On Friday 19 September 2008 11:36:17 Alan G Isaac wrote:
> On 9/19/2008 11:09 AM Stefan Van der Walt apparently wrote:
> > Masked arrays. Using NaN's for missing values is dangerous. You may
> > do some operation, which generates invalid results, and then you have
> > a mixed bag of missing and invalid values.
>
> That rather evades my full question, I think?
>
> In the case I mentioned,
> I am filling an array inside a loop,
> and the possible fill values are not constrained.
> So I cannot mask based on value,
> and I cannot mask based on position
> (at least until after the computations are complete).
No, but you may do the opposite: just start with an array completely masked,
and unmasked it as you need:
Say, you have 4x5 array, and want to unmask (0,0), (1,2), (3,4)
>>> a = ma.empty((4,5), dtype=float)
>>> a.mask=True
>>> a[0,0] = 0
>>> a[1,2]=1
>>> a[3,4]=3
>>>a
masked_array(data =
[[0.0 -- -- -- --]
[-- -- 1.0 -- --]
[-- -- -- -- --]
[-- -- -- -- 3.0]],
mask =
[[False True True True True]
[ True True False True True]
[ True True True True True]
[ True True True True False]],
fill_value=1e+20)
>>>a.max(axis=0)
masked_array(data = [0.0 -- 1.0 -- 3.0],
mask = [False True False True False],
fill_value=1e+20)
> It seems to me that there are pragmatic reasons
> why people work with NaNs for missing values,
> that perhaps shd not be dismissed so quickly.
> But maybe I am overlooking a simple solution.
nansomething solutions tend to be considerably faster, that might be one
reason. A lack of visibility of numpy.ma could be a second. In any case, I
can't but agree with other posters: a NaN in an array usually means something
went astray.
> PS I confess I do not understand NaNs.
> E.g., why could there not be a value np.miss
> that would be a NaN that represents a missing value?
You can't compare NaNs to anything. How do you know this np.miss is a masked
value, when np.sqrt(-1.) is NaN ?
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