quantrs2-device 0.1.3

//! Signal processing components for crosstalk analysis

use std::collections::HashMap;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::Complex64;

use super::*;
use crate::DeviceResult;

impl SignalProcessor {
    pub fn new(config: &SignalProcessingConfig) -> Self {
        Self {
            config: config.clone(),
            filter_bank: FilterBank::new(&config.filtering_config),
            spectral_analyzer: SpectralAnalyzer::new(&config.spectral_config),
            timefreq_analyzer: TimeFrequencyAnalyzer::new(&config.timefreq_config),
            wavelet_analyzer: WaveletAnalyzer::new(&config.wavelet_config),
        }
    }

    pub fn process_signals(&mut self, characterization: &CrosstalkCharacterization) -> DeviceResult<SignalProcessingResult> {
        Ok(SignalProcessingResult {
            filtered_signals: HashMap::new(),
            spectral_analysis: SpectralAnalysisResult {
                power_spectral_density: HashMap::new(),
                cross_spectral_density: HashMap::new(),
                coherence: HashMap::new(),
                transfer_functions: HashMap::new(),
                spectral_peaks: HashMap::new(),
            },
            timefreq_analysis: TimeFrequencyAnalysisResult {
                stft_results: HashMap::new(),
                cwt_results: HashMap::new(),
                hht_results: None,
                instantaneous_frequency: HashMap::new(),
                instantaneous_amplitude: HashMap::new(),
            },
            wavelet_analysis: WaveletAnalysisResult {
                coefficients: HashMap::new(),
                reconstructed_signals: HashMap::new(),
                energy_distribution: HashMap::new(),
                denoising_results: HashMap::new(),
            },
            noise_characteristics: NoiseCharacteristicsResult {
                noise_power: HashMap::new(),
                snr: HashMap::new(),
                noise_color: HashMap::new(),
                stationarity: HashMap::new(),
            },
        })
    }

    /// Apply filtering to signals
    pub fn apply_filtering(&mut self, signals: &HashMap<String, Array1<f64>>) -> DeviceResult<HashMap<String, Array1<f64>>> {
        let mut filtered_signals = HashMap::new();

        for (signal_name, signal_data) in signals {
            let filtered = self.filter_bank.apply_filters(signal_data)?;
            filtered_signals.insert(signal_name.clone(), filtered);
        }

        Ok(filtered_signals)
    }

    /// Perform spectral analysis
    pub fn analyze_spectrum(&mut self, signals: &HashMap<String, Array1<f64>>) -> DeviceResult<SpectralAnalysisResult> {
        self.spectral_analyzer.analyze_signals(signals)
    }

    /// Perform time-frequency analysis
    pub fn analyze_time_frequency(&mut self, signals: &HashMap<String, Array1<f64>>) -> DeviceResult<TimeFrequencyAnalysisResult> {
        self.timefreq_analyzer.analyze_signals(signals)
    }

    /// Perform wavelet analysis
    pub fn analyze_wavelets(&mut self, signals: &HashMap<String, Array1<f64>>) -> DeviceResult<WaveletAnalysisResult> {
        self.wavelet_analyzer.analyze_signals(signals)
    }

    /// Analyze noise characteristics
    pub fn analyze_noise(&self, signals: &HashMap<String, Array1<f64>>) -> DeviceResult<NoiseCharacteristicsResult> {
        let mut noise_power = HashMap::new();
        let mut snr = HashMap::new();
        let mut noise_color = HashMap::new();
        let mut stationarity = HashMap::new();

        for (signal_name, signal_data) in signals {
            // Estimate noise power
            let power = self.estimate_noise_power(signal_data)?;
            noise_power.insert(signal_name.clone(), power);

            // Calculate SNR
            let signal_power = self.estimate_signal_power(signal_data)?;
            let snr_value = 10.0 * (signal_power / power).log10();
            snr.insert(signal_name.clone(), snr_value);

            // Analyze noise color
            let color = self.analyze_noise_color(signal_data)?;
            noise_color.insert(signal_name.clone(), color);

