numpy.ma.masked_values()

numpy.ma.masked_values(x, value, rtol=1e-05, atol=1e-08, copy=True, shrink=True) [source]

Mask using floating point equality.

Return a MaskedArray, masked where the data in array x are approximately equal to value, i.e. where the following condition is True

(abs(x - value) <= atol+rtol*abs(value))

The fill_value is set to value and the mask is set to nomask if possible. For integers, consider using masked_equal.

Parameters:

Parameters:	x : array_like Array to mask. value : float Masking value. rtol : float, optional Tolerance parameter. atol : float, optional Tolerance parameter (1e-8). 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 `value`.

x : array_like

Array to mask.

value : float

Masking value.

rtol : float, optional

Tolerance parameter.

atol : float, optional

Tolerance parameter (1e-8).

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 value.

Examples

>>> 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, 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.

>>> x = np.arange(5)
>>> x
array([0, 1, 2, 3, 4])
>>> ma.masked_values(x, 2)
masked_array(data = [0 1 -- 3 4],
      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=999999)

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.ma.masked_values.html

doc_NumPy

2025-01-10 15:47:30

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