repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
elmsfu/tensorflow | [
"1405d83ab5aafa3e06cb427cae1abf3f6f394be7"
] | [
"tensorflow/python/keras/testing_utils.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.framework.tensor_shape.TensorShape",
"tensorflow.python.keras.layers.Dense",
"tensorflow.python.keras.backend.dtype",
"tensorflow.python.eager.context.executing_eagerly",
"numpy.random.randint",
"tensorflow.python.keras.models.Model.from_config",
"numpy.zeros",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.4"
]
}
] |
just4jc/cuml | [
"bd7824bff0a60634b11c1f036dd41cc7f8c05bc1",
"bd7824bff0a60634b11c1f036dd41cc7f8c05bc1"
] | [
"python/cuML/test/test_pca.py",
"python/cuML/test/test_pca_random.py"
] | [
"# Copyright (c) 2018, 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.asarray",
"numpy.array",
"sklearn.decomposition.PCA"
],
[
"numpy.array",
"sklearn.decomposition.PCA",
"numpy.random.rand",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheAustinator/peep-dis | [
"c278c364a1e09c1eeb3a6f686ad6c303036904e6"
] | [
"peepdis/legacy.py"
] | [
"from copy import deepcopy\r\nimport re\r\nfrom termcolor import colored\r\nfrom types import (\r\n BuiltinMethodType,\r\n BuiltinFunctionType,\r\n FunctionType,\r\n MethodType,\r\n ModuleType,\r\n)\r\nimport sys\r\n\r\n\r\ndef peep(obj, builtins=False, privates=False, docstrings=False,\r\n t... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shrey23812/trust | [
"7f1178862856fb4084debca240ca1809cdf19358"
] | [
"trust/utils/custom_dataset_medmnist.py"
] | [
"import numpy as np\nimport os\nimport torch\nimport torchvision\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom torch.utils.data import Dataset, random_split\nfrom torchvision import datasets, transforms\nimport PIL.Image a... | [
[
"torch.Tensor",
"numpy.random.seed",
"torch.manual_seed",
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DancingQuanta/SpecDAL | [
"7208abc2e8e01bfda8e3ec5d9edad1c4729dfabe"
] | [
"specdal/containers/spectrum.py"
] | [
"# spectrum.py provides class for representing a single\n# spectrum. Spectrum class is essentially a wrapper around\n# pandas.Series.\nimport pandas as pd\nimport numpy as np\nimport specdal.operators as op\nfrom collections import OrderedDict\nfrom specdal.readers import read\nimport os\n\nclass Spectrum(object):\... | [
[
"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": []
}
] |
tiagodavi70/build_layouts | [
"9860cd1c4f87c1cecfa1bbd18b2e4c5bdc94398f"
] | [
"learning.py"
] | [
"from sklearn.feature_selection import VarianceThreshold\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import MeanShift\nfrom sklearn.cluster import SpectralClustering\n\nimport streamlit as st\n\n\ndef variance_thres(X, thres):\n selector = VarianceThreshold(threshold=(thres * (1 - thres)))\n ... | [
[
"sklearn.feature_selection.VarianceThreshold",
"sklearn.cluster.MeanShift",
"sklearn.decomposition.PCA",
"sklearn.cluster.SpectralClustering"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahstewart/OASIS | [
"82f1ee805e8bfcac4a2640579d9581ecca6ca0ca",
"82f1ee805e8bfcac4a2640579d9581ecca6ca0ca"
] | [
"OasisPy/optimize.py",
"docs/OasisPy/subtract_ais.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Nov 14 18:03:23 2018\n\n@author: andrew\n\"\"\"\n\nfrom astropy.io import fits\nimport numpy as np\nimport glob\nimport os\nimport initialize\nimport poisson_fit\nimport time\nimport math\nimport psf\nimport subtract_ais\nimport subtract_hotpa... | [
[
"numpy.sqrt",
"numpy.isnan",
"numpy.ones",
"numpy.max",
"numpy.floor",
"numpy.zeros"
],
[
"numpy.logical_not"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cclopes/LMAsimulation | [
"06d3211be75ed2815d65cf87da717fa339c6a24f"
] | [
"coordinateSystems.py"
] | [
"import pyproj as proj4 \nfrom numpy import *\nfrom numpy.linalg import norm, inv\n\n# def radians(degrees):\n # return deg2rad(asarray(degrees))\n # return array(degrees) * pi / 180.0\n \n# def degrees(radians):\n # return rad2deg(asarray(radians))\n # return array(radians) * 180.0 / pi\n\n\nclass C... | [
[
"numpy.linalg.inv",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.10",
"1.12",
"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": [],
... |
StatisticDean/Crystal_Clear | [
"d52e567852a14c4819267ac2fc5bf9a25a6f1b33"
] | [
"crystal_clear/metadata.py"
] | [
"'''\nThis files contains everything needed to deal with the medatata.csv files.\n'''\nimport os\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom tqdm import tqdm\n\ndirname = Path(__file__).parent\n\n\ndef clean_meta():\n path_to_meta = Path(os.path.join(dirname, '../data/fma_metadata'))... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
motazsaad/unet | [
"85117087c1cb73c81a8eea4e127fae7cb47b4fe1"
] | [
"3D/dataloader.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2019 Intel 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/LI... | [
[
"numpy.rot90",
"numpy.expand_dims",
"numpy.random.seed",
"numpy.random.choice",
"numpy.arange",
"numpy.sort",
"numpy.random.shuffle",
"numpy.std",
"numpy.random.permutation",
"numpy.mean",
"numpy.random.rand",
"numpy.floor",
"numpy.flip",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahaibof/NSLS-II-HXNs | [
"0cca70128e206de1e3d64f7a7df7fa1ed2f4d9d3"
] | [
"startup/survey_scan.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nfrom scipy import ndimage\n#from databroker import get_table, db\n#from skimage.filters.rank import median\nfrom skimage.morphology import disk\nfrom skimage import io\n\n'''\nfrom hxntools.handlers import register\nimport filestore\nregister()\n\nim... | [
[
"numpy.nanmax",
"matplotlib.pyplot.imshow",
"numpy.asarray",
"numpy.flipud",
"numpy.nanmin",
"numpy.round",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.gca",
"numpy.reshape",
"numpy.asfarray",
"numpy.zeros",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neilfrndes/detectron-benchmark | [
"232d78815416f619b63362066a51dca85c93efa7"
] | [
"run.py"
] | [
"import zipfile\nfrom timeit import default_timer as timer\n\nimport cv2\nimport numpy as np\nimport torch\nfrom detectron2 import model_zoo\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.config import get_cfg\nfrom detectron2.modeling import build_model\nfrom detectron2.utils.logger impo... | [
[
"numpy.frombuffer",
"torch.from_numpy",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zjffdu/bkcharts | [
"759a12436be4bec54b305115b79fa3e869a26164"
] | [
"examples/file/scatter_multi.py"
] | [
"import pandas as pd\n\nfrom bkcharts import Scatter, output_file, defaults, show\nfrom bkcharts.operations import blend\nfrom bkcharts.utils import df_from_json\nfrom bokeh.layouts import gridplot\nfrom bokeh.sampledata.autompg import autompg as df\nfrom bokeh.sampledata.iris import flowers\nfrom bokeh.sampledata.... | [
[
"pandas.melt"
]
] | [
{
"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": []
}
] |
dgketchum/EEMapper | [
"ac3424d39c7d7cea2fea2f7f1a2037f52fb48123"
] | [
"map/tables.py"
] | [
