repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
LequnWang/Improve-Screening-via-Calibrated-Subset-Selection | [
"de397a600b7ac1d4a2a844c58bb7ebde29841b2a"
] | [
"src/ucss.py"
] | [
"\"\"\"\nSelect a Shortlist of Applicants Based on the Uncalibrated Scores\n\"\"\"\nimport argparse\nimport pickle\nimport numpy as np\nfrom train_LR import NoisyLR\nfrom utils import calculate_expected_qualified, calculate_expected_selected, transform_except_last_dim\n\nif __name__ == \"__main__\":\n parser = a... | [
[
"numpy.mean",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
masumis/pymgrit | [
"0dc2f32025e109306c7e131dfd2fd777f5e8fc51"
] | [
"src/pymgrit/advection/advection_1d.py"
] | [
"\"\"\"\nVector and application class for the 1D advection problem\n\"\"\"\n\nimport numpy as np\nfrom scipy import sparse as sp\nfrom scipy.sparse.linalg import spsolve\nfrom scipy.sparse import identity\n\nfrom pymgrit.core.application import Application\nfrom pymgrit.core.vector import Vector\n\n\nclass VectorAd... | [
[
"numpy.linspace",
"scipy.sparse.linalg.spsolve",
"scipy.sparse.diags",
"numpy.linalg.norm",
"scipy.sparse.csr_matrix",
"numpy.ones",
"scipy.sparse.identity",
"numpy.random.rand",
"numpy.exp",
"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"
... |
TengHoo3/ANN-implementation | [
"6368bc3f6adc2f91ca8bceedc3bf072267b75aaf"
] | [
"src/utils/model.py"
] | [
"import tensorflow as tf\n\n\ndef create_model(LOSS_FUNCTION, OPTIMIZER, METRICS, NUM_CLASSES):\n\n LAYERS = [tf.keras.layers.Flatten(input_shape=[28, 28], name=\"inputLayer\"),\n tf.keras.layers.Dense(300, activation=\"relu\", name=\"hiddenLayer1\"),\n tf.keras.layers.Dense(100, activation=\"r... | [
[
"tensorflow.keras.layers.Dense",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.layers.Flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
LeGamermc/ursina_tutorials | [
"f0ad518be3a02cdb52f27c87f2f70817b4d0e8b0",
"f0ad518be3a02cdb52f27c87f2f70817b4d0e8b0"
] | [
"python_minecraft_tut_2021/pyCraft_tut_4.py",
"python_solarSystem_tut_2021/tut_ursina_5_solar PREP.py"
] | [
"\"\"\"\nMinecraft in Python, with Ursina, tut 4\n\n3) building and 'mining' + change building block type\n\"\"\"\n\nfrom random import randrange\nfrom ursina import *\nfrom ursina.prefabs.first_person_controller import FirstPersonController\nfrom numpy import floor\nfrom numpy import abs\nimport time\nfrom perlin_... | [
[
"numpy.abs",
"numpy.floor"
],
[
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
agarwalutkarsh554/PyAutoLens | [
"72d2f5c39834446e72879fd119b591e52b36cac4"
] | [
"test_autolens/unit/pipeline/phase/dataset/test_phase_dataset.py"
] | [
"from os import path\r\n\r\nimport numpy as np\r\nimport pytest\r\n\r\nimport autofit as af\r\nfrom autofit.mapper.prior.prior import TuplePrior\r\nimport autolens as al\r\nfrom autolens import exc\r\nfrom autolens.mock import mock\r\n\r\npytestmark = pytest.mark.filterwarnings(\r\n \"ignore:Using a non-tuple se... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
1eg1on/dimreducers-crusher | [
"744a91e0f8712acbfcad27b8e4d040dfe9716c05"
] | [
"dimreducers_crusher/reducers/LLE.py"
] | [
"from .AbstractReducer import AbstractReducer\nfrom sklearn.manifold import LocallyLinearEmbedding as sklle\nimport numpy as np\n\n\nclass LLE(AbstractReducer):\n def __init__(self, d: int = 2, random_state: int = 0, **kwargs):\n super().__init__(d, random_state)\n self._main = sklle(n_components=d... | [
[
"sklearn.manifold.LocallyLinearEmbedding"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adityaapte/oct2py | [
"27b70154dfe63326e85be51688cd49751b5a3b72"
] | [
"example/roundtrip.py"
] | [
"\"\"\"Send a numpy array roundtrip to Octave using an m-file.\r\n\"\"\"\r\nfrom oct2py import octave\r\nimport numpy as np\r\n\r\nif __name__ == '__main__':\r\n x = np.array([[1, 2], [3, 4]], dtype=float)\r\n #use nout='max_nout' to automatically choose max possible nout\r\n out, oclass = octave.roundtrip... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
j4lley/compression | [
"b758903c6df5eeafb5d444e8cec85d1b2bc132d3"
] | [
"tensorflow_compression/python/all_test.py"
] | [
"# Copyright 2019 Google LLC. 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 required by appl... | [
[
"tensorflow.test.main"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andrewlenz/topography-touchpad | [
"390b61df1e38391b0c4069decf3d39572f448abc"
] | [
"robot_final_code/detect_aruco_video.py"
] | [
"#!/usr/bin/env python3\n\nfrom imutils.video import VideoStream\nimport cv2\nimport argparse\nimport imutils\nimport time\nimport sys\nimport socket\nimport imagezmq\nfrom math import acos, pi, cos, asin, sin, sqrt\n\nfrom threading import Thread\nimport robot_controller\nimport asyncio\nimport numpy as np\n\niter... | [
[
"numpy.reshape",
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huananfshi/EV_metaproteomics | [
"17339eeaaca12ea1ad228a0a33e8c3eb7c65a402"
] | [
"scripts/post_unipept.py"
] | [
"#!/usr/bin/env python\nimport numpy as np\nimport pandas as pd\nimport sys\nquant_file = sys.argv[1] # path to quant file\nunipept_file = sys.argv[2] # path to unipept output (saved as csv with \\t sep)\noutput_file = sys.argv[3] # output path/name to combined quant_unipept file\nEV_peptides = pd.read_csv(quant_fi... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
kenjimarshall/conn2res | [
"24b4fd4741d6eab23d9bb021addd807bf8b3d2ae"
] | [
"examples/echo_state_network.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nConnectome-informed reservoir - Echo-State Network\n=================================================\nThis example demonstrates how to use the conn2res toolbox \nto perform a task using a human connectomed-informed\nEcho-State network while playing with the dynamics of the \nreser... | [
[
"matplotlib.pyplot.legend",
"pandas.concat",
"numpy.linspace",
"numpy.random.choice",
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.plot",
"scipy.linalg.eigh",
"numpy.max",
"matplotlib.pyplot.subplot",
"numpy.round",
"matplotlib.pyplot.close",
"m... | [
{
"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": [
"0.13",
"0.14",
"0.15",
"0.12",
"... |
DaiJitao/simbert | [
"6b562985db4004768613833c08d664a69a8a5294"
] | [
"qt_sim/train_consin_shared_twin_noNSP_middle.py"
] | [
"#! -*- coding:utf-8 -*-\n# qt sim 任务训练:非冻结， 最后做cosin\n\nimport os\nimport sys\nimport tensorflow as tf\nfrom keras.models import Sequential\nfrom bert4keras.backend import keras, set_gelu, search_layer, K\nfrom bert4keras.tokenizers import Tokenizer\nfrom bert4keras.models import build_transformer_model\nfrom bert... | [
[
"tensorflow.compat.v1.ConfigProto",
"tensorflow.config.list_logical_devices",
"tensorflow.compat.v1.Session",
"tensorflow.python.keras.backend.set_session",
"tensorflow.distribute.MirroredStrategy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HowcanoeWang/EasyRIC | [
"a3420bc7b1e0f1013411565cf0e66dd2d2ba5371"
] | [
"easyric/io/pix4d.py"
] | [
