Convert a list to a numpy mask array

Question

Given list like indice = [1, 0, 2] and dimension m = 3, I want to get the mask array like this

>>> import numpy as np
>>> mask_array = np.array([ [1, 1, 0], [1, 0, 0], [1, 1, 1] ])  
>>> mask_array
  [[1, 1, 0],    
   [1, 0, 0],
   [1, 1, 1]]

Given m = 3, so the axis=1 of mask_array is 3, the row of mask_array indicates the length of indice.

For converting the indice to mask_array, the rule is marking the item values whose index is less or equal to the each entry of inside to value 1. For example, indice[0]=1, so the output is [1, 1, 0], given dimension is 3.

In NumPy, are there any APIs which can be used to do this?

juanpa.arrivillaga · Accepted Answer · 2020-07-20 05:38:34Z

1

Sure, just use broadcasting with arange(m), make sure to use an np.array for the indices, not a list...

>>> indice = [1, 0, 2]
>>> m = 3
>>> np.arange(m) <= np.array(indice)[..., None]
array([[ True,  True, False],
       [ True, False, False],
       [ True,  True,  True]])

Note, the [..., None] just reshapes the indices array so that the broadcasting works like we want, like this:

>>> indices = np.array(indice)
>>> indices
array([1, 0, 2])
>>> indices[...,None]
array([[1],
       [0],
       [2]])

edited Jul 20, 2020 at 5:38

answered Jul 20, 2020 at 5:28

juanpa.arrivillaga

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1 Comment

GoingMyWay Over a year ago

I cannot stop accepting your answer, brilliant idea

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