repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
TheGreatRefrigerator/joerd | [
"0a010c81c29ee0258261e516420375b39d8f8010"
] | [
"joerd/output/tiff.py"
] | [
"from builtins import str\nfrom builtins import range\nfrom builtins import object\nfrom joerd.util import BoundingBox\nfrom joerd.region import RegionTile\nfrom joerd.mkdir_p import mkdir_p\nfrom tempfile import NamedTemporaryFile as Tmp\nfrom osgeo import osr, gdal\nimport re\nimport logging\nimport os\nimport os... | [
[
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GitHK/CarND-Vehicle-Detection- | [
"2c55a14e449cdd0bf79e10151a3c0d591bca2f3a"
] | [
"code/features.py"
] | [
"import cv2\nimport matplotlib.image as mpimg\nimport numpy as np\nfrom skimage.feature import hog\n\nCOLORS_SPACE_MAP = {\n 'RGB': lambda x: np.copy(x),\n 'HSV': lambda x: cv2.cvtColor(x, cv2.COLOR_RGB2HSV),\n 'LUV': lambda x: cv2.cvtColor(x, cv2.COLOR_RGB2LUV),\n 'HLS': lambda x: cv2.cvtColor(x, cv2.C... | [
[
"numpy.hstack",
"numpy.concatenate",
"matplotlib.image.imread",
"numpy.copy",
"numpy.ravel",
"numpy.histogram"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hudsonchase/datacube-core | [
"e327e8f3508111635dfee2cffa36ae079384737e"
] | [
"datacube/utils/dates.py"
] | [
"\"\"\"\nDate and time utility functions\n\nIncludes sequence generation functions to be used by statistics apps\n\n\"\"\"\nfrom typing import Union\nfrom datetime import datetime\n\nimport dateutil\nimport dateutil.parser\nfrom dateutil.relativedelta import relativedelta\nfrom dateutil.rrule import YEARLY, MONTHLY... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
janchorowski/JEM | [
"5d98611951cf3c38645cb818f535a6036896db71"
] | [
"train_pgan_ebm_simple.py"
] | [
"import argparse\nimport torch\nimport torch.nn as nn\nimport torch.utils\nimport numpy as np\nimport torch.distributions as distributions\nfrom torch.utils.data import DataLoader, Dataset, TensorDataset\nfrom torchvision import datasets, transforms\nimport torchvision\nimport sklearn.datasets as skdatasets\ndevice... | [
[
"torch.nn.functional.upsample",
"torch.randn_like",
"torch.svd",
"torch.rand_like",
"torch.cat",
"torch.zeros",
"torch.utils.data.DataLoader",
"numpy.mean",
"torch.cuda.is_available",
"torch.nn.functional.tanh",
"torch.nn.init.calculate_gain",
"torch.nn.Softplus",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xiedidan/Mask_RCNN_Pytorch | [
"8db33e8b072c3f41f81c06caf624a61c99f4bdcc"
] | [
"demo_coco.py"
] | [
"import os\nimport sys\nimport random\nimport math\nimport numpy as np\nimport skimage.io\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nimport coco\nimport utils\nimport model as modellib\nimport visualize\n\nimport torch\n\n\n# Root directory of the project\nROOT_DIR = os.getcwd()\n\n# Directory to save l... | [
[
"torch.device",
"matplotlib.pyplot.show",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
roachsinai/dlcv_for_beginners | [
"691d010405438a476809389f6b2273c8f2494bc6"
] | [
"chap6/6.2_opencv_basic/gamma_change.py"
] | [
"import numpy as np\nimport cv2\n\nimg = cv2.imread('img/faraway.jpg')\n\n# 分通道计算每个通道的直方图\nhist_b = cv2.calcHist([img], [0], None, [256], [0, 256])\nhist_g = cv2.calcHist([img], [1], None, [256], [0, 256])\nhist_r = cv2.calcHist([img], [2], None, [256], [0, 256])\n\n# 定义Gamma矫正的函数\ndef gamma_trans(img, gamma):\n ... | [
[
"numpy.array",
"matplotlib.pyplot.show",
"numpy.power",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miiio/Person_reID_baseline_pytorch | [
"15f266c0f6329c0ba7b99514f8ab792b926997c8"
] | [
"train.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function, division\n\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.autograd import Variable\nfrom torchvision import datasets, transforms\nimport torch.backends.cudnn as cu... | [
[
"torch.nn.Softmax",
"torch.nn.CrossEntropyLoss",
"torch.norm",
"torch.max",
"torch.cuda.set_device",
"matplotlib.use",
"matplotlib.pyplot.figure",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.autograd.Variable",
"torch.no_grad",
"torch.cuda.is_available",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IAIK/ProcHarvester | [
"556e85dc238326d7b2538938bbb6938f60f7048a"
] | [
"code/analysis tool/main_combined_sc_vectors.py"
] | [
"import input_parser\nimport pandas as pd\nimport features as ft\nimport app_classifier\nimport config\nimport timing\n\n\ndef main():\n timing.start_measurement()\n\n print(\"Do combined classification using all input files\")\n file_contents, labels = input_parser.parse_input_files(config.get_record_dir(... | [
[
"pandas.Series"
]
] | [
{
"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": []
}
] |
csbisa/poogie | [
"9ea89a50eec8be4ceda7f4af034d70d0705d6403"
] | [
"examples/plot-monster-health.py"
] | [
"#!/usr/bin/python\n\n# Generates monster health graph given a pcap file.\n# This script doesn't detect quest boundaries, so you'll get funny results if\n# you feed a pcap file that contains multiple quests.\n#\n# ./plot-monster-health.py <pcap file>\n\nimport matplotlib.pyplot as plt\nfrom mh_types.generated.mh4u... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mcoughlin/kowalski | [
"4e69dbc4565b346c59814aafb839f06cb58464e8"
] | [
"tools/fetch_ztf_matchfiles.py"
] | [
"from bs4 import BeautifulSoup\nimport fire\nimport multiprocessing as mp\nimport os.path\nimport pandas as pd\nimport pathlib\nimport requests\nimport subprocess\nfrom tqdm import tqdm\nfrom typing import Sequence\nfrom urllib.parse import urljoin\n\nfrom utils import load_config\n\n\nconfig = load_config(\n pa... | [
[
"pandas.DataFrame.from_records",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
andersonbrito/miscCodes | [
"2052e3627867a4f90f359840edb690e2a463ae74"
] | [
"webDataExtract/genomeStats.py"
] | [
"#!/usr/bin/python\n\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n# Created by: Anderson Brito\n#\n# genomeStats.py -> This code searches for stats from viral genomes\n# available on NCBI website, and retrieves these data\n# in TSV format. Additional ... | [
[
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MagdaW19/Sentiment_Analysis | [
"535cd086882a45c7ce2abff96bb5ec0be53b466f"
] | [
"scripts/utils_visualizations.py"
] | [
"# Copyright 2021 Magda Wójcicka\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 applica... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yippp/SY-GNN | [
"65a1e2566dce549724cef080dfac7efc00fbe91b"
] | [
"pack/data.py"
] | [
"\"\"\"\nThis module contains all functionalities required to load atomistic data,\ngenerate batches and compute statistics. It makes use of the ASE database\nfor atoms [#ase2]_.\n\nReferences\n----------\n.. [#ase2] Larsen, Mortensen, Blomqvist, Castelli, Christensen, Dułak, Friis,\n Groves, Hammer, Hargus:\n ... | [
[
"numpy.savez",
"numpy.sqrt",
"torch.sum",
"torch.zeros_like",
"torch.from_numpy",
"torch.FloatTensor",
"torch.no_grad",
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ericmbell1/standardizedinventories | [
"770880023bb8a5b1b993b59e5c2b20a2f9b83f2d"
