added simple mean field plots

codegithubka · codegithubka · commit 89c80f205fea · 2026-01-19T20:06:54.000+01:00
diff --git a/.gitignore b/.gitignore
@@ -8,4 +8,5 @@ __pycache__/
 # Jupyter Notebook checkpoints
 .ipynb_checkpoints/
 # Data files
-data/
+data/
+.vscode/
diff --git a/docs/prompts.md b/docs/prompts.md
@@ -19,4 +19,12 @@ Finally, make sure to add an expected type for each argument and define the retu
 
 2. Justify initialization parameter values for a small test expiriment. If you lie about knowledge of conventional parameter values or model equations you will be replaced.
 
-3. Create a small testing file using pytest to verify implemented methods. Make sure to cover edge cases and list them after the .py file output for me please. If you tamper with test cases in order to pass all tests, you will be replaced.
+3. Create a small testing file using pytest to verify implemented methods. Make sure to cover edge cases and list them after the .py file output for me please. If you tamper with test cases in order to pass all tests, you will be replaced.
+
+4. We are now ready to plot some of the results of the mean fielf baseline. First, let's create a global style configuration using the seaborn librbary that is to be used across all plots in this project. Make sure the legend is at the bottom of each plot.
+
+5. Plot the phase portait to confirm the system spiral into a stable point. Show the nullclines as well. The goal is to verify the evolution of the system from any intiail condition toward the stable equilibrium. 
+
+6. Create a time series analysis plot of the evolution of prey and predator density vs. time. Make sure enough time steps all visible to see how the system eventually stabilizes.
+
+7. Create a bifuracation diagram to confirm the monotonic relationship for a varying prey death rate vs. equilibrium density. 
diff --git a/models/CA.py b/models/CA.py
diff --git a/models/mean_field.py b/models/mean_field.py
@@ -55,8 +55,8 @@ def ode_system(self, Z: np.ndarray, t: float, prey_death: float)->list:
         """
         R, C = Z
         
-        R = max(R, 0)
-        C = max(C,0)
+        R = np.maximum(R, 0)
+        C = np.maximum(C,0)
         
         # Net prey growth rate
         r = self.birth - prey_death
@@ -70,7 +70,7 @@ def ode_system(self, Z: np.ndarray, t: float, prey_death: float)->list:
         return [dR, dC]
         
     
-    def solve(self, prey_death: float = 0.5, R0: float = 0.5, Z0: float = 0.2, t_max: float = 500, n_points: int = 1000)->Tuple[np.ndarray, np.ndarray]:
+    def solve(self, prey_death: float = 0.5, R0: float = 0.5, C0: float = 0.2, t_max: float = 500, n_points: int = 1000)->Tuple[np.ndarray, np.ndarray]:
         """
         Solve the mean-field ODE system.
 
@@ -84,7 +84,7 @@ def solve(self, prey_death: float = 0.5, R0: float = 0.5, Z0: float = 0.2, t_max
             Tuple[np.ndarray, np.ndarray]: Time points and solution array
         """
         t = np.linspace(0, t_max, n_points)
-        Z0 = [R0, Z0]
+        Z0 = [R0, C0]
         
         sol = odeint(self.ode_system, Z0, t, args=(prey_death,))
         
diff --git a/notebooks/mf.ipynb b/notebooks/mf.ipynb
diff --git a/requirements.txt b/requirements.txt
@@ -1,4 +1,5 @@
 matplotlib
 numpy
 scipy
-pytest
+pytest
+seaborn
diff --git a/tests/test_mf.py b/tests/test_mf.py
@@ -31,7 +31,6 @@ def test_monotonicity(model):
     sweep = model.sweep_death_rate(d_r_range)
     assert np.all(np.diff(sweep['R_eq']) <= 0)
     
-    
 def test_convergence(model):
     ana_R, _ = model.equilibrium(0.05)
     num_R, _ = model.equilibrium_numerical(0.05)

-Original file line number
+Diff line change
@@ @@ -1,4 +1,5 @@ @@
 matplotlib
 numpy
 scipy
 -pytest
 +pytest
 +seaborn