"import numpy as np\nimport pandas as pd\n\ndef read_xyz(xyz_path):\n '''\n read pix4d file PROJECTNAME_offset.xyz\n :param xyz_path: the path to target offset.xyz file\n :return: x, y, z float results\n '''\n with open(xyz_path, 'r') as f:\n x, y, z = f.read().split(' ')\n return float(... | [
[
"numpy.vstack",
"pandas.read_csv",
"numpy.loadtxt",
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
YangXiaoo/NoteBook | [
"37056acad7a05b876832f72ac34d3d1a41e0dd22"
] | [
"MachineLearning/Softmax Regression/main.py"
] | [
"# 2018-9-30\n# Softmax Regression\n# python 机器学习算法\nimport numpy as np\nimport random as rd\nimport matplotlib.pyplot as plt\ndef loadData(file_name, skip=True):\n \"\"\"\n 载入数据\n \"\"\"\n feature = []\n label = []\n file = open(file_name)\n\n for line in file.readlines():\n feature_tmp... | [
[
"numpy.ones",
"numpy.shape",
"numpy.exp",
"numpy.mat",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tejashwatiwari/Emotional_Audio | [
"6d057637651891528d58eefe9a16b1ef3d0e0cb1"
] | [
"Audio_prep.py"
] | [
"import librosa, librosa.display\r\nimport matplotlib.pyplot as plt\r\nimport plotly.graph_objects as go\r\nimport numpy as np\r\n\r\nfile = 'sample_audio.wav'\r\n\r\n# Waveform_____\r\n\r\n# We'll first load the file using librosa.load(), which will take in the path of the file and the sample rate\r\n# It shall re... | [
[
"numpy.arange",
"numpy.array",
"numpy.abs",
"numpy.fft.fft"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.21",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
3ZadeSSG/Reinforcement-Learning-Agents | [
"162534fc6caab32089b8f3d3b2761a9bad1e707f"
] | [
"2. Tennis/model.py"
] | [
"import numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ndef hidden_init(layer):\n fan_in = layer.weight.data.size()[0]\n lim = 1. / np.sqrt(fan_in)\n return (-lim, lim)\n\nclass Actor(nn.Module):\n \"\"\"Actor (Policy) Model.\"\"\"\n\n def __init__(self, state_s... | [
[
"torch.nn.Linear",
"torch.manual_seed",
"torch.nn.BatchNorm1d",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cversteeg/garage-1 | [
"f582f9dbfde000d5f7e59afcf1a0fcd31fb4a407"
] | [
"tests/garage/tf/q_functions/test_discrete_cnn_q_function.py"
] | [
"import pickle\nfrom unittest import mock\n\nimport numpy as np\nimport pytest\nimport tensorflow as tf\n\nfrom garage.envs import GarageEnv\nfrom garage.tf.models import CNNModel\nfrom garage.tf.q_functions import DiscreteCNNQFunction\n\nfrom tests.fixtures import TfGraphTestCase\nfrom tests.fixtures.envs.dummy im... | [
[
"tensorflow.Graph",
"numpy.array_equal",
"tensorflow.compat.v1.get_variable",
"tensorflow.ones_like",
"numpy.full",
"tensorflow.compat.v1.placeholder",
"tensorflow.compat.v1.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
reshma-katkar/ibis | [
"53ef3cefc4ae90d61f3612310cb36da2bcd11305"
] | [
"ci/datamgr.py"
] | [
"#!/usr/bin/env python\nimport io\nimport json\nimport logging\nimport os\nimport warnings\nimport zipfile\nfrom pathlib import Path\n\nimport click\nimport pandas as pd\nimport sqlalchemy as sa\nfrom plumbum import local\nfrom toolz import dissoc\n\nSCRIPT_DIR = Path(__file__).parent.absolute()\nDATA_DIR_NAME = 'i... | [
[
"pandas.to_datetime",
"pandas.read_csv",
"pandas.isnull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
AlbertoCastelo/bayesian-dl-medical-diagnosis | [
"82b0efc7147d88663b81cc066d5cd41189860e43"
] | [
"deep_gp/models/resnet_bw.py"
] | [
"import math\n\nfrom torch import nn\nfrom torchvision.models.resnet import BasicBlock\n\n\nclass ResNetBW(nn.Module):\n\n def __init__(self, block=BasicBlock, layers=[2, 2, 2, 2], num_classes=1000):\n self.inplanes = 64\n super(ResNetBW, self).__init__()\n self.conv1 = nn.Conv2d(1, 64, kern... | [
[
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d",
"torch.nn.Linear",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ma-schmidt/hdfe | [
"ff169487b8c25c2a10e1404c8702dcf5c182f2e2"
] | [
"tests/test_multicollinearity_funcs.py"
] | [
"import numpy as np\nfrom scipy import sparse as sps\nfrom hdfe.multicollinearity import (\n remove_cols_from_csc,\n find_collinear_cols,\n remove_collinear_cols,\n)\n\n\ndef test_remove_cols_from_csc() -> None:\n x = sps.eye(4, dtype=int).tocsc()\n cols_to_remove = [1, 2]\n result = remove_cols_f... | [
[
"scipy.sparse.eye",
"numpy.testing.assert_equal",
"numpy.array"
]
] | [
{
"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"... |
loiseaujc/Parametized_ROM_Cylinder_Flow | [
"86750fec29e869d07886ea59a386b70c868af7dd"
] | [
"scripts/stability_post_process.py"
] | [
"import os\n\nimport numpy as np\n\nfrom scipy.linalg import svd\nfrom scipy.sparse.linalg import svds\n\nfrom sklearn.utils.extmath import svd_flip\n\nfrom glob import glob\nfrom tqdm import trange, tqdm\nimport h5py\n\nfrom nekio import *\n\ndef load_stability_results():\n # -->\n fname = \"./data/STABILITY... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChenKuanSun/TheObstacleTowerChallenge | [
"c2de16930dd88949c0bc6a460f378beae3a04204"
] | [
"keepitpossible/backup/ck_cnnlstm_oppo.py"
] | [
"# 這個文件直接執行是給GCP用的\r\nfrom obstacle_tower_env import ObstacleTowerEnv\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport threading\r\nimport queue\r\n\r\n# 運行環境設定：\r\n# 設置幾個代理\r\nN_WORKER = 6\r\n# 代理人自己更新的步數\r\nEP_LEN = 500\r\n# 最大訓練回合數(每個代理人加起來的回合)\r\nEP_MAX = N_WORKER * 200\r\n# 設定更新整個模型：每個代理走了N步就更新\r\nUP... | [
[
"numpy.sqrt",
"tensorflow.cast",
"tensorflow.train.AdamOptimizer",
"numpy.random.randint",
"tensorflow.nn.conv2d",
"numpy.linalg.svd",
"numpy.clip",
"numpy.reshape",
"tensorflow.get_collection",
"tensorflow.layers.dense",
"tensorflow.Session",
"tensorflow.square",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
wyattpfeil/OpencvTemplateMatching | [
"8ee5bf5d54decd60aa503269538814755596b325"
] | [
"BloxburgBot.py"
] | [
"import cv2\nimport numpy as np\nimport os\nimport pyautogui\n\ndef checkIfImageExists(small_image, large_image):\n res = cv2.matchTemplate(small_image, large_image, cv2.TM_CCOEFF_NORMED)\n _, max_val, _, max_loc = cv2.minMaxLoc(res)\n threshold = 0.8\n flag = False\n if np.amax(res) > threshold:\n ... | [
[
"numpy.amax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
out-running-27/divvy_pagerank | [
"5840b0d19bad14d66c99f6ab9f8d496efad6a152"
] | [
"divvy_pagerank/get_data.py"
] | [
"import requests\nimport zipfile\nimport io\nimport os\nimport pandas as pd\n\n\ncurrent_dir = os.path.dirname(__file__)\ndata_dir = os.path.join(current_dir, \"../data/\")\n\ndef get_ride_data():\n divvy_file = 'https://s3.amazonaws.com/divvy-data/tripdata/Divvy_Trips_2018_Q2.zip'\n\n r = requests.get(divvy_... | [
[
"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": []
}
] |
jbhopkins/pyFAI | [
"cf10f043fb97f55cf45a0bd406772499bfb432d0"
] | [
"pyFAI/worker.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Project: Azimuthal integration\n# https://github.com/silx-kit/pyFAI\n#\n# Copyright (C) 2015-2018 European Synchrotron Radiation Facility, Grenoble, France\n#\n# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)\n#\n# Permissio... | [
[
"numpy.sqrt",
"numpy.ascontiguousarray",
