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355 lines
11 KiB
355 lines
11 KiB
/// \ingroup tutorial_fit
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/// \notebook
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/// Convoluted Landau and Gaussian Fitting Function
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/// (using ROOT's Landau and Gauss functions)
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///
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/// Based on a Fortran code by R.Fruehwirth (fruhwirth@hephy.oeaw.ac.at)
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///
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/// to execute this example, do:
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///
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/// ~~~{.cpp}
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/// root > .x langaus.C
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/// ~~~
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///
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/// or
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///
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/// ~~~{.cpp}
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/// root > .x langaus.C++
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/// ~~~
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///
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/// \macro_image
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/// \macro_output
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/// \macro_code
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///
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/// \authors H.Pernegger, Markus Friedl
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#include "TH1.h"
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#include "TF1.h"
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#include "TROOT.h"
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#include "TStyle.h"
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#include "TMath.h"
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#include <TH2.h>
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#include <TStyle.h>
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#include <TCanvas.h>
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#include <iostream>
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#include <fstream>
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#include <iomanip>
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#include <cstdlib>
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#include <cmath>
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#include <string.h>
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#include <TLorentzVector.h>
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#include <vector>
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#include <TApplication.h>
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#include <TAxis.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <TFile.h>
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#include <TTree.h>
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Double_t langaufun(Double_t *x, Double_t *par) {
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//Fit parameters:
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//par[0]=Width (scale) parameter of Landau density
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//par[1]=Most Probable (MP, location) parameter of Landau density
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//par[2]=Total area (integral -inf to inf, normalization constant)
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//par[3]=Width (sigma) of convoluted Gaussian function
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//
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//In the Landau distribution (represented by the CERNLIB approximation),
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//the maximum is located at x=-0.22278298 with the location parameter=0.
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//This shift is corrected within this function, so that the actual
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//maximum is identical to the MP parameter.
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// Numeric constants
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Double_t invsq2pi = 0.3989422804014; // (2 pi)^(-1/2)
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Double_t mpshift = -0.22278298; // Landau maximum location
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// Control constants
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Double_t np = 200.0; // number of convolution steps
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Double_t sc = 4.0; // convolution extends to +-sc Gaussian sigmas
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// Variables
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Double_t xx;
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Double_t mpc;
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Double_t fland;
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Double_t sum = 0.0;
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Double_t xlow,xupp;
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Double_t step;
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Double_t i;
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// MP shift correction
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mpc = par[1] - mpshift * par[0];
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// Range of convolution integral
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xlow = x[0] - sc * par[3];
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xupp = x[0] + 2*sc * par[3];
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step = (xupp-xlow) / np;
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// Convolution integral of Landau and Gaussian by sum
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for(i=1.0; i<=np/2; i++) {
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xx = xlow + (i-.5) * step;
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fland = TMath::Landau(xx,mpc,par[0]) / par[0];
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sum += fland * TMath::Gaus(x[0],xx,par[3]);
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xx = xupp - (i-.5) * step;
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fland = TMath::Landau(xx,mpc,par[0]) / par[0];
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sum += fland * TMath::Gaus(x[0],xx,par[3]);
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}
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return (par[2] * step * sum * invsq2pi / par[3]);
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}
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TF1 *langaufit(TH1D *his, Double_t *fitrange, Double_t *startvalues, Double_t *parlimitslo, Double_t *parlimitshi, Double_t *fitparams, Double_t *fiterrors, Double_t *ChiSqr, Int_t *NDF)
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{
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// Once again, here are the Landau * Gaussian parameters:
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// par[0]=Width (scale) parameter of Landau density
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// par[1]=Most Probable (MP, location) parameter of Landau density
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// par[2]=Total area (integral -inf to inf, normalization constant)
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// par[3]=Width (sigma) of convoluted Gaussian function
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//
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// Variables for langaufit call:
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// his histogram to fit
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// fitrange[2] lo and hi boundaries of fit range
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// startvalues[4] reasonable start values for the fit
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// parlimitslo[4] lower parameter limits
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// parlimitshi[4] upper parameter limits
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// fitparams[4] returns the final fit parameters
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// fiterrors[4] returns the final fit errors
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// ChiSqr returns the chi square
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// NDF returns ndf
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Int_t i;
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Char_t FunName[100];
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sprintf(FunName,"Fitfcn_%s",his->GetName());
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TF1 *ffitold = (TF1*)gROOT->GetListOfFunctions()->FindObject(FunName);
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if (ffitold) delete ffitold;
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TF1 *ffit = new TF1(FunName,langaufun,fitrange[0],fitrange[1],4);
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ffit->SetParameters(startvalues);
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ffit->SetParNames("Width","MP","Area","GSigma");
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for (i=0; i<4; i++) {
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ffit->SetParLimits(i, parlimitslo[i], parlimitshi[i]);
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}
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his->Fit(FunName,"RB0"); // fit within specified range, use ParLimits, do not plot
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ffit->GetParameters(fitparams); // obtain fit parameters
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for (i=0; i<4; i++) {
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fiterrors[i] = ffit->GetParError(i); // obtain fit parameter errors
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}
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ChiSqr[0] = ffit->GetChisquare(); // obtain chi^2
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NDF[0] = ffit->GetNDF(); // obtain ndf
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return (ffit); // return fit function
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}
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Int_t langaupro(Double_t *params, Double_t &maxx, Double_t &FWHM) {
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// Seaches for the location (x value) at the maximum of the
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// Landau-Gaussian convolute and its full width at half-maximum.
