repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
ahzz1207/ALBERT-TINYBERT | [
"d7065a8c7316fd46bc6855ed238c45533e926c0e"
] | [
"utils/tf_utils.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.convert_to_tensor",
"tensorflow.nn.sigmoid",
"tensorflow.constant",
"tensorflow.range",
"tensorflow.shape",
"tensorflow.pow",
"tensorflow.nest.flatten",
"tensorflow.gather",
"tensorflow.keras.backend.reshape",
"tensorflow.keras.activations.get"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
HeosSacer/pyautomonkey | [
"f0863aab0e6464f50659c9ff183558739f56a772"
] | [
"pyautomonkey/image_tools.py"
] | [
"from desktopmagic.screengrab_win32 import getScreenAsImage\nimport win32gui, win32con\nfrom PIL import Image\nfrom time import sleep\nfrom timeit import default_timer as timer\nimport numpy as np\nimport cv2\n\n\ndef retrieve_image(window_name=None):\n \"\"\"\n brings specified window to foreground and retur... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Martinjoh1/Ticket-Maker | [
"71fb2004d28c6bbd4d071df8b1fc785f866bb1d1"
] | [
"ticket_scanner.py"
] | [
"import csv\nimport os\nimport os.path\nimport pandas as pd\n\n\ndef create_scanned_csv():\n csvData = ['ID']\n\n with open('scanned_tickets.csv', 'a') as csvFile:\n writer = csv.writer(csvFile)\n writer.writerow(csvData)\n\n csvFile.close()\n\ndef add_to_csv(id):\n with open('scanned_tickets.... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
xeb/transformers | [
"43fda01d35739622cad1dd190b170c052c5959c8"
] | [
"examples/run_language_modeling.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace 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.nn.utils.rnn.pad_sequence",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"torch.device",
"torch.distributed.get_rank",
"torch.distributed.init_process_group",
"torch.utils.data.distributed.DistributedSampler",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chouxianyu/TFA | [
"8802aeb66fbc3a5081fa3dfb2830ca517718afe1"
] | [
"datasets/prepare_voc_few_shot.py"
] | [
"\"\"\"\n基于VOC，生成多组（29组）不同seed的few-shot dataset\n\n代码逻辑如下：\n遍历训练集（07、12的train和val合起来）的标注。对于每个class，知道哪些图片包含（至少有1个）属于该class的object\n对于某个seed、某个class的多个k-shot：3-shot dataset包括1-shot dataset、5-shot dataset包括3-shot dataset，以此类推……\n对于某个seed、某个class、某个k-shot（以5-shot为例）：\n 基于上个shot（3-shot）选取的图片（m张图片，最多3张，可以少于3张，最少1张；n个... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raunak274/greyatom-python-for-data-science | [
"180158a75a67d124d4b3737d0ce1ca5e972c36bc"
] | [
"Wrangling-With-Pandas/code.py"
] | [
"# --------------\n# Importing header files\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom scipy.stats import mode \r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\n#Reading file\r\nbank_data = pd.read_csv(path)\r\n\r\n#Code starts here\r\nbank = pd.DataFrame(bank_data)\r\n\r\ncategorical_var = ... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"pandas.pivot_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ElementAI/SRKORNIA | [
"a0be8884bc79d9fdbc8e9145606a8b634c392d94"
] | [
"code/trainval.py"
] | [
"import os\nimport argparse\nimport pandas as pd\nimport pprint\nimport torch \n\nimport exp_configs\nfrom src.backbones import get_backbone\nfrom src import datasets\nfrom src import models\n\nfrom haven import haven_utils as hu\nfrom haven import haven_chk as hc\nfrom haven import haven_jobs as hj\n\n\ndef trainv... | [
[
"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": []
}
] |
ncclementi/pygbe_lspr | [
"0a73406701a74369206c33e0a3fd31d7b5f0cf55"
] | [
"analysis_notebooks/scripts/data_analysis_helper.py"
] | [
"import numpy \nfrom matplotlib import pyplot, rcParams\nfrom scipy.interpolate import interp1d, splev, splrep\n\ndef wave_filter_interp(lambda_eval, lambda_interp):\n '''It removes the data points of a wavelength array, that are out of the\n range where the resulting interpolation is valid. \n\n Argum... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.loglog",
"matplotlib.pyplot.plot",
"scipy.interpolate.interp1d",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.text",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"sci... | [
{
"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"
... |
bmd2007/benchmark_eval | [
"aa42bb3369e79db4cb63e1963afcc8af6d8f5696"
] | [
"Methods/GenericPairwisePredictor.py"
] | [
"import os\r\nimport sys\r\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\r\nparentdir = os.path.dirname(currentdir)\r\nsys.path.append(parentdir)\r\nimport time\r\nimport numpy as np\r\nfrom joblib import dump, load\r\nimport torch\r\nimport copy\r\nfrom ProteinFeaturesHolder import ProteinFeaturesHolde... | [
[
"numpy.hstack",
"numpy.log",
"numpy.log2",
"numpy.abs",
"numpy.asarray",
"numpy.isnan",
"numpy.sign",
"numpy.log10",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flatM/AlephNull | [
"796edec7e106cd76a5a69cb6e67a1a96c7a22cf6"
] | [
"alephnull/examples/buystock.py"
] | [
"#!/usr/bin/env python\n#\n# Copyright 2013 Carter Bain Wealth Management\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# ... | [
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.gcf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pkretzschmar/commons-config-backend | [
"a5abf9294e0b2fbb8f5d5bf5d3db2b1c55ebbc91"
] | [
"models/augmented_bonding_curve.py"
] | [
"import os\nimport pandas as pd\nimport numpy as np\n\n\n#These numbers are fixed from the hatch results (all in thousands except the token price)\nTOTAL_HATCH_FUNDING = 1571.22357 \nTOTAL_INITIAL_TECH_SUPPLY= 2035.918945 \nHATCH_FINAL_TECH_PRICE = 0.754743 \n\n\nclass BondingCurveInitializer:\n\n def __init__(s... | [
[
"numpy.linspace",
"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": []
}
] |
HugoTessier-lab/SWD | [
"f382166d0231d1bfce50392acefd3634e6d50734"
] | [
"experiment.py"
] | [
"import torch\nfrom utils.parse_arguments import parse_arguments\nimport sys\nfrom utils.datasets import get_dataset\nfrom utils.regularization_and_pruning import Regularization, get_mask_function\nimport math\nimport numpy as np\nfrom utils.checkpoint import Checkpoint\nimport time\n\nif torch.backends.cudnn.enabl... | [
[
"numpy.random.seed",
"torch.manual_seed",
"torch.nn.functional.cross_entropy",
"torch.no_grad",
"torch.argsort",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lbruand/tensorflow | [
"02159bbe1f87638bb6cde6a6f4aaa2fc0362e53b"
] | [
"tensorflow/python/eager/def_function.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.resource_variable_ops.var_is_initialized_op",
"tensorflow.python.util.tf_decorator.make_decorator",
"tensorflow.python.eager.context.context",
"tensorflow.python.eager.context.executing_eagerly",
"tensorflow.python.eager.context.ensure_initialized",
"tensorflow.pytho... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"2.7",
"2.4",
"2.3",
"2.5",
