diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/ma/core.py | 28 |
1 files changed, 11 insertions, 17 deletions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py index 91309bb3d..be213ebf3 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -2081,9 +2081,11 @@ def masked_equal(x, value, copy=True): """ Mask an array where equal to a given value. - This function is a shortcut to ``masked_where``, with - `condition` = (x == value). For floating point arrays, - consider using ``masked_values(x, value)``. + Return a MaskedArray, masked where the data in array `x` are + equal to `value`. The fill_value of the returned MaskedArray + is set to `value`. + + For floating point arrays, consider using ``masked_values(x, value)``. See Also -------- @@ -2303,25 +2305,17 @@ def masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True): Note that `mask` is set to ``nomask`` if possible. - >>> ma.masked_values(x, 1.5) + >>> ma.masked_values(x, 2.1) masked_array(data=[1. , 1.1, 2. , 1.1, 3. ], mask=False, - fill_value=1.5) + fill_value=2.1) - For integers, the fill value will be different in general to the - result of ``masked_equal``. + Unlike `masked_equal`, `masked_values` can perform approximate equalities. - >>> x = np.arange(5) - >>> x - array([0, 1, 2, 3, 4]) - >>> ma.masked_values(x, 2) - masked_array(data=[0, 1, --, 3, 4], + >>> ma.masked_values(x, 2.1, atol=1e-1) + masked_array(data=[1.0, 1.1, --, 1.1, 3.0], 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=2) + fill_value=2.1) """ xnew = filled(x, value) |