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 = 0
sizeStates = 5
sizeConstants = 15
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] = "x in component x (dimensionless)"
legend_constants[0] = "r1 in component x (rate)"
legend_constants[1] = "r2 in component x (rate)"
legend_constants[2] = "c1 in component x (rate)"
legend_states[1] = "z in component z (dimensionless)"
legend_states[2] = "y in component y (dimensionless)"
legend_constants[3] = "r3 in component y (rate)"
legend_constants[4] = "r4 in component y (rate)"
legend_constants[5] = "c2 in component y (rate)"
legend_constants[6] = "c3 in component y (rate)"
legend_constants[7] = "epsilon in component model_constants (dimensionless)"
legend_states[3] = "u in component u (dimensionless)"
legend_constants[8] = "r5 in component z (rate)"
legend_constants[9] = "r6 in component z (rate)"
legend_constants[10] = "r7 in component z (rate)"
legend_constants[11] = "z_ in component z (dimensionless)"
legend_constants[12] = "y_ in component z (dimensionless)"
legend_constants[13] = "delta in component z (dimensionless)"
legend_constants[14] = "omega in component u (rate)"
legend_states[4] = "v in component u (dimensionless)"
legend_rates[0] = "d/dt x in component x (dimensionless)"
legend_rates[2] = "d/dt y in component y (dimensionless)"
legend_rates[1] = "d/dt z in component z (dimensionless)"
legend_rates[3] = "d/dt u in component u (dimensionless)"
legend_rates[4] = "d/dt v in component u (dimensionless)"
return (legend_states, legend_algebraic, legend_voi, legend_constants)
def initConsts():
constants = [0.0] * sizeConstants; states = [0.0] * sizeStates;
states[0] = 5
constants[0] = 0.15
constants[1] = 0.12
constants[2] = 0.1
states[1] = 1
states[2] = 0
constants[3] = 0.05
constants[4] = 0.03
constants[5] = 0.1
constants[6] = 0.005
constants[7] = 0.1
states[3] = 1
constants[8] = 0.09
constants[9] = 0.1
constants[10] = 0.05
constants[11] = 1.01
constants[12] = 1.08
constants[13] = 0.01
constants[14] = 0.05
states[4] = 0
return (states, constants)
def computeRates(voi, states, constants):
rates = [0.0] * sizeStates; algebraic = [0.0] * sizeAlgebraic
rates[0] = states[1]*(constants[0]*states[2]+-constants[1]*states[0]+constants[2])
rates[2] = constants[7]*(constants[3]/states[1]+-constants[4]*states[0]+constants[5]+constants[6]*states[3])
rates[1] = constants[7]*constants[13]*((constants[8]*(states[2]-constants[12])*(constants[11]-states[1])+constants[9]*states[1]*(constants[11]-states[1]))-constants[10]*states[1])
rates[3] = -constants[14]*states[4]
rates[4] = constants[14]*states[3]
return(rates)
def computeAlgebraic(constants, states, voi):
algebraic = array([[0.0] * len(voi)] * sizeAlgebraic)
states = array(states)
voi = array(voi)
return algebraic
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)
