Improve the execution speed using Numpy

Surabhi Amit Chembra

I have the following for loop and want to use numpy vectorizing, boolean, mask arrays etc to improve the execution speed. arr is a numpy array

def translate(arr, x=0):
    arr1 = arr.copy()
    for i in range(arr1.shape[0]):
        for j in range(arr1.shape[1]):
            if i + x < arr1.shape[0] and i + x > 0:
                arr1[i,j] = arr[i+x,j]
            if i + x < 0 or i + x > arr1.shape[0]:
                arr1[i,j] = 255
    return arr1

Any help or suggestions will be much appreciated.

Edit: instead of 255, when (i+x) <0, it should be arr[i,j]

def translate(arr, x=0):
    arr1 = arr.copy()
    for i in range(arr1.shape[0]):
        for j in range(arr1.shape[1]):
            if i + x < arr1.shape[0] and i + x > 0:
                arr1[i,j] = arr[i+x,j]
            if i + x < 0 or i + x > arr1.shape[0]:
                arr1[i,j] = arr[i,j]
    return arr1

R.yan

Since @Paul Panzer solution didn't give out the same output as your function, I revised on his work by trying boolean array. Hopefully its what you want.

Code:

def my_translate(arr, x=0):
    arr = arr.copy()
    if x == 0:
        return arr.copy()
    elif x > 0:
        replacement1 = np.zeros(arr.shape)
        replacement1[:-x] = arr[x:]
    else:
        replacement1 = np.zeros(arr.shape)
        replacement1[-x:] = arr[:x]
    replacement2 = np.zeros(arr.shape)+255 # Array filled with 255 for second logic
    l = [np.repeat(i,arr.shape[1]) for i in range(arr.shape[0])]
    firstlooplogic1 = np.asarray(l)+x < arr.shape[0]
    firstlooplogic2 = np.asarray(l)+x > 0
    secondlooplogic1 = np.asarray(l)+x > arr.shape[0]
    secondlooplogic2 = np.asarray(l)+x < 0

    part1logic = np.logical_and(firstlooplogic1,firstlooplogic2)
    part2logic = np.logical_or(secondlooplogic1,secondlooplogic2)

    part1 = part1logic*replacement1
    part2 = part2logic*replacement2
    part3 = ((part1 == 0) * (part2 == 0)) * arr       
    return (part1 + part2 +part3).astype(arr.dtype)

Result:

arr = 3 - np.maximum.outer(*2*(np.abs(np.arange(-3, 4)),))
output1 = my_translate(arr,-2) #output from my function
output2 = translate(arr,-2)    #output from your function above
np.array_equal(output1,output2)
>Out[10]: True

Basically, I just break down your nested for-loop into boolean array and do the operation.

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