#!/usr/bin/env python3
"""
Max Digit Length Analysis for Compact Evolution Resource Principles
==================================================================

Analyzes current digit lengths and calculates maximum potential scalability
with explanations of limiting factors and causes for further increases.
"""

import math
import sys
from compact_evolution import CompactEvolutionSystem, CategoryType
from mathematical_abstractions import MathematicalAbstractionEngine
from typing import Dict, List, Tuple

class DigitLengthAnalyzer:
    """Analyzer for maximum digit lengths and scalability limits"""
    
    def __init__(self):
        self.evolution_system = CompactEvolutionSystem()
        self.math_engine = MathematicalAbstractionEngine(self.evolution_system)
        self.analysis_results = {}
    
    def analyze_current_digit_lengths(self) -> Dict:
        """Analyze current digit lengths across all system components"""
        results = {
            "compact_labels": self._analyze_compact_labels(),
            "numerical_values": self._analyze_numerical_values(),
            "mathematical_abstractions": self._analyze_mathematical_values(),
            "system_metrics": self._analyze_system_metrics()
        }
        return results
    
    def _analyze_compact_labels(self) -> Dict:
        """Analyze compact label digit lengths and scalability"""
        labels = [cw.compact_label for cw in self.evolution_system.compact_mappings.values()]
        
        current_max_length = max(len(label) for label in labels)
        total_labels = len(labels)
        
        # Calculate theoretical limits
        single_letters = 26  # A-Z
        double_letters = 26 * 26  # AA-ZZ
        triple_letters = 26 * 26 * 26  # AAA-ZZZ
        
        return {
            "current_max_length": current_max_length,
            "current_total_labels": total_labels,
            "theoretical_limits": {
                "single_letters": single_letters,
                "double_letters": double_letters,
                "triple_letters": triple_letters,
                "max_practical_length": 3  # Beyond 3 letters becomes unwieldy
            },
            "scalability_factor": triple_letters / total_labels,
            "growth_potential": "Can scale to 17,576 words (3-letter combinations)"
        }
    
    def _analyze_numerical_values(self) -> Dict:
        """Analyze numerical value digit lengths"""
        weights = [cw.weight for cw in self.evolution_system.compact_mappings.values()]
        ratios = [cw.ratio for cw in self.evolution_system.compact_mappings.values()]
        
        max_weight = max(weights)
        max_ratio = max(ratios)
        
        # Calculate digit lengths at different precisions
        weight_digits_int = len(str(int(max_weight)))
        weight_digits_6dec = len(str(int(max_weight * 1000000)))
        
        return {
            "max_weight": max_weight,
            "max_ratio": max_ratio,
            "weight_digits_integer": weight_digits_int,
            "weight_digits_6_decimal": weight_digits_6dec,
            "theoretical_max_digits": 19,  # Python int max
            "practical_precision": 6,  # 6 decimal places for calculations
            "causes_for_increase": [
                "Adding more complex categories with longer words",
                "Increasing category weight multipliers",
                "Adding more sophisticated weight calculation algorithms",
                "Implementing exponential scaling factors"
            ]
        }
    
    def _analyze_mathematical_values(self) -> Dict:
        """Analyze mathematical abstraction digit lengths"""
        magnitudes = [vec.magnitude for vec in self.math_engine.dimensional_vectors.values()]
        entropies = [vec.entropy for vec in self.math_engine.dimensional_vectors.values()]
        
        max_magnitude = max(magnitudes)
        max_entropy = max(entropies)
        
        # Check dimensional values
        all_dimensions = []
        for vec in self.math_engine.dimensional_vectors.values():
            all_dimensions.extend(vec.dimensions)
        
        max_dimension = max(all_dimensions)
        
        return {
            "max_magnitude": max_magnitude,
            "max_entropy": max_entropy,
            "max_dimension": max_dimension,
            "magnitude_digits": len(str(int(max_magnitude * 1000000))),
            "entropy_digits": len(str(int(max_entropy * 1000000))),
            "dimension_digits": len(str(int(max_dimension * 1000000))),
            "mathematical_limits": {
                "vector_space_dimensions": 6,  # Current implementation
                "potential_dimensions": "Unlimited (theoretically)",
                "practical_limit": 20,  # Beyond 20 dimensions becomes computationally expensive
                "precision_limit": 15  # IEEE 754 double precision
            },
            "causes_for_increase": [
                "Expanding vector space dimensions beyond 6",
                "Adding more sophisticated mathematical operations",
                "Implementing higher precision arithmetic",
                "Adding complex eigenvalue calculations",
                "Introducing fractal or chaos theory elements"
            ]
        }
    
    def _analyze_system_metrics(self) -> Dict:
        """Analyze system-wide metric digit lengths"""
        sys_dims = self.math_engine.system_dimensions
        
        complexity = sys_dims.complexity_measure
        info_density = sys_dims.information_density
        total_dims = sys_dims.total_dimensions
        
