How to normalize a NumPy array to within a certain range?

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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.

convenience-methods scipy numpy arrays python
8 Answers

You are trying to min-max scale the values of audio between -1 and +1 and image between 0 and 255.

Using sklearn.preprocessing.minmax_scale, should easily solve your problem.

e.g.:

audio_scaled = minmax_scale(audio, feature_range=(-1,1))

and

shape = image.shape
image_scaled = minmax_scale(image.ravel(), feature_range=(0,255)).reshape(shape)

note: Not to be confused with the operation that scales the norm (length) of a vector to a certain value (usually 1), which is also commonly referred to as normalization.

user_63276580
user_110732340

I tried following this, and got the error 

TypeError: ufunc 'true_divide' output (typecode 'd') could not be coerced to provided output parameter (typecode 'l') according to the casting rule ''same_kind''

The numpy array I was trying to normalize was an integer array. It seems they deprecated type casting in versions > 1.10, and you have to use numpy.true_divide() to resolve that.

arr = np.array(img)
arr = np.true_divide(arr,[255.0],out=None)

img was an PIL.Image object.

user_85071200
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