torsh-series 0.1.2

//! Wavelet decomposition for time series analysis
//!
//! Wavelets provide a time-frequency representation of signals, making them ideal
//! for analyzing non-stationary time series with features at different scales.
//!
//! NOTE: This module provides the structure for wavelet analysis.
//! Full implementation will use scirs2-signal when the wavelet API is available.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::TimeSeries;
use scirs2_core::ndarray::Array1;
// TODO: Uncomment when scirs2-signal wavelet API is available
// use scirs2_signal::wavelets::{dwt, idwt, wavelet_decompose, wavelet_reconstruct, WaveletType};
use torsh_core::error::{Result, TorshError};
use torsh_tensor::Tensor;

/// Wavelet type enumeration (placeholder until scirs2-signal provides it)
#[derive(Debug, Clone, Copy)]
pub enum WaveletType {
    Haar,
    Daubechies4,
    Symlet4,
    Morlet,
}

/// Wavelet decomposition result
#[derive(Debug, Clone)]
pub struct WaveletDecomposition {
    /// Approximation coefficients (low frequency)
    pub approximation: Tensor,
    /// Detail coefficients (high frequency) for each level
    pub details: Vec<Tensor>,
    /// Wavelet family used
    pub wavelet_family: String,
    /// Decomposition level
    pub level: usize,
}

/// Continuous Wavelet Transform (CWT) result
#[derive(Debug, Clone)]
pub struct CWTResult {
    /// Wavelet coefficients [scale x time]
    pub coefficients: Tensor,
    /// Scales used
    pub scales: Vec<f64>,
    /// Frequencies corresponding to scales
    pub frequencies: Vec<f64>,
}

/// Wavelet-based time series decomposer
pub struct WaveletDecomposer {
    wavelet_type: WaveletType,
    level: Option<usize>,
    mode: String,
}

impl WaveletDecomposer {
    /// Create a new wavelet decomposer
    ///
    /// # Arguments
    /// * `wavelet_type` - Type of wavelet to use (e.g., Haar, Daubechies, Symlet)
    pub fn new(wavelet_type: WaveletType) -> Self {
        Self {
            wavelet_type,
            level: None,
            mode: "symmetric".to_string(),
        }
    }

    /// Set decomposition level
    pub fn with_level(mut self, level: usize) -> Self {
        self.level = Some(level);
        self
    }

    /// Set boundary extension mode
    pub fn with_mode(mut self, mode: &str) -> Self {
        self.mode = mode.to_string();
        self
    }

    /// Perform Discrete Wavelet Transform (DWT) decomposition
    pub fn decompose(&self, series: &TimeSeries) -> Result<WaveletDecomposition> {
        // Convert TimeSeries to Array1
        let data = series.values.to_vec().map_err(|e| {
            TorshError::InvalidArgument(format!("Failed to convert tensor to vec: {}", e))
        })?;
        let _ts_array = Array1::from_vec(data.clone());

        // Determine decomposition level if not specified
        let level = self
            .level
            .unwrap_or_else(|| Self::max_decomposition_level(series.len()));

        // TODO: Use scirs2-signal wavelet_decompose when available
        // For now, return placeholder decomposition
        let n = series.len();
        let mut current_len = n;
        let mut details = Vec::with_capacity(level);

        // Create placeholder detail coefficients (simplified Haar-like decomposition)
        for _lev in 0..level {
            let detail_len = current_len / 2;
            let detail_data = vec![0.0f32; detail_len];
            let detail_tensor = Tensor::from_vec(detail_data, &[detail_len])?;
            details.push(detail_tensor);
            current_len = detail_len;
        }

        // Approximation coefficients (same length as last detail level)
        let approx_data = vec![0.0f32; current_len];
        let approximation = Tensor::from_vec(approx_data, &[current_len])?;

        Ok(WaveletDecomposition {
            approximation,
            details,
            wavelet_family: format!("{:?}", self.wavelet_type),
            level,
        })
    }

    /// Reconstruct time series from wavelet decomposition
    pub fn reconstruct(&self, decomposition: &WaveletDecomposition) -> Result<TimeSeries> {
        // TODO: Use scirs2-signal wavelet_reconstruct when available
        // For now, return placeholder reconstruction
        let approx_len = decomposition.approximation.shape().dims()[0];
        let total_len = approx_len * (2_usize.pow(decomposition.level as u32));

        let recon_data = vec![0.0f32; total_len];
        let tensor = Tensor::from_vec(recon_data, &[total_len])?;

        Ok(TimeSeries::new(tensor))
    }

    /// Calculate maximum useful decomposition level
    fn max_decomposition_level(n: usize) -> usize {
        // Maximum level is floor(log2(n))
        ((n as f64).log2().floor() as usize).max(1)
    }

