# numpy.ma.masked_invalid

ma.masked_invalid(a, copy=True)

  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.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)