            // Test stationarity
            let stationarity_result = self.test_stationarity(signal_data)?;
            stationarity.insert(signal_name.clone(), stationarity_result);
        }

        Ok(NoiseCharacteristicsResult {
            noise_power,
            snr,
            noise_color,
            stationarity,
        })
    }

    fn estimate_noise_power(&self, signal: &Array1<f64>) -> DeviceResult<f64> {
        // Estimate noise power using robust methods
        let variance = signal.var(0.0);
        Ok(variance * 0.1) // Simplified - assume 10% is noise
    }

    fn estimate_signal_power(&self, signal: &Array1<f64>) -> DeviceResult<f64> {
        // Estimate signal power
        let variance = signal.var(0.0);
        Ok(variance * 0.9) // Simplified - assume 90% is signal
    }

    fn analyze_noise_color(&self, signal: &Array1<f64>) -> DeviceResult<NoiseColor> {
        // Analyze noise color by fitting power law to spectrum
        // Simplified implementation
        Ok(NoiseColor::White) // Default to white noise
    }

    fn test_stationarity(&self, signal: &Array1<f64>) -> DeviceResult<StationarityResult> {
        // Augmented Dickey-Fuller test or similar
        Ok(StationarityResult {
            test_statistic: -3.5,
            p_value: 0.01,
            is_stationary: true,
            confidence: 0.99,
        })
    }
}

impl FilterBank {
    pub fn new(config: &FilteringConfig) -> Self {
        Self {
            filters: HashMap::new(),
            adaptive_filters: HashMap::new(),
            noise_reducer: NoiseReducer::new(&config.noise_reduction),
        }
    }

    /// Apply all filters to input signal
    pub fn apply_filters(&mut self, signal: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        let mut filtered_signal = signal.clone();

        // Apply digital filters
        for (_, filter) in &mut self.filters {
            filtered_signal = filter.apply(&filtered_signal)?;
        }

        // Apply adaptive filters
        for (_, adaptive_filter) in &mut self.adaptive_filters {
            filtered_signal = adaptive_filter.apply(&filtered_signal)?;
        }

        // Apply noise reduction
        filtered_signal = self.noise_reducer.reduce_noise(&filtered_signal)?;

        Ok(filtered_signal)
    }

    /// Add a digital filter
    pub fn add_filter(&mut self, name: String, filter_type: FilterType, params: FilterParameters) {
        let filter = DigitalFilter::new(filter_type, params);
        self.filters.insert(name, filter);
    }

    /// Add an adaptive filter
    pub fn add_adaptive_filter(&mut self, name: String, algorithm: LearningAlgorithm, length: usize) {
        let filter = AdaptiveFilter::new(algorithm, length);
        self.adaptive_filters.insert(name, filter);
    }

    /// Update adaptive filters
    pub fn update_adaptive_filters(&mut self, desired_signal: &Array1<f64>, input_signal: &Array1<f64>) -> DeviceResult<()> {
        for (_, adaptive_filter) in &mut self.adaptive_filters {
            adaptive_filter.update(desired_signal, input_signal)?;
        }
        Ok(())
    }
}

impl DigitalFilter {
    pub fn new(filter_type: FilterType, parameters: FilterParameters) -> Self {
        let coefficients = Self::design_filter(&filter_type, &parameters);
        let state = Array1::zeros(coefficients.len());

        Self {
            filter_type,
            coefficients,
            state,
            parameters,
        }
    }

    /// Apply filter to input signal
    pub fn apply(&mut self, input: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        let mut output = Array1::zeros(input.len());

        for (i, &x) in input.iter().enumerate() {
            output[i] = self.filter_sample(x);
        }

        Ok(output)
    }

    fn filter_sample(&mut self, input: f64) -> f64 {
        // Shift state
        for i in (1..self.state.len()).rev() {
            self.state[i] = self.state[i - 1];
        }
        self.state[0] = input;

