- Author: Jake Vanderplas vanderplas@astro.washington.edu
- License: BSD
This is a Python wrapper for the PROPACK package, which implements efficient partial singular value decompositions of large sparse matrices and linear operators. The package is meant to stand alone, but may be included in Scipy at a later date.
This is a work in progress: not all routines are currently wrapped, and the code should be considered unsupported
To install in-place, type
python setup.py build_ext --inplace
To run the few unit tests that currently exist, type
nosetests pypropack/tests
Currently very straightforward. You can do
>>> from pypropack import svdp
>>> import numpy as np
>>> A = np.random.random((10, 20))
>>> u, s, vt = svdp(A, k=3) # compute top k singular values
The svdp documentation has more information.
use the script plot_benchmarks.py for a comparison of PROPACK, ARPACK,
SVDLIBC, and the standard LAPACK approaches to computing the svd.
PROPACK had to be slightly modified to play well with gfortran. The PROPACK code used is version 2.1, downloaded from http://soi.stanford.edu/~rmunk/PROPACK/
For compatibility with gfortran, the files PROPACK/*/second.f
needed to be slightly modified. The line
REAL ETIME
EXTERNAL ETIME
was changed to
EXTERNAL REAL ETIME