repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
iml1111/air_delay_project | [
"4d0b9eee7f84eb79db73d07b4c2c5508593a66c4"
] | [
"src/main.py"
] | [
"from preprocess import *\nfrom lgb import *\nimport pandas as pd\n\ndf = p_proc()\ndf2 = p_proc2()\ndf3 = label(pd.concat([df, df2], ignore_index=True))\n#Y_pred = l_proc2(df3)\nY_pred, result = load_model(df3)\nprint(len([i for i in result if i > 0.4]))"
] | [
[
"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": []
}
] |
ruza-net/Korbit | [
"b33bedbf9169296f33331bfc27d03d3891459ebe"
] | [
"korbit.py"
] | [
"import numpy as np\nfrom turtle import *\n\n\n# Gravitational constant\nG = 6.67428e-11\n\n# Distance scale\nSCALE = 1e-9\n\n# A step\ndphi = 0.05 * 1 / np.pi\n\n\nclass Simulation(Turtle):\n '''\n Draws the orbit based on the parameters\n - mechanical energy, masses, and angular momentum.\n '''\n\n\n ... | [
[
"numpy.cos",
"numpy.sqrt",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
volpatto/dit | [
"a8b0ffd72e0829c4b239419e85b9e0cfd20085dd"
] | [
"dit/pid/pid.py"
] | [
"\"\"\"\nClasses implementing the partial information decomposition.\n\"\"\"\n\nfrom __future__ import division\n\nfrom abc import ABCMeta, abstractmethod\n\nfrom six import with_metaclass\n\nfrom sys import version_info\n\nfrom itertools import product\n\nimport networkx as nx\nimport numpy as np\n\nimport prettyt... | [
[
"numpy.all",
"numpy.isnan",
"numpy.linalg.solve",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KailinLi/pyrender | [
"cd943dac32ea943b464b0e37262367c593bbd1c9"
] | [
"pyrender/viewer.py"
] | [
"\"\"\"A pyglet-based interactive 3D scene viewer.\n\"\"\"\nimport copy\nimport os\nimport sys\nimport time\nfrom threading import RLock, Thread\n\nimport imageio\nimport numpy as np\nimport OpenGL\nimport trimesh\n\ntry:\n from Tkinter import Tk\n from Tkinter import tkFileDialog as filedialog\nexcept Except... | [
[
"numpy.sqrt",
"numpy.clip",
"numpy.eye",
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin",
"numpy.all",
"numpy.tan",
"numpy.ones",
"numpy.cross",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saiskee/python3-geoplotlib | [
"8e108c7c4b90ebe056fd18c48cc064120321973e"
] | [
"geoplotlib/utils.py"
] | [
"from collections import defaultdict\nimport csv\nfrom datetime import datetime\nimport json\nfrom math import radians, cos, sin, asin, sqrt\nimport urllib.request, urllib.error, urllib.parse\nimport numpy as np\n\n\ndef haversine(lon1, lat1, lon2, lat2):\n \"\"\"\n Distance between geodesic coordinates http:... | [
[
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brianchiang-tw/HackerRank | [
"02a30a0033b881206fa15b8d6b4ef99b2dc420c8",
"02a30a0033b881206fa15b8d6b4ef99b2dc420c8"
] | [
"Python/Numpy/Dot and Cross/dot_and_corss.py",
"Python/Numpy/Transpose and Flatten/tranpose_and_flatten.py"
] | [
"import numpy as np\n\nif __name__ == '__main__':\n\n dim = int(input())\n\n arr_a = []\n arr_b = []\n\n for i in range(dim):\n\n arr_a += list( map(int, input().split() ) ) \n\n for i in range(dim):\n\n arr_b += list( map(int, input().split() ) ) \n\n\n np_arr_a = np.array( arr_a)\n... | [
[
"numpy.reshape",
"numpy.array",
"numpy.matmul"
],
[
"numpy.zeros",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fochoao/cpython | [
"3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9",
"3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9"
] | [
"Lib/site-packages/ginga/util/plots.py",
"Lib/site-packages/ginga/AstroImage.py"
] | [
"#\n# plots.py -- Utility functions for plotting.\n#\n# This is open-source software licensed under a BSD license.\n# Please see the file LICENSE.txt for details.\n#\nimport numpy\nimport matplotlib as mpl\nfrom matplotlib.figure import Figure\n# fix issue of negative numbers rendering incorrectly with default font... | [
[
"numpy.nanmax",
"numpy.polyfit",
"numpy.poly1d",
"numpy.sqrt",
"matplotlib.figure.Figure",
"numpy.isnan",
"numpy.arange",
"numpy.median",
"numpy.nanmin",
"numpy.min",
"numpy.max",
"numpy.append",
"numpy.array",
"numpy.histogram"
],
[
"numpy.nanmax",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
newby-jay/AshbyaTracking | [
"9e80513d52281cf51d35bfef1e164148a9d3439e"
] | [
"Python/csv2DT.py"
] | [
"#!/opt/local/bin/python2.7\nfrom __future__ import division\nfrom __future__ import print_function\nfrom pylab import *\nimport pandas as pd\nimport os\nimport subprocess\nimport time\nimport sys\nfrom scipy.io import savemat, loadmat\n# tdpath = '/Users/jaynewby/Dropbox/CF/PT/Net-Tracker/TrackingTools/'\n# sys.pa... | [
[
"pandas.read_csv",
"scipy.io.savemat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"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"... |
deepmind/distrax | [
"c3f04c64325024bd5a93a4dfe4690249717f753f"
] | [
"distrax/_src/distributions/mvn_diag_test.py"
] | [
"# Copyright 2021 DeepMind Technologies Limited. 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# Unle... | [
[
"numpy.asarray",
"numpy.ones",
"numpy.random.randn",
"numpy.random.rand",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
enneract/colour | [
"27470c16f7a5bf388d0f0798884e8b7abdceafa4"
] | [
"colour/models/rgb/transfer_functions/tests/test_log.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nDefines unit tests for :mod:`colour.models.rgb.transfer_functions.log`\nmodule.\n\"\"\"\n\nfrom __future__ import division, unicode_literals\n\nimport numpy as np\nimport unittest\n\nfrom colour.models.rgb.transfer_functions import (log_encoding_Log2,\n ... | [
[
"numpy.reshape",
"numpy.array",
"numpy.tile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mrirecon/spreco | [
"e9c720fb0d8a9c59a0e83696d2b7efcdc90b2cc3"
] | [
"spreco/model/nn.py"
] | [
"from spreco.model.utils import *\n\nimport tensorflow.compat.v1 as tf\ntf.disable_eager_execution()\nfrom tf_slim import add_arg_scope\nimport numpy as np\n\n\n@add_arg_scope\ndef dense(x_, num_units, nonlinearity=None, init_scale=1., counters={}, init=False, use_bias=True, ema=None, **kwargs):\n ''' fully conn... | [
[
"tensorflow.compat.v1.sqrt",
"tensorflow.compat.v1.reshape",
"tensorflow.compat.v1.einsum",
"tensorflow.compat.v1.constant_initializer",
"tensorflow.compat.v1.zeros_like",
"tensorflow.compat.v1.layers.batch_normalization",
"tensorflow.compat.v1.variable_scope",
"tensorflow.compat.v... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SK-tklab/MES | [
"d1422f7d709e185ffc63f1b8dc1c6ddb91da5eba"
] | [
"src/models/RBFFB.py"
] | [
"import numpy as np\n\n\nclass RBFFourierBasis:\n def __init__(self, n_features: int, n_dim: int, rbf_ls: float = 1.,\n rng: np.random.Generator = None):\n if rng is None:\n rng = np.random.default_rng()\n self.__n_features = n_features\n self.__n_dim = n_dim\n ... | [
[