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//
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// The search is probably not very efficient, but it's a first try.
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Double_t p,x,fy,fxr,fxl;
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Double_t step;
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Double_t l,lold;
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Int_t i = 0;
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Int_t MAXCALLS = 10000;
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// Search for maximum
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p = params[1] - 0.1 * params[0];
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step = 0.05 * params[0];
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lold = -2.0;
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l = -1.0;
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while ( (l != lold) && (i < MAXCALLS) ) {
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i++;
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lold = l;
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x = p + step;
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l = langaufun(&x,params);
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if (l < lold)
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step = -step/10;
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p += step;
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}
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if (i == MAXCALLS)
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return (-1);
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maxx = x;
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fy = l/2;
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// Search for right x location of fy
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p = maxx + params[0];
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step = params[0];
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lold = -2.0;
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l = -1e300;
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i = 0;
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while ( (l != lold) && (i < MAXCALLS) ) {
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i++;
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lold = l;
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x = p + step;
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l = TMath::Abs(langaufun(&x,params) - fy);
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if (l > lold)
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step = -step/10;
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p += step;
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}
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if (i == MAXCALLS)
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return (-2);
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fxr = x;
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// Search for left x location of fy
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p = maxx - 0.5 * params[0];
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step = -params[0];
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lold = -2.0;
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l = -1e300;
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i = 0;
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while ( (l != lold) && (i < MAXCALLS) ) {
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i++;
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lold = l;
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x = p + step;
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l = TMath::Abs(langaufun(&x,params) - fy);
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if (l > lold)
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step = -step/10;
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p += step;
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}
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if (i == MAXCALLS)
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return (-3);
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fxl = x;
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FWHM = fxr - fxl;
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return (0);
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}
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void langaus() {
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// Fill Histogram
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/* Int_t data[100] = {10,20,50,3,10,5,2,6,11,18,18,55,90,141,255,323,454,563,681,
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737,821,796,832,720,637,558,519,460,357,291,279,241,212,
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153,164,139,106,95,91,76,80,80,59,58,51,30,49,23,35,28,23,
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22,27,27,24,20,16,17,14,20,12,12,13,10,17,7,6,12,6,12,4,
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9,9,10,3,4,5,2,4,1,5,5,1,7,1,6,3,3,3,4,5,4,4,2,2,7,2,4};
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TH1F *hSNR = new TH1F("snr","Signal-to-noise",400,0,400);
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*/
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// for (Int_t i=0; i<100; i++) hSNR->Fill(i,data[i]);
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TH1D * hSNR;
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Double_t graph_x[30], graph_y[30], graph_xerr[30], graph_yerr[30];
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Double_t beta_proton[30] = {0.308525262, 0.340993523, 0.366173485, 0.386743776, 0.404197708, 0.4194131, 0.43294541, 0.445179695, 0.456345494, 0.466616582, 0.476154213, 0.48502264, 0.493348242, 0.501186212, 0.50863865, 0.515744144, 0.522562549, 0.52911069, 0.535379917, 0.541397728, 0.549575745, 0.557428612, 0.564849395, 0.571867977, 0.578541117, 0.584900169};
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Double_t beta_helium[30] = {0.316661966, 0.34727202, 0.371222186, 0.391037227, 0.408018455, 0.422922098, 0.436235455, 0.44827542, 0.459299095, 0.469441244, 0.478845524, 0.487649369, 0.495886825, 0.503656244, 0.510990953, 0.517959457, 0.52457247, 0.530888884, 0.536925849, 0.54269899, 0.550671248, 0.55845178, 0.565814653, 0.572798702, 0.579448698, 0.585785313};
