efa8184f06e677a343713a7f66e4fdaed4969f66"
] | [
"distance.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf8 -*-\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\nfrom math import *\nfrom decimal import Decimal\nfrom scipy.spatial import distance\n\ndef get_all_params(items):\n all_params = {}\n for item in items:\n params = item... | [
[
"pandas.Series",
"scipy.spatial.distance.cosine",
"pandas.DataFrame",
"numpy.column_stack",
"numpy.array",
"numpy.sum",
"sklearn.preprocessing.MinMaxScaler",
"pandas.get_dummies"
]
] | [
{
"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... |
bsmrvl/DS-Unit-3-Sprint-2-SQL-and-Databases | [
"d04faad599946d98dccc9d0f3a6adfc72db136e6"
] | [
"module2-sql-for-analysis/insert_titanic.py"
] | [
"\"\"\"\nExecuting this module on its own will connect to a postgreSQL\ndatabase and insert the Titanic data into a new table called \n'titanic'. First create a file called creds.txt with the \nfollowing:\n\nhost\ndbname\nuser\npassword\n\nNo extra lines at the beginning or end. Put this file in the same\ndirectory... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
atidem/fuzzyAnalyticHierarchyProcess | [
"9ffe29d08f8a464dea8fc494ca77bfc2fe6c66c6"
] | [
"ahp.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu May 9 11:38:48 2019\r\n\r\n@author: atidem\r\n\"\"\"\r\n\r\n\"\"\"\r\nBu proje Atakan Demir(2016280050) ve Kaan Karavar(2016280022) tarafından yapılmıştır.\r\nBu program anaconda framework içindeki spyder editörü kullanılarak hazırlandı.\r\nKütüphaneler komut sa... | [
[
"numpy.array",
"numpy.sum",
"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": []
}
] |
zhangganjun87/models | [
"58deb0599f10dc5b33570103339fb7fa5bb876c3"
] | [
"official/resnet/keras/resnet_model.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.keras.layers.BatchNormalization",
"tensorflow.python.keras.layers.Activation",
"tensorflow.python.keras.backend.image_data_format",
"tensorflow.python.keras.layers.ZeroPadding2D",
"tensorflow.python.keras.layers.MaxPooling2D",
"tensorflow.python.keras.regularizers.l2",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.4"
]
}
] |
MTG/jamendo-dataset | [
"472a14524bc8f257c9ad921307180a80cc5fd1f3"
] | [
"scripts/baseline/solver.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nimport time\nimport numpy as np\nimport datetime\nimport tqdm\nfrom sklearn import metrics\nimport pickle\nimport csv\n\nimport torch\nimport torch.nn as nn\n\nfrom model import CNN\n\n\nclass Solver(object):\n def __init__(self, data_loader, valid_loader, config):\n #... | [
[
"sklearn.metrics.roc_auc_score",
"torch.load",
"torch.nn.BCELoss",
"sklearn.metrics.average_precision_score",
"torch.cuda.is_available",
"numpy.load",
"numpy.array",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
em3ndez/open_spiel | [
"ce3c25a788319a58076732cdf28c7ef5651b2ae7"
] | [
"open_spiel/python/algorithms/cfr_test.py"
] | [
"# Copyright 2019 DeepMind Technologies Ltd. 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 r... | [
[
"numpy.asarray",
"numpy.arange",
"numpy.testing.assert_array_equal",
"numpy.testing.assert_allclose",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kirill888/datacube-core | [
"996b395e15f975decb77c0ca9fa0555177674b2f",
"996b395e15f975decb77c0ca9fa0555177674b2f"
] | [
"tests/test_utils_docs.py",
"datacube_apps/movie_generator.py"
] | [
"\"\"\"\nTest utility functions from :module:`datacube.utils`\n\n\n\"\"\"\nimport os\nfrom pathlib import Path\nfrom collections import OrderedDict\nfrom types import SimpleNamespace\nfrom typing import Tuple, Iterable\n\nimport numpy as np\nimport pytest\nimport toolz\n\nfrom datacube.model import MetadataType\nfr... | [
[
"numpy.asarray",
"numpy.dtype"
],
[
"numpy.isnan",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emilyso-99/TCRP_data | [
"bd1a82a7affb83d35d7e800e19728f0faa73a4d2"
] | [
"code/utils /data_loading.py"
] | [
"import numpy as np\nimport random\nimport copy\nimport torch\nimport torch.utils.data as du\n\n'''\ndef get_unseen_data_loader( feature, label, cat_label, K ):\n\n\tnum_sample = feature.shape[0]\n\tindex_list = np.random.permutation( num_sample )\n\t\n\ttrain_index_list = index_list[0:K]\n\ttest_index_list = index... | [
[
"torch.utils.data.TensorDataset",
"torch.utils.data.DataLoader",
"numpy.random.permutation",
"torch.FloatTensor",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ricagj/train_your_own_game_AI | [
"37cff4733536e882c8b9f35ccf4a1eb5f550132c"
] | [
"game_player/grab_screen.py"
] | [
"import cv2\nimport numpy as np\nimport win32gui, win32ui, win32con, win32api\n\n# ---*---\n\ndef grab_screen(x, x_w, y, y_h):\n\n # 获取桌面\n hwin = win32gui.GetDesktopWindow()\n\n w = x_w - x\n h = y_h - y\n\n # 返回句柄窗口的设备环境、覆盖整个窗口，包括非客户区，标题栏，菜单，边框\n hwindc = win32gui.GetWindowDC(hwin)\n\n # 创建设备... | [
[
