File indexing completed on 2023-03-17 11:14:49
0001 #include <string>
0002 #include <vector>
0003 #include "TF1.h"
0004 #include "TH1D.h"
0005 #include "TH1F.h"
0006 #include "TH2.h"
0007 #include "TCanvas.h"
0008 #include "TCutG.h"
0009 #include "TFile.h"
0010 #include "TLegend.h"
0011 #include "TMath.h"
0012 #include "TNtuple.h"
0013 #include "TPad.h"
0014 #include "TPaveText.h"
0015 #include "TROOT.h"
0016 #include "TString.h"
0017 #include "TSystem.h"
0018 #include <MuonAnalysis/MomentumScaleCalibration/test/Macros/RooFit/TkAlStyle.cc>
0019
0020
0021 using namespace ROOT::Math;
0022
0023 struct Plot_1D{
0024 TString directory;
0025 TString histoname;
0026 TString plotname;
0027 TString xtitle;
0028 TString ytitle;
0029 float xmin;
0030 float xmax;
0031 float ymin;
0032 float ymax;
0033 float absMin;
0034 float absMax;
0035
0036 vector<TH1D*> histogram;
0037 vector<TString> histogram_label;
0038 vector<pair<float, float>> mean_sigma_Y;
0039 vector<pair<int, float>> chi2Y;
0040
0041 Plot_1D(TString directory_, TString histoname_, TString plotname_, TString xtitle_, TString ytitle_, float xmin_, float xmax_, float ymin_, float ymax_) :
0042 directory(directory_),
0043 histoname(histoname_),
0044 plotname(plotname_),
0045 xtitle(xtitle_),
0046 ytitle(ytitle_),
0047 xmin(xmin_),
0048 xmax(xmax_),
0049 ymin(ymin_),
0050 ymax(ymax_),
0051 absMin(9e9),
0052 absMax(-9e9)
0053 {}
0054 ~Plot_1D(){}
0055
0056 void addMeanSigmaChi2Y(TH1D* htmp){
0057 double mean=0;
0058 double sigma=0;
0059 for (int bin=1; bin<=htmp->GetNbinsX(); bin++){
0060 float bincontent = htmp->GetBinContent(bin);
0061 float binerror = htmp->GetBinError(bin);
0062 if (binerror==0. && bincontent==0.) continue;
0063 absMin = min(absMin, bincontent - binerror);
0064 absMax = max(absMax, bincontent + binerror);
0065 mean += bincontent/pow(binerror, 2);
0066 sigma += 1./pow(binerror, 2);
0067 }
0068 mean /= sigma;
0069 sigma = sqrt(1./sigma);
0070 mean_sigma_Y.push_back(pair<float, float>(mean, sigma));
0071
0072 float chi2=0; int ndof=-1;
0073 for (int bin=1; bin<=htmp->GetNbinsX(); bin++){
0074 float bincontent = htmp->GetBinContent(bin);
0075 float binerror = htmp->GetBinError(bin);
0076 if (binerror==0. && bincontent==0.) continue;
0077 ndof++;
0078 chi2 += pow((bincontent-mean)/binerror, 2);
0079 }
0080 chi2Y.push_back(pair<int, float>(ndof, chi2));
0081 }
0082
0083 void addHistogramFromFile(TFile* finput, TString label, int color, int linestyle, int markerstyle){
0084 if (finput!=0 && !finput->IsZombie()){
0085 TH1D* htmp = (TH1D*)finput->Get(histoname);
0086 if (htmp!=0){
0087 htmp->SetTitle("");
0088 htmp->GetXaxis()->SetTitle(xtitle);
0089 htmp->GetYaxis()->SetTitle(ytitle);
0090
0091 htmp->SetLineWidth(1);
0092 htmp->SetLineColor(color);
0093 htmp->SetLineStyle(linestyle);
0094 htmp->SetMarkerSize(1.2);
