repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
dataplumber/nexus | [
"f25a89e85eba098da9c6db1ff3d408dae8a6b310",
"f25a89e85eba098da9c6db1ff3d408dae8a6b310"
] | [
"analysis/webservice/Filtering.py",
"analysis/webservice/NexusHandler.py"
] | [
"\"\"\"\nCopyright (c) 2016 Jet Propulsion Laboratory,\nCalifornia Institute of Technology. All rights reserved\n\"\"\"\n\nimport math\n\nimport logging\nimport traceback\n\nimport numpy as np\nfrom scipy import stats\nfrom scipy.fftpack import fft\nfrom scipy.ndimage.interpolation import zoom\nfrom scipy.interpol... | [
[
"numpy.shape",
"scipy.signal.butter",
"numpy.average",
"scipy.signal.filtfilt"
],
[
"numpy.linspace",
"numpy.min",
"numpy.ma.min",
"numpy.flipud",
"numpy.all",
"numpy.max",
"numpy.ma.max",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
aniket-gupta1/Reinforcement_Learning_Swarm | [
"d333606b8d275e41985ecc3015cd841b0e30fce5",
"d333606b8d275e41985ecc3015cd841b0e30fce5"
] | [
"Environment_iteration9.py",
"Environment_iteration6.py"
] | [
"import copy\nimport time\nimport numpy as np\nfrom utils import *\nimport matplotlib.pyplot as plt\nfrom shapely.geometry import Point\nimport matplotlib.animation as animation\nfrom shapely.geometry.polygon import Polygon\n\n# Formation reward changed to a negative function based on distance from mean center\ncla... | [
[
"matplotlib.pyplot.scatter",
"numpy.reshape",
"numpy.linalg.norm",
"matplotlib.pyplot.clf",
"numpy.random.permutation",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"numpy.random.uniform",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DarlanAjlune/Projeto-Controle-PI | [
"417e544b9f94cb4d4e5f672556fe25ae08e8d891"
] | [
"Projeto_com_interface/Utils/all.py"
] | [
"from Utils.AjustePI import PI\nfrom control.timeresp import step_info\nimport scipy.io \nfrom Utils.SintoniaRF import RF\nimport matplotlib.pyplot as plt\nfrom Utils.MinimosQuadrados import MQ\nfrom Utils.SintoniaLugarRaizes import LR\nimport numpy as np\n# from SaidaMalhaFechadaPI import SMF\nfrom control.matlab ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Niccolo-Marcucci/meep_objects | [
"8f4efe54eb206f536331864987624c98de374ef8"
] | [
"python_utils.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n% Copyright 2020 Niccolò Marcucci <niccolo.marcucci@polito.it>\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% ht... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"numpy.linspace",
"numpy.transpose",
"numpy.int_",
"matplotlib.pyplot.axes",
"numpy.round",
"matplotlib.widgets.TextBox",
"numpy.shape",
"numpy.floor",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.xt... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
megvii-model/RLNAS | [
"a7e2ef9debcd06a93b075181a027b806b737b106"
] | [
"darts_search_space/cifar10/rlnas/evolution_search/super_model.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom operations import *\r\nfrom torch.autograd import Variable\r\nfrom genotypes import PRIMITIVES\r\nfrom genotypes import Genotype\r\nimport math\r\nimport numpy as np\r\nfrom config import config\r\nimport copy\r\nfrom utils import che... | [
[
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.rand",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Arindam-1991/deep_reid | [
"ab68d95c2229ef5b832a6a6b614a9b91e4984bd5"
] | [
"torchreid/engine/engine.py"
] | [
"from __future__ import division, print_function, absolute_import\r\nimport time\r\nimport numpy as np\r\nimport os.path as osp\r\nimport datetime\r\nfrom collections import OrderedDict\r\nimport torch\r\nfrom torch.nn import functional as F\r\nfrom torch.utils.tensorboard import SummaryWriter\r\n\r\nfrom torchreid... | [
[
"torch.nn.functional.normalize",
"torch.cat",
"numpy.asarray",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShunlongHu/wows_aim | [
"4d1da293b2f0eaf657a30a6de4663b90cd2c359d"
] | [
"digitStrip.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Mar 7 22:54:01 2022\r\n\r\n@author: HU\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom os import walk\r\n\r\nsaveDir = './Digits/'\r\nwidth = 7\r\nheight = 11\r\nyLoc = 557\r\nxLocs = (877,884,895,902)\r\n\r\n\r\n\r\nfor (dirpath, dirnames, filenames) i... | [
[
"matplotlib.pyplot.imread"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jie-Yuan/myrun | [
"de6f60c609f598636d782e61271ee90aa192e155"
] | [
"myrun/run.py"
] | [
"#!/usr/bin/env python\n#-*- coding: utf-8 -*-\n\"\"\"\n__title__ = 'run'\n__author__ = 'JieYuan'\n__mtime__ = '2019/4/17'\n\"\"\"\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import RandomForestClassifier\n\n\ndef main(n_iter):\n data = make_classification(10 ** 6, 10 ** 3)\n cl... | [
[
"sklearn.datasets.make_classification",
"sklearn.ensemble.RandomForestClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
evanfebrianto/night2day | [
"77ae4ecf15f0b147d23c7bd01a5f8cb75660fdb7"
] | [
"options/test_options.py"
] | [
"import os\nfrom util import util\nimport torch\nimport easydict\n\nclass BaseTestOptions():\n def __init__(self):\n self.parser = None\n self.initialized = False\n \n def initialize(self):\n self.parser = easydict.EasyDict({\n \"name\" : 'robotcar_2day',\n \"... | [
[
"torch.cuda.set_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fred5577/VAE-IW | [
"f3fe7c8e8e6786517aea0cc5c78f978739243b35"
] | [
"util/plots_first.py"
] | [
"import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotinpy as pnp\nfrom matplotlib.patches import Patch\nimport numpy as np\nimport numpy.ma as ma\n\nsns.set()\nsns.set_style(\"ticks\")\nsns.palplot(sns.color_palette(\"Paired\"))\ndata = pd.read_csv(\"barplotstuff.csv\") \ndata = ... | [
[
"numpy.ma.where",
"matplotlib.pyplot.xticks",
"pandas.read_csv",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2... |
ZzuGiser/yolov4-pytorch | [
"d0526ef75fbc3aab8bca946c090d3a16668bb385"
] | [
"road_train.py"
] | [
"#-------------------------------------#\n# 对数据集进行训练\n#-------------------------------------#\nimport os\nimport numpy as np\nimport pandas as pd\nimport time\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.b... | [
[
"numpy.random.seed",
"torch.load",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"numpy.reshape",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"numpy.random.shuffle",
"pandas.DataFrame",
"torch.no_grad",
"numpy.shape",
"torch.cuda.is_available",
"torch.nn.Data... | [
{
"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": []
}
] |
VictoriaNguyenMD/42-wildcard | [
"5a33dd8faeaaec5cd699d05ec87046bb67189ac5"
] | [
"tweet_analyzer.py"
] | [
"import numpy as np\nimport pandas as pd\n\nfrom textblob import TextBlob\nimport re\n\nclass TweetAnalyzer():\n \"\"\"\n Functionality for analyzing and categorizing content from tweets.\n \"\"\"\n def clean_tweet(self, tweet):\n return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:... | [
[
"numpy.array",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
burhanmudassar/pytorch-action-detection | [
"16afb9312248d73c0e2be56ac733e0a33040307e"
