AngularCorrelations.cxx

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#include <iostream>
#include <iomanip>
#include <vector>
#include <string>
#include <cassert>
 
#include "TFile.h"
#include "TH2.h"
#include "TH1.h"
#include "TGraphErrors.h"
#include "TMultiGraph.h"
#include "TCanvas.h"
#include "TLine.h"
#include "TLegend.h"
#include "Fit/Fitter.h"
#include "TMatrixD.h"
#include "TPaveText.h"
 
#include "ArgParser.h"
#include "TUserSettings.h"
#include "TGriffinAngles.h"
#include "TPeakFitter.h"
#include "TRWPeak.h"
 
TGraph*             MixingMethod(TGraphErrors* data, TGraphErrors* z0, TGraphErrors* z2, TGraphErrors* z4, int twoJhigh, int twoJmid, int twoJlow, std::vector<double>& bestParameters);
std::vector<double> A2a4Method(TGraphErrors* data, TGraphErrors* z0, TGraphErrors* z2, TGraphErrors* z4);
 
double GetYError(TGraphErrors* graph, const double& x)
{
   /// general function to get the error of a graph at point x (takes the maximum of the errors of the bracketing points)
   for(int i = 0; i < graph->GetN(); ++i) {
#if ROOT_VERSION_CODE >= ROOT_VERSION(6, 20, 0)
      // exact match: return the error at this point
      if(graph->GetPointX(i) == x) { return graph->GetErrorY(i); }
      // first point with larger x: take maximum of this point and previous point
      // TGraphErrors::GetErrorY returns -1 if index is negative, so we don't need to check for this
      if(graph->GetPointX(i) > x) { return std::max(graph->GetErrorY(i - 1), graph->GetErrorY(i)); }
#else
      double px, py;
      graph->GetPoint(i, px, py);
      // exact match: return the error at this point
      if(px == x) { return graph->GetErrorY(i); }
      // first point with larger x: take maximum of this point and previous point
      // TGraphErrors::GetErrorY returns -1 if index is negative, so we don't need to check for this
      if(px > x) { return std::max(graph->GetErrorY(i - 1), graph->GetErrorY(i)); }
#endif
   }
   return 0.;
}
 
/// program to read in 2D matrices from AngularCorrelationHelper
/// project and fit peaks to create angular correlation plots
/// and to create chi-square plots
int main(int argc, char** argv)
{
   // --------------------------------------------------------------------------------
   // Reading and verifying inputs and settings.
   // --------------------------------------------------------------------------------
 
   // input parameters
   bool        help           = false;
   double      projGateLow    = -1.;
   double      projGateHigh   = -1.;
   double      projBgLow      = -1.;
   double      projBgHigh     = -1.;
   double      timeRandomNorm = -1.;
   double      peakPos        = -1.;
   double      peakLow        = -1.;
   double      peakHigh       = -1.;
   std::string baseName       = "AngularCorrelation";
   std::string inputFile;
   std::string theoryFile;
   std::string outputFile = "AngularCorrelations.root";
   std::string settingsFile;
 
   // set up the argument parser
   ArgParser parser;
   parser.option("h help ?", &help, true).description("Show this help message.");
   parser.option("projection-low proj-low", &projGateLow, true).description("Low edge of projection gate.");
   parser.option("projection-high proj-high", &projGateHigh, true).description("High edge of projection gate.");
   parser.option("background-low bg-low", &projBgLow, true).description("Low edge of background gate (for multiple gates use settings file).");
   parser.option("background-high bg-high", &projBgHigh, true).description("High edge of background gate (for multiple gates use settings file).");
   parser.option("time-random-normalization", &timeRandomNorm, true).description("Normalization factor for subtraction of time-random matrix. If negative (default) it will be calculated automatically.");
   parser.option("base-name", &baseName, true).description("Base name of matrices.").default_value("AngularCorrelation");
   parser.option("peak-pos peak", &peakPos, true).description("Peak position (for multiple peaks use settings file).");
   parser.option("peak-low", &peakLow, true).description("Low edge of peak fit.");
   parser.option("peak-high", &peakHigh, true).description("High edge of peak fit.");
   parser.option("settings", &settingsFile, true).description("Settings file with user settings, these do not overwrite anything provided on command line!");
   parser.option("input", &inputFile, true).description("Input file with gamma-gamma matrices for each angle (coincident, time-random, and event mixed).");
   parser.option("theory", &theoryFile, true).description("File with simulated z0-, z2-, and z4-graphs.");
   parser.option("output", &outputFile, true).description("Name of output file, default is \"AngularCorrelations.root\".");
 
   parser.parse(argc, argv, true);
 
   if(help) {
      std::cout << parser << std::endl;
      return 1;
   }
 
   // open the input root file and read any settings stored there, then add the ones potentially read in from command line
   if(inputFile.empty()) {
      std::cerr << "Need an input file!" << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   TFile input(inputFile.c_str());
 
   if(!input.IsOpen()) {
      std::cerr << "Failed to open input file " << inputFile << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   auto* settings = static_cast<TUserSettings*>(input.Get("UserSettings"));
 
   // if we have a path for a settings file provided on command line, we either add it to the ones read
   // from the root file or (if there weren't any) create a new instance from it
   if(!settingsFile.empty()) {
      if(settings == nullptr) {
         settings = new TUserSettings(settingsFile);
      } else {
         settings->ReadSettings(settingsFile);
      }
   }
 
   // check that we got settings either from the root file or a path provided on command line
   if(settings == nullptr || settings->empty()) {
      std::cerr << "Failed to get user settings from input file." << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   // set all variables from settings if they haven't been set from command line
   if(projGateLow == -1.) { projGateLow = settings->GetDouble("Projection.Low"); }
   if(projGateHigh == -1.) { projGateHigh = settings->GetDouble("Projection.High"); }
   if(timeRandomNorm == -1.) { timeRandomNorm = settings->GetDouble("TimeRandomNormalization"); }
   if(peakPos == -1.) { peakPos = settings->GetDouble("Peak.Position"); }
   if(peakLow == -1.) { peakLow = settings->GetDouble("Peak.Low"); }
   if(peakHigh == -1.) { peakHigh = settings->GetDouble("Peak.High"); }
   // for the background-peak positions and background gates we could have multiple, so we create vectors for them
   std::vector<double> bgPeakPos;
   std::vector<double> bgLow;
   std::vector<double> bgHigh;
   // we only fill the background gates from the settings if there were none provided on the command line
   if(projBgLow == -1. || projBgHigh == -1. || projBgLow >= projBgHigh) {
      for(int i = 1;; ++i) {
         try {
            auto low  = settings->GetDouble(Form("Background.%d.Low", i), true);
            auto high = settings->GetDouble(Form("Background.%d.High", i), true);
            if(low >= high) {
               std::cout << i << ". background gate, low edge not lower than the high edge: " << low << " >= " << high << std::endl;
               break;
            }
            bgLow.push_back(low);
            bgHigh.push_back(high);
         } catch(std::out_of_range& e) {
            break;
         }
      }
   } else {
      bgLow.push_back(projBgLow);
      bgHigh.push_back(projBgHigh);
   }
   // background-peak positions can only be set via settings file
   // we loop until we fail to find an entry
   for(int i = 1;; ++i) {
      try {
         auto pos = settings->GetDouble(Form("BackgroundPeak.Position.%d", i), true);
         if(pos <= peakLow || pos >= peakHigh) {
            std::cout << i << ". background peak outside of fit range: " << pos << " <= " << peakLow << " or " << pos << " >= " << peakHigh << std::endl;
            break;
         }
         bgPeakPos.push_back(pos);
      } catch(std::out_of_range& e) {
         break;
      }
   }
 