"numpy.dtype",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nkhn37/python-tech-sample-source | [
"e8aea7ed3d810494682b3c2dde952ddd0f7acf84"
] | [
"python-data-analysis/matplotlib/hist/hist2d_basic.py"
] | [
"\"\"\"matplotlib\nhist2dによる2次元ヒストグラムの描画\n\n[説明ページ]\nhttps://tech.nkhn37.net/matplotlib-hist-hist2d-hexbin/#hist2d2\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nplt.style.use('seaborn-whitegrid')\n\n# 2次元正規分布の平均と共分散を定義\n# 平均\nm = [0, 0]\n# 共分散\ncov = [[1, 1], [1, 2]]\n# データの生成\nx, y = np.random.m... | [
[
"numpy.random.multivariate_normal",
"matplotlib.pyplot.hist2d",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Open-MSS/mss-test | [
"a41b33bc610f23949ce0228a3502fdb1338f9330"
] | [
"mslib/mswms/utils.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n\n mslib.mslib.utils\n ~~~~~~~~~~~~~~~~~\n\n This module provides functions for the wms server\n\n This file is part of MSS.\n\n :copyright: Copyright 2016 Joern Ungermann\n :copyright: Copyright 2016-2022 by the MSS team, see AUTHORS.\n :license: APACHE-2.0, s... | [
[
"numpy.log",
"matplotlib.colors.BoundaryNorm",
"numpy.linspace",
"numpy.arange",
"matplotlib.patheffects.withStroke",
"matplotlib.pyplot.cm.colors.ListedColormap",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Toyuri453/RSSP-Python-demo | [
"0adf92ad765b5a9334d7e2830611b98c8c4eb26d"
] | [
"Visualization.py"
] | [
"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport scipy.stats as stats\r\nimport math\r\nimport seaborn as sns\r\nimport Calculation\r\nfrom matplotlib.ticker import MultipleLocator, FormatStrFormatter\r\nimport GenNorm\r\nimport Terminal\r\nimport AxesFrame\r\nfrom scipy.interpolate import Univariat... | [
[
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Splinter1984/broken_lines | [
"8ce714c367e487dbad92ee8ab8b7e6411bb012e2"
] | [
"script/view_polygons.py"
] | [
"from matplotlib import pyplot as plt\nimport random\nimport pylab\n\nINPUT_FILEPATH = '../data/output.txt'\n\ndef random_color():\n return (random.random(), random.random(), random.random())\n\nfile = open(INPUT_FILEPATH, 'r')\nfig = plt.figure()\nax = fig.add_subplot(1, 1, 1)\nlayer = 0\nxs = []\nys = []\ndata... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.fill",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlouiAmine/Synonym-Finder | [
"6e0ef70f7b920c43b20c4109da29a7c395817322"
] | [
"synonym_finder/synonym_finder.py"
] | [
"import pywikibot\nimport pandas as pd\nimport requests\nfrom itertools import chain\nfrom nltk.corpus import wordnet\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.stem import PorterStemmer\nfrom SPARQLWrapper import SPARQLWrapper, JSON\nfrom sentence_transformers import SentenceTransformer, util, models, los... | [
[
"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": []
}
] |
Radio-Camera-Initiative/bifrost | [
"d4891bcc97335fb6e7955314c40631ef9136f556"
] | [
"python/bifrost/blocks/transpose.py"
] | [
"\n# Copyright (c) 2016, The Bifrost Authors. 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\n# are met:\n# * Redistributions of source code must retain the above copyright\n# notice, this list of ... | [
[
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShNadi/eBay_SClassifier | [
"aac1c9815968e3dfddea1bb2c882e308d4d0fadf"
] | [
"eBay_SClassifier/Main.py"
] | [
"import pandas as pd\nimport SentimentClass\nfrom sklearn.metrics import confusion_matrix\nfrom pprint import pprint\n\nimport multiprocessing\nimport string\n# import translate\n\n\npd.set_option('display.expand_frame_repr', False)\n\n\n\ndef sentiment(preprocess = 'False', sentiment_de = False, translate_to_en =F... | [
[
"pandas.read_stata",
"pandas.set_option",
"sklearn.metrics.confusion_matrix",
"pandas.concat"
]
] | [
{
"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": []
}
] |
2018233031/kfac | [
"62756a95d7fe605c4772fad860a345b3957670f1"
] | [
"kfac/python/ops/optimizer.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.cond",
"tensorflow.get_variable",
"tensorflow.control_dependencies",
"tensorflow.colocate_with",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.equal",
"tensorflow.math.logical_and",
"tensorflow.squeeze",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ivitan/LearnPython | [
"f7c1c8f450f5cbcbd8cabe03711c5e0d81dfdee3"
] | [
"Crawl/Code/com.vitan.test/Boston.py"
] | [
"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# @Time : 18-12-13 下午3:23\r\n# @Author : Vitan\r\n# @File : Boston.py\r\n\r\nimport numpy as np\r\nfrom sklearn.datasets import load_boston\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.model_selection import train_test_split\r\nimpo... | [
[
"matplotlib.pyplot.scatter",
"numpy.min",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.plot",
"numpy.mean",
"sklearn.linear_model.LinearRegression",
"sklearn.datasets.load_boston",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cation98/py-image-search-engine | [
"88e0d93595a7ab58b2f88d6d0fa471d4a4d2dac1"
] | [
"playground/image-search-engine.py"
] | [
"\"\"\"\n @author Victor I. Afolabi\n A.I. Engineer & Software developer\n javafolabi@gmail.com\n \n Created on 25 December, 2017 @ 7:18 PM.\n \n Copyright © 2017. Victor. All rights reserved.\n\"\"\"\nimport argparse\nimport glob\nimport os\nimport pickle\n\nimport cv2\nfrom scipy.spatial.distance import eu... | [
[
"scipy.spatial.distance.euclidean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
vmironovich/generic-onell | [
"78ab448b248ad9a498bd28d9a34759e325a68be6"
] | [
"run-benchmarks.py"
] | [
"from numpy import arange\nimport subprocess\n\nepi = 2\nfor p in range(3,8):\n n = 2**p\n for dum in arange(1-1.0/n,0.8,-1.0/n):\n for neu in range(0,int(n/5)+1):\n print('p {} dum {} epi {} neu {}'.format(n,dum,epi,neu))\n #subprocess.run(['srun','--cpus-per-task=16', '--mem=32G... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kurt-hectic/dython | [
"4f585cd115aeb5138f93084fb32840897d1ae15c"
] | [
"dython/examples.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom sklearn import svm, datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import label_binarize\nfrom sklearn.multiclass import OneVsRestClassifier\n\nfrom .data_utils import split_hist\nfrom .model_utils import roc_graph\nfrom .nom... | [
[
"pandas.concat",
"pandas.read_csv",
"sklearn.datasets.load_breast_cancer",
"sklearn.preprocessing.label_binarize",
"sklearn.datasets.load_iris",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.svm.SVC",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
fangguanya/bullet3 | [
"8cc9213dd4452abffb9b1798f26151d36ed2667c"
] | [
"examples/pybullet/gym/pybullet_envs/agents/utility.py"
] | [
"# Copyright 2017 The TensorFlow Agents 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 appli... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.local_variables_initializer",
"tensorflow.Variable",
"tensorflow.gfile.Exists",
"tensorflow.gfile.GFile",
"tensorflow.global_variables",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.gfile.M... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