] | [
"lib/utils/nms_utils.py"
] | [
"import numpy as np\nfrom .box_utils import nms2d\n\ndef classAgnosticNMS(dets, scores, overlapThresh=0.3, top_k=None):\n \"\"\"Compute the NMS for a set of scored tubelets\n scored tubelets are numpy array with 4K+1 columns, last one being the score\n return the indices of the tubelets to keep\n \"\"\"... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.argsort",
"numpy.where",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TransitionProjects/ServicesConsistencyReport | [
"9c3b8b53abf391c482170649306875baf211b3bf"
] | [
"ConsistancyCheck.py"
] | [
"from tkinter.filedialog import askopenfilename\r\nfrom tkinter.filedialog import asksaveasfilename\r\n\r\nimport pandas as pd\r\n\r\n\r\nclass ConsistencyCheck:\r\n \"\"\"\r\n\r\n \"\"\"\r\n def __init__(self):\r\n pd.options.mode.chained_assignment = None\r\n self.raw_consistency_report = a... | [
[
"pandas.read_excel",
"pandas.isnull"
]
] | [
{
"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": []
}
] |
jettom/numpy-unittest-100 | [
"006226bf0be5e1aba56fe4da75be1e378391a1d4"
] | [
"test_array_slicing.py"
] | [
"import unittest\nimport numpy as np\nfrom numpy.testing import assert_array_equal\n\nclass TestArraySlicing(unittest.TestCase):\n\n def test_slicing_1d(self):\n vector = np.arange(10)\n assert_array_equal(vector[2:5], np.array([2, 3, 4]))\n\n def test_slicing_1d_with_step(self):\n vector... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
usafchn/models | [
"480a33914d40822ce65df88f5b98d0e2372629f2"
] | [
"fluid/metric_learning/losses/metrics.py"
] | [
"import numpy as np\ndef recall_topk(fea, lab, k = 1):\n fea = np.array(fea)\n fea = fea.reshape(fea.shape[0], -1)\n n = np.sqrt(np.sum(fea**2, 1)).reshape(-1, 1)\n fea = fea/n\n a = np.sum(fea ** 2, 1).reshape(-1, 1)\n b = a.T\n ab = np.dot(fea, fea.T)\n d = a + b - 2*ab\n d = d + np.eye... | [
[
"numpy.dot",
"numpy.arange",
"numpy.argsort",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
keesh0410/SkillTree | [
"33478b328e501c5937bb16427266af62089c70d4"
] | [
"Machine_Learning_Code_Implementation-master/charpter15_random_forest/cart.py"
] | [
"import numpy as np\r\nfrom utils import feature_split, calculate_gini\r\n\r\n### 定义树结点\r\nclass TreeNode():\r\n def __init__(self, feature_i=None, threshold=None,\r\n leaf_value=None, left_branch=None, right_branch=None):\r\n # 特征索引\r\n self.feature_i = feature_i \r\n ... | [
[
"numpy.expand_dims",
"numpy.unique",
"numpy.concatenate",
"numpy.mean",
"numpy.shape",
"numpy.var"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ellagale/testing_object_detectors_in_deepCNNs | [
"fbc15e0b8f7e023c1e3e139feec185b36f68e6f6"
] | [
"analysis.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport itertools\nimport threading\nimport imp\n\n\"\"\"This module is for developing the machinery required to make neural nets and analyse local and global codes\n\nThis module does stuff.\n\"\"\"\n\n__version__ = '0.1'\n__author__ = 'Ella Gale'\n__date__ = 'J... | [
[
"matplotlib.pyplot.Rectangle",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.axhline",
"numpy.log",
"numpy.abs",
"matplotlib.pyplot.title",
"numpy.ones",
"matplotlib.pyplot.plot",
"numpy.random.uniform",
"matplotlib.pyplot.ylabel",
"matplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlchemistPrimus/data_crunchers_knbs | [
"b6d5a73bdbfed3f4a99e7047bd0747f3653a7fd2"
] | [
"data_cleaning.py"
] | [
"# Importing the necessary library\nimport pandas as pd\nimport numpy as np\nimport geopandas as gpd\n\n# Shapefile function\n\ndef shapefile_func(data):\n \n shapefile_data = gpd.read_file(\"Data/County.shp\") # Shapefiles\n \n # Shapefile data preprocessing\n shapefile_data.replace(to_replace='Keiy... | [
[
"pandas.read_csv",
"numpy.subtract"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mrdvince/tensorflowdevcert | [
"6300521c738b0a646a47627e9e8d656ed072cad3"
] | [
"udacity/l02c01_celsius_to_fahrenheit.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport logging\n\nlogger = tf.get_logger()\nlogger.setLevel(logging.ERROR)\n\n# training data\ncelsius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)\nfahrenheit_a = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float)\n\nfor i, c in enumerate(celsius_q):\n ... | [
[
"tensorflow.keras.layers.Dense",
"tensorflow.keras.Sequential",
"tensorflow.get_logger",
"matplotlib.pyplot.plot",
"tensorflow.keras.optimizers.Adam",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
astromme/classify-handwritten-characters | [
"d55c572c8604b898deeb874c24d18af3e15807a0"
] | [
"libgnt/gnt.py"
] | [
"#!/usr/bin/env python3\n\nimport os\nimport numpy as np\n\nfrom .tagcode import tagcode_to_unicode\n\ndef samples_from_gnt(gnt_filepath):\n \"\"\"\n Given a gnt file path,\n returns generater that yields samples of (bitmap, character)\n \"\"\"\n header_size = 10\n\n with open(gnt_filepath, 'rb') ... | [
[
"numpy.fromfile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ashwinipokle/deq | [
"955560601ac7b9dd3088e918850efd9ba14b7610"
] | [
"MDEQ-Vision/lib/models/mdeq_xt.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\n\nimport os\nimport sys\nimport logging\nimport functools\nfrom termcolor import colored\n\nfrom collections import OrderedDict\n\nimport numpy as np\n\nimport torch\nimport torch.nn as nn... | [
[
"torch.cos",
"torch.sigmoid",
"torch.cat",
"torch.sin",
"torch.load",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.arange",
"torch.nn.Module",
"torch.nn.Linear",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"numpy.sum",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lilyminium/xarray | [
"4bad455a801e91b329794895afa0040c868ff128"
] | [
"xarray/backends/netCDF4_.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport functools\nimport operator\nimport warnings\nfrom distutils.version import LooseVersion\n\nimport numpy as np\n\nfrom .. import Variable, coding\nfrom ..coding.variables import pop_to\nfrom ..core import indexing\nfrom ..core.pycompat impor... | [
[
"numpy.asarray",
"numpy.string_",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WiraDKP/pytorch_gru_speaker_diarization | [
"b2c170e38f23eebd1bb168441b2e9111baef2461"
] | [
"src/model.py"
] | [
"from torch import nn\n\nclass SpeakerDiarization(nn.Module):\n \"\"\"\n N (n_batch), I (n_input), S (n_seq), L (n_layer), H (n_hidden), C (n_output)\n \"\"\"\n def __init__(self, n_input, n_output, n_hidden=128, n_layer=4, dropout=0.2):\n super().__init__()\n dropout = 0 if n_layer == 0 e... | [
[
"torch.nn.Linear",
"torch.nn.GRU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oyuka1112/Stocks | [
"40e9b7c163ec512d5a4c03aca62b7554d1f2d4c3"
] | [
"Xy.py"
] | [
"import pandas as pd\nimport numpy as np\n\n#region Import data\n\ndef get_prices_df(percent_change=False, path='data/stock_prices.csv'):\n prices = pd.read_csv(path).set_index('date')\n prices.index = pd.to_datetime(prices.index)\n if percent_change:\n prices = prices.pct_change()\n return price... | [
