`numpy.ma.core:make_mask`

source: build-install/usr/lib/python3.14/site-packages/numpy/ma/core.py :1596

Signature

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

Summary

Create a boolean mask from an array.

Extended Summary

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.

Examples

import numpy as np
import numpy.ma as ma
m = [True, False, True, True]
ma.make_mask(m)
m = [1, 0, 1, 1]
ma.make_mask(m)
m = [1, 0, 2, -3]
ma.make_mask(m)

Effect of the `shrink` parameter.

m = np.zeros(4)
m
ma.make_mask(m)
ma.make_mask(m, shrink=False)

Using a flexible `dtype`.

m = [1, 0, 1, 1]
n = [0, 1, 0, 0]
arr = []
for man, mouse in zip(m, n):
    arr.append((man, mouse))
arr
dtype = np.dtype({'names':['man', 'mouse'],
                  'formats':[np.int64, np.int64]})
arr = np.array(arr, dtype=dtype)
arr
ma.make_mask(arr, dtype=dtype)

Aliases

numpy.ma.make_mask