repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
RyanRizzo96/custom-DDPG | [
"bcdd5dfd31f01d481473bd9c696a7a7b6747e1d2"
] | [
"csv_plot.py"
] | [
"from baselines.common import plot_util as plot\nfrom baselines.common import plot_util as pu\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\n\n# osprint(os.environ['OPENAI_LOGDIR'])\n\n# OPENAI_LOGDIR=\".log/FetchReach/HER5k/trial1\"\n\n# results = plot.load_results(\".log/HER/test_5k/2019-10-27-1... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GiocomoLab/phy | [
"b97d510b5414f65733e3d50ba205d736d42962cd"
] | [
"phy/cluster/views/amplitude.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"Amplitude view.\"\"\"\n\n\n# -----------------------------------------------------------------------------\n# Imports\n# -----------------------------------------------------------------------------\n\nimport logging\n\nimport numpy as np\n\nfrom phylib.utils.color import selected_... | [
[
"numpy.quantile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mgasvoda/ThreatMatrix-Backend | [
"87fbe15a69c0815fc1718ec18c2d6c9b59cd3e18"
] | [
"threatmatrix_backend/utils.py"
] | [
"import logging\nimport os\nimport time\nimport itertools\n\nimport sqlalchemy\nimport pandas as pd\nimport numpy as np\n\nfrom sqlalchemy import exc\nfrom db import db_location\n\nlog = logging.getLogger(os.path.dirname(__file__))\nengine = sqlalchemy.create_engine(db_location)\nconn = engine.connect()\n\n\ndef up... | [
[
"pandas.notnull"
]
] | [
{
"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": []
}
] |
alan-y-han/subtitlemaster | [
"519c7f7a78d603d81f219daf8b3cc2372eb1a75a"
] | [
"ScrubFrame.py"
] | [
"import numpy as np\nimport cv2\n\nx_start = 254\nx_end = 1030\ny_start = 595\ny_end = 680\n\n\ndef erase_subs(img):\n greyscale = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n cropped = greyscale[y_start:y_end, x_start:x_end].copy()\n\n _, thresholded = cv2.threshold(cropped, 200, 255, cv2.THRESH_BINARY)\n\n ... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anhtu96/image-captioning | [
"bc504f27662c128346e26724ea24e1208ad3c983"
] | [
"model.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 23 22:06:35 2019\n\n@author: tungo\n\"\"\"\nimport torch\nimport torch.nn as nn\nfrom torchvision import models\n\nclass Encoder(nn.Module):\n \"\"\"\n CNN model for image encoding, using pretrained VGG19 network\n \"\"\"\n def __init__(self):\n ... | [
[
"torch.nn.Sequential",
"torch.nn.Softmax",
"torch.mean",
"torch.max",
"torch.Tensor",
"torch.nn.LSTM",
"torch.cat",
"torch.sum",
"torch.from_numpy",
"torch.nn.Embedding",
"torch.tensor",
"torch.nn.Linear",
"torch.no_grad",
"torch.log",
"torch.topk"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HarryQL/TuRBO-Penicillin | [
"1e9464bccaaa9818b3a2e3ff7d357e5b4e8982db"
] | [
"TuRBO-Penicillin/turbo/utils.py"
] | [
"###############################################################################\n# Copyright (c) 2019 Uber Technologies, Inc. #\n# #\n# Licensed under the Uber Non-Commercial License (the \"License\"); ... | [
[
"numpy.arange",
"numpy.all",
"numpy.random.permutation",
"numpy.random.uniform",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zharmad/SpinRelax | [
"5075c666f9c43e4418a41f724d5548f53e533908"
] | [
"calculate-dq-distribution-multi.py"
] | [
"#!/usr/bin/python\n\nfrom math import *\nimport sys, os, argparse, time\nimport numpy as np\n#from scipy.optimize import curve_fit\nfrom scipy.optimize import fmin_powell\nimport transforms3d.quaternions as qops\nimport transforms3d_supplement as qs\nimport plumedcolvario as pl\nimport general_scripts as gs\nimpor... | [
[
"numpy.dot",
"numpy.take",
"numpy.einsum",
"numpy.concatenate",
"numpy.mean",
"scipy.optimize.fmin_powell",
"numpy.exp",
"numpy.square",
"numpy.reshape",
"numpy.std",
"numpy.apply_along_axis",
"numpy.outer",
"numpy.zeros",
"numpy.multiply",
"numpy.linalg... | [
{
"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"
... |
tdenkler0002/BFPWeb | [
"11d4ef8eebef78ee781f3b7d7915a77480557712"
] | [
"src/media/module1.py"
] | [
"#######################################################\n## TEAM BFP\n## Sprint 2\n## Team 1 - Tiffany D. & Apoorva T.\n## Module 1\n#######################################################\n\nimport pandas as pd\nimport numpy as np\n\n# Create data frame from CSV_input_file with fields required\ndf = pd.read_csv('... | [
[
"pandas.read_csv",
"pandas.isnull",
"numpy.column_stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
crvernon/mosartwmpy | [
"e6769fe6fe9ebd069905dde4d5ffba54378f0f40"
] | [
"mosartwmpy/validate.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport os\nfrom pathlib import Path\nfrom xarray import open_mfdataset\n\n# TODO accept command line path input as alternative\n# TODO accept command line year input\n# TODO allow easily toggling between scenarios for variables of interest (no-wm, wm, heat, etc)... | [
[
"matplotlib.pyplot.show",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mortacious/numba-progress | [
"3d98e51d9ef8c1ed2f34cd79333799df3044e1ec"
] | [
"numba_progress/progress.py"
] | [
"import numba as nb\nimport numpy as np\nimport sys\nfrom tqdm import tqdm\nfrom threading import Thread, Event\n\nfrom numba.extending import overload_method, typeof_impl, as_numba_type, models, register_model, \\\n make_attribute_wrapper, overload_attribute, unbox, NativeValue, box\nfrom .numba_atomic import a... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shrinath-suresh/serve | [
"66216ce945d99a32fb281707eb88791215a44dd5"
] | [
"ts/torch_handler/text_handler.py"
] | [
"# pylint: disable=W0223\n# Details : https://github.com/PyCQA/pylint/issues/3098\n\"\"\"\nBase module for all text based default handler.\nContains various text based utility methods\n\"\"\"\nimport os\nimport re\nimport string\nimport unicodedata\nfrom abc import ABC\nimport torch\nfrom torchtext.data.utils impor... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Yoshinobu-Ishizaki/calcimpy | [
"4103c3f0e01d406569859f3a50ea0cb05d42aefb"
] | [
"test_xmen.py"
] | [
"# -*- coding: utf-8 -*-\n'''\ntest program to show various calculation result of xmensur\n'''\n__version__ = '0.1'\n\nimport xmensur as xmn\nimport imped\n\nimport argparse\n\nimport numpy as np\n\nif __name__ == \"__main__\" :\n # exec this as standalone program.\n\n parser = argparse.ArgumentParser(descrip... | [
[
"numpy.arange",
"numpy.real",
"numpy.imag",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stral0/used_cars_ads_check | [
"194921791c1a75c0b93325fda34d24a837fd06b3"
] | [
"script.py"
] | [
"from bs4 import BeautifulSoup as BS\nfrom bs4 import NavigableString, Comment\n\nimport urllib.request as urllib2 #using urllib.request bc this script is originally written for python2, and this is the way to run it with python3\nfrom urllib.request import URLError\n\nfrom time import sleep\nfrom tqdm import tqdm\... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BoyuGuan/aim | [
