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 formasked_values
are the same as those forisclose
.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.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)