On 6/23/06, Paul Dubois firstname.lastname@example.org wrote:
In the original MA in Numeric, I decided that to constantly check for masks that didn't actually mask anything was not a good idea. It punishes normal use with a very expensive check that is rarely going to be true.
If you are in a setting where you do not want this behavior, but instead want masks removed whenever possible, you may wish to wrap or replace things like masked_array so that they call make_mask with flag = 1:
y = masked_array(data, make_mask(maskdata, flag=1))
y will have no mask if maskdata is all false.
Hi Paul. If this is purely for optimisation, is there perhaps a better way to do so in which the optimisation is hidden? For example if the optimisation is in regard to the case in which none of the elements is masked, an alternative approach may be to make a subclass of ndarray and cache the result of the any method. e.g.
import numpy as N
def asmask(data): if isinstance(data, Mask): return data else: return Mask(data)
class Mask(N.ndarray): __array_priority__ = 10.0 def __new__(subtype, data): ret = N.array(data, N.bool_) return ret.view(Mask)
def __init__(self, data): self._any = None
def any(self): if self._any is None: self._any = N.ndarray.any(self) return self._any
def __setitem__(self, index, value): self._any = None N.ndarray.__setitem__(self, index, value)
The biggest problem I have with the current setup is the inconsistency between the behaviour of the array when the mask is nomask vs a boolarray with all False. Another example of this is when one changes the mask on an element. This is not possible when the mask is nomask
print N.version.version ma1 = N.ma.array([1,2,3], mask=[False, False, False]) print ma1.mask ma1.mask = True ma2 = N.ma.array([1,2,3], mask=False) print ma2.mask ma2.mask = True
----- output 0.9.8 [False False False] [False False True] False Traceback (most recent call last): File "D:\eclipse\Mask\src\mask__init__.py", line 111, in ? ma2.mask = True TypeError: object does not support item assignment