        // Compute output
        let mut output = 0.0;
        for (i, &coeff) in self.coefficients.iter().enumerate() {
            if i < self.state.len() {
                output += coeff * self.state[i];
            }
        }

        output
    }

    fn design_filter(filter_type: &FilterType, params: &FilterParameters) -> Array1<f64> {
        match filter_type {
            FilterType::Butterworth { order, cutoff } => {
                Self::design_butterworth(*order, *cutoff, params.sampling_frequency)
            },
            FilterType::Chebyshev1 { order, rp, cutoff } => {
                Self::design_chebyshev1(*order, *rp, *cutoff, params.sampling_frequency)
            },
            FilterType::Chebyshev2 { order, rs, cutoff } => {
                Self::design_chebyshev2(*order, *rs, *cutoff, params.sampling_frequency)
            },
            FilterType::Elliptic { order, rp, rs, cutoff } => {
                Self::design_elliptic(*order, *rp, *rs, *cutoff, params.sampling_frequency)
            },
            FilterType::Kalman { process_noise, measurement_noise } => {
                Self::design_kalman(*process_noise, *measurement_noise)
            },
            FilterType::Wiener { noise_estimate } => {
                Self::design_wiener(*noise_estimate)
            },
        }
    }

    fn design_butterworth(order: usize, cutoff: f64, fs: f64) -> Array1<f64> {
        // Simplified Butterworth filter design
        Array1::from_vec(vec![0.1, 0.2, 0.4, 0.2, 0.1]) // Placeholder coefficients
    }

    fn design_chebyshev1(order: usize, rp: f64, cutoff: f64, fs: f64) -> Array1<f64> {
        // Simplified Chebyshev Type I filter design
        Array1::from_vec(vec![0.05, 0.2, 0.5, 0.2, 0.05])
    }

    fn design_chebyshev2(order: usize, rs: f64, cutoff: f64, fs: f64) -> Array1<f64> {
        // Simplified Chebyshev Type II filter design
        Array1::from_vec(vec![0.08, 0.18, 0.48, 0.18, 0.08])
    }

    fn design_elliptic(order: usize, rp: f64, rs: f64, cutoff: f64, fs: f64) -> Array1<f64> {
        // Simplified elliptic filter design
        Array1::from_vec(vec![0.06, 0.19, 0.5, 0.19, 0.06])
    }

    fn design_kalman(process_noise: f64, measurement_noise: f64) -> Array1<f64> {
        // Simplified Kalman filter coefficients
        Array1::from_vec(vec![0.2, 0.6, 0.2])
    }

    fn design_wiener(noise_estimate: f64) -> Array1<f64> {
        // Simplified Wiener filter coefficients
        Array1::from_vec(vec![0.15, 0.7, 0.15])
    }
}

impl AdaptiveFilter {
    pub fn new(algorithm: LearningAlgorithm, filter_length: usize) -> Self {
        Self {
            algorithm,
            filter_length,
            weights: Array1::zeros(filter_length),
            learning_curve: VecDeque::with_capacity(1000),
        }
    }

    /// Apply adaptive filter to input
    pub fn apply(&self, input: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        let mut output = Array1::zeros(input.len());

        for (i, &x) in input.iter().enumerate() {
            // Simplified adaptive filtering
            let mut y = 0.0;
            let start_idx = if i >= self.filter_length { i - self.filter_length + 1 } else { 0 };

            for (j, &w) in self.weights.iter().enumerate() {
                let input_idx = start_idx + j;
                if input_idx < input.len() {
                    y += w * input[input_idx];
                }
            }

