mean field model base

codegithubka · codegithubka · commit a36a8c7804a6 · 2026-01-19T17:03:41.000+01:00
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,9 @@
+# Virtual environment
+.venv/
+__pycache__/
+# Python bytecode
+*.pyc
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+# Data files
+data/
diff --git a/models/mean_field.py b/models/mean_field.py
@@ -0,0 +1,74 @@
+import numpy as np
+from scipy.integrate import odeint
+from scipy.ndimage import label
+from scipy.optimize import curve_fit
+from scipy.stats import kstest
+import matplotlib.pyplot as plt
+from typing import Tuple, List, Dict, Optional
+
+class MeanFieldModel:
+    """
+    Mean-field (non-spatial) predator-prey model.
+    """
+    
+    def __init__(self, birth: float, consumption: float, predator_death: float):
+        """
+        Initialize the mean-field model with given parameters.
+        Args:
+            birth (float): Prey birth rate (b)
+            consumption (float): predator consumption rate (c)
+            predator_death (float): predator death rat (d_c)
+        """
+        
+    def ode_system(self, Z: np.ndarray, t: float, prey_death: float)->List[float]:
+        """
+        Mean-field ODE system for predator prey dynamics
+
+        Args:
+            Z (np.ndarray): _description_
+            t (float): _description_
+            prey_death (float): _description_
+
+        Returns:
+            List[float]: _description_
+        """
+        pass
+    
+    def solve(self, prey_death: float, Z0: Tuple[float, float], t_max: float, n_points: int)->Tuple[np.ndarray, np.ndarray]:
+        """
+        Solve the mean-field ODE system.
+
+        Args:
+            prey_death (float): Prey death rate (d)
+            Z0 (Tuple[float, float]): Initial conditions (prey, predator)
+            t_max (float): Maximum time
+            n_points (int): Number of time points
+
+        Returns:
+            Tuple[np.ndarray, np.ndarray]: Time points and solution array
+        """
+        pass
+    
+    def equilibrium(self, prey_death: float)->Tuple[float, float]:
+        """
+        Calculate the equilibrium point of the system.
+
+        Args:
+            prey_death (float): Prey death rate (d)
+
+        Returns:
+            Tuple[float, float]: Equilibrium populations (prey, predator)
+        """
+        pass
+    
+    def sweep_death_rate(self, d_r_values: np.ndarray, t_equilibrium: float) -> Dict[str, np.ndarray]:
+        """
+        Sweep prey death rate and record equilibrium densities.
+        """
+        pass
+    
+    def nullclines(self, prey_death: float, Z_r_range: np.ndarray)->Dict[str, np.ndarray]:
+        """
+        Compute nullclines for phase portrait. 
+        """
+        pass
diff --git a/requirements.txt b/requirements.txt
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+matplotlib
+numpy
+scipy
