/// Module de génération de waveform pour visualisation audio
/// 
/// Features :
/// - Génération de waveform optimisée
/// - Format peaks.js compatible
/// - Analyse spectrale avancée
/// - Support multi-résolution
/// - Export JSON/binaire

use std::sync::Arc;
use std::path::Path;
use std::collections::HashMap;

use serde::{Serialize, Deserialize};
use tokio::sync::RwLock;
// Note: Use tracing::info! macro directly instead of importing

use crate::error::AppError;

/// Générateur de waveform principal
#[derive(Debug)]
pub struct WaveformGenerator {
    config: WaveformConfig,
    cache: Arc<RwLock<HashMap<String, WaveformData>>>,
}

/// Configuration du générateur
#[derive(Debug, Clone)]
pub struct WaveformConfig {
    pub samples_per_pixel: u32,
    pub bit_depth: u8,
    pub amplitude_scale: f32,
    pub enable_spectral_analysis: bool,
    pub peak_detection_threshold: f32,
    pub cache_enabled: bool,
    pub output_formats: Vec<WaveformFormat>,
}

/// Formats de sortie supportés
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum WaveformFormat {
    /// Format JSON compatible peaks.js
    PeaksJS,
    /// Format binaire compact
    Binary,
    /// Format SVG vectoriel
    SVG,
    /// Format PNG image
    PNG { width: u32, height: u32 },
}

/// Données de waveform générées
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WaveformData {
    /// Métadonnées de base
    pub duration: f64,
    pub sample_rate: u32,
    pub channels: u8,
    pub length: usize,
    
    /// Données de pics (min/max par pixel)
    pub peaks: Vec<WaveformPeak>,
    
    /// Données spectrales (optionnel)
    pub spectral_data: Option<SpectralData>,
    
    /// Statistiques audio
    pub audio_stats: AudioStatistics,
    
    /// Format d'export
    pub format: WaveformFormat,
    
    /// Version du générateur
    pub version: String,
}

/// Pic de waveform (min/max pour un pixel)
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WaveformPeak {
    pub min: f32,
    pub max: f32,
    pub rms: f32,    // Root Mean Square pour volume perçu
    pub peak: f32,   // Pic absolu
}

/// Données spectrales pour analyse fréquentielle
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpectralData {
    /// Spectrogramme par bandes de fréquence
    pub spectrogram: Vec<SpectralFrame>,
    /// Fréquences centrales des bandes
    pub frequency_bins: Vec<f32>,
    /// Résolution temporelle (ms par frame)
    pub time_resolution_ms: f32,
}

/// Frame spectrale à un instant donné
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpectralFrame {
    /// Magnitude par bande de fréquence
    pub magnitudes: Vec<f32>,
    /// Timestamp en millisecondes
    pub timestamp_ms: f32,
}

/// Statistiques audio globales
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AudioStatistics {
    /// Niveau RMS moyen
    pub average_rms: f32,
    /// Pic maximum absolu
    pub peak_amplitude: f32,
    /// Dynamic range en dB
    pub dynamic_range_db: f32,
    /// Loudness intégré LUFS
    pub integrated_loudness: f32,
    /// Facteur de crête
    pub crest_factor: f32,
    /// Détection de silence (pourcentage)
    pub silence_percentage: f32,
    /// BPM détecté (optionnel)
    pub estimated_bpm: Option<f32>,
    /// Clé détectée (optionnel) 
    pub estimated_key: Option<String>,
}

/// Analyseur de pics et événements audio
#[derive(Debug)]
pub struct PeakAnalyzer {
    config: PeakAnalyzerConfig,
    detection_state: PeakDetectionState,
}

/// Configuration de l'analyseur de pics
#[derive(Debug, Clone)]
pub struct PeakAnalyzerConfig {
    pub threshold_db: f32,
    pub min_peak_distance_ms: f32,
    pub attack_time_ms: f32,
    pub release_time_ms: f32,
}

