"""
Causal Rules Core Module
=======================

This module provides advanced causal rule processing capabilities,
including causal inference algorithms and graph operations.
"""

import networkx as nx
from typing import Dict, List, Set
import copy

from .engine import CausalRuleSet


class CausalInference:
    """
    Implements causal inference algorithms for reasoning with causal rules.
    
    This class provides methods for performing causal inference using
    the do-calculus, counterfactual reasoning, and other causal methods.
    """
    
    @staticmethod
    def do_operation(
        ruleset: CausalRuleSet,
        intervention: Dict[str, float]
    ) -> CausalRuleSet:
        """
        Perform Pearl's do-operation on a causal ruleset.
        
        This simulates an intervention where we set variables to specific values,
        breaking their dependencies on parent nodes in the causal graph.
        
        Args:
            ruleset: The original causal ruleset
            intervention: Dictionary mapping variable names to intervention values
            
        Returns:
            Modified causal ruleset after intervention
        """
        # Create a copy of the ruleset to avoid modifying the original
        new_ruleset = copy.deepcopy(ruleset)
        
        # For each variable in the intervention
        for var in intervention:
            # Remove all incoming edges to the intervened variable
            # This simulates setting the variable to a fixed value
            for rule in list(new_ruleset.rules):
                if rule.effect == var:
                    new_ruleset.remove_rule(rule.cause, rule.effect)
        
        return new_ruleset
    
    @staticmethod
    def counterfactual(
        ruleset: CausalRuleSet,
        factual_values: Dict[str, float],
        intervention: Dict[str, float],
        query_variables: List[str]
    ) -> Dict[str, float]:
        """
        Perform counterfactual inference.
        
        This answers "what would have happened if" questions by considering
        both the factual world and a hypothetical world with interventions.
        
        Args:
            ruleset: The causal ruleset
            factual_values: Observed values in the factual world
            intervention: Dictionary mapping variable names to intervention values
            query_variables: Variables to query in the counterfactual world
            
        Returns:
            Dictionary mapping query variables to their counterfactual values
        """
        # Step 1: Abduction - compute posterior of exogenous variables
        # (In a real implementation, this would use actual inference algorithms)
        exogenous = CausalInference._compute_exogenous(ruleset, factual_values)
        
        # Step 2: Action - apply intervention to get modified graph
        modified_ruleset = CausalInference.do_operation(ruleset, intervention)
        
        # Step 3: Prediction - use exogenous and modified graph to predict counterfactuals
        counterfactual_values = CausalInference._predict_from_exogenous(
            modified_ruleset,
            exogenous,
            intervention,
            query_variables
        )
        
        return counterfactual_values
    
    @staticmethod
    def _compute_exogenous(
        ruleset: CausalRuleSet,
        observed_values: Dict[str, float]
    ) -> Dict[str, float]:
        """
        Compute the values of exogenous variables given observations.
        
        In a real implementation, this would use probabilistic inference algorithms.
        This is a simplified placeholder version.
        
        Args:
            ruleset: The causal ruleset
            observed_values: Observed variable values
            
        Returns:
            Dictionary mapping exogenous variables to their inferred values
        """
        # Placeholder implementation
        # In real causal inference, this would be much more sophisticated
        exogenous = {}
        
        # Find root nodes (exogenous variables) in the causal graph
        all_effects = {rule.effect for rule in ruleset.rules}
        all_variables = ruleset.variables
        
        # Variables that are never effects are exogenous
        exogenous_vars = all_variables - all_effects
        
        # Assign random values to exogenous variables
        # In reality, these would be inferred to be consistent with observations
        for var in exogenous_vars:
            if var in observed_values:
                exogenous[var] = observed_values[var]
            else:
                exogenous[var] = 0.0  # Default value
        
        return exogenous
    
    @staticmethod
    def _predict_from_exogenous(
        ruleset: CausalRuleSet,
        exogenous: Dict[str, float],
        intervention: Dict[str, float],
        query_variables: List[str]
    ) -> Dict[str, float]:
        """
        Predict query variable values in a modified graph given exogenous values.
        
        Args:
            ruleset: The (possibly modified) causal ruleset
            exogenous: Values of exogenous variables
            intervention: Intervention values
            query_variables: Variables to predict
            
        Returns:
            Dictionary mapping query variables to their predicted values
        """
        # Create a combined dict of known values
        known_values = {**exogenous, **intervention}
        
        # Get a causal ordering of variables
        ordering = CausalInference._get_causal_ordering(ruleset)
        
        # Predict values in causal order
        for var in ordering:
            if var not in known_values:
                # Compute value based on causal parents
                parent_rules = ruleset.get_rules_for_effect(var)
                
                # Simple weighted sum model for illustration
                # Real systems would use more complex functional relationships
                value = 0.0
                for rule in parent_rules:
                    if rule.cause in known_values:
                        value += known_values[rule.cause] * rule.strength
                
                known_values[var] = value
        
        # Return only requested variables
        return {var: known_values.get(var, 0.0) for var in query_variables}
    
    @staticmethod
    def _get_causal_ordering(ruleset: CausalRuleSet) -> List[str]:
        """
        Get a causal ordering of variables (topological sort).
        
        Args:
            ruleset: The causal ruleset
            
        Returns:
            List of variables in causal order
        """
        # Build directed graph
        G = nx.DiGraph()
        
        # Add all variables as nodes
        for var in ruleset.variables:
            G.add_node(var)
        
        # Add edges from causes to effects
        for rule in ruleset.rules:
            G.add_edge(rule.cause, rule.effect)
        
        # Get topological sort (causal ordering)
        try:
            return list(nx.topological_sort(G))
        except nx.NetworkXUnfeasible:
            # Graph has cycles
            return list(ruleset.variables)


class CausalGraphAnalysis:
    """
    Tools for analyzing causal graphs and extracting insights.
    """
    
    @staticmethod
    def identify_confounders(ruleset: CausalRuleSet, var1: str, var2: str) -> Set[str]:
        """
        Identify potential confounding variables between two variables.
        
        Args:
            ruleset: The causal ruleset
            var1: First variable
            var2: Second variable
            
        Returns:
            Set of potential confounding variables
        """
        # Convert ruleset to graph
        G = nx.DiGraph()
        
        # Add edges
        for rule in ruleset.rules:
            G.add_edge(rule.cause, rule.effect)
        
        # Find common ancestors
        ancestors1 = nx.ancestors(G, var1) if var1 in G else set()
        ancestors2 = nx.ancestors(G, var2) if var2 in G else set()
        
        # Common ancestors are potential confounders
        return ancestors1.intersection(ancestors2)
    
    @staticmethod
    def check_d_separation(
        ruleset: CausalRuleSet, 
        var1: str, 
        var2: str, 
        conditioned_on: Set[str] = None
    ) -> bool:
        """
        Check if two variables are d-separated given a conditioning set.
        
        Args:
            ruleset: The causal ruleset
            var1: First variable
            var2: Second variable
            conditioned_on: Variables to condition on
            
        Returns:
            True if var1 and var2 are d-separated given conditioned_on
        """
        conditioned_on = conditioned_on or set()
        
        # Convert ruleset to graph
        G = nx.DiGraph()
        
        # Add edges
        for rule in ruleset.rules:
            G.add_edge(rule.cause, rule.effect)
        
        # Check d-separation
        # This is a simplification - real d-separation would account for all paths
        try:
            return not nx.has_path(G, var1, var2)
        except nx.NetworkXError:
            # One or both variables not in graph
            return True