   // parameter limits and fixed parameters for the peak and the background peaks
   // if the limits are the same, the parameter is fixed to that value, if the high limit is lower than the low limit there is no limit
   // right now we are hard coded to use TRWPeaks which have 6 parameters
   std::vector<double> peakParameter(6, -2.);
   std::vector<double> peakParameterLow(6, 0.);
   std::vector<double> peakParameterHigh(6, -1.);
   std::vector<double> bgPeakParameter(6, -2.);
   std::vector<double> bgPeakParameterLow(6, 0.);
   std::vector<double> bgPeakParameterHigh(6, -1.);
   for(size_t i = 0; i < peakParameterLow.size(); ++i) {
      peakParameter[i]       = settings->GetDouble(Form("Peak.Parameter.%d", static_cast<int>(i)), -2.);
      peakParameterLow[i]    = settings->GetDouble(Form("Peak.Parameter.%d.Low", static_cast<int>(i)), 0.);
      peakParameterHigh[i]   = settings->GetDouble(Form("Peak.Parameter.%d.High", static_cast<int>(i)), -1.);
      bgPeakParameter[i]     = settings->GetDouble(Form("BackgroundPeak.Parameter.%d", static_cast<int>(i)), -2.);
      bgPeakParameterLow[i]  = settings->GetDouble(Form("BackgroundPeak.Parameter.%d.Low", static_cast<int>(i)), 0.);
      bgPeakParameterHigh[i] = settings->GetDouble(Form("BackgroundPeak.Parameter.%d.High", static_cast<int>(i)), -1.);
      // check that the result makes sense, i.e. if the limits are in the righ order that the parameter itself is within the limits
      // only output a warning that the parameter is changed if it's not the default value
      if(peakParameterLow[i] <= peakParameterHigh[i] && (peakParameter[i] < peakParameterLow[i] || peakParameterHigh[i] < peakParameter[i])) {
         bool output = peakParameter[i] != -2.;
         if(output) { std::cout << "Warning, " << i << ". peak parameter (" << peakParameter[i] << ") is out of range " << peakParameterLow[i] << " - " << peakParameterHigh[i] << ", resetting it to "; }
         peakParameter[i] = (peakParameterHigh[i] + peakParameterLow[i]) / 2.;
         if(output) { std::cout << peakParameter[i] << std::endl; }
      }
      if(bgPeakParameterLow[i] <= bgPeakParameterHigh[i] && (bgPeakParameter[i] < bgPeakParameterLow[i] || bgPeakParameterHigh[i] < bgPeakParameter[i])) {
         bool output = bgPeakParameter[i] != -2.;
         if(output) { std::cout << "Warning, " << i << ". background peak parameter (" << bgPeakParameter[i] << ") is out of range " << bgPeakParameterLow[i] << " - " << bgPeakParameterHigh[i] << ", resetting it to "; }
         bgPeakParameter[i] = (bgPeakParameterHigh[i] + bgPeakParameterLow[i]) / 2.;
         if(output) { std::cout << bgPeakParameter[i] << std::endl; }
      }
   }
   // parameter limits for the background (A + B*(x-o) + C*(x-o)^2)
   // same idea as above, lower limit = higher limit means fixed parameter, higher limit < lower limit means parameter not set
   std::vector<double> backgroundParameter(4, -2.);
   std::vector<double> backgroundParameterLow(4, 0.);
   std::vector<double> backgroundParameterHigh(4, -1.);
   backgroundParameter[0]     = settings->GetDouble("Background.Offset", -2.);
   backgroundParameterLow[0]  = settings->GetDouble("Background.Offset.Low", 0.);
   backgroundParameterHigh[0] = settings->GetDouble("Background.Offset.High", -1.);
   backgroundParameter[1]     = settings->GetDouble("Background.Linear", -2.);
   backgroundParameterLow[1]  = settings->GetDouble("Background.Linear.Low", 0.);
   backgroundParameterHigh[1] = settings->GetDouble("Background.Linear.High", -1.);
   backgroundParameter[2]     = settings->GetDouble("Background.Quadratic", -2.);
   backgroundParameterLow[2]  = settings->GetDouble("Background.Quadratic.Low", 0.);
   backgroundParameterHigh[2] = settings->GetDouble("Background.Quadratic.High", -1.);
   backgroundParameter[3]     = settings->GetDouble("Background.Xoffset", -2.);
   backgroundParameterLow[3]  = settings->GetDouble("Background.Xoffset.Low", 0.);
   backgroundParameterHigh[3] = settings->GetDouble("Background.Xoffset.High", -1.);
   for(size_t i = 0; i < backgroundParameter.size(); ++i) {
      if(backgroundParameterLow[i] <= backgroundParameterHigh[i] && (backgroundParameter[i] < backgroundParameterLow[i] || backgroundParameterHigh[i] < backgroundParameter[i])) {
         bool output = backgroundParameter[i] != -2.;
         if(output) { std::cout << "Warning, " << i << ". background parameter (" << backgroundParameter[i] << ") is out of range " << backgroundParameterLow[i] << " - " << backgroundParameterHigh[i] << ", resetting it to "; }
         backgroundParameter[i] = (backgroundParameterHigh[i] + backgroundParameterLow[i]) / 2.;
         if(output) { std::cout << backgroundParameter[i] << std::endl; }
      }
   }
 
   // the spins of the low, middle, and high levels, to be used for the mixing method
   // two of these need to be vectors of length one (meaning the settings file should have an entry with "name: <value>,"), the third can have a length larger than 1
   std::vector<int> twoJLow    = settings->GetIntVector("TwoJ.Low");
   std::vector<int> twoJMiddle = settings->GetIntVector("TwoJ.Middle");
   std::vector<int> twoJHigh   = settings->GetIntVector("TwoJ.High");
 
   // confidence level (this value is used to draw a line at the confidence level for the mixing method)
   double confidenceLevel = settings->GetDouble("ConfidenceLevel", 1.535);   // 1.535 is 99% confidence level for 48 degrees of freedom
 
