scirs2_series/features/
frequency.rs

1//! Frequency domain and spectral analysis features for time series
2//!
3//! This module provides comprehensive frequency domain feature extraction including
4//! FFT analysis, power spectral density estimation, spectral peak detection,
5//! frequency band analysis, and advanced periodogram methods.
6
7use scirs2_core::ndarray::{s, Array1};
8use scirs2_core::numeric::{Float, FromPrimitive};
9use std::fmt::Debug;
10
11use super::config::{EnhancedPeriodogramConfig, SpectralAnalysisConfig};
12use super::utils::{
13    BispectrumFeatures, PhaseSpectrumFeatures, PhaseSpectrumResult, ScaleSpectralFeatures,
14};
15use crate::error::Result;
16use crate::utils::autocorrelation;
17
18/// Comprehensive frequency domain features for time series analysis
19#[derive(Debug, Clone)]
20pub struct FrequencyFeatures<F> {
21    /// Spectral centroid (center of mass of spectrum)
22    pub spectral_centroid: F,
23    /// Spectral spread (variance around centroid)
24    pub spectral_spread: F,
25    /// Spectral skewness
26    pub spectral_skewness: F,
27    /// Spectral kurtosis
28    pub spectral_kurtosis: F,
29    /// Spectral entropy
30    pub spectral_entropy: F,
31    /// Spectral rolloff (95% of energy)
32    pub spectral_rolloff: F,
33    /// Spectral flux (change in spectrum)
34    pub spectral_flux: F,
35    /// Dominant frequency
36    pub dominant_frequency: F,
37    /// Number of spectral peaks
38    pub spectral_peaks: usize,
39    /// Power in different frequency bands
40    pub frequency_bands: Vec<F>,
41    /// Advanced spectral analysis features
42    pub spectral_analysis: SpectralAnalysisFeatures<F>,
43    /// Enhanced periodogram analysis features
44    pub enhanced_periodogram_features: EnhancedPeriodogramFeatures<F>,
45    /// Wavelet-based features
46    pub wavelet_features: WaveletFeatures<F>,
47    /// Hilbert-Huang Transform (EMD) features
48    pub emd_features: EMDFeatures<F>,
49}
50
51impl<F> Default for FrequencyFeatures<F>
52where
53    F: Float + FromPrimitive + Default,
54{
55    fn default() -> Self {
56        Self {
57            spectral_centroid: F::zero(),
58            spectral_spread: F::zero(),
59            spectral_skewness: F::zero(),
60            spectral_kurtosis: F::zero(),
61            spectral_entropy: F::zero(),
62            spectral_rolloff: F::zero(),
63            spectral_flux: F::zero(),
64            dominant_frequency: F::zero(),
65            spectral_peaks: 0,
66            frequency_bands: Vec::new(),
67            spectral_analysis: SpectralAnalysisFeatures::default(),
68            enhanced_periodogram_features: EnhancedPeriodogramFeatures::default(),
69            wavelet_features: WaveletFeatures::default(),
70            emd_features: EMDFeatures::default(),
71        }
72    }
73}
74
75/// Comprehensive spectral analysis features
76#[derive(Debug, Clone)]
77pub struct SpectralAnalysisFeatures<F> {
78    // Power Spectral Density (PSD) features
79    /// Power spectral density using Welch's method
80    pub welch_psd: Vec<F>,
81    /// Power spectral density using periodogram
82    pub periodogram_psd: Vec<F>,
83    /// Power spectral density using autoregressive method
84    pub ar_psd: Vec<F>,
85    /// Frequency resolution of PSD estimates
86    pub frequency_resolution: F,
87    /// Total power across all frequencies
88    pub total_power: F,
89    /// Normalized power spectral density
90    pub normalized_psd: Vec<F>,
91
92    // Spectral peak detection and characterization
93    /// Peak frequencies (in Hz or normalized units)
94    pub peak_frequencies: Vec<F>,
95    /// Peak magnitudes (power/amplitude at peaks)
96    pub peak_magnitudes: Vec<F>,
97    /// Peak widths (FWHM - Full Width Half Maximum)
98    pub peak_widths: Vec<F>,
99    /// Peak prominence (relative height above surroundings)
100    pub peak_prominences: Vec<F>,
101    /// Number of significant peaks
102    pub significant_peaks_count: usize,
103    /// Spectral peak density (peaks per frequency unit)
104    pub peak_density: F,
105    /// Average peak spacing
106    pub average_peak_spacing: F,
107    /// Peak asymmetry measures
108    pub peak_asymmetry: Vec<F>,
109
110    // Frequency band analysis and decomposition
111    /// Delta band power (0.5-4 Hz)
112    pub delta_power: F,
113    /// Theta band power (4-8 Hz)
114    pub theta_power: F,
115    /// Alpha band power (8-12 Hz)
116    pub alpha_power: F,
117    /// Beta band power (12-30 Hz)
118    pub beta_power: F,
119    /// Gamma band power (30-100 Hz)
120    pub gamma_power: F,
121    /// Low frequency power (custom band)
122    pub low_freq_power: F,
123    /// High frequency power (custom band)
124    pub high_freq_power: F,
125    /// Relative band powers (normalized)
126    pub relative_band_powers: Vec<F>,
127    /// Band power ratios (e.g., alpha/theta)
128    pub band_power_ratios: Vec<F>,
129    /// Frequency band entropy
130    pub band_entropy: F,
131
132    // Spectral entropy and information measures
133    /// Spectral entropy (Shannon entropy of PSD)
134    pub spectral_shannon_entropy: F,
135    /// Spectral Rényi entropy
136    pub spectral_renyi_entropy: F,
137    /// Spectral permutation entropy
138    pub spectral_permutation_entropy: F,
139    /// Frequency domain sample entropy
140    pub spectral_sample_entropy: F,
141    /// Spectral complexity (Lempel-Ziv in frequency domain)
142    pub spectral_complexity: F,
143    /// Spectral information density
144    pub spectral_information_density: F,
145    /// Frequency domain approximate entropy
146    pub spectral_approximate_entropy: F,
147
148    // Spectral shape and distribution measures
149    /// Spectral flatness (Wiener entropy)
150    pub spectral_flatness: F,
151    /// Spectral crest factor (peak-to-average ratio)
152    pub spectral_crest_factor: F,
153    /// Spectral irregularity measure
154    pub spectral_irregularity: F,
155    /// Spectral smoothness index
156    pub spectral_smoothness: F,
157    /// Spectral slope (tilt of spectrum)
158    pub spectral_slope: F,
159    /// Spectral decrease measure
160    pub spectral_decrease: F,
161    /// Spectral brightness (high frequency content)
162    pub spectral_brightness: F,
163    /// Spectral roughness (fluctuation measure)
164    pub spectral_roughness: F,
165
166    // Advanced spectral characteristics
167    /// Spectral autocorrelation features
168    pub spectral_autocorrelation: Vec<F>,
169    /// Cross-spectral features (if applicable)
170    pub cross_spectral_coherence: Vec<F>,
171    /// Spectral coherence measures
172    pub spectral_coherence_mean: F,
173    /// Phase spectrum features
174    pub phase_spectrum_features: PhaseSpectrumFeatures<F>,
175    /// Bispectrum features (third-order statistics)
176    pub bispectrum_features: BispectrumFeatures<F>,
177
178    // Frequency stability and variability
179    /// Frequency stability measure
180    pub frequency_stability: F,
181    /// Spectral variability index
182    pub spectral_variability: F,
183    /// Frequency modulation index
184    pub frequency_modulation_index: F,
185    /// Spectral purity measure
186    pub spectral_purity: F,
187
188    // Multi-scale and cross-frequency coupling
189    /// Multiscale spectral features
190    pub multiscale_spectral_features: Vec<ScaleSpectralFeatures<F>>,
191    /// Cross-frequency coupling measures
192    pub cross_frequency_coupling: Vec<F>,
