scribe-graph 0.5.1

//! # Scribe Graph - Advanced Code Dependency Analysis
//!
//! High-performance graph-based code analysis with PageRank centrality computation.
//! This crate provides sophisticated tools for understanding code structure, dependency
//! relationships, and file importance through research-grade graph algorithms.
//!
//! ## Key Features
//!
//! ### PageRank Centrality Analysis
//! - **Research-grade PageRank implementation** optimized for code dependency graphs
//! - **Reverse edge emphasis** (importance flows to imported files)
//! - **Convergence detection** with configurable precision
//! - **Multi-language import detection** (Python, JavaScript, TypeScript, Rust, Go, Java)
//!
//! ### Graph Construction and Analysis
//! - **Efficient dependency graph** representation with adjacency lists
//! - **Comprehensive statistics** (degree distribution, connectivity, structural patterns)
//! - **Performance optimized** for large codebases (10k+ files)
//! - **Concurrent processing** support for multi-core systems
//!
//! ### Integration with Scribe Heuristics
//! - **Seamless V2 integration** with existing heuristic scoring system
//! - **Configurable centrality weighting** in final importance scores
//! - **Multiple normalization methods** (min-max, z-score, rank-based)
//! - **Entrypoint boosting** for main/index files
//!
//! ## Quick Start
//!
//! ```ignore
//! use scribe_graph::{CentralityCalculator, PageRankConfig};
//! # use scribe_analysis::heuristics::ScanResult;
//! # use std::collections::HashMap;
//! #
//! # // Mock implementation for documentation
//! # #[derive(Debug)]
//! # struct MockScanResult {
//! #     path: String,
//! #     relative_path: String,
//! # }
//! #
//! # impl ScanResult for MockScanResult {
//! #     fn path(&self) -> &str { &self.path }
//! #     fn relative_path(&self) -> &str { &self.relative_path }
//! #     fn depth(&self) -> usize { 1 }
//! #     fn is_docs(&self) -> bool { false }
//! #     fn is_readme(&self) -> bool { false }
//! #     fn is_entrypoint(&self) -> bool { false }
//! #     fn is_examples(&self) -> bool { false }
//! #     fn is_tests(&self) -> bool { false }
//! #     fn priority_boost(&self) -> f64 { 0.0 }
//! #     fn get_documentation_score(&self) -> f64 { 0.0 }
//! #     fn get_file_size(&self) -> usize { 1000 }
//! #     fn get_imports(&self) -> Vec<String> { vec![] }
//! #     fn get_git_churn(&self) -> usize { 0 }
//! # }
//! #
//! # fn example() -> Result<(), Box<dyn std::error::Error>> {
//! // Create centrality calculator optimized for code analysis
//! let calculator = CentralityCalculator::for_large_codebases()?;
//!
//! // Example scan results (replace with actual scan results)
//! let scan_results = vec![
//!     MockScanResult { path: "main.rs".to_string(), relative_path: "main.rs".to_string() },
//!     MockScanResult { path: "lib.rs".to_string(), relative_path: "lib.rs".to_string() },
//! ];
//! let heuristic_scores = HashMap::new();
//!
//! // Calculate PageRank centrality for scan results
//! let centrality_results = calculator.calculate_centrality(&scan_results)?;
//!
//! // Get top files by centrality
//! let top_files = centrality_results.top_files_by_centrality(10);
//!
//! // Integrate with existing heuristic scores
//! let integrated_scores = calculator.integrate_with_heuristics(
//!     &centrality_results,
//!     &heuristic_scores
//! )?;
//! # Ok(())
//! # }
//! ```
//!
//! ## Performance Characteristics
//!
//! - **Memory usage**: ~2MB for 1000-file codebases, ~20MB for 10k+ files
//! - **Computation time**: ~10ms for small projects, ~100ms for large codebases
//! - **Convergence**: Typically 8-15 iterations for most dependency graphs
//! - **Parallel efficiency**: Near-linear speedup on multi-core systems

// Core modules
pub mod centrality;
pub mod graph;
pub mod pagerank;
pub mod statistics;