0095 htmp->SetMarkerColor(color);
0096 htmp->SetMarkerStyle(markerstyle);
0097
0098 histogram.push_back(htmp);
0099 histogram_label.push_back(label);
0100 addMeanSigmaChi2Y(htmp);
0101 }
0102 }
0103 }
0104
0105 void plot(TString fitFormula="", bool AutoSetRange=false){
0106
0107 unsigned int nValidHistos = histogram.size();
0108 const unsigned int nValidHistosLimit = 4;
0109 float rangeFactor[2]={ 1.01, 1.05 };
0110 float dampingFactor = 0;
0111 float deviationThreshold = 1.04;
0112 if (nValidHistos>nValidHistosLimit) dampingFactor = 0.07/nValidHistosLimit*(nValidHistos-nValidHistosLimit);
0113 double rangeMaxReduction = 0.02;
0114 if (nValidHistos>nValidHistosLimit) rangeMaxReduction = rangeMaxReduction*nValidHistosLimit/nValidHistos;
0115 double dampingFactorEff = dampingFactor;
0116
0117 float overallAvg=0;
0118 float overallSigmaSqInv=0;
0119 for (unsigned int f=0; f<nValidHistos; f++){
0120 float avgY = mean_sigma_Y.at(f).first;
0121 float devY = mean_sigma_Y.at(f).second;
0122 devY = 1./pow(devY, 2);
0123 avgY *= devY;
0124 overallAvg += avgY;
0125 overallSigmaSqInv += devY;
0126 }
0127 overallAvg /= overallSigmaSqInv;
0128
0129
0130 float MaxY=-1000.;
0131 float MinY=1000.;
0132
0133
0134 for (unsigned int f=0; f<nValidHistos; f++){
0135 for (int bin=1; bin<=histogram.at(f)->GetNbinsX(); bin++){
0136 float bincontent = histogram.at(f)->GetBinContent(bin);
0137 float binerror = histogram.at(f)->GetBinError(bin);
0138 if (binerror==0 && bincontent==0) continue;
0139 if ((bincontent + binerror)>deviationThreshold*overallAvg) rangeMaxReduction = 0;
0140 if(bincontent>=MaxY)MaxY=bincontent;
0141 if(bincontent<=MinY)MinY=bincontent;
0142
0143 }
0144 }
0145 if (nValidHistos>nValidHistosLimit && rangeMaxReduction!=0) dampingFactorEff = dampingFactorEff*0.7;
0146
0147 float Yrange = MaxY-MinY;
0148 if(AutoSetRange){
0149 ymin=MinY-Yrange/10.;
0150 ymax=Yrange*1.6+ymin;
0151 }
0152
0153
0154 if (ymin>=ymax){
0155 ymin = absMin/rangeFactor[0];
0156 ymax = absMax*(rangeFactor[1]+dampingFactorEff-rangeMaxReduction);
0157 }
0158 for (unsigned int f=0; f<nValidHistos; f++){
0159 histogram.at(f)->GetXaxis()->SetRangeUser(xmin, xmax);
0160 histogram.at(f)->GetYaxis()->SetRangeUser(ymin, ymax);
0161 }
0162
0163
0164 vector<TF1*> hfit;
0165 TCanvas* canvas;
0166 TLegend* legend;
0167 TString cname = Form("c%s", plotname.Data());
0168 canvas = new TCanvas(cname, cname, 8, 30, 800, 800);
0169 canvas->cd();
0170 legend = TkAlStyle::legend("topleft", nValidHistos);
0171 for (unsigned int f=0; f<nValidHistos; f++){
0172 TString legendlabel = histogram_label.at(f);
0173 if (f==0) histogram.at(f)->Draw();
0174 else histogram.at(f)->Draw("same");
0175
0176 if (fitFormula==""){
0177 if (plotname.Contains("mean")){
0178 float normchi2=0.;
0179 if (chi2Y.at(f).first>0) normchi2=chi2Y.at(f).first/chi2Y.at(f).second;