"numpy.cos",
"numpy.sqrt",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dangz90/Deep-Learning-for-Multi-Modal-Hidden-Emotion-Analysis | [
"66c50292cac9a66eace32a040c49267b06c45de5"
] | [
"Models/CNN+GRU/preprocessing/image.py"
] | [
"\"\"\"Fairly basic set of tools for real-time data augmentation on image data.\nCan easily be extended to include new transformations,\nnew preprocessing methods, etc...\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nimport numpy as np\nimport re\nimport h5py\nfrom scipy ... | [
[
"numpy.rollaxis",
"numpy.dot",
"scipy.linalg.svd",
"numpy.sqrt",
"numpy.asarray",
"numpy.max",
"numpy.mean",
"numpy.random.randint",
"numpy.reshape",
"numpy.arange",
"numpy.stack",
"numpy.sin",
"numpy.copy",
"numpy.std",
"numpy.zeros",
"scipy.ndimage... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
hyeonjames/ray | [
"4ab80eafb9d79ffa2f1ba149fc48d1fdd037c14a"
] | [
"python/ray/tests/test_reference_counting.py"
] | [
"# coding: utf-8\nimport asyncio\nimport copy\nimport json\nimport logging\nimport gc\nimport time\nimport weakref\n\nimport numpy as np\n\nimport pytest\n\nimport ray\nimport ray.cluster_utils\nfrom ray.test_utils import SignalActor, put_object, wait_for_condition\nfrom ray.internal.internal_api import global_gc\n... | [
[
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ducouloa/ml4ir | [
"75aeecaff11682a7bd71c5521e59c449c43c3f9f"
] | [
"python/ml4ir/base/model/architectures/dnn.py"
] | [
"import tensorflow as tf\nfrom tensorflow.keras import layers\nfrom typing import List\n\nfrom ml4ir.base.features.feature_config import FeatureConfig\nfrom ml4ir.base.features.feature_fns.categorical import get_vocabulary_info\nfrom ml4ir.base.io.file_io import FileIO\n\n\nOOV = 1\n\n\n\nclass DNNLayer:\n DENSE... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.Dense",
"tensorflow.squeeze",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.Dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
cemlyn007/mindboggle | [
"947d4b3f41fb7a24c079550c7255c4d16939d740"
] | [
"mindboggle/shapes/zernike/pipelines.py"
] | [
"from __future__ import division\n\nimport logging\n\nimport numpy as np\nimport scipy\nfrom scipy.special import (factorial,\n comb as nchoosek,\n )\n\nfrom mindboggle.shapes.zernike.helpers import nest, autocat\n\nLOG = logging.getLogger(__name__)\n\nIMAG_CONST ... | [
[
"numpy.imag",
"numpy.sqrt",
"numpy.conj",
"numpy.power",
"numpy.flipud",
"numpy.linalg.norm",
"scipy.special.comb",
"scipy.sqrt",
"numpy.real",
"numpy.concatenate",
"numpy.zeros_like",
"scipy.special.factorial",
"numpy.mod",
"numpy.roll",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
... |
shaymargolis/PCC-RL | [
"947a0016480db57a3bd1f96f3f892180d707496b"
] | [
"src/gym/test_vis_multiple.py"
] | [
"# Copyright 2019 Nathan Jay and Noga Rotman\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... | [
[
"matplotlib.use",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
matthewzimmer/trajectory-contact-networks | [
"b70e12052447899cea2e21c1dda85aea2f62a469"
] | [
"app/lib/ops/tiles.py"
] | [
"from math import radians, cos, sin, asin, pi, sqrt\nimport itertools\nimport networkx as nx\nimport numpy as np\n\n\nfrom app.lib.datasets import GeolifeData\nfrom app.lib.pipeline_ops import PipelineOp\nfrom app.lib.points import TrajectoryPoint\n\n\nclass GenerateTilesOp(PipelineOp):\n EARTH_CIRCUMFERENCE_AT_... | [
[
"numpy.asarray",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qingyundou/tacotron_qdou | [
"aca014e8ea73bbab617029b81368cee235f47ce2",
"aca014e8ea73bbab617029b81368cee235f47ce2"
] | [
"lib/sigproc/scripts/bndspec2spec.py",
"datasets/ljspeech.py"
] | [
"#!/usr/bin/python\n\n'''\nCopyright(C) 2016 Engineering Department, University of Cambridge, UK.\n\nLicense\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apach... | [
[
"numpy.fromfile"
],
[
"numpy.fromfile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sufe-nlp/transformer-alignment | [
"0763129d8b1065eb671a516d0e14459cdec44271"
] | [
"fairseq/models/transformer.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom collections import namedtuple\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom fair... | [
[
"torch.nn.functional.softmax",
"torch.Tensor",
"torch.nn.functional.dropout",
"torch.nn.init.constant_",
"torch.nn.functional.log_softmax",
"torch.nn.ModuleList",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.FloatTensor",
"torch.nn.init.xavie... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zweifel/SyncMap | [
"7ad5a08e15cc49807a2c965a3b70e795948b256f"
] | [
"FixedChunkTest.py"
] | [
"from keras.utils import np_utils\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\n\nclass FixedChunkTest:\n\t\n\tdef __init__(self, time_delay, filename=\"fixed_chunk2.txt\"):\n\t\t'''\n\t\tChunks are written in the filename in which every line is a sequence of outputs followed by the number of... | [
[
"numpy.array_equal",
"numpy.asarray",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"numpy.loadtxt",
"numpy.argmax",
"numpy.random.randn",
"matplotlib.pyplot.close",
"numpy.exp",
"matplotlib.pyplot.show",
"numpy.empty",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
atulyakumar97/gender-classifier-using-voice | [
"58a5aa035d42f0e295b0210e0a5fd5336ced3a6f"
] | [
"ML_testing/knn.py"
] | [
"#k nearest neighbours\n\nimport pandas as pd\n\n#Importing the dataset\ndataset = pd.read_csv('C:\\\\Users\\\\Atulya\\\\Documents\\\\GitHub\\\\gender-classifier-using-voice\\\\Data Preprocessing\\\\feature extraction\\\\features.csv')\nX = dataset.iloc[:,1:-1].values\ny = dataset.iloc[:,-1:].values\n\n#Taking care... | [
[
"pandas.read_csv",
"sklearn.impute.SimpleImputer",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.confusion_matrix",
"sklearn.neighbors.KNeighborsClassifier",
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.LabelEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
KennCoder7/DNN-numpy | [
"4b31624d442acc7854734c3d51e6ddcd66271f82"
] | [
"model/ActivationBlock.py"
] | [
"\"\"\"\r\nauthor: Kun Wang (Kenn)\r\ne-mail: iskenn7@gmail.com\r\n\"\"\"\r\nimport numpy as np\r\nfrom utils import *\r\n\r\n\r\nclass Activation(object):\r\n def __init__(self, name, method):\r\n self.name = name\r\n self.__method = method\r\n self.__input_dim = None\r\n\r\n def initial... | [
[
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carnivorouspeanut/isovar_comp | [
"74fcc12ef52d08eb4cfa85bdcda8903970babbda"
] | [
"isovar/dataframe_builder.py"
] | [
"# Copyright (c) 2016. Mount Sinai School of Medicine\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 a... | [
[
"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": []
}
] |
biorover/MAGOT2 | [
