["python","arrays","numpy","scipy","convenience-methods"]
How to normalize a NumPy array to within a certain range?

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.
# Normalize audio channels to between -1.0 and +1.0 audio /= np.max(np.abs(audio),axis=0) # Normalize image to between 0 and 255 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. In-place operations do not change the dtype of the container array. Since the desired normalized values are floats, the audio and image arrays need to have floating-point point dtype before the in-place operations are performed. If they are not already of floating-point dtype, you'll need to convert them using astype. For example, image = image.astype('float64')