//! Advanced-advanced spectral analysis methods for statistical signal processing
//!
//! This module implements state-of-the-art spectral analysis techniques including:
//! - Multi-taper spectral estimation with adaptive bandwidth
//! - Wavelet-based time-frequency analysis
//! - Higher-order spectral analysis (bispectra, trispectra)
//! - Coherence analysis for multivariate signals
//! - Spectral clustering and manifold learning
//! - Non-stationary signal analysis
//! - Compressed sensing spectral recovery
//! - Machine learning enhanced spectral methods

use crate::error::{StatsError, StatsResult};
use scirs2_core::ndarray::{Array1, Array2, Array3, Array4, ArrayView1, ArrayView2};
use scirs2_core::numeric::{Float, FloatConst, NumCast, One, Zero};
use scirs2_core::{simd_ops::SimdUnifiedOps, validation::*};
use scirs2_linalg::parallel_dispatch::ParallelConfig;
use std::collections::HashMap;
use std::marker::PhantomData;

/// Advanced-advanced spectral analysis framework
pub struct AdvancedSpectralAnalyzer<F> {
    /// Analysis configuration
    config: AdvancedSpectralConfig<F>,
    /// Cached basis functions and transforms
    cache: SpectralCache<F>,
    /// Performance metrics
    performance: SpectralPerformanceMetrics,
    _phantom: PhantomData<F>,
}

/// Configuration for advanced spectral analysis
pub struct AdvancedSpectralConfig<F> {
    /// Sampling frequency
    pub fs: F,
    /// Window functions to use
    pub windows: Vec<WindowFunction>,
    /// Multi-taper configuration
    pub multitaper_config: MultiTaperConfig<F>,
    /// Wavelet analysis configuration
    pub wavelet_config: WaveletConfig<F>,
    /// Higher-order spectral analysis settings
    pub hos_config: HigherOrderSpectralConfig<F>,
    /// Coherence analysis settings
    pub coherence_config: CoherenceConfig<F>,
    /// Non-stationary analysis settings
    pub nonstationary_config: NonStationaryConfig<F>,
    /// Machine learning enhancement settings
    pub ml_config: MLSpectralConfig<F>,
    /// Parallel processing settings
    pub parallel_config: ParallelConfig,
}

/// Multi-taper spectral estimation configuration
#[derive(Debug, Clone)]
pub struct MultiTaperConfig<F> {
    /// Time-bandwidth product
    pub nw: F,
    /// Number of tapers to use
    pub k: usize,
    /// Adaptive bandwidth selection
    pub adaptive: bool,
    /// Jackknife confidence intervals
    pub jackknife: bool,
    /// F-test for line components
    pub f_test: bool,
}

/// Wavelet analysis configuration
#[derive(Debug, Clone)]
pub struct WaveletConfig<F> {
    /// Wavelet type
    pub wavelet_type: WaveletType,
    /// Number of scales
    pub scales: usize,
    /// Minimum frequency
    pub f_min: F,
    /// Maximum frequency
    pub f_max: F,
    /// Time localization vs frequency resolution tradeoff
    pub q_factor: F,
    /// Enable continuous wavelet transform
    pub continuous: bool,
    /// Enable discrete wavelet packet transform
    pub packet_transform: bool,
}

/// Higher-order spectral analysis configuration
#[derive(Debug, Clone)]
pub struct HigherOrderSpectralConfig<F> {
    /// Compute bispectrum (third-order)
    pub compute_bispectrum: bool,
    /// Compute trispectrum (fourth-order)
    pub compute_trispectrum: bool,
    /// Maximum lag for higher-order statistics
    pub max_lag: usize,
    /// Overlap for segmented analysis
    pub overlap: F,
    /// Window size for segmentation
    pub segment_length: usize,
}

/// Coherence analysis configuration
#[derive(Debug, Clone)]
pub struct CoherenceConfig<F> {
    /// Compute magnitude-squared coherence
    pub magnitude_squared: bool,
    /// Compute complex coherence
    pub complex_coherence: bool,
    /// Compute partial coherence
    pub partial_coherence: bool,
    /// Compute multiple coherence
    pub multiple_coherence: bool,
    /// Frequency resolution
    pub frequency_resolution: F,
    /// Confidence level for significance testing
    pub confidence_level: F,
}

/// Non-stationary signal analysis configuration
#[derive(Debug, Clone)]
pub struct NonStationaryConfig<F> {
    /// Short-time Fourier transform window size
    pub stft_windowsize: usize,
    /// STFT overlap percentage
    pub stft_overlap: F,
    /// Spectrogram type
    pub spectrogram_type: SpectrogramType,
    /// Time-varying spectral estimation
    pub time_varying: bool,
    /// Adaptive window sizing
    pub adaptive_window: bool,
}

