Using Python for programming algorithms

[sturlamolden]

On May 18, 12:32 am, Vicent Giner <vgi... at gmail.com> wrote:

> * As far as I understand, the fact that Python is not a compiled
> language makes it slower than C, when performing huge amounts of
> computations within an algorithm or program.

First of all: whatever you do, use NumPy for all numerical work (and
possibly Scipy). Remember that Python with NumPy tend to be faster
than Matlab. Anything that applies to Matlab regarding vectorization
for speed also applies to NumPy.

If your program runs too slowly, try to use the psyco jit compiler
first. If that doesn't help, try one of the following:

- use Cython or Pyrex and compile your (modified) Python code
- inline C++ using scipy.weave
- write a function in C and call it using ctypes
- write a function in Fortran and make it callable from Python using
f2py

Usually, only small bottlenecks matter when it comes to overall
performance. It is also notoriously difficult to guess where they are.
Therefore: write everything in Python first, then profile your code to
identify bottlenecks. Only important bottlenecks need to be translated
to Pyrex, C or Fortran.

http://www.scipy.org/PerformancePython


> * I know Python is a "serious" and mature programming language, of
> course. But I do not know if it is seen as "just funny" in a research
> context.

Google NumPy, SciPy, Matplolib and Sage.

NASA uses Python to process image data from the Hubble telescope.





> Thank you in advance.