For lazy data loading I use memory-mapped array (numpy.memmap): I use it to
process multi-image files that are much larger than the available RAM.
Nadav.
From: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org]
On Behalf Of Craig Yoshioka
ok, that was an alternative strategy I was going to try... but not my favorite
as I'd have to explicitly perform all operations on the data portion of the
object, and given numpy's mechanics, assignment would also have to be explicit,
and creating new image objects implicitly would be trickier:
Similar to what Matthew said, I often find that it's cleaner to make a
seperate class with a "data" (or somesuch) property that lazily loads the
numpy array.
For example, something like:
class DataFormat(object):
def __init__(self, filename):
self.filename = filename
for key,
Hi,
On Tue, Jul 26, 2011 at 5:11 PM, Craig Yoshioka wrote:
> I want to subclass ndarray to create a class for image and volume data, and
> when referencing a file I'd like to have it load the data only when accessed.
> That way the class can be used to quickly set and manipulate header values,
I want to subclass ndarray to create a class for image and volume data, and
when referencing a file I'd like to have it load the data only when accessed.
That way the class can be used to quickly set and manipulate header values, and
won't load data unless necessary. What is the best way to do
On Mon, Jul 25, 2011 at 2:29 PM, Charles R Harris
wrote:
>> Why? Users can simply do
>>
>> import numpy.io as npyio ?
>>
>
> It caused problems with 2to3 for one thing because it was getting imported
> as io in the package. It is just a bad idea to shadow python modules and
> best avoided.
Call
On 7/25/11 1:00 PM, Ian Stokes-Rees wrote:
> As best I can tell, I have Python 2.7.2 for my system Python:
>
> [ijstokes@moose ~]$ python -V
> Python 2.7.2
>
> [ijstokes@moose ~]$ which python
> /Library/Frameworks/Python.framework/Versions/2.7/bin/python
yup -- that is probably the python.org pyt
The current release, version 0.82.0, contains fixes for two major bugs.
The first bug is a show-stopping segmentation fault under some versions
of Linux and arises from a variable type mismatch in calls to the numpy
api. The second bug causes bad spectral values at the Nyquist frequency
for serie
