The Azimuth Project
Experiments in predator-prey with Sage (Rev #7, changes)

Showing changes from revision #6 to #7: Added | Removed | Changed



Showing an easy way of doing predator-prey modeling in Sage . Right now it is a generic version and one version which is the competitiveLotka-Volterra.


Original Lotka-Volterra

This is the original Lotka-Volterra phase map for a non-dimensional form. This was posted on Marshall Hampon on the ask.sage site. Lotka-Volterra has many coarse “flaws” and has also been modified over time as we’ll see below. Dimensionless format of Lotka-Volterra. default for Sage ode_solver is to use runga-kutta-felhberg (4,5)

Plot and Code


T = ode_solver()
T.function = lambda t, y: [y[0]-y[0]*y[1], -y[1]+y[0]*y[1]]
sol_lines = Graphics()
for i in srange(0.1,1.1,.1):
    y = T.solution
    sol_lines += line([x[1] for x in y], rgbcolor = (i,0,1-i))
show(sol_lines+point((1,1),rgbcolor=(0,0,0)), figsize = [6,6], xmax = 6, ymax = 6)

Interactive and more realistic Lotka-Volterra

Here we enable choice of the exponential growth in the original Lotka-Volterra and logistic growth. We also added a parameter g which is used in both and k which is the scaled carrying capacity. See if you canfind the fixed point for the latter (using the code below)?

See if you can find the fixed point for the latter (using the code below)?

It should look like this:


def lv(g,k,growth):
   Tg = ode_solver()
   if growth == "Malthusian":
       Tg.function = lambda t, y: [g*y[0]*(1-y[1]), (-1.0/g)*(1-y[0])]
       Tg.function = lambda t, y: [g*y[0]*(1-y[0]/k - y[1]), (-1.0/g)*(1-y[0])]
   sol_lines = Graphics()
   for i in srange(0.1,1.1,.2):
      y = Tg.solution
      sol_lines += line([x[1] for x in y], rgbcolor = (i,0,1-i))
   return sol_lines

def _(g = (0.1,1.,0.1), k = (0.1,4.0,0.1), Growth=["Malthusian","Logistic"]):

Next step

We will look at cases where Lotka-Volterra might lead to Hopf bifurcation and also see if we can add the Allee effect