/// Machine learning enhanced spectral configuration
#[derive(Debug, Clone)]
pub struct MLSpectralConfig<F> {
    /// Use neural networks for spectral enhancement
    pub neural_enhancement: bool,
    /// Use autoencoder for noise reduction
    pub autoencoder_denoising: bool,
    /// Use adversarial training for super-resolution
    pub adversarial_sr: bool,
    /// Use reinforcement learning for adaptive parameterization
    pub rl_adaptation: bool,
    /// Network architecture parameters
    pub network_params: NetworkParams<F>,
}

/// Network architecture parameters
#[derive(Debug, Clone)]
pub struct NetworkParams<F> {
    /// Hidden layer sizes
    pub hiddensizes: Vec<usize>,
    /// Activation function
    pub activation: ActivationFunction,
    /// Learning rate
    pub learning_rate: F,
    /// Regularization strength
    pub regularization: F,
    /// Number of epochs
    pub epochs: usize,
}

/// Window functions for spectral analysis
#[derive(Debug, Clone, Copy)]
pub enum WindowFunction {
    /// Rectangular window
    Rectangular,
    /// Hann window
    Hann,
    /// Hamming window
    Hamming,
    /// Blackman window
    Blackman,
    /// Blackman-Harris window
    BlackmanHarris,
    /// Kaiser window with beta parameter
    Kaiser(f64),
    /// Tukey window with alpha parameter
    Tukey(f64),
    /// Gaussian window with sigma parameter
    Gaussian(f64),
    /// Dolph-Chebyshev window
    DolphChebyshev(f64),
    /// Adaptive optimal window
    AdaptiveOptimal,
}

/// Wavelet types for time-frequency analysis
#[derive(Debug, Clone, Copy)]
pub enum WaveletType {
    /// Morlet wavelet
    Morlet,
    /// Mexican hat (Ricker) wavelet
    MexicanHat,
    /// Daubechies wavelets
    Daubechies(usize),
    /// Biorthogonal wavelets
    Biorthogonal(usize, usize),
    /// Coiflets
    Coiflets(usize),
    /// Complex Morlet
    ComplexMorlet,
    /// Gabor wavelets
    Gabor,
    /// Meyer wavelets
    Meyer,
    /// Shannon wavelets
    Shannon,
}

/// Spectrogram types
#[derive(Debug, Clone, Copy)]
pub enum SpectrogramType {
    /// Power spectral density
    PowerSpectralDensity,
    /// Cross spectral density
    CrossSpectralDensity,
    /// Phase spectrogram
    Phase,
    /// Instantaneous frequency
    InstantaneousFrequency,
    /// Group delay
    GroupDelay,
    /// Reassigned spectrogram
    Reassigned,
    /// Synchrosqueezed transform
    Synchrosqueezed,
}

/// Activation functions for neural networks
#[derive(Debug, Clone, Copy)]
pub enum ActivationFunction {
    ReLU,
    LeakyReLU(f64),
    ELU(f64),
    Swish,
    GELU,
    Tanh,
    Sigmoid,
    Softplus,
}

/// Spectral analysis results
#[derive(Debug, Clone)]
pub struct AdvancedSpectralResults<F> {
    /// Power spectral density
    pub psd: Array2<F>,
    /// Frequency bins
    pub frequencies: Array1<F>,
    /// Time bins (for time-frequency analysis)
    pub times: Option<Array1<F>>,
    /// Confidence intervals
    pub confidence_intervals: Option<Array3<F>>,
    /// Coherence results
    pub coherence: Option<CoherenceResults<F>>,
    /// Higher-order spectral results
    pub higher_order: Option<HigherOrderResults<F>>,
    /// Wavelet analysis results
    pub wavelet: Option<WaveletResults<F>>,
    /// Machine learning enhanced results
    pub ml_enhanced: Option<MLSpectralResults<F>>,
    /// Performance metrics
    pub performance: SpectralPerformanceMetrics,
}

/// Coherence analysis results
#[derive(Debug, Clone)]
pub struct CoherenceResults<F> {
    /// Magnitude-squared coherence
    pub magnitude_squared: Option<Array2<F>>,
    /// Complex coherence
    pub complex_coherence: Option<Array2<scirs2_core::numeric::Complex<F>>>,
    /// Partial coherence
    pub partial_coherence: Option<Array3<F>>,
    /// Multiple coherence
    pub multiple_coherence: Option<Array2<F>>,
    /// Significance levels
    pub significance: Option<Array2<F>>,
}

/// Higher-order spectral analysis results
#[derive(Debug, Clone)]
pub struct HigherOrderResults<F> {
    /// Bispectrum
    pub bispectrum: Option<Array3<scirs2_core::numeric::Complex<F>>>,
    /// Bicoherence
    pub bicoherence: Option<Array3<F>>,
    /// Trispectrum
    pub trispectrum: Option<Array4<scirs2_core::numeric::Complex<F>>>,
    /// Tricoherence
    pub tricoherence: Option<Array4<F>>,
}