/// État de détection des pics
#[derive(Debug)]
struct PeakDetectionState {
    last_peak_time: f32,
    envelope_follower: f32,
    peak_candidates: Vec<PeakCandidate>,
}

/// Candidat de pic détecté
#[derive(Debug, Clone)]
struct PeakCandidate {
    timestamp_ms: f32,
    amplitude: f32,
    duration_ms: f32,
}

impl Default for WaveformConfig {
    fn default() -> Self {
        Self {
            samples_per_pixel: 1024,
            bit_depth: 16,
            amplitude_scale: 1.0,
            enable_spectral_analysis: true,
            peak_detection_threshold: -20.0, // -20dB
            cache_enabled: true,
            output_formats: vec![
                WaveformFormat::PeaksJS,
                WaveformFormat::Binary,
            ],
        }
    }
}

impl WaveformGenerator {
    /// Crée un nouveau générateur de waveform
    pub fn new() -> Self {
        Self::with_config(WaveformConfig::default())
    }
    
    /// Crée un générateur avec configuration personnalisée
    pub fn with_config(config: WaveformConfig) -> Self {
        Self {
            config,
            cache: Arc::new(RwLock::new(HashMap::new())),
        }
    }
    
    /// Génère une waveform depuis un fichier audio
    pub async fn generate_from_file<P: AsRef<Path>>(&self, file_path: P) -> Result<WaveformData, AppError> {
        let path_str = file_path.as_ref().to_string_lossy().to_string();
        
        // Vérifier le cache d'abord
        if self.config.cache_enabled {
            if let Some(cached) = self.get_from_cache(&path_str).await {
                tracing::debug!("Waveform trouvée en cache pour: {}", path_str);
                return Ok(cached);
            }
        }
        
        tracing::info!("Génération de waveform pour: {}", path_str);
        
        // Simulation de lecture du fichier audio
        let audio_data = self.load_audio_file(&path_str).await?;
        
        // Générer la waveform
        let waveform = self.generate_waveform_data(&audio_data).await?;
        
        // Mettre en cache
        if self.config.cache_enabled {
            self.store_in_cache(&path_str, &waveform).await;
        }
        
        Ok(waveform)
    }
    
    /// Génère une waveform depuis des échantillons audio bruts
    pub async fn generate_from_samples(
        &self,
        samples: &[f32],
        sample_rate: u32,
        channels: u8,
    ) -> Result<WaveformData, AppError> {
        let audio_data = AudioData {
            samples: samples.to_vec(),
            sample_rate,
            channels,
            duration: samples.len() as f64 / (sample_rate as f64 * channels as f64),
        };
        
        self.generate_waveform_data(&audio_data).await
    }
    
    /// Charge un fichier audio (simulation)
    async fn load_audio_file(&self, _file_path: &str) -> Result<AudioData, AppError> {
        // Simulation de chargement - en production, utiliser symphonia ou similar
        let sample_rate = 44100;
        let channels = 2;
        let duration_seconds = 180.0; // 3 minutes
        let total_samples = (sample_rate as f64 * channels as f64 * duration_seconds) as usize;
        
        // Générer des échantillons de test (sinusoïde modulée)
        let mut samples = Vec::with_capacity(total_samples);
        for i in 0..total_samples {
            let t = i as f64 / (sample_rate as f64 * channels as f64);
            let frequency = 440.0 + 100.0 * (t * 0.1).sin(); // Fréquence modulée
            let amplitude = 0.5 * (1.0 + (t * 0.05).sin()); // Amplitude modulée
            let sample = (amplitude * (2.0 * std::f64::consts::PI * frequency * t).sin()) as f32;
            samples.push(sample);
        }
        
        Ok(AudioData {
            samples,
            sample_rate,
            channels,
            duration: duration_seconds,
        })
    }
    
    /// Génère les données de waveform
    async fn generate_waveform_data(&self, audio_data: &AudioData) -> Result<WaveformData, AppError> {
        let start_time = std::time::Instant::now();
        
