# 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.

## Answers:

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()
```

```
# 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')
```

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.

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)
```

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()

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.

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.

This answer to a similar question solved the problem for me with

```
np.interp(a, (a.min(), a.max()), (-1, +1))
```