Martin Spacek wrote:
Kurt Smith wrote:
You might try numpy.memmap -- others have had success with it for
large files (32 bit should be able to handle a 1.3 GB file, AFAIK).
Yeah, I looked into numpy.memmap. Two issues with that. I need to
eliminate as much disk access as possible while
On Dec 1, 2007 12:09 AM, Martin Spacek [EMAIL PROTECTED] wrote:
Kurt Smith wrote:
You might try numpy.memmap -- others have had success with it for
large files (32 bit should be able to handle a 1.3 GB file, AFAIK).
Yeah, I looked into numpy.memmap. Two issues with that. I need to
Ivan Vilata i Balaguer (el 2007-11-30 a les 19:19:38 +0100) va dir::
Well, one thing you could do is dump your data into a PyTables_
``CArray`` dataset, which you may afterwards access as if its was a
NumPy array to get slices which are actually NumPy arrays. PyTables
datasets have no
Hallo!
* A new ARGOUTVIEW suite of typemaps is provided that allows your
wrapped function
to provide a pointer to internal data and that returns a numpy
array encapsulating
it.
Thanks for integrating it !
* New typemaps are provided that correctly handle FORTRAN ordered 2D
On Samstag 01 Dezember 2007, Martin Spacek wrote:
Kurt Smith wrote:
You might try numpy.memmap -- others have had success with it for
large files (32 bit should be able to handle a 1.3 GB file, AFAIK).
Yeah, I looked into numpy.memmap. Two issues with that. I need to
eliminate as much
On Freitag 30 November 2007, Joe Harrington wrote:
I was misinformed about the status of numdisplay's pages. The package
is available as both part of stsci_python and independently, and its
(up-to-date) home page is here:
http://stsdas.stsci.edu/numdisplay/
I had a look at ds9/numdisplay,
These corrections have been committed.
Thanks.
On Dec 1, 2007, at 9:21 AM, Georg Holzmann wrote:
* A new ARGOUTVIEW suite of typemaps is provided that allows your
wrapped function
to provide a pointer to internal data and that returns a numpy
array encapsulating
it.
Thanks for