"2.6"
]
}
] |
Juancarlos1983/Pymop | [
"7b7e789e640126c6d254e86ede5d7f4baad7eaa5"
] | [
"tests/archive/wfg.py"
] | [
"import numpy as np\n\nfrom pymop.problem import Problem\n\n\nclass WFG(Problem):\n def __init__(self, name, n_var, n_obj, k=None):\n Problem.__init__(self)\n self.n_obj = n_obj\n self.n_var = n_var\n self.k = 2 * (self.n_obj - 1) if k is None else k\n self.func = self._evaluat... | [
[
"numpy.ones",
"matplotlib.pyplot.show",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LaudateCorpus1/caladrius | [
"9094d7542fb1796a690fed2b8b0083533dcac67e"
] | [
"graph/analysis/heron/io_ratios.py"
] | [
"# Copyright 2018 Twitter, Inc.\n# Licensed under the Apache License, Version 2.0\n# http://www.apache.org/licenses/LICENSE-2.0\n\n\"\"\" This module contains methods for calculating the input output (I/O) ratio\nfor the instances of a given topology. \"\"\"\n\nimport logging\n\nimport datetime as dt\n\nfrom typing... | [
[
"numpy.linalg.lstsq",
"pandas.Grouper",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [
... |
Mire-Group/GMCORE | [
"d6743305aacfa3051b6ab0a51033b03804347848"
] | [
"tools/calc_errors.py"
] | [
"#!/usr/bin/env python3\n\nfrom netCDF4 import Dataset\nimport numpy as np\nimport sys\n\nf = Dataset(sys.argv[1], 'r')\n\nh0 = f.variables['h'][0,:]\nh1 = f.variables['h'][-1,:]\ncos_lat = np.cos(np.radians(f.variables['lat'][:]))\n\nL1 = np.sum(np.sum(np.abs(h1 - h0), axis=1) * cos_lat) / np.sum(np.sum(np.abs(h0)... | [
[
"numpy.radians",
"numpy.sum",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arbab97/deepcrime | [
"d85b0b30d11cd858655d8ef2519cd54a6af24c3c"
] | [
"test_models/custom_model_orig.py"
] | [
"\nfrom __future__ import print_function\nimport keras, sys\nfrom utils import mutation_utils\nimport os\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras import backend as K\nimport ten... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.Graph",
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"tensorflow.compat.v1.Session",
"sklearn.preprocessing.Normalizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
trh3/clpipe | [
"e783fccc1ed530ea59c2047d0b8ecb26a2f98204"
] | [
"clpipe/fmri_postprocess.py"
] | [
"import os\nimport glob\nimport click\nimport pandas\nimport nibabel as nib\nfrom .batch_manager import BatchManager, Job\nfrom .config_json_parser import ClpipeConfigParser\nimport json\nfrom pkg_resources import resource_stream, resource_filename\nimport clpipe.postprocutils\nimport numpy\nimport logging\nimport ... | [
[
"numpy.convolve",
"pandas.concat",
"pandas.read_csv",
"numpy.expand_dims",
"numpy.logical_and",
"numpy.asarray",
"numpy.arange",
"pandas.read_table",
"numpy.linalg.pinv",
"numpy.shape",
"numpy.float32",
"numpy.transpose",
"numpy.array",
"numpy.zeros",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
pimier15/pyMLBasic | [
"11421946ac574362b0371b8af3c277dc7e9dbc0b"
] | [
"pyMLBasic/pyMLBasic/3 SimpleClassifier/RandomForest.py"
] | [
"import numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\nfrom Common.LoadIris import LoadIris\n\nforest = RandomForestClassifier(criterion = 'entropy' , n_estimators = 10 , random_state = 0 , n_jobs = 2)\n \n"
] | [
[
"sklearn.ensemble.RandomForestClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Covid19-US-Dashboard/COVID19_Dashboard | [
"fa5ef6bdc6fab7f2e5903077b335a78991524a00"
] | [
"python_dash/app.py"
] | [
"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport pandas as pd\n\nfrom dash.dependencies import Input, Output, State\nfrom plotly import graph_objs as go\nfrom plotly.graph_objs import *\nfrom datetime import datetime as dt\n\napp = dash.Dash(\n __name__, meta_tags=[{\... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mathigatti/theBeautyFormula | [
"b5d9cf73af8ca36d3172311aa8e24126116a531f"
] | [
"number2automata.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image\n\nON = 255\nOFF = 0\nN = 250\n\ndef rule(neighbours,rules):\n if neighbours in rules:\n return ON\n else:\n return OFF\n\ndef update(grid,rules):\n newGrid = grid.copy()\n for i in range(1,N-1):\n for j in range(1,N-1)... | [
[
"numpy.array",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mincloud1501/Python | [
"d6bbd7e22c8b8ff83217199b8af178f07b4cd724"
] | [
"Data_Analytics_Pandas/gonggongInfoAnalysis2.py"
] | [
"import os\nimport sys\nimport urllib.request\nimport datetime\nimport time\nimport json\n\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom matplotlib import font_manager, rc\n\n# https://www.data.go.kr/\n# 출입국 관광 통계 서비스\ndef get_request_url(url):\n req = urllib.request.Request(url)\n\n try:\n ... | [
[
"matplotlib.font_manager.FontProperties",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.rc",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shapedbyiris/audio-super-res | [
"f69a9dbdd4452dc5521ae383ead5f71d08a84664"
] | [
"src/models/audiotfilm.py"
] | [
"import numpy as np\nimport tensorflow as tf\n\nfrom scipy import interpolate\nfrom .model import Model, default_opt\n\nfrom .layers.subpixel import SubPixel1D, SubPixel1D_v2\n\nfrom keras import backend as K\nfrom keras.layers import merge, MaxPooling1D, MaxPooling2D, AveragePooling1D\nfrom keras.layers.core impor... | [
[
"scipy.interpolate.splrep",
"tensorflow.shape",
"numpy.arange",
"tensorflow.reshape",
"tensorflow.cast",
"scipy.interpolate.splev",
"tensorflow.name_scope",
"tensorflow.trainable_variables",
"numpy.zeros"
]
] | [
{
"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"
... |
yaochaorui/mmpose | [
"056d8db55373e933a971eadc66f92f1d1e773332"
] | [
"mmpose/datasets/datasets/mesh/mesh_h36m_dataset.py"
] | [
"import os\nfrom collections import OrderedDict\n\nimport json_tricks as json\nimport numpy as np\n\nfrom mmpose.datasets.builder import DATASETS\nfrom ....core.evaluation import compute_similarity_transform\nfrom .mesh_base_dataset import MeshBaseDataset\n\n\n@DATASETS.register_module()\nclass MeshH36MDataset(Mesh... | [
[
"numpy.array",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
nomad-coe/workflow-parsers | [
"29a47d873ae921c23b58b95f8496fa0a2d503f15"
] | [
"tests/test_lobsterparser.py"
] | [
"#\n# Copyright The NOMAD Authors.\n#\n# This file is part of NOMAD. See https://nomad-lab.eu for further info.\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.... | [
[
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tenokraat/pyarubacentral | [
"a8c04f1e55f0f7a6091d4bf66a8d1ad9970d97f8"
] | [
"nwseg_submodule_getGwDetails.py"
] | [
"\n# Import Aruba Central Base\nfrom pycentral.base import ArubaCentralBase\nfrom pycentral.configuration import Groups\nfrom pycentral.monitoring import Devices\nfrom pprint import pprint\nimport json\nimport csv\nimport pandas\nimport collections\n\n\n# Create the following files by refering to the samples.\ncent... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
