`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. result : MaskedArray The result of masking a where condition is True.

`masked_values`
`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`
`masked_outside`
`masked_invalid`
Mask invalid values (NaNs or infs).

Examples

```>>> import numpy.ma as ma
>>> a = np.arange(4)
>>> a
array([0, 1, 2, 3])
masked_array(data = [-- -- -- 3],
mask = [ True  True  True False],
fill_value=999999)
```

Mask array b conditional on a.

```>>> b = ['a', 'b', 'c', 'd']
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)