[
"pandas.to_datetime",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ttm/kolmogorov-smirnov | [
"2e13d22cb77e1916d639d4583d01f8e435792eee"
] | [
"scripts/cumSumKS.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\n# create some randomly ddistributed data:\ndata = np.random.randn(10000)\n\n# sort the data:\ndata_sorted = np.sort(data)\n\n# calculate the proportional values of samples\np = 1. * arange(len(data)) / (len(data) - 1)\n\n# plot the sorted data:\nfig = figure()... | [
[
"numpy.random.randn",
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jthois/Temporal-Auditory-Coding-Features-for-Causal-Speech-Enhancement | [
"c33db76aa168bdfb19d23e36bb72843dd79b0a36"
] | [
"processing.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom utils import *\nfrom matplotlib import pyplot as plt\nfrom scipy.signal import stft\nimport scipy\nfrom config import *\nimport librosa\nimport numpy as np\nfrom six.moves import range # pylint: ... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.squeeze",
"numpy.cumsum",
"numpy.concatenate",
"numpy.random.randn",
"numpy.where",
"numpy.square",
"numpy.clip",
"numpy.sin",
"numpy.finfo",
"numpy.diff",
"scipy.signal.lfilter",
"numpy.repeat",
"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"... |
Plutoyxt/BboxToolkit | [
"16539fc574f209b0d5ec527ca11381dff3380b4d"
] | [
"BboxToolkit/datasets/io.py"
] | [
"import os\nimport os.path as osp\nimport pickle\nimport time\nimport numpy as np\n\nfrom ..utils import get_bbox_dim\nfrom .misc import (read_img_info, change_cls_order, get_classes,\n prog_map)\n\n\ndef load_imgs(img_dir, ann_dir=None, classes=None, nproc=10,\n def_bbox_type='poly')... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bgoli/pysces | [
"94e4734824e8f0eb9cfa9489e853afd4c9a37d23"
] | [
"pysces/PyscesJWSParse.py"
] | [
"\"\"\"\nPySCeS - Python Simulator for Cellular Systems (http://pysces.sourceforge.net)\n\nCopyright (C) 2004-2020 B.G. Olivier, J.M. Rohwer, J.-H.S Hofmeyr all rights reserved,\n\nBrett G. Olivier (bgoli@users.sourceforge.net)\nTriple-J Group for Molecular Cell Physiology\nStellenbosch University, South Africa.\n\... | [
[
"scipy.MachAr"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daxixi/Context-Aware-Compilation-of-DNN-Training-Pipelines-across-Edge-and-Cloud | [
"bcdd23ba8424717db67dd0a09f40d0126b7c9851"
] | [
"computation.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nfrom torch.autograd import Variable\r\nimport copy\r\nimport time\r\nimport pickle\r\nimport lz4.frame\r\n\r\nimport decision.engine as decision_engine\r\n\r\ndef edge_forward(models,inputs,use_Q):\r\n x=i... | [
[
"torch.cuda.synchronize",
"torch.max",
"torch.optim.zero_grad",
"torch.min",
"torch.nn.functional.cross_entropy",
"torch.sum",
"torch.dequantize",
"torch.optim.step",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhizhangxian/DeepLab-v3-plus-cityscapes | [
"c61f5a80ee35b92abf37e73176ecc20068933fe2"
] | [
"demo.py"
] | [
"# from cityscapes import CityScapes, collate_fn2\n\n# from tqdm import tqdm\n# from torch.utils.data import DataLoader\n# from configs.configurations import Config\n\n\n\n# # def get_overlaps(cur_cors, ori_cors):\n# # overlaps = []\n# # up = max(ori_cors[0][0], ori_cors[1][0])\n# # left = max(ori_cors[... | [
[
"torch.flip",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Innovativaltd/RiskAdjustedReturn | [
"a2eacbf8ea7d24f81c9eb2d5ab49aba6d028166b"
] | [
"notebooks/risk_adjusted_return.py"
] | [
"# To add a new cell, type '# %%'\n# To add a new markdown cell, type '# %% [markdown]'\n# %%\n# standard imports\nimport warnings\nimport math\nimport cmath\nwarnings.filterwarnings('ignore')\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nimport yfinance as yf\nimport matplotlib.pyplot as plt\nim... | [
[
"matplotlib.pyplot.legend",
"numpy.minimum",
"numpy.sqrt",
"pandas.Series",
"numpy.nan_to_num",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.add",
"numpy.nanmean",
"numpy.where",
"numpy.divide",
"numpy.hstack",
"matplotlib.pyplot.gca",
"pandas.read_csv",
... | [
{
"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": []
}
] |
moyiming1/Retrosynthesis-pathway-ranking | [
"380f31189d09395d0de911759b8bcea436b559b2"
] | [
"tree_lstm/train_valid_test.py"
] | [
"import os, sys\n\nproject_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nsys.path.append(project_path)\n\nimport torch\nimport math\n\nfrom tree_lstm.treeLSTM_model import topk_accuracy\nfrom utils.scheduler import NoamLR, SinexpLR\nimport time\n\n\ndef train(model,\n ... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
keberhart/python-skyfield | [
"16fa511c86092aa88003b493d0f1b0708c0ec4de"
] | [
"skyfield/tests/test_topos.py"
] | [
"from assay import assert_raises\nfrom numpy import abs, arange, sqrt\n\nfrom skyfield import constants\nfrom skyfield.api import Distance, load, wgs84, wms\nfrom skyfield.functions import length_of\nfrom skyfield.positionlib import Apparent, Barycentric\nfrom skyfield.toposlib import ITRSPosition, iers2010\n\nangl... | [
[
"numpy.arange",
"numpy.sqrt",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hieubkvn123/CenterNet | [
"438c1e8d0424122ece353bb20e64ff51f9444b6f"
] | [
"src/lib/detectors/exdet.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n\nimport cv2\nimport numpy as np\nimport time\nimport torch\n\nfrom models.decode import exct_decode, agnex_ct_decode\nfrom models.utils import flip_tensor\nfrom utils.image import get_affin... | [
[
"numpy.partition",
"torch.cuda.synchronize",
"numpy.maximum",
"numpy.concatenate",
"torch.no_grad",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ramonemiliani93/torchio | [
"b6ba3d168d5abb021dc19c4c71f7af72229b9cb8",
"b6ba3d168d5abb021dc19c4c71f7af72229b9cb8"
] | [
"torchio/data/image.py",
"tests/data/test_subject.py"
] | [
"import warnings\nfrom pathlib import Path\nfrom collections import Counter\nfrom collections.abc import Iterable\nfrom typing import Any, Dict, Tuple, Optional, Union, Sequence, List, Callable\n\nimport torch\nimport humanize\nimport numpy as np\nimport nibabel as nib\nimport SimpleITK as sitk\nfrom deprecated imp... | [
[
"numpy.array_equal",
"torch.cat",
"numpy.asarray",
"torch.isnan",
"numpy.eye",
"numpy.prod",
"numpy.array",
"torch.as_tensor"
],
[
"numpy.array",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yardenas/la-mbda | [
"17304eabb51aabd3eed43867277ee6a00ec42ce8"
] | [
"la_mbda/replay_buffer.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.mixed_precision import experimental as prec\nfrom tensorflow_probability import stats as tfps\nfrom tf_agents.replay_buffers import episodic_replay_buffer\n\nimport la_mbda.utils as utils\n\n\nclass EpisodeBuffer(object):\n def __init__(self, sa... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.Variable",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.keras.mixed_precision.experimental.global_policy",
"numpy.array",
"tensorflow.TensorSpec"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