            output[i] = y;
        }

        Ok(output)
    }

    /// Update filter weights
    pub fn update(&mut self, desired: &Array1<f64>, input: &Array1<f64>) -> DeviceResult<()> {
        match &self.algorithm {
            LearningAlgorithm::LMS { step_size } => {
                self.lms_update(desired, input, *step_size)
            },
            LearningAlgorithm::RLS { forgetting_factor } => {
                self.rls_update(desired, input, *forgetting_factor)
            },
            LearningAlgorithm::GradientDescent { momentum } => {
                self.gradient_descent_update(desired, input, *momentum)
            },
            LearningAlgorithm::Adam { beta1, beta2, epsilon } => {
                self.adam_update(desired, input, *beta1, *beta2, *epsilon)
            },
            LearningAlgorithm::KalmanFilter { process_noise, measurement_noise } => {
                self.kalman_update(desired, input, *process_noise, *measurement_noise)
            },
        }
    }

    fn lms_update(&mut self, desired: &Array1<f64>, input: &Array1<f64>, step_size: f64) -> DeviceResult<()> {
        // LMS (Least Mean Squares) algorithm
        let output = self.apply(input)?;
        let error = desired - &output;

        for i in 0..self.weights.len() {
            // Simplified weight update
            self.weights[i] += step_size * error.mean().unwrap_or(0.0);
        }

        self.learning_curve.push_back(error.mapv(|x| x * x).mean().unwrap_or(0.0));
        Ok(())
    }

    fn rls_update(&mut self, desired: &Array1<f64>, input: &Array1<f64>, forgetting_factor: f64) -> DeviceResult<()> {
        // RLS (Recursive Least Squares) algorithm
        // Simplified implementation
        Ok(())
    }

    fn gradient_descent_update(&mut self, desired: &Array1<f64>, input: &Array1<f64>, momentum: f64) -> DeviceResult<()> {
        // Gradient descent with momentum
        // Simplified implementation
        Ok(())
    }

    fn adam_update(&mut self, desired: &Array1<f64>, input: &Array1<f64>, beta1: f64, beta2: f64, epsilon: f64) -> DeviceResult<()> {
        // Adam optimizer
        // Simplified implementation
        Ok(())
    }

    fn kalman_update(&mut self, desired: &Array1<f64>, input: &Array1<f64>, process_noise: f64, measurement_noise: f64) -> DeviceResult<()> {
        // Kalman filter-based adaptation
        // Simplified implementation
        Ok(())
    }
}

impl NoiseReducer {
    pub fn new(config: &NoiseReductionConfig) -> Self {
        Self {
            method: config.method.clone(),
            noise_estimator: NoiseEstimator::new(&config.noise_estimation),
            reduction_history: VecDeque::with_capacity(1000),
        }
    }

    /// Apply noise reduction to signal
    pub fn reduce_noise(&mut self, signal: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        if !self.method.is_enabled() {
            return Ok(signal.clone());
        }

        match &self.method {
            NoiseReductionMethod::SpectralSubtraction { over_subtraction_factor } => {
                self.spectral_subtraction(signal, *over_subtraction_factor)
            },
            NoiseReductionMethod::WienerFiltering { noise_estimate } => {
                self.wiener_filtering(signal, *noise_estimate)
            },
            NoiseReductionMethod::WaveletDenoising { wavelet, threshold_method } => {
                self.wavelet_denoising(signal, wavelet, threshold_method)
            },
            NoiseReductionMethod::AdaptiveFiltering { step_size, filter_length } => {
                self.adaptive_filtering(signal, *step_size, *filter_length)
            },
        }
    }

    fn spectral_subtraction(&mut self, signal: &Array1<f64>, over_subtraction_factor: f64) -> DeviceResult<Array1<f64>> {
        // Spectral subtraction noise reduction
        let noise_estimate = self.noise_estimator.estimate_noise(signal)?;

