On Feb 16, 2008 3:21 PM, Pierre GM <[EMAIL PROTECTED]> wrote:
> > Can I safely carry around the data, mask and MaskedArray? I'm
> > considering working along the lines of the following conceptual
> > outline:
>
> That depends a lot on what calculate_results does, and whether you update the
> arrays
> Can I safely carry around the data, mask and MaskedArray? I'm
> considering working along the lines of the following conceptual
> outline:
That depends a lot on what calculate_results does, and whether you update the
arrays in place or not.
> d = numpy.array(shape, dtype)
> m = numpy.array(sh
On Feb 16, 2008 12:25 PM, Pierre GM <[EMAIL PROTECTED]> wrote:
> Alexander,
> You get the gist here: process your _data and _mask separately and recombine
> them into a MaskedArray at the end. That way, you'll skip most of the
> overhead costs brought by some tests in the package (in __getitem__,
>
Alexander,
You get the gist here: process your _data and _mask separately and recombine
them into a MaskedArray at the end. That way, you'll skip most of the
overhead costs brought by some tests in the package (in __getitem__,
__setitem__...).
___
Nump
In part of some code I'm rewriting from carrying around a data and
mask array to using MaskedArray, I read data into an array from an
input stream. By its nature this a "one at a time" process, so it is
basically a loop over assigning single elements (in no predetermined
order) of already allocated