        // Calculer le nombre de pixels nécessaires
        let total_samples = audio_data.samples.len();
        let samples_per_frame = self.config.samples_per_pixel as usize * audio_data.channels as usize;
        let pixel_count = (total_samples + samples_per_frame - 1) / samples_per_frame;
        
        // Générer les pics pour chaque pixel
        let mut peaks = Vec::with_capacity(pixel_count);
        
        for pixel_index in 0..pixel_count {
            let start_sample = pixel_index * samples_per_frame;
            let end_sample = (start_sample + samples_per_frame).min(total_samples);
            
            if start_sample < total_samples {
                let pixel_samples = &audio_data.samples[start_sample..end_sample];
                let peak = self.calculate_pixel_peak(pixel_samples);
                peaks.push(peak);
            }
        }
        
        // Calculer les statistiques audio
        let audio_stats = self.calculate_audio_statistics(&audio_data.samples, audio_data.sample_rate);
        
        // Générer les données spectrales si activé
        let spectral_data = if self.config.enable_spectral_analysis {
            Some(self.generate_spectral_data(audio_data).await?)
        } else {
            None
        };
        
        let generation_time = start_time.elapsed();
        tracing::info!("Waveform générée en {:?}: {} pixels, {} échantillons", 
              generation_time, peaks.len(), total_samples);
        
        Ok(WaveformData {
            duration: audio_data.duration,
            sample_rate: audio_data.sample_rate,
            channels: audio_data.channels,
            length: peaks.len(),
            peaks,
            spectral_data,
            audio_stats,
            format: WaveformFormat::PeaksJS,
            version: "1.0.0".to_string(),
        })
    }
    
    /// Calcule le pic pour un groupe d'échantillons (pixel)
    fn calculate_pixel_peak(&self, samples: &[f32]) -> WaveformPeak {
        if samples.is_empty() {
            return WaveformPeak {
                min: 0.0,
                max: 0.0,
                rms: 0.0,
                peak: 0.0,
            };
        }
        
        let mut min_val = f32::MAX;
        let mut max_val = f32::MIN;
        let mut sum_squares = 0.0;
        let mut peak_val: f32 = 0.0;
        
        for &sample in samples {
            min_val = min_val.min(sample);
            max_val = max_val.max(sample);
            sum_squares += sample * sample;
            peak_val = peak_val.max(sample.abs());
        }
        
        let rms = (sum_squares / samples.len() as f32).sqrt();
        
        WaveformPeak {
            min: min_val * self.config.amplitude_scale,
            max: max_val * self.config.amplitude_scale,
            rms: rms * self.config.amplitude_scale,
            peak: peak_val * self.config.amplitude_scale,
        }
    }
    
    /// Calcule les statistiques audio globales
    fn calculate_audio_statistics(&self, samples: &[f32], sample_rate: u32) -> AudioStatistics {
        if samples.is_empty() {
            return AudioStatistics::default();
        }
        
        // Calculs de base
        let mut sum_squares = 0.0;
        let mut peak_amplitude: f32 = 0.0;
        let mut silence_samples = 0;
        let silence_threshold = 0.001; // -60dB environ
        
        for &sample in samples {
            let abs_sample = sample.abs();
            sum_squares += sample * sample;
            peak_amplitude = peak_amplitude.max(abs_sample);
            
            if abs_sample < silence_threshold {
                silence_samples += 1;
            }
        }
        
        let average_rms = (sum_squares / samples.len() as f32).sqrt();
        let silence_percentage = (silence_samples as f32 / samples.len() as f32) * 100.0;
        
        // Dynamic range (approximation)
        let dynamic_range_db = if average_rms > 0.0 && peak_amplitude > 0.0 {
            20.0 * (peak_amplitude / average_rms).log10()
        } else {
            0.0
        };
        
        // Crest factor
        let crest_factor = if average_rms > 0.0 {
            peak_amplitude / average_rms
        } else {
            0.0
        };
        
        // Loudness intégré (approximation simple)
        let integrated_loudness = if average_rms > 0.0 {
            -0.691 + 10.0 * average_rms.log10()
        } else {
            -70.0 // Silence
        };
        