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pcorless/inference-sdk | [
"4a69d91e9f544bcf8d6e6cccffd33918bdbab11f"
] | [
"inference-test-tool/test_inference_mask.py"
] | [
"\"\"\"\nThis script lets you test if the inference outputs will be processed correctly by the Arterys server.\n\"\"\"\n\nimport os\nimport argparse\nimport random\nfrom io import BytesIO\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pydicom\nfrom utils import load_image_data, sort_images, create_fol... | [
[
"matplotlib.pyplot.imsave",
"numpy.fromfile",
"numpy.reshape",
"numpy.full",
"numpy.append",
"numpy.iinfo",
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abrammer/windspharm | [
"a4a297216ca6faa63234fcd5deae5a3c28b1132b"
] | [
"windspharm/tests/test_error_handling.py"
] | [
"\"\"\"Tests for error handling in `windspharm`.\"\"\"\n# Copyright (c) 2012-2016 Andrew Dawson\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 with... | [
[
"numpy.ma.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChadPro/DataToTFRecord | [
"54ea5ef5a512c8ce3d43cecb6da85ed9090e7747",
"54ea5ef5a512c8ce3d43cecb6da85ed9090e7747"
] | [
"cifar10/cifar10_224.py",
"VOC/pascalvoc_common.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport time\nfrom scipy.misc import imread,imresize\nfrom os import walk\nfrom os.path import join\nimport sys\nimport cv2\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n# Image \nIMG_SIZE = 224\nIMG_CHANNELS = 3\n\ndef read_and_decode(filename_queue):\n ... | [
[
"tensorflow.concat",
"tensorflow.FixedLenFeature",
"tensorflow.range",
"tensorflow.stack",
"tensorflow.decode_raw",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.expand_dims",
"tensorflow.train.string_input_producer",
"tensorflow.name_scope",
"tensorflow.TFRecord... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
... |
cc-ai/pytorch_GAN_zoo | [
"e1281b176f0f4b356dbd2c31b0cf867678b6409d"
] | [
"visualization/np_visualizer.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport numpy as np\nimport scipy\nimport scipy.misc\nimport imageio\nimport torch\nfrom PIL import Image\n\ndef make_numpy_grid(arrays_list, gridMaxWidth=2048,\n imgMinSize=128,\n interpolation='nearest'):... | [
[
"numpy.reshape",
"torch.clamp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PKUfudawei/cmssw | [
"8fbb5ce74398269c8a32956d7c7943766770c093"
] | [
"RecoEgamma/ElectronIdentification/python/FWLite.py"
] | [
"import ROOT\nimport ctypes\nimport pprint\nfrom numpy import exp\n\n# Python wrappers around the Electron MVAs.\n# Usage example in RecoEgamma/ElectronIdentification/test\n\nclass ElectronMVAID:\n \"\"\" Electron MVA wrapper class.\n \"\"\"\n\n def __init__(self, name, tag, categoryCuts, xmls, variablesFi... | [
[
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tandon-A/ufldl-python-solutions | [
"f58be0f49c4e2266c21316915853404a1a0df55f"
] | [
"Logitsic_Regression.py"
] | [
"import numpy as np \nimport scipy.optimize\nimport time \nimport matplotlib.pyplot as plt \nimport pandas as pd \nimport math\n\n\ndef log_reg(theta,x,y):\n \"\"\"\n Arguments:\n theta - A vector containing the parameter values to optimize.\n X - The examples stored in a matrix.\n X(i,j) is the ... | [
[
"numpy.dot",
"numpy.log",
"pandas.read_csv",
"numpy.isinf",
"numpy.isnan",
"numpy.random.shuffle",
"matplotlib.pyplot.plot",
"numpy.logical_or",
"numpy.insert",
"numpy.equal",
"numpy.transpose",
"numpy.exp",
"matplotlib.pyplot.xlabel",
"numpy.random.rand",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
aecgabriel/uuv_test | [
"d0729639808a0616881723116aebd62d299aabb9"
] | [
"uuv_control/uuv_trajectory_control/src/uuv_trajectory_generator/path_generator/cs_interpolator.py"
] | [
"# Copyright (c) 2016 The UUV Simulator Authors.\n# 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# U... | [
[
"scipy.interpolate.splrep",
"numpy.arange",
"numpy.cumsum",
"scipy.interpolate.splev",
"numpy.array",
"numpy.sum"
]
] | [
{
"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"
... |
jaysonfig/EXOSIMS | [
"d81436c2b17cdb779cad519b1932d3e3ad49b55e",
"d81436c2b17cdb779cad519b1932d3e3ad49b55e"
] | [
"EXOSIMS/PlanetPopulation/JupiterTwin.py",
"EXOSIMS/SurveySimulation/tieredScheduler.py"
] | [
"from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation\nimport numpy as np\nimport astropy.units as u\n\nclass JupiterTwin(PlanetPopulation):\n \"\"\"\n Population of Jupiter twins (11.209 R_Earth, 317.83 M_Eearth, 1 p_Earth)\n On eccentric orbits (0.7 to 1.5 AU)*5.204.\n Numbers pulled from ... | [
[
"numpy.random.uniform",
"numpy.array",
"numpy.mean",
"numpy.ones"
],
[
"numpy.dot",
"numpy.sqrt",
"numpy.arctan",
"numpy.asarray",
"numpy.in1d",
"numpy.vstack",
"numpy.all",
"numpy.max",
"numpy.round",
"numpy.nanargmin",
"numpy.mean",
"numpy.any"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lidongyv/3dndiv | [