/// Wavelet analysis results
#[derive(Debug, Clone)]
pub struct WaveletResults<F> {
    /// Continuous wavelet transform coefficients
    pub cwt_coefficients: Option<Array3<scirs2_core::numeric::Complex<F>>>,
    /// Discrete wavelet transform coefficients
    pub dwt_coefficients: Option<Vec<Array1<F>>>,
    /// Wavelet packet coefficients
    pub packet_coefficients: Option<HashMap<String, Array1<F>>>,
    /// Ridge detection results
    pub ridges: Option<Array2<usize>>,
    /// Instantaneous frequency
    pub instantaneous_frequency: Option<Array2<F>>,
}

/// Machine learning enhanced spectral results
#[derive(Debug, Clone)]
pub struct MLSpectralResults<F> {
    /// Denoised spectrum
    pub denoised_spectrum: Option<Array2<F>>,
    /// Super-resolution spectrum
    pub super_resolution: Option<Array2<F>>,
    /// Learned features
    pub learned_features: Option<Array2<F>>,
    /// Anomaly detection scores
    pub anomaly_scores: Option<Array1<F>>,
    /// Uncertainty estimates
    pub uncertainty: Option<Array2<F>>,
}

/// Performance metrics for spectral analysis
#[derive(Debug, Clone)]
pub struct SpectralPerformanceMetrics {
    /// Computation time breakdown
    pub timing: HashMap<String, f64>,
    /// Memory usage statistics
    pub memory_usage: MemoryUsageStats,
    /// Numerical accuracy metrics
    pub accuracy: AccuracyMetrics,
    /// Algorithm convergence information
    pub convergence: ConvergenceMetrics,
}

/// Memory usage statistics
#[derive(Debug, Clone)]
pub struct MemoryUsageStats {
    /// Peak memory usage in bytes
    pub peak_usage: usize,
    /// Average memory usage
    pub average_usage: usize,
    /// Cache efficiency
    pub cache_efficiency: f64,
    /// Memory allocation count
    pub allocation_count: usize,
}

/// Numerical accuracy metrics
#[derive(Debug, Clone)]
pub struct AccuracyMetrics {
    /// Relative error
    pub relative_error: f64,
    /// Absolute error
    pub absolute_error: f64,
    /// Signal-to-noise ratio improvement
    pub snr_improvement: f64,
    /// Frequency resolution achieved
    pub frequency_resolution: f64,
}

/// Algorithm convergence metrics
#[derive(Debug, Clone)]
pub struct ConvergenceMetrics {
    /// Number of iterations
    pub iterations: usize,
    /// Final residual
    pub final_residual: f64,
    /// Convergence rate
    pub convergence_rate: f64,
    /// Stability measure
    pub stability: f64,
}

/// Spectral cache for performance optimization
struct SpectralCache<F> {
    /// Cached window functions
    windows: HashMap<String, Array1<F>>,
    /// Cached FFT plans
    fft_plans: HashMap<usize, Vec<u8>>, // Placeholder for FFT plans
    /// Cached wavelets
    wavelets: HashMap<String, Array2<scirs2_core::numeric::Complex<F>>>,
    /// Cached tapers
    tapers: HashMap<String, Array2<F>>,
}

impl<F> AdvancedSpectralAnalyzer<F>
where
    F: Float
        + NumCast
        + FloatConst
        + SimdUnifiedOps
        + One
        + Zero
        + PartialOrd
        + Copy
        + Send
        + Sync
        + std::fmt::Display,
{
    /// Create a new advanced spectral analyzer
    pub fn new(config: AdvancedSpectralConfig<F>) -> Self {
        let cache = SpectralCache {
            windows: HashMap::new(),
            fft_plans: HashMap::new(),
            wavelets: HashMap::new(),
            tapers: HashMap::new(),
        };

        let performance = SpectralPerformanceMetrics {
            timing: HashMap::new(),
            memory_usage: MemoryUsageStats {
                peak_usage: 0,
                average_usage: 0,
                cache_efficiency: 0.0,
                allocation_count: 0,
            },
            accuracy: AccuracyMetrics {
                relative_error: 0.0,
                absolute_error: 0.0,
                snr_improvement: 0.0,
                frequency_resolution: 0.0,
            },
            convergence: ConvergenceMetrics {
                iterations: 0,
                final_residual: 0.0,
                convergence_rate: 0.0,
                stability: 0.0,
            },
        };

        Self {
            config,
            cache,
            performance: SpectralPerformanceMetrics {
                timing: HashMap::new(),
                memory_usage: MemoryUsageStats {
                    peak_usage: 0,
                    average_usage: 0,
                    cache_efficiency: 0.0,
                    allocation_count: 0,
                },
                accuracy: AccuracyMetrics {
                    relative_error: 0.0,
                    absolute_error: 0.0,
                    snr_improvement: 0.0,
                    frequency_resolution: 0.0,
                },
                convergence: ConvergenceMetrics {
                    iterations: 0,
                    final_residual: 0.0,
                    convergence_rate: 1.0,
                    stability: 1.0,
                },
            },
            _phantom: PhantomData,
        }
    }

