Fitting data to system of ODEs using Python via Scipy & Numpy

Question

I am having some trouble translating my MATLAB code into Python via Scipy & Numpy. I am stuck on how to find optimal parameter values (k0 and k1) for my system of ODEs to fi

Answer 1

For these kind of fitting tasks you could use the package lmfit. The outcome of the fit would look like this; as you can see, the data are reproduced very well:

For now, I fixed the initial concentrations, you could also set them as variables if you like (just remove the vary=False in the code below). The parameters you obtain are:

[[Variables]]
    x10:   5 (fixed)
    x20:   0 (fixed)
    x30:   0 (fixed)
    k0:    0.12183301 +/- 0.005909 (4.85%) (init= 0.2)
    k1:    0.77583946 +/- 0.026639 (3.43%) (init= 0.3)
[[Correlations]] (unreported correlations are <  0.100)
    C(k0, k1)                    =  0.809 

The code that reproduces the plot looks like this (some explanation can be found in the inline comments):

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from lmfit import minimize, Parameters, Parameter, report_fit
from scipy.integrate import odeint


def f(y, t, paras):
    """
    Your system of differential equations
    """

    x1 = y[0]
    x2 = y[1]
    x3 = y[2]

    try:
        k0 = paras['k0'].value
        k1 = paras['k1'].value

    except KeyError:
        k0, k1 = paras
    # the model equations
    f0 = -k0 * x1
    f1 = k0 * x1 - k1 * x2
    f2 = k1 * x2
    return [f0, f1, f2]


def g(t, x0, paras):
    """
    Solution to the ODE x'(t) = f(t,x,k) with initial condition x(0) = x0
    """
    x = odeint(f, x0, t, args=(paras,))
    return x


def residual(paras, t, data):

    """
    compute the residual between actual data and fitted data
    """

    x0 = paras['x10'].value, paras['x20'].value, paras['x30'].value
    model = g(t, x0, paras)

    # you only have data for one of your variables
    x2_model = model[:, 1]
    return (x2_model - data).ravel()


# initial conditions
x10 = 5.
x20 = 0
x30 = 0
y0 = [x10, x20, x30]

# measured data
t_measured = np.linspace(0, 9, 10)
x2_measured = np.array([0.000, 0.416, 0.489, 0.595, 0.506, 0.493, 0.458, 0.394, 0.335, 0.309])

plt.figure()
plt.scatter(t_measured, x2_measured, marker='o', color='b', label='measured data', s=75)

# set parameters including bounds; you can also fix parameters (use vary=False)
params = Parameters()
params.add('x10', value=x10, vary=False)
params.add('x20', value=x20, vary=False)
params.add('x30', value=x30, vary=False)
params.add('k0', value=0.2, min=0.0001, max=2.)
params.add('k1', value=0.3, min=0.0001, max=2.)

# fit model
result = minimize(residual, params, args=(t_measured, x2_measured), method='leastsq')  # leastsq nelder
# check results of the fit
data_fitted = g(np.linspace(0., 9., 100), y0, result.params)

# plot fitted data
plt.plot(np.linspace(0., 9., 100), data_fitted[:, 1], '-', linewidth=2, color='red', label='fitted data')
plt.legend()
plt.xlim([0, max(t_measured)])
plt.ylim([0, 1.1 * max(data_fitted[:, 1])])
# display fitted statistics
report_fit(result)

plt.show()

If you have data for additional variables, you can simply update the function residual.