"d14a52626d9e036b55d8a7283fa983a3e38970f4"
] | [
"MAGOT2/lib.py"
] | [
"#!/usr/bin/env python\r\nimport sys, pysam, os, re, glob\r\nimport numpy as np\r\nfrom intervaltree import IntervalTree, Interval\r\nfrom collections import OrderedDict\r\nfrom typing import Union\r\nfrom pathlib import Path\r\nimport pandas as pd\r\n\r\ndef mode(a,prec = 2) -> float:\r\n vals,counts = np.uniq... | [
[
"numpy.around",
"pandas.read_csv",
"numpy.mean",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
YasserAlBarbary/Real-time-face-tracking-and-recognition | [
"dbee9e28e167e2b380a7cf9809cfd40ca7ebe4ff"
] | [
"recognition/FaceRecognition.py"
] | [
"import os\r\nimport cv2\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom recognition import facenet\r\n\r\nBASE_DIR = os.path.dirname(__file__) + '/'\r\n\r\n# Path to frozen detection graph. This is the actual model that is used for the object detection.\r\nPATH_TO_CKPT = 'model/20170512-110547.pb'\r\ninpu... | [
[
"tensorflow.Graph",
"numpy.zeros",
"tensorflow.GPUOptions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Yipeng-Sun/Paddle | [
"e08b6e70e948f1a4ad45675b866aa48621ff3b78"
] | [
"python/paddle/fluid/framework.py"
] | [
"# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless ... | [
[
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mirekphd/Vertica-ML-Python | [
"1660471dee371808eb88cf38bbd59cb619741322"
] | [
"vertica_ml_python/rvd.py"
] | [
"# (c) Copyright [2018] Micro Focus or one of its affiliates. \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... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imshow",
"numpy.linspace",
"matplotlib.pyplot.barh",
"numpy.ndarray",
"pandas.DataFrame",
"numpy.random.randint",
"matplotlib.pyplot.gca",
"numpy.arange",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"matplotl... | [
{
"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": []
}
] |
RyanTorant/AGIO | [
"634e38cf5013821c78dad53d242da8202f632aa0"
] | [
"process_interrel.py"
] | [
"import csv\nfrom scipy.stats import shapiro, normaltest, norm, ks_2samp\nimport numpy as np\n\nspecies_data = {}\nspecies_types = {}\n\nbaseline = {}\nspecies_count = 0\nwith open('bin/baseline.csv','r') as csvfile:\n freader = csv.reader(csvfile)\n for sid,data in enumerate(freader):\n baseline[sid] ... | [
[
"scipy.stats.ks_2samp",
"numpy.mean"
]
] | [
{
"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"
... |
dbrainio/catalyst | [
"d6c0908f17f2a53e360b50415b4849354c40e3fc"
] | [
"rl/offpolicy/algorithms/core.py"
] | [
"import copy\nimport torch\n\nfrom catalyst.dl.utils import UtilsFactory\nfrom catalyst.rl.agents import AGENTS\nfrom .utils import soft_update\n\n\nclass Algorithm:\n def __init__(\n self,\n actor,\n critic,\n gamma,\n n_step,\n actor_optimizer_params,\n critic_o... | [
[
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CaiYingFeng/ASLFeat | [
"97f7375d0ded92204551b917f30c46951f3b5516"
] | [
"models/cnn_wrapper/homo_utils.py"
] | [
"\"\"\"\nAdapted from:\nhttps://github.com/tynguyen/unsupervisedDeepHomographyRAL2018\n\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\n\n#######################################################\n# Auxiliary matrices used to solve DLT\nAux_M1 = np.array([\n [0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, ... | [
[
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.equal",
"tensorflow.matrix_solve",
"tensorflow.squeeze",
"tensorflow.Session",
"tensorflow.math.divide_no_nan",
"tensorflow.matrix_transpose",
"tensorflow.tile",
"tensorflow.... | [
{
"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... |
SimonSuster/lxmls-toolkit | [
"78413c1ee61752ca33988c454e3b2c27326e7063"
] | [
"lxmls/sequences/discriminative_sequence_classifier.py"
] | [
"import numpy as np\nimport lxmls.sequences.sequence_classifier as sc\nimport pdb\n\nclass DiscriminativeSequenceClassifier(sc.SequenceClassifier):\n\n def __init__(self, observation_labels, state_labels, feature_mapper):\n sc.SequenceClassifier.__init__(self, observation_labels, state_labels)\n\n ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
heyoh-app/gestures-detector | [
"b052382a9899d771502b79a97cea28ff51bdfa0e"
] | [
"multitask_lightning/lightning_module.py"
] | [
"import json\nimport torch\nimport numpy as np\nimport pytorch_lightning as pl\nfrom torch.utils.data import DataLoader\nfrom dataset.dataset import Dataset\nfrom losses.loss import get_loss\nfrom models.model import UnetClipped\nfrom utils.train_utils import get_optim\nfrom metrics.metric import MeanAveragePrecisi... | [
[
"torch.tensor",
"torch.utils.data.DataLoader",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
slaclab/lume-elegant | [
"b4c1e9d2ab72c2502bd6b937ae5b518116aa2675"
] | [
"elegant/parsers.py"
] | [
"import numpy as np\n\nimport subprocess\nimport os\n\n\ndef parse_sdds_table(sddsfile, columns, sdds2plaindata_exe='sdds2plaindata'):\n \"\"\"\n Get tabular data from SDDS file.\n \n Example:\n get_table('LCLS2scH.twi', ['s', 'betax', 'betay', 'etax'])\n \"\"\"\n \n assert os.path.exist... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tishacy/MTSpider | [
"2a0e046477f59bd29b6512645d356f02c021eb88"
] | [
"mspider/mtd.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport os\nimport requests\nimport wget\nimport numpy as np\nimport pandas as pd\nimport json\nimport time\nfrom threading import Thread\nfrom queue import Queue\nfrom bs4 import BeautifulSoup\nfrom tqdm import tqdm\n\n\n# CLASS Downloader\n\nclass Downloader(object):\n\t\"\"\"A download... | [
[
"numpy.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iraquitan/stanford-cnn-visual-recog | [
"28d515661b65dc525f4d0532eeb0f37741d1c6d0"
] | [
"assignment1/cs231n/classifiers/linear_classifier.py"
] | [
"from __future__ import print_function\n\nimport numpy as np\nfrom cs231n.classifiers.linear_svm import *\nfrom cs231n.classifiers.softmax import *\n\n\nclass LinearClassifier(object):\n def __init__(self):\n self.W = None\n\n def train(\n self,\n X,\n y,\n learning_rate=1e-... | [
[
"numpy.max",
"numpy.zeros",
"numpy.random.randn",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
imjal/CenterNet | [
"295ad5c620c58a8ade3ae48dae2b8b3f5147fd4e"
] | [
"src/lib/datasets/dataset/driving_fourth_dataset.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport pycocotools.coco as coco\nfrom pycocotools.cocoeval import COCOeval\nimport numpy as np\nimport json\nimport os\n\nimport torch.utils.data as data\n\nclass DrivingFourth(data.Dataset):\n num_cl... | [
[
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lamhoangtung/kornia | [
"9cfee5a83a9b7f371f7581ca9a1a67128ca872bc"
] | [
"test/feature/test_siftdesc.py"
] | [