"6a585103fcb9642f6650bd941f74140c1ee50024"
] | [
"quantization/range_learning.py"
] | [
"# =============================================================================\n#\n# @@-COPYRIGHT-START-@@\n#\n# Copyright (c) 2021, Qualcomm Innovation Center, Inc. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the f... | [
[
"torch.device",
"torch.rand",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pisalore/DeepLayout | [
"74d4252e0af8f1d6935b59d4b278e31977331dd8"
] | [
"layout_transformer/dataset.py"
] | [
"import numpy as np\nimport torch\nfrom torchvision.datasets.mnist import MNIST\nfrom torch.utils.data.dataset import Dataset\nfrom PIL import Image, ImageDraw, ImageOps\nimport json\n\nfrom utils import trim_tokens, gen_colors\n\n\nclass Padding(object):\n def __init__(self, max_length, vocab_size):\n se... | [
[
"torch.zeros",
"numpy.clip",
"numpy.lexsort",
"torch.tensor",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abhishekyana/espresso | [
"698fd3810c377c277cebae78406761e1b2770d80"
] | [
"fairseq/tasks/fairseq_task.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\n\nfrom fairseq import tokenizer\nfrom fairseq.data import data_utils, FairseqDataset, iterators, D... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
akondrahman/IaC_Defect_Categ_Revamp | [
"9a8d0a88218530299fdc5b2b31255cff7913c17d"
] | [
"src_categ_revamp/empirical/temporal_data_prep.py"
] | [
"'''\nRun script to get month wise defects \nAkond Rahman \nJune 15, 2019 \n'''\n\nimport numpy as np \nimport pandas as pd \nimport os \n\ncateg_list = ['SERVICE_DEFECT', 'SECU_DEFECT', 'DEP_DEFECT', 'DOC_DEFECT', 'CONFIG_DEFECT', 'SYNTAX_DEFECT', 'CONDI_DEFECT', 'IDEM_DEFECT']\ninvalid_months = ['2019-06', '2... | [
[
"pandas.read_csv",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
phanxuanphucnd/Arizona-QA | [
"c68f10b9694ca9fe81061c023a7cfe28294ba077"
] | [
"utils.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. 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 ... | [
[
"torch.utils.data.TensorDataset",
"torch.utils.data.SequentialSampler",
"torch.utils.data.DataLoader",
"torch.tensor",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JordanHyatt/nelson_check | [
"ede3dc86fd54188cdcaa4812cdfbb9d74c3227a8"
] | [
"NelsonCheck/NelsonCheck.py"
] | [
"from pandas import DataFrame as DF\nfrom pandas import Series\nimport numpy as np\n\nclass NelsonRule:\n RULES = {\n 1:{\n 'num':1,\n 'desc':'One point is more than 3 standard deviations from the mean',\n 'problem':'One sample is grossly out of control.',\n },\n ... | [
[
"numpy.std",
"numpy.mean",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
actively-ai/fastcluster | [
"569c3e38e749ba2f3c3a665a04f5f8e0a71d6d37"
] | [
"tests/test.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nprint('''\nTest program for the 'fastcluster' package.\nCopyright:\n * Until package version 1.1.23: (c) 2011 Daniel Müllner <http://danifold.net>\n * All changes from version 1.1.24 on: (c) Google Inc. <http://google.com>''')\nimport sys\nimport fastcluster as fc\... | [
[
"numpy.minimum",
"numpy.nanmin",
"numpy.round",
"numpy.seterr",
"numpy.max",
"numpy.random.randn",
"numpy.fill_diagonal",
"numpy.any",
"numpy.mean",
"scipy.spatial.distance.squareform",
"numpy.random.randint",
"numpy.square",
"numpy.arange",
"numpy.min",
... | [
{
"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"
... |
wengfeicn/ocr | [
"3e00381f39cdd64518b3e14000318140c47516e1"
] | [
"ctpn/ctpn/cfg.py"
] | [
"import sys\nimport numpy as np\n\nclass Config:\n MEAN=np.float32([102.9801, 115.9465, 122.7717])\n #MEAN=np.float32([100.0, 100.0, 100.0])\n TEST_GPU_ID=0\n SCALE=900\n MAX_SCALE=1500\n TEXT_PROPOSALS_WIDTH=0\n MIN_RATIO=0.01\n LINE_MIN_SCORE=0.6\n TEXT_LINE_NMS_THRESH=0.2\n MAX_HORI... | [
[
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nouldine/Text-Classification-with-CNN | [
"26d79e66a13fadc3993b30bbbba4f1030e9b6a9b"
] | [
"20newsgroups_non_static.py"
] | [
"import numpy as np\nimport os\nimport matplotlib.pyplot as plt\n\n\nos.environ['KERAS_BACKEND'] = 'tensorflow'\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.utils.np_utils import to_categorical\nfrom keras.layers import Dense, Input, Flatten\nfr... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.asarray",
"numpy.arange",
"numpy.random.shuffle",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"sklearn.datasets.fetch_20newsgroups"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Yuansurex/DL-NLP | [
"1d04cf92b469377e43bd0dd28c4b1f2f1e0bbb8f"
] | [
"dataset/ptb.py"
] | [
"# coding: utf-8\nimport sys\nimport os\nsys.path.append('..')\ntry:\n import urllib.request\nexcept ImportError:\n raise ImportError('Use Python3!')\nimport pickle\nimport numpy as np\n\n\nurl_base = 'https://raw.githubusercontent.com/tomsercu/lstm/master/data/'\nkey_file = {\n 'train':'ptb.train.txt',\n ... | [
[
"numpy.load",
"numpy.array",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vingelklang/DELPHI | [
"1f75ad8047fb22f386105a7d621025d744967916"
] | [
"CDC/DELPHI_model_CDC.py"
] | [
"# Authors: Hamza Tazi Bouardi (htazi@mit.edu), Michael L. Li (mlli@mit.edu), Omar Skali Lami (oskali@mit.edu)\nimport pandas as pd\nimport numpy as np\nfrom scipy.integrate import solve_ivp\nfrom scipy.optimize import minimize\nfrom datetime import datetime, timedelta\nimport multiprocessing as mp\nimport time\nfr... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime",
"numpy.log",
"numpy.arctan",
"numpy.multiply",
"scipy.optimize.minimize",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"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... |
mmohrhard/devito | [
"715f5eb9925c0faf42295ba92e2ec5cdfc294c26"
] | [
"tests/test_pickle.py"
] | [
"import pytest\nimport numpy as np\nfrom sympy import Symbol, Min\nimport pickle\n\nfrom conftest import skipif\nfrom devito import (Constant, Eq, Function, TimeFunction, SparseFunction, Grid,\n Dimension, SubDimension, ConditionalDimension, IncrDimension,\n TimeDimension, Step... | [
[
"numpy.all",
"numpy.array",
"numpy.linalg.norm",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
encount/quantipy3 | [
"01fe350b79594ba162cd48ce91f6e547e74265fe",
"01fe350b79594ba162cd48ce91f6e547e74265fe",
"01fe350b79594ba162cd48ce91f6e547e74265fe"
] | [
"quantipy/core/weights/rim.py",
"tests/test_rules.py",
"quantipy/core/builds/powerpoint/pptx_painter.py"
] | [
"\nimport pandas as pd\nimport numpy as np\nimport io\nimport re\nimport itertools\nimport pdb\nimport copy\nimport warnings\nimport time\n\nfrom quantipy.core.tools.view.logic import (\n has_any, has_all, has_count,\n not_any, not_all, not_count,\n is_lt, is_ne, is_gt,\n is_le, is_eq, is_ge,\n union... | [
[
"pandas.concat",
"pandas.Series",
"pandas.np.isnan",
"numpy.sum",
"pandas.np.mean"
],
[
"pandas.DataFrame.from_csv",
"pandas.DataFrame"
],
[
"numpy.round",
"pandas.concat",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy... |
mcStargazer/hf | [
