visualization

LotkaVolterraModel.py

@@ -1,3 +1,10 @@
+"""
+Module containing simulation code of the Lotka Volterra model.
+
+Adapted from:
+https://github.com/scipy/scipy-cookbook/blob/master/ipython/LotkaVolterraTutorial.ipynb
+"""
+
 import numpy as np
 
 

Visualization.py

@@ -0,0 +1,41 @@
+"""
+Module containing matlab code for visualization of the Lotka Volterra model.
+
+Adapted from:
+https://github.com/scipy/scipy-cookbook/blob/master/ipython/LotkaVolterraTutorial.ipynb
+"""
+
+import matplotlib.pyplot as plt
+import os
+
+
+def _createfolder(folder="graphs"):
+    """ Make directory named folder in current working directory,
+    if it doesn't exist. Returns name of folder.
+    """
+    if not os.path.isdir(folder):
+        os.mkdir(folder)
+
+    return folder
+
+
+def evolution(t, X, savefig=True, showfig=True):
+    """ Simple function to plot temporal evolution of X. """
+    rabbits, foxes = X.T
+
+    fig1 = plt.figure()
+    plt.plot(t, rabbits, "r-", label="Rabbits")
+    plt.plot(t, foxes, "b-", label="Foxes")
+
+    plt.grid()
+    plt.legend(loc="best")
+    plt.xlabel("time")
+    plt.ylabel("population")
+    plt.title("Evolution of fox and rabbit populations")
+
+    if savefig:
+        foldername = _createfolder()
+        fig1.savefig(os.path.join(foldername, "rabbits_and_foxes_1.png"))
+
+    if showfig:
+        fig1.show()

main.py

@@ -1,22 +1,25 @@
-# Code adapted from https://scipy-cookbook.readthedocs.io/items/LoktaVolterraTutorial.html
+# Code adapted from https://github.com/scipy/scipy-cookbook/blob/master/ipython/LotkaVolterraTutorial.ipynb
+
 import numpy as np
 from scipy import integrate
 from LotkaVolterraModel import dX_dt, check_equilibrium
-
+from Visualization import evolution
 
 # Definition of parameters
-a = 1.0   # natural growth rate of rabbits (prey)
-b = 0.1   # natural dying rate of rabbits
-c = 1.5   # natural dying rate of foxes
+a = 1.0  # natural growth rate of rabbits (prey)
+b = 0.1  # natural dying rate of rabbits
+c = 1.5  # natural dying rate of foxes
 d = 0.75  # factor describing growth of foxes based on caught rabbits
 
-"""
 # Running dynamical system:
 t = np.linspace(0, 15, 1000)  # time
 X0 = np.array([10, 5])  # initial conditions: 10 rabbits and 5 foxes
-X, infodict = integrate.odeint(lambda x, _: dX_dt(x, a, b, c, d), X0, t, full_output=True)
-"""
+X, infodict = integrate.odeint(
+    lambda x, _: dX_dt(x, a, b, c, d), X0, t, full_output=True
+)
+
+evolution(t, X)
 
 
-print("Checking fix points of ODE.")
-check_equilibrium(a, b, c, d)
+# print("Checking fix points of ODE.")
+# check_equilibrium(a, b, c, d)