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 = 4
sizeConstants = 13
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 (day)"
legend_states[0] = "T in component T (dimensionless)"
legend_constants[0] = "s in component T (first_order_rate_constant)"
legend_constants[1] = "dT in component T (first_order_rate_constant)"
legend_constants[2] = "b in component kinetic_parameters (first_order_rate_constant)"
legend_states[1] = "I in component I (dimensionless)"
legend_constants[3] = "p in component I (first_order_rate_constant)"
legend_constants[4] = "dI in component I (first_order_rate_constant)"
legend_constants[5] = "ql in component kinetic_parameters (first_order_rate_constant)"
legend_constants[6] = "qa in component kinetic_parameters (first_order_rate_constant)"
legend_states[2] = "E in component E (dimensionless)"
legend_states[3] = "Il in component Il (dimensionless)"
legend_constants[7] = "al in component Il (first_order_rate_constant)"
legend_constants[8] = "c in component E (first_order_rate_constant)"
legend_constants[9] = "dE in component E (first_order_rate_constant)"
legend_constants[10] = "K in component E (dimensionless)"
legend_constants[11] = "dE_ in component E (first_order_rate_constant)"
legend_constants[12] = "K_ in component E (dimensionless)"
legend_rates[0] = "d/dt T in component T (dimensionless)"
legend_rates[1] = "d/dt I in component I (dimensionless)"
legend_rates[3] = "d/dt Il in component Il (dimensionless)"
legend_rates[2] = "d/dt E in component E (dimensionless)"
return (legend_states, legend_algebraic, legend_voi, legend_constants)
def initConsts():
constants = [0.0] * sizeConstants; states = [0.0] * sizeStates;
states[0] = 1.0
constants[0] = 10.0
constants[1] = 0.01
constants[2] = 0.001
states[1] = 1.0
constants[3] = 0.05
constants[4] = 0.3
constants[5] = 0.001
constants[6] = 0.001
states[2] = 1.0
states[3] = 1.0
constants[7] = 0.01
constants[8] = 0.3
constants[9] = 0.1
constants[10] = 0.1
constants[11] = 0.25
constants[12] = 05.0
return (states, constants)
def computeRates(voi, states, constants):
rates = [0.0] * sizeStates; algebraic = [0.0] * sizeAlgebraic
rates[0] = constants[0]-(constants[1]*states[0]+constants[2]*states[0]*states[1])
rates[1] = (constants[2]*states[0]*states[1]-(constants[4]*states[1]+constants[3]*states[2]*states[1]+constants[5]*states[1]))+constants[6]*states[1]
rates[3] = constants[5]*states[1]-(constants[7]*states[3]+constants[6]*states[3])
rates[2] = (constants[8]*states[2]*states[1])/(states[1]+constants[10])-(constants[9]*states[2]+(constants[11]*states[2]*states[1])/(constants[12]+states[1]))
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)
