scipy.signal.normalize#

scipy.signal.normalize(b, a)[source]#

Normalize numerator/denominator of a continuous-time transfer function.

If values of b are too close to 0, they are removed. In that case, a BadCoefficients warning is emitted.

Parameters:

b: array_like: Numerator of the transfer function. Can be a 2-D array to normalize multiple transfer functions.
a: array_like: Denominator of the transfer function. At most 1-D.

Returns:

num: array: The numerator of the normalized transfer function. At least a 1-D array. A 2-D array if the input num is a 2-D array.
den: 1-D array: The denominator of the normalized transfer function.

Notes

Coefficients for both the numerator and denominator should be specified in descending exponent order (e.g., s^2 + 3s + 5 would be represented as [1, 3, 5]).

Examples

>>> from scipy.signal import normalize

Normalize the coefficients of the transfer function (3*s^2 - 2*s + 5) / (2*s^2 + 3*s + 1):

>>> b = [3, -2, 5]
>>> a = [2, 3, 1]
>>> normalize(b, a)
(array([ 1.5, -1. ,  2.5]), array([1. , 1.5, 0.5]))

A warning is generated if, for example, the first coefficient of b is 0. In the following example, the result is as expected:

>>> import warnings
>>> with warnings.catch_warnings(record=True) as w:
...     num, den = normalize([0, 3, 6], [2, -5, 4])

>>> num
array([1.5, 3. ])
>>> den
array([ 1. , -2.5,  2. ])

>>> print(w[0].message)
Badly conditioned filter coefficients (numerator): the results may be meaningless