193    /// Phase-amplitude coupling indices
194    pub phase_amplitude_coupling: Vec<F>,
195    /// Cross-scale correlation measures
196    pub cross_scale_correlations: Vec<F>,
197
198    // Time-frequency analysis (advanced)
199    /// Short-time Fourier transform features
200    pub stft_features: Vec<F>,
201    /// Spectrogram-based measures
202    pub spectrogram_entropy: F,
203    /// Time-frequency localization measures
204    pub time_frequency_localization: F,
205    /// Instantaneous frequency measures
206    pub instantaneous_frequency_stats: Vec<F>,
207}
208
209impl<F> Default for SpectralAnalysisFeatures<F>
210where
211    F: Float + FromPrimitive + Default,
212{
213    fn default() -> Self {
214        Self {
215            // Power Spectral Density features
216            welch_psd: Vec::new(),
217            periodogram_psd: Vec::new(),
218            ar_psd: Vec::new(),
219            frequency_resolution: F::zero(),
220            total_power: F::zero(),
221            normalized_psd: Vec::new(),
222
223            // Spectral peak detection
224            peak_frequencies: Vec::new(),
225            peak_magnitudes: Vec::new(),
226            peak_widths: Vec::new(),
227            peak_prominences: Vec::new(),
228            significant_peaks_count: 0,
229            peak_density: F::zero(),
230            average_peak_spacing: F::zero(),
231            peak_asymmetry: Vec::new(),
232
233            // Frequency band analysis
234            delta_power: F::zero(),
235            theta_power: F::zero(),
236            alpha_power: F::zero(),
237            beta_power: F::zero(),
238            gamma_power: F::zero(),
239            low_freq_power: F::zero(),
240            high_freq_power: F::zero(),
241            relative_band_powers: Vec::new(),
242            band_power_ratios: Vec::new(),
243            band_entropy: F::zero(),
244
245            // Spectral entropy and information measures
246            spectral_shannon_entropy: F::zero(),
247            spectral_renyi_entropy: F::zero(),
248            spectral_permutation_entropy: F::zero(),
249            spectral_sample_entropy: F::zero(),
250            spectral_complexity: F::zero(),
251            spectral_information_density: F::zero(),
252            spectral_approximate_entropy: F::zero(),
253
254            // Spectral shape measures
255            spectral_flatness: F::zero(),
256            spectral_crest_factor: F::zero(),
257            spectral_irregularity: F::zero(),
258            spectral_smoothness: F::zero(),
259            spectral_slope: F::zero(),
260            spectral_decrease: F::zero(),
261            spectral_brightness: F::zero(),
262            spectral_roughness: F::zero(),
263
264            // Advanced spectral characteristics
265            spectral_autocorrelation: Vec::new(),
266            cross_spectral_coherence: Vec::new(),
267            spectral_coherence_mean: F::zero(),
268            phase_spectrum_features: PhaseSpectrumFeatures::default(),
269            bispectrum_features: BispectrumFeatures::default(),
270
271            // Frequency stability and variability
272            frequency_stability: F::zero(),
273            spectral_variability: F::zero(),
274            frequency_modulation_index: F::zero(),
275            spectral_purity: F::zero(),
276
277            // Multi-scale and cross-frequency coupling
278            multiscale_spectral_features: Vec::new(),
279            cross_frequency_coupling: Vec::new(),
280            phase_amplitude_coupling: Vec::new(),
281            cross_scale_correlations: Vec::new(),
282
283            // Time-frequency analysis
284            stft_features: Vec::new(),
285            spectrogram_entropy: F::zero(),
286            time_frequency_localization: F::zero(),
287            instantaneous_frequency_stats: Vec::new(),
288        }
289    }
290}
291
292/// Enhanced periodogram analysis features
293#[derive(Debug, Clone)]
294pub struct EnhancedPeriodogramFeatures<F> {
295    // Advanced periodogram methods
296    /// Bartlett's method periodogram (averaged periodograms)
297    pub bartlett_periodogram: Vec<F>,
298    /// Enhanced Welch's method periodogram
299    pub welch_periodogram: Vec<F>,
300    /// Multitaper periodogram using Thomson's method
301    pub multitaper_periodogram: Vec<F>,
302    /// Blackman-Tukey periodogram
303    pub blackman_tukey_periodogram: Vec<F>,
304    /// Capon's minimum variance periodogram
305    pub capon_periodogram: Vec<F>,
306    /// MUSIC (Multiple Signal Classification) periodogram
307    pub music_periodogram: Vec<F>,
308    /// Enhanced autoregressive periodogram
309    pub ar_periodogram: Vec<F>,
310
311    // Window analysis and optimization
312    /// Window type information and characteristics
313    pub window_type: WindowTypeInfo<F>,
314    /// Window effectiveness metrics
315    pub window_effectiveness: F,
316    /// Spectral leakage measurements
317    pub spectral_leakage: F,
318    /// Optimal window selection results
319    pub optimal_window_type: String,
320    /// Window comparison metrics
321    pub window_comparison_metrics: Vec<F>,
322
323    // Cross-periodogram analysis
324    /// Cross-periodogram values
325    pub cross_periodogram: Vec<F>,
326    /// Coherence function values
327    pub coherence_function: Vec<F>,
328    /// Phase spectrum analysis results
329    pub phase_spectrum_result: PhaseSpectrumResult<F>,
330    /// Cross-correlation from periodogram
331    pub periodogram_xcorr: Vec<F>,
332
333    // Statistical analysis and confidence
334    /// Confidence intervals for periodogram estimates
335    pub confidence_intervals: Vec<(F, F)>,
336    /// Statistical significance of peaks
337    pub peak_significance: Vec<F>,
338    /// Goodness-of-fit test results
339    pub goodness_of_fit_statistics: Vec<F>,
340    /// Variance and bias estimates
341    pub variance_estimates: Vec<F>,
342    /// Bias estimates
343    pub bias_estimates: Vec<F>,
344
345    // Bias correction and variance reduction
346    /// Bias-corrected periodogram
347    pub bias_corrected_periodogram: Vec<F>,
348    /// Variance-reduced periodogram
349    pub variance_reduced_periodogram: Vec<F>,
350    /// Smoothed periodogram
351    pub smoothed_periodogram: Vec<F>,
352
353    // Frequency resolution enhancement
354    /// Zero-padded periodogram for improved resolution
355    pub zero_padded_periodogram: Vec<F>,
356    /// Zero-padding effectiveness measure
357    pub zero_padding_effectiveness: F,
358    /// Interpolated periodogram
359    pub interpolated_periodogram: Vec<F>,
360    /// Interpolation effectiveness measure
361    pub interpolation_effectiveness: F,
362
363    // Quality and performance metrics
364    /// Signal-to-noise ratio estimate
365    pub snr_estimate: F,
366    /// Dynamic range of periodogram
367    pub dynamic_range: F,
368    /// Spectral purity measure
369    pub spectral_purity_measure: F,
370    /// Frequency stability measures
371    pub frequency_stability_measures: Vec<F>,
372    /// Estimation error bounds
373    pub error_bounds: Vec<F>,
374    /// Computational efficiency metrics
375    pub computational_efficiency: F,
376    /// Memory efficiency metrics
377    pub memory_efficiency: F,
378
379    // Advanced features
380    /// Multiscale coherence analysis
381    pub multiscale_coherence: Vec<F>,
382    /// Cross-scale correlation results
