Warning, /analysis/HF-Particle/D0/D0_BUP2020.ipynb is written in an unsupported language. File is not indexed.
0001 {
0002 "cells": [
0003 {
0004 "cell_type": "markdown",
0005 "metadata": {},
0006 "source": [
0007 "# HF D0 RAA v2 production for BUP2020\n",
0008 "\n",
0009 "Run with Jupyter Lab, e.g. BNL SDCC Jupyter Lab https://github.com/sPHENIX-Collaboration/tutorials/tree/master/JupyterLab "
0010 ]
0011 },
0012 {
0013 "cell_type": "code",
0014 "execution_count": 1,
0015 "metadata": {},
0016 "outputs": [],
0017 "source": [
0018 "%%cpp -d\n",
0019 "\n",
0020 "// $Id: $\n",
0021 "\n",
0022 "/*!\n",
0023 " * \\file makeRAA_BUP2020.C\n",
0024 " * \\brief \n",
0025 " * \\author Jin Huang <jhuang@bnl.gov>\n",
0026 " * \\version $Revision: $\n",
0027 " * \\date $Date: $\n",
0028 " */\n",
0029 "\n",
0030 "#include \"SaveCanvas.C\"\n",
0031 "#include \"sPhenixStyle.C\"\n",
0032 "\n",
0033 "#include <TChain.h>\n",
0034 "#include <TCut.h>\n",
0035 "#include <TEfficiency.h>\n",
0036 "#include <TF1.h>\n",
0037 "#include <TGraphAsymmErrors.h>\n",
0038 "#include <TGraphErrors.h>\n",
0039 "#include <TH2.h>\n",
0040 "#include <TH3.h>\n",
0041 "#include <TPolyLine.h>\n",
0042 "\n",
0043 "#include <TFile.h>\n",
0044 "\n",
0045 "#include <TColor.h>\n",
0046 "#include <TLatex.h>\n",
0047 "#include <TLegend.h>\n",
0048 "#include <TLine.h>\n",
0049 "#include <TStyle.h>\n",
0050 "\n",
0051 "#include <TMath.h>\n",
0052 "#include <TPad.h>\n",
0053 "#include <TString.h>\n",
0054 "#include <TTree.h>\n",
0055 "#include <TVectorD.h>\n",
0056 "#include <TVirtualFitter.h>\n",
0057 "\n",
0058 "#include <cmath>\n",
0059 "#include <iostream>\n",
0060 "\n",
0061 "using namespace std;\n",
0062 "// ROOT6 disabled assert. Well....\n",
0063 "#ifdef assert\n",
0064 "#undef assert\n",
0065 "#endif\n",
0066 "#define assert(exp) \\\n",
0067 " { \\\n",
0068 " if (!(exp)) \\\n",
0069 " { \\\n",
0070 " cout << \"Assert (\" << #exp << \") failed at \" << __FILE__ << \" line \" << __LINE__ << endl; \\\n",
0071 " } \\\n",
0072 " }"
0073 ]
0074 },
0075 {
0076 "cell_type": "code",
0077 "execution_count": 2,
0078 "metadata": {},
0079 "outputs": [
0080 {
0081 "name": "stdout",
0082 "output_type": "stream",
0083 "text": [
0084 "sPhenixStyle: Applying nominal settings.\n",
0085 "sPhenixStyle: ROOT6 mode\n"
0086 ]
0087 }
0088 ],
0089 "source": [
0090 "SetsPhenixStyle();\n",
0091 "gStyle->SetOptStat(0);\n",
0092 "gStyle->SetOptFit(1111);\n",
0093 "TVirtualFitter::SetDefaultFitter(\"Minuit2\");"
0094 ]
0095 },
0096 {
0097 "cell_type": "markdown",
0098 "metadata": {},
0099 "source": [
0100 "# Constants and inputs"
0101 ]
0102 },
0103 {
0104 "cell_type": "code",
0105 "execution_count": 3,
0106 "metadata": {},
0107 "outputs": [],
0108 "source": [
0109 "\n",
0110 "const double refAuAuMB = 240e9;\n",
0111 "const double refAuAuXSec = 6.8252; // b\n",
0112 "TString refD0_significance_TFile = \"D0_significance.root\";\n",
0113 "const double Psi2_resolution = 0.5;\n",
0114 "\n",
0115 "const double AuAu_Ncoll_C0_10 = 960.2; // [DOI:?10.1103/PhysRevC.87.034911?]\n",
0116 "const double AuAu_Ncoll_C0_20 = 770.6; // [DOI:?10.1103/PhysRevC.91.064904?]\n",
0117 "const double AuAu_Ncoll_60_70 = 29.8; //PHYSICAL REVIEW C 87, 034911 (2013)\n",
0118 "const double AuAu_Ncoll_70_80 = 12.6; //PHYSICAL REVIEW C 87, 034911 (2013)\n",
0119 "const double AuAu_Ncoll_C0_100 = 238.5; // BUP2020\n",
0120 "\n",
0121 "const double AuAu_rec_3year = (5.7 + 15) * 1e9; // BUP2020\n",
0122 "const double AuAu_rec_5year = AuAu_rec_3year + 30e9; // BUP2020\n",
0123 "\n",
0124 "const double AuAu_rec_3year_20wk = (1.7 + 10) * 1e9; // BUP2021 1st release\n",
0125 "\n",
0126 "const double pp_inelastic_crosssec = 42e-3; // 42 mb [sPH-TRG-000]\n",
0127 "const double pp_rec_3year = 6.2e12; // BUP2020\n",
0128 "const double pp_rec_5year = pp_rec_3year + 80e12; // BUP2020\n",
0129 "const double pp_beam_pol = 0.57;\n",
0130 "\n",
0131 "const double OO_rec_5year = 37e9; // BUP2020\n",
0132 "const double OO_Ncoll_C0_100 = 9.6; // BUP2020\n",
0133 "const double OO_inelastic_crosssec = 1.12214; // BUP2020\n",
0134 "const double OO_Psi2_resolution = 0.3;\n",
0135 "\n",
0136 "const double ArAr_rec_5year = 12e9; // BUP2020\n",
0137 "const double ArAr_Ncoll_C0_100 = 28.5; // BUP2020\n",
0138 "const double ArAr_inelastic_crosssec = 2.3423; // BUP2020\n",
0139 "const double ArAr_Psi2_resolution = 0.4;\n",
0140 "\n",
0141 "const double pAu_C0_5_trig_3year = 0.05e12; // 4kHz \n",
0142 "const double pAu_rec_3year = 0.01e12; // BUP2020\n",
0143 "const double pAu_Ncoll_C0_100 = 4.7; // [sPH-TRG-000]\n",
0144 "const double pAu_Ncoll_C0_5 = 10.7-1; // [10.1103/PhysRevLett.121.222301], NPart = 10.7 \\pm 0.6; [10.1103/PhysRevC.95.034910]\n",
0145 "const double pAu_inelastic_crosssec = 1.7; // barn [sPH-TRG-000]\n",
0146 "const double pAu_Psi2_resolution = 0.171; // [10.1103/PhysRevC.95.034910] : FVTX-S"
0147 ]
0148 },
0149 {
0150 "cell_type": "markdown",
0151 "metadata": {},
0152 "source": [
0153 "# ROOT data Inputs\n",
0154 "\n",
0155 "Based on sPH-HF-2017-002"
0156 ]
0157 },
0158 {
0159 "cell_type": "code",
0160 "execution_count": 4,
0161 "metadata": {},
0162 "outputs": [],
0163 "source": [
0164 "TFile *fin1 = new TFile(refD0_significance_TFile);\n",
0165 "assert(fin1);\n",
0166 "\n",
0167 "TGraph *gProD0_0_10_noPid = (TGraph *) fin1->Get(\"gProD0_0_10_noPid\");\n",
0168 "assert(gProD0_0_10_noPid);\n",
0169 "TGraph *gNonProD0_0_10_noPid = (TGraph *) fin1->Get(\"gNonProD0_0_10_noPid\");\n",
0170 "assert(gNonProD0_0_10_noPid);\n",
0171 "\n",
0172 "TGraph *gProD0_10_40_noPid = (TGraph *) fin1->Get(\"gProD0_10_40_noPid\");\n",
0173 "assert(gProD0_10_40_noPid);\n",
0174 "TGraph *gNonProD0_10_40_noPid = (TGraph *) fin1->Get(\"gNonProD0_10_40_noPid\");\n",
0175 "assert(gNonProD0_10_40_noPid);\n",
0176 "\n",
0177 "TGraph *gProD0_60_80_noPid = (TGraph *) fin1->Get(\"gProD0_60_80_noPid\");\n",
0178 "assert(gProD0_60_80_noPid);\n",
0179 "TGraph *gNonProD0_60_80_noPid = (TGraph *) fin1->Get(\"gNonProD0_60_80_noPid\");\n",
0180 "assert(gNonProD0_60_80_noPid);\n",
0181 "\n",
0182 "TGraph *gProD0_0_80_noPid = (TGraph *) fin1->Get(\"gProD0_0_80_noPid\");\n",
0183 "assert(gProD0_0_80_noPid);\n",
0184 "TGraph *gNonProD0_0_80_noPid = (TGraph *) fin1->Get(\"gNonProD0_0_80_noPid\");\n",
0185 "assert(gNonProD0_0_80_noPid);\n",
0186 "\n",
0187 "TFile *_file0 = new TFile(\"RAA_DB_theory.root\");\n",
0188 "assert(_file0);\n",
0189 "\n",
0190 "TGraph *RAA_pi = (TGraph *) _file0->GetObjectChecked(\"RAA_pi\", \"TGraph\");\n",
0191 "assert(RAA_pi);\n",
0192 "TGraph *RAA_D = (TGraph *) _file0->GetObjectChecked(\"RAA_D\", \"TGraph\");\n",
0193 "assert(RAA_D);\n",
0194 "TGraph *RAA_B = (TGraph *) _file0->GetObjectChecked(\"RAA_B\", \"TGraph\");\n",
0195 "assert(RAA_B);\n",
0196 "TGraph *RAA_D0_B = (TGraph *) _file0->GetObjectChecked(\"RAA_D0_B\", \"TGraph\");\n",
0197 "assert(RAA_D0_B);\n",
0198 "TGraphErrors *RAA_proj_D = (TGraphErrors *) _file0->GetObjectChecked(\"RAA_proj_D\", \"TGraph\");\n",
0199 "assert(RAA_proj_D);\n",
0200 "TGraphErrors *RAA_proj_D_B = (TGraphErrors *) _file0->GetObjectChecked(\"RAA_proj_D_B\", \"TGraph\");\n",
0201 "assert(RAA_proj_D_B);\n",
0202 "\n",
0203 "TFile *_file1 = new TFile(\"v2_DB_10_40.root\");\n",
0204 "assert(_file1);\n",
0205 "\n",
0206 "TGraph *v2_D = (TGraph *) _file1->GetObjectChecked(\"v2_D\", \"TGraph\");\n",
0207 "assert(v2_D);\n",
0208 "TGraph *v2_B = (TGraph *) _file1->GetObjectChecked(\"v2_B\", \"TGraph\");\n",
0209 "assert(v2_B);\n",
0210 "TGraph *v2_D_B = (TGraph *) _file1->GetObjectChecked(\"v2_D_B\", \"TGraph\");\n",
0211 "assert(v2_D_B);\n",
0212 "TGraphErrors *v2_proj_D = (TGraphErrors *) _file1->GetObjectChecked(\"v2_proj_D\", \"TGraph\");\n",
0213 "assert(v2_proj_D);\n",
0214 "TGraphErrors *v2_proj_D_B = (TGraphErrors *) _file1->GetObjectChecked(\"v2_proj_D_B\", \"TGraph\");\n",
0215 "assert(v2_proj_D_B);"
0216 ]
0217 },
0218 {
0219 "cell_type": "code",
0220 "execution_count": 5,
0221 "metadata": {},
0222 "outputs": [
0223 {
0224 "data": {
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\n",
0226 "text/plain": [
0227 "<IPython.core.display.Image object>"
0228 ]
0229 },
0230 "metadata": {},
0231 "output_type": "display_data"
0232 },
0233 {
0234 "name": "stdout",
0235 "output_type": "stream",
0236 "text": [
0237 "Save TH1 hframe\n",
0238 "Save TGraph Graph\n",
0239 "Save TGraph Graph\n",
0240 "Save TGraph Graph\n",
0241 "Save TGraph Graph\n",
0242 "Save TH1 hframe\n",
0243 "Save TGraph Graph\n",
0244 "Save TGraph Graph\n",
0245 "Save TGraph Graph\n",
0246 "Save TGraph Graph\n",
0247 "removed ‘fig_BUP2020/D0_BUP2020pp_significance_sPH_HF_2017_002.svg’\n"
0248 ]
0249 },
0250 {
0251 "name": "stderr",
0252 "output_type": "stream",
0253 "text": [
0254 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020pp_significance_sPH_HF_2017_002.png has been created\n",
0255 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020pp_significance_sPH_HF_2017_002.root has been created\n",
0256 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020pp_significance_sPH_HF_2017_002.eps has been created\n",
0257 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020pp_significance_sPH_HF_2017_002.svg has been created\n",
0258 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020pp_significance_sPH_HF_2017_002.C has been generated\n"
0259 ]
0260 }
0261 ],
0262 "source": [
0263 "{\n",
0264 " \n",
0265 " gProD0_0_10_noPid->SetMarkerColor(kRed+1);\n",
0266 " gProD0_10_40_noPid->SetMarkerColor(kBlue+1);\n",
0267 " gProD0_60_80_noPid->SetMarkerColor(kGreen+1);\n",
0268 " gProD0_0_80_noPid->SetMarkerColor(kBlack);\n",
0269 " \n",
0270 " gNonProD0_0_10_noPid->SetMarkerColor(kRed+1);\n",
0271 " gNonProD0_10_40_noPid->SetMarkerColor(kBlue+1);\n",
0272 " gNonProD0_60_80_noPid->SetMarkerColor(kGreen+1);\n",
0273 " gNonProD0_0_80_noPid->SetMarkerColor(kBlack);\n",
0274 " \n",
0275 " \n",
0276 " TString s_suffix = \"_sPH_HF_2017_002\";\n",
0277 "\n",
0278 " TCanvas *c1 = new TCanvas(\"D0_BUP2020pp_significance\" + s_suffix,\n",
0279 " \"D0_BUP2020pp_significance\" + s_suffix, 1100, 800);\n",
0280 " c1->Divide(2, 1);\n",
0281 " int idx = 1;\n",
0282 " TPad *p;\n",
0283 "\n",
0284 " p = (TPad *) c1->cd(idx++);\n",
0285 " c1->Update();\n",
0286 " p->DrawFrame(0, 0, 12, 3000)->SetTitle(\";p_{T} [GeV];Significance, Prompt D^{0}\");\n",
0287 " gProD0_0_80_noPid->DrawClone(\"p\");\n",
0288 " gProD0_0_10_noPid->DrawClone(\"p\");\n",
0289 " gProD0_10_40_noPid->DrawClone(\"p\");\n",
0290 " gProD0_60_80_noPid->DrawClone(\"p\");\n",
0291 "\n",
0292 " TLegend * leg = new TLegend(.5, .52, .9, .9);\n",
0293 " leg->SetFillStyle(0);\n",
0294 " leg->SetHeader(\"sPH-HF-2017-002\");\n",
0295 " leg->AddEntry(gProD0_0_80_noPid, \"0-80%\", \"p\");\n",
0296 " leg->AddEntry(gProD0_0_10_noPid, \"0-10%\", \"p\");\n",
0297 " leg->AddEntry(gProD0_10_40_noPid, \"10-40%\", \"p\");\n",
0298 " leg->AddEntry(gProD0_60_80_noPid, \"60-80%\", \"p\");\n",
0299 " leg->Draw();\n",
0300 " \n",
0301 " p = (TPad *) c1->cd(idx++);\n",
0302 " c1->Update();\n",
0303 " p->DrawFrame(0, 0, 12, 150)->SetTitle(\";p_{T} [GeV];Significance, NonPrompt D^{0}\");\n",
0304 " gNonProD0_0_80_noPid->DrawClone(\"p\");\n",
0305 " gNonProD0_0_10_noPid->DrawClone(\"p\");\n",
0306 " gNonProD0_10_40_noPid->DrawClone(\"p\");\n",
0307 " gNonProD0_60_80_noPid->DrawClone(\"p\");\n",
0308 "\n",
0309 " c1->Draw();\n",
0310 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
0311 "}"
0312 ]
0313 },
0314 {
0315 "cell_type": "markdown",
0316 "metadata": {},
0317 "source": [
0318 "# Significance calculations"
0319 ]
0320 },
0321 {
0322 "cell_type": "markdown",
0323 "metadata": {},
0324 "source": [
0325 "## Utilities"
0326 ]
0327 },
0328 {
0329 "cell_type": "code",
0330 "execution_count": 6,
0331 "metadata": {},
0332 "outputs": [],
0333 "source": [
0334 "%%cpp -d\n",
0335 "\n",
0336 "TGraph *GetSignificance(const TGraph *refAuAuSignificance, const double AuAu_centrality_ncoll, const double N_Collision, const double centrality_ncoll)\n",
0337 "{\n",
0338 " TGraph *ret = new TGraph(*refAuAuSignificance);\n",
0339 "\n",
0340 " double max(0);\n",
0341 " for (int i = 0; i < ret->GetN(); ++i)\n",
0342 " {\n",
0343 " ret->GetY()[i] *= sqrt(N_Collision * centrality_ncoll / refAuAuMB / AuAu_centrality_ncoll);\n",
0344 "\n",
0345 " max = std::max(max, ret->GetY()[i]);\n",
0346 " }\n",
0347 "\n",
0348 " ret->SetMaximum(max * 1.2);\n",
0349 " ret->SetTitle(Form(\"Significance for N_Collision = %.4e centrality_ncoll = %.4e, based on %s\",\n",
0350 " N_Collision, centrality_ncoll, refAuAuSignificance->GetTitle()));\n",
0351 " return ret;\n",
0352 "}"
0353 ]
0354 },
0355 {
0356 "cell_type": "markdown",
0357 "metadata": {},
0358 "source": [
0359 "## 3 year pp runs"
0360 ]
0361 },
0362 {
0363 "cell_type": "code",
0364 "execution_count": 7,
0365 "metadata": {},
0366 "outputs": [],
0367 "source": [
0368 " const TGraph *gProD0_Significance_pp_3year = GetSignificance(\n",
0369 " gProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0370 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
0371 " pp_inelastic_crosssec * pp_rec_3year, // const double N_Collision,\n",
0372 " 1 // const double centrality_ncoll\n",
0373 " );\n",
0374 " const TGraph *gNonProD0_Significance_pp_3year = GetSignificance(\n",
0375 " gNonProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0376 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
0377 " pp_inelastic_crosssec * pp_rec_3year, // const double N_Collision,\n",
0378 " 1 // const double centrality_ncoll\n",
0379 " );"
0380 ]
0381 },
0382 {
0383 "cell_type": "code",
0384 "execution_count": 8,
0385 "metadata": {},
0386 "outputs": [
0387 {
0388 "data": {
0389 "image/png": 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\n",
0390 "text/plain": [
0391 "<IPython.core.display.Image object>"
0392 ]
0393 },
0394 "metadata": {},
0395 "output_type": "display_data"
0396 },
0397 {
0398 "name": "stdout",
0399 "output_type": "stream",
0400 "text": [
0401 "Save TH1 hframe\n",
0402 "Save TGraph Graph\n",
0403 "Save TH1 hframe\n",
0404 "Save TGraph Graph\n",
0405 "removed ‘fig_BUP2020/D0_BUP2020pp_significance_3yr.svg’\n"
0406 ]
0407 },
0408 {
0409 "name": "stderr",
0410 "output_type": "stream",
0411 "text": [
0412 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020pp_significance_3yr.png has been created\n",
0413 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020pp_significance_3yr.root has been created\n",
0414 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020pp_significance_3yr.eps has been created\n",
0415 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020pp_significance_3yr.svg has been created\n",
0416 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020pp_significance_3yr.C has been generated\n"
0417 ]