    /// Perform comprehensive spectral analysis on input signal
    pub fn analyze_comprehensive(
        &mut self,
        signal: &ArrayView1<F>,
    ) -> StatsResult<AdvancedSpectralResults<F>> {
        checkarray_finite(signal, "signal")?;
        check_min_samples(signal, 2, "signal")?;

        let start_time = std::time::Instant::now();
        let mut results = AdvancedSpectralResults {
            psd: Array2::zeros((0, 0)),
            frequencies: Array1::zeros(0),
            times: None,
            confidence_intervals: None,
            coherence: None,
            higher_order: None,
            wavelet: None,
            ml_enhanced: None,
            performance: self.performance.clone(),
        };

        // Multi-taper spectral estimation
        let (psd, frequencies) = self.multitaper_psd(signal)?;
        results.psd = psd;
        results.frequencies = frequencies;

        // Compute confidence intervals if requested
        if self.config.multitaper_config.jackknife {
            results.confidence_intervals = Some(self.compute_confidence_intervals(signal)?);
        }

        // Wavelet analysis if enabled
        if self.config.wavelet_config.continuous || self.config.wavelet_config.packet_transform {
            results.wavelet = Some(self.wavelet_analysis(signal)?);
        }

        // Higher-order spectral analysis
        if self.config.hos_config.compute_bispectrum || self.config.hos_config.compute_trispectrum {
            results.higher_order = Some(self.higher_order_analysis(signal)?);
        }

        // Machine learning enhancement
        if self.config.ml_config.neural_enhancement {
            results.ml_enhanced = Some(self.ml_spectral_enhancement(signal, &results.psd)?);
        }

        // Update performance metrics
        let elapsed = start_time.elapsed();
        self.performance
            .timing
            .insert("total_analysis".to_string(), elapsed.as_secs_f64());

        results.performance = self.performance.clone();
        Ok(results)
    }

    /// Multi-channel coherence analysis
    pub fn analyze_coherence(
        &mut self,
        signals: &ArrayView2<F>,
    ) -> StatsResult<CoherenceResults<F>> {
        checkarray_finite(signals, "signals")?;
        let (_n_samples_, n_channels) = signals.dim();

        if n_channels < 2 {
            return Err(StatsError::InvalidArgument(
                "Need at least 2 channels for coherence analysis".to_string(),
            ));
        }

        let mut coherence_results = CoherenceResults {
            magnitude_squared: None,
            complex_coherence: None,
            partial_coherence: None,
            multiple_coherence: None,
            significance: None,
        };

        // Magnitude-squared coherence
        if self.config.coherence_config.magnitude_squared {
            coherence_results.magnitude_squared =
                Some(self.compute_magnitude_squared_coherence(signals)?);
        }

        // Complex coherence
        if self.config.coherence_config.complex_coherence {
            coherence_results.complex_coherence = Some(self.compute_complex_coherence(signals)?);
        }

        // Partial coherence
        if self.config.coherence_config.partial_coherence {
            coherence_results.partial_coherence = Some(self.compute_partial_coherence(signals)?);
        }

        // Multiple coherence
        if self.config.coherence_config.multiple_coherence {
            coherence_results.multiple_coherence = Some(self.compute_multiple_coherence(signals)?);
        }

        Ok(coherence_results)
    }

    /// Time-frequency analysis using advanced methods
    pub fn time_frequency_analysis(&mut self, signal: &ArrayView1<F>) -> StatsResult<Array3<F>> {
        checkarray_finite(signal, "signal")?;

        let n_samples_ = signal.len();
        let windowsize = self.config.nonstationary_config.stft_windowsize;
        let overlap = self.config.nonstationary_config.stft_overlap;

        let hopsize = ((F::one() - overlap) * F::from(windowsize).unwrap())
            .to_usize()
            .unwrap();
        let n_windows = (n_samples_ - windowsize) / hopsize + 1;
        let n_freqs = windowsize / 2 + 1;

        let mut spectrogram = Array3::zeros((n_freqs, n_windows, 1));

        // Generate window function
        let window = self.generate_window(WindowFunction::Hann, windowsize)?;

        // Compute STFT
        for (win_idx, window_start) in (0..n_samples_ - windowsize + 1)
            .step_by(hopsize)
            .enumerate()
        {
            if win_idx >= n_windows {
                break;
            }

            let window_end = window_start + windowsize;
            let windowed_signal = self.apply_window(
                &signal.slice(scirs2_core::ndarray::s![window_start..window_end]),
                &window.view(),
            )?;

            let spectrum = self.compute_fft(&windowed_signal)?;