VMarty/CityEnergyAnalyst | [
"5ab4385fc008f3b23f5bd5f9ba683d401cfcef38"
] | [
"cea/demand/metamodel/nn_generator/nn_trainer_evaluate.py"
] | [
"# coding=utf-8\n\"\"\"\n'input_matrix.py' script hosts the following functions:\n (1) collect CEA inputs\n (2) collect CEA outputs (demands)\n (3) add delay to time-sensitive inputs\n (4) return the input and target matrices\n\"\"\"\n\nfrom math import sqrt\n\nimport pandas as pd\nfrom cea.demand.metam... | [
[
"sklearn.metrics.mean_absolute_error",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error"
]
] | [
{
"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": []
}
] |
RNubla/FeatureFlow | [
"cadd40547f4db900615c8cfa12316ff4d2fc89d3"
] | [
"src/loss.py"
] | [
"import torch\r\nimport torch.nn as nn\r\n\r\n\r\nclass CharbonnierLoss(nn.Module):\r\n \"\"\"Charbonnier Loss (L1)\"\"\"\r\n\r\n def __init__(self, eps=1e-6):\r\n super(CharbonnierLoss, self).__init__()\r\n self.eps = eps\r\n\r\n def forward(self, x, y):\r\n diff = x - y\r\n lo... | [
[
"torch.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ishine/kaldi-tflite | [
"5674da672dd621a42c22e578103975e032c6f079"
] | [
"kaldi_tflite/lib/layers/normalization/batchnorm.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright (2021-) Shahruk Hossain <shahruk10@gmail.com>\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... | [
[
"tensorflow.keras.initializers.Constant",
"numpy.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
csarron/xlnet | [
"d7b9809c40c94fd9d4512d5be74f4dde753359d3"
] | [
"modeling.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nfrom util import logger\nfrom util import tf\n\n\ndef gelu(x):\n \"\"\"Gaussian Error Linear Unit.\n\n This is a smoother version of the RELU.\n Original paper: https://a... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AaronBlare/dnam | [
"4d97c879cb24447eee0852eaf48fc5b3ef8e159b",
"4d97c879cb24447eee0852eaf48fc5b3ef8e159b"
] | [
"scripts/python/preprocessing/serialization/GSE53740.py",
"src/inference/dnam.py"
] | [
"import pandas as pd\nfrom scripts.python.preprocessing.serialization.routines.filter import get_forbidden_cpgs, manifest_filter, betas_pvals_filter\nfrom scripts.python.preprocessing.serialization.routines.pheno_betas_checking import get_pheno_betas_with_common_subjects\nfrom scripts.python.preprocessing.serializa... | [
[
"pandas.read_excel"
],
[
"torch.utils.data.DataLoader",
"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": []
},
{
"matplotlib": [],
"nump... |
ctrl-gaurav/Brain-Tumor-Detection | [
"e7622208014f781b841153a3455f429b52ad62bc"
] | [
"Scripts/Train.py"
] | [
"import cv2\nimport os\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom PIL import Image\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n# from keras.utils import normalize\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers i... | [
[
"tensorflow.keras.utils.to_categorical",
"numpy.array",
"sklearn.model_selection.train_test_split",
"tensorflow.keras.utils.normalize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
applied-exploration/multi-drl | [
"3e5d76e174469693ab7a748d0db0a7fc30a9e760"
] | [
"agents/agent_multi_ddpg/multi.py"
] | [
"# main code that contains the neural network setup\n# policy + critic updates\n# see ddpg.py for other details in the network\n\nfrom ddpg_w_memory import DDPG_Agent\nimport torch\nfrom utilities import soft_update, transpose_to_tensor, transpose_list\nimport numpy as np\n#DEVICE = torch.device(\"cuda\" if torch.c... | [
[
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dsrim/mats | [
"0a2105f5e05a10785c6936c832a7ace98c8a4b3f"
] | [
"examples/color/clawpack/run_snapshot.py"
] | [
"# collect snapshots\nimport numpy as np\nimport os\nimport setrun\nimport matplotlib.pyplot as plt\n\nmake_solplots = 0 # plots for each snapshot, save in _plots_all\nmake_plots = 0 # plot collected snapshots\nsave_shots = 1 # collect all snapshots, save in ../_output/\n\nnp.random.seed(12345)\nrundata... | [
[
"numpy.linalg.svd",
"numpy.random.seed",
"numpy.linspace",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.save",
"numpy.random.rand",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mtaruno/chatbot_matt | [
"3d0a94aa04d2663fb88680ecea7992372c1712be"
] | [
"deploy/generate_train.py"
] | [
"import streamlit as st\n\nimport pandas as pd\nimport numpy as np\n\n# Word Embeddings\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport gensim\nimport builtins\n\n# Text\nfrom nltk.tokenize import word_tokenize\nfrom nltk.tokenize imp... | [
[
"pandas.concat",
"pandas.Series",
"matplotlib.pyplot.title",
"pandas.DataFrame",
"sklearn.feature_extraction.text.CountVectorizer",
"pandas.read_pickle",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
markshih91/pelee_keras | [
"8e4401aeb8f14949c9668786c7e02e2b53ef41e3",
"0d387ba834c4501e885e5f7c8b1ad67003c58620"
] | [
"utils/object_detection_2d_image_boxes_validation.py",
"experiments/inference.py"
] | [
"\"\"\"\nUtilities for 2D object detection related to answering the following questions:\n1. Given an image size and bounding boxes, which bounding boxes meet certain\n requirements with respect to the image size?\n2. Given an image size and bounding boxes, is an image of that size valid with\n respect to the b... | [
[
"numpy.clip",
"numpy.random.choice",
"numpy.ones",
"numpy.copy",
"numpy.array"
],
[
"matplotlib.pyplot.Rectangle",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.imshow",
"numpy.linspace",
"numpy.set_printoptions",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liruiqi0515/mask-rcnn-point | [
"8a4da7067b7eb0c638199a7af988a7573a9fffbc"
] | [
"samples/guidewire/guidewire.py"
] | [
"#!/usr/bin/env python\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\"\"\"\nMask R-CNN\nTrain on the toy Balloon dataset and implement color splash effect.\n\nCopyright (c) 2018 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for de... | [
[
"numpy.where",
"numpy.zeros",
"numpy.sum",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SoftwareDevEngResearch/PyFAT | [
"7d444cc9276fe7cf7aa2052f61b772ca17ade9c2"
] | [
"pyfat/tests/test_monotonic.py"
] | [
"import os, sys, numpy as np, pytest\nfrom pathlib import Path\n\n\nmain_dir = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, os.path.join(main_dir,'..'))\n\nimport monotonic\n\n@pytest.fixture\ndef test_run():\n cwd = os.path.dirname(os.path.abspath(__file__))\n file1 = Path(cwd,\"inputs\",\"... | [
[
"numpy.log",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
symuvia/symupy | [
"e6604c59bb4474f594ef5c997508f0407c9b3870"
] | [
"symupy/tsc/vehicles.py"
] | [
"\"\"\"\nVehicle Model\n=============\nThis module implements a vehicle model.\n\nVehicle model acts as an instance to trace individual vehicle data and modify vehicle behavior according to given dynamics\n\"\"\"\n\n# ============================================================================\n# STANDARD IMPORTS\... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