"0dc9f835bcaf43e7f1f3b9e1e03a39a0de6f29a5"
] | [
"make_metrics.py"
] | [
"#!/home/mcollier/miniconda3/bin/python\n# -*- coding: utf-8 -*-\n\n# Typical use cases:\n#hf> /media/mcollier/ONYX/ONYX/W/portfolio/scripts/make_metrics.py -v\n#hf> /media/mcollier/ONYX/ONYX/W/portfolio/scripts/make_metrics.py -s WMT\n#hf> /media/mcollier/ONYX/ONYX/W/portfolio/scripts/make_metrics.py -h\n\n\n#####... | [
[
"pandas.concat",
"pandas.notnull"
]
] | [
{
"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": []
}
] |
chjort/detr | [
"c4f149f2b47a72b3d541d9dea8a31059a7324ffb"
] | [
"drivers/instre_driver.py"
] | [
"import glob\nimport os\n\nimport torch\nfrom torch.utils.data import Dataset, DistributedSampler, DataLoader\n\nfrom datasets.coco import make_coco_transforms_query, make_coco_transforms_target\nfrom datasets.osd_dataset import OSDDataset\nfrom util.misc import collate_fn_os\nfrom util.plot_utils import plot_resul... | [
[
"torch.utils.data.DistributedSampler",
"torch.utils.data.SequentialSampler",
"torch.utils.data.DataLoader",
"torch.utils.data.RandomSampler",
"matplotlib.pyplot.show",
"torch.utils.data.BatchSampler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sisl/InteractionImitation | [
"9c9ee8f21b53e71bbca86b0b79c6e6d913a20567",
"9c9ee8f21b53e71bbca86b0b79c6e6d913a20567"
] | [
"tests/test_metrics.py",
"src/util/rollout.py"
] | [
"import torch\nimport numpy as np\n\nfrom src import metrics\nfrom src.metrics import divergence, evaluate_histogram\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.neighbors import KernelDensity\n\ndef test_kl_divergence():\n p = torch.randn(1000000)\n q = 1.0 + 2.0 * torch.randn(1000000)\n\n... | [
[
"torch.randn",
"numpy.isfinite",
"numpy.isclose"
],
[
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SizheWei/pytorch-3dunet | [
"02f825eb415fe8cec0d90e22d8d71dd27899fa8f"
] | [
"unet3d/predictor.py"
] | [
"import h5py\nimport hdbscan\nimport numpy as np\nimport time\nimport torch\nfrom sklearn.cluster import MeanShift\n\nfrom datasets.hdf5 import SliceBuilder\nfrom unet3d.utils import get_logger\nfrom unet3d.utils import unpad\n\nlogger = get_logger('UNet3DTrainer')\n\n\nclass _AbstractPredictor:\n def __init__(s... | [
[
"numpy.expand_dims",
"sklearn.cluster.MeanShift",
"numpy.unique",
"numpy.max",
"numpy.bitwise_and",
"numpy.argmax",
"torch.no_grad",
"numpy.zeros",
"numpy.bitwise_or"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mkolodziejczyk-piap/racecar_gym | [
"e117432572bb5744884ad6059e55282b03aea929"
] | [
"racecar_gym/tasks/tracking.py"
] | [
"from .task import Task\nimport numpy as np\n\n\nclass WaypointFollow(Task):\n def __init__(self, laps: int, time_limit: float, terminate_on_collision: bool, n_min_rays: int = 1080,\n collision_reward: float = 0.0, state_gain: float = 0.1, action_gain: float = 0.5):\n self._time_limit = ti... | [
[
"numpy.exp",
"numpy.matmul",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Slbalderrama/OnSSET-2016 | [
"458caf306389758a064257a15bddd9f79656ecdb"
] | [
"ONNSET_Bolivia/Test.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 17 17:10:06 2019\n\n@author: balderrama\n\"\"\"\n\nimport pandas as pd\nfrom joblib import load\nimport numpy as np\n\nData = pd.read_csv('bo-1_0_0_0_0_0_0_0_0.csv',index_col=0) \nData['HouseHolds'] = Data['Pop2025']/Data['NumPeoplePerH... | [
[
"pandas.read_excel",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
helmutsimon/pymc3 | [
"ea263f6caaf81960792eb664fc5494659524b451",
"ea263f6caaf81960792eb664fc5494659524b451"
] | [
"pymc3/tests/test_starting.py",
"pymc3/step_methods/hmc/hmc.py"
] | [
"# Copyright 2020 The PyMC Developers\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 appli... | [
[
"numpy.std",
"numpy.random.randn",
"numpy.mean",
"numpy.testing.assert_allclose",
"numpy.array"
],
[
"numpy.abs",
"numpy.isfinite",
"numpy.isnan",
"numpy.random.rand",
"numpy.random.uniform",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SulmanK/eBay-web-crawler-phone-auctions | [
"149c0f8d2bccc71b661a4a18e2a212d8a883273f"
] | [
"Scraping_Application_Docker/scraper.py"
] | [
"# --------------------- Packages\nfrom sqlalchemy import create_engine\nfrom sqlalchemy_utils import database_exists, create_database\nimport bs4\nimport datetime as dt\nimport pandas as pd\nimport psycopg2\nimport re\nimport requests\nimport time\n# --------------------- Function\n\n\ndef eBayscrapper_daily(phone... | [
[
"pandas.DatetimeIndex",
"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": []
}
] |
TileDB-Inc/TileDB-CF-Py | [
"9aab0fe9ba7346a1846c7458a5d08b123dcf90a8",
"9aab0fe9ba7346a1846c7458a5d08b123dcf90a8"
] | [
"tiledb/cf/netcdf_engine/_array_converters.py",
"tests/core/test_virtual_group.py"
] | [
"# Copyright 2021 TileDB Inc.\n# Licensed under the MIT License.\n\"\"\"Classes for converting NetCDF4 files to TileDB.\"\"\"\n\nimport itertools\nfrom typing import Any, Dict, Optional, Sequence, Tuple, Union\n\nimport netCDF4\nimport numpy as np\n\nimport tiledb\nfrom tiledb.cf.creator import (\n ArrayCreator,... | [
[
"numpy.meshgrid"
],
[
"numpy.testing.assert_equal",
"numpy.array",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
openPMD/openPMD-updater | [
"1f334c99093b7175af990d57879ea24ff5b4cb01",
"1f334c99093b7175af990d57879ea24ff5b4cb01"
] | [
"openpmd_updater/transforms/v2_0_0/ExtensionString.py",
"openpmd_updater/transforms/v2_0_0/Version.py"
] | [
"\"\"\"\nThis file is part of the openPMD-updater.\n\nCopyright 2018 openPMD contributors\nAuthors: Axel Huebl\nLicense: ISC\n\"\"\"\n\nfrom openpmd_updater.transforms.ITransform import ITransform\nimport numpy as np\n\n\nclass ExtensionString(ITransform):\n \"\"\"\n Transforms an extension ID to a string att... | [
[
"numpy.uint32"
],
[
"numpy.string_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
barbmarques/classification-project-telco | [
"4b53faf5dd4cd10237966ddcf07ba68d2fb221fc"
] | [
"explore.py"
] | [
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom scipy import stats\n\ndef train_validate_test_split(df, target, seed=123):\n '''\n This function takes in a dataframe, the name of the target variable\n ... | [
[
"matplotlib.pyplot.boxplot",
"pandas.crosstab",
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"scipy.stats.chi2_contingency",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"matplotlib.pyplot.subplots",
"scipy.sta... | [
{
"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... |
brocki/GamestonkTerminal | [
"d7bb5a9456250114da83172e5d49beb242df5eed"
] | [
"gamestonk_terminal/government/gov_controller.py"
] | [
"\"\"\"Government Controller Module\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nfrom matplotlib import pyplot as plt\nfrom prompt_toolkit.completion import NestedCompleter\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.helper_funcs import get_flair\n... | [
[
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
helegraf/cs-ranking | [
"a635d59a254e7d4cad06c3e04f7593392e0b9cec",
