numpy.ma.masked_invalid#

ma.masked_invalid(a, copy=True)[source]#

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

masked_where: Mask where a condition is met.

Examples

>>> import numpy.ma as ma
>>> a = np.arange(5, dtype=float)
>>> a[2] = np.nan
>>> a[3] = np.inf
>>> 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],
       fill_value=1e+20)