Superposition (Stacking) of ALSSMs [code101.1]

Generates two discrete-time autonomous linear state space models and stacks them into a single combined model that produces a two-channel stacked output.

dump_tree returns the internal structure of the stacked ALSSM and helps with debugging.

Note: replacing AlssmStacked with AlssmSum generates a summed (scalar) output instead of a stacked (multi-channel) output.

Note: No graphical output.

Console Output

-- Print --
AlssmStacked(A=[[1. 1. 1. 1. 0. 0.] [0. 1. 2. 3. 0. 0.] [0. 0. 1. 3. 0. 0.] [0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1.]], C=[[1 0 0 0 0 0] [0 0 0 0 1 0]], label=alssm-stacked)

-- Structure --
└-AlssmStacked, A: (6, 6), C: (2, 6), label: alssm-stacked
  └-AlssmPoly, A: (4, 4), C: (4,), label: alssm-polynomial
  └-Alssm, A: (2, 2), C: (2,), label: alssm-line

Code

"""
Superposition (Stacking) of ALSSMs [code101.1]
============================================

Generates two discrete-time autonomous linear state space models and stacks
them into a single combined model that produces a two-channel stacked output.

[`dump_tree`][lmlib.statespace.model.ModelBase.dump_tree] returns the internal
structure of the stacked ALSSM and helps with debugging.

Note: replacing [`AlssmStacked`][lmlib.statespace.model.AlssmStacked] with
[`AlssmSum`][lmlib.statespace.model.AlssmSum] generates a summed (scalar)
output instead of a stacked (multi-channel) output.

"""
import lmlib as lm

A = [[1, 1], [0, 1]]
C = [1, 0]
alssm_line = lm.Alssm(A, C, label="alssm-line")

alssm_poly = lm.AlssmPoly(poly_degree=3, label="alssm-polynomial")

# stacking ALSSM
alssm_stacked = lm.AlssmStacked((alssm_poly, alssm_line), label="alssm-stacked")

# print content and structure
print("-- Print --")
print(alssm_stacked)

print("\n-- Structure --")
print(alssm_stacked.dump_tree())