Simple CTMC solver for Python
Project description
Markon
Continous Time Markov Chain solver for Python
Markon is a small Python utility for performing analytic solution of CTMCs. Both quantitative and symbolic analysis is possible.
Example:
from markon import solve_ctmc
# Define a CTMC as a dict of state-tuples (from_state, to_state) to a transition rate
t = {('up', 'down'): 1, # Transition rate from 'up' to 'down' is 1
('down', 'up'): 5} # Transition rate from 'down' to 'up' is 5
# Obtain steady state probabilities
p = solve_ctmc(t)
print(p)
will output
{'down': 1/6, 'up': 5/6}
Individual state probabilities can be obtained, both in exact form or as a single rounded float:
print(p['up'])
print(float(p['up']))
will output
5/6
0.8333333333333334
General symbolic analysis can also be done via Sympy:
from markon import solve_ctmc
import sympy
# Define Sympy symbols
l, m = sympy.symbols('l, m')
t = {('up', 'down'): l,
('down', 'up'): m}
# Obtain steady state probabilities with defined order
p = solve_ctmc(t, ['up', 'down'])
print(p)
will output
{'up': m/(l + m), 'down': l/(l + m)}
We can then use Sympy functionality to substitute values
values = [(l, 1), (m,5)]
print(p['up'].subs(values))
will output
5/6
Refer to the Sympy documentation for more on how to enable nice formatting of expressions, output to Latex, etc.
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