0418 }
0419 ],
0420 "source": [
0421 "{\n",
0422 " TString s_suffix = \"_3yr\";\n",
0423 "\n",
0424 " TCanvas *c1 = new TCanvas(\"D0_BUP2020pp_significance\" + s_suffix,\n",
0425 " \"D0_BUP2020pp_significance\" + s_suffix, 1100, 800);\n",
0426 " c1->Divide(2, 1);\n",
0427 " int idx = 1;\n",
0428 " TPad *p;\n",
0429 "\n",
0430 " p = (TPad *) c1->cd(idx++);\n",
0431 " c1->Update();\n",
0432 " p->DrawFrame(0, 0, 12, gProD0_Significance_pp_3year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0433 " gProD0_Significance_pp_3year->DrawClone(\"p\");\n",
0434 "\n",
0435 " p = (TPad *) c1->cd(idx++);\n",
0436 " c1->Update();\n",
0437 " p->DrawFrame(0, 0, 12, gNonProD0_Significance_pp_3year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0438 " gNonProD0_Significance_pp_3year->DrawClone(\"p\");\n",
0439 "\n",
0440 " c1->Draw();\n",
0441 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
0442 "}"
0443 ]
0444 },
0445 {
0446 "cell_type": "markdown",
0447 "metadata": {},
0448 "source": [
0449 "## 5 year pp runs"
0450 ]
0451 },
0452 {
0453 "cell_type": "code",
0454 "execution_count": 9,
0455 "metadata": {},
0456 "outputs": [],
0457 "source": [
0458 " const TGraph *gProD0_Significance_pp_5year = GetSignificance(\n",
0459 " gProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0460 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
0461 " pp_inelastic_crosssec * pp_rec_5year, // const double N_Collision,\n",
0462 " 1 // const double centrality_ncoll\n",
0463 " );\n",
0464 " const TGraph *gNonProD0_Significance_pp_5year = GetSignificance(\n",
0465 " gNonProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0466 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
0467 " pp_inelastic_crosssec * pp_rec_5year, // const double N_Collision,\n",
0468 " 1 // const double centrality_ncoll\n",
0469 " );"
0470 ]
0471 },
0472 {
0473 "cell_type": "code",
0474 "execution_count": 10,
0475 "metadata": {},
0476 "outputs": [
0477 {
0478 "data": {
0479 "image/png": 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\n",
0480 "text/plain": [
0481 "<IPython.core.display.Image object>"
0482 ]
0483 },
0484 "metadata": {},
0485 "output_type": "display_data"
0486 },
0487 {
0488 "name": "stdout",
0489 "output_type": "stream",
0490 "text": [
0491 "Save TH1 hframe\n",
0492 "Save TGraph Graph\n",
0493 "Save TH1 hframe\n",
0494 "Save TGraph Graph\n",
0495 "removed ‘fig_BUP2020/D0_BUP2020pp_significance_5yr.svg’\n"
0496 ]
0497 },
0498 {
0499 "name": "stderr",
0500 "output_type": "stream",
0501 "text": [
0502 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020pp_significance_5yr.png has been created\n",
0503 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020pp_significance_5yr.root has been created\n",
0504 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020pp_significance_5yr.eps has been created\n",
0505 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020pp_significance_5yr.svg has been created\n",
0506 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020pp_significance_5yr.C has been generated\n"
0507 ]
0508 }
0509 ],
0510 "source": [
0511 "{\n",
0512 " TString s_suffix = \"_5yr\";\n",
0513 "\n",
0514 " TCanvas *c1 = new TCanvas(\"D0_BUP2020pp_significance\" + s_suffix,\n",
0515 " \"D0_BUP2020pp_significance\" + s_suffix, 1100, 800);\n",
0516 " c1->Divide(2, 1);\n",
0517 " int idx = 1;\n",
0518 " TPad *p;\n",
0519 "\n",
0520 " p = (TPad *) c1->cd(idx++);\n",
0521 " c1->Update();\n",
0522 " p->DrawFrame(0, 0, 12, gProD0_Significance_pp_5year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0523 " gProD0_Significance_pp_5year->DrawClone(\"p\");\n",
0524 "\n",
0525 " p = (TPad *) c1->cd(idx++);\n",
0526 " c1->Update();\n",
0527 " p->DrawFrame(0, 0, 12, gNonProD0_Significance_pp_5year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0528 " gNonProD0_Significance_pp_5year->DrawClone(\"p\");\n",
0529 "\n",
0530 " c1->Draw();\n",
0531 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
0532 "}"
0533 ]
0534 },
0535 {
0536 "cell_type": "markdown",
0537 "metadata": {},
0538 "source": [
0539 "## 3 year AuAu runs, 0-10%"
0540 ]
0541 },
0542 {
0543 "cell_type": "code",
0544 "execution_count": 11,
0545 "metadata": {},
0546 "outputs": [],
0547 "source": [
0548 "const TGraph *gProD0_Significance_AuAu_0_10_3year = GetSignificance(\n",
0549 " gProD0_0_10_noPid, // const TVectorD &refAuAuSignificance,\n",
0550 " 0.1 * AuAu_Ncoll_C0_10, // const double AuAu_centrality_ncoll,\n",
0551 " AuAu_rec_3year * refAuAuXSec, // const double N_Collision,\n",
0552 " 0.1 * AuAu_Ncoll_C0_10 // const double centrality_ncoll\n",
0553 ");\n",
0554 "const TGraph *gNonProD0_Significance_AuAu_0_10_3year = GetSignificance(\n",
0555 " gNonProD0_0_10_noPid, // const TVectorD &refAuAuSignificance,\n",
0556 " 0.1 * AuAu_Ncoll_C0_10, // const double AuAu_centrality_ncoll,\n",
0557 " AuAu_rec_3year * refAuAuXSec, // const double N_Collision,\n",
0558 " 0.1 * AuAu_Ncoll_C0_10 // const double centrality_ncoll\n",
0559 ");\n",
0560 "\n",
0561 "const TGraph *gProD0_Significance_AuAu_0_10_3year_20wk = GetSignificance(\n",
0562 " gProD0_0_10_noPid, // const TVectorD &refAuAuSignificance,\n",
0563 " 0.1 * AuAu_Ncoll_C0_10, // const double AuAu_centrality_ncoll,\n",
0564 " AuAu_rec_3year_20wk * refAuAuXSec, // const double N_Collision,\n",
0565 " 0.1 * AuAu_Ncoll_C0_10 // const double centrality_ncoll\n",
0566 ");\n",
0567 "const TGraph *gNonProD0_Significance_AuAu_0_10_3year_20wk = GetSignificance(\n",
0568 " gNonProD0_0_10_noPid, // const TVectorD &refAuAuSignificance,\n",
0569 " 0.1 * AuAu_Ncoll_C0_10, // const double AuAu_centrality_ncoll,\n",
0570 " AuAu_rec_3year_20wk * refAuAuXSec, // const double N_Collision,\n",
0571 " 0.1 * AuAu_Ncoll_C0_10 // const double centrality_ncoll\n",
0572 ");"
0573 ]
0574 },
0575 {
0576 "cell_type": "code",
0577 "execution_count": 12,
0578 "metadata": {},
0579 "outputs": [
0580 {
0581 "name": "stdout",
0582 "output_type": "stream",
0583 "text": [
0584 "(double) 1.4128164e+11\n"
0585 ]
0586 }
0587 ],
0588 "source": [
0589 "AuAu_rec_3year * refAuAuXSec"
0590 ]
0591 },
0592 {
0593 "cell_type": "code",
0594 "execution_count": 13,
0595 "metadata": {},
0596 "outputs": [
0597 {
0598 "data": {
0599 "image/png": 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\n",
0600 "text/plain": [
0601 "<IPython.core.display.Image object>"
0602 ]
0603 },
0604 "metadata": {},
0605 "output_type": "display_data"
0606 },
0607 {
0608 "name": "stdout",
0609 "output_type": "stream",
0610 "text": [
0611 "Save TH1 hframe\n",
0612 "Save TGraph Graph\n",
0613 "Save TH1 hframe\n",
0614 "Save TGraph Graph\n",
0615 "removed ‘fig_BUP2020/D0_BUP2020AuAu_0_10_significance_3yr.svg’\n"
0616 ]
0617 },
0618 {
0619 "name": "stderr",
0620 "output_type": "stream",
0621 "text": [
0622 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020AuAu_0_10_significance_3yr.png has been created\n",
0623 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020AuAu_0_10_significance_3yr.root has been created\n",
0624 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020AuAu_0_10_significance_3yr.eps has been created\n",
0625 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020AuAu_0_10_significance_3yr.svg has been created\n",
0626 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020AuAu_0_10_significance_3yr.C has been generated\n"
0627 ]
0628 }
0629 ],
0630 "source": [
0631 "{\n",
0632 " TString s_suffix = \"_3yr\";\n",
0633 "\n",
0634 " TCanvas *c1 = new TCanvas(\"D0_BUP2020AuAu_0_10_significance\" + s_suffix,\n",
0635 " \"D0_BUP2020AuAu_0_10_significance\" + s_suffix, 1100, 800);\n",
0636 " c1->Divide(2, 1);\n",
0637 " int idx = 1;\n",
0638 " TPad *p;\n",
0639 "\n",
0640 " p = (TPad *) c1->cd(idx++);\n",
0641 " c1->Update();\n",
0642 " p->DrawFrame(0, 0, 12, gProD0_Significance_AuAu_0_10_3year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0643 " gProD0_Significance_AuAu_0_10_3year->DrawClone(\"p\");\n",
0644 "\n",
0645 " p = (TPad *) c1->cd(idx++);\n",
0646 " c1->Update();\n",
0647 " p->DrawFrame(0, 0, 12, gNonProD0_Significance_AuAu_0_10_3year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0648 " gNonProD0_Significance_AuAu_0_10_3year->DrawClone(\"p\");\n",
0649 "\n",
0650 " c1->Draw();\n",
0651 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
0652 "}"
0653 ]
0654 },
0655 {
0656 "cell_type": "markdown",
0657 "metadata": {},
0658 "source": [
0659 "## 3 year AuAu runs, 10-40%"
0660 ]
0661 },
0662 {
0663 "cell_type": "code",
0664 "execution_count": 14,
0665 "metadata": {},
0666 "outputs": [],
0667 "source": [
0668 "const TGraph *gProD0_Significance_AuAu_10_40_3year = GetSignificance(\n",
0669 " gProD0_10_40_noPid, // const TVectorD &refAuAuSignificance,\n",
0670 " 1, // const double AuAu_centrality_ncoll,\n",
0671 " AuAu_rec_3year * refAuAuXSec, // const double N_Collision,\n",
0672 " 1 // const double centrality_ncoll\n",
0673 ");\n",
0674 "\n",
0675 "const TGraph *gNonProD0_Significance_AuAu_10_40_3year = GetSignificance(\n",
0676 " gNonProD0_10_40_noPid, // const TVectorD &refAuAuSignificance,\n",
0677 " 1, // const double AuAu_centrality_ncoll,\n",
0678 " AuAu_rec_3year * refAuAuXSec, // const double N_Collision,\n",
0679 " 1 // const double centrality_ncoll\n",
0680 ");"
0681 ]
0682 },
0683 {
0684 "cell_type": "code",
0685 "execution_count": 15,
0686 "metadata": {},
0687 "outputs": [
0688 {
0689 "data": {
0690 "image/png": 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\n",
0691 "text/plain": [
0692 "<IPython.core.display.Image object>"
0693 ]
0694 },
0695 "metadata": {},
0696 "output_type": "display_data"
0697 },
0698 {
0699 "name": "stdout",
0700 "output_type": "stream",
0701 "text": [
0702 "Save TH1 hframe\n",
0703 "Save TGraph Graph\n",
0704 "Save TH1 hframe\n",
0705 "Save TGraph Graph\n",
0706 "removed ‘fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.svg’\n"
0707 ]
0708 },
0709 {
0710 "name": "stderr",
0711 "output_type": "stream",
0712 "text": [
0713 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.png has been created\n",
0714 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.root has been created\n",
0715 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.eps has been created\n",
0716 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.svg has been created\n",
0717 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.C has been generated\n"
0718 ]
0719 }
0720 ],
0721 "source": [
0722 "{\n",
0723 " TString s_suffix = \"_3yr\";\n",
0724 "\n",
0725 "\n",
0726 " TCanvas *c1 = new TCanvas(\"D0_BUP2020AuAu_0_80_significance\" + s_suffix,\n",
0727 " \"D0_BUP2020AuAu_0_80_significance\" + s_suffix, 1100, 800);\n",
0728 " c1->Divide(2, 1);\n",
0729 " int idx = 1;\n",
0730 " TPad *p;\n",
0731 "\n",
0732 " p = (TPad *) c1->cd(idx++);\n",
0733 " c1->Update();\n",
0734 " p->DrawFrame(0, 0, 12, gProD0_Significance_AuAu_10_40_3year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0735 " gProD0_Significance_AuAu_10_40_3year->DrawClone(\"p\");\n",
0736 "\n",
0737 " p = (TPad *) c1->cd(idx++);\n",
0738 " c1->Update();\n",
0739 " p->DrawFrame(0, 0, 12, gNonProD0_Significance_AuAu_10_40_3year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0740 " gNonProD0_Significance_AuAu_10_40_3year->DrawClone(\"p\");\n",
0741 "\n",
0742 " c1->Draw();\n",
0743 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
0744 "}"
0745 ]
0746 },
0747 {
0748 "cell_type": "markdown",
0749 "metadata": {},
0750 "source": [
0751 "## 3 year AuAu runs, 0-80%"
0752 ]
0753 },
0754 {
0755 "cell_type": "code",
0756 "execution_count": 16,
0757 "metadata": {},
0758 "outputs": [],
0759 "source": [
0760 "const TGraph *gProD0_Significance_AuAu_0_80_3year = GetSignificance(\n",
0761 " gProD0_0_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0762 " 0.8 * AuAu_Ncoll_C0_100, // const double AuAu_centrality_ncoll,\n",
0763 " AuAu_rec_3year * refAuAuXSec, // const double N_Collision,\n",
0764 " 0.8 * AuAu_Ncoll_C0_100 // const double centrality_ncoll\n",
0765 ");\n",
0766 "const TGraph *gNonProD0_Significance_AuAu_0_80_3year = GetSignificance(\n",
0767 " gNonProD0_0_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0768 " 0.8 * AuAu_Ncoll_C0_100, // const double AuAu_centrality_ncoll,\n",
0769 " AuAu_rec_3year * refAuAuXSec, // const double N_Collision,\n",
0770 " 0.8 * AuAu_Ncoll_C0_100 // const double centrality_ncoll\n",
0771 ");\n",
0772 "\n",
0773 "const TGraph *gProD0_Significance_AuAu_0_80_3year_20wk = GetSignificance(\n",
0774 " gProD0_0_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0775 " 0.8 * AuAu_Ncoll_C0_100, // const double AuAu_centrality_ncoll,\n",
0776 " AuAu_rec_3year_20wk * refAuAuXSec, // const double N_Collision,\n",
0777 " 0.8 * AuAu_Ncoll_C0_100 // const double centrality_ncoll\n",
0778 ");\n",
0779 "const TGraph *gNonProD0_Significance_AuAu_0_80_3year_20wk = GetSignificance(\n",
0780 " gNonProD0_0_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0781 " 0.8 * AuAu_Ncoll_C0_100, // const double AuAu_centrality_ncoll,\n",
0782 " AuAu_rec_3year_20wk * refAuAuXSec, // const double N_Collision,\n",
0783 " 0.8 * AuAu_Ncoll_C0_100 // const double centrality_ncoll\n",
0784 ");"
0785 ]
0786 },
0787 {
0788 "cell_type": "code",
0789 "execution_count": 17,
0790 "metadata": {},
0791 "outputs": [
0792 {
0793 "data": {
0794 "image/png": 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\n",
0795 "text/plain": [
0796 "<IPython.core.display.Image object>"
0797 ]
0798 },
0799 "metadata": {},
0800 "output_type": "display_data"
0801 },
0802 {
0803 "name": "stdout",
0804 "output_type": "stream",
0805 "text": [
0806 "Save TH1 hframe\n",
0807 "Save TGraph Graph\n",
0808 "Save TH1 hframe\n",
0809 "Save TGraph Graph\n",
0810 "removed ‘fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.svg’\n"
0811 ]
0812 },
0813 {
0814 "name": "stderr",
0815 "output_type": "stream",
0816 "text": [
0817 "Warning in <TCanvas::Constructor>: Deleting canvas with same name: D0_BUP2020AuAu_0_80_significance_3yr\n",
0818 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.png has been created\n",
0819 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.root has been created\n",
0820 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.eps has been created\n",
0821 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.svg has been created\n",
0822 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020AuAu_0_80_significance_3yr.C has been generated\n"
0823 ]
0824 }
0825 ],
0826 "source": [
0827 "{\n",
0828 " TString s_suffix = \"_3yr\";\n",
0829 "\n",
0830 "\n",
0831 " TCanvas *c1 = new TCanvas(\"D0_BUP2020AuAu_0_80_significance\" + s_suffix,\n",
0832 " \"D0_BUP2020AuAu_0_80_significance\" + s_suffix, 1100, 800);\n",
0833 " c1->Divide(2, 1);\n",
0834 " int idx = 1;\n",
0835 " TPad *p;\n",
