src/bio_knowledge/tcl_quantum_integrator.py

"""
TCL-Quantum H-bond Integration System

This module integrates Thought-Compression Language (TCL) with the quantum
hydrogen bond protein folding engine to enable superhuman hypothesis generation.

Key innovation: TCL compresses complex biological causality into symbolic
representations, while quantum H-bond analysis reveals hidden molecular mechanisms.
"""

import time
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field

from ..thought_compression.tcl_engine import ThoughtCompressionEngine
from ..thought_compression.tcl_types import TCLExecutionContext, CognitiveMetrics
from ..thought_compression.tcl_symbols import TCLSymbol, SymbolType
from ..multiversal.protein_folding_engine import (
    ProteinFoldingEngine, 
    ProteinStructure,
    FoldingParameters
)
from .biological_database import (
    BiologicalKnowledgeBase,
    CancerPathway,
    Drug,
    Protein,
    MolecularInteraction,
    InteractionType,
    PathwayType
)


@dataclass
class QuantumProteinAnalysis:
    """Results from quantum H-bond analysis of a protein"""
    protein: Protein
    structure: Optional[ProteinStructure] = None
    quantum_hbond_energy: float = 0.0
    classical_hbond_energy: float = 0.0
    quantum_advantage: float = 0.0
    coherence_strength: float = 0.0
    topological_protection: float = 0.0
    collective_effects: float = 0.0
    
    # TCL compression results
    compressed_symbols: List[str] = field(default_factory=list)
    causality_depth: int = 0
    
    def to_dict(self) -> Dict:
        return {
            "protein": self.protein.gene_name,
            "quantum_hbond_energy": self.quantum_hbond_energy,
            "classical_hbond_energy": self.classical_hbond_energy,
            "quantum_advantage": self.quantum_advantage,
            "coherence_strength": self.coherence_strength,
            "topological_protection": self.topological_protection,
            "collective_effects": self.collective_effects,
            "compressed_symbols": self.compressed_symbols,
            "causality_depth": self.causality_depth
        }


@dataclass
class TCLCausalChain:
    """Compressed causal chain in TCL notation"""
    chain_id: str
    symbols: List[TCLSymbol]
    causal_steps: List[str]  # Human-readable causal steps
    tcl_expression: str  # Compressed TCL expression
    quantum_enhancement: float  # How much quantum analysis improves this chain
    biological_validity: float  # How biologically plausible (0-1)
    novelty_score: float  # How novel is this hypothesis (0-1)
    
    def to_dict(self) -> Dict:
        return {
            "chain_id": self.chain_id,
            "symbols": [s.name for s in self.symbols],
            "causal_steps": self.causal_steps,
            "tcl_expression": self.tcl_expression,
            "quantum_enhancement": self.quantum_enhancement,
            "biological_validity": self.biological_validity,
            "novelty_score": self.novelty_score
        }


class TCLQuantumIntegrator:
    """
    Integrates TCL with quantum H-bond analysis for cancer hypothesis generation
    
    This system:
    1. Compresses biological knowledge into TCL symbols
    2. Uses quantum H-bond analysis to find hidden molecular mechanisms
    3. Generates causal chains from cancer to cure
    4. Scores hypotheses by biological validity and novelty
    """
    
    def __init__(self, bio_kb: BiologicalKnowledgeBase):
        self.bio_kb = bio_kb
        
        # Initialize TCL engine
        self.tcl_engine = ThoughtCompressionEngine(enable_quantum_mode=True)
        self.session_id = self.tcl_engine.create_session("cancer_researcher", cognitive_level=0.8)
        
        # Initialize quantum H-bond engine
        self.quantum_engine = ProteinFoldingEngine(
            artifacts_dir="./protein_folding_analysis",
            params=FoldingParameters()
        )
        
        # Store analysis results
        self.quantum_analyses: Dict[str, QuantumProteinAnalysis] = {}
        self.causal_chains: List[TCLCausalChain] = []
        
        # Load biological knowledge into TCL
        self._load_biological_knowledge_into_tcl()
    
    def _load_biological_knowledge_into_tcl(self):
        """Compress biological knowledge into TCL symbols"""
        
        context = self.tcl_engine.sessions[self.session_id]
        
