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 <matthew.brett@gmail.com> wrote:
Hi,

On Tue, Jul 26, 2011 at 5:11 PM, Craig Yoshioka <craigyk@me.com> 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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