MuhammadEzzatHBK/AUG---Problem-Solving-For-Bioinformatics-Level-1- | [
"55e129c0c5c6441f88775bd4eab6e67fface77eb"
] | [
"Session 6/GC-Skew.py"
] | [
"import matplotlib.pyplot as plt\n\nseq = \"TTGATTACCTTATTTGATCATTACACATTGTACGCTTGTGTCAAAATATCACATGTGCCT\"\nC = 0\nG = 0\nGCSkew = []\n\nfor ch in seq:\n if ch == 'C':\n C += 1\n elif ch == 'G':\n G += 1\n if G != 0 or C != 0:\n GCSkew.append((G-C)/(G+C))\n else:\n GCSkew.app... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
m0000Amir/BST_BS_types_set | [
"c455aaafa632a0de8a1075262097f042993721c7"
] | [
"bab/evaluation.py"
] | [
"\"\"\"\nCALCULATION OF EVALUATIONS\n- node noncoverage;\n- node cost;\n- node delay\n\"\"\"\nfrom .bst import Node\nfrom typing import Tuple, Any\n\nimport numpy as np\n\n\ndef noncov_btwn_sta(place1: float, place2: float,\n cov1: float, cov2: float) -> float:\n \"\"\"\n Calculate noncover... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
i-usalko/torri | [
"4e533d8e13cd9db7694c9983c73f874856e4cd69"
] | [
"tests/test_methods.py"
] | [
"import os\nimport unittest\nfrom torri import Torri, TorriException\nfrom timeit import default_timer as timer\nfrom cv2 import cv2\nimport numpy as np\n\n\nclass TestMethods(unittest.TestCase):\n\n def test_case_one(self):\n t = Torri()\n with self.assertRaises(TorriException):\n obj ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
midhunexeter/sklearn-porter | [
"945a67a1509b5c42d83e2a2f4a82d20aee2cfbce"
] | [
"tests/estimator/classifier/ExtraTreesClassifier/ExtraTreesClassifierJSTest.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom unittest import TestCase\n\nfrom sklearn.ensemble import ExtraTreesClassifier\n\nfrom tests.estimator.classifier.Classifier import Classifier\nfrom tests.language.JavaScript import JavaScript\n\n\nclass ExtraTreesClassifierJSTest(JavaScript, Classifier, TestCase):\n\n def setUp(s... | [
[
"sklearn.ensemble.ExtraTreesClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anonymous-authors-iclr2022-481/ltcl | [
"0d8902228fa6c37f875bb60c4d16988462a9655a"
] | [
"ltcl/tools/gen_ball.py"
] | [
"import argparse\nimport torchvision.transforms as transforms\n\nimport os\nimport numpy as np\nfrom ltcl.datasets.physics_dataset import PhysicsDataset\nfrom ltcl.tools.utils import load_yaml\nimport yaml\nimport ipdb as pdb\n\nclass Namespace(object):\n def __init__(self, **kwds):\n self.__dict__.update... | [
[
"numpy.zeros",
"numpy.random.rand",
"numpy.random.choice",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kw-lee/pytorch-DCRNN | [
"49fbf64705360f2d2586a94fb1bde1b2f9599a70"
] | [
"model/dcrnn_cell.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nfrom base import BaseModel\n\n\nclass DiffusionGraphConv(BaseModel):\n def __init__(self, input_dim, hid_dim, max_diffusion_step, output_dim, filter_type=\"rando... | [
[
"torch.transpose",
"torch.add",
"torch.cat",
"torch.nn.init.constant_",
"torch.zeros",
"torch.reshape",
"torch.nn.init.xavier_normal_",
"torch.unsqueeze",
"torch.matmul",
"torch.nn.Linear",
"torch.FloatTensor",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kaland313/A3C-CarRacingGym | [
"62fe7e033a0fc4a58101e8a06716cbf386c8bd9f"
] | [
"Scripts/CartPole/a3c_cartpole.py"
] | [
"import os\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n\nimport threading\nimport gym\nimport multiprocessing\nimport numpy as np\nfrom queue import Queue\nimport argparse\nimport matplotlib.pyplot as plt\n\nimport tensorflow as tf\nfrom tensorflow.python import keras\nfrom tensorflow.python.keras import layers... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.enable_eager_execution",
"tensorflow.python.keras.layers.Dense",
"matplotlib.pyplot.plot",
"tensorflow.train.AdamOptimizer",
"tensorflow.stop_gradient",
"numpy.argmax",
"tensorflow.one_hot",
"numpy.array",
"matplotlib.pyplot.show"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
fgsect/fexm | [
"ca6629bbcbf79639871d3ec52bc2a7de9ae453a4"
] | [
"fexm/evalscripts/dependency_graph.py"
] | [
"import sys\n\nimport networkx as nx\nimport pandas as pd\n\n\ndef main(package_csv: str, package: str):\n package_dict = {}\n count = 0\n df = pd.read_csv(package_csv)\n df.fillna(\"\")\n dep_graph = nx.DiGraph()\n for index, row in df.iterrows():\n dependencies = str(row[\"depends\"]).spl... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
twangnh/Distilling-Object-Detectors-Shuffledet | [
"6ad638451aac52cc6f4fb94944c0c394a5ad6139"
] | [
"lib/config/kitti_shuffledet_config.py"
] | [
"import numpy as np\n\nfrom config import base_model_config\n\ndef kitti_shuffledet_config():\n \"\"\"Specify the parameters to tune below.\"\"\"\n mc = base_model_config('KITTI')\n\n mc.IMAGE_WIDTH = 1248\n mc.IMAGE_HEIGHT = 384\n # mc.IMAGE_WIDTH = 560\n # ... | [
[
"numpy.reshape",
"numpy.arange",
"numpy.array",
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
asijit123/Python | [
"30050ab3aa7f89eb75e142bd5dfc9987861284a6"
] | [
"pan.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom matplotlib import *\n\n#.........................Series.......................#\n\nx1=np.array([1,2,3,4])\ns=pd.Series(x1,index=[1,2,3,4])\nprint(s)\n\n#.......................DataFrame......................#\n\nx2=np.array([1,2,3,4,5,6])\ns=pd.DataFrame(x2)\nprint(s)\... | [
[
"pandas.read_csv",
"pandas.Series",
"pandas.DataFrame",
"numpy.random.randn",
"pandas.date_range",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
knitemblazor/MaskRCNN_pytorch_1.8_non_detectron | [
"2956d1d831ee0c518bd5cf588a952cd9c91a74c1"
] | [
"tests/crop_and_resize_example.py"
] | [
"import torch\nfrom torch import nn\nfrom torchvision import transforms, utils\nfrom torch.autograd import Variable, gradcheck\nfrom roi_align.crop_and_resize import CropAndResizeFunction\nimport matplotlib.pyplot as plt\nfrom skimage.io import imread\n\n\ndef to_varabile(tensor, requires_grad=False, is_cuda=True):... | [
[
"matplotlib.pyplot.imshow",
"torch.cat",
"torch.autograd.Variable",
"matplotlib.pyplot.subplot",
"torch.FloatTensor",
"torch.cuda.is_available",
"torch.IntTensor",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mkoculak/mne-python | [
"c6291eb1b9bc943e7e294b3147e4f5aafd82cbd8"
] | [
"mne/viz/tests/test_evoked.py"
] | [
"# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Denis Engemann <denis.engemann@gmail.com>\n# Martin Luessi <mluessi@nmr.mgh.harvard.edu>\n# Eric Larson <larson.eric.d@gmail.com>\n# Cathy Nangini <cnangini@gmail.com>\n# Mainak Jas <mainak@neuro.hut.fi>\n# ... | [
[
"matplotlib.pyplot.gca",
"numpy.allclose",
"numpy.min",
"numpy.arange",
"numpy.linalg.norm",
"numpy.ones",
"matplotlib.pyplot.axes",
"numpy.zeros_like",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.close",
"matplotlib.cm.get_cmap",
"numpy.errstate",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
roachsinai/CVND---Image-Captioning-Project | [
"fa885ed601d0e106b98b081959dae25d196846aa"
] | [
"data_loader.py"
] | [
