repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
mabirck/dqn-EWC | [
"ed97ad9b382e93e9c9fbfc8d7a2772cf8de992ff"
] | [
"utils.py"
] | [
"import os\nimport csv\nimport torch\nimport torch.optim as optim\nfrom torch.autograd import Variable as V\nimport torch.nn.functional as F\nfrom myDataLoader import getDataLoader\n\ndef saveLog(test_loss, test_acc, correct, dropout, args, epoch, test_task, continual=False):\n #print(test_task, continual)\n ... | [
[
"torch.nn.functional.cross_entropy",
"torch.max",
"torch.cuda.is_available",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eddyfortier/fortier_project | [
"586e35b5c417b4ae454ee4fe40755112b29b97a0"
] | [
"code/other_functions.py"
] | [
"import numpy as np\nimport plotly.graph_objects as go\n\n\nif __name__ == \"__main__\":\n print(\"This script is not designed to be used by itself.\",\n \"Please use the graph_generator.py script instead\")\n\n\nelse:\n def retrieve_spreadsheet():\n \"\"\"\n INPUTS\n -none\n ... | [
[
"numpy.log10"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jhlee9010/espnet | [
"a3c7c50bccf84c1f40bf79d40bdd735f1b02d79f"
] | [
"espnet2/asr/espnet_model.py"
] | [
"from contextlib import contextmanager\nfrom distutils.version import LooseVersion\nimport logging\nfrom typing import Dict\nfrom typing import List\nfrom typing import Optional\nfrom typing import Tuple\nfrom typing import Union\n\nimport torch\nfrom typeguard import check_argument_types\n\nfrom espnet.nets.e2e_as... | [
[
"torch.cat",
"torch.cuda.amp.autocast"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mizeri/SolveSudoku | [
"e66be4a7cbebf835b672781bb6c9dd998d94c442"
] | [
"SolveSudoku.py"
] | [
"import numpy as np\n\nSudoku = np.array([0] * 9, [0] * 9, [0] * 9, [0] * 9, [0] * 9, [0] * 9,\n [0] * 9, [0] * 9, [0] * 9)\n\n\ndef findNextCell(sudoku):\n for i in range(0, 9):\n for j in range(0, 9):\n if sudoku[i][j] == 0:\n return i, j\n return -1, -1\n\n... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
md-arif-shaikh/publication-plot-settings | [
"55d851646b492fc58c8011e1047bfcaccc947415"
] | [
"examples/example.py"
] | [
"import numpy as np\nimport plotsettings as plts\n\nx = np.arange(-np.pi, np.pi, 0.01)\nfig, ax = plts.set(\"APS\", left=0.17)\nfor idx in range(5):\n ax.plot(x, np.sin(x + idx), label=r\"$i = %.f$\" % idx)\nax.set_xlabel(r\"$\\theta$\")\nax.set_ylabel(r\"$\\sin(\\theta + i)$\")\nax.legend(loc=\"upper left\")\na... | [
[
"numpy.arange",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
scott-mao/DenseDepth_Pruning | [
"88bcff70e93dd68058a5cf0dfeac119a57abc6de"
] | [
"src/models/utils.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Utils for handling models.\n\n- Author: Curt-Park\n- Email: jwpark@jmarple.ai\n\"\"\"\n\nfrom collections import OrderedDict\nimport hashlib\nimport os\nimport re\nimport tarfile\nfrom typing import Any, Dict, List, Set, Tuple\n\nimport gdown\nimport numpy as np\nimport torch\nimport... | [
[
"torch.sum",
"torch.nn.utils.prune.global_unstructured",
"torch.nn.utils.prune.remove",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ibaiGorordo/PyTorch-High-Res-Stereo-Depth-Estimation | [
"f8860766369efe1586344af7f0a70fc760bc82cc"
] | [
"highres_stereo/hsm/hsm.py"
] | [
"from __future__ import print_function\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom .submodule import disparityregression, decoderBlock\nfrom .unet import unet\nimport numpy as np\n\nclass HSMNet(nn.Module):\n def __init__(self, maxdisp,clean,le... | [
[
"torch.abs",
"torch.nn.functional.softmax",
"torch.squeeze",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oztalha/News-Commentary-Tweets-of-Elites | [
"cd93caccd7a725e9c34f3bf7be6f0b9ecd88faf1"
] | [
"scrapers/scrape-nediyor.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 08 15:41:01 2015\n\n@author: Talha\n\"\"\"\n\nfrom selenium import webdriver\nimport pandas as pd\nimport time\n\n#initialize variables\ndf = pd.DataFrame(columns=('title', 'twcount', 'href'))\ndriver = webdriver.Firefox()\n\n# nediyor.com gundem sayfasi\n#driver... | [
[
"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": []
}
] |
chkra/calc_batch_correct | [
"4dafcf77bc37539a4944b64658c85318c7a6b5b8"
] | [
"hello_genomics/main.py"
] | [
"#!/usr/bin/env python\r\n# coding: utf-8\r\n'''\r\n Combat batch correction app for FASTGenomics\r\n'''\r\nimport json\r\nimport pathlib\r\nimport random\r\nimport csv\r\nimport jinja2\r\nimport logging\r\nimport enum\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nimport matplotlib\r\nmatplotlib.use('Agg'... | [
[
"numpy.ix_",
"pandas.read_csv",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.Circle",
"numpy.round",
"scipy.spatial.distance.pdist",
"numpy.mean",
"numpy.array",
"sklearn.decomposition.PCA"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2... |
nantutech/ntcore | [
"2daacad2435c30f116b76685aa579b4665bff9f7"
] | [
"client/examples/tf_keras_example.py"
] | [
"import tensorflow as tf\nprint(\"TensorFlow version:\", tf.__version__)\n\nfrom ntcore import client\nclient.set_endpoint('http://localhost:8000')\nclient.autolog('C8W60XEPH7DA3AAH3S41PJZ3OV')\n\nmnist = tf.keras.datasets.mnist\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nx_train, x_test = x_train / ... | [
[
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.layers.Dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
mattmcdermott/custodian | [
"ae3d3f903337ff5753db9288bd6f7c8f360f2f25"
] | [
"custodian/vasp/jobs.py"
] | [
"# coding: utf-8\n\nfrom __future__ import unicode_literals, division\nimport subprocess\nimport os\nimport shutil\nimport math\nimport logging\n\nimport numpy as np\n\nfrom pymatgen import Structure\nfrom pymatgen.io.vasp import VaspInput, Incar, Poscar, Outcar, Kpoints, Vasprun\nfrom monty.os.path import which\nf... | [
[
"numpy.polyfit",
"numpy.poly1d",
"numpy.linalg.norm",
"scipy.optimize.minimize",
"numpy.array"
]
] | [
{
"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"
... |
seung-lab/seuron | [
"81e462f8ef71f2e28b4c2ad4c835b27b251ae25a"
] | [
"dags/segmentation_dags.py"
] | [
"from airflow import DAG\n\nfrom airflow.operators.python_operator import PythonOperator\n\nfrom airflow.operators.dagrun_operator import TriggerDagRunOperator\nfrom airflow.utils.weight_rule import WeightRule\nfrom airflow.models import Variable\n\nfrom chunkiterator import ChunkIterator\n\nfrom slack_message impo... | [
[
"matplotlib.pyplot.title",
"numpy.logspace",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.savefig",
"numpy.dtype",
"numpy.frombuffer",
"matplotlib.pyplot.clf",
"numpy.mean",
"matplotlib.pyplot.xlabel",
"numpy.argsort",
"matplotlib.pyplot.xscale",
"numpy.sum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
spatialucr/MapLinksPlot | [
"471dbc328858ad0bafd78d5b4b495379f8e69de9"
] | [