// Primary API exports - PageRank centrality system
pub use centrality::{
    CentralityCalculator, CentralityConfig, CentralityResults, ImportDetectionStats,
    ImportResolutionConfig, IntegrationConfig, IntegrationMetadata, NormalizationMethod,
};

pub use pagerank::{
    PageRankComputer, PageRankConfig, PageRankResults, PerformanceMetrics, ScoreStatistics,
};

pub use graph::{
    ConcurrentDependencyGraph, DegreeInfo, DependencyGraph, GraphStatistics, NodeMetadata,
    TraversalDirection,
};

pub use statistics::{
    ConnectivityAnalysis, DegreeDistribution, GraphAnalysisResults, GraphStatisticsAnalyzer,
    ImportInsights, PerformanceProfile, StatisticsConfig, StructuralPatterns,
};

// Maintain compatibility alias for existing integrations
pub use graph::DependencyGraph as CodeGraph;

use scribe_analysis::heuristics::ScanResult;
use scribe_core::Result;
use std::collections::HashMap;

/// Main entry point for PageRank centrality analysis
///
/// This is the primary interface for computing PageRank centrality scores
/// and integrating them with the Scribe heuristic system.
pub struct PageRankAnalysis {
    calculator: CentralityCalculator,
}

impl PageRankAnalysis {
    /// Create a new PageRank analysis instance with default configuration
    pub fn new() -> Result<Self> {
        Ok(Self {
            calculator: CentralityCalculator::new()?,
        })
    }

    /// Create with custom centrality configuration
    pub fn with_config(config: CentralityConfig) -> Result<Self> {
        Ok(Self {
            calculator: CentralityCalculator::with_config(config)?,
        })
    }

    /// Create optimized for code dependency analysis
    pub fn for_code_analysis() -> Result<Self> {
        Ok(Self {
            calculator: CentralityCalculator::new()?,
        })
    }

    /// Create optimized for large codebases (>5k files)
    pub fn for_large_codebases() -> Result<Self> {
        Ok(Self {
            calculator: CentralityCalculator::for_large_codebases()?,
        })
    }

    /// Compute PageRank centrality scores for a collection of files
    pub fn compute_centrality<T>(&self, scan_results: &[T]) -> Result<CentralityResults>
    where
        T: ScanResult + Sync,
    {
        self.calculator.calculate_centrality(scan_results)
    }

    /// Integrate centrality scores with existing heuristic scores
    ///
    /// This combines PageRank centrality with Scribe heuristic scores using
    /// configurable weights. The default configuration uses 15% centrality weight
    /// and 85% heuristic weight.
    pub fn integrate_with_heuristics(
        &self,
        centrality_results: &CentralityResults,
        heuristic_scores: &HashMap<String, f64>,
    ) -> Result<HashMap<String, f64>> {
        self.calculator
            .integrate_with_heuristics(centrality_results, heuristic_scores)
    }

    /// Get a summary of centrality computation results
    pub fn summarize_results(&self, results: &CentralityResults) -> String {
        results.summary()
    }
}

impl Default for PageRankAnalysis {
    fn default() -> Self {
        Self::new().expect("Failed to create PageRankAnalysis")
    }
}

/// Utility functions for PageRank analysis
pub mod utils {
    use super::*;

    /// Quick function to compute centrality scores for scan results
    ///
    /// This is a convenience function for simple use cases. For more control
    /// over configuration, use `PageRankAnalysis` directly.
    pub fn compute_file_centrality<T>(scan_results: &[T]) -> Result<HashMap<String, f64>>
    where
        T: ScanResult + Sync,
    {
        let analysis = PageRankAnalysis::for_code_analysis()?;
        let results = analysis.compute_centrality(scan_results)?;
        Ok(results.pagerank_scores)
    }

    /// Quick function to get top-K most important files
    pub fn get_top_important_files<T>(
        scan_results: &[T],
        top_k: usize,
    ) -> Result<Vec<(String, f64)>>
    where
        T: ScanResult + Sync,
    {
        let analysis = PageRankAnalysis::for_code_analysis()?;
        let results = analysis.compute_centrality(scan_results)?;
        Ok(results.top_files_by_centrality(top_k))
    }

    /// Combine centrality and heuristic scores with default configuration
    pub fn combine_scores<T>(
        scan_results: &[T],
        heuristic_scores: &HashMap<String, f64>,
    ) -> Result<HashMap<String, f64>>
    where
        T: ScanResult + Sync,
    {
        let analysis = PageRankAnalysis::for_code_analysis()?;
        let centrality_results = analysis.compute_centrality(scan_results)?;
        analysis.integrate_with_heuristics(&centrality_results, heuristic_scores)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_pagerank_analysis_creation() {
        let analysis = PageRankAnalysis::new();
        assert!(analysis.is_ok());
    }
}