# Plotting trajectories and direction fields for a system of ODEs in Python

The aim of this post is to guide the reader through plotting trajectories and direction fields for a system of equations in Python. This is useful when investigating the equilibria and stability of the system, and to facilitate in understanding the general behavior of a system under study. I will use a system of predator-prey equations, that my most devoted online readers are already familiar with from my previous posts on identifying equilibria and stability, and on nondimensionalization. Specifically, I’ll be using the Lotka-Volterra set of equations with Holling’s Type II functional response:

$\frac{\mathrm{d} x}{\mathrm{d} t}=bx\left ( 1-\frac{x}{K} \right )-\frac{axy}{1+ahx}$

$\frac{\mathrm{d} y}{\mathrm{d} t}=\frac{caxy}{1+ahx}-dy$

where:

x: prey abundance

y: predator abundance

b: prey growth rate

d: predator death rate

c: rate with which consumed prey is converted to predator

a: rate with which prey is killed by a predator per unit of time

K: prey carrying capacity given the prey’s environmental conditions

h: handling time

This system has 3 equilibria: when both species are dead (0,0), when predators are dead and the prey grows to its carrying capacity (K,0) and a non-trivial equilibrium where both species coexist and is generally more interesting, given by:

$y^*=\frac{b}{a}(1+ahx^*)\left(1-\frac{x^*}{K} \right)$

$x^*=\frac{d}{a(c-dh)}$

The following code should produce both trajectories and direction fields for this system of ODEs (python virtuosos please excuse the extensive commenting, I try to comment as much as possible for people new to python):

import numpy as np
from matplotlib import pyplot as plt
from scipy import integrate

# I'm using this style for a pretier plot, but it's not actually necessary
plt.style.use('ggplot')

"""
This is to ignore RuntimeWarning: invalid value encountered in true_divide
I know that when my populations are zero there's some division by zero and
the resulting error terminates my function, which I want to avoid in this case.
"""
np.seterr(divide='ignore', invalid='ignore')

# These are the parameter values we'll be using
a = 0.005
b = 0.5
c = 0.5
d = 0.1
h = 0.1
K = 2000

# Define the system of ODEs
# P[0] is prey, P[1] is predator
def fish(P, t=0):
return ([b*P[0]*(1-P[0]/K) - (a*P[0]*P[1])/(1+a*h*P[0]),
c*(a*P[0]*P[1])/(1+a*h*P[0]) - d*P[1] ])

# Define equilibrium point
EQ = ([d/(a*(c-d*h)),b*(1+a*h*(d/(a*(c-d*h))))*(1-(d/(a*(c-d*h)))/K)/a])

"""
I need to define the possible values my initial points will take as they
relate to the equilibrium point. In this case I chose to plot 10 trajectories
ranging from 0.1 to 5
"""
values = np.linspace(0.1, 5, 10)
# I want each trajectory to have a different color
vcolors = plt.cm.autumn_r(np.linspace(0.1, 1, len(values)))

# Open figure
f = plt.figure()
"""
I need to define a range of time over which to integrate the system of ODEs
The values don't really matter in this case because our system doesn't have t
on the right hand side of dx/dt and dy/dt, but it is a necessary input for
integrate.odeint.
"""
t = np.linspace(0, 150, 1000)