        # Create primitive symbols for cancer biology
        primitives = [
            # Cancer concepts
            TCLSymbol("Κ", "cancer", SymbolType.CONCEPT, "Cancer disease state", 
                     {"proliferation": 0.9, "metastasis": 0.7}, [], 0.95, 1.0),
            TCLSymbol("Ω", "cure", SymbolType.CONCEPT, "Therapeutic cure", 
                     {"remission": 0.95, "survival": 0.9}, [], 0.98, 1.0),
            TCLSymbol("Δ", "mutation", SymbolType.CONCEPT, "Genetic mutation", 
                     {"dna_damage": 0.8, "alteration": 0.75}, [], 0.85, 0.9),
            
            # Molecular mechanisms
            TCLSymbol("Ψ", "protein", SymbolType.CONCEPT, "Protein molecular entity", 
                     {"structure": 0.9, "function": 0.95}, [], 0.9, 1.0),
            TCLSymbol("Φ", "phosphorylation", SymbolType.PRIMITIVE, "Phosphate group transfer", 
                     {"activation": 0.85, "regulation": 0.9}, [], 0.8, 0.85),
            TCLSymbol("Θ", "hydrogen_bond", SymbolType.PRIMITIVE, "Quantum hydrogen bond", 
                     {"coherence": 0.9, "binding": 0.95}, [], 0.92, 1.0),
            TCLSymbol("Λ", "quantum_coherence", SymbolType.PRIMITIVE, "Quantum coherent state", 
                     {"delocalization": 0.95, "entanglement": 0.9}, [], 0.97, 1.0),
            
            # Pathway symbols
            TCLSymbol("Π", "proliferation", SymbolType.CONCEPT, "Cell proliferation pathway", 
                     {"growth": 0.9, "division": 0.85}, [], 0.88, 0.9),
            TCLSymbol("Α", "apoptosis", SymbolType.CONCEPT, "Programmed cell death", 
                     {"death": 0.95, "elimination": 0.9}, [], 0.92, 0.95),
            TCLSymbol("Ξ", "angiogenesis", SymbolType.CONCEPT, "Blood vessel formation", 
                     {"vascularization": 0.9, "tumor_growth": 0.85}, [], 0.87, 0.9),
            
            # Drug action symbols
            TCLSymbol("Δρ", "inhibition", SymbolType.CAUSALITY, "Molecular inhibition", 
                     {"block": 0.9, "suppress": 0.85}, [], 0.85, 0.9),
            TCLSymbol("Σα", "activation", SymbolType.CAUSALITY, "Molecular activation", 
                     {"enhance": 0.9, "stimulate": 0.85}, [], 0.85, 0.9),
            TCLSymbol("Ξη", "target", SymbolType.CAUSALITY, "Molecular targeting", 
                     {"binding": 0.95, "specificity": 0.9}, [], 0.9, 0.95),
        ]
        
        for primitive in primitives:
            context.symbols.add_symbol(primitive)
        
        # Add causality relationships
        causality_map = context.causality
        # Use symbol IDs for causal links
        cancer_id = next((s.id for s in primitives if s.name == "Κ"), "")
        proliferation_id = next((s.id for s in primitives if s.name == "Π"), "")
        mutation_id = next((s.id for s in primitives if s.name == "Δ"), "")
        cure_id = next((s.id for s in primitives if s.name == "Ω"), "")
        apoptosis_id = next((s.id for s in primitives if s.name == "Α"), "")
        inhibition_id = next((s.id for s in primitives if s.name == "Δρ"), "")
        activation_id = next((s.id for s in primitives if s.name == "Σα"), "")
        quantum_id = next((s.id for s in primitives if s.name == "Λ"), "")
        hbond_id = next((s.id for s in primitives if s.name == "Θ"), "")
        protein_id = next((s.id for s in primitives if s.name == "Ψ"), "")

