# Computing

Most of the code of the numerical computations for our research
is written in

Python
(employing efficient libraries in the back
with some parts using embedded

`C++` or

`cython`).

Why Python? Python is a high-level, dynamic, object-oriented
programming language. In addition to scripts/programs
python can be used interactively.

Together with NumPy, SciPy and matplotlib it is an
excellent free alternative to commercial tools.
In addition one has the advantage of a complete programming
language.

For a simple example, see

Quantum maps
providing code to compute the eigenvalues of quantum maps and the
level spacing distribution.

## Python packages for scientific computing

Python itself is already installed on most Unix systems during
the standard installation.
For Windows see, e.g.,

Anaconda.

Additionally needed/recommended packages are

- Numpy
(Base array package)
- Scipy
(Library for scientific computing )
- Matplotlib
(Interactive plotting and more)
- Ipython
(Enhanced interactive shell)

Highly recommended are

- Mayavi
(excellent 3D visualization)
- PyX
(publication-ready .eps/.pdf graphics)

A good explanation and
an overview of further packages can be found at

Scipy - getting starting.

## Useful tools for scientific computation

py4science - talk given at IIT Madras, February 2017

Last modified: 08 November 2017, 20:23:52 ,
© Arnd Bäcker