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

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`. result : MaskedArray The result of masking x where approximately equal to value.

`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])
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. ],
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])
masked_array(data = [0 1 -- 3 4],
mask = [False False  True False False],
fill_value=2)
masked_array(data = [0 1 -- 3 4],
mask = [False False  True False False],
fill_value=999999)
```