Maths: {
    difference: ((x0: number, x1: number) => null | number);
    differences: ((x0: number[], x1: number[]) => (null | number)[]);
    erf: ((x: number) => number);
    interpolate: ((xySequence: XySequence, value: number, mode?: InterpolateMode) => number);
    minMax: ((data: number[]) => number[]);
    normalCcdf: ((μ: number, σ: number, x: number) => number);
    percentDifference: ((x0: number, x1: number) => number);
    percentDifferences: ((x0: number[], x1: number[]) => number[]);
    responseSpectrum: ((hazards: Map<Imt, XySequence>, returnPeriod: number) => XySequence);
    round: ((value: number, scale: number) => number);
} = ...

Export functions

Type declaration

  • difference: ((x0: number, x1: number) => null | number)
      • (x0, x1): null | number
      • Calculate the difference of two values.

        Parameters

        • x0: number

          First value

        • x1: number

          Second value

        Returns null | number

  • differences: ((x0: number[], x1: number[]) => (null | number)[])
      • (x0, x1): (null | number)[]
      • Calculate the difference for two arrays.

        Parameters

        • x0: number[]

          First pair of values

        • x1: number[]

          Second pair of values

        Returns (null | number)[]

  • erf: ((x: number) => number)
      • (x): number
      • Error function approximation of Abramowitz and Stegun, formula 7.1.26 in the Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Although the approximation is only valid for x ≥ 0, because erf(x) is an odd function, erf(x) = −erf(−x) and negative values are supported.

        Parameters

        • x: number

        Returns number

  • interpolate: ((xySequence: XySequence, value: number, mode?: InterpolateMode) => number)
      • (xySequence, value, mode?): number
      • Interpolate.

        Parameters

        • xySequence: XySequence

          The Xy sequence

        • value: number

          The value to interpolate at.

        • mode: InterpolateMode = InterpolateMode.LOG_LOG

          The interpolation mode

          Default interpolation model

        Returns number

        InterpolateMode.LOG_LOG
        
  • minMax: ((data: number[]) => number[])
      • (data): number[]
      • Returns the min and max value of a large data array.

        Parameters

        • data: number[]

          The data

        Returns number[]

  • normalCcdf: ((μ: number, σ: number, x: number) => number)
      • (μ, σ, x): number
      • Normal complementary cumulative distribution function.

        Parameters

        • μ: number

          mean

        • σ: number

          standard deviation

        • x: number

          variate

        Returns number

  • percentDifference: ((x0: number, x1: number) => number)
      • (x0, x1): number
      • Calculate the percent difference of two values.

        Parameters

        • x0: number

          First value

        • x1: number

          Second value

        Returns number

  • percentDifferences: ((x0: number[], x1: number[]) => number[])
      • (x0, x1): number[]
      • Calculate the percent difference for two arrays.

        Parameters

        • x0: number[]

          First pair of values

        • x1: number[]

          Second pair of values

        Returns number[]

  • responseSpectrum: ((hazards: Map<Imt, XySequence>, returnPeriod: number) => XySequence)
      • (hazards, returnPeriod): XySequence
      • Calculate the response sepectrum for each IMT.

        Parameters

        • hazards: Map<Imt, XySequence>

          The hazard curves by imt

        • returnPeriod: number

          The return period (in years) to calculate at

        Returns XySequence

  • round: ((value: number, scale: number) => number)
      • (value, scale): number
      • Round a number to specific format

        Parameters

        • value: number

          Value to round

        • scale: number

          Format scale

        Returns number