] | [
"stewi/globals.py"
] | [
"# globals.py (stewi)\n# !/usr/bin/env python3\n# coding=utf-8\n\"\"\"\nSupporting variables and functions used in stewi\n\"\"\"\n\nimport pandas as pd\nimport json\nimport logging as log\nimport os\nimport numpy as np\nimport yaml\nimport time\nimport subprocess\nfrom datetime import datetime\n\nfrom esupy.process... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"pandas.ExcelFile",
"pandas.read_json",
"pandas.DataFrame.from_records",
"pandas.api.types.is_string_dtype",
"pandas.to_numeric"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Het-Shah/genrl | [
"5371b98cb7deac35558a63cbde14e55de5e0636f"
] | [
"genrl/agents/deep/base/base.py"
] | [
"from abc import ABC\nfrom typing import Any, Dict, Tuple\n\nimport numpy as np\nimport torch\n\nfrom genrl.utils import set_seeds\n\n\nclass BaseAgent(ABC):\n \"\"\"Base Agent Class\n\n Attributes:\n network (str): The network type of the Q-value function.\n Supported types: [\"cnn\", \"mlp... | [
[
"torch.device",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smarx/ethshardingpoc | [
"62420e26f00be20369e05690792e957a131e68f0"
] | [
"visualizer.py"
] | [
"import random\nimport hashlib\nimport matplotlib as mpl\nmpl.use('TkAgg')\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport networkx as nx\nfrom blocks import Block, SwitchMessage_BecomeAParent, SwitchMessage_ChangeParent, SwitchMessage_Orbit\nfrom config import *\nimport copy\n\nfrom PIL import Image, ... | [
[
"matplotlib.use",
"matplotlib.pyplot.draw",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eklem1/ares | [
"df39056065f0493e3c922fb50ced2dc6d1bc79a2"
] | [
"input/hmf/generate_halo_histories.py"
] | [
"\"\"\"\n\nrun_trajectories.py\n\nAuthor: Jordan Mirocha\nAffiliation: McGill\nCreated on: Sat 9 Mar 2019 15:48:15 EST\n\nDescription: This script may be obsolete.\n\n\"\"\"\n\nimport os\nimport sys\nimport ares\nimport h5py\nimport numpy as np\nimport matplotlib.pyplot as pl\n\ntry:\n fn_hmf = sys.argv[1]\nexc... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HerambD/ga-dsmp | [
"2e03b5eee7565ca11ed7fbf4daca3cf74d0d593f"
] | [
"Linear-regression/code.py"
] | [
"# --------------\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n# code starts here\ndf = pd.read_csv(path)\n# Display top 5 \ndf.head(5)\n# Store all the features(independent values) in a variable called X\nX = df[['ages','num_reviews','piece_count','play_star_ratin... | [
[
"pandas.read_csv",
"sklearn.metrics.r2_score",
"matplotlib.pyplot.subplots",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.mean_squared_error",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplot.hist"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Jonathan56/CyDER | [
"58cc88f5140585b6d4d9073eca29e3d3b324eb5e"
] | [
"front_end/worker/cosimulation/source/monitor.py"
] | [
"from __future__ import division\nimport matplotlib\nfrom matplotlib.ticker import FormatStrFormatter\nimport matplotlib.pyplot as plt\nfrom matplotlib.dates import DateFormatter\nimport numpy\nimport datetime\nimport json\nplt.switch_backend('Qt4Agg')\n\n\nclass Monitor(object):\n \"\"\"Monitor simulation progr... | [
[
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.switch_backend",
"matplotlib.ticker.FormatStrFormatter",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hyun-jin-park/deep-text-recognition-benchmark | [
"03580ec69c05e659ffbcdd968c7ab3ffa9ccd7b5"
] | [
"test.py"
] | [
"import os\nimport time\nimport string\nimport argparse\nimport re\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.utils.data\nimport torch.nn.functional as F\nimport numpy as np\nfrom nltk.metrics.distance import edit_distance\n\nfrom utils import CTCLabelConverter, AttnLabelConverter, Averager... | [
[
"torch.nn.functional.softmax",
"torch.nn.CrossEntropyLoss",
"torch.LongTensor",
"torch.load",
"torch.nn.DataParallel",
"torch.no_grad",
"torch.nn.CTCLoss",
"torch.cuda.is_available",
"torch.IntTensor",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qilei123/mmdetection | [
"6c18ff0866938d982f4555a8c43979733dba1c7a"
] | [
"tools/analysis_tools/analyze_results.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport os.path as osp\n\nimport mmcv\nimport numpy as np\nfrom mmcv import Config, DictAction\n\nfrom mmdet.core.evaluation import eval_map\nfrom mmdet.core.visualization import imshow_gt_det_bboxes\nfrom mmdet.datasets import build_dataset, get_loa... | [
[
"numpy.round"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pyplan/pyplan-core | [
"21b991a16feb1141b3ff7e3ac75a3aee54f80d0d"
] | [
"pyplan_core/classes/evaluators/CubepyEvaluator.py"
] | [
"import json\n\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\n\nfrom pyplan_core import cubepy\nfrom pyplan_core.classes.evaluators.BaseEvaluator import BaseEvaluator\nfrom pyplan_core.classes.common.filterChoices import filterChoices\nfrom pyplan_core.classes.common.indexValuesReq import IndexValue... | [
[
"pandas.isnull",
"numpy.isnan",
"pandas.Index",
"numpy.append",
"numpy.isinf"
]
] | [
{
"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": []
}
] |
kerryeon/relu | [
"f778cca6d973b80c6bb163506db94c5e7d775eae"
] | [
"model/cell/nalu.py"
] | [
"from torch import Tensor, exp, log, nn\nfrom torch.nn.parameter import Parameter\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.functional import linear, hardtanh\nfrom .nac import NacCell\n\n\n# Source from https://github.com/bharathgs/NALU\nclass NaluCell(nn.Module):\n \"\"\"Basic NALU unit impleme... | [
[
"torch.exp",
"torch.nn.functional.linear",
"torch.Tensor",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nedoko-maki/Internet-Voicechat | [
"c5c764350039147890288ed3373d091f39c94fc0"
] | [
"client.py"
] | [
"import logging\r\nimport queue\r\nimport socket\r\nimport threading\r\nimport traceback\r\n\r\nimport numpy\r\nimport pyflac\r\nimport select\r\nimport sounddevice as sd\r\n\r\nimport config\r\n\r\nlogging.basicConfig(\r\n format='%(asctime)s.%(msecs)03d %(levelname)s:\\t%(message)s',\r\n level=logging.INFO,... | [
[
"numpy.frombuffer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Willianwatch/ObjectDetectionPL | [
"58b13dc107248f3d6c268fc76b216671a917520b",
"58b13dc107248f3d6c268fc76b216671a917520b"
] | [
"retinanet/methods/networks/blocks/residual.py",
"retinanet/materials/lit_data_loader.py"
] | [
"import torch.nn as nn\n\n\ndef conv3x3(in_planes, out_planes, stride=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\n\n\nclass BasicBlock(nn.Module):\n expansion = 1\n\n def __init__(sel... | [
[
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d"
],
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
csong27/embedding-tests | [
"07248c8038ce4cf229320cf5672ea323afeed477"
] | [
"models/albert/modeling.py"
] | [
"# coding=utf-8\n# Copyright 2019 Google LLC\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... | [
[
"tensorflow.get_variable",
"numpy.sqrt",
"tensorflow.control_dependencies",
"tensorflow.zeros",
"tensorflow.gfile.GFile",
