`scipy.signal._signaltools:residue`

source: /scipy/signal/_signaltools.py :3243

Signature

def residue ( b , a , tol = 0.001 , rtype = avg )

Summary

Compute partial-fraction expansion of b(s) / a(s).

Extended Summary

If M is the degree of numerator b and N the degree of denominator a:

        b(s)     b[0] s**(M) + b[1] s**(M-1) + ... + b[M]
H(s) = ------ = ------------------------------------------
        a(s)     a[0] s**(N) + a[1] s**(N-1) + ... + a[N]

then the partial-fraction expansion H(s) is defined as

    r[0]       r[1]             r[-1]
= -------- + -------- + ... + --------- + k(s)
  (s-p[0])   (s-p[1])         (s-p[-1])

If there are any repeated roots (closer together than tol), then H(s) has terms like

  r[i]      r[i+1]              r[i+n-1]
-------- + ----------- + ... + -----------
(s-p[i])  (s-p[i])**2          (s-p[i])**n

This function is used for polynomials in positive powers of s or z, such as analog filters or digital filters in controls engineering. For negative powers of z (typical for digital filters in DSP), use residuez.

See Notes for details about the algorithm.

Parameters

b : array_like: Numerator polynomial coefficients.
a : array_like: Denominator polynomial coefficients.
tol : float, optional: The tolerance for two roots to be considered equal in terms of the distance between them. Default is 1e-3. See unique_roots for further details.
rtype : {'avg', 'min', 'max'}, optional: Method for computing a root to represent a group of identical roots. Default is 'avg'. See unique_roots for further details.

Returns

r : ndarray: Residues corresponding to the poles. For repeated poles, the residues are ordered to correspond to ascending by power fractions.
p : ndarray: Poles ordered by magnitude in ascending order.
k : ndarray: Coefficients of the direct polynomial term.

Notes

The "deflation through subtraction" algorithm is used for computations --- method 6 in ^[1].

The form of partial fraction expansion depends on poles multiplicity in the exact mathematical sense. However there is no way to exactly determine multiplicity of roots of a polynomial in numerical computing. Thus you should think of the result of residue with given tol as partial fraction expansion computed for the denominator composed of the computed poles with empirically determined multiplicity. The choice of tol can drastically change the result if there are close poles.

Array API Standard Support

residue has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ⛔                     ⛔                   
JAX                   ⛔                     ⛔                   
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Aliases

scipy.signal.residue