"ccd7cf75963abf861a49443323585fc0ef08c229"
] | [
"auxiliary/surface_loss.py"
] | [
"# -*- coding: utf-8 -*- \r\n#@Author: Lidong Yu \r\n#@Date: 2019-11-18 20:53:24 \r\n#@Last Modified by: Lidong Yu \r\n#@Last Modified time: 2019-11-18 21:44:1\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn.functional as F\r\nimport os\r\nimport time\r\n\r\ndef compute_pairwise_distance(x):\r\n\t'... | [
[
"torch.abs",
"torch.mean",
"numpy.sqrt",
"torch.sum",
"torch.zeros_like",
"torch.nn.functional.relu",
"torch.rand",
"torch.clamp",
"numpy.array",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wang518hongyu/PyGEM | [
"1c9fa133133b3d463b1383d4792c535fa61c5b8d"
] | [
"run_preprocessing.py"
] | [
"\"\"\"\npygemfxns_preprocessing.py is a list of the model functions that are used to preprocess the data into the proper format.\n\n\"\"\"\n\n# Built-in libraries\nimport os\nimport glob\nimport argparse\n# External libraries\nimport pandas as pd\nimport numpy as np\nimport xarray as xr\nimport netCDF4 as nc\nfrom... | [
[
"numpy.polyfit",
"pandas.Series",
"pandas.DataFrame",
"numpy.round",
"numpy.concatenate",
"numpy.nanmean",
"numpy.moveaxis",
"numpy.where",
"pandas.read_csv",
"numpy.arange",
"numpy.zeros",
"numpy.log",
"pandas.concat",
"numpy.isnan",
"numpy.append",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kipronokoech/Mask-R-CNN-for-Fruit-Detection | [
"8604dc0c617711be336cb67b1b929b310d785942"
] | [
"evaluation/runMain-One.py"
] | [
"import os\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport cv2 as cv\nfrom MaskReconstruction import MaskConstruction\nfrom Evaluation import MaskRCNN_Evaluation\nimport random\nimport json\nimport pandas as pd\n\n# changing the font size in matplotlib\nimport matplotlib\nfont = {'weight' : 'norma... | [
[
"pandas.DataFrame",
"numpy.mean",
"numpy.load",
"numpy.array",
"matplotlib.rc"
]
] | [
{
"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": []
}
] |
ZachMontgomery/AirfoilDatabase | [
"165ee387a7b8ae9642feedf64315081c7a1718c7"
] | [
"zach/airfoil.py"
] | [
"import numpy as np\nfrom numpy import pi, sqrt, cos, sin, tan, sign\nfrom numpy import arctan2 as atan2\nfrom numpy import arctan as atan\nfrom numpy import arcsinh as asinh\nfrom numpy import log as ln\n\nimport scipy.optimize as opt\nfrom timeit import default_timer as timer\n\nimport matplotlib.pyplot as plt\np... | [
[
"numpy.sqrt",
"numpy.arctan",
"numpy.linspace",
"numpy.cos",
"numpy.sin",
"numpy.sign",
"numpy.tan",
"numpy.copy",
"numpy.zeros",
"scipy.optimize.newton"
]
] | [
{
"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"... |
dendisuhubdy/Greedy_InfoMax | [
"8c9b0de0663d9eb2936e082250685b41710aedb8"
] | [
"GreedyInfoMax/utils/logger.py"
] | [
"import os\nimport torch\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport copy\n\n\nclass Logger:\n \"\"\"\n Logging class that keeps track of the training parameters and progress, and saves log files and model checkpoints\n \"\"\"\n def __init__(self, opt):\n self.opt = opt\n\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fang2hou/ABGAN | [
"518a959e5d1e6191721d859cff02971e1a3105ea"
] | [
"abganlibs/models/sagan.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.parallel\n\n\nclass Self_Attn(nn.Module):\n \"\"\" Self attention Layer\"\"\"\n\n def __init__(self, in_dim, activation):\n super(Self_Attn, self).__init__()\n self.chanel_in = in_dim\n self.activation = activation\n\n self.quer... | [
[
"torch.nn.Softmax",
"torch.nn.Sequential",
"torch.nn.ConvTranspose2d",
"torch.zeros",
"torch.nn.Conv2d",
"torch.bmm",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
loco-3d/quadruped-walkgen | [
"d78c205bcfcc69919eacb7cb4b51e426e2bc626f"
] | [
"exemple/exemple_simple.py"
] | [
"# coding: utf8\n\nimport time\n\nimport numpy as np\nimport matplotlib.pylab as plt\n\nfrom GaitProblem import GaitProblem\n\n####################\n# Initialization #\n####################\n\n# Time step of the MPC\ndt_mpc = 0.02\n\n# Period of the MPC\nT_mpc = 0.32\n\n# Creation crocoddyl problem\ngaitProblem =... | [
[
"matplotlib.pylab.show",
"numpy.set_printoptions",
"numpy.int",
"matplotlib.pylab.subplot",
"matplotlib.pylab.figure",
"matplotlib.pylab.plot",
"matplotlib.pylab.legend",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haibinzheng/NeuronFair | [
"5f6affd6fb378058bb0d2a0fd0ea413d2c8bd3cf"
] | [
"nf_model/model_train.py"
] | [
"import copy\nimport os\nimport numpy as np\nimport sys\n# sys.path.append(\"../\")\nimport tensorflow as tf\nfrom tensorflow.python.platform import flags\nfrom nf_data.census import census_data\nfrom nf_data.bank import bank_data\nfrom nf_data.credit import credit_data\nfrom nf_data.compas import compas_data\nfrom... | [
[
"tensorflow.placeholder",
"tensorflow.ConfigProto",
"tensorflow.python.platform.flags.DEFINE_float",
"tensorflow.global_variables_initializer",