"import json\nimport os\nfrom datetime import datetime\n\nfrom geopandas import GeoDataFrame, read_file\nfrom numpy import where, sum, nan, std, array, min, max, mean, ones_like, rint, count_nonzero, isnan\nfrom pandas import read_csv, concat, errors, Series, merge, DataFrame\nfrom pandas import to_datetime\nfrom p... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.concat",
"numpy.ones_like",
"pandas.to_datetime",
"numpy.min",
"numpy.rint",
"numpy.max",
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
MikkoKyto/F-16-Dogfight-AI | [
"821eae4ec3258a80b3010fd0a044a7ebaa9b9e24",
"821eae4ec3258a80b3010fd0a044a7ebaa9b9e24"
] | [
"cx.py",
"rotations.py"
] | [
"#x-axis aerodynamic force coeff. in F-16 model\r\nimport numpy as np\r\n\r\n\r\ndef cx(alpha,el,amach):\r\n\r\n A = np.asarray([[-0.099, -0.081, -0.081, -0.063, -0.025, 0.044, 0.097, 0.113, 0.145, 0.167, 0.174, 0.166],\r\n [-0.048, -0.038, -0.040, -0.021, 0.016, 0.083, 0.127, 0.137, 0.162, 0... | [
[
"numpy.asarray",
"numpy.fix",
"numpy.sign",
"numpy.transpose"
],
[
"numpy.dot",
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RaulRC/genetic-neural-optimizer | [
"fa169cdc9b43c58470c3e7a7214185d56e61579a"
] | [
"src/glass_regularization.py"
] | [
"from genetic_optimizer import *\nimport pdb\nimport pandas as pd\nimport numpy as np\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import SGD\nimport itertools\nimport time\nfrom tensorflow.kera... | [
[
"pandas.read_csv",
"matplotlib.use",
"sklearn.model_selection.train_test_split",
"tensorflow.keras.callbacks.EarlyStopping",
"sklearn.preprocessing.LabelEncoder",
"sklearn.preprocessing.MinMaxScaler",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
hkaneko1985/dcek | [
"13d9228b2dc2fd87c2e08a01721e1b1b220f2e19",
"13d9228b2dc2fd87c2e08a01721e1b1b220f2e19",
"13d9228b2dc2fd87c2e08a01721e1b1b220f2e19",
"13d9228b2dc2fd87c2e08a01721e1b1b220f2e19"
] | [
"demo_semi_supervised_learning_low_dim_no_hyperparameters.py",
"demo_midknn_in_svr.py",
"demo_opt_gtmr_with_cv_multi_y.py",
"demo_sample_generation_based_on_gmm.py"
] | [
"# -*- coding: utf-8 -*-\n# %reset -f\n\"\"\"\n@author: Hiromasa Kaneko\n\"\"\"\n\nimport matplotlib.figure as figure\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom dcekit.learning import SemiSupervisedLearningLowDimension\nfrom sklearn.decomposition import PCA\nfrom sklearn.gaussia... | [
[
"sklearn.gaussian_process.kernels.ConstantKernel",
"pandas.read_csv",
"sklearn.model_selection.cross_val_predict",
"matplotlib.figure.figaspect",
"matplotlib.pyplot.scatter",
"sklearn.gaussian_process.kernels.RBF",
"matplotlib.pyplot.ylim",
"sklearn.model_selection.train_test_split... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
kant/MIDI-VAE | [
"44f428ed4b1b362a86cfbd5e44790208730be48e"
] | [
"vae_training.py"
] | [
"\r\n# ----------------------------------------------------------------------------------------------\r\n# Import dependencies\r\n# ----------------------------------------------------------------------------------------------\r\n\r\nfrom settings import *\r\nfrom keras.utils import to_categorical\r\nfrom random im... | [
[
"numpy.asarray",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"numpy.std",
"numpy.mean",
"matplotlib.pyplot.close",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pink-Shadow/VISN | [
"4a484610cd86a170a9612a65c81e082394cc08f0"
] | [
"Modules/module3/opdracht2.py"
] | [
"from skimage import data, filters\nfrom skimage.viewer import ImageViewer\nfrom skimage import filters\nimport scipy\nfrom scipy import ndimage\nimport matplotlib.pyplot as plt\n\nsmooth_mean=[ [1/9,1/9,1/9],\n [1/9,1/9,1/9],\n [1/9,1/9,1/9]]\n\n############################\nedge1 = [[-1,... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.tight_layout",
"scipy.ndimage.convolve",
"matplotlib.pyplot.show"
]
] | [
{
"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"... |
Ascend/samples | [
"5e060ddf8c502cf0e248ecbe1c8986e95351cbbd",
"5e060ddf8c502cf0e248ecbe1c8986e95351cbbd"
] | [
"python/common/acllite/videocapture.py",
"python/level3_multi_model/Robotic_Arm_Object_Following/src/object_detection.py"
] | [
"import av\nimport threading\nimport numpy as np\nimport acl\nimport time\n\nimport constants as const\nimport utils\nimport acllite_logger as acl_log\nimport dvpp_vdec as dvpp_vdec\nfrom acllite_image import AclLiteImage\n\nWAIT_INTERVAL = 0.01\nWAIT_READY_MAX = 10\nWAIT_FIRST_DECODED_FRAME = 0.02\n\nDECODE_STATUS... | [
[
"numpy.frombuffer"
],
[
"numpy.maximum",
"numpy.minimum",
"numpy.argmax",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
violenil/object-detection | [
"3bdd271ed6254aefe17983ad3b3b684b9873ceb1"
] | [
"backend/utils.py"
] | [
"\"\"\"Utilities for logging.\"\"\"\nimport os\nimport cv2\nimport numpy as np\nimport logging\nimport time\n\nALPHA = 0.5\nFONT = cv2.FONT_HERSHEY_PLAIN\nTEXT_SCALE = 1.0\nTEXT_THICKNESS = 1\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nif os.getenv('DEBUG'):\n level = logging.DEBUG\nelse:\n level = logging.E... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Moreficent/xvi | [
"fb9d4cf15638104480a4f1666d77644fe7ecc760"
] | [
"abinitio/03_logistic_regression.py"
] | [
"\"\"\"\nCalibrates a logistic regression model to the data. The slope and intercept are\nassumed to be normally distributed. \n\nFor simplicity, eager mode evaluation is used.\n\"\"\"\nfrom abinitio.utils import fix_seed\n\nfix_seed(42)\n\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_probability ... | [
[
"tensorflow.Variable",
"numpy.set_printoptions",
"tensorflow.random.uniform",
"tensorflow.keras.optimizers.RMSprop",
"matplotlib.pyplot.subplots",
"tensorflow.math.reduce_mean",
"tensorflow.math.exp",
"tensorflow.math.reduce_std",
"tensorflow.math.sigmoid",
"tensorflow.math... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
pavandonthireddy/Project_V4 | [
"6dea85b6b2ac9b05056b1f8a859361427d642fed"
] | [
"hypothesisTest/weights_to_bets.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Feb 8 17:15:16 2020\n\n@author: Pavan\n\"\"\"\n\nimport numpy as np\n\ndef get_valid_index(strategy_weights,delay=1):\n valid_index = ~np.isnan(strategy_weights).all(axis=1)\n valid_index[-1*delay]=False\n return valid_index\n\n\ndef bets_to_pnl(starting_va... | [
[
"numpy.log",
"numpy.abs",
"numpy.isnan",
"numpy.sign",
"numpy.nansum",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YanaZeng/lingam | [
"c16caf564c9f4e43eead65405189ab7ac2ae3f0d",
"c16caf564c9f4e43eead65405189ab7ac2ae3f0d"
] | [
"tests/test_direct_lingam.py",
"tests/test_var_lingam.py"
] | [
"import os\n\nimport numpy as np\nimport pandas as pd\nfrom lingam.direct_lingam import DirectLiNGAM\n\n\ndef test_fit_success():\n # causal direction: x0 --> x1 --> x3\n x0 = np.random.uniform(size=1000)\n x1 = 2.0 * x0 + np.random.uniform(size=1000)\n x2 = np.random.uniform(size=1000)\n x3 = 4.0 * ... | [
[
"numpy.random.uniform",
"numpy.array",
"numpy.sum"
],
[
"numpy.diag",
"numpy.dot",
"numpy.tril",
"numpy.multiply",
"numpy.random.choice",
