masked_values(x, value, rtol=1e-05, atol=1e-08, copy=True, shrink=True)

Return a MaskedArray, masked where the data in array x are approximately equal to :None:None:`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 :None:None:`value` and the mask is set to nomask if possible.

Parameters

x : array_like

Array to mask.

value : float

Masking value.

rtol, atol : float, optional

Tolerance parameters passed on to isclose

copy : bool, optional

Whether to return a copy of x.

shrink : bool, optional

Whether to collapse a mask full of False to nomask .

Returns

result : MaskedArray

The result of masking x where approximately equal to :None:None:`value`.

Mask using floating point equality.

See Also

masked_equal

Mask where equal to a given value (integers).

masked_where

Mask where a condition is met.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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 :None:None:`mask` is set to nomask if possible.

This example is valid syntax, but we were not able to check execution

>>> ma.masked_values(x, 1.5)
masked_array(data=[1. , 1.1, 2. , 1.1, 3. ],
             mask=False,
       fill_value=1.5)

For integers, the fill value will be different in general to the result of masked_equal .

This example is valid syntax, but we were not able to check execution

>>> x = np.arange(5)
... x
array([0, 1, 2, 3, 4])

This example is valid syntax, but we were not able to check execution

>>> ma.masked_values(x, 2)
masked_array(data=[0, 1, --, 3, 4],
             mask=[False, False,  True, False, False],
       fill_value=2)

This example is valid syntax, but we were not able to check execution

>>> ma.masked_equal(x, 2)
masked_array(data=[0, 1, --, 3, 4],
             mask=[False, False,  True, False, False],
       fill_value=2)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.ma.masked_values numpy.ma.core.masked_values numpy.ma.core.masked_object numpy.ma.core.masked_equal numpy.ma.core.masked_where

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GitHub : /numpy/ma/core.py#2251
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