0836 "\n",
0837 " p = (TPad *) c1->cd(idx++);\n",
0838 " c1->Update();\n",
0839 " p->DrawFrame(0, 0, 12, gProD0_Significance_AuAu_0_80_3year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0840 " gProD0_Significance_AuAu_0_80_3year->DrawClone(\"p\");\n",
0841 "\n",
0842 " p = (TPad *) c1->cd(idx++);\n",
0843 " c1->Update();\n",
0844 " p->DrawFrame(0, 0, 12, gNonProD0_Significance_AuAu_0_80_3year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0845 " gNonProD0_Significance_AuAu_0_80_3year->DrawClone(\"p\");\n",
0846 "\n",
0847 " c1->Draw();\n",
0848 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
0849 "}"
0850 ]
0851 },
0852 {
0853 "cell_type": "markdown",
0854 "metadata": {},
0855 "source": [
0856 "## 5 year AuAu runs, 0-80%"
0857 ]
0858 },
0859 {
0860 "cell_type": "code",
0861 "execution_count": 18,
0862 "metadata": {},
0863 "outputs": [],
0864 "source": [
0865 "const TGraph *gProD0_Significance_AuAu_0_80_5year = GetSignificance(\n",
0866 " gProD0_0_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0867 " 0.8 * AuAu_Ncoll_C0_100, // const double AuAu_centrality_ncoll,\n",
0868 " AuAu_rec_5year * refAuAuXSec, // const double N_Collision,\n",
0869 " 0.8 * AuAu_Ncoll_C0_100 // const double centrality_ncoll\n",
0870 ");\n",
0871 "const TGraph *gNonProD0_Significance_AuAu_0_80_5year = GetSignificance(\n",
0872 " gNonProD0_0_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0873 " 0.8 * AuAu_Ncoll_C0_100, // const double AuAu_centrality_ncoll,\n",
0874 " AuAu_rec_5year * refAuAuXSec, // const double N_Collision,\n",
0875 " 0.8 * AuAu_Ncoll_C0_100 // const double centrality_ncoll\n",
0876 ");"
0877 ]
0878 },
0879 {
0880 "cell_type": "code",
0881 "execution_count": 19,
0882 "metadata": {},
0883 "outputs": [
0884 {
0885 "data": {
0886 "image/png": 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\n",
0887 "text/plain": [
0888 "<IPython.core.display.Image object>"
0889 ]
0890 },
0891 "metadata": {},
0892 "output_type": "display_data"
0893 },
0894 {
0895 "name": "stdout",
0896 "output_type": "stream",
0897 "text": [
0898 "Save TH1 hframe\n",
0899 "Save TGraph Graph\n",
0900 "Save TH1 hframe\n",
0901 "Save TGraph Graph\n",
0902 "removed ‘fig_BUP2020/D0_BUP2020AuAu_0_80_significance_5yr.svg’\n"
0903 ]
0904 },
0905 {
0906 "name": "stderr",
0907 "output_type": "stream",
0908 "text": [
0909 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_5yr.png has been created\n",
0910 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_5yr.root has been created\n",
0911 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_5yr.eps has been created\n",
0912 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020AuAu_0_80_significance_5yr.svg has been created\n",
0913 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020AuAu_0_80_significance_5yr.C has been generated\n"
0914 ]
0915 }
0916 ],
0917 "source": [
0918 "{\n",
0919 " TString s_suffix = \"_5yr\";\n",
0920 "\n",
0921 "\n",
0922 " TCanvas *c1 = new TCanvas(\"D0_BUP2020AuAu_0_80_significance\" + s_suffix,\n",
0923 " \"D0_BUP2020AuAu_0_80_significance\" + s_suffix, 1100, 800);\n",
0924 " c1->Divide(2, 1);\n",
0925 " int idx = 1;\n",
0926 " TPad *p;\n",
0927 "\n",
0928 " p = (TPad *) c1->cd(idx++);\n",
0929 " c1->Update();\n",
0930 " p->DrawFrame(0, 0, 12, gProD0_Significance_AuAu_0_80_5year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0931 " gProD0_Significance_AuAu_0_80_5year->DrawClone(\"p\");\n",
0932 "\n",
0933 " p = (TPad *) c1->cd(idx++);\n",
0934 " c1->Update();\n",
0935 " p->DrawFrame(0, 0, 12, gNonProD0_Significance_AuAu_0_80_5year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
0936 " gNonProD0_Significance_AuAu_0_80_5year->DrawClone(\"p\");\n",
0937 "\n",
0938 " c1->Draw();\n",
0939 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
0940 "}"
0941 ]
0942 },
0943 {
0944 "cell_type": "markdown",
0945 "metadata": {},
0946 "source": [
0947 "## 3 year pAu runs"
0948 ]
0949 },
0950 {
0951 "cell_type": "code",
0952 "execution_count": 20,
0953 "metadata": {},
0954 "outputs": [],
0955 "source": [
0956 "const TGraph *gProD0_Significance_pAu_3year = GetSignificance(\n",
0957 " gProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0958 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
0959 " pAu_rec_3year * pAu_inelastic_crosssec, // const double N_Collision,\n",
0960 " pAu_Ncoll_C0_100 // const double centrality_ncoll\n",
0961 ");\n",
0962 "\n",
0963 "const TGraph *gNonProD0_Significance_pAu_3year = GetSignificance(\n",
0964 " gNonProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0965 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
0966 " pAu_rec_3year * pAu_inelastic_crosssec, // const double N_Collision,\n",
0967 " pAu_Ncoll_C0_100 // const double centrality_ncoll\n",
0968 ");\n",
0969 "\n",
0970 "const TGraph *gProD0_Significance_pAu_C0_5_3year = GetSignificance(\n",
0971 " gProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0972 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
0973 " pAu_C0_5_trig_3year * pAu_inelastic_crosssec, // const double N_Collision,\n",
0974 " 0.05 * pAu_Ncoll_C0_5 // const double centrality_ncoll\n",
0975 ");\n",
0976 "\n",
0977 "const TGraph *gNonProD0_Significance_pAu_C0_5_3year = GetSignificance(\n",
0978 " gNonProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
0979 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
0980 " pAu_C0_5_trig_3year * pAu_inelastic_crosssec, // const double N_Collision,\n",
0981 " 0.05 * pAu_Ncoll_C0_5 // const double centrality_ncoll\n",
0982 ");"
0983 ]
0984 },
0985 {
0986 "cell_type": "markdown",
0987 "metadata": {},
0988 "source": [
0989 "## 5 year OO runs"
0990 ]
0991 },
0992 {
0993 "cell_type": "code",
0994 "execution_count": 21,
0995 "metadata": {},
0996 "outputs": [],
0997 "source": [
0998 "const TGraph *gProD0_Significance_OO_5year = GetSignificance(\n",
0999 " gProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
1000 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
1001 " OO_rec_5year * OO_inelastic_crosssec, // const double N_Collision,\n",
1002 " OO_Ncoll_C0_100 // const double centrality_ncoll\n",
1003 ");\n",
1004 "\n",
1005 "const TGraph *gNonProD0_Significance_OO_5year = GetSignificance(\n",
1006 " gNonProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
1007 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
1008 " OO_rec_5year * OO_inelastic_crosssec, // const double N_Collision,\n",
1009 " OO_Ncoll_C0_100 // const double centrality_ncoll\n",
1010 ");"
1011 ]
1012 },
1013 {
1014 "cell_type": "code",
1015 "execution_count": 22,
1016 "metadata": {},
1017 "outputs": [
1018 {
1019 "data": {
1020 "image/png": 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\n",
1021 "text/plain": [
1022 "<IPython.core.display.Image object>"
1023 ]
1024 },
1025 "metadata": {},
1026 "output_type": "display_data"
1027 },
1028 {
1029 "name": "stdout",
1030 "output_type": "stream",
1031 "text": [
1032 "Save TH1 hframe\n",
1033 "Save TGraph Graph\n",
1034 "Save TH1 hframe\n",
1035 "Save TGraph Graph\n",
1036 "removed ‘fig_BUP2020/D0_BUP2020OO_significance_5yr.svg’\n"
1037 ]
1038 },
1039 {
1040 "name": "stderr",
1041 "output_type": "stream",
1042 "text": [
1043 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020OO_significance_5yr.png has been created\n",
1044 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020OO_significance_5yr.root has been created\n",
1045 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020OO_significance_5yr.eps has been created\n",
1046 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020OO_significance_5yr.svg has been created\n",
1047 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020OO_significance_5yr.C has been generated\n"
1048 ]
1049 }
1050 ],
1051 "source": [
1052 "{\n",
1053 " TString s_suffix = \"_5yr\";\n",
1054 "\n",
1055 "\n",
1056 " TCanvas *c1 = new TCanvas(\"D0_BUP2020OO_significance\" + s_suffix,\n",
1057 " \"D0_BUP2020OO_significance\" + s_suffix, 1100, 800);\n",
1058 " c1->Divide(2, 1);\n",
1059 " int idx = 1;\n",
1060 " TPad *p;\n",
1061 "\n",
1062 " p = (TPad *) c1->cd(idx++);\n",
1063 " c1->Update();\n",
1064 " p->DrawFrame(0, 0, 12, gProD0_Significance_OO_5year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
1065 " gProD0_Significance_OO_5year->DrawClone(\"p\");\n",
1066 "\n",
1067 " p = (TPad *) c1->cd(idx++);\n",
1068 " c1->Update();\n",
1069 " p->DrawFrame(0, 0, 12, gNonProD0_Significance_OO_5year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
1070 " gNonProD0_Significance_OO_5year->DrawClone(\"p\");\n",
1071 "\n",
1072 " c1->Draw();\n",
1073 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
1074 "}"
1075 ]
1076 },
1077 {
1078 "cell_type": "code",
1079 "execution_count": 23,
1080 "metadata": {},
1081 "outputs": [
1082 {
1083 "name": "stdout",
1084 "output_type": "stream",
1085 "text": [
1086 "(double) 41.519180\n"
1087 ]
1088 }
1089 ],
1090 "source": [
1091 "OO_rec_5year*OO_inelastic_crosssec/1e9"
1092 ]
1093 },
1094 {
1095 "cell_type": "code",
1096 "execution_count": 24,
1097 "metadata": {},
1098 "outputs": [
1099 {
1100 "name": "stdout",
1101 "output_type": "stream",
1102 "text": [
1103 "(double) 398.58413\n"
1104 ]
1105 }
1106 ],
1107 "source": [
1108 "OO_rec_5year*OO_inelastic_crosssec/1e9*OO_Ncoll_C0_100"
1109 ]
1110 },
1111 {
1112 "cell_type": "code",
1113 "execution_count": 25,
1114 "metadata": {},
1115 "outputs": [
1116 {
1117 "name": "stdout",
1118 "output_type": "stream",
1119 "text": [
1120 "(double) 1467.1996\n"
1121 ]
1122 }
1123 ],
1124 "source": [
1125 "AuAu_rec_5year*refAuAuXSec*.1*(AuAu_Ncoll_60_70+AuAu_Ncoll_70_80)/1e9"
1126 ]
1127 },
1128 {
1129 "cell_type": "code",
1130 "execution_count": 26,
1131 "metadata": {},
1132 "outputs": [
1133 {
1134 "name": "stdout",
1135 "output_type": "stream",
1136 "text": [
1137 "(double) 4.0000000\n"
1138 ]
1139 }
1140 ],
1141 "source": [
1142 "pow(2,2)"
1143 ]
1144 },
1145 {
1146 "cell_type": "markdown",
1147 "metadata": {},
1148 "source": [
1149 "## 5 year ArAr runs"
1150 ]
1151 },
1152 {
1153 "cell_type": "code",
1154 "execution_count": 27,
1155 "metadata": {},
1156 "outputs": [],
1157 "source": [
1158 "const TGraph *gProD0_Significance_ArAr_5year = GetSignificance(\n",
1159 " gProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
1160 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
1161 " ArAr_rec_5year * ArAr_inelastic_crosssec, // const double N_Collision,\n",
1162 " ArAr_Ncoll_C0_100 // const double centrality_ncoll\n",
1163 ");\n",
1164 "\n",
1165 "const TGraph *gNonProD0_Significance_ArAr_5year = GetSignificance(\n",
1166 " gNonProD0_60_80_noPid, // const TVectorD &refAuAuSignificance,\n",
1167 " 0.1 * (AuAu_Ncoll_60_70 + AuAu_Ncoll_70_80), // const double AuAu_centrality_ncoll,\n",
1168 " ArAr_rec_5year * ArAr_inelastic_crosssec, // const double N_Collision,\n",
1169 " ArAr_Ncoll_C0_100 // const double centrality_ncoll\n",
1170 ");"
1171 ]
1172 },
1173 {
1174 "cell_type": "code",
1175 "execution_count": 28,
1176 "metadata": {},
1177 "outputs": [
1178 {
1179 "data": {
1180 "image/png": 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\n",
1181 "text/plain": [
1182 "<IPython.core.display.Image object>"
1183 ]
1184 },
1185 "metadata": {},
1186 "output_type": "display_data"
1187 },
1188 {
1189 "name": "stdout",
1190 "output_type": "stream",
1191 "text": [
1192 "Save TH1 hframe\n",
1193 "Save TGraph Graph\n",
1194 "Save TH1 hframe\n",
1195 "Save TGraph Graph\n",
1196 "removed ‘fig_BUP2020/D0_BUP2020ArAr_significance_5yr.svg’\n"
1197 ]
1198 },
1199 {
1200 "name": "stderr",
1201 "output_type": "stream",
1202 "text": [
1203 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020ArAr_significance_5yr.png has been created\n",
1204 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020ArAr_significance_5yr.root has been created\n",
1205 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020ArAr_significance_5yr.eps has been created\n",
1206 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020ArAr_significance_5yr.svg has been created\n",
1207 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020ArAr_significance_5yr.C has been generated\n"
1208 ]
1209 }
1210 ],
1211 "source": [
1212 "{\n",
1213 " TString s_suffix = \"_5yr\";\n",
1214 "\n",
1215 "\n",
1216 " TCanvas *c1 = new TCanvas(\"D0_BUP2020ArAr_significance\" + s_suffix,\n",
1217 " \"D0_BUP2020ArAr_significance\" + s_suffix, 1100, 800);\n",
1218 " c1->Divide(2, 1);\n",
1219 " int idx = 1;\n",
1220 " TPad *p;\n",
1221 "\n",
1222 " p = (TPad *) c1->cd(idx++);\n",
1223 " c1->Update();\n",
1224 " p->DrawFrame(0, 0, 12, gProD0_Significance_ArAr_5year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
1225 " gProD0_Significance_ArAr_5year->DrawClone(\"p\");\n",
1226 "\n",
1227 " p = (TPad *) c1->cd(idx++);\n",
1228 " c1->Update();\n",
1229 " p->DrawFrame(0, 0, 12, gNonProD0_Significance_ArAr_5year->GetMaximum())->SetTitle(\";p_{T} [GeV];Significance\");\n",
1230 " gNonProD0_Significance_ArAr_5year->DrawClone(\"p\");\n",
1231 "\n",
1232 " c1->Draw();\n",
1233 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
1234 "}"
1235 ]
1236 },
1237 {
1238 "cell_type": "markdown",
1239 "metadata": {},
1240 "source": [
1241 "# RAA projection"
1242 ]
1243 },
1244 {
1245 "cell_type": "markdown",
1246 "metadata": {},
1247 "source": [
1248 "## Utilities"
1249 ]
1250 },
1251 {
1252 "cell_type": "code",
1253 "execution_count": 29,
1254 "metadata": {},
1255 "outputs": [],
1256 "source": [
1257 "%%cpp -d\n",
1258 "\n",
1259 "TGraphErrors *Significance2RAA(const TGraph *AASignificance, const TGraph *refSignificance, const TGraph *RAACentroid)\n",
1260 "{\n",
1261 " assert(AASignificance);\n",
1262 " assert(refSignificance);\n",
1263 " assert(RAACentroid);\n",
1264 " \n",
1265 "// AASignificance->Print();\n",
1266 "// refSignificance->Print();\n",
1267 "// RAACentroid->Print();\n",
1268 " \n",
1269 " assert(AASignificance->GetN() == refSignificance->GetN());\n",
1270 " \n",
1271 " const int npoint = AASignificance->GetN() ;\n",
1272 " \n",
1273 " TVectorD x(npoint, AASignificance->GetX());\n",
1274 " TVectorD y(npoint);\n",
1275 " TVectorD ex(npoint);\n",
1276 " TVectorD ey(npoint);\n",
1277 " \n",
1278 " for (int i = 0; i<npoint; ++i)\n",
1279 " {\n",
1280 " y[i] = RAACentroid->Eval(x[i]); \n",
1281 " ey[i] = y[i] * sqrt(1/pow( AASignificance->GetY()[i], 2) + 1/pow( refSignificance->GetY()[i], 2)); \n",
1282 " } \n",
1283 " \n",
1284 " TGraphErrors * gr = new TGraphErrors(x, y, ex, ey);\n",
1285 " \n",
1286 " return gr;\n",
1287 "}\n",
1288 "\n",
1289 "TGraphErrors *Significance2RAA(const TGraph *AASignificance, const TGraph *refSignificance, const double RAACentroid)\n",
1290 "{\n",
1291 " assert(AASignificance);\n",
1292 " assert(refSignificance);\n",
1293 " \n",
1294 " \n",
