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I have a 3 dimensional uint8 numpy array. I want to increase all the elements by 1 which has these values: 0,1,3,16,17,18. And set others to 0. I have tried using traditional for loops which is really really slow.

Tried using python indexing techniques to set other values to 255 (which will later be changed to 0)

mask[(mask[:,:,:] != 0) & (mask[:,:,:] != 1) & (mask[:,:,:] != 3) & (mask[:,:,:] != 16) & (mask[:,:,:] != 17) & (mask[:,:,:] != 18)] = 255

Then increasing the values by 1

mask[(mask[:,:,:] == 0) & (mask[:,:,:] == 1) & (mask[:,:,:] == 3) & (mask[:,:,:] == 16) & (mask[:,:,:] == 17) & (mask[:,:,:] == 18)] = mask[:,:,:]+1 #gives me error

Then changing the 255 to 0

mask[mask[:,:,:] == 255] = 0

The middle operation gives me error

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

How to do that efficiently. Preferably in one go. Not 3 times iterating the array.

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1 Answer 1

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Use np.isin and then boolean indexing to change values as:

arr = np.arange(0,255)
mask = np.isin(arr,[0,1,3,16,17,18])

arr[mask]+=1
arr[~mask]=0

Or use np.where as:

arr = np.where(np.isin(arr,[0,1,3,16,17,18]),arr+1,0)

arr
array([ 1,  2,  0,  4,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 17,
       18, 19,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0])
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2 Comments

That's just marvelous! I was converting and writing the arrays to disk like 50 arrays per seconds using traditional for loop. Now its more like 500 per seconds or more!
@Tahlil Numpy array methods are always fast. We need to know how to use it and where to use it properly.

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