0180 legendlabel = Form("%s (#chi^{2}/dof=%.2f)", legendlabel.Data(), normchi2);
0181 }
0182 }
0183 else{
0184 TF1* ftmp = new TF1(Form("fit_%i", f), fitFormula, xmin, xmax);
0185 ftmp->SetParameter(0, 91.0);
0186 ftmp->SetParameter(1, 0);
0187 if (fitFormula.Contains("[2]")) ftmp->SetParameter(2, 0);
0188 ftmp->SetLineColor(1);
0189
0190 histogram.at(f)->Fit(ftmp, "R", "same", xmin, xmax);
0191 ftmp->Draw("same");
0192 hfit.push_back(ftmp);
0193 }
0194
0195 legend->AddEntry(histogram.at(f), legendlabel, "lp");
0196 }
0197 legend->Draw("same");
0198 TkAlStyle::drawStandardTitle();
0199 canvas->RedrawAxis();
0200 canvas->Modified();
0201 canvas->Update();
0202 canvas->SaveAs(Form("%s/%s%s", directory.Data(), plotname.Data(), ".png"));
0203 canvas->SaveAs(Form("%s/%s%s", directory.Data(), plotname.Data(), ".pdf"));
0204 canvas->SaveAs(Form("%s/%s%s", directory.Data(), plotname.Data(), ".root"));
0205
0206 for (unsigned int hf=0; hf<hfit.size(); hf++) delete hfit.at(hf);
0207 delete legend;
0208 canvas->Close();
0209 delete canvas;
0210 }
0211
0212 };
0213
0214
0215 void splitOption(string rawoption, string& wish, string& value, char delimiter){
0216 size_t posEq = rawoption.find(delimiter);
0217 if (posEq!=string::npos){
0218 wish=rawoption;
0219 value=rawoption.substr(posEq+1);
0220 wish.erase(wish.begin()+posEq, wish.end());
0221 }
0222 else{
0223 wish="";
0224 value=rawoption;
0225 }
0226 }
0227 void splitOptionRecursive(string rawoption, vector<string>& splitoptions, char delimiter){
0228 string suboption=rawoption, result=rawoption;
0229 string remnant;
0230 while (result!=""){
0231 splitOption(suboption, result, remnant, delimiter);
0232 if (result!="") splitoptions.push_back(result);
0233 suboption = remnant;
0234 }
0235 if (remnant!="") splitoptions.push_back(remnant);
0236 }
0237
0238 void getCustomRanges(TString type, TString resonance, int iP, float minmax_plot[2]);
0239 void MultiHistoOverlapAll_Base_one(string files, string labels, string colors, string linestyles, string markerstyles, TString directory, TString resonance, TString type, bool switchONfit, bool AutoSetRange=false, float CustomMinY=90.85, float CustomMaxY=91.4);
0240
0241 void MultiHistoOverlapAll_Base(string files, string labels, string colors, string linestyles, string markerstyles, TString directory, TString resonance, bool switchONfit=false, bool AutoSetRange=false, float CustomMinY=90.85, float CustomMaxY=91.4){
0242 gSystem->mkdir(directory, true);
0243 MultiHistoOverlapAll_Base_one(files, labels, colors, linestyles, markerstyles, directory, resonance, "mean", switchONfit, AutoSetRange, CustomMinY, CustomMaxY);
0244 MultiHistoOverlapAll_Base_one(files, labels, colors, linestyles, markerstyles, directory, resonance, "sigma", switchONfit, AutoSetRange, CustomMinY, CustomMaxY);
