ml-spectra-fitting/lib/index.js

Fit spectra using gaussian or lorentzian

import { xMinMaxValues } from 'ml-spectra-processing';
import { getSumOfShapes } from "./shapes/getSumOfShapes.js";
import { getInternalPeaks } from "./util/internalPeaks/getInternalPeaks.js";
import { selectMethod } from "./util/selectMethod.js";
/**
 * Fits a set of points to the sum of a set of bell functions.
 *
 * @param data - An object containing the x and y data to be fitted.
 * @param peaks - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].
 * @param options - Options for optimize
 * @returns - An object with fitting error and the list of optimized parameters { parameters: [ {x, y, width} ], error } if the kind of shape is pseudoVoigt mu parameter is optimized.
 */
export function optimize(data, peaks, options = {}) {
    // rescale data
    const temp = xMinMaxValues(data.y);
    const minMaxY = { ...temp, range: temp.max - temp.min };
    const internalPeaks = getInternalPeaks(peaks, minMaxY, options);
    // need to rescale what is related to Y
    const { baseline: shiftValue = minMaxY.min } = options;
    const normalizedY = new Float64Array(data.y.length);
    for (let i = 0; i < data.y.length; i++) {
        normalizedY[i] = (data.y[i] - shiftValue) / minMaxY.range;
    }
    const nbParams = internalPeaks[internalPeaks.length - 1].toIndex + 1;
    const minValues = new Float64Array(nbParams);
    const maxValues = new Float64Array(nbParams);
    const initialValues = new Float64Array(nbParams);
    const gradientDifferences = new Float64Array(nbParams);
    let index = 0;
    for (const peak of internalPeaks) {
        for (let i = 0; i < peak.parameters.length; i++) {
            minValues[index] = peak.propertiesValues.min[i];
            maxValues[index] = peak.propertiesValues.max[i];
            initialValues[index] = peak.propertiesValues.init[i];
            gradientDifferences[index] = peak.propertiesValues.gradientDifference[i];
            index++;
        }
    }
    const { algorithm, optimizationOptions } = selectMethod(options.optimization);
    const sumOfShapes = getSumOfShapes(internalPeaks);
    const fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapes, {
        minValues,
        maxValues,
        initialValues,
        gradientDifference: gradientDifferences,
        ...optimizationOptions,
    });
    const fittedValues = fitted.parameterValues;
    const newPeaks = [];
    for (const peak of internalPeaks) {
        const { id, shape, parameters, fromIndex } = peak;
        let newPeak = { x: 0, y: 0, shape };
        if (id) {
            newPeak = { ...newPeak, id };
        }
        newPeak.x = fittedValues[fromIndex];
        newPeak.y = fittedValues[fromIndex + 1] * minMaxY.range + shiftValue;
        for (let i = 2; i < parameters.length; i++) {
            //@ts-expect-error should be fixed once
            newPeak.shape[parameters[i]] = fittedValues[fromIndex + i];
        }
        newPeaks.push(newPeak);
    }
    return {
        error: fitted.parameterError,
        iterations: fitted.iterations,
        peaks: newPeaks,
    };
}
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