""" Main BioFirm implementation. Integrates all components for bioregional evaluation and stewardship. """ from typing import Dict, List, Optional, Tuple, Union, Any import numpy as np import yaml from pathlib import Path import logging import time import copy import networkx as nx from collections import defaultdict from .core.state_spaces import StateSpace, BioregionalState from .core.observation import ( ObservationModel, HierarchicalObservation, ObservationAggregator ) from .core.transition import ( TransitionModel, HierarchicalTransition, EcologicalConstraints ) from .core.stewardship import ( Intervention, StewardshipMetrics, StewardshipMode, AdaptiveComanagement, BioregionalStewardship ) from .visualization.plotting import BioregionalVisualization logger = logging.getLogger(__name__) class BioFirm: """Main BioFirm class for bioregional active inference.""" def __init__(self, config_path: Union[str, Path]): """ Initialize BioFirm framework. Args: config_path: Path to configuration file """ self.config = self._load_config(config_path) self._setup_components() self._initialize_state() self.visualization = BioregionalVisualization() def _load_config(self, config_path: Union[str, Path]) -> Dict: """Load configuration from file.""" try: with open(config_path, 'r') as f: config = yaml.safe_load(f) self._validate_config(config) return config except Exception as e: logger.error(f"Error loading config: {str(e)}") raise def _validate_config(self, config: Dict) -> bool: """Validate configuration structure.""" required_sections = [ 'system', 'state_spaces', 'observation', 'transition', 'stewardship', 'visualization' ] for section in required_sections: if section not in config: raise ValueError(f"Missing required config section: {section}") return True def _setup_components(self): """Set up all framework components.""" # Set up state spaces self.state_spaces = self._setup_state_spaces() # Set up observation model self.observation_model = self._setup_observation_model() # Set up transition model self.transition_model = self._setup_transition_model() # Set up stewardship self.stewardship = self._setup_stewardship() def _setup_state_spaces(self) -> Dict[str, StateSpace]: """Set up state spaces for each scale.""" spaces = {} for scale, config in self.config['state_spaces'].items(): spaces[scale] = StateSpace( dimensions=config['dimensions'], labels=config['labels'], mappings=config['mappings'], scale=scale ) return spaces def _setup_observation_model(self) -> HierarchicalObservation: """Set up hierarchical observation model.""" models = {} for scale, config in self.config['observation'].items(): models[scale] = ObservationModel( state_space=self.state_spaces[scale], observation_space=StateSpace(**config['observation_space']), likelihood_matrix=np.array(config['likelihood_matrix']), noise_model=config.get('noise_model', 'gaussian'), precision=config.get('precision', 1.0) ) scale_couplings = { tuple(k.split('_to_')): v for k, v in self.config['observation']['couplings'].items() } return HierarchicalObservation(models, scale_couplings) def _setup_transition_model(self) -> HierarchicalTransition: """Set up hierarchical transition model.""" models = {} for scale, config in self.config['transition'].items(): models[scale] = TransitionModel( state_space=self.state_spaces[scale], action_space=StateSpace(**config['action_space']), transition_matrices={ action: np.array(matrix) for action, matrix in config['transition_matrices'].items() }, temporal_horizon=config['temporal_horizon'], control_modes=config.get('control_modes', ['homeostatic']) ) scale_couplings = { tuple(k.split('_to_')): v for k, v in self.config['transition']['couplings'].items() } return HierarchicalTransition(models, scale_couplings) def _setup_stewardship(self) -> BioregionalStewardship: """Set up stewardship framework.""" config = self.config['stewardship'] # Create stewardship mode mode = AdaptiveComanagement( stakeholder_weights=config['stakeholder_weights'], learning_rate=config.get('learning_rate', 0.1) ) return BioregionalStewardship( observation_model=self.observation_model, transition_model=self.transition_model, stewardship_mode=mode ) def _initialize_state(self): """Initialize system state.""" self.current_states = {} self.target_states = {} for scale, config in self.config['system']['initial_states'].items(): self.current_states[scale] = BioregionalState(**config) for scale, config in self.config['system']['target_states'].items(): self.target_states[scale] = BioregionalState(**config) def evaluate_system(self) -> Dict[str, float]: """Evaluate current system state.""" return self.stewardship.evaluate_system( self.current_states, self.target_states ) def plan_interventions(self, constraints: