Logarithmic Smoothing - A Julia Demo

This Julia demo shows how to smooth data on a logarithmic scale.

View the demo here: https://firionus.github.io/logarithmic_smoothing_julia_demo/

Quick Summary (TL;DR)

Switch Julia REPL to Pkg mode (hit ]) and install the packages:

add https://github.com/Firionus/NonuniformResampling1D.jl
add https://github.com/Firionus/NonlinearSequences.jl

Run:

using NonuniformResampling1D, NonlinearSequences

x = range(1, 24e3, length=10_000)
y = rand(length(xin))

resolution = 1/12 # octaves
oversampling = 8 # results in `oversampling/resolution` many points per octave

xlog = octspace(20, 20e3, resolution/oversampling)
ylog = nuresample(x, y, xlog, rect_window(.5*oversampling))

Smoother results can be achieved with a different window, e.g. hann_window(1*oversampling).

Other kinds of nonlinear smoothing can be performed by replacing xlog with an appropriately spaced vector.

Run the Notebook Yourself

To run the Pluto notebook yourself, clone the repository:

git clone https://github.com/Firionus/logarithmic_smoothing_julia_demo.git

In Julia, start Pluto with:

import Pluto; Pluto.run()

In Pluto, open the notebook at logarithmic_smoothing_julia_demo/logarithmic_smoothing_demo.jl.

The code should start running automatically. Cloning the repository is needed because next to the notebook file, the Project.toml needs to be placed. Using Pluto's integrated package management is not possible with unregistered packages.

If you have any problems or feedback, feel free to open an issue.