"tensorflow.cast",
"tensorflow.assert_less_equal",
"tensorflow.truncated_normal_initializer",
"tensorflow.squeeze",
"tensorflow.logging.warn",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
derrowap/MA490-MachineLearning-FinalProject | [
"2f6003edd985cefdddfba8f64c2c0effdd51a92e"
] | [
"adder.py"
] | [
"from tensorflow.contrib import skflow\nimport numpy as np\nimport time\n\nregressor = skflow.TensorFlowEstimator.restore('/home/sanderkd/Data/adderSkFlow')\n\nwhile True:\n val = int(input(\"Enter val to add: \"))\n start = time.clock()\n prediction = regressor.predict(np.array([[val]]))\n end = time.c... | [
[
"numpy.array",
"tensorflow.contrib.skflow.TensorFlowEstimator.restore"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rolveb/modin | [
"d0c84590798f33358dc896eef9d7cd9c519b6289"
] | [
"modin/backends/base/query_compiler.py"
] | [
"# Licensed to Modin Development Team under one or more contributor license agreements.\n# See the NOTICE file distributed with this work for additional information regarding\n# copyright ownership. The Modin Development Team licenses this file to you under the\n# Apache License, Version 2.0 (the \"License\"); you... | [
[
"pandas.concat",
"numpy.conj",
"pandas.core.dtypes.common.is_scalar",
"pandas.get_dummies"
]
] | [
{
"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": []
}
] |
andreped/MONAILabel | [
"0f5dc4991036fb671a8ce8bf5f24da85ebded1fe"
] | [
"tests/unit/endpoints/test_train.py"
] | [
"# Copyright 2020 - 2021 MONAI Consortium\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# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agre... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
domdfcoding/aoc2015 | [
"539ac24144db3893ae2247266016057b1abd15d3"
] | [
"14/code.py"
] | [
"\"\"\"\n--- Day 14: Reindeer Olympics ---\n\nThis year is the Reindeer Olympics!\nReindeer can fly at high speeds, but must rest occasionally to recover their energy.\nSanta would like to know which of his reindeer is fastest, and so he has them race.\n\nReindeer can only either be flying (always at their top spee... | [
[
"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": []
}
] |
suyash622/Smart_cleaning_robot | [
"658a12912323049c10f01f3a9438a610336a167c"
] | [
"code/sample.py"
] | [
"import plotly\nfrom plotly.graph_objs import Scatter, Layout\n\n# plotly.offline.plot({\n# \"data\": [Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])],\n# \"layout\": Layout(title=\"hello world\")\n# })\n\n\n\n# from plotly.offline import init_notebook_mode, iplot\n# from IPython.display import display, HTML\nimpo... | [
[
"numpy.max",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Lucasc-99/Meta-set | [
"14bd29aa3facb9e44d7eb7af66b45b626cbd4ed5"
] | [
"image_transformation/synthesize_set_coco.py"
] | [
"import os\nimport random\n\nimport cv2\nimport imgaug as ia\nimport imgaug.augmenters as iaa\nimport numpy as np\nfrom pycocotools.coco import COCO\nfrom tqdm import trange\n\n# ===================================================== #\n# ----------- Image Transformations ----------- #\n# ===================... | [
[
"numpy.random.choice",
"numpy.int32",
"numpy.ones",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DavidNovo/ExplorationsWithPython | [
"56ed90d3bdb58df6013412de81ada2598cdf2ad4"
] | [
"scatterCharts.py"
] | [
"__author__ = 'davidnovogrodsky_wrk'\n__author__ = 'davidnovogrodsky_wrk'\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib import style\n\n# adding a predefined style\nstyle.use('ggplot')\n\nx = [0,2,7,8]\ny = [0,3,5,9]\n\nx2 = [0,4,6,9]\ny2 = [0,2,4,6]\n\nprint(len(x))\nprint(len(y))\n\n\nplt.scatter(x,y,c... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.style.use",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ranothil/pyfolio | [
"e164a6628c176189d4fef29a60493d3e2caed366"
] | [
"pyfolio/tears.py"
] | [
"#\n# Copyright 2019 Quantopian, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or... | [
[
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.subplot",
"matplotlib.gridspec.GridSpec",
"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": []
}
] |
richpaulyim/L2PSync | [
"81138245c1b50584476be83722ee1044ef023ce6"
] | [
"ml-models/random-forest/random_forest.py"
] | [
"\r\nimport numpy as np\r\nimport csv\r\nfrom tqdm import trange, tqdm\r\nimport math\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nimport matplotlib.pyplot as plt\r\nimport sklea... | [
[
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"numpy.reshape",
"numpy.ravel",
"sklearn.model_selection.train_test_split",
"numpy.load",
"numpy.array",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
haroldadmin/transportation-analytics-platform | [
"366891dc422d3a72287b3224fbf5b0daf3d14751"
] | [
"analytics/MAP.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef main():\n def read(file):\n df = pd.read_csv(file)\n return df.values\n\n X = read(\"/home/divyakshi/Documents/2000x.csv\")\n Y = read(\"/home/divyakshi/Documents/2000y.csv\")\n\n X = X.reshape((X.shape[0],))\... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"numpy.min",
"numpy.asarray",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.mean",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
iphysresearch/lfi-gw | [
"afcfbb68ca6f8f4b5bdcf7f2e7fe71d4ff19f6fb"
] | [
"lfigw/waveform_generator.py"
] | [
"from .reduced_basis import SVDBasis\nimport h5py\nimport numpy as np\nfrom pathlib import Path\nimport json\nimport functools\nfrom tqdm import tqdm\n\nfrom pycbc.waveform import (get_td_waveform, get_fd_waveform,\n get_waveform_filter_length_in_time)\nfrom pycbc.types.frequencyseries im... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.zeros_like",
"numpy.exp",
"numpy.hstack",
"numpy.arcsin",
"torch.from_numpy",
"numpy.sin",
"torch.tensor",
"numpy.std",
"numpy.apply_along_axis",
"numpy.float32",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ckmganesh/dask-sql | [
"5a056cc5e3e80463fb3d16dc45f1feffbf278b65"
] | [
"dask_sql/physical/rex/core/call.py"
] | [
"from dask_sql.mappings import sql_to_python_type\nimport operator\nfrom functools import reduce\nfrom typing import Any, Union, Callable\nimport re\nimport logging\nfrom dask.base import tokenize\nfrom dask.dataframe.core import Series\nfrom dask.highlevelgraph import HighLevelGraph\nfrom dask.utils import random_... | [
[
"pandas.api.types.is_float_dtype",
"numpy.isnan",
"pandas.Timestamp.now",
"pandas.isna",
"numpy.random.RandomState",
"pandas.tseries.offsets.MonthEnd"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.24",
"1.0",
"0.25"
],
"scipy": [],
"tensorflow": []
}
] |
Livioni/Reinforcement-Learning-of-Sequential-Price-Mechanisms | [
"91d7007c82782035828b398bc7ea51e6e29420d8"
] | [
"Env/Stackelberg.py"
] | [
"import numpy as np\nimport gym,random\nfrom torch import rand\nimport torch\nfrom gym import spaces\nfrom torch.distributions import Categorical\n\nclass Stackelberg(gym.Env):\n metadata = {\"render.modes\": [\"human\", \"rgb_array\"], \"video.frames_per_second\": 30}\n\n def __init__(self):\n ## 初始化动... | [