0245 }
0246
0247 void MultiHistoOverlapAll_Base_one(string files, string labels, string colors, string linestyles, string markerstyles, TString directory, TString resonance, TString type, bool switchONfit, bool AutoSetRange, float CustomMinY, float CustomMaxY){
0248 gROOT->Reset();
0249 if (TkAlStyle::status() == NO_STATUS) TkAlStyle::set(INTERNAL);
0250 gROOT->ForceStyle();
0251
0252 vector<string> strValidation_file;
0253 vector<string> strValidation_label;
0254 vector<string> strValidation_color;
0255 vector<string> strValidation_linestyle;
0256 vector<string> strValidation_markerstyle;
0257 splitOptionRecursive(files, strValidation_file, ',');
0258 splitOptionRecursive(labels, strValidation_label, ',');
0259 splitOptionRecursive(colors, strValidation_color, ',');
0260 splitOptionRecursive(linestyles, strValidation_linestyle, ',');
0261 splitOptionRecursive(markerstyles, strValidation_markerstyle, ',');
0262 int nfiles = strValidation_file.size();
0263 int nlabels = strValidation_label.size();
0264 int ncolors = strValidation_color.size();
0265 int nlinestyles = strValidation_linestyle.size();
0266 int nmarkerstyles = strValidation_markerstyle.size();
0267 if (nlabels!=nfiles){
0268 cout << "nlabels!=nfiles" << endl;
0269 return;
0270 }
0271 if (ncolors!=0 && ncolors!=nfiles){
0272 cout << "ncolors!=nfiles" << endl;
0273 return;
0274 }
0275 if (nlinestyles!=0 && nlinestyles!=nfiles){
0276 cout << "nlinestyles!=nfiles" << endl;
0277 return;
0278 }
0279 if (nmarkerstyles!=0 && nmarkerstyles!=nfiles){
0280 cout << "nmarkerstyles!=nfiles" << endl;
0281 return;
0282 }
0283
0284 const int nhistos=8;
0285 const int n2Dhistos = 2;
0286 TFile** file = new TFile*[nfiles];
0287 for (int f=0; f<nfiles; f++) file[f] = TFile::Open((strValidation_file[f]).c_str(), "read");
0288
0289 TString histoname[nhistos] ={
0290 TString("MassVsPt/allHistos/") + type + TString("Histo"),
0291 TString("MassVsPhiPlus/allHistos/") + type + TString("Histo"),
0292 TString("MassVsPhiMinus/allHistos/") + type + TString("Histo"),
0293 TString("MassVsEtaPlus/allHistos/") + type + TString("Histo"),
0294 TString("MassVsEtaMinus/allHistos/") + type + TString("Histo"),
0295 TString("MassVsEtaPlusMinusDiff/allHistos/") + type + TString("Histo"),
0296 TString("MassVsCosThetaCS/allHistos/") + type + TString("Histo"),
0297 TString("MassVsPhiCS/allHistos/") + type + TString("Histo")
0298 };
0299 TString histo2Dname[n2Dhistos] ={
0300 TString("MassVsEtaPhiPlus/allHistos/") + type + TString("Histo"),
0301 TString("MassVsEtaPhiMinus/allHistos/") + type + TString("Histo"),
0302 };
0303 TString xtitle[nhistos] ={
0304 "p^{T}_{#mu} (GeV)",
0305 "#phi_{#mu+}",
0306 "#phi_{#mu-}",
0307 "#eta_{#mu+}",
0308 "#eta_{#mu-}",
0309 "#eta_{#mu+} - #eta_{#mu-}",
0310 "cos #theta_{CS}",
0311 "#phi_{CS}"
0312 };
0313 TString x2Dtitle[n2Dhistos] ={
0314 "#phi_{#mu+}",
0315 "#phi_{#mu-}"
0316 };
0317 TString y2Dtitle[n2Dhistos] ={
0318 "#eta_{#mu+}",
0319 "#eta_{#mu-}"
0320 };
0321 TString ytitle;
0322 if (type=="mean") ytitle = "M_{#mu#mu} (GeV)";
0323 else if (type=="sigma") ytitle = "#sigma_{#mu#mu} (GeV)";
0324 TString plotname[nhistos] ={
0325 type + "MassVsPt_ALL",
0326 type + "MassVsPhiPlus_ALL",
0327 type + "MassVsPhiMinus_ALL",
0328 type + "MassVsEtaPlus_ALL",
0329 type + "MassVsEtaMinus_ALL",
0330 type + "MassVsDeltaEta_ALL",
0331 type + "MassVsCosThetaCS_ALL",
0332 type + "MassVsPhiCS_ALL"
0333 };
0334 TString plotname2D[n2Dhistos] ={
0335 type + "MassVsEtaPhiPlus",
0336 type + "MassVsEtaPhiMinus"
0337 };
0338 TString fitFormula[nhistos]={
0339 "[0]+[1]*cos(x+[2])",
0340 "[0]+[1]*cos(x+[2])",
0341 "[0]+[1]*cos(x+[2])",
0342 "[0]+[1]*x",
0343 "[0]+[1]*x",
0344 "[0]+[1]*x",
0345 "[0]+[1]*cos(x+[2])",
0346 "[0]+[1]*cos(x+[2])"
0347 };
0348 float plot_xmax[nhistos]={
0349 static_cast<float>((resonance=="Z" ? 100. : (resonance=="Y1S" ? 20. : (resonance=="Y2S" ? 20. : (resonance=="Y3S" ? 20. : (resonance=="JPsi" ? 20. : 20.)))))),
0350 static_cast<float>(TMath::Pi()),
0351 static_cast<float>(TMath::Pi()),
0352 2.4,
0353 2.4,
0354 4.8,
0355 1,
0356 static_cast<float>(TMath::Pi())
0357 };
0358 float plot_xmin[nhistos]={
0359 0.,
0360 static_cast<float>(-TMath::Pi()),
0361 static_cast<float>(-TMath::Pi()),
0362 -2.4,
0363 -2.4,
0364 -4.8,
0365 -1,
0366 static_cast<float>(-TMath::Pi())
0367 };
0368
0369
0370
0371
0372 Double_t zMass(91.1876);
0373 Double_t Y1SMass(9.46);
0374 Double_t maxDist(0.);
0375 Double_t Mass=zMass;
0376 Double_t fixrange=1.5;
0377 if (resonance=="Y1S"){
0378 Mass=Y1SMass;
0379 fixrange=0.15;
0380 }
0381 for (int f=0; f<nfiles; f++){
0382
0383 if (file[f]!=0 && !file[f]->IsZombie()){
0384
0385
0386 TH2D* histo2D[2];
0387 histo2D[0]= (TH2D*)file[f]->Get(histo2Dname[0]);
0388 histo2D[1]= (TH2D*)file[f]->Get(histo2Dname[1]);
0389 for(int s=0;s<2;s++){
0390 TCanvas dummycanvas;
0391 dummycanvas.SetFillColor(0);
0392 dummycanvas.cd()->SetTopMargin(0.07);
0393 dummycanvas.cd()->SetRightMargin(0.15);
0394 dummycanvas.cd()->SetLeftMargin(0.12);
0395 histo2D[s]->SetTitle("");
0396 histo2D[s]->GetZaxis()->SetLabelFont(42);
0397 histo2D[s]->GetXaxis()->SetTitle(x2Dtitle[s]);
0398 histo2D[s]->GetYaxis()->SetTitle(y2Dtitle[s]);
0399 histo2D[s]->GetYaxis()->SetTitleOffset(0.9);
0400 if(type=="mean"){
0401 Double_t zMin=Mass;
0402 Double_t zMax=Mass;
0403
0404 for(int nbinX=1;nbinX<=histo2D[s]->GetNbinsX();nbinX++){
0405 for(int nbinY=1;nbinY<=histo2D[s]->GetNbinsY();nbinY++){
0406 Double_t value = histo2D[s]->GetBinContent(nbinX,nbinY);
0407 if(value<zMin)zMin=value;
0408 if(value>zMax)zMax=value;
0409 }
0410 }
0411 maxDist=fabs(zMax-Mass);