minhtcuet/creditscoring | [
"5a00f110b1936c7151a6e471cfb52b907b199f4d"
] | [
"credit_scoring/metrics/_ks.py"
] | [
"import numpy as np\r\nimport pandas as pd\r\nimport plotly.express as px\r\n\r\nfrom credit_scoring.metrics.credit_score import CreditScore\r\n\r\n\r\nclass CalculatedKS(CreditScore):\r\n\r\n\tdef ks(self) -> tuple:\r\n\t\t'''\r\n\t\tCalculate the KS table of each decile, KS value, the deicile at KS\r\n\t\t:return... | [
[
"numpy.round",
"pandas.qcut",
"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": []
}
] |
Zilv1128/test1 | [
"49fbb1392e69b5194c077df9847505ec995b4e3d"
] | [
"recipes/sota/2019/lm_analysis/generate_shuffle_dev_other_tts.py"
] | [
"import os\nimport sys\n\nimport numpy\n\n\nnumpy.random.seed(42)\n\n\nwith open(os.path.join(sys.argv[1], \"dev-other.lst\"), \"r\") as f:\n data = [line.strip() for line in f]\n\nfor n, seed_val in enumerate([0, 2, 3, 4, 5]):\n numpy.random.seed(42 + seed_val)\n data = numpy.random.permutation(data)\n\n ... | [
[
"numpy.random.permutation",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZircXes/uncmac854course | [
"0b8845889cabb07098a7186120ed56ee0ef392a7"
] | [
"Week 01/Week 01 - Examples.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# # Week 01 Example Statistics in Python 3\n# This Notebook introduces basic statistical analysis from week 01 using Python 3.x.\n# \n# Jupyter notebooks blend Markdown (rich formatted text) with software code and output to ease the learning process.\n# \n\n# Version: 01\n... | [
[
"scipy.stats.norm.ppf",
"pandas.read_excel",
"scipy.stats.norm.interval",
"scipy.stats.t.ppf",
"scipy.stats.t.cdf",
"scipy.stats.t.interval"
]
] | [
{
"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": []
}
] |
stebranchi/Incremental-predictive-monitoring-python3 | [
"114b080df4afa0653ce03d8eb0059ceda096752b"
] | [
"src/evaluation/prepare_data_resource.py"
] | [
"\"\"\"\nThis script prepares data in the format for the testing\nalgorithms to run\n\nThe script is expanded to the resource attribute\n\"\"\"\n\nfrom __future__ import division\n\nimport copy\nimport csv\nimport re\nimport time\nfrom queue import PriorityQueue\nfrom datetime import datetime\n\nimport numpy as np\... | [
[
"numpy.math.exp",
"numpy.log",
"numpy.cumsum",
"numpy.max",
"numpy.argsort",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shingte/CarND-Vehicle-Detection | [
"38b0f9645f1300836f5877a33c612f004e0aaa5b"
] | [
"mrcnn/visualize.py"
] | [
"\"\"\"\nMask R-CNN\nDisplay and Visualization Functions.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\nAdd plot param to display_instances() - S.Li\n\"\"\"\n\nimport os\nimport sys\nimport logging\nimport random\nimport itertools\nimp... | [
[
"matplotlib.pyplot.imshow",
"numpy.concatenate",
"numpy.any",
"numpy.where",
"matplotlib.patches.Polygon",
"matplotlib.pyplot.tight_layout",
"numpy.unique",
"numpy.fliplr",
"numpy.arange",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.axis",
"numpy.zeros",
"ma... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pramenku/tensorflow-upstream | [
"1464c9b4823d88c19e2969170c6149a58d4ec6cd"
] | [
"tensorflow/python/keras/backend_test.py"
] | [
"# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.keras.backend.zeros",
"tensorflow.python.keras.backend.softmax",
"tensorflow.core.protobuf.config_pb2.RunMetadata",
"tensorflow.python.keras.backend.reset_uids",
"tensorflow.python.keras.backend.variable",
"tensorflow.python.keras.backend.function",
"tensorflow.pytho... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.3",
"2.5",
"2.2",
"2.4"
]
}
] |
jamienoss/hcipy | [
"49604bd0dab5653cea2b934f7c1b327c4b5d4a67"
] | [
"hcipy/plotting/error_bars.py"
] | [
"import numpy as np\nimport matplotlib as mpl\n\ndef errorfill(x, y, y_err_pos, y_err_neg=None, color=None, alpha_fill=0.25, ax=None, **kwargs):\n\t\"\"\"\n\tPlot a line with filled errorbars.\n\t\"\"\"\n\timport matplotlib.pyplot as plt\n\n\tif ax is None:\n\t\tax = plt.gca()\n\n\tif y_err_neg is None:\n\t\ty_err_... | [
[
"matplotlib.colors.colorConverter.to_rgba",
"matplotlib.pyplot.gca",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
feifeibear/ColossalAI | [
"4c4388c46ed1cee9e535bf9ea5a5e5fb61d9a769"
] | [
"tests/test_zero/test_zero_engine.py"
] | [
"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\nfrom functools import partial\n\nimport colossalai\nimport pytest\nimport torch\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\nfrom colossalai.core import global_context as gpc\nfrom colossalai.testing import rerun_if_address_is_in_use\nfr... | [
[
"torch.distributed.get_world_size",
"torch.multiprocessing.spawn",
"torch.cuda.current_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jorgepiloto/lamberthub | [
"fedf6aaa939ee08e3bccb754b519397352cb5a84"
] | [
"src/lamberthub/universal_solvers/izzo.py"
] | [
"\"\"\" A module hosting all algorithms devised by Izzo \"\"\"\n\nimport time\n\nimport numpy as np\nfrom numpy import cross, pi\nfrom numpy.linalg import norm\nfrom scipy.special import hyp2f1\n\nfrom lamberthub.utils.assertions import assert_parameters_are_valid\n\n\ndef izzo2015(\n mu,\n r1,\n r2,\n ... | [
[
"numpy.log2",
"numpy.sqrt",
"numpy.abs",
"numpy.min",
"numpy.arccos",
"numpy.linalg.norm",
"numpy.max",
"scipy.special.hyp2f1",
"numpy.floor",
"numpy.cross"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dsosnoski/morepork | [
"669e53827052549c402b8f5b0ad24a374a7b3559"
] | [
"latest/loop_trainer_rnn.py"
] | [
"\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport tensorflow as tf\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n\nimport morepork_support\nfrom sampler_dataset import DataSampler\nfrom resnet_rnn import ResnetRNNBuilder\nimport training_parameters\n\ntrain_fra... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"tensorflow.keras.callbacks.ReduceLROnPlateau",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"tensorflow.keras.optimizers.Adam",
"matplotlib.pyplot.subplot",
"tensorflow.keras.backend.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
kbaseapps/mags_mash | [
"40f66e6b6ca7d96adf2f1cb6f83718c3dae19c4f"
] | [
"lib/mags_mash/utils/mags_filter.py"
] | [
"from installed_clients.WorkspaceClient import Workspace\nfrom installed_clients.DataFileUtilClient import DataFileUtil\n\nimport numpy as np\nimport pandas as pd\nimport os\nfrom collections import defaultdict\nfrom scipy.cluster.hierarchy import linkage, leaves_list\n\n\ndef create_tree(GOLD, tree_cols, dist_comp... | [
[
"pandas.concat",
"scipy.cluster.hierarchy.leaves_list",
"pandas.read_csv",
"pandas.DataFrame.from_dict",
"scipy.cluster.hierarchy.linkage",
"numpy.array"
]
] | [
{
"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",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2"... |