"a635d59a254e7d4cad06c3e04f7593392e0b9cec"
] | [
"csrank/core/cmpnet_core.py",
"csrank/dataset_reader/objectranking/sushi_object_ranking_dataset_reader.py"
] | [
"import logging\nfrom itertools import permutations\n\nimport numpy as np\nimport tensorflow as tf\nfrom keras import optimizers, Input, Model, backend as K\nfrom keras.layers import Dense, concatenate\nfrom keras.optimizers import SGD\nfrom keras.regularizers import l2\nfrom sklearn.utils import check_random_state... | [
[
"tensorflow.Session",
"sklearn.utils.check_random_state",
"numpy.empty"
],
[
"sklearn.utils.check_random_state",
"sklearn.model_selection.StratifiedKFold",
"sklearn.model_selection.ShuffleSplit"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
ialifinaritra/Text_Summarization | [
"c3edc144a71b663e99fc3c4e9e148117b5cfda85"
] | [
"Summarizer.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom transformers import FlaubertModel, FlaubertTokenizer\nfrom scipy.spatial.distance import cosine\nfrom sklearn.manifold import TSNE\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\nimport plotly.express as px... | [
[
"torch.mean",
"pandas.Series",
"sklearn.cluster.KMeans",
"torch.sum",
"pandas.DataFrame",
"sklearn.manifold.TSNE",
"numpy.mean",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.where",
"torch.tensor",
"numpy.zeros",
"torch.squeeze",
"matplotlib.pyplot.fig... | [
{
"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... |
zdebeurs/rv_net | [
"38a3cfbc6cf5670b7bff7ec084f5c46bf32bcd08"
] | [
"ops/dataset_ops_test.py"
] | [
"# Copyright 2018 The Exoplanet ML 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 required by applicable ... | [
[
"tensorflow.constant",
"tensorflow.range",
"numpy.arange",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.test.main",
"tensorflow.data.Dataset.range",
"tensorflow.tables_initializer",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
zzshou/mt-en2zh | [
"0a9855d8265c7349e9ca108a6c1d1836ce570e20"
] | [
"baseline/trainer.py"
] | [
"import datetime\nimport logging\nimport math\nimport os\nimport re\nimport time\nimport traceback\nfrom contextlib import contextmanager\nfrom typing import Any, Dict, Iterator, List, Optional, Tuple, Union, Type\n\nfrom allennlp.common.util import int_to_device\n\nimport torch\nimport torch.distributed as dist\nf... | [
[
"torch.isnan",
"torch.distributed.barrier",
"torch.tensor",
"torch.nn.utils.clip_grad_norm_",
"torch.cuda.amp.autocast",
"torch.cuda.amp.GradScaler",
"torch.no_grad",
"torch.device",
"torch.distributed.get_rank",
"torch.cuda.device_count",
"torch.distributed.all_reduce"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kangkulee/DeepLearning-project | [
"be2447ec1b5d9ba22a6b46b86a44668b80d774df"
] | [
"train.py"
] | [
"# -*- coding: utf-8 -*-\n# Char-RNN 예제\n# Author : solaris33\n# Project URL : http://solarisailab.com/archives/2487\n# GitHub Repository : https://github.com/solaris33/char-rnn-tensorflow/\n# Reference : https://github.com/sherjilozair/char-rnn-tensorflow\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom util... | [
[
"tensorflow.nn.dynamic_rnn",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"numpy.squeeze",
"numpy.cumsum",
"tensorflow.train.AdamOptimizer",
"tensorflow.gradients",
"tensorflow.contrib.rnn.MultiRNNCell",
"numpy.argmax",
"tensorflow.Session",
"tensorflow.trainable_vari... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
willwheelera/pyqmc | [
"0c8d1f308bbccb1560aa680a5a75e7a4fe7a69fb"
] | [
"pyqmc/hdftools.py"
] | [
"import h5py\nimport numpy as np\n\n\ndef setup_hdf(f, data, attr):\n \"\"\"\n f should be an h5py file object\n data should be a dictionary of numpy arrays.\n attr is a dictionary that should go into attributes\n\n It's assumed that data consists of representative sizes.\n This function will not ... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jhanley634/testing-tools | [
"3f3f8a34df53015347e1e1cc37d20c8d03652cad"
] | [
"problem/pop_map/demographic/num_reps.py"
] | [
"#! /usr/bin/env python\n\n# Copyright 2020 John Hanley.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, co... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
alvinwan/shiftresnet-cifar | [
"7fcb8f48aed3f9c887cd9fc775d8f1cb9b280c59"
] | [
"eval.py"
] | [
"'''Test CIFAR10 with PyTorch.'''\nfrom __future__ import print_function\n\nimport glob\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\n\nimport torchvision\nimport torchvision.transforms as transforms\n\nimport os\nimport a... | [
[
"torch.nn.CrossEntropyLoss",
"torch.max",
"torch.load",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.device_count",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iShohei220/kaggle-pku-autonomous-driving | [
"647f1c48044f0c2cebcc5cb71854cb39ace0078c",
"647f1c48044f0c2cebcc5cb71854cb39ace0078c"
] | [
"test.py",
"lib/utils/wbf.py"
] | [
"import time\nimport os\nimport math\nimport argparse\nfrom glob import glob\nfrom collections import OrderedDict\nimport random\nimport warnings\nfrom datetime import datetime\nimport json\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport pandas as pd\nimport joblib\nimport cv2\... | [
[
"torch.cos",
"matplotlib.pyplot.imshow",
"pandas.read_csv",
"torch.load",
"torch.sin",
"torch.atan2",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.flip",
"numpy.array",
"numpy.sum",
"matplotlib.pyplot.show",
"torch.save"
],
[
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YunxiaoRen/deep_transfer_learning_AMR | [
"49fe7a19edc8b8e6fa0cb4e17b81ab7f8fffd546"
] | [
"02_transfer_learning.py"
] | [
"\"\"\"\r\n=================================\r\n1. Load module\r\n=================================\r\n\"\"\"\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import train_test_split,KFold, StratifiedKFold, RepeatedStratifiedKFold, cross_val_score\r\nfr... | [
[
"matplotlib.pyplot.legend",
"sklearn.metrics.matthews_corrcoef",
"sklearn.metrics.confusion_matrix",
"pandas.DataFrame",
"sklearn.model_selection.KFold",
"matplotlib.pyplot.plot",
"numpy.max",
"pandas.core.frame.DataFrame",
"sklearn.metrics.classification_report",
"pandas.r... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
Qkley/elephant | [
"731bbe4b3e3111b58fbcdbf8ba400f988df1c440"
] | [
"elephant/unitary_event_analysis.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nUnitary Event (UE) analysis is a statistical method that\n enables to analyze in a time resolved manner excess spike correlation\n between simultaneously recorded neurons by comparing the empirical\n spike coincidences (precision of a few ms) to the expected number\n based on the f... | [
[
"numpy.dot",
"scipy.special.gammaincc",
"numpy.expand_dims",
"numpy.asarray",
"numpy.arange",
"numpy.tile",
"numpy.random.shuffle",
"numpy.all",
"numpy.int64",
"numpy.copy",
"numpy.log10",
"numpy.mean",
"numpy.shape",
"numpy.prod",
"numpy.where",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lcylcy/GLM_copa | [
"dbb28be27cd48905986ab5db6e29620eff05984c",