outofculture/talk-like-me | [
"11cad990d2e85c8d32a5dd5a3762f1b2f314cb27"
] | [
"audio.py"
] | [
"from __future__ import division\n\nimport gzip\nimport os\nimport wave\nfrom functools import reduce\nfrom math import ceil\n\nimport numpy as np\nimport pyaudio\nimport scipy.signal\nfrom keras import Input\nfrom scipy.signal import stft, istft\n\nfrom utils import sliding_window\n\n\ndef load_digits(sample_rate=... | [
[
"numpy.log",
"numpy.absolute",
"numpy.abs",
"numpy.clip",
"scipy.signal.stft",
"numpy.arange",
"numpy.dstack",
"numpy.random.shuffle",
"numpy.concatenate",
"numpy.sign",
"numpy.fromstring",
"numpy.hamming",
"numpy.dsplit",
"scipy.signal.istft",
"numpy.ze... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"1.7",
"1.0",
"1.2",
"1.8"
],
"tensorflow": []
}
] |
ScottHull/Chimera | [
"6124425c8ae373b58174c91f3a74a073dff5d3f2"
] | [
"Chimera/Chimera_1D/linear.py"
] | [
"from . import mesh\nfrom . import console\nfrom . import neighbors\nfrom . import backends\nfrom . import heat\nimport os\nimport pandas as pd\nimport numpy as np\nimport time\n\nclass Line:\n \"\"\"\n Handles and tracks all functions with the construction and evolution of the linear Chimera model.\n \"\"... | [
[
"pandas.DataFrame",
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
amitbcp/machine_learning_with_Scikit_Learn_and_TensorFlow | [
"37dda063e316503d53ac45f3b104a5cf1aaa4d78"
] | [
"scikit-learn/5_question-classification/benchmark.py"
] | [
"\nimport pandas as pd\nfrom sklearn.model_selection import StratifiedShuffleSplit\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn import metrics\n\nfrom time import time\n\ndef load_data(file=\"questions.txt\"):\n df = pd.r... | [
[
"pandas.read_csv",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.model_selection.StratifiedShuffleSplit",
"sklearn.metrics.classification_report",
"sklearn.feature_extraction.text.TfidfVectorizer",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dimitrivinet/advanced_ia_project | [
"f8693e74e3418ddd7837a584ae5784ce46eb99a3"
] | [
"webmnist/train.py"
] | [
"\nfrom torch.optim import AdamW\nfrom tqdm import tqdm\nfrom webmnist.model import Model, LeNet5\nfrom webmnist.data import MNISTDataset, MNISTLoader\n\nimport torch\nimport torch.nn as nn\n\n\ndef train(path: str, save_all: bool, epochs: int = 3,) -> None:\n dataset = MNISTDataset()\n loader = MNISTLoader()... | [
[
"torch.nn.CrossEntropyLoss",
"torch.no_grad",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tudelft-cda-lab/GROOT | [
"514bd29487aa31209c4e851ddee96111ec3c2c36"
] | [
"groot/toolbox.py"
] | [
"import json\n\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.tree import DecisionTreeClassifier\n\nfrom .util import convert_numpy\n\nimport numpy as np\n\n\nclass Model:\n... | [
[
"numpy.log",
"numpy.argmax",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shuoyangd/sockeye | [
"8ee6fd87b0c9c6bfa691bd3efb29e2eda7219480"
] | [
"sockeye/lexical_constraints.py"
] | [
"# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You may not\n# use this file except in compliance with the License. A copy of the License\n# is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in the \"l... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JakubPetriska/moving-sprites-object-detection | [
"3dc48a8c5b46b0ee9b08b90b0bf1607753e5c659"
] | [
"detect.py"
] | [
"import os\nimport sys\n\nimport numpy as np\nfrom scipy import misc\nfrom scipy.cluster.vq import kmeans\n\nfrom detection import constants\nfrom detection.model import Model\nfrom detection.utils import annotate_image\nfrom detection.utils import save_mask\nfrom detection.utils import save_mask_colored\n\nFILTER_... | [
[
"numpy.nonzero",
"scipy.misc.imsave",
"numpy.subtract",
"scipy.misc.imread",
"numpy.random.rand",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
borgr/active_learning_coreset | [
"5b9d49197138671ccccce5fe2346a562866068cd"
] | [
"tf_base/src/adverserial_trainer.py"
] | [
"import tensorflow as tf\nimport numpy\nfrom network.vgg_adverserial import VGG16Adverserial\nfrom network.vgg_adverserial_100 import VGG16Adverserial100\nfrom network.mnist_adverserial import MNISTAdverserial\n\nclass AdverserialTrainer(object):\n \"\"\"\n Train a network using robust objective\n \"\"\"\n... | [
[
"tensorflow.device",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.control_dependencies",
"tensorflow.cast",
"numpy.concatenate",
"tensorflow.nn.l2_loss",
"numpy.exp",
"tensorflow.summary.scalar",
"tensorflow.get_collection",
"numpy.ceil",
"tensorflow.t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mwtoews/modflow6 | [
"3426f524fba90b8b6186d09272226a941b97cef7",
"3426f524fba90b8b6186d09272226a941b97cef7",
"3426f524fba90b8b6186d09272226a941b97cef7"
] | [
"autotest/test_gwf_auxvars.py",
"autotest/test_gwf_libmf6_rch01.py",
"autotest/test_gwf_csub_subwt02.py"
] | [
"import os\nimport sys\nimport numpy as np\n\ntry:\n import pymake\nexcept:\n msg = \"Error. Pymake package is not available.\\n\"\n msg += \"Try installing using the following command:\\n\"\n msg += \" pip install https://github.com/modflowpy/pymake/zipball/master\"\n raise Exception(msg)\n\ntry:\n ... | [
[
"numpy.allclose"
],
[
"numpy.arange",
"numpy.sin"
],
[
"numpy.abs",
"numpy.arange",
"numpy.genfromtxt",
"numpy.count_nonzero",
"numpy.recarray",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lucasw/rqt_pcl_visualizer | [
"6d1207c3400e174c263599c64c55ed251195dbcf"
] | [
"rqt_pcl_visualizer/scripts/gen_pc2.py"
] | [
"#!/usr/bin/env python\n# Sample code to publish a pcl2 with python\n\nimport math\nimport numpy\nimport rospy\nimport sensor_msgs.point_cloud2 as pcl2\nimport sys\n\nfrom sensor_msgs.msg import PointCloud2, PointField\nfrom std_msgs.msg import Header, UInt32\n\n\nclass GenPC2:\n def __init__(self):\n sel... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GEOS-ESM/AeroApps | [
"874dad6f34420c014d98eccbe81a061bdc0110cf"
] | [
"src/Components/missions/PACE/hyperTest/o3_bremen/rsr_weighted_pengwang.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nread and plot o3 spectra from text file\n\"\"\"\n\nimport os\nimport sys\nimport matplotlib.pyplot as plt\nfrom scipy.interpolate import interp1d\nimport numpy as np\nfrom netCDF4 import Dataset\nfrom pyhdf.SD import SD, SDC\n\ndef read_o3(inFile,te):\n f = open(inFile)\n nhead... | [
[
"numpy.append",
"scipy.interpolate.interp1d",
"numpy.array",
"numpy.zeros",
"numpy.trapz"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
yas-sim/simple-annotation-toolkiw | [
"9c451ed0ce6bd1425532f577a136b8a8499ff032"
] | [
"annot.py"
] | [
"# -*- coding: utf-8 -*-\n# # ROI annotation tool\nimport sys\nimport os\nimport shutil\nimport re\n\nimport cv2\nimport numpy as np \n\nimport argparse\n\ng_colorTbl = [\n ( 0, 0, 0), (255, 0, 0), ( 0, 0,255), (255, 0,255),\n ( 0,255, 0), (255,255, 0), ( 0,255,255), (255,255,255) \n]\n\ndef dispC... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rena-nong/carculator | [
"072225f8eb20f6dfe14d3985da42c5cd61ff41a8"
