Adding a new fitting function to IDA

The tabs in Indirect Data Analysis use each fit spectra to a predefined list of functions, adding new functions to the list of available ones can seem daunting at first but it can be done by following some steps.


In the FitTabConstants.h file, there are sets of maps of {“Fit function class name”, “Fit function class initialization string”}, e.g. {std::string(“TeixeiraWater”), std::string(“name=TeixeiraWater, Tau=1, L=1.5, constraints=(Tau>0, L>0)”)}. To add a new fit function to FqFit you simply need to add it to this list of available functions. Currently, we divide up the functions into Width, EISF, and All with the intent that the list of functions would change depending on what data is loaded into the tab, however as it is not yet implemented for the workspace input it uses the list for All.


The fit function strings for MSDFit are also stored in the FitTabConstants.h file.


The fitting functions in IqtFit are hard coded into the template browser. These should be moved into the FitTabConstants.h file to allow us to add more functions easily in the future.


ConvFit has the most involved process of adding a function to it’s library and it needs to be added in its entirety.

Firstly, in ConvTypes.h add the function name to the enumerated list FitType e.g. TeixeiraWater, and in the paramID enum add an entry for each parameter in the function along with a unique prefix denoting which function they are for. e.g. TW_HEIGHT, TW_DIFFCOEFF, TW_TAU, TW_CENTRE. Make sure to preserve the ordering of the current enum and add the parameters of the function along with each other.

Secondly, in ConvTypes.cpp add QDependency for the function as either true or false in the FitTypeQDepends map based on if the function is Q dependant.

{FitType::TeixeiraWater, true},

Then, in the FitTypeEnumToString and FitTypeStringToEnum add the mapping of FitType from the enum type onto the function name

{FitType::TeixeiraWater, "TeixeiraWaterSQE"}

{"TeixeiraWaterSQE", FitType::TeixeiraWater}

After that, in the g_paramName add ParamID from the header and parameter names for each parameter in the function. The order of these is important so add them in the same order as they appear in the header. Make sure the function is in the right range. If it is Lorentzian add it between the range from LOR1_AMPLITUDE to LOR2_FWHM_2 and if it is a fit function add it between the range from TW_HEIGHT to IIRD_CENTRE and if it is a background function add it between the range from FLAT_BG_A0 to LINEAR_BG_A1. Note that, background functions have _BG_ format.

{ParamID::TW_HEIGHT, "Height"},
{ParamID::TW_DIFFCOEFF, "DiffCoeff"},
{ParamID::TW_TAU, "Tau"},
{ParamID::TW_CENTRE, "Centre"},

Moreover, to tie it all together add param ranges to g_typeMap in the form {FitType, {“Function name displayed in tab”, “Function class name”, {ParamID::first, ParamID::last}}} this allows the template to construct a function out of the related parameters. There are several places where this can be added, those being FitType, LorentzianType, and BackgroundType. ConvFit can run fits with one of each Fit, Lorentzian, and Background but only one of each.

{FitType::TeixeiraWater, {"Teixeira Water SQE", "TeixeiraWaterSQE", {ParamID::TW_HEIGHT, ParamID::TW_CENTRE}}},

Finally, in FitTabConstants.h add the fit function name and shortened key to the FUNCTION_STRINGS variable. This key will be used in the output workspace from the fit.

In ConvFunctionModel add the build function string function

std::string ConvFunctionModel::buildTeixeiraFunctionString() const {
  return "name=TeixeiraWaterSQE, Height=1, DiffCoeff=2.3, Tau=1.25, Centre=0, "
  "constraints=(Height>0, DiffCoeff>0, Tau>0)";

and then add else if case to buildPeaksFunctionString, buildLorentzianPeaksString or buildFitTypeString.

else if (m_fitType == FitType::TeixeiraWater) {