enhanced_ecosystem_with_psi_and_frequency.py

import numpy as np

class EnhancedHumanQuantumEcosystem:
    def __init__(self, num_groups=5, interaction_strength=0.1, base_frequency=20):
        self.num_groups = num_groups
        self.growth_rates = np.random.uniform(0.01, 0.05, num_groups)
        self.carrying_capacities = np.random.uniform(0.5, 1.0, num_groups)
        self.innovation_rates = np.random.uniform(0.001, 0.01, num_groups)
        self.adaptation_rates = np.random.uniform(0.01, 0.05, num_groups)
        self.interaction_strength = interaction_strength
        
        # Psi and frequency-related parameters
        self.base_frequency = base_frequency  # Hz, super low frequency
        self.psi = np.random.uniform(0, 2*np.pi, num_groups)  # Random initial phase for each group
        self.golden_ratio = (1 + np.sqrt(5)) / 2
        
    def update(self, state, t):
        # Existing dynamics
        growth = self.growth_rates * state * (1 - state / self.carrying_capacities)
        innovation = self.innovation_rates * state
        adaptation = self.adaptation_rates * (1 - state)
        
        # New dynamics incorporating Psi and frequency
        frequency = self.base_frequency * (1 + np.sin(t / self.golden_ratio))  # Time-varying frequency
        psi_effect = np.sin(self.psi + 2 * np.pi * frequency * t)  # Time-dependent phase
        balancer_effect = self.interaction_strength * (psi_effect - np.mean(psi_effect))
        
        # Update Psi based on the state of the system
        self.psi += 0.1 * np.tanh(state)  # State-dependent phase adjustment
        
        # Combine all effects
        total_effect = growth + innovation + adaptation + balancer_effect
        return total_effect