383    pub cross_scale_correlations: Vec<F>,
384    /// Hierarchical structure analysis results
385    pub hierarchical_analysis: Vec<F>,
386    /// Scale-dependent statistics
387    pub scale_dependent_statistics: Vec<F>,
388}
389
390impl<F> Default for EnhancedPeriodogramFeatures<F>
391where
392    F: Float + FromPrimitive + Default,
393{
394    fn default() -> Self {
395        Self {
396            // Advanced periodogram methods
397            bartlett_periodogram: Vec::new(),
398            welch_periodogram: Vec::new(),
399            multitaper_periodogram: Vec::new(),
400            blackman_tukey_periodogram: Vec::new(),
401            capon_periodogram: Vec::new(),
402            music_periodogram: Vec::new(),
403            ar_periodogram: Vec::new(),
404
405            // Window analysis
406            window_type: WindowTypeInfo::default(),
407            window_effectiveness: F::zero(),
408            spectral_leakage: F::zero(),
409            optimal_window_type: String::new(),
410            window_comparison_metrics: Vec::new(),
411
412            // Cross-periodogram analysis
413            cross_periodogram: Vec::new(),
414            coherence_function: Vec::new(),
415            phase_spectrum_result: (
416                Vec::new(),
417                Vec::new(),
418                F::zero(),
419                PhaseSpectrumFeatures::default(),
420                BispectrumFeatures::default(),
421            ),
422            periodogram_xcorr: Vec::new(),
423
424            // Statistical analysis
425            confidence_intervals: Vec::new(),
426            peak_significance: Vec::new(),
427            goodness_of_fit_statistics: Vec::new(),
428            variance_estimates: Vec::new(),
429            bias_estimates: Vec::new(),
430
431            // Bias correction and variance reduction
432            bias_corrected_periodogram: Vec::new(),
433            variance_reduced_periodogram: Vec::new(),
434            smoothed_periodogram: Vec::new(),
435
436            // Frequency resolution enhancement
437            zero_padded_periodogram: Vec::new(),
438            zero_padding_effectiveness: F::zero(),
439            interpolated_periodogram: Vec::new(),
440            interpolation_effectiveness: F::zero(),
441
442            // Quality and performance metrics
443            snr_estimate: F::zero(),
444            dynamic_range: F::zero(),
445            spectral_purity_measure: F::zero(),
446            frequency_stability_measures: Vec::new(),
447            error_bounds: Vec::new(),
448            computational_efficiency: F::zero(),
449            memory_efficiency: F::zero(),
450
451            // Advanced features
452            multiscale_coherence: Vec::new(),
453            cross_scale_correlations: Vec::new(),
454            hierarchical_analysis: Vec::new(),
455            scale_dependent_statistics: Vec::new(),
456        }
457    }
458}
459
460/// Window type information for spectral analysis
461#[derive(Debug, Clone)]
462pub struct WindowTypeInfo<F> {
463    /// Window name/type
464    pub window_name: String,
465    /// Main lobe width
466    pub main_lobe_width: F,
467    /// Side lobe level
468    pub side_lobe_level: F,
469    /// Scalloping loss
470    pub scalloping_loss: F,
471    /// Processing gain
472    pub processing_gain: F,
473    /// Noise bandwidth
474    pub noise_bandwidth: F,
475    /// Coherent gain
476    pub coherent_gain: F,
477    /// Window length
478    pub window_length: usize,
479    /// Equivalent noise bandwidth
480    pub equivalent_noise_bandwidth: F,
481    /// Overlap correlation factor
482    pub overlap_correlation: F,
483}
484
485impl<F> Default for WindowTypeInfo<F>
486where
487    F: Float + FromPrimitive,
488{
489    fn default() -> Self {
490        Self {
491            window_name: "Hanning".to_string(),
492            main_lobe_width: F::zero(),
493            side_lobe_level: F::zero(),
494            scalloping_loss: F::zero(),
495            processing_gain: F::zero(),
496            noise_bandwidth: F::zero(),
497            coherent_gain: F::zero(),
498            window_length: 0,
499            equivalent_noise_bandwidth: F::zero(),
500            overlap_correlation: F::zero(),
501        }
502    }
503}
504
505/// Placeholder wavelet features (to be implemented in wavelet.rs)
506#[derive(Debug, Clone, Default)]
507pub struct WaveletFeatures<F> {
508    /// Energy in different wavelet scales
509    pub scale_energies: Vec<F>,
510    /// Wavelet entropy
511    pub wavelet_entropy: F,
512}
513
514/// Placeholder EMD features (to be implemented in temporal.rs or separate EMD module)
515#[derive(Debug, Clone, Default)]
516pub struct EMDFeatures<F> {
517    /// Number of IMFs extracted
518    pub num_imfs: usize,
519    /// Energy distribution across IMFs
520    pub imf_energies: Vec<F>,
521    /// Instantaneous frequency statistics
522    pub instantaneous_frequencies: Vec<F>,
523}
524
525// =============================================================================
526// Core Frequency Analysis Functions
527// =============================================================================
528
529/// Calculate comprehensive frequency domain features
530#[allow(dead_code)]
531pub fn calculate_frequency_features<F>(
532    ts: &Array1<F>,
533    config: &SpectralAnalysisConfig,
534) -> Result<FrequencyFeatures<F>>
535where
536    F: Float
537        + FromPrimitive
538        + Debug
539        + scirs2_core::ndarray::ScalarOperand
540        + std::iter::Sum
541        + Default,
542    for<'a> F: std::iter::Sum<&'a F>,
543{
544    let n = ts.len();
545    if n < 4 {
546        return Ok(FrequencyFeatures::default());
547    }
548
549    // Calculate basic spectrum
550    let spectrum = calculate_simple_periodogram(ts)?;
551    let frequencies = (0..spectrum.len())
552        .map(|i| F::from(i).unwrap() / F::from(spectrum.len() * 2).unwrap())
553        .collect::<Vec<_>>();
554
555    // Calculate spectral moments
556    let _total_power = spectrum.iter().fold(F::zero(), |acc, &x| acc + x);
557    let spectral_centroid = calculate_spectral_centroid(&spectrum, &frequencies);
558    let spectral_spread = calculate_spectral_spread(&spectrum, &frequencies, spectral_centroid);
559    let spectral_skewness =
560        calculate_spectral_skewness(&spectrum, &frequencies, spectral_centroid, spectral_spread);
561    let spectral_kurtosis =
562        calculate_spectral_kurtosis(&spectrum, &frequencies, spectral_centroid, spectral_spread);
563
564    // Calculate other spectral features
565    let spectral_entropy = calculate_spectral_entropy(&spectrum);
566    let spectral_rolloff =
567        calculate_spectral_rolloff(&spectrum, &frequencies, F::from(0.95).unwrap());
568    let spectral_flux = F::zero(); // Would need previous spectrum for comparison
569    let dominant_frequency = find_dominant_frequency(&spectrum, &frequencies);
570
571    // Calculate spectral peaks
572    let (peak_frequencies, _peak_magnitudes) = find_spectral_peaks(&spectrum, &frequencies)?;
573    let spectral_peaks = peak_frequencies.len();
574
575    // Calculate frequency bands
576    let frequency_bands = calculate_frequency_bands(&spectrum, &frequencies);
577
578    // Calculate advanced spectral analysis
579    let spectral_analysis = if config.calculate_welch_psd || config.calculate_periodogram_psd {
580        calculate_spectral_analysis_features(ts, config)?
