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# numpy.ma.masked_invalid

`numpy.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=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)

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