I've cooked a very rudimentary implementation of what I would like to have, however I've got a small problem concerning array shape. The idea is to have a parent array (a group) that can be instantiated like a regular array and that build internally all the relevant contiguous child arrays. I would like those child arrays to not be reshapable (at least not without telling their parent first) and I naively overloaded the shape property but got a lot of problems with infinite recursion. Is there a way to do that ?

Nicolas

I put the code below (around 100 lines):

class array(np.ndarray):

def __new__(subtype, shape=(1,1), dtype=np.float32, order=None, group=None): obj = np.ndarray.__new__(subtype, shape=shape, dtype=dtype, order=order) obj._group = group return obj

def _get_group(self): return self._group or self def _set_group(self, group): if self.size == group.size: self.shape = group.shape self._group = group else: raise ValueError, \ 'shape mismatch: objects cannot be broadcast to a single shape' group = property(_get_group, _set_group, doc = '''Group to which this array belongs to''')

def _get_shape(self): return self.ctypes.shape def _set_shape(self, shape): if self.group == None: self.ctypes.shape = shape else: raise AttributeError, \ '''Cannot reshape a child array (''group'' is not None)''' # shape = property(_get_shape, _set_shape, doc='''c-types shape''')

class group(object): def __init__(self, shape, dtype=None, order=None): object.__setattr__(self,'_arrays', {}) self._shape = shape self._dtype = dtype if len(dtype) == 0: self._dtype = np.dtype('f0',dtype) for i in range(len(self._dtype)): name, dtype = self._dtype[i] self._arrays[name] = array(shape=shape,dtype=dtype,order=order)

def __getattr__(self, key): if key in self._arrays.keys(): return self._arrays[key] else: return object.__getattribute__(self, key) def __setattr__(self, key, value): if key in self._arrays.keys(): self._arrays[key][...] = value else: object.__setattr__(self, key, value)

def __getitem__(self, key): return self._arrays[key] def __setitem__(self, key, value): self._arrays[key][...] = value def __len__(self): return len(self._arrays[self._arrays.keys()[0]])

def _get_shape(self): return self._shape def _set_shape(self, shape): for key in self._arrays.keys(): self._arrays[key].shape = shape self._shape = shape shape = property(_get_shape, _set_shape)

def _get_dtype(self): return self._dtype def _set_dtype(self): raise AttributeError, \ '''attribute 'dtype' of 'group' objects is not writable''' dtype = property(_get_dtype, _set_dtype)

def _get_size(self): return self._arrays[self._arrays.keys()[0]].size def _set_size(self): raise AttributeError, \ '''attribute 'size' of 'group' objects is not writable''' size = property(_get_size, _set_size)

def __repr__(self): s = 'group(\n' for i in range(len(self._dtype)): name,dtype = self._dtype[i] t = "'%s': " % name a = repr(self._arrays[name]).replace('\n', '\n' +' '*len(t)) s += t+a+'\n' s += ')' return s

if __name__ == '__main__':

G = group((3,3), dtype = [('r',np.float32),('g',np.int32),('b',np.bool)]) G['r'] = G.g = G.b = 0 print G print G.r.shape = (9,1) print G.r.shape print G.shape

On Thu, 2009-07-30 at 20:01 +0200, Nicolas Rougier wrote:

Thanks for the quick answer. It makes sense. I will have to find some other way to do it then.

Nicolas

On 30 Jul, 2009, at 18:52 , David Cournapeau wrote:

...

On Fri, Jul 31, 2009 at 12:53 AM, Nicolas RougierNicolas.Rougier@loria.fr wrote:

...

Hello,

I've been using record arrays to create arrays with different types and since I'm doing a lot of computation on each of the different fields, the default memory layout does not serve my computations. Ideally, I would like to have record arrays where each field is a contiguous block of memory.

I don't think you can do it with record arrays: one of the fundamental design choice of numpy array layout is that the data pointer points to one block of N items, where each item is described by the dtype. To have contiguous layout for each member of the structured dtype, you need two arrays with the corresponding dtype.

cheers,

David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

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