Revision 5ca32e744596c2c4c54e6d5ea2aab7bf35858f4c authored by Matt Newville on 13 November 2013, 14:45:19 UTC, committed by Matt Newville on 13 November 2013, 14:45:19 UTC
1 parent a21c859
setup.py
#!/usr/bin/env python
# from distutils.core import setup
from setuptools import setup
import lmfit as lmfit
import numpy, scipy
long_desc = """A library for least-squares minimization and data fitting in
Python. Built on top of scipy.optimize, lmfit provides a Parameter object
which can be set as fixed or free, can have upper and/or lower bounds, or
can be written in terms of algebraic constraints of other Parameters. The
user writes a function to be minimized as a function of these Parameters,
and the scipy.optimize methods are used to find the optimal values for the
Parameters. The Levenberg-Marquardt (leastsq) is the default minimization
algorithm, and provides estimated standard errors and correlations between
varied Parameters. Other minimization methods, including Nelder-Mead's
downhill simplex, Powell's method, BFGS, Sequential Least Squares, and
others are also supported. Bounds and contraints can be placed on
Parameters for all of these methods.
In addition, methods for explicitly calculating confidence intervals are
provided for exploring minmization problems where the approximation of
estimating Parameter uncertainties from the covariance matrix is
questionable. """
setup(name = 'lmfit',
version = lmfit.__version__,
author = 'Matthew Newville',
author_email = 'newville@cars.uchicago.edu',
url = 'http://cars9.uchicago.edu/software/python/lmfit/',
download_url = 'http://newville.github.com/lmfit-py/',
requires = ('numpy', 'scipy'),
license = 'BSD',
description = "Least-Squares Minimization with Bounds and Constraints",
long_description = long_desc,
platforms = ('Windows', 'Linux', 'Mac OS X'),
classifiers=['Intended Audience :: Science/Research',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Topic :: Scientific/Engineering',
],
# test_suite='nose.collector',
# test_requires=['Nose'],
package_dir = {'lmfit': 'lmfit'},
packages = ['lmfit', 'lmfit.uncertainties'],
)
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