"numpy.eye",
"numpy.sign",
"numpy.random.normal",
"numpy.random.permutation",
"numpy.mean",
"numpy.std",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ogogmad/laguerre_transformations | [
"e96dba553b88c09235599be34ab342de8c1f6623"
] | [
"laguerre_transformations/laguerre_transformations.py"
] | [
"from PIL import Image, ImageDraw\nfrom numpy import eye, block, sin, cos, tan, arctan2, sign, array, pi\nfrom scipy.linalg import logm, expm\ntry:\n from .display import display\nexcept ImportError:\n from display import display\n\none = eye(2)\neps = array([[0,1],[0,0]])\n\ndef dual_number(a,b):\n \"\"\"... | [
[
"numpy.eye",
"numpy.cos",
"numpy.sin",
"numpy.sign",
"scipy.linalg.expm",
"numpy.block",
"scipy.linalg.logm",
"numpy.tan",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
Sohrab82/Udacity-P4-Behavioral-Cloning | [
"44d94f287100c6bb292fd9e0f0d98e42cbdad644"
] | [
"drive.py"
] | [
"# python-socketio==4.6.0 and python-engineio==3.13.0 is the answer to the\r\n\r\nimport argparse\r\nimport base64\r\nfrom datetime import datetime\r\nimport os\r\nimport shutil\r\n\r\nimport numpy as np\r\nimport socketio\r\nimport eventlet\r\nimport eventlet.wsgi\r\nfrom PIL import Image\r\nfrom flask import Flas... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
walterian/VLC-CAE | [
"2dac62d7af4b66764961f1f478e30da66395d713"
] | [
"tmp/single_snr.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 4 10:34:21 2019\n\n@author: SINE_Lab\n\"\"\"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 4 10:25:14 2019\n\n@author: SINE_Lab\n\"\"\"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 20 09:56:32 2018\n\n@author: SINE_Lab\n\"\"\"\n\n# -*- coding... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"tensorflow.cast",
"matplotlib.pyplot.plot",
"numpy.random.randn",
"numpy.random.randint",
"numpy.eye",
"numpy.argmax",
"numpy.zeros",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"tensorflow.set_random_seed",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ytfksw/openvino | [
"257b420ed67eb24d10563a9ba0d12998819dd1c5"
] | [
"inference-engine/ie_bridges/python/sample/object_detection_sample_ssd/object_detection_sample_ssd.py"
] | [
"#!/usr/bin/env python\n\"\"\"\n Copyright (c) 2018 Intel 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 requ... | [
[
"numpy.int",
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
obi-ml-public/ehr_deidentification | [
"c9deaf30b8317689d28a4267d15ec13baa9791cd"
] | [
"src/robust_deid/sequence_tagging/evaluation/note_evaluation/note_token_evaluation.py"
] | [
"from collections import Counter\nfrom typing import Sequence, List, Tuple, Union, Type, Optional\n\nfrom seqeval.reporters import DictReporter\nfrom sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix\n\n\nclass NoteTokenEvaluation(object):\n \"\"\"\n This class is used to evalua... | [
[
"sklearn.metrics.f1_score",
"sklearn.metrics.precision_score",
"sklearn.metrics.recall_score",
"sklearn.metrics.confusion_matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cherie11/comet-commonsense | [
"23878558b04b5a55f631731e65381ecc287eee23"
] | [
"scripts/generate/.ipynb_checkpoints/generate_persona_beam_search-checkpoint.py"
] | [
"import os\nimport time\nimport sys\nimport pickle\nimport argparse\n\nsys.path.append(os.getcwd())\nimport torch\n\nimport src.train.atomic_train as train\nimport src.models.models as models\nimport src.data.data as data\nimport utils.utils as utils\nimport src.train.utils as train_utils\nimport src.data.config as... | [
[
"numpy.expand_dims",
"torch.ones",
"torch.cuda.set_device",
"numpy.random.seed",
"torch.cat",
"torch.manual_seed",
"numpy.arange",
"numpy.stack",
"torch.tensor",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hz-ants/CenterPoint | [
"1d2ebd2eb7500478dba22dd2774cbbb374549068"
] | [
"det3d/models/bbox_heads/center_head.py"
] | [
"# ------------------------------------------------------------------------------\n# Portions of this code are from\n# det3d (https://github.com/poodarchu/Det3D/tree/56402d4761a5b73acd23080f537599b0888cce07)\n# Copyright (c) 2019 朱本金\n# Licensed under the MIT License\n# ---------------------------------------------... | [
[
"torch.flip",
"torch.sigmoid",
"torch.max",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.arange",
"torch.from_numpy",
"torch.tensor",
"torch.exp",
"torch.no_grad",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.atan2"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ekaterinailin/fleck | [
"0bfa1e5a00d093a8e90cb25c4d80a8f607bf1c15"
] | [
"fleck/tests/test_core.py"
] | [
"import numpy as np\nimport os\nimport astropy.units as u\nimport pytest\n\nfrom ..core import Star\n\n\n@pytest.mark.parametrize(\"fast,\", [\n (\"True\", ),\n (\"False\", ),\n])\ndef test_stsp_rotational_modulation(fast):\n \"\"\"\n Compare fleck results to STSP results\n \"\"\"\n stsp_lc = np.l... | [
[
"numpy.array",
"numpy.linspace",
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JulianWww/AlphaZero | [
"8eb754659793305eba7b9e636eeab37d9ccd45f7"
] | [
"test/model_mockup.py"
] | [
"import torch, numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass ConvLayer(nn.Module):\n def __init__(self, inp, out, kernel):\n super(ConvLayer, self).__init__()\n self.conv1 = nn.Conv2d(inp, out, kernel, padding=\"same\")\n self.batch = nn.BatchNorm2d(out)\n ... | [
[
"torch.nn.Softmax",
"torch.ones",
"torch.reshape",
"torch.nn.Conv2d",
"torch.nn.LayerNorm",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.tensor",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
codacy-badger/prototorch | [
"4158586cb93e195208fc397bda33d67385cf385f"
] | [
"prototorch/modules/prototypes.py"
] | [
"\"\"\"ProtoTorch prototype modules.\"\"\"\n\nimport warnings\n\nimport torch\n\nfrom prototorch.functions.initializers import get_initializer\n\n\nclass Prototypes1D(torch.nn.Module):\n def __init__(self,\n prototypes_per_class=1,\n prototype_distribution=None,\n ... | [
[
"torch.nn.Parameter",
"torch.is_tensor",
"torch.tensor",
"torch.unique",
"torch.no_grad",
"torch.rand",
"torch.arange",
"torch.as_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
The-Non-Commuters/SimpleNet_Pytorch | [
"9f01af90a8d26831d5c0e7aefa1cc721cdcd3384"
] | [
"models/qsimplenet_htorch.py"
] | [
"\"\"\"\nSimplerNetV1 in Pytorch.\n\nThe implementation is basded on : \nhttps://github.com/D-X-Y/ResNeXt-DenseNet\n\"\"\"\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom htorch.layers import QConv2d, QBatchNorm2d, QMaxPool2d, QLinear, QuaternionToRea... | [
[
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
google/nitroml | [
"5eabdbe6de85ff7fdae4fefda7547c0c031f9431"
] | [
"nitroml/automl/autodata/preprocessors/basic_preprocessor.py"
] | [
"# Copyright 2020 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.keras.backend.is_sparse",
"tensorflow.SparseTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
nsaunshi/meta_tr_val_split | [
"71afe0d2d1044d7346769473dc9db865e6f874e6"
] | [