        // BPM et clé (simulation - en production, utiliser des algos dédiés)
        let estimated_bpm = self.estimate_bpm(samples, sample_rate);
        let estimated_key = self.estimate_key(samples, sample_rate);
        
        AudioStatistics {
            average_rms,
            peak_amplitude,
            dynamic_range_db,
            integrated_loudness,
            crest_factor,
            silence_percentage,
            estimated_bpm,
            estimated_key,
        }
    }
    
    /// Estime le BPM (simulation)
    fn estimate_bpm(&self, _samples: &[f32], _sample_rate: u32) -> Option<f32> {
        // Simulation - en production, utiliser des algorithmes de détection de tempo
        Some(128.0)
    }
    
    /// Estime la clé musicale (simulation)
    fn estimate_key(&self, _samples: &[f32], _sample_rate: u32) -> Option<String> {
        // Simulation - en production, utiliser des algorithmes de détection de tonalité
        Some("C major".to_string())
    }
    
    /// Génère les données spectrales
    async fn generate_spectral_data(&self, audio_data: &AudioData) -> Result<SpectralData, AppError> {
        let fft_size = 2048;
        let hop_size = fft_size / 4; // 75% overlap
        let window_count = (audio_data.samples.len() + hop_size - 1) / hop_size;
        
        let mut spectrogram = Vec::with_capacity(window_count);
        let frequency_bins = self.generate_frequency_bins(fft_size, audio_data.sample_rate);
        let time_resolution_ms = (hop_size as f32 / audio_data.sample_rate as f32) * 1000.0;
        
        // Simulation de FFT - en production, utiliser rustfft
        for window_index in 0..window_count {
            let start_sample = window_index * hop_size;
            let end_sample = (start_sample + fft_size).min(audio_data.samples.len());
            
            if start_sample < audio_data.samples.len() {
                let window_samples = &audio_data.samples[start_sample..end_sample];
                let magnitudes = self.calculate_fft_magnitudes(window_samples, fft_size);
                
                let frame = SpectralFrame {
                    magnitudes,
                    timestamp_ms: window_index as f32 * time_resolution_ms,
                };
                
                spectrogram.push(frame);
            }
        }
        
        Ok(SpectralData {
            spectrogram,
            frequency_bins,
            time_resolution_ms,
        })
    }
    
    /// Génère les bins de fréquence
    fn generate_frequency_bins(&self, fft_size: usize, sample_rate: u32) -> Vec<f32> {
        let bin_count = fft_size / 2 + 1;
        (0..bin_count)
            .map(|i| i as f32 * sample_rate as f32 / fft_size as f32)
            .collect()
    }
    
    /// Calcule les magnitudes FFT (simulation)
    fn calculate_fft_magnitudes(&self, samples: &[f32], fft_size: usize) -> Vec<f32> {
        let bin_count = fft_size / 2 + 1;
        let mut magnitudes = Vec::with_capacity(bin_count);
        
        // Simulation simple - en production, utiliser une vraie FFT
        for i in 0..bin_count {
            let frequency = i as f32 / bin_count as f32;
            let magnitude = if !samples.is_empty() {
                let avg_amplitude = samples.iter().map(|&s| s.abs()).sum::<f32>() / samples.len() as f32;
                avg_amplitude * (1.0 - frequency) // Décroissance avec la fréquence
            } else {
                0.0
            };
            magnitudes.push(magnitude);
        }
        
        magnitudes
    }
    
    /// Récupère depuis le cache
    async fn get_from_cache(&self, key: &str) -> Option<WaveformData> {
        self.cache.read().await.get(key).cloned()
    }
    
    /// Stocke en cache
    async fn store_in_cache(&self, key: &str, waveform: &WaveformData) {
        self.cache.write().await.insert(key.to_string(), waveform.clone());
    }
    