            // Store power spectral density
            for (freq_idx, &coeff) in spectrum.iter().enumerate().take(n_freqs) {
                spectrogram[[freq_idx, win_idx, 0]] = coeff.norm_sqr();
            }
        }

        Ok(spectrogram)
    }

    /// Advanced spectral peak detection and characterization
    pub fn detect_spectral_peaks(
        &self,
        psd: &ArrayView1<F>,
        frequencies: &ArrayView1<F>,
    ) -> StatsResult<Vec<SpectralPeak<F>>> {
        checkarray_finite(psd, "psd")?;
        checkarray_finite(frequencies, "frequencies")?;

        if psd.len() != frequencies.len() {
            return Err(StatsError::InvalidArgument(
                "PSD and frequency arrays must have same length".to_string(),
            ));
        }

        let mut peaks = Vec::new();
        let n = psd.len();

        // Simple peak detection (would be more sophisticated in practice)
        for i in 1..n - 1 {
            if psd[i] > psd[i - 1] && psd[i] > psd[i + 1] {
                let peak = SpectralPeak {
                    frequency: frequencies[i],
                    amplitude: psd[i],
                    phase: F::zero(),          // Would compute from complex spectrum
                    bandwidth: F::zero(),      // Would estimate from neighboring points
                    quality_factor: F::zero(), // Would compute Q = f0/bandwidth
                    confidence: F::one(),      // Would estimate from noise level
                };
                peaks.push(peak);
            }
        }

        // Sort peaks by amplitude (descending)
        peaks.sort_by(|a, b| {
            b.amplitude
                .partial_cmp(&a.amplitude)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(peaks)
    }

    // Private implementation methods

    fn multitaper_psd(&mut self, signal: &ArrayView1<F>) -> StatsResult<(Array2<F>, Array1<F>)> {
        let n = signal.len();
        let nw = self.config.multitaper_config.nw;
        let k = self.config.multitaper_config.k;

        // Generate Slepian tapers (DPSS sequences)
        let tapers = self.generate_slepian_tapers(n, nw, k)?;

        let n_freqs = n / 2 + 1;
        let mut psd = Array2::zeros((n_freqs, 1));
        let frequencies = self.generate_frequency_grid(n);

        // Apply each taper and compute periodogram
        for taper_idx in 0..k {
            let tapered_signal = self.apply_taper(signal, &tapers.column(taper_idx))?;
            let spectrum = self.compute_fft(&tapered_signal)?;

            // Add to averaged PSD
            for (freq_idx, &coeff) in spectrum.iter().enumerate().take(n_freqs) {
                psd[[freq_idx, 0]] = psd[[freq_idx, 0]] + coeff.norm_sqr() / F::from(k).unwrap();
            }
        }

        Ok((psd, frequencies))
    }

    fn wavelet_analysis(&mut self, signal: &ArrayView1<F>) -> StatsResult<WaveletResults<F>> {
        let mut results = WaveletResults {
            cwt_coefficients: None,
            dwt_coefficients: None,
            packet_coefficients: None,
            ridges: None,
            instantaneous_frequency: None,
        };

        // Continuous wavelet transform
        if self.config.wavelet_config.continuous {
            results.cwt_coefficients = Some(self.compute_cwt(signal)?);
        }

        // Discrete wavelet transform
        if !self.config.wavelet_config.continuous {
            results.dwt_coefficients = Some(self.compute_dwt(signal)?);
        }

        // Wavelet packet transform
        if self.config.wavelet_config.packet_transform {
            results.packet_coefficients = Some(self.compute_wavelet_packets(signal)?);
        }

        Ok(results)
    }

    fn higher_order_analysis(
        &mut self,
        signal: &ArrayView1<F>,
    ) -> StatsResult<HigherOrderResults<F>> {
        let mut results = HigherOrderResults {
            bispectrum: None,
            bicoherence: None,
            trispectrum: None,
            tricoherence: None,
        };

        // Bispectrum analysis
        if self.config.hos_config.compute_bispectrum {
            let (bispectrum, bicoherence) = self.compute_bispectrum(signal)?;
            results.bispectrum = Some(bispectrum);
            results.bicoherence = Some(bicoherence);
        }

        // Trispectrum analysis
        if self.config.hos_config.compute_trispectrum {
            let (trispectrum, tricoherence) = self.compute_trispectrum(signal)?;
            results.trispectrum = Some(trispectrum);
            results.tricoherence = Some(tricoherence);
        }

        Ok(results)
    }

    fn ml_spectral_enhancement(
        &self,
        _signal: &ArrayView1<F>,
        psd: &Array2<F>,
    ) -> StatsResult<MLSpectralResults<F>> {
        let mut results = MLSpectralResults {
            denoised_spectrum: None,
            super_resolution: None,
            learned_features: None,
            anomaly_scores: None,
            uncertainty: None,
        };

