jiangchengfeiyi-xiaochengxu/node_modules/mathjs/lib/cjs/function/matrix/fft.js
2025-01-02 11:13:50 +08:00

134 lines
4.3 KiB
JavaScript

"use strict";
Object.defineProperty(exports, "__esModule", {
value: true
});
exports.createFft = void 0;
var _array = require("../../utils/array.js");
var _factory = require("../../utils/factory.js");
const name = 'fft';
const dependencies = ['typed', 'matrix', 'addScalar', 'multiplyScalar', 'divideScalar', 'exp', 'tau', 'i', 'dotDivide', 'conj', 'pow', 'ceil', 'log2'];
const createFft = exports.createFft = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => {
let {
typed,
matrix,
addScalar,
multiplyScalar,
divideScalar,
exp,
tau,
i: I,
dotDivide,
conj,
pow,
ceil,
log2
} = _ref;
/**
* Calculate N-dimensional Fourier transform
*
* Syntax:
*
* math.fft(arr)
*
* Examples:
*
* math.fft([[1, 0], [1, 0]]) // returns [[{re:2, im:0}, {re:2, im:0}], [{re:0, im:0}, {re:0, im:0}]]
*
*
* See Also:
*
* ifft
*
* @param {Array | Matrix} arr An array or matrix
* @return {Array | Matrix} N-dimensional Fourier transformation of the array
*/
return typed(name, {
Array: _ndFft,
Matrix: function (matrix) {
return matrix.create(_ndFft(matrix.valueOf()), matrix.datatype());
}
});
/**
* Perform an N-dimensional Fourier transform
*
* @param {Array} arr The array
* @return {Array} resulting array
*/
function _ndFft(arr) {
const size = (0, _array.arraySize)(arr);
if (size.length === 1) return _fft(arr, size[0]);
// ndFft along dimension 1,...,N-1 then 1dFft along dimension 0
return _1dFft(arr.map(slice => _ndFft(slice, size.slice(1))), 0);
}
/**
* Perform an 1-dimensional Fourier transform
*
* @param {Array} arr The array
* @param {number} dim dimension of the array to perform on
* @return {Array} resulting array
*/
function _1dFft(arr, dim) {
const size = (0, _array.arraySize)(arr);
if (dim !== 0) return new Array(size[0]).fill(0).map((_, i) => _1dFft(arr[i], dim - 1));
if (size.length === 1) return _fft(arr);
function _transpose(arr) {
// Swap first 2 dimensions
const size = (0, _array.arraySize)(arr);
return new Array(size[1]).fill(0).map((_, j) => new Array(size[0]).fill(0).map((_, i) => arr[i][j]));
}
return _transpose(_1dFft(_transpose(arr), 1));
}
/**
* Perform an 1-dimensional non-power-of-2 Fourier transform using Chirp-Z Transform
*
* @param {Array} arr The array
* @return {Array} resulting array
*/
function _czt(arr) {
const n = arr.length;
const w = exp(divideScalar(multiplyScalar(-1, multiplyScalar(I, tau)), n));
const chirp = [];
for (let i = 1 - n; i < n; i++) {
chirp.push(pow(w, divideScalar(pow(i, 2), 2)));
}
const N2 = pow(2, ceil(log2(n + n - 1)));
const xp = [...new Array(n).fill(0).map((_, i) => multiplyScalar(arr[i], chirp[n - 1 + i])), ...new Array(N2 - n).fill(0)];
const ichirp = [...new Array(n + n - 1).fill(0).map((_, i) => divideScalar(1, chirp[i])), ...new Array(N2 - (n + n - 1)).fill(0)];
const fftXp = _fft(xp);
const fftIchirp = _fft(ichirp);
const fftProduct = new Array(N2).fill(0).map((_, i) => multiplyScalar(fftXp[i], fftIchirp[i]));
const ifftProduct = dotDivide(conj(_ndFft(conj(fftProduct))), N2);
const ret = [];
for (let i = n - 1; i < n + n - 1; i++) {
ret.push(multiplyScalar(ifftProduct[i], chirp[i]));
}
return ret;
}
/**
* Perform an 1-dimensional Fourier transform
*
* @param {Array} arr The array
* @return {Array} resulting array
*/
function _fft(arr) {
const len = arr.length;
if (len === 1) return [arr[0]];
if (len % 2 === 0) {
const ret = [..._fft(arr.filter((_, i) => i % 2 === 0), len / 2), ..._fft(arr.filter((_, i) => i % 2 === 1), len / 2)];
for (let k = 0; k < len / 2; k++) {
const p = ret[k];
const q = multiplyScalar(ret[k + len / 2], exp(multiplyScalar(multiplyScalar(tau, I), divideScalar(-k, len))));
ret[k] = addScalar(p, q);
ret[k + len / 2] = addScalar(p, multiplyScalar(-1, q));
}
return ret;
} else {
// use chirp-z transform for non-power-of-2 FFT
return _czt(arr);
}
// throw new Error('Can only calculate FFT of power-of-two size')
}
});