"tensorflow.python.platform.flags.DEFINE_integer",
"tensorflow.Session",
"tensorflow.set_random_seed",
"tensorflow.python.platform.flags.D... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NejcHirci/material-addon | [
"c08e2081413c3319b712c2f7193ac8013f601382",
"c08e2081413c3319b712c2f7193ac8013f601382"
] | [
"materialGAN/tools/generate_fake_inputs.py",
"materialGAN/PerceptualSimilarity/models/__init__.py"
] | [
"import os\nimport glob\nimport sys\nimport numpy as np\nsys.path.insert(1, 'materialGAN/src/')\nfrom optim import loadLightAndCamera\nfrom render import *\n\ndef gyListNames(in_dir):\n dir_list = sorted(glob.glob(in_dir))\n fn_list = []\n for dir in dir_list:\n fn_list.append(os.path.split(dir)[1])... | [
[
"numpy.array"
],
[
"numpy.maximum",
"numpy.arange",
"torch.sum",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.prod",
"numpy.transpose",
"numpy.where",
"numpy.sum",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shgold/apls | [
"4ac612e5d26580268e76d8b1a3721de2d7d18c38"
] | [
"apls/apls.py"
] | [
"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 29 12:32:19 2017\n\n@author: avanetten\n\n\"\"\"\n\n# from __future__ import print_function\n# from . import apls_utils\n# from . import apls_plots\n# from . import osmnx_funcs\n# import sp_metric\n# import topo_metric\n# import apls_tools... | [
[
"pandas.read_csv",
"numpy.abs",
"numpy.linspace",
"numpy.min",
"numpy.isnan",
"numpy.arange",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"numpy.round",
"numpy.max",
"numpy.argmin",
"numpy.mean",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
sbhaktha/ruletaker-1 | [
"61ab221c3bd409d4aa46def30cb788042cc56969"
] | [
"theory_generator.py"
] | [
"import argparse\nimport common\nfrom common import (\n Example,\n Fact,\n Rule,\n Theory,\n TheoryAssertionInstance,\n TheoryAssertionRepresentationWithLabel,\n)\nimport json\n\nimport nltk\nfrom nltk import Nonterminal, PCFG\nfrom numpy.random import choice\nimport random\n\nimport problog\nfrom... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jgolebiowski/kitkopt | [
"0b46d38004b75799dd1e8603a445b1d711c03735",
"0b46d38004b75799dd1e8603a445b1d711c03735"
] | [
"examples/random-minimize/control.py",
"tst/test_bayesian_optimizer.py"
] | [
"import numpy as np\n\nfrom kitkopt.random_optimizer import minimize_function\nfrom kitkopt.hyper_parameter import HyperParameter\nfrom kitkopt.kernels import rbf\n\n\ndef funct(x):\n return np.sum(np.square(x))\n\n\ndef main():\n # ------ Define hyperparameters with bounds and stepsize\n hyperparam_config... | [
[
"numpy.square"
],
[
"numpy.square",
"numpy.testing.assert_almost_equal",
"numpy.mean",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zxChouSean/crispr_bedict_reproduce | [
"8590ddaeaefedd370a60c1f61043333371526766",
"8590ddaeaefedd370a60c1f61043333371526766"
] | [
"criscas/model.py",
"tools/statistic.py"
] | [
"import numpy as np\nimport torch\nfrom torch import nn\n\nclass SH_SelfAttention(nn.Module):\n \"\"\" single head self-attention module\n \"\"\"\n def __init__(self, input_size):\n \n super().__init__()\n # define query, key and value transformation matrices\n # usually input_s... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.LogSoftmax",
"torch.nn.init.uniform_",
"torch.cat",
"torch.zeros",
"torch.sin",
"torch.nn.ModuleList",
"torch.randn",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goru97/horovod | [
"56afed8666e0ec8b54d84ac202890393c0c1a8a7"
] | [
"setup.py"
] | [
"# Copyright 2017 Uber Technologies, Inc. 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.python.framework.load_library.load_op_library",
"torch.compiled_with_cxx11_abi",
"torch.utils.cpp_extension.include_paths",
"tensorflow.sysconfig.get_link_flags",
"torch.utils.ffi.create_extension",
"torch.utils.cpp_extension.CppExtension",
"tensorflow.sysconfig.get_lib",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
jay-johnson/donkeycar | [
"6968ee50ae5a43b28ce21d660245c008337885b8"
] | [
"donkeycar/management/base.py"
] | [
"\nimport sys\nimport os\nimport socket\nimport shutil\nimport argparse\nimport json\nimport time\n\nimport donkeycar as dk\nfrom donkeycar.parts.datastore import Tub\nfrom donkeycar.utils import *\nfrom donkeycar.management.tub import TubManager\nfrom donkeycar.management.joystick_creator import CreateJoystick\nim... | [
[
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"tensorflow.python.keras.models.load_model",
"tensorflow.python.keras.models.Model",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"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": [
"1.5",
"1.4"
]
}
] |
Sun-Joong/aifh | [
"1b6363d26f54b77348020ce88ced0670568ed736"
] | [
"vol3/vol3-python-examples/examples/example_iris.py"
] | [
