How to normalize a NumPy array to within a certain range

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

User · Accepted Answer

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     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

User · Answer

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

User · Answer

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 · Answer

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  don t forget the parenthesis before astype int  c    255  a - np min a   np ptp a   astype int             Normalised  -1 1  d   2   a - np min a   np ptp a -1  If the array contains nan  one solution could be to just remove them as  def nan ptp a       return np ptp a np isfinite a     b    a - np nanmin a   nan ptp a   However  depending on the context you might want to treat nan differently  E g  interpolate the value  replacing in with e g  0  or raise an error  Finally  worth mentioning even if it s not OP s question  standardization  e    a - np mean a     np std a

User · Answer

A simple solution is using the scalers offered by the sklearn preprocessing library    scaler   sk MinMaxScaler feature range  0  250   scaler   scaler fit X  X scaled   scaler transform X    Checking reconstruction X rec   scaler inverse transform X scaled    The error X rec-X will be zero  You can adjust the feature range for your needs  or even use a standart scaler sk StandardScaler

User · Answer

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 · Answer

You can also rescale using sklearn  The advantages are that you can adjust normalize the standard deviation  in addition to mean-centering the data  and that you can do this on either axis  by features  or by records   from sklearn preprocessing import scale X   scale  X  axis 0  with mean True  with std True  copy True     The keyword arguments axis  with mean  with std are self explanatory  and are shown in their default state  The argument copy performs the operation in-place if it is set to False  Documentation here

[python] How to normalize a NumPy array to within a certain range?

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