I think it is unlikely to be possible (or even desirable?) to completely
emulate the behaviour of numpy arrays in flex arrays. The underlying data
structure and behaviour of numpy arrays is significantly different to flex
arrays. For example, when you do a slice in numpy you don't get a copy, but
a 'view' - i.e. you get a new object, but that object points to the same
underlying data in memory as the original array:
http://www.scipy.org/Tentative_NumPy_Tutorial#head-4306be2ef34ad134f9fa0b87d...
When I implemented the multidimensional slicing for flex arrays, I only did
so because I required that functionality in code I was developing, not for
the fun or challenge of emulating numpy arrays. I am unlikely to have time
to add numpy-style assignment to slices in the near future, but if James,
or indeed anyone else, requires and is willing to implement such behaviour,
I'm sure Nat can grant you svn commit access in order to make the changes
you require (providing that the changes play well with the rest of the
cctbx code and Phenix, etc.).
Cheers,
Richard
On 11 April 2013 10:08, James Stroud
On Apr 11, 2013, at 9:18 AM, Luc Bourhis wrote:
On 11 Apr 2013, at 09:49, James Stroud wrote:
It seems that flex arrays do not support slice assignment:
it would not be too difficult to code 1-dimensional slice assignment but ...
On 11 Apr 2013, at 10:24, [email protected] wrote:
[...]
array[k,:,:] = image.as_numpy_array()[y0:y1, x0:x1]
that's way more difficult!
In python this type of magic is fairly straightforward because the slice is just a tuple:
py> class Example(object): ... def __setitem__(self, i, v): ... print "setting %s to %s" % (i, v) ... py> e = Example() py> e[5:6, 4, 8:9, "bob"] = 2 setting (slice(5, 6, None), 4, slice(8, 9, None), 'bob') to 2
This should actually be the case on the C side, too, not that it wouldn't take a few lines of code to build an iterator based on the slice.
But as a way to circumvent all of this magic with slices (which would be very cool, by the way), it might be easiest for the programmer (and somewhat intuitive for those familiar with flex) just to add a signature to set_selected() that uses a flex.grid to represent a slice because flex.grid already encapsulates the most useful aspects of slicing semantics.
Here is a prototype for the suggested behavior in python using ndarray as a back-end:
class Prototype(object): def __init__(self, data): self.data = numpy.array(data) def __getitem__(self, i): if isinstance (i, flex.grid): slices = [slice(*t) for t in zip(i.origin(), i.last())] return self.__class__(self.data[slices]) else: return self.__class__(self.data[i]) def __setitem__(self, i, v): if isinstance (i, flex.grid): slices = [slice(*t) for t in zip(i.origin(), i.last())] self.data[slices] = v else: self.data[i] = v def __repr__(self): return repr(self.data)
And here it is in action:
py> grd = flex.grid((1, 1), (3, 3)) # <== selects [1:3, 1:3] py> ary = Prototype(numpy.arange(25).reshape((5,5))) py> ary array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) py> ary[grd] array([[ 6, 7], [11, 12]]) py> ary[grd] = [[41, 42], [43, 44]] py> ary[grd] array([[41, 42], [43, 44]]) py> ary array([[ 0, 1, 2, 3, 4], [ 5, 41, 42, 8, 9], [10, 43, 44, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]])
James
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