[Some background: we're talking about numpy.lib.recfunctions, a set of
functions to manipulate structured arrays]
Ryan,
If the two files have the same structure, you can use that fact and
specify the dtype of the output directly with the dtype parameter of
mafromtxt. That way, you're sure that the two arrays will have the
same dtype. If you don't know the structure beforehand, you could try
to load one array and use its dtype as input of mafromtxt to load the
second one.
Now, we could also try to modify stack_arrays so that it would take
the largest dtype when several fields have the same name. I'm not
completely satisfied by this approach, as it makes dtype conversions
under the hood. Maybe we could provide the functionality as an option
(w/ a forced_conversion boolean input parameter) ?
I'm a bit surprised by the error message you get. If I try:
>>> a = ma.array([(1,2,3)], mask=[(0,1,0)], dtype=[('a',int),
('b',bool), ('c',float)])
>>> b = ma.array([(4, 5, 6)], dtype=[('a', int), ('b', float), ('c',
float)])
>>> test = np.stack_arrays((a, b))
I get a TypeError instead (the field 'b' hasn't the same type in a and
b). Now, I get the 'two fields w/ the same name' when I use
np.merge_arrays (with the flatten option). Could you send a small
example ?
>
> P.S. Thanks so much for your work on putting those utility
> functions in
> recfunctions.py It makes it so much easier to have these functions
> available in
> the library itself rather than needing to reinvent the wheel over
> and over.
Indeed. Note that most of the job had been done by John Hunter and the
matplotlib developer in their matplotlib.mlab module, so you should
thank them and not me. I just cleaned up some of the functions.
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