"dbb28be27cd48905986ab5db6e29620eff05984c"
] | [
"change_mp.py",
"tasks/data_utils.py"
] | [
"import sys\nimport os\nimport torch\nimport copy\n\ncheckpoint = sys.argv[1]\ntarget_mp = int(sys.argv[2])\n\nassert os.path.isdir(checkpoint)\niteration_file = os.path.join(checkpoint, 'latest_checkpointed_iteration.txt')\nif os.path.exists(iteration_file):\n with open(iteration_file) as fin:\n iteratio... | [
[
"torch.no_grad",
"torch.save",
"torch.cat",
"torch.load"
],
[
"numpy.array",
"torch.utils.data.DataLoader",
"torch.utils.data.distributed.DistributedSampler",
"torch.utils.data.dataloader.default_collate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bkommineni/Activity-Recognition-Using-Sensordata | [
"94effff8295e5314ac097a777b01013e540c75de"
] | [
"NeuronBinaryClassification/Graph/k-foldSelectedFeaturesPlot.py"
] | [
"from sklearn.linear_model import Perceptron\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedKFold\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndef plotGraph(obj1,obj2):\n AxisX = obj2[1:44]\n AxisY = obj1[0]\n y_pos = np.... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.barh",
"sklearn.model_selection.train_test_split",
"sklearn.model_selection.StratifiedKFold",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.yticks",
"numpy.array",
"matplotlib.pyplot.show",
"sklearn.linear_mo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
carolgaudeoso/PyTTa | [
"13a5d2a8f30a096897baaffe98ebb84b324d6891"
] | [
"pytta/classes/measurement.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport os\nimport json\nimport zipfile\nimport numpy as np\nimport sounddevice as sd\nimport time\nfrom pytta.classes import _base\nfrom pytta.classes.signal import SignalObj, ImpulsiveResponse\nfrom pytta import _h5utils as _h5\nimport traceback\n\n\n# Measurement class\nclass Measureme... | [
[
"numpy.round",
"numpy.squeeze",
"numpy.log2",
"numpy.log10"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LiuXiaoxuanPKU/actnn-mmcls | [
"75b087f27eed3125cb879d2b8bde10f5e371fbb3"
] | [
"mmcls/models/backbones/tnt.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport torch\nimport torch.nn as nn\nfrom mmcv.cnn import build_norm_layer\nfrom mmcv.cnn.bricks.transformer import FFN, MultiheadAttention\nfrom mmcv.cnn.utils.weight_init import trunc_normal_\nfrom mmcv.runner.base_module import BaseModule, ModuleLi... | [
[
"torch.nn.Dropout",
"torch.linspace",
"torch.zeros",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.Unfold"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rawrawiz/ICNN | [
"145be374d690bdec32d3a358259584125d08f246"
] | [
"tools/get_cubsampleimdb.py"
] | [
"import os\r\nimport cv2\r\nimport h5py\r\nimport matplotlib.image as mpimg\r\nimport math\r\nimport numpy as np\r\n\r\n# different get+dataset+imdb.py has deffernet readAnnotation getNegObjSet and getI functions\r\n\r\ndef getI(obj,image_size, IsFlip):\r\n I = cv2.imread(obj[\"filename\"])\r\n if(len(I.shape... | [
[
"numpy.expand_dims",
"numpy.tile",
"numpy.ones",
"numpy.concatenate",
"numpy.mean",
"numpy.repeat",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bartek-wojcik/graph_agregations | [
"12305e4ffdf4db60da041689f04d96b48e9e72e5",
"12305e4ffdf4db60da041689f04d96b48e9e72e5",
"12305e4ffdf4db60da041689f04d96b48e9e72e5"
] | [
"src/models/modules/gat.py",
"src/models/modules/vector_sage_module.py",
"src/models/sp_classifier_model_test.py"
] | [
"from torch import nn\nfrom torch_geometric.nn import (\n GATConv,\n global_max_pool,\n)\nimport torch.nn.functional as F\n\n\nclass GAT(nn.Module):\n\n def __init__(self, hparams: dict):\n super().__init__()\n self.hparams = hparams\n\n if hparams[\"num_conv_layers\"] < 1:\n ... | [
[
"torch.nn.Linear",
"torch.nn.ModuleList",
"torch.nn.functional.relu",
"torch.nn.functional.dropout"
],
[
"torch.nn.Linear",
"torch.nn.ModuleList",
"torch.nn.functional.relu",
"torch.nn.functional.dropout"
],
[
"torch.nn.CrossEntropyLoss",
"torch.cat",
"torch.arg... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
franekmagiera/manigp | [
"4b05ae013e3c5b2e376b1e8dcbe9f33e61e8795b"
] | [
"methods/LDAClassifier.py"
] | [
"'''\nCopyright: 2019-present Patryk Orzechowski\nLicence: MIT\n'''\n\n\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n\nhyper_params={\n 'solver' : ['svd','lsqr', 'eigen']\n}\n\nest=LinearDiscriminantAnalysis(n_components=2)"
] | [
[
"sklearn.discriminant_analysis.LinearDiscriminantAnalysis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seraphlabs-ca/MIM | [
"97fb8241f9a91ab6ec5fcdde7231e23fa100878e"
] | [
"src/vae-as-mim-image.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nCode is based on https://github.com/jmtomczak/vae_vampprior.git\n\n@article{TW:2017,\n title={{VAE with a VampPrior}},\n author={Tomczak, Jakub M and Welling, Max},\n journal={arXiv},\n year={2017}\n}\n\"\"\"\nfrom __future__ import print_function\nimport argparse\nimport torch\n... | [
[
"torch.manual_seed",
"torch.cuda.manual_seed",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuenishi/chainer-compiler | [
"942c3b09759d0857ddc7c6bb93b81117d011ff50",
"942c3b09759d0857ddc7c6bb93b81117d011ff50"
] | [
"elichika/sandbox.py",
"elichika/elichika/parser/functions.py"
] | [
"import chainer\nimport chainer.functions as F\nimport chainer.links as L\nimport numpy as np\nimport os\n\nimport elichika\nimport elichika.parser as parser\nimport elichika.parser.visualizer as visualizer\n\nclass MultiLayerPerceptron(chainer.Chain):\n def __init__(self, n_in, n_hidden, n_out):\n super(... | [
[
"numpy.zeros"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yanlai00/reward-learning-rl | [
"3400e4cafe1596e5517c5c933c6c3c6324652ead"
] | [
"softlearning/algorithms/rl_algorithm.py"
] | [
"import abc\nfrom collections import OrderedDict\nfrom itertools import count\nimport gtimer as gt\nimport math\nimport os\n\nimport tensorflow as tf\nfrom tensorflow.python.training import training_util\nimport numpy as np\n\nfrom softlearning.samplers import rollouts\nfrom softlearning.misc.utils import save_vide... | [
[
"tensorflow.python.training.training_util._increment_global_step",
"numpy.min",
"tensorflow.python.training.training_util.get_or_create_global_step",
"tensorflow.keras.backend.get_session",
"numpy.max",
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
deephdc/audio-classification-tf | [
"415adb7bdbfc406f87b375563f643506946af92f"
] | [
"audioclas/embeddings/vggish_postprocess.py"
] | [
"# Copyright 2017 The TensorFlow Authors All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requi... | [
[
"numpy.load",
"numpy.dot",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sembrestels/Aragon_Conviction_Voting | [
"222d5cda250ed2adf6897db5df6591bad4dcc657"
] | [
"models/v3/model/parts/system.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom .utils import * \nimport networkx as nx\nfrom scipy.stats import expon, gamma\n\n\n\n# Behaviors\ndef driving_process(params, step, sL, s):\n '''\n Driving process for adding new participants (their funds) and new proposals.\n '''\n arrival_rate = 10/(1+s['... | [
