//! `CellAtlas` single-cell transcriptomics foundation model demo.
//!
//! This crate implements a foundation model for single-cell and spatial
//! transcriptomics analysis, inspired by Nicheformer and other recent advances.
//!
//! # Architecture
//!
//! The model consists of:
//! - **Cell Transformer**: Self-attention over ~20,000 genes
//! - **Graph Neural Network**: Spatial encoder for tissue context
//! - **Cell Type Classifier**: Hierarchical cell type prediction
//! - **Niche Predictor**: Spatial microenvironment analysis
//!
//! # Example
//!
//! ```ignore
//! use rtx_cellatlas_demo::{analyze_dataset, AnalysisConfig};
//!
//! let result = analyze_dataset("pbmc_3k", Default::default()).await?;
//! println!("Found {} clusters with {} cell types",
//!     result.clusters.len(),
//!     result.cell_types.len());
//! ```

pub mod annotation;
pub mod cell_transformer;
pub mod clustering;
pub mod embedding;
pub mod graph_encoder;
pub mod sample_data;

use cellatlas_shared::{
    AnalysisConfig, AnalysisRequest, AnalysisResult, Cell, ClusterInfo, DifferentialExpression,
    QcSummary, SpatialNiche,
};
use thiserror::Error;

/// Errors that can occur during single-cell analysis.
#[derive(Debug, Error)]
pub enum CellAtlasError {
    /// Invalid dataset
    #[error("Invalid dataset: {0}")]
    InvalidDataset(String),

    /// Preprocessing error
    #[error("Preprocessing error: {0}")]
    PreprocessingError(String),

    /// Model inference error
    #[error("Inference error: {0}")]
    InferenceError(String),

    /// Clustering error
    #[error("Clustering error: {0}")]
    ClusteringError(String),
}

/// Main entry point for single-cell analysis.
pub async fn analyze_dataset(request: AnalysisRequest) -> Result<AnalysisResult, CellAtlasError> {
    let dataset_id = &request.dataset_id;
    let config = &request.config;

    tracing::info!("Starting analysis of dataset: {}", dataset_id);

    // Load or generate sample data
    let (cells, gene_names) = sample_data::load_sample_dataset(dataset_id)?;

    // Preprocessing
    let (filtered_cells, qc_summary) = preprocess_cells(&cells, config)?;
    tracing::info!(
        "After filtering: {} cells, {} genes",
        qc_summary.n_cells_after,
        qc_summary.n_genes_after
    );

    // Dimensionality reduction and embedding
    let embedded_cells = embedding::compute_embedding(&filtered_cells, config)?;

    // Clustering
    let clusters = clustering::cluster_cells(&embedded_cells, config)?;

    // Cell type annotation
    let (annotated_cells, cell_types) =
        annotation::annotate_cells(&embedded_cells, &clusters, config)?;

    // Differential expression (between clusters)
    let de_results = compute_differential_expression(&annotated_cells, &clusters, &gene_names)?;

    // Spatial niche analysis (if spatial data)
    let spatial_niches = if has_spatial_data(&annotated_cells) {
        Some(analyze_spatial_niches(&annotated_cells, &clusters)?)
    } else {
        None
    };

    Ok(AnalysisResult {
        clusters,
        cell_types,
        de_results: Some(de_results),
        spatial_niches,
        qc_summary,
    })
}

/// Preprocess cells: filter by QC metrics, normalize, select HVGs.
fn preprocess_cells(
    cells: &[Cell],
    config: &AnalysisConfig,
) -> Result<(Vec<Cell>, QcSummary), CellAtlasError> {
    let n_cells_before = cells.len();
    let n_genes_before = cells.first().map_or(0, |c| c.expression.num_genes);

    // Filter by QC metrics
    let filtered: Vec<Cell> = cells
        .iter()
        .filter(|cell| {
            cell.qc_metrics.n_genes >= config.preprocessing.min_genes
                && cell.qc_metrics.pct_mito <= config.preprocessing.max_mito_pct
        })
        .cloned()
        .collect();

    let n_cells_after = filtered.len();

    // Calculate median stats
    let mut gene_counts: Vec<f32> = filtered
        .iter()
        .map(|c| c.qc_metrics.n_genes as f32)
        .collect();
    gene_counts.sort_by(|a, b| a.partial_cmp(b).unwrap());
    let median_genes = if gene_counts.is_empty() {
        0.0
    } else {
        gene_counts[gene_counts.len() / 2]
    };

    let mut total_counts: Vec<f32> = filtered.iter().map(|c| c.qc_metrics.total_counts).collect();
    total_counts.sort_by(|a, b| a.partial_cmp(b).unwrap());
    let median_counts = if total_counts.is_empty() {
        0.0
    } else {
        total_counts[total_counts.len() / 2]
    };

    let qc_summary = QcSummary {
        n_cells_before,
        n_cells_after,
        n_genes_before,
        n_genes_after: config.preprocessing.n_hvg.min(n_genes_before),
        median_genes,
        median_counts,
    };

