The goal of this project is to provides binaries for the following packages:
* NETLIB BLAS and LAPACK
* Atlas
* Umfpack
* fftw3
* numpy and scipy
* matplotlib
* ipython

That is to give packages for a nice scipy environment. I intend to support a least the following distributions:

* debian etch
* ubuntu (dapper and other versions if the build system enables it)
* last Fedora (6 ATM)
* last openSUSE (10.2 ATM)

For now, this is really experimental, because I am still learning how to build rpm, which have different convention than debian packages which I know much better.

Blah blah blah

First try to build trivial package (one binary file build from C sources + Makefile) using open build

LAPACK is written in Fortran77 and provides routines for solving systems of
simultaneous linear equations, least-squares solutions of linear systems of
equations, eigenvalue problems, and singular value problems. The associated
matrix factorizations (LU, Cholesky, QR, SVD, Schur, generalized Schur) are
also provided, as are related computations such as reordering of the Schur
factorizations and estimating condition numbers. Dense and banded matrices are
handled, but not general sparse matrices. In all areas, similar functionality
is provided for real and complex matrices, in both single and double precision.

Blah blah

The fundamental package needed for scientific computing with Python
is called NumPy. This package contains:

* a powerful N-dimensional array object sophisticated
* (broadcasting) functions tools for integrating C/C++ and
* Fortran code useful linear algebra, Fourier transform, and
* random number capabilities.

It derives from the old Numeric code base and can be
used as a replacement for Numeric. It also adds the
features introduced by numarray and can be used to
replace numarray.

SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science,
and engineering. It is also the name of a very popular conference on scientific
programming with Python. The core library is NumPy which provides convenient
and fast N-dimensional array manipulation. The SciPy library is built to work
with NumPy arrays, and provides many user-friendly and efficient numerical
routines such as routines for numerical integration and optimization.
Together, they run on all popular operating systems, are quick to install, and
are free of charge. NumPy and SciPy are easy to use, but powerful enough to be
depended upon by some of the world's leading scientists and engineers. If you
need to manipulate numbers on a computer and display or publish the results,
give SciPy a try!

The BLAS (Basic Linear Algebra Subprograms) are routines
that provide standard building blocks for performing basic vector and matrix
operations. The Level 1 BLAS perform scalar, vector and vector-vector
operations, the Level 2 BLAS perform matrix-vector operations, and the Level 3
BLAS perform matrix-matrix operations. Because the BLAS are efficient,
portable, and widely available, they are commonly used in the development of
high quality linear algebra software, LAPACK for example.