1295 " assert(AASignificance->GetN() == refSignificance->GetN());\n",
1296 " \n",
1297 " const int npoint = AASignificance->GetN() ;\n",
1298 " \n",
1299 " TVectorD x(npoint, AASignificance->GetX());\n",
1300 " TVectorD y(npoint);\n",
1301 " TVectorD ex(npoint);\n",
1302 " TVectorD ey(npoint);\n",
1303 " \n",
1304 " for (int i = 0; i<npoint; ++i)\n",
1305 " {\n",
1306 " y[i] = RAACentroid; \n",
1307 " ey[i] = y[i] * sqrt(1/pow( AASignificance->GetY()[i], 2) + 1/pow( refSignificance->GetY()[i], 2)); \n",
1308 " } \n",
1309 " \n",
1310 " TGraphErrors * gr = new TGraphErrors(x, y, ex, ey);\n",
1311 " \n",
1312 " return gr;\n",
1313 "}\n",
1314 "\n",
1315 "\n",
1316 "TGraphErrors *GraphShiftCut(TGraphErrors * gr_src, const double x_shift, const double x_min, const double x_max)\n",
1317 "{\n",
1318 " assert(gr_src);\n",
1319 " \n",
1320 " \n",
1321 " const int npoint = gr_src->GetN() ;\n",
1322 " \n",
1323 " TVectorD vx(npoint);\n",
1324 " TVectorD vy(npoint);\n",
1325 " TVectorD vex(npoint);\n",
1326 " TVectorD vey(npoint);\n",
1327 " \n",
1328 " int nfilled = 0;\n",
1329 " for (int i = 0; i<npoint; ++i)\n",
1330 " {\n",
1331 " const double & x = gr_src->GetX()[i];\n",
1332 " if (x<x_min or x>x_max) continue;\n",
1333 " \n",
1334 " vx[nfilled] = x + x_shift;\n",
1335 " vy[nfilled] = gr_src->GetY()[i];\n",
1336 " vex[nfilled] = gr_src->GetEX()[i];\n",
1337 " vey[nfilled] = gr_src->GetEY()[i];\n",
1338 " \n",
1339 " ++nfilled;\n",
1340 " } \n",
1341 " \n",
1342 " TGraphErrors * gr = new TGraphErrors(nfilled, vx. GetMatrixArray (), vy. GetMatrixArray (), \n",
1343 " vex.GetMatrixArray (), vey. GetMatrixArray ());\n",
1344 " \n",
1345 " return gr;\n",
1346 "}"
1347 ]
1348 },
1349 {
1350 "cell_type": "markdown",
1351 "metadata": {},
1352 "source": [
1353 "## Projections"
1354 ]
1355 },
1356 {
1357 "cell_type": "code",
1358 "execution_count": 30,
1359 "metadata": {},
1360 "outputs": [
1361 {
1362 "name": "stdout",
1363 "output_type": "stream",
1364 "text": [
1365 "(const TGraph *) 0x7f76b2526760\n"
1366 ]
1367 }
1368 ],
1369 "source": [
1370 "gProD0_Significance_pp_3year"
1371 ]
1372 },
1373 {
1374 "cell_type": "code",
1375 "execution_count": 31,
1376 "metadata": {},
1377 "outputs": [
1378 {
1379 "data": {
1380 "image/png": 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\n",
1381 "text/plain": [
1382 "<IPython.core.display.Image object>"
1383 ]
1384 },
1385 "metadata": {},
1386 "output_type": "display_data"
1387 },
1388 {
1389 "name": "stdout",
1390 "output_type": "stream",
1391 "text": [
1392 "Save TH1 hframe\n",
1393 "Save TGraph RAA_pi\n",
1394 "Save TGraph RAA_B\n",
1395 "Save TGraph RAA_D\n",
1396 "Save TGraph RAA_D0_B\n",
1397 "Save TGraph Graph\n",
1398 "Save TGraph Graph\n",
1399 "removed ‘fig_BUP2020/D0_BUP2020_AuAu_RAA_3yr.svg’\n"
1400 ]
1401 },
1402 {
1403 "name": "stderr",
1404 "output_type": "stream",
1405 "text": [
1406 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1407 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1408 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1409 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1410 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1411 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1412 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1413 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1414 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1415 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1416 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1417 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1418 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1419 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1420 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1421 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1422 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1423 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1424 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1425 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1426 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1427 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1428 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1429 "Warning in <TFile::Append>: Replacing existing TH1: Graph (Potential memory leak).\n",
1430 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020_AuAu_RAA_3yr.png has been created\n",
1431 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020_AuAu_RAA_3yr.root has been created\n",
1432 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020_AuAu_RAA_3yr.eps has been created\n",
1433 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020_AuAu_RAA_3yr.svg has been created\n",
1434 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020_AuAu_RAA_3yr.C has been generated\n"
1435 ]
1436 }
1437 ],
1438 "source": [
1439 "{\n",
1440 " TString s_suffix = \"_3yr\";\n",
1441 "\n",
1442 " TGraphErrors * grProD0_RAA_3year \n",
1443 " = GraphShiftCut(\n",
1444 " Significance2RAA( gProD0_Significance_AuAu_0_10_3year, gProD0_Significance_pp_3year, RAA_D),\n",
1445 " 0., 0,100);\n",
1446 " TGraphErrors * grNonProD0_RAA_3year \n",
1447 " = GraphShiftCut(\n",
1448 " Significance2RAA( gNonProD0_Significance_AuAu_0_10_3year, gNonProD0_Significance_pp_3year, RAA_D0_B),\n",
1449 " 0., 1.9,100);\n",
1450 " \n",
1451 " grProD0_RAA_3year->SetMarkerStyle(kFullCircle);\n",
1452 " grNonProD0_RAA_3year->SetMarkerStyle(kFullSquare);\n",
1453 " \n",
1454 " \n",
1455 " grProD0_RAA_3year->SetMarkerSize(2);\n",
1456 " grNonProD0_RAA_3year->SetMarkerSize(2);\n",
1457 " \n",
1458 " grProD0_RAA_3year->SetLineWidth(4);\n",
1459 " grNonProD0_RAA_3year->SetLineWidth(4);\n",
1460 "// grProD0_RAA_3year->SetLineStyle(kDashed);\n",
1461 "// grNonProD0_RAA_3year->SetLineStyle(kDashed);\n",
1462 " \n",
1463 " grProD0_RAA_3year->SetLineColorAlpha(kBlack, 1);\n",
1464 " grNonProD0_RAA_3year->SetLineColorAlpha(kBlue+2, 1);\n",
1465 " \n",
1466 " grProD0_RAA_3year->SetMarkerColorAlpha(kBlack, 1);\n",
1467 " grNonProD0_RAA_3year->SetMarkerColorAlpha(kBlue+1, 1);\n",
1468 " \n",
1469 " RAA_pi->SetLineColorAlpha(kGreen+2, 1);\n",
1470 " RAA_B->SetLineColorAlpha(kBlue-4, 1);\n",
1471 " RAA_D->SetLineColorAlpha(kBlack, 1);\n",
1472 " RAA_D0_B->SetLineColorAlpha(kBlue+1, 1);\n",
1473 " \n",
1474 " \n",
1475 " RAA_pi->SetLineStyle(kSolid );\n",
1476 " RAA_B->SetLineStyle(kSolid );\n",
1477 " RAA_D->SetLineStyle(kDashed);\n",
1478 " RAA_D0_B->SetLineStyle(kDashed);\n",
1479 " \n",
1480 " TCanvas *c1 = new TCanvas(\"D0_BUP2020_AuAu_RAA\" + s_suffix,\n",
1481 " \"D0_BUP2020_AuAu_RAA\" + s_suffix, 1100, 800);\n",
1482 " c1->Divide(1, 1);\n",
1483 " int idx = 1;\n",
1484 " TPad *p;\n",
1485 "\n",
1486 " p = (TPad *) c1->cd(idx++);\n",
1487 " c1->Update();\n",
1488 " p->DrawFrame(0, 0, 11, 1.6)->SetTitle(\";#it{p}_{T} [GeV];#it{R}_{AA}\");\n",
1489 " \n",
1490 " RAA_pi->Draw();\n",
1491 " RAA_B->Draw();\n",
1492 " RAA_D->Draw();\n",
1493 " RAA_D0_B->Draw();\n",
1494 " \n",
1495 " grProD0_RAA_3year->DrawClone(\"p\");\n",
1496 " grNonProD0_RAA_3year->DrawClone(\"p\");\n",
1497 " \n",
1498 " TLegend *leg = new TLegend(0, .78, .85, .9);\n",
1499 " leg->SetFillStyle(0);\n",
1500 " leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection, 0-10% Au+Au, Years 1-3\", \"\");\n",
1501 " leg->AddEntry(\"\", Form(\"%.1f pb^{-1} str. #it{p}+#it{p}, %.0f nb^{-1} rec. Au+Au\", pp_rec_3year/1e12,\n",
1502 " AuAu_rec_3year/1e9 )\n",
1503 " , \"\");\n",
1504 " leg->Draw();\n",
1505 " \n",
1506 " \n",
1507 " leg = new TLegend(.65, .52, .9, .77);\n",
1508 " leg->SetFillStyle(0);\n",
1509 " leg->AddEntry(RAA_B, \"#it{B}-meson\", \"l\");\n",
1510 " leg->AddEntry(grNonProD0_RAA_3year, \"#it{B}#rightarrow#it{D}^{0}\", \"lp\");\n",
1511 " leg->AddEntry(grProD0_RAA_3year, \"Prompt #it{D}^{0}\", \"lp\");\n",
1512 " leg->AddEntry(RAA_pi, \"#pi\", \"l\");\n",
1513 " leg->Draw();\n",
1514 "\n",
1515 " c1->Draw();\n",
1516 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
1517 "}"
1518 ]
1519 },
1520 {
1521 "cell_type": "code",
1522 "execution_count": 32,
1523 "metadata": {},
1524 "outputs": [
1525 {
1526 "data": {
1527 "image/png": 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\n",
1528 "text/plain": [
1529 "<IPython.core.display.Image object>"
1530 ]
1531 },
1532 "metadata": {},
1533 "output_type": "display_data"
1534 },
1535 {
1536 "name": "stdout",
1537 "output_type": "stream",
1538 "text": [
1539 "Save TH1 hframe\n",
1540 "Save TGraph Graph\n",
1541 "Save TGraph Graph\n",
1542 "Save TGraph Graph\n",
1543 "Save TGraph Graph\n",
1544 "removed ‘fig_BUP2020/D0_BUP2020OOArAr_RAA_5yr.svg’\n"
1545 ]
1546 },
1547 {
1548 "name": "stderr",
1549 "output_type": "stream",
1550 "text": [
1551 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020OOArAr_RAA_5yr.png has been created\n",
1552 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020OOArAr_RAA_5yr.root has been created\n",
1553 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020OOArAr_RAA_5yr.eps has been created\n",
1554 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020OOArAr_RAA_5yr.svg has been created\n",
1555 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020OOArAr_RAA_5yr.C has been generated\n"
1556 ]
1557 }
1558 ],
1559 "source": [
1560 "{\n",
1561 " TString s_suffix = \"_5yr\";\n",
1562 "\n",
1563 " TGraphErrors * grProD0_RAA_OO_5year \n",
1564 " = GraphShiftCut(\n",
1565 " Significance2RAA( gProD0_Significance_OO_5year, gProD0_Significance_pp_5year, RAA_D),\n",
1566 " 0.2, 0,100);\n",
1567 " TGraphErrors * grNonProD0_RAA_OO_5year \n",
1568 " = GraphShiftCut(\n",
1569 " Significance2RAA( gNonProD0_Significance_OO_5year, gNonProD0_Significance_pp_5year, RAA_D0_B),\n",
1570 " 0.2, 1.9,100);\n",
1571 " \n",
1572 "\n",
1573 " TGraphErrors * grProD0_RAA_ArAr_5year \n",
1574 " = GraphShiftCut(Significance2RAA( gProD0_Significance_ArAr_5year, gProD0_Significance_pp_5year, RAA_D),\n",
1575 " 0., 0,100);\n",
1576 " TGraphErrors * grNonProD0_RAA_ArAr_5year \n",
1577 " = GraphShiftCut(\n",
1578 " Significance2RAA( gNonProD0_Significance_ArAr_5year, gNonProD0_Significance_pp_5year, RAA_D0_B),\n",
1579 " 0., 1.9,100);\n",
1580 " \n",
1581 " grProD0_RAA_OO_5year->SetMarkerStyle(kOpenCircle);\n",
1582 " grNonProD0_RAA_OO_5year->SetMarkerStyle(kOpenSquare);\n",
1583 " grProD0_RAA_ArAr_5year->SetMarkerStyle(kFullCircle);\n",
1584 " grNonProD0_RAA_ArAr_5year->SetMarkerStyle(kFullSquare);\n",
1585 " \n",
1586 " \n",
1587 " grProD0_RAA_OO_5year->SetMarkerSize(2);\n",
1588 " grNonProD0_RAA_OO_5year->SetMarkerSize(2);\n",
1589 " grProD0_RAA_ArAr_5year->SetMarkerSize(2);\n",
1590 " grNonProD0_RAA_ArAr_5year->SetMarkerSize(2);\n",
1591 " \n",
1592 " grProD0_RAA_OO_5year->SetLineWidth(4);\n",
1593 " grNonProD0_RAA_OO_5year->SetLineWidth(4);\n",
1594 " grProD0_RAA_ArAr_5year->SetLineWidth(4);\n",
1595 " grNonProD0_RAA_ArAr_5year->SetLineWidth(4);\n",
1596 " \n",
1597 " grProD0_RAA_OO_5year->SetLineColorAlpha(kBlack, 1);\n",
1598 " grNonProD0_RAA_OO_5year->SetLineColorAlpha(kBlue+2, 1);\n",
1599 " grProD0_RAA_ArAr_5year->SetLineColorAlpha(kBlack, 1);\n",
1600 " grNonProD0_RAA_ArAr_5year->SetLineColorAlpha(kBlue+2, 1);\n",
1601 " \n",
1602 " grProD0_RAA_OO_5year->SetMarkerColorAlpha(kBlack, 1);\n",
1603 " grNonProD0_RAA_OO_5year->SetMarkerColorAlpha(kBlue+2, 1);\n",
1604 " grProD0_RAA_ArAr_5year->SetMarkerColorAlpha(kBlack, 1);\n",
1605 " grNonProD0_RAA_ArAr_5year->SetMarkerColorAlpha(kBlue+2, 1);\n",
1606 " \n",
1607 " TCanvas *c1 = new TCanvas(\"D0_BUP2020OOArAr_RAA\" + s_suffix,\n",
1608 " \"D0_BUP2020OOArArO_RAA\" + s_suffix, 1100, 800);\n",
1609 " c1->Divide(1, 1);\n",
1610 " int idx = 1;\n",
1611 " TPad *p;\n",
1612 "\n",
1613 " p = (TPad *) c1->cd(idx++);\n",
1614 " c1->Update();\n",
1615 " p->DrawFrame(0, 0, 11, 1.2)->SetTitle(\";#it{p}_{T} [GeV];#it{R}_{AA}\");\n",
1616 " \n",
1617 " grProD0_RAA_OO_5year->DrawClone(\"p\");\n",
1618 " grNonProD0_RAA_OO_5year->DrawClone(\"p\");\n",
1619 " \n",
1620 " grProD0_RAA_ArAr_5year->DrawClone(\"p\");\n",
1621 " grNonProD0_RAA_ArAr_5year->DrawClone(\"p\");\n",
1622 " \n",
1623 " TLegend *leg = new TLegend(.37, .7, .85, .9);\n",
1624 " leg->SetFillStyle(0);\n",
1625 " leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection, Years 1-5\", \"\");\n",
1626 " leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. O+O\", OO_rec_5year/1e9), \"\");\n",
1627 " leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. Ar+Ar\", ArAr_rec_5year/1e9), \"\");\n",
1628 " leg->AddEntry(\"\", Form(\"%.0f pb^{-1} str. #it{p}+#it{p}\", pp_rec_5year/1e12), \"\");\n",
1629 " leg->Draw();\n",
1630 " \n",
1631 " leg = new TLegend(.18, .17, .55, .37, \"O+O\");\n",
1632 " leg->SetFillStyle(0);\n",
1633 " leg->AddEntry(grProD0_RAA_OO_5year, \" \", \"p\");\n",
1634 " leg->AddEntry(grNonProD0_RAA_OO_5year, \" \", \"p\");\n",
1635 " leg->Draw();\n",
1636 " \n",
1637 " leg = new TLegend(.28, .17, .55, .37, \"Ar+Ar\");\n",
1638 " leg->SetFillStyle(0);\n",
1639 " leg->AddEntry(grProD0_RAA_ArAr_5year, \"Prompt #it{D}^{0}\", \"p\");\n",
1640 " leg->AddEntry(grNonProD0_RAA_ArAr_5year, \"#it{B}#rightarrow#it{D}^{0}\", \"p\");\n",
1641 " leg->Draw();\n",
1642 "\n",
1643 " c1->Draw();\n",
1644 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
1645 "}"
1646 ]
1647 },
1648 {
1649 "cell_type": "markdown",
1650 "metadata": {},
1651 "source": [
1652 "### RAA flatted centroid"
1653 ]
1654 },
1655 {
1656 "cell_type": "code",
1657 "execution_count": 33,
1658 "metadata": {},
1659 "outputs": [
1660 {
1661 "data": {
1662 "image/png": 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\n",
1663 "text/plain": [
1664 "<IPython.core.display.Image object>"
1665 ]
1666 },
1667 "metadata": {},
1668 "output_type": "display_data"
1669 },
1670 {
1671 "name": "stdout",
1672 "output_type": "stream",
1673 "text": [
1674 "Save TH1 hframe\n",
1675 "Save TGraph Graph\n",
1676 "Save TGraph Graph\n",
1677 "Save TGraph Graph\n",
1678 "Save TGraph Graph\n",
1679 "removed ‘fig_BUP2020/D0_BUP2020OOArAr_RAA_flat_5yr.svg’\n"
1680 ]
1681 },
1682 {
1683 "name": "stderr",
1684 "output_type": "stream",
1685 "text": [
1686 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020OOArAr_RAA_flat_5yr.png has been created\n",
1687 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020OOArAr_RAA_flat_5yr.root has been created\n",
1688 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020OOArAr_RAA_flat_5yr.eps has been created\n",
1689 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020OOArAr_RAA_flat_5yr.svg has been created\n",
1690 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020OOArAr_RAA_flat_5yr.C has been generated\n"
1691 ]
1692 }
1693 ],
1694 "source": [
1695 "{\n",
1696 " TString s_suffix = \"_5yr\";\n",
1697 "\n",