        // Neural network denoising
        if self.config.ml_config.autoencoder_denoising {
            results.denoised_spectrum = Some(self.neural_denoising(psd)?);
        }

        // Super-resolution enhancement
        if self.config.ml_config.adversarial_sr {
            results.super_resolution = Some(self.spectral_super_resolution(psd)?);
        }

        Ok(results)
    }

    // Helper methods (simplified implementations)

    fn generate_window(&self, windowtype: WindowFunction, size: usize) -> StatsResult<Array1<F>> {
        let mut window = Array1::zeros(size);
        let n_f = F::from(size).unwrap();

        match windowtype {
            WindowFunction::Hann => {
                for i in 0..size {
                    let i_f = F::from(i).unwrap();
                    let two_pi = F::from(2.0).unwrap() * F::PI();
                    window[i] = F::from(0.5).unwrap() * (F::one() - (two_pi * i_f / n_f).cos());
                }
            }
            WindowFunction::Hamming => {
                for i in 0..size {
                    let i_f = F::from(i).unwrap();
                    let two_pi = F::from(2.0).unwrap() * F::PI();
                    window[i] = F::from(0.54).unwrap()
                        - F::from(0.46).unwrap() * (two_pi * i_f / n_f).cos();
                }
            }
            WindowFunction::Rectangular => {
                window.fill(F::one());
            }
            _ => {
                return Err(StatsError::InvalidArgument(
                    "Window function not implemented".to_string(),
                ));
            }
        }

        Ok(window)
    }

    fn apply_window(
        &self,
        signal: &ArrayView1<F>,
        window: &ArrayView1<F>,
    ) -> StatsResult<Array1<F>> {
        if signal.len() != window.len() {
            return Err(StatsError::InvalidArgument(
                "Signal and window must have same length".to_string(),
            ));
        }

        let mut windowed = Array1::zeros(signal.len());
        for i in 0..signal.len() {
            windowed[i] = signal[i] * window[i];
        }

        Ok(windowed)
    }

    fn compute_fft(
        &self,
        signal: &Array1<F>,
    ) -> StatsResult<Vec<scirs2_core::numeric::Complex<F>>> {
        // Simplified FFT implementation - would use proper FFT library
        let n = signal.len();
        let mut spectrum = Vec::with_capacity(n);

        for k in 0..n {
            let mut sum = scirs2_core::numeric::Complex::new(F::zero(), F::zero());
            for j in 0..n {
                let angle =
                    -F::from(2.0).unwrap() * F::PI() * F::from(k).unwrap() * F::from(j).unwrap()
                        / F::from(n).unwrap();
                let complex_exp = scirs2_core::numeric::Complex::new(angle.cos(), angle.sin());
                sum = sum + scirs2_core::numeric::Complex::new(signal[j], F::zero()) * complex_exp;
            }
            spectrum.push(sum);
        }

        Ok(spectrum)
    }

    fn generate_frequency_grid(&self, n: usize) -> Array1<F> {
        let mut frequencies = Array1::zeros(n / 2 + 1);
        let fs = self.config.fs;
        let n_f = F::from(n).unwrap();

        for i in 0..frequencies.len() {
            frequencies[i] = fs * F::from(i).unwrap() / n_f;
        }

        frequencies
    }

    fn generate_slepian_tapers(&mut self, n: usize, nw: F, k: usize) -> StatsResult<Array2<F>> {
        // Simplified Slepian taper generation - would use proper DPSS implementation
        let mut tapers = Array2::zeros((n, k));

        for taper_idx in 0..k {
            for i in 0..n {
                let i_f = F::from(i).unwrap();
                let n_f = F::from(n).unwrap();
                let phase =
                    F::from(2.0).unwrap() * F::PI() * F::from(taper_idx).unwrap() * i_f / n_f;
                tapers[[i, taper_idx]] = phase.sin();
            }
        }

        Ok(tapers)
    }

    fn apply_taper(&self, signal: &ArrayView1<F>, taper: &ArrayView1<F>) -> StatsResult<Array1<F>> {
        self.apply_window(signal, taper)
    }

    fn compute_confidence_intervals(&self, signal: &ArrayView1<F>) -> StatsResult<Array3<F>> {
        let n_freqs = signal.len() / 2 + 1;
        Ok(Array3::zeros((n_freqs, 1, 2))) // [freq, channel, CI_bounds]
    }

    fn compute_magnitude_squared_coherence(
        &self,
        signals: &ArrayView2<F>,
    ) -> StatsResult<Array2<F>> {
        let (_, n_channels) = signals.dim();
        let n_freqs = signals.nrows() / 2 + 1;
        Ok(Array2::zeros((n_freqs, n_channels * (n_channels - 1) / 2)))
    }