        if cancer_id and proliferation_id:
            causality_map.add_causal_link(cancer_id, proliferation_id, 0.95)  # Cancer causes proliferation
        if mutation_id and cancer_id:
            causality_map.add_causal_link(mutation_id, cancer_id, 0.85)  # Mutations cause cancer
        if proliferation_id and cancer_id:
            causality_map.add_causal_link(proliferation_id, cancer_id, 0.90)  # Proliferation causes cancer
        if apoptosis_id and cure_id:
            causality_map.add_causal_link(apoptosis_id, cure_id, 0.75)  # Apoptosis leads to cure
        if inhibition_id and proliferation_id:
            causality_map.add_causal_link(inhibition_id, proliferation_id, 0.85)  # Inhibition suppresses proliferation
        if activation_id and apoptosis_id:
            causality_map.add_causal_link(activation_id, apoptosis_id, 0.90)  # Activation induces apoptosis
        if quantum_id and hbond_id:
            causality_map.add_causal_link(quantum_id, hbond_id, 0.92)  # Quantum coherence enhances H-bonds
        if hbond_id and protein_id:
            causality_map.add_causal_link(hbond_id, protein_id, 0.95)  # H-bonds stabilize proteins
        
        print(f"✅ Loaded {len(primitives)} TCL symbols from biological knowledge")
        print(f"✅ Established causal relationships between cancer biology concepts")
    
    def analyze_protein_quantum_properties(self, protein: Protein, 
                                          sequence: Optional[str] = None) -> QuantumProteinAnalysis:
        """
        Analyze a protein using quantum H-bond force law
        
        This uses the REAL quantum H-bond analysis from protein_folding_engine
        """
        
        # Use default sequence if not provided (simulated)
        if sequence is None:
            # Create a representative sequence for analysis
            # In real system, this would fetch from UniProt
            sequence = "ACDEFGHIKLMNPQRSTVWY" * (len(protein.gene_name) + 2)
        
        # Create initial structure
        structure = self.quantum_engine.initialize_extended_chain(sequence, seed=42)
        
        # Analyze quantum H-bond properties
        energy_result = self.quantum_engine.energy(structure, return_breakdown=True)
        breakdown = energy_result["energy_breakdown"]
        quantum_stats = energy_result["quantum_hbond_stats"]
        
        # Extract quantum properties
        quantum_hbond = breakdown.get("hydrogen_bond_quantum_coherence", 0.0)
        classical_hbond = breakdown.get("hydrogen_bond_classical", 0.0)
        quantum_advantage = classical_hbond - quantum_hbond  # More negative is better
        
        analysis = QuantumProteinAnalysis(
            protein=protein,
            structure=structure,
            quantum_hbond_energy=quantum_hbond,
            classical_hbond_energy=classical_hbond,
            quantum_advantage=quantum_advantage,
            coherence_strength=quantum_stats.get("avg_coherence_strength", 0.0),
            topological_protection=quantum_stats.get("avg_topological_protection", 0.0),
            collective_effects=quantum_stats.get("avg_collective_effect", 0.0)
        )
        
        # Compress protein concept into TCL
        compression_result = self.tcl_engine.compress_concept(
            self.session_id, 
            f"{protein.gene_name} protein: {protein.function}"
        )
        
        analysis.compressed_symbols = compression_result["compressed_symbols"]
        
        # Get causality depth
        causality_result = self.tcl_engine.generate_causal_chain(
            self.session_id, 
            protein.gene_name,
            depth=5
        )
        analysis.causality_depth = causality_result.get("chain_complexity", 0)
        
        # Store analysis
        self.quantum_analyses[protein.uniprot_id] = analysis
        
        return analysis
    
    def generate_cancer_to_cure_causal_chain(self, 
                                            target_protein: Protein,
                                            pathway: CancerPathway,
                                            drug: Optional[Drug] = None) -> TCLCausalChain:
        """
        Generate a causal chain from cancer to cure using TCL compression
        
        This is the CORE innovation: compressing complex biology into
        symbolic causal chains that reveal novel therapeutic strategies
        """
        
        chain_id = f"chain_{target_protein.gene_name}_{int(time.time())}"
        
        # Analyze target protein quantum properties
        quantum_analysis = self.analyze_protein_quantum_properties(target_protein)
        
        # Build causal steps based on biological knowledge
        causal_steps = []
        
        # Step 1: Cancer initiation
        if target_protein.is_oncogene:
            causal_steps.append(f"Cancer mutations activate {target_protein.gene_name} (oncogene)")
        else:
            causal_steps.append(f"Cancer mutations inactivate {target_protein.gene_name} (tumor suppressor)")
        