"#!/usr/bin/env python\n\"\"\"\n Artificial Intelligence for Humans\n Volume 3: Deep Learning and Neural Networks\n Python Version\n http://www.aifh.org\n http://www.jeffheaton.com\n Code repository:\n https://github.com/jeffheaton/aifh\n Copyright 2015 by Jeff Heaton\n Licensed under the... | [
[
"pandas.read_csv",
"numpy.argmax",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
neelabh17/MAVI-Face | [
"5dbf105b51a8b90203cd144f2fe671770d38eb81"
] | [
"vimak.py"
] | [
"\n # 'newOneToOneOhem_lr-beg=1.3e-03_lr-sch=1e-03_shuffle=False',\n # 'newOneToOneOhem_lr-beg=1.3e-03_lr-sch=None_shuffle=False',\nfrom torch.utils.tensorboard import SummaryWriter\nfrom toolbox.pickleOpers import loadup\nls=['5XOhemWithShuffleNoScheduler_epoch_36',\n 'new_1xOhem_shuffle_true_scheduler_e2... | [
[
"torch.utils.tensorboard.SummaryWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hasselg/pysubtracking | [
"e3666d89c98562361ed0521c366fb0a299f2fdae"
] | [
"subtracking/grouse.py"
] | [
"# Copyright 2015 Gregory Hasseler\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed... | [
[
"numpy.nonzero",
"numpy.linalg.norm",
"numpy.cos",
"numpy.sin",
"numpy.random.rand",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
volpatto/pysodes | [
"48add3ce16ee48e2f3af7a928935f9b22d74d908"
] | [
"tests/test_odeint_integrate_const.py"
] | [
"import pytest\nfrom pytest import approx\nimport numpy as np\nfrom scipy.integrate import solve_ivp\n\nfrom pysodes.odeint import integrate_const\n\n\ndef lotka_volterra(z, dzdt, t):\n x, y = z\n\n a = 1.5\n b = 1.0\n c = 3.0\n d = 1.0\n\n dzdt[0] = a*x - b*x*y\n dzdt[1] = -c*y + d*x*y\n\n ... | [
[
"scipy.integrate.solve_ivp",
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
kw-0/MyGrad | [
"307f1bb5f2391e7f4df49fe43a7acf9d1e8ea141"
] | [
"src/mygrad/indexing_routines/ops.py"
] | [
"import numpy as np\n\nfrom mygrad.operation_base import Operation\n\n\nclass Where(Operation):\n def __call__(self, a, b, *, condition):\n self.variables = (a, b)\n self.condition = np.asarray(condition, dtype=bool)\n return np.where(condition, a.data, b.data)\n\n def backward_var(self, ... | [
[
"numpy.asarray",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tripti-agarwal/MapperInteractive | [
"466b6b133f279cdd67b19e4c9dee6632f0d82b0b"
] | [
"app/views.py"
] | [
"from flask import render_template,request, url_for, jsonify, redirect, Response, send_from_directory\nfrom app import app\nfrom app import APP_STATIC\nfrom app import APP_ROOT\nimport json\nfrom mogutda import SimplicialComplex\nimport numpy as np\nimport pandas as pd\nimport os\nimport re\n# from kmapper import K... | [
[
"pandas.read_csv",
"numpy.sum",
"sklearn.cluster.MeanShift",
"sklearn.cluster.KMeans",
"numpy.unique",
"numpy.asarray",
"pandas.DataFrame",
"numpy.round",
"numpy.concatenate",
"numpy.delete",
"sklearn.preprocessing.normalize",
"numpy.mean",
"scipy.spatial.distan... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"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... |
deepneuro/Document_Clustering | [
"347d6db613c1c1df9da71e87fb4221a7915dfb5a"
] | [
"reader.py"
] | [
"import pandas as pd\nimport pather\n\n\ndef find_txt_files(initial_path):\n \"\"\"\n For a given path, returns the paths to the .txt files\n :param initial_path: the initial directory of search\n :return: list of paths inside the initial path\n \"\"\"\n\n path_list = pather.find_paths(initial_pat... | [
[
"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": []
}
] |
anukchat/cassava_leaf_classifier | [
"f5c2f9d1fceae2a504d0119b3ea078786db6f8c1"
] | [
"app/main.py"
] | [
"from fastapi import FastAPI, File, UploadFile, HTTPException\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom pydantic import ValidationError\nimport uvicorn\n\nfrom PIL import Image\nimport io\nimport sys\nimport logging\nimport cv2\nimport numpy as np\n\nfrom response_dto.prediction_response_dto impor... | [
[
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HenriARM/co-mod-gan | [
"6b0e8be87aafe8af3de19f796aac1b7cd4cc2d37"
] | [
"training/misc.py"
] | [
"# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.\n#\n# This work is made available under the Nvidia Source Code License-NC.\n# To view a copy of this license, visit\n# https://nvlabs.github.io/stylegan2/license.html\n\n\"\"\"Miscellaneous utility functions.\"\"\"\n\nimport os\nimport pickle\nimport n... | [
[
"numpy.split",
"numpy.sqrt",
"numpy.clip",
"numpy.rint",
"numpy.argmax",
"numpy.random.rand",
"numpy.float32",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JohannesSchuele/unet-keras | [
"6cc081dae917a97310ffba7309dae48196e88192"
] | [
"tools/utilz_graph.py"
] | [
"import glob\nimport skimage.io as io\nimport skimage.transform as trans\nimport numpy as np\nimport pylab as plt\nfrom natsort import natsorted\nimport glob\nimport os\nimport tensorflow as tf\ndef get_sorted_data_names_from_paths(path_to_image,path_to_mask):\n image_names = glob.glob(path_to_image + \"/*.png\"... | [
