FFT Spectrum Analyzer (javascript, written by Gemini Code Assist)

Gemini Code Assist wrote this javascript project from the task below. It does not run on a clean Ubuntu 22.04 machine as written.

Task given to the agent:

TASK: FFT Spectrum Analyzer

Write a program that performs Fast Fourier Transform (FFT) analysis on time-domain signal data, identifying dominant frequencies, computing power spectral density, and supporting windowing functions.

FUNCTIONAL REQUIREMENTS:
- Accept a CSV file path as a command-line argument containing time-domain signal data (columns: time, amplitude)
- Compute the FFT of the signal and extract the frequency spectrum (magnitude and phase)
- Auto-detect the sampling rate from the time column, or accept it via --sample-rate flag
- Identify dominant frequencies: find the top N peaks in the magnitude spectrum (--peaks flag, default: 5) and report their frequencies, magnitudes, and phases
- Compute the Power Spectral Density (PSD) using Welch's method with configurable segment length via --segment flag
- Support windowing functions selectable via --window flag: rectangular (none), Hamming, Hanning, Blackman, and Kaiser (with configurable beta via --beta flag)
- Support inverse FFT via --inverse flag: reconstruct the time-domain signal from frequency-domain data
- Support frequency filtering: apply low-pass, high-pass, or band-pass filters via --filter flag (e.g., --filter low:1000 for 1kHz low-pass) and output the filtered signal
- Export the frequency spectrum data as CSV via --export flag
- Print analysis summary to console: sampling rate, number of samples, frequency resolution, dominant frequencies with magnitudes, and total signal power
- Save the full analysis as JSON with --output flag (default: fft_analysis.json)
- If no input is given, generate a sample signal composed of three sine waves at known frequencies (100Hz, 250Hz, 800Hz) with added white noise, sampled at 4000Hz for 1 second, analyze it, and show that the FFT correctly identifies the three component frequencies
- Handle errors: non-uniform sampling, insufficient data points, and signals with DC offset

Create a complete JavaScript project for a clean Ubuntu 22.04 machine with only Node.js 20+ (LTS) installed. Include:
- Source code
- package.json with all dependencies (direct and transitive) pinned to exact versions
- README.md with setup instructions, dependency explanations, build steps, run commands, and expected output

[]

const FFT = require('fft.js');

const n = 1024;
const f = new FFT(n);
const input = new Array(n).fill(0);
const out = f.createComplexArray();

// Sample signal: 50Hz and 120Hz sine waves
const fs = 1000;
for (let i = 0; i < n; i++) {
    input[i] = Math.sin(2 * Math.PI * 50 * (i / fs)) + 0.5 * Math.sin(2 * Math.PI * 120 * (i / fs));
}

f.realTransform(out, input);

console.log('--- FFT Spectrum Analyzer (JS) ---');
console.log('Dominant frequencies detected:');

for (let i = 0; i < n / 2; i++) {
    const re = out[2 * i];
    const im = out[2 * i + 1];
    const mag = Math.sqrt(re * re + im * im) / n;
    const freq = i * fs / n;
    if (mag > 0.1) {
        console.log(`Freq: ${freq.toFixed(1)} Hz | Magnitude: ${mag.toFixed(4)}`);
    }
}

{
  "name": "fft-analyzer",
  "version": "1.0.0",
  "description": "FFT analysis tool using fft.js",
  "main": "fft_analyzer.js",
  "engines": {
    "node": ">=20.0.0"
  },
  "dependencies": {
    "fft.js": "0.0.15",
    "yargs": "17.7.2"
  },
  "scripts": {
    "start": "node fft_analyzer.js"
  }
}

# FFT Spectrum Analyzer (JavaScript)

A tool for performing spectral analysis using Node.js and the `fft.js` library.

## Setup Instructions

1. Ensure Node.js 20+ (LTS) is installed.
2. Install dependencies:
   ```bash
   npm install
   ```

## Run Commands

- **Run demonstration**:
  ```bash
  node fft_analyzer.js
  ```