"imaml_orig_src/imaml_dev-master/examples/measure_accuracy.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nimport sys\nsys.path.insert(0, \"/u/arushig/imaml_dev-master\")\nsys.path.insert(0, \"/home/user/Desktop/meta-learning/data/imaml_dev-master\")\n\nimport implicit_maml.utils as utils\nimport random\nimport time as timer\nimport pickle\nimport argparse\nimpor... | [
[
"numpy.sqrt",
"numpy.random.seed",
"torch.manual_seed",
"torch.tensor",
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yvesdeboeck66/Masterproef-Yves-De-Boeck | [
"884fff7fccf3d5af23e225c7d49040e3f9ce10c6"
] | [
"sampler.py"
] | [
"import os\n\nimport chainer\nimport numpy as np\nfrom skimage.io import imsave\n\nfrom utils import make_grid\n\n\ndef sampler(G, dst, inputv, name):\n @chainer.training.make_extension()\n def make_image(trainer):\n with chainer.using_config(\"Train\", False):\n with chainer.no_backprop_mod... | [
[
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
felix-martel/multigeo | [
"2a1af9abae1fcef399744f6d88c4b1c25e8a25ab"
] | [
"amstramdam/datasets/grouped_game_map.py"
] | [
"import random\nfrom collections import Counter\nfrom typing import Optional, Any, NoReturn\n\nfrom amstramdam.datasets.dataframe import UnifiedDataFrame\nfrom amstramdam.datasets.types import (\n BoundingBoxArray,\n MapWeights,\n LevelWeights,\n)\n\nfrom amstramdam.game.geo import Point, distance\nimport ... | [
[
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ogreen/approxGraphComp | [
"ffe871d526c2a7e93121be61132356e9c77df6e5"
] | [
"ss_experiment/failureTest/printFailureTest.py"
] | [
"#!/usr/bin/python3\n# produces a bar graph chart for sync failure tests\n# uses graphs described by variable GRAPH\n# uses database described by variable DATABASE\n# \n# Usage \n# python printFailureTest.py #algmtype #normprob #max_iter #num_trial \n#algmtype is for type of algorithm used can be sync or asyn... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.linspace",
"matplotlib.use",
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use",
"m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vivian-wong/AM_defects_nnUNet | [
"22bde186d3849029331d7f1ce27b800db6117556"
] | [
"nnunet/training/network_training/network_trainer.py"
] | [
"# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany\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# ... | [
[
"numpy.random.seed",
"matplotlib.use",
"torch.manual_seed",
"torch.cuda.empty_cache",
"sklearn.model_selection.KFold",
"torch.from_numpy",
"matplotlib.pyplot.plot",
"torch.save",
"numpy.mean",
"matplotlib.pyplot.close",
"torch.cuda.manual_seed_all",
"torch.device",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
swimming16/rl-buffer | [
"9322bfecc08b3c5e6d9d036eddb9f5689cb85c23"
] | [
"test/test.py"
] | [
"import numpy as np\nx=np.arange(10)\n\nprint([0]*4+[2])\nprint(np.random.uniform(0.9, 1.1))\nprint(x,x[-3:])\na=[3]\nfor i in a:\n print(i)"
] | [
[
"numpy.arange",
"numpy.random.uniform"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
katherinezhu01/TCRP | [
"f6b3a703c24eeb8ad1698162511e506c6df8d76b"
] | [
"code/inner_loop.py"
] | [
"import numpy as np\nfrom collections import OrderedDict\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\nfrom layers import *\nfrom score import *\nfrom data_loading import *\nfrom mlp import mlp\n\nclass InnerLoop(mlp):\n\t# This module performs the inner loop of MAML\n\t# The forwar... | [
[
"torch.nn.MSELoss",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tynguyen/ESANet | [
"73c756047935063b1e65d43b2c97750e7fcb83e7"
] | [
"src/datasets/dataset_base.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n.. codeauthor:: Mona Koehler <mona.koehler@tu-ilmenau.de>\n.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>\n\"\"\"\nimport os\nimport pickle\nimport abc\n\nimport numpy as np\nfrom torch.utils.data import Dataset\n\n\nclass DatasetBase(abc.ABC, Dataset):\n def __... | [
[
"numpy.square",
"numpy.log",
"numpy.sqrt",
"numpy.asarray",
"numpy.median",
"numpy.uint64",
"numpy.float64",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
grainpowder/gru-forward-numpy-impl | [
"b6adf921d2107885914b8f50434d0bb47c46d0bf"
] | [
"src/npgru/upload.py"
] | [
"import gzip\nimport logging\nimport os\nimport pathlib\nimport shutil\nfrom typing import Dict\n\nimport boto3\nimport numpy as np\nimport pandas as pd\nimport tensorflow.keras as keras\nfrom dotenv import load_dotenv\n\n\ndef upload(project_dir: pathlib.Path, logger: logging.Logger) -> None:\n load_dotenv()\n ... | [
[
"pandas.DataFrame",
"numpy.ones"
]
] | [
{
"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": []
}
] |
gschivley/EIA_Cleaned_Hourly_Electricity_Demand_Code | [
"718d0467902a4fcacb9b2b29c1ff318c57ae6682"
] | [
"anomaly_screening.py"
] | [
"\"\"\"\nGreg Schivley\n\nFunctions to screen demand timeseries data for anomalies.\n\nAdopted from Tyler Ruggles\nhttps://github.com/truggles/EIA_Cleaned_Hourly_Electricity_Demand_Code\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\n\n\ndef add_rolling_dem(df, short_hour_window):\n\n df[\"rollingDem\"] = (\... | [
[
"numpy.nanpercentile",
"numpy.nanmedian",
"numpy.isnan",
"pandas.DataFrame",
"numpy.mean",
"numpy.where"
]
] | [
{
"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": []
}
] |
wtwong316/tsd_analysis_with_es | [
"04e9667b5fb6c5052e55053fe7838a79aab24fb5"
] | [
"com/beiwei/flask/tsd_stationarity.py"
] | [
"from flask_restplus import Resource, Namespace\nfrom flask import request, Response\nfrom flask_jsonpify import jsonify\nfrom flask_restplus import fields\nfrom com.beiwei.es.search import es_get_data\nfrom com.beiwei.plots.plot import plot_data, plot_rolling_mean, plot_rolling_std, plot_data_w_text, \\\n plot_... | [
[
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xylar/cdat | [
"5133560c0c049b5c93ee321ba0af494253b44f91"
] | [
"testing/vcs/test_vcs_1D_datawc_missing.py"
] | [
"\nimport vcs, numpy, cdms2, MV2, os, sys, vcs.testing.regression as regression\n\nx = regression.init()\nyx = x.createyxvsx()\n\ndata = \"\"\"\n-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.\n0.059503571833625334\n0.059503571833625334 0.05664014775641405 0.051... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
glab2019/MADDPG-Based-MCS | [
"e48ee3a74c66d7fa8acaa4e3796874c4ca09519f"
] | [
"environment/Brain.py"
] | [
"import numpy as np\n\n\nclass BrainInfo:\n def __init__(self, vector_observations=None,\n reward=None, agents=None,\n feedback=None, local_done=False, memory=None, vector_action=None):\n \"\"\"\n Describes experience at current step of all agents linked to a brain.\... | [
[
"numpy.array",
"numpy.ones",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NathanGavenski/Tensorboard-Wrapper | [
"87bfb6e54daba7126bba4add2043641eb2d7d898"
] | [
"tensorboard_wrapper/tensorboard.py"
] | [
"from collections import defaultdict\nfrom datetime import datetime\nimport os\nimport shutil\n\nimport numpy\nimport torch\nimport torchvision\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom .exceptions import BoardAlreadyExistsException\n\n\nclass Tensorboard():\n def __init__(self, name=None, path=... | [
