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.
audio /= np.max(np.abs(audio),axis=0) image *= (255.0/image.max())
*= 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
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')
If the array contains both positive and negative data, I'd go with:
import numpy as np a = np.random.rand(3,2) # Normalised [0,1] b = (a - np.min(a))/np.ptp(a) # Normalised [0,255] as integer c = 255*(a - np.min(a))/np.ptp(a).astype(int) # Normalised [-1,1] d = 2.*(a - np.min(a))/np.ptp(a)-1
also, worth mentioning even if it's not OP's question, standardization:
e = (a - np.mean(a)) / np.std(a)