"PYTHON_Quantitative_Data_VIZ/Adaptive_Choropleth_Mapper.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n#!/usr/bin/env python\n# coding: utf-8\n\nimport json, math, copy\nfrom geosnap.io import store_ltdb\nfrom geosnap import Community, datasets\nfrom geosnap.io import store_census\nimport pandas as pd\nimport shapely.wkt\nimport shapely.geometry\nfrom datetime... | [
[
"pandas.notnull",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
kentaro/CodeXGLUE | [
"d956beaca2902f305ce229fb97737146f9bc1df5"
] | [
"Code-Code/Defect-detection/code/model.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT License.\nimport torch\nimport torch.nn as nn\nimport torch\nfrom torch.autograd import Variable\nimport copy\nfrom transformers.modeling_bert import BertLayerNorm\nimport torch.nn.functional as F\nfrom torch.nn import CrossEntropyLoss, MSELoss\n\n ... | [
[
"torch.nn.functional.sigmoid",
"torch.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
josephmaa/pyuoi | [
"f546900791c3c2176e5ac2ac6c553bae065a3127",
"f546900791c3c2176e5ac2ac6c553bae065a3127"
] | [
"pyuoi/linear_model/base.py",
"classifier/rat7m_classifier.py"
] | [
"import abc as _abc\nimport numpy as np\nimport logging\nfrom sklearn.linear_model._base import SparseCoefMixin\nfrom sklearn.metrics import r2_score, accuracy_score, log_loss\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import check_X_y\nfrom sklearn.preprocessing import StandardScaler... | [
[
"numpy.dot",
"sklearn.metrics.r2_score",
"numpy.squeeze",
"numpy.zeros_like",
"numpy.any",
"sklearn.utils.check_X_y",
"scipy.sparse.issparse",
"numpy.unique",
"numpy.arange",
"numpy.argmax",
"numpy.count_nonzero",
"numpy.zeros",
"numpy.median",
"scipy.sparse... | [
{
"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"... |
ricklentz/nglod | [
"6cb8d10cc4b44f709f855dc98fa8e6e58ba7c1fd"
] | [
"sdf-net/lib/tracer/BaseTracer.py"
] | [
"# The MIT License (MIT)\n#\n# Copyright (c) 2021, NVIDIA CORPORATION.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of\n# this software and associated documentation files (the \"Software\"), to deal in\n# the Software without restriction, including without limitation the rights... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alasdairtran/facenet | [
"ab0b64d1dd5c978cc57ac0d85579a97018de02d1"
] | [
"tmp/align_dlib.py"
] | [
"# Copyright 2015-2016 Carnegie Mellon University\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appli... | [
[
"numpy.max",
"numpy.array",
"numpy.float32",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oricalworks/openpilot-1 | [
"aec0baa6f36a614c90ce2a757339d63b5354c4d6"
] | [
"selfdrive/controls/lib/lateral_planner.py"
] | [
"import os\nimport math\nimport numpy as np\nfrom common.realtime import sec_since_boot, DT_MDL\nfrom common.numpy_fast import interp, clip\nfrom selfdrive.swaglog import cloudlog\nfrom selfdrive.controls.lib.lateral_mpc import libmpc_py\nfrom selfdrive.controls.lib.drive_helpers import MPC_COST_LAT, MPC_N, CAR_ROT... | [
[
"numpy.arange",
"numpy.linalg.norm",
"numpy.ones",
"numpy.column_stack",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChengZheWu/Medical-Image-Analysis | [
"83b7e34eaa9691d35ac29d52ca26656cec3e411d"
] | [
"kflod.py"
] | [
"from trainer import Trainer\nfrom preprocessing import get_dataset\nfrom os import path\nfrom torch.utils.data import DataLoader\nimport torch as T\nimport numpy as np\nimport random\nfrom sklearn import metrics\nfrom os import path\n\ndef get_metrics(target, pred):\n prec, recall, _, _ = metrics.precision_reca... | [
[
"torch.cuda.manual_seed",
"numpy.random.seed",
"torch.zeros",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"sklearn.metrics.roc_curve",
"sklearn.metrics.precision_recall_fscore_support",
"sklearn.metrics.auc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TrueOctopus/classifierBack | [
"5ebe20b8bd18aa74bbeb229403963b331c5013c2"
] | [
"textRank/textrank4zh/util.py"
] | [
"import os\nimport math\nimport networkx as nx\nimport numpy as np\n\nsentence_delimiters = ['?', '!', ';', '？', '！', '。', '；', '……', '…', '\\n']\nallow_speech_tags = ['an', 'i', 'j', 'l', 'n', 'nr', 'nrfg', 'ns', 'nt', 'nz', 't', 't', 'vd', 'vn', 'eng']\n\ntext_type = str\nstring_types = (str,)\nxrange = range\n\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
istepanov/CarND-Capstone | [
"b5ead07b7a8f513208418be178c3efad70bc138b"
] | [
"ros/src/tl_detector/tl_detector.py"
] | [
"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import Int32\nfrom geometry_msgs.msg import PoseStamped, Pose\nfrom styx_msgs.msg import TrafficLightArray, TrafficLight\nfrom styx_msgs.msg import Lane\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\nfrom light_classification.tl_classifier... | [
[
"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"... |
tommyod/paretoset | [
"1744f1ece7ac188570c35d585c364a9263cf3852"
] | [
"paretoset/algorithms_numpy.py"
] | [
"import numpy as np\nimport paretoset.user_interface # Import like this to avoid circular import issues\n\n\ndef paretoset_naive(costs, distinct=True):\n \"\"\"Naive implementation.\n\n Parameters\n ----------\n costs : (np.ndarray) Array of shape (n_costs, n_objectives).\n\n Returns\n -------\n ... | [
[
"numpy.take_along_axis",
"numpy.logical_not",
"numpy.arange",
"numpy.divide",
"numpy.ones",
"numpy.all",
"numpy.logical_or",
"numpy.zeros_like",
"numpy.any",
"numpy.argsort",
"numpy.logical_and",
"numpy.zeros",
"numpy.sum",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rafikk/data | [
"ee6de10a53922df0257c0b6e395a05cc337b0b64"
] | [
"scripts/us_census/geojsons_low_res/plotter.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# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"matplotlib.use",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
laloc2496/cdn_configuration_optimization | [
"58cf2278456d0ef8796570f12f1d00fd68aec686"
] | [
"nsga2.py"
] | [
"import os, sys\nos.environ['OMP_NUM_THREADS'] = '1' # speed up\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))\n\nfrom argparse import ArgumentParser\nimport numpy as np\nfrom time import time\nimport yaml\nfrom multiprocessing import cpu_count\nfrom pymoo.algorithms.nsga2 import N... | [
[
"numpy.concatenate",
"numpy.max",
"numpy.vstack",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
microsoft/fnl_paper | [
"a93a8081dab472fb53effd26a384e28670383485"
] | [
"deficient-efficient/models/blocks.py"
] | [
"# blocks and convolution definitions\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.autograd import Variable\nfrom torch.utils.checkpoint import checkpoint, checkpoint_sequential\n\nif __name__ == 'blocks' or __name__ == '__main__':\n from hashed import HashedCo... | [
[
"torch.nn.Sequential",
"torch.nn.functional.dropout",
"torch.randn",
"torch.nn.functional.conv2d",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maglab-uconn/EARShot_TF2 | [