[
"tensorflow.convert_to_tensor",
"numpy.triu_indices",
"numpy.asarray",
"numpy.fliplr",
"numpy.squeeze",
"numpy.full",
"numpy.argwhere",
"numpy.all",
"numpy.transpose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
sytham/rnn-benchmark | [
"0e78860972d71fac1a0bbbc1d3ffc198d2c3b4d6"
] | [
"sandbox/rocky/tf/samplers/vectorized_sampler.py"
] | [
"import pickle\n\nimport tensorflow as tf\nfrom rllab.sampler.base import BaseSampler\nfrom sandbox.rocky.tf.envs.parallel_vec_env_executor import ParallelVecEnvExecutor\nfrom sandbox.rocky.tf.envs.vec_env_executor import VecEnvExecutor\nfrom rllab.misc import tensor_utils\nimport numpy as np\nfrom rllab.sampler.st... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alimhanif/blocksparse | [
"89074c5ccf78e3a88b4aa2aefc9e208d4773dcbc"
] | [
"blocksparse/matmul.py"
] | [
"\n\"\"\"Cuda op Python library.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport tensorflow as tf\nimport scipy.sparse as sparse\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops.init_ops impor... | [
[
"numpy.dot",
"numpy.sqrt",
"tensorflow.control_dependencies",
"tensorflow.get_default_graph",
"numpy.square",
"numpy.linalg.svd",
"scipy.sparse.find",
"numpy.arange",
"tensorflow.name_scope",
"numpy.zeros",
"numpy.nonzero",
"scipy.sparse.csr_matrix",
"numpy.arra... | [
{
"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"... |
mattwigway/DiscreteChoiceModels.jl | [
"82cc136dcd7dd9cbeb2d5b83c19fe08b0f5dcdd1"
] | [
"benchmark/foreign/python/benchmarkable.py"
] | [
"import timeit\nimport numpy as np\nimport multiprocessing\nimport tempfile\nimport os\n\n\"\"\"\nThis class represents something that can be benchmarked. Subclasses should\noverride the setup() and measurable() methods. Only the time taken by measurable() will be benchmarked.\n\"\"\"\n\n\nclass Benchmarkable(multi... | [
[
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mhaseeb123/hicops | [
"e40ab4e4e737cdb7f5921e1743c95bab7e3e3e27"
] | [
"tools/ms2prep/mspartition.py"
] | [
"#\n# imports\n#\n\nimport os\nimport sys\nimport glob\nimport copy\nimport numpy as np\nimport argparse\n\n#\n# main function\n#\n\nif __name__ == '__main__':\n\n # initialize arg parser\n parser = argparse.ArgumentParser(description='Split/merge the MS/MS data set into/from partitions')\n\n # data root d... | [
[
"numpy.array_split",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
azuki-miho/RFCR | [
"039733f25818d0d3db6af8c9e00e7ad989b69ee1",
"039733f25818d0d3db6af8c9e00e7ad989b69ee1"
] | [
"KPConv_deform_ScanNet/visualize_scannet.py",
"KPConv_deform_ScanNet/models/KPFCNN_model.py"
] | [
"\"\"\"\nAuthor: Jingyu Gong\nDate: May 2021\n\"\"\"\n\nimport numpy as np\nimport plyfile\nfrom plyfile import PlyData\nimport os\nimport glob\nfrom utils.ply import write_ply\n\n#result_path = \"results/Log_2020-08-26_08-38-15_add_weight_697/val_preds_449/\"\nresult_path = \"test/Log_2020-10-05_09-36-55_regionce_... | [
[
"numpy.array",
"numpy.zeros"
],
[
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.global_variables",
"tensorflow.equal",
"tensorflow.nn.l2_loss",
"tensorflow.get_default_graph",
"tensorflow.add_n",
"tensorflow.gather",
"tensorflow.square",
"tensorflow.logic... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
soham1024/Handwritten-Digit-Recognition | [
"065557e94a46cac75d0439604f7dd93784b6cffe"
] | [
"app.py"
] | [
"from flask import Flask, render_template, request\nfrom scipy.misc import imsave,imread, imresize\nimport numpy as np\nimport keras.models\nimport re\nimport base64\n\nimport sys \nimport os\nsys.path.append(os.path.abspath(\"./model\"))\nfrom load import *\n\napp = Flask(__name__)\nglobal model, graph\nmodel, gra... | [
[
"scipy.misc.imresize",
"numpy.argmax",
"scipy.misc.imread",
"numpy.invert"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
qyz96/Legion | [
"b12f0d1bd759788356385fbb6ae8b6321068e3b7"
] | [
"bindings/python/examples/struct.py"
] | [
"#!/usr/bin/env python\n\n# Copyright 2019 Stanford University\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.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
linklab-uva/deepracing | [
"fc25c47658277df029e7399d295d97a75fe85216",
"fc25c47658277df029e7399d295d97a75fe85216"
] | [
"deepracing_py/plot_frame_rate.py",
"DCNN-Pytorch/shape_testing.py"
] | [
"import numpy as np\nimport numpy.linalg as la\nimport scipy\nimport scipy.stats\nimport skimage\nimport PIL\nfrom PIL import Image as PILImage\nimport TimestampedPacketMotionData_pb2\nimport PoseSequenceLabel_pb2\nimport TimestampedImage_pb2\nimport Vector3dStamped_pb2\nimport argparse\nimport os\nimport google.pr... | [
[
"matplotlib.pyplot.plot",
"scipy.stats.linregress",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"torch.rand"
]
] | [