beukueb/leopard | [
"0b1c8d267397ee3daa7b7e713a765d24a6c645f6"
] | [
"leopard/utils.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Leopard utility functions and classes\n\"\"\"\n\nimport pandas as pd, re\nfrom collections import OrderedDict\n\ndef makeFigFromFile(filename,*args,**kwargs):\n \"\"\"\n Renders an image in a matplotlib figure, so it can be added to reports \n args and kwargs are passed to p... | [
[
"matplotlib.pyplot.imread",
"matplotlib.pyplot.subplots",
"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": []
}
] |
caomw/pydro | [
"00286cb99a58e4c49fe79b08d8ae041cf8ee173c",
"00286cb99a58e4c49fe79b08d8ae041cf8ee173c"
] | [
"tests/features_test.py",
"src/pydro/features.py"
] | [
"import numpy.linalg\nimport scipy.io\nimport scipy.misc\nimport numpy\nimport itertools\n\nfrom pydro.features import *\nfrom pydro.io import *\n\ndef resize_test():\n image = scipy.misc.imread('tests/lenna.png').astype(numpy.float32)\n\n image_resized = ResizeImage(image, image.shape[0]/2, image.shape[1]/2)... | [
[
"numpy.isnan",
"numpy.array",
"numpy.fabs"
],
[
"numpy.dstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
doongu/pnu_opensource_hack | [
"1abcce865776182b677ee468135a86bb462e89a8"
] | [
"secondary-voice-server-AI/assem-vc/gta_extractor.py"
] | [
"import os\r\nimport tqdm\r\nimport torch\r\nfrom torch.utils.data import DataLoader\r\nimport shutil\r\nimport argparse\r\nimport pytorch_lightning as pl\r\nfrom omegaconf import OmegaConf\r\n\r\nfrom synthesizer import Synthesizer\r\nfrom datasets.text import Language\r\nfrom datasets import TextMelDataset, text_... | [
[
"torch.cat",
"torch.load",
"torch.utils.data.DataLoader",
"torch.cuda.empty_cache",
"torch.no_grad",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haharay/mesa | [
"449185f7ceb461c4df60a6721ecc6f021146c8aa"
] | [
"mesa/space.py"
] | [
"\"\"\"\nMesa Space Module\n=================\n\nObjects used to add a spatial component to a model.\n\nGrid: base grid, a simple list-of-lists.\nSingleGrid: grid which strictly enforces one object per cell.\nMultiGrid: extension to Grid where each cell is a set of objects.\n\n\"\"\"\n# Instruction for PyLint to su... | [
[
"numpy.minimum",
"numpy.abs",
"numpy.sqrt",
"numpy.delete",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sbassi/metodos-numericos | [
"d21b09f384f326793eb59bbaa28505561698d719"
] | [
"python/bernstein.py"
] | [
"#!/usr/bin/env python3\n# Aproximación a una función continua mediante los polinomios de Bernstein\n# (prueba del teorema de aproximación de Weierstrass)\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.stats as st\n\n\n# La función a aproximar\ndef f(x):\n return np.abs(1 / 2 - x)\n\n\ndef be... | [
[
"matplotlib.pyplot.legend",
"numpy.abs",
"numpy.linspace",
"scipy.stats.binom.pmf",
"numpy.arange",
"matplotlib.pyplot.plot",
"numpy.vectorize",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KejiaChen/assembly | [
"dbaa3eeb40709c4a2033b6a603a68c17e60a0477"
] | [
"furniture/env/controllers/baxter_ik_controller.py"
] | [
"\"\"\"\nNOTE: requires pybullet module.\n\nRun `pip install pybullet==1.9.5`.\n\"\"\"\n\nimport os\nimport numpy as np\n\ntry:\n import pybullet as p\nexcept ImportError:\n raise Exception(\n \"Please make sure pybullet is installed. Run `pip install pybullet==1.9.5`\"\n )\n\nfrom .. import transfo... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
matthew-z/MatchZoo-py | [
"014e33d7c4ae05dc5296ebd1d600baf7e9cb9dec"
] | [
"matchzoo/engine/base_model.py"
] | [
"\"\"\"Base Model.\"\"\"\n\nimport abc\nimport typing\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom matchzoo import preprocessors\nfrom matchzoo import tasks\nfrom matchzoo.dataloader import callbacks\nfrom matchzoo.engine import hyper_spaces\nfrom matchzoo.engine.base_callback import BaseCallbac... | [
[
"torch.nn.Sequential",
"torch.Tensor",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YanickT/astonomische-methoden | [
"e1c35265f64ed1bb6310ba89f975f45b2c42a9cb"
] | [
"tf_versions/main.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom astroquery.sdss import SDSS\nfrom network import Network\n\n\ndef get_data(class_, n_train=1000, n_test=1000):\n \"\"\"\n Load data for training and evaluation.\n :param class_: str = Type of astronomical objects\n :param n_train: int = Number o... | [
[
"numpy.abs",
"matplotlib.pyplot.ticklabel_format",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carsondahlberg/Kashgari | [
"7111d2d2959f234c05a0db06073c6142f826a8f7"
] | [
"kashgari/tasks/seq_labeling/base_model.py"
] | [
"# encoding: utf-8\n\"\"\"\n@author: BrikerMan\n@contact: eliyar917@gmail.com\n@blog: https://eliyar.biz\n\n@version: 1.0\n@license: Apache Licence\n@file: base_model\n@time: 2019-01-21\n\n\"\"\"\nimport os\nimport random\nimport json\nimport pathlib\nimport logging\nfrom typing import Tuple, Dict\n\nimport numpy a... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dantegd/dask-cuml | [
"f69539c5bc88963824d51e5cc558512a00c62a48"
] | [
"dask_cuml/neighbors/nearest_neighbors.py"
] | [
"# Copyright (c) 2019, NVIDIA CORPORATION.\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 l... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpchiodini/Grasp-Planning | [
"e31234244b8f934743605ebea59d9d98a258957e"
] | [
"Grasping.py"
] | [
"import numpy as np\nimport math\nimport itertools\n\n\ndef GraspPointFiltering(numPts, P, N, C):\n # create superset of all possibilities:\n counter = list(range(0, numPts))\n points = list(itertools.combinations(counter, 2))\n curvatureVals = []\n\n for i in range(0, len(points)):\n x = poin... | [
[
"numpy.asarray",
"numpy.dot",
"numpy.subtract",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
tussedrotten/pylie | [
"df34b820b9d9273bc9c4287e559e5d5837faf794"
] | [
"examples/vis_perturbations.py"
] | [
"import visgeom as vg\nfrom pylie import SO3, SE3\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons\n\n\"\"\"Example - Visualizes different perturbations and the path they take along the manifold\"\"\"\n\n\ndef vis_pert... | [
[
"numpy.linspace",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.subplots_adjust",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
muzi2018/DRL_RTD | [
"518100c4c48f66fd38ef9877f3e4903c9cc2a237"
] | [
"Multi-Agent-Path-Finding/cbs_mapf/planner.py"
] | [
"#!/usr/bin/env python3\n'''\nAuthor: Haoran Peng\nEmail: gavinsweden@gmail.com\n\nAn implementation of multi-agent path finding using conflict-based search\n[Sharon et al., 2015]\n'''\nfrom typing import List, Tuple, Dict, Callable, Set\nimport multiprocessing as mp\nfrom heapq import heappush, heappop\nfrom itert... | [
[
"numpy.array",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
chenghlee/imagecodecs | [
"926078fe406bfa4e6c0d4452f1eccf7201e105fd"
] | [
"imagecodecs/imagecodecs.py"