] | [
"carculator/background_systems.py"
] | [
"from . import DATA_DIR\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\n\n\nclass BackgroundSystemModel:\n \"\"\"\n Retrieve and build dictionaries that contain important information to model in the background system:\n\n * gross electricity production mixes from nearly all countries in ... | [
[
"numpy.arange",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
timgates42/xarray | [
"bf0fe2caca1d2ebc4f1298f019758baa12f68b94"
] | [
"xarray/tests/test_dask.py"
] | [
"import operator\nimport pickle\nimport sys\nfrom contextlib import suppress\nfrom distutils.version import LooseVersion\nfrom textwrap import dedent\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport xarray as xr\nimport xarray.ufuncs as xu\nfrom xarray import DataArray, Dataset, Variable\nfrom xa... | [
[
"numpy.maximum",
"numpy.arange",
"pandas.Index",
"pandas.MultiIndex.from_arrays",
"numpy.ones",
"numpy.testing.assert_array_equal",
"numpy.sin",
"numpy.random.randn",
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"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": []
}
] |
soufiomario/labs-Deep-learning | [
"0ae8be9afb69a4761a4901f694480bb16947d52a"
] | [
"labs/07_seq2seq/translate.py"
] | [
"# copyright 2017 Olivier Grisel\n# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Original code from:\n#\n# https://www.tensorflow.org/tutorials/seq2seq/\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#... | [
[
"tensorflow.models.rnn.translate.seq2seq_model.Seq2SeqModel",
"tensorflow.train.get_checkpoint_state",
"tensorflow.gfile.GFile",
"tensorflow.app.flags.DEFINE_integer",
"numpy.random.random_sample",
"tensorflow.compat.as_bytes",
"tensorflow.global_variables_initializer",
"numpy.argm... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
vindex10/AmpliGraph | [
"1aa91a4b32081bcaad4e7386032b9ac85deb99d6"
] | [
"ampligraph/latent_features/regularizers.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport abc\nimport logging\n\nREGULARIZER_REGISTRY = {}\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\n\ndef register_regularizer(name, external_params=[], class_params={}):\n def insert_in_registry(class_handle):\n REGULARIZER_REGI... | [
[
"tensorflow.abs",
"numpy.isscalar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
RichardGrumitt/pymc3 | [
"af65e0bce1628edc4828eff65ccada84ed4377d6"
] | [
"pymc3/sinf/SINF.py"
] | [
"import torch\nimport torch.nn as nn\nimport numpy as np\nimport time\nimport math\nfrom pymc3.sinf.SlicedWasserstein import *\nfrom pymc3.sinf.RQspline import *\nimport torch.multiprocessing as mp\nimport copy\n\n\nclass SINF(nn.Module):\n\n #Sliced Iterative Normalizing Flow model\n \n def __init__(self,... | [
[
"torch.mean",
"torch.max",
"torch.zeros",
"torch.sum",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.cuda.synchronize",
"torch.ones",
"torch.randn",
"torch.clone",
"torch.einsum",
"torch.eye",
"torch.tensor",
"torch.qr",
"torch.s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xiye17/diffmask | [
"6ae62ce58bf9bf5ea0b0ac23b196c52b7ee97c48"
] | [
"extract_rob_attribution_yesno.py"
] | [
"import os\nfrom os.path import join\nimport argparse\nimport torch\nimport json\nfrom collections import defaultdict\n\nimport matplotlib.pyplot as plt\nfrom tqdm.notebook import tqdm, trange\n\nfrom diffmask.models.question_answering_rob_squad_diffmask import (\n RobertaQuestionAnsweringSquadDiffMask,\n)\nfrom... | [
[
"torch.stack",
"torch.manual_seed",
"torch.sum",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HaroldMills/NFC | [
"356b2234dc3c7d180282a597fa1e039ae79e03c6",
"76e5931ca0c7fbe070c53b1362ec246ec9007beb",
"356b2234dc3c7d180282a597fa1e039ae79e03c6"
] | [
"scripts/show_recording_stats.py",
"vesper/mpg_ranch/nfc_species_classifier_2_0/analyze_classifications.py",
"vesper/psw/nogo_coarse_classifier_0_0/create_datasets_from_hdf5_files.py"
] | [
"\"\"\"\nScript that computes and displays audio recording statistics.\n\nThe statistics computed for a recording include an *ampligram*. An\nampligram is an image whose X axis is time, whose Y axis is waveform\namplitude, and in which color indicates the sample count in a particular\ntime/amplitude cell. It is use... | [
[
"matplotlib.backends.backend_pdf.PdfPages",
"matplotlib.colors.LogNorm",
"numpy.abs",
"matplotlib.pyplot.subplots",
"numpy.stack",
"matplotlib.pyplot.close",
"matplotlib.cm.get_cmap",
"numpy.array",
"numpy.histogram",
"numpy.zeros"
],
[
"matplotlib.pyplot.legend",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
heinoko/PVN3D | [
"2dc8d089a84eab4f52a05972c82c1e728c13f9e2"
] | [
"pvn3d/write_dpt_choose_norms.py"
] | [
"import sys\n#sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')\t\t#This is to remove ROS-python from the PYTHONPATH which messes up the Python 3 env this project works with\nfrom lib.utils.basic_utils import Basic_Utils\nfrom common import Config\nimport numpy as np\nfrom PIL import Image\nimport pcl... | [
[
"numpy.array",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mjg41/gusto | [
"f8b1480f942b701e765d008329bb6fe86f7747ea"
] | [
"gusto/state.py"
] | [
"from os import path, makedirs\nimport itertools\nfrom netCDF4 import Dataset\nimport sys\nimport time\nfrom gusto.diagnostics import Diagnostics, Perturbation, SteadyStateError\nfrom firedrake import (FiniteElement, TensorProductElement, HDiv, DirichletBC,\n FunctionSpace, MixedFunctionSpace,... | [
[
"numpy.arctan2",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xzlxiao/malmo | [
"3518f172d3063d2a9873181611c4be6cafc1b5b8"
] | [
"MalmoEnv/malmoenv/core.py"
] | [
"# ------------------------------------------------------------------------------------------------\n# Copyright (c) 2018 Microsoft Corporation\n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and\n# associated documentation files (the \"Software\"), to deal in th... | [
[
"numpy.frombuffer",
"numpy.zeros",
"numpy.iinfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
benedictlee21/FYP_SCSE21_0204 | [
"b5fdefac0fbec1291def5d47c780e8e7dced3b50",
"b5fdefac0fbec1291def5d47c780e8e7dced3b50"
] | [
"evaluation/pointnet.py",
"external/emd/emd_module.py"
] | [
"from __future__ import print_function\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport numpy as np\nimport torch.nn.functional as F\n\n\"\"\"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation\"\n github code : (https://github.com/charlesq34/pointnet)\n... | [
[
"torch.nn.BatchNorm1d",
"torch.max",
"torch.nn.functional.log_softmax",
"torch.cat",
"torch.nn.Linear",
"torch.bmm",
"torch.rand",
"torch.nn.Conv1d",
"torch.nn.ReLU",
"numpy.array"
],
[
"numpy.take_along_axis",
"numpy.expand_dims",
"torch.rand",
"torch.z... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shuuchen/lsun-room-dsc | [
"e7841fae2fb1c6695ed4e82f622cebfafe6c878d"
] | [
"test/test_dataloader.py"
] | [
