# GSL Capabilites not yet wrapped by PyGSL

## Sorting

Is covered by the list sorting methods of python.
See
Python Mini Sorting howto
and python stable sort
## Fourier Transforms.

is covered by e.g. the Numeric module.
## N-tuples

If you do not know PAW or ROOT you probably will not need them. If you need
them, assistance will be provided if required.
# GSL Capabilites partly implemented

## Series Acceleration

SWIG wrapped, but do you know what it is supposed to do? If so write a test
and an example!
## Discrete Hankel Transforms

SWIG wrapped, but do you know what it is supposed to do? If so write a test
and an example!
# PyGSL related Capabilites

## Blas Memory handling

Blas is basically wrapped, but:
NumPy and GSL vectors and matrices differ slightly. 1-dimensional arrays and
GSL Vectors can be interchanged. The difference between the two is mainly that
Numpy counts the stride in bytes whereas GSL counts it in the basis type.

2-dimensional Numpy arrays use the stride concept for both dimensions whereas
the GSL matrices use an offset tda. So they assume contiunes lines, with some
elements on the line not used. Here one could think of a full featured matrix
object, which can interoperate with a numeric array simulating an Matrix
object. Such a object would be useful to allow prototyping of linear algebra
systems in python. The next point needs to be implemented as well.

optional reused return object: Numpy allows to add an optional argument to
many functions, which is to be used as return argument. This avoids the need
to delete and reallocate objects.