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CommonJS consumers need Node.js >= 22.12 or any 24.x\nor later, where `require()` of a synchronous ES module is supported.\n\n## [API documentation](https://mljs.github.io/global-spectral-deconvolution/)\n\n`gsd` is 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).\n\nPreprocessing of the data involves the following parameters\n\n- `maxCriteria`: search either for maxima or minima. We will invert the data and the results if searching for a minima\n- `noiseLevel`: specifies the noise level. All the peaks below 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.\n- `sgOptions`: Savitzky-Golay filter that is used to smooth the data for the calculation of the derivatives\n- `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\n\n### gsd({x:[], y:[]}, options)\n\nThe result of GSD is an array of GSDPeak:\n\n- x: position of the peak on the x axis\n- y: the height of the peak\n- width: width at the level of the inflection points\n- index: index in the 'x' and 'y' array of the peak\n- ddY: second derivative value at the level of the peak. Allows to identify 'large' peaks\n- inflectionPoints: an object with the position of the inflection points\n  - from: { x, index }\n  - to: { x, index }\n\n### Parameters\n\n#### minMaxRatio=0.00025 (0-1)\n\nThreshold to determine if a given peak should be considered as a noise, bases on its relative height compared to the highest peak.\n\n#### maxAbsoluteRatio=0 (0-1)\n\nUse an absolute threshold based on a fraction of the maximum absolute Y value. When set to a value in (0,1] the threshold will be `maxAbsoluteRatio * maxAbsoluteValue(y)`. When `0` (default) this option is ignored and `noiseLevel` is used. Value must be between 0 and 1.\n\n#### maxCriteria=true [true||false]\n\nPeaks are local maximum(true) or minimum(false)\n\n#### smoothY=true [true||false]\n\nSelect the peak intensities from a smoothed version of the independent variables?\n\n#### realTopDetection=false [true||false]\n\nUse a quadratic optimizations with the peak and its 3 closest neighbors to determine the true x,y values of the peak?\n\n#### sgOptions={windowSize: 5, polynomial: 3}\n\nSavitzky-Golay parameters. windowSize should be odd; polynomial is the degree of the polynomial to use in the approximations. It should be bigger than 2.\n\n#### ids=true [true||false]\n\nGive every peak a random `id`. Minting one costs more than finding the peak did — on a mass-spectrometry imaging run picking millions of peaks it is around 20% of the whole deconvolution — so a caller that reads only the coordinates should pass `ids: false`, and the `id` property is then left off the peaks entirely.\n\n### Post methods\n\n#### GSD.broadenPeaks(peakList, {factor=2, overlap=false})\n\nWe enlarge the peaks and add the properties from and to.\nBy default we enlarge of a factor 2 and we don't allow overlap.\n\n#### GSD.optimizePeaks(data, peakList, options)\n\nOptimize 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\n\n## Example\n\n```js\nimport { IsotopicDistribution } from 'mf-global';\nimport { gsd, optimizePeaks } from 'ml-gsd';\n\n// generate a sample spectrum of the form {x:[], y:[]}\nconst data = new IsotopicDistribution('C').getGaussian();\n\nconst peaks = gsd(data, {\n  minMaxRatio: 0.00025, // Threshold to determine if a given peak should be considered as a noise\n  realTopDetection: true, // Correction of the x and y coordinates using a quadratic optimizations\n  maxCriteria: true, // Are we looking for maxima or minima\n  smoothY: false, // should we smooth the spectra and return smoothed peaks ? Default false.\n  sgOptions: { windowSize: 7, polynomial: 3 }, // Savitzky-Golay smoothing parameters for first and second derivative calculation\n  ids: true, // Give each peak a random id. Pass false when you only read the coordinates\n});\nconsole.log(peaks);\n/*\n  array of peaks containing {x, y, width, ddY, inflectionPoints}\n  - width = distance between inflection points\n  - ddY = second derivative on the top of the peak\n */\n\nconst optimized = optimizePeaks(data, peaks);\nconsole.log(optimized);\n/*\n[\n  {\n    x: 11.999999999607912,\n    y: 0.9892695155316565,\n    shape: { fwhm: 0.010000209739248308, kind: 'gaussian' },\n    id: '80b85a44-e14f-4bfd-a317-78a0b27feb39',\n    width: 0.008493396138996155\n  },\n  {\n    x: 13.003354834677824,\n    y: 0.010699670320385445,\n    shape: { fwhm: 0.010000209728620733, kind: 'gaussian' },\n    id: '4deb729b-c44c-4713-b7c7-f630621fc44d',\n    width: 0.008493396129969924\n  }\n]\n*/\n```\n\n## License\n\n[MIT](./LICENSE)\n","readmeFilename":"README.md"}