"import pdb\nimport unittest\n\nimport torch\nimport matplotlib.pyplot as plt\nimport torchvision.transforms as transforms\nfrom PIL import Image\n\nfrom datasets.lsun_room.folder import ImageFolderDataset\nfrom datasets.transform import ToLabel\n\n\nclass TestDataloader(unittest.TestCase):\n\n image_size = (404... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mdreves/model-analysis | [
"73760b27b763e322a92ea80ff0a768ad9ef74526"
] | [
"tensorflow_model_analysis/eval_saved_model/example_trainers/fixed_prediction_estimator.py"
] | [
"# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"tensorflow.metrics.mean",
"tensorflow.losses.mean_squared_error",
"tensorflow.estimator.export.build_parsing_serving_input_receiver_fn",
"tensorflow.estimator.Estimator",
"tensorflow.FixedLenFeature",
"tensorflow.constant",
"tensorflow.train.get_global_step",
"tensorflow.estimator... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
myinxd/cavdet | [
"7a3eddfb9c94223ce35dc7133bfa7e2e7545df25"
] | [
"gcnn/getSampleSgl.py"
] | [
"# Copyright (C) 2017 Zhixian MA <zxma_sjtu@qq.com>\n\n\"\"\"\nA script that process on the single observations.\n\nSteps\n=====\n[1] Load the true cavity regions\n[2] Generate the mask image\n[3] Generate samples of this observation\n[4] Generate recovered image by the true mask image\n[5] Do edge detection\n[6] L... | [
[
"scipy.misc.imsave",
"scipy.misc.imread",
"scipy.io.savemat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": []
}
] |
suchoss/Dotace | [
"9aa6804b6ec2c933af7731a17f483dfc85a3a004"
] | [
"jsontocsv.py"
] | [
"# script pro zploštění dotace z hlídače do csv\n\nimport pandas as pd\nimport json\n\npd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)\npd.set_option('display.max_colwidth', None)\npd.set_option('display.width', None)\n\n# df = pd.read_json(\"expo.json\", orient='records')\n\nwit... | [
[
"pandas.set_option",
"pandas.json_normalize",
"pandas.merge"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0"
],
"scipy": [],
"tensorflow": []
}
] |
Aparajith-S/donors_project | [
"ec5cb7abb5180b438dcd5350beb7fddfd5beee02"
] | [
"finding_donors.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# ## Supervised Learning\n# ## Project: Finding Donors for *CharityML*\n\n# In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections t... | [
[
"sklearn.model_selection.GridSearchCV",
"pandas.read_csv",
"numpy.log",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.ensemble.HistGradientBoostingClassifier",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.base.clone",
"sklearn.metrics.fbeta_sco... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
shaheryarmalik212/Opencv-Face-Detection | [
"83af7bf4aeb7fd8ba6dc8b8561adc4dae5db4174"
] | [
"app.py"
] | [
"import numpy as np\nimport cv2\nimport cfg\nimport time\n\ncap = cv2.VideoCapture(0)\nnet = cv2.dnn.readNetFromCaffe(cfg.prototxt, cfg.caffemodel)\nstart_time = time.time()\nwhile True:\n ret, frame = cap.read()\n frame = cv2.resize(frame, (640, 480))\n cv2.waitKey(1000)\n (h, w) = frame.shape[:2]\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flavio-assis/marvel-characters-consumer | [
"abcb9274cf6375d9304881916de347d5862cccc7"
] | [
"tests/integration/test_clean_dataframe.py"
] | [
"import os\nfrom unittest import TestCase\n\nimport pandas as pd\n\nfrom src.data_processing.cleaning import CharacterDataFrameCleaning\n\n\nclass TestCleanDataframe(TestCase):\n def setUp(self) -> None:\n self.raw_path = os.path.join(os.path.dirname(__file__), 'files/raw')\n self.cleaned_path = os... | [
[
"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": []
}
] |
mikiec84/tensorflow | [
"bbc8c15b85358b9df029f16e31bc663b8f9d1fef"
] | [
"tensorflow/python/keras/engine/network.py"
] | [
"# Copyright 2015 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.models.save_model",
"tensorflow.python.util.tf_inspect.getfullargspec",
"tensorflow.python.keras.backend.batch_get_value",
"tensorflow.python.keras.backend.int_shape",
"tensorflow.python.training.checkpointable.layer_utils.gather_non_trainable_weights",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"2.7",
"2.6",
"2.4",
"2.3",
"2.9",
"2.5",
"2.2",
"2.10"
]
}
] |
ygnn123/training_extensions | [
"c3aeba9359b0d4e0ef9c054de777d3ec081a9892"
] | [
"misc/pytorch_toolkit/text_recognition/text_recognition/datasets/dataset.py"
] | [
"\"\"\"\nMIT License\n\nCopyright (c) 2019 luopeixiang\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify,... | [
[
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Lornatang/PyTorch-PGGAN | [
"a5ad433968641cafc13e2d0c2d9780872071b262"
] | [
"utils/data.py"
] | [
"# -*- coding: utf-8 -*-\nimport os, scipy.misc\nfrom glob import glob\nimport numpy as np \nimport h5py\n\n\n#prefix = 'C:\\\\Users\\\\yuan\\\\Downloads'\n# prefix = '/Users/yuan/Downloads/'\nprefix = './datasets/'\n\ndef get_img(img_path, is_crop=True, crop_h=256, resize_h=64, normalize=False):\n img = scipy.m... | [
[
"numpy.ones",
"numpy.ceil",
"numpy.random.randn",
"numpy.transpose",
"numpy.random.uniform",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
VictorDarvariu/solving-graph-pgg | [
"30428dec0783aa71a8ff955862e95ae5b3cca9b4"
] | [
"relnet/agent/mcts/mcts_tree_node.py"
] | [
"import numpy as np\nfrom relnet.utils.config_utils import local_np_seed\n\n\"\"\"\nSimple tree node representation.\n\"\"\"\nclass MCTSTreeNode(object):\n def __init__(self, state, parent_node, parent_action, valid_actions, depth=-1):\n self.state = state\n self.parent_node = parent_node\n ... | [
[
"numpy.array",
"numpy.random.choice",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hx123123/express-order-detection | [
"a1fa92a6bb02b5c47ad4b36e6f01602660527ac7"
] | [
"evaluation_model/statistics_height.py"
] | [
"# -*- coding:utf-8 -*-\nimport glob, os\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n\"\"\"\n此文件的作用就是可视化出框的高度分布情况，来选择合适的anchor\n\"\"\"\n\n\ndef statictic():\n \"\"\"\n 统计出所有的框的height并且保存下来\n :return:\n \"\"\"\n # txt_dir = '/media/haoxin/A1/data/AdvancedEAST/txt_550'\n txt_dir = '/dat... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xxchenxx/LoRA | [
"c16967ba0b644fecfb71df92d8c3c89a85441976"
] | [
"src/optimizer.py"
] | [
"import logging\r\nimport math\r\nimport os\r\n\r\nimport torch\r\nfrom torch import nn\r\nfrom torch.nn import CrossEntropyLoss, MSELoss\r\nimport torch.nn.functional as F\r\n\r\nfrom torch.optim import Optimizer\r\nfrom torch.optim.lr_scheduler import LambdaLR\r\n\r\nfrom collections import OrderedDict \r\nimport... | [
[
"torch.optim.lr_scheduler.LambdaLR",
"torch.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ravenlocke/mcpt | [
"942e8491c3c030e285e0b851d76418ad8f532b7e"
] | [
"mcpt/correlation_permutation_test.py"
] | [
"import random as _rd\nimport multiprocessing as _mp\n\nimport scipy.stats as _st\n\nfrom mcpt import _GT, _LT, _BOTH, _RESULT\nfrom mcpt.ci import wilson\nfrom mcpt.plot import plot_histogram\n\n\ndef _correlation_greater(tup):\n x, y, stat_0, f, seed = tup\n _rd.seed(seed)\n _rd.shuffle(y)\n stat = f(... | [
[
"scipy.stats.spearmanr",
"scipy.stats.pearsonr"
]
] | [
{
"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"
... |