1698 " TGraphErrors * grProD0_RAA_OO_5year \n",
1699 " = GraphShiftCut(\n",
1700 " Significance2RAA( gProD0_Significance_OO_5year, gProD0_Significance_pp_5year, .7),\n",
1701 " 0.2, 0,100);\n",
1702 " TGraphErrors * grNonProD0_RAA_OO_5year \n",
1703 " = GraphShiftCut(\n",
1704 " Significance2RAA( gNonProD0_Significance_OO_5year, gNonProD0_Significance_pp_5year, 1),\n",
1705 " 0.2, 1.9,100);\n",
1706 " \n",
1707 "\n",
1708 " TGraphErrors * grProD0_RAA_ArAr_5year \n",
1709 " = GraphShiftCut(Significance2RAA( gProD0_Significance_ArAr_5year, gProD0_Significance_pp_5year, .6),\n",
1710 " 0., 0,100);\n",
1711 " TGraphErrors * grNonProD0_RAA_ArAr_5year \n",
1712 " = GraphShiftCut(\n",
1713 " Significance2RAA( gNonProD0_Significance_ArAr_5year, gNonProD0_Significance_pp_5year, .9),\n",
1714 " 0., 1.9,100);\n",
1715 " \n",
1716 " grProD0_RAA_OO_5year->SetMarkerStyle(kOpenCircle);\n",
1717 " grNonProD0_RAA_OO_5year->SetMarkerStyle(kOpenSquare);\n",
1718 " grProD0_RAA_ArAr_5year->SetMarkerStyle(kFullCircle);\n",
1719 " grNonProD0_RAA_ArAr_5year->SetMarkerStyle(kFullSquare);\n",
1720 " \n",
1721 " \n",
1722 " grProD0_RAA_OO_5year->SetMarkerSize(2);\n",
1723 " grNonProD0_RAA_OO_5year->SetMarkerSize(2);\n",
1724 " grProD0_RAA_ArAr_5year->SetMarkerSize(2);\n",
1725 " grNonProD0_RAA_ArAr_5year->SetMarkerSize(2);\n",
1726 " \n",
1727 " grProD0_RAA_OO_5year->SetLineWidth(4);\n",
1728 " grNonProD0_RAA_OO_5year->SetLineWidth(4);\n",
1729 " grProD0_RAA_ArAr_5year->SetLineWidth(4);\n",
1730 " grNonProD0_RAA_ArAr_5year->SetLineWidth(4);\n",
1731 " \n",
1732 " grProD0_RAA_OO_5year->SetLineColorAlpha(kBlack, 1);\n",
1733 " grNonProD0_RAA_OO_5year->SetLineColorAlpha(kBlue+2, 1);\n",
1734 " grProD0_RAA_ArAr_5year->SetLineColorAlpha(kBlack, 1);\n",
1735 " grNonProD0_RAA_ArAr_5year->SetLineColorAlpha(kBlue+2, 1);\n",
1736 " \n",
1737 " grProD0_RAA_OO_5year->SetMarkerColorAlpha(kBlack, 1);\n",
1738 " grNonProD0_RAA_OO_5year->SetMarkerColorAlpha(kBlue+2, 1);\n",
1739 " grProD0_RAA_ArAr_5year->SetMarkerColorAlpha(kBlack, 1);\n",
1740 " grNonProD0_RAA_ArAr_5year->SetMarkerColorAlpha(kBlue+2, 1);\n",
1741 " \n",
1742 " TCanvas *c1 = new TCanvas(\"D0_BUP2020OOArAr_RAA_flat\" + s_suffix,\n",
1743 " \"D0_BUP2020OOArArO_RAA_flat\" + s_suffix, 1100, 800);\n",
1744 " c1->Divide(1, 1);\n",
1745 " int idx = 1;\n",
1746 " TPad *p;\n",
1747 "\n",
1748 " p = (TPad *) c1->cd(idx++);\n",
1749 " c1->Update();\n",
1750 " p->DrawFrame(0, 0, 11, 1.2)->SetTitle(\";#it{p}_{T} [GeV];#it{R}_{AA}\");\n",
1751 " \n",
1752 " grProD0_RAA_OO_5year->DrawClone(\"p\");\n",
1753 " grNonProD0_RAA_OO_5year->DrawClone(\"p\");\n",
1754 " \n",
1755 " grProD0_RAA_ArAr_5year->DrawClone(\"p\");\n",
1756 " grNonProD0_RAA_ArAr_5year->DrawClone(\"p\");\n",
1757 " \n",
1758 " TLegend *leg = new TLegend(.1, .2, .55, .5);\n",
1759 " leg->SetFillStyle(0);\n",
1760 " leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection, Years 1-5\", \"\");\n",
1761 " leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. O+O\", OO_rec_5year/1e9), \"\");\n",
1762 " leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. Ar+Ar\", ArAr_rec_5year/1e9), \"\");\n",
1763 " leg->AddEntry(\"\", Form(\"%.0f pb^{-1} str. #it{p}+#it{p}\", pp_rec_5year/1e12), \"\");\n",
1764 " leg->Draw();\n",
1765 " \n",
1766 " leg = new TLegend(.58, .2, .95, .425,\"O+O\");\n",
1767 " leg->SetFillStyle(0);\n",
1768 " leg->AddEntry(grProD0_RAA_OO_5year, \" \", \"p\");\n",
1769 " leg->AddEntry(grNonProD0_RAA_OO_5year, \" \", \"p\");\n",
1770 " leg->Draw();\n",
1771 " \n",
1772 " leg = new TLegend(.68, .2, .95, .425,\"Ar+Ar\");\n",
1773 " leg->SetFillStyle(0);\n",
1774 " leg->AddEntry(grProD0_RAA_ArAr_5year, \"Prompt #it{D}^{0}\", \"p\");\n",
1775 " leg->AddEntry(grNonProD0_RAA_ArAr_5year, \"#it{B}#rightarrow#it{D}^{0}\", \"p\");\n",
1776 " leg->Draw();\n",
1777 "\n",
1778 " c1->Draw();\n",
1779 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
1780 "}"
1781 ]
1782 },
1783 {
1784 "cell_type": "markdown",
1785 "metadata": {},
1786 "source": [
1787 "# v2 projection"
1788 ]
1789 },
1790 {
1791 "cell_type": "markdown",
1792 "metadata": {},
1793 "source": [
1794 "## Utilities\n",
1795 "\n",
1796 "v2 formula is Eq (3) of sPH-HF-2017-002"
1797 ]
1798 },
1799 {
1800 "cell_type": "code",
1801 "execution_count": 34,
1802 "metadata": {},
1803 "outputs": [],
1804 "source": [
1805 "%%cpp -d\n",
1806 "\n",
1807 "Double_t v2_err(Double_t sig, Double_t v2, Double_t Res)\n",
1808 "{\n",
1809 " const Double_t Pi = 3.1415927;\n",
1810 " return Pi/4 / sig * sqrt(1 - 16 * v2 * v2 / Pi / Pi) / Res; //Eq (3) of sPH-HF-2017-002\n",
1811 "}\n",
1812 "\n",
1813 "TGraphErrors *Significance2v2(const TGraph *AASignificance, double v2_centroid, Double_t Res, const int n_rebin=1, const double x_shift = 0)\n",
1814 "{\n",
1815 " assert(AASignificance);\n",
1816 " \n",
1817 " \n",
1818 " const int npoint = AASignificance->GetN() ;\n",
1819 " assert(npoint%n_rebin == 0);\n",
1820 " \n",
1821 " TVectorD significance(npoint/n_rebin);\n",
1822 " TVectorD x_center(npoint/n_rebin);\n",
1823 " \n",
1824 " \n",
1825 " for (int i = 0; i<npoint/n_rebin; ++i)\n",
1826 " {\n",
1827 " significance[i] = 0;\n",
1828 " x_center[i] = 0;\n",
1829 " for (int j = 0; j<n_rebin; ++j)\n",
1830 " {\n",
1831 " \n",
1832 " significance[i] += pow(AASignificance->GetY()[i*n_rebin + j],2);\n",
1833 " x_center[i] += AASignificance->GetX()[i*n_rebin + j] + x_shift ;\n",
1834 " \n",
1835 " }\n",
1836 " \n",
1837 " significance[i] = sqrt(significance[i]);\n",
1838 " x_center[i] /= n_rebin;\n",
1839 " }\n",
1840 " \n",
1841 " \n",
1842 " TVectorD y(npoint/n_rebin);\n",
1843 " TVectorD ex(npoint/n_rebin);\n",
1844 " TVectorD ey(npoint/n_rebin);\n",
1845 " \n",
1846 " for (int i = 0; i<npoint/n_rebin; ++i)\n",
1847 " {\n",
1848 " y[i] = v2_centroid; \n",
1849 " ey[i] = v2_err (significance[i], y[i], Res); \n",
1850 " } \n",
1851 " \n",
1852 " TGraphErrors * gr = new TGraphErrors(x_center, y, ex, ey);\n",
1853 " \n",
1854 " return gr;\n",
1855 "}\n"
1856 ]
1857 },
1858 {
1859 "cell_type": "markdown",
1860 "metadata": {},
1861 "source": [
1862 "## Projections"
1863 ]
1864 },
1865 {
1866 "cell_type": "markdown",
1867 "metadata": {},
1868 "source": [
1869 "### BUP2020 28 week runs"
1870 ]
1871 },
1872 {
1873 "cell_type": "code",
1874 "execution_count": 35,
1875 "metadata": {},
1876 "outputs": [
1877 {
1878 "data": {
1879 "image/png": 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\n",
1880 "text/plain": [
1881 "<IPython.core.display.Image object>"
1882 ]
1883 },
1884 "metadata": {},
1885 "output_type": "display_data"
1886 },
1887 {
1888 "name": "stdout",
1889 "output_type": "stream",
1890 "text": [
1891 "Save TH1 hframe\n",
1892 "Save TGraph v2_B\n",
1893 "Save TGraph v2_D\n",
1894 "Save TGraph Graph\n",
1895 "Save TGraph Graph\n",
1896 "removed ‘fig_BUP2020/D0_BUP2020_AuAu_v2_3yr.svg’\n"
1897 ]
1898 },
1899 {
1900 "name": "stderr",
1901 "output_type": "stream",
1902 "text": [
1903 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020_AuAu_v2_3yr.png has been created\n",
1904 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020_AuAu_v2_3yr.root has been created\n",
1905 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020_AuAu_v2_3yr.eps has been created\n",
1906 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020_AuAu_v2_3yr.svg has been created\n",
1907 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020_AuAu_v2_3yr.C has been generated\n"
1908 ]
1909 }
1910 ],
1911 "source": [
1912 "{\n",
1913 " TString s_suffix = \"_3yr\";\n",
1914 "\n",
1915 " TGraphErrors * grProD0_v2_AuAu_3year \n",
1916 " = GraphShiftCut(\n",
1917 " Significance2v2( gProD0_Significance_AuAu_0_80_3year, 0.025, Psi2_resolution, 1),\n",
1918 " 0, 0,100);\n",
1919 " TGraphErrors * grNonProD0_v2_AuAu_3year \n",
1920 " = GraphShiftCut(\n",
1921 " Significance2v2( gNonProD0_Significance_AuAu_0_80_3year, 0, Psi2_resolution, 1),\n",
1922 " 0, 1.9,100); \n",
1923 "\n",
1924 " \n",
1925 " grProD0_v2_AuAu_3year->SetMarkerStyle(kFullCircle);\n",
1926 " grNonProD0_v2_AuAu_3year->SetMarkerStyle(kFullSquare);\n",
1927 " \n",
1928 " grProD0_v2_AuAu_3year->SetMarkerSize(2);\n",
1929 " grNonProD0_v2_AuAu_3year->SetMarkerSize(2);\n",
1930 " \n",
1931 " grProD0_v2_AuAu_3year->SetLineWidth(4);\n",
1932 " grNonProD0_v2_AuAu_3year->SetLineWidth(4);\n",
1933 "// grProD0_v2_AuAu_3year->SetLineStyle(kDashed);\n",
1934 "// grNonProD0_v2_AuAu_3year->SetLineStyle(kDashed);\n",
1935 " \n",
1936 " grProD0_v2_AuAu_3year->SetLineColorAlpha(kBlack, 1);\n",
1937 " grNonProD0_v2_AuAu_3year->SetLineColorAlpha(kBlue+1, 1);\n",
1938 " \n",
1939 " grProD0_v2_AuAu_3year->SetMarkerColorAlpha(kBlack, 1);\n",
1940 " grNonProD0_v2_AuAu_3year->SetMarkerColorAlpha(kBlue+1, 1);\n",
1941 " \n",
1942 "// RAA_pi->SetLineColorAlpha(kGreen+2, 1);\n",
1943 " v2_B->SetLineColorAlpha(kBlue-4, 1);\n",
1944 " v2_D->SetLineColorAlpha(kBlack, 1);\n",
1945 " v2_D_B->SetLineColorAlpha(kBlue+1, 1);\n",
1946 " \n",
1947 " \n",
1948 "// RAA_pi->SetLineStyle(kSolid );\n",
1949 " v2_B->SetLineStyle(kSolid );\n",
1950 " v2_D->SetLineStyle(kDashed);\n",
1951 " v2_D_B->SetLineStyle(kDashed);\n",
1952 " \n",
1953 " TCanvas *c1 = new TCanvas(\"D0_BUP2020_AuAu_v2\" + s_suffix,\n",
1954 " \"D0_BUP2020_AuAu_v2\" + s_suffix, 1100, 800);\n",
1955 " c1->Divide(1, 1);\n",
1956 " int idx = 1;\n",
1957 " TPad *p;\n",
1958 "\n",
1959 " p = (TPad *) c1->cd(idx++);\n",
1960 " c1->Update();\n",
1961 " p->DrawFrame(0, -.1, 11, 0.25)->SetTitle(\";#it{p}_{T} [GeV];v_{2}\");\n",
1962 " (new TLine(0, -.0, 11, .0))->Draw();\n",
1963 " \n",
1964 " v2_B->DrawClone( );\n",
1965 " v2_D->DrawClone();\n",
1966 "// v2_D_B->DrawClone();\n",
1967 " \n",
1968 " grProD0_v2_AuAu_3year->DrawClone(\"p\");\n",
1969 " grNonProD0_v2_AuAu_3year->DrawClone(\"p\");\n",
1970 " \n",
1971 " TLegend *leg = new TLegend(0, .78, .85, .9);\n",
1972 " leg->SetFillStyle(0);\n",
1973 " leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection, 0-80% Au+Au, Years 1-3\", \"\");\n",
1974 " leg->AddEntry(\"\", Form(\"%.0f nb^{-1} rec. Au+Au, Res(#Psi_{2})=%.1f\",\n",
1975 " AuAu_rec_3year /1e9 \n",
1976 " , Psi2_resolution)\n",
1977 " , \"\");\n",
1978 " leg->Draw();\n",
1979 " \n",
1980 " \n",
1981 " leg = new TLegend(.65, .6, .9, .77);\n",
1982 " leg->SetFillStyle(0);\n",
1983 " leg->AddEntry(grNonProD0_v2_AuAu_3year, \"#it{B}#rightarrow#it{D}^{0}\", \"lp\");\n",
1984 " leg->AddEntry(grProD0_v2_AuAu_3year, \"Prompt #it{D}^{0}\", \"lp\");\n",
1985 "// leg->AddEntry(v2_B, \"#it{B}-meson\", \"l\");\n",
1986 "// leg->AddEntry(v2_D, \"#it{D}-meson\", \"l\");\n",
1987 "// leg->AddEntry(RAA_pi, \"#pi\", \"l\");\n",
1988 " leg->Draw();\n",
1989 " \n",
1990 " leg = new TLegend(.2, .2, .7, .3);\n",
1991 " leg->SetFillStyle(0);\n",
1992 " leg->AddEntry(v2_D, \"#it{D}-meson (fit to STAR PRL#bf{118})\", \"l\");\n",
1993 " leg->AddEntry(v2_B, \"#it{B}-meson (m_{T} scaling)\", \"l\");\n",
1994 "// leg->AddEntry(RAA_pi, \"#pi\", \"l\");\n",
1995 " leg->Draw();\n",
1996 "\n",
1997 " c1->Draw();\n",
1998 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
1999 "}"
2000 ]
2001 },
2002 {
2003 "cell_type": "markdown",
2004 "metadata": {},
2005 "source": [
2006 "### BUP2021 20-week run"
2007 ]
2008 },
2009 {
2010 "cell_type": "code",
2011 "execution_count": 36,
2012 "metadata": {},
2013 "outputs": [
2014 {
2015 "data": {
2016 "image/png": 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/0K6igc04Kw/mYa0ogeciLRnkJfkMYG/plvLiRdq10l/x0tYN6Xxi7iW8vcjKn50QZtcy6y7UT9TjsH7mAMmcQkBYMA2aJjCw8dzmR/cRUHnu6NpGzC1YV5zsT+La4+TddDKbnlXCM+XPgpp4f8cV/4kLNzewWVDp0eIxUWlrXhxwZW7Wf5YFVzEuFI17uudlNf5AnpaZyYN5emDjuT74y+cYR2CjFjmxi8ACPNme5tpO+IpalrV4yd927f92ZUByQGAz+ewpXTtdRo6UfAawt0RLuachRdBqutoVsHiRqMRoCsNkO0A4bVP+Qf8uWvOLK1Bx9UbzzBww+WpOjTmFQLFFVDMa7TAmc7+e+pknI3KByee75kasn8sPQ+p85rN5z4quKr4nj9avQmIPVzVoMg70p2eBkJ5R4+pKj797z4LKnLmY63OV9czuVI3zpH/yLJ8e2HhKxWTxjiGwGd09OT2rhDzumYylXTmyNhiOYa+KWl9aQuwX2BQ2OUU1Y7JVPlXyGcDeEi3lZuBh7T1i7UQb0jXZStv+so1YU2vOHLAgPa5AxdUbTYsKls0cIFgDG/8qgeaeLGsdRX47GSr4ky2XCWkqCdmg9U4/uaKrQuZZ0fpVSP3AVT0KCTNCjkC4wNrMykqP9YmGv/wEZtOzmNZS5KpQLsjOJH8VefJ3cXpgIze+oIkgksDGeonz725u6+Ws9IQENpN73DUYODKw2aonbTx2upIc6SXzPAFZ6rquLMu6rtu2FZ/UCnXJYRgWbF+7/ci9SGabj6f9Rwsq7vf7giRptI3IXXzpS19SP//6179uXV5d7K/+6q/Ur7SJBExf/vKXzQ/feuutiRRvre97/8nVSoIk1/LXa+UhnSxCcgF/f0WZHrm8LFeeFeu6Ftf3ZSVZkgXAdVi0r6y786ybnLquu64Tf1u7oomLzOb7bdtW3rOHYdAOqUiStfo7DMPKMuAvb+pVzrzinU5Nkr8crjxKu6rr2uyAEJgd/0kJv16pW568GYX0wb7dbhEWmEnix65m8H6/l2WZYl4y9vLZCQB20batrIKo7vf7/X4XXzVNY70ezb3JVVVlrqJ9UlWV59pnVkput5v6T+1e8p3vfEeLRkJ4ApW3337bFc9Yfe9731P/6RpCI7iqeu+88843vvGNyX2Fc7XeHFa3rutaHL3wwOZ+v4ckb5PIdrHAA2jWucPX3dCCSqqriPZ9L9MvT675udip/M2WZTmua7ANJ/Zu1nTV1BaOa1QIdSP+2lvXdbFV79THB9a0VVUlMnju72uSdjubPJUyO/4zIhez/nLVfYkIef1R6vte/ky6ruu6ThRd8/Hi3lw1hMI2N6a6lvpP+Svruu74LMCFwAbZUq+hVuLCqtZRhMDLt7giu+4c2kb8l7y5N4wFUY0597Sq6zoZ2BS2dgn/QzhPYONf8a233towtll/X3FtIaTtYrFZZ1/WaeK5icraYRKWJVWt1ZlXjKIo6rpWLzjWmuJk9XEBkSr1KqSGWOpiZVlaU+6hpko8EppcfqtiaT0g8sPAtkGZ4JDEt20bW2Cmkr+ykPYQafEPs23bTSIZjfYzKV581Cjiz3iubJNEgZe/wZjb/S6FwAbZEtfQtm39V+fb7eZ/vDq3NmDlvxuZzTvqP/0xSSBXPzT1E7nM17/+dW0B9Z/mgBlrTzOxltZpTbN5o8251K5KOJjWRhGyvPx7srKoPvt3XQ3qupY/omNqOdbmGllrlM0U8ho4ea3TzK3obxjY+K/b1idW2oV6buIjbHE6hTUwLh6KWV3XwzCsucqJjrLDMJin+H6/3263xa2Lc5OxSdfZtm3F4Uro4U72CGyQM3nx8j9/Up/VmRe7Bbfqya5lGn/zzmRMEmJyI2+//fZv//Zvhyzvj1Wkt956S1tyHEcz1Pn2t7/9O7/zOyEbPJGssPqrbgvux3Pv4uG93Q4TyR1dRpUL0hPeddB/NZBnR+sGFlJ45pIXNDXx8jqm1vJFPyJxCZrVLiFz0TSNJ+WyhhpVbKCOhvKkavLJl188P0OrBU/l1FuVOO/aFjbJsrw1Dw/U5jVX1LGhkF2IBSZ/8lslCVshsMElyBubp2et4G88CbSyqqftVO0ktlWSzI34u7d5Zg4wZ0griuLb3/72O++8o34i2nmePHmiNe+888478Qc2geLpJ7ZtSgJr5Ofmeu7e90itv8nOU5FSW4QC9yWuadryskuPOeRGxFfhsUf4xADyoXWxXfBmPVby6j3Z7qQFe/6HEXKEVeS90Q6gZv+YcWJq4GR9LnAWteUq8FBs++QCizErGnLTvsj81n+R0m6oy65Ts6Kjuc07ywIbjTWMcW1Z+3xy5oBvf/vbWvTy5MkTEb2YMUxg+8+5ZDHwR8XhQ3FkyfTfwsWdUn24KLfsWVHckgV/MvxCJk1Sv4rnpu5KcMiccsu27Pk25FzLchVYsbY210jWEzH37KgtHv4l13QA2+nB/KxiqU5qN3dHMVTBTXKQpP/wipBPPT5yec/0j4tPmeh+5umlqX5+elNY4O8lzgvgxdFig9yYN6fJe616PZo16N9lw5kDzAE2IQNhi6KoqkpGL9pGXDUVrTfa5PKCOcBG++SNN95QB9I8efJEa8zZdgqBPagNfa7Hcmoxm1VsPM+JzchEq0RaV1zw+N+qVub8caVwfaiwockhLosbsgKfJctCotYLtYqjuesFdSNRMFy1T3/e5wqJVRZPL7ZTC4n1RLjUyugs6wny9GFenMJdyZZDzy93WDpUZnEsJ/fYdd1h0wZuwtMUc3oABosjX5qDFCVXSMwKlnjdxPjwRk7/r2CTH4i2ke9+97vhCd7k1ZzFiy8Oc72aczLlgvpqznfffdd/iMzVtQW2fVlnyDu2J4VsYfKd39Yj79l++MsN564ovw15T6j/K0+mxhcPvrYv11v8QnYqXxax4OV3rvSMq4uK/1CM3hLiP2UhLzO1bs2zKVf65+4i8Fi5SoJ/O9rVODxVyxLjIZfXLr/+7aiJNxeQZXiTl1HOLSSTxdWa5ck3e6qFXDtWk+tOnpdNLubjdi/onLzmqwne/GWgZ1l58GOQfAawtyLY2Sn9ByGP6FTqFdYMexYkwGxj8S+vLaxFQYsfhKvRi7YRNVDRWOModQFtRM3rr7+ufmt2S3vvvfcms+xaLMRhgc3orsdM1s9cX6lrqffFvu89FQj/ip6ULEih55332lfaimsCm8n6hIe6riu1y6og2oXFUwCs23cdqwWZ9ezFWndcU9EPjy1dq7g+Ny/Uk7sIDGwWHFLXKoFn3Dywm1d55wY2agK0X67nKuFJtpll1wn1pFBd3RUXhefRZavAZvReabUCvHJHByiCnZ3StZLPAPaWYikPDwa0u5H5gucFe5+7Ef81JfxipFGjF/8uJvfo+fZrX/ua/OrJkyfat0+ePLHuwlzyjTfe8KfK5cjAxvUCUJX/ya7nK5fJppLwlJjLhKQw5NfkWevgwMaaTdWaOlPIoQh5XG0VGHJMVu49T3YC63kLAqHRfdb8B62qqsBwZQwObObmd3zxoLniYddZsx6o0wObkMRbz+9kIdc2K3MXEtiEXLiK1VWODQObYxIclQyyk3wGsLdES/nkNd168fX3Cgs0ayOTzTshV9XJS61/F/49up6/Cu+++674fG6s4k9wuCMDm3HqPueqBfq376lJ+AuPZ0VrSjzPyP0p9PyaJrusHB/YjO4js76i47+w+LfvKTzh8UPI8tZELqvlB64yhvVLtCZp28Bm8QVBrjXZsqQuFnNgMy69XrnOl9y19cOQwGYySSvbarT0b3LkD0hwVIo0q3yq5DOAvSVdymVnfXEB2rDHM65MdBVTy5W/UMm7oGeDoqwWD8+wwx+uqytOpkR2chMLh+xC0n5NzcPQtZ2IHS1e3Tyk2yVtdgGwrivMSphcN2Rheb5i6P2/yenoH2ycuACzfmWSqBOffvy14hryy/WfL/Uysux0yO2rP4SYb83mT/7007qTpKt8QjkmNTcFjleWFBJgFfmiDH5K4cRB44ghXWI2xaZpop0/DdBkUOXjPTaREhPMl2WpvcViDTGLvNig2LJnRnkAALASrzcBjpR8ZJYf9Y0Zqqqq1gQh6lT9s7acQfgOnGjBG6whrlc86ka65K2cXz0SkkGVL/kMZMYTfgjLzpfsCePR9731wVIGpRw4kfxRr3w2cR1ENUideJzBTx7JyaDKR1e0iLRtK6MadXioOivLgkZtdRVtIvZKmfzEfMc5gMWGYSjLsixL+aOmR0qgYRjGcSSqQbrquh7HkagGOF7ykVlOZLuK+ahS7c3ialqxmlxRXcD6iDSD8B04nvrLKmiuAQBEL4MqX/IZyIY6tMZ6UpZ1aJncbPFi/zdzmQxKOXAKMVFHURQ0PgAA4pdBlS/5DGRjMm5ZNgTZ0woUuOUMSjkAAAD8MqjyJZ+BbMgIxNPTLGSZZat43rORQSkHAACAXwZVPiYPiE5IxLKgs75ns3T9BwAAQOoIbKIQGFqok5gFknOgeZaRXdQWbB8xG4aBqBUAAFzEy2cnAEUxv81kw9rqMAxy5gCGOGfDfM1r6o3LAAAAfrTYpGTz92BoM9Lyno08yKimqirZChfyklYAAIB0EdjE5cjOYG3bqlGN54l+ualDMndpIqppmkZ0RRvHUZQrAlcAAHJCDU1DV7QrmttPiV5MCZH9CdWOhcMwlGUp+xwCAIAMbFtDyyC2ocXmWkQFV41qmqYhbsmJGH/VNI3nWwAAgPwQ2MTF/0x9Za1U63tWVdU4jkwYkBlRhMxeZ6I3GoENAADIFYFNFOYOflgwWEJtqKmqqu976riXwgAbAACQN8bYRGHvSqfaabLv+6vVcUNapeq6juewhLx/xp9g11dbRbPhb8iJ6sBaieJxbtOlOJ7xHysAAKI2Ig7yjPR9v2YZz1rLTncGhST85yC6550ufHI8M8GuEy1G3WyVQdcYnsgPrEkc6lk/qJ24TtxOmqYReRdzgq88AoEldqO0AwB2kcGFmhabWFRVJUZHiAe35gLqA/Lwx7rqc+jxkpMEzGqjuN/vZVmefqDCpy+bm+CzGgREOmNrLZRvp63rWkwVWFVV27ZaIv2NOdq3wzC0bXu/3+dmVlwB2rbdu+1ImxRRHAEx+m5xyWfCPQBADAhsYlHXtagcdF1nrdnID2e960bWYPq+X5nCRKmBjXkQxLeygiscULkM1DSNVjmWfcDUBJuxjRke7zekylq0rAf2drudHjSqRG1epF/EM7fb7Xa7VVWlHS4tI4J5duSveEEIJ2cs3LVDmkyh1bKontF6AIBYnNhaBI08KWIKZpVad7R2GqkeaN+uP9GpFxIZB072horkd6Ge68DF1DIj8msWoW37XMmuaCF9zNRQ3EzYWWQW1A89vzLtgHs2uLjfnewbtmz1SWoW1L2oHQsX7F1uVlx/PLbLCgBge6lX+cZxTD4DOVGrF2r9z1UdsS7gWjGQufHUS3l4lVo9/ockbYNkmOfONZZm23zNrcT7y9gprKXCHwZP/lICS5qL/MHuFAPIFJq5m3x04iELQzxRKwBggXju0Ysx3XNE2raV9aqu68oH6stnZvX6oIuIKqrRHR7yrIX0OVTro2JF0Ynufr+rZ198OKsT47bmTjawN9nV0NXn0D9oZKcjKYvoHj0h1fJgXhnqupaZmrt3ubVUfmIAgFwR2MRlGAZXFVA8ZD04PalbNuPCiQFAoVSpQxKsLiMzK4rQ7XZr21aMZRdDrSIZOOQhhgbJkF6M6Q9ZS4yQUdfyR/XigGx4ord6iCDO3R5j8ScH6ckFFu+dwAYAcLKzm4xgp07G2jTNid3Tky4ky7p1bXi0F5y7ucmQy6sdgczK67ZFaG5XtMkT4e826Um8Jzhxpc3T40vdmvmtZ8uu7qALbLIR12b9W15WWvxnFgCQigyu5MyKFqn4H64nIbxbl3rAt3rwPAxD13Vd143BTW3LmpjMdYcH4p+nFyf/iRiGQe1vabrdbtZ5xtQ3z3LZ6eoAACAASURBVJru93td12Zbyh5dpzbv9rlfP1JPYZicdD4QndMAAKegKxpyFtitS32zR3gQMkmOW/DXv1WzBtiYu9P+2T6Yu6ltqXMlWxOjRjVqW4HaDGJGPuqmXHMAaGONBPUtLv5khyy2OZH4bXujBeZFFqEFeRdzZMthgYLoGchgPwDAMWixwSWIYRjmh8WLNcgNoxq5CxHVBFbvZJ07m0fdatBovvileDGn2vGv63ocRxkWasdQfUeT+dIeuZar8cEaOvpf8+KxpqnNlYaVzSbHkBm/3+/WZjfxedM0pwfYAIDsEdggW2pd836/T85ztdNzZVE1d3WLcllQoz2lEny/3wPbo1xVW3leXMNs+r4XNWbXGbRmvGkaa8uMv5eUGobNKg9q2tYHNiINJwY24dGdeZTkeCq1pS6V6SsAAEkjsMG0wJrr5s0dK82tmO7XZ0ZUzSd3seCp/4YNBbtyRTVq1ztX+kWPPk8jhvVDVx+8yeEfMiIS3dhCjqq6owUvj9K4+oMtKJxzu5bJmGoZ8yyro6e6riOwAYBThHeJTx2BDabFFrEEUivNrhqVGGEvKs2i8WGPzNZ1HRLbLKi5pjJ6oes6dTIDSZ4Xf/xg7Z0lo51tezqJ7Yj6fciW1UYe6wwHW2nbdm43uV3TI4lOfWLGbe0rf2dCAMAxAus2OcQ/R0y9hpSlW0hkIZ+cOVedjHi/mbUnp0j2v/beKjyP2wqc7rnve3Uoi5lINct+5u6sbSOT82vLTfkX01I+KfyUhbBenxfMJyHzqB4rz35nTY8ebqfNAgC2lcFVepfn08jJTo0YB5APHkLSLxeWT+it8w2sJJ+4W4f0mGnwU/v5HHyOZDNFyNgkz6Rzcx8OabvzzBMtmumsbQjiLAQeMbWhSe2mJSvrdV1v3ioyq+iGCCwqs07r5nsHAJwr3SqfRFc05GmTatkeL4CXW/aM3wisKE++Sz4S2oj8DcMA0dNJhKDayRKTca2voKtxi8yFp3MjTOoZT2KqNwAriQsvP3Ycj8AGeVrzQhhBDIzZLEFFUSgvY7HOUKzuenJTw9TLYeKk1WvlOJk1o0HkyI3hgTwynkFNl6phz52L4jpHBsAeNnmuBCxAYIM8LX4hjLr8ttU7uTVrJX7uDUDGSJ75xOIhAxiXTcIMtXVFdjnT9rv4TTXrzX2EqcXka2ZF07bjSkPgC23XiL+sAlgpoWdtyA+BDTIXUpHaL5hRN+tvmpjVxKT17dkgfSdRpzvzLNa2rWyTEZ/IoS+uIS5DwDs6D6N2xhMWTOO2clY0GVvKg+kR3sITMngm6SIKIJy1YzBwpJfOTgCwvVndutSeS+r0TRuSF3pPh6vwh+Vqm8PmneUOJiv3YtCRa7Gu67RXrA7D0HVd13WuyQMKR4g499Uu68mXw1RV1TSNSFXXda4TvVN/MPVQ+xcI770ZGGCnMhgMwBplWYq3GpydEFzbOZOxIR0pFpLA6WW1KX23nbFX3YvYvn9+YZkM12LmBMTrp3iWkynP3VTgdM/qjlzLq5kKX1eN6FxHzLXZxad7QVGxnn1/kZDHdvOZx/0lx388ZVHRvp08fcz1DFxE80BcFna6pWJXGVyok88A9pZiKV/wYHjvqMYfOSxoeNmk1rs4+3MDG//yrnxph0XLsvqVdnjVyrQnlApJuSsxgWuJfZlnX6TQejSWJS+EmgU1SernrhPqOtSj+0RMvsUIQJY81zdEbo9bz8HoioYMzW0Kb5pmp45JoqPU5ICKWXsXd4tEB2Fbu5ypFevb7VY+ULuZmb341LW6risV6oAWc3cLDp35whzXZGsaURRn7VGsskevrbqu5WbVI6ZmbcEPQQ0jtc2qL25iPDH20D44OyHpEeNhOHTIzdmRFWKXYiEJL/+7PkUWNe+Qp1YhtdgFHcYmqRufteLcFpvxxZNifutvs/J0z/MfMf9aIcdzctjVZFucp7+ZmcJZaVvGlSP/qfTn138iaKtJRd/3sh9RMfOas3jFlWQxO2Z3Knm4ZM+rpmnmtqWH3yY2J8/X5r1eabFJ1yk/pW0lnwHsLYNSfqLNbxh7iOcOJDovza0lqNULOWQoZETTibl21Sdk1LF3AtR+8ObImWW007fVZnGAZc8IBE/wf8BP7KzAxvM0alau9+t6Gr7rWb/T/kXWZQhs0pVBla8c5zzexgWVJYUkc2VZVhd7k5qcf/n4si17tVmPuZii+mqn4wrK8ums5cfx8U4pMZk9La2sP5aQF0Pt+iuTs7of+VuWO/UITM8p6RcmX0JgpeXderESF1iuYynKoMrHGBvg0ua+NTIPslv5wf3L67r2RDXqW3qOTBUuTo1qPNM/mFcJ9Y0lWgOdf8WkqdnxHK6Q4Cdkmdg0L2KIDqKze5sQEkchyZjsf3J2Qk5wWKcvbXeFu+MHc6RmrCjen/XfYQmbLJmeBeTn1kJ7zEzfB1dmJiead809qC1j9t/bNdlWjLGBKYP6AC02mFaGOTuZmE08qU39LZ/LyAeNBzSPyInaREXH/5JWmmtwJFneqqqylkz1kbz6t1pQrYXWtWLS1LfNWg+XNveg+a2YMND8CtjVdSpyBDaYFhgln51MzCZOXGYdRcKJiC5kdMEactjMOI6e6p04C5OTsAHbkn3JPIVTFsu5M+mbI+wHhXWVwG/D43/X8gs2VShHwHPZ9BzJuQcwhJmLtm1FBFWWZV3X4VHl4hVX0rIwDINIhisBWjoDJ983V8wm3g5xoYrc3CYeXA2FBBlzvUBzK4FdMq7cJ/Aiou2KFlIZUBt1/R9qZGAjf2L+meLVwN76q1RnlJ5Mv//Ns56vPALrTq7FtCnFNumtp86r5ml+N/ubqV3RZq04aW5XNPWM+1+vvHgKPs+KzEevWlMUI/Gy60wDQPbEK+r2a7MSHU5Cngs2TXPZprNUzJ3Z7Nx9hcyrpvZDm7uYWlzFI3ZzRbN9o65r8aOwtl2oz927rjN/OCHtS3JTsjH24Om5PPsyj9K2fdI87c+32210PI8fhsGTjNvtdtgkAXK+ygXf3u9314xe/rn7uq6b23CHmNEVDcCl7RrYCLfbzdqbWa3tHZAMQFPXtXjG6a/VyW+1IirjHGt9Wl1YLermZqXwnlr+H8t+UY18KuxZRh2Hs9V+Q6i9XiW1pcIVnMhoQW2cUVcUVf/wlLRtO1moTGp8Jd5Fpo3pkt9qU/CpSfXP3ac1zsgTdL/fCWyyQWADAADshmFwtZOodUExIqJtW9EKWpalXEsLA2RtUqtKWofBqP8MDBgCoxrZH2zbtgjP4TpA0zTmQZP1fn/dfXxxvKUITtR/bpZKB3HcROwhHvSo6ZHnVORRS6rsSmeGKGqwZBZgWZYuNd4mbwQ2ALAXf1dgnhEiclqQYC6gPvbuuq7rOnXKL9F6oK0ia5BatyL5c3BVxF0NR54E+3uFSa5l5tL2fnwbrLV2LpPhaRBzjbFZPG/EMlVVWbMwOcNeXdcyqa6FrSvKEnJMBnGE8OE4uCYKCQCsFO3kAR5qJOMZme1vP7EOPbfWQNRB8NYFJj/0zxawN+04hKwSMgFD+H49g+D9B9y/d/+p3MTkLkLyONqyGbKiWvCQwXGgxQYAAPyS1pfM1fQxDIO2mHwhvVzmdruZT8qtvdHEdlxh0mTzptpaUkwNwtmWdrgKo/dd5PyzzLu6Du7B/46vYn6HsZAVh2EQFeJZW0a0mBUNAAAUhREeFLaRCZKn05cYbyM6m3Vdp/X4krNUiTE5hdHNrKoq+b5a8YnZUc2VGME6qdrmZh2uve0Uy8mTFUPX2cA8uqbpwxUQ2AAAAH1W3MkxKv7FRNAiNqjNNSxjHrk7LbBp21YEDK7Ix6VpGjl0J/y9jctosw8fPKn0hhKKAWaNhEn0dGAluqIBAHBpZqeycWpyi8BOPta/PUuKSrY53t18JY5JtJZ4JsjaUFmW2uzDuVajEwp7gILABgCAK9P6U82toy+r+GojNwIH2HgmKpB9wOq69r9gZz3xxhhBHK6ka/+BMWcMAoePa+ExLoXABgCAfY3j41n/HZk2dajM+OLLTFwWVHZdL6VRG3zUXauTPof0Q9Ne5mj9fBNqVBN4uJIW2AkwXcODsxOCbRDYAABwUZNDZfxrLdiR9s/7/S5jD22CAfGHHGZTzIlSZFzUdd2GdVY1AdnMo6W9UEgT0glwb4Gv0RRzVEy+CUdze0Bsk4nNJ5BGZigkAJCrxZUBuaLnDSH+97SY/cpcu/BsJOSrDe9icoObvNRl2/fYeJJk3Yt6/F3rqnPQLU7hpMldhCRDLqO+wihkxQMymJAMjgMtNgAAXNGaR9QhTSJqJzfzW60FwP+iz2LqdSsmNWwwH9jXD8JbgdRsZtYvyzoYaRgG2Zgz9+BvSz1H1vOlJtW1sHVFtcVS26AsIbTkJObsyAqxoywBQJbm1lbVZ+Hji3cHMTOY+Lzve23LrgSoy5gtP9pGrK0K/l14Hti7MuWhRkqBwjcYmAbTJi025hYCz+AmQvaipkctbJNJ1VaUn/d972mzUtfapHXudFsV2vjxHhtMG3PpSQwAkFY+ih7HUY6kv9/vrinIAuMBsw2krmt1+MeCRhL1VTPrH73n+uRevvxn5RnclfpaJE9hM6sr2orq3A9S0zSZNcGZAity1uOTFrqiAQCAJUbbg39JPFn3v3lG/m0NbNRNLUvhhq+1yTWwadvW03bnP4NHGobBk07RGrNgRTlROPJQ8jAefmVJIQEA+Mi5y+73e1VVcnzCycnCHLJxQ4SRbdvGeQbVifIKZbjUghUJaTQZVPmSzwD2lkEpBwAAgF8GVT66ogEAAABIHoENAAAAgOQR2AAAAABIHoENAAAAgOQR2AAAAABIHoENAAAAgOQR2AAAAABIHoENAAAAgOQR2AAAAABIHoENAAAAgOQR2AAAAABIHoENAAAAgOS9fHYCkICyLEMWG8dx75QAAABglsCKXAYIbDCNiAUAACBRgRW5DOIfuqIBAAAASB6BDQAAAIDkEdgAAAAASB6Bzb7atq3ruizLuq7ruh6GYdvti41PbrZt2zLA5skDAAAAjsHkAXtp27brOvnP+/1eFMXtdquqasP4QWx2EhELAAAA8kZgs4u6rl0hx/1+L8tyk3nG6roOXDIw/gEAAAASRWCzvbZtZSChts+obTjru6UNw7AgXOn73vNteKQEAAAARGWbpgOo5CzgTdO0bat+NQzD7XYTf/d9vyCQEOGQGjuFbEomacHp3qp9CQAAANHKoMpHi83G1EhGi2qKoqjruqoqEZO0bTur0UYNimaRe6mqasHqAAAAQPyYFW1jk1GEjHYOG/cik0RPMwAAAOSKFpuNyXDFbK4R1OhiGIbwYKOua22EzLIGHAAAACA/BDZ7CYlYZgU25jZlr7bJvWir04YDAACAzNAVbUuBY2YOHuuiBj/ihZ63B+K9nK7GJQAAACAVBDZbmjuD88HvzbzdbtYWnq7r5LRpAAAAQIroinYCz+s7D1BVleiBpr0JJ4M5/gAAAHBZBDa7iGdiZbVRSH1bqKRGWW3bWrulbdueQ/gEAACwHj1uNHRFy19VVVVVNU1j7fk2DIMMw7qus25h3NR+OQUAALgOamgaWmwyV9f15EieYRhkxD93ojYAAAAgBrTY7MI/hObgOQNmiTltAAAAgAuBzZbmtnXE0zYie6MR2CAS5YOzE5K8117T/wOAXQ3DQHUCp6Ar2pbiCVSALMk4J4+uwHtzxTDWz99/f9e0ALiQ2+1mna8I2BuBzV48g1VkR7UIA6EIk4TrUOMWf+jC7OQeZtyiBi2zop2CgAfATLz1GycisNlYVVUibnEFNuoDjAOiCFlT7Pves7uYYy1cREivMxHMmEvSklMEt8NYAxVP/zSadwAEGoahbdsT39QHENhsTL4Wpus660ML+eEx77qRgVbbtq5GYTWdBDY4V0hkEhi9qPFP3gGPv4kmhGt5OrMBCMSQSMSAwGZjbdvKt8GY77schkF9G6a5uowr2rbdJMZo2/Z2uxVFcb/frY1IwzDIBDdNs36PQCBxF5Qhx5rYY9a62n7TtT6emTSreYfObMCVySqEWtUBDkZgs72maUSoIP4vA5hhGESMURRFVVXWGENeC7Z6n0xd17LRRhvMJyYtUV/KSb9YHOaAZ3sZj9I5IKTxoDMbAJOsQtAbDScisNme6PQlO6SpkYN05FQh6vs37/e7q0KZbiUP6Tql1I3j6BmlU0T8Wzg3nvFb2ZktnowgLWpl+sx0JEjOyMyhQ054j80uhmFwdeuqqur4atM4jp4hPackCRc3Oe/ZiXuPrae49f0z77+fRjAg0qn9Z+IFOzEQI7/ruhavkKrrOrzKK3oZLFhxpe7BMbtzEXkPf2QpjrNYS/xx/MzIoud813VMyoycJNwZIwmy9Ub0PRNOT09RFDJJk7efpHvsIBIJDWuJKqkxN9Fs6MqtN/f//t+rf/yPz07FCz2lTf4XknjW9c/GuYkYZkSURyAkv+pAXFPTNIfFhHKuo1mnSSsJ1hVFHnmPTYoyqPLRFW1fsbXwxpYeXEFsDSB+MVzTLxLPSCJ3Mtfij4yz/KPnz//ipz/9y5/97Ht/93fik9/65Cd/91Of+v1Pf/pzjx4dnx5/VFM89GG2/jT8695utyNr6mcJz6A/qimMobkR0k430QtiQ1c0AEeIIWBYpnxwwL7S7XK2npbTLDun/eKjj775k598/oMP/tPz59/7+c+LshT//fXPf/6D588//8EHf/qTn/zio48OTpVaVW2aZnzQ973ah9n6bF5dt+97c8XTO4ntLXwGMG22Hnm4xBGTn0feN6x5UcwxGK6JFhsAWzJ7hqQb0hzmak00HtbWmyKLA/KLjz764w8//LNnz4qy/OazZ9q333z2rCjLP/7ww//693//1Vdf/fhLBz12VCumWpckMfBDNjKY7wxQ11V/5mJFeSk4ZQDJ3oYH4dN/qdOimh261ElNbrdbtJdNIhlEjhYbTCvDnJ1MnC/LYiCfqm6+5aRnBdiVeRAyaMD582fP/uzZsw+eP/cs8/3nz//82bP/1wh79iNr2NaXEBQvVmS1Sq3/HWiyFSKzmX9FzHa73bquW5Y1V2ygttvkFwriXNepyBHYYNoY5uxkIhbZl4f1NwBXPENIozKPSbrhzY+eP3/zRz/yRzXC958//1c/+tGPApbchP+d0YKMW9R6vFrz9r9vunjxfW6SdV+B34bX+13LL9jUGuohco3UVz9fk8HFU9stXnElLQtyhj1XArR0hjcJnpXBGFyoIheYVVwWhQTQLL5+Pn6s/4dwSR+6//PHP/7DH/7QPvu18d+bP/zhH/34x8ckLKQwqy0J8kMZ7YgXBljJkTZyGf9aasuPOtrHs0FP+tVkm7vzfDWpf5HclDpsxpU1/+5kBq3Z968yvphljZk2uaKWi8kVJ4nMhh9YNctqGTC34HqRxuTuPCuGH+orKNKv8jHGBsByUU2OfJjR9opPP0bRrHf65Gnl06fLVx7HIrjM/OmzZ8U4/u8//vHy3RXF+Pjx5DJqP7S5i8kPPTMFt20rBpbIpp66ruWIHc+OiqLous58oB7SviQ35RnTstKaOaz966oHasHG/TPUuS7U2qwG5oqHTRLgnzXO/61n7j45sbWVmK2Bvn/ZoCsagIXy6I+7jHgyNLmYOYqGLmcrZdM5LQZizPo4jv5anTWGWTa8xN/bKnyb/nr/rlHNMm3bikO9X4QgLshaw4XaUuHatYwW1MYZdcW5E7WJzM497Gp81TRN3/dqgtVvq6pyJdUsGG3bynKlNc7IQF1MjDErtYgWgQ2AVa7WXOOiDrxhVoC9WcObeCOcZb+ROH5Z6sRfk2NpQr6StUmtKmkdBqP+U+59sn0pJKqRHcniGWghD5drIodJTdOYB03W+/1193EctWFR6rX9gKMkipmIPcRgGDU98pyKPGpJVWeq0LKpBktaLoZhkGUpnmKAlQhsAAQxR8wHtlpcSlmWzApwmGQmT1vWthlBi6gWJGyyTVmD1LoVyfqoqyIe0vktvK2mVgSmfFdqd6nFlWx/5OlpEHONsbHOG7GfqqqsWfBMzSfUdS2T6lrYuqIsIZnN3Xdl9v6IgOTqtIpLMd9OA81rrxVPn5ZFUTx+PBYMoTmDGVLG4/Uf/OCvf/7z8OV/8xOf+Ot//s/3S08ItaptBgmB1wS5mPqSHOu6cnfqGDZ1gckPz+qBZs1jODXZha1hwU8eN8+K/gNeeE/iytyFmNxFSB4LWzZDVlQL3sIMZCSDKh8tNgAmENVMElVqMVsX7TNniXn4ze9+6lN/+MorgQu/+corv/upT+2aHr+2bcuy9EQ1K1l7o4ndudqFJhOghQeRtMP4iS5Va6KaTXhmDCvcXQf34Dpri9uyQlYchoHeBzlhVjQAQbjuu8jasxbPEBCe4vTJ06x+/9Of/vwHHwT2LvvT//bffviFL+ydJCstPCgCqtragAcXdRn5jLxtW1Fd1rqZVVUlFpAbNzuqabRkWydVi4o5VdfKC8VOsZxMZwzD6wPzGFgmkSUCGwATqJd7uKIanMsa3hTnnabPPXr07z73uT/+8MPvT7158zcePfrKZz/7+UePjkmYSqtq79ebS87bK3enBTZynmhX5OPSNI0cuhP+3saDLYged5VQDDBrJEycZx97oysaACzkry7LSUWPTBJUUc0u8AevvPIvX3nl170Ry288evR7n/70HwR3WtvKMAxa37MF0/VaNzt3SVHJNse7h7zgRYQHngmyYiBjNkFOAnZeiiYkFPYABYENACyzrBHAnFwOezMnpjslvPn4Sy999dVX/82rrxbj+KYRurz5yivFOH7ls5/96quv/srHPnZkwrQGhL7vQ+KBlUMvtNUDB9h4ZmaTjR51XcvFPC+sPIV8P2nxED3GENIEvsUoBv5XzktaeIxLIbABYEcV3GNl1yYO7ClOD28+/tJLb37mM//5C1/4Xx89+q1PfKIYR/Hfb37iE7/26NEPv/CFf/VP/snBUU2h1P5FVTuwOigX81R8PQGJrNCrNXt11+qkzyH90LSXOVo/P5f6psjA6DEGgZ0A0zU8ODsh2Ehg+IvLopBcE5cIDzH72ePHC1fnwMZAnsQ1p3K9/mc/O23f4zgqL1/X3lg/SX3zyeTGtTe+C3J1GcOor5OX26+qSm7HsxHtc3WOAXWzewjc0U7pkQfHs1nrUVKjTc/2DziMKwuSulhVVeoyahmb3Pve5SQJGdybks8A9pZBKccCVL5dTq8KY0ORhDcnCqnaTq7rqhH6N24247hW92wn5Ku9L2UhNWMZaM0NICdtEti41lXjw01TPZ28ucmwHuGQFY8pJKnI4DjQFQ2AhbhAnJ2K6OwxuRZd/k50eue0c63sfiNrjdahLLLnkmtgjNa1yTN+RttdILVNyeyQVj84pq9a5B26rGdwGAY5Imjuwd+Weo6s50tNqmth64quUiomjBbopZaYc+MqxI+yBAg7PdfnFxSJC7bezK2tmq0Nrq/mdnMqbJ2FtORZWxX8e/E8sPdkai5/CrVlAoV3i9qkxcbcgnbwAxOzTMhe1PRUVRWeVG1F+Xnf9542qyO7Mh4jvOydndK1aLHBtMDCdHYygR3t9yIUfjuRuGDrzfpH0ercyqVCHSUfuCmzKUP7ZEFbh2tmAqjUljd5BmUDSDHnJO6nbVsZh9zvd1dSzcuptqJc63a7yVLaNE32xeM6FTkCGwD/gD5RLnu/3jGbO0oGLhjerFHXtafW2/f95Ps01U2ZG5d/T3ZU86RB/BHna21i0Latp+1u8iQeZhgGTzpFa8yCFc99Oyo2V3I3hV9ZUkguQYY0nG7N8S+tF+eCE3E6NaQ57OwnahgGrXmEymJy2rYdhuF+v4swsm3bSEIajUin/KccDLNgRUqpJoMqX/IZwN4yKOUIQWXa6viopiDIjAzhDYCLyKDKl3wGsLcMSjmwzClRjUCcGRWtNxrhDYAsZVDlSz4D2FsGpRxY4MSoBnGi6QZA3jKo8iWfAewtg1IOzEVUAxfCGwC5yqDKl3wGsLcMSjk86PJkijCq4TRFhZ5pALKUQZUv+QxgbxmUcrgwSN0UYVRTcKaiRHgDIDMZVPmSzwD2lkEphwvtAJo4oxqBkxUneqYByEYGVb7kM4C9ZVDKgRAxRzWIHOENgAxkUOVLPgPYWwalHJiUXFRDA05s6JkGIHUZVPmSzwD2lkEpB/ySi2oKRt3EiqYbAOnKoMqXfAawtwxKOVQ86dekGNUInMpoEd4ASFEGVb7kM4C9ZVDKoaI2rEo3qkHk6JkGIDkZVPmSzwD2lkEph4rARiKqwd4IbwAkJIMqX/IZwN5kV/5JlKUkENgUmfYU4sxGK8vyBiAh16nLEdhgQgbhO6DKtZbJdAKRy7XgAchGBlW+5DOAvWVQygEp7+5nNNpEjp5pAGKWQZUv+QxgbxmUclDfFfKOapAKmm4AxCmDKl/yGcDeMijlF0cPJeGCUQ0BbcwIbwDEJoMqX/IZwN4yKOUXR+22uGRUUxDTRo+eaQCikkGVL/kMYG8ZlPKLI7C5ZlQjcPbjR3gDIBIZVPmSzwD2lkEpx5VdOapBQuiZBuB0GVT5ks8A9pZBKcdlEdUgLYQ3AE6UQZUv+QxgbxmU8muiDxJRjYlSET96pgE4SwZVvuQzgL1lUMoviFHjRDVW6sunL1s2kkDTDYDjZVDlSz4D2FsGpfyCLv5gnqjGg9gmIYQ3AI6UQZUv+QxgbxmU8gu6cmBDVBPiyiUkLfRMA3CYDKp8yWcAe8uglOM6iGqQJZpuABwggypf8hnA3jIo5bgIoprFaMBJAuENgF1lUOVLPgPYWwalHFdAVLMGs02kgp5pAPaTQZUv+QxgbxmU8ou48kN3opr1rlx+kkPTDYA9ZFDlSz4D2Js6h5IfZelEV37iTlSDayK8ARDoOnU5AhtMTtvn3wAAIABJREFUyCB8v4LLPm4nqsGVEdsA2FAGVb7kM4C9ZVDKkSuimv1cNlROEeENgE1kUOVLPgPYWwalHFkiqtnVlTs3pohJBQCsl0GVL/kMYG8ZlHLkh6jmADTaJIemGwBrZFDle+nsBCBU27Z1XZdlWdd1XdfDMGy7fbHxzTeLXZVlGT4iMBtENccYxzH1O9zVvP/+L38Ur72mN+MAQPaSj8yuoG3bruvMz6uq2jAOEfXjvu/rutY+p5DE6ZqdhYhqzkIDTkLomQZggQyqfC+fnQBMqOv6fr9bv7rf71sVQS2YQUJSvwbNQlRzugxue1cgfiDy9/Laa/xkcGnl06fy7/Hx4xNTgr3RFS1qbdvKqKaqqvFB0zRymfUxyTAMrtgJMbtaTyGimnNdqrDlgZ5pAK6GwCZqsgda0zRqr7O2bfu+F3/f7/dlHdKGYRiGoa7r2+22NqHAzohqYnC1WDoP6k+G8AZA3ghs4tW2rfVvoa7rqqpc3/oNw1CW5e12u91utNUk54ITBhDVxKl8cHZCMEFtuimMETgAkA0Cm3jJdhgZwGhkPENwch0XrEQS1cTvgsUyRfRMA5A9hoHGS9YVzJnKZi1jpfVek73RmBUtclebmYqoJn5XK5MZYM40XA2TBwTKoMrHrGgJCIlYxGiZxdusqopmnySkfsVZhopXzK5ZJpPGnGkAckVXtEgFzgfg6qUGZICuMsB+6JkGID8ENpGaO9HZhm/qBGJAJ7QUMZ1AcphUAEBOCGzSxos1kSWimtQR2ySEphsA2WCMTexi6Gy2bR2FHvnLXHCINlFNckT5JKpJ0fvvvzDqpuAHCKSA662GwAbTLlWZjtOlrlw8ME4dV4xEMakAkJxtr7cZVDboigYk4wr1RTqh5YdRN2mhZxqAdBHYxM4/CzNzBlzEOI5ENUgasU1atEkFCG8AJIHAJlJzZwVgFgFkg6gmMzImJ7ZJi9p0U9BHFEAKCGwiRaCCq6HalDFim3TRMw1AQghsEuDpbyY7qhEIZek6gxPohJa9K/SlzBg90wAkgcAmXnKiZ1dgo35OYIN0EdVcxEWGiuXK7JlGeAMgNgQ28ZKxStd11gXathV/xPCuG2AlopqrKR+cnRDMQHgDIGYENvGScYv2tzAMg+yHZn5bFEX9gJnTEDNqRSC2SQ7zCgCIE4FN1JqmEX90XadGL8Mw3G438XdVVWY/NBH2CAQ26cq+6w6d0K4s++KdPeYVABAbApuotW0ru5l1XSd7bsiopuBVNkgWUQ0Kwpv00TMNQDwIbGI3DINst9FUVUWFAKkjqoGKUTcpYuANgEiU1IxT0bat6GAm+p4JB+y3LCkkRxMVu7wPO801sJJRTd7lP2NaSMMPHDEonz6Vf4+PH5+YkshlUOVLPgPYWwalPC1XqNgR1cDjCoF99tTwhp85TkdgEyiDKt/LZycAgEXqVxYPohr4ZVz4r0P8usWPXfyf3zuAAzDGBsAJqOUgEKNu0sXAGwAHo8UG0wJrFTxn3UTeh5GaDZbJoIPENalNNwWtN8BJrvN4iMAG06hPYBN0QsMC4zhe55acK8Ib4FyBFbkMLrZ0RQNwBKIaLMa7bvJgzgoNANsisAFicYWxBEQ1WO8Kv5SMqeENA28AbIvABohC3hU16i7YXN4/mewxrwCAPRDYABHJsr8NndCwrSx/Jhdk9kwjvAGwEpMHAFHIta5GVIM95Pp7uSDmFQCwIVpsAOyOagoAD+YVALAJWmwA7IXaCY6hjrehMSddausNTTcAFqDFBsAu6IQGYAEG3gBYjBYb4EziSXN+z5iJanCk/H5BF8fAm8OUT5/Kv8fHj09MCbAJWmwA7IWKCIDFmDYNwFwENgA2RuUDMeA9nnkgvAEQjsAGONM4jpn1oqETGqJCbJMHwhsAIQhsAGyGqAbxyOyRAQrCGwBTmDwAwMaIahAJYpssMbUAABcCG+AEWU6GxqNTxE/2TMvs13dBhDcATAQ2mBbYSZ2KQqAsO/3TCQ3A8QhvgBBZVjysCGwwjYhlDzkdVaIapCKn3x0kwhvAL/DSl0H8Q2ADYBtUI5AiOqdlwxreFFyagCthVjTgaJlN8czQGuQhg0eVKIyZ0womTwOuhBYbAMvxTBSpE08ZiGoyI69I9E8DLoXABsBCRDXIRk6NqFAx/Aa4FAIb4DhZzvJMFQH5YeBNZghvgItgjA1wkMz6utBnHUBatOE3jL0B8kOLDXCoPB4A0wkNecvjdworWm+AjBHYAAfJpqpEVIMLonNaZpgbGsgSXdEALMHtH9eUWZ/Si2NuaCAztNgAmIFbPq6JWaEzxtzQQDYIbACEorcGLo5+aHlj+A2QOgIbYF/ZTPFMVANoGHiTJcIbIF2MsQEwA3d3AFfA3NBAimixATCNOzpgoqEme7TeAGkhsAH2lUHVh05oQKBsup6ifPpU/j2+/7hgbmggBQQ2mBY4ERD38ixxCwfmKsuS62F+tNabggYcpOM6MzoS2GAad2hw5wZCjON4nQrENTE3NFIUWJHL4PJFYAPAiaE1wFxmBYL+aVmyDr8piHCAUzErGrCLsixTf/LBfRrYUOoXBFhpk6cVzJ8GnIoWG2B7GdRgiGqArdA/LXv0TwMiQWAD7CWDnifclYFNZHA1QAj6pwHnIrABtpd6JYZ+FMCu1Aac1C8XMDF/GnAWAhsAL+ARIwCsR/804HgENgB+iagGOACtNJdC/zTgMMyKBmwpg8nQCm63wBnyuHrAhfnTgAPQYgNsJvVKCbdY4HRlWdKekzH6pwG7IrABNpZopSSq3hHl06fy7/Hx4xNTAhyGWaEvxdo/LYbLL5A0AhsAcUU1wGWZj0VEqJPo4xJMYvgNsC3G2ACbGccx6foHt1IgTrTk5I3hN8BWaLEBro7bJxAt+qddB8NvgPUIbIBLo+cDELmk24GxAP3TgMUIbIC10u0Ez/0SSJFsw0nxsoNAWnhT0IBTFGWpzCszMq8MLAhsgKu78m0SAGJG/zRgFgIbTAvs4c2zw7QwtAZIlHqxTbfFGLPQPw1rXGeoHoENpnHL9Ei0VsFNEcgJr/W8CFf/tIKLObwCrw8ZxD8ENsByiV4CuBEC2WDatAsy+6cVXNiBoigIbID10npQys0PyIzrtZ7Wr5ATIhxAQ2ADLJdcpYEbHgDkxxPhcLXHpbx0dgIy17ZtXddlWdZ1Xdf1MAynbLZt2zLAVslDnIhqgIsYH6gfiuv8+o2XT5/K/9ZvDdt6//1/+E967bV/+A+4Alps9tK2bdd18p/3+70oitvtVlXVmvjBtdnC23pAxAKiGgAF0wxcBtMM4JoIbHZR17UIOUz3+33xfUWLajRlWfZ9X9e1dacLdgeX5GZC434GgGkGLohBOLgaApvttW0rAwm1fUYNSxZ0SxuGQa5eVZXojSY+Fy02RVHcbjd/bbvve8+31qAImuRqBtzDAAiuaQYSekyDZZwRzr89JTnAXmiS3p6s+DZN07at+pUahLhaVyY3a+3M5tlpsW6GHPotaNKqByQa1ah998fHj09MCZC3BXeHi/w8s8+muDs8/be/zObj/+1x5HeKslROyjjjpGR/NreSQZWPyQM2pgYVZoBR13VVVa5vPdRIxtrU0zSN+MPsqyaXl7vGRSQa1QA4TOqVGCymzTEgMNMAUkdgs7HJKELGM7PGvYTMeza5Lj3NNmHONRQnohoAIVzzpyXX7RYbIrxBoghsNibDFVekoUYX4cNs5JKyZSZkYYCoBgAQgqmikQECm72ENI+ERyAyXvJs1tXJzWyxGR4E7h3J4T4EYLFdX4OD+Jm91IhwkAoCmy0Fhgprxrp4AhvXV2qfN/Faz9sDcaOaNdrnshK6qdMJDcBOUrkMYj2zAacgwkH0mO55SwtmcN5js67RO3JCNk3XdV3XJTFuBJOIagDsgdfgXBYvw0FCCGxO4Hl958rNel7fKVVVJV+AoyYjgzn+wJ0GOFH2U8qKe4SaTVyKP8IpuPUgAgQ2u4hnYmW1tcf6Ahw1ymrb1totbdundImGT/Enm6gGwInWvDANabFGOAW3oTPQjqohsMmfiLLqurYGLcMwyNim6zrrMtyl4sftBEAkaP+/Dm0KNevf3JV2te1vLYMwicAmc3VdTw7RGYZBFmUR5+ydKmyLqCZd2XdewnX8Q0e1FytGNONcx2QzTsFNCvsjsNmFfwjNTvMsb7JZAhuVuCVHfj8mqgEQD9cFk2ac63A14xTcsLA/pnve0tyQIHD5uZtdMMJHrsLLbdLCTQJAzMz34eBS5JzRrmmjmTka26LFZksHtHXQogKJqAapoMfdxZmxDV3ULogpB3AAApu9eCIQ2VFtQYji2axsbFkT+RA1pYI7AQAgRUw5gP3QFW1jk3261M/Do4imafybLZR4SZ3ZrHzg72O2JtbKWLSdKIhqACRtfKB+KG9YZ6UKx7N2VCvoq4alCGw2JgMD17syZdQxaySM3KxrWgLrNM3qXlwLaF8R2MSPqAYAkBnXaJyiIMLBDAQ2G1ODBDOWGIbB2q4i1Q+0BhY13rDGHjKOkm072l7u97u10WYYBte6iBBRDYBc0YyzWFk+lf+dnZYNeIIcwI/AZnsyPNDedzkMw+12E39XVWXGJyLsEcwgpO978cf9ftc2q17xtXiprmvZaHO73dSdDsPQtq1MkrnuZUV7EyWqAXBZRDjXRF81zEJgs722bWUs0XWdvBarIcSCWZXrulZDJutmrQNC1H3d73d1RbW/XJyDSY4X7V2TqAbABVmbcXBBnnsfQQ4kAptdDMPg6tZVVdXiC3Tbtp7eYp7NjuPoGdKzJkm5iu2AENUAuDh/R7Von0lhJ7TkwIrAZi9t247j2DSNiCiqqmqapu97T1tNXdfywu3qFWZutqqqvu8nK+LDMMgV1SSN48hLOVURPhokqgEAwIpZB6DiPTb72mnUyuLNMoomOUQ1AODieg7FC0CvyfWGHNVrr3E/zRmBDRAvohoAABZQ75vaXHFazMMdNicENkBRPDzei+rZHlENACxjvZjTjAMrNc7hhps6AhtMCxyUme6tIuZRp1xkAQDYinZX1Vpvcg1yYq7nbIvABtPSjVhmiSqbDHYEgG3RjAOTZ1hOTkFOYPHOIP4hsAGiu5/RCQ0AgINdJMjJG4ENEBeiGgA4jLMZh0vwtQUGOQU368gQ2AARIaoBACAq/lmkiXOiQmADxIKoBgBiMI5j+fSp9St1EEJs3ZhxAP/cAwWd1s5GYIPrimqKZ6IaAEhXVDcUHCZ8jjVzYeyBwAYXFdXUH0Q1AJAEf+hSliWxzZXRae10BDa4tBjuQEQ1AJC6cRzN52XMJX1lszqtmctjGQIbXFQktxmiGgDYT1n+cqjMOD7edV+R3FYQJ+KcYxDYAKchqgGAjPlfCepaAFfAJAQ7IbABzkFUAwCQmH7gypiEYCsENriWSO4cRDUAcE2T0w+ELIa8MQnBYgQ2uJBIZkIjqgEAaKzTDwhEO1c2d3COucqlENjgcs69MRDVAACsJm9PTCeNyTjH+uF1qhwENriQ0+8HRDUAgLnEzcsznXRRcFO5KLM6ERLqZFwJIbABDkJUM9s4FnH0HtzdRXJKNnNCNg8X+GxuyaxrMWVzR9fIZkiok3GTDoENpgUOTTm9PSRmRDWBfvT8+V/89KeFLEvj+PoPfvC7n/rU73/605979OjUpG3sIjklm2QzOQllU9521df1uGhz5ySUzTUukk2/999/oSL3+LGltiZqKdav0vLS2QlAAsYwZyfTqSzLc6cNuGxUU5ZP5X+TC//io4+++ZOffP6DD/7T8+dFWcr//vrnP//B8+ef/+CDP/3JT37x0UcHJHtvF8kp2SSbyckjm/77clmWeWRz0kWyGUgtFe+/X2j/5YRRaJiQ+lDF0yeTuWxUU8x55/cvPvrojz/88M+ePfvg+XPXMr/+6NG/fOWVr7766sdfSviJzEVySjYlsnmui1yFArNZlmXxsY/90X/5L3lns0j8bJ4o9SpfQVc0XARRTeT+/Nkz/x2oKIrvP39ePHv2P/+jf/TmZz5zWMI2d5Gckk2JbKbiCtkcx/GbP/nJ//Xhh5PZ/P4w/B9/9mfFX/5lkWY/8yucTVglH5lhb6mH7ye+kZOoJvDp2o+eP//8Bx+Ejukcx//8hS8k2jf6IjklmxZk8yQXuQrtks2PPir+xb8o/vZv1RvouS+5vsjZPFHqVb6CMTbI3unjfy4b1YT7i5/+9A9/9VcDF37zV3/1Wz/96a7p2c9Fcko2TWQzcmTT9OZnPvNH//E/ugbqaP88fSyr6iJnE1YENsAurBPJw+ovf/azbz57Frjwnz579pc/+9mu6dnPRXJKNk1kM3Jk02TNpv9BYSSxzUXOJqwIbIDt0Qltlu/93d/NWv6vZy4fj4vklGxakc2YkU0razbNfhCeGdhOacm5yNmEFYEN8nRiszhRzSw/+x//Y8Fa/9+itc51kZySTQ+yGSey6RGezcCO3+WLFiTJ7yJnEy7MioYMndgaTlQz16deXnIV+p8WrXWui+SUbHqQzTiRTY812Zw7wFW9dy8eHHuRswkXWmyQrePnDCCqWea3PvnJWcv/5szl43GRnJJNq9iyGfj+3NSzGYhsWu2RzQWv9p7bvBNDNnEWIlRkiMmd0/K7n/rU//IrvxI41vPNV175tWTn5bxITsmmiWxGjmyajs/mrHu3p3kn8mxiV8nPV429ZTCp+QGIaqx2eufAD7/whc+neR+6SE7JpkV82bzIz5NsqnLKZlH8cu5R/U07//SfFv/+3xcvhXVKii+bJ8qgykdXNGAtopqVPvfo0b/73Od+PeC+8huPHv0//+yfpXsHukhOyaaGbMaPbGriz6avP9vf/m3RdXlkE3PRFQ35OOWNyEQ1m/iDV175r3//98WzZ99//ty1zG88evR7n/70H7zyypEJ29xFcko2JbKZCrIpJZ1NUQf4xUcf/d8ffphxNuFCiw2w0GuvEdVs5uMvvfTVV1/9N6++Wozjm8Zt5s1XXinG8Suf/exXX331Vz72sVNSuJWL5JRsFmQzNWSzIJtIHy02yMSRUzy/9toL/ySq2cTHX3rpzc98pv7kJ7/1058WSrPbb37yk7/26FFOfaAvklOySTaTQzbJJlKX/CAh7C08YDixLG0y+X0IQppZAsezWlZ8+lSsU5Tl+HjGism5SE7JZoQu8vMkmxMrks1rSKIutwlabDAtlVJOSJOb8160erSL5DSRbC6uPMn1t0xNtMhmTshm7sJfGbR3SvZGYIMcENIAAABcHIENknTMBGiENAAAAKkgsEF69m4q1eKZgpAGXmt7LgEAgC0Q2CBVezTXENIAAAAkisAG6TkgpCGeATQ0TAEAIkdgg6sjpAEAAMgAgQ0SsNNUAYQ0AAAA2SCwQez2mCqAkAYAACAzBDZIwybNNcwNAAAAkCsCG8SOkAYAAACTCGwQnW1H1NDrDAAA4AoIbBCXDUfUENIAAABcB4ENYrSyuYaQBgAA4GoIbBCXNSENA2kAAAAui8AGOSCkAQAAuDgCG0wLHPeyrLFl5VQB9DoDAADw2OOVgHEisMG0rSYoM635pRHSAAAATAqsyGUQ/xDY4HxzAydCGgAAAGgIbHAmQhoAAABsgsAGCWBuAAAAAPi9dHYCcLS2beu6Lsuyruu6rodhOGzX5YPwVV57zdJKQ1QDAAAADS02F9K2bdd18p/3+70oitvtVlXVkeFNIHqdAQAAIByBzVXUdS0iGdP9fi/Lcr+pz6TAXRDSAAAAYC66ol1C27YyqqmqanzQNI1cpq7rcxKn0Dqe0esMAAAAgWixuQTZA61pmrZt5edivM3tdiuK4n6/D8OwbXgT/vJNWmkyUJZPZ307jo/3TM5eyKb120SzWVwmp2TT+i3ZjNlFsolt0WKTPy2S0b6t67qqKte3awROEkArDQAAANYjsMmfnBhABjAaGc+4BuGsET50h5AGAAAAi9EVLX8yXHE1yKjdzzbsjRYY0hDMANkbH9NFBACwO1psLiQkYolw3mcAAABgEoFN5gIDFVcvNQAAACAJBDaZm9sCQ4sNAAAAUsQYGxSF9/WdAPbD4BMAALZCYHMVazqbBU7cjMubNxGEr1wpc0rEV/y2y2bULpLNgnJrRTbJZgQukk1sicAG08KnbMaV+V+mZvKUq/Lp05DFTrFhNmN2kWwWlFsHskk2T3eRbEYlg6iPwAYAgA3QsRAAzsXkAVfhH0LDnAEAAABIGoFN5ua+bXOrt3MCAAAARyKwyRyBCgAAAK6AwOZCPP3NZEc1AiEAAACkiMAmf3KiZ1dgo35OYAMAAIAUMSta/uTLN7uua9vWXEB+uOZdN8A46lNCqZN1mt8mimxav03XRXJKNq3fJopsWr8FClpsrkANZszAZhgG2Q/NGvYAAAAA8SOwuYSmacQfWqPNMAy32038XVUV/dAAAACQKAKbS2jbVnYz67qufCCjmoJX2QAAACBlBDZXMQyDbLfRVFU1juPB6QEApGh8/Fj+d3ZaAOAFBDYX0rbtOI5N04jWm6qqmqbp+562GgAAAKSOWdEuhxkCAAAAkB9abAAAAAAkj8AGAAAAQPLoioZpZVmGLMYMBAAAALEJrMhlgMAG04hYAAAAEhVYkcsg/qErGgAAAIDkEdgAAAAASB6BDQAAAIDkEdgAAAAASB6BDQAAAIDkEdgAAAAASB7TPQOIzvj48dlJAAAAiaHFBgAAAEDyCGwAAAAAJI/ABgAAAEDyGGMDAJg2jgx8AgBEjRYbAAAAAMmjxQYAVqEpAwCAGNBiAwAAACB5BDYAAAAAkkdXNEwryzJksXEc904JAOyNvoUAMhNYkcsAgQ2mEbEAAAAkKrAil0H8Q2ADAADwgvExDXdAehhjAwAAACB5BDYAAAAAkkdgAwAAACB5BDYAAAAAkkdgAwAAACB5zIoGAMDl8LoeAPmhxQYAAABA8ghsAAAAACSPwAYAAABA8ghsAAAAACSPwAYAAABA8ghsAAAAACSPwAYAAABA8ghsAAAAACSPF3RiWlmWIYuN47h3SpAW3gAIAMDpAityGSCwwTQiFgAAgEQFVuQyiH/oigYAAAAgeQQ2AAAAAJJHYAMAAAAgeQQ2AAAAAJJHYAMAAAAgeQQ2AAAAAJLHdM8AAABXND7mbWPICoENAADIE68JBi6FrmgAAAAAkkdgAwAAACB5BDYAAAAAkkdgAwAAACB5BDYAAAAAksesaAA2MwzDMAz+Zeq6ruv6iNRgBXEqrSdreFDXddu24kPzjwwMw9C27f1+Vz+sqqptW1cZXpN9/7rqt4E/ovDErPxVhuxochchV4+5m0r0aiOPgyfxf/EX3/6bv/mrL37xjd/7vd85JlVAGkbAi0KCcFVVBV55qqo6O7Hwcd0gtFOsLZ/N5aJpmsky3DSNuWLordfGn6RZCy9LjDVH2+6oqqq+760bCb96yE250iM3tcl1pu97uVNX4jdJiavUuc6L+Pbh7/flf7NSmJbF2Szef1/+t1Pa8lCkfw2nKxqmlWHOTibOpz3b9i9JmYmWeE5sVjSHYQg/xVLbtm3bBj6M13Yn1p274kp1XXddN7lY13WHtQaYB2HB8Zx0QI7u9/vtdtvknIpryB7HQbM4teG/l2EYyrJ0lbqu66w5/eIX3yiK4l//67eWJQ+Xcp2KHF3RMG1c9xgSF9Q0jVZDkp1D1Dt9WZaUrtjI2phZkZI1vKqqAiuUwzCI6lp4RyPpdrtp+z2A1ves73utJLdtKyug9/tdC73Up/sqmRfRk21uqswqr+gKGLi6+XuUGylerH/f7/e6rhdHC9YdiVOvHlURQbnS7zpEZmqLorjdbrteQ8y+iOHkSd9kSTOn/+E/3Muy/JM/eeeLX3yjKF5ZkkRcRuDPJIfY5tT2IiSAQoJAapUucLFiRe8X7MRzXhbcOOTpXtAp6JT7lNypP8Fqc1bIZld2jlJTFb5fueRkByrtV7k4ef4dqV2tzOMw6xCpx8Esq1t1RTMbLQO7opnxbUjHOW1564fauqLR5otffIOuaBMr0hUtzIKff2zoigZgG+EDf0elJhTS5weHCRm1nDG1DPvLs/rt3g1K6vbDUzhLXddqXXyn/l1t28r6+uJmEGGn4yB6P4rWpLIsZyVStN2JFcPbagrjUIjYaRgGUUvzLFkUxVe+8qQoir/5m7/61reehe8RyBiBDYBtyOpFyCDgA2pRWEDWoQlsJslyvncBlsG/Nrh824BKPeP75cgVpC2wVYykEv0M7/f7gm12XSfWnbWWdhDMro9abKOddDkl2p/8yTuz9gvkisBmX/L5jXgCtNXdYu5m27YNGTRG/RJryDt6SJ14cS1KzjUs/yl+C4JZ1ZM/FvF7CakLymmOZ61l3VdI1rQsTP6itcEq6urrrzPiJJqzM2k7FVnTkuH/5H6/m8u4TO5OW3jWAfTvV/wREpwfE/uZTUPy7GxYoT/Mhgctj9hbK6sLMiUbbTZKEZC483rBZc41b+PKLr+eSUg9awVOo2ntPUwhQSB/QfIsP2uYjVrgXT8HkQDXMO7FPxZPOj1reX7ynhS6DqNcRSTGtd9l1xl5PMPTKZcxP1mctvATt+AABh4Bc18rLR71YV0xMI9zD4Vcfu7It1k7ci089xB5hhstPtqT03x7Muhf0ZWSkNFNkwkQX33lK08YY+NckTE2YVyFMCG02OzCM1vomllu1dl4TJ4mlxQf7CEtatmb+9Bx2fN19eeg1aHFfLKebu6uGaL8veq7rrM23fjXEtNMWXfn74g/OStuXddyv9oRELN1eda1Smu8U8gBnFu01DMVSYOAvyF0p+E9++V9w34BrsK/Rtu2WiUpfOPaiv6oWzLn3NMWCG/SodEGKIr0I7MIuSZ+8U8IM0m9SqrvOJv1vKf3cq07N6m4IFm8w8u2LJbLWmxkkTbT4Pqh+WeazW+DAAAgAElEQVSUcm1W+4lpvxR1p1pG1K/M31fg7rS1zNpS+LqTJs9gyNFzJXjzWdF2PQiS54WSsyxrQ3DNNBjYsmQ9PpMpXHPQJnfkOTWzDpF/DsYNX9CpBTbhhUErh56UaBcu9TKifeXaiJgbrSgKWmycK9JiE2bBzz82yWcgQtbLk7DmBcb+S5tnp+NU/WByvwvWwtX4S+BWq4zeitH4Yi3E/0vxxCfW/bqqSv5cuNaarES6cjHrQYb1Wxetk9vcLbu+2imwWXwAJ7ketFdVtWZq8mVVbc9a/oKnLeO/3fR9vyaqCdzRZEU//BB5nibM3dSkAwIbcy+uEuhaXQyzKYriW996b0beEkRgs7dlV4CoJJ+B2CyuHvn5n07597umYjFmUcpxgAU3/gWrjFP1ufAfoLbTkMRYt+zfnStamNyd6yevfu6q0bry6CcP3dwj4P9qp8Bm8QGcu2urBc04y678nmyqiVmWC6tlzVPawTGZOzI3sqBH2eRPIJXAxtyRxr/6t771nljsK195Epi8RBHY7M3620wLY2w2NjmpjuxBO2vcS8i8Z5PrRtJlHPlZMMBmzZgcz1rLNhWYGPmjtv7crB+Kl/aM4+ia5da1O/Vz16972/EVCV0o1IyHHMAF4zrGcez73jWU/H6/32436yx8G/KXkz3mRhvHcf3Zv9toywSOP/Hr+37vNwgdRh0vZ+UaqifISZ8ZZgMQ2GxMXptcF9xlt1u55OScLbM2C2xiQZFbX0r9NbBZT38DJ/m17lGu4ppawCQX8+9O/tiP+UUnNMVI4PVQHt5lB1BM2C3iUq2zltR13X5xoCwn1myqhW2r+v0Bk/6LNpZNDtrk7BqpMKMa0e/RnBFk8cxDwHW8fHYCshVy4RavXwjZWsgbQqqqEotpb88wH8Qm9GgWSZj19g/BfOegZ9K/cVGnmnBzm4+035esbYg39BVF0TSNeKWMfztrfoAbzgSVK1lf3CSKlhvRCqqYgG6PGrZa2fVvfzKo7o3XPqrU+eVut9uan5so+davwkt7VVWeUzb8/+3dTc7zOHooUBuoHXUGnUksN5BsobOMZH4vLANZQLZRa2igJN9JMkjW5DtgvyyWLFH0r0T5HNTgK7+WRYmWzEd8SPZ927bh5ISKqDq8iccSDc7/IJjJf9n02ED1uXSrUpjVHVskDwyzzrxnalqquO3UQ8d8MXxJmFX4XYpGr5TCNZry18LsiI7R8ScPBAlThzMwOhijcAzM6FkqGbLy2Bib/Imdfc/Un94xxia+XngCnx9okfnw2ZMW3Dvqo6RzfrYAhSdq8OZnauqZGeTuOkX52Q4qGmNzVz2WvKeweJUyxubdNvAVkor2Svc+Fyx8/70fO5VScjweR/90Pp+32sEdFyP3yugrL1f4UHY0F6tpmtFhxyvsmhgUKQykGX1wEAZj6BrdnqZp0pbryzsN7l1T6PkCvGPQzvuMZiUASEVbwOwwwYc/tuS38HA4hGZW3/dpMfb7/fXNCT9s0gN5XKND0dJsn6WcTqeHG4ix/P2PeJhh4O/t0eWTUR9I8PsSMe228AQWSjOySm6GT85PUCiT3LXb7WKBy4d4TSn8BeGFBl+bqSs9fuHjVp6VwBSBzVuspyGS3jdHE5fTKGsqefe1z/g/GT7d7ssrL3dvky42xWKMvbgXDsYInxaPK47HuFwut82RxQO5bfvA6R20OF+lfGKA0+kUo5En27sruRgfoKEfJSt18i22mnHzMKlo2xeSeU6n0+jPfN/36bROo5/w2vTH9x0pi7irY+EzD7nvFUuVb6S2bRuClljyMI55MF3HYJPbubkK53yP1+NnGm3reRwzK56QfA/D7ByVUx+7K/5ylkzr8oB4XLOVMjWN+FepN6oZlHzqhjB4vd7j5R200Ab02Aw9/NuwzntNSXpPOq2TR1/cq7xtl3YPvmQhi1cZtGinDmQ00ggvns/n8p+Ekt2ll+1nJn1Ku61WfhNI5yUrOYF3zccVTkLJ5GCP7WLWvVUfy/x8Nlql1v+NLXf7k11yaL/++rfwDz02ILAZup17scQgyyv/CW96rvaSj93SLwQfNvXNSadnDfLDBhYx26KNBR5MeBD/PXXtjAZ+s7tLE/buOIwnxCEWVdwE3nQC27aNG+afCqUDcnZvC2xKPjZ9XlBF3b3Km/IAPyxNJtz9LFYT0nQH42Dj+28/JM7yLLABgc3QY78KYat7B18W7uveIj3QEkoH4967Ld8s/cKk7byM/JIaS0n7Lff7fVrIQWA29VD8eDwO5h7ItH3zu0u3+tglmRbgHZ//TBt0dHjSO05g0zTxZhibmKFOm585V3Y3z79em79xu8RTXtp/lcmKvEtdAVLmkAsXtVwwA2d0/a7L5TJ1vYzef2Jg89e//vNLSwf1EdgMPdOV/4Ffgrp+b9i8u1pR+XX3Ftd1XbpM4eh7bvua0q3iAp2jH/7A7j6csBfa9K99Cp6ertDELP8apN0y4ZW0AfqmExjusemMdvmI/bV19GQG3avq7sllOj8jVtPozBx3WfaH9Xq9Fo7/nqqUENjoroGdyQPeJ/8MKfzjgTtpPjXi4Y+NRE3cpaSFGuauuF6va45qdjcrk9zquu72wcfsVruJTqqS3X34enzT7h7Opss/ZnrfCez7viRcCUsuvvakDSZAf2CrZ66ytI9o/cN10vNT2F28Wte5JVnDLXT0TwbYQMrSJS8WnyFNLYjR37lUQhB7qzMPO+Mjn/Rj44v53/jM26xvw7fpf1wul5DsHhRuFV8p3zDcK8Ludj/Trz1zCA8Lt4Jn1vMZFVKk4vks//CYBxjOzOjd730nMK3T+OGF1QoPuP0yz14v//7v//c///M/dosm1H3Mfv+/8d/X65/u2PB/kw3/dMeG32YDTb7qD2Bt0nzZqSHIIfK5Ky1nNhya2m/J7vJl3sC3HCgU7xiueqhCeBjx5z//03/9V/VTKcwS2LzbBpp8UtFeLL+wWj+x5noUnwX20xM+jj4pnBpvmq6YMfWw896xqsBWxTvGyvMGgV2Sh/YNUQ2UENi8XgwPBqsKpL0uo2uuh7AnuG1VxJzvy+Uy+Nh03OEgXgqT/IR/H4/HwbxM6dymt9sC3ybeItwNYP1CEprRNRBV3+W0TumkOqNGT3sa+YzmuI/OCzn7sbtkCM1j2/qSwPeINyIXPqxc+HH/nktVKtq7baDJp8fmLfq+n0rrChPpPPaxbdtmssUyH3u9XjMTEz1TJGBjmqYJ9xnj42HN/vEfD7vd7t/+7f8sXZAKXP/0p/jf0mXhvaqPzFZuMBfQq+bSST92d88UQHH5tvLpiTYQvgP3+rYnwVCXX3/927/+67/svuwifbjHhkIbaPJVfwC82wa+5cC9wjTH5jWGdfr117/993//vz//+Z/++td/XrosnyOwebcNNPmqPwDebQPfcgCgdgKbd9tAk88YGwAAoHq/LF0AKlAyqdruyzJ9AQCqUNiQ2wCBDfNELAAAlSpsyG0g/hHYAC9Tsqqj8egrZ9qA1wrnc3fzzY8Xy+JroYYCLF4MgOdVP0iId9vASDI+pvxhz+FwCK091sZEz68V12vuui4NbKZe/7yw9LNLEthAk8/kAcBr3NUqulwuG+jy3p7Qws6s58v2hL6ay+UisAFqJxUNeI20VdR13ehfw8Ky8cW2bSXArEesHQ3cb3M6nc7n8/F4rP1hLfDlqu9y4t020C/JZ8TUmtmclrSvxrdrPUK9nE4n0eYLrSflLE/tAxto8klFA14jdsXMtt5Op9PbS8OdYiy65sY37xOuyvP5vHRBAB4nsAFeTMu4RvE5ver7TrHeJSIC9RLYAC+QNobKW8Z3DVLvf8T/bZpm/+M2f6Zt2/iGpmlKEmziNMd3bTW6r5LW4eAQmqbJb5Ue/mDzUOxnmqShw22qM22w68HxDvZ7W7CHS1UuFCnsN/yjvMbvqrjbL0n592TwOYOzOvX6w9/k2036xGCTWE1S0YCKXSHLl4QSaYN49s3xnV3Xle8i/fyp9nf4wNupC0rKlomyTqfTA1sdDoeprTIlnDotcZNQmKn9ZnaaEc9n/q9d102VPO53qmCZc/ikB07m7FYPbDJ1jPGEDEoy9XpahsweHziurutiVY4WNf81ADZvA5d/9QfAu23gW84HxFbabMP6rhAolTYf479vm9H5ATyjxZttsE41BGe3euHu0sAmPerRM3DXib3+sTF9K20N58ucL9i9pSpRMl7rNgYoqYLbfc1uMrqvhwOb2ULelrAk9Ir/yGz+vigUWLPRG0tdqj8A3m0D33I+IG05Zd72cFRzvWlWps3B29btIKJIG9n5T04/dtBMHDRA050Ojjr9021Lt3B3g61u26zl284aPWmjh3N7sLsbU1XzjuZyWuNTJ+T2uEpKm/kKDQ4kf/IfDmxGi5E/n1PHdfvlmaqLqTMGfIPbO1h1qj8A3m0D33I+IG2HnW4cDofB8/tndjG6+aCvILN5Jj4Z3e9UZ1S+jTi1VT7Euv7xTKavl4Quj53e2ef0+cZ0Pvy73tObd6901+V/zVfB1FaZA8yX5JnA5q6v1uw3efaTr49+hYBt2MC1X/0B8G4b+JbzbiWJPdHDrdt8m6w8Pslk/szuevbFaCpamN3dVBM5fX2qVTp1jHnpEJr8G6YONl+5+fDjGbFgU1+qeEJGSzt1vHGr9DzPHsLUG54JbEZ3NBt6PRag5ksFfIOX36U/z6xowLPumozrcrk8OU3W6OaPfWbhZG6xtTc6YdToi03ThJts+teS3aWvT81P9dp5q8pXsJmdxW6pCbXiGkoDfd+HWoivpCUsqYIo/mo+Ucz7TA0fGi3eYBa10Q1Lasdk30DVBDbAs2KjKoxzGJWOLL9cLmGZ88fk2173TiFdstXoHuMm5/O5fFbokt3FFu1nVhSZigpuPdbqfV9bOf3