numpy.ma.masked_where(condition, a, copy=True)
[source]
Mask an array where a condition is met.
Return a
as an array masked where condition
is True. Any masked values of a
or condition
are also masked in the output.
Parameters: |
condition : array_like Masking condition. When a : array_like Array to mask. copy : bool If True (default) make a copy of |
---|---|
Returns: |
result : MaskedArray The result of masking |
See also
masked_values
masked_equal
masked_not_equal
not
equal to a given value.masked_less_equal
masked_greater_equal
masked_less
masked_greater
masked_inside
masked_outside
masked_invalid
>>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_where(a <= 2, a) masked_array(data = [-- -- -- 3], mask = [ True True True False], fill_value=999999)
Mask array b
conditional on a
.
>>> b = ['a', 'b', 'c', 'd'] >>> ma.masked_where(a == 2, b) masked_array(data = [a b -- d], mask = [False False True False], fill_value=N/A)
Effect of the copy
argument.
>>> c = ma.masked_where(a <= 2, a) >>> c masked_array(data = [-- -- -- 3], mask = [ True True True False], fill_value=999999) >>> c[0] = 99 >>> c masked_array(data = [99 -- -- 3], mask = [False True True False], fill_value=999999) >>> a array([0, 1, 2, 3]) >>> c = ma.masked_where(a <= 2, a, copy=False) >>> c[0] = 99 >>> c masked_array(data = [99 -- -- 3], mask = [False True True False], fill_value=999999) >>> a array([99, 1, 2, 3])
When condition
or a
contain masked values.
>>> a = np.arange(4) >>> a = ma.masked_where(a == 2, a) >>> a masked_array(data = [0 1 -- 3], mask = [False False True False], fill_value=999999) >>> b = np.arange(4) >>> b = ma.masked_where(b == 0, b) >>> b masked_array(data = [-- 1 2 3], mask = [ True False False False], fill_value=999999) >>> ma.masked_where(a == 3, b) masked_array(data = [-- 1 -- --], mask = [ True False True True], fill_value=999999)
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Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.ma.masked_where.html