"43fcc570b78a80b245d745aebc19f341c7e283fc"
] | [
"earshot-tf2/patterns.py"
] | [
"import os, io, librosa, pickle, json\nimport numpy as np\nfrom random import sample\nimport concurrent.futures\n\nfrom .audio import *\n\nclass PatternGenerator(object):\n '''\n Creates spectrogam/semantic vector pairs, using the parameters in pattern_params,\n and writes them to the directory specifed in... | [
[
"numpy.random.permutation",
"numpy.zeros",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jimgoo/Merlion | [
"2239f4ba6fc4fc08b3f88be842908851ee17ddbf"
] | [
"ts_datasets/ts_datasets/forecast/ECL.py"
] | [
"#\n# Copyright (c) 2021 salesforce.com, inc.\n# All rights reserved.\n# SPDX-License-Identifier: BSD-3-Clause\n# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n#\nimport glob\nimport logging\nimport os\n\nimport pandas as pd\n\nfrom ts_datasets.base im... | [
[
"pandas.read_csv",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Kitware/netharn | [
"9ebc8ddb33c56fe890684f3a0a6369c52ebe4742"
] | [
"netharn/data/collate.py"
] | [
"\"\"\"\nFIXME 0 dimension tensors\n\"\"\"\nimport torch.utils.data as torch_data\nimport torch\nimport ubelt as ub\nimport numpy as np # NOQA\nimport re\n\n# if six.PY2:\n# import collections\n# container_abcs = collections\n# elif six.PY3:\n# import collections.abc\n# container_abcs = collections... | [
[
"torch.LongTensor",
"torch.full",
"torch.cat",
"torch.from_numpy",
"torch.is_tensor",
"numpy.full",
"torch.FloatTensor",
"torch.__version__.startswith",
"torch.DoubleTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wheeltune/kid-neuro | [
"131ec888e4f0c3ee1d7b4c4ebf57a6b1d5323d8a"
] | [
"learner/generator.py"
] | [
"import pytorch_lightning as pl\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n__all__ = [\"KeystrokesGeneratorLearner\"]\n\n#===============================================================================\n\nclass KeystrokesGeneratorLearner(pl.LightningModule):\n\n #----... | [
[
"torch.zeros",
"torch.nn.L1Loss",
"torch.nn.functional.pad",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Globe-Eater/Geographic-Duplicate-Detection- | [
"ec467fc41cb456959da87fd913465dc9daa27d80"
] | [
"SVM/mapping.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 3 15:04:11 2019\n\n@author: kellenbullock\n\nGEOGRAPHY part\n\n\nThis file will be for making the \ncoordiantes column, \nzipping latitude and longitude together, \ncreating point geometry, \nreading in shapefile of ok counties,\nsetting ... | [
[
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
sourabbapusridhar/object-detection-and-tracking-with-multiple-cameras | [
"4674ec5129125e7e329d4638edf4e0518e592525"
] | [
"tracking/opticalflow.py"
] | [
"# Optical Flow\n\n# RUN object_tracker.py\n# conda activate yolov4-cpu\n# python3 object_tracker.py --weights ./checkpoints/yolov4-tiny-416 --model yolov4 --video ./data/video/test.mp4 --output ./outputs/tiny.avi --tiny\n\nimport cv2\nimport numpy as np\n\n\"\"\"Solve optical flow problem\n\n Parameters \n -... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
notaJiminLee/pycoral | [
"d04eabadb69b57899c429d808633969444985ff2"
] | [
"benchmarks/inference_benchmarks.py"
] | [
"# Lint as: python3\n# Copyright 2019 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 appli... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamjstewart/evolution | [
"ba202fd857e9883b8b374ac263f7154a2e58fec0"
] | [
"evolution.py"
] | [
"#!/usr/bin/env python\n\n# Hello World program written using evolutionary computation\n\n# Requires Python 2.7+ and NumPy\n\nimport numpy\nimport random\nimport string\nimport sys\nimport time\n\n\nPOP_SIZE = 1000\nEND_GOAL = list('Hello, World!')\nRATE_OF_INSERTION = 0.1\nRATE_OF_DELETION = 0.1\nRATE_OF_SU... | [
[
"numpy.asarray",
"numpy.arange",
"numpy.minimum",
"numpy.add"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SCAII/SCAII | [
"7ab302f788556392850d104d3df6e0b4a556414d"
] | [
"backends/sky-rts/demos/agent_layers_multi.py"
] | [
"from scaii.env.sky_rts.env.scenarios.tower_example import TowerExample\nfrom scaii.env.explanation import Explanation, BarChart, BarGroup, Bar\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nlabels = [\"Health\", \"Agent Location\",\n \"Small Towers\", \"Big Towers\", \"Friend\", \"Enemy\"]\n\n\nd... | [
[
"matplotlib.pyplot.imshow",
"numpy.random.random",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplot",
"numpy.random.rand",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheDr1ver/fintulib | [
"8ed45562d1b5452696288d22363691fbe085afd5"
] | [
"fintulib/wrangle/FuzzyMatcher.py"
] | [
"import pandas as pd\nimport numpy as np\nimport dill as pickle\nimport re\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom scipy.sparse import csr_matrix\nfrom scipy.sparse import rand\n\n\ndef cossim_top(A, B, ntop, lower_bound=0):\n try:\n import sparse_dot_topn.sparse_dot_topn as ct\... | [
[
"numpy.multiply",
"numpy.asarray",
"scipy.sparse.csr_matrix",
"pandas.DataFrame",
"sklearn.feature_extraction.text.TfidfVectorizer",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.1... |
rnpatien/O-CNN | [
"d29647768d9dd05ebdabafb20db418008d397098"
] | [
"tensorflow/libs/build.py"
] | [
"import os\nimport sys\nimport tensorflow as tf\nimport subprocess\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--octree\", type=str, required=False,\n default='../../octree')\nparser.add_argument(\"--cuda\", type=str, required=False,\n default=... | [
[
"tensorflow.sysconfig.get_compile_flags",
"tensorflow.sysconfig.get_link_flags"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
gomerudo/openai-baselines | [
"42adc549f3a7b3241227dfa2ebb1188784c4da06"
] | [
"baselines/common/policies.py"
] | [
"import tensorflow as tf\nfrom baselines.common import tf_util\nfrom baselines.meta_a2c.utils import fc\nfrom baselines.common.distributions import make_pdtype\nfrom baselines.common.input import observation_placeholder, encode_observation\nfrom baselines.common.tf_util import adjust_shape\nfrom baselines.common.mp... | [
[
"tensorflow.get_default_session",
"tensorflow.layers.flatten",
"tensorflow.constant",
"tensorflow.placeholder",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
jpmorgen/IoIO | [
"c22523d77bc38162cabf3ddb6d7446e4005864e8"
] | [
"flat_ratio.py"
] | [
"\"\"\"Get on-off ratios for [SII] and Na filters from flats\"\"\"\n\nimport os\nimport glob\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import AutoMinorLocator\nimport pandas as pd\n\nfrom astropy.stats import mad_std, biweight_location\nfrom astropy.time import Time\n\nfrom co... | [
[
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.title",
"numpy.median",
"matplotlib.ticker.AutoMinorLocator",
"pandas.DataFrame",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.plot",
"numpy.std",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.... | [
