numpy.ma.masked_values#

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:

xarray_like: Array to mask.
valuefloat: Masking value.
rtol, atolfloat, optional: Tolerance parameters passed on to isclose
copybool, optional: Whether to return a copy of x.
shrinkbool, optional: Whether to collapse a mask full of False to nomask.

Returns:

resultMaskedArray: 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 as np
>>> 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, 2.1)
masked_array(data=[1. , 1.1, 2. , 1.1, 3. ],
             mask=False,
       fill_value=2.1)

Unlike masked_equal, masked_values can perform approximate equalities.

>>> ma.masked_values(x, 2.1, atol=1e-1)
masked_array(data=[1.0, 1.1, --, 1.1, 3.0],
             mask=[False, False,  True, False, False],
       fill_value=2.1)