0412 if(fabs(Mass-zMin) >= maxDist) maxDist=fabs(Mass-zMin);
0413 histo2D[s]->GetZaxis()->SetRangeUser(Mass-fixrange,Mass+fixrange);
0414 if(AutoSetRange)histo2D[s]->GetZaxis()->SetRangeUser(zMin,zMax);
0415
0416 }
0417 histo2D[s]->Draw("COLZ");
0418 TString alignment_label = strValidation_label.at(f);
0419 alignment_label.ReplaceAll(" ","_");
0420 dummycanvas.SaveAs(Form("%s/%sc%s%s%s", directory.Data(),alignment_label.Data(),"_", plotname2D[s].Data(), ".png"));
0421 dummycanvas.SaveAs(Form("%s/%sc%s%s%s", directory.Data(),alignment_label.Data(),"_", plotname2D[s].Data(), ".pdf"));
0422 dummycanvas.SaveAs(Form("%s/%sc%s%s%s", directory.Data(),alignment_label.Data(),"_", plotname2D[s].Data(), ".root"));
0423 }
0424
0425 }
0426
0427 };
0428
0429
0430 for (int iP=0; iP<nhistos; iP++){
0431 TString theFitFormula="";
0432 if (switchONfit && type=="mean") theFitFormula = fitFormula[iP];
0433
0434 float minmax_plot[2]={ -1, -1 };
0435
0436 if (type=="mean") {
0437 minmax_plot[0]=CustomMinY;
0438 minmax_plot[1]=CustomMaxY;
0439 }
0440 Plot_1D thePlot(directory, histoname[iP], plotname[iP], xtitle[iP], ytitle, plot_xmin[iP], plot_xmax[iP], minmax_plot[0], minmax_plot[1]);
0441 for (int f=0; f<nfiles; f++){
0442 int color=0, linestyle=0, markerstyle=0;
0443 if (ncolors>f) color = stoi(strValidation_color.at(f));
0444 else if (f==0) color = (int)kBlack;
0445 else if (f==(nfiles-1)) color = (int)kViolet;
0446 else if (f==1) color = (int)kBlue;
0447 else if (f==2) color = (int)kRed;
0448 else if (f==3) color = (int)(kGreen+2);
0449 else if (f==4) color = (int)(kOrange+3);
0450 else if (f==5) color = (int)kGreen;
0451 else if (f==6) color = (int)kYellow;
0452 else if (f==7) color = (int)(kPink+9);
0453 else if (f==8) color = (int)kCyan;
0454 else if (f==9) color = (int)(kGreen+3);
0455 else color = (int)kBlack;
0456 if (nlinestyles>f){
0457 linestyle = stoi(strValidation_linestyle.at(f));
0458 if (linestyle > 100) markerstyle = (linestyle / 100);
0459 linestyle = linestyle % 100;
0460 }
0461 else linestyle=1;
0462 if (nmarkerstyles>f) markerstyle = stoi(strValidation_markerstyle.at(f));
0463 else if (markerstyle==0){
0464 if (strValidation_label.at(f).find("reference")!=string::npos || strValidation_label.at(f).find("Reference")!=string::npos) markerstyle=1;
0465 else markerstyle=20;
0466 }
0467 thePlot.addHistogramFromFile(file[f], strValidation_label.at(f), color, linestyle, markerstyle);
0468 }
0469 if(type=="mean")thePlot.plot(theFitFormula,AutoSetRange);
0470 else thePlot.plot(theFitFormula,true);
0471
0472 }
0473
0474 for (int f=nfiles-1; f>=0; f--){ if (file[f]!=0 && file[f]->IsOpen()) file[f]->Close(); }
0475 delete[] file;
0476 }
0477
0478 void getCustomRanges(TString type, TString resonance, int iP, float minmax_plot[2]){
0479 if (type=="mean"){
0480
0481 if (resonance=="Z"){
0482 if (iP==7){