{
"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": []
}
] |
timsainb/spikesorters | [
"0d4ca7718e786720bdf55285d0f0bc3bf9dff9c2"
] | [
"spikesorters/hdsort/hdsort.py"
] | [
"from pathlib import Path\nimport os\nfrom typing import Union\nimport numpy as np\nimport sys\n\nimport spikeextractors as se\nfrom ..basesorter import BaseSorter\nfrom ..utils.shellscript import ShellScript\nfrom ..sorter_tools import recover_recording\n\nPathType = Union[str, Path]\n\n\ndef check_if_installed(hd... | [
[
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
douglasdavis/awkward-1.0 | [
"f00775803a5568efb0a8e2dae3b1a4f23228fa40",
"f00775803a5568efb0a8e2dae3b1a4f23228fa40",
"f00775803a5568efb0a8e2dae3b1a4f23228fa40",
"f00775803a5568efb0a8e2dae3b1a4f23228fa40"
] | [
"tests/test_0084-start-unionarray.py",
"tests/v2/test_1192-iterables-in-__array_function__.py",
"tests/v2/test_0979-where-multidimentional-numpy-array.py",
"tests/test_0072-fillna-operation.py"
] | [
"# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE\n\nimport pytest # noqa: F401\nimport numpy as np # noqa: F401\nimport awkward as ak # noqa: F401\n\n\ndef test_getitem():\n content0 = ak.from_iter([[1.1, 2.2, 3.3], [], [4.4, 5.5]], highlevel=False)\n content1 = ak.f... | [
[
"numpy.asarray",
"numpy.array",
"numpy.dtype"
],
[
"numpy.array"
],
[
"numpy.array"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
Kandy990125/PointClouds_cls | [
"23efc75e9f38a1a9c931711c64d3feffc3246424"
] | [
"PointCNN_Pytorch/train.py"
] | [
"import numpy as np\nimport torch\nfrom torch import nn\n\nfrom PointCNN_Pytorch import provider\nfrom PointCNN_Pytorch.model.classifier import Classifier\nfrom dataset.ModelNet40 import get_data_loader\nfrom torch.autograd import Variable\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nbl... | [
[
"torch.nn.CrossEntropyLoss",
"torch.from_numpy",
"torch.save",
"torch.cuda.is_available",
"numpy.zeros",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Bilal-A-Qureshi/Point-GNN | [
"401eba8f7afaaa811ee1d001f38405629071a42a"
] | [
"models/nms.py"
] | [
"\"\"\"This file defines nms functions to merge boxes\"\"\"\n\nimport time\n\nimport cv2\nimport numpy as np\nfrom shapely.geometry import Polygon\n\ndef boxes_3d_to_corners(boxes_3d):\n all_corners = []\n for box_3d in boxes_3d:\n x3d, y3d, z3d, l, h, w, yaw = box_3d\n R = np.array([[np.cos(yaw... | [
[
"numpy.expand_dims",
"numpy.concatenate",
"numpy.max",
"numpy.zeros_like",
"numpy.any",
"numpy.where",
"numpy.sin",
"numpy.float32",
"numpy.zeros",
"numpy.logical_not",
"numpy.min",
"numpy.median",
"numpy.logical_or",
"numpy.transpose",
"numpy.argsort",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anaruse/dask | [
"72304a94c98ace592f01df91e3d9e89febda307c"
] | [
"dask/dataframe/io/tests/test_parquet.py"
] | [
"import math\nimport glob\nimport os\nimport sys\nimport warnings\nfrom decimal import Decimal\nfrom distutils.version import LooseVersion\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport dask\nimport dask.multiprocessing\nimport dask.dataframe as dd\nfrom dask.dataframe.utils import assert_eq\nf... | [
[
"pandas.to_datetime",
"numpy.linspace",
"numpy.isnat",
"pandas.RangeIndex",
"pandas.DataFrame",
"numpy.dtype",
"pandas.read_parquet",
"numpy.random.randn",
"numpy.random.randint",
"numpy.unique",
"numpy.arange",
"pandas.Index",
"numpy.full",
"numpy.repeat",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MichQiu/PreSumm | [
"b826ac7d23cd78da5a5293c2ac625b7bd7af1364"
] | [
"src/models/encoder.py"
] | [
"import math\n\nimport torch\nimport torch.nn as nn\n\nfrom models.neural import MultiHeadedAttention, PositionwiseFeedForward\n\n\nclass Classifier(nn.Module):\n def __init__(self, hidden_size):\n super(Classifier, self).__init__()\n self.linear1 = nn.Linear(hidden_size, 1)\n self.sigmoid =... | [
[
"torch.nn.Dropout",
"torch.zeros",
"torch.nn.LayerNorm",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jluttine/bayespy | [
"d31d9c395c2cd6c276671dd9f13fd951fd19065a"
] | [
"bayespy/inference/vmp/nodes/expfamily.py"
] | [
"################################################################################\n# Copyright (C) 2013-2014 Jaakko Luttinen\n#\n# This file is licensed under the MIT License.\n################################################################################\n\n\nimport warnings\n\nimport numpy as np\n\nfrom bayespy... | [
[
"numpy.logical_not",
"numpy.reshape",
"numpy.all",
"numpy.ndim",
"numpy.copy",
"numpy.shape",
"numpy.prod",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ullrichd21/CantmeraOS | [
"d17a6e918132e7f19045e710d1b278779fd22c42"
] | [
"Cantmera/generate_grayscale.py"
] | [
"from matplotlib import pyplot as plt\nimport matplotlib.image as mpimg\nimport numpy\n\n#image = \"ct34\"\nimage_ext = \".jpg\"\nimage_list = [\"ct1\",\"ct3\",\"ct4\",\"ct5\",\"ct6\",\"ct7\",\"ct8\",\"ct9\",\"ct10\",\"ct11\",\"ct12\",\"ct13\",\n\t\t\t\"ct14\",\"ct15\",\"ct16\",\"ct17\",\"ct18\",\"ct19\",\"ct20\",\... | [
[
"matplotlib.image.imsave",
"matplotlib.pyplot.title",
"matplotlib.pyplot.gcf",
"matplotlib.image.imread",
"numpy.copy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
evandropp10/predict_energy_star_score_nyc | [
"fed291c6714d8be14abade686efc04a3ef93625a"
] | [
"model02.py"
] | [
"# encoding: utf-8\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import PolynomialFeatures\n\n\ndef resultToInt(x):\n xint = int(x)\n\n if xint > 100:\n xint = 100\n if x... | [
[
"sklearn.linear_model.LinearRegression",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.PolynomialFeatures",
"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": []
}
] |
PhantomJoker07/BastardSword | [
"62b9832136c05516c14c7acd65c2edcba3cfb0d0"
] | [
"imageviewer.py"
] | [
"import matplotlib\nimport matplotlib.pyplot as plt\nimport sys\nimport numpy as np\nimport struct\n\ndef draw_image(fileName):\n with open(fileName, 'rb') as file:\n width, height = struct.unpack('ii', file.read(4*2))\n image_data_bytes = file.read((width*height*4) * 4)\n image_data_float =... | [
[
"numpy.array",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dcxSt/mcgill_physics_hackathon_2019 | [
"925b2322dae988d0b14d444a61d7ece4116c9b98"
] | [
"main.py"
] | [
"from aiohttp import web\nimport socketio\nimport numpy as np\n\ndef load_eigenvector(k,d):\n vec_path = \"eigenvectors/eigen_k=\" + str(k) + \",d=\" + str(d) + \".npy\"\n eigenvector_np = np.load(vec_path)\n eigenvector_str = \"\"\n for x in np.nditer(eigenvector_np):\n eigenvector_str += str(x)... | [
[
"numpy.load",
"numpy.nditer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
debnoob/em_examples | [
"c137108b51118855296d02ac8c31ed299bb23e34"
] | [
"em_examples/MT.py"
] | [
