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
, i.e. where the following condition is True
(abs(x - value) <= atol+rtol*abs(value))
The fill_value is set to value
and the mask is set to nomask
if possible. For integers, consider using masked_equal
.
Parameters: |
x : array_like Array to mask. value : float Masking value. rtol : float, optional Tolerance parameter. atol : float, optional Tolerance parameter (1e-8). copy : bool, optional Whether to return a copy of shrink : bool, optional Whether to collapse a mask full of False to |
---|---|
Returns: |
result : MaskedArray The result of masking |
See also
masked_where
masked_equal
>>> 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=999999)
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https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.ma.masked_values.html