dist-javascript-algorithms-and-data-structures
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Algorithms and data-structures implemented on JavaScript
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JavaScript
;
Object.defineProperty(exports, "__esModule", {
value: true
});
exports.default = dft;
var _ComplexNumber = _interopRequireDefault(require("../complex-number/ComplexNumber"));
function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
const CLOSE_TO_ZERO_THRESHOLD = 1e-10;
/**
* Discrete Fourier Transform (DFT): time to frequencies.
*
* Time complexity: O(N^2)
*
* @param {number[]} inputAmplitudes - Input signal amplitudes over time (complex
* numbers with real parts only).
* @param {number} zeroThreshold - Threshold that is used to convert real and imaginary numbers
* to zero in case if they are smaller then this.
*
* @return {ComplexNumber[]} - Array of complex number. Each of the number represents the frequency
* or signal. All signals together will form input signal over discrete time periods. Each signal's
* complex number has radius (amplitude) and phase (angle) in polar form that describes the signal.
*
* @see https://gist.github.com/anonymous/129d477ddb1c8025c9ac
* @see https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/
*/
function dft(inputAmplitudes, zeroThreshold = CLOSE_TO_ZERO_THRESHOLD) {
const N = inputAmplitudes.length;
const signals = []; // Go through every discrete frequency.
for (let frequency = 0; frequency < N; frequency += 1) {
// Compound signal at current frequency that will ultimately
// take part in forming input amplitudes.
let frequencySignal = new _ComplexNumber.default(); // Go through every discrete point in time.
for (let timer = 0; timer < N; timer += 1) {
const currentAmplitude = inputAmplitudes[timer]; // Calculate rotation angle.
const rotationAngle = -1 * (2 * Math.PI) * frequency * (timer / N); // Remember that e^ix = cos(x) + i * sin(x);
const dataPointContribution = new _ComplexNumber.default({
re: Math.cos(rotationAngle),
im: Math.sin(rotationAngle)
}).multiply(currentAmplitude); // Add this data point's contribution.
frequencySignal = frequencySignal.add(dataPointContribution);
} // Close to zero? You're zero.
if (Math.abs(frequencySignal.re) < zeroThreshold) {
frequencySignal.re = 0;
}
if (Math.abs(frequencySignal.im) < zeroThreshold) {
frequencySignal.im = 0;
} // Average contribution at this frequency.
// The 1/N factor is usually moved to the reverse transform (going from frequencies
// back to time). This is allowed, though it would be nice to have 1/N in the forward
// transform since it gives the actual sizes for the time spikes.
frequencySignal = frequencySignal.divide(N); // Add current frequency signal to the list of compound signals.
signals[frequency] = frequencySignal;
}
return signals;
}