dask.array.ma.masked_values
dask.array.ma.masked_values¶
- dask.array.ma.masked_values(x, value, rtol=1e-05, atol=1e-08, shrink=True)[source]¶
Mask using floating point equality.
This docstring was copied from numpy.ma.masked_values.
Some inconsistencies with the Dask version may exist.
Return a MaskedArray, masked where the data in array x are approximately equal to value, determined using isclose. The default tolerances for masked_values are the same as those for isclose.
For integer types, exact equality is used, in the same way as masked_equal.
The fill_value is set to value and the mask is set to
nomask
if possible.- Parameters
- xarray_like
Array to mask.
- valuefloat
Masking value.
- rtol, atolfloat, optional
Tolerance parameters passed on to isclose
- copybool, optional (Not supported in Dask)
Whether to return a copy of x.
- shrinkbool, optional
Whether to collapse a mask full of False to
nomask
.
- Returns
- resultMaskedArray
The result of masking x where approximately equal to value.
See also
masked_where
Mask where a condition is met.
masked_equal
Mask where equal to a given value (integers).
Examples
>>> import numpy as np >>> import numpy.ma as ma >>> x = np.array([1, 1.1, 2, 1.1, 3]) >>> ma.masked_values(x, 1.1) masked_array(data=[1.0, --, 2.0, --, 3.0], mask=[False, True, False, True, False], fill_value=1.1)
Note that mask is set to
nomask
if possible.>>> ma.masked_values(x, 2.1) masked_array(data=[1. , 1.1, 2. , 1.1, 3. ], mask=False, fill_value=2.1)
Unlike masked_equal, masked_values can perform approximate equalities.
>>> 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=2.1)