Python for scientific programming?

Asked by katie townley

I heard about the NumPy, SciPy and Matplotlib libraries, I heard it read on wikipedia :-) Who used them, what are the advantages over MatLab or MatCad, is it possible to process large amounts of data using complex mat transformations, and beautifully display the results.

Answers

shannon haupt
advantage over mat. packages - you can compile the code, say, on the same one, and this will not require every user to buy and install a matlab.

Can be programmed in parallel for the cluster.

Data can be processed, but it is slow - I ended up staying on Scala, there are all the buns of modern languages ​​(for example, I love tuples), and the speed is almost siplusplusovskaya.
Python will be 40 times slower. Actually, like a matlab, how I remember him. This significantly limits the range of conveniently solvable tasks.
For beautiful output and heaps of embedded scientific primitives, the same GnuScienceLibrary (including gnuplot, as its component) is attached to all possible languages, as far as I know ...

In general, I use a dynamic language (true, Ruby, not Python) to quickly (without recompilation) debug the algorithm on a small data array, and then I write the final version on Scala and compile it into a regular .jar.
mehul thakkar
If judged by my specialization (computational mechanics), then unfortunately, python is of little use in current form to scientific developments. Many scientists have grid calculation functions written and tested many years ago. These functions are not even written in C, but on fortran. It is not surprising, but on it they are considered faster than in C. And then with the help of a wrapper on C and MPI, tasks are counted on super-computers.

To rewrite them into another programming language is an ungrateful task, since there is a lot of math, formulas, logic and other things in these functions.

You can use ctypes, but python then becomes just a convenient control construct. Actually, libraries are being implemented. For example NumPy.
Replies:
in numpy there is a library for easy connection of the Fortran code (www.scipy.org/F2py) - now it’s clear why) - penny clasper
As far as I remember, it just makes a wrapper for the usual dynamic library, which can be connected via ctypes - robert mood
judging by the description, a slightly different approach is to give it input the fortran file hello.f and you get a pythonnier extension hello.so, which can be imported and used as import hello. In this case, the arrays in the function can be passed as numpy-arrays and so on. - cody russ
jonathan webb
For me personally, plus python, in that you can quickly write a simple puzzle and build graphics through Matplotlib. And something difficult scientific still worth writing to Fortran.
Replies:
then already in assembler, if it is very complicated and very scientific - jeryl hayes
rita linden
It depends on the scientific goals, the python is still a brake, cumbersome calculations are dumped on him - suicide)
ahmed mamdouh
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