subclassing ndarray : i want slice to return ndarray, not subclass
Hello, all my question is in the title. More precisely, like in scipy doc i try this : ====================================== import sys import numpy as np class ArrayChild(np.ndarray): def __new__(cls, array, info=None): obj = np.asarray(array).view(cls) obj.info = info return obj def __array_finalize__(self, obj): print>>sys.stderr, "__array_finalize__" if obj is None: return self.info = getattr(obj, 'info', None) if __name__=='__main__': a = np.arange(6) a.shape=2,3 a_child = ArrayChild(a) for x in a_child : print>>sys.stderr, x, type(x) ===================================== and i have the answer : ===================================== __array_finalize__ __array_finalize__ [0 1 2] <class '__main__.ArrayChild'> __array_finalize__ [3 4 5] <class '__main__.ArrayChild'> ===================================== but i want ===================================== __array_finalize__ __array_finalize__ [0 1 2] <class '__main__.np.ndarray'> __array_finalize__ [3 4 5] <class '__main__.np.ndarray'> ===================================== I've tried to redefine : __getslice__ like this, but it does not work. ===================================== def __getslice__(self, *args, **kwargs): return np.ndarray.__getslice__(self, *args, **kwargs) ===================================== Some idea ? Thanks to numpy/scipy team for that great job. ===================================== Pierre Puiseux Laboratoire de Mathématiques Appliquées Université de Pau et des Pays de l'Adour pierre.puiseux@univ-pau.fr http://www.univ-pau.fr/~puiseux
On 05/21/2012 11:56 AM, pierre puiseux UPPA wrote:
Hello, all my question is in the title. More precisely, like in scipy doc i try this : ====================================== importsys import numpy as np
class ArrayChild(np.ndarray):
Subclassing ndarray is a very tricky business -- I did it once and regretted having done it for years, because there's so much you can't do etc.. You're almost certainly better off with embedding an array as an attribute, and then forward properties etc. to it. But your answer: You want to override __getitem__, __getslice__ is deprecated in Python. Dag
def __new__(cls, array, info=None): obj = np.asarray(array).view(cls) obj.info = info return obj
def __array_finalize__(self, obj): print>>sys.stderr, "__array_finalize__" if obj is None: return self.info = getattr(obj, 'info', None)
if __name__=='__main__': a = np.arange(6) a.shape=2,3 a_child = ArrayChild(a) for x in a_child : print>>sys.stderr, x, type(x)
===================================== and i have the answer : ===================================== __array_finalize__ __array_finalize__ [0 1 2] <class '__main__.ArrayChild'> __array_finalize__ [3 4 5] <class '__main__.ArrayChild'>
===================================== but i want ===================================== __array_finalize__ __array_finalize__ [0 1 2] <class '__main__.np.ndarray'> __array_finalize__ [3 4 5] <class '__main__.np.ndarray'>
===================================== I've tried to redefine : __getslice__ like this, but it does not work. ===================================== def __getslice__(self, *args, **kwargs): return np.ndarray.__getslice__(self, *args, **kwargs)
On Mon, May 21, 2012 at 12:42 PM, Dag Sverre Seljebotn <d.s.seljebotn@astro.uio.no> wrote:
On 05/21/2012 11:56 AM, pierre puiseux UPPA wrote:
Hello, all my question is in the title. More precisely, like in scipy doc i try this : ====================================== importsys import numpy as np
class ArrayChild(np.ndarray):
Subclassing ndarray is a very tricky business -- I did it once and regretted having done it for years, because there's so much you can't do etc.. You're almost certainly better off with embedding an array as an attribute, and then forward properties etc. to it.
Yes, it's almost always the wrong thing...
But your answer: You want to override __getitem__, __getslice__ is deprecated in Python.