"import pytest\nimport torch\nfrom torch.autograd import gradcheck\n\nimport kornia.testing as utils # test utils\nfrom kornia.feature.siftdesc import SIFTDescriptor, get_sift_bin_ksize_stride_pad, get_sift_pooling_kernel\nfrom kornia.testing import assert_close\n\n\n@pytest.mark.parametrize(\"ksize\", [5, 13, 25]... | [
[
"torch.jit.script",
"torch.ones",
"torch.tensor",
"torch.rand",
"torch.autograd.gradcheck"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shiduo-zh/pybulletSim | [
"a51c71adc328d2071d7faf53e4bc5cd695f03ab2"
] | [
"exptools/logging/_logger.py"
] | [
"from contextlib import contextmanager\nfrom exptools.logging.tabulate import tabulate\nfrom exptools.logging.console import mkdir_p, colorize\nfrom exptools.logging.autoargs import get_all_parameters\nimport numpy as np\nfrom collections import OrderedDict, defaultdict\nimport os, shutil\nimport os.path as osp\nim... | [
[
"numpy.nanmax",
"numpy.isnan",
"numpy.nanmin",
"pandas.DataFrame",
"numpy.nanmean",
"numpy.transpose",
"numpy.nanstd",
"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": []
}
] |
stjordanis/rlmeta | [
"f407c60362d8e46c48ff6fd180bde523a6eb7737"
] | [
"examples/atari/ppo/atari_ppo_model.py"
] | [
"# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport rlmeta.core.remote as r... | [
[
"torch.nn.Sequential",
"torch.nn.functional.log_softmax",
"torch.nn.Conv2d",
"torch.nn.Flatten",
"torch.nn.Linear",
"torch.no_grad",
"torch.where",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
awtkns/openapi-perf | [
"63151ee87383efabea6d6b4fb5c05c6c621c7f0f"
] | [
"openapi_perf/core/results.py"
] | [
"from typing import Union, Any\n\nfrom pandas import DataFrame, read_csv\nfrom matplotlib.figure import Figure\n\nfrom ._types import TEST_RESULTS, FILE_PATH\nfrom .analysis import graphing\n\n\nclass PerfResults:\n def __init__(self, results: Union[DataFrame, TEST_RESULTS]) -> None:\n if type(results) is... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
salt-die/Labyrinth | [
"772f7635101b7132df696332eaed2473bd460f17"
] | [
"labyrinth.py"
] | [
"# -*- coding: utf-8 -*\nfrom random import choice\nfrom kivy.app import App\nfrom kivy.uix.widget import Widget\nfrom kivy.graphics.texture import Texture\nfrom kivy.graphics import Rectangle\nfrom kivy.core.window import Window\nimport numpy as np\nimport networkx as nx\nfrom maze_gen import gen_maze, maze_to_arr... | [
[
"numpy.array",
"numpy.random.random",
"numpy.dstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
s4sarath/tf-transformers | [
"f26d440a4de0557e0e481279bfd70a732aaa8825"
] | [
"src/tf_transformers/layers/layer_normalization.py"
] | [
"# Copyright 2019 The TensorFlow/tf_transformers 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... | [
[
"tensorflow.reduce_mean",
"tensorflow.keras.initializers.serialize",
"tensorflow.math.rsqrt",
"tensorflow.keras.utils.register_keras_serializable",
"tensorflow.square",
"tensorflow.keras.initializers.get"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
saumyaborwankar/self_supervised_AHC | [
"ae8595c9e33383eacd1172917603c6cc4f34218e"
] | [
"services/generate_groundtruth_label_sequence.py"
] | [
"import os\nimport argparse\nimport numpy as np\nfrom pdb import set_trace as bp\n\ndef indices_with_intersecting_durs(seg_time_boundaries, rttm_bins, threshold):\n \n rttm_bins[:,1] += rttm_bins[:,0]\n\n intersect_values = np.minimum(seg_time_boundaries[1], rttm_bins[:,1]) - np.maximum(seg_time_boundaries[... | [
[
"numpy.maximum",
"numpy.minimum",
"numpy.unique",
"numpy.asarray",
"numpy.genfromtxt",
"numpy.argmax",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
easeml/snoopy | [
"bd087cbd512d2f1872fe156a6b0f0223a77c9419"
] | [
"snoopy/embedding/base.py"
] | [
"from __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom time import time\nfrom typing import Callable, NamedTuple, Optional, Tuple\n\nimport numpy as np\nimport tensorflow as tf\nimport torch as pt\n\nfrom .._logging import get_logger\nfrom .._utils import get_num_splits, get_tf_device\nfro... | [
[
"tensorflow.image.resize_with_crop_or_pad",
"tensorflow.shape",
"tensorflow.minimum",
"tensorflow.reshape",
"tensorflow.random.uniform",
"torch.narrow",
"tensorflow.image.resize",
"tensorflow.random.uniform_candidate_sampler",
"tensorflow.image.convert_image_dtype",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
RishirajKanungo/semantic-segmentation-pytorch | [
"e74993792b4e4b737ca2c8b42e65427bcabe7bbb"
] | [
"models/resnet.py"
] | [
"import torch.nn as nn\nimport math\nfrom .utils import load_url\nfrom lib.nn import SynchronizedBatchNorm2d\nBatchNorm2d = SynchronizedBatchNorm2d\n\n\n__all__ = ['ResNet', 'resnet18', 'resnet50', 'resnet101'] # resnet101 is coming soon!\n\n\nmodel_urls = {\n 'resnet18': 'http://sceneparsing.csail.mit.edu/model... | [
[
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shrita10/Text-AI | [
"af4b55aa4378b0bd9ee0c1de27aa40070ddd8bd7"
] | [
"05 - PRODUCT TO LAUNCH/app.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 9 03:49:28 2021\n\n@author: Shrita\n\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\nfrom flask import Flask, request, jsonify, render_template\nimport pickle\nimport re\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.prep... | [
[
"tensorflow.keras.preprocessing.text.Tokenizer",
"tensorflow.keras.models.load_model",
"tensorflow.keras.preprocessing.sequence.pad_sequences"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
PatrickFeng/RPNet | [
"83d6003950553e3563b4318d44d91294bac34821"
] | [
"net.py"
] | [
"import torch\nimport torch.nn as nn\nimport math\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\nfrom layers.slice_pool_layer.slice_pool_layer import *\nfrom layers.slice_unpool_layer.slice_unpool_layer import *\n\n\n\n\nclass RSNet(nn.Module):\n def __init__(self, pool_type, num_slice=... | [
[
"torch.nn.Dropout",
"torch.nn.GRU",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mbrenohd/Python-Baseball | [
"4984392ed520856109ff675a8c2e227cf5acd3c1"
] | [
"stats/offense.py"
] | [
"import pandas as pd\nimport matplotlib.pyplot as plt\n\n# Task 1\nfrom data import games\n\nplays = games[games['type'] == 'play']\n\nplays.columns = ['type', 'inning', 'team', 'player', 'count', 'pitches', 'event', 'game_id', 'year']\n\n# Task 2\nhits = plays.loc[plays['event'].str.contains('^(?:S(?!B)|D|T|HR)'),... | [
[
"pandas.Categorical",
"matplotlib.pyplot.show",
"pandas.to_numeric"
]
] | [
{
"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": []
}
] |
icecube/simweights | [
"f8a7c35a8f54a7cca17ff1e1cd73cc3424b57980"
] | [
"examples/nugen_plot.py"
] | [
"import pandas as pd\nimport pylab as plt\n\nimport simweights\n\n# load the hdf5 file that we just created using pandas\nhdffile = pd.HDFStore(\"Level2_IC86.2016_NuMu.021217.hdf5\", \"r\")\n\n# instantiate the weighter object by passing the pandas file to it\nweighter = simweights.NuGenWeighter(hdffile, nfiles=10)... | [
