dask.array.ma.masked_where

dask.array.ma.masked_where¶

dask.array.ma.masked_where(condition, a)[source]¶

Mask an array where a condition is met.

This docstring was copied from numpy.ma.masked_where.

Some inconsistencies with the Dask version may exist.

Return a as an array masked where condition is True. Any masked values of a or condition are also masked in the output.

Parameters

conditionarray_like: Masking condition. When condition tests floating point values for equality, consider using masked_values instead.
aarray_like: Array to mask.
copybool (Not supported in Dask): If True (default) make a copy of a in the result. If False modify a in place and return a view.

Returns

resultMaskedArray: The result of masking a where condition 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 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',
            dtype='<U1')

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)

dask.array.ma.masked_values

dask.array.ma.nonzero