ml-peak-shape-generator
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    ml-peak-shape-generator

    ml-peak-shape-generator

    NPM version build status Test coverage npm download

    Generate various peak shapes.

    The current supported kinds of shapes:

    Name kind Equation
    Gaussian gaussian
    Lorentzian lorentzian
    Lorentzian Dispersive lorentzianDispersive
    Generalized Lorentzian generalizedLorentzian
    Pseudo Voigt pseudoVoigt
    Pseudo Voigt (TCH) pseudoVoigtTCH The pseudo Voigt above, with independent gaussian and lorentzian widths fwhmG and fwhmL. The effective fwhm and mu are derived from them through the Thompson–Cox–Hastings approximation.
    Split Gaussian (asymmetric) splitGaussian Two gaussian halves sharing the apex: the lower-x half (t ≤ x) uses fwhmLow, the higher-x half (t > x) uses fwhmHigh.

    The only 2D shape is gaussian, whose widths are set per axis.

    where

    $ npm i ml-peak-shape-generator

    This package allows to calculate various shapes. By default they will have a height of 1.

    demo.png

    You see the resulting functions using this playground

    import {
      getGaussianData,
      getLorentzianData,
      getPseudoVoigtData,
    } from 'ml-peak-shape-generator';

    // It's possible to specify the windows size with factor option
    let data = getGaussianData({ sd: 500 }, { factor: 3.5 });
    // or fix the number of points as Full Width at Half Maximum
    let data = getGaussianData({ fwhm: 500 }, { factor: 3.5 });

    // It's possible to specify the windows size with factor option
    let data = getLorentzianData({ fwhm: 500 }, { factor: 5 });

    // It's possible to specify the windows size with factor option
    let data = getPseudoVoigtData({ fwhm: 500 }, { factor: 5 });

    It is also possible to take an instance of each kind of shape:

    import { Gaussian, gaussianFct, Gaussian2D } from 'ml-peak-shape-generator';

    const gaussianShape = new Gaussian({ fwhm: 500 });
    // It is possible to set a new value for fwhm
    gaussianShape.fwhm = 300;

    // By default the height value ensure a volume equal 1.
    const symmetric2DShape = new Gaussian2D({ fwhm: 500 });

    // It is possible to set values for sd, fwhm and factor for each axes.
    const gaussian2DShape = new Gaussian2D({ fwhm: { x: 300, y: 500 } });

    // It is possible to set new value for fwhm by:
    gaussian2DShape.fwhm = { x: 300, y: 500 };
    // or set the same value for both axes.
    gaussian2DShape.fwhm = 400;

    // An instance of any shape has the same methods accessible for each
    // shape e.g. fct or getData, but these use the internal parameters. e.g:

    gaussianShape.fct(5);
    gaussianFct(5, 500);
    // getData
    gaussianShape.getData({ factor: 3.5 });

    import { getShape1D, getShape2D } from 'ml-peak-shape-generator';

    // If you want to dynamically select a shape you can use `getShape1D` /
    // `getShape2D`. They return an instance of the required kind of shape.

    const lorentzian = getShape1D({ kind: 'lorentzian', fwhm: 500 });
    const gaussian2D = getShape2D({ kind: 'gaussian', sd: 500 });

    A shape exists in two forms: a descriptor — a plain object such as { kind: 'gaussian', fwhm: 500 } — and an instance, the class that computes the curve. getShape1D / getShape2D turn a descriptor into an instance.

    An instance carries its own kind and serializes back to a descriptor, so a shape survives a trip through JSON:

    import { getShape1D } from 'ml-peak-shape-generator';

    const shape = getShape1D({ kind: 'pseudoVoigt', fwhm: 500, mu: 0.3 });

    shape.kind; // 'pseudoVoigt'
    JSON.stringify(shape); // '{"kind":"pseudoVoigt","fwhm":500,"mu":0.3}'

    const restored = getShape1D(JSON.parse(JSON.stringify(shape)));
    restored.fct(5) === shape.fct(5); // true

    toJSON emits the parameters the shape is defined by — those getParameters() reports — so a round trip preserves both the curve and its analytical derivatives. A splitGaussian therefore emits fwhmLow and fwhmHigh rather than its mean fwhm, and a pseudoVoigtTCH emits its component widths fwhmG and fwhmL. Options that are alternative ways to express a width, such as sd, are resolved first and emitted as the resulting fwhm.

    Because an instance is itself a valid descriptor, handing one back to the factory copies it:

    const copy = getShape1D(shape); // a new instance with the same parameters

    The kind strings are exported as types:

    import type { Shape1DKind, Shape2DKind } from 'ml-peak-shape-generator';

    // Shape1DKind: 'gaussian' | 'lorentzian' | 'lorentzianDispersive' |
    //              'pseudoVoigt' | 'pseudoVoigtTCH' | 'generalizedLorentzian' |
    //              'splitGaussian'
    // Shape2DKind: 'gaussian'

    It is also possible to get a function that allows to calculate y for any x

    import { gaussianFct } from 'ml-peak-shape-generator';
    const func = gaussianFct(x - mean, fwhm);

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