        // Simplified implementation: apply gain reduction
        let reduced_signal = signal.mapv(|x| x * (1.0 - over_subtraction_factor * noise_estimate));

        self.reduction_history.push_back(noise_estimate);
        Ok(reduced_signal)
    }

    fn wiener_filtering(&mut self, signal: &Array1<f64>, noise_estimate: f64) -> DeviceResult<Array1<f64>> {
        // Wiener filtering for noise reduction
        let signal_power = signal.var(0.0);
        let wiener_gain = signal_power / (signal_power + noise_estimate);

        let filtered_signal = signal.mapv(|x| x * wiener_gain);

        self.reduction_history.push_back(noise_estimate);
        Ok(filtered_signal)
    }

    fn wavelet_denoising(&mut self, signal: &Array1<f64>, wavelet: &str, threshold_method: &str) -> DeviceResult<Array1<f64>> {
        // Wavelet denoising
        // Simplified implementation: apply soft thresholding
        let threshold = signal.std(0.0) * 0.1; // Simple threshold estimate

        let denoised_signal = signal.mapv(|x| {
            if x.abs() > threshold {
                x - threshold * x.signum()
            } else {
                0.0
            }
        });

        Ok(denoised_signal)
    }

    fn adaptive_filtering(&mut self, signal: &Array1<f64>, step_size: f64, filter_length: usize) -> DeviceResult<Array1<f64>> {
        // Adaptive filtering for noise reduction
        let mut adaptive_filter = AdaptiveFilter::new(
            LearningAlgorithm::LMS { step_size },
            filter_length,
        );

        // Use delayed version as reference (simplified)
        let delayed_signal = if signal.len() > 1 {
            let mut delayed = Array1::zeros(signal.len());
            delayed.slice_mut(scirs2_core::ndarray::s![1..]).assign(&signal.slice(scirs2_core::ndarray::s![..signal.len()-1]));
            delayed
        } else {
            signal.clone()
        };

        adaptive_filter.apply(signal)
    }
}

impl NoiseEstimator {
    pub fn new(method: &NoiseEstimationMethod) -> Self {
        Self {
            method: method.clone(),
            noise_estimate: 0.01,
            adaptation_rate: 0.1,
        }
    }

    /// Estimate noise level in signal
    pub fn estimate_noise(&mut self, signal: &Array1<f64>) -> DeviceResult<f64> {
        let new_estimate = match &self.method {
            NoiseEstimationMethod::VoiceActivityDetection => {
                self.vad_noise_estimation(signal)
            },
            NoiseEstimationMethod::MinimumStatistics => {
                self.minimum_statistics_estimation(signal)
            },
            NoiseEstimationMethod::MCRA { alpha } => {
                self.mcra_estimation(signal, *alpha)
            },
            NoiseEstimationMethod::IMCRA { alpha_s, alpha_d } => {
                self.imcra_estimation(signal, *alpha_s, *alpha_d)
            },
        }?;

        // Update estimate with adaptation
        self.noise_estimate = (1.0 - self.adaptation_rate) * self.noise_estimate
                            + self.adaptation_rate * new_estimate;

        Ok(self.noise_estimate)
    }

    fn vad_noise_estimation(&self, signal: &Array1<f64>) -> DeviceResult<f64> {
        // Voice Activity Detection based noise estimation
        // Simplified: assume first 10% of signal is noise
        let noise_samples = signal.len() / 10;
        let noise_portion = signal.slice(scirs2_core::ndarray::s![..noise_samples]);
        Ok(noise_portion.var(0.0))
    }

    fn minimum_statistics_estimation(&self, signal: &Array1<f64>) -> DeviceResult<f64> {
        // Minimum statistics noise estimation
        let window_size = 100;
        let mut min_powers = Vec::new();

        for i in (0..signal.len()).step_by(window_size) {
            let end_idx = std::cmp::min(i + window_size, signal.len());
            let window = signal.slice(scirs2_core::ndarray::s![i..end_idx]);
            let power = window.mapv(|x| x * x).mean().unwrap_or(0.0);
            min_powers.push(power);
        }

        let min_power = min_powers.iter().cloned().fold(f64::INFINITY, f64::min);
        Ok(min_power)
    }

    fn mcra_estimation(&self, signal: &Array1<f64>, alpha: f64) -> DeviceResult<f64> {
        // Minima Controlled Recursive Averaging
        // Simplified implementation
        let current_power = signal.mapv(|x| x * x).mean().unwrap_or(0.0);
        Ok(alpha * self.noise_estimate + (1.0 - alpha) * current_power)
    }

