LTISM.py

from lib601.sm import *

def LTISM(cCoeffs, dCoeffs):
  def parallelAddMany(machines):
    return reduce(lambda a,b: ParallelAdd(a,b), machines, Gain(0))

  def CascadeMany(machines):
    return reduce(lambda a,b: Cascade(a,b), machines, Gain(1))

  def RPower(power):
    return CascadeMany([R(0) for _ in xrange(power)])

  def delayArray(gains):
    return parallelAddMany([Cascade(Gain(gain), RPower(i)) for (gain,i) in zip(gains, xrange(len(gains)))])

  cArray = delayArray(cCoeffs)
  dArray = delayArray([0] + dCoeffs)
  return Cascade(cArray, FeedbackAdd(Gain(1), dArray))


if __name__ == "__main__":
  # print parallelAddMany([Gain(1), Gain(1)]).transduce([0,1,1,0])
  # print RPower(4).transduce([1,0,0,0,0,0,0,0])
  # print RPower(0).transduce([1,0,0,0,0,0,0,0])
  # print delayArray([1,2,2]).transduce([1,0,0,0,0,0,0,0])
  # print LTISM([1],[2]).transduce([1,0,0,0,0,0,0,0])
  # print LTISM([0,2],[1]).transduce([1,0,0,0,0,0,0,0])
  # print LTISM([4, 1], [4, 3]).transduce([1,2,3,4])

  unit_sample_signal = [1,0,0,0,0,0,0,0,0,0]
  m = LTISM([3, 3, 4, 5],[3, 4, 1, 4])
  m.start()
  m.step(1)
  m.step(2)
  # print m.getNextValues(m.startState(),0)
  print m.getNextValues(m.startState,0)
  # o = [m.getNextValues(m.startState,i)[1] for i in unit_sample_signal]
  # print o