fcwt2 1.6.0

Python bindings for the Rust fCWT implementation

fCWT in Rust

Rust implementation of the fast Continuous Wavelet Transform.

It uses rustfft for FFT planning and execution, and nightly Rust portable_simd for the daughter-wavelet frequency multiplication.

Toolchain

The crate is pinned to nightly in rust-toolchain.toml because std::simd/portable_simd is still unstable.

cargo test
cargo clippy --all-targets
cargo fmt --check
cargo bench --bench cwt

If you want to call the toolchain explicitly:

cargo +nightly-2026-04-03 test

Usage

use fcwt2::{Fcwt, Morlet, ScaleType, Scales};

let signal = vec![0.0_f32; 1024];
let scales = Scales::new(ScaleType::LinearFrequencies, 1_000, 1.0, 100.0, 64)?;
let mut fcwt = Fcwt::new(Morlet::new(2.0));

let coefficients = fcwt.cwt_real(&signal, &scales);
assert_eq!(coefficients.len(), signal.len() * scales.len());
# Ok::<(), fcwt2::ScaleError>(())

Output is scale-major: coefficients[scale_index * signal.len() + sample_index].

Benchmarks

Criterion benchmarks cover real and complex CWT transforms at 1024 and 4096 samples:

cargo bench --bench cwt

Examples

Run the wavelet packet denoising example to see article-style basis selection combined with soft-threshold reconstruction:

cargo run --example wavelet_packet_denoiser

My homelab benchmarks

Running benches/cwt.rs (target/release/deps/cwt-6c2d4260722aeb78)
cwt_real/1024           time:   [320.71 µs 321.58 µs 322.68 µs]
                        thrpt:  [203.10 Melem/s 203.79 Melem/s 204.34 Melem/s]
Found 3 outliers among 100 measurements (3.00%)
  3 (3.00%) high severe
Benchmarking cwt_real/4096: Warming up for 3.0000 s
cwt_real/4096           time:   [1.6010 ms 1.6288 ms 1.6622 ms]
                        thrpt:  [157.71 Melem/s 160.95 Melem/s 163.74 Melem/s]
Found 15 outliers among 100 measurements (15.00%)
  2 (2.00%) high mild
  13 (13.00%) high severe

cwt_complex/1024        time:   [325.21 µs 326.07 µs 327.12 µs]
                        thrpt:  [200.34 Melem/s 200.99 Melem/s 201.52 Melem/s]
Found 3 outliers among 100 measurements (3.00%)
  2 (2.00%) high mild
  1 (1.00%) high severe
Benchmarking cwt_complex/4096: Warming up for 3.0000 s
cwt_complex/4096        time:   [1.4950 ms 1.4991 ms 1.5039 ms]
                        thrpt:  [174.31 Melem/s 174.86 Melem/s 175.35 Melem/s]
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high mild

Python bindings

PyO3 bindings are available behind the optional python feature:

cargo build --features python

The module exposes Morlet, Scales, Fcwt/FCWT, WaveletPacketTransform, StationaryWaveletTransform, and DualTreeComplexWaveletTransform. Real transforms accept a list of floats; complex transforms accept Python complex values or (real, imag) tuples and return Python complex values in the same scale-major layout as the Rust API.

To build/install the Python extension with maturin:

pip install maturin
maturin develop

Example:

import fcwt2

scales = fcwt2.Scales.linear_frequencies(1000, 1.0, 120.0, 64)
transform = fcwt2.Fcwt(2.0)
coefficients = transform.cwt_real([0.0] * 1024, scales)

complex_coefficients = transform.cwt_complex([1.0 + 0.0j] * 1024, scales)

packet = fcwt2.WaveletPacketTransform(3)
tree = packet.decompose([0.0] * 8)
reconstructed = tree.reconstruct()

swt = fcwt2.StationaryWaveletTransform(2)
swt_coefficients = swt.decompose([0.0] * 8)

dtcwt = fcwt2.DualTreeComplexWaveletTransform(2)
dtcwt_tree = dtcwt.decompose([0.0] * 8)

Tagged releases matching v* build Python wheels on Linux, macOS, and Windows with GitHub Actions and upload the wheels plus source distribution to the corresponding GitHub Release.

Acknowledgements

• Original library fCWT: https://github.com/fastlib/fCWT
• Fixed fCWT in C++: https://github.com/ThirdLetterC/fCWT
• PyWavelets - Wavelet Transforms in Python: https://pywavelets.readthedocs.io/en/latest/
• Wavelet Basis Selection in Signal Denoising Based on Wavelet-Coefficient Distribution Shape: https://www.mdpi.com/2624-6120/7/3/39
• dtcwt library: https://dtcwt.readthedocs.io/en/0.12.0/