[
"numpy.hstack",
"torch.softmax",
"torch.tensor",
"numpy.ones",
"numpy.all",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fwtan/text2scene_public | [
"cfb11d617e001c5434d5a7cbc17116dc937c8da3"
] | [
"lib/datasets/composites_loader.py"
] | [
"#!/usr/bin/env python\n\nimport os, sys, cv2, json\nimport math, PIL, cairo\nimport numpy as np\nimport pickle, random\nimport os.path as osp\nfrom time import time\nfrom copy import deepcopy\nfrom glob import glob\nfrom collections import OrderedDict\nfrom scipy import ndimage, misc\nfrom skimage.transform import... | [
[
"numpy.array",
"numpy.zeros_like",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alex-kennedy/asteroids | [
"f08661d33d4d9ef4593228c8e27d80172a8a0656"
] | [
"pipeline/asteroids/mpcorb.py"
] | [
"\"\"\"Module to download and parse the MPCORB.dat file to a pandas.DataFrame.\"\"\"\n\nimport gzip\nimport json\nimport logging\nimport os\nimport shutil\nfrom datetime import datetime, timezone\n\nimport numpy as np\nimport pandas as pd\nimport requests\nfrom tqdm import tqdm\n\nURL = 'http://www.minorplanetcente... | [
[
"numpy.deg2rad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wdian/ppq | [
"58bd1271ea6f0dfaf602eb72bdca63ea79f191b8"
] | [
"ppq/api/__init__.py"
] | [
"import os\nfrom typing import Any, Callable, List\n\nimport torch\nfrom ppq.core import (NetworkFramework, TargetPlatform, empty_ppq_cache,\n ppq_warning)\nfrom ppq.executor import TorchExecutor\nfrom ppq.IR import (BaseGraph, GraphCommand, GraphCommandType, GraphFormatter,\n ... | [
[
"torch.onnx.export",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kerenruzal/hw5 | [
"da538a3457868bc9a64f9e12019d9ecd4a774663"
] | [
"hw5.py"
] | [
"import json\r\nimport pathlib\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom typing import Union\r\n\r\nBAD_TYPE_MESSAGE = \"Invalid input: ({value})! Only pathlib.Path and strings are accepted.\"\r\nDIRECTORY_NOT_EXISTING_MESSAGE = \"Invalide input: ({value})! Directory do... | [
[
"numpy.empty_like",
"matplotlib.pyplot.show",
"numpy.nanmean",
"pandas.read_json"
]
] | [
{
"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": []
}
] |
huhuman/crack-segmentation | [
"ae72a5e11f0e11d5e5dba8841ae8a3682fc0f27d"
] | [
"test_maskrcnn.py"
] | [
"# import some common libraries\nimport numpy as np\nimport cv2\nimport os\nimport argparse\n\n# import some common detectron2 utilities\nfrom detectron2 import model_zoo\nfrom detectron2.config import get_cfg\nfrom detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_test_loader\nfrom dataset.cr... | [
[
"numpy.diag",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
VasilyevEvgeny/experimental_spectrum | [
"c95ff89989d04df5eb2aa2908d5b71bbb2405466"
] | [
"core/compare_ifs.py"
] | [
"import numpy as np\nfrom numpy import log10, where\nfrom matplotlib import pyplot as plt\nfrom os import path\nfrom datetime import datetime\nfrom time import sleep\n\n\ndef plot_ifs_comparison(data, colors, labels, res_dir, log_scale=True):\n\n # get data\n lambdas_list = []\n ifs_list = []\n for obj ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.yticks",
"numpy.min",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.log10",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SharifAmit/SAIN2018_DilatedFCNSegmentation | [
"28481a28c70faafce37949ec2381cd4a1417cc27"
] | [
"solvedilated.py"
] | [
"import caffe\nimport surgery, score\n\nimport numpy as np\nimport os\n\nimport setproctitle\nsetproctitle.setproctitle(os.path.basename(os.getcwd()))\n\n\n\nweights = 'Dilated_FCN-2s_VGG16/snapshot/vgg16surgery.caffemodel' # Comment this to resume training\n# weights = 'Dilated_FCN-2s_VGG16/snapshot/voctraining_it... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tailintalent/ar-pde-cnn | [
"88c130d7296af4ef7c13ec28a287fec4af3639f7"
] | [
"ONED_KS_SWAG/post/plotSpectralContour.py"
] | [
"'''\nThis script plots two spectral simulations of the Kuramoto-Sivashinsky equation.\nThe produced graphic is seen in Figure 4 of the paper.\n===\nDistributed by: Notre Dame CICS (MIT Liscense)\n- Associated publication:\nurl: http://www.sciencedirect.com/science/article/pii/S0021999119307612\ndoi: https://doi.or... | [
[
"numpy.linspace",
"numpy.meshgrid",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.savefig",
"numpy.loadtxt",
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplot2grid",
"matplotlib.rc",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KnutAM/cyclic_data | [
"027d342aeb42a0eaeba014b025ca4f208eb766ef"
] | [
"test/test_von_mises.py"
] | [
"import numpy as np\nfrom pytest import approx\n\nimport cyclic_data.von_mises as vm\n\n\ndef test_vm_stress():\n # Test both for single float and np.array\n for val in [np.random.rand(), np.random.rand(10)]:\n assert vm.vm(val, 0.0) == approx(np.abs(val))\n assert vm.vm(0.0, val) == approx(np.a... | [
[
"numpy.logical_xor",
"numpy.abs",
"numpy.sqrt",
"numpy.random.rand",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
svaisakh/captioner | [
"5c1144a9ed3986e1d8acdd7bb2732b252b3982d3"
] | [
"captioner/data.py"
] | [
"from torchvision.datasets import CocoCaptions\nfrom numpy.random import randint\n\ndef get_extract_dataloaders(data_path, image_shape=None, batch_size=1, num_workers=0):\n\t\"\"\"\n\tMakes PyTorch DataLoaders for extraction.\n\n\t:param data_path: The root path of the COCO dataset.\n\t:param image_shape: The shape... | [
[
"torch.utils.data.dataloader.DataLoader",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
junmokane/offline-PEARL | [
"b6119ea2d56b3dbf039f4e4f2c84b5a21f5c11fe"
] | [
"rlkit/envs/half_cheetah_dir.py"
] | [
"import numpy as np\n\nfrom .half_cheetah import HalfCheetahEnv\nfrom . import register_env\n\n\n@register_env('cheetah-dir')\nclass HalfCheetahDirEnv(HalfCheetahEnv):\n \"\"\"Half-cheetah environment with target direction, as described in [1]. The\n code is adapted from\n https://github.com/cbfinn/maml_rl... | [
[
"numpy.square"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cbonesteel/bookCollector | [
"c194120862c999b1628f593cb04d53e07c7b5692"
] | [
"app.py"
] | [
"import requests\nfrom random import randrange\nimport pandas\n\ncolNames = ['isbn', 'title', 'author', 'year']\ndata = pandas.read_csv('books.csv', names=colNames)\n\nisbns = data.isbn.tolist()\n\nbookID = 5\n\nbooks = open('books.txt', 'w')\ninventory = open('inventory.txt', 'w')\n\nfor k in isbns:\n url = \"h... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hassan-mahmood/Layout-Agnostic-Object-Alignment-and-Image-Generation | [
"c526cb365b6fe383bb85423afcbc914e3e791790"
] | [
"models/layout_model.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom .norm_module import *\nfrom .mask_regression import *\nfrom .sync_batchnorm import SynchronizedBatchNorm2d\nimport numpy as np\nfrom tqdm import tqdm \nimport sg2im.box_utils as box_utils\nfrom sg2im.graph import GraphTripleConv, GraphTripl... | [
[
"torch.abs",
"torch.range",
"torch.max",
"torch.add",