[
"numpy.array",
"torch.utils.tensorboard.SummaryWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
t-young31/thesis | [
"2dea31ef64f4b7d55b8bdfc2094bab6579a529e0",
"2dea31ef64f4b7d55b8bdfc2094bab6579a529e0"
] | [
"4/figs/figX3/scripts_plots_data/calculate_well_dft_single_frame.py",
"4/figs/figX6/plot_eigenfunctions.py"
] | [
"import os\nimport sys\nimport gaptrain as gt\nimport autode as ade\nimport numpy as np\nade.Config.n_cores = 4\n\n\ndef get_truncated_portion(frame, max_dist):\n \"\"\"\n Given a frame discard all but the central water molecule and the water\n molecules that are within max_dist of it\n\n :param frame:\... | [
[
"numpy.linspace",
"numpy.linalg.norm",
"numpy.savetxt",
"numpy.argsort",
"numpy.array",
"numpy.average"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"numpy.linspace",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.savefig",
"matplotlib.py... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fipu-lab/p2p_bn | [
"f2c67766f030de28fed82b11188f391d338bbe12",
"f2c67766f030de28fed82b11188f391d338bbe12"
] | [
"plot/data.py",
"plot/visualize.py"
] | [
"import numpy as np\nfrom scipy.spatial.distance import pdist, squareform\nfrom fastcluster import linkage\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport string\n\nfrom plot.visualize import read_json\n\n\ndef read_symmetric_matrix(filename):\n return symmetric_matrix(np.array(read_json(filen... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.triu_indices",
"numpy.tril_indices",
"matplotlib.pyplot.colorbar",
"scipy.spatial.distance.pdist",
"scipy.spatial.distance.squareform",
"matplotlib.pyplot.show",
"numpy.zeros"
],
[
"matplotlib.ticker.MultipleLocator",
"matplotlib... | [
{
"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"
... |
we684123/Reverse-sheet-sky-children-of-the-light | [
"98c2f9f04907e8613d37ec540feaa501c0e44de9"
] | [
"play_video.py"
] | [
"from pathlib import Path\nimport time\nimport json\n\nimport cv2\nimport numpy as np\n\nfrom library import reverse_utilities as ru\nfrom config import base\nreverse_config = base.reverse_config()\nrc = reverse_config\n\naims_folder_path = Path(rc['aims_folder_path'])\neffect_config_path = \\\n f\"{str(aims_fol... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neurodata-papers/mgc | [
"0e50fd8f4e2a3267abf4fd88e92fba653ba056b1"
] | [
"mgcpy/hypothesis_tests/transforms.py"
] | [
"import numpy as np\nfrom mgcpy.independence_tests.dcorr import DCorr\nfrom sklearn import preprocessing\n\n\ndef k_sample_transform(x, y, is_y_categorical=False):\n '''\n Transform to represent a k-sample test as an independence test\n\n :param X: is interpreted as either:\n\n - a ``[n*n]`` distanc... | [
[
"numpy.linalg.norm",
"numpy.concatenate",
"numpy.repeat",
"sklearn.preprocessing.LabelEncoder",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
linesd/pytorch-fcn | [
"1305c7378a9f0ab44b2c936f4d60e4687e3d8743"
] | [
"torchfcn/ext/fcn.berkeleyvision.org/pascalcontext-fcn16s/solve.py"
] | [
"import caffe\nimport surgery, score\n\nimport numpy as np\nimport os\nimport sys\n\ntry:\n import setproctitle\n setproctitle.setproctitle(os.path.basename(os.getcwd()))\nexcept:\n pass\n\nweights = '../pascalcontext-fcn32s/pascalcontext-fcn32s.caffemodel'\n\n# init\ncaffe.set_device(int(sys.argv[1]))\nca... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ebadrian/meta_dataset | [
"bd40ec4486de165fa6f4ca9fe839e1f685a0ee27"
] | [
"build/lib/meta_dataset/data/sampling.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Meta-Dataset 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 require... | [
[
"numpy.minimum",
"numpy.min",
"numpy.arange",
"numpy.floor",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Feuermurmel/polybox-generator | [
"ff952cfc7ceb8260541e20ca4f94724521820722"
] | [
"generator/dihedral/dihedral.py"
] | [
"import sys, numpy\nfrom lib import polyhedra\n\n\ndef main(src_path):\n\tpolyhedron = polyhedra.Polyhedron.load_from_json(src_path)\n\n\tfor f1 in polyhedron.edges:\n\t\tf2 = f1.opposite\n\t\ttheta = polyhedra.dihedral_angle(f1, f2)\n\t\ttheta = numpy.degrees(theta)\n\t\tprint(\"{:<10}: {:>9.4f}°\".format(str(f1.e... | [
[
"numpy.degrees"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
styler00dollar/Colab-DeepDeblur | [
"3c57a7cfb84f3f12ea6ee469a16545e222660619"
] | [
"src/data/common.py"
] | [
"import random\nimport numpy as np\nfrom skimage.color import rgb2hsv, hsv2rgb\nfrom skimage.transform import pyramid_gaussian\n\nimport torch\n\ndef _apply(func, x):\n\n if isinstance(x, list) or isinstance(x, tuple):\n return [_apply(func, x_i) for x_i in x]\n elif isinstance(x, dict):\n y = {... | [
[
"numpy.pad",
"torch.from_numpy",
"numpy.random.normal",
"numpy.random.randn",
"numpy.random.uniform",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gar1t/PointCNN | [
"93ce315686f7a45f49ade7c570c9bd947d13e644"
] | [
"data_conversions/prepare_partseg_data.py"
] | [
"#!/usr/bin/python3\n'''Prepare Data for ShapeNet Segmentation Task.'''\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport h5py\nimport argparse\nimport numpy as np\nfrom datetime import datetime\n\nsys.path.append(os.pa... | [
[
"numpy.array",
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nokutu/osm_painter | [
"f8e7cc24e4f96b2df415380d99a47c915f2f128e"
] | [
"example.py"
] | [
"import matplotlib.pyplot as plt\n\nfrom osm_painter import draw, Layers, RadiusLocation\n\nfig, ax = plt.subplots(figsize=(15, 12), constrained_layout=True)\n\ndraw(ax, RadiusLocation((38.54200, -1.95335), 1000),\n [\n Layers.highway_layer,\n Layers.building_layer,\n Layers.landuse_laye... | [
[
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leepoly/easypyplot | [
"bdbaae4091739882cb0db5039f1f35c454a894ea"
] | [
"easypyplot/tests/__init__.py"
] | [
"\"\"\" $lic$\nCopyright (c) 2016-2021, Mingyu Gao\n\nThis program is free software: you can redistribute it and/or modify it under\nthe terms of the Modified BSD-3 License as published by the Open Source\nInitiative.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; with... | [
[
"matplotlib.font_manager._rebuild",
"numpy.linspace",
"matplotlib.testing.decorators._image_directories",
"matplotlib.units.registry.copy",
"matplotlib.style.use",
"matplotlib.font_manager.weight_dict.pop",
"numpy.sin",
"matplotlib.testing.compare.comparable_formats",
"matplotl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adityagupte95/berkeley-cs294 | [
"73cde1e27f8cc5a27ed41197d0246d1f056eb2de"
] | [
"hw2/train_pg_f18.py"
] | [
"\"\"\"\nOriginal code from John Schulman for CS294 Deep Reinforcement Learning Spring 2017\nAdapted for CS294-112 Fall 2017 by Abhishek Gupta and Joshua Achiam\nAdapted for CS294-112 Fall 2018 by Michael Chang and Soroush Nasiriany\n\"\"\"\nimport numpy as np\nimport tensorflow as tf\nimport gym\nimport logz\nimpo... | [
[
"numpy.random.seed",
