scipy.signal.

invresz#

scipy.signal.invresz(r, p, k, tol=0.001, rtype='avg')[source]#

Compute b(z) and a(z) from partial fraction expansion.

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

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

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

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

If there are any repeated roots (closer than tol), then the partial fraction expansion has terms like:

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

This function is used for polynomials in negative powers of z, such as digital filters in DSP. For positive powers, use invres.

Parameters:

rarray_like: Residues corresponding to the poles. For repeated poles, the residues must be ordered to correspond to ascending by power fractions.
parray_like: Poles. Equal poles must be adjacent.
karray_like: Coefficients of the direct polynomial term.
tolfloat, 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:

bndarray: Numerator polynomial coefficients.
andarray: Denominator polynomial coefficients.

See also

residuez, unique_roots, invres

Notes

Array API Standard Support

invresz 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 Support for the array API standard for more information.