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:
image3 = Image(image1)
image3.data = ( image1.data + 19.0 ) * image2.data
vs.
image3 = ( image1 + 19 ) * image2
I suppose option A isn't that bad though and getting lazy loading would be very
straightforward....
--
On a side note, I prefer this construct for lazy operations... curious to see
what people's reactions are, ie: that's horrible!
class lazy_property(object):
'''
meant to be used for lazy evaluation of object attributes.
should represent non-mutable return value, as whatever is returned replaces
itself permanently.
'''
def __init__(self,fget):
self.fget = fget
def __get__(self,obj,cls):
value = self.fget(obj)
setattr(obj,self.fget.func_name,value)
return value
class DataFormat(object):
def __init__(self,loader):
self.loadData = loader
@lazy_property
def data(self):
return self.loadData()
On Jul 26, 2011, at 5:45 PM, Joe Kington wrote:
> 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, value in self._read_header().iteritems():
> setattr(self, key, value)
>
> @property
> def data(self):
> try:
> return self._data
> except AttributeError:
> self._data = self._read_data()
> return self._data
>
> Hope that helps,
> -Joe
>
> On Tue, Jul 26, 2011 at 4:15 PM, Matthew Brett <[email protected]>
> wrote:
> Hi,
>
> On Tue, Jul 26, 2011 at 5:11 PM, Craig Yoshioka <[email protected]> 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, and won't load data unless necessary. What is the best way
> > to do this? Are there any hooks I can use to load the data when an array's
> > values are first accessed or manipulated? I tried some trickery with
> > __array_interface__ but couldn't get it to work very well. Should I just
> > use a memmapped array, and give up on a purely 'lazy' approach?
>
> What kind of images are you loading? We do lazy loading in nibabel,
> for medical image type formats:
>
> http://nipy.sourceforge.net/nibabel/
>
> - but our images _have_ arrays and headers, rather than (appearing to
> be) arrays. Thus something like:
>
> import nibabel as nib
>
> img = nib.load('my_image.img')
> # data not loaded at this point
> data = img.get_data()
> # data loaded now. Maybe memmapped if the format allows
>
> If you think you might have similar needs, I'd be very happy to help
> you get going in nibabel...
>
> Best,
>
> Matthew
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