# Reasoning tool for scientific analysis

from typing import Dict, Any, List, Optional
from .base_tool import BaseTool

class ReasoningTool(BaseTool):
    """Tool for structured scientific reasoning and analysis.
    
    This tool helps agents perform structured reasoning about scientific hypotheses,
    including causal analysis, counter-factual reasoning, and identification of
    potential confounding variables.
    """
    
    def __init__(self):
        """Initialize the reasoning tool."""
        super().__init__(
            name="reasoning",
            description="Perform structured scientific reasoning and analysis"
        )
    
    def execute(
        self, 
        reasoning_type: str, 
        hypothesis: str, 
        context: Optional[str] = None,
        depth: int = 2
    ) -> Dict[str, Any]:
        """Execute structured scientific reasoning on a hypothesis.
        
        Args:
            reasoning_type: Type of reasoning to perform (causal, counterfactual, confounders)
            hypothesis: The scientific hypothesis to analyze
            context: Optional additional context to consider
            depth: Depth of reasoning (1-3, with 3 being the deepest)
            
        Returns:
            A dictionary containing the reasoning results
        """
        self.logger.info(f"Performing {reasoning_type} reasoning on hypothesis: {hypothesis[:50]}...")
        
        # TODO: Implement actual reasoning functionality
        # This would use structured reasoning frameworks and potentially specialized models
        
        # Mock implementation for now
        result = {
            "reasoning_type": reasoning_type,
            "hypothesis": hypothesis,
            "analysis": f"Structured {reasoning_type} analysis of the hypothesis.",
            "insights": [
                f"Key insight 1 about {hypothesis[:30]}...",
                f"Key insight 2 about {hypothesis[:30]}...",
                f"Key insight 3 about {hypothesis[:30]}..."
            ],
            "confidence": 0.85,
            "recommendations": [
                "Consider examining related factor X",
                "The hypothesis may be strengthened by addressing Y"
            ]
        }
        
        if reasoning_type == "causal":
            result["causal_factors"] = [
                {"factor": "Factor A", "effect_direction": "positive", "confidence": 0.9},
                {"factor": "Factor B", "effect_direction": "negative", "confidence": 0.7}
            ]
        
        elif reasoning_type == "counterfactual":
            result["counterfactual_scenarios"] = [
                {"scenario": "If X were not present", "outcome": "Y would likely decrease"},
                {"scenario": "If Z were increased", "outcome": "Y would likely increase"}
            ]
        
        elif reasoning_type == "confounders":
            result["potential_confounders"] = [
                {"confounder": "Confounder A", "impact": "high", "explanation": "Explains both X and Y"},
                {"confounder": "Confounder B", "impact": "medium", "explanation": "Partially explains Z"}
            ]
        
        return result
    
    def get_parameters_schema(self) -> Dict[str, Any]:
        """Get the JSON schema for the tool's parameters.
        
        Returns:
            A dictionary containing the JSON schema
        """
        return {
            "type": "object",
            "properties": {
                "reasoning_type": {
                    "type": "string",
                    "description": "Type of reasoning to perform",
                    "enum": ["causal", "counterfactual", "confounders", "general"]
                },
                "hypothesis": {
                    "type": "string",
                    "description": "The scientific hypothesis to analyze"
                },
                "context": {
                    "type": "string",
                    "description": "Optional additional context to consider"
                },
                "depth": {
                    "type": "integer",
                    "description": "Depth of reasoning (1-3, with 3 being the deepest)",
                    "minimum": 1,
                    "maximum": 3,
                    "default": 2
                }
            },
            "required": ["reasoning_type", "hypothesis"]
        }