large arrays in python (scientific)

[phys137 at mailcity.com]

I second that 'R' comment. For the last couple of weeks I've been routinely
running _very_ funny stuff on datasets with 4 million numbers (some even had
10). R had no problem handling that. [But I have 1 gb mem :-)]

Having said that, I don't think that numpy would have any problem handling
matrices that size - guys at LLLabs who wrote it are probably handling sets
ten times that size daily. (All these solutions seem to be using LINPACK
underneath anyway.) So the underlying accuracy etc problems should be the
same in both cases.

The reason I switched to R for that is plenty of libraries for various
advanced algorithms and nice plots, so you can spot a problem quickly as
well as compare various methods. The other option is SCILAB (very nice), but
R has better set of libraries for statistical and similar data analysis.

<Tae_kyon at digilander.iol.it> wrote in message
news:slrna3le0j.31l.Tae_kyon at localhost.localdomain...
> Il 7 Jan 2002 15:25:23 -0800, Mark Grant <markrgrant at yahoo.com> scrisse:
>
> > I have a data set that contains values for positions in 3D space.
> > There are about 2 million data points
> > (128x128x128).
> >
> > I'm trying to fit a function to the data. I want to use the
> > LeastSquaresFit procedure in ScientificPython,
>
> I know nothing about Python, but you sure it's the best instrument for
> this sort of stuff ? Sounds more like something for R to me.
> ( http://www.cran.org ).
> Anyway you have a huge data set .... going to need a lot of memory.
>
> --
> Tae Kyon
> Non c'è nulla di più animale
> della coscienza pulita,
> sul terzo pianeta del sistema solare