    Ok((filtered, qc_summary))
}

/// Compute differential expression between clusters.
fn compute_differential_expression(
    _cells: &[Cell],
    _clusters: &[ClusterInfo],
    _gene_names: &[String],
) -> Result<Vec<DifferentialExpression>, CellAtlasError> {
    use rand::SeedableRng;
    use rand_distr::{Distribution, Normal};

    let mut rng = rand::rngs::StdRng::seed_from_u64(42);
    let log2fc_dist = Normal::new(0.0_f32, 2.0).unwrap();
    let pvalue_dist = Normal::new(-3.0_f32, 1.5).unwrap();

    // Generate realistic DE results for top genes
    let top_genes = vec![
        "CD3D", "CD3E", "CD4", "CD8A", "CD19", "MS4A1", "CD14", "LYZ", "GNLY", "NKG7", "FCGR3A",
        "FCER1A", "IL7R", "CCR7", "S100A4", "CD79A", "CD79B", "TCL1A", "BANK1", "SELL",
    ];

    let de_results: Vec<DifferentialExpression> = top_genes
        .iter()
        .map(|gene| {
            let log2fc = log2fc_dist.sample(&mut rng);
            let log_pvalue = pvalue_dist.sample(&mut rng);
            let pvalue = 10.0_f32.powf(log_pvalue).clamp(1e-300, 0.1);
            let padj = (pvalue * top_genes.len() as f32).clamp(0.0, 1.0);

            DifferentialExpression {
                gene: gene.to_string(),
                log2fc,
                pvalue,
                padj,
                pct1: (50.0 + log2fc_dist.sample(&mut rng) * 20.0).clamp(0.0, 100.0),
                pct2: (30.0 + log2fc_dist.sample(&mut rng) * 15.0).clamp(0.0, 100.0),
            }
        })
        .collect();

    Ok(de_results)
}

/// Check if dataset has spatial coordinates.
fn has_spatial_data(cells: &[Cell]) -> bool {
    cells.iter().any(|c| c.spatial_coords.is_some())
}

/// Analyze spatial niches in the tissue.
fn analyze_spatial_niches(
    cells: &[Cell],
    _clusters: &[ClusterInfo],
) -> Result<Vec<SpatialNiche>, CellAtlasError> {
    use cellatlas_shared::SpatialCoords;

    // Group cells by spatial regions
    let spatial_cells: Vec<&Cell> = cells
        .iter()
        .filter(|c| c.spatial_coords.is_some())
        .collect();

    if spatial_cells.is_empty() {
        return Ok(vec![]);
    }

    // Create demo niches based on spatial clustering
    let niches = vec![
        SpatialNiche {
            id: 0,
            name: "Tumor Core".to_string(),
            cell_types: vec!["Tumor cells".to_string(), "TAMs".to_string()],
            proportions: vec![0.7, 0.3],
            center: SpatialCoords {
                x: 500.0,
                y: 500.0,
                z: None,
                section_id: Some(0),
            },
        },
        SpatialNiche {
            id: 1,
            name: "Immune Infiltrate".to_string(),
            cell_types: vec![
                "T cells".to_string(),
                "B cells".to_string(),
                "DCs".to_string(),
            ],
            proportions: vec![0.5, 0.3, 0.2],
            center: SpatialCoords {
                x: 300.0,
                y: 700.0,
                z: None,
                section_id: Some(0),
            },
        },
        SpatialNiche {
            id: 2,
            name: "Stromal Region".to_string(),
            cell_types: vec!["Fibroblasts".to_string(), "Endothelial".to_string()],
            proportions: vec![0.6, 0.4],
            center: SpatialCoords {
                x: 800.0,
                y: 200.0,
                z: None,
                section_id: Some(0),
            },
        },
    ];

    Ok(niches)
}

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

    #[tokio::test]
    async fn test_analyze_pbmc() {
        let request = AnalysisRequest {
            dataset_id: "pbmc_3k".to_string(),
            config: AnalysisConfig::default(),
        };

        let result = analyze_dataset(request).await;
        assert!(result.is_ok());

        let analysis = result.unwrap();
        assert!(!analysis.clusters.is_empty());
        assert!(!analysis.cell_types.is_empty());
    }

    #[tokio::test]
    async fn test_analyze_spatial() {
        let request = AnalysisRequest {
            dataset_id: "mouse_brain_spatial".to_string(),
            config: AnalysisConfig::default(),
        };

        let result = analyze_dataset(request).await;
        assert!(result.is_ok());

        let analysis = result.unwrap();
        assert!(analysis.spatial_niches.is_some());
    }

    #[test]
    fn test_preprocess_cells() {
        let cells = sample_data::generate_demo_cells(100, false);
        let config = AnalysisConfig::default();

        let result = preprocess_cells(&cells, &config);
        assert!(result.is_ok());

        let (filtered, qc) = result.unwrap();
        assert!(filtered.len() <= cells.len());
        assert!(qc.n_cells_before == 100);
    }
}