ml-peak-shape-generator
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    Class PseudoVoigtTCH

    TCH-style pseudo-Voigt where gaussian and lorentzian widths are independent. The effective fwhm and mixing parameter mu are derived from fwhmG and fwhmL via the Thompson–Cox–Hastings approximation.

    Implements

    Index

    Constructors

    Properties

    kind: "pseudoVoigtTCH" = ...

    Kind of shape, so that an instance is also a valid Shape1D descriptor.

    Accessors

    Methods

    • Analytical partial derivatives of fct at x, with respect to x and to each shape parameter (in the same order as getParameters()). Every shape provides a closed-form Jacobian, so consumers (e.g. curve fitting) can use it directly instead of numerical differentiation.

      Parameters

      • x: number

        position at which to evaluate the derivatives.

      Returns Shape1DDerivative

    • Return a parameterized function of a gaussian shape (see README for equation).

      Parameters

      • x: number

      Returns number

      • the y value of gaussian with the current parameters.
    • Descriptor of this shape, so JSON.stringify round-trips through getShape1D. The component widths are emitted rather than fwhm/mu, because they are the state this shape is defined by: getParameters reports them and derivative differentiates with respect to them. The effective width and the mixing ratio are re-derived from them exactly.

      Returns PseudoVoigtTCHShape1D

      the shape descriptor.