numpy.ma.masked_invalid¶

numpy.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=np.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],
      fill_value=1e+20)