[
"pandas.HDFStore"
]
] | [
{
"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": []
}
] |
Slyne/wenet | [
"de74d8acf40f47a3c503bff5cf4ed6808a9dad14"
] | [
"wenet/transformer/subsampling.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Copyright 2019 Mobvoi Inc. All Rights Reserved.\n# Author: di.wu@mobvoi.com (DI WU)\n\"\"\"Subsampling layer definition.\"\"\"\n\nfrom typing import Tuple, Union\n\nimport torch\n\n\nclass BaseSubsampling(torch.nn.Module):\n def __init__(self):\n super... | [
[
"torch.nn.Dropout",
"torch.nn.Conv2d",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EnriqueNueve/BirdCall_Project2021 | [
"2a0d6a5ae4706f9f8461f50b4acf88de54ed9064"
] | [
"feature_extraction/nn_v2.py"
] | [
"import numpy as np\nfrom tensorflow import keras\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Activation\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.layers import Dense, Dropout\n\n# load the features\nX = np.load('feat_with_... | [
[
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.Dense",
"sklearn.model_selection.train_test_split",
"numpy.max",
"numpy.load",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.utils.to_categorical"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
xub-alt/MuseumAR | [
"063ee437425a588d592a004872ece6272d16ee41",
"063ee437425a588d592a004872ece6272d16ee41"
] | [
"yolo_utils/torch_utils.py",
"yolo_utils/plots.py"
] | [
"# YOLOv5 PyTorch yolo_utils\n\nimport datetime\nimport logging\nimport math\nimport os\nimport platform\nimport subprocess\nimport time\nfrom contextlib import contextmanager\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport to... | [
[
"torch.cuda.get_device_properties",
"torch.cuda.synchronize",
"torch.mm",
"torch.zeros",
"torch.sqrt",
"torch.manual_seed",
"torch.nn.utils.prune.remove",
"torch.nn.Conv2d",
"torch.distributed.barrier",
"torch.nn.utils.prune.l1_unstructured",
"torch.no_grad",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
... |
Computational-Plant-Science/plant_image_analysis | [
"321eaae9531cd5f8eaebf3ee6c68b99eb53e420c"
] | [
"dev_code/marker_roi_crop.py"
] | [
"'''\nName: sticker_detection.py\n\nVersion: 1.0\n\nSummary: Detect sticker shape markers in image and cropp image based on marker location\n \nAuthor: suxing liu\n\nAuthor-email: suxingliu@gmail.com\n\nCreated: 2018-03-09\n\nUSAGE:\n\ntime python3 marker_roi_crop.py -p ~/plant-image-analysis/test/ -ft png \n\n'... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SergioLordano/hoshi | [
"b14d9d26ebb92c283cc7026ba85bb70135046cf7"
] | [
"hoshi/srw_beam_caustic.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 20 17:15:24 2018\n\n@author: sergio.lordano\n\"\"\"\n\n\ndef SRW_beam_caustic(wfr=None, zStart=1.0, zFin=2.0, zStep=0.1, zOffset=0.0, extract_parameter=1, useMPI=True, save_hdf5=False, h5_filename='test.h5', buffer=False, matrix=True, ppFi... | [
[
"numpy.linspace",
"numpy.max",
"scipy.integrate.simps",
"numpy.array",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"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"
... |
llxSKyWALKeRxll/Video_Manipulation | [
"7531c34735be4f68ec76700858e2faa809fa837a"
] | [
"FlashArtPy_VideoCol.py"
] | [
"import pygame\r\nimport cv2\r\nimport numpy\r\n\r\nclass FlashArt:\r\n def __init__(self, vid_path, font_size=8, colour_lvl = 16):\r\n pygame.init()\r\n self.video_path = vid_path\r\n self.capture_video = cv2.VideoCapture(vid_path)\r\n self.colour_level = colour_lvl\r\n self.i... | [
[
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bossm0n5t3r/deep-learning-from-scratch | [
"8b06ffc7067f54ca54cc23822ec4a86d33c5e10a"
] | [
"ch06/multi_layer_net_extend.py"
] | [
"# coding: utf-8\nimport numpy as np\nfrom collections import OrderedDict\nfrom layers import *\nfrom gradient import numerical_gradient\n\nclass MultiLayerNetExtend:\n \"\"\"완전 연결 다층 신경망(확장판)\n 가중치 감소, 드롭아웃, 배치 정규화 구현\n\n Parameters\n ----------\n input_size : 입력 크기（MNIST의 경우엔 784）\n hidden_size_... | [
[
"numpy.sqrt",
"numpy.ones",
"numpy.argmax",
"numpy.random.randn",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ColasGael/Autonomous-Aircraft | [
"f45510c9f892ca6a08171f63ba5e3e08fe6ef23c"
] | [
"strategy_supervisor/scripts/util.py"
] | [
"import numpy as np\nfrom math import sin, cos, sqrt, atan2, radians\n\n'''\nUseful classes and functions are defined here\n'''\n\ndef coord_to_dist(loc1, loc2):\n\t'''Compute distance (m) beteen two GPS lat/long positions using Haversine formula\n\t'''\n\tlat1 = radians(loc1[0])\n\tlon1 = radians(loc1[1])\n\tlat2 ... | [
[
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HXWAndCL/mmgeneration | [
"9afb1d740bf56a4ecde5064d5bb2a4e2d777638b"
] | [
"apps/conditional_interpolate.py"
] | [
"import argparse\nimport os\n\nimport mmcv\nimport torch\nimport torch.nn as nn\nfrom mmcv import Config, DictAction\nfrom mmcv.runner import load_checkpoint\nfrom torchvision.utils import save_image\n\nfrom mmgen.apis import set_random_seed\nfrom mmgen.core.evaluation import slerp\nfrom mmgen.models import build_m... | [
[
"torch.linspace",
"torch.lerp",
"torch.cat",
"torch.nn.Identity",
"torch.no_grad",
"torch.split",
"torch.stack",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shizhouxing/Robustness-Verification-for-Transformers | [
"b1758c10826b7e00ebf3f030020548809be558a1"
] | [
"data_utils.py"
] | [
"# Copyright (c) 2020, Zhouxing shi <zhouxingshichn@gmail.com>\n# Licenced under the BSD 2-Clause License.\n\nimport numpy as np\nimport json, re, os, nltk, pickle, gzip, random, csv\nimport torch\nfrom tqdm import tqdm\nfrom multiprocessing import Pool\n\nif not os.path.exists(\"tmp\"): os.mkdir(\"tmp\") \n\nde... | [
[
"torch.manual_seed",
"numpy.random.seed",
"torch.cuda.manual_seed_all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DrSnowbird/texar-docker | [
"a087880f29c0aa08473c6af5554165dedb3f9453"
] | [
"texar/tf/modules/pretrained/xlnet_model_utils_test.py"
] | [
"\"\"\"\nUnit tests for xlnet model utils.\n\"\"\"\nimport tensorflow as tf\n\nfrom texar.tf.modules.pretrained.xlnet_model_utils import \\\n PositionWiseFF, RelativePositionalEncoding, RelativeMutiheadAttention\n\n\nclass XLNetModelUtilsTest(tf.test.TestCase):\n r\"\"\"Tests xlnet model utils.\n \"\"\"\n\... | [
[
"tensorflow.random_uniform",
"tensorflow.test.main",
"tensorflow.random_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
Ehsan-Yaghoubi/reid-strong-baseline | [
"51b3e4933082f6a6539da3f05cf0d248971f079e"
] | [
"tools/train.py"
] | [