   // check if all necessary settings have been provided
   if(projGateLow >= projGateHigh) {
      std::cerr << "Need a projection gate with a low edge that is smaller than the high edge, " << projGateLow << " >= " << projGateHigh << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   if(peakLow >= peakHigh) {
      std::cerr << "Need a fit range with a low edge that is smaller than the high edge, " << peakLow << " >= " << peakHigh << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   if(peakPos >= peakHigh || peakPos <= peakLow) {
      std::cerr << "Need a peak within the fit range, " << peakPos << " not within " << peakLow << " - " << peakHigh << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   if(timeRandomNorm <= 0.) {
      std::cerr << "Need a positive normalization factor for time random subtraction" << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   // for the background gates we checked that the low edge is below the high edge before adding them to the vector
   // so we only need to check that the vectors aren't empty (sizes should always be the same, but we check anyway)
   if(bgLow.empty() || bgLow.size() != bgHigh.size()) {
      std::cerr << "Background gate information missing, either no low/high edges or a mismatching amount of low and high edges: " << bgLow.size() << " low edges, and " << bgHigh.size() << " high edges" << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   // get the angles from the input file
   auto* angles = static_cast<TGriffinAngles*>(input.Get("GriffinAngles"));
 
   if(angles == nullptr) {
      std::cerr << "Failed to find 'GriffinAngles' in '" << inputFile << "'" << std::endl;
      std::cout << parser << std::endl;
      return 1;
   }
 
   // --------------------------------------------------------------------------------
   // Create the angular distribution.
   // --------------------------------------------------------------------------------
 
   // open output file and create graphs
   TFile output(outputFile.c_str(), "recreate");
 
   auto* rawAngularDistribution = new TGraphErrors(angles->NumberOfAngles());
   rawAngularDistribution->SetName("RawAngularDistribution");
   auto* angularDistribution = new TGraphErrors(angles->NumberOfAngles());
   angularDistribution->SetName("AngularDistribution");
   auto* mixedAngularDistribution = new TGraphErrors(angles->NumberOfAngles());
   mixedAngularDistribution->SetName("MixedAngularDistribution");
   auto* rawChiSquares = new TGraph(angles->NumberOfAngles());
   rawChiSquares->SetName("RawChiSquares");
   auto* mixedChiSquares = new TGraph(angles->NumberOfAngles());
   mixedChiSquares->SetName("MixedChiSquares");
 
   // write the user settings to the output file
   settings->Write();
 
#if ROOT_VERSION_CODE >= ROOT_VERSION(6, 20, 0)
   auto* fitDir = output.mkdir("fits", "Projections with fits", true);
#else
   auto* fitDir = output.mkdir("fits", "Projections with fits");
#endif
 
   // loop over all matrices
   for(int i = 0; i < angles->NumberOfAngles(); ++i) {
      // get the three histograms we need: prompt, time random, and event mixed
      auto* prompt = static_cast<TH2*>(input.Get(Form("AngularCorrelation%d", i)));
      if(prompt == nullptr) {
         std::cerr << "Failed to find histogram '" << Form("AngularCorrelation%d", i) << "', should have " << angles->NumberOfAngles() << " angles in total!" << std::endl;
         std::cout << parser << std::endl;
         return 1;
      }
      auto* bg = static_cast<TH2*>(input.Get(Form("AngularCorrelationBG%d", i)));
      if(bg == nullptr) {
         std::cerr << "Failed to find histogram '" << Form("AngularCorrelationBG%d", i) << "', should have " << angles->NumberOfAngles() << " angles in total!" << std::endl;
         std::cout << parser << std::endl;
         return 1;
      }
      auto* mixed = static_cast<TH2*>(input.Get(Form("AngularCorrelationMixed%d", i)));
      if(mixed == nullptr) {
         std::cerr << "Failed to find histogram '" << Form("AngularCorrelationMixed%d", i) << "', should have " << angles->NumberOfAngles() << " angles in total!" << std::endl;
         std::cout << parser << std::endl;
         return 1;
      }
 
      // first subtract time random background from prompt data and enable proper error propagation for prompt and mixed data
      prompt->Sumw2();
      prompt->Add(bg, -timeRandomNorm);
      mixed->Sumw2();
 
      // project onto x-axis
      auto* proj      = prompt->ProjectionX(Form("proj%d", i), prompt->GetYaxis()->FindBin(projGateLow), prompt->GetYaxis()->FindBin(projGateHigh));
      auto* projMixed = mixed->ProjectionX(Form("projMixed%d", i), mixed->GetYaxis()->FindBin(projGateLow), mixed->GetYaxis()->FindBin(projGateHigh));
      // project background gate(s) onto x-axis
      auto*  projBg           = prompt->ProjectionX(Form("projBg%d", i), prompt->GetYaxis()->FindBin(bgLow[0]), prompt->GetYaxis()->FindBin(bgHigh[0]));
      auto*  projMixedBg      = mixed->ProjectionX(Form("projMixedBg%d", i), mixed->GetYaxis()->FindBin(bgLow[0]), mixed->GetYaxis()->FindBin(bgHigh[0]));
      double bgGateWidth      = prompt->GetYaxis()->FindBin(bgHigh[0]) - prompt->GetYaxis()->FindBin(bgLow[0]) + 1;
      double mixedBgGateWidth = mixed->GetYaxis()->FindBin(bgHigh[0]) - mixed->GetYaxis()->FindBin(bgLow[0]) + 1;
      for(size_t g = 1; g < bgLow.size(); ++g) {
         projBg->Add(prompt->ProjectionX(Form("projBg%d", i), prompt->GetYaxis()->FindBin(bgLow[g]), prompt->GetYaxis()->FindBin(bgHigh[g])));
         projMixedBg->Add(mixed->ProjectionX(Form("projMixedBg%d", i), mixed->GetYaxis()->FindBin(bgLow[g]), mixed->GetYaxis()->FindBin(bgHigh[g])));
         bgGateWidth += prompt->GetYaxis()->FindBin(bgHigh[g]) - prompt->GetYaxis()->FindBin(bgLow[g]) + 1;
         mixedBgGateWidth += mixed->GetYaxis()->FindBin(bgHigh[g]) - mixed->GetYaxis()->FindBin(bgLow[g]) + 1;
      }
 
      // subtract background gate (+1 because the projection includes first and last bin)
      proj->Add(projBg, -(prompt->GetYaxis()->FindBin(projGateHigh) - prompt->GetYaxis()->FindBin(projGateLow) + 1) / bgGateWidth);
      projMixed->Add(projMixedBg, -(mixed->GetYaxis()->FindBin(projGateHigh) - mixed->GetYaxis()->FindBin(projGateLow) + 1) / mixedBgGateWidth);
 