"import nltk\nimport os\nimport torch\nimport torch.utils.data as data\nfrom vocabulary import Vocabulary\nfrom PIL import Image\nfrom pycocotools.coco import COCO\nimport numpy as np\nfrom tqdm import tqdm\nimport random\nimport json\n\ndef get_loader(transform,\n mode='train',\n batch_... | [
[
"torch.Tensor",
"numpy.random.choice",
"torch.utils.data.DataLoader",
"torch.utils.data.sampler.SubsetRandomSampler",
"numpy.array",
"torch.utils.data.sampler.BatchSampler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
agonzale34/sdc-advanced-lane-detection | [
"10bb208deabfb5a654418d3f4d5404a7c57363af"
] | [
"src/models/line.py"
] | [
"import numpy as np\n\nfrom src.utils.params import *\n\n\n# Define a class to receive the characteristics of each line detection\nclass Line:\n\n def __init__(self):\n # was the line detected in the last iteration?\n self.detected = False\n # x values of the last n fits of the line\n ... | [
[
"numpy.polyfit",
"numpy.absolute",
"numpy.argmax",
"numpy.mean",
"numpy.average",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ljjcoder/CSEI | [
"1dec6205c234e229629bdc2e88ccc3350f26f620"
] | [
"torchFewShot/models/resnet12.py"
] | [
"import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\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 BasicBl... | [
[
"torch.nn.Sequential",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GinkoBalboa/xgboost | [
"29bfa94bb6a99721f3ab9e0c4f05ee2df4345294"
] | [
"tests/python/test_callback.py"
] | [
"from typing import Union\nimport xgboost as xgb\nimport pytest\nimport os\nimport testing as tm\nimport tempfile\n\n# We use the dataset for tests.\npytestmark = pytest.mark.skipif(**tm.no_sklearn())\n\n\nclass TestCallbacks:\n @classmethod\n def setup_class(cls):\n from sklearn.datasets import load_b... | [
[
"sklearn.datasets.load_breast_cancer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
owenmwilliams/land_search | [
"630e71672a5fff21c833d9905406b9ef70571b28"
] | [
"models/est/fltr/comps.py"
] | [
"import psycopg2\nfrom datetime import datetime\nfrom psycopg2 import sql\nfrom est.fltr import county_return\nfrom est.db.cur import con_cur\nimport numpy as np\nimport pandas as pd\n\ndef comp_find(est, a, b):\n temp1 = est\n temp2 = np.array(temp1[0])\n county = temp2[0].strip()\n state = temp2[1].st... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qianqian121/puma | [
"4a5980fcd302fc794f50e782e478a3bdd77f57b2"
] | [
"apps/pipelines/slam/puma_pipeline.py"
] | [
"#!/usr/bin/env python3\nimport copy\nimport glob\nimport os\nfrom collections import deque\nfrom pathlib import Path\n\nimport click\nimport numpy as np\nimport open3d as o3d\n\nfrom puma.mesh import create_mesh_from_map\nfrom puma.preprocessing import preprocess\nfrom puma.registration import register_scan_to_mes... | [
[
"numpy.linalg.inv",
"numpy.eye"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZhengL97/frites | [
"48d752a8c316d6e4cf2e7c795af539c42eea8cd6"
] | [
"frites/core/gcmi_nd.py"
] | [
"\"\"\"Multi-dimentional Gaussian copula mutual information estimation.\"\"\"\nimport numpy as np\nfrom scipy.special import psi\nfrom itertools import product\n\nfrom frites.core.copnorm import copnorm_nd\n\n###############################################################################\n##########################... | [
[
"numpy.log",
"numpy.einsum",
"numpy.unique",
"numpy.multiply",
"numpy.arange",
"numpy.concatenate",
"numpy.all",
"numpy.zeros_like",
"numpy.linalg.cholesky",
"numpy.moveaxis",
"numpy.sum",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gamcoh/U-2-Net | [
"a116a06ebdc46772b2b400ad180319b89c8ce72c"
] | [
"u2net_test.py"
] | [
"import os\nfrom skimage import io, transform\nimport torch\nimport torchvision\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms#, utils\nimport argparse\n# import torch.optim as optim\n... | [
[
"torch.max",
"torch.min",
"torch.utils.data.DataLoader",
"torch.cuda.is_available",
"torch.device",
"numpy.array",
"numpy.where",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChipWan/pytorch-lightning | [
"2c31beccfbfe0752306122a2ba6f9822ec5cb6b8"
] | [
"pytorch_lightning/trainer/training_io.py"
] | [
"\"\"\"\nLightning can automate saving and loading checkpoints\n=====================================================\n\nCheckpointing is enabled by default to the current working directory.\nTo change the checkpoint path pass in::\n\n Trainer(default_root_dir='/your/path/to/save/checkpoints')\n\n\nTo modify the... | [
[
"torch.save",
"torch.cuda.empty_cache",
"torch.distributed.barrier",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anadeba/AV-AMEXPERT | [
"c69b738b6055c2d31b471ebda53bf4599457981e"
] | [
"data_preprocessing2.py"
] | [
"import pandas as pd\r\nimport numpy as np\r\nfrom datetime import datetime\r\n\r\ndata_processed_train = pd.read_csv(r'X:\\Hackathon\\AV - AMEXPERT\\train_amex\\data_processed_train.csv')\r\n\r\ndata_processed_train['DateTime'] = pd.to_datetime(data_processed_train['DateTime'])\r\ndata_processed_train['Dayofweek']... | [
[
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
obserthinker/FDTD-CPML | [
"7f2351d0d81b4fcbba9bc6a47d677d4c1f6fccc4"
] | [
"1D/1D/Ex.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib import animation\n\ndz = 0.015\nspace = 30 \ntime = 300 \ndata = np.loadtxt('Ex.txt')\n\nfig = plt.figure()\nax = plt.axes(xlim = (0,1), ylim = (-0.1,1))\nline, = ax.plot([],[],lw = 2)\n\ndef init():\n\tline.set_da... | [
[
"numpy.arange",
"matplotlib.pyplot.axes",
"matplotlib.animation.FuncAnimation",
"matplotlib.pyplot.show",
"numpy.loadtxt",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
VinceBaz/neuromaps | [
"6758b53e127d1563fa06eb26bc5f08a4e24ae7e7"
] | [
"neuromaps/nulls/tests/test_spins.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nFor testing neuromaps.nulls.spins functionality\n\"\"\"\n\nimport numpy as np\nimport pytest\n\nfrom neuromaps.nulls import spins\n\n\ndef test_load_spins():\n out = np.random.randint(1000, size=(100, 100), dtype='int32')\n assert out is spins.load_spins(out)\n assert np.a... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thomaswmorris/sirepo-bluesky | [
"05be2df43d56f2b5e69fb9511de9a424ad1e8b79"
] | [
"sirepo_bluesky/srw_handler.py"
] | [
"import numpy as np\nimport srwpy.uti_plot_com as srw_io\n\n\ndef read_srw_file(filename, ndim=2):\n data, mode, ranges, labels, units = srw_io.file_load(filename)\n data = np.array(data)\n if ndim == 2:\n data = data.reshape((ranges[8], ranges[5]), order='C')\n photon_energy = ranges[0]\n ... | [
[
"numpy.array",
"numpy.mean",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hammond756/redbull-f1 | [
"70fe15356b7798f11b8c7eafe1e6b432d7ccc546"
] | [
"models/simple_network.py"
] | [
"import os, sys, math\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\nimport numpy as np\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable as var\nimport matplotlib.pyplot as plt\n\n\ndef train_model():\n dataset = TorcsDataLoade... | [
[
"torch.nn.Linear",
