ml-gsd


Global spectral deconvolution and peak optimizer.

API documentation

gsdis using an algorithm that is searching for inflection points to determine the position and width of peaks. The width is defined as the distance between the 2 inflection points. Depending the shape of the peak this width may differ from 'fwhm' (Full Width Half Maximum).

Preprocessing of the data involves the following parameters

- maxCriteria: search either for maxima or minima. We will invert the data and the results if searching for a minima
- noiseLevel: specifies the noise level. All the peaks bellow this value (or above in case of maxCriteria=false) are ignored. By default the noiseLevel will be set to the median + 3 x sd. This is a good value when not too many peaks are present in the spectrum.
- sgOptions: Savitzky-Golay filter that is used to smooth the data for the calculation of the derivatives
- smoothY: If this value is true the SG filter is not only applied during the calculation of the derivatives but also on the original data

$3

The result of GSD is an array of GSDPeak:

- x: position of the peak on the x axis
- y: the height of the peak
- width: width at the level of the inflection points
- index: index in the 'x' and 'y' array of the peak
- ddY: second derivative value at the level of the peak. Allows to identify 'large' peaks
- inflectionPoints: an object with the position of the inflection points
- from: { x, index }
- to: { x, index }

$3

#### 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.

#### 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.

$3

#### GSD.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.optimizePeaks(data, peakList, options)

Optimize the position (x), max intensity (y), full width at half maximum (fwhm) and the ratio of gaussian contribution (mu) if it's required. It currently supports three kind of shapes: gaussian, lorentzian and pseudovoigt

Example

``js
import { IsotopicDistribution } from 'mf-global';
import { gsd, optimizePeaks } from 'ml-gsd';

// generate a sample spectrum of the form {x:[], y:[]}
const data = new IsotopicDistribution('C').getGaussian();

let peaks = gsd(data, {
minMaxRatio: 0.00025, // Threshold to determine if a given peak should be considered as a noise
realTopDetection: true, // Correction of the x and y coordinates using a quadratic optimizations
maxCriteria: true, // Are we looking for maxima or minima
smoothY: false, // should we smooth the spectra and return smoothed peaks ? Default false.
sgOptions: { windowSize: 7, polynomial: 3 }, // Savitzky-Golay smoothing parameters for first and second derivative calculation
});
console.log(peaks);
/*
array of peaks containing {x, y, width, ddY, inflectionPoints}
- width = distance between inflection points
- ddY = second derivative on the top of the peak
*/

let optimized = optimizePeaks(data, peaks);
console.log(optimized);
/*
[
{
x: 11.99999999960885,
y: 0.9892695646808637,
shape: { kind: 'gaussian' },
fwhm: 0.010000209455943584,
width: 0.008493395898379276
},
{
x: 13.003354834590702,
y: 0.010699637653261198,
shape: { kind: 'gaussian' },
fwhm: 0.010000226962299321,
width: 0.008493410766908847
}
]
*/
``

License

MIT