# Stability analysis with Python¶

The following provides Python-code for analysing the system \{f(x,y), g(x,y)\} as discussed here: http://systems-sciences.uni-graz.at/etextbook/sw2/ph_plane_analysis.html

$f(x,y) = \frac{dx}{dt} = x(2-x-y)$

$g(x,y) = \frac{dy}{dt} = y(-1+x)$

The deployed Python-modules Matplotlib, Numpy, Scipy and Sympy are included in Python's Anaconda (http://docs.continuum.io/anaconda/install.html) installation

### 1. Plot the system in time and in phase plane¶

In [4]:
# import necessary libraries
import matplotlib.pyplot as plt
# show plots in notebook
% matplotlib inline

# define system in terms of separated differential equations
def f(x,y):
return 2*x - x**2 - x*y
def g(x,y):
return - y + x*y

# initialize lists containing values
x = []
y = []

#iv1, iv2 = initial values, dt = timestep, time = range
def sys(iv1, iv2, dt, time):
# initial values:
x.append(iv1)
y.append(iv2)
#z.append(iv3)
# compute and fill lists
for i in range(time):
x.append(x[i] + (f(x[i],y[i])) * dt)
y.append(y[i] + (g(x[i],y[i])) * dt)
#z.append(z[i] + (h(x[i],y[i],z[i])) * dt)
return x, y

sys(10, 2, 0.01, 1000)

#plot
fig = plt.figure(figsize=(15,5))
fig.subplots_adjust(wspace = 0.5, hspace = 0.3)

ax1.plot(x, 'r-', label='predator')
ax1.plot(y, 'b-', label='prey')
#ax1.plot(z, 'g-', label='prey')
ax1.set_title("Dynamics in time")
ax1.set_xlabel("time")
ax1.grid()
ax1.legend(loc='best')

ax2.plot(x, y, color="blue")
ax2.set_xlabel("x")
ax2.set_ylabel("y")
ax2.set_title("Phase space")
ax2.grid()

#### Alternatively, use the Python libraries Numpy (http://www.numpy.org) for numerical mathematics and Scipy (http://www.scipy.org/scipylib/index.html) for scientific computation¶

Note the differences in the plots due to different integration-steps and methods

In [5]:
import numpy as np
from scipy import integrate

# define system in terms of a Numpy array
def Sys(X, t=0):
# here X[0] = x and x[1] = y
return np.array([ 2*X[0] - X[0]**2 - X[0]*X[1] , - X[1] + X[0]*X[1] ])

# generate 1000 linearly spaced numbers for x-axes
t = np.linspace(0, 20,  1000)
# initial values: x0 = 10, y0 = 2
Sys0 = np.array([10, 2])

X, infodict = integrate.odeint(Sys, Sys0, t, full_output=True)
# infodict['message']                      # integration successful

x,y = X.T

#plot
fig = plt.figure(figsize=(15,5))
fig.subplots_adjust(wspace = 0.5, hspace = 0.3)

ax1.plot(x, 'r-', label='predator')
ax1.plot(y, 'b-', label='prey')
ax1.set_title("Dynamics in time")
ax1.set_xlabel("time")
ax1.grid()
ax1.legend(loc='best')

ax2.plot(x, y, color="blue")
ax2.set_xlabel("x")
ax2.set_ylabel("y")
ax2.set_title("Phase space")
ax2.grid()

In [6]:
# brute force: iterate through possibility space (r)
fp = []

def find_fixed_points(r):
for x in range(r):
for y in range(r):
if ((f(x,y) == 0) and (g(x,y) == 0)):
fp.append((x,y))
print('The system has a fixed point in %s,%s' % (x,y))
return fp

find_fixed_points(10)
The system has a fixed point in 0,0
The system has a fixed point in 1,1
The system has a fixed point in 2,0

Out[6]:
[(0, 0), (1, 1), (2, 0)]

#### Alternatively, use Python's Sympy-modul (http://sympy.org/) for symbolic mathematics:¶

In [7]:
import sympy as sm
# define the system in this way (asuming a predator-prey-system with no negative values)
# to avoid interference x = r (for resource) and y = c (for consumer)
r, c = sm.symbols('r, c', negative=False)
R = 2*r - r**2 - r*c
C = - c + r*c

# use sympy's way of setting equations to zero
REqual = sm.Eq(R, 0)
CEqual = sm.Eq(C, 0)

# compute fixed points
equilibria = sm.solve( (REqual, CEqual), r, c )
print(equilibria)
[(0, 0), (1, 1), (2, 0)]

### 3. Draw Nullclines and Quiver plot¶

In [8]:
#plot
fig2 = plt.figure(figsize=(8,6))

x = np.linspace(0,2,20)
y = np.arange(0,2,20)

