Eberly College of Science Mathematics Department, Center for Interdisciplinary Mathematics
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Python part 2, solving ordinary differential equations

References

Numerical solution of differential equations.

The simplest autonomous ordinary differential equation of interest in science is the exponential decay equation

\[ \begin{gather*} dn/dt = r n \quad \text{ where $r < 0$.} \end{gather*} \]

Implement the following script

# Numerical integration of the exponential decay model
#
#       dn/dt = r n, where r < 0
#

from numpy import linspace, array
import scipy.integrate
from scipy import exp
from matplotlib.pyplot import \
        plot, figure, text, show, savefig, xlabel, ylabel

r = -0.05 # decay_rate per day

def vector_field(X, t):
    n = X[0]
    return array([ r * n ])


time_start = 0.
time_end = 10.
numsteps = 20

observation_times = linspace(time_start, time_end, numsteps)

X_initial = array([32.])

X = scipy.integrate.odeint(vector_field, X_initial, observation_times)

X_final_calc = X[-1, 0]
X_final_exct = X_initial[0]*exp(r*time_end)
X_final_error = abs( X_final_calc - X_final_exct )
error_str = "Final Error = %1.5g"%(X_final_error)

figure(1)
options = {'fontsize': 18}

plot(observation_times, X[:, 0],'x-')
xlabel('Time (days)',options)
ylabel('Mass (micrograms)',options)
text(2,20,error_str,options)
savefig('exponential.pdf')
show()

Analysis of the dampled pendulum

A pendulum on a rod, hanging from a pivot with weak friction should move according to the following system of equations, according to Newton's laws.

\[ \begin{align*} \frac{d \theta}{dt} &= \omega, \\ \frac{d \omega}{dt} &= -\sin(\theta) - a \omega. \end{align*} \]

The angle is measured as different from the rest position. In our case, let's assume the friction coefficient is \(a=0.05\).