    /// Exporte la waveform dans un format spécifique
    pub fn export_waveform(&self, waveform: &WaveformData, format: WaveformFormat) -> Result<Vec<u8>, AppError> {
        match format {
            WaveformFormat::PeaksJS => {
                let json = serde_json::to_string_pretty(waveform)
                    .map_err(|_| AppError::SerializationError)?;
                Ok(json.into_bytes())
            },
            WaveformFormat::Binary => {
                // Format binaire compact pour la performance
                let mut data = Vec::new();
                
                // Header
                data.extend(&(waveform.peaks.len() as u32).to_le_bytes());
                data.extend(&waveform.sample_rate.to_le_bytes());
                data.extend(&(waveform.channels as u32).to_le_bytes());
                data.extend(&waveform.duration.to_le_bytes());
                
                // Peaks data
                for peak in &waveform.peaks {
                    data.extend(&peak.min.to_le_bytes());
                    data.extend(&peak.max.to_le_bytes());
                    data.extend(&peak.rms.to_le_bytes());
                    data.extend(&peak.peak.to_le_bytes());
                }
                
                Ok(data)
            },
            WaveformFormat::SVG { .. } => {
                // Génération SVG simple
                let svg = self.generate_svg_waveform(waveform)?;
                Ok(svg.into_bytes())
            },
            WaveformFormat::PNG { width, height } => {
                // Génération PNG (simulation)
                let _png_data = self.generate_png_waveform(waveform, width, height)?;
                Ok(Vec::new()) // Placeholder
            },
        }
    }
    
    /// Génère une représentation SVG de la waveform
    fn generate_svg_waveform(&self, waveform: &WaveformData) -> Result<String, AppError> {
        let width = 800;
        let height = 200;
        let center_y = height / 2;
        
        let mut svg = format!(
            "<svg width=\"{}\" height=\"{}\" xmlns=\"http://www.w3.org/2000/svg\">\n<rect width=\"100%\" height=\"100%\" fill=\"#f0f0f0\"/>\n<g stroke=\"#007cba\" stroke-width=\"1\" fill=\"none\">",
            width, height
        );
        
        // Dessiner la waveform
        let x_scale = width as f32 / waveform.peaks.len() as f32;
        let y_scale = center_y as f32;
        
        let mut path = String::from("M");
        for (i, peak) in waveform.peaks.iter().enumerate() {
            let x = i as f32 * x_scale;
            let y_top = center_y as f32 - (peak.max * y_scale);
            let y_bottom = center_y as f32 - (peak.min * y_scale);
            
            if i == 0 {
                path.push_str(&format!("{},{}", x, y_top));
            } else {
                path.push_str(&format!(" L{},{}", x, y_top));
            }
        }
        
        // Fermer le chemin
        for (i, peak) in waveform.peaks.iter().enumerate().rev() {
            let x = i as f32 * x_scale;
            let y_bottom = center_y as f32 - (peak.min * y_scale);
            path.push_str(&format!(" L{},{}", x, y_bottom));
        }
        path.push('Z');
        
        svg.push_str(&format!("<path d=\"{}\" fill=\"#007cba\" opacity=\"0.6\"/>", path));
        svg.push_str("</g></svg>");
        
        Ok(svg)
    }
    
    /// Génère une image PNG de la waveform
    fn generate_png_waveform(&self, _waveform: &WaveformData, _width: u32, _height: u32) -> Result<Vec<u8>, AppError> {
        // Simulation - en production, utiliser une lib comme `image` ou `skia`
        Ok(Vec::new())
    }
}

/// Données audio brutes
#[derive(Debug)]
struct AudioData {
    samples: Vec<f32>,
    sample_rate: u32,
    channels: u8,
    duration: f64,
}

impl Default for AudioStatistics {
    fn default() -> Self {
        Self {
            average_rms: 0.0,
            peak_amplitude: 0.0,
            dynamic_range_db: 0.0,
            integrated_loudness: -70.0,
            crest_factor: 0.0,
            silence_percentage: 100.0,
            estimated_bpm: None,
            estimated_key: None,
        }
    }
} 