] | [
"# imagecodecs.py\n\n# Copyright (c) 2008-2021, Christoph Gohlke\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# 1. Redistributions of source code must retain the above copyright not... | [
[
"numpy.load",
"numpy.savez_compressed",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nayan-Das/augur | [
"857f4a4e7d688fd54356aa0f546834071fbabbf2"
] | [
"workers/linux_badge_worker/linux_badge_worker/worker.py"
] | [
"import os\nfrom datetime import datetime\nimport logging\nimport requests\nimport json\nfrom urllib.parse import quote\nfrom multiprocessing import Process, Queue\n\nfrom linux_badge_worker import __data_source__, __tool_source__, __tool_version__\nimport pandas as pd\nimport sqlalchemy as s\nfrom sqlalchemy.ext.a... | [
[
"pandas.read_sql"
]
] | [
{
"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": []
}
] |
rodekruis/Covid-SEIR | [
"c1f0d5d23b1bb82d5ac6298193dbcae139a77b0f"
] | [
"bin/confidencecurves.py"
] | [
"import os\nimport warnings\n\nfrom src.io_func import load_config, load_data\nfrom src.tools import calc_axis_interval\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport datetime\nimport matplotlib.dates as mdates\n\nfrom src.parse import get_mean\nfrom src.tools import generate_hos_actual, g... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.plot",
"numpy.max",
"matplotlib.dates.DayLocator",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.gcf",
"numpy.size",
"numpy.diff",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"matplotlib.dates.DateFormatter",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mitkosl/OcadoHackaton | [
"45067799bf3178d7460d143ff1a2f265c610f7be"
] | [
"recognize_video.py"
] | [
"# USAGE\n# python recognize_video.py --detector face_detection_model \\\n#\t--embedding-model openface_nn4.small2.v1.t7 \\\n#\t--recognizer output/recognizer.pickle \\\n#\t--le output/le.pickle\n\n# import the necessary packages\nfrom imutils.video import VideoStream\nfrom imutils.video import FPS\nimport numpy as... | [
[
"numpy.array",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jisuk500/ML_learning | [
"4f77eb34bd652753e63fb75fa2be5bd252232f80"
] | [
"tensorflow examples/3 neural networks/neural_network_raw.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 3 17:15:11 2020\n\n@author: jisuk\n\"\"\"\n\n# %% immport some modules\nfrom __future__ import absolute_import, division, print_function\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.datasets import mnist\n\nimport tensorflow as tf\nimport numpy as np\... | [
[
"tensorflow.clip_by_value",
"tensorflow.matmul",
"tensorflow.nn.softmax",
"tensorflow.nn.sigmoid",
"tensorflow.zeros",
"tensorflow.initializers.RandomNormal",
"numpy.reshape",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.cast",
"tensorflow.keras.datasets.mnist.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13",
"1.10",
"1.12"
]
}
] |
wzzheng/DCML | [
"01a7220bac7ebb1e70416ef663f3ba7cee9e8bf5",
"01a7220bac7ebb1e70416ef663f3ba7cee9e8bf5"
] | [
"criteria/oproxy.py",
"criteria/imrot.py"
] | [
"import numpy as np\nimport torch, torch.nn as nn, torch.nn.functional as F\nimport batchminer\nimport criteria\n\n\"\"\"=================================================================================================\"\"\"\nALLOWED_MINING_OPS = None\nREQUIRES_BATCHMINER = False\nREQUIRES_OPTIM = True\n\n\nc... | [
[
"torch.nn.functional.normalize",
"torch.nn.Parameter",
"torch.nn.Softplus",
"torch.randn",
"torch.tensor",
"torch.arange",
"torch.logsumexp"
],
[
"torch.nn.Linear",
"torch.nn.CrossEntropyLoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TangJiahui/AC215-Advanced_Practical_Data_Science | [
"904e595630ddb9b2348889ce0d2cc777bb90f071"
] | [
"mushroom-app/api-service/api/tracker.py"
] | [
"\nimport os\nimport asyncio\nfrom glob import glob\nimport json\nimport pandas as pd\n\nimport tensorflow as tf\nfrom google.cloud import storage\n\n\ngcp_project = os.environ[\"GCP_PROJECT\"]\nbucket_name = \"ac215-mushroom-app-models-small\"\nlocal_experiments_path = \"/persistent/experiments\"\n\n# Setup experi... | [
[
"pandas.read_csv",
"tensorflow.io.gfile.glob",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
kentaroy47/UniTrack | [
"e18aece2a9046225ec3b3dd595a35490d594c699"
] | [
"tracker/mot/box.py"
] | [
"###################################################################\n# File Name: box.py\n# Author: Zhongdao Wang\n# mail: wcd17@mails.tsinghua.edu.cn\n# Created Time: Fri Jan 29 15:16:53 2021\n###################################################################\n\nimport torch\nfrom torchvision import ops\n\nfrom ... | [
[
"torch.from_numpy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
InkGenius/MLA | [
"c0e73eff9923f38b5c2c3f4f643d0da6084b372e"
] | [
"clustering/nmi.py"
] | [
"# coding=utf-8\nimport numpy as np\nimport math\n\n\ndef compute_cost(A, B):\n # len(A) should be equal to len(B)\n total = len(A)\n A_ids = set(A)\n B_ids = set(B)\n\n # Mutual information\n MI = 0\n eps = 1.4e-45\n for idA in A_ids:\n for idB in B_ids:\n idAOccur = np.wh... | [
[
"numpy.intersect1d",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eedeleon/pytorch-dp | [
"0a5404f700f67a3e823bf64cf267fbb2d6d95f7a"
] | [
"torchdp/test/per_sample_gradient_clip_test.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport unittest\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom torchdp import PerSampleGradientClipper\nfrom torchvision import transforms\nfrom to... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Conv2d",
"torch.utils.data.DataLoader",
"torch.zeros_like",
"torch.nn.Linear",
"torch.nn.Conv1d",
"torch.allclose",
"torch.nn.functional.max_pool2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SubmissionSNIPit/SNIP-it | [
"0358ec4e7d94a08be73d5d7710f9608684efc861"
] | [
"models/trainers/TickTock.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom models.networks.assisting_layers.GateDecoratorLayers import GatedBatchNorm\nfrom models.trainers.DefaultTrainer import DefaultTrainer\nfrom utils.constants import OPTIMS\nfrom utils.model_utils import find_right_model\n\n\nclass TickTock(DefaultTrainer):\n\n \"\"\"\n ... | [
[
"torch.BoolTensor",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nv-research-israel/On-Learning-Sets-of-Symmetric-Elements | [
"3a72f3287a66f04388fd713a5546f9496e5ae5ed",
"3a72f3287a66f04388fd713a5546f9496e5ae5ed"
] | [
"color_matching_exp/layers.py",
"image_selection_exp/layers.py"
] | [
"# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\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 ... | [
[
"torch.nn.functional.upsample",
"torch.nn.BatchNorm1d",
"torch.nn.Dropout2d",
"torch.max",
"torch.nn.Conv2d",
"torch.nn.init.xavier_normal_",
"torch.sum",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.BatchNorm2d",