"from __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nfrom scipy.constants import epsilon_0, mu_0\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ipywidgets import *\nimport warnings\nwarnings.filterwarnings('ignore') # ignore warnings:... | [
[
"numpy.matrix",
"numpy.ones_like",
"numpy.abs",
"numpy.arange",
"numpy.ones",
"numpy.log10",
"numpy.zeros_like",
"numpy.angle",
"numpy.exp",
"numpy.array",
"matplotlib.pyplot.show",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jglaser2/ssa | [
"35326b1d9e96501ebda664f9fe4a0225fbbc1168"
] | [
"ssa/util.py"
] | [
"############ Import packages\n\nimport numpy as np\nimport numpy.random as npr\nimport copy\nimport time\n\nimport torch\n\n\n\n############ Utilities\n\ndef get_sample_weights(Y,eps=.1):\n\n \"\"\"\n Function for getting weights for each sample (So different time points can be upweighted/downweighted in the... | [
[
"numpy.mean",
"numpy.sum",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
2runo/dl_numpy | [
"3aa498146189fd3e5b4b0c0a97830253edba12af"
] | [
"dl_numpy/core/base.py"
] | [
"\"\"\"\n연산 magic method 정의\n\"\"\"\nimport numpy as np\nfrom .decorators import for_all_methods, _graph_debugging_decorator\nfrom ..autograd.backward import Backward\nfrom ..utils.core import ValueRepr\n\n\ndef check_tensor_decorator(class_method=True):\n # 인자를 Tensor로 변환하는 decorator\n def _check_tensor_deco... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
awsaf49/keras_cv_attention_models | [
"242aaf02fd46f68d57f710b9e805afe96e3067e5"
] | [
"keras_cv_attention_models/resnet_family/resnet_quad.py"
] | [
"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import backend as K\nfrom keras_cv_attention_models.download_and_load import reload_model_weights\nfrom keras_cv_attention_models.attention_layers import batchnorm_with_activation, conv2d_no_bias, drop_block\n\n\nPRETRAINED_DICT = {\n ... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.layers.GlobalAveragePooling2D",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.ZeroPadding2D",
"tensorflow.keras.layers.Add",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.kera... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
gvanhorn38/active_neurofinder | [
"e10bd893ff3224925b3cf2f4329959c6c19714d8"
] | [
"movie.py"
] | [
"\"\"\"\nCreate a video out of the images\n\"\"\"\n\nfrom matplotlib import pyplot as plt\nimport matplotlib.animation as animation\nimport numpy as np\nfrom scipy import interpolate\n\nfrom glob import glob\nfrom util import load_images, load_regions\nimport os\nfrom scipy.misc import imread\nfrom PIL import Image... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.min",
"numpy.dstack",
"numpy.max",
"scipy.interpolate.interp1d",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"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"
... |
cjdjr/cosp_nas | [
"f54243176e8398d79df25d295704f24238f24cc7"
] | [
"search/COSP/search.py"
] | [
"import os\nimport time\nimport glob\nimport numpy as np\nimport pickle\nimport torch\nimport logging\nimport argparse\nimport torch\nimport random\nimport json\nimport pprint as pp\nfrom tqdm import tqdm\nfrom torch.nn import DataParallel\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variabl... | [
[
"torch.set_rng_state",
"torch.optim.lr_scheduler.LambdaLR",
"torch.cat",
"torch.cuda.get_rng_state_all",
"torch.utils.data.DataLoader",
"torch.argmin",
"torch.is_tensor",
"torch.get_rng_state",
"torch.cuda.set_rng_state_all",
"torch.no_grad",
"torch.cuda.is_available",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
awalker88/auto-ts | [
"7935394dbf7ce386f574ccf83f4e2ae748ae2790"
] | [
"auto_bots/AutoTS.py"
] | [
"import warnings\nimport datetime as dt\nfrom functools import reduce\nfrom typing import Union, List, Tuple\n\nimport pandas as pd\nimport numpy as np\nfrom pmdarima import auto_arima\nfrom statsmodels.tsa.holtwinters import ExponentialSmoothing\n\nfrom tbats import BATS\n\nfrom auto_bots.utils.error_metrics impor... | [
[
"numpy.concatenate",
"pandas.merge",
"pandas.DataFrame",
"pandas.date_range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
terrencetec/kontrol | [
"ba6461784e38d01399efeb7a42911259f9254db0"
] | [
"tests/core/test_spectral.py"
] | [
"\"\"\"Tests for kontrol.core.spectral\n\"\"\"\nimport control\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.signal\n\nimport kontrol\n\n\nnp.random.seed(123)\n\n# Time axis and sampling frequency\nfs = 128\nt0 = 0\nt_end = 512\nt = np.arange(t0, t_end, 1/fs)\n\n# The coherent signal\nA = 1\nsi... | [
[
"numpy.allclose",
"numpy.random.seed",
"numpy.arange",
"numpy.all",
"numpy.log10",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
moj-analytical-services/data_engineering_utils | [
"fb05b48553184f3a9e38d9d20ef14e4009335b4a"
] | [
"dataengineeringutils/pd_metadata_conformance.py"
] | [
"import pkg_resources\nimport json\nimport pandas as pd\nimport numpy as np\n\ndef _remove_paritions_from_table_metadata(table_metadata):\n\n if \"partitions\" in table_metadata:\n table_metadata[\"columns\"] = [c for c in table_metadata[\"columns\"] if c[\"name\"] not in table_metadata[\"partitions\"]]\n... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
droidiyann/tensorflow_tensorflow | [
"ed10660cf38306921faaa67ddbc3f369441bcb6d"
] | [
"tensorflow/lite/testing/op_tests/resize_nearest_neighbor.py"
] | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.lite.testing.zip_test_utils.make_zip_of_tests",
"tensorflow.lite.testing.zip_test_utils.create_tensor_data",
"tensorflow.image.resize_nearest_neighbor",
"tensorflow.compat.v1.placeholder",
"tensorflow.lite.testing.zip_test_utils.register_make_test_function"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marcelonyc/demos | [
"00451b64816a2ab32d2b4b5307f1dd3c80c2d4e6"
] | [
"image_classification/utils.py"
] | [
"import os\nimport zipfile\nimport json\nfrom tempfile import mktemp\nimport pandas as pd\n\n\ndef open_archive(context, \n target_dir='content',\n archive_url=''):\n \"\"\"Open a file/object archive into a target directory\"\"\"\n \n os.makedirs(target_dir, exist_ok=Tru... | [
[
"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": []
}
] |
AndreiRoibu/basic_numpy | [
"4bcaf2a5b628937f481de9d5b7a7c9061c1eb816"
] | [
"simple_exercises/MNIST_flip.py"
] | [
"'''MNIST flip\n\nAuthor: Andrei-Claudiu Roibu, 2019\n\nThis code has been created to compare the speed difference between a numpy dot product and a slow dot product. This code was written in support of my learning.\n\nThe original code comes from these two sources: \n\n # https://deeplearningcourses.com/c/deep-... | [
[
"numpy.rot90",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stevegolton/toppra | [
"846e2a7f5b87e0e1884b244b07d5fd661edcd9bd"
] | [
"tests/tests/solverwrapper/test_basic_can_linear.py"
] | [