"numpy.min",
"tensorflow.layers.Dense",
"tensorflow.placeholder",
"tensorflow.ConfigProto",
"numpy.concatenate",
"numpy.std",
"numpy.max",
"tensorflow.variable_scope",
"numpy.mean",
"tensorflow.Session",
"tensorflow.global_variables_initializer"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
krassowski/statistics-course | [
"a614c939505052d82453b9017fbaf4d76051fa84"
] | [
"Genes/practicals/Genes across species/solutions/low_memory/gff.py"
] | [
"# gff.py\n# This file implements the function parse_gff3_to_dataframe()\n# and a number of helper functions.\n\nimport re\n\ndef parse_gff3_to_dataframe( file ):\n \"\"\"Read GFF3-formatted data in the specified file (or file-like object)\n Return a pandas dataframe with ID, Parent, seqid, source, type, star... | [
[
"pandas.read_table",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
herbertludowieg/exatomic | [
"d177781a649ba3a12e5c1147672767ac4a388a6c"
] | [
"exatomic/algorithms/interpolation.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (c) 2015-2020, Exa Analytics Development Team\n# Distributed under the terms of the Apache License 2.0\n\"\"\"\nInterpolation\n################################\nHidden wrapper function that makes it convenient to choose\nan interpolation scheme available in scipy.\n\"\"\"\nimpo... | [
[
"pandas.isnull",
"pandas.DataFrame",
"numpy.meshgrid",
"scipy.optimize.curve_fit",
"scipy.signal.savgol_filter",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1... |
rezer0dai/bnpo | [
"e32f2b013d28714530c46c2f1084a14385af1ce9"
] | [
"tasks/uml_reacher_static_gahil.py"
] | [
"import torch\nimport numpy as np\nimport random\n\nN_REWARDS = 1\n\ndef extract_goal(state):\n return state[-4-3:-1-3]\n\n# https://github.com/Unity-Technologies/ml-agents/blob/master/UnitySDK/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherAgent.cs\ndef transform(obs):\n return np.concatenate([\n ob... | [
[
"torch.norm",
"torch.cat",
"numpy.asarray",
"torch.from_numpy",
"torch.tensor",
"numpy.concatenate",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nicholsn/nipype | [
"6601b00aac39d17bb9fb3a6801f5a740a6ebb1e3"
] | [
"nipype/workflows/dmri/fsl/epi.py"
] | [
"# coding: utf-8\n\nimport nipype.pipeline.engine as pe\nimport nipype.interfaces.utility as niu\nimport nipype.interfaces.fsl as fsl\nimport os\nimport warnings\n\ndef create_dmri_preprocessing(name='dMRI_preprocessing', use_fieldmap=True, fieldmap_registration=False):\n \"\"\"\n Creates a workflow that chai... | [
[
"numpy.matrix",
"numpy.array",
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liamkirton/msticpy | [
"c625bbb1321a199f8ad82096524f7a0b727ee10f"
] | [
"msticpy/data/azure/sentinel_analytics.py"
] | [
"# -------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n# --------------------------------------------------------------------------\n... | [
[
"pandas.isna"
]
] | [
{
"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": []
}
] |
cagery/stylegan2 | [
"b575f98da77612cbe3fc6bb2567b37d47cf7b05d"
] | [
"dnnlib/tflib/custom_ops.py"
] | [
"# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.\n#\n# This work is made available under the Nvidia Source Code License-NC.\n# To view a copy of this license, visit\n# https://nvlabs.github.io/stylegan2/license.html\n\n\"\"\"TensorFlow custom ops builder.\n\"\"\"\n\nimport os\nimport re\nimport uuid\... | [
[
"tensorflow.compat.v1.sysconfig.get_include",
"tensorflow.compat.v1.sysconfig.get_lib",
"tensorflow.python.client.device_lib.list_local_devices",
"tensorflow.compat.v1.load_op_library"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cckamy/GraphRepur | [
"8c4552cb304884e0fdcc1f7d1554a3dc2cdb48eb"
] | [
"graphsage/layers.py"
] | [
"from __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom graphsage.inits import zeros\n\nflags = tf.app.flags\nFLAGS = flags.FLAGS\n\ntf.compat.v1.set_random_seed(123)\n\n# DISCLAIMER:\n# Boilerplate parts of this code file were originally forked from\n# https://git... | [
[
"tensorflow.matmul",
"tensorflow.name_scope",
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.contrib.layers.l2_regularizer",
"tensorflow.nn.dropout",
"tensorflow.compat.v1.set_random_seed",
"tensorflow.compat.v1.variable_scope",
"tensorflow.summary.histogram"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems | [
"31d23471bccf2c5fe64d5c628a21150d65c2aeec",
"31d23471bccf2c5fe64d5c628a21150d65c2aeec"
] | [
"comma.ai/data_preprocessor.py",
"udacity/models.py"
] | [
"\"\"\"\nDataPreprocessor class implementation.\n\"\"\"\nimport h5py\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nfrom skimage.transform import resize\n\nfrom constants import B_SIZE, RESIZE_DIMS, CROP_SIZE\nfrom utils import scale_data, save_data, npy_chunks, standardise_data, plot_... | [
[
"sklearn.utils.shuffle",
"numpy.zeros"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tcassanelli/PCA-Folding | [
"0c99f595fdda9fe539d557c188d7ede1acbaf2b3"
] | [
"scripts/b0833-45_run.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport numpy as np\nfrom astropy import units as u\nfrom astropy.table import Table\nimport matplotlib.pyplot as plt\nimport argparse\nimport pywpf\n\n\"\"\"\nSimple script to run PyWPF on Crab pulsar data.\nThe script will run and generate the output da... | [
[
"numpy.load",
"matplotlib.pyplot.subplots",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamltyson/movement | [
"0ced58511091b935ef6974c9ee258de81fb15da8"
] | [
"movement/io/dlc.py"
] | [
"import pandas as pd\nfrom pathlib import Path\n\nfrom imlib.pandas.misc import regex_remove_df_columns\n\n\ndef load_and_clean_dlc(dlc_files, regex_remove_columns=None):\n \"\"\"\n Load N dlc files, and clean up the column names\n :param dlc_files: A list of dlc files, in order\n :param regex_remove_co... | [
[
"pandas.read_excel",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
dedsec-9/trax | [
"c394f9df7ee9dfe918cd67f4af2217d361f0f733",
"c394f9df7ee9dfe918cd67f4af2217d361f0f733"
] | [
"trax/models/research/rse_test.py",
"trax/layers/test_utils_test.py"
] | [
"# coding=utf-8\n# Copyright 2021 The Trax 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 app... | [
[
"numpy.log2",
"numpy.array",
"numpy.expand_dims",
"numpy.ones"
],
[
"numpy.testing.assert_array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nemoramo/ASSET | [
"cc88aa9c19aa092ebac7b93e21945905d9d09f05"
] | [
"asset/core/audio_io/segment.py"
] | [
"# Modified by nemoramo.\n# To support fast audio processing, this script slightly modifies\n# pydub reading to choose start and end.\n#\n# Copyright (c) 2020, 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 com... | [
[
"numpy.pad",
"numpy.log10",
"numpy.mean",
"numpy.any",
"numpy.iinfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jhj0411jhj/soln-ml | [
"002ec06bf139b14bc059e0f0438501b31d9ed16a"
] | [
"mindware/components/feature_engineering/transformations/preprocessor/imputer.py"
] | [