"torch.nn.functional.max_pool2d"
],
[
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JorgeSaNel/Predicci-n-de-valores-y-gesti-n-de-cartera-en-el-mercado-burs-til | [
"02660a9d141e8e82533cfc115f0dcc787c8661b3"
] | [
"py/import_data_stock_prediction.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"1_Import_Data_Stock_Prediction.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/16M9YfgtSjMARmvqPtTB3nMXsUoMhH3wb\n\"\"\"\n\n# Descomentar esta línea si se quiere ejecutar en local\n# !pip install yfinance\n... | [
[
"matplotlib.dates.DateFormatter",
"pandas.Series",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.dates.ConciseDateFormatter",
"matplotlib.dates.AutoDateLocator"
]
] | [
{
"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": []
}
] |
MikeMork/tracepy | [
"42e1e9a43f79b266a22dc07ba76ec215673e427f"
] | [
"tracepy/transforms.py"
] | [
"# Authors: Gavin Niendorf <gavinniendorf@gmail.com>\n#\n# Functions for transforming between reference frames.\n#\n# License: MIT\n\nimport numpy as np\nfrom numpy import cos, sin\n\ndef gen_rot(ang):\n \"\"\"Returns a rotation matrix from 3 rotation angles.\n\n Note\n ----\n np.matrix is deprecated an... | [
[
"numpy.matrix",
"numpy.dot",
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jinfenglin/SparkTrace | [
"19cbb1cc9dfccf0746b49c80c9b22e7bf933263a"
] | [
"scripts/query2.py"
] | [
"import math\n\nimport pandas as pd\n\nfrom Preprocessor import Preprocessor\nfrom VSM import VSM\n\n\ndef get_req(query, reqs):\n query = query.lower()\n query_tokens = query.split()\n preprocessor = Preprocessor()\n docs = []\n for x in reqs.values():\n if not isinstance(x, str):\n ... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
panpiort8/MolBERT | [
"b312700733d6f14ba5bf90347adb59b0d99be65d"
] | [
"molbert/apps/base.py"
] | [
"import logging\nimport pprint\nfrom abc import ABC\nfrom argparse import ArgumentParser, Namespace\n\nimport torch\nfrom pytorch_lightning import Trainer, seed_everything\nfrom pytorch_lightning.callbacks import ModelCheckpoint\n\nfrom molbert.apps.args import get_default_parser\nfrom molbert.models.base import Mo... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ModelTC/mqbench-paper | [
"8d25a3b63c0cde4d904f77439fc435b49b0b33d4"
] | [
"prototype/utils/misc.py"
] | [
"import os\nimport logging\nimport torch\nimport spring.linklink as link\nfrom collections import defaultdict\nimport numpy as np\ntry:\n from sklearn.metrics import precision_score, recall_score, f1_score\nexcept ImportError:\n print('Import metrics failed!')\n\nfrom .dist import simple_group_split\nimport y... | [
[
"numpy.random.beta",
"numpy.sqrt",
"numpy.clip",
"torch.randperm",
"numpy.int",
"numpy.mean",
"torch.nn.BatchNorm2d",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChenjunZou/deep-rl-tensorflow | [
"95d3e2dde77d4a7a393ec418fe3537094d08c2ba"
] | [
"agents/experience.py"
] | [
"\"\"\"Modification of https://github.com/tambetm/simple_dqn/blob/master/src/replay_memory.py\"\"\"\n\nimport random\nimport numpy as np\n\nclass Experience(object):\n def __init__(self, data_format, batch_size, history_length, memory_size, observation_dims):\n self.data_format = data_format\n self.batch_siz... | [
[
"numpy.empty",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ktavabi/autoreject | [
"9571e1f35497bc5484af8f2cfafcc3c91b6b6f18"
] | [
"autoreject/tests/test_utils.py"
] | [
"# Author: Mainak Jas <mainak.jas@telecom-paristech.fr>\n# License: BSD-3-Clause\n\nfrom numpy.testing import assert_array_equal, assert_array_almost_equal\nimport pytest\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.bem import _check_origin\nfrom mne import io\n\nfrom autoreject.utils import clean_by_in... | [
[
"numpy.testing.assert_array_equal",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
exile79/my_ml_service | [
"e912cb169b817fd8dfd490e1ffbdc71727a34c29"
] | [
"backend/server/apps/endpoints/views.py"
] | [
"from django.shortcuts import render\n\n# Create your views here.\nfrom rest_framework import viewsets\nfrom rest_framework import mixins\n\nfrom apps.endpoints.models import Endpoint\nfrom apps.endpoints.serializers import EndpointSerializer\n\nfrom apps.endpoints.models import MLAlgorithm\nfrom apps.endpoints.ser... | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mCodingLLC/magnum-bindings | [
"5994150a68a216621582f76ecf394d1b42758abc"
] | [
"src/python/corrade/test/test_containers_numpy.py"
] | [
"#\n# This file is part of Magnum.\n#\n# Copyright © 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,\n# 2020, 2021 Vladimír Vondruš <mosra@centrum.cz>\n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xmuyzz/IVContrast | [
"f3100e54f1808e1a796acd97ef5d23d0a2fd4f6c"
] | [
"src/go_model/old_version/call_backs2.py"
] | [
"#----------------------------------------------------------------------\n# Deep learning for classification for contrast CT;\n# Transfer learning using Google Inception V3;\n#-------------------------------------------------------------------------------------------\n\nimport os\nimport numpy as np\nimport pandas ... | [
[
"tensorflow.keras.callbacks.TensorBoard",
"tensorflow.keras.callbacks.EarlyStopping",
"tensorflow.math.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
baajur/catalyst | [
"a35297ecab8d1a6c2f00b6435ea1d6d37ec9f441"
] | [
"catalyst/core/callbacks/metrics.py"
] | [
"from typing import Any, Callable, Dict, List, Union\nfrom abc import ABC, abstractmethod\nfrom collections import defaultdict\nimport logging\n\nimport torch\n\nfrom catalyst.core import utils\nfrom catalyst.core.callback import Callback, CallbackNode, CallbackOrder\nfrom catalyst.core.runner import IRunner\nfrom ... | [
[
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SCUT-AILab/CRN_tvqa | [
"0680ed828208ec8c104965438fa0b1cd2010df1f"
] | [
"pythia/scripts/features/extract_resnet152_feat.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\r\nfrom __future__ import (absolute_import, division, print_function,\r\n unicode_literals)\r\n\r\nimport argparse\r\nimport os\r\nfrom glob import glob\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nimport torchvision... | [
[
"torch.nn.Sequential",
"numpy.save",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IOdevelop/composite | [
"878cc8319d4b988f0205969b41f58235f9926514"
] | [
"onadata/apps/api/tools.py"
] | [
"import os\n\nfrom datetime import datetime\nimport numpy as np\nimport inspect\nimport re\n\nfrom django import forms\nfrom django.conf import settings\nfrom django.core.files.storage import get_storage_class\nfrom django.contrib.auth.models import Permission\nfrom django.contrib.auth.models import User\nfrom djan... | [
[
"numpy.maximum",
"numpy.sum",
"numpy.min",
"numpy.max",
"numpy.ravel",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JahodaPaul/FIT_CTU | [