      // fit the projections, we create separate peak fitters and peaks for the prompt and mixed histograms
      TPeakFitter pf(peakLow, peakHigh);
      TRWPeak     peak(peakPos);
      for(size_t p = 0; p < peakParameterLow.size(); ++p) {
         if(peakParameterLow[p] == peakParameterHigh[p]) {
            peak.GetFitFunction()->FixParameter(p, peakParameter[p]);
         } else if(peakParameterLow[p] < peakParameterHigh[p]) {
            peak.GetFitFunction()->SetParameter(p, peakParameter[p]);
            peak.GetFitFunction()->SetParLimits(p, peakParameterLow[p], peakParameterHigh[p]);
         }
      }
      pf.AddPeak(&peak);
      for(auto bgPeak : bgPeakPos) {
         auto* bgP = new TRWPeak(bgPeak);
         for(size_t p = 0; p < bgPeakParameterLow.size(); ++p) {
            if(bgPeakParameterLow[p] == bgPeakParameterHigh[p]) {
               bgP->GetFitFunction()->FixParameter(p, bgPeakParameter[p]);
            } else if(bgPeakParameterLow[p] < bgPeakParameterHigh[p]) {
               bgP->GetFitFunction()->SetParameter(p, bgPeakParameter[p]);
               bgP->GetFitFunction()->SetParLimits(p, bgPeakParameterLow[p], bgPeakParameterHigh[p]);
            }
         }
         pf.AddPeak(bgP);
      }
      for(size_t p = 0; p < backgroundParameterLow.size(); ++p) {
         if(backgroundParameterLow[p] == backgroundParameterHigh[p]) {
            pf.GetBackground()->FixParameter(p, backgroundParameter[p]);
         } else if(backgroundParameterLow[p] < backgroundParameterHigh[p]) {
            pf.GetBackground()->SetParameter(p, backgroundParameter[p]);
            pf.GetBackground()->SetParLimits(p, backgroundParameterLow[p], backgroundParameterHigh[p]);
         }
      }
      pf.Fit(proj, "qretryfit");
 
      TPeakFitter pfMixed(peakLow, peakHigh);
      TRWPeak     peakMixed(peakPos);
      for(size_t p = 0; p < peakParameterLow.size(); ++p) {
         if(peakParameterLow[p] == peakParameterHigh[p]) {
            peakMixed.GetFitFunction()->FixParameter(p, peakParameter[p]);
         } else if(peakParameterLow[p] < peakParameterHigh[p]) {
            peakMixed.GetFitFunction()->SetParameter(p, peakParameter[p]);
            peakMixed.GetFitFunction()->SetParLimits(p, peakParameterLow[p], peakParameterHigh[p]);
         }
      }
      pfMixed.AddPeak(&peakMixed);
      for(auto bgPeak : bgPeakPos) {
         auto* bgP = new TRWPeak(bgPeak);
         for(size_t p = 0; p < bgPeakParameterLow.size(); ++p) {
            if(bgPeakParameterLow[p] == bgPeakParameterHigh[p]) {
               bgP->GetFitFunction()->FixParameter(p, bgPeakParameter[p]);
            } else if(bgPeakParameterLow[p] < bgPeakParameterHigh[p]) {
               bgP->GetFitFunction()->SetParameter(p, bgPeakParameter[p]);
               bgP->GetFitFunction()->SetParLimits(p, bgPeakParameterLow[p], bgPeakParameterHigh[p]);
            }
         }
         pfMixed.AddPeak(bgP);
      }
      for(size_t p = 0; p < backgroundParameterLow.size(); ++p) {
         if(backgroundParameterLow[p] == backgroundParameterHigh[p]) {
            pfMixed.GetBackground()->FixParameter(p, backgroundParameter[p]);
         } else if(backgroundParameterLow[p] < backgroundParameterHigh[p]) {
            pfMixed.GetBackground()->SetParameter(p, backgroundParameter[p]);
            pfMixed.GetBackground()->SetParLimits(p, backgroundParameterLow[p], backgroundParameterHigh[p]);
         }
      }
      pfMixed.Fit(projMixed, "qretryfit");
 
      // save the fitted histograms to the output file and the areas of the peaks
      fitDir->cd();
      proj->Write();
      projMixed->Write();
 
      // TODO: set an error for the angles?
      rawAngularDistribution->SetPoint(i, angles->AverageAngle(i), peak.Area());
      rawAngularDistribution->SetPointError(i, 0., peak.AreaErr());
      mixedAngularDistribution->SetPoint(i, angles->AverageAngle(i), peakMixed.Area());
      mixedAngularDistribution->SetPointError(i, 0., peakMixed.AreaErr());
      angularDistribution->SetPoint(i, angles->AverageAngle(i), peak.Area() / peakMixed.Area());
      angularDistribution->SetPointError(i, 0., peak.Area() / peakMixed.Area() * TMath::Sqrt(TMath::Power(peak.AreaErr() / peak.Area(), 2) + TMath::Power(peakMixed.AreaErr() / peakMixed.Area(), 2)));
 
      rawChiSquares->SetPoint(i, angles->AverageAngle(i), peak.GetReducedChi2());
      mixedChiSquares->SetPoint(i, angles->AverageAngle(i), peakMixed.GetReducedChi2());
 
      std::cout << std::setw(3) << i << " of " << angles->NumberOfAngles() << " done\r" << std::flush;
   }
   std::cout << "Fitting of projections done." << std::endl;
 
   // correct the raw and mixed graphs for the number of combinations that contribute to each angle
   auto* rawAngularDistributionCorr = static_cast<TGraphErrors*>(rawAngularDistribution->Clone("RawAngularDistributionCorrected"));
   for(int i = 0; i < rawAngularDistributionCorr->GetN(); ++i) {
#if ROOT_VERSION_CODE >= ROOT_VERSION(6, 20, 0)
      if(angles->Count(rawAngularDistributionCorr->GetPointX(i)) != 0) {
         rawAngularDistributionCorr->SetPointY(i, rawAngularDistributionCorr->GetPointY(i) / angles->Count(rawAngularDistributionCorr->GetPointX(i)));
         rawAngularDistributionCorr->SetPointError(i, rawAngularDistributionCorr->GetErrorX(i), rawAngularDistributionCorr->GetErrorY(i) / angles->Count(rawAngularDistributionCorr->GetPointX(i)));
      } else {
         rawAngularDistributionCorr->SetPointY(i, 0);
      }
#else
      double px, py;
      rawAngularDistributionCorr->GetPoint(i, px, py);
      if(angles->Count(px) != 0) {
         rawAngularDistributionCorr->SetPoint(i, px, py / angles->Count(px));
         rawAngularDistributionCorr->SetPointError(i, rawAngularDistributionCorr->GetErrorX(i), rawAngularDistributionCorr->GetErrorY(i) / angles->Count(px));
      } else {
         rawAngularDistributionCorr->SetPoint(i, px, 0);
      }
#endif
   }
   auto* mixedAngularDistributionCorr = static_cast<TGraphErrors*>(mixedAngularDistribution->Clone("MixedAngularDistributionCorrected"));
   for(int i = 0; i < mixedAngularDistributionCorr->GetN(); ++i) {
#if ROOT_VERSION_CODE >= ROOT_VERSION(6, 20, 0)
      if(angles->Count(mixedAngularDistributionCorr->GetPointX(i)) != 0) {
         mixedAngularDistributionCorr->SetPointY(i, mixedAngularDistributionCorr->GetPointY(i) / angles->Count(mixedAngularDistributionCorr->GetPointX(i)));
         mixedAngularDistributionCorr->SetPointError(i, mixedAngularDistributionCorr->GetErrorX(i), mixedAngularDistributionCorr->GetErrorY(i) / angles->Count(mixedAngularDistributionCorr->GetPointX(i)));
      } else {
         mixedAngularDistributionCorr->SetPointY(i, 0);
      }
#else
      double px, py;
      mixedAngularDistributionCorr->GetPoint(i, px, py);
      if(angles->Count(px) != 0) {
         mixedAngularDistributionCorr->SetPoint(i, px, py / angles->Count(px));
         mixedAngularDistributionCorr->SetPointError(i, mixedAngularDistributionCorr->GetErrorX(i), mixedAngularDistributionCorr->GetErrorY(i) / angles->Count(px));
      } else {
         mixedAngularDistributionCorr->SetPoint(i, px, 0);
      }
#endif
   }
 