[
"scipy.stats.expon.rvs",
"numpy.median",
"numpy.sum",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdss/lvmutil | [
"1938f6e1d7f4074a90a55570a316886850c5c6af"
] | [
"py/lvmutil/test/test_bitmask.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n# -*- coding: utf-8 -*-\n\"\"\"Test lvmutil.bitmask.\n\"\"\"\nfrom __future__ import (absolute_import, division,\n print_function, unicode_literals)\n# The line above will help with 2to3 support.\nimport sys\nimport unittest\nf... | [
[
"numpy.int",
"numpy.array",
"numpy.uint64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DennisSoemers/open_spiel | [
"88a645f7e562c5b276cd215dd7b26eb0645d22aa"
] | [
"open_spiel/python/tests/games_bridge_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.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MarketGarden/armory | [
"75c6121deb21f0bddc60eeef52e21206db9f825f"
] | [
"blender/arm/exporter.py"
] | [
"\"\"\"\nArmory Scene Exporter\nhttp://armory3d.org/\n\nBased on Open Game Engine Exchange\nhttp://opengex.org/\nExport plugin for Blender by Eric Lengyel\nCopyright 2015, Terathon Software LLC\n\nThis software is licensed under the Creative Commons\nAttribution-ShareAlike 3.0 Unported License:\nhttp://creativecomm... | [
[
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
praeclarumjj3/deocclusion | [
"580621fe943008f838987162e62e9459f5489e10"
] | [
"tester.py"
] | [
"import os\nimport cv2\nimport time\nimport numpy as np\n\nimport torch\nimport torch.optim\nfrom torch.utils.data import DataLoader\nfrom colorama import init\nfrom colorama import Fore, Style\ninit(autoreset=True)\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nimport models\nimport utils\nimport datasets\... | [
[
"torch.mean",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
merlzbert/SkinScan | [
"684129c20b671db0a338ab832fa512c095f0cb60"
] | [
"CameraProjector/Projections/SLMDisplay.py"
] | [
"import ctypes\nimport numpy as np\nfrom .slm_utils import _slm_win as slm\nfrom PIL import Image\nfrom pathlib import Path\n\nfrom .Projection import Projection\nfrom abc import ABC\n\nclass NoSLMError(Exception):\n pass\n\nclass SLMDisplay(ABC, Projection):\n \"\"\"\n Class to handle SLM input-ouput. \n ... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iskorini/tpu | [
"e99cbb85d5d43d5d13dad89e4b8ba123fe1ce0e4"
] | [
"models/official/detection/dataloader/maskrcnn_parser.py"
] | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.compat.v1.expand_dims",
"tensorflow.compat.v1.control_dependencies",
"tensorflow.compat.v1.gather",
"tensorflow.compat.v1.shape",
"tensorflow.compat.v1.size",
"tensorflow.compat.v1.range",
"tensorflow.compat.v1.cast",
"tensorflow.compat.v1.squeeze",
"tensorflow.comp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YulioTech/pyrender | [
"341606d378fdb9105e6ab69a99138007429f1fba"
] | [
"pyrender/primitive.py"
] | [
"\"\"\"Primitives, conforming to the glTF 2.0 standards as specified in\nhttps://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-primitive\n\nAuthor: Matthew Matl\n\"\"\"\nimport numpy as np\n\nfrom OpenGL.GL import *\n\nfrom .material import Material, MetallicRoughnessMaterial\nfrom .constants... | [
[
"numpy.hstack",
"numpy.min",
"numpy.ascontiguousarray",
"numpy.eye",
"numpy.linalg.norm",
"numpy.max",
"numpy.asanyarray",
"numpy.mean",
"numpy.diff",
"numpy.any",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
narroyo1/sffnn | [
"a3d7d8dd7eec76c0dca3aa57e18844b30b75b3b1"
] | [
"experiments.py"
] | [
"\"\"\"\nThis module has a set of preset experiments to test the model.\n\"\"\"\nimport numpy as np\n\nimport functions as func\n\nfrom datasets import DataSets\n\nEXPERIMENT_1 = {\n # datasets\n \"x_range_train\": np.array([[-5.0, 5.0]]),\n \"x_range_test\": np.array([[-4.0, 4.0]]),\n \"base_function\"... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UtkarshPandey55557/Face_detcetion | [
"d95d8cc6ea00a66f39cd94c91bf1a6ac3e97c31d"
] | [
"TwoD_Square_PCA.py"
] | [
"import numpy as np\r\nimport cv2\r\nimport scipy.linalg as s_linalg\r\n\r\n\r\nclass two_d_square_pca_class:\r\n\r\n\r\n def give_p(self, d):\r\n print(\"D\", d)\r\n sum = np.sum(d)\r\n sum_85 = 0.95 * sum\r\n temp = 0\r\n p = 0\r\n while temp < sum_85:\r\n t... | [
[
"numpy.dot",
"numpy.asarray",
"numpy.linalg.eig",
"numpy.linalg.norm",
"numpy.mean",
"numpy.argmin",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jamt9000/DVE | [
"208514419dd1eb0d27ce60876ca836d1ab8c4f4a"
] | [
"model/folded_correlation.py"
] | [
"import torch\nimport argparse\nimport torch.nn.functional as F\nimport copy\nfrom utils import tps\nimport time\nfrom collections import defaultdict\nfrom torch.autograd import gradcheck\n\nLOCAL_CHECKS = False\nPROFILE = False\n\n\nclass DenseCorr(torch.autograd.Function):\n\n @staticmethod\n def forward(ct... | [
[
"torch.abs",
"torch.nn.functional.softmax",
"torch.autograd.enable_grad",
"torch.ones",
"torch.enable_grad",
"torch.zeros",
"torch.cuda.HalfTensor",
"torch.randn",
"torch.sum",
"torch.cuda.FloatTensor",
"torch.matmul",
"torch.FloatTensor",
"torch.no_grad",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sorelyss/somber | [
"2d9cb85c32c7fe30b39f1c2ce29ddd2570e3c12b"
] | [
"somber/som.py"
] | [
"\"\"\"The standard SOM.\"\"\"\nimport json\nimport logging\nfrom collections import Counter, defaultdict\nfrom typing import Callable, Dict, List, Optional, Tuple\n\nimport numpy as np\n\nfrom somber.base import Base, _T\nfrom somber.components.initializers import range_initialization\nfrom somber.components.utili... | [
[
"numpy.nonzero",
"numpy.asarray",
"numpy.arange",
"numpy.cumprod",
"numpy.copy",
"numpy.argmin",
"numpy.mean",
"numpy.prod",
"numpy.broadcast_to",
"numpy.exp",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JNDanielson/mplstereonet | [
"6196e3fd8fff5b2868f50dbcc96eef804024f62e"
] | [
"examples/fault_slip_plot.py"
] | [
"\"\"\"\nIllustrates two different methods of plotting fault slip data.\n\nA fault-and-striae diagram is the traditional method. The tangent-lineation\ndiagram follows Twiss & Unruh, 1988 (this style was originally introduced by\nGoldstein & Marshak, 1988 and also by Hoeppener, 1955, but both used the opposite\nco... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"numpy.hypot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YC-wind/XL-NET- | [
"84b718a9af69bde98629bc5c3da316a13ea2e9e1"
] | [
"model_utils.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport os\nimport re\nimport numpy as np\nimport six\nfrom os.path import join\nfrom six.moves import zip\nfrom absl import flags\nimport tensorflow as tf\n\n\ndef configure_tpu(FLA... | [
[
"tensorflow.contrib.cluster_resolver.TPUClusterResolver",
"tensorflow.get_variable",
"tensorflow.gfile.Exists",
"tensorflow.global_variables",
"tensorflow.train.init_from_checkpoint",
"tensorflow.cast",
"tensorflow.contrib.tpu.CrossShardOptimizer",