"2d96f18c7787ddfe340a15a36da6eea910225461"
] | [
"Thesis - Autonomous Car Chasing/RC_Version/SemanticSegmentation.py"
] | [
"import numpy as np\nimport math\n\nclass SemanticSegmentation:\n def __init__(self):\n self.counter = 0\n self.imageWidth = 0\n self.imageHeight = 0\n\n self.CoordRectangles = [] #y,x\n\n self.lastN = 5\n self.lastNX = []\n self.lastNY = []\n self.exponent... | [
[
"numpy.max",
"numpy.array",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
piiq/SlicerNotebooks | [
"c02ccab403723aa48fa205a171b54cbdf08ed9a6"
] | [
"SlicerPlayground/playground_utils.py"
] | [
"\"\"\"\nUtility fuctions for Slicer Playground.\n\nThese functions are copies from notebooks where they were created to enable reuse.\n\"\"\"\nimport numpy as np\nimport vtk\nimport slicer\nfrom emoji import UNICODE_EMOJI\n\n\ndef create_np_text_img(text: str, size: tuple = (128, 128),\n font... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saalfeldlab/gunpowder-stardist | [
"eb099c7a095d6f5adb0cb3e1626cbb2bd6bf260c"
] | [
"setup.py"
] | [
"import os\nfrom setuptools import find_packages, setup, Extension\nfrom numpy.distutils.misc_util import get_numpy_include_dirs\n\nNAME = \"gpstardist\"\nDESCRIPTION = \"Gunpowder node for stardist computation\"\nURL = \"https://github.com/saalfeldlab/gunpowder-stardist\"\nEMAIL = \"heinrichl@janelia.hhmi.org\"\nA... | [
[
"numpy.distutils.misc_util.get_numpy_include_dirs"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.19",
"1.24",
"1.16",
"1.23",
"1.20",
"1.7",
"1.12",
"1.21",
"1.22",
"1.14",
"1.6",
"1.13",
"1.9",
"1.17",
"1.10",
"1.18",
"1.15",
"1.8"
],
"pand... |
tweigel-dev/CSIKit | [
"6c8037dddca8ed87b8e1f86d0e0255f95c8a86d1"
] | [
"tests/matlab/nexmon/test_nexmon.py"
] | [
"from CSIKit.reader import NEXBeamformReader\n\nimport numpy as np\n\nimport errno\nimport glob\nimport os\nimport scipy.io\n\n\nclass InconsistentOutputError(RuntimeError):\n def __init__(self, arg):\n self.args = arg\n\ndef test_nexmon_matlab_consistency():\n\n example_dir = os.environ[\"NEX_TEST_EXA... | [
[
"numpy.squeeze",
"numpy.array",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
knebiolo/emergent | [
"a6edb20c9907d4122d165eecaeeff33782d24a48"
] | [
"scripts/simple_sim.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 17 21:21:42 2021\n\n@author: KNebiolo\n\"\"\"\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import animation\nfrom matplotlib.patches import Polygon\nfrom matplotlib import colors\nfrom matplotlib.collections import PatchCollection\nimport os\nimport numpy ... | [
[
"matplotlib.collections.PatchCollection",
"matplotlib.animation.FuncAnimation",
"numpy.ndarray.tolist",
"numpy.array",
"matplotlib.patches.Polygon",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dominicpanarello/inky | [
"fa59811bb5a9aad9eac82da3e58b6dc2ead75b8c"
] | [
"library/inky/inky_uc8159.py"
] | [
"\"\"\"Inky e-Ink Display Driver.\"\"\"\nimport time\nimport struct\n\ntry:\n from PIL import Image\nexcept ImportError:\n Image = None\n\nfrom . import eeprom\n\ntry:\n import numpy\nexcept ImportError:\n raise ImportError('This library requires the numpy module\\nInstall with: sudo apt install python-... | [
[
"numpy.rot90",
"numpy.fliplr",
"numpy.flipud",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BeaAnn/pyfoamsetup | [
"5908aea41bfbde3fad7d6957feab58a50d58e262"
] | [
"pyfoamsetup/PropellerSimulation/PropellerSimulation.py"
] | [
"import numpy as np\nimport os\nimport subprocess\nimport shutil\nimport multiprocessing\nimport sys\nimport copy\nfrom collections import OrderedDict\n\nfrom pyfoamsetup.coreLibrary import *\nimport pyfoamsetup.coreLibrary.CaseSetup as CaseSetup\n\nclass PropellerSimulation(CaseSetup.CaseSetup):\n\tdef __init__(se... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.cos",
"numpy.sin",
"numpy.round",
"numpy.ceil",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adehecq/xdem | [
"674c94785df07097da1aeedbdb60ae87392a6636"
] | [
"tests/test_dem.py"
] | [
"\"\"\" Functions to test the DEM tools.\"\"\"\nimport inspect\nimport os\nimport warnings\n\nimport geoutils.georaster as gr\nimport geoutils.satimg as si\nimport numpy as np\nimport pyproj\nimport pytest\n\nimport xdem\nfrom xdem.dem import DEM\n\nDO_PLOT = False\n\n\nclass TestDEM:\n\n def test_init(self):\n ... | [
[
"numpy.abs",
"numpy.greater",
"numpy.array_equal",
"numpy.isfinite",
"numpy.less",
"numpy.all",
"numpy.nanmean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
QianWanghhu/IES-FF | [
"c57d25806a034b1d0478a94715ade4ad5c96accd"
] | [
"src/plot_gp.py"
] | [
"#!/usr/bin/env python\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport pickle\nimport seaborn as sns\n\nfrom scipy import stats\n# from scipy.optimize import root\nfrom pyapprox import generate_independent_random_samples\nimport matplotlib as mpl\nfrom scipy import stats\nfrom scip... | [
[
"sklearn.metrics.r2_score",
"numpy.asarray",
"numpy.cumsum",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"numpy.zeros_like",
"scipy.stats.spearmanr",
"numpy.where",
"pandas.read_csv",
"numpy.arange",
"numpy.zeros",
"matplotlib.pyplot.figure",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Shigangli/mindspore | [
"351ce03fb2721335695afd77e8535d15670571f4"
] | [
"tests/ut/python/dataset/test_random_auto_contrast.py"
] | [
"# Copyright 2021 Huawei Technologies Co., Ltd\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 l... | [
[
"numpy.array",
"numpy.fromfile",
"numpy.zeros",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bernhard-42/jupyrter-cadquery | [
"ff773855f480624f30ae7bf874e728ec9ebddd68"
] | [
"jupyter_cadquery/tessellator.py"
] | [
"\"\"\"Tessellator class\"\"\"\n\nimport os\nimport sys\n\nfrom cachetools import LRUCache, cached\n\nimport numpy as np\n\n# pylint: disable=no-name-in-module,import-error\nfrom OCP.gp import gp_Vec, gp_Pnt\nfrom OCP.BRep import BRep_Tool\nfrom OCP.BRepTools import BRepTools\nfrom OCP.BRepGProp import BRepGProp_Fa... | [
[
"numpy.asarray",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CharlesThut/sportsipy | [
"eaed3442ca7bf8836cacedba00c051d58a5ca539"
] | [
"sportsipy/ncaab/boxscore.py"
] | [
"import pandas as pd\nimport re\nfrom datetime import timedelta\nfrom pyquery import PyQuery as pq\nfrom urllib.error import HTTPError\nfrom .. import utils\nfrom ..constants import AWAY, HOME\nfrom ..decorators import float_property_decorator, int_property_decorator\nfrom .constants import (BOXSCORE_ELEMENT_INDEX,... | [
[
"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": []
}
] |
linzehui/fastNLP | [
"9577a9acc47182c4fd4196e05d0e7290a5213f38"
] | [
"fastNLP/embeddings/embedding.py"
] | [