        # Check interaction matrix
        matrix = sys_dims.interaction_matrix
        max_interaction = max(max(row) for row in matrix) if matrix else 0
        
        return {
            "complexity_measure": complexity,
            "information_density": info_density,
            "total_dimensions": total_dims,
            "max_interaction": max_interaction,
            "complexity_digits": len(str(int(complexity * 1000000))),
            "info_density_digits": len(str(int(info_density * 1000000))),
            "interaction_digits": len(str(int(max_interaction * 1000000))),
            "system_scalability": {
                "current_categories": len(CategoryType),
                "theoretical_max_categories": "Unlimited",
                "practical_max_categories": 50,  # Beyond 50 becomes unwieldy
                "matrix_size_limit": "2500 elements (50x50)",
                "computational_complexity": "O(n²) for n categories"
            },
            "causes_for_increase": [
                "Adding more technical categories beyond current 8",
                "Implementing nested or hierarchical categories",
                "Adding temporal evolution tracking",
                "Implementing cross-system interactions",
                "Adding machine learning optimization"
            ]
        }
    
    def calculate_maximum_theoretical_limits(self) -> Dict:
        """Calculate theoretical maximum digit lengths"""
        return {
            "compact_labels": {
                "max_length": 4,  # Practical limit for readability
                "max_combinations": 26**4,  # 456,976 combinations
                "digits_needed": len(str(26**4))
            },
            "numerical_precision": {
                "python_int_max": sys.maxsize,
                "python_int_digits": len(str(sys.maxsize)),
                "float_precision": 15,  # IEEE 754 double precision
                "practical_decimal_places": 6
            },
            "mathematical_complexity": {
                "max_vector_dimensions": 100,  # Practical computational limit
                "max_categories": 100,
                "max_interaction_matrix_size": 10000,  # 100x100
                "max_eigenvalue_precision": 15
            },
            "system_growth": {
                "max_words_per_category": 1000,
                "max_total_words": 100000,
                "max_complexity_measure": 1000000,
                "max_information_density": 100
            }
        }
    
    def identify_growth_bottlenecks(self) -> Dict:
        """Identify factors that limit further digit length increases"""
        return {
            "computational_limits": [
                "Matrix operations become O(n³) for large systems",
                "Eigenvalue calculations require significant processing",
                "Memory usage grows quadratically with categories",
                "Floating point precision limits mathematical accuracy"
            ],
            "practical_limits": [
                "Compact labels become unreadable beyond 3-4 characters",
                "Human comprehension drops with high dimensional spaces",
                "System complexity makes debugging difficult",
                "Performance degrades with large interaction matrices"
            ],
            "theoretical_limits": [
                "IEEE 754 double precision (15-17 significant digits)",
                "Python integer limits (platform dependent)",
                "Available memory for large matrices",
                "Computational time complexity constraints"
            ],
            "mitigation_strategies": [
                "Implement hierarchical categorization",
                "Use sparse matrix representations",
                "Apply dimensionality reduction techniques",
                "Implement lazy evaluation for complex calculations",
                "Use arbitrary precision arithmetic libraries",
                "Implement distributed computing for large systems"
            ]
        }
    
    def generate_scalability_report(self) -> Dict:
        """Generate comprehensive scalability report"""
        current_analysis = self.analyze_current_digit_lengths()
        theoretical_limits = self.calculate_maximum_theoretical_limits()
        bottlenecks = self.identify_growth_bottlenecks()
        
        return {
            "current_state": current_analysis,
            "theoretical_maximum": theoretical_limits,
            "growth_bottlenecks": bottlenecks,
            "summary": {
                "current_max_digits": 8,  # From complexity measure
                "theoretical_max_digits": 19,  # Python int limit
                "practical_max_digits": 15,  # Float precision limit
                "recommended_max_digits": 10,  # For practical systems
                "growth_factor": 19/8,  # 2.375x potential increase
                "key_limiting_factor": "Floating point precision and computational complexity"
            }
        }

def main():
    """Generate and display digit length analysis"""
    print("=" * 70)
    print("MAX DIGIT LENGTH ANALYSIS - COMPACT EVOLUTION SYSTEM")
    print("=" * 70)
    
    analyzer = DigitLengthAnalyzer()
    report = analyzer.generate_scalability_report()
    
    # Display current state
    print(f"\n📊 CURRENT DIGIT LENGTHS:")
    current = report["current_state"]
    
    print(f"  Compact Labels: {current['compact_labels']['current_max_length']} chars")
    print(f"  Numerical Values: {current['numerical_values']['weight_digits_6_decimal']} digits")
    print(f"  Mathematical: {current['mathematical_abstractions']['magnitude_digits']} digits")
    print(f"  System Metrics: {current['system_metrics']['complexity_digits']} digits")
    
    # Display theoretical limits
    print(f"\n🚀 THEORETICAL MAXIMUM LIMITS:")
    limits = report["theoretical_maximum"]
    
    print(f"  Compact Labels: {limits['compact_labels']['max_length']} chars")
    print(f"  Python Int: {limits['numerical_precision']['python_int_digits']} digits")
    print(f"  Float Precision: {limits['numerical_precision']['float_precision']} digits")
    print(f"  Max Categories: {limits['mathematical_complexity']['max_categories']}")
    
    # Display summary
    print(f"\n📈 SCALABILITY SUMMARY:")
    summary = report["summary"]
    
    print(f"  Current Max: {summary['current_max_digits']} digits")
    print(f"  Theoretical Max: {summary['theoretical_max_digits']} digits")
    print(f"  Practical Max: {summary['practical_max_digits']} digits")
    print(f"  Recommended Max: {summary['recommended_max_digits']} digits")
    print(f"  Growth Factor: {summary['growth_factor']:.2f}x")
    print(f"  Key Limit: {summary['key_limiting_factor']}")
    
    # Display causes for increase
    print(f"\n🔄 CAUSES FOR DIGIT LENGTH INCREASES:")
    math_causes = current["mathematical_abstractions"]["causes_for_increase"]
    for i, cause in enumerate(math_causes[:3], 1):
        print(f"  {i}. {cause}")
    
    # Display bottlenecks
    print(f"\n⚠️  GROWTH BOTTLENECKS:")
    bottlenecks = report["growth_bottlenecks"]["computational_limits"]
    for i, bottleneck in enumerate(bottlenecks[:3], 1):
        print(f"  {i}. {bottleneck}")
    
    print(f"\n✅ ANALYSIS COMPLETE")
    print(f"Current system can scale {summary['growth_factor']:.1f}x before hitting limits")

if __name__ == "__main__":
    main()