0483 minmax_plot[0] = 90.9;
0484 minmax_plot[1] = 91.3;
0485 }
0486 else if (iP==6){
0487 minmax_plot[0] = 90.95;
0488 minmax_plot[1] = 91.4;
0489 }
0490 else if (iP==5){
0491 minmax_plot[0] = 90.9;
0492 minmax_plot[1] = 91.6;
0493 }
0494 else if (iP==4){
0495 minmax_plot[0] = 90.8;
0496 minmax_plot[1] = 91.5;
0497 }
0498 else if (iP==3){
0499 minmax_plot[0] = 90.8;
0500 minmax_plot[1] = 91.5;
0501 }
0502 else if (iP==2){
0503 minmax_plot[0] = 90.8;
0504 minmax_plot[1] = 91.7;
0505 }
0506 else if (iP==1){
0507 minmax_plot[0] = 90.8;
0508 minmax_plot[1] = 91.7;
0509 }
0510 else{
0511 minmax_plot[0] = 90.;
0512 minmax_plot[1] = 94.;
0513 }
0514 }
0515 else if (resonance=="Y1S"){
0516 if (iP==7){
0517 minmax_plot[0] = 9.42;
0518 minmax_plot[1] = 9.50;
0519 }
0520 else if (iP==6){
0521 minmax_plot[0] = 9.43;
0522 minmax_plot[1] = 9.49;
0523 }
0524 else if (iP==5){
0525 minmax_plot[0] = 9.43;
0526 minmax_plot[1] = 9.49;
0527 }
0528 else if (iP==4){
0529 minmax_plot[0] = 9.40;
0530 minmax_plot[1] = 9.52;
0531 }
0532 else if (iP==3){
0533 minmax_plot[0] = 9.41;
0534 minmax_plot[1] = 9.52;
0535 }
0536 else if (iP==2){
0537 minmax_plot[0] = 9.435;
0538 minmax_plot[1] = 9.475;
0539 }
0540 else if (iP==1){
0541 minmax_plot[0] = 9.435;
0542 minmax_plot[1] = 9.475;
0543 }
0544 else{
0545 minmax_plot[0] = 9.4;
0546 minmax_plot[1] = 9.65;
0547 }
0548 }
0549
0550 }
0551 else if (type=="sigma"){
0552
0553 if (resonance=="Z"){
0554 if (iP==7){
0555 minmax_plot[0] = 1.1;
0556 minmax_plot[1] = 1.7;
0557 }
0558 else if (iP==6){
0559 minmax_plot[0] = 1.0;
0560 minmax_plot[1] = 1.65;
0561 }
0562 else if (iP==5){
0563 minmax_plot[0] = 0.8;
0564 minmax_plot[1] = 2.3;
0565 }
0566 else if (iP==4){
0567 minmax_plot[0] = 0.8;
0568 minmax_plot[1] = 3.2;
0569 }
0570 else if (iP==3){
0571 minmax_plot[0] = 0.8;
0572 minmax_plot[1] = 3.2;
0573 }
0574 else if (iP==2){
0575 minmax_plot[0] = 1.0;
0576 minmax_plot[1] = 1.7;
0577 }
0578 else if (iP==1){
0579 minmax_plot[0] = 1.0;
0580 minmax_plot[1] = 1.7;
0581 }
0582 else{
0583 minmax_plot[0] = 0;
0584 minmax_plot[1] = 3.0;
0585 }
0586 }
0587 else if (resonance=="Y1S"){
0588 if (iP==7){
0589 minmax_plot[0] = 0.05;
0590 minmax_plot[1] = 0.16;
0591 }
0592 else if (iP==6){
0593 minmax_plot[0] = 0.06;
0594 minmax_plot[1] = 0.12;
0595 }
0596 else if (iP==5){
0597 minmax_plot[0] = 0.04;
0598 minmax_plot[1] = 0.16;
0599 }
0600 else if (iP==4){
0601 minmax_plot[0] = 0.05;
0602 minmax_plot[1] = 0.22;
0603 }
0604 else if (iP==3){
0605 minmax_plot[0] = 0.05;
0606 minmax_plot[1] = 0.22;
0607 }
0608 else if (iP==2){
0609 minmax_plot[0] = 0.06;
0610 minmax_plot[1] = 0.11;
0611 }
0612 else if (iP==1){
0613 minmax_plot[0] = 0.06;
0614 minmax_plot[1] = 0.11;
0615 }
0616 else{
0617 minmax_plot[0] = 0.0;
0618 minmax_plot[1] = 0.26;
0619 }
0620 }
0621
0622 }
0623
0624 }