"\"\"\"A test suite for solverwrappers that implement solve methods for\ncanonical linear constraints. Wrapppers considered include:\n'cvxpy', 'qpOASES', \"ecos\", 'hotqpOASES', 'seidel'.\n\n\"\"\"\nimport pytest\nimport numpy as np\nimport numpy.testing as npt\nimport toppra\nimport toppra.constraint as constraint... | [
[
"numpy.random.seed",
"numpy.linspace",
"numpy.isnan",
"numpy.random.randn",
"numpy.random.rand",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Joiner12/TimeVisual | [
"c83c5e88adade5d2a4d75f414e051ab09d4bca8a"
] | [
"python/DrawMap.py"
] | [
"# -*- coding:utf-8 -*-\n\"\"\"\n\n1.机场数据\nhttps://ourairports.com/data/\n\n\"\"\"\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Geo\nfrom pyecharts.globals import ChartType, SymbolType\nimport pandas as pd\nfrom datetime import datetime\n\nextraPosition = {\n \"多哈\": (25.261101, 51.565102... | [
[
"pandas.read_excel"
]
] | [
{
"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": []
}
] |
shivam1212-shivam/OPEN_CV | [
"c2c1765b4dea1a97ae4e6f12eaa49ccf80e08cd3"
] | [
"spaces.py"
] | [
"import cv2 as cv\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#color spaces --> rgb spaces,grayscal\nimg = cv.imread('photos/dog.jpg')\ncv.imshow('Dog',img)\n\n# plt.imshow(img)\n# plt.show()\n# point to note --> there is conversion of colour spaces in matplotlib\n\n# BGR to grayscale -->\n\ngray = cv.c... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pfnet/pynif3d | [
"da3680cce7e8fc4c194f13a1528cddbad9a18ab0"
] | [
"pynif3d/models/idr/sample_network.py"
] | [
"import torch\n\nfrom pynif3d.common.verification import check_true\nfrom pynif3d.log.log_funcs import func_logger\n\n\nclass IDRSampleNetwork(torch.nn.Module):\n \"\"\"\n Computes the differentiable intersection between the viewing ray and the surface.\n Please check equation 3 from the paper and section ... | [
[
"torch.bmm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
damien-petit/perception | [
"a8d6588f3e123797ccf5a777926b62498b8b882f"
] | [
"tests/kinect2_sensor_bridge.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nInterface to the Ensenso N* Sensor\nAuthor: Jeff Mahler\n\"\"\"\nimport IPython\nimport logging\nimport numpy as np\nimport os\nimport struct\nimport sys\nimport time\nimport signal\n\ntry:\n from cv_bridge import CvBridge, CvBridgeError\n import rospy\n import sensor_msgs.m... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
skn123/BASS | [
"a2ac050a09d550d0ad121eb97d21f7e8aff40a84"
] | [
"pytorch_version/Conn_Functions.py"
] | [
"from BASS import *\n\n\nimport numpy as np\nimport cv2\n\ndef Create_LookupTable():\n return np.load('./lookup.npy')\n\ndef binary(num, length=8):\n return int(format(num, '#0{}b'.format(length + 2)))\n\ndef Create_Matrix(padded_matrix,mod):\n if(mod==1):\n pixels = padded_matrix.index_select(0, Gl... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sainiprathmesh/Bitcoin_Price_Alert_and_Prediction_System | [
"6ca7803185fa1bde570a4a6b67937242de3450a5"
] | [
"prediction/deep_learning_models.py"
] | [
"import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.layers import LSTM, GRU\nfrom tensor... | [
[
"pandas.read_csv",
"sklearn.metrics.r2_score",
"tensorflow.keras.layers.Dense",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.plot",
"tensorflow.keras.layers.GRU",
"matplotlib.pyplot.xlim",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
TomHacker/VTD | [
"3009fd53cec8a86493b5f1960e8879e5a0c7345c"
] | [
"Detection/CTPN_vertical/CP.py"
] | [
"import numpy as np\nimport os,xml.etree.ElementTree as ET\nimport cv2\nimport matplotlib.pyplot as plt\nfrom glob import glob\nfrom concurrent.futures import ThreadPoolExecutor\nimport tensorflow as tf\nfrom PIL import Image,ImageDraw\nfrom math import *\nanchor_scale = 16\n#\nIOU_NEGATIVE = 0.3\nIOU_POSITIVE = 0.... | [
[
"matplotlib.pyplot.imshow",
"numpy.expand_dims",
"numpy.minimum",
"numpy.polyfit",
"numpy.sqrt",
"numpy.vstack",
"numpy.fabs",
"numpy.max",
"tensorflow.GPUOptions",
"numpy.mean",
"numpy.exp",
"numpy.where",
"numpy.random.randint",
"numpy.hstack",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aj-ames/NSAC-NULLPointers | [
"36945b0f9678509213b9b17d93c36f2a3b92285c"
] | [
"firedetectionserver/run.py"
] | [
"import time\nimport numpy as np\nimport cv2\nimport sys\nimport requests\nfrom videocaptureasync import VideoCaptureAsync\nimport traceback\nimport ast\nimport random\nimport sys\n\nclass Theia:\n ''' Class to hold member methods and variables\n for performing Inference. '''\n\n def __init__(self):\n ... | [
[
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eyecan-dev/HRNet-for-Fashion-Landmark-Estimation.PyTorch | [
"63dcb75836ebb8896307c9e5a62be8a475de8323"
] | [
"lib/dataset/deepfashion2.py"
] | [
"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Written by Bin Xiao (Bin.Xiao@microsoft.com)\n# ------------------------------------------------------------------------------\n\nfrom __future__ import absolute_import\... | [
[
"numpy.multiply",
"numpy.arange",
"numpy.ones",
"numpy.max",
"numpy.exp",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shamanDevel/fV-SRN | [
"966926ee678a0db0f1c67661537c4bb7eec0c56f"
] | [
"applications/volnet/eval_world_DensityVsColorGrid_NoImportance.py"
] | [
"\"\"\"\nScript to evaluate the network quality if it is trained for densities or for colors.\nThey are tested with world-space training and the best configuration from eval_network_configs\n\"\"\"\n\nimport numpy as np\nimport sys\nimport os\nimport subprocess\nimport itertools\nimport imageio\nimport json\nimport... | [
[
"numpy.linspace",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.std",
"numpy.mean",
"torch.device",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
duducosmos/blockmatching | [
"c37949cce79b58ae658d5814ba3cff906dc4ab4e"
] | [
"src/blockmatching/blockmatching.py"
] | [
"#!/usr/bin/env python3.6\n# -*- Coding: UTF-8 -*-\n\"\"\"\nBlock Matching Algorithm.\n------------------------\nAccording to [cuevs2013]_ in a block matching (BM) approach:\n\n '...image frames in a video sequence are divided into blocks. For each\n block in the current frame, the best matching block is iden... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Richard-cpu2333/tx2dl | [
"985d9f9f24004271e85745a49252ab9922aec655"
] | [
"vision/ssd/config/vgg_ssd_config.py"
] | [
"import numpy as np\n\nfrom vision.utils.box_utils import SSDSpec, SSDBoxSizes, generate_ssd_priors\n\n\nimage_size = 300\nimage_mean = np.array([123, 117, 104]) # RGB layout\nimage_std = 1.0\n\niou_threshold = 0.75\ncenter_variance = 0.1\nsize_variance = 0.2\n\nspecs = [\n SSDSpec(38, 8, SSDBoxSizes(30, 60), [... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thailengthol/PyAEZ | [
"1ff1456c27fd8742a85114a5a538796bc249c8d9"
] | [
"code/SoilConstraints.py"
] | [