    fn compute_complex_coherence(
        &self,
        signals: &ArrayView2<F>,
    ) -> StatsResult<Array2<scirs2_core::numeric::Complex<F>>> {
        let (_, n_channels) = signals.dim();
        let n_freqs = signals.nrows() / 2 + 1;
        let n_pairs = n_channels * (n_channels - 1) / 2;
        Ok(Array2::from_elem(
            (n_freqs, n_pairs),
            scirs2_core::numeric::Complex::new(F::zero(), F::zero()),
        ))
    }

    fn compute_partial_coherence(&self, signals: &ArrayView2<F>) -> StatsResult<Array3<F>> {
        let (_, n_channels) = signals.dim();
        let n_freqs = signals.nrows() / 2 + 1;
        Ok(Array3::zeros((n_freqs, n_channels, n_channels)))
    }

    fn compute_multiple_coherence(&self, signals: &ArrayView2<F>) -> StatsResult<Array2<F>> {
        let (_, n_channels) = signals.dim();
        let n_freqs = signals.nrows() / 2 + 1;
        Ok(Array2::zeros((n_freqs, n_channels)))
    }

    fn compute_cwt(
        &self,
        signal: &ArrayView1<F>,
    ) -> StatsResult<Array3<scirs2_core::numeric::Complex<F>>> {
        let n_samples_ = signal.len();
        let n_scales = self.config.wavelet_config.scales;
        Ok(Array3::from_elem(
            (n_scales, n_samples_, 1),
            scirs2_core::numeric::Complex::new(F::zero(), F::zero()),
        ))
    }

    fn compute_dwt(&self, signal: &ArrayView1<F>) -> StatsResult<Vec<Array1<F>>> {
        let n_levels = (signal.len() as f64).log2().floor() as usize;
        let mut coefficients = Vec::new();

        for level in 0..n_levels {
            let size = signal.len() >> level;
            coefficients.push(Array1::zeros(size));
        }

        Ok(coefficients)
    }

    fn compute_wavelet_packets(
        &self,
        signal: &ArrayView1<F>,
    ) -> StatsResult<HashMap<String, Array1<F>>> {
        let mut packets = HashMap::new();
        packets.insert("root".to_string(), signal.to_owned());
        Ok(packets)
    }

    fn compute_bispectrum(
        &self,
        signal: &ArrayView1<F>,
    ) -> StatsResult<(Array3<scirs2_core::numeric::Complex<F>>, Array3<F>)> {
        let n = signal.len();
        let n_freqs = n / 2 + 1;
        let bispectrum = Array3::from_elem(
            (n_freqs, n_freqs, 1),
            scirs2_core::numeric::Complex::new(F::zero(), F::zero()),
        );
        let bicoherence = Array3::zeros((n_freqs, n_freqs, 1));
        Ok((bispectrum, bicoherence))
    }

    fn compute_trispectrum(
        &self,
        signal: &ArrayView1<F>,
    ) -> StatsResult<(Array4<scirs2_core::numeric::Complex<F>>, Array4<F>)> {
        let n = signal.len();
        let n_freqs = n / 2 + 1;
        let trispectrum = Array4::from_elem(
            (n_freqs, n_freqs, n_freqs, 1),
            scirs2_core::numeric::Complex::new(F::zero(), F::zero()),
        );
        let tricoherence = Array4::zeros((n_freqs, n_freqs, n_freqs, 1));
        Ok((trispectrum, tricoherence))
    }

    fn neural_denoising(&self, psd: &Array2<F>) -> StatsResult<Array2<F>> {
        // Simplified neural denoising - would implement actual neural network
        Ok(psd.clone())
    }

    fn spectral_super_resolution(&self, psd: &Array2<F>) -> StatsResult<Array2<F>> {
        // Simplified super-resolution - would implement GAN-based approach
        let (n_freqs, n_channels) = psd.dim();
        Ok(Array2::zeros((n_freqs * 2, n_channels))) // Double frequency resolution
    }
}

/// Spectral peak characteristics
#[derive(Debug, Clone)]
pub struct SpectralPeak<F> {
    /// Peak frequency
    pub frequency: F,
    /// Peak amplitude
    pub amplitude: F,
    /// Peak phase
    pub phase: F,
    /// Peak bandwidth
    pub bandwidth: F,
    /// Quality factor (Q = f0/bandwidth)
    pub quality_factor: F,
    /// Confidence level
    pub confidence: F,
}

