I have a 2D NumPy array and would like to replace all values in it greater than or equal to a threshold T with 255.0. To my knowledge, the most fundamental way would be:
shape = arr.shape
result = np.zeros(shape)
for x in range(0, shape[0]):
for y in range(0, shape[1]):
if arr[x, y] >= T:
result[x, y] = 255
What is the most concise and pythonic way to do this?
Is there a faster (possibly less concise and/or less pythonic) way to do this?
This will be part of a window/level adjustment subroutine for MRI scans of the human head. The 2D numpy array is the image pixel data.
I think both the fastest and most concise way to do this is to use NumPy's built-in Fancy indexing. If you have an ndarray named arr, you can replace all elements >255 with a value x as follows:
arr[arr > 255] = x
I ran this on my machine with a 500 x 500 random matrix, replacing all values >0.5 with 5, and it took an average of 7.59ms.
In [1]: import numpy as np
In [2]: A = np.random.rand(500, 500)
In [3]: timeit A[A > 0.5] = 5
100 loops, best of 3: 7.59 ms per loop
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