Scientific Libraries in Python

Fernando Pérez fperez528 at
Sun Nov 11 15:44:21 CET 2001

>>>> s = ShamelessPlug()
>>>> s.start()
> VTK is library and some folks have trouble using it because it does
> take some effort to get the hang of it.  There is one tool/application
> out there that helps such folks out by attempting to make data
> visualization very easy.  Its written in 100% pure Python and uses VTK
> and Tkinter.  Its available here:
> To see specific examples in action look here:

YES! That's *exactly* what I had in mind. The problem I see with most other 
systems out there is that at some point, your problem gets ugly enough that 
they fail you. And you're stuck learning a new syntax from scratch. The 
reason why I'm so interested in python is because, being such a clean 
language, I think it's a great base layer for a lot of scientific work. I'm 
sick and tired of having a project made up of: shell 
scripts/sed/awk/perl/Fortran/C/C++/IDL/Mathematica... In the end I end up 
spending more time switching mentally from syntax to syntax than getting work 

I don't mind that VTK is a complex toolkit: for something to scale well to 
really serious tasks, it better be. Otherwise it will crack open beyond a 
certain point. I think the challenge is writing a tool that:

1) can be easily used from the interactive python command line for 'basic' 2d 
and 3d plotting.

2) has itself a gui for fine tuning of things.

3) is scriptable from python with access to the mid and low-level routines.

The gracePlot module to me is an excellent illustration of the model I have 
in mind, even thought the plotting tool itself is very limited (only 2d, 
though good at what it does). You can quickly fire it up from the command 
line to plot some data, then fine-tune it from the gui and save to eps the 
finished plots. Quick, efficient and clean.

So best of luck with mayavi. I'll be keeping a close eye on this, it seems 
like you're well on  your way to putting together a truly first-rate 
visualization environment for python. I just posted an announcement I found 
about the Gnu Scientific Library. Using that as a basis for SciPy/Scientific 
and your system, we're well on our way to a kick-ass scientific working 
environment: useful for things from interactive data exploration to job 
scripting/control, all the way up to full-scale simulation writing (with 
appropriate use of C/C++ components when needed).



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