This solution does not work for me since I have an offset before the data that
is not a multiple of the datatype (it's a header containing various stuff).
I'll at pytables.
# Exploit the operating system's virtual memory manager to get a
virtual copy of the entire file in memory
# (This does not
I see that pytables deals with hdf5 data. It would be very nice if the data
were in such a standard format, but that is not the case, and that can't be
changed.
Da: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org] per
conto di
On Wed, Mar 13, 2013 at 2:18 PM, Andrea Cimatoribus
andrea.cimatori...@nioz.nl wrote:
This solution does not work for me since I have an offset before the data
that is not a multiple of the datatype (it's a header containing various
stuff).
np.memmap takes an offset= argument.
-n
Thanks a lot for the feedback, I'll try to modify my function to overcome this
issue.
Since I'm in the process of buying new hardware too, a slight OT (but
definitely related).
Does an ssd provide substantial improvement in these cases?
Da:
On 13 March 2013 16:54, Andrea Cimatoribus andrea.cimatori...@nioz.nlwrote:
Since I'm in the process of buying new hardware too, a slight OT (but
definitely related).
Does an ssd provide substantial improvement in these cases?
It should help. Nevertheless, when talking about performance, it
On Wed, Mar 13, 2013 at 9:54 AM, Andrea Cimatoribus
andrea.cimatori...@nioz.nl wrote:
Thanks a lot for the feedback, I'll try to modify my function to overcome
this issue.
Since I'm in the process of buying new hardware too, a slight OT (but
definitely related).
Does an ssd provide