    fn imcra_estimation(&self, signal: &Array1<f64>, alpha_s: f64, alpha_d: f64) -> DeviceResult<f64> {
        // Improved Minima Controlled Recursive Averaging
        // Simplified implementation
        let current_power = signal.mapv(|x| x * x).mean().unwrap_or(0.0);
        let alpha = if current_power > self.noise_estimate { alpha_s } else { alpha_d };
        Ok(alpha * self.noise_estimate + (1.0 - alpha) * current_power)
    }
}

impl SpectralAnalyzer {
    pub fn new(config: &SpectralAnalysisConfig) -> Self {
        Self {
            config: config.clone(),
            window_function: config.window_function.clone(),
            spectral_cache: HashMap::new(),
        }
    }

    /// Analyze signals and compute spectral properties
    pub fn analyze_signals(&mut self, signals: &HashMap<String, Array1<f64>>) -> DeviceResult<SpectralAnalysisResult> {
        let mut power_spectral_density = HashMap::new();
        let mut cross_spectral_density = HashMap::new();
        let mut coherence = HashMap::new();
        let mut transfer_functions = HashMap::new();
        let mut spectral_peaks = HashMap::new();

        // Compute PSD for each signal
        for (signal_name, signal_data) in signals {
            let psd = self.compute_psd(signal_data)?;
            power_spectral_density.insert(signal_name.clone(), psd);

            let peaks = self.find_spectral_peaks(signal_data)?;
            spectral_peaks.insert(signal_name.clone(), peaks);
        }

        // Compute cross-spectral properties for all pairs
        let signal_names: Vec<_> = signals.keys().collect();
        for i in 0..signal_names.len() {
            for j in (i + 1)..signal_names.len() {
                let signal1_name = signal_names[i];
                let signal2_name = signal_names[j];
                let signal1_data = &signals[signal1_name];
                let signal2_data = &signals[signal2_name];

                let csd = self.compute_cross_spectral_density(signal1_data, signal2_data)?;
                cross_spectral_density.insert((signal1_name.clone(), signal2_name.clone()), csd);

                let coh = self.compute_coherence(signal1_data, signal2_data)?;
                coherence.insert((signal1_name.clone(), signal2_name.clone()), coh);

                let tf = self.compute_transfer_function(signal1_data, signal2_data)?;
                transfer_functions.insert((signal1_name.clone(), signal2_name.clone()), tf);
            }
        }

        Ok(SpectralAnalysisResult {
            power_spectral_density,
            cross_spectral_density,
            coherence,
            transfer_functions,
            spectral_peaks,
        })
    }

    fn compute_psd(&self, signal: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        // Compute power spectral density using specified method
        match &self.config.estimation_method {
            SpectralEstimationMethod::Periodogram => self.periodogram(signal),
            SpectralEstimationMethod::Welch { nperseg, noverlap } => {
                self.welch_method(signal, *nperseg, *noverlap)
            },
            SpectralEstimationMethod::Bartlett => self.bartlett_method(signal),
            SpectralEstimationMethod::Multitaper { nw, k } => {
                self.multitaper_method(signal, *nw, *k)
            },
        }
    }

    fn periodogram(&self, signal: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        // Compute periodogram
        let windowed_signal = self.apply_window(signal)?;
        let fft_result = self.compute_fft(&windowed_signal)?;
        let psd = fft_result.mapv(|x| x.norm_sqr() / signal.len() as f64);
        Ok(psd)
    }

    fn welch_method(&self, signal: &Array1<f64>, nperseg: usize, noverlap: usize) -> DeviceResult<Array1<f64>> {
        // Welch's method for PSD estimation
        let step_size = nperseg - noverlap;
        let mut psd_accumulator = Array1::zeros(nperseg / 2 + 1);
        let mut num_segments = 0;

        for start in (0..signal.len()).step_by(step_size) {
            let end = std::cmp::min(start + nperseg, signal.len());
            if end - start < nperseg {
                break;
            }

            let segment = signal.slice(scirs2_core::ndarray::s![start..end]).to_owned();
            let windowed_segment = self.apply_window(&segment)?;
            let fft_result = self.compute_fft(&windowed_segment)?;
            let segment_psd = fft_result.mapv(|x| x.norm_sqr());

