-
numpy.ma.masked_where(condition, a, copy=True)
[source] -
Mask an array where a condition is met.
Return
a
as an array masked wherecondition
is True. Any masked values ofa
orcondition
are also masked in the output.Parameters: condition : array_like
Masking condition. When
condition
tests floating point values for equality, consider usingmasked_values
instead.a : array_like
Array to mask.
copy : bool
If True (default) make a copy of
a
in the result. If False modifya
in place and return a view.Returns: result : MaskedArray
The result of masking
a
wherecondition
is True.See also
-
masked_values
- Mask using floating point equality.
-
masked_equal
- Mask where equal to a given value.
-
masked_not_equal
- Mask where
not
equal to a given value. -
masked_less_equal
- Mask where less than or equal to a given value.
-
masked_greater_equal
- Mask where greater than or equal to a given value.
-
masked_less
- Mask where less than a given value.
-
masked_greater
- Mask where greater than a given value.
-
masked_inside
- Mask inside a given interval.
-
masked_outside
- Mask outside a given interval.
-
masked_invalid
- Mask invalid values (NaNs or infs).
Examples
>>> 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 ona
.>>> 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
ora
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)
-
numpy.ma.masked_where()
2017-01-10 18:15:37
Please login to continue.