   // --------------------------------------------------------------------------------
   // If a theory/simulation file has been provided, we use the a2/a4 and mixing
   // methods to fit the angular distribution.
   // --------------------------------------------------------------------------------
 
   // check if we have a theory file
   if(!theoryFile.empty()) {
      TFile theory(theoryFile.c_str());
 
      if(theory.IsOpen()) {
         // read graphs from file
         auto* z0 = static_cast<TGraphErrors*>(theory.Get("graph000"));
         auto* z2 = static_cast<TGraphErrors*>(theory.Get("graph010"));
         auto* z4 = static_cast<TGraphErrors*>(theory.Get("graph100"));
 
         if(z0 != nullptr && z2 != nullptr && z4 != nullptr && z0->GetN() == z2->GetN() && z0->GetN() == z4->GetN()) {
            // check if the sizes of the provided graphs match what we expect:
            // if we have folding and grouping enabled we either need the graphs to match the angular distribution or have a size of either 51 (singles) or 49 (addback)
            // otherwise the size needs to match the angular distribution
            if(z0->GetN() == angularDistribution->GetN() || ((angles->Grouping() || angles->Folding()) && z0->GetN() == (angles->Addback() ? 49 : 51))) {
               // if the theory needs to be folded and/or grouped, do so now
               if(z0->GetN() != angularDistribution->GetN()) {
                  angles->FoldOrGroup(z0, z2, z4);
               }
               // calculate chi2 vs mixing graphs
               std::vector<TGraph*>             spin;
               std::vector<double>              spinLabel;
               std::vector<std::vector<double>> parameters;
               // first check which of the vectors we iterate over
               if(twoJLow.size() > 1 && twoJMiddle.size() == 1 && twoJHigh.size() == 1) {
                  for(auto twoJ : twoJLow) {
                     parameters.emplace_back();
                     spin.push_back(MixingMethod(angularDistribution, z0, z2, z4, twoJHigh.at(0), twoJMiddle.at(0), twoJ, parameters.back()));
                     spinLabel.push_back(twoJ / 2.);
                  }
               } else if(twoJLow.size() == 1 && twoJMiddle.size() > 1 && twoJHigh.size() == 1) {
                  for(auto twoJ : twoJMiddle) {
                     parameters.emplace_back();
                     spin.push_back(MixingMethod(angularDistribution, z0, z2, z4, twoJHigh.at(0), twoJ, twoJLow.at(0), parameters.back()));
                     spinLabel.push_back(twoJ / 2.);
                  }
               } else if(twoJLow.size() == 1 && twoJMiddle.size() == 1 && twoJHigh.size() > 1) {
                  for(auto twoJ : twoJHigh) {
                     parameters.emplace_back();
                     spin.push_back(MixingMethod(angularDistribution, z0, z2, z4, twoJ, twoJMiddle.at(0), twoJLow.at(0), parameters.back()));
                     spinLabel.push_back(twoJ / 2.);
                  }
               } else {
                  parameters.emplace_back();
                  spin.push_back(MixingMethod(angularDistribution, z0, z2, z4, twoJHigh.at(0), twoJMiddle.at(0), twoJLow.at(0), parameters.back()));
                  spinLabel.push_back(twoJHigh.at(0) / 2.);
               }
 
               // create canvas and plot graphs on it
               auto* canvas = new TCanvas;
 
               // determine minimum and maximum y-value
               double min = TMath::MinElement(spin.at(0)->GetN(), spin.at(0)->GetY());
               double max = TMath::MaxElement(spin.at(0)->GetN(), spin.at(0)->GetY());
               for(size_t i = 1; i < spin.size(); ++i) {
                  min = TMath::Min(min, TMath::MinElement(spin.at(i)->GetN(), spin.at(i)->GetY()));
                  max = TMath::Max(max, TMath::MaxElement(spin.at(i)->GetN(), spin.at(i)->GetY()));
               }
               min = TMath::Min(min, confidenceLevel);
 
               // find first graph with more than one data point
               size_t first = 0;
               for(first = 0; first < spin.size(); ++first) {
                  if(spin[first]->GetN() > 1) { break; }
               }
 
               spin[first]->SetTitle("");
               spin[first]->SetMinimum(0.9 * min);
               spin[first]->SetMaximum(1.1 * max);
 
               for(size_t i = 0; i < spin.size(); ++i) {
                  spin[i]->SetLineColor(i + 1);
                  spin[i]->SetMarkerColor(i + 1);
                  spin[i]->SetLineWidth(2);
               }
 
               spin[first]->Draw("ac");
               for(size_t i = 0; i < spin.size(); ++i) {
                  if(i == first) { continue; }
                  if(spin[i]->GetN() > 1) {
                     spin[i]->Draw("c");
                  } else {
                     spin[i]->Draw("*");
                  }
               }
 
               auto* confidenceLevelLine = new TLine(-1.5, confidenceLevel, 1.5, confidenceLevel);
 
               confidenceLevelLine->Draw();
 
#if ROOT_VERSION_CODE >= ROOT_VERSION(6, 20, 0)
               auto* legend = new TLegend(0.1, 0.3);
#else
               auto* legend = new TLegend(0.7, 0.6, 0.8, 0.9);
#endif
               for(size_t i = 0; i < spin.size(); ++i) {
                  if(spin[i]->GetN() == 1) {
                     legend->AddEntry(spin[i], Form("J = %.1f", spinLabel[i]), "p");
                  } else {
                     legend->AddEntry(spin[i], Form("J = %.1f", spinLabel[i]), "l");
                  }
               }
 
               legend->Draw();
 
               canvas->SetLogy();
 
               spin[first]->GetHistogram()->GetXaxis()->SetRangeUser(-1.5, 1.5);
               spin[first]->GetHistogram()->GetXaxis()->SetTitle("atan(#delta) [rad]");
               spin[first]->GetHistogram()->GetXaxis()->CenterTitle();
               spin[first]->GetHistogram()->GetYaxis()->SetTitle("red. #chi^{2}");
               spin[first]->GetHistogram()->GetYaxis()->CenterTitle();
 