            // Take only positive frequencies
            let half_length = segment_psd.len() / 2 + 1;
            psd_accumulator = psd_accumulator + segment_psd.slice(scirs2_core::ndarray::s![..half_length]);
            num_segments += 1;
        }

        if num_segments > 0 {
            psd_accumulator = psd_accumulator / num_segments as f64;
        }

        Ok(psd_accumulator)
    }

    fn bartlett_method(&self, signal: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        // Bartlett's method (non-overlapping Welch)
        self.welch_method(signal, signal.len() / 8, 0)
    }

    fn multitaper_method(&self, signal: &Array1<f64>, nw: f64, k: usize) -> DeviceResult<Array1<f64>> {
        // Multitaper method using DPSS sequences
        // Simplified implementation
        self.periodogram(signal)
    }

    fn apply_window(&self, signal: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        let window = self.generate_window(signal.len())?;
        Ok(signal * &window)
    }

    fn generate_window(&self, length: usize) -> DeviceResult<Array1<f64>> {
        let mut window = Array1::zeros(length);

        match &self.window_function {
            WindowFunction::Rectangular => {
                window.fill(1.0);
            },
            WindowFunction::Hanning => {
                for (i, w) in window.iter_mut().enumerate() {
                    *w = 0.5 * (1.0 - (2.0 * std::f64::consts::PI * i as f64 / (length - 1) as f64).cos());
                }
            },
            WindowFunction::Hamming => {
                for (i, w) in window.iter_mut().enumerate() {
                    *w = 0.54 - 0.46 * (2.0 * std::f64::consts::PI * i as f64 / (length - 1) as f64).cos();
                }
            },
            WindowFunction::Blackman => {
                for (i, w) in window.iter_mut().enumerate() {
                    let n = i as f64 / (length - 1) as f64;
                    *w = 0.42 - 0.5 * (2.0 * std::f64::consts::PI * n).cos()
                        + 0.08 * (4.0 * std::f64::consts::PI * n).cos();
                }
            },
            WindowFunction::Kaiser { beta } => {
                // Kaiser window implementation
                for (i, w) in window.iter_mut().enumerate() {
                    let n = i as f64 - (length - 1) as f64 / 2.0;
                    let alpha = (length - 1) as f64 / 2.0;
                    *w = self.modified_bessel_i0(*beta * (1.0 - (n / alpha).powi(2)).sqrt())
                        / self.modified_bessel_i0(*beta);
                }
            },
            WindowFunction::Tukey { alpha } => {
                // Tukey window implementation
                let transition_samples = (*alpha * length as f64 / 2.0) as usize;

                for (i, w) in window.iter_mut().enumerate() {
                    if i < transition_samples {
                        *w = 0.5 * (1.0 + (std::f64::consts::PI * i as f64 / transition_samples as f64 - std::f64::consts::PI).cos());
                    } else if i < length - transition_samples {
                        *w = 1.0;
                    } else {
                        let idx = i - (length - transition_samples);
                        *w = 0.5 * (1.0 + (std::f64::consts::PI * idx as f64 / transition_samples as f64).cos());
                    }
                }
            },
        }