# plot nullclines
ax4.plot([0,2],[2,0], 'r-', lw=2, label='x-nullcline')
ax4.plot([1,1],[0,2], 'b-', lw=2, label='y-nullcline')

# plot fixed points
for point in fp:
ax4.plot(point[0],point[1],"red", marker = "o", markersize = 10.0)
ax4.set_title("Quiverplot with nullclines")
ax4.legend(loc='best')

# quiverplot
# define a grid and compute direction at each point
x = np.linspace(0, 2, 20)
y = np.linspace(0, 2, 20)

X1 , Y1  = np.meshgrid(x, y)                    # create a grid
DX1, DY1 = Sys([X1, Y1])                        # compute growth rate on the grid
M = (np.hypot(DX1, DY1))                        # norm growth rate
M[ M == 0] = 1.                                 # avoid zero division errors
DX1 /= M                                        # normalize each arrows
DY1 /= M

ax4.quiver(X1, Y1, DX1, DY1, M, pivot='mid')
ax4.legend()
ax4.grid()

### 4. Calculate eigenvalues from Jacobian matrix¶

\$A = ( ) = (

)

Determinant: $detA = a_{11}a_{22} - a_{12}a_{21}$

Trace: $trA=a_{11} + a_{22}$

Eigenvalues: $\lambda = \frac{trA \pm \sqrt{(trA)^2-4detA}}{2}$

In [9]:
# needed to calculate (complex) squareroots of negative numbers
from scipy import sqrt

def eigenvalues(x,y):
# 2*x - x**2 - x*y
a11 = 2 - 2*x - y                # differentiated with respect to x
a12 = - x                        # differentiated with respect to y
# - y + x*y
a21 = y                          # differentiated with respect to x
a22 = - 1 + x                    # differentiated with respect to y

tr = a11 + a22
det = a11*a22 - a12*a21
lambda1 = (tr - sqrt(tr**2 - 4*det))/2
lambda2 = (tr + sqrt(tr**2 - 4*det))/2
print('Check the fixed point  %s, %s' % (x,y))
print('The real part of the first eigenvalue is %s' %lambda1.real)
print('The real part of the second eigenvalue is %s' % lambda2.real)

if (lambda1.real < 0 and lambda2.real < 0):
print('The fixed point in %s, %s is a sink. It is stable' % (x,y))
if (lambda1.real > 0 and lambda2.real > 0):
print('The fixed point in %s, %s is a source. It is unstable' % (x,y))
if (lambda1.real > 0 and lambda2.real < 0):
print('The fixed point in %s, %s is a saddle. It is unstable' % (x,y))
if (lambda1.real < 0 and lambda2.real > 0):
print('The fixed point in %s, %s is unstable' % (x,y))
print '----------------------------'
return lambda1 , lambda2

# iterate through list of fixed points
for x,y in fp:
eigenvalues(x,y)
Check the fixed point  0, 0
The real part of the first eigenvalue is -1.0
The real part of the second eigenvalue is 2.0
The fixed point in 0, 0 is unstable
----------------------------
Check the fixed point  1, 1
The real part of the first eigenvalue is -0.5
The real part of the second eigenvalue is -0.5
The fixed point in 1, 1 is a sink. It is stable
----------------------------
Check the fixed point  2, 0
The real part of the first eigenvalue is -2.0
The real part of the second eigenvalue is 1.0
The fixed point in 2, 0 is unstable
----------------------------

#### Alternatively, again use Python's Sympy-modul (http://sympy.org/) for symbolic mathematics:¶

In [11]:
# here again, in order to avoid interference x = r (for resource) and y = c (for consumer) (see above)

# put equations into matrix-form and compute Jacobian-matrix
eqMat = sm.Matrix([ R, C ])
Mat = sm.Matrix([ r, c ])
jacMat = eqMat.jacobian(Mat)
print('Jacobian %s' % jacMat)
print('---------------------')

# iterate through list of equilibria
for item in equilibria:
eqmat = jacMat.subs([ (r, item[0]), (c, item[1]) ])
print('The eigenvalues for the fixed point (%s, %s) are %s and %s:'
%(item[0], item[1], eqmat.eigenvals().keys()[0], eqmat.eigenvals().keys()[1]))
print('-------------------------------------------')
Jacobian Matrix([[-c - 2*r + 2, -r], [c, r - 1]])
---------------------
The eigenvalues for the fixed point (0, 0) are 2 and -1:
-------------------------------------------
The eigenvalues for the fixed point (1, 1) are -1/2 - sqrt(3)*I/2 and -1/2 + sqrt(3)*I/2:
-------------------------------------------
The eigenvalues for the fixed point (2, 0) are 1 and -2:
-------------------------------------------