pysparse 1.1-1 source package in Ubuntu

Changelog

pysparse (1.1-1) unstable; urgency=low

  * New upstream release.
  * Small changes to package description.
  * Bounds-checking eliminates many crashes (closes: #535318).
  * Added --install-layout=deb to setup.py install (closes: #547839).
  * Using quilt for patches, and added README.source describing it.
  * Updated Standards-Version.
 -- Alessio Treglia <email address hidden>   Tue,  24 Nov 2009 00:08:18 +0000

Upload details

Uploaded by:
Alessio Treglia on 2009-11-24
Uploaded to:
Lucid
Original maintainer:
hazelsct
Component:
universe
Architectures:
any
Section:
python
Urgency:
Low Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Lucid release on 2009-11-24 universe python

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pysparse_1.1.orig.tar.gz 891.1 KiB b6d52b9b34824be138f75dd790d52598
pysparse_1.1-1.diff.gz 6.5 KiB 31c95608032d6bc1224d62fb3c3f7743
pysparse_1.1-1.dsc 1.2 KiB 668dcbf2d520181553f1141370e9dfe4

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Binary packages built by this source

python-sparse: Sparse linear algebra extension for Python

 This provides a set of sparse matrix types for Python, with modules which
 implement:
  - Iterative methods for solving linear systems of equations
  - A set of standard preconditioners
  - An interface to a direct solver for sparse linear systems of equations
  - The JDSYM eigensolver
 .
 All of these modules are implemented as C extension modules based on standard
 sparse and dense matrix libraries (UMFPACK/AMD, SuperLU, BLAS/LAPACK) for
 maximum performance and robustness.

python-sparse-examples: Sparse linear algebra extension for Python: documentation

 This package provides documents and examples for python-sparse, a set of
 sparse matrix types for Python, with modules which implement:
  - Iterative methods for solving linear systems of equations
  - A set of standard preconditioners
  - An interface to a direct solver for sparse linear systems of equations
  - The JDSYM eigensolver
 .
 All of these modules are implemented as C extension modules based on standard
 sparse and dense matrix libraries (UMFPACK/AMD, SuperLU, BLAS/LAPACK) for
 maximum performance.