sample_evolution_example.py

import stokepy as sp
import numpy   as np

# instantiate class
fmc = sp.FiniteMarkovChain()

# create initial distribution vector
phi = np.array([0, 0, 1, 0, 0])

# generate Markov chain with no boundary conditions
fmc.gen_from_params(phi, p = 0.6, num_states = 5, dim = 1)

# apply boundary condition: absorbing, reflecting, semi-reflecting
# only works for 1 dimension Markov chains at the moment
fmc.apply_boundary_condition(condition='semi-reflecting')

# choose solution method like Sample Evolution
sample_evolution = sp.SampleEvolution(fmc, phi, samples = 2000, steps = 2000,\
                                      rec_class_states = [])

# run the solution
sample_evolution.run()

# get data from the run
average_distribution   = sample_evolution.pi
epdf                   = sample_evolution.epdf
absorption_proportions = sample_evolution.absorption_proportions
apbrc                  = sample_evolution.recurrent_class_absorbed_proportions
mean_absorption_time   = sample_evolution.mean_absorption_time