numpy.ma.masked_values

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

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)