RitujaPawas/ivy | [
"595788507aca609e868cb3d17edd815463af28e4"
] | [
"ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py"
] | [
"\"\"\"Collection of tests for elementwise functions.\"\"\"\n\n# global\nimport numpy as np\nfrom hypothesis import given, assume, strategies as st\nfrom numbers import Number\n\n# local\nimport ivy\nimport ivy_tests.test_ivy.helpers as helpers\nimport ivy.functional.backends.numpy as ivy_np\n\n\n# abs\n@given(\n ... | [
[
"numpy.asarray",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flying0712/Person_reID_baseline_pytorch | [
"b91fa4e6145585abb893ee544b59b0adf8cb3076"
] | [
"evaluate_gpu.py"
] | [
"import scipy.io\nimport torch\nimport numpy as np\n#import time\nimport os\n\n#######################################################################\n# Evaluate\ndef evaluate(qf,ql,qc,gf,gl,gc):\n query = qf.view(-1,1)\n print(query.shape)\n # print(query.shape)\n score = torch.mm(gf,query)\n score... | [
[
"torch.mean",
"torch.mm",
"numpy.in1d",
"numpy.argwhere",
"numpy.intersect1d",
"numpy.append",
"torch.FloatTensor",
"numpy.argsort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BACKPGJ/models | [
"d312801f52be5fc9d530cba98a863abd096f4885"
] | [
"official/resnet/imagenet_main.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.concat",
"tensorflow.transpose",
"tensorflow.FixedLenFeature",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.cast",
"tensorflow.reshape",
"tensorflow.expand_dims",
"tensorflow.logging.set_verbosity",
"tensorflow.VarLenFeature",
"tensorflow.parse_sing... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
guessable/machineLearning | [
"69be1f53918c29d27c1a8707ea92ce71c0cc1bfb",
"69be1f53918c29d27c1a8707ea92ce71c0cc1bfb",
"69be1f53918c29d27c1a8707ea92ce71c0cc1bfb"
] | [
"sampler/M_H.py",
"clustering/kmeans.py",
"linear_model/linear_reg.py"
] | [
"#!/usr/bin/env python3\n# _*_ coding:utf-8 _*_\n# Filename : tes.py\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef Gauss(x):\n g = (1/(np.sqrt(2*np.pi))*np.exp(-1*(x-1)**2/(2)))\n return g\n\n\nT = 10000\nsample = [0. for i in range(T)]\n\nfor t in range(T-1):\n\n t = t + 1\n x_st... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.random.normal",
"numpy.exp",
"numpy.random.uniform",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist"
],
[
"numpy.sqrt",
"matplotlib.pyplot.scatter",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",... |
daptablade/pyNastran | [
"247d4ae7a1a3f6a25cc1c3f3c286d6b32f1f84e7"
] | [
"pyNastran/op2/tables/oef_forces/oef_force_objects.py"
] | [
"#pylint disable=C0301\nfrom struct import Struct, pack\nfrom abc import abstractmethod\nimport inspect\nfrom typing import List\n\nimport numpy as np\nfrom numpy import zeros, searchsorted, allclose\n\nfrom pyNastran.utils.numpy_utils import integer_types, float_types\nfrom pyNastran.op2.result_objects.op2_objects... | [
[
"pandas.Series",
"numpy.array_equal",
"numpy.unique",
"numpy.isnan",
"numpy.allclose",
"pandas.Panel",
"numpy.union1d",
"pandas.MultiIndex.from_arrays",
"numpy.full",
"pandas.DataFrame",
"numpy.searchsorted",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20"
],
"scipy": [],
"tensorflow": []
}
] |
Mehul2203/dashboard1-group-e | [
"300d8db176c24c46008e01147818cfeca04470c9",
"300d8db176c24c46008e01147818cfeca04470c9"
] | [
"src/plot_6.py",
"src/plot_5.py"
] | [
"from ctypes import alignment\nfrom dash import Dash, dcc, html, Input, Output\nimport plotly.express as px\nimport pandas as pd\nimport dash_bootstrap_components as dbc\n\n# Read data\ndf = pd.read_csv('../data/Superstore.csv')\nsubcat = sorted(df['Sub-Category'].unique())\n\n# Create instance of app\napp = Dash(_... | [
[
"pandas.read_csv"
],
[
"pandas.merge",
"pandas.read_csv",
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1... |
Rishit-dagli/GLOM-TensorFlow | [
"9d8e4562414eedf69161a4c799e66798dee762b1"
] | [
"glomtf/pairwisedist.py"
] | [
"import tensorflow as tf\n\n\ndef pairwise_dist(A, B):\n \"\"\"Write an algorithm that computes batched the p-norm distance between each pair of two collections of row vectors.\n\n We use the euclidean distance metric.\n For a matrix A [m, d] and a matrix B [n, d] we expect a matrix of\n pairwise distan... | [
[
"tensorflow.matmul",
"tensorflow.reshape",
"tensorflow.square"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
JRSuckert/pymatgen | [
"f3f54792219035604f39e8ecbc544a410f4b7c97"
] | [
"pymatgen/io/cif.py"
] | [
"# coding: utf-8\n# Copyright (c) Pymatgen Development Team.\n# Distributed under the terms of the MIT License.\n\n\nimport math\nimport re\nimport os\nimport textwrap\nimport warnings\nfrom collections import OrderedDict, deque\nfrom io import StringIO\nimport numpy as np\nfrom functools import partial\nfrom pathl... | [
[
"numpy.array",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ycool/ai | [
"c0b3d3d1871a7389caf52fa1a9c8247effec3d8b"
] | [
"carla_python/camera_demo.py"
] | [
"import glob\nimport os\nimport sys\ntry:\n sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (\n sys.version_info.major,\n sys.version_info.minor,\n 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])\nexcept IndexError:\n pass\nimport carla\n\nimport random\nimport ti... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haochiche/pysplit | [
"df6f8ebe93dd81ff8925529b8dfaaea2f446f2e5"
] | [
"pysplit/hyfile_handler.py"
] | [
"from __future__ import division, print_function\n\nimport os\nimport fnmatch\nimport numpy as np\nimport pandas as pd\nimport re\nimport datetime as dt\n\n\ndef hysplit_filelister(signature):\n \"\"\"\n List all HYSPLIT files matching a given signature.\n\n Parameters\n ----------\n signature : stri... | [
[
"numpy.split",
"numpy.nonzero",
"numpy.unique",
"pandas.DatetimeIndex",
"numpy.full",
"numpy.max",
"numpy.diff",
"numpy.argsort",
"numpy.array"
]
] | [
{
"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": []
}
] |
anonymous-scholar/ICDM2021-TCube | [
"be10c73a455c98fccc03e7d28003c8a3adcfc324"
] | [
"Finetune PLMs/preprocess_dart.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n\nfrom xml.dom import minidom\nfrom pathlib import Path\nimport re\nimport unidecode\nimport os\nimport pandas as pd\n\ndef clean_node(node):\n\tnode = node.strip()\n\tnode = node.replace('(', '')\n\tnode = node.replace('\\\"', '')\n\tnode = node.replace(')', '')\n\tnode =... | [
[
"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": []
}
] |
JiminKong/ActiveThief-attack-MLaaS | [
"542ac43a94ec8b96b4dffc101f561e85ce564865"
] | [
"cfg.py"
] | [
"\"\"\"\nMIT License\n\nCopyright (c) 2019 Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade,\nShirish Shevade, Vinod Ganapathy. Indian Institute of Science.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to... | [
[
"tensorflow.flags.DEFINE_boolean",
"tensorflow.flags.DEFINE_string",
"tensorflow.ConfigProto",
"tensorflow.flags.DEFINE_float",
"tensorflow.flags.DEFINE_integer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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