"torch.nn.functional.tanh",
"torch.FloatTensor",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gfwm0502/Paddle | [
"cbce0e603ac2900258dfe29218860f30448aa53e"
] | [
"python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/sharding_optimizer_stage2.py"
] | [
"# Copyright (c) 2021 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 ... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mawright/flow | [
"6e3f3da04b289a3f9e754c84915b60f0689dc78d"
] | [
"flow/envs/base_env.py"
] | [
"\"\"\"Base environment class. This is the parent of all other environments.\"\"\"\n\nfrom copy import deepcopy\nimport os\nimport atexit\nimport time\nimport traceback\nimport numpy as np\nimport random\nfrom flow.renderer.pyglet_renderer import PygletRenderer as Renderer\n\nimport gym\nfrom gym.spaces import Box\... | [
[
"numpy.asarray",
"numpy.copy",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlvinWen428/keyframe-focused-imitation-learning | [
"0aa9abb663b5351ec7dd52df87313e53e6a0d2f4"
] | [
"carla08/driving_benchmark/driving_benchmark.py"
] | [
"# Copyright (c) 2017 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see <https://opensource.org/licenses/MIT>.\n\nimport os\nimport abc\nimport logging\nimport math\nimport time\nimport numpy as np\nfrom... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
psindhuja98/analyzing-weather-dataset | [
"f9d5fd6568669c96cf6fc5fdd865af9495ce484b"
] | [
"code.py"
] | [
"# --------------\n#Importing the modules\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom scipy.stats import mode \r\n\r\n\r\n#Code for categorical variable\r\ndef categorical(df):\r\n \"\"\" Extract names of categorical column\r\n \r\n This function accepts a dataframe and returns categorical list,\r... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"pandas.pivot_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
awilliamson1889/Simple-SPAM-classificator | [
"4094ea1daa18bac71894158e2105d0e8398c3bd9"
] | [
"src/email_classifier.py"
] | [
"\"\"\"Email classifier\"\"\"\nimport os.path\nimport sys\nfrom joblib import dump\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom reader import DSReader\n\n\nsys.path.append('src')\n\ndataset_path = os.path.abs... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.naive_bayes.MultinomialNB"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brodderickrodriguez/strategy_learning_system | [
"62d91c5ba19d41ba6768210638d26f83e56166b9"
] | [
"examples/prey_pred_model.py"
] | [
"# Brodderick Rodriguez\n# Auburn University - CSSE\n# 27 Aug. 2019\n\nimport strategy_learning_system as sls\nimport numpy as np\nimport sys\n\n# for bcr \nif sys.platform == 'darwin':\n\tROOT = '/Users/bcr/Dropbox/Projects'\n\tNETLOGO_HOME = '/Applications/NetLogo-6.0.4/'\n\nelse:\n\tROOT = '/home/bcr/Dropbox/Pro... | [
[
"numpy.max",
"numpy.std",
"numpy.zeros",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miyamotost/hand_object_detector | [
"34c8c6ad53306d4a12c12857e71bcd73bd6a68bf"
] | [
"lib/datasets/voc_eval.py"
] | [
"# --------------------------------------------------------\n# Fast/er R-CNN\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Bharath Hariharan\n# --------------------------------------------------------\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.arange",
"numpy.cumsum",
"numpy.sort",
"numpy.finfo",
"numpy.concatenate",
"numpy.max",
"numpy.round",
"numpy.argmax",
"numpy.where",
"numpy.argmin",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.sum"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GwydionJon/Group_1_Developing_Scientific_Software | [
"ca15bbadd8b311f2ba50425bf9c13c501f5c9af4"
] | [
"src/module_draft/main.py"
] | [
"# Author: Tobias Kaczun, Leonie Kreis, Gwydion Daskalakis\n# Date: 19.03.21\n# Package: DSS Analysis Package\n\nimport sys\nimport numpy as np\nimport reader\nfrom user_input import user_input\nimport analysis\n\n\ndef main():\n \"\"\"Main function for commandline call\n \"\"\"\n # end user version for us... | [
[
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
homeostasie/petits-pedestres | [
"bf20d94a5f2b12d2bb860ebb06a6b18641271020"
] | [
"2014.d/3-differential-equations-in-action.d/1-houston-we-have-a-problem/pb1-houston-we-have-a-problem.py"
] | [
"\"\"\" \n\tDifferential Equations in Action\n\tLesson 1 - Houston We have a problem\n\"\"\"\t\n# Import\nimport math # import math.cos(), math.sin(), math.pi\nimport numpy # import distance\nimport matplotlib.pyplot as plt # import plot\n\n\"\"\" \n------------------------------------------------------------------... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.scatter",
"numpy.linspace",
"numpy.linalg.norm",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.axis",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carthurs/plotly.py | [
"23cbf9bef63ffaf898f89d7e0d6862ca25f5eae0"
] | [
"packages/python/plotly/plotly/tests/test_core/test_px/test_px_input.py"
] | [
"import plotly.express as px\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport plotly.graph_objects as go\nimport plotly\nfrom plotly.express._core import build_dataframe\nfrom pandas.util.testing import assert_frame_equal\n\nattrables = (\n [\"x\", \"y\", \"z\", \"a\", \"b\", \"c\", \"r\", \"theta... | [
[
"numpy.random.random",
"numpy.arange",
"pandas.DataFrame",
"numpy.all",
"pandas.MultiIndex.from_product",
"numpy.array"
]
] | [
{
"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": []
}
] |
uberdeveloper/fastbt | [
"0a056e269b9ce65a282bfb764e3b0de2245739f0"
] | [
"tests/test_metrics.py"
] | [
"import unittest\nimport pandas as pd\n\nfrom fastbt.metrics import *\n\n\nclass TestSpread(unittest.TestCase):\n def setUp(self):\n dates = pd.date_range(\"2016-01-01\", \"2019-12-31\")\n s = pd.Series(index=dates)\n s.loc[:] = 1\n self.s = s\n\n def test_default(self):\n s... | [
[
"pandas.Series",
"pandas.DataFrame",
"pandas.date_range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
aburnap/DS2016_DesignFreedom_and_BrandRecognition | [
"a2cdf9d39d795d3f0a4361210d0249cb35f0350a"
] | [
"code/l1_logit_brand_recognition/l1_logit_brand_recognition.py"
] | [
"\nimport time\nimport numpy as np\nfrom sklearn import linear_model, cross_validation, preprocessing, svm\nfrom sklearn.grid_search import GridSearchCV\n\nDESIGNS = [elm+str(num) for elm in ['a','b','c','l'] for num in range(0,5)]\nmorphed_DESIGNS = [elm+str(num) for elm in ['A','B','C','L'] for num in range(0,8)]... | [
[
"sklearn.linear_model.LogisticRegression",
"sklearn.linear_model.SGDClassifier",
"sklearn.cross_validation.ShuffleSplit",
"numpy.loadtxt",
"numpy.shape",
"numpy.mean",
"sklearn.svm.LinearSVC",
"numpy.savetxt",
"sklearn.preprocessing.scale",
"numpy.array",
"numpy.empty"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
switiz/las.pytorch | [
"357f602f05e3dbde84f8cc37f97dabd7dc6397fe"
] | [