kwtmu43sysw60bXsblOa9I6K767wVfpMf+ECAivyydAGA6sWWcWbRkvB627ZxafOw1MbgxYF3PyC/d/mdwTIyMTw7n8/hEE6nU8nKLc+09V/Vcn2HDz/vb5rmcDiEr9/lcjkej7vd7nA4ND/et+u4FEzf9+WR4eLi6ZrSNM3UxQiwfgIb5hU+XP9kkgbrNPvcejSwWYPCtungbV3XhcZ0FCOcw+EQ1lUc/Zz8gcfGZUUt5qX0fT8IjC+Xy+VyiXHm4DuZBuEP7Kvv+4+1+x/rsXnVNaXHBrbkmSyJughsmFdvxPIP/7B0CVbsf/7nNZ/zfAOoMIloDQa9JWEgTWhbD4KQ0IFwOBxqObR6tT9uQ44QZ3Zd93xzf6pfMXQQtW37Pe0GoDqFDbkN3MeMsQGe8nxyfwhsRr2qkLMKl1gZLVIo//V6HQwl2k3PlJA/tJePl/gGcWBM13WD83Y8Hm9P6V3frkFHTfptCWHtc2V/gfg1e9VVs57eVIByemzYsld1SpARG3z3toTW0HJqmib0tLykOZiO64gP+C+Xy23SnW6c94kdgH3fx0TBtm3vjWTip+3+OOtD1XU3m9xY9dEB6LEBXqMkUBlMY/W2spSKZcg3+MJomTRlLjynzzSX27a97RyI7eP87h6OFR9Tb79QTEIb/WvTNHHJl9uhNZmhMm3bHo/H4/F4W19r6JwZVfhNfuADASoisAEed9esYun8UZnJdj8pLXPmWfX5fA5D0tP3h/Ebg8kDnt9dyYIkr/XyLKaPOf8oL/nUskKpzED8qS/54qcuLdgz69gsfiAAzxDYAI8rbAaFXKwYA4QZw95XqrvE/oqpECU2GdOejZIQZXQCrtndpWcpV+7XqTewmT2ZowsH5bdKw+/bb+nUKborvn2T+LBgqjOqZD63Z2aNA1icwAZ4XNrO2087Ho+xwbS2UQqDQ0j/N8Rjs2lIac5S3DCdW2YqCrrdXbrVx85SvYFNetoHc+tl6i5TBSEJLfx7KhYanKXb+dCWOo3pMe73+8Eh3zXZUb3ZicCXE9gAj7s3of90Oq2w9RxHYux2u+PxOBqPhZU3p7Y6n8+DQG70bbO7y2z1VqEhW92yOWGBzvDvML/2VCydqbt0q7RPYxAmjb4/bnI4HNLgZ6kZzKe+k3Hp0nwKaCYHD6AKAhvgE8IMuevJQEulo8xHdV13W/LZrcKGo+M0Znf34ZZlvQ3Zvu/z3QujsXS+Cg6Hw+2aD5n3h12k35ClQsTMcYWe0nzocjvLBUBd9vWuvchn7Pe+JHyL/sflcgmP+dMZnGe3iq+UbxhakGF3u5/p1545hIeFVKXT6VRpozbOUHdv3aVVMLvVYB68pmnS05X+ddmeyUGgFb9XMT1vNHgO34G1JYsCH7OBJl/1B8C7beBbDsyKTd63Xu9xeZ+8euOrlYsjbW5ruf9Z9ufzHYbASmygyScVDYDfs4/e+rS+fBq995Vhq+KImqk35M9q/AKIaoB6CWwA+L05+9aukr7vr38UXu+6Ln1RYPOAONZodh2b0SkEQn/dShaYAnhM9V1OvNsG+iWBEjEZ6ZOXfOhhcJN5Xqy+3dg4mXTy69uzHVMEVQR8sw00+fTYALDb7XZN04QH9pKRajSY/HqwllSMakanTQtRzYcnGQd4OYENAH8XpwhbuiA8ou/7B2YSD5V+u1ITQHWq73Li3crXq/Zdgg0IU1eXzJX8ElLR3iHOPx6qcvczBfnom0NgYxo62LDvacsJbJixgYRLYLUENgArsYEmn1Q0AACgegIbAACgegIbAACgegIbAACgegIbAACgegIbAACgegIbAACgegIbAACgegIbAACger8sXQAAvlftq1wDsB56bAAAgOoJbAAAgOoJbAAAgOoJbAAAgOoJbOC77Pf7pYvAy6jNLVGbW6I2N0aF1sKsaMwrvJ7NbgQAsDbfE5gJbJgnYgEAqFRhQ24D8Y9UNAAAoHoCGwAAoHoCGwAAoHoCm2q0bds0zX6/b5qmaZq+71/7+eHDu6577ccCAMAH7I0LX7+2bc/n8+3rh8PhheFNGDH222+/HY/Hweu+JFuiQrdEbW6J2twStbkxX1KhGzhMs6KtXdM0l8tl9E+Xy+VVX8GmaZ7/EAAAWIpUtFVr2zZGNYfD4frjdDrF9zwfk3RdNxU7LeXzEw4uMsXhBuZVLPEl51Zt2mmNvuRKUZtb2qnaJENgs2oxA+10OqVZZ23bxsEwl8vlsYS0ruu6rmua5i9/+cuzBQUAgEVJRVuvtm1H/x00TXM4HEJPS9u2d8U2XdcJZgAA2BI9NusVY5XD4TD6hhjtrC2RDAAAPqz62Q82LKZXhoSxh98zajCtc+zAWcmsaJ/f6Zcc5iI7dZhb2umXHOYiO3WYW9rplxzmIjt1mBvb6WtJRatAScTS9/1dgc0geolZbQAAUCOpaCtVOGZmKksNAAC+isBmpe6d6OyFK3UCAEB1BDZ1s7AmAADsjLFZvzUkm1nzy06r2+MiO3WYdlrdHhfZqcO00+r2uMhOrdH5AIENM2qfHwMAgG8gsHmjh8e9SDADAIC7CGzeqG3bB+ZQPhwOaUSU/wRzBgAAwE5g81aPdbyErZqmOZ/P794XAABsg8Dmjdq2fXhbgQoAAJQz3XMFMvlmMVFNIAQAwDcT2KxXnOh5KrBJXxfYAADwzQQ26xVjlanBNjHVbQ1r3QAAwIIENuuVDtG5Ha7T933MQxsdzNP8eHjmtLZtm6bZ7/dPfg5r0Pd9rNBQp8+MAWNVQp2q0Hr1fR9us67N2qV3Wj+d1Qn30vL3hyvXr+q6XFmx0+kUa+p0OsXXu66Lrx8Oh9sN0zekG06JfT6//fbbYL+p0X2xcumXQZ1uT7x4S6501iZzearQuuTvtF3XLV1AZsQavOvN6npt9NisWtu2sdVyPp/3P47HY3zPy58GZWaavlwudz3MYHF936ffllvqtGqPLZbFSuQvz/P57AFwLWbvtMfjUW2uXHkF5atbXS9LYLN2fd/n+09evsfYTkqf5adlMFFBRdKb76DTLx2apU5r1Pf9XatdsTbp5Rmf8qbX5vl8lshUhcydNr6uNtfsrodEJVfu64tIofd2CPE6p9MpXDOHw+F0Or2przN+MW6zINIbtJ7WKqTh6GiVzb6BNRvczGUu1SW2gUbTQdPnDh8vGveZvZHGv0r9XZuu626fHec3Sd9/+1d1vTh3TH6Xv1yvc7/ErE1JfbkLV+p2LkSBTV1mW1ECm1rM3mnTx4KfLRqTpnJhZusof8tV14uTisbvYi/51PzRMW1UWn8V8vPmBfHmrk4rEidFDP23SxeHu5VM1h966Q+Hg/yllZtdKVuu72akF+PUhLT5N/BuAht+N9sOTq9Yv7UVyfys+sWtUczwdhlWKqbgZ5o+bdv2fR/mk/1MqXjM7FJyrtMVatu2+6OSrWaf/+4KVlfnrX5ZugCsUcnvqJ/blSu5/5a/jfWI113hLzFr5i66JVMNWQ3cdXrg6otVmdm2bdvw7EkexCL02PB3hXdezd9aNE0T8k3zNVtym2Y94tQ9p9NJlVVq9JIMnTOhl+bTBeI5scoul8voatqxg07iaO3EKusnsOHv7v019eu7AXGoxk42cA3SFpL62oy+78PqZMfj8Xw+H4/HsF6Ze2xFYvdpWHEuBKht2zZNExNHD4eDy3YzJHivlsCG+7hiNyNdYkxHXBVifV3fsIAVH5Pmf8aslVuW+atI0zSDJWtCmBqfHJ1OJ5EqfIAxNgxp436DpmnSlVj94q5ffKYgm2UzLpdLehmGKk77UUMHnfCmCu6iX6XwIa/RyJ8nsIHv0rZtuiiyqKYKcWiNbJbtGb0G9/t9+Mf5fFbj6zd4VNQ0TdM0YdxUeP18Pp/PZ32t8G4CG/gWae5ZcDqdtJmqEGNRUej2jNbp9XqNsU3btq7TNUujmq7r4hP62AUXb7z7/V5ssw2FXTG6az7PGBuG8pN+aFdVKh3DuvtZIVtrqQqxgWt+5+3J1KnVMGoxGtVEYYLK+L/uuvBWemz4u6Zp0gylkve/rSy80qCjRu5ZvUabROl4jFizqnjN0pttfm6lULlmmF2z9KrM1ObpdAqV7trcPFW8LIENfydQ2aRBVDP6QJFazDZw4xuMWN2Aex82sYjCNY5jbQpTq3Y4HEINuseullQ0RmSeN8Sbsku6CumEztfrVa3B4lyGm6Rav0Gs5UwzqTDW5U0ENvxuNqU7fd1NfP1iHUk/q9o1K162p9MpvujyrIXm0Wbk77HuwNsQb62ZnrdY1+7DixDY8Lt4EU7lP8RkYj+0VUgTkxYtCG/kt7NGcTGizFDyknE4LC7WYD7HLNamX8+qpRfj1A9r/CaYKGIRAht+l16Etxdkumycy3X9BDOwWmlrePR2mraf3G/XLK2pqRA0/9tKXeJTicHyCUGaKPGxIpES2PAH8YodrAqXjkGPK2SzZmlgsy+gTuGT0pttWMwx/G/f9/v9Pj5Fim9jteKc3ZfLZb/fp/feUJtpd407be0y8+ClKxp5trgUa0UxlF6Zo3xnqjBbjwPG4VSqbdvQbLLcanVmL1JXZS3iZZihNlcurhg228i5Xe16wASkC9Jjw1Df91PPCMPMWh8uD8Am9X2fyVc5nU7awbVo2za/fq7a3JKmaTLVLapZlh4bJrVtG8bVhN7zYOlCAWxNerMN/+tmW6m+70PHaazNpml0pW5VrO5AXa+BwAYAAKieVDQAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6AhsAAKB6vyxdAAAAWF7btoNXmqZpmia/Sd/3u93ucrnEFw+HQ9u2+Q0fFgs5W7bMVrdHuhs7/Orsr9fr0mUAAICF7ff7wSun02mqud80TRrMjMps/rB0v4XN+L7vj8dj+HfXdSG2uS38BoICqWgAAFCq7/v9fj8b1ex2u/P5/PJ+mzRSCp1Fd23ypn6kldBjAwAAv/fYZJrHae/Hbrc7HA6DzK6+7/u+P5/P6VavbW/Hch4Oh5LYJr5/tAep5KhroccGAADmDaKaruv6vh+ECiHOuV6vh8MhvvjahLTT6RT+UdJrlEY+2+6u2emxAQCAXUHfRToIJwxWyX/gA+NhCsWSzBajbdvYfTRaBj02AADwRdJel5KoZvfH3pI3zTk2+7Exqon9PBsmsAEAYBX6H/F/wxRe+/1+v983TVM4XP4dYoQQxtUUbhUT0gajbgbCke5/zB5pYTbaByKrVZGKBgDA8uIIljDGfWo+5XfMoRzkk7LKs78GYnQxutVg3M5AZl8l5ZnNQ9tJRQMAgNdKuxcy8ymfz+fPdz48M2Ny8+P2T/moZrfbHY/Hqa6b2BeUORtflYe2E9gAALAGsQUfmuOHw6HruuuPtGmeT+t6a9nSuc6eN5hjLR5s13Wj70nFeGYqAvy2PLSdwAYAgDVIG+hhhZa0l6Nt27S5/+HBNrFsL5wxOf2o6/Wa/m/TNGli2GhYMjg5t294UzC2Zr8sXQAAAL5dGqhMrTuZNuUHYc9K5DtGBglpszNBd10Xumumsu8Oh0P4kNHTFfu1Vnii3kRgAwDAwtKmeaY3Jjbll5IJEvq+z+fIjfaxZLpTZgO5tm1D5HN7Tr4wD20nFQ0AgMUV5k3Fxv1S8z6/ar/5qdKieDZmu7AG0csX5qHt9NgAALC42OdQ2L2wVHpVJrBpmmZ08rG+7297VOIr+Zy62e6pqWy02Hf0Pd01O4ENAADrkY9YluqoKUyBmx3lf6sws27qwDPZaCV73xipaAAALKk8XHnH7GQl4u6WGuEzdbyj2WglA3g2SWADAMCSCgeEpPHPUoHN7v5eo3wslC5fk5HJKIv5bzH97Dvz0NvemAcAAAL3SURBVHYCGwAAllUYKiw4ID4NbKZWzBw1dWj5WQHukolevioPbSewAQBgWYX5Xct2RKQTA5QXYCoKKpzerW3bweo3eW3bfm0e2m63208tCQQAAB+w3+/jv6eapk3ThPhnavnOFxZjqgxpObuum403YplvN+n7PsY8Ux+VviffYm/b9nYJnZIS7gqOuiJ6bAAAWMwgShntDGnbNp0f+e1lmpA2/Y/HY77fZhDV3P419qhMfU6Mamb7Xh6YjW2TBDYAACzmdgGWtJkelnmJ3RFd132waCPSApzP5/1+37Zt3/fxKEKB9/t97F8aXdxml0Qjl8tlv9+n56Hv+7R3qCSWGwQ/X5iHtpOKBgDAgmLPRtd1+XH5hblVDytMykozxPJOp1OIXsIn35a/5KMKj3rwUeXnakupaAIbAAAWkzaspxr67xtXM1WS2TePDmtJpaHFVGCzm4tt7orlSoYqZbbaQFAgsAEAYDG3DeuQ3HW5XA6HQ/NjkZLM6vs+9MmE0u52u6ZpHpi0bfA5Kz/q1RLYAACwjNjv8Zk+mbwtNfHLbemoTR4AAMAyYjDzQO9E27b7AossesMi9NgAALCMZ7oL8vMpR+V9QVvquyi3paP+ZekCAADA3W7DlcwYfUYtnv73WlLRAABYQGxVv3bRleejms2nsYWVdvb7feG81bUQ2AAAsIBnBtjALaloAAAsIMYzKwlsTqfT4JWVFOzlwoTa6SvbOFKTBwAAsAVhjI3G7deSigYAAFRPYAMAAFRPYAMAAFRPYAMAAFRPYAMAAFRPYAMAAFRPYAMAAFRPYAMAAFRPYAMAAFRPYAMAAFRvf71ely4DAADAU/TYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1RPYAAAA1fv/rpTL4q5c8tQAAAAASUVORK5CYII=\n",
2017 "text/plain": [
2018 "<IPython.core.display.Image object>"
2019 ]
2020 },
2021 "metadata": {},
2022 "output_type": "display_data"
2023 },
2024 {
2025 "name": "stdout",
2026 "output_type": "stream",
2027 "text": [
2028 "Save TH1 hframe\n",
2029 "Save TGraph v2_B\n",
2030 "Save TGraph v2_D\n",
2031 "Save TGraph Graph\n",
2032 "Save TGraph Graph\n",
2033 "removed ‘fig_BUP2021/D0_BUP2020_AuAu_v2_3yr_20wk_comp.svg’\n"
2034 ]
2035 },
2036 {
2037 "name": "stderr",
2038 "output_type": "stream",
2039 "text": [
2040 "Info in <TCanvas::Print>: png file fig_BUP2021/D0_BUP2020_AuAu_v2_3yr_20wk_comp.png has been created\n",
2041 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2021/D0_BUP2020_AuAu_v2_3yr_20wk_comp.root has been created\n",
2042 "Info in <TCanvas::Print>: eps file fig_BUP2021/D0_BUP2020_AuAu_v2_3yr_20wk_comp.eps has been created\n",
2043 "Info in <TCanvas::Print>: SVG file fig_BUP2021/D0_BUP2020_AuAu_v2_3yr_20wk_comp.svg has been created\n",
2044 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2021/D0_BUP2020_AuAu_v2_3yr_20wk_comp.C has been generated\n"
2045 ]
2046 }
2047 ],
2048 "source": [
2049 "{\n",
2050 " TString s_suffix = \"_3yr_20wk_comp\";\n",
2051 "\n",
2052 " TGraphErrors * grProD0_v2_AuAu_3year \n",
2053 " = GraphShiftCut(\n",
2054 " Significance2v2( gProD0_Significance_AuAu_0_80_3year, 0.025, Psi2_resolution, 1),\n",
2055 " 0, 0,100);\n",
2056 " TGraphErrors * grNonProD0_v2_AuAu_3year \n",
2057 " = GraphShiftCut(\n",
2058 " Significance2v2( gNonProD0_Significance_AuAu_0_80_3year, 0, Psi2_resolution, 1),\n",
2059 " 0, 1.9,100); \n",
2060 "\n",
2061 " TGraphErrors * grProD0_v2_AuAu_3year_20wk \n",
2062 " = GraphShiftCut(\n",
2063 " Significance2v2( gProD0_Significance_AuAu_0_80_3year_20wk, 0.025, Psi2_resolution, 1),\n",
2064 " 0, 0,100);\n",
2065 " TGraphErrors * grNonProD0_v2_AuAu_3year_20wk \n",
2066 " = GraphShiftCut(\n",
2067 " Significance2v2( gNonProD0_Significance_AuAu_0_80_3year_20wk, 0.025, Psi2_resolution, 1, 0.25),\n",
2068 " 0, 1.9,100); \n",
2069 "\n",
2070 " \n",
2071 " grProD0_v2_AuAu_3year->SetMarkerStyle(kFullCircle);\n",
2072 " grNonProD0_v2_AuAu_3year->SetMarkerStyle(kFullSquare);\n",
2073 " \n",
2074 " grProD0_v2_AuAu_3year->SetMarkerSize(2);\n",
2075 " grNonProD0_v2_AuAu_3year->SetMarkerSize(2);\n",
2076 " \n",
2077 " grProD0_v2_AuAu_3year->SetLineWidth(4);\n",
2078 " grNonProD0_v2_AuAu_3year->SetLineWidth(4);\n",
2079 "// grProD0_v2_AuAu_3year->SetLineStyle(kDashed);\n",
2080 "// grNonProD0_v2_AuAu_3year->SetLineStyle(kDashed);\n",
2081 " \n",
2082 " grProD0_v2_AuAu_3year->SetLineColorAlpha(kBlack, 1);\n",
2083 " grNonProD0_v2_AuAu_3year->SetLineColorAlpha(kBlue+1, 1);\n",
2084 " \n",
2085 " grProD0_v2_AuAu_3year->SetMarkerColorAlpha(kBlack, 1);\n",
2086 " grNonProD0_v2_AuAu_3year->SetMarkerColorAlpha(kBlue+1, 1);\n",
2087 " \n",
2088 " \n",
2089 " grProD0_v2_AuAu_3year_20wk->SetMarkerStyle(kFullCircle);\n",
2090 " grNonProD0_v2_AuAu_3year_20wk->SetMarkerStyle(kFullCircle);\n",
2091 " \n",
2092 " grProD0_v2_AuAu_3year_20wk->SetMarkerSize(2);\n",
2093 " grNonProD0_v2_AuAu_3year_20wk->SetMarkerSize(2);\n",
2094 " \n",
2095 " grProD0_v2_AuAu_3year_20wk->SetLineWidth(4);\n",
2096 " grNonProD0_v2_AuAu_3year_20wk->SetLineWidth(4);\n",
2097 "// grProD0_v2_AuAu_3year->SetLineStyle(kDashed);\n",
2098 "// grNonProD0_v2_AuAu_3year->SetLineStyle(kDashed);\n",
2099 " \n",
2100 " grProD0_v2_AuAu_3year_20wk->SetLineColorAlpha(kBlack, 1);\n",
2101 " grNonProD0_v2_AuAu_3year_20wk->SetLineColorAlpha(kCyan+1, 1);\n",
2102 " \n",
2103 " grProD0_v2_AuAu_3year_20wk->SetMarkerColorAlpha(kBlack, 1);\n",
2104 " grNonProD0_v2_AuAu_3year_20wk->SetMarkerColorAlpha(kCyan+1, 1);\n",
2105 " \n",
2106 " \n",
2107 " \n",
2108 "// RAA_pi->SetLineColorAlpha(kGreen+2, 1);\n",
2109 " v2_B->SetLineColorAlpha(kBlue-4, 1);\n",
2110 " v2_D->SetLineColorAlpha(kBlack, 1);\n",
2111 " v2_D_B->SetLineColorAlpha(kBlue+1, 1);\n",
2112 " \n",
2113 " \n",
2114 "// RAA_pi->SetLineStyle(kSolid );\n",
2115 " v2_B->SetLineStyle(kSolid );\n",
2116 " v2_D->SetLineStyle(kDashed);\n",
2117 " v2_D_B->SetLineStyle(kDashed);\n",
2118 " \n",
2119 " TCanvas *c1 = new TCanvas(\"D0_BUP2020_AuAu_v2\" + s_suffix,\n",
2120 " \"D0_BUP2020_AuAu_v2\" + s_suffix, 1100, 800);\n",
2121 " c1->Divide(1, 1);\n",
2122 " int idx = 1;\n",
2123 " TPad *p;\n",
2124 "\n",
2125 " p = (TPad *) c1->cd(idx++);\n",
2126 " c1->Update();\n",
2127 " p->DrawFrame(0, -.1, 11, 0.25)->SetTitle(\";#it{p}_{T} [GeV];v_{2}\");\n",
2128 " (new TLine(0, -.0, 11, .0))->Draw();\n",
2129 " \n",
2130 " v2_B->DrawClone( );\n",
2131 " v2_D->DrawClone();\n",
2132 "// v2_D_B->DrawClone();\n",
2133 " \n",
2134 " //grProD0_v2_AuAu_3year->DrawClone(\"p\");\n",
2135 " grNonProD0_v2_AuAu_3year->DrawClone(\"p\");\n",
2136 " //grProD0_v2_AuAu_3year_20wk->DrawClone(\"p\");\n",
2137 " grNonProD0_v2_AuAu_3year_20wk->DrawClone(\"p\");\n",
2138 " \n",
2139 " TLegend *leg = new TLegend(0, .78, .85, .9);\n",
2140 " leg->SetFillStyle(0);\n",
2141 " leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection, 0-80% Au+Au, Years 1-3\", \"\");\n",
2142 " leg->AddEntry(\"\", Form(\"#it{B}#rightarrow#it{D}^{0}, Res(#Psi_{2})=%.1f\"\n",
2143 " , Psi2_resolution)\n",
2144 " , \"\");\n",
2145 " leg->Draw();\n",
2146 " \n",
2147 " \n",
2148 " leg = new TLegend(.55, .65, .85, .83);\n",
2149 " leg->SetFillStyle(0);\n",
2150 " leg->AddEntry(grNonProD0_v2_AuAu_3year, Form(\"28wk: %.0f nb^{-1} rec. \",\n",
2151 " AuAu_rec_3year /1e9 ), \"lp\");\n",
2152 " leg->AddEntry(grNonProD0_v2_AuAu_3year_20wk, Form(\"20wk: %.0f nb^{-1} rec.\",\n",
2153 " AuAu_rec_3year_20wk /1e9 ), \"lp\");\n",
2154 "// leg->AddEntry(v2_B, \"#it{B}-meson\", \"l\");\n",
2155 "// leg->AddEntry(v2_D, \"#it{D}-meson\", \"l\");\n",
2156 "// leg->AddEntry(RAA_pi, \"#pi\", \"l\");\n",
2157 " leg->Draw();\n",
2158 " \n",
2159 " leg = new TLegend(.2, .2, .7, .3);\n",
2160 " leg->SetFillStyle(0);\n",
2161 " leg->AddEntry(v2_D, \"#it{D}-meson (fit to STAR PRL#bf{118})\", \"l\");\n",
2162 " leg->AddEntry(v2_B, \"#it{B}-meson (m_{T} scaling)\", \"l\");\n",
2163 "// leg->AddEntry(RAA_pi, \"#pi\", \"l\");\n",
2164 " leg->Draw();\n",
2165 "\n",
2166 " c1->Draw();\n",
2167 " SaveCanvas(c1, \"fig_BUP2021/\" + TString(c1->GetName()), kTRUE);\n",
2168 "}"
2169 ]
2170 },
2171 {
2172 "cell_type": "code",
2173 "execution_count": 37,
2174 "metadata": {},
2175 "outputs": [
2176 {
2177 "data": {
2178 "image/png": 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\n",
2179 "text/plain": [
2180 "<IPython.core.display.Image object>"
2181 ]
2182 },
2183 "metadata": {},
2184 "output_type": "display_data"
2185 },
2186 {
2187 "name": "stdout",
2188 "output_type": "stream",
2189 "text": [
2190 "Save TH1 hframe\n",
2191 "Save TGraph Graph\n",
2192 "Save TGraph Graph\n",
2193 "Save TGraph Graph\n",
2194 "Save TGraph Graph\n",
2195 "removed ‘fig_BUP2020/D0_BUP2020OOArAr_v2_5yr.svg’\n"
2196 ]
2197 },
2198 {
2199 "name": "stderr",
2200 "output_type": "stream",
2201 "text": [
2202 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020OOArAr_v2_5yr.png has been created\n",
2203 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020OOArAr_v2_5yr.root has been created\n",
2204 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020OOArAr_v2_5yr.eps has been created\n",
2205 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020OOArAr_v2_5yr.svg has been created\n",
2206 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020OOArAr_v2_5yr.C has been generated\n"
2207 ]
2208 }
2209 ],
2210 "source": [
2211 "{\n",
2212 " TString s_suffix = \"_5yr\";\n",
2213 "\n",
2214 " TGraphErrors * grProD0_v2_OO_5year \n",
2215 " = GraphShiftCut(\n",
2216 " Significance2v2( gProD0_Significance_OO_5year, 0.0, OO_Psi2_resolution, 2),\n",
2217 " 0.2, 0,100);\n",
2218 " TGraphErrors * grNonProD0_v2_OO_5year \n",
2219 " = GraphShiftCut(\n",
2220 " Significance2v2( gNonProD0_Significance_OO_5year, 0, OO_Psi2_resolution, 5),\n",