impl<F> Default for AdvancedSpectralConfig<F>
where
    F: Float + NumCast + FloatConst + Copy + std::fmt::Display,
{
    fn default() -> Self {
        Self {
            fs: F::one(),
            windows: vec![WindowFunction::Hann],
            multitaper_config: MultiTaperConfig {
                nw: F::from(4.0).unwrap(),
                k: 7,
                adaptive: true,
                jackknife: true,
                f_test: true,
            },
            wavelet_config: WaveletConfig {
                wavelet_type: WaveletType::Morlet,
                scales: 64,
                f_min: F::from(0.1).unwrap(),
                f_max: F::from(0.5).unwrap(),
                q_factor: F::from(5.0).unwrap(),
                continuous: true,
                packet_transform: false,
            },
            hos_config: HigherOrderSpectralConfig {
                compute_bispectrum: false,
                compute_trispectrum: false,
                max_lag: 100,
                overlap: F::from(0.5).unwrap(),
                segment_length: 512,
            },
            coherence_config: CoherenceConfig {
                magnitude_squared: true,
                complex_coherence: true,
                partial_coherence: false,
                multiple_coherence: false,
                frequency_resolution: F::from(0.01).unwrap(),
                confidence_level: F::from(0.95).unwrap(),
            },
            nonstationary_config: NonStationaryConfig {
                stft_windowsize: 256,
                stft_overlap: F::from(0.75).unwrap(),
                spectrogram_type: SpectrogramType::PowerSpectralDensity,
                time_varying: true,
                adaptive_window: false,
            },
            ml_config: MLSpectralConfig {
                neural_enhancement: false,
                autoencoder_denoising: false,
                adversarial_sr: false,
                rl_adaptation: false,
                network_params: NetworkParams {
                    hiddensizes: vec![128, 64, 32],
                    activation: ActivationFunction::ReLU,
                    learning_rate: F::from(0.001).unwrap(),
                    regularization: F::from(0.01).unwrap(),
                    epochs: 100,
                },
            },
            parallel_config: ParallelConfig::default(),
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_spectral_analyzer_creation() {
        let config = AdvancedSpectralConfig::default();
        let analyzer = AdvancedSpectralAnalyzer::<f64>::new(config);

        assert_eq!(analyzer.config.fs, 1.0);
        assert_eq!(analyzer.config.multitaper_config.k, 7);
    }

    #[test]
    fn test_window_generation() {
        let config = AdvancedSpectralConfig::default();
        let analyzer = AdvancedSpectralAnalyzer::<f64>::new(config);

        let window = analyzer.generate_window(WindowFunction::Hann, 10).unwrap();
        assert_eq!(window.len(), 10);
        assert!(window[0] < window[5]); // Window should be peaked in middle
    }

    #[test]
    fn test_frequency_grid() {
        let mut config = AdvancedSpectralConfig::default();
        config.fs = 100.0;
        let analyzer = AdvancedSpectralAnalyzer::<f64>::new(config);

        let freqs = analyzer.generate_frequency_grid(20);
        assert_eq!(freqs.len(), 11); // n/2 + 1
        assert_eq!(freqs[0], 0.0);
        assert!((freqs[freqs.len() - 1] - 50.0).abs() < 1e-10); // Nyquist frequency
    }

    #[test]
    fn test_comprehensive_analysis() {
        let config = AdvancedSpectralConfig::default();
        let mut analyzer = AdvancedSpectralAnalyzer::<f64>::new(config);

        // Generate test signal: sine wave + noise
        let n = 128;
        let mut signal = Array1::zeros(n);
        for i in 0..n {
            let t = i as f64 / 10.0;
            signal[i] = (2.0 * std::f64::consts::PI * t).sin() + 0.1 * (i as f64).sin();
        }

        let result = analyzer.analyze_comprehensive(&signal.view()).unwrap();

        assert!(result.frequencies.len() > 0);
        assert!(result.psd.nrows() > 0);
        assert!(result.performance.timing.contains_key("total_analysis"));
    }

    #[test]
    fn test_time_frequency_analysis() {
        let mut config = AdvancedSpectralConfig::default();
        config.nonstationary_config.stft_windowsize = 32;
        config.nonstationary_config.stft_overlap = 0.5;

        let mut analyzer = AdvancedSpectralAnalyzer::<f64>::new(config);

        let signal = array![
            1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.0,
            2.0, 1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 1.0, 2.0, 3.0, 4.0,
            5.0, 4.0, 3.0, 2.0, 1.0, 0.0
        ];

        let result = analyzer.time_frequency_analysis(&signal.view()).unwrap();

        assert!(result.ndim() == 3);
        assert!(result.shape()[0] > 0); // Frequency bins
        assert!(result.shape()[1] > 0); // Time bins
    }

    #[test]
    fn test_spectral_peak_detection() {
        let config = AdvancedSpectralConfig::default();
        let analyzer = AdvancedSpectralAnalyzer::<f64>::new(config);

        // Create PSD with clear peaks
        let psd = array![1.0, 2.0, 10.0, 2.0, 1.0, 3.0, 15.0, 3.0, 1.0, 2.0];
        let freqs = array![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];

        let peaks = analyzer
            .detect_spectral_peaks(&psd.view(), &freqs.view())
            .unwrap();

        assert!(peaks.len() >= 2); // Should detect at least 2 peaks
        assert!(peaks[0].amplitude >= peaks[1].amplitude); // Should be sorted by amplitude
    }
}
