Generated Code
The following is python code generated by the CellML API from this CellML file. (Back to language selection)
The raw code is available.
# Size of variable arrays:
sizeAlgebraic = 4
sizeStates = 4
sizeConstants = 5
from math import *
from numpy import *
def createLegends():
legend_states = [""] * sizeStates
legend_rates = [""] * sizeStates
legend_algebraic = [""] * sizeAlgebraic
legend_voi = ""
legend_constants = [""] * sizeConstants
legend_voi = "time in component environment (second)"
legend_states[0] = "M in component M (dimensionless)"
legend_states[1] = "AM in component AM (dimensionless)"
legend_states[2] = "Mp in component Mp (dimensionless)"
legend_algebraic[0] = "k1 in component model_parameters (first_order_rate_constant)"
legend_constants[0] = "k2 in component model_parameters (first_order_rate_constant)"
legend_constants[1] = "k7 in component model_parameters (first_order_rate_constant)"
legend_states[3] = "AMp in component AMp (dimensionless)"
legend_constants[2] = "k3 in component model_parameters (first_order_rate_constant)"
legend_constants[3] = "k4 in component model_parameters (first_order_rate_constant)"
legend_constants[4] = "k5 in component model_parameters (first_order_rate_constant)"
legend_algebraic[3] = "k6 in component model_parameters (first_order_rate_constant)"
legend_algebraic[1] = "phosphorylation in component phosphorylation (dimensionless)"
legend_algebraic[2] = "stress in component stress (dimensionless)"
legend_rates[0] = "d/dt M in component M (dimensionless)"
legend_rates[2] = "d/dt Mp in component Mp (dimensionless)"
legend_rates[3] = "d/dt AMp in component AMp (dimensionless)"
legend_rates[1] = "d/dt AM in component AM (dimensionless)"
return (legend_states, legend_algebraic, legend_voi, legend_constants)
def initConsts():
constants = [0.0] * sizeConstants; states = [0.0] * sizeStates;
states[0] = 1.0
states[1] = 0.0
states[2] = 0.0
constants[0] = 0.5
constants[1] = 0.01
states[3] = 0.0
constants[2] = 0.4
constants[3] = 0.1
constants[4] = 0.5
return (states, constants)
def computeRates(voi, states, constants):
rates = [0.0] * sizeStates; algebraic = [0.0] * sizeAlgebraic
algebraic[0] = custom_piecewise([greater_equal(voi , 0.00000) & less(voi , 5.00000), 0.550000 , True, 0.300000])
rates[0] = -(algebraic[0]*states[0])+constants[0]*states[2]+constants[1]*states[1]
rates[2] = (constants[3]*states[3]+algebraic[0]*states[0])-(constants[0]+constants[2])*states[2]
algebraic[3] = algebraic[0]
rates[3] = (constants[2]*states[2]+algebraic[3]*states[1])-(constants[4]+constants[3])*states[3]
rates[1] = constants[4]*states[3]-(algebraic[3]+constants[1])*states[1]
return(rates)
def computeAlgebraic(constants, states, voi):
algebraic = array([[0.0] * len(voi)] * sizeAlgebraic)
states = array(states)
voi = array(voi)
algebraic[0] = custom_piecewise([greater_equal(voi , 0.00000) & less(voi , 5.00000), 0.550000 , True, 0.300000])
algebraic[3] = algebraic[0]
algebraic[1] = states[3]+states[2]
algebraic[2] = states[3]+states[1]
return algebraic
def custom_piecewise(cases):
"""Compute result of a piecewise function"""
return select(cases[0::2],cases[1::2])
def solve_model():
"""Solve model with ODE solver"""
from scipy.integrate import ode
# Initialise constants and state variables
(init_states, constants) = initConsts()
# Set timespan to solve over
voi = linspace(0, 10, 500)
# Construct ODE object to solve
r = ode(computeRates)
r.set_integrator('vode', method='bdf', atol=1e-06, rtol=1e-06, max_step=1)
r.set_initial_value(init_states, voi[0])
r.set_f_params(constants)
# Solve model
states = array([[0.0] * len(voi)] * sizeStates)
states[:,0] = init_states
for (i,t) in enumerate(voi[1:]):
if r.successful():
r.integrate(t)
states[:,i+1] = r.y
else:
break
# Compute algebraic variables
algebraic = computeAlgebraic(constants, states, voi)
return (voi, states, algebraic)
def plot_model(voi, states, algebraic):
"""Plot variables against variable of integration"""
import pylab
(legend_states, legend_algebraic, legend_voi, legend_constants) = createLegends()
pylab.figure(1)
pylab.plot(voi,vstack((states,algebraic)).T)
pylab.xlabel(legend_voi)
pylab.legend(legend_states + legend_algebraic, loc='best')
pylab.show()
if __name__ == "__main__":
(voi, states, algebraic) = solve_model()
plot_model(voi, states, algebraic)