    /// Perform single-level DWT
    pub fn single_level_dwt(&self, series: &TimeSeries) -> Result<(Tensor, Tensor)> {
        let data = series.values.to_vec()?;
        let n = data.len();

        // TODO: Use scirs2-signal dwt when available
        // Placeholder implementation (simplified Haar transform)
        let half_n = n / 2;
        let mut approx_data = vec![0.0f32; half_n];
        let mut detail_data = vec![0.0f32; half_n];

        for i in 0..half_n {
            if 2 * i + 1 < n {
                approx_data[i] = (data[2 * i] + data[2 * i + 1]) / 2.0;
                detail_data[i] = (data[2 * i] - data[2 * i + 1]) / 2.0;
            }
        }

        let approx_tensor = Tensor::from_vec(approx_data, &[half_n])?;
        let detail_tensor = Tensor::from_vec(detail_data, &[half_n])?;

        Ok((approx_tensor, detail_tensor))
    }

    /// Perform single-level inverse DWT
    pub fn single_level_idwt(&self, approx: &Tensor, detail: &Tensor) -> Result<TimeSeries> {
        let approx_data = approx.to_vec()?;
        let detail_data = detail.to_vec()?;

        // TODO: Use scirs2-signal idwt when available
        // Placeholder implementation (simplified inverse Haar transform)
        let half_n = approx_data.len();
        let mut recon_data = vec![0.0f32; half_n * 2];

        for i in 0..half_n {
            recon_data[2 * i] = approx_data[i] + detail_data[i];
            recon_data[2 * i + 1] = approx_data[i] - detail_data[i];
        }

        let tensor = Tensor::from_vec(recon_data.clone(), &[recon_data.len()])?;
        Ok(TimeSeries::new(tensor))
    }
}

/// Continuous Wavelet Transform (CWT) analyzer
pub struct CWTAnalyzer {
    wavelet_type: WaveletType,
    scales: Option<Vec<f64>>,
    sampling_period: f64,
}

impl CWTAnalyzer {
    /// Create a new CWT analyzer
    pub fn new(wavelet_type: WaveletType) -> Self {
        Self {
            wavelet_type,
            scales: None,
            sampling_period: 1.0,
        }
    }

    /// Set scales for CWT
    pub fn with_scales(mut self, scales: Vec<f64>) -> Self {
        self.scales = Some(scales);
        self
    }

    /// Set sampling period
    pub fn with_sampling_period(mut self, period: f64) -> Self {
        self.sampling_period = period;
        self
    }

    /// Perform Continuous Wavelet Transform
    pub fn analyze(&self, series: &TimeSeries) -> Result<CWTResult> {
        let _data = series.values.to_vec()?;

        // Generate scales if not provided
        let scales = self
            .scales
            .clone()
            .unwrap_or_else(|| Self::generate_scales(series.len(), 1.0, 128.0, 64));

        // TODO: Use scirs2-signal cwt when available
        // Placeholder implementation
        let n_scales = scales.len();
        let n_time = series.len();
        let coef_data = vec![0.0f32; n_scales * n_time];
        let coefficients = Tensor::from_vec(coef_data, &[n_scales, n_time])?;

        // Calculate frequencies from scales
        let frequencies = self.scales_to_frequencies(&scales);

        Ok(CWTResult {
            coefficients,
            scales,
            frequencies,
        })
    }

    /// Generate logarithmically spaced scales
    fn generate_scales(_n: usize, min_scale: f64, max_scale: f64, n_scales: usize) -> Vec<f64> {
        let log_min = min_scale.ln();
        let log_max = max_scale.ln();
        let step = (log_max - log_min) / (n_scales - 1) as f64;

        (0..n_scales)
            .map(|i| (log_min + i as f64 * step).exp())
            .collect()
    }

    /// Convert scales to frequencies
    fn scales_to_frequencies(&self, scales: &[f64]) -> Vec<f64> {
        // For Morlet wavelet, center frequency is typically ~1.0
        // Frequency = center_freq / (scale * sampling_period)
        let center_freq = 1.0;
        scales
            .iter()
            .map(|&scale| center_freq / (scale * self.sampling_period))
            .collect()
    }
}

/// Wavelet packet decomposition for more flexible time-frequency analysis
pub struct WaveletPacketDecomposer {
    wavelet_type: WaveletType,
    level: usize,
}

impl WaveletPacketDecomposer {
    /// Create a new wavelet packet decomposer
    pub fn new(wavelet_type: WaveletType, level: usize) -> Self {
        Self {
            wavelet_type,
            level,
        }
    }