581    } else {
582        SpectralAnalysisFeatures::default()
583    };
584
585    // Enhanced periodogram features would be calculated separately
586    let enhanced_periodogram_features = EnhancedPeriodogramFeatures::default();
587    let wavelet_features = WaveletFeatures::default();
588    let emd_features = EMDFeatures::default();
589
590    Ok(FrequencyFeatures {
591        spectral_centroid,
592        spectral_spread,
593        spectral_skewness,
594        spectral_kurtosis,
595        spectral_entropy,
596        spectral_rolloff,
597        spectral_flux,
598        dominant_frequency,
599        spectral_peaks,
600        frequency_bands,
601        spectral_analysis,
602        enhanced_periodogram_features,
603        wavelet_features,
604        emd_features,
605    })
606}
607
608/// Calculate enhanced periodogram analysis features
609#[allow(dead_code)]
610pub fn calculate_enhanced_periodogram_features<F>(
611    ts: &Array1<F>,
612    config: &EnhancedPeriodogramConfig,
613) -> Result<EnhancedPeriodogramFeatures<F>>
614where
615    F: Float
616        + FromPrimitive
617        + Debug
618        + Default
619        + scirs2_core::ndarray::ScalarOperand
620        + std::iter::Sum,
621    for<'a> F: std::iter::Sum<&'a F>,
622{
623    let n = ts.len();
624    if n < 8 {
625        return Ok(EnhancedPeriodogramFeatures::default());
626    }
627
628    let mut features = EnhancedPeriodogramFeatures::default();
629
630    // Calculate advanced periodogram methods
631    if config.enable_bartlett_method {
632        features.bartlett_periodogram = calculate_bartlett_periodogram(ts, config)?;
633    }
634
635    if config.enable_enhanced_welch {
636        features.welch_periodogram = calculate_enhanced_welch_periodogram(ts, config)?;
637    }
638
639    if config.enable_multitaper {
640        features.multitaper_periodogram = calculate_multitaper_periodogram(ts, config)?;
641    }
642
643    if config.enable_blackman_tukey {
644        features.blackman_tukey_periodogram = calculate_blackman_tukey_periodogram(ts, config)?;
645    }
646
647    if config.enable_enhanced_ar {
648        features.ar_periodogram = calculate_enhanced_ar_periodogram(ts, config)?;
649    }
650
651    // Calculate window analysis
652    if config.enable_window_analysis {
653        features.window_type = calculate_window_analysis(ts, config)?;
654        features.window_effectiveness = calculate_window_effectiveness(&features.window_type);
655        features.spectral_leakage = calculate_spectral_leakage(&features.window_type);
656    }
657
658    // Calculate statistical analysis and confidence intervals
659    if config.enable_confidence_intervals {
660        features.confidence_intervals =
661            calculate_periodogram_confidence_intervals(&features.welch_periodogram, config)?;
662    }
663
664    if config.enable_significance_testing {
665        features.peak_significance =
666            calculate_peak_significance(&features.welch_periodogram, config)?;
667    }
668
669    // Calculate bias correction and variance reduction
670    if config.enable_bias_correction {
671        features.bias_corrected_periodogram =
672            calculate_bias_corrected_periodogram(&features.welch_periodogram, config)?;
673    }
674
675    if config.enable_variance_reduction {
676        features.variance_reduced_periodogram =
677            calculate_variance_reduced_periodogram(&features.welch_periodogram, config)?;
678    }
679
680    if config.enable_smoothing {
681        features.smoothed_periodogram =
682            calculate_smoothed_periodogram(&features.welch_periodogram, config)?;
683    }
684
685    // Calculate frequency resolution enhancement
686    if config.enable_zero_padding {
687        features.zero_padded_periodogram = calculate_zero_padded_periodogram(ts, config)?;
688        features.zero_padding_effectiveness = calculate_zero_padding_effectiveness(
689            &features.zero_padded_periodogram,
690            &features.welch_periodogram,
691        );
692    }
693
694    // Note: Interpolation functionality would be enabled based on config if available
695    // For now, using default values
696
697    // Calculate quality and performance metrics (simplified for compatibility)
698    // SNR estimation
699    features.snr_estimate = calculate_snr_from_periodogram(&features.welch_periodogram)?;
700
701    // Dynamic range calculation
702    features.dynamic_range = calculate_dynamic_range(&features.welch_periodogram);
703
704    if config.enable_enhanced_welch {
705        features.spectral_purity_measure = calculate_spectral_purity(&features.welch_periodogram);
706    }
707
708    Ok(features)
709}
710
711// =============================================================================
712// Periodogram Calculation Functions
713// =============================================================================
714
715/// Calculate Bartlett's periodogram using averaged periodograms
716#[allow(dead_code)]
717pub fn calculate_bartlett_periodogram<F>(
718    ts: &Array1<F>,
719    config: &EnhancedPeriodogramConfig,
720) -> Result<Vec<F>>
721where
722    F: Float + FromPrimitive + Debug + std::iter::Sum,
723    for<'a> F: std::iter::Sum<&'a F>,
724{
725    let n = ts.len();
726    let segment_length = n / config.bartlett_num_segments;
727
728    if segment_length < 4 {
729        return Ok(vec![F::zero(); n / 2]);
730    }
731
732    let mut averaged_periodogram = vec![F::zero(); segment_length / 2];
733    let mut segment_count = 0;
734
735    for i in 0..config.bartlett_num_segments {
736        let start_idx = i * segment_length;
737        let end_idx = std::cmp::min(start_idx + segment_length, n);
738
739        if end_idx - start_idx >= 4 {
740            let segment = ts.slice(s![start_idx..end_idx]).to_owned();
741            let segment_periodogram = calculate_simple_periodogram(&segment)?;
742
743            for (j, &value) in segment_periodogram.iter().enumerate() {
744                if j < averaged_periodogram.len() {
745                    averaged_periodogram[j] = averaged_periodogram[j] + value;
746                }
747            }
748            segment_count += 1;
749        }
750    }
751
752    // Average the periodograms
753    if segment_count > 0 {
754        let count_f = F::from_usize(segment_count).unwrap();
755        for value in averaged_periodogram.iter_mut() {
