The Fastest Fourier Transform on the Web!
We welcome feedback via GitHub Issues and PRs!
WebFFT is a metalibrary containing many FFT libraries, both javascript and webassembly based. We'll refer to these as sub-libraries.
There is a default sub-library that is used, but if you run
import webfft from "webfft";
const fft = new webfft(1024);
fft.profile(); // optional arg sets number of seconds spent profilingit will benchmark them all and use the best one for future calls.
As part of importing the library we will run a check to see if wasm is even supported, so the profiler and default can know which pool to pull from.
const webfft = require('webfft');
// Instantiate
const fftsize = 1024; // must be power of 2
const fft = new webfft(fftsize);
// Profile
profileResults = fft.profile(); // results object can be used to make visualizations of the benchmarking results
// Create Input
const input = new Float32Array(2048); // interleaved complex array (IQIQIQIQ...), so it's twice the size
input.fill(0);
// Run FFT
const out = fft.fft(input); // out will be a Float32Array of size 2048
// or
const out = fft.fft(input, 'kissWasm');
fft.dispose(); // release Wasm memoryWebFFT also supports 2D FFTs, using an array of arrays. The inner arrays should be length 2*size and the outter array length should be a power of 2 but does not need to match the inner.
import webfft from "webfft";
const fftsize = 1024;
const outterSize = 128;
const fft = new webfft(fftsize);
let inputArr = [];
for (let j = 0; j < outterSize; j++) {
const subArray = new Float32Array(fftsize * 2);
for (let i = 0; i < fftsize * 2; i++) {
subArray[i] = i * j * 1.12312312; // Arbitrary
}
inputArr.push(subArray); // add inner array
}
const out = fft.fft2d(inputArr);
fft.dispose(); // cleanup wasmUse fftr() for real-valued input, the output will still be complex but only the positive frequencies will be returned.
You don't have to pass fft/fftr/fft2d typed arrays, they can be regular javascript arrays.
Run unit tests with npm run test
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