"torch.tile",
"torch.reshape",
"torch.nn.Conv2d",
"torch.arange",
"torch.nn.Sigmoid",
"torch.matmul",
"torch.nn.Linear",
"torch.nn.functional.sigmoid",
"torch.nn.functional.relu",
"torch.nn.BatchNorm2d",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hoechenberger/pyCompare | [
"99f9bb4d98872164166c607b4553cfae5ec765ef"
] | [
"pyCompare/_plotBlandAltman.py"
] | [
"import numpy\nimport matplotlib.pyplot as plt\nimport matplotlib.transforms as transforms\nimport matplotlib.ticker as ticker\nimport warnings\n\nfrom ._rangeFrameLocator import rangeFrameLocator\nfrom ._detrend import detrend as detrendFun\nfrom ._calculateConfidenceIntervals import calculateConfidenceIntervals\n... | [
[
"matplotlib.pyplot.text",
"numpy.asarray",
"matplotlib.pyplot.subplots",
"numpy.std",
"numpy.mean",
"matplotlib.pyplot.close",
"matplotlib.ticker.FixedLocator",
"matplotlib.pyplot.show",
"matplotlib.transforms.blended_transform_factory"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Paraboly/libosrmc | [
"3f5bec2c379869b23f4f33e9dfa5a3105b53d406"
] | [
"osrmcpy/examples/osrm_python3_route.py"
] | [
"import sys\n\nimport pandas as pd\n\nfrom osrmcpy import OSRM, Coordinate\n\n\n# Example User Code\ndef main():\n if '--help' in sys.argv or '-h' in sys.argv:\n sys.exit('Usage: {} [OSRM data base path]'.format(sys.argv[0]))\n\n osrm = OSRM(sys.argv[1].encode('utf-8') if len(sys.argv) >= 2 else None, ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
DarrenGebler/FlapPyBird | [
"af9ced03daefca2c88d582500c26b599753b2522"
] | [
"flappy.py"
] | [
"from itertools import cycle\nimport random\nimport sys\n\nimport pygame\nfrom pygame.locals import *\nfrom pygame.surfarray import array3d, pixels_alpha\nfrom pygame.event import pump\n\nimport numpy as np\n\nFPS = 30\nSCREENWIDTH = 288\nSCREENHEIGHT = 512\nPIPEGAPSIZE = 100 # gap between upper and lower part of ... | [
[
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fmfi-compbio/heron | [
"6d643118441e48f01a96fed9d3d9a36f67c0529a"
] | [
"basecall.py"
] | [
"#!/usr/bin/env python\n\nfrom ont_fast5_api.fast5_interface import get_fast5_file\nimport argparse\nimport os\nimport numpy as np\nimport datetime\nimport torch.multiprocessing as mp\nimport sys\nimport gzip\nimport backend\nimport time\nimport queue\nimport multiprocessing\n\ndef write_output(read_id, basecall, q... | [
[
"torch.multiprocessing.Queue"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
architsakhadeo/Offline-Hyperparameter-Tuning-for-RL | [
"94b8f205b12f0cc59ae8e19b2e6099f34be929d6"
] | [
"plot/hyperparam/rank_corr.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\ndef subplot_label(xy, text):\n y = xy[1] - 0.15 # shift y-value for label so that it's below the artist\n plt.text(xy[0], y, text, ha=\"center\", family='sans-serif', size=14)\n\ndef corr_group(true_order, offline_order_all, envs, title):\n algs = li... | [
[
"numpy.corrcoef",
"matplotlib.pyplot.text"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
forcast-open/federated-api | [
"cbcbeacf00a7cb53a22b26c8170bf146f476ac1a"
] | [
"client/client.py"
] | [
"import os\nimport sys\nimport requests\nfrom sklearn.model_selection import train_test_split\nimport jsonpickle as jpk\nimport time\nimport numpy as np\nimport pandas as pd\n# Federated imports\nimport forcast_federated_learning as ffl\n\n# Parameters\nBASE = 'http://127.0.0.1:5000/'\nSERVER_ID = int( os.... | [
[
"pandas.concat",
"sklearn.model_selection.train_test_split",
"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": []
}
] |
KevYuen/mlflow | [
"74d75109aaf2975f5026104d6125bb30f4e3f744"
] | [
"tests/pyfunc/test_model_export.py"
] | [
"from __future__ import print_function\n\nimport os\nimport time\nimport six\nimport pickle\nimport shutil\nimport tempfile\nimport unittest\nimport requests\nimport signal\nfrom subprocess import Popen, STDOUT\n\nfrom click.testing import CliRunner\nimport numpy as np\nimport pandas\nimport sklearn.datasets\nimpor... | [
[
"numpy.testing.assert_array_equal",
"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": []
}
] |
nakujaproject/n2Basestation | [
"6e02f3210dc49a481398773ee00f892fc3426957"
] | [
"Services/testStandDashboard/dataFetcher.py"
] | [
"\n# Python program killing\n# a thread using ._stop()\n# function\n \nimport time\nimport threading\nimport RPi.GPIO as GPIO\nimport numpy as np\nimport random\nfrom datetime import datetime\nfrom logData import Publish\n\nclass FetchData(threading.Thread):\n \n # Thread class with a _stop() method. \n # Th... | [
[
"numpy.savetxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pd-perry/alf | [
"a6e6f26334c4d02b9733fe11f281d7cad8e1d5ca"
] | [
"alf/experience_replayers/replay_buffer.py"
] | [
"# Copyright (c) 2020 Horizon Robotics and ALF Contributors. 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... | [
[
"torch.all",
"torch.mean",
"torch.ones",
"torch.full",
"torch.cat",
"torch.zeros",
"torch.randperm",
"torch.min",
"torch.zeros_like",
"numpy.finfo",
"torch.tensor",
"torch.no_grad",
"torch.rand",
"numpy.float32",
"torch.arange",
"torch.where",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
konung-yaropolk/pyABF | [
"b5620e73ac5d060129b844da44f8b2611536ac56",
"b5620e73ac5d060129b844da44f8b2611536ac56"
] | [
"tests/cookbook/gettingStarted.py",
"src/pyabf/tools/ap.py"
] | [
"\"\"\"\nThis file contains functions to demonstrate core functionality of the pyABF\nmodule. Functions prefaced with \"demo_\" may be run automatically to generate \na markdown document. In this case their docstrings will be included in the \ngetting started guide and their code will be added to the \nreadme along... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"matplotlib.pyplot.marg... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rony2303/IML.HUJI | [
"9a9cebcfc7c3ff1fcc6e55674d0735bca578ea34",
"9a9cebcfc7c3ff1fcc6e55674d0735bca578ea34"
] | [
"exercises/house_price_prediction.py",
"IMLearn/metrics/loss_functions.py"
] | [
"#from asyncio.windows_events import NULL\nfrom audioop import add\nfrom re import X\nfrom turtle import title\nfrom urllib import response\nfrom IMLearn.utils import split_train_test\nfrom IMLearn.learners.regressors import LinearRegression\n\nfrom typing import NoReturn\nimport numpy as np\nimport pandas as pd\ni... | [
[
"pandas.read_csv",
"numpy.random.seed",
"numpy.linspace",
"numpy.std",
"numpy.cov",
"numpy.array"
],
[
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mrahnis/rio-terrain | [
"5603aa7805c72e66973cd3e879179f953993175e"
] | [
"rio_terrain/cli/std.py"
] | [
"\"\"\"Calculate a standard-deviation raster.\"\"\"\n\nimport time\nimport warnings\nimport concurrent.futures\nfrom math import ceil\n\nimport click\nimport numpy as np\nimport rasterio\n\nimport rio_terrain as rt\nimport rio_terrain.tools.messages as msg\nfrom rio_terrain.core import focalstatistics\nfrom rio_ter... | [
[
"numpy.sqrt",
"numpy.warnings.filterwarnings"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MaximilianBeckers/FDRthresholding | [