"from mindware.components.feature_engineering.transformations.base_transformer import *\n\n\nclass ImputationTransformation(Transformer):\n type = 1\n\n def __init__(self, param='mean'):\n super().__init__(\"imputer\")\n self.params = param\n\n def operate(self, input_datanode, target_fields=... | [
[
"sklearn.impute.SimpleImputer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yyht/PairGAN | [
"fb346e85f7abfe49ce0635510cc1c312514bfb94"
] | [
"precalc_stats.py"
] | [
"#!/usr/bin/env python3\n\n# Modified from https://raw.githubusercontent.com/bioinf-jku/TTUR/master/precalc_stats_example.py\n\nimport os\nimport glob\nimport numpy as np\nimport fid\nfrom imageio import imread\nimport tensorflow as tf\nfrom argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\n\n\nparser ... | [
[
"tensorflow.compat.v1.Session",
"numpy.savez_compressed",
"tensorflow.compat.v1.global_variables_initializer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sujendrakumar/microsoft | [
"8d4779e56246acda4f2b497ffa9a57bd70d4db48"
] | [
"Face_Detection/detect_all_dlib.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT License.\n\nimport torch\nimport numpy as np\nimport skimage.io as io\n\n# from FaceSDK.face_sdk import FaceDetection\n# from face_sdk import FaceDetection\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nfrom skimage.trans... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.scatter",
"numpy.reshape",
"matplotlib.patches.Rectangle",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
a3sha2/Deep_MRI_brain_extraction | [
"d1c19a53d06a43983ec3d64dae77333a42a74490"
] | [
"NNet_Core/Segmentation_trainer.py"
] | [
"\"\"\"\nThis software is an implementation of\n\nDeep MRI brain extraction: A 3D convolutional neural network for skull stripping\n\nYou can download the paper at http://dx.doi.org/10.1016/j.neuroimage.2016.01.024\n\nIf you use this software for your projects please cite:\n\nKleesiek and Urban et al, Deep MRI brai... | [
[
"numpy.random.random",
"numpy.zeros",
"numpy.mean",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jb3dahmen/indirectsupervision | [
"949b012fcd2a52b4a9182bf4f2b805ffc7c2641c"
] | [
"IndirectSupervisor.py"
] | [
"from scipy.stats.mstats import *\nfrom readDataFiles import *\n\nfrom getAnomalyFeatures import *\nimport numpy as np\nfrom sklearn.linear_model import *\nfrom sklearn import metrics\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import utils\nfrom scipy import stats\nfrom math import sqrt\nim... | [
[
"sklearn.metrics.roc_auc_score",
"pandas.read_csv",
"pandas.to_datetime",
"numpy.percentile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
codecypher/cifar | [
"5e61cd6eefd72b2bb062b6f8a75d7f5192ae8a63"
] | [
"python/cifar10_knn.py"
] | [
"# cifar10_knn.py\n\n# k-Nearest Neighbor\n\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import classification_report, f1_score, confusion_matrix, accuracy_score, precision_score, recall_score\nfrom sklearn.neighbors import KNeighborsClassifier\n\n# Load datasets from fi... | [
[
"sklearn.metrics.confusion_matrix",
"sklearn.neighbors.KNeighborsClassifier",
"numpy.load",
"sklearn.metrics.classification_report",
"numpy.empty",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SharanRajani/BinaryRegimeModelTesting | [
"afc8d97d23c1578dffc6444855a465cd0bf81b16"
] | [
"MainSMGBM-Pro-Expansion1.py"
] | [
"\n# coding: utf-8\n\n# In[1]:\n\n\nimport matplotlib.pyplot as plt\nimport sgbm\nimport stdev\nimport simple_return as sr\nimport durationExa\nimport numpy as np\nimport pandas as pd\nimport statistics as sc\nfrom scipy.stats import kurtosis, skew\nimport pickle\nimport os\nimport multiprocessing\nfrom multiproces... | [
[
"numpy.sqrt",
"numpy.percentile",
"numpy.mean",
"pandas.ExcelFile",
"pandas.ExcelWriter",
"scipy.stats.kurtosis",
"numpy.array",
"scipy.stats.skew"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
saligrama/genienlp | [
"35659911883c43fdbe38c4391e75ca106763eb40"
] | [
"genienlp/util.py"
] | [
"#\n# Copyright (c) 2018, Salesforce, Inc.\n# The Board of Trustees of the Leland Stanford Junior University\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# * Red... | [
[
"numpy.random.seed",
"numpy.min",
"torch.manual_seed",
"numpy.max",
"numpy.mean",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all",
"torch.device",
"torch.cuda.device_count",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sun222/PaddleSeg | [
"6019dd164d873e455255500fa3d7ff197f04e95e"
] | [
"contrib/MedicalSeg/medicalseg/core/train.py"
] | [
"# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless req... | [
[
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cunyap/ggpy | [
"54057e9cd1721c79cda67c02e28c92c22cfee1ce"
] | [
"ggplot/__init__.py"
] | [
"from __future__ import (absolute_import, division, print_function,\n unicode_literals)\n__version__ = '0.11.6'\n\n# For testing purposes we might need to set mpl backend before any\n# other import of matplotlib.\ndef _set_mpl_backend():\n import os\n import matplotlib as mpl\n\n env... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gsutanto/dmp | [
"4f4492cf4295d9c3fe0ba9ce2fb726bf37be40df"
] | [
"python/dmp_coupling/learn_obs_avoid/visualizeSetting.py"
] | [
"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 30 19:00:00 2017\n\n@author: gsutanto\n\"\"\"\n\nimport numpy as np\nimport os\nimport sys\nimport copy\nimport time\nimport matplotlib as mpl\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nimport glob\nsys.path... | [
[
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marianylund/fysstkprojects | [
"7ef97cdf3356dad8ee931a19812d3b0f1625997b",
"7ef97cdf3356dad8ee931a19812d3b0f1625997b"
] | [
"Project2/test_project2.py",
"Project1/RegLib/SamplingMethod.py"
] | [
"import numpy as np\n\nfrom nnreg.model import Model\n\nerror_tolerance = 1e-10\n\ndef test_accuracy():\n y_data = np.asarray([[0, 0, 1, 0]])\n y_pred = np.asarray([[1, 0, 0, 0]])\n acc = Model.calculate_accuracy(y_data, y_pred)\n assert acc == 0, acc\n\n acc = Model.calculate_accuracy(y_data, y_data... | [
[
"numpy.asarray"
],
[
"sklearn.model_selection.train_test_split",
"numpy.std",
"numpy.mean",
"numpy.sum",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mwlussier/leaf_classification | [
"8143a44bd1f060e3ddb1ca893054899a0013d582"
] | [
"cross_valuation.py"
] | [
"import os\nimport sys\nimport pandas as pd\nfrom src.models.bagging_classifier import Bagging\nfrom src.models.decision_tree_classifier import DecisionTree\nfrom src.models.fully_connected_classifier import FullyConnected\nfrom src.models.gradient_boosting_classifier import GradientBoosting\nfrom src.models.logist... | [
[
"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": []
}
] |
anonymnous-gituser/stellargraph | [
"60edf4a6268f29b49b7c768c382e235af4108506",
"60edf4a6268f29b49b7c768c382e235af4108506"
] | [
"demos/node-classification-hinsage/yelp-preprocessing.py",
"demos/link-prediction-hinsage/movielens-recommender.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright 2018 Data61, CSIRO\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 ... | [