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Double_t beta_carbon[30] = {0.407931067, 0.448122448, 0.479020432, 0.504000457, 0.524971648, 0.543076553, 0.559041917, 0.573329576, 0.586254563, 0.598061846, 0.608917559, 0.618952311, 0.62829287, 0.637039726, 0.645286945, 0.65311609, 0.660570813, 0.667689852, 0.67446932, 0.680943928, 0.689677353, 0.698000799, 0.70580765, 0.71315081, 0.720086739, 0.726650602};
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Double_t beta_oxygen[30] = {0.43638582, 0.479000679, 0.51134888, 0.537325331, 0.559061572, 0.57764689, 0.594123989, 0.608730698, 0.621997639, 0.63402408, 0.645050809, 0.655226738, 0.664724227, 0.673475951, 0.681810969, 0.689681788, 0.697243281, 0.704219104, 0.710968918, 0.717442538, 0.726111033, 0.734356548, 0.742073831, 0.749383281, 0.756156282, 0.762562424};
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// hSNR = h_beamSignal_b0[13];
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// Fitting SNR histo
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printf("Fitting...\n");
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TCanvas * c1 = new TCanvas("c1","c1", 800, 600);
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// Setting fit range and start values
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Double_t fr[2];
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Double_t sv[4], pllo[4], plhi[4], fp[4], fpe[4];
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Double_t chisqr;
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Int_t ndf;
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TF1 *fitsnr;
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Double_t SNRPeak, SNRFWHM;
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char rootfilename[50] = "";
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char saveplotname[50] = "";
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char fout_mpv_name[50] = "";
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Double_t norm;
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ofstream myfile, fout_mpv;
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int j = 1;
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myfile.open ("MPVcorrection_helium0mm05.txt");
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sprintf(fout_mpv_name, "jobs%i/plots/mpv.txt",j);
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fout_mpv.open (fout_mpv_name);
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for (int i = 0; i<26;i++){
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sprintf(rootfilename, "jobs%i/runjob%i001_eventdata_out.root",j,i);
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TFile *rootFile = new TFile(rootfilename,"OPEN");
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TH1D * hSNR = (TH1D*)rootFile->Get("h_spratio");
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norm = hSNR->GetEntries();
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hSNR->Scale(1/norm);
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fr[0]=hSNR->GetMean() - 2*hSNR->GetRMS();
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fr[1]=hSNR->GetMean() + 3*hSNR->GetRMS();
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pllo[0]=0.0006; pllo[1]=0.50; pllo[2]=0.001; pllo[3]=0.01;
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plhi[0]=0.01; plhi[1]=1.5; plhi[2]=0.055; plhi[3]=0.05;
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sv[0]= hSNR->GetRMS()/10.;;
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//sv[1]=2.1;
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sv[2]=0.01;
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// sv[3]=0.03;
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sv[3] = hSNR->GetRMS()/2.;
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sv[1] = hSNR->GetMean();
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fitsnr = langaufit(hSNR,fr,sv,pllo,plhi,fp,fpe,&chisqr,&ndf);
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langaupro(fp,SNRPeak,SNRFWHM);
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graph_x[i] = beta_helium[i];
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graph_y[i] = fp[1];
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graph_yerr[i] = sqrt(fitsnr->GetParError(1)*fitsnr->GetParError(1)+0.012*0.012*graph_y[i]*graph_y[i]);
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fout_mpv << graph_x[i] << " " << graph_y[i] << " " << graph_yerr[i] << endl;
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printf("Fitting done\nPlotting results...\n");
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myfile << i << " " << graph_y[i] << endl;
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// Global style settings
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gStyle->SetOptStat(1111);
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gStyle->SetOptFit(111);
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//gStyle->SetLabelSize(0.03,"x");
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//gStyle->SetLabelSize(0.03,"y");
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// hSNR->GetXaxis()->SetRange(0,70);
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hSNR->Draw();
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fitsnr->Draw("lsame");
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c1->Update();
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sprintf(saveplotname, "jobs%i/plots/runjob%i001_h_spratio.pdf",j,i);
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c1->SaveAs(saveplotname);
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// c1->WaitPrimitive();
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hSNR->Delete();
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rootFile->Close();
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}
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TGraphErrors * graph_1 = new TGraphErrors(23,graph_x, graph_y, 0, graph_yerr);
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TCanvas * c2 = new TCanvas("c2","c2", 800, 600);
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c2->cd();
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graph_1->Draw("A*");
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c2->SaveAs("MPVcorrection_helium0mm05.pdf");
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myfile.close();
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fout_mpv.close();
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}
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