""" Earth System Simulator implementation. Provides multi-scale simulation of ecological, climate and human systems. """ import logging from typing import Dict, List, Optional, Tuple, Union, Any import numpy as np from dataclasses import dataclass from earth_systems import ( SystemState, EcologicalState, ClimateState, HumanImpactState, SimulationConfig ) logger = logging.getLogger(__name__) @dataclass class ModelState: """Class representing the state of an Active Inference model.""" beliefs: np.ndarray policies: np.ndarray precision: float free_energy: float prediction_error: float class EarthSystemSimulator: """Earth System Simulator implementation.""" def __init__(self, config_dict: Dict[str, Any]): """Initialize simulator with configuration.""" try: self.config = SimulationConfig.from_dict(config_dict) logger.info("Initialized Earth System Simulator with configuration") # Set random seed if provided if self.config.random_seed is not None: np.random.seed(self.config.random_seed) logger.info(f"Set random seed to {self.config.random_seed}") except Exception as e: logger.error(f"Failed to initialize simulator: {str(e)}") raise def run_scale_simulation(self, scale: str, **kwargs) -> Dict[str, Any]: """Run simulation at specified scale.""" try: logger.info(f"Running simulation at {scale} scale") # TODO: Implement scale-specific simulation logic return {"scale": scale, "status": "simulated"} except Exception as e: logger.error(f"Error in scale simulation: {str(e)}") raise def test_intervention(self, intervention_type: str, **kwargs) -> Dict[str, Any]: """Test intervention strategy.""" try: logger.info(f"Testing {intervention_type} intervention") # TODO: Implement intervention testing logic return {"intervention": intervention_type, "status": "tested"} except Exception as e: logger.error(f"Error in intervention test: {str(e)}") raise def analyze_stability(self, scenario: str) -> Dict[str, Any]: """Analyze system stability.""" try: logger.info(f"Analyzing stability for scenario: {scenario}") # TODO: Implement stability analysis logic return {"scenario": scenario, "status": "analyzed"} except Exception as e: logger.error(f"Error in stability analysis: {str(e)}") raise def analyze_resilience(self, disturbance_type: str) -> Dict[str, Any]: """Analyze system resilience.""" try: logger.info(f"Analyzing resilience for disturbance: {disturbance_type}") # TODO: Implement resilience analysis logic return {"disturbance": disturbance_type, "status": "analyzed"} except Exception as e: logger.error(f"Error in resilience analysis: {str(e)}") raise def _compute_ecological_health(self, state: SystemState) -> float: """Compute ecological health metric.""" # TODO: Implement ecological health computation return 0.0 def _compute_climate_stability(self, state: SystemState) -> float: """Compute climate stability metric.""" # TODO: Implement climate stability computation return 0.0 def _compute_social_wellbeing(self, state: SystemState) -> float: """Compute social wellbeing metric.""" # TODO: Implement social wellbeing computation return 0.0 def test_belief_update(self, method: str, initial_beliefs: np.ndarray, observation: np.ndarray, **kwargs) -> Dict[str, Any]: """Test belief updating mechanisms.""" try: logger.info(f"Testing {method} belief updates") if method == "variational": updated_beliefs = self._variational_belief_update( initial_beliefs, observation, **kwargs) elif method == "sampling": updated_beliefs = self._sampling_belief_update( initial_beliefs, observation, **kwargs) else: raise ValueError(f"Unknown belief update method: {method}") return { "method": method, "initial_beliefs": initial_beliefs.tolist(), "observation": observation.tolist(), "updated_beliefs": updated_beliefs.tolist() } except Exception as e: logger.error(f"Error in belief update test: {str(e)}") raise def test_policy_inference(self, initial_state: ModelState, goal_prior: np.ndarray, goal_type: str, **kwargs) -> Dict[str, Any]: """Test policy inference mechanisms.""" try: logger.info(f"Testing policy inference for {goal_type} goal") # Compute expected free energy expected_free_energy = self._compute_expected_free_energy( initial_state, goal_prior) # Infer policies inferred_policies = self._softmax(-expected_free_energy) return { "goal_type": goal_type, "initial_state": self._state_to_dict(initial_state), "goal_prior": goal_prior.tolist(), "inferred_policies": inferred_policies.tolist(), "expected_free_energy": expected_free_energy.tolist() } except Exception as e: logger.error(f"Error in policy inference test: {str(e)}") raise def test_free_energy(self, component: str, temporal_horizon: int, precision: float) -> Dict[str, Any]: """Test free energy computation.""" try: logger.info(f"Testing {component} free energy computation") # Generate example trajectory time = np.linspace(0, temporal_horizon, 100) state = 0.5 + 0.2 * np.sin(time/10) prediction = 0.5 + 0.2 * np.sin((time-1)/10) if component == "accuracy": energy = -0.5 * precision * (state - prediction)**2 elif component == "complexity": energy = -np.log(np.abs(np.gradient(state)) + 1e-8) elif component == "expected": energy = self._compute_expected_free_energy_trajectory( state, prediction, precision) elif component == "full": accuracy = -0.5 * precision * (state - prediction)**2 complexity = -np.log(np.abs(np.gradient(state)) + 1e-8) energy = accuracy + complexity else: raise ValueError(f"Unknown free energy component: {component}") return { "component": component, "time": time.tolist(), "state": state.tolist(), "prediction": prediction.tolist(), "energy": energy.tolist() } except Exception as e: logger.error(f"Error in free energy test: {str(e)}") raise def test_hierarchical_inference(self, level: str, coupling_strength: float, top_down_weight: float, bottom_up_weight: float) -> Dict[str, Any]: """Test hierarchical inference.""" try: logger.info(f"Testing hierarchical inference at {level} level") # Generate example multi-scale dynamics time = np.linspace(0, 100, 100) # Generate beliefs at different scales micro = 0.5 + 0.2 * np.sin(time/5) + np.random.normal(0, 0.02, 100) meso = 0.5 + 0.2 * np.sin(time/10) + np.random.normal(0, 0.02, 100) macro = 0.5 + 0.2 * np.sin(time/20) + np.random.normal(0, 0.02, 100) # Compute coupling effects if level == "micro": state = micro top_down = meso bottom_up = None elif level == "meso": state = meso top_down = macro bottom_up = micro elif level == "macro": state = macro top_down = None bottom_up = meso else: raise ValueError(f"Unknown hierarchical level: {level}") # Compute information flow prediction_error = np.zeros_like(time) information_flow = np.zeros_like(time) for t in range(1, len(time)): # Prediction error prediction_error[t] = state[t] - state[t-1] # Information flow (approximated by mutual information) if top_down is not None and bottom_up is not None: information_flow[t] = ( coupling_strength * ( top_down_weight * (state[t] - top_down[t]) + bottom_up_weight * (state[t] - bottom_up[t]) ) ) elif top_down is not None: information_flow[t] = ( coupling_strength * top_down_weight * (state[t] - top_down[t]) ) elif bottom_up is not None: information_flow[t] = ( coupling_strength * bottom_up_weight * (state[t] - bottom_up[t]) ) return { "level": level, "time": time.tolist(), "state": state.tolist(), "prediction_error": prediction_error.tolist(), "information_flow": information_flow.tolist(), "coupling_params": { "strength": coupling_strength, "top_down_weight": top_down_weight, "bottom_up_weight": bottom_up_weight } } except Exception as e: logger.error(f"Error in hierarchical inference test: {str(e)}") raise def _variational_belief_update(self, beliefs: np.ndarray, observation: np.ndarray, learning_rate: float = 0.1) -> np.ndarray: """Implement variational belief updates.""" prediction = beliefs # Simple identity mapping for demonstration prediction_error = observation - prediction updated_beliefs = beliefs + learning_rate * prediction_error return updated_beliefs / np.sum(updated_beliefs) def _sampling_belief_update(self, beliefs: np.ndarray, observation: np.ndarray, num_samples: int = 1000) -> np.ndarray: """Implement sampling-based belief updates.""" # Simple particle filter implementation particles = np.random.dirichlet(beliefs * num_samples, size=num_samples) weights = np.exp(-0.5 * np.sum((particles - observation[None, :])**2, axis=1)) weights /= np.sum(weights) resampled = particles[np.random.choice(num_samples, num_samples, p=weights)] return np.mean(resampled, axis=0) def _compute_expected_free_energy(self, state: ModelState, goal_prior: np.ndarray) -> np.ndarray: """Compute expected free energy for policy selection.""" # Simple implementation combining goal-seeking and uncertainty reduction goal_seeking = -np.abs(state.beliefs - goal_prior) uncertainty = -np.sum(state.beliefs * np.log(state.beliefs + 1e-8)) return goal_seeking - 0.5 * uncertainty def _compute_expected_free_energy_trajectory(self, state: np.ndarray, prediction: np.ndarray, precision: float) -> np.ndarray: """Compute expected free energy over a trajectory.""" accuracy = -0.5 * precision * (state - prediction)**2 entropy = -state * np.log(state + 1e-8) return accuracy - entropy def _state_to_dict(self, state: ModelState) -> Dict[str, Any]: """Convert ModelState to dictionary.""" return { "beliefs": state.beliefs.tolist(), "policies": state.policies.tolist(), "precision": state.precision, "free_energy": state.free_energy, "prediction_error": state.prediction_error } def _softmax(self, x: np.ndarray) -> np.ndarray: """Compute softmax probabilities.""" exp_x = np.exp(x - np.max(x)) return exp_x / np.sum(exp_x)