"1c482c2b699cf5411b3d7d30c916d1df6416c3af"
] | [
"confidenceMapUtil/confidenceMapMain.py"
] | [
"import numpy as np\nimport subprocess\nimport math\nimport gc\nimport os\nimport sys\nfrom confidenceMapUtil import mapUtil, locscaleUtil, FDRutil\n\n#--------------------------------------------------------------------------\ndef calculateConfidenceMap(em_map, apix, noiseBox, testProc, ecdf, lowPassFilter_resolut... | [
[
"numpy.multiply",
"numpy.nonzero",
"numpy.subtract",
"numpy.max",
"numpy.copy",
"numpy.isscalar",
"numpy.fft.rfftn"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.21",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chandra-iiitmk/resume-filtering | [
"d7247de28a37568077b1e96b3cfeb69fded4176a"
] | [
"matchingTerm.py"
] | [
"import os\nimport sys\nfrom pdfminer.pdfparser import PDFParser, PDFDocument\nimport subprocess\nimport re\nimport numpy as np\nfrom tqdm import tqdm\nimport pandas as pd\n\n\ndef term_match(string_to_search, term):\n \"\"\"\n A utility function which return the first match to the `regex_pattern` in the `str... | [
[
"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": []
}
] |
qurrata111/OpenNMT-py | [
"71cdd038ebe484b3a8b3f7021c93d910185f030a"
] | [
"onmt/inputters/inputter.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nimport codecs\nimport math\n\nfrom collections import Counter, defaultdict, OrderedDict\n\nimport torch\nfrom torchtext.data import Field, RawField, LabelField\nfrom torchtext.vocab import Vocab\n\nfrom onmt.constants import DefaultTokens, ModelTask\nfrom onmt.inputters.text_dat... | [
[
"torch.device",
"torch.load",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
R-fred/awesome-streamlit | [
"10f2b132bc8e61a82edfacb4b3bb36d0da6c63d3",
"10f2b132bc8e61a82edfacb4b3bb36d0da6c63d3"
] | [
"gallery/sentiment_analyzer/sentiment_analyzer.py",
"gallery/kickstarter_dashboard/kickstarter_dashboard.py"
] | [
"\"\"\"Fork of Sentiment-Analyzer-Tool by Paras Patidar. Improvements by Marc Skov Madsen\n\nOriginal Source: https://github.com/patidarparas13/Sentiment-Analyzer-Tool\nOriginal Author: https://github.com/patidarparas13,\n\"\"\"\n\nimport itertools\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pand... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.naive_bayes.BernoulliNB"
],
[
"pandas.read_csv",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bedapisl/tensorflow-onnx | [
"62f1e704928e18c96e8d1f167af6bc82298fb636"
] | [
"tests/run_pretrained_models.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT license.\n\n\"\"\"Tool to convert and test pre-trained tensorflow models.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_lit... | [
[
"tensorflow.import_graph_def",
"numpy.linspace",
"tensorflow.as_dtype",
"numpy.stack",
"numpy.ones",
"numpy.testing.assert_array_equal",
"numpy.prod",
"numpy.random.sample",
"numpy.array",
"numpy.zeros",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1.2",
"2.... |
IPL-UV/sakame | [
"e660c4ac82ac52d5136d3cd765d59d409b74b4ce"
] | [
"src/models/regression.py"
] | [
"from sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.base import BaseEstimator\nfrom sklearn.gaussian_process.kernels import WhiteKernel, RBF, ConstantKernel as C\nimport numpy as np\n\n\nfrom typing import Tuple, Optional\n\nfrom sklearn.utils import gen_batches\nfrom sklearn.metrics import... | [
[
"sklearn.gaussian_process.kernels.ConstantKernel",
"numpy.concatenate",
"sklearn.gaussian_process.GaussianProcessRegressor",
"sklearn.gaussian_process.kernels.WhiteKernel",
"sklearn.gaussian_process.kernels.RBF",
"sklearn.utils.gen_batches"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
imagejan/biobeam | [
"360465bb0979c125ed1b0df1e0dbdd53bf8aba8b"
] | [
"biobeam/simlsm/sim_lattice.py"
] | [
"\"\"\"\n\n\nmweigert@mpi-cbg.de\n\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nimport numpy as np\nfrom biobeam.simlsm.simlsm import SimLSM_Base\nfrom six.moves import range\n\n\nclass SimLSM_Lattice(SimLSM_Base):\n def __init__(self, dn=None,\n signal=... | [
[
"numpy.arange",
"numpy.roll",
"numpy.isscalar",
"numpy.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YorkSu/hat | [
"b646b6689f3d81c985ed13f3d5c23b6c717fd07d"
] | [
"models/advance/extendrgb.py"
] | [
"# pylint: disable=attribute-defined-outside-init\n\nfrom tensorflow.python.keras import backend as K\nfrom tensorflow.python.keras.layers import Layer\nfrom tensorflow.python.keras.utils import conv_utils\n\n\nclass ExtendRGB(Layer):\n \"\"\"\n Extend the RGB channels\n\n Input:\n (batch, ..., 3)\n ... | [
[
"tensorflow.python.keras.backend.variable",
"tensorflow.python.keras.backend.transpose",
"tensorflow.python.keras.backend.reshape",
"tensorflow.python.keras.utils.conv_utils.normalize_tuple"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.2",
"2.10"
]
}
] |
WangLabTHU/DeSP | [
"1213cebe0b2adaa291d9feced340af1838afc318"
] | [
"files/encode_re.py"
] | [
"import streamlit as st\nimport plotly.io as pio\nimport matplotlib.pyplot as plt\nfrom Model.Model import * \nimport plotly.express as px\nfrom Analysis.Analysis import dna_chunk, plot_oligo_number_distribution, plot_error_distribution,save_simu_result\nfrom Analysis.Helper_Functions import preprocess\nfrom Encode... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rh-ia/color-information | [
"e912a1667e4fffb339dbc574c85020ec6cf78b02"
] | [
"lib/layers/act_norm.py"
] | [
"import torch\nimport torch.nn as nn\nfrom torch.nn import Parameter\nimport pdb\n\n__all__ = ['ActNorm1d', 'ActNorm2d']\n\n\nclass ActNormNd(nn.Module):\n\n def __init__(self, num_features, eps=1e-12):\n super(ActNormNd, self).__init__()\n self.num_features = num_features\n self.eps = eps\n... | [
[
"torch.mean",
"torch.Tensor",
"torch.tensor",
"torch.exp",
"torch.no_grad",
"torch.log",
"torch.var"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CU-NESS/distpy | [
"279ba7e46726a85246566401fca19b8739d18d08",
"279ba7e46726a85246566401fca19b8739d18d08",
"279ba7e46726a85246566401fca19b8739d18d08"
] | [
"examples/distribution/multivariate_custom_discrete_distribution.py",
"examples/distribution/poisson_distribution.py",
"distpy/distribution/InfiniteUniformDistribution.py"
] | [
"\"\"\"\nFile: examples/distribution/multivariate_custom_discrete_distribution.py\nAuthor: Keith Tauscher\nDate: Oct 15 2019\n\nDescription: Example showing how to use the CustomDiscreteDistribution class to\n represent a multivative discrete distribution with a simple\n (linear) varying pro... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"numpy.arange",
"matplotlib.pyplot.hist2d",
"numpy.concatenate",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.ylabel",
"numpy.mean",
"numpy.cov",
"numpy.exp",
"matplotlib.pyplot.xlabe... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dongdu3/VIPNet | [
"6baa617522289a412055ee745ba824de57997e42"
] | [
"model.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import distributions as dist\nfrom layers import (CResnetBlockConv1d, CBatchNorm1d, CBatchNorm1d_legacy)\nimport resnet\nimport numpy as np\n\nclass VoxelDecoder64(nn.Module):\n ''' Voxel64 Decoder with batch normalization (BN) cla... | [