"model/las.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom model.encoder import Listener\nfrom model.decoder import Speller\n\nclass ListenAttendSpell(nn.Module):\n def __init__(self, listener, speller):\n super(ListenAttendSpell, self).__init__()\n self.listener = listener\n ... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abcdvzz/pytorch-image-models | [
"aebc5b58c90adef48bdeef681f3f5f3d3936e1a0"
] | [
"timm/models/twins.py"
] | [
"\"\"\" Twins\nA PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`\n - https://arxiv.org/pdf/2104.13840.pdf\n\nCode/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below\n\n\"\"\"\n# -------------------------------------------------... | [
[
"torch.nn.Dropout",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ben0110/frustum-pointnets_RSC_RADAR_fil_PC_batch_para | [
"a796fd4a775179c4daa342872ea1bbc5ba5d5026"
] | [
"train/provider_dfd_test.py"
] | [
"''' Provider class and helper functions for Frustum PointNets.\n\nAuthor: Charles R. Qi\nDate: September 2017\n'''\nfrom __future__ import print_function\n\n#import cPickle as pickle\n#import pcl\nimport sys\nimport csv\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d impo... | [
[
"numpy.expand_dims",
"numpy.concatenate",
"numpy.random.randn",
"numpy.where",
"numpy.sin",
"numpy.copy",
"numpy.argmax",
"numpy.count_nonzero",
"numpy.zeros",
"numpy.isin",
"numpy.median",
"numpy.transpose",
"numpy.array",
"numpy.sum",
"numpy.random.ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
labscript-suite-temp-archive/lyse-fork--rpanderson-lyse--forked-from--labscript_suite-lyse | [
"ca5bfe2f1f8dd98cc5b4505736a74d08d562e1fd"
] | [
"dataframe_utilities.py"
] | [
"#####################################################################\r\n# #\r\n# /dataframe_utilities.py #\r\n# #\r\n# Copyright 2013, Monas... | [
[
"pandas.concat",
"pandas.Series",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"pandas.Int64Index",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FINNGEN/hail | [
"03fabf5dad71415aeca641ef1618e5352639d683"
] | [
"benchmark-service/benchmark/benchmark.py"
] | [
"import asyncio\nimport os\nimport aiohttp\nfrom aiohttp import web\nimport logging\nfrom gear import setup_aiohttp_session, web_authenticated_developers_only\nfrom hailtop.config import get_deploy_config\nfrom hailtop.tls import internal_server_ssl_context\nfrom hailtop.hail_logging import AccessLogger, configure_... | [
[
"numpy.median",
"pandas.DataFrame",
"scipy.stats.mstats.gmean",
"numpy.mean",
"scipy.stats.mstats.hmean"
]
] | [
{
"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": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.1... |
chaneyddtt/Coarse-to-fine-3D-Animal | [
"b3f9b1031b5761838c94ca091095636101747fd9"
] | [
"model/graph_hg.py"
] | [
"\"\"\"\nThis file contains the Definition of GraphCNN\nGraphCNN includes ResNet50 as a submodule\n\"\"\"\nfrom __future__ import division\n\nimport torch\nimport torch.nn as nn\n\nfrom model.networks.graph_layers import GraphResBlock, GraphLinear\nfrom smal.mesh import Mesh\nfrom smal.smal_torch import SMAL\n\n# e... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.functional.grid_sample",
"torch.nn.GroupNorm",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yangg1224/AI-102-AIEngineer | [
"340202b4a37fd0c1e730322bc641c0606bf7e3c1"
] | [
"15-computer-vision/Python/image-analysis/image-analysis.py"
] | [
"from dotenv import load_dotenv\nimport os\nfrom array import array\nfrom PIL import Image, ImageDraw\nimport sys\nimport time\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\n# Import namespaces\n# import namespaces\nfrom azure.cognitiveservices.vision.computervision import ComputerVisionClient\nfrom a... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.annotate",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Veence/robosat.pink | [
"9d336c06df456ea11cd6c7532eff1dcc5adad883"
] | [
"robosat_pink/tools/tile.py"
] | [
"import os\nimport sys\nimport math\nimport argparse\nfrom tqdm import tqdm\n\nimport numpy as np\nfrom PIL import Image\n\nimport mercantile\n\nfrom rasterio import open as rasterio_open\nfrom rasterio.vrt import WarpedVRT\nfrom rasterio.enums import Resampling\nfrom rasterio.warp import transform_bounds, calculat... | [
[
"numpy.all",
"numpy.uint8",
"numpy.squeeze",
"numpy.moveaxis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jcvasquezc/Disvoice | [
"ed9dbd42c3f01f041f90848f96004be8ebb78d8d"
] | [
"disvoice/phonological/phonological.py"
] | [
"\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Jun 24 2020\n\n@author: J. C. Vasquez-Correa\n\"\"\"\nimport os\nimport sys\n\nimport numpy as np\nimport pandas as pd\nfrom phonet.phonet import Phonet\nfrom phonet.phonet import Phonological as phon\nimport scipy.stats as st\nimport matplotlib.pyplot as plt\nplt.rcPa... | [
[
"numpy.hstack",
"numpy.expand_dims",
"torch.from_numpy",
"numpy.stack",
"pandas.DataFrame",
"numpy.repeat",
"numpy.vstack"
]
] | [
{
"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": []
}
] |
tomfisher/embeddedml | [
"37b67d3489c38936f1693f459e62a80b965cbfc4"
] | [
"convtrees/convtrees.py"
] | [
"import math\n\nimport mnist\nimport numpy\nimport pandas\nimport scipy.signal\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.preprocessing import FunctionTransformer, StandardScaler\nfrom sklearn import p... | [
[
"sklearn.model_selection.GridSearchCV",
"numpy.random.random",
"sklearn.ensemble.RandomForestClassifier",
"numpy.random.choice",
"sklearn.metrics.accuracy_score",
"matplotlib.pyplot.subplots",
"numpy.stack",
"pandas.DataFrame",
"numpy.repeat",
"numpy.array",
"numpy.sum"... | [
{
"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": []
}
] |
feihuidiqiu/HRNet-Facial-Landmark-Detection | [
"cc9d86a7b1270bec1aa25da0c81ef27f1b8b69a0"
] | [
"lib/utils/transforms.py"
] | [
"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Created by Tianheng Cheng(tianhengcheng@gmail.com), Yang Zhao\n# ------------------------------------------------------------------------------\n\nimport cv2\nimport tor... | [
[
"numpy.dot",
"torch.zeros",
"numpy.linalg.inv",
"numpy.arange",
"numpy.eye",
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin",
"numpy.float32",
"numpy.exp",
"numpy.array",
"numpy.zeros",
"numpy.math.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Galaxy-SynBioCAD/rpSelenzyme_image | [
"b6e1f3db18a89d9d84ff2f8285cb8d26e2a9db95"
] | [
"selenzy/tools/storefingerprints.py"
] | [
"\n\nfrom rdkit import Chem\nfrom rdkit.Chem.rdMolDescriptors import GetMorganFingerprint, GetAtomPairFingerprint, GetTopologicalTorsionFingerprint\nfrom rdkit.Chem.rdmolops import PatternFingerprint, RDKFingerprint\nfrom rdkit import DataStructs\nfrom os import path\nimport numpy as np\nimport csv, sys\n\n\ndef re... | [
[
"numpy.load",
"numpy.savez_compressed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jglaser/arrow | [
"c547f4d44997a73feb11ed85c195b22f557b42b7"
] | [
"python/pyarrow/tests/test_pandas.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"pandas.to_datetime",
"pandas.testing.assert_series_equal",
"pandas.Series",
"numpy.linspace",