"# encoding: utf-8\n\"\"\"\n@author: sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport argparse\nimport os\nimport sys\nimport torch\n\nfrom torch.backends import cudnn\n\nsys.path.append('.')\nfrom config import cfg\nfrom data import make_data_loader\nfrom engine.trainer import do_train, do_train_wit... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pelingundogdu/signalization_prior_knowledge_based_nn | [
"677870d51e87dda2601622d0c152aa6fa2421e8b"
] | [
"scripts/dataset_scripts.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# Required libraries\n# import os\n# import pyreadr\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, FunctionTransformer\n\ndef dataframe_modification(experiment_dataset, target_col_index):\n '''\n Data uploadi... | [
[
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.MinMaxScaler",
"sklearn.preprocessing.FunctionTransformer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
michaelsouza/bp_loop | [
"8a6eb7a55f836661409b2f9adc889c256bb6a71a"
] | [
"read_sol.py"
] | [
"import pandas as pd\nimport numpy as np\n\ndef read_sol(fsol):\n print('Reading ' + fsol)\n df = {}\n colname = {}\n with open(fsol, 'r') as fid:\n for k, row in enumerate(fid.readlines()):\n if k == 0: # header\n for i, col in enumerate(row.split()):\n ... | [
[
"numpy.array",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
adamrvfisher/TechnicalAnalysisLibrary | [
"38a22b2b2b5052623f81edb11b3c5460fc254e45",
"38a22b2b2b5052623f81edb11b3c5460fc254e45"
] | [
"NormChaikinTester.py",
"YahooSourceDailies+Div.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n\r\n@author: Adam Reinhold Von Fisher - https://www.linkedin.com/in/adamrvfisher/\r\n\r\n\"\"\"\r\n\r\n#This is a strategy tester\r\n#Pandas_datareader is deprecated, use YahooGrabber\r\n\r\n#Import modules\r\nimport numpy as np\r\nfrom pandas_datareader import data\r\nimport p... | [
[
"numpy.where",
"pandas.Series"
],
[
"pandas.to_numeric",
"pandas.to_datetime",
"pandas.read_csv",
"numpy.sqrt",
"pandas.Series",
"pandas.to_pickle",
"pandas.read_pickle",
"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": []
},
{
"matplotlib": [],
"nump... |
WayneFerrao/autofocus | [
"80a5d2366639177dbd16708a79b88df17528054c",
"80a5d2366639177dbd16708a79b88df17528054c"
] | [
"onehot.py",
"linreg.py"
] | [
"import pandas as pd\r\nimport numpy as np\r\nfrom numpy import argmax\r\nimport seaborn as sns\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.preprocessing import OneHotEncoder\r\n\r\nout_path = \"dummy.csv\"\r\n\r\ndf = pd.read_csv(out_path)\r\n\r\n#use dff to do basic charts to show trends\r\n... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"pandas.get_dummies"
],
[
"pandas.read_csv",
"sklearn.linear_model.LinearRegression",
"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... |
ziofil/strawberryfields | [
"2c3241c1831f396ade208a3d4b8ff5786f3cf806"
] | [
"tests/backend/test_beamsplitter_operation.py"
] | [
"# Copyright 2019 Xanadu Quantum Technologies Inc.\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 applica... | [
[
"numpy.allclose",
"numpy.einsum",
"numpy.linspace",
"numpy.conj",
"numpy.arange",
"numpy.abs",
"scipy.special.factorial",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
... |
roshniRam/Tensorflow-for-poets2 | [
"d2208873da0fdf7348c2af1887afab77ae143280"
] | [
"scripts/retrain.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.framework.tensor_shape.scalar",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.logging.warning",
"tensorflow.gfile.DeleteRecursively",
"tensorflow.zeros",
"tensorflow.gfile.Exists",
"tensorflow.stack",
"numpy.squeeze",
"tensorflow.cast",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ksarangmath/prolip | [
"145235cdc084b382830a8a471ed99383ca4c76fb"
] | [
"experiment.py"
] | [
"import os\nimport torch\nimport numpy as np\nimport time\nimport csv\nimport matplotlib as mpl \nfrom matplotlib import rc\nimport matplotlib.pyplot as plt\nimport argparse\nimport onnx\nfrom onnx import numpy_helper\nfrom boxprop import Box\nfrom onnx_to_boxprop import boxprop\n\ndef isnetworkfile(fname):\n _,... | [
[
"matplotlib.pyplot.title",
"torch.manual_seed",
"numpy.arange",
"torch.randn",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.ylabel",
"matplotlib.rcParams.update",
"matplotlib.pyplot.xlabel",
"matplotlib.rc",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sthagen/pyvista-pyvista | [
"c49a6abae7cc62d242f12ec45a6b22b524db1ec8"
] | [
"tests/plotting/test_widgets.py"
] | [
"import numpy as np\nimport pytest\n\nimport pyvista\nfrom pyvista.plotting import system_supports_plotting\n\n# skip all tests if unable to render\nif not system_supports_plotting():\n pytestmark = pytest.mark.skip\n\n\ndef test_widget_box(uniform):\n p = pyvista.Plotter()\n func = lambda box: box # Does... | [
[
"numpy.array",
"numpy.random.random"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WorksApplications/omni_torch | [
"10b689d794c8f485e38c765303ef018da17bc641"
] | [
"networks/train.py"
] | [
"\"\"\"\n# Copyright (c) 2018 Works Applications Co., Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless require... | [
[
"numpy.asarray",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
grapesmoker/ketl | [
"328a807bd1a439ae45540cf3d96597c803689a73"
] | [
"ketl/transformer/Transformer.py"
] | [
"from abc import abstractmethod\nfrom itertools import chain\nfrom pathlib import Path\nfrom typing import List, Union, Dict, Callable\n\nimport pandas as pd\nimport json\n\n\nclass AdapterError(Exception):\n pass\n\n\nclass NoValidSourcesError(AdapterError):\n pass\n\n\nclass BaseTransformer:\n \"\"\"\n ... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.read_json",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
deraevejames/data-generation_FS | [
"0f245f5456cec09119a4bd034d81a731df029b51"
] | [
"MVPA_sim_data_generation.py"
] | [
"import numpy as np\r\n\r\n# this code generates data that simulates two tasks which elicit a \r\n# shared activation pattern for certain voxels(default is features 10-20)\r\n# this is presented here as each task having two \"rules\" with one rule in common for both tasks\r\n\r\n#### arguments:\r\n# signal = SNR, d... | [
[
"numpy.hstack",
"numpy.random.normal",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dachoc/nnUNet | [
"fc6bfb78fc728509a5e79e78ece34fc9c421785c"
] | [
"nnunet/training/dataloading/dataset_loading.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.choice",
"numpy.unique",
"numpy.save",
"numpy.concatenate",
"numpy.savez_compressed",
"numpy.copy",
"numpy.random.random_integers",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HarshitR612/-Robust-Real-Time-LPR | [
"17a0296fdc1c41e52b3a69cde7e55daf7156cb10"
] | [
"src/yolo/segmentation-recognition/benchmark.py"
] | [