"tensorflow.gfile.MakeDirs",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
garciamorenc/signature-object-detection | [
"8a91e98c50ecb2e7ead2961caf3b8d4b0f89a956"
] | [
"XML_preprocesor.py"
] | [
"import glob\nfrom xml.etree import ElementTree\nimport numpy as np\n\n\nclass XmlPreprocessor(object):\n\n def __init__(self, data_path):\n self.path_prefix = data_path\n self.num_classes = 1\n self.data = dict()\n self._preprocess()\n\n def _preprocess(self):\n files = glo... | [
[
"numpy.asarray",
"numpy.hstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jakeKonrad/ogb | [
"c8f0d2aca80a4f885bfd6ad5258ecf1c2d0ac2d9"
] | [
"examples/lsc/mag240m/sgc.py"
] | [
"import time\nimport argparse\n\nimport torch\nimport numpy as np\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.nn import ModuleList, Linear, BatchNorm1d, Identity\n\nfrom ogb.lsc import MAG240MDataset, MAG240MEvaluator\nfrom root import ROOT\n\n\nclass MLP(torch.nn.Module):\... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.functional.dropout",
"torch.manual_seed",
"torch.nn.ModuleList",
"torch.from_numpy",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rajadain/gwlf-e | [
"ba2fb9dbc08a3d7a4ced4b83b6f0f1307814e2a3",
"ba2fb9dbc08a3d7a4ced4b83b6f0f1307814e2a3"
] | [
"gwlfe/gwlfe.py",
"test/unittests/test_AvErosion.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\n\"\"\"\nRuns the GWLF-E MapShed model.\n\nImported from GWLF-E.frm\n\"\"\"\n\nimport logging\n\nfrom numpy import zeros\nfrom numpy import seterr\n\nfro... | [
[
"numpy.seterr",
"numpy.zeros"
],
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SSamDav/ecco | [
"11f8f2e9f110f4fecf40873d4f7f28ecd5cfe38f"
] | [
"tests/lm_test.py"
] | [
"from ecco.lm import LM, _one_hot, sample_output_token, activations_dict_to_array\nimport ecco\nimport torch\nimport numpy as np\nfrom transformers import PreTrainedModel\n\n\nclass TestLM:\n def test_one_hot(self):\n expected = torch.tensor([[1., 0., 0.], [0., 1., 0.]])\n actual = _one_hot(torch.t... | [
[
"torch.eq",
"numpy.zeros",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
decarsg/pykoop | [
"6a8b7c83bdc7de3419e2fac48c1035fa06966e24"
] | [
"pykoop/tsvd.py"
] | [
"\"\"\"Truncated singular value decomposition.\"\"\"\n\nimport logging\n\nimport numpy as np\nimport optht\nimport sklearn.base\nfrom scipy import linalg\n\nlog = logging.getLogger(__name__)\n\n\nclass Tsvd(sklearn.base.BaseEstimator):\n \"\"\"Truncated singular value decomposition.\n\n Attributes\n ------... | [
[
"numpy.max",
"scipy.linalg.svd",
"numpy.where",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
abonawas/cuda-slic | [
"5264b81b12fa3049ccb9cab59257740422a13d21"
] | [
"src/cuda_slic/slic.py"
] | [
"import os\n\nimport numpy as np\n\nfrom jinja2 import Template\nfrom skimage.color import rgb2lab\nfrom skimage.segmentation.slic_superpixels import (\n _enforce_label_connectivity_cython,\n)\n\nfrom .types import float3, int3\n\n\ndef line_kernel_config(threads_total, block_size=128):\n block = (block_size,... | [
[
"numpy.prod",
"numpy.max",
"numpy.array",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dailyfine/nlp_summary_prob | [
"7b953aa47ff9184bea1c93dc210d848539cdb7e9"
] | [
"util.py"
] | [
"# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n# Modifications Copyright 2017 Abigail See\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.apach... | [
[
"tensorflow.ConfigProto",
"tensorflow.train.get_checkpoint_state",
"tensorflow.logging.info"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Fluid-Dynamics-Group/gpsearch | [
"8c5758c9fb2b623ef79952c3e9c113cb157d79bc"
] | [
"gpsearch/tests/test_sample.py"
] | [
"import numpy as np\nimport GPy\nfrom GPy.models import GradientChecker\n\n\ndef test_predictive_gradients_with_normalizer():\n \"\"\"\n Check that model.predictive_gradients returns the gradients of\n model.predict when normalizer=True \n \"\"\"\n N, M, Q = 4, 15, 3\n X = np.random.rand(M,Q)\n ... | [
[
"numpy.random.rand",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JuehuiYang/Fandango | [
"d625716020ae1a116b87468694af24bf49b4e9c0"
] | [
"fandango_serv/fandango_algo/mytools/infer/predict_system.py"
] | [
"# Copyright (c) 2020 PaddlePaddle 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 re... | [
[
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
scipp/scippnexus | [
"978b3b671355f55f94eb9d79f2ffe5cf793605ba"
] | [
"src/scippnexus/nxobject.py"
] | [
"# SPDX-License-Identifier: BSD-3-Clause\n# Copyright (c) 2022 Scipp contributors (https://github.com/scipp)\n# @author Simon Heybrock\nfrom __future__ import annotations\nimport re\nimport warnings\nimport datetime\nimport dateutil.parser\nfrom enum import Enum, auto\nimport functools\nfrom typing import List, Uni... | [
[
"numpy.asarray",
"numpy.array",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CAPTAIN-WHU/SCD | [
"ae9914a25b540f019c74717744c37fec60eb8895"
] | [
"SCD_files/Metric.py"
] | [
"from PIL import Image\nimport numpy as np\nimport math\nimport os\n\n\nnum_class = 37\nIMAGE_FORMAT = '.png'\nINFER_DIR = './prediction_dir/'\nLABEL_DIR = './label_dir/'\n\n\ndef fast_hist(a, b, n):\n k = (a >= 0) & (a < n)\n return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)\n\n... | [
[
"numpy.diag",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mkroutikov/FCOS | [
"b0608f02422c31e96f3ca0120a43e1670a82280c"
] | [
"maskrcnn_benchmark/utils/comm.py"
] | [
"\"\"\"\nThis file contains primitives for multi-gpu communication.\nThis is useful when doing distributed training.\n\"\"\"\n\nimport pickle\nimport time\n\nimport torch\nimport torch.distributed as dist\n\n\ndef get_world_size():\n if dist.is_available() and dist.is_initialized():\n return dist.get_worl... | [
[
"torch.ByteTensor",
"torch.cat",
"torch.distributed.all_gather",
"torch.distributed.is_initialized",
"torch.distributed.barrier",
"torch.distributed.reduce",
"torch.distributed.is_available",
"torch.no_grad",
"torch.IntTensor",
"torch.stack",
"torch.distributed.get_rank... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SeSodesa/fractal_christmas_tree | [
"2a99f62e27f9a029256f82172053387491a4151d"
] | [
"barnsley_tree.py"
] | [
"import numpy as np\nfrom PIL import Image, ImageDraw # Run 'pip3 install pillow'\nw, h = 1280, 1920 # width, height\ndata = [([[ 0.1, 0.0], [ 0.0, 0.2]], [ 0.0, 0.3]), # trunk\n ([[ 0.1, 0.0], [ 0.0, 0.2]], [ 0.0, 0.37]), # trunk\n ([[ 0.87, 0.01],[-0.01, 0.87]], [ 0.0, 0.8]), # copy bra... | [
[
"numpy.array",
"numpy.zeros",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hubert482/torch2trt | [
"eaeb4b58d69bae0af6f1b710fb7a0623e4810290"
] | [
"torch2trt/torch2trt.py"
] | [
"import torch\nimport tensorrt as trt\nimport copy\nimport numpy as np\nimport io\nfrom collections import defaultdict\nimport importlib\n\nfrom .calibration import (\n TensorBatchDataset,\n DatasetCalibrator,\n DEFAULT_CALIBRATION_ALGORITHM,\n)\n\n# UTILITY FUNCTIONS\n\n\ndef trt_version():\n return tr... | [
