Fast Konno-Ohmachi Spectral Smoothing

Implemented in Rust with a Python interface. The performance gain measured against the widely used Python/numpy implementation that comes with obspy approaches approximately a factor of 2.5 for large and 10 for small vectors (see Benchmarks).

Installation

Installation from pypi:

bash pip install konnoohmachi

Installation from source:

bash pip install .

Usage

This smoothes some random numbers:

Python

```python import konnoohmachi

bandwidth = 40

using fake random data

frequencies = np.arange(1000) amplitudes = np.random.rand(1000)

smoothed_amplitudes = konnoohmachi.smooth(frequencies, amplitudes, bandwidth) ```

Rust

```rust use konnoohmachi;

let frequencies = Array1::::zeros(10); let amplitudes = Array1::::ones(10); let bandwidth = 40.0; konnoohmachismooth( frequencies.view().intodyn(), amplitudes.view().into_dyn(), bandwidth, ); ```

Benchmarks

Measuring the execution time based of increasing sized spectra yields:

``` ❯ python3 benchmark.py

nsamples | Rust | Python | Performance Gain

256 | 0.00017 | 0.00192 | 11.30802 512 | 0.00054 | 0.00431 | 7.97596 1024 | 0.00198 | 0.01117 | 5.63623 2048 | 0.00775 | 0.03143 | 4.05371 4096 | 0.03067 | 0.10024 | 3.26844 8192 | 0.12212 | 0.35058 | 2.87080 16384 | 0.49391 | 1.29653 | 2.62506 32768 | 1.98499 | 5.05335 | 2.54578 ```