"import cv2\nimport numpy as np\nimport os\nfrom PIL import Image\nfrom skimage.morphology import opening\nfrom skimage.morphology import disk\n\nconf_threshold = 0.5\nnms_threshold = 0.4\n\ndef getOutputLayers(net):\n\tlayerNames = net.getLayerNames()\n\toutputLayers = [layerNames[i[0] - 1] for i in net.getUnconne... | [
[
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KernelA/ldgcnn | [
"e58017a5436738c159376c44ffb91930685990a2"
] | [
"evaluate.py"
] | [
"\"\"\"\nEvaluate the classification accuracy on the ModelNet40 based on our ldgcnn \ntrained feature extractor and classifier. We borrow the evaluation code \nfrom the DGCNN, and add the code of combining the classifier with the \nfeature extractor. \nReference code: https://github.com/WangYueFt/dgcnn\n@author: Ku... | [
[
"numpy.reshape",
"numpy.squeeze",
"tensorflow.global_variables",
"tensorflow.placeholder",
"tensorflow.ConfigProto",
"numpy.argmax",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
linkian209/dnd_map | [
"76c15c632576524cef40b2dc9a6ac56a7fdbb902"
] | [
"map/util.py"
] | [
"'''map.util\n\nThis module contains functions to help generate maps. It also contains\nfunctions to display them.\n'''\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.spatial import Voronoi, voronoi_plot_2d # noqa\n\n\n# in_bounds and generate_voronoi create a bounded set of voronoi regions\n# htt... | [
[
"matplotlib.pyplot.gca",
"scipy.spatial.Voronoi",
"numpy.logical_and",
"matplotlib.pyplot.triplot",
"numpy.copy",
"numpy.append",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zork777/ts_summer | [
"f29e4a24a4df14d9a8af6d12eef400e8ddcb8cd2"
] | [
"stability_index.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom datetime import timedelta\n\nimport isodate\n\n\ndef to_relative(multi_ts: pd.DataFrame):\n \"\"\"\n Transforms multi-dimensional time series into relative values\n \"\"\"\n return multi_ts.apply(lambda x: x / x.sum(), axis=1)\n\n\ndef calculate_si(real, re... | [
[
"numpy.log",
"numpy.sum",
"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": []
}
] |
kpj/numpyro | [
"eb03c3db993cff8a1ab7613ea334d80a26de42ff"
] | [
"examples/bnn.py"
] | [
"# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\n\"\"\"\nExample: Bayesian Neural Network\n================================\n\nWe demonstrate how to use NUTS to do inference on a simple (small)\nBayesian neural network with two hidden layers.\n\n.. image:: ../_static/img/exam... | [
[
"numpy.random.seed",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"numpy.percentile",
"matplotlib.pyplot.savefig",
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sahitpj/Gaussian-EM | [
"2a1ef6afedb1b527637332140a15bd4b892aa613"
] | [
"sample.py"
] | [
"from utils import cov\nimport torch, random, math\nfrom torch.distributions.multivariate_normal import MultivariateNormal\nfrom multiprocessing import Queue\nfrom torch.multiprocessing import Process\n\n\nclass GaussianMMSampler(object):\n '''Gaussian Mixture Model Sampler\n\n Gaussian mixture models, is a s... | [
[
"torch.mean",
"torch.norm",
"torch.zeros",
"torch.rand",
"torch.distributions.multivariate_normal.MultivariateNormal",
"torch.multiprocessing.Process"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abau171/shiny-things | [
"3cf9cd710c4402c9e64496220b7e28d8133b8107"
] | [
"setup.py"
] | [
"from distutils.core import setup, Extension\nimport numpy\n\nmodule = Extension(\"_shinythings\",\n sources=[\"_shinythings/_shinythings.c\",\n \"_shinythings/_shinythings_Scene.c\",\n \"_shinythings/_shinythings_Matrix.c\",\n ... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nfgallimore/microsoft-bonsai-api | [
"932f657df4dd771af6da2cce2c300870aa8bde53"
] | [
"Python/samples/microgrid/main.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"\nMSFT Bonsai SDK3 Template for Simulator Integration using Python\nCopyright 2020 Microsoft\n\nUsage:\n For registering simulator with the Bonsai service for training:\n python simulator_integration.py \n Then connect your registered simulator to a Brain via UI, or using t... | [
[
"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": []
}
] |
shreyayadav/traDSSAT | [
"cc9650f896910c0d0a7a382aff36bef89aba70f2"
] | [
"tradssat/tmpl/file.py"
] | [
"import os\nimport re\n\nimport numpy as np\n\nfrom tradssat.utils import detect_encod\nfrom .vals import FileValueSet, ValueSubSection\nfrom .var import VariableSet, CODE_MISS\n\n\nclass File(object):\n \"\"\"\n Parent class for all file objects.\n \"\"\"\n\n def __init__(self, file):\n \"\"\"\n... | [
[
"numpy.cumsum",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
drib861204/Soft-Actor-Critic-and-Extensions | [
"3075df7430c1c49177b3798d753a9e3f6226672e"
] | [
"run_v2.py"
] | [
"import numpy as np\nimport random\nimport gym\n# import pybulletgym # to run e.g. HalfCheetahPyBullet-v0\n# import pybullet_envs # to run e.g. HalfCheetahBullet-v0 different reward function bullet-v0 starts ~ -1500. pybullet-v0 starts at 0\nfrom collections import deque\nimport torch\nimport time\nfrom torch.utils... | [
[
"numpy.expand_dims",
"numpy.random.seed",
"numpy.clip",
"torch.load",
"torch.manual_seed",
"matplotlib.pyplot.subplots",
"numpy.asmatrix",
"numpy.append",
"numpy.mean",
"numpy.argmin",
"torch.cuda.is_available",
"numpy.shape",
"matplotlib.pyplot.show",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wildomonges/artificial-intelligence | [
"2f4d1d8b5ac8990abb89f69dc3096c7ddf9fe459"
] | [
"informed_and_uninformed_search/search.py"
] | [
"\"\"\"\n@autor Wildo Monges\nThis project is to apply the knowledge learned in the course \"Artificial Intelligence\" in edx.org\nI've implemented uninformed search BFS, DFS and informed search A* and IDA\nNote:\n The problem is called n-puzzle. You have an input state and using a search algorithm, the program\... | [
[
"numpy.reshape",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
toshohirasawa/mst | [
"2a6ebf13b1ba3b878064038ffaa0728fc6673852"
] | [
"nmtpytorch/layers/encoders/bilstmp.py"
] | [
"# -*- coding: utf-8 -*-\nimport logging\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom ..ff import FF\n\nlogger = logging.getLogger('nmtpytorch')\n\n\nclass BiLSTMp(nn.Module):\n \"\"\"A bidirectional LSTM encoder for speech features. A batch should\n only contain samples that have the s... | [
[
"torch.nn.Dropout",
"torch.nn.ModuleList",
"torch.nn.functional.pad",
"torch.nn.LSTM"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
crkaushik93/autonomous-system-integration---ROS-Autonomous-system | [
"226f992e4d94e790aa8deec13cbb64c377a78188"
] | [
"ros/src/waypoint_updater/waypoint_updater.py"
] | [
