[Numpy-discussion] [SciPy-dev] SciPy Sprint results
Pierre GM
pgmdevlist@gmail....
Thu Dec 20 17:52:38 CST 2007
> > * cumsum(cumprod) works as if the _data array was filled with 0 (1). The
> > mask is preserved, but not updated. (the output of numpy.core.ma has
> > nomask).
>
> I don't understand what you mean here. So, the mask effectively
> removes those elements from the sum(product) computation? What does it
> mean that the mask is not updated?
Quick example:
>>> x= masked_array([1,2,3],mask=[0,1,0])
>>> x
masked_array(data = [1 -- 3],
mask = [False True False],
fill_value=999999)
>>> x.cumsum()
masked_array(data = [1 -- 4],
mask = [False True False],
fill_value=999999)
So, cumsum works as if the masked values were 0. The mask stays the same as it
was initially (that's what I meant by "mask not updated"). An alternative
would be to set the mask to True for all the values past the first masked:
with our example, we would have:
masked_array(data = [1 -- --],
mask = [False True True],
fill_value=999999)
I prefer the first version (the one that is currently implemented).
> > * bool(x) raises a ValueError, as it does for ndarrays.
>
> What does bool(x) raise for numpy.core.ma.
True
> If we can document exactly what the compatibility issues are (and it
> looks like we are almost there), we should move forward.
OK, I'll take care of that this week-end. Stefan, feel free to beat me to
it...
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