A Wednesday 01 July 2009 15:04:08 Francesc Alted escrigué:
> However, you can still speed-up out-of-core computations by using the
> recently introduced tables.Expr class (PyTables 2.2b1, see [2]), which uses
> a combination of the Numexpr [3] and PyTables advanced computing
> capabilities:
>
>
On Wed, Jul 1, 2009 at 6:14 PM, Pauli Virtanen wrote:
> Wed, 01 Jul 2009 10:17:51 +0200, Emmanuelle Gouillart kirjoitti:
>> I'm using numpy.memmap to open big 3-D arrays of Xray tomography
>> data. After I have created a new array using memmap, I modify the
>> contrast of every Z-slice (along
Hi Francesc,
many thanks for this very detailed and informative answer! This
list is really great :D.
I'm going to install pytables at once and I will try your scripts
with my data. As your computations were made with files of the same size
as mine, hopefully it should run
Hi Pauli,
thank you for your answer! I was indeed measuring the memory used
with top, which is not the best tool for understanding what really
happens. I monitored "free" during the execution of my program and
indeed, the used numbers on the "+/-buffers/cache" line stays roughly
c
A Wednesday 01 July 2009 10:17:51 Emmanuelle Gouillart escrigué:
> Hello,
>
> I'm using numpy.memmap to open big 3-D arrays of Xray tomography
> data. After I have created a new array using memmap, I modify the
> contrast of every Z-slice (along the first dimension) inside a for loop,
>
Wed, 01 Jul 2009 10:17:51 +0200, Emmanuelle Gouillart kirjoitti:
> I'm using numpy.memmap to open big 3-D arrays of Xray tomography
> data. After I have created a new array using memmap, I modify the
> contrast of every Z-slice (along the first dimension) inside a for loop,
> for a better vis
Hello,
I'm using numpy.memmap to open big 3-D arrays of Xray tomography
data. After I have created a new array using memmap, I modify the
contrast of every Z-slice (along the first dimension) inside a for loop,
for a better visualization of the data. Although I call memmap.flush
af