Optional[Dict[str, Any]] = None ) -> Dict[str, List[Intervention]]: """Plan interventions across scales.""" if constraints is None: constraints = self.config['stewardship']['default_constraints'] return self.stewardship.plan_interventions( self.current_states, constraints ) def apply_intervention(self, intervention: Intervention, scale: str) -> BioregionalState: """Apply intervention and update state.""" # Get current state current_state = self.current_states[scale] # Predict next state next_state = self.transition_model.models[scale].predict_next_state( current_state, intervention.type ) # Apply ecological constraints constraints = EcologicalConstraints( self.config['system']['ecological_constraints'] ) constrained_state = constraints.apply_constraints(next_state) # Update current state self.current_states[scale] = constrained_state # Update metrics self.stewardship.update_metrics( self.current_states, {scale: [intervention]} ) return constrained_state def visualize_state(self, time_series: Optional[np.ndarray] = None, scale: Optional[str] = None): """Visualize system state.""" if scale is None: scale = list(self.current_states.keys())[0] return self.visualization.plot_system_state( self.current_states[scale], time_series ) def visualize_intervention(self, before_state: BioregionalState, after_state: BioregionalState, intervention: Intervention): """Visualize intervention impacts.""" return self.visualization.plot_intervention_impacts( before_state, after_state, intervention.__dict__ ) def visualize_cross_scale(self, states: Optional[Dict[str, np.ndarray]] = None): """Visualize cross-scale dynamics.""" if states is None: states = { scale: np.array([state.to_vector()]) for scale, state in self.current_states.items() } scales = list(states.keys()) interactions = np.array([ [self.transition_model.scale_couplings.get( (scale1, scale2), 0.0 ) for scale2 in scales] for scale1 in scales ]) return self.visualization.plot_cross_scale_dynamics( states, scales, interactions ) class HierarchicalProcessor: """Implements hierarchical processing across scales""" def __init__(self, scales: List[str]): self.scales = scales self.processors = { scale: ScaleProcessor(scale) for scale in scales } self.couplings = self._initialize_couplings() def _initialize_couplings(self) -> Dict[Tuple[str, str], float]: """Initialize cross-scale coupling strengths""" return { (scale1, scale2): self._compute_coupling(scale1, scale2) for scale1 in self.scales for scale2 in self.scales if scale1 != scale2 } def _compute_coupling(self, scale1: str, scale2: str) -> float: """Compute coupling strength between scales""" # Implementation based on scale relationship pass def _get_messages(self, scale: str, beliefs: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]: """Get messages from other scales""" messages = {} for other_scale in self.scales: if other_scale != scale: messages[other_scale] = self._compute_message( scale, other_scale, beliefs.get(other_scale) ) return messages def process_hierarchy(self, observations: Dict[str, np.ndarray] ) -> Dict[str, np.ndarray]: """Process observations across hierarchical levels""" beliefs = {} for scale in self.scales: beliefs[scale] = self.processors[scale].update_beliefs( observations[scale], self._get_messages(scale, beliefs) ) return beliefs class BeliefPropagator: """Implements belief propagation across network""" def __init__(self, network: nx.Graph): self.network = network self.messages = defaultdict(dict) self.convergence_threshold = 1e-6 def _update_messages(self): """Update messages between nodes""" new_messages = defaultdict(dict) for node in self.network.nodes(): for neighbor in self.network.neighbors(node): new_messages[node][neighbor] = self._compute_message( node, neighbor ) self.messages = new_messages def _update_beliefs(self): """Update node beliefs based on messages""" for node in self.network.nodes(): self.network.nodes[node]['belief'] = self._compute_belief(node) def _check_convergence(self) -> bool: """Check if belief propagation has converged""" # Implementation of convergence check pass def propagate_beliefs(self, initial_beliefs: Dict[str, np.ndarray], max_iterations: int = 100): """Propagate beliefs through network""" self._initialize_beliefs(initial_beliefs) for iteration in range(max_iterations): self._update_messages() self._update_beliefs() if self._check_convergence(): logger.info(f"Converged after {iteration + 1} iterations") break return self._get_final_beliefs() class AdaptiveController: """Implements adaptive control mechanisms""" def __init__(self, control_params: Dict[str, Any], learning_rate: float = 0.01): self.params = control_params self.learning_rate = learning_rate self.history = [] def _compute_gradient(self, performance: PerformanceMetrics) -> Dict[str, np.ndarray]: """Compute gradient for parameter updates""" gradients = {} for param_name, param_value in self.params.items(): gradients[param_name] = self._estimate_gradient( param_name, param_value, performance ) return gradients def _update_params(self, gradient: Dict[str, np.ndarray]): """Update control parameters using gradient""" for param_name, grad in gradient.items(): self.params[param_name] -= self.learning_rate * grad def _store_adaptation(self, context: SystemContext): """Store adaptation history""" self.history.append({ 'timestamp': context.timestamp, 'params': copy.deepcopy(self.params), 'context': context.to_dict() }) def adapt_control(self, performance: PerformanceMetrics, context: SystemContext): """Adapt control parameters based on performance""" gradient = self._compute_gradient(performance) self._update_params(gradient) self._store_adaptation(context) return self.params class MetaLearner: """Implements meta-learning capabilities""" def __init__(self, base_learners: List[BaseLearner], meta_params: Dict[str, Any]): self.learners = base_learners self.meta_params = meta_params self.meta_model = self._initialize_meta_model() def _compute_meta_gradient(self, experience: Experience) -> Dict[str, np.ndarray]: """Compute meta-learning gradients""" # Implementation of meta-gradient computation pass def _update_meta_params(self, gradient: Dict[str, np.ndarray]): """Update meta-learning parameters""" # Implementation of meta-parameter updates pass def _adapt_learners(self, performance: Dict[str, float]): """Adapt base learners using meta-knowledge""" # Implementation of learner adaptation pass def meta_update(self, experience: Experience, performance: Dict[str, float]): """Update meta-learning parameters""" meta_gradient = self._compute_meta_gradient(experience) self._update_meta_params(meta_gradient) self._adapt_learners(performance) return self.meta_params class StateManager: """Manages bioregional state transitions""" def __init__(self, state_config: Dict[str, Any], constraints: List[Constraint]): self.config = state_config self.constraints = constraints self.state_history = [] def _compute_next_state(self, current_state: BioregionalState, action: Action) -> BioregionalState: """Compute next state based on current state and action""" # Implementation of state transition pass def _apply_constraints(self, proposed_state: BioregionalState) -> BioregionalState: """Apply constraints to proposed state""" valid_state = proposed_state for constraint in self.constraints: valid_state = constraint.apply(valid_state) return valid_state def _record_transition(self, current_state: BioregionalState, action: Action, next_state: BioregionalState): """Record state transition""" self.state_history.append({ 'timestamp': time.time(), 'current_state': current_state.to_dict(), 'action': action.to_dict(), 'next_state': next_state.to_dict() }) def transition_state(self, current_state: BioregionalState, action: Action) -> BioregionalState: """Execute state transition with constraints""" proposed_state = self._compute_next_state(current_state, action) valid_state = self._apply_constraints(proposed_state) self._record_transition(current_state, action, valid_state) return valid_state class ObservationProcessor: """Processes multi-scale observations""" def __init__(self, observation_models: Dict[str, ObservationModel], aggregator: ObservationAggregator): self.models = observation_models self.aggregator = aggregator def process_observations(self, raw_observations: Dict[str, np.ndarray], uncertainty: Dict[str, float]) -> Dict[str, np.ndarray]: """Process and aggregate observations""" processed = { scale: model.process(obs, uncertainty[scale]) for scale, (obs, model) in zip( raw_observations.items(), self.models.items() ) } return self.aggregator.aggregate(processed) class InterventionPlanner: """Plans multi-scale interventions""" def __init__(self, planning_horizon: int, objective_weights: Dict[str, float]): self.horizon = planning_horizon self.weights = objective_weights self.planner = self._initialize_planner() def _compute_objectives(self, current_state: BioregionalState) -> Dict[str, float]: """Compute objective values for current state""" # Implementation of objective computation pass def _validate_plan(self, plan: List[Intervention]) -> List[Intervention]: """Validate and adjust intervention plan""" # Implementation of plan validation pass def plan_interventions(self, current_state: BioregionalState, constraints: Dict[str, Any]) -> List[Intervention]: """Generate intervention plan""" objectives = self._compute_objectives(current_state) plan = self.planner.optimize(objectives, constraints) return self._validate_plan(plan)