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Dharhas Pothina | 27 Jul 2009 22:24
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Ubuntu vs Fedora for scientific work?

Hi All,

This is slightly off topic but I felt that this lists membership would have good input on this question. I am
presently running fedora 8 on my main workstation and it is getting a bit long in the tooth. Back when I set
this machine up, RHEL was too much of a pain to use because scientific packages were always extremely
outdated and difficult to install. Ubuntu was nice to use at home but installing scientific packages like
the Intel Fortran compiler etc was complicated (not impossible, just more work than I wanted). So I went
with Fedora which has worked pretty well so far. 

Recently, I've noticed that a lot of scientific packages now have ubuntu repositories and even the intel
compiler has an ubuntu option. So I'm trying to decide whether to go with Fedora 11 or Ubuntu Jaunty. 

I'm not trying to start a flame war but I'm interested in what people's experience has been. 

thanks,

- dharhas
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josef.pktd | 27 Jul 2009 22:45
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Re: Scipy and statistics: probability density function

On Mon, Jul 27, 2009 at 11:59 AM, Robert Kern<robert.kern <at> gmail.com> wrote:
> On Mon, Jul 27, 2009 at 02:33, Daniel J
> Farrell<daniel.farrell <at> imperial.ac.uk> wrote:
>> Dear list,
>>
>> I am looking for some of the functionality provided by the GNU
>> Scientific Library histogram module (http://www.gnu.org/software/gsl/manual/html_node/Histograms.html
>> ).
>>
>> In particular, a need to be able to create a probability density
>> function from my histogram of data. This will allow inverse look-ups
>> to be performed, i.e. for a random number (0-->1) find the associated
>> probability (http://www.gnu.org/software/gsl/manual/html_node/The-histogram-probability-distribution-struct.html
>> ). This allows the distribution and for samples to be returned
>> weighted by the probability of the distribution -- which is a common
>> task!
>
> It looks like you want a CDF (or rather it's inverse, the PPF) rather
> than a PDF. Anyways, this is straightforward. Compute the histogram
> using normed=False. Find the cumulative sum and divide by the sum to
> get the (smoothed) empirical CDF. Prepend a 0.0 to this, and then this
> will align with the edges array that is also returned. Then you can
> use linear interpolation to do lookups. If you use the edges array as
> "X" and the empirical CDF as "Y", then this is a CDF. If you use the
> empircal CDF array as "X" and the edges as "Y", then this is a PPF.
>
>
> In [12]: x = np.random.uniform(0, 10, size=1000)
>
> In [13]: hist, edges = np.histogram(x)
(Continue reading)

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Stef Mientki | 28 Jul 2009 00:29
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Re: optical ray tracing


Elliot Hallmark wrote:
> howdy,
>
> I'm looking to do some raytracing for a nonimaging design and there
> are no opensource packages that address the geometry needed.  Scipy
> seems like a good package to become familiar with anyway (I miss
> mathematica now that i'm no longer a university student), but i was
> wondering if anyone has already done some optical ray tracing work in
> scipy that i could build off of.
>
> Wouldn't be to hard to do from scratch, but my focus is on the design
> and the fewer excursions into building new tools the better.
>
>   
This might be interesting
  http://www-ee.eng.hawaii.edu/~zqyun/caevp.html

I'ld love to integrate this in PyLab_Works,
so the optical system can be manipulated both through interacting with 
the graphical window and by code.
I tried to connect the guy, but he doesn't respond.

cheers,
Stef

> thanks,
>
> elliot
> _______________________________________________
(Continue reading)

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Yosef Meller | 28 Jul 2009 08:42
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Re: optical ray tracing

On Monday 27 July 2009 19:34:37 Elliot Hallmark wrote:
> I'm looking to do some raytracing for a nonimaging design and there
> are no opensource packages that address the geometry needed.  Scipy
> seems like a good package to become familiar with anyway (I miss
> mathematica now that i'm no longer a university student), but i was
> wondering if anyone has already done some optical ray tracing work in
> scipy that i could build off of.
>
> Wouldn't be to hard to do from scratch, but my focus is on the design
> and the fewer excursions into building new tools the better.

I have an intern writing a ray-tracer for optical ray tracing right now. In 
about a month we expect to have the basic framework in releasable shape, but 
if you're impatient you can have a look at her git tree:

http://github.com/jasminef/tracer/tree/optic_tests

Corrently some of the tests fail, but we have the following:
* Construction of assemblies of objects
* surfaces: flat and spherical (parabolic on the way)
* specular reflection and refraction

We are focusing on modular design and programmability, not speed. It should, 
then, be not too hard to add a new surface geometry.

Again, this is still not in releasable shape, but maybe it'll help.
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