ezfft
v1.1.2
Published
An easy way to get the FFT from a signal
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planetslightningarresterReadme
ezFFT - Node JS
Easy as fun
let fft = require("ezfft").fft;
let ifft = require("ezfft").ifft;
// import { fft, ifft } from "ezfft"; // Or import on ES5+
...
let data = fft(signal, fs); // OMG ITS EZ AS F*
console.log(data.frequency.amplitude); // Amplitude axis
console.log(data.frequency.phase); // Phase axis
console.log(data.frequency.frequency); // Frequency axisWhereas data has the following properties.
data = {
// Time domain data
time: {
real: [], // Real portion
imag: [], // Imaginary portion
time: [] // Time axis
},
// Frequency domain data
frequency:{
real: [], // FFT real portion
imag: [], // FFT imaginary portion
amplitude: [], // Amplitude module
phase: [], // Phase [rad]
frequency: [] // Frequency axis [Hz]
},
fs: fs, // Sample rate in Hz
samplingTime: st // Sampling time in seconds
}Usage
const fft = require('ezfft').fft;
const ifft = require('ezfft').ifft;
const signal = []; // My awesome signal
const fs = 1000; // My awesome sample rate
const f = 20; // My signal's awesome frequency
for (let t = 0; t < 1; t += 1/fs) {
signal.push(3*Math.sin(2*Math.PI*f*t)); // Let's make some sin ;-) (oh yeah go with it)
}
let data = fft(signal, fs); // Returns the whole signal with frequency and time domain axis
data = ifft(data.frequency.amplitude, data.frequency.frequency); // Get the time from frequency domain
HELL YEAH! So easy. If you want an easy way to plot your data in the browser, go to the bonus at the end of this readme.
Install
npm i ezfftFFT
fft (signal, fs, imag = [], ignoreFftAmplitudesLowerThan = 1e-3)- Input
- signal: Time signal [Array]
- fs: Sample frequency (Hz) [Array]
- [Optional] imag: Imaginary portion of the signal (if any) [Array]
- [Optional] ignoreFftAmplitudesLowerThan: Threshold to make fft value equals to zero [Value]
- Output
- data: Data object [Object]
IFFT
ifft(amplitude, frequency, phase = [], fftReal = [], fftImag = [], ignoreImagAmplitudesLowerThan = 1e-3)- Input
- amplitude: Amplitude axis [Array]
- frequency: Frequency axis (Hz) [Array]
- [Optional] phase: Phase axis (if any) [Array]
- [Optional] fftReal: Real portion of FFT (overrides the parameters amplitude and phase) [Array]
- [Optional] fftImag: Imaginary portion of FFT (overrides the parameters amplitude and phase) [Array]
- [Optional] ignoreImagAmplitudesLowerThan: Threshold to make imag value equals to zero [Value]
- Output
- data: Data object [Object]
[BONUS] Plot your data with express and socket.io in 3 steps!
Install express
npm install expressInstall socket.io
npm install socket.ioCreate a index.html. You can change the your graph size below.
<html>
<head>
<title>Data display</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.8.0/Chart.bundle.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/2.3.0/socket.io.dev.js"></script>
</head>
<body>
<!-- Change the size below -->
<canvas id="time" width="600" height="400"></canvas>
<canvas id="fft" width="600" height="400"></canvas>
<script>
let ctxTime = document.getElementById('time').getContext('2d');
let time = new Chart(ctxTime, {
type: 'line',
data: {
label: "Time (s)",
labels: [1, 2, 3, 4],
datasets: [{
label: "Signal (Unit)",
fill: false,
borderColor: 'rgba(255, 99, 132, 1)',
data: [12, 9, 13, 13]
}]
},
options: {
animation: {
duration: 0
},
hover: {
animationDuration: 0
},
responsiveAnimationDuration: 0,
responsive: false,
title: {
display: true,
text: "Sensors"
},
elements: {
point: {
radius: 0
}
}
}
});
let ctxFFT = document.getElementById('fft').getContext('2d');
let fft = new Chart(ctxFFT, {
type: 'line',
data: {
label: "Frequency (Hz)",
labels: [1, 2, 3, 4],
datasets: [{
label: "Acceleration (mg)",
fill: false,
borderColor: 'rgba(255, 99, 132, 1)',
data: [12, 9, 13, 13]
}]
},
options: {
animation: {
duration: 0
},
hover: {
animationDuration: 0
},
responsiveAnimationDuration: 0,
responsive: false,
title: {
display: true,
text: "Sensor"
},
elements: {
point: {
radius: 0
}
}
}
});
let socket = io('http://localhost:8013');
socket.on("data", function(data, _time){
time.data.labels = data.time.time;
time.data.datasets[0].data = data.time.real;
fft.data.labels = data.frequency.frequency;
fft.data.datasets[0].data = data.frequency.amplitude;
time.update();
fft.update();
});
</script>
</body>
</html>
Create a main.js file with the content below and run it with node main.js.
And access by your browser the localhost:8013
// npm install ezfft
let fft = require("ezfft").fft;
// import { fft } from "ezfft";
// npm install express
var express = require('express');
var appExpress = express();
var http = require('http').Server(appExpress);
/*Express configuration*/
appExpress.use("/", express.static(__dirname + "/"));
appExpress.get('/', function (req, res) {
res.sendFile(__dirname + '/index.html'); // Name of your local web page
});
http.listen(8013, function () {
console.log('With your browser, access "localhost:8013"');
});
// npm install socket.io
var io = require('socket.io')(http);
/*Socket configuration*/
io.on('connection', function (socket) {
console.log("LES'GO");
setInterval(function () {
const signal = []; // Array with the Y axis (amplitude in time)
const time = []; // Array with the X axis (time)
const data;
const f = 60; // Your signal frequency
const fs = 1000; // Your sample rate
const samplingTime = 1; // Period that the signal was sampled
for (let t = 0; t < samplingTime; t += 1/fs){
signal.push(180*Math.sin(2*Math.PI*f*t) + 20*Math.sin(2*Math.PI*2*f*t) + 2*Math.sin(2*Math.PI*3*f*t)); // The generated signal
time.push(t); //Append time axis
}
data = fft(signal, fs); // Get signal's FFT
socket.emit("data", data); // Send data to Browser
}, 2000); // Update rate in sec
});
LICENSE
It all is just a wrapper to Project Nayuki (MIT License). Thank you so much for doing this in many languages including JS. I did change some parts of the code, but the original one can be found below. The main logic is the same.
https://www.nayuki.io/page/free-small-fft-in-multiple-languages
Free FFT and convolution (JavaScript)
Copyright (c) 2017 Project Nayuki. (MIT License) https://www.nayuki.io/page/free-small-fft-in-multiple-languages
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
- The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- The Software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the Software or the use or other dealings in the Software.