{
"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"
... |
ZhaozhiQIAN/neurawkes | [
"1a3caa837b34f77ac9d078bc9bf10ff10a3bf959"
] | [
"test_models_and_save.py"
] | [
"# -*- coding: utf-8 -*-\n# !/usr/bin/python\n\"\"\"\nCreated on Mar 18th 10:58:37 2016\n\ntest a continuous-time sequential model\n\n@author: hongyuan\n\"\"\"\n\nimport pickle\nimport time\nimport numpy\nimport theano\nfrom theano import sandbox\nimport theano.tensor as tensor\nimport os\nimport sys\nfrom collecti... | [
[
"numpy.int32",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
benmoseley/seismic-simulation-wavenet | [
"c2a136077bba29cab772b88c7c03b4911c9185c9"
] | [
"models.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jan 12 16:57:30 2019\n\n@author: bmoseley\n\"\"\"\n\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom loss_functions import lp_mean_loss\nfrom tfutils import w, b\nfrom wavenet import Wavenet1D\nimport plot_... | [
[
"matplotlib.pyplot.legend",
"numpy.expand_dims",
"numpy.sqrt",
"tensorflow.train.AdamOptimizer",
"tensorflow.pad",
"tensorflow.summary.scalar",
"matplotlib.pyplot.subplot2grid",
"numpy.pad",
"tensorflow.Variable",
"tensorflow.summary.image",
"numpy.arange",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
synml/horovod-tutorial | [
"75fcfd12c0eb23ad5fde1724c45fd795beb9f0d3"
] | [
"main.py"
] | [
"import os\nimport random\nimport time\n\nimport filelock\nimport horovod.torch as hvd\nimport numpy as np\nimport torch.backends.cudnn\nimport torch.nn as nn\nimport torch.utils.data\nimport torch.utils.tensorboard\nimport torchvision\nimport tqdm\n\n\ndef train(model, trainloader, criterion, optimizer, device, sc... | [
[
"torch.nn.CrossEntropyLoss",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
charliec443/feast | [
"8b3a97a9774fa6a2cefcf64c237a36212a158ab1"
] | [
"sdk/python/tests/test_historical_retrieval.py"
] | [
"import os\nimport random\nimport string\nimport time\nfrom datetime import datetime, timedelta\nfrom tempfile import TemporaryDirectory\n\nimport assertpy\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom google.cloud import bigquery\nfrom pandas.testing import assert_frame_equal\nfrom pytz import utc\... | [
[
"numpy.random.seed",
"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": []
}
] |
binyao2020/ElegantRL | [
"bf79f0d071d00cd93be03f1ca005020c3ab8dfe0"
] | [
"BetaWarning/Tutorial.py"
] | [
"import time\nimport numpy as np\nimport numpy.random as rd\n\nimport gym\nimport torch\nimport torch.nn as nn\n\n\nclass EvaluateRewardSV: # SV: Simplify Version. Only for tutorial.\n def __init__(self, env):\n self.env = env\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else... | [
[
"torch.nn.SmoothL1Loss",
"torch.cat",
"torch.tensor",
"torch.nn.Tanh",
"torch.nn.Linear",
"numpy.random.normal",
"torch.no_grad",
"numpy.random.rand",
"torch.cuda.is_available",
"numpy.random.uniform",
"torch.nn.ReLU",
"numpy.average",
"torch.nn.MSELoss",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishalbelsare/UGFraud | [
"29f9486802eb1d57705028b3e9db704570f5b66f"
] | [
"UGFraud/Utils/helper.py"
] | [
"from sklearn.metrics import average_precision_score\nfrom sklearn.metrics import roc_auc_score\nimport gzip\nimport numpy as np\nimport networkx as nx\nimport time\nimport functools\nimport warnings\n\n\ndef create_ground_truth(user_data):\n \"\"\"Given user data, return a dictionary of labels of users and revi... | [
[
"sklearn.metrics.roc_auc_score",
"numpy.array",
"sklearn.metrics.average_precision_score",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
winiar93/viadot | [
"7d8f8b0a30de3d40da161615639532012de072b0"
] | [
"viadot/utils.py"
] | [
"import re\nfrom typing import Any, Dict, List\n\nimport pandas as pd\nimport prefect\nimport pyodbc\nimport requests\nfrom prefect.utilities.graphql import EnumValue, with_args\nfrom requests.adapters import HTTPAdapter\nfrom requests.exceptions import ConnectionError, HTTPError, ReadTimeout, Timeout\nfrom request... | [
[
"pandas.Int64Dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
eM7RON/Image-Evolution | [
"9e554d038c14269a3ad600b38b62e8d627cbf656"
] | [
"main.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport sys, os, time, copy, re #, shutil\n\nfrom PyQt5.QtWidgets import QMainWindow, QWidget, QApplication, QFileDialog, QDesktopWidget, QStyleFactory, \\\n QDialog, QPushButton, QVBoxLayout, QToolBar, QMessageBox, QProgressBar\nfrom PyQt5.QtCore import pyqtSig... | [
[
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SCIInstitute/shapeworks | [
"cbd44fdeb83270179c2331f2ba8431cf7330a4ff"
] | [
"Python/ShapeCohortGenPackage/ShapeCohortGen/CohortGenUtils.py"
] | [
"import os\nimport numpy as np\nimport scipy\nimport shutil\nimport vtk\nfrom vtk.util.numpy_support import vtk_to_numpy\nimport shapely\nimport matplotlib.pyplot as plt\nimport shapeworks as sw\n\n'''\nMake folder\n'''\ndef make_dir(dir_path):\n if os.path.exists(dir_path):\n shutil.rmtree(dir_path)\n ... | [
