Pedal Tone Graph

This repo contains the code of the Pedal Tone Graph app, which lets you browse and compare the frequency response of different guitar pedals.

Prerequisites

Node

Use nvm to install node.js. We use LTS version node 16.

$ curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.1/install.sh | bash
$ nvm install LTS

Global npm dependencies

A few global npm dependencies are needed. you can install them after installing node.js

$ npm install -g typescript next next-cli expo-cli ts-node yarn

Environment variables

You'll need tokens to make the contentFul API works. if you just want to use the analyze scripts, you don't need it, but if you want to run the app locally, you can ask for one.

After this is done, you can set up the repo and run the commands below to run the app locally.

Set up

This repo is bootstrapped with Expo and Next.js, using React Native Web, and typescript. It is set up with Eslint & Jest and requires node LTS 16 and yarn 1.x

• clone this repo
• run yarn to install dependencies

Run the app locally

• run yarn next dev
• open http://localhost:3000 in your browser

Analyze data

This repo contains scripts to analyze data provided either as CSV files exported from R, or raw text files from Audacity.

From R csv

The expectation here is that the columns are [frequency, db, pedal name, gain value, tone value] You should skip the header row so the csv file only contains the datapoints.

Create a folder in <rootDir>/data with the name of the pedal. Place the file there with the name data.csv

Then run

yarn analyze:csv data/<folder name>

this will generate a data.json file in the folder you created, which will contain the datapoints, formatted so that it can be loaded in the Graph engine of the website. you can then go to https://tonegraphs.com/preview and paste the json data in the textarea. Change the tone / curve and see the result !

From Audacity text files

Create a folder in <rootDir>/data with the name of the pedal. You should have one file with the data for pink noise named pink noise.txt, and then separate files for each tone and gain value. The file name should follow the t<toneValue>_g<gainValue>.txt format.

Then run

yarn analyze data/<folder name>

This will generate a data.json file in the folder, which you can use to preview the graph, as explained in the paragraph above