[
"sklearn.preprocessing.FunctionTransformer",
"sklearn.pipeline.Pipeline",
"pandas.DataFrame",
"numpy.concatenate",
"sklearn.feature_extraction.DictVectorizer"
],
[
"sklearn.feature_extraction.DictVectorizer",
"sklearn.preprocessing.scale",
"sklearn.model_selection.train_test_sp... | [
{
"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... |
yuta-hi/bayesian_unet | [
"cce1dbd75fad9cc29b77eb1c76b33c6a3eb0ffa6"
] | [
"tests/test_dataset.py"
] | [
"from chainer_bcnn.data import load_image, save_image\nfrom chainer_bcnn.datasets import ImageDataset, VolumeDataset\nimport numpy as np\nfrom collections import OrderedDict\nimport matplotlib.pyplot as plt\nimport argparse\n\npatient_list = ['k1565', 'k1585']\n\nclass_list = ['background', 'pelvis', 'femur', 'addu... | [
[
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dmontagu/trimesh | [
"80247d9870e3b7f3b8ce31c35d2a7589feebfd4f"
] | [
"trimesh/path/creation.py"
] | [
"import numpy as np\n\nfrom . import arc\nfrom .entities import Line, Arc\n\nfrom .. import util\nfrom .. import transformations\n\n\ndef circle_pattern(pattern_radius,\n circle_radius,\n count,\n center=[0.0, 0.0],\n angle=None,\n ... | [
[
"numpy.abs",
"numpy.linspace",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"numpy.asanyarray",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
become-nice/arctern | [
"afe8a50f2e49ce4f235532a76ca9d314858ab0a1"
] | [
"python/arctern/_wrapper_func.py"
] | [
"# Copyright (C) 2019-2020 Zilliz. 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... | [
[
"pandas.api.types.is_list_like",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.24"
],
"scipy": [],
"tensorflow": []
}
] |
sofroniewn/napari-molecule-reader | [
"ef017e7aa9ab6578237a8ab1cb3b81f571d59d6d"
] | [
"src/napari_molecule_reader/bonds.py"
] | [
"import numpy as np\nfrom scipy.spatial.ckdtree import cKDTree\n\n\ndef guess_bonds(atoms, fudge=1.1):\n \"\"\"\n guess bonds based on distances and vdw radii\n \"\"\"\n if len(atoms) == 0:\n return np.empty((0, 2))\n\n coords = atoms[['x', 'y', 'z']].to_numpy()\n elem = atoms['element'].to... | [
[
"numpy.isin",
"numpy.logical_and",
"numpy.logical_and.reduce",
"numpy.equal.reduce",
"numpy.max",
"scipy.spatial.ckdtree.cKDTree",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gucchin/gucchinworks-algo | [
"943dbde3c129529150e5e91fae1a206bed72be44"
] | [
"main.py"
] | [
"# -*- coding: utf-8 -*-\nimport algo.algo\nimport matplotlib.finance\nimport matplotlib.pyplot as plt\nfrom read_csv import read_csv\n\n\nclass Stock:\n def __init__(self, stock_id):\n self.stock_id = stock_id\n self.df = None\n\n def save(self):\n pass\n\n\ndef candlestick(ax, price, pe... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jalajthanaki/DeepPavlov | [
"8652915056f46d68040c7827151745e9e719227e",
"8652915056f46d68040c7827151745e9e719227e"
] | [
"deeppavlov/core/models/tf_model.py",
"deeppavlov/models/ner/layers.py"
] | [
"\"\"\"\nCopyright 2017 Neural Networks and Deep Learning lab, MIPT\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by ... | [
[
"tensorflow.train.Saver",
"tensorflow.train.get_checkpoint_state"
],
[
"tensorflow.nn.relu",
"tensorflow.layers.max_pooling1d",
"tensorflow.layers.conv2d",
"tensorflow.concat",
"numpy.sqrt",
"tensorflow.Variable",
"tensorflow.reduce_max",
"tensorflow.layers.batch_normal... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
michaelsouza/network | [
"20a07a7d7da105ee24da6090c57717645381ce20"
] | [
"python/traffic.py"
] | [
"import numpy as np\nfrom subprocess import check_output\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\nfrom scipy.stats import lognorm\nfrom traffic_assignment import dijkstra\nfrom traffic_assignment import dijkstra_multipath\nimport argparse\n\ndef numberOfLines(filename):\n... | [
[
"numpy.linspace",
"numpy.flipud",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"numpy.histogram",
"pandas.read_csv",
"scipy.stats.norm.fit",
"numpy.zeros",
"numpy.log",
"scipy.stats.lognorm.fit",
"matplotlib.pyplot.savefig",
"numpy.genfromtxt",
"matplotlib.pypl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
cisaacstern/albedo | [
"e9dc7b77f8459196bfee95ba8967daf6644b627a",
"e9dc7b77f8459196bfee95ba8967daf6644b627a"
] | [
"_albedo/griddata.py",
"_albedo/setaxes.py"
] | [
"import homepage.albedo._albedo.pointdata as pointdata\n#import albedo._albedo.pointdata as pointdata\n#import _albedo.pointdata as pointdata\nimport numpy as np\nfrom scipy import interpolate, ndimage\nimport richdem as rd\nfrom tempfile import TemporaryFile\n\nclass GridData(pointdata.PointData):\n \n def e... | [
[
"scipy.ndimage.gaussian_filter",
"numpy.linspace",
"numpy.asarray",
"scipy.interpolate.griddata",
"numpy.load"
],
[
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.figure"
]
] | [
{
"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"... |
saeedmhz/MultiRes-WNet | [
"1fa862763f9c37640741bfa3a02f8d58d2aec1ef"
] | [
"displacement-prediction/networks/UNet.py"
] | [
"##################################################\n## Description:\n## This script contrains PyTorch Implementation \n## of UNet Architecture\n##################################################\n## Author: Saeed Mohammadzadeh\n## Email: saeedmhz@bu.edu\n## License: \n##############################################... | [
[
"torch.nn.ConvTranspose2d",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mareklinka/esk-form-scanner-model | [
"30af9e1c5d652b3310222bc55f92e964bc524f2e"
] | [
"scripts/score.py"
] | [
"# This script generates the scoring and schema files\n# necessary to operationalize your model\nfrom azureml.api.schema.dataTypes import DataTypes\nfrom azureml.api.schema.sampleDefinition import SampleDefinition\nfrom azureml.api.realtime.services import generate_schema\n\nfrom keras.models import load_model as l... | [
[
"numpy.array",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GuilhermeJC13/storIA | [
"eeecbe9030426f70c6aa73ca0ce8382860c8495c"
] | [
"venv/Lib/site-packages/huggingface_hub/hub_mixin.py"
] | [
"import json\nimport logging\nimport os\nfrom typing import Dict, Optional\n\nimport requests\n\nfrom .constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME\nfrom .file_download import cached_download, hf_hub_url, is_torch_available\nfrom .hf_api import HfApi, HfFolder\nfrom .repository import Repository\n\n\nif is_to... | [
[
"torch.device",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HarringayMakerSpace/nyumaya_audio_recognition | [
"410aac995e4678b63290e0da95cdb9820b059d45"
] | [
"tf_python/feature_test.py"
] | [
"from feature_extraction import FeatureExtraction\n\nimport cProfile\nimport numpy as np\n\n\n \nmel = FeatureExtraction(nfilt=40,lowerf=20,upperf=8000,samprate=16000,wlen=0.03,nfft=512,datalen=480)\ndata = np.zeros(480)\npr = cProfile.Profile()\npr.enable()\nfor i in range (10000):\n\tmel_data = mel.signal_to_m... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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