[
"matplotlib.pyplot.imsave",
"numpy.random.choice",
"numpy.min",
"numpy.arange",
"matplotlib.pyplot.imread",
"numpy.ones",
"numpy.max",
"numpy.random.normal",
"scipy.ndimage.filters.gaussian_filter",
"numpy.float32",
"numpy.array",
"numpy.where",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
s183983/HodgeNet | [
"09a84d5bf4c1cc5b01d4583a685a257d12ceb5ae"
] | [
"hodgenet.py"
] | [
"import scipy\nimport scipy.sparse.linalg\nimport torch\nimport torch.nn as nn\n\nfrom hodgeautograd import HodgeEigensystem\n\n\nclass HodgeNetModel(nn.Module):\n \"\"\"Main HodgeNet model.\n\n The model inputs a batch of meshes and outputs features per vertex or \n pooled to faces or the entire mesh.\n ... | [
[
"torch.nn.BatchNorm1d",
"torch.cat",
"torch.zeros",
"torch.einsum",
"torch.eye",
"torch.nn.Linear",
"torch.nn.LeakyReLU",
"scipy.sparse.vstack",
"torch.split",
"torch.stack"
]
] | [
{
"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"... |
toastisme/dials | [
"6bc8ababc33bfe334513677f8adb65c0e90003f3"
] | [
"command_line/background.py"
] | [
"# LIBTBX_SET_DISPATCHER_NAME dials.background\n# LIBTBX_PRE_DISPATCHER_INCLUDE_SH export PHENIX_GUI_ENVIRONMENT=1\n\n\nimport math\n\nimport iotbx.phil\nfrom libtbx.phil import parse\nfrom scitbx import matrix\n\nimport dials.util.masking\nfrom dials.algorithms.spot_finding.factory import SpotFinderFactory\nfrom d... | [
[
"matplotlib.pyplot.gca",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"matplotlib.ticker.FixedLocator",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TarrySingh/scipy | [
"0c42ad1b50c1d6c738c964e2aa60351e34ac2812"
] | [
"scipy/stats/tests/test_morestats.py"
] | [
"# Author: Travis Oliphant, 2002\n#\n# Further enhancements and tests added by numerous SciPy developers.\n#\nfrom __future__ import division, print_function, absolute_import\n\nimport warnings\n\nimport numpy as np\nfrom numpy.random import RandomState\nfrom numpy.testing import (assert_array_equal,\n assert_a... | [
[
"scipy.stats.norm.ppf",
"scipy._lib._numpy_compat.suppress_warnings",
"numpy.linspace",
"scipy.stats.ppcc_max",
"scipy.stats.circstd",
"numpy.seterr",
"scipy.stats.kstat",
"scipy.stats.tukeylambda.rvs",
"numpy.random.randn",
"scipy.stats.gumbel_l.logsf",
"scipy.stats.me... | [
{
"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"
... |
anajikadam17/prediction-of-segments | [
"eaf50b1bdc2cfafce121310ea588796fbcd10d3f"
] | [
"code.py"
] | [
"# --------------\n#import the packages\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nimport xgboost as xgb\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.feature_selection import SelectFromModel\r\nfrom sklearn.feat... | [
[
"sklearn.model_selection.GridSearchCV",
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.f1_score",
"sklearn.metrics.classification_report",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
arunsundar022/Corona-tweet-sentiment-analysis | [
"2fa37b3f1046bdbd230ce6be7a5f07c73a7d82b2"
] | [
"nlp.py"
] | [
"import nltk\nfrom nltk.corpus import stopwords\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n\n\ndef nlp_ready(string):\n sentence = nltk.word_tokenize(string)\n lemma = nltk.WordNetLemmatizer()\n lem_output = ' '.join([lemma... | [
[
"tensorflow.keras.preprocessing.text.Tokenizer",
"tensorflow.keras.preprocessing.sequence.pad_sequences"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
DemirTonchev/iambandit | [
"8b445ebb7d1c65b8c27381fb574e499c07bebff6"
] | [
"policy.py"
] | [
"import numpy as np\nfrom base import bernuolli, random_argmax, kl_bernulli\nfrom itertools import cycle\nfrom numpy.random import beta\nfrom functools import partial\nfrom numpy import linalg\n\ndef safe_min_1d(array):\n \"\"\"Useful for taking min arm index for some policies\n \"\"\"\n if len(array) > 0:... | [
[
"numpy.dot",
"numpy.log",
"numpy.random.beta",
"numpy.linspace",
"numpy.min",
"numpy.random.choice",
"numpy.linalg.inv",
"numpy.full",
"numpy.vectorize",
"numpy.identity",
"numpy.random.rand",
"numpy.argmax",
"numpy.where",
"numpy.outer",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tomo-2525/Python | [
"b605ef7cc5b691afd603d289433ee8c7ba64c774"
] | [
"02_library/pandas/pandas2.py"
] | [
"import pandas\nimport numpy\nimport matplotlib\nnumpy.random.seed(42)\n# 10行5列の配列（要素は乱数）を作る\ndata = numpy.random.randn(10, 5)\nprint(data)\n\n# dataを要素とするDataFrameを作る\ndf = pandas.DataFrame(data, columns=['A', 'B', 'C', 'D', 'E'])\nprint(df)\n# 先頭3行を取得する\nprint(df.head(3))\n\n# 後尾３行を取得する\nprint(df.tail(3))\nprint(... | [
[
"numpy.random.randn",
"numpy.random.seed",
"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": []
}
] |
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