        Ok(window)
    }

    fn modified_bessel_i0(&self, x: f64) -> f64 {
        // Approximation of modified Bessel function of the first kind, order 0
        let t = x / 3.75;
        if x.abs() < 3.75 {
            let t2 = t * t;
            1.0 + 3.5156229 * t2 + 3.0899424 * t2 * t2 + 1.2067492 * t2 * t2 * t2
                + 0.2659732 * t2 * t2 * t2 * t2 + 0.0360768 * t2 * t2 * t2 * t2 * t2
                + 0.0045813 * t2 * t2 * t2 * t2 * t2 * t2
        } else {
            let t_inv = 1.0 / t;
            (x.exp() / x.sqrt()) * (0.39894228 + 0.01328592 * t_inv + 0.00225319 * t_inv * t_inv
                - 0.00157565 * t_inv * t_inv * t_inv + 0.00916281 * t_inv * t_inv * t_inv * t_inv
                - 0.02057706 * t_inv * t_inv * t_inv * t_inv * t_inv)
        }
    }

    fn compute_fft(&self, signal: &Array1<f64>) -> DeviceResult<Array1<Complex64>> {
        // Simplified FFT implementation (would use proper FFT library in practice)
        let n = signal.len();
        let mut result = Array1::zeros(n);

        for k in 0..n {
            let mut sum = Complex64::new(0.0, 0.0);
            for j in 0..n {
                let angle = -2.0 * std::f64::consts::PI * k as f64 * j as f64 / n as f64;
                sum += signal[j] * Complex64::new(angle.cos(), angle.sin());
            }
            result[k] = sum;
        }

        Ok(result)
    }

    fn compute_cross_spectral_density(&self, signal1: &Array1<f64>, signal2: &Array1<f64>) -> DeviceResult<Array1<Complex64>> {
        // Compute cross-spectral density
        let fft1 = self.compute_fft(signal1)?;
        let fft2 = self.compute_fft(signal2)?;

        let csd = Array1::from_iter(fft1.iter().zip(fft2.iter()).map(|(x1, x2)| x1 * x2.conj()));
        Ok(csd)
    }

    fn compute_coherence(&self, signal1: &Array1<f64>, signal2: &Array1<f64>) -> DeviceResult<Array1<f64>> {
        // Compute magnitude-squared coherence
        let csd = self.compute_cross_spectral_density(signal1, signal2)?;
        let psd1 = self.compute_psd(signal1)?;
        let psd2 = self.compute_psd(signal2)?;

        let coherence = Array1::from_iter(
            csd.iter().zip(psd1.iter().zip(psd2.iter()))
                .map(|(csd_val, (psd1_val, psd2_val))| {
                    csd_val.norm_sqr() / (psd1_val * psd2_val)
                })
        );

        Ok(coherence)
    }

    fn compute_transfer_function(&self, input: &Array1<f64>, output: &Array1<f64>) -> DeviceResult<Array1<Complex64>> {
        // Compute transfer function H(f) = Sxy(f) / Sxx(f)
        let csd = self.compute_cross_spectral_density(input, output)?;
        let psd_input = self.compute_psd(input)?;

        let transfer_function = Array1::from_iter(
            csd.iter().zip(psd_input.iter())
                .map(|(csd_val, psd_val)| {
                    if psd_val.abs() > 1e-12 {
                        csd_val / psd_val
                    } else {
                        Complex64::new(0.0, 0.0)
                    }
                })
        );

        Ok(transfer_function)
    }

    fn find_spectral_peaks(&self, signal: &Array1<f64>) -> DeviceResult<Vec<SpectralPeak>> {
        let psd = self.compute_psd(signal)?;
        let mut peaks = Vec::new();

        // Simple peak detection
        for i in 1..(psd.len() - 1) {
            if psd[i] > psd[i - 1] && psd[i] > psd[i + 1] && psd[i] > 0.1 {
                let frequency = i as f64 * self.config.sampling_frequency / (2.0 * psd.len() as f64);
                peaks.push(SpectralPeak {
                    frequency,
                    amplitude: psd[i],
                    width: 1.0, // Simplified
                    significance: 0.9, // Simplified
                    q_factor: frequency / 1.0, // Simplified
                });
            }
        }

        Ok(peaks)
    }
}

// Implementation helpers for NoiseReductionMethod
impl NoiseReductionMethod {
    fn is_enabled(&self) -> bool {
        true // All methods are enabled by default
    }
}