"numpy.asarray",
"pandas.RangeIndex",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.dtype",
"numpy.random.random_sample",
"pandas.testing.assert_frame_equal",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tomscolaro/l5kit | [
"92c88c4050b946b6828a47ddb3d13da9d87f9f7d"
] | [
"l5kit/l5kit/geometry/transform.py"
] | [
"from typing import Optional, Sequence, Tuple, Union, cast\n\nimport numpy as np\nimport pymap3d as pm\nimport transforms3d\n\n\ndef rotation33_as_yaw(rotation: np.ndarray) -> float:\n \"\"\"Compute the yaw component of given 3x3 rotation matrix.\n\n Args:\n rotation (np.ndarray): 3x3 rotation matrix (... | [
[
"numpy.eye",
"numpy.array",
"numpy.matmul",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andrewrech/CellSeg | [
"2235a0e15ccc106cc0781d377f95c9e7db22ea9f"
] | [
"src/cvmask.py"
] | [
"# cvmask.py\n# ---------------------------\n# Wrapper class for masks. See class doc for details.\n\nimport numpy as np\nfrom scipy.linalg import lstsq\nfrom scipy.spatial import distance\nfrom operator import itemgetter\nfrom skimage.measure import find_contours\nfrom skimage.morphology import disk, dilation\nfr... | [
[
"scipy.ndimage.morphology.binary_dilation",
"numpy.sum",
"numpy.nonzero",
"numpy.unique",
"numpy.reshape",
"numpy.arange",
"numpy.fliplr",
"sklearn.neighbors.kneighbors_graph",
"scipy.spatial.distance.cdist",
"scipy.linalg.lstsq",
"numpy.max",
"numpy.copy",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
rohm1/CatalogScanner | [
"19b9846e0a0ecb94342f7fafdcc2d32fbc007335"
] | [
"critters.py"
] | [
"import itertools\nfrom common import ScanMode, ScanResult\n\nimport collections\nimport cv2\nimport enum\nimport functools\nimport json\nimport numpy\nimport os\n\nfrom typing import Dict, Iterator, List, Tuple\n\n# The expected color for the video background.\nBG_COLOR = numpy.array([207, 238, 240])\n\n\nclass Cr... | [
[
"numpy.partition",
"numpy.fmod",
"numpy.arange",
"numpy.median",
"numpy.linalg.norm",
"numpy.argmin",
"numpy.array",
"numpy.roll"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaymody/transformers | [
"2f64fd85dcc61a9e1156843daa978f7dbe480c1e"
] | [
"src/transformers/training_args.py"
] | [
"import dataclasses\nimport json\nimport logging\nimport os\nfrom dataclasses import dataclass, field\nfrom typing import Any, Dict, Optional, Tuple\n\nfrom .file_utils import cached_property, is_torch_available, is_torch_tpu_available, torch_required\n\n\nif is_torch_available():\n import torch\n\nif is_torch_t... | [
[
"torch.distributed.init_process_group",
"torch.cuda.set_device",
"torch.cuda.is_available",
"torch.device",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mxbonn/zigzag_fork | [
"250ee5e22904ba846dfb106983d46b83bd9ee230"
] | [
"cost_model_funcs.py"
] | [
"import numpy as np\nimport copy\nimport sys\nimport math\nfrom numpy import prod\n\n\"\"\"\n\nThis file includes all the functions used in the cost model.\n\n\"\"\"\n\n\ndef get_operand_level_energy_cost(operand, level, mem_word_cost, mac_array_info, schedule_info, loop, mem_fifo,\n ... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lonelycorn/machine-learning | [
"812b4d4f214dc28463cb87bada4e88d0d0cf4184"
] | [
"plsr/Plsr.py"
] | [
"import numpy as np\nimport copy\n\n\ndef decompose(X_raw, Y_raw, M_star):\n \"\"\"\n :param [in] X_raw: M-by-N matrix where each COL is a sample input\n :param [in] Y_raw: L-by-N matrix where each COL is a sample output\n :return (v, p, q, C_YY_history, C_XX_history)\n \"\"\"\n assert(X_raw.shape... | [
[
"matplotlib.pyplot.legend",
"numpy.linalg.svd",
"matplotlib.pyplot.title",
"numpy.multiply",
"numpy.matmul",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.supti... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alan-turing-institute/DSSG19-DNCP-PUBLIC | [
"1f8b037dd2fd79c9729ece970ba23a7b615acb74"
] | [
"notebooks/annajulia-objects-stats.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:light\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.4'\n# jupytext_version: 1.1.7\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # O... | [
[
"pandas.read_sql_query",
"matplotlib.pyplot.subplots"
]
] | [
{
"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": []
}
] |
alexlib/partracking3D | [
"e3bb7aa48d20de8bb02a2f3549f07f3a411249f4"
] | [
"STMPython/STMFunctions.py"
] | [
"# 2017-04-12 included [0,0,0] in the default neighbours should not be too critical, just a backup measure.\r\nimport sys\r\nimport math\r\nimport collections as col\r\nimport numpy as np\r\nimport itertools as it\r\nimport copy\r\nimport datetime\r\n#import scipy.spatial as sps\r\n\r\n# Take a position p and a bun... | [
[
"numpy.dot",
"numpy.linalg.solve",
"numpy.identity",
"numpy.cross",
"numpy.outer",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manvhah/pyksvd | [
"2b17a2892c0cdcc7a53b7ddbadc764b0af7a8dd9"
] | [
"setup.py"
] | [
"\n#!/usr/bin/env python\n\n################################################################################\n# All the control parameters should go here\n\nsource_directory_list = ['ksvd']\ncompiler_args = []\nlink_args = []\nversion = \"0.01\"\ndescription=\"Implementation of the K-SVD algorithm.\" \nauthor = \"H... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LSSTDESC/ImageProcessingPipelines | [
"55eac5471fbc90fae884d8723e201da4d1bdffea"
] | [
"workflows/srs/pipe_scripts/run_makeFpSummary.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\n.. _run_makeFpSummary:\n\nRun makeFpSummary.py for a list of visits\n=========================================\n\"\"\"\n\nfrom __future__ import print_function\nimport os\nimport glob\nimport numpy as N\nimport libRun as LR\n\n\n__author__ = 'Nicolas Chotard <nchotard@in2p3.fr>'\n_... | [
[
"numpy.savetxt",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
elma16/floe | [
"840c78758035af541374607b2686df9ac5d11ee0"
] | [
"examples/evp-error-conv.py"
] | [
"from seaice import *\nfrom firedrake import *\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\n\nplt.rcParams.update({\"font.size\": 12})\n\npath = \"./output/evp-error-conv\"\nPath(path).mkdir(parents=True, exist_ok=True)\n\n\"\"\"\nTEST 2 : EVP Error Convergence\n\nManufactured solutions.\nCoriolis fo... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.loglog",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.rcParams.update"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kshithijiyer/TensorNetwork | [
"bf47f8635eca33edf95c73d50d48d861f628aaec"
] | [
"tensornetwork/tensornetwork_test.py"
] | [
"# Copyright 2019 The TensorNetwork 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.convert_to_tensor",
"numpy.diag",
"tensorflow.zeros",
"tensorflow.map_fn",
"numpy.trace",
"numpy.linalg.svd",
"numpy.arange",
"numpy.eye",
"numpy.matmul",
"tensorflow.test.main",
"numpy.tensordot",
"numpy.zeros",
"tensorflow.enable_v2_behavior",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13"
]
}
] |
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