Crate scirs2_signal 

SciRS2 Signal - Digital Signal Processing

scirs2-signal provides comprehensive signal processing capabilities modeled after SciPy’s signal module, offering filtering, spectral analysis, wavelet transforms, system identification, and time-frequency analysis with SIMD acceleration and parallel processing.

🎯 Key Features

• SciPy Compatibility: Drop-in replacement for scipy.signal functions
• Digital Filters: FIR, IIR, Butterworth, Chebyshev, elliptic, Bessel
• Spectral Analysis: FFT-based PSD, spectrograms, Lomb-Scargle periodograms
• Wavelet Transforms: DWT, CWT, dual-tree complex wavelets, 2D transforms
• Convolution: Fast 1D/2D convolution with SIMD and parallel support
• LTI Systems: Transfer functions, state-space, frequency response
• Advanced Methods: EMD, Hilbert transform, system identification

📦 Module Overview

SciRS2 Module	SciPy Equivalent	Description
filter	scipy.signal.butter, cheby1	Digital filter design (FIR/IIR)
convolve	scipy.signal.convolve	1D/2D convolution and correlation
spectral	scipy.signal.periodogram	Power spectral density, spectrograms
dwt	pywt.dwt	Discrete wavelet transform
wavelets	pywt.cwt	Continuous wavelet transform
window	scipy.signal.get_window	Window functions (Hann, Hamming, etc.)
lti	scipy.signal.TransferFunction	LTI system representation
lombscargle	scipy.signal.lombscargle	Lomb-Scargle periodogram

🚀 Quick Start

[dependencies]
scirs2-signal = "0.1.0-rc.4"

use scirs2_signal::{convolve, filter, spectral};

// Convolution
let signal = vec![1.0, 2.0, 3.0];
let kernel = vec![0.25, 0.5, 0.25];
let filtered = convolve(&signal, &kernel, "same").unwrap();

🔒 Version: 0.1.0-rc.4 (December 21, 2025)

Re-exports

pub use error::SignalError;

pub use error::SignalResult;

pub use convolve::convolve;

pub use convolve::convolve_simd_ultra;

pub use convolve::correlate;

pub use convolve_parallel::parallel_convolve1d;

pub use convolve_parallel::parallel_convolve_simd_ultra;

pub use measurements::peak_to_peak;

pub use measurements::peak_to_rms;

pub use measurements::rms;

pub use measurements::snr;

pub use measurements::thd;

pub use filter::analyze_filter;

pub use filter::butter;

pub use filter::filtfilt;

pub use filter::firwin;

pub use filter::FilterType;

pub use lti::design_tf;

pub use lti::impulse_response;

pub use lti::lsim;

pub use lti::step_response;

pub use lti::TransferFunction;

pub use spectral::get_window_simd_ultra;

pub use spectral::periodogram;

pub use spectral::spectrogram;

pub use spectral::stft;

pub use spectral::welch;

pub use dwt::dwt_decompose;

pub use dwt::dwt_reconstruct;

pub use dwt::wavedec;

pub use dwt::waverec;

pub use dwt::DecompositionResult;

pub use dwt::Wavelet;

pub use dwt::WaveletFilters;

pub use wavelets::complex_morlet;

pub use wavelets::cwt;

pub use wavelets::morlet;

pub use wavelets::ricker;

pub use wavelets::scalogram;

pub use parametric::ar_spectrum;

pub use parametric::burg_method;

pub use parametric::yule_walker;

pub use parametric_advanced_enhanced::adaptive_ar_spectral_estimation;

pub use parametric_advanced_enhanced::advanced_enhanced_arma;

pub use parametric_advanced_enhanced::high_resolution_spectral_estimation;

pub use parametric_advanced_enhanced::multitaper_parametric_estimation;

pub use parametric_advanced_enhanced::robust_parametric_spectral_estimation;

pub use parametric_advanced_enhanced::AdaptiveARConfig;

pub use parametric_advanced_enhanced::AdvancedEnhancedConfig;

pub use parametric_advanced_enhanced::HighResolutionConfig;

pub use parametric_advanced_enhanced::MultitaperParametricConfig;

pub use parametric_advanced_enhanced::RobustParametricConfig;

pub use swt::iswt;

pub use swt::swt;

pub use swt::swt_decompose_simd_pipelined;

pub use tv::tv_denoise_1d;

pub use tv::tv_denoise_2d;

pub use waveforms::chirp;

pub use waveforms::sawtooth;

pub use waveforms::square;

Modules

convolve

convolve_parallel

denoise_enhanced

Enhanced wavelet denoising with advanced thresholding methods

dwt

dwt2d_enhanced

Enhanced 2D Discrete Wavelet Transform (DWT) Module

dwt2d_super_refined

Advanced-refined 2D wavelet transforms with memory efficiency and adaptive basis selection

emd

error

filter

hilbert

lombscargle

lombscargle_enhanced

lombscargle_scipy_validation

Comprehensive Lomb-Scargle validation against SciPy reference implementation

lti

measurements

median

parametric

Parametric spectral estimation methods

parametric_advanced

parametric_advanced_enhanced

Advanced-enhanced parametric spectral estimation with SIMD acceleration

simd_advanced

Advanced SIMD-optimized signal processing operations

spectral

spline

swt

sysid

tv

utils

waveforms

wavelets

window

Window Functions for Signal Processing