You have to override both, actually. After overriding __getitem__, then add def __getslice__(self, i, j): return self.__getitem__(slice(i, j)) or otherwise your __getitem__ will get skipped in some cases. The other option would be to just accept that your subclass will be returned from slices. If the only difference between your subclass and the base ndarray is that your arrays have an extra ".info" attribute, then you can just not define __array_finalize__ and check for the presence of a ".info" attribute instead of checking for the subclass directly. This depends on what your subclass is actually supposed to do, obviously. -- Nathaniel
First, my problem is : Imagine ArrayChild is a subclass of np.ndarray and 'ac' is an instance of ArrayChild. I call very often ac[i] or ac[1:12], which calls methods ArrayChild.__getislice__() or ArrayChild.__getiitem__() But for each call Python/NumPy create a new instance of ArrayChild, and calls my (very expansive) ArrayChild.__array_finalize__ I'd like to avoid this creation, and I want a more direct access to ArrayChild's slice or item Finally, i've found a solution (I'm not sure it's less expansive, anyway) : first, i create a ndarray view of my ArrayChild in __array_finalize__. where i add this line : ====================================== self.array_view = obj.view(np.ndarray) ====================================== Then i define methods ArrayChild.__getslice__ and ArrayChild.__getitem__ to call the getters of array_view instead of default getters : ====================================== def __getslice__(self, *args, **kwargs): return np.ndarray.__getslice__(self.array_view, *args, **kwargs) --------------- def __getitem__(self, *args, **kwargs): return np.ndarray.__getitem__(self.array_view, *args, **kwargs) --------------- ====================================== That's it, i have my results : subarray of ArrayChild are now np.ndarray s. ====================================== __array_finalize__ [0 1 2] <type 'numpy.ndarray'> [3 4 5] <type 'numpy.ndarray'> ====================================== The final test program is : ====================================== import numpy as np class ArrayChild(np.ndarray): def __new__(cls, array, info=None): obj = np.asarray(array).view(cls) obj.info = info return obj def __array_finalize__(self, obj): print>>sys.stderr, "__array_finalize__" if obj is None: return self.info = getattr(obj, 'info', None) self.array_view = obj.view(np.ndarray) def __getslice__(self, *args, **kwargs): return np.ndarray.__getslice__(self.array_view, *args, **kwargs) def __getitem__(self, *args, **kwargs): return np.ndarray.__getitem__(self.array_view, *args, **kwargs) if __name__=='__main__': a = np.arange(6) a.shape=2,3 a_child = ArrayChild(a) for x in a_child : print>>sys.stderr, x, type(x) ====================================== Le 21 mai 2012 à 11:56, pierre puiseux UPPA a écrit :
Hello, all my question is in the title. More precisely, like in scipy doc i try this : ====================================== import sys import numpy as np
class ArrayChild(np.ndarray):
def __new__(cls, array, info=None): obj = np.asarray(array).view(cls) obj.info = info return obj
def __array_finalize__(self, obj): print>>sys.stderr, "__array_finalize__" if obj is None: return self.info = getattr(obj, 'info', None)
if __name__=='__main__': a = np.arange(6) a.shape=2,3 a_child = ArrayChild(a) for x in a_child : print>>sys.stderr, x, type(x)
===================================== and i have the answer : ===================================== __array_finalize__ __array_finalize__ [0 1 2] <class '__main__.ArrayChild'> __array_finalize__ [3 4 5] <class '__main__.ArrayChild'>
===================================== but i want ===================================== __array_finalize__ __array_finalize__ [0 1 2] <class '__main__.np.ndarray'> __array_finalize__ [3 4 5] <class '__main__.np.ndarray'>
===================================== I've tried to redefine : __getslice__ like this, but it does not work. ===================================== def __getslice__(self, *args, **kwargs): return np.ndarray.__getslice__(self, *args, **kwargs)
===================================== Some idea ? Thanks to numpy/scipy team for that great job. =====================================
Pierre Puiseux Laboratoire de Mathématiques Appliquées Université de Pau et des Pays de l'Adour pierre.puiseux@univ-pau.fr http://www.univ-pau.fr/~puiseux
pierre puiseux pierre@puiseux.name http://puiseux.name
participants (4)
-
Dag Sverre Seljebotn -
Nathaniel Smith -
pierre puiseux -
pierre puiseux UPPA