# numpy.ma.masked_values

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, determined using `isclose`. The default tolerances for `masked_values` are the same as those for `isclose`.

  For integer types, exact equality is used, in the same way as `masked_equal`.

  The fill_value is set to value and the mask is set to `nomask` if possible.

  Parameters
    x array_like

      Array to mask.

    value float

      Masking value.

    rtol, atol float, optional

      Tolerance parameters passed on to `isclose`

    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=2)

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