756            *value = *value / count_f;
757        }
758    }
759
760    Ok(averaged_periodogram)
761}
762
763/// Calculate enhanced Welch's periodogram with advanced windowing
764#[allow(dead_code)]
765pub fn calculate_enhanced_welch_periodogram<F>(
766    ts: &Array1<F>,
767    config: &EnhancedPeriodogramConfig,
768) -> Result<Vec<F>>
769where
770    F: Float + FromPrimitive + Debug + std::iter::Sum,
771    for<'a> F: std::iter::Sum<&'a F>,
772{
773    let n = ts.len();
774    let window_length = (n as f64 * 0.25).round() as usize; // 25% window length
775    let overlap = (window_length as f64 * 0.5).round() as usize; // 50% overlap
776
777    if window_length < 4 {
778        return calculate_simple_periodogram(ts);
779    }
780
781    let window = create_window(&config.primary_window_type, window_length)?;
782    let step_size = window_length - overlap;
783    let num_segments = (n - overlap) / step_size;
784
785    if num_segments == 0 {
786        return calculate_simple_periodogram(ts);
787    }
788
789    let mut averaged_periodogram = vec![F::zero(); window_length / 2];
790    let mut segment_count = 0;
791
792    for i in 0..num_segments {
793        let start_idx = i * step_size;
794        let end_idx = std::cmp::min(start_idx + window_length, n);
795
796        if end_idx - start_idx == window_length {
797            let mut segment = ts.slice(s![start_idx..end_idx]).to_owned();
798
799            // Apply window
800            for (j, &w) in window.iter().enumerate() {
801                segment[j] = segment[j] * w;
802            }
803
804            let segment_periodogram = calculate_simple_periodogram(&segment)?;
805
806            for (j, &value) in segment_periodogram.iter().enumerate() {
807                if j < averaged_periodogram.len() {
808                    averaged_periodogram[j] = averaged_periodogram[j] + value;
809                }
810            }
811            segment_count += 1;
812        }
813    }
814
815    // Average and normalize
816    if segment_count > 0 {
817        let count_f = F::from_usize(segment_count).unwrap();
818        for value in averaged_periodogram.iter_mut() {
819            *value = *value / count_f;
820        }
821    }
822
823    Ok(averaged_periodogram)
824}
825
826/// Calculate multitaper periodogram using Thomson's method (simplified)
827#[allow(dead_code)]
828pub fn calculate_multitaper_periodogram<F>(
829    ts: &Array1<F>,
830    config: &EnhancedPeriodogramConfig,
831) -> Result<Vec<F>>
832where
833    F: Float + FromPrimitive + Debug + std::iter::Sum,
834    for<'a> F: std::iter::Sum<&'a F>,
835{
836    let n = ts.len();
837    if n < 8 {
838        return calculate_simple_periodogram(ts);
839    }
840
841    // Simplified multitaper using multiple Hanning windows with different phases
842    let num_tapers = config.multitaper_num_tapers;
843    let mut averaged_periodogram = vec![F::zero(); n / 2];
844
845    for taper_idx in 0..num_tapers {
846        let phase_shift =
847            F::from(taper_idx as f64 * std::f64::consts::PI / num_tapers as f64).unwrap();
848        let mut tapered_signal = ts.clone();
849
850        for (i, value) in tapered_signal.iter_mut().enumerate() {
851            let t = F::from(i).unwrap() / F::from(n).unwrap();
852            let taper_weight = (F::from(std::f64::consts::PI).unwrap() * t + phase_shift).sin();
853            *value = *value * taper_weight.abs();
854        }
855
856        let taper_periodogram = calculate_simple_periodogram(&tapered_signal)?;
857
858        for (j, &value) in taper_periodogram.iter().enumerate() {
859            if j < averaged_periodogram.len() {
860                averaged_periodogram[j] = averaged_periodogram[j] + value;
861            }
862        }
863    }
864
865    // Average across tapers
866    let num_tapers_f = F::from_usize(num_tapers).unwrap();
867    for value in averaged_periodogram.iter_mut() {
868        *value = *value / num_tapers_f;
869    }
870
871    Ok(averaged_periodogram)
872}
873
874/// Calculate Blackman-Tukey periodogram
875#[allow(dead_code)]
876pub fn calculate_blackman_tukey_periodogram<F>(
877    ts: &Array1<F>,
878    config: &EnhancedPeriodogramConfig,
879) -> Result<Vec<F>>
880where
881    F: Float + FromPrimitive + Debug + std::iter::Sum,
882    for<'a> F: std::iter::Sum<&'a F>,
883{
884    let n = ts.len();
885    let max_lag = (n as f64 * config.blackman_tukey_max_lag_factor).round() as usize;
886
887    // Calculate autocorrelation
888    let acf = autocorrelation(ts, Some(max_lag))?;
889
890    // Apply windowing to autocorrelation
891    let window = create_window("Blackman", acf.len())?;
892    let mut windowed_acf = acf.clone();
893    for (i, &w) in window.iter().enumerate() {
894        if i < windowed_acf.len() {
895            windowed_acf[i] = windowed_acf[i] * w;
896        }
897    }
898
899    // Calculate periodogram from windowed autocorrelation
900    calculate_simple_periodogram(&windowed_acf)
901}
902
903/// Calculate enhanced autoregressive periodogram
904#[allow(dead_code)]
905pub fn calculate_enhanced_ar_periodogram<F>(
906    ts: &Array1<F>,
907    config: &EnhancedPeriodogramConfig,
908) -> Result<Vec<F>>
909where
910    F: Float + FromPrimitive + Debug + std::iter::Sum,
911    for<'a> F: std::iter::Sum<&'a F>,
912{
913    // Simplified AR periodogram - would need full AR parameter estimation
914    // For now, return a smoothed version of the regular periodogram
915    let periodogram = calculate_simple_periodogram(ts)?;
916    let order = config.enhanced_ar_order.min(periodogram.len() / 4);
917
918    let mut ar_periodogram = periodogram.clone();
919
920    // Apply simple smoothing as placeholder for proper AR method
921    for i in order..(ar_periodogram.len() - order) {
922        let sum = (0..2 * order + 1).fold(F::zero(), |acc, j| acc + periodogram[i - order + j]);
923        ar_periodogram[i] = sum / F::from(2 * order + 1).unwrap();
924    }
925
926    Ok(ar_periodogram)
927}
928
929/// Calculate simple periodogram using FFT
930#[allow(dead_code)]
931pub fn calculate_simple_periodogram<F>(ts: &Array1<F>) -> Result<Vec<F>>
932where
933    F: Float + FromPrimitive + Debug + std::iter::Sum,
934    for<'a> F: std::iter::Sum<&'a F>,
935{
936    let n = ts.len();
937    if n < 2 {
938        return Ok(vec![F::zero()]);
939    }
940
941    // Simplified FFT-based periodogram calculation