"\"\"\"\n该模块中的Embedding主要用于随机初始化的embedding(更推荐使用 :class:`fastNLP.embeddings.StaticEmbedding` )，或按照预训练权重初始化Embedding。\n\n\"\"\"\n\n__all__ = [\n \"Embedding\",\n \"TokenEmbedding\"\n]\n\nfrom abc import abstractmethod\n\nimport torch\nimport torch.nn as nn\n\nfrom .utils import get_embeddings\n\n\nclass Embedd... | [
[
"torch.Size",
"torch.nn.Dropout",
"torch.bernoulli",
"torch.full_like",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mingg8/turtlebot3 | [
"14e5c9dd64fc1019b18a8d8e8f75b445d71ab278"
] | [
"turtlebot3_sandbot/scripts/turtlebot3_sandbot_pointop.py"
] | [
"#!/usr/bin/env python\n#################################################################################\n# Copyright 2018 ROBOTIS CO., LTD.\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 Li... | [
[
"numpy.deg2rad",
"numpy.rad2deg"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mit-han-lab/amc-compressed-models | [
"49f83edfca6533a7e5464bbd2d5025835811c690"
] | [
"models/mobilenet_v2.py"
] | [
"import torch\nimport torch.nn as nn\nimport math\n\n\ndef conv_bn(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n nn.BatchNorm2d(oup),\n nn.ReLU6(inplace=True)\n )\n\n\ndef conv_1x1_bn(inp, oup):\n return nn.Sequential(\n nn.Conv2d(... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.ReLU6",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aks-research-team/HPC-research | [
"195fca1f40ebdace76747814378578e97afb9c33"
] | [
"test.py"
] | [
"import torch\nimport numpy as np\n\n\ngamma = torch.Tensor([7/5]).cuda()\nk = torch.Tensor([200]).cuda()\nR = torch.Tensor([8.31]).cuda()\nmu = torch.Tensor([0.029]).cuda()\nc = torch.Tensor([R / ((gamma - 1) * mu)]).cuda()\nv_sound = torch.Tensor([343]).cuda()\n\n\ndef U2params(U):\n ro = U[0]\n vx = U[1] /... | [
[
"torch.zeros",
"torch.Tensor",
"numpy.char.add"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hysts/pytorch_yolov3 | [
"6d4c7a1e42d366894effac8ca52f7116f891b5ab",
"6d4c7a1e42d366894effac8ca52f7116f891b5ab"
] | [
"yolov3/transforms/transforms.py",
"yolov3/models/yolov3.py"
] | [
"import random\nimport cv2\nimport numpy as np\nimport torch\n\n\nclass Compose:\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, image, targets):\n for transform in self.transforms:\n image, targets = transform(image, targets)\n return im... | [
[
"numpy.asarray",
"torch.from_numpy",
"numpy.ones",
"numpy.concatenate",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.vstack",
"numpy.random.randint"
],
[
"torch.zeros",
"torch.cat",
"torch.tensor",
"torch.no_grad",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kslam07/RWA2-LLT | [
"392fc78fc82a36a8ff88d1af94dabdd577397796"
] | [
"double_rotor_tip_effect.py"
] | [
"\"\"\"\nPlotting function\n\"\"\"\nfrom create_geometry import BladeGeometry\nfrom lifting_line_solver import LiftingLineSolver\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom read_BEMdata_into_Python import read_matlab_data\nimport numpy as np\n\n\nphase_shifts = np.linspace(0, 180... | [
[
"numpy.resize",
"numpy.linspace",
"numpy.degrees",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.close",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gwpark-git/dynamics_of_networks_and_colloids | [
"0b0a3687533379ec75171ae6b906aeff5bedfbba"
] | [
"post_processing/plot_position_dumbbell_multiprocessing.py"
] | [
"from numpy import *\nimport matplotlib.pyplot as plt\nimport sys\nfrom multiprocessing import Pool\nfrom functools import partial\n\nfn = sys.argv[1]\nout_path = sys.argv[2]\n\ndef plot_t(traj, t):\n dimension = 2\n Np = int((shape(traj)[1] - 1)/(2*dimension)) \n box_dimension = [10.0, 10.0]\n\n marker... | [
[
"matplotlib.pyplot.close",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
opveclib/opveclib | [
"2d70ec1857437426d488d924fe05a8260c6e69bd"
] | [
"opveclib/test/test_limits.py"
] | [
"# Copyright 2016 Hewlett Packard Enterprise Development LP\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by appli... | [
[
"numpy.array",
"numpy.equal",
"numpy.finfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
equinor/Marmot | [
"99ab6336f920f460df335d4a1b3e4f36d847ae46"
] | [
"marmot/plottingmodules/storage.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Energy storage plots.\n\nThis module creates energy storage plots.\n\"\"\"\n\nimport logging\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\nimport marmot.config.mconfig as mconfig\nimport marmot.plottingmodules.plotutils.plot_library as plotlib\nfr... | [
[
"pandas.notna",
"pandas.concat",
"pandas.DataFrame",
"matplotlib.pyplot.subplots_adjust"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ajavadia/qiskit-ignis | [
"c03d1bd22f49f461e7f3112cb8b854e92f6d961f"
] | [
"qiskit/ignis/verification/tomography/fitters/mle_fit.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Copyright 2018, IBM.\n#\n# This source code is licensed under the Apache License, Version 2.0 found in\n# the LICENSE.txt file in the root directory of this source tree.\n\n\n\"\"\"\nMaximum-Likelihood estimation quantum tomography fitter\n\"\"\"\n\nimport numpy as np\nfrom scipy.linal... | [
[
"numpy.sqrt",
"scipy.linalg.lstsq",
"numpy.shape",
"numpy.array",
"numpy.trace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
colonelzentor/GENN | [
"65376ddbb4084a7b687d3c059a09ed3dae065b1d"
] | [
"src/jenn/tests/test_utils.py"
] | [
"import numpy as np\nfrom jenn.tests.test_problems import rosenbrock\nfrom jenn._utils import grad_check\n\n\ndef test_grad_check():\n \"\"\"\n Test that method to check gradient accuracy works, using the\n banana Rosenbrock function as a test example\n \"\"\"\n x0 = [np.array([1.25, -1.75]).reshape(... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jeromedockes/nistats | [
"45894a76794d24051221951e346a9a2e882b98d1"
] | [
"nistats/contrasts.py"
] | [
"\"\"\"\nThis module is for contrast computation and operation on contrast to\nobtain fixed effect results.\n\nAuthor: Bertrand Thirion, Martin Perez-Guevara, 2016\n\"\"\"\n\nfrom warnings import warn\n\nimport numpy as np\nimport scipy.stats as sps\nimport pandas as pd\n\nfrom .utils import z_score\n\nDEF_TINY = 1... | [
[
"numpy.dot",
"numpy.maximum",
"numpy.minimum",
"numpy.asarray",
"numpy.all",
"scipy.linalg.sqrtm",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
kiat/debs2019 | [
"b1231a0995a154f8549ef23a00f635b81cc3c689"
] | [
"src/ssh-kd/plugin/kde_seg.py"
] | [
"import numpy as np\nfrom sklearn.cluster import DBSCAN\nfrom scipy import stats\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\ndef get_different_sectors(temp, threshold=0.7):\n \"\"\" \n Divides the scene into sectors. \n \n In a given 3D scene, from the t... | [
[
"numpy.split",
"numpy.sqrt",
"numpy.linspace",
"numpy.unique",
"sklearn.cluster.DBSCAN",
"numpy.arctan2",
"numpy.max",
"scipy.stats.gaussian_kde",
"numpy.diff",
"numpy.array",
"numpy.sum"
]
] | [
{
"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"... |
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