fix_invalid(a, fill_value=None)
This docstring was copied from numpy.ma.fix_invalid.
Some inconsistencies with the Dask version may exist.
Invalid data means values of :None:None:`nan`, :None:None:`inf`, etc.
A copy is performed by default.
Input array, a (subclass of) ndarray.
Mask. Must be convertible to an array of booleans with the same shape as :None:None:`data`. True indicates a masked (i.e. invalid) data.
Whether to use a copy of a (True) or to fix a in place (False). Default is True.
Value used for fixing invalid data. Default is None, in which case the a.fill_value
is used.
The input array with invalid entries fixed.
Return input with invalid data masked and replaced by a fill value.
>>> x = np.ma.array([1., -1, np.nan, np.inf], mask=[1] + [0]*3) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... x # doctest: +SKIP masked_array(data=[--, -1.0, nan, inf], mask=[ True, False, False, False], fill_value=1e+20)
>>> np.ma.fix_invalid(x) # doctest: +SKIP masked_array(data=[--, -1.0, --, --], mask=[ True, False, True, True], fill_value=1e+20)This example is valid syntax, but we were not able to check execution
>>> fixed = np.ma.fix_invalid(x) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... fixed.data # doctest: +SKIP array([ 1.e+00, -1.e+00, 1.e+20, 1.e+20])
>>> x.data # doctest: +SKIP array([ 1., -1., nan, inf])See :
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