[
"torch.onnx.export",
"torch.ones",
"torch.empty",
"torch.cuda.current_stream",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Smalltarget108/gs-quant | [
"bed3dd1a183e001c73c14765158aeb8908fe1d5d"
] | [
"gs_quant/timeseries/measures.py"
] | [
"# Copyright 2019 Goldman Sachs.\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 agreed to in wr... | [
[
"pandas.tseries.offsets.CustomBusinessDay",
"numpy.power",
"numpy.unique",
"pandas.tseries.holiday.Holiday",
"numpy.timedelta64",
"numpy.diff",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20",
"1.0",
"0.25"
],
"scipy": [],
"tensorflow": []
}
] |
raudie/depthai-experiments | [
"20fa06a59466f33d733d5d9c0b977d2bffb02710"
] | [
"gaze-estimation/main.py"
] | [
"import argparse\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\nimport cv2\nimport numpy as np\nfrom math import cos, sin\nimport depthai\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-nd', '--no-debug', action=\"store_true\", help=\"Prevent debug output\")\nparser.add_argument('... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zdyshine/XXX | [
"62d4ed910f0014ba0e6b4083b436672601870e0a"
] | [
"dataset/imfdb.py"
] | [
"#!/usr/bin/env python\r\n# encoding: utf-8\r\n'''\r\n@author: wujiyang\r\n@contact: wujiyang@hust.edu.cn\r\n@file: casia_webface.py\r\n@time: 2018/12/21 19:09\r\n@desc: CASIA-WebFace dataset loader\r\n'''\r\n\r\nimport torchvision.transforms as transforms\r\nimport torch.utils.data as data\r\nimport numpy as np\r\... | [
[
"torch.utils.data.DataLoader",
"torch.from_numpy",
"numpy.stack",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Stark-developer01/pandas | [
"e218f059caaf5d921c4c71b7a7889adfe6c74b9d"
] | [
"pandas/core/indexes/datetimelike.py"
] | [
"\"\"\"\nBase and utility classes for tseries type pandas objects.\n\"\"\"\nfrom __future__ import annotations\n\nfrom datetime import datetime\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Sequence,\n TypeVar,\n cast,\n final,\n)\nimport warnings\n\nimport numpy as np\n\nfrom pan... | [
[
"numpy.asarray",
"pandas.core.dtypes.common.is_dtype_equal",
"pandas.core.indexes.base.Index",
"pandas._libs.tslibs.Resolution.from_attrname",
"pandas.core.indexes.extension.inherit_names",
"pandas.compat.numpy.function.validate_take",
"pandas.core.common.asarray_tuplesafe",
"panda... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tksmatsubara/deep-energy-based-modeling-of-discrete-time-physics | [
"e101a090510be1bed6926a2efa5bea5fb9a07e7c"
] | [
"experiment-pend/train_dgnet.py"
] | [
"# Hamiltonian Neural Networks | 2019\n# Sam Greydanus, Misko Dzamba, Jason Yosinski\n\nimport torch\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\ntorch.set_default_dtype(torch.float32)\nimport argparse\nimport numpy as np\n\nimport os\nimport sys\nTHIS_DIR = os.path.dirname(os... | [
[
"numpy.ones_like",
"torch.enable_grad",
"numpy.random.seed",
"numpy.linspace",
"torch.set_default_dtype",
"torch.manual_seed",
"numpy.asarray",
"numpy.stack",
"torch.tensor",
"numpy.sign",
"torch.set_grad_enabled",
"numpy.mean",
"numpy.random.rand",
"torch.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NEAT-RL/Expectation-Maximisation | [
"68751946aba5db96910cdedb1d9a068f1d794971"
] | [
"NEATAgent/NEATEMAgent.py"
] | [
"from ValueFunction.ValueFunction import ValueFunction\nfrom Policy.SoftmaxPolicy import SoftmaxPolicy\nimport numpy as np\nfrom . import Feature\nimport logging\nimport theano\nimport theano.tensor as T\nimport math\nlogger = logging.getLogger()\n\"\"\"\nMountain car\nstate = (position, velocity)\n self.min... | [
[
"numpy.asarray",
"numpy.copy",
"numpy.zeros",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ankitrajixr/Shotifier-Pipeline | [
"db32d16bb36d54b0a5e92c5bd02e98392b85f37b"
] | [
"Filtering_Events_Subevents.py"
] | [
"\r\n#Importing libraries\r\nimport pymongo \r\nimport pprint\r\nimport pandas as pd\r\n\r\n#connecting mongodb and python\r\nmongo_uri = \"mongodb://localhost:27017/\" \r\nclient = pymongo.MongoClient(mongo_uri)\r\nclient.list_database_names()\r\ndb = client.Wyscout\r\ndb.list_collection_names()\r\n\r\n\r\n#Filte... | [
[
"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": []
}
] |
resuly/Traffic-Embedding | [
"4e27f8663e60585f5048ede3c0907a5890e22264"
] | [
"model/model/data_loader.py"
] | [
"import random\nimport os\nimport pandas as pd\nimport torch\nimport numpy as np\nimport pickle\n\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\nwith open(\"data/data_sample.pickle\", 'rb') as f:\n x_train, x_test, y_train, y_test = pickle.load(f)\n... | [
[
"torch.utils.data.DataLoader",
"torch.utils.data.sampler.SubsetRandomSampler",
"torch.from_numpy",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ikomia-dev/infer_colorful_image_colorization | [
"37b8333fbcab69b2cd26cce956e9e893358bbc98"
] | [
"infer_colorful_image_colorization_process.py"
] | [
"from ikomia import core, dataprocess\nimport copy\nimport numpy as np\nimport cv2\nimport os\n\n\n# --------------------\n# - Class to handle the process parameters\n# - Inherits PyCore.CProtocolTaskParam from Ikomia API\n# --------------------\nclass ColorfulImageColorizationParam(core.CWorkflowTaskParam):\n\n ... | [
[
"numpy.concatenate",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
podpearson/vcfnp | [
"c90b6c5f1a5314f0a60a8300a54a961be42effad"
] | [
"vcfnp/test/test_array.py"
] | [
"from __future__ import print_function, division, absolute_import\n\n\nimport os\nfrom nose.tools import eq_, assert_almost_equal, assert_raises\nimport numpy as np\nimport logging\n\n\nimport vcfnp\nfrom vcfnp.array import variants, calldata, calldata_2d\n\n\nlogger = logging.getLogger(__name__)\ndebug = logger.de... | [
[
"numpy.all",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NiZiL/esnpy | [
"8a29b5fefb67d5fd2f1f3efcbd1dbcd4b8cb6e7f"
] | [
"esnpy/init.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\nimport scipy.sparse\n\n\ndef _uniform(shape, bmin, bmax):\n return np.random.rand(*shape).dot(bmax-bmin) + bmin\n\n\ndef _normal(shape, mu, sigma):\n return np.random.randn(*shape).dot(sigma) + mu\n\n\nclass CompositeInit():\n def __init__(self, *args):\n ... | [
[
"numpy.random.randn",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iriszero/AI-Study | [
"4bd6a30ca595828263d87a54b546661523345d0c"
] | [
"dev/cs231n/assignment2/cs231n/layers.py"
] | [
"from builtins import range\nimport numpy as np\n\n\ndef affine_forward(x, w, b):\n \"\"\"\n Computes the forward pass for an affine (fully-connected) layer.\n\n The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N\n examples, where each example x[i] has shape (d_1, ..., d_k). We will\... | [
[
"numpy.log",
"numpy.maximum",
"numpy.sqrt",
"numpy.random.seed",
"numpy.reshape",
"numpy.arange",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"numpy.random.randn",
"numpy.array",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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