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Mask an array where invalid values occur (NaNs or infs).

This function is a shortcut to masked_where, with condition = ~(np.isfinite(a)). Any pre-existing mask is conserved. Only applies to arrays with a dtype where NaNs or infs make sense (i.e. floating point types), but accepts any array_like object.

See also


Mask where a condition is met.


>>> import as ma
>>> a = np.arange(5, dtype=float)
>>> a[2] = np.NaN
>>> a[3] = np.PINF
>>> a
array([ 0.,  1., nan, inf,  4.])
>>> ma.masked_invalid(a)
masked_array(data=[0.0, 1.0, --, --, 4.0],
             mask=[False, False,  True,  True, False],