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# numpy.ma.masked_values

`numpy.ma.``masked_values`(x, value, rtol=1e-05, atol=1e-08, copy=True, shrink=True)

  Mask using floating point equality.

  Return a MaskedArray, masked where the data in array x are approximately equal to value, i.e. where the following condition is True

  (abs(x - value) <= atol+rtol*abs(value))

  The fill_value is set to value and the mask is set to `nomask` if possible. For integers, consider using `masked_equal`.

  Parameters: | x : array_like Array to mask. value : float Masking value. rtol : float, optional Tolerance parameter. atol : float, optional Tolerance parameter (1e-8). copy : bool, optional Whether to return a copy of x. shrink : bool, optional Whether to collapse a mask full of False to `nomask`.
  Returns: | result : MaskedArray The result of masking x where approximately equal to value.

  See also

  `masked_where`
    Mask where a condition is met.
  `masked_equal`
    Mask where equal to a given value (integers).

  Examples

      >>> import numpy.ma as ma
      >>> x = np.array([1, 1.1, 2, 1.1, 3])
      >>> ma.masked_values(x, 1.1)
      masked_array(data = [1.0 -- 2.0 -- 3.0],
            mask = [False  True False  True False],
            fill_value=1.1)

  Note that mask is set to `nomask` if possible.

      >>> ma.masked_values(x, 1.5)
      masked_array(data = [ 1.   1.1  2.   1.1  3. ],
            mask = False,
            fill_value=1.5)

  For integers, the fill value will be different in general to the result of `masked_equal`.

      >>> x = np.arange(5)
      >>> x
      array([0, 1, 2, 3, 4])
      >>> ma.masked_values(x, 2)
      masked_array(data = [0 1 -- 3 4],
            mask = [False False  True False False],
            fill_value=2)
      >>> ma.masked_equal(x, 2)
      masked_array(data = [0 1 -- 3 4],
            mask = [False False  True False False],
            fill_value=999999)