               // write graphs and canvas to output file
               output.cd();
               z0->Write("graph000");
               z2->Write("graph010");
               z4->Write("graph100");
               for(size_t i = 0; i < spin.size(); ++i) {
                  spin[i]->Write(Form("spin%d", static_cast<int>(i)));
               }
               canvas->Write("MixingCanvas");
 
               // create theory graphs with best fit for each spin, and write them to file
               std::vector<TGraphErrors*> spinFit(spin.size(), new TGraphErrors(angularDistribution->GetN()));
               auto*                      x = angularDistribution->GetX();
               for(int p = 0; p < angularDistribution->GetN(); ++p) {
                  for(size_t i = 0; i < spinFit.size(); ++i) {
                     spinFit[i]->SetPoint(p, x[p], parameters[i][0] * ((1. - parameters[i][1] - parameters[i][2]) * z0->Eval(x[p]) + parameters[i][1] * z2->Eval(x[p]) + parameters[i][2] * z4->Eval(x[p])));
                     spinFit[i]->SetPointError(p, 0., TMath::Sqrt(parameters[i][0] * (TMath::Power((1. - parameters[i][1] - parameters[i][2]) * GetYError(z0, x[p]), 2) + TMath::Power(parameters[i][1] * GetYError(z2, x[p]), 2) + TMath::Power(parameters[i][2] * GetYError(z4, x[p]), 2))));
                  }
               }
               for(size_t i = 0; i < spin.size(); ++i) {
                  spinFit[i]->SetLineColor(i + 1);
                  spinFit[i]->SetMarkerColor(i + 1);
                  spinFit[i]->SetLineWidth(2);
                  spinFit[i]->Write(Form("SpinFit%d", static_cast<int>(i)));
                  output.WriteObject(&parameters[i], Form("Parameters%d", static_cast<int>(i)));
               }
 
               auto a2a4Parameters = A2a4Method(angularDistribution, z0, z2, z4);
               output.WriteObject(&a2a4Parameters, "ParametersA2a4Fit");
            } else {   // if(z0->GetN() == angularDistribution->GetN() || ((angles->Grouping() || angles->Folding) && z0->GetN() == (angles->Addback() ? 49:51)))
               std::cerr << "Mismatch between theory and data (" << z0->GetN() << " != " << angularDistribution->GetN() << "?) or neither grouping and folding are enabled (" << std::boolalpha << angles->Grouping() << ", " << angles->Folding() << ") or the ungrouped/folded theory doesn't match the required size for " << (angles->Addback() ? "addback" : "singles") << " data (" << (angles->Addback() ? 49 : 51) << ")" << std::endl;
            }
         } else {   // if(z0 != nullptr && z2 != nullptr && z4 != nullptr && z0->GetN() == z2->GetN() && z0->GetN() == z4->GetN())
            std::cerr << "Failed to find z0 (" << z0 << ", \"graph000\"), z2 (" << z2 << ", \"graph010\"), or z4 (" << z4 << ", \"graph100\") in " << theoryFile << ", or they had mismatched sizes" << std::endl;
         }
      } else {   // if(theory.IsOpen())
         std::cerr << "Failed to open " << theoryFile << std::endl;
      }
   } else {   // if(!theoryFile.empty())
      std::cout << "No file with simulation results (--theory flag), so we won't produce chi2 vs mixing angle plot." << std::endl;
   }
   output.cd();
 
   rawAngularDistribution->Write();
   angularDistribution->Write();
   mixedAngularDistribution->Write();
   rawChiSquares->Write();
   mixedChiSquares->Write();
   rawAngularDistributionCorr->Write();
   mixedAngularDistributionCorr->Write();
 
   angles->Write();
 
   output.Close();
   input.Close();
 
   return 0;
}
 
class Ac {
public:
   explicit Ac(TGraphErrors* data = nullptr, TGraphErrors* z0 = nullptr, TGraphErrors* z2 = nullptr, TGraphErrors* z4 = nullptr)
      : fData(data), fZ0(z0), fZ2(z2), fZ4(z4) {}
 
   void Data(TGraphErrors* data) { fData = data; }
   void Z0(TGraphErrors* z0) { fZ0 = z0; }
   void Z2(TGraphErrors* z2) { fZ2 = z2; }
   void Z4(TGraphErrors* z4) { fZ4 = z4; }
   void SetZ(TGraphErrors* z0, TGraphErrors* z2, TGraphErrors* z4)
   {
      fZ0 = z0;
      fZ2 = z2;
      fZ4 = z4;
   }
 
   int Np() { return fData->GetN(); }
 
   double operator()(const double* p)
   {
      double chi2 = 0;
      for(int point = 0; point < fData->GetN(); ++point) {
         // get data values
         double x = 0.;
         double y = 0.;
         fData->GetPoint(point, x, y);
         double yError = fData->GetErrorY(point);
 
         // get simulation values
         // for the y-values we can use Eval (uses linear interpolation)
         double functionValue = p[0] * ((1. - p[1] - p[2]) * fZ0->Eval(x) + p[1] * fZ2->Eval(x) + p[2] * fZ4->Eval(x));
         // for the y uncertainties we need to find the index (for each graph)
         double errorSquare = TMath::Power(yError, 2) + TMath::Power(p[0], 2) * (TMath::Power((1. - p[1] - p[2]) * GetYError(fZ0, x), 2) + TMath::Power(p[1] * GetYError(fZ2, x), 2) + TMath::Power(p[2] * GetYError(fZ4, x), 2));
 
         // calculate chi^2
         chi2 += TMath::Power((y - functionValue), 2) / errorSquare;
      }
      return chi2;
   }
 
private:
   TGraphErrors* fData{nullptr};
   TGraphErrors* fZ0{nullptr};
   TGraphErrors* fZ2{nullptr};
   TGraphErrors* fZ4{nullptr};
};
 
TGraph* MixingMethod(TGraphErrors* data, TGraphErrors* z0, TGraphErrors* z2, TGraphErrors* z4, int twoJhigh, int twoJmid, int twoJlow, std::vector<double>& bestParameters)
{
   TGraph*           result = nullptr;
   Ac                ac(data, z0, z2, z4);
   ROOT::Fit::Fitter fitter;
   int               nPar = 3;
   fitter.SetFCN(nPar, ac);
   for(int i = 0; i < nPar; ++i) {
      // parameter settings arguments are parameter name, initial value, step size, minimum, and maximum
      fitter.Config().ParSettings(i) = ROOT::Fit::ParameterSettings(Form("a_{%d}", 2 * i), 0.5, 0.0001, -10., 10.);
   }
   fitter.Config().MinimizerOptions().SetPrintLevel(0);
   fitter.Config().SetMinimizer("Minuit2", "Migrad");   // or simplex?
 