"\"\"\"\r\nPyAEZ\r\nWritten by N. Lakmal Deshapriya\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport csv\r\n\r\nimport ALL_REDUCTION_FACTORS_IRR as crop_P_IRR\r\nimport ALL_REDUCTION_FACTORS_RAIN as crop_P_RAIN\r\n\r\nclass SoilConstraints(object):\r\n\r\n '''functions for soil qualities'''\r\n\r\n def soil_qty_... | [
[
"numpy.min",
"numpy.copy",
"numpy.interp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
locvdnguyen/python-training | [
"5a415e413d036e5305ec75879684a839fc4d2c86"
] | [
"exercise06/filehandler.py"
] | [
"\"\"\"\nExercise 06: Mock interview\nRequirements:\n 1/ Load data from .csv file as a list of dictionaries\n 2/ Group the data based on license number:\n US: first 6 characters are alphabetic,\n next 5 characters are numbers\n UK: first character is in alphabet, followed by hyphen,\n... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
broadinstitute/scRNA-Seq | [
"03aafb92274a97f4d634ac9e42f0e0feca91ed98"
] | [
"scCloud/scCloud/tools/visualization.py"
] | [
"import time\nimport numpy as np\nimport pandas as pd\nfrom subprocess import check_call\n\nimport os\nimport pkg_resources\n\nfrom MulticoreTSNE import MulticoreTSNE as TSNE\nfrom umap import UMAP\nfrom fitsne import FItSNE\n\n\nfrom . import calculate_affinity_matrix, calculate_nearest_neighbors, select_cells, co... | [
[
"pandas.read_csv",
"numpy.zeros",
"numpy.insert"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jtwhite79/flopy | [
"41302901e4db38455c28a68153b49a8466da3027"
] | [
"flopy/utils/datautil.py"
] | [
"import os\nimport numpy as np\n\n\ndef clean_name(name):\n # remove bad characters\n clean_string = name.replace(\" \", \"_\")\n clean_string = clean_string.replace(\"-\", \"_\")\n # remove anything after a parenthesis\n index = clean_string.find(\"(\")\n if index != -1:\n clean_string = c... | [
[
"numpy.savetxt",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emitra17/PyBNF | [
"61138d8533556eeec262203170053a591fe3633c"
] | [
"pybnf/data.py"
] | [
"\"\"\"Class with methods to manage experimental and simulation data\"\"\"\n\n\nimport logging\nimport math\nimport numpy as np\nimport re\nfrom .printing import PybnfError\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass Data(object):\n \"\"\"Top level class for managing data\"\"\"\n\n def __init__(self,... | [
[
"numpy.isfinite",
"numpy.random.choice",
"numpy.min",
"numpy.stack",
"numpy.ones",
"numpy.max",
"numpy.delete",
"numpy.std",
"numpy.mean",
"numpy.errstate",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fabiommendes/easymunk | [
"420dfc4a006997c47887f6876876249674feb3cd"
] | [
"examples/matplotlib_util_demo.py"
] | [
"import matplotlib.pyplot as plt\n\nimport easymunk.matplotlib\nfrom shapes_for_draw_demos import fill_space\n\nspace = easymunk.Space()\ncaptions = fill_space(space, (1, 1, 0, 1))\n\nfig = plt.figure(figsize=(14, 10))\nax = plt.axes(xlim=(0, 1000), ylim=(0, 700))\nax.set_aspect(\"equal\")\nfor caption in captions:... | [
[
"matplotlib.pyplot.axes",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
astrojuanlu/ropy | [
"e01948cf7bfbbf8bca16df921b453e19a6a8cc04"
] | [
"tests/transform/test_utils3d.py"
] | [
"import pytest\nimport numpy as np\nfrom scipy.spatial.transform import Rotation as ScipyRotation\n\nimport ropy.transform as rtf\n\n\n@pytest.mark.parametrize(\n \"sequence, angles, degrees\",\n [\n (\"xyz\", (0, 0, np.pi / 4), False),\n (\"xyz\", (0, 0, 45), True),\n (\"xyz\", (0, 0, 90... | [
[
"numpy.allclose",
"scipy.spatial.transform.Rotation.from_quat",
"numpy.asarray",
"numpy.eye",
"scipy.spatial.transform.Rotation.from_euler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.5",
"1.3",
"1.2",
"1.4"
],
"tensorflow": []
}
] |
swc-17/Cylinder3D | [
"fffc642b3950a8e80d553aa6de07f0652ce96068"
] | [
"network/segmentator_3d_asymm_spconv.py"
] | [
"# -*- coding:utf-8 -*-\n# author: Xinge\n# @file: segmentator_3d_asymm_spconv.py\n\nimport numpy as np\nimport spconv\nimport torch\nfrom torch import nn\n\n\ndef conv3x3(in_planes, out_planes, stride=1, indice_key=None):\n return spconv.SubMConv3d(in_planes, out_planes, kernel_size=3, stride=stride,\n ... | [
[
"torch.nn.BatchNorm1d",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.Sigmoid",
"torch.nn.LeakyReLU",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mushrifshahreyar/NoC-Routing | [
"c744968697d10bf096368d117c4de71d5a62643c"
] | [
"DQN_Hops_rewards.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Activation, Flatten\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import TensorBoard\nfrom collections import deque... | [
[
"tensorflow.keras.models.load_model",
"numpy.random.random",
"tensorflow.keras.Input",
"tensorflow.keras.layers.Dense",
"numpy.max",
"numpy.argmax",
"tensorflow.keras.optimizers.Adam",
"numpy.array",
"tensorflow.keras.models.Sequential",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
cdknorow/modelstore | [
"f08839478432b89e828a8dcb41adf27b0e3aa66b"
] | [
"tests/models/test_pytorch_lightning.py"
] | [
"# Copyright 2020 Neal Lathia\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... | [
[
"torch.utils.data.TensorDataset",
"torch.utils.data.DataLoader",
"torch.nn.functional.mse_loss",
"torch.nn.Linear",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShivangMathur1/Small-Pytorch-Projects | [
"aebc6e81103fe2a6830caeedc1b17227e211a6e5"
] | [
"MNIST/train.py"
] | [
"import torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision import transforms, datasets\n\n# Neural Network\nclass Net(nn.Module):\n def __init__(self):\n super().__init__()\n\n #layers: \n self.fc1 = nn.Linear(28*28, 64)\n self... | [
[
"torch.nn.Linear",
"torch.nn.functional.nll_loss",
"torch.utils.data.DataLoader",
"torch.nn.functional.log_softmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emilydolson/interpreting_the_tape_of_life | [
"48fd46f3cd2a7f07ce4ea1fd6fa120b520e34925"
] | [
"analysis/cec_python_library/extract_lineage_fitness.py"
] | [
"#!/home/emily/anaconda2/bin/python\n###############################################################################\n# Version: 1.1\n# Last modified on: 3 April, 2016 \n# Developers: Michael G. Epitropakis\n# email: m_(DOT)_epitropakis_(AT)_lancaster_(DOT)_ac_(DOT)_uk \n#######################################... | [
[
"numpy.array",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
JackWBoynton/ecg-augmentation | [
"7590c510231e387d77908e2e2b418f83cb2ae621"
] | [
"ecgaugmentation/augment.py"
] | [
"from scipy import signal\nfrom scipy import stats\nimport numpy as np\nfrom pylab import rcParams\nfrom glob import glob\nimport random as rd\nimport sys\nimport math\nimport matplotlib.pyplot as plt\n\nNUM_PEAKS = 5\n\nVARIANCE_MIN = 0.45\nVARIANCE_MAX = 0.5 # for smoothing\n\nAUGMENT_MAX = 1.5 # max peak multipl... | [
[
"numpy.isnan",
"numpy.arange",
"numpy.save",
"scipy.signal.peak_prominences",
"numpy.copy",
"numpy.nanmean",
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
KuangjuX/Algorithm-Lab | [
"02fb617b7639185120b08edf9826ecc106118f55"