# Plot trajectories by looping through the possible values
for v, col in zip(values, vcolors):
# Starting point of each trajectory
P0 = [E*v for E in EQ]
# Integrate system of ODEs to get x and y values
P = integrate.odeint(fish, P0, t)
# Plot each trajectory
plt.plot( P[:,0], P[:,1],
# Different line width for different trajectories (optional)
lw=0.5*v,
# Different color for each trajectory
color=col,
# Assign starting point to trajectory label
label='P0=(%.f, %.f)' % ( P0[0], P0[1]) )
"""
To plot the direction fields we first need to define a grid in order to
compute the direction at each point
"""
# Get limits of trajectory plot
ymax = plt.ylim(ymin=0)[1]
xmax = plt.xlim(xmin=0)[1]
# Define number of points
nb_points = 20
# Define x and y ranges
x = np.linspace(0, xmax, nb_points)
y = np.linspace(0, ymax, nb_points)
# Create meshgrid
X1 , Y1 = np.meshgrid(x,y)
# Calculate growth rate at each grid point
DX1, DY1 = fish([X1, Y1])
# Direction at each grid point is the hypotenuse of the prey direction and the
# predator direction.
M = (np.hypot(DX1, DY1))
# This is to avoid any divisions when normalizing
M[ M == 0] = 1.
# Normalize the length of each arrow (optional)
DX1 /= M
DY1 /= M

plt.title('Trajectories and direction fields')
"""
This is using the quiver function to plot the field of arrows using DX1 and
DY1 for direction and M for speed
"""
Q = plt.quiver(X1, Y1, DX1, DY1, M, pivot='mid', cmap=plt.cm.plasma)
plt.xlabel('Prey abundance')
plt.ylabel('Predator abundance')
plt.legend(bbox_to_anchor=(1.05, 1.0))
plt.grid()
plt.xlim(0, xmax)
plt.ylim(0, ymax)
plt.show()



This should produce the following plot. All P0s are the initial conditions we defined.

We can also see that this parameter combination produces limit cycles in our system. If we change the parameter values to:

a = 0.005
b = 0.5
c = 0.5
d = 0.1
h = 0.1
K = 200


i.e. reduce the available resources to the prey, our trajectories look like this:

The equilibrium becomes stable, attracting the trajectories to it.

The same can be seen if we increase the predator death rate:

a = 0.005
b = 0.5
c = 0.5
d = 1.5
h = 0.1
K = 2000


The implication of this observation is that an initially stable system, can become unstable given more resources for the prey or less efficient predators. This has been referred to as the Paradox of Enrichment and other predator-prey models have tried to address it (more on this in future posts).

P.S: I would also like to link to this scipy tutorial, that I found very helpful and that contains more plotting tips.

# Exploring the stability of systems of ordinary differential equations – an example using the Lotka-Volterra system of equations

Stability when dealing with dynamical systems is important
because we generally like the systems we make decisions on to be predictable.
As such, we’d like to know whether a small change in initial conditions could
lead to similar behavior. Do our solutions all tend to the same point? Would
slightly different initial conditions lead to the same or to a completely
different point for our systems.

This blogpost will consider the stability of dynamical systems of the form:

The equilibria of which are denoted by x* and y*,
respectively.

I will use the example of the Lotka-Volterra system of
equations, which is the most widely known method of modelling many predator-prey/parasite-host interactions encountered in natural systems. The Lotka-Volterra predator-prey equations were discovered independently by both Alfred Lotka and Vito Volterra in 1925-26. Volterra got to these equations while trying to explain why, immediately after WWI, the number of predatory fish was much larger than before the war.

The system is described by the following equations:

Where a, b, c, d > 0 are the parameters describing the
growth, death, and predation of the fish.

In the absence of predators, the prey population (x) grows
exponentially with an intrinsic rate of growth b.

Total predation is proportional to the abundance of prey and
the abundance of predators, at a constant predation rate a.

New predator abundance is proportional to the total
predation (axy) at a constant conversion rate c.

In the absence of prey, the predator population decreases at
a mortality rate d.

The system demonstrates an oscillating behavior, as
presented in the following figure for parameters a=1, b=1, c=2, d=1.

Volterra’s explanation for the rise in the numbers of
predatory fish was that fishing reduces the rate of increase of the prey
numbers and thus increases the rate of decrease of the predator. Fishing does
not change the interaction coefficients. So, the number of predators is
decreased by fishing and the number of prey increases as a consequence. Without
any fishing activity (during the war), the number of predators increased which
also led to a decrease in the number of prey fish.

