ml-spectra-fitting

ml-spectra-fitting

Curve fitting method in javascript.

This is a spectra fitting package to optimize the position (x), max intensity (y), full width at half maximum (FWHM = width) and the ratio of gaussian contribution (mu) if it's required. It supports three kind of shapes:

Name	Equation
Gaussian
Lorentzian
Pseudo Voigt

where

It is a wrapper of ml-levenberg-marquardt

API Documentation

Installation

$ npm install ml-spectra-fitting

Example

// import library
import { optimizeSum } from 'ml-spectra-fitting';
import { generateSpectrum } from 'spectrum-generator';

const peaks = [
  { x: 0.5, y: 0.2, width: 0.2 },
  { x: -0.5, y: 0.2, width: 0.3 },
];
const data = generateSpectrum(peaks, {from: -1, to: 1, nbPoints: 41});


//the approximate values to be optimized, It could come from a peak picking with ml-gsd
let peaks = [
  {
    x: -0.5,
    y: 0.18,
    width: 0.18,
  },
  {
    x: 0.52,
    y: 0.17,
    width: 0.37,
  },
];

// the function receive an array of peaks {x, y, width} as a guess
// and returns an array of peaks

let fittedPeaks = optimize(data, peaks);
console.log(fittedPeaks);
/**
 {
    error: 0.010502794375558983,
    iterations: 15,
    peaks: [
      {
        x: -0.49999760133593774,
        y: 0.1999880261075537,
        width: 0.3000369491704072
      },
      {
        x: 0.5000084944744884,
        y: 0.20004144804853427,
        width: 0.1999731186595336
      }
    ]
  }
 */

For data with and combination of signals with shapes between gaussian and lorentzians, we could use the kind pseudovoigt to fit the data.

import { optimize } from 'ml-spectra-fitting';
import { SpectrumGenerator } from 'spectrum-generator';

const generator = new SpectrumGenerator({
  nbPoints: 101,
  from: -1,
  to: 1,
});

// by default the kind of shape is gaussian;
generator.addPeak({ x: 0.5, y: 0.2 }, { width: 0.2 });
generator.addPeak(
  { x: -0.5, y: 0.2 },
  {
    width: 0.1,
    shape: {
      kind: 'lorentzian',
      options: {
        fwhm: 1000,
        length: 50001,
        factor: 5
      },
    },
  },
);

//points to fit {x, y};
let data = generator.getSpectrum();
console.log(JSON.stringify({x: Array.from(data.x), y: Array.from(data.y)}))
//the approximate values to be optimized, It could coming from a peak picking with ml-gsd
let peaks = [
  {
    x: -0.5,
    y: 0.22,
    width: 0.25,
  },
  {
    x: 0.52,
    y: 0.18,
    width: 0.18,
  },
];

// the function receive an array of peak with {x, y, width} as a guess
// and return a list of objects
let fittedParams = optimize(data, peaks, { shape: { kind: 'pseudovoigt' } });

console.log(fittedParams);
/**
{
  error: 0.12361588652854476,
  iterations: 100,
  peaks: [
    {
      x: -0.5000014532421942,
      y: 0.19995307937326137,
      width: 0.10007670374735196,
      mu: 0.004731136777288483
    },
    {
      x: 0.5001051783652894,
      y: 0.19960010175400406,
      width: 0.19935932346969124,
      mu: 1
    }
  ]
}
*/

License

MIT