942    // In a real implementation, you would use a proper FFT library
943    let mut periodogram = vec![F::zero(); n / 2];
944
945    // Calculate power spectrum (simplified)
946    let mean = ts.iter().fold(F::zero(), |acc, &x| acc + x) / F::from_usize(n).unwrap();
947    let variance = ts
948        .iter()
949        .fold(F::zero(), |acc, &x| acc + (x - mean) * (x - mean))
950        / F::from_usize(n).unwrap();
951
952    // For demonstration, create a simple spectrum based on autocorrelation
953    for (k, item) in periodogram.iter_mut().enumerate() {
954        let mut power = F::zero();
955        let freq = F::from(k).unwrap() / F::from(n).unwrap()
956            * F::from(2.0 * std::f64::consts::PI).unwrap();
957
958        for lag in 0..std::cmp::min(n / 4, 50) {
959            let mut autocorr = F::zero();
960            let mut count = 0;
961
962            for i in 0..(n - lag) {
963                autocorr = autocorr + (ts[i] - mean) * (ts[i + lag] - mean);
964                count += 1;
965            }
966
967            if count > 0 {
968                autocorr = autocorr / F::from_usize(count).unwrap();
969                let lag_f = F::from(lag).unwrap();
970                power = power + autocorr * (freq * lag_f).cos();
971            }
972        }
973
974        *item = power.abs() / variance;
975    }
976
977    Ok(periodogram)
978}
979
980// =============================================================================
981// Window Functions
982// =============================================================================
983
984/// Create a window function
985#[allow(dead_code)]
986pub fn create_window<F>(_windowtype: &str, length: usize) -> Result<Vec<F>>
987where
988    F: Float + FromPrimitive,
989{
990    let mut window = vec![F::zero(); length];
991
992    match _windowtype {
993        "Rectangular" => {
994            window.fill(F::one());
995        }
996        "Hanning" | "Hann" => {
997            for (i, w) in window.iter_mut().enumerate() {
998                let arg =
999                    F::from(2.0 * std::f64::consts::PI * i as f64 / (length - 1) as f64).unwrap();
1000                *w = F::from(0.5).unwrap() * (F::one() - arg.cos());
1001            }
1002        }
1003        "Hamming" => {
1004            for (i, w) in window.iter_mut().enumerate() {
1005                let arg =
1006                    F::from(2.0 * std::f64::consts::PI * i as f64 / (length - 1) as f64).unwrap();
1007                *w = F::from(0.54).unwrap() - F::from(0.46).unwrap() * arg.cos();
1008            }
1009        }
1010        "Blackman" => {
1011            for (i, w) in window.iter_mut().enumerate() {
1012                let arg =
1013                    F::from(2.0 * std::f64::consts::PI * i as f64 / (length - 1) as f64).unwrap();
1014                let arg2 = F::from(2.0).unwrap() * arg;
1015                *w = F::from(0.42).unwrap() - F::from(0.5).unwrap() * arg.cos()
1016                    + F::from(0.08).unwrap() * arg2.cos();
1017            }
1018        }
1019        _ => {
1020            // Default to Hanning
1021            for (i, w) in window.iter_mut().enumerate() {
1022                let arg =
1023                    F::from(2.0 * std::f64::consts::PI * i as f64 / (length - 1) as f64).unwrap();
1024                *w = F::from(0.5).unwrap() * (F::one() - arg.cos());
1025            }
1026        }
1027    }
1028
1029    Ok(window)
1030}
1031
1032// =============================================================================
1033// Spectral Analysis Helper Functions
1034// =============================================================================
1035
1036/// Calculate spectral centroid
1037#[allow(dead_code)]
1038pub fn calculate_spectral_centroid<F>(spectrum: &[F], frequencies: &[F]) -> F
1039where
1040    F: Float + FromPrimitive,
1041{
1042    let total_power = spectrum.iter().fold(F::zero(), |acc, &x| acc + x);
1043    if total_power == F::zero() {
1044        return F::zero();
1045    }
1046
1047    let weighted_sum = spectrum
1048        .iter()
1049        .zip(frequencies.iter())
1050        .fold(F::zero(), |acc, (&power, &freq)| acc + power * freq);
1051
1052    weighted_sum / total_power
1053}
1054
1055/// Calculate spectral spread
1056#[allow(dead_code)]
1057pub fn calculate_spectral_spread<F>(spectrum: &[F], frequencies: &[F], centroid: F) -> F
1058where
1059    F: Float + FromPrimitive,
1060{
1061    let total_power = spectrum.iter().fold(F::zero(), |acc, &x| acc + x);
1062    if total_power == F::zero() {
1063        return F::zero();
1064    }
1065
1066    let weighted_variance =
1067        spectrum
1068            .iter()
1069            .zip(frequencies.iter())
1070            .fold(F::zero(), |acc, (&power, &freq)| {
1071                let diff = freq - centroid;
1072                acc + power * diff * diff
1073            });
1074
1075    (weighted_variance / total_power).sqrt()
1076}
1077
1078/// Calculate spectral skewness
1079#[allow(dead_code)]
1080pub fn calculate_spectral_skewness<F>(
1081    spectrum: &[F],
1082    frequencies: &[F],
1083    centroid: F,
1084    spread: F,
1085) -> F
1086where
1087    F: Float + FromPrimitive,
1088{
1089    if spread == F::zero() {
1090        return F::zero();
1091    }
1092
1093    let total_power = spectrum.iter().fold(F::zero(), |acc, &x| acc + x);
1094    if total_power == F::zero() {
1095        return F::zero();
1096    }
1097
1098    let weighted_third_moment =
1099        spectrum
1100            .iter()
1101            .zip(frequencies.iter())
1102            .fold(F::zero(), |acc, (&power, &freq)| {
1103                let standardized = (freq - centroid) / spread;
1104                acc + power * standardized * standardized * standardized
1105            });
1106
1107    weighted_third_moment / total_power
1108}
1109
1110/// Calculate spectral kurtosis
1111#[allow(dead_code)]
1112pub fn calculate_spectral_kurtosis<F>(
1113    spectrum: &[F],
1114    frequencies: &[F],
1115    centroid: F,
1116    spread: F,
1117) -> F
1118where
1119    F: Float + FromPrimitive,
1120{
1121    if spread == F::zero() {
1122        return F::zero();
1123    }
1124
1125    let total_power = spectrum.iter().fold(F::zero(), |acc, &x| acc + x);
1126    if total_power == F::zero() {
1127        return F::zero();
1128    }
1129
1130    let weighted_fourth_moment =
1131        spectrum
1132            .iter()
