numpy.ma.masked_values — NumPy v1.11 Manual (original) (raw)
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 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. |
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=999999)