[
"torch.nn.BatchNorm1d",
"torch.cat",
"torch.nn.ConvTranspose3d",
"torch.distributions.Bernoulli",
"torch.sum",
"torch.nn.Sigmoid",
"torch.nn.Conv3d",
"torch.nn.LeakyReLU",
"torch.nn.functional.leaky_relu",
"torch.nn.Conv1d",
"numpy.array",
"torch.nn.BatchNorm3d",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lzx0014/STOPS | [
"da73bb00cee53d2bc7b866dfdedc7ce014898c8c"
] | [
"deep_rl/utils/torch_utils.py"
] | [
"#######################################################################\n# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################... | [
[
"torch.Size",
"torch.nn.Parameter",
"torch.zeros",
"torch.arange",
"torch.tensor",
"torch.distributions.Categorical",
"torch.set_num_threads",
"torch.distributions.Normal",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rpazuki/algos | [
"bca46326f58eb983db6efe55320bf95fcf2b895f"
] | [
"information_theory/measures.py"
] | [
"import numpy as np\n\n\ndef entropy(distribution, unit=2):\n \"\"\"Finds the entropy of a distribution.\n Its default unit is 'bit'.\n\n Args:\n distribution ([FrequencyTable]): A FrequencyTable or any of\n its sub-classes\n unit (int, optional): U... | [
[
"numpy.log2"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KingJamesSong/FastDifferentiableMatSqrt | [
"ab5278195e25df0192096581e0c0c288e0c66bd2",
"abcef0181c07415c7e6a1d3596d84c44e3a0cf77",
"ab5278195e25df0192096581e0c0c288e0c66bd2"
] | [
"Decorrelated BN/main_cifar10.py",
"TACP/ops/TCP/TSA.py",
"So-ViT/src/normalization/svPN.py"
] | [
"'''Train CIFAR10 with PyTorch.'''\nfrom __future__ import print_function\nimport os\nos.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\nimport time\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.backends.cudnn as cudnn\nimport torchvision\nimport torchvision.transforms as transforms\nimpo... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.nn.CrossEntropyLoss",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.DataParallel"
],
[
"torch.einsum",
"torch.nn.functional.softmax",
"torch.nn.Conv2d"
],
[
"torch.ones",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
atechnicolorskye/regain | [
"6879a3066bf8bb690d809b746e612e5446c4c1e1",
"6879a3066bf8bb690d809b746e612e5446c4c1e1"
] | [
"regain/covariance/latent_graphical_lasso_.py",
"regain/norm.py"
] | [
"# BSD 3-Clause License\n\n# Copyright (c) 2017, Federico T.\n# All rights reserved.\n\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n\n# * Redistributions of source code must retain the above copyright notice, th... | [
[
"numpy.sqrt",
"numpy.zeros_like",
"numpy.linalg.norm",
"scipy.linalg.pinvh"
],
[
"numpy.diag",
"numpy.abs",
"numpy.einsum",
"numpy.linalg.norm",
"numpy.full",
"numpy.random.randn"
]
] | [
{
"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": [],
... |
kyrarivest/ACRES_REU_Project_2021 | [
"fc3fd121fbffb630e3652bacdc74d1b1bddab50e"
] | [
"src/LogpPredictor_MMFF94.py"
] | [
"import sys\nimport os.path as osp\nimport numpy as np\nfrom joblib import load\n\nimport torch\nfrom classicalgsg.molreps_models.gsg import GSG\nfrom classicalgsg.classicalgsg import OBFFGSG\nfrom classicalgsg.molreps_models.utils import scop_to_boolean\n\n\nPRETRAINED_MODEL_PATH = 'classicalgsg/pretrained_models'... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
umairaziz112221/text | [
"c9c93b7ff5bb718ebf56f6795554bb7aa35f31e3"
] | [
"torchtext/experimental/vocab.py"
] | [
"from collections import OrderedDict\nimport logging\nfrom typing import Dict, List\n\nimport torch\nimport torch.nn as nn\nfrom tqdm import tqdm\n\nlogger = logging.getLogger(__name__)\n\n\ndef _infer_shape(f):\n num_lines = 0\n for line in f:\n num_lines += 1\n f.seek(0)\n return num_lines\n\n\... | [
[
"torch.classes.torchtext.Vocab"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ibrahemesam/Fos | [
"f2f284a2c7bdc24dafafebb8aa3141ebf225e451"
] | [
"examples/nx_network.py"
] | [
"import networkx as nx\nimport numpy as np\n\nn = 100\n\ng=nx.gnp_random_graph(n,0.3)\nedges = np.array(g.edges())\n\nret = nx.spring_layout(g, 3)\n\ne=np.zeros( (n,3))\n\nfor i in range(n):\n e[i,:] = ret[i]\n\nfrom fos import Window, WindowManager\nfrom fos.actor.network import AttributeNetwork\n\nwi = Window(... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
statsu1990/kaggle_understanding_clouds | [
"756c8271855d232167a76bd25f8bb81e7505a422",
"756c8271855d232167a76bd25f8bb81e7505a422",
"756c8271855d232167a76bd25f8bb81e7505a422",
"756c8271855d232167a76bd25f8bb81e7505a422"
] | [
"script/mygenerator.py",
"script/cloud_images_segmentation_utillity_script.py",
"model/deeplab_v3.py",
"model/cos_similarity.py"
] | [
"from keras.utils import Sequence\r\nimport numpy as np\r\nimport cv2\r\n\r\n#from script.cloud_images_segmentation_utillity_script import build_masks\r\nfrom script.my_util import build_masks\r\n\r\n\r\nclass DataGenerator(Sequence):\r\n def __init__(self, images, imageName_to_imageIdx_dict, dataframe, batch_si... | [
[
"numpy.random.seed",
"numpy.random.shuffle",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.random.rand",
"numpy.sum",
"numpy.empty"
],
[
"numpy.asarray",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.mean",
"numpy.where",
"numpy.clip",
"numpy.zeros",... | [
{
"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",
"... |
lhlmgr/tensor2tensor | [
"8c1a703f6ec644703734a6ea233ed5bdbc4d9997"
] | [
"tensor2tensor/data_generators/sst_binary.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Tensor2Tensor Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requir... | [
[
"tensorflow.gfile.Exists",
"tensorflow.gfile.Open"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
makistsantekidis/testing_blocks | [
"b0edfd16748e31edd983d283451e78610253dfc4"
] | [
"prototyping.py"
] | [
"__author__ = 'mike'\n\n## WIP not to be used\n\nimport sys\n\nfrom blocks.bricks.recurrent import BaseRecurrent, LSTM, recurrent\nfrom blocks.bricks import Linear, Tanh, Initializable\nfrom blocks.initialization import IsotropicGaussian, Constant\nimport theano\nimport numpy as np\nfrom theano.sandbox.rng_mrg impo... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Full-Data-Alchemist/twitt-off | [
"8e7016dcd6a3341e0812a53b0abe226a953f4a8b"
] | [
"twitoff/prediction.py"
] | [
"\"\"\"\nPreediction for users based on tweet embeddings\n\"\"\"\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom .models import User\nfrom .twitter import vectorize_tweets\nimport pickle\n\n\ndef predict_user(user0_name, user1_name, hypo_tweet_text):\n \"\"\"\n Determine and ret... | [
[
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sliwy/moabb | [
"48c538b3a505efacecd26332ab79826705d515e2"
] | [
"moabb/datasets/bnci.py"
] | [
"\"\"\"\nBNCI 2014-001 Motor imagery dataset.\n\"\"\"\n\nfrom moabb.datasets.base import BaseDataset\nfrom moabb.datasets import download as dl\n\nfrom mne import create_info\nfrom mne.io import RawArray\nfrom mne.channels import make_standard_montage\nfrom mne.utils import verbose\nimport numpy as np\n\nBNCI_URL =... | [