        # Step 2: Pathway dysregulation
        causal_steps.append(f"Dysregulation in {pathway.name} pathway")
        
        # Step 3: Molecular mechanism
        if pathway.pathway_type == PathwayType.PROLIFERATION:
            causal_steps.append("Uncontrolled cell proliferation")
        elif pathway.pathway_type == PathwayType.APOPTOSIS:
            causal_steps.append("Inhibition of programmed cell death")
        elif pathway.pathway_type == PathwayType.ANGIOGENESIS:
            causal_steps.append("Tumor vascularization and growth")
        
        # Step 4: Quantum H-bond insight
        if quantum_analysis.quantum_advantage < -0.1:  # Significant quantum effect
            causal_steps.append(f"Quantum H-bond coherence in {target_protein.gene_name} enables enhanced binding")
        
        # Step 5: Therapeutic intervention
        if drug:
            causal_steps.append(f"Drug {drug.name} targets {target_protein.gene_name}")
            if drug.affects_quantum_coherence:
                causal_steps.append(f"Drug modulates quantum H-bond networks")
        else:
            causal_steps.append(f"Therapeutic intervention targeting {target_protein.gene_name}")
        
        # Step 6: Cure outcome
        causal_steps.append("Restoration of normal cellular behavior")
        causal_steps.append("Tumor regression and patient recovery")
        
        # Compress causal chain into TCL expression
        tcl_expression = self._compress_causal_chain_to_tcl(
            causal_steps, 
            target_protein, 
            pathway,
            drug
        )
        
        # Calculate scores
        biological_validity = self._calculate_biological_validity(
            target_protein, pathway, drug, quantum_analysis
        )
        
        novelty_score = self._calculate_novelty_score(
            target_protein, pathway, drug, quantum_analysis
        )
        
        quantum_enhancement = abs(quantum_analysis.quantum_advantage) if quantum_analysis.quantum_advantage < 0 else 0.0
        
        # Extract symbols for the chain
        context = self.tcl_engine.sessions[self.session_id]
        symbols = [
            context.symbols.symbols.get("Κ"),  # Cancer
            context.symbols.symbols.get("Ω"),  # Cure
            context.symbols.symbols.get("Ψ"),  # Protein
        ]
        symbols = [s for s in symbols if s is not None]
        
        chain = TCLCausalChain(
            chain_id=chain_id,
            symbols=symbols,
            causal_steps=causal_steps,
            tcl_expression=tcl_expression,
            quantum_enhancement=quantum_enhancement,
            biological_validity=biological_validity,
            novelty_score=novelty_score
        )
        
        self.causal_chains.append(chain)
        
        return chain
    
    def _compress_causal_chain_to_tcl(self, 
                                     causal_steps: List[str],
                                     protein: Protein,
                                     pathway: CancerPathway,
                                     drug: Optional[Drug]) -> str:
        """Compress causal chain into compact TCL expression"""
        
        # Build TCL expression step by step
        tcl_parts = []
        
        # Start with cancer symbol
        tcl_parts.append("Κ")  # Cancer
        
        # Add mutation effect
        tcl_parts.append("→")
        tcl_parts.append("Δ")  # Mutation
        
        # Add protein
        tcl_parts.append("→")
        tcl_parts.append(f"Ψ({protein.gene_name})")
        
        # Add pathway effect
        if pathway.pathway_type == PathwayType.PROLIFERATION:
            tcl_parts.append("→")
            tcl_parts.append("Π")  # Proliferation
        elif pathway.pathway_type == PathwayType.APOPTOSIS:
            tcl_parts.append("→")
            tcl_parts.append("~Α")  # Inhibited apoptosis
        
        # Add quantum enhancement if relevant
        quantum_analysis = self.quantum_analyses.get(protein.uniprot_id)
        if quantum_analysis and quantum_analysis.quantum_advantage < -0.1:
            tcl_parts.append("∧")
            tcl_parts.append("Λ(Θ)")  # Quantum coherence of H-bonds
        
        # Add therapeutic intervention
        tcl_parts.append("→")
        if drug:
            tcl_parts.append(f"Δρ({drug.name})")  # Inhibition by drug
        else:
            tcl_parts.append("Δρ")  # Inhibition
        
        # Add apoptosis induction
        tcl_parts.append("→")
        tcl_parts.append("Σα")  # Activation
        tcl_parts.append("→")
        tcl_parts.append("Α")  # Apoptosis
        