   // j1 is the spin of the highest level
   // j2 is the spin of the middle level
   // j3 is the spin of the bottom level
   double j1 = 0.5 * twoJhigh;
   double j2 = 0.5 * twoJmid;
   double j3 = 0.5 * twoJlow;
   // l1 is the transition between j1 and j2
   // a is the lowest allowed spin
   // b is the mixing spin
   int l1a = TMath::Abs(twoJhigh - twoJmid) / 2;
   if(l1a == 0) { l1a = 1; }
   int l1b = l1a + 1;
   // l2 is the transition between j2 and j3
   // a is the lowest allowed spin
   // b is the mixing spin
   int l2a = TMath::Abs(twoJmid - twoJlow) / 2;
   if(l2a == 0) { l2a = 1; }
   int l2b = l2a + 1;
 
   // run some quick checks on the mixing ratios
   if((twoJhigh == 0 && twoJmid == 0) || (twoJmid == 0 && twoJlow == 0)) {
      std::cout << "!!!!!!!!!!!!!!! ERROR !!!!!!!!!!!!!!!" << std::endl
                << "Can't have gamma transition between J=0 states (high " << twoJhigh << ", mid " << twoJmid << ", low " << twoJlow << ")." << std::endl
                << "Aborting..." << std::endl;
      return result;
   }
   if(l1a == TMath::Abs(twoJhigh + twoJmid) / 2) {
      //std::cout<<"!!!!!!!!!!!!!!! ALERT !!!!!!!!!!!!!!!"<<std::endl
      // <<"Only one angular momentum allowed for high->middle transition (l1a "<<l1a<<" == "<<TMath::Abs(twoJhigh+twoJmid)/2<<")."<<std::endl
      // <<"That mixing ratio (delta1) will be fixed at zero."<<std::endl
      // <<"!!!!!!!!!!!!! END ALERT !!!!!!!!!!!!!"<<std::endl;
      l1b = l1a;
   }
   if(l2a == TMath::Abs(twoJmid + twoJlow) / 2) {
      //std::cout<<"!!!!!!!!!!!!!!! ALERT !!!!!!!!!!!!!!!"<<std::endl
      // <<"Only one angular momentum allowed for middle->low transition (l2a "<<l2a<<" == "<<TMath::Abs(twoJmid+twoJlow)/2<<")."<<std::endl
      // <<"That mixing ratio (delta2) will be fixed at zero."<<std::endl
      // <<"!!!!!!!!!!!!! END ALERT !!!!!!!!!!!!!"<<std::endl;
      l2b = l2a;
   }
 
   // -------------------------------------------------------------------//
   //                       Constrained fitting
   // -------------------------------------------------------------------//
   // The basic idea is to select a particular set of physical quantities,
   // calculate a2/a4, fix the a2/a4 parameters, and fit the scaling
   // factor a0. Then output the specifications for that set of physical
   // quantities and the chi^2 for further analysis.
   // -------------------------------------------------------------------//
 
   // delta runs from -infinity to infinity (unless constrained by known physics)
   // in this case, it then makes more sense to sample evenly from tan^{-1}(delta)
   // The next few lines are where you may want to include limits to significantly speed up calculations
   // mixing for the high-middle transition
   double mixingAngle1Minimum = -TMath::Pi() / 2;
   double mixingAngle1Maximum = TMath::Pi() / 2;
   int    steps1              = 100;
   double stepSize1           = (mixingAngle1Maximum - mixingAngle1Minimum) / steps1;
   // mixing for the middle-low transition
   double mixingAngle2Minimum = -TMath::Pi() / 2;
   double mixingAngle2Maximum = TMath::Pi() / 2;
   int    steps2              = 100;
   double stepSize2           = (mixingAngle2Maximum - mixingAngle2Minimum) / steps2;
 
   // if appropriate, constrain the delta values
   if(l1a == l1b) {
      mixingAngle1Minimum = 0;
      steps1              = 1;
   }
   if(l2a == l2b) {
      mixingAngle2Minimum = 0;
      steps2              = 1;
   }
 
   result         = new TGraph(steps1);
   double minChi2 = 1e6;
   for(int i = 0; i < steps1; i++) {
      double mixangle1 = mixingAngle1Minimum + i * stepSize1;
      double delta1    = TMath::Tan(mixangle1);
      for(int j = 0; j < steps2; j++) {
         double mixangle2 = mixingAngle2Minimum + j * stepSize2;
         double delta2    = TMath::Tan(mixangle2);
         // calculate a2
         double a2 = TGRSIFunctions::CalculateA2(j1, j2, j3, l1a, l1b, l2a, l2b, delta1, delta2);
         // fix a2
         fitter.Config().ParSettings(1).Set("a_{2}", a2);
         fitter.Config().ParSettings(1).Fix();
         // calculate a4
         double a4 = TGRSIFunctions::CalculateA4(j1, j2, j3, l1a, l1b, l2a, l2b, delta1, delta2);
         // fix a4
         fitter.Config().ParSettings(2).Set("a_{4}", a4);
         fitter.Config().ParSettings(2).Fix();
         if(!fitter.FitFCN()) {
            std::cerr << i << ", " << j << ": Fit failed using a2 " << a2 << ", a4 " << a4 << std::endl;
            continue;
         }
         auto fitResult = fitter.Result();
         // MinFcnValue() is the minimum chi2, ac.Np gives the number of data points
         result->SetPoint(i, mixangle1, fitResult.MinFcnValue() / (ac.Np() - fitResult.NFreeParameters()));
         if(fitResult.MinFcnValue() / (ac.Np() - fitResult.NFreeParameters()) < minChi2) {
            minChi2 = fitResult.MinFcnValue() / (ac.Np() - fitResult.NFreeParameters());
            bestParameters.assign(fitResult.GetParams(), fitResult.GetParams() + nPar);
         }
      }
   }
   return result;
}
 
std::vector<double> A2a4Method(TGraphErrors* data, TGraphErrors* z0, TGraphErrors* z2, TGraphErrors* z4)
{
   assert(data->GetN() == z0->GetN());
   assert(data->GetN() == z2->GetN());
   assert(data->GetN() == z4->GetN());
   // create a copy of the data with cos(theta) as x-axis and fit it with a legenre polynomial
   // this is to get initial conditions for our fit
   auto* cosTheta = new TGraphErrors(*data);
   auto* x        = cosTheta->GetX();
   for(int i = 0; i < cosTheta->GetN(); ++i) {
      x[i] = TMath::Cos(x[i] / 180. * TMath::Pi());
   }
 
   int   nPar     = 3;
   auto* legendre = new TF1("legendre", TGRSIFunctions::LegendrePolynomial, -1., 1., nPar);
   legendre->SetParNames("a_{0}", "a_{2}", "a_{4}");
   legendre->SetParameters(1., 0.5, 0.5);
   cosTheta->Fit(legendre, "QN0");
 