[
"scipy.io.loadmat",
"numpy.zeros_like",
"numpy.unique"
]
] | [
{
"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"... |
danelee2601/RL-based-automatic-berthing | [
"625ba52322d2e7b2d120a46fdd42fac45c1d4938"
] | [
"utils/berthing_simulator/Simulation_turning_test.py"
] | [
"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom Berthing_Simulator_PythonCode.Berthing_Simulator_rev04 import BerthingSimulator\r\n\r\n# Initial setting\r\n# =====================================================\r\nL = 175 # [m]\r\nd = 8.5 # [m]\r\nB = 25.4 # [m]\r\nC_b = 0.559\r\nm = (L * B * d) ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LiderMyHand/A2J | [
"0f90d2916a0880ee39fe34f39ccb6c8e7615341c"
] | [
"src/itop_top.py"
] | [
"import cv2\r\nimport torch\r\nimport torch.nn as nn\r\nimport numpy as np\r\nimport scipy.io as scio\r\nimport os\r\nfrom PIL import Image\r\nfrom torch.autograd import Variable\r\nimport torch.utils.data\r\nimport sys\r\nimport model as model\r\nimport anchor as anchor\r\nfrom tqdm import tqdm\r\n\r\nos.environ[\... | [
[
"numpy.square",
"torch.load",
"numpy.asarray",
"scipy.io.loadmat",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"numpy.ones",
"torch.FloatTensor",
"numpy.shape",
"torch.no_grad",
"numpy.load",
"numpy.zeros"
]
] | [
{
"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"... |
kanishkaganguly/QuadGARL | [
"2e995861cab98d9623dd36155cb472dcb4e21cd0"
] | [
"quad_garl/quad_controller.py"
] | [
"import numpy as np\n\n\nclass ManualController:\n def __init__(self, actuate_motors, quad_identifier, target_pose, update_rate, time_scaling, log):\n self.log = log\n self.quad_identifier = quad_identifier\n self.actuate_motors = actuate_motors\n self.update_rate = update_rate\n ... | [
[
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
babylonhealth/corrsim | [
"7f0841fc4906f5823d12dd597ea9337f5cac5ce0"
] | [
"evaluation/corrset_eval.py"
] | [
"# Copyright 2019 Babylon Partners Limited. 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 re... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gaborfodor/wave-bird-recognition | [
"6feafdbae82746e3e7b0f6588a9158aa8336309a"
] | [
"birds/display_utils.py"
] | [
"import base64\nimport io\n\nimport librosa\nimport markdown\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom plotly import graph_objects as go, io as pio\n\nfrom birds.pann import SAMPLE_RATE, N_FFT, MIN_FREQ, MAX_FREQ, HOP_SIZE, CHUNK_DURATION, read_audio_fast\n\n\ndef get_taxo... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"matplotlib.pyplot.box",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.log10",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.suptitle",
"numpy.random.randin... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
sjruan/malmcs | [
"4bf00ae16ea4d29a33e90c7b851062f86c45b196"
] | [
"utils/distance.py"
] | [
"import numpy as np\n\n\ndef distance(src_x, src_y, dest_x, dest_y):\n return np.sqrt(np.square(src_x - dest_x) + np.square(src_y - dest_y))\n\n\ndef point_distance(matrix_pt_1, matrix_pt_2):\n return distance(matrix_pt_1[1], matrix_pt_1[0], matrix_pt_2[1], matrix_pt_2[0])\n"
] | [
[
"numpy.square"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WhitePhosphorus4/xh-learning-code | [
"025e31500d9f46d97ea634d7fd311c65052fd78e"
] | [
"python/python project/testgpu2.py"
] | [
"# tensorflow安装成功与否的小测试\nimport tensorflow as tf\ntf.compat.v1.disable_eager_execution()#保证sess.run()能够正常运行\nhello = tf.constant('hello,tensorflow')\nsess= tf.compat.v1.Session()#版本2.0的函数\nprint(sess.run(hello))"
] | [
[
"tensorflow.compat.v1.Session",
"tensorflow.constant",
"tensorflow.compat.v1.disable_eager_execution"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
longhathuc/crocoddyl | [
"07a35d9c2d97f443c3e3665d33e80dae0720af9b"
] | [
"bindings/python/crocoddyl/utils/__init__.py"
] | [
"import crocoddyl\nimport pinocchio\nimport numpy as np\nimport scipy.linalg as scl\n\n\ndef a2m(a):\n return np.matrix(a).T\n\n\ndef m2a(m):\n return np.array(m).squeeze()\n\n\ndef rev_enumerate(lname):\n return reversed(list(enumerate(lname)))\n\n\ndef absmax(A):\n return np.max(abs(A))\n\n\ndef raise... | [
[
"numpy.matrix",
"scipy.linalg.LinAlgError",
"numpy.dot",
"scipy.linalg.cho_factor",
"numpy.asarray",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.fill_diagonal",
"numpy.cross",
"numpy.hstack",
"numpy.asscalar",
"numpy.eye",
"scipy.linalg.cho_solve",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
schubergphilis/akamai_etp_downloader | [
"c92c42077dffd2e26dc24451c2c2fb39d4990ebb"
] | [
"etp_downloader.py"
] | [
"#! /usr/bin/env python\n\n\nimport requests, logging, json\nfrom http_calls import EdgeGridHttpCaller\n\nfrom akamai.edgegrid import EdgeGridAuth\nimport os\nimport datetime\nfrom datetime import timedelta\nimport math\nimport argparse\nfrom logging.handlers import RotatingFileHandler\nimport time\nfrom flatten_js... | [
[
"pandas.io.json.json_normalize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20",
"0.25"
],
"scipy": [],
"tensorflow": []
}
] |
benchiverton/imfpredict | [
"21d848e6c7eb966c494addda3785addedb92e786"
] | [
"imfprefict/dataPreperation.py"
] | [
"import numpy as np\n\n\ndef window_data(x, window_size):\n # perform windowing\n n_row = x.shape[0] - window_size + 1\n output = np.lib.stride_tricks.as_strided(x, shape=(n_row, window_size), strides=(x.strides[0], x.strides[0]))\n return output[:-1], output[-1]\n\n\ndef prepare_data_y(y, window_size):... | [
[
"numpy.lib.stride_tricks.as_strided"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.19",
"1.24",
"1.16",
"1.23",
"1.20",
"1.7",
"1.12",
"1.21",
"1.22",
"1.14",
"1.6",
"1.13",
"1.9",
"1.17",
"1.10",
"1.18",
"1.15",
"1.8"
],
"pand... |
erikwii/sscode | [
"f060493398a6a138fa2b0df01f7e06506d68fa32"
] | [
"coba.py"
] | [
"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport threading\nimport math\nimport glob\nimport os\n\n# helper generator\ndef split_tol(test_list, tol):\n res = []\n last = test_list[0]\n for ele in test_list:\n if ele-last > tol:\n yield res\n res = [... | [
[
"numpy.hstack",
"numpy.abs",
"matplotlib.pyplot.title",
"numpy.asarray",
"numpy.ones",
"matplotlib.pyplot.plot",
"numpy.std",
"matplotlib.pyplot.subplot",
"numpy.mean",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.show",
"numpy.zeros"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sunshinnnn/DSMnet | [
"eed7a4de12f93cd625e1e68758aa4c3193a60d28",
"eed7a4de12f93cd625e1e68758aa4c3193a60d28"
] | [
"myTransforms/aug_color-t1.py",
"models/util_fun.py"
] | [
"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nimport torch\nimport random\n\nimagenet_pca = {\"eigval\": torch.Tensor([0.2175, 0.0188, 0.0045]),\n \"eigvec\": torch.Tensor([\n [-0.5675, 0.7192, 0.4009],\n [-0.5808, -0.0045, -0.8140],\n ... | [
[
"torch.Tensor"
],
[
"torch.rand",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.