1133            .zip(frequencies.iter())
1134            .fold(F::zero(), |acc, (&power, &freq)| {
1135                let standardized = (freq - centroid) / spread;
1136                let standardized_squared = standardized * standardized;
1137                acc + power * standardized_squared * standardized_squared
1138            });
1139
1140    weighted_fourth_moment / total_power - F::from(3.0).unwrap()
1141}
1142
1143/// Calculate spectral entropy
1144#[allow(dead_code)]
1145pub fn calculate_spectral_entropy<F>(spectrum: &[F]) -> F
1146where
1147    F: Float + FromPrimitive,
1148{
1149    let total_power = spectrum.iter().fold(F::zero(), |acc, &x| acc + x);
1150    if total_power == F::zero() {
1151        return F::zero();
1152    }
1153
1154    let mut entropy = F::zero();
1155    for &power in spectrum.iter() {
1156        if power > F::zero() {
1157            let prob = power / total_power;
1158            entropy = entropy - prob * prob.ln();
1159        }
1160    }
1161
1162    entropy
1163}
1164
1165/// Calculate spectral rolloff
1166#[allow(dead_code)]
1167pub fn calculate_spectral_rolloff<F>(spectrum: &[F], frequencies: &[F], threshold: F) -> F
1168where
1169    F: Float + FromPrimitive,
1170{
1171    let total_power = spectrum.iter().fold(F::zero(), |acc, &x| acc + x);
1172    let target_power = total_power * threshold;
1173
1174    let mut cumulative_power = F::zero();
1175    for (i, &power) in spectrum.iter().enumerate() {
1176        cumulative_power = cumulative_power + power;
1177        if cumulative_power >= target_power {
1178            return frequencies[i];
1179        }
1180    }
1181
1182    frequencies.last().copied().unwrap_or(F::zero())
1183}
1184
1185/// Find dominant frequency
1186#[allow(dead_code)]
1187pub fn find_dominant_frequency<F>(spectrum: &[F], frequencies: &[F]) -> F
1188where
1189    F: Float + FromPrimitive,
1190{
1191    let max_idx = spectrum
1192        .iter()
1193        .enumerate()
1194        .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
1195        .map(|(idx_, _)| idx_)
1196        .unwrap_or(0);
1197
1198    frequencies[max_idx]
1199}
1200
1201/// Find spectral peaks
1202#[allow(dead_code)]
1203pub fn find_spectral_peaks<F>(spectrum: &[F], frequencies: &[F]) -> Result<(Vec<F>, Vec<F>)>
1204where
1205    F: Float + FromPrimitive,
1206{
1207    let mut peak_frequencies = Vec::new();
1208    let mut peak_magnitudes = Vec::new();
1209
1210    if spectrum.len() < 3 {
1211        return Ok((peak_frequencies, peak_magnitudes));
1212    }
1213
1214    // Simple peak detection: find local maxima
1215    for i in 1..(spectrum.len() - 1) {
1216        if spectrum[i] > spectrum[i - 1] && spectrum[i] > spectrum[i + 1] {
1217            peak_frequencies.push(frequencies[i]);
1218            peak_magnitudes.push(spectrum[i]);
1219        }
1220    }
1221
1222    Ok((peak_frequencies, peak_magnitudes))
1223}
1224
1225/// Calculate frequency bands
1226#[allow(dead_code)]
1227pub fn calculate_frequency_bands<F>(spectrum: &[F], frequencies: &[F]) -> Vec<F>
1228where
1229    F: Float + FromPrimitive,
1230{
1231    let mut bands = Vec::new();
1232
1233    // Standard EEG frequency bands (normalized)
1234    let band_boundaries = [
1235        (F::from(0.0).unwrap(), F::from(0.05).unwrap()), // Delta (0-4Hz normalized to 0-0.05)
1236        (F::from(0.05).unwrap(), F::from(0.1).unwrap()), // Theta (4-8Hz)
1237        (F::from(0.1).unwrap(), F::from(0.15).unwrap()), // Alpha (8-12Hz)
1238        (F::from(0.15).unwrap(), F::from(0.375).unwrap()), // Beta (12-30Hz)
1239        (F::from(0.375).unwrap(), F::from(0.5).unwrap()), // Gamma (30-100Hz)
1240    ];
1241
1242    for (low, high) in band_boundaries.iter() {
1243        let mut band_power = F::zero();
1244        for (i, &freq) in frequencies.iter().enumerate() {
1245            if freq >= *low && freq < *high && i < spectrum.len() {
1246                band_power = band_power + spectrum[i];
1247            }
1248        }
1249        bands.push(band_power);
1250    }
1251
1252    bands
1253}
1254
1255// =============================================================================
1256// Placeholder Functions (to be fully implemented)
1257// =============================================================================
1258
1259/// Calculate spectral analysis features (placeholder)
1260#[allow(dead_code)]
1261pub fn calculate_spectral_analysis_features<F>(
1262    ts: &Array1<F>,
1263    config: &SpectralAnalysisConfig,
1264) -> Result<SpectralAnalysisFeatures<F>>
1265where
1266    F: Float + FromPrimitive + Debug + Default + std::iter::Sum,
1267    for<'a> F: std::iter::Sum<&'a F>,
1268{
1269    // Simplified implementation - would need full spectral analysis
1270    let spectrum = calculate_simple_periodogram(ts)?;
1271    let frequencies = (0..spectrum.len())
1272        .map(|i| F::from(i).unwrap() / F::from(spectrum.len() * 2).unwrap())
1273        .collect::<Vec<_>>();
1274
1275    let mut features = SpectralAnalysisFeatures::default();
1276
1277    if config.calculate_welch_psd {
1278        features.welch_psd = spectrum.clone();
1279    }
1280
1281    if config.calculate_periodogram_psd {
1282        features.periodogram_psd = spectrum.clone();
1283    }
1284
1285    features.total_power = spectrum.iter().fold(F::zero(), |acc, &x| acc + x);
1286    features.frequency_resolution = F::from(1.0).unwrap() / F::from(ts.len()).unwrap();
1287
1288    // Calculate frequency bands
1289    let bands = calculate_frequency_bands(&spectrum, &frequencies);
1290    if bands.len() >= 5 {
1291        features.delta_power = bands[0];
1292        features.theta_power = bands[1];
1293        features.alpha_power = bands[2];
1294        features.beta_power = bands[3];
1295        features.gamma_power = bands[4];
1296    }
1297
1298    features.spectral_shannon_entropy = calculate_spectral_entropy(&spectrum);
1299    features.spectral_flatness = calculate_spectral_flatness(&spectrum);
1300
1301    Ok(features)
1302}
1303
1304/// Calculate spectral flatness
1305#[allow(dead_code)]
1306pub fn calculate_spectral_flatness<F>(spectrum: &[F]) -> F
1307where
1308    F: Float + FromPrimitive,
1309{
1310    if spectrum.is_empty() {
1311        return F::zero();
1312    }
1313
1314    // Geometric mean / Arithmetic mean
1315    let mut geometric_mean = F::one();
1316    let mut arithmetic_mean = F::zero();
1317    let mut count = 0;
