API reference

Contents

API reference#

Core module summary#

`Fitter`()	Main class for performing fits and organising data
`Source`(x, y, yerr, name[, xerr])	Initializes a source of data
`Model`(name[, prefunc])	Base Model class

Models module summary#

`ExponentialDecay`(a, tau[, name, prefunc])	Model for an exponential decay
`Polynomial`(p[, name, prefunc])	Model class for a polynomial response
`SkewedVoigt`(A, mu, FWHMG, FWHML, skew[, ...])	Model for a skewed Voigt peak by the error function.
`PiecewiseConstant`(values, bounds[, name, ...])	Model class for a PiecewiseConstant response
`Voigt`(A, mu, FWHMG, FWHML[, name, prefunc])	Model for a Voigt lineshape
`HFS`(I, J[, A, B, C, df, fwhmg, fwhml, name, ...])	Initializes a hyperfine spectrum Model with the given hyperfine parameters.

Interface module summary#

`HFSModel`(I, J, ABC[, centroid, fwhm, scale, ...])	Initializes a hyperfine spectrum Model with the given hyperfine parameters.
`SumModel`(models, background_params[, name, ...])	Initializes a hyperfine spectrum for the sum of multiple Models with the given models and a step background.
`chisquare_fit`(model, x, y, yerr[, xerr, method])	Perform a fit of the provided model to the data provided in this function.

Plotting module summary#

`generateCorrelationPlot`(filename[, filter, ...])	Given the random walk data, creates a triangle plot: distribution of a single parameter on the diagonal axes, 2D contour plots with 1, 2 and 3 sigma contours on the off-diagonal.
`generateWalkPlot`(filename[, filter, burnin, ...])	Given the random walk data, the random walk for the selected parameters is plotted.

Utilities module summary#

`generateSpectrum`(models, x[, generator])	Generates a dataset based on the models and x-values provided.
`poissonInterval`(data[, sigma, alpha, mean])	Calculates the confidence interval for the mean of a Poisson distribution.
`weightedAverage`(x, sigma[, axis])	Takes the weighted average of an array of values and the associated errors.

Subpages#