    /// Decompose into wavelet packet tree
    pub fn decompose(&self, series: &TimeSeries) -> Result<WaveletPacketTree> {
        let _data = series.values.to_vec()?;

        // TODO: Use scirs2-signal wavelet_packet_decompose when available
        // Placeholder implementation
        let mut nodes = std::collections::HashMap::new();

        // Create root node
        let root_data = vec![0.0f32; series.len()];
        let root_tensor = Tensor::from_vec(root_data, &[series.len()])?;
        nodes.insert("".to_string(), root_tensor);

        Ok(WaveletPacketTree {
            nodes,
            level: self.level,
            wavelet_family: format!("{:?}", self.wavelet_type),
        })
    }
}

/// Wavelet packet tree structure
#[derive(Debug, Clone)]
pub struct WaveletPacketTree {
    /// Nodes indexed by path (e.g., "a" for approximation, "d" for detail)
    pub nodes: std::collections::HashMap<String, Tensor>,
    /// Decomposition level
    pub level: usize,
    /// Wavelet family
    pub wavelet_family: String,
}

impl WaveletPacketTree {
    /// Get node at specific path
    pub fn get_node(&self, path: &str) -> Option<&Tensor> {
        self.nodes.get(path)
    }

    /// Get all leaf nodes at the deepest level
    pub fn leaf_nodes(&self) -> Vec<&Tensor> {
        self.nodes
            .iter()
            .filter(|(path, _)| path.len() == self.level)
            .map(|(_, tensor)| tensor)
            .collect()
    }
}

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

    fn create_test_series() -> TimeSeries {
        // Create a simple time series with multiple frequencies
        let mut data = Vec::with_capacity(128);
        for i in 0..128 {
            let t = i as f32 * 0.1;
            // Low frequency + high frequency components
            let val = (t).sin() + 0.5 * (5.0 * t).sin();
            data.push(val);
        }
        let tensor = Tensor::from_vec(data, &[128]).expect("Tensor should succeed");
        TimeSeries::new(tensor)
    }

    #[test]
    fn test_wavelet_decomposer_creation() {
        let decomposer = WaveletDecomposer::new(WaveletType::Haar);
        assert_eq!(decomposer.mode, "symmetric");
    }

    #[test]
    fn test_wavelet_decomposition() {
        let series = create_test_series();
        let decomposer = WaveletDecomposer::new(WaveletType::Haar).with_level(3);
        let decomposition = decomposer
            .decompose(&series)
            .expect("decomposition should succeed");

        assert_eq!(decomposition.level, 3);
        assert_eq!(decomposition.details.len(), 3);
        assert!(decomposition.approximation.shape().dims()[0] > 0);
    }

    #[test]
    fn test_wavelet_reconstruction() {
        let series = create_test_series();
        let decomposer = WaveletDecomposer::new(WaveletType::Haar).with_level(2);
        let decomposition = decomposer
            .decompose(&series)
            .expect("decomposition should succeed");
        let reconstructed = decomposer
            .reconstruct(&decomposition)
            .expect("reconstruction should succeed");

        // Reconstructed should have similar length to original
        assert!(reconstructed.len() >= series.len() - 10); // Allow small difference due to padding
    }

    #[test]
    fn test_single_level_dwt() {
        let series = create_test_series();
        let decomposer = WaveletDecomposer::new(WaveletType::Haar);
        let (approx, detail) = decomposer
            .single_level_dwt(&series)
            .expect("single-level DWT should succeed");

        assert!(approx.shape().dims()[0] > 0);
        assert!(detail.shape().dims()[0] > 0);
    }

    #[test]
    fn test_cwt_analyzer() {
        let series = create_test_series();
        let analyzer = CWTAnalyzer::new(WaveletType::Morlet).with_sampling_period(0.1);
        let result = analyzer.analyze(&series).expect("analysis should succeed");

        assert!(result.coefficients.shape().dims()[0] > 0); // scales
        assert_eq!(result.coefficients.shape().dims()[1], series.len()); // time
        assert!(!result.scales.is_empty());
        assert!(!result.frequencies.is_empty());
    }

    #[test]
    fn test_max_decomposition_level() {
        assert_eq!(WaveletDecomposer::max_decomposition_level(128), 7);
        assert_eq!(WaveletDecomposer::max_decomposition_level(256), 8);
        assert_eq!(WaveletDecomposer::max_decomposition_level(64), 6);
    }

    #[test]
    fn test_wavelet_packet_decomposer() {
        let series = create_test_series();
        let decomposer = WaveletPacketDecomposer::new(WaveletType::Daubechies4, 2);
        let tree = decomposer
            .decompose(&series)
            .expect("decomposition should succeed");

        assert_eq!(tree.level, 2);
        assert!(!tree.nodes.is_empty());
    }
}