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setup.py
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setup.py
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#!/usr/bin/env python
import re
import sys
from subprocess import CalledProcessError, check_output
from setuptools import setup, find_packages
PROJECT = "auDeep"
VERSION = "0.9.6a1"
LICENSE = "GPLv3+"
AUTHOR = "Maurice Gerczuk"
AUTHOR_EMAIL = "maurice.gerczuk@informatik.uni-augsburg.de"
URL = "https://github.com/auDeep/auDeep"
with open("DESCRIPTION.md", "r") as fh:
LONG_DESCRIPTION = fh.read()
dependencies = [
"cliff>=3.3, <3.4",
"liac-arff>=2.4",
"matplotlib>=3.2",
"netCDF4==1.4.2",
"liac-arff>=2.4",
"matplotlib>=3.2",
"netCDF4==1.4.2",
"pandas>=1.0, <1.2",
"pysoundfile>=0.9",
"scipy>=1.4",
"scikit-learn>=0.23",
"xarray==0.10.0",
]
if sys.platform.startswith("darwin"):
dependencies.append("tensorflow>=1.15.2,<2")
else:
dependencies.append("tensorflow-gpu>=1.15.2,<2")
setup(
name=PROJECT,
version=VERSION,
license=LICENSE,
author=AUTHOR,
author_email=AUTHOR_EMAIL,
description="auDeep is a Python toolkit for unsupervised feature learning with deep neural networks (DNNs)",
long_description=LONG_DESCRIPTION,
long_description_content_type="text/markdown",
url=URL,
platforms=["Any"],
scripts=[],
provides=[],
install_requires=dependencies,
namespace_packages=[],
packages=find_packages(),
include_package_data=True,
entry_points={
"console_scripts": [
"audeep = audeep.main:main"
],
"audeep.commands": [
"preprocess = audeep.cli.extract_spectrograms:ExtractSpectrograms",
"export = audeep.cli.export:Export",
"import = audeep.cli.import_data:Import",
"modify = audeep.cli.modify:Modify",
"upsample = audeep.cli.upsample:Upsample",
"fuse = audeep.cli.fuse:FuseDataSets",
"fuse chunks = audeep.cli.fuse:FuseChunks",
"validate = audeep.cli.validate:Validate",
"visualize tsne = audeep.cli.visualize:VisualizeTSNE",
"inspect raw = audeep.cli.inspect:InspectRaw",
"inspect netcdf = audeep.cli.inspect:InspectNetCDF",
"t-rae train = audeep.cli.train:TrainTimeAutoencoder",
"t-rae generate = audeep.cli.generate:GenerateTimeAutoencoder",
"f-rae train = audeep.cli.train:TrainFrequencyAutoencoder",
"f-rae generate = audeep.cli.generate:GenerateFrequencyAutoencoder",
"ft-rae train = audeep.cli.train:TrainFrequencyTimeAutoencoder",
"ft-rae generate = audeep.cli.generate:GenerateFrequencyTimeAutoencoder",
"mlp evaluate = audeep.cli.evaluate:MLPEvaluation",
"svm evaluate = audeep.cli.evaluate:SVMEvaluation",
"mlp predict = audeep.cli.predict:MLPPrediction",
"svm predict = audeep.cli.predict:SVMPrediction",
],
},
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 4 - Beta',
'Environment :: GPU :: NVIDIA CUDA :: 10.0',
# Indicate who your project is intended for
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Intended Audience :: Science/Research',
# Pick your license as you wish (should match "license" above)
'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
'Programming Language :: Python :: 3.7',
],
keywords='machine-learning audio-analysis science research',
project_urls={
'Source': 'https://github.com/auDeep/auDeep/',
'Tracker': 'https://github.com/auDeep/auDeep/issues',
},
zip_safe=False,
)