Files
lynchmark/tests/3_signal_pipeline/outputs/deepseek_deepseek-v3.2.js
2026-03-05 08:30:33 +00:00

59 lines
2.0 KiB
JavaScript

const analyzeSignal = async (yamlString) => {
const { load } = await import('https://cdn.jsdelivr.net/npm/js-yaml@4.1.0/+esm');
const math = await import('https://cdn.jsdelivr.net/npm/mathjs@11.9.1/+esm');
const ndarray = await import('https://cdn.jsdelivr.net/npm/ndarray@1.0.19/+esm');
const fft = await import('https://cdn.jsdelivr.net/npm/ndarray-fft@1.0.6/+esm');
const DOMPurify = await import('https://cdn.jsdelivr.net/npm/dompurify@3.0.6/+esm');
const config = load(yamlString);
const { sampleRate, duration, components } = config;
const N = sampleRate * duration;
const signal = new Float64Array(N);
for (let i = 0; i < N; i++) {
let t = i / sampleRate;
let value = 0;
for (const comp of components) {
value += comp.amplitude * math.sin(2 * math.pi * comp.frequency * t);
}
signal[i] = value;
}
const real = ndarray.default(signal.slice());
const imag = ndarray.default(new Float64Array(N));
fft.default(1, real, imag);
const magnitudes = [];
const halfN = Math.floor(N / 2);
for (let k = 0; k <= halfN; k++) {
const re = real.get(k);
const im = imag.get(k);
magnitudes[k] = Math.sqrt(re * re + im * im) / halfN;
}
const peaks = [];
for (let k = 0; k <= halfN; k++) {
if (magnitudes[k] > 0.1) {
peaks.push({
frequencyHz: Math.round(k * sampleRate / N),
magnitude: Math.round(magnitudes[k] * 100) / 100
});
}
}
peaks.sort((a, b) => b.magnitude - a.magnitude);
let html = `<table><tr><th>Frequency (Hz)</th><th>Magnitude</th></tr>`;
peaks.forEach(p => {
html += `<tr><td>${p.frequencyHz}</td><td>${p.magnitude}</td></tr>`;
});
html += `</table>`;
return {
peaks,
html: DOMPurify.default.sanitize(html),
signalLength: N
};
};
export default analyzeSignal;
// Generation time: 19.364s
// Result: FAIL