1318
1319    for &power in spectrum.iter() {
1320        if power > F::zero() {
1321            geometric_mean = geometric_mean * power;
1322            arithmetic_mean = arithmetic_mean + power;
1323            count += 1;
1324        }
1325    }
1326
1327    if count == 0 {
1328        return F::zero();
1329    }
1330
1331    let count_f = F::from_usize(count).unwrap();
1332    geometric_mean = geometric_mean.powf(F::one() / count_f);
1333    arithmetic_mean = arithmetic_mean / count_f;
1334
1335    if arithmetic_mean == F::zero() {
1336        F::zero()
1337    } else {
1338        geometric_mean / arithmetic_mean
1339    }
1340}
1341
1342// Additional placeholder functions for completeness
1343
1344/// Calculate window analysis for enhanced periodogram
1345#[allow(dead_code)]
1346pub fn calculate_window_analysis<F>(
1347    _ts: &Array1<F>,
1348    config: &EnhancedPeriodogramConfig,
1349) -> Result<WindowTypeInfo<F>>
1350where
1351    F: Float + FromPrimitive,
1352{
1353    Ok(WindowTypeInfo {
1354        window_name: config.primary_window_type.clone(),
1355        ..Default::default()
1356    })
1357}
1358
1359/// Calculate window effectiveness metrics
1360#[allow(dead_code)]
1361pub fn calculate_window_effectiveness<F>(_windowinfo: &WindowTypeInfo<F>) -> F
1362where
1363    F: Float + FromPrimitive,
1364{
1365    F::from(0.8).unwrap() // Placeholder
1366}
1367
1368/// Calculate spectral leakage measures
1369#[allow(dead_code)]
1370pub fn calculate_spectral_leakage<F>(_windowinfo: &WindowTypeInfo<F>) -> F
1371where
1372    F: Float + FromPrimitive,
1373{
1374    F::from(0.1).unwrap() // Placeholder
1375}
1376
1377/// Calculate confidence intervals for periodogram
1378#[allow(dead_code)]
1379pub fn calculate_periodogram_confidence_intervals<F>(
1380    _periodogram: &[F],
1381    _config: &EnhancedPeriodogramConfig,
1382) -> Result<Vec<(F, F)>>
1383where
1384    F: Float + FromPrimitive,
1385{
1386    Ok(Vec::new()) // Placeholder
1387}
1388
1389/// Calculate peak significance for periodogram
1390#[allow(dead_code)]
1391pub fn calculate_peak_significance<F>(
1392    _periodogram: &[F],
1393    _config: &EnhancedPeriodogramConfig,
1394) -> Result<Vec<F>>
1395where
1396    F: Float + FromPrimitive,
1397{
1398    Ok(Vec::new()) // Placeholder
1399}
1400
1401/// Calculate bias-corrected periodogram
1402#[allow(dead_code)]
1403pub fn calculate_bias_corrected_periodogram<F>(
1404    periodogram: &[F],
1405    _config: &EnhancedPeriodogramConfig,
1406) -> Result<Vec<F>>
1407where
1408    F: Float + FromPrimitive,
1409{
1410    Ok(periodogram.to_vec()) // Placeholder
1411}
1412
1413/// Calculate variance-reduced periodogram
1414#[allow(dead_code)]
1415pub fn calculate_variance_reduced_periodogram<F>(
1416    periodogram: &[F],
1417    _config: &EnhancedPeriodogramConfig,
1418) -> Result<Vec<F>>
1419where
1420    F: Float + FromPrimitive,
1421{
1422    Ok(periodogram.to_vec()) // Placeholder
1423}
1424
1425/// Calculate smoothed periodogram
1426#[allow(dead_code)]
1427pub fn calculate_smoothed_periodogram<F>(
1428    periodogram: &[F],
1429    _config: &EnhancedPeriodogramConfig,
1430) -> Result<Vec<F>>
1431where
1432    F: Float + FromPrimitive,
1433{
1434    Ok(periodogram.to_vec()) // Placeholder
1435}
1436
1437/// Calculate zero-padded periodogram
1438#[allow(dead_code)]
1439pub fn calculate_zero_padded_periodogram<F>(
1440    ts: &Array1<F>,
1441    _config: &EnhancedPeriodogramConfig,
1442) -> Result<Vec<F>>
1443where
1444    F: Float + FromPrimitive + Debug + std::iter::Sum,
1445    for<'a> F: std::iter::Sum<&'a F>,
1446{
1447    calculate_simple_periodogram(ts) // Placeholder
1448}
1449
1450/// Calculate interpolated periodogram
1451#[allow(dead_code)]
1452pub fn calculate_interpolated_periodogram<F>(
1453    periodogram: &[F],
1454    _config: &EnhancedPeriodogramConfig,
1455) -> Result<Vec<F>>
1456where
1457    F: Float + FromPrimitive,
1458{
1459    Ok(periodogram.to_vec()) // Placeholder
1460}
1461
1462/// Calculate zero padding effectiveness
1463#[allow(dead_code)]
1464pub fn calculate_zero_padding_effectiveness<F>(_padded: &[F], original: &[F]) -> F
1465where
1466    F: Float + FromPrimitive,
1467{
1468    F::from(0.9).unwrap() // Placeholder
1469}
1470
1471/// Calculate interpolation effectiveness
1472#[allow(dead_code)]
1473pub fn calculate_interpolation_effectiveness<F>(_interpolated: &[F], original: &[F]) -> F
1474where
1475    F: Float + FromPrimitive,
1476{
1477    F::from(0.85).unwrap() // Placeholder
1478}
1479
1480/// Calculate signal-to-noise ratio from periodogram
1481#[allow(dead_code)]
1482pub fn calculate_snr_from_periodogram<F>(periodogram: &[F]) -> Result<F>
1483where
1484    F: Float + FromPrimitive,
1485{
1486    if periodogram.is_empty() {
1487        return Ok(F::zero());
1488    }
1489
1490    let max_power = periodogram.iter().fold(F::neg_infinity(), |a, &b| a.max(b));
1491    let avg_power = periodogram.iter().fold(F::zero(), |acc, &x| acc + x)
1492        / F::from_usize(periodogram.len()).unwrap();
1493
1494    if avg_power == F::zero() {
1495        Ok(F::zero())
1496    } else {
1497        Ok((max_power / avg_power).log10())
1498    }
1499}
1500
1501/// Calculate dynamic range of periodogram
1502#[allow(dead_code)]
1503pub fn calculate_dynamic_range<F>(periodogram: &[F]) -> F
1504where
1505    F: Float + FromPrimitive,
1506{
1507    if periodogram.is_empty() {
1508        return F::zero();
1509    }
1510
1511    let max_power = periodogram.iter().fold(F::neg_infinity(), |a, &b| a.max(b));
1512    let min_power = periodogram.iter().fold(F::infinity(), |a, &b| a.min(b));
1513
1514    if min_power == F::zero() || max_power == F::zero() {
1515        F::zero()
1516    } else {
1517        (max_power / min_power).log10()
1518    }
1519}
1520
1521/// Calculate spectral purity measure
1522#[allow(dead_code)]
1523pub fn calculate_spectral_purity<F>(periodogram: &[F]) -> F
1524where
1525    F: Float + FromPrimitive,
1526{
1527    if periodogram.len() < 2 {
1528        return F::zero();
1529    }
1530
1531    let max_power = periodogram.iter().fold(F::neg_infinity(), |a, &b| a.max(b));
1532    let total_power = periodogram.iter().fold(F::zero(), |acc, &x| acc + x);
1533
1534    if total_power == F::zero() {
1535        F::zero()
1536    } else {
1537        max_power / total_power
1538    }
1539}
scirs2_series/features/frequency.rs

scirs2_series/features/
frequency.rs
