numpy.ma.masked_where

numpy.ma.masked_where(condition, a, copy=True)[源代码]

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 condition tests floating point values for equality, consider using masked_values instead.

a : array_like

Array to mask.

copy : bool

If True (default) make a copy of a in the result. If False modify a in place and return a view.

Returns:
result : MaskedArray

The result of masking a where condition is True.

参见

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 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)