ml-gsd

global-spectral-deconvolution

Global Spectra Deconvolution + Peak optimizer

API documentation

Parameters

minMaxRatio=0.00025 (0-1)

Threshold to determine if a given peak should be considered as a noise, bases on its relative height compared to the highest peak.

broadRatio=0.00 (0-1)

If broadRatio is higher than 0, then all the peaks which second derivative smaller than broadRatio * maxAbsSecondDerivative will be marked with the soft mask equal to true.

noiseLevel=0 (-inf, inf)

Noise threshold in spectrum units

maxCriteria=true [true||false]

Peaks are local maximum(true) or minimum(false)

smoothY=true [true||false]

Select the peak intensities from a smoothed version of the independent variables?

realTopDetection=false [true||false]

Use a quadratic optimizations with the peak and its 3 closest neighbors to determine the true x,y values of the peak?

sgOptions={windowSize: 5, polynomial: 3}

Savitzky-Golay parameters. windowSize should be odd; polynomial is the degree of the polynomial to use in the approximations. It should be bigger than 2.

heightFactor=0

Factor to multiply the calculated height (usually 2).

boundaries=false

Return also the inflection points of the peaks

derivativeThreshold=0

Filters based on the amplitude of the first derivative

Post methods

GSD.post.broadenPeaks(peakList, {factor=2, overlap=false})

We enlarge the peaks and add the properties from and to. By default we enlarge of a factor 2 and we don't allow overlap.

GSD.post.joinBroadPeaks

GSD.post.optimizePeaks

Example

var CC = require('chemcalc');
var Stat = require('ml-stat');
var peakPicking = require('ml-gsd');

var spectrum = CC.analyseMF('Cl2.Br2', {
  isotopomers: 'arrayXXYY',
  fwhm: 0.01,
  gaussianWidth: 11
});
var xy = spectrum.arrayXXYY;
var x = xy[0];
var y = xy[1];
//Just a fake noiseLevel
var noiseLevel =
  Stat.array.median(
    y.filter(function(a) {
      return a > 0;
    })
  ) * 3;

var options = {
  noiseLevel: noiseLevel,
  minMaxRatio: 0,
  broadRatio: 0,
  smoothY: false,
  realTopDetection: true
};
var result = peakPicking.gsd(x, y, options);
result = peakPicking.post.optimizePeaks(result, x, y, 1, 'gaussian');

License

MIT