   // the actual fitting
   Ac                ac(data, z0, z2, z4);
   ROOT::Fit::Fitter fitter;
   fitter.SetFCN(nPar, ac);
   // this is a good guess for the initial scale
   fitter.Config().ParSettings(0) = ROOT::Fit::ParameterSettings("a_{0}", data->GetMaximum() / z0->GetMaximum(), 0.0001, -10., 10.);
   for(int i = 1; i < nPar; ++i) {
      // parameter settings arguments are parameter name, initial value, step size, minimum, and maximum
      fitter.Config().ParSettings(i) = ROOT::Fit::ParameterSettings(Form("a_{%d}", 2 * i), legendre->GetParameter(i), 0.0001, -10., 10.);
   }
   fitter.Config().MinimizerOptions().SetPrintLevel(0);
   fitter.Config().SetMinimizer("Minuit2", "Migrad");   // or simplex?
   if(!fitter.FitFCN()) {
      std::cerr << "Fit failed" << std::endl;
      return {};
   }
 
   // get the fit result and print chi^2 and parameters
   auto fitResult = fitter.Result();
   // MinFcnValue() is the minimum chi2, ac.Np gives the number of data points
   std::cout << "Reduced chi^2: " << fitResult.MinFcnValue() / (ac.Np() - fitResult.NFreeParameters()) << std::endl;
   std::vector<double> parameters(fitResult.GetParams(), fitResult.GetParams() + nPar);
   const auto*         errors = fitResult.GetErrors();
   std::cout << "Parameters a_0: " << parameters[0] << " +- " << errors[0] << ", a_2: " << parameters[1] << " +- " << errors[1] << ", a_4: " << parameters[2] << " +- " << errors[2] << std::endl;
   TMatrixD covariance(nPar, nPar);
   fitResult.GetCovarianceMatrix(covariance);
 
   // create fit and residual graphs
   auto* fit = static_cast<TGraphErrors*>(z0->Clone("fit"));
   for(int i = 0; i < fit->GetN(); ++i) {
#if ROOT_VERSION_CODE >= ROOT_VERSION(6, 20, 0)
      fit->SetPointY(i, parameters[0] * ((1. - parameters[1] - parameters[2]) * z0->GetPointY(i) + parameters[1] * z2->GetPointY(i) + parameters[2] * z4->GetPointY(i)));
#else
      fit->SetPoint(i, fit->GetX()[i], parameters[0] * ((1. - parameters[1] - parameters[2]) * z0->GetY()[i] + parameters[1] * z2->GetY()[i] + parameters[2] * z4->GetY()[i]));
#endif
      fit->SetPointError(i, 0., std::abs(parameters[0]) * TMath::Sqrt(TMath::Power((1. - parameters[1] - parameters[2]) * z0->GetErrorY(i), 2) + TMath::Power(parameters[1] * z2->GetErrorY(i), 2) + TMath::Power(parameters[2] * z4->GetErrorY(i), 2)));
   }
 
   auto* residual = static_cast<TGraphErrors*>(data->Clone("residual"));
   for(int i = 0; i < residual->GetN(); ++i) {
#if ROOT_VERSION_CODE >= ROOT_VERSION(6, 20, 0)
      residual->SetPointY(i, data->GetPointY(i) - fit->GetPointY(i));
#else
      residual->SetPoint(i, residual->GetX()[i], data->GetY()[i] - fit->GetY()[i]);
#endif
      residual->SetPointError(i, 0., TMath::Sqrt(TMath::Power(data->GetErrorY(i), 2) + TMath::Power(fit->GetErrorY(i), 2)));
   }
 
   // This text box will display the fit statistics
   // with the margins of the pads, the center in x is at 0.55 (0.545 to be exact_, so we center around that point
   auto* stats = new TPaveText(0.35, 0.7, 0.75, 0.95, "NDC");
   stats->SetTextFont(133);
   stats->SetTextSize(20);
   stats->SetFillStyle(0);
   stats->SetBorderSize(0);
   stats->AddText(Form("a_{2} = %f #pm %f", parameters[1], errors[1]));
   stats->AddText(Form("a_{4} = %f #pm %f", parameters[2], errors[2]));
   stats->AddText(Form("#chi^{2}/NDF = %.2f", fitResult.MinFcnValue() / (ac.Np() - fitResult.NFreeParameters())));
 
   // create canvas and two pads (big one for comparison and small one for residuals)
   // wider left margin for y-axis labels and title
   // same for the bottom margin of the residuals
   auto* canvas = new TCanvas;
   auto* resPad = new TPad("resPad", "resPad", 0., 0., 1., 0.3);
   resPad->SetTopMargin(0.);
   resPad->SetBottomMargin(0.22);
   resPad->SetLeftMargin(0.1);
   resPad->SetRightMargin(0.01);
   resPad->Draw();
   auto* compPad = new TPad("compPad", "compPad", 0., 0.3, 1., 1.);
   compPad->SetTopMargin(0.01);
   compPad->SetBottomMargin(0.);
   compPad->SetLeftMargin(0.1);
   compPad->SetRightMargin(0.01);
   compPad->Draw();
 
   // plot comparison of fit and data
   compPad->cd();
 
   auto* multiGraph = new TMultiGraph;
   fit->SetLineColor(kRed);
   fit->SetFillColor(kRed);
   fit->SetMarkerColor(kRed);
   multiGraph->Add(fit, "l3");   // 3 = filled contour between upper and lower error bars
   data->SetMarkerStyle(kFullDotLarge);
   multiGraph->Add(data, "p");
 
   multiGraph->SetTitle(";;Normalized Counts;");
 
   multiGraph->GetXaxis()->SetRangeUser(0., 180.);
 
   multiGraph->GetYaxis()->CenterTitle();
   multiGraph->GetYaxis()->SetTitleSize(0.05);
   multiGraph->GetYaxis()->SetTitleOffset(1.);
 
   multiGraph->Draw("a");
 
   stats->Draw();
 
   // plot residuals
   resPad->cd();
   residual->SetTitle(";#vartheta [^{o}];Residual");
 
   residual->GetXaxis()->CenterTitle();
   residual->GetXaxis()->SetTitleSize(0.1);
   residual->GetXaxis()->SetLabelSize(0.1);
   residual->GetXaxis()->SetRangeUser(0., 180.);
 
   residual->GetYaxis()->CenterTitle();
   residual->GetYaxis()->SetTitleSize(0.1);
   residual->GetYaxis()->SetTitleOffset(0.5);
   residual->GetYaxis()->SetLabelSize(0.08);
 
   residual->Draw("ap");
   auto* zeroLine = new TLine(0., 0., 180., 0.);
   zeroLine->Draw("same");
 
   fit->Write("A2a4Fit");
   residual->Write("Residual");
   multiGraph->Write("FitComparison");
   canvas->Write("A2a4Canvas");
 
   return parameters;
}