] | [
"Test.py"
] | [
"import matplotlib.pyplot as plt\nimport os\nimport random\nimport numpy as np\n\ndef generateData(filePath,fileName):\n file = str(filePath + '/' +fileName)\n with open(file ,'w+') as f:\n for i in range(10,100):\n flag = []\n up = random.randint(10,10000)\n f.write(str(... | [
[
"matplotlib.pyplot.show",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
heyamrita/Python | [
"ebfd55d0c21fd50dc3376030ec90c1c67b5df48f"
] | [
"Face_Mask_detection (haarcascade)/mask_detection.py"
] | [
"import tensorflow.keras\nfrom PIL import Image, ImageOps\nimport numpy as np\nimport cv2\n\nstr = ''\nfaceCascade= cv2.CascadeClassifier(\"Resources/haarcascade_frontalface_default.xml\")\n\nnp.set_printoptions(suppress=True)\nmodel = tensorflow.keras.models.load_model('Resources/keras_model.h5')\ndata = np.ndarra... | [
[
"numpy.set_printoptions",
"numpy.ndarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DSciLab/mlutils | [
"352af36f2b34218b6551254f641427b7bbdd0f31"
] | [
"mlutils/inspector.py"
] | [
"import cv2\nimport numpy as np\nimport torch\n\n\nclass ModuleNode(object):\n def __init__(self, name) -> None:\n super().__init__()\n self.name = name\n self.children = {}\n \n def remove_child(self, name):\n del self.children[name]\n\n @property\n def empty(self):\n ... | [
[
"torch.mean",
"numpy.maximum",
"torch.zeros",
"numpy.uint8",
"torch.sum",
"numpy.float32",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jjcordano/elkans_kmeans | [
"21ac72896cdf2a54b951cb06fb45575befdc8dc7"
] | [
"kmeans.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\nimport numpy as np\n\nclass Kmeans:\n def __init__(self, k=2, max_iter=250, tolerance=0, method='Elkan'):\n \"\"\"\n Initialises a Kmeans clustering algorithm.\n \n Args:\n - k: Number of clusters. Defaults to 2.\n - max_iter: M... | [
[
"numpy.min",
"numpy.linalg.norm",
"numpy.random.random_sample",
"numpy.max",
"numpy.append",
"numpy.average",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdeneefe/tensor2tensor | [
"d9f807cf2738323d19aba0a20a8cf0c7f7da8b27"
] | [
"tensor2tensor/tpu/tpu_trainer_lib.py"
] | [
"# coding=utf-8\n# Copyright 2017 The Tensor2Tensor 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 requir... | [
[
"tensorflow.contrib.tpu.TPUEstimator",
"tensorflow.estimator.Estimator",
"tensorflow.ConfigProto",
"tensorflow.contrib.learn.Experiment",
"tensorflow.contrib.tpu.TPUConfig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7"
]
}
] |
ElementAI/seq2struct | [
"06c6bd29a0b7d4db799c2fbbacc41ea30eadbecc"
] | [
"seq2struct/models/spider_enc_modules.py"
] | [
"import collections\nimport itertools\nimport operator\n\nimport numpy as np\nimport torch\nfrom torch import nn\nimport torchtext\n\ntry:\n from seq2struct.models import lstm\nexcept ImportError:\n pass\nfrom seq2struct.models import transformer\nfrom seq2struct.utils import batched_sequence\nfrom seq2struct... | [
[
"torch.LongTensor",
"torch.nn.Dropout",
"numpy.pad",
"torch.cat",
"torch.nn.LSTM",
"numpy.cumsum",
"numpy.stack",
"torch.tensor",
"torch.nn.Linear",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cwharris/custrings | [
"78b52db8a07163e170b6728217b2e1e85d6b9007"
] | [
"python/tests/test_strip.py"
] | [
"# Copyright (c) 2018-2019, NVIDIA CORPORATION.\n\nimport pandas as pd\nimport nvstrings\n\nfrom utils import assert_eq\n\n\ndef test_strip():\n s = [\" hello \", \" there \", \" world \", None, \" accénté \", \"\"]\n strs = nvstrings.to_device(s)\n pstrs = pd.Series(s)\n got = strs.strip()\n ... | [
[
"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": []
}
] |
krai/ck-qaic | [
"c84964bb019fe1b12ba952fafd92274ecdc64380"
] | [
"package/model-qaic-calibrate/ssd-resnet34/modifications/aimet_mod.py"
] | [
"import types\nfrom typing import Tuple, List, Union, Dict\nimport copy\n\nimport numpy as np\nimport torch\n\nimport libpymo\nimport aimet_torch\nimport aimet_torch.onnx_utils\nfrom aimet_torch import utils\nimport aimet_torch.bias_correction\nfrom aimet_torch.qc_quantize_op import QcQuantizeWrapper, QcPostTrainin... | [
[
"numpy.ceil",
"numpy.array",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
monodera/seir_model | [
"1a5a9880b2124f87280af8998a3ebc43c2b07665"
] | [
"main.py"
] | [
"#!/usr/bin/env python\n\n# %%\nimport numpy as np\nfrom scipy.integrate import solve_ivp\nfrom bokeh.layouts import column, row\nfrom bokeh.models import CustomJS, Slider, Range1d\nfrom bokeh.plotting import (\n ColumnDataSource,\n figure,\n output_file,\n show,\n save,\n curdoc,\n)\nfrom bokeh.i... | [
[
"scipy.integrate.solve_ivp",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
itouchz/Conversational-AutoML | [
"01b7f8428d517a6d2b91f2a72e672c1b3627a992"
] | [
"server/AutoML.py"
] | [
"from flask import Flask, request, send_from_directory\nfrom flask_cors import CORS\nfrom tensorflow.keras.models import load_model\n\nimport tensorflow as tf\nimport autokeras as ak\nimport numpy as np\n\nimport dl_model as dl\nimport ml_model as ml\n\nimport os\nimport joblib\n\napp = Flask(__name__)\nCORS(app)\n... | [
[
"tensorflow.expand_dims",
"tensorflow.keras.models.load_model",
"tensorflow.keras.preprocessing.image.img_to_array",
"tensorflow.keras.preprocessing.image.load_img"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
gomesfernanda/lxmls_lab | [
"74b60b9e79aaa2994aee9428b623c04e93807bda"
] | [
"lxmls/parsing/dependency_features.py"
] | [
"import sys\nimport numpy as np\nfrom scipy.sparse import lil_matrix\nfrom lxmls.parsing.dependency_reader import *\n\n\nclass DependencyFeatures:\n \"\"\"\n Dependency features class\n \"\"\"\n\n def __init__(self, use_lexical=False, use_distance=False, use_contextual=False):\n self.feat_dict = ... | [
[
"numpy.size",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Chucooleg/vnla | [
"b9c1367b263f00a38828ff24cefc8becc149be7a"
] | [
"code/tasks/VNLA/history_buffer.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport pickle\nfrom collections import defaultdict\nimport numpy as np\nimport os\nimport torch\n\n\ndef nested_defaultdict():\n '''Use this rather than lambda to allow defaultdict objects to be pickled'''\n return defaultdict(list)... | [
[
"torch.ones",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tobifinn/py_bacy | [
"f550876fe0303eb1711866268871f6fd478ef1c1"
] | [
"py_bacy/tasks/pytassim/cosmo.py"
] | [
"#!/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Created on 14.01.21\n#\n# Created for py_bacy\n#\n# @author: Tobias Sebastian Finn, tobias.sebastian.finn@uni-hamburg.de\n#\n# Copyright (C) {2021} {Tobias Sebastian Finn}\n\n\n# System modules\nfrom typing import Dict, Any, List, Tuple\nimport os.path\nimport g... | [
[
"numpy.log",
"numpy.sum",
"numpy.abs",
"numpy.power"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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