How to normalize a NumPy array in Python?

Question

After doing some processing on an audio or image array, it needs to be normalized within a range before it can be written back to a file. This can be done like so:

# Normalize audio channels to between -1.0 and +1.0
audio[:,0] = audio[:,0]/abs(audio[:,0]).max()
audio[:,1] = audio[:,1]/abs(audio[:,1]).max()

# Normalize image to between 0 and 255
image = image/(image.max()/255.0)

Is there a less verbose, convenience function way to do this? matplotlib.colors.Normalize() doesn't seem to be related.

Answer 1

You can use the "i" (as in idiv, imul..) version, and it doesn't look half bad:

image /= (image.max()/255.0)

For the other case you can write a function to normalize an n-dimensional array by colums:

def normalize_columns(arr):
    rows, cols = arr.shape
    for col in xrange(cols):
        arr[:,col] /= abs(arr[:,col]).max()

Answer 2

audio /= np.max(np.abs(audio),axis=0)
image *= (255.0/image.max())

Using /= and *= allows you to eliminate an intermediate temporary array, thus saving some memory. Multiplication is less expensive than division, so

image *= 255.0/image.max()    # Uses 1 division and image.size multiplications

is marginally faster than

image /= image.max()/255.0    # Uses 1+image.size divisions

Since we are using basic numpy methods here, I think this is about as efficient a solution in numpy as can be.