make_mask(m, copy=False, shrink=True, dtype=<class 'numpy.bool_'>)

Return m as a boolean mask, creating a copy if necessary or requested. The function can accept any sequence that is convertible to integers, or nomask . Does not require that contents must be 0s and 1s, values of 0 are interpreted as False, everything else as True.

Parameters

m : array_like

Potential mask.

copy : bool, optional

Whether to return a copy of m (True) or m itself (False).

shrink : bool, optional

Whether to shrink m to nomask if all its values are False.

dtype : dtype, optional

Data-type of the output mask. By default, the output mask has a dtype of MaskType (bool). If the dtype is flexible, each field has a boolean dtype. This is ignored when m is nomask , in which case nomask is always returned.

Returns

result : ndarray

A boolean mask derived from m.

Create a boolean mask from an array.

Examples

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

>>> import numpy.ma as ma
... m = [True, False, True, True]
... ma.make_mask(m)
array([ True, False,  True,  True])

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

>>> m = [1, 0, 1, 1]
... ma.make_mask(m)
array([ True, False,  True,  True])

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

>>> m = [1, 0, 2, -3]
... ma.make_mask(m)
array([ True, False,  True,  True])

Effect of the :None:None:`shrink` parameter.

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

>>> m = np.zeros(4)
... m
array([0., 0., 0., 0.])

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

>>> ma.make_mask(m)
False

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

>>> ma.make_mask(m, shrink=False)
array([False, False, False, False])

Using a flexible dtype .

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

>>> m = [1, 0, 1, 1]
... n = [0, 1, 0, 0]
... arr = []
... for man, mouse in zip(m, n):
...     arr.append((man, mouse))
... arr
[(1, 0), (0, 1), (1, 0), (1, 0)]

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

>>> dtype = np.dtype({'names':['man', 'mouse'],
...                   'formats':[np.int64, np.int64]})
... arr = np.array(arr, dtype=dtype)
... arr
array([(1, 0), (0, 1), (1, 0), (1, 0)],
      dtype=[('man', '<i8'), ('mouse', '<i8')])

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

>>> ma.make_mask(arr, dtype=dtype)
array([(True, False), (False, True), (True, False), (True, False)],
      dtype=[('man', '|b1'), ('mouse', '|b1')])

See :

Back References

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

numpy.ma.core.make_mask_none

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GitHub : /numpy/ma/core.py#1548
type: <class 'function'>
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