        # End with cure
        tcl_parts.append("→")
        tcl_parts.append("Ω")  # Cure
        
        # Add quantifiers for causality
        tcl_expression = f"∀x(Δx → Κx ∧ Ψ({protein.gene_name})x)"
        tcl_expression += f" ∧ ∃y(Δρy → Αy → Ωy)"
        
        # Add quantum enhancement
        if quantum_analysis and quantum_analysis.quantum_advantage < -0.1:
            tcl_expression += f" ∧ Λ(Θ)"
        
        return tcl_expression
    
    def _calculate_biological_validity(self, 
                                       protein: Protein,
                                       pathway: CancerPathway,
                                       drug: Optional[Drug],
                                       quantum_analysis: QuantumProteinAnalysis) -> float:
        """Calculate how biologically valid a hypothesis is"""
        
        validity_score = 0.0
        
        # Protein relevance (high if known oncogene/tumor suppressor)
        if protein.is_oncogene or protein.is_tumor_suppressor:
            validity_score += 0.3
        else:
            validity_score += 0.1
        
        # Pathway relevance
        if pathway.pathway_type in [PathwayType.PROLIFERATION, PathwayType.APOPTOSIS]:
            validity_score += 0.25
        else:
            validity_score += 0.15
        
        # Drug relevance (higher if FDA approved)
        if drug:
            if drug.fda_approved:
                validity_score += 0.25
            elif drug.clinical_status == "clinical_trial":
                validity_score += 0.15
            else:
                validity_score += 0.05
            
            # Check if drug actually targets this protein
            if protein.uniprot_id in drug.target_proteins:
                validity_score += 0.1
        
        # Quantum enhancement (higher if quantum effects are significant)
        if quantum_analysis.quantum_advantage < -0.1:
            validity_score += 0.1
        
        return min(1.0, validity_score)
    
    def _calculate_novelty_score(self,
                                 protein: Protein,
                                 pathway: CancerPathway,
                                 drug: Optional[Drug],
                                 quantum_analysis: QuantumProteinAnalysis) -> float:
        """Calculate how novel a hypothesis is"""
        
        novelty_score = 0.0
        
        # Novelty from quantum H-bond insights (high novelty)
        if quantum_analysis.quantum_advantage < -0.2:
            novelty_score += 0.4
        elif quantum_analysis.quantum_advantage < -0.1:
            novelty_score += 0.25
        
        # Novelty from targeting under-explored proteins
        if drug is None:
            novelty_score += 0.3  # Novel target
        elif not drug.fda_approved and drug.clinical_status == "research":
            novelty_score += 0.25
        
        # Novelty from quantum mechanisms
        if drug and drug.affects_quantum_coherence:
            novelty_score += 0.15
        
        # Novelty from pathway combinations
        if pathway.pathway_type == PathwayType.METABOLISM or pathway.pathway_type == PathwayType.DNA_REPAIR:
            novelty_score += 0.15
        
        return min(1.0, novelty_score)
    
    def get_top_hypotheses(self, n: int = 10) -> List[TCLCausalChain]:
        """Get top hypotheses by combined score"""
        
        scored_chains = []
        for chain in self.causal_chains:
            # Combined score: 60% biological validity + 40% novelty
            combined_score = (0.6 * chain.biological_validity + 
                             0.4 * chain.novelty_score)
            scored_chains.append((combined_score, chain))
        
        scored_chains.sort(key=lambda x: x[0], reverse=True)
        
        return [chain for score, chain in scored_chains[:n]]
    
    def generate_summary_report(self) -> Dict[str, Any]:
        """Generate comprehensive summary of analysis"""
        
        top_hypotheses = self.get_top_hypotheses(5)
        
        return {
            "session_id": self.session_id,
            "bio_knowledge_stats": self.bio_kb.get_statistics(),
            "quantum_analyses_performed": len(self.quantum_analyses),
            "causal_chains_generated": len(self.causal_chains),
            "avg_biological_validity": sum(c.biological_validity for c in self.causal_chains) / len(self.causal_chains) if self.causal_chains else 0.0,
            "avg_novelty_score": sum(c.novelty_score for c in self.causal_chains) / len(self.causal_chains) if self.causal_chains else 0.0,
            "avg_quantum_enhancement": sum(c.quantum_enhancement for c in self.causal_chains) / len(self.causal_chains) if self.causal_chains else 0.0,
            "top_hypotheses": [h.to_dict() for h in top_hypotheses]
        }