To determine the stability of a system of this form, we
first need to estimate its equilibria, i.e. the values of x and y for which:

An obvious equilibrium exists at x=0 and y=0, which kinda

We’ll first look at a system that’s still alive, i.e x>0
and y>0:

And

Looking at these expressions for the equilibria we can also
see that the isoclines for zero growth for each of the species are straight
lines given by b/a for the prey and d/ca for the predator, one horizontal and
one vertical in the (x,y) plane.

In dynamical systems, the behavior of the system near an
equilibrium relates to the eigenvalues of the Jacobian (J) of F(x,y) at the equilibrium.
If the eigenvalues all have real parts that are negative, then the equilibrium
is considered to be a stable node; if the eigenvalues all have real parts that
are positive, then the equilibrium is considered to be an unstable node. In the
case of complex eigenvalues, the equilibrium is considered a focus point and
its stability is determined by the sign of the real part of the eigenvalue.

I found the following graphic from scholarpedia to be a
useful illustration of these categorizations.

So we can now evaluate the stability of our equilibria.
First we calculate the Jacobian of our system and then plug in our estimated
equilibrium.

To find the eigenvalues of this matrix we need to find the
values of λ that satisfy: det⁡(J-λI)=0  where I is
the identity matrix and det denotes the determinant.

Our eigenvalues are therefore complex with their real parts equal to 0. The equilibrium is therefore a focus point, right between instability and asymptotic stability. What this means for the points that start out near the equilibrium is that they tend to both converge towards the equilibrium and away from it. The solutions of this system are therefore periodic, oscillating around the equilibrium point, with a period , with no trend either towards the
equilibrium or away from it.

One can arrive at the same conclusion by looking at the
trace (τ) of the Jacobian and its determinant (Δ).

The trace is exactly zero and the determinant is positive
(both d,b>0) which puts the system right in between stability and
instability.

Now let’s look into the equilibrium where x*=0 and y*=0, aka
the total death.

Both b and d are positive real numbers which means that the
eigenvalues will always have real values of different signs. This makes the
(0,0) an unstable saddle point. This is important because if the equilibrium of
total death were a stable point, initial low population levels would tend to
converge towards their extinction. The fact that this equilibrium is unstable
means that the dynamics of the system make it difficult to achieve total death
and that prey and predator populations could be infinitesimally close to zero
and still recover.

Now consider a system where we’ve somehow killed all the
predators (y=0). The prey would continue to grow exponentially with a growth
rate b. This is generally unrealistic for real-life systems because it assumed
infinite resources for the prey. A more realistic model would consider the prey
to exhibit a logistic growth, with a carrying capacity K. The carrying capacity of a biological species is the maximum population size of the species that can be sustained indefinitely given the necessary resources.

The model therefore becomes:

Where a, b, c, d, K > 0.

To check for this system’s stability we have to go through
the same exercise.

The predator equation has remained the same so:

For zero prey growth:

Calculating the eigenvalues becomes a tedious exercise at
this point and the time of writing is 07:35PM on a Friday. I’d rather apply a
small trick instead and use the isoclines to derive the stability of the system. The isocline for the predator zero-growth has remained the same (d/ca), which is a straight line (vertical on the (x,y) vector plane we draw before). The isocline for the prey’s zero-growth has changed to:

Which is again a straight line with a slope of –b/aK, i.e.,
it’s decreasing when moving from left to right (when the prey is increasing). Now looking at the signs in the Jacobian of the first system:

We see no self-dependence for each of the two species (the
two 0), we see that as the predator increases the prey decreases (-) and that
as the prey increases the predator increases too (+).

For our logistic growth the signs in the Jacobian change to:

Because now there’s a negative self-dependence for the prey-as its numbers increase its rate of growth decreases. This makes the trace (τ) of the Jacobian negative and the determinant positive, which implies that our system is now a stable system. Plotting the exact same dynamic system but now including a carrying capacity, we can see how the two populations converge to specific numbers.