"#!/usr/bin/env python\n\nimport math\nimport numpy as np\n\nimport rospy\nfrom geometry_msgs.msg import PoseStamped\nfrom scipy.spatial import KDTree\nfrom std_msgs.msg import Int32\nfrom styx_msgs.msg import Lane, Waypoint\n\n'''\nThis node will publish waypoints from the car's current position to some `x` distan... | [
[
"numpy.dot",
"numpy.array",
"scipy.spatial.KDTree"
]
] | [
{
"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"... |
vantuan5644/ReceiptOCR | [
"5e167b55a73c935622ecdfb05f502855d8d44004"
] | [
"labelText.py"
] | [
"import cv2\r\nimport os\r\nimport pandas as pd\r\nfrom shutil import copyfile\r\n\r\ns1 = u'ÀÁÂÃÈÉÊÌÍÒÓÔÕÙÚÝàáâãèéêìíòóôõùúýĂăĐđĨĩŨũƠơƯưẠạẢảẤấẦầẨẩẪẫẬậẮắẰằẲẳẴẵẶặẸẹẺẻẼẽẾếỀềỂểỄễỆệỈỉỊịỌọỎỏỐốỒồỔổỖỗỘộỚớỜờỞởỠỡỢợỤụỦủỨứỪừỬửỮữỰựỲỳỴỵỶỷỸỹ'\r\ns0 = u'AAAAEEEIIOOOOUUYaaaaeeeiioooouuyAaDdIiUuOoUuAaAaAaAaAaAaAaAaAaAaAaAaEeEeEeEeE... | [
[
"pandas.read_excel",
"pandas.to_datetime"
]
] | [
{
"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": []
}
] |
salimelawad/recession_predictor | [
"df7c94999125fe2c573d9bbfea3ff80d0b36024b"
] | [
"main.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\r\nfrom sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\r\nfrom sklearn.neural_network import... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.da... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jamesobutler/ITK | [
"ddbaeae13c1711fcb3010b6e81b108cb10677875"
] | [
"Wrapping/Generators/Python/itkExtras.py"
] | [
"#==========================================================================\n#\n# Copyright Insight Software Consortium\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.issubdtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
udibr/LRE | [
"2571ba133ec8ac276e36074915bfa7d2113e5baa"
] | [
"nn.py"
] | [
"from collections import OrderedDict\nimport logging\n\nimport scipy\nimport numpy as np\nfrom theano import tensor\nfrom theano.tensor.signal.downsample import max_pool_2d, DownsampleFactorMax\n\nfrom blocks.extensions import SimpleExtension\nfrom blocks.extensions.monitoring import (DataStreamMonitoring,\n ... | [
[
"numpy.dot",
"numpy.product",
"numpy.maximum",
"numpy.sqrt",
"numpy.reshape",
"numpy.isnan",
"scipy.linalg.eigh",
"numpy.copy",
"scipy.sparse.identity",
"numpy.mean",
"numpy.cov",
"numpy.float32",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
alexfikl/arraycontext | [
"fe059e66e90c159029caaaa3e6df4fe3ad9fd9da"
] | [
"test/test_utils.py"
] | [
"\"\"\"Testing for internal utilities.\"\"\"\n\n\n__copyright__ = \"Copyright (C) 2021 University of Illinois Board of Trustees\"\n\n__license__ = \"\"\"\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\ni... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carlos-ramos-mv/laboratoriouno | [
"e057b1fb1a747f619c7ad45d1d5c5d77c751bd7a"
] | [
"backup/shellManager.py"
] | [
"import sys, re, subprocess, os\nfrom datetime import datetime\nimport pandas as pd\n\nclass LogsHandler():\n def __init__(self, path=\"/var/log/VirtualLab\"):\n self.file = os.path.join(self.file, \"VirtualLab-\" + self.getTime() + \".log\")\n # If path does not exist, make it\n if not os.p... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
arpitvaghela/PySyft | [
"be534ad861dfddf2f314c5cb346b6b6319719ca3"
] | [
"packages/syft/src/syft/lib/torch/module.py"
] | [
"# stdlib\nimport ast\nfrom collections import OrderedDict\nimport copy\nimport inspect\nfrom itertools import islice\nimport os\nfrom pathlib import Path\nimport sys\nfrom typing import Any\nfrom typing import Callable\nfrom typing import Dict\nfrom typing import Iterator\nfrom typing import List\nfrom typing impo... | [
[
"torch.load",
"torch.rand",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mascondosa/MateUBA_PDE- | [
"e9313e2ead43004f4a2efe3001fdc810294288dd",
"e9313e2ead43004f4a2efe3001fdc810294288dd"
] | [
"minifemlib.py",
"ej_CalorExplCBPeriodicas.py"
] | [
"import numpy as np\nfrom scipy.spatial import Delaunay\n\ndef Elements( Egeom, order ):\n # Devuelve bases de Lagrange y cuadraturas en el elemento de referencia\n\n if ( Egeom == 'triangular' and order==1 ):\n def phi(j,Xnodes):\n if (j==0): return 1-Xnodes[:,0]-Xnodes[:,1]\n el... | [
[
"numpy.ix_",
"numpy.meshgrid",
"numpy.linspace",
"scipy.spatial.Delaunay",
"numpy.matmul",
"numpy.linalg.det",
"numpy.shape",
"numpy.array",
"numpy.zeros",
"numpy.sum"
],
[
"numpy.matmul",
"matplotlib.pyplot.subplots",
"numpy.size",
"numpy.shape",
"m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nasser-glx/openpilot-1 | [
"33fdc9abb75aec5817408a589097844b6f83c6b1"
] | [
"selfdrive/test/longitudinal_maneuvers/test_longitudinal.py"
] | [
"#!/usr/bin/env python3\nimport os\nos.environ['OLD_CAN'] = '1'\nos.environ['NOCRASH'] = '1'\n\nimport unittest\nimport matplotlib\nmatplotlib.use('svg')\n\nfrom selfdrive.config import Conversions as CV\nfrom selfdrive.car.honda.values import CruiseButtons as CB\nfrom selfdrive.test.longitudinal_maneuvers.maneuver... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mingwayzhang/tvm | [
"3b287c4d4e6d83e6fd30db47ffa3d5481a332a63"
] | [
"topi/tests/python/test_topi_vision.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.clip",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZHAW-Audio-Style-Transfer/melgan | [
"f0249fca167ee866c70e4a0b4ed943e869e634c9"
] | [
"src/models/base.py"
] | [
"from __future__ import annotations\nfrom abc import ABC, abstractmethod\nfrom logger.logger import Logger\nfrom config.config import Config\nfrom pathlib import Path\nfrom tensorflow import keras as K\n\n\nclass BaseModel(ABC):\n def __init__(self):\n Logger.log(f\"Initializing {self.__class__.__name__}.... | [
[
"tensorflow.keras.callbacks.TensorBoard"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
leofang/qutip | [
"67c46870749b6e5925feed106a493eac9124bcc3"
] | [
"qutip/correlation.py"
] | [
"# This file is part of QuTiP: Quantum Toolbox in Python.\n#\n# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.\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... | [
[
"numpy.linalg.solve",
"numpy.sqrt",
"numpy.asarray",
"numpy.ones",
"numpy.linalg.pinv",
"numpy.size",
"numpy.real",
"numpy.identity",
"numpy.diff",
"numpy.prod",
"numpy.transpose",
"numpy.exp",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
indy-lab/ProtoTransfer | [
"2e186ffd5bd795244c6dd7192575b84f935c5749"
] | [
"omni-mini/prototransfer/protonet.py"
] | [
"\"\"\"Adapted from https://github.com/jakesnell/prototypical-networks/blob/master/protonets/models/few_shot.py\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom prototransfer.utils_proto import prototypical_loss, get_prototypes\n\nclass Protonet(nn.Module):\n \"\"\"Prototypical network\n\n Parameters:\n ... | [
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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