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setup.py
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"""The PyRhO package setup script"""
import os
from setuptools import setup, find_packages # Prefer setuptools over distutils
# from codecs import open # To use a consistent encoding
# Download and install setuptools if not installed
#from ez_setup import use_setuptools
#use_setuptools()
#python -m ensurepip --upgrade
here = os.path.abspath(os.path.dirname(__file__))
home = os.path.expanduser("~")
# print(home)
prwd = os.path.join(home, 'pyrho') # pyrho working directory
# TODO: Test changes to package_data and include notebooks and license without MANIFEST
# TODO: Fix this to remove redundant long_description text
# Get the long description from the relevant file
#with open(os.path.join(here, 'DESCRIPTION.rst'), encoding='utf-8') as f:
#with open('DESCRIPTION.rst', encoding='utf-8') as f:
# long_description = f.read()
long_description = """
PyRhO - A Virtual Optogenetics Laboratory
=========================================
A Python module to fit and characterise rhodopsin photocurrents.
Background
----------
Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behaviour. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these rhodopsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO.
PyRhO enables users to:
(i) characterize new (and existing) rhodopsins by automatically fitting a minimal set of experimental data to three, four or six-state kinetic models,
(ii) simulate these models at the channel, neuron & network levels and
(iii) gain functional insights through model selection and virtual experiments *in silico*.
The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments for these virtual opsins will enable (neuro)scientists to gain a comprehensive understanding of their behaviour and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the rhodopsin genetic code in a neuro-engineering feed-back loop. In this way, we hope PyRhO will help to significantly improve optogenetics as a tool for transforming biological sciences.
Further Information
-------------------
If you use PyRhO please cite our paper:
Evans, B. D., Jarvis, S., Schultz, S. R. & Nikolic K. (2016) "PyRhO: A Multiscale Optogenetics Simulation Platform", *Frontiers in Neuroinformatics, 10* (8). `doi:10.3389/fninf.2016.00008 <https://dx.doi.org/10.3389/fninf.2016.00008>`_
The PyRhO project website with additional documentation may be found here: `www.imperial.ac.uk/bio-modelling/pyrho <http://www.imperial.ac.uk/a-z-research/bio-modelling/pyrho>`_
Finally, don't forget to follow us on twitter for updates: `@ProjectPyRhO <https://twitter.com/ProjectPyRhO>`_!
"""
setup(
name='PyRhO',
# Versions should comply with PEP440. For a discussion on single-sourcing
# the version across setup.py and the project code, see
# https://packaging.python.org/en/latest/single_source_version.html
version='0.9.5',
description='Fit and characterise rhodopsin photocurrents',
long_description=long_description,
# The project's main homepage.
url='https://github.com/ProjectPyRhO/PyRhO/',
# download_url='https://github.com/ProjectPyRhO/PyRhO/archive/master.zip',
# download_url='https://github.com/ProjectPyRhO/PyRhO/tarball/' + version,
# Author details
author='Benjamin D. Evans',
author_email='[email protected]',
license='BSD',
platforms=['Linux', 'Mac OS X', 'Windows'],
# See https://pypi.python.org/pypi?%3Aaction=list_classifiers
classifiers=[
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 4 - Beta',
'Intended Audience :: Science/Research',
'Intended Audience :: Education',
'Topic :: Scientific/Engineering',
'Topic :: Scientific/Engineering :: Medical Science Apps.',
'Topic :: Scientific/Engineering :: Bio-Informatics',
# The license should match "license" above
'License :: OSI Approved :: BSD License',
# Supported Python versions
'Programming Language :: Python',
'Programming Language :: Python :: 3',
# 3.7 EOL: 27/6/23
# 'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
'Programming Language :: Python :: 3.11',
'Framework :: IPython',
'Natural Language :: English',
'Operating System :: OS Independent',
],
#keywords='optogenetics rhodopsin opsin brain neuroscience neuron brian jupyter',
keywords=['optogenetics', 'rhodopsin', 'opsin', 'brain', 'neuroscience',
'neuron', 'brian', 'jupyter'],
# You can just specify the packages manually here if your project is
# simple. Or you can use find_packages().
packages=find_packages(exclude=['contrib', 'docs', 'tests*']),
# package_dir = {'': '.'},
# package_dir = {'pyrho': 'pyrho'}, # Relative to this script
python_requires='>=3.8',
# List run-time dependencies here. These will be installed by pip when your
# project is installed. For an analysis of "install_requires" vs pip's
# requirements files see:
# https://packaging.python.org/en/latest/requirements.html
# ipython is used for latex repr - remove from requirements and have a fallback repr?
install_requires=[
'numpy>=1.8',
'scipy>=0.15',
'matplotlib>=1.3',
# 'lmfit>=0.9.3,<1.0.3',
'lmfit>=1.0.3',
'brian2>=2.0'
# 'ipython>=4.1'
],
# List additional groups of dependencies here (e.g. development dependencies).
# You can install these using the following syntax, for example:
# $ pip install -e .[dev,test]
extras_require={
# 'dev': ['check-manifest'],
# 'test': ['coverage'],
'test': ['pytest'],
# 'brian' : ['brian2'],
# 'docs' : ['sphinx>=1.3'],
'extras': ['seaborn>=0.7', 'pandas>=0.17'], # 'cython>=0.23'
# traitlets is a dependency of ipywidgets and can be removed if 4.1 entails traitlets>=4.1
'GUI' : ['jupyter>=1.0', 'notebook>=4.1', 'ipywidgets>=4.1,<5',
'seaborn>=0.7'], # , 'traitlets>=4.1,<5'
'full': ['jupyter>=1.0', 'notebook>=4.1', 'ipywidgets>=4.1,<5',
'seaborn>=0.7', 'pandas>=0.17', 'pytest'], # 'cython>=0.23'
},
tests_require=['pytest'],
include_package_data=True,
# If there are data files included in your packages that need to be
# installed, specify them here. If using Python 2.6 or less, then these
# have to be included in MANIFEST.in as well.
package_data={
# TODO: Try this without MANIFEST
'NEURON' : ['*.mod', '*.hoc', '*.sh'],
'gui' : ['*.png'],
'datasets': ['*.pkl'],
},
# Although 'package_data' is the preferred approach, in some case you may
# need to place data files outside of your packages.
# see http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files
# In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
#data_files=[('my_data', ['data/data_file'])],
#data_files=[#(prwd, []),
# (prwd, [os.path.join(prwd, 'gui/*.png'), ])],
# To provide executable scripts, use entry points in preference to the
# "scripts" keyword. Entry points provide cross-platform support and allow
# pip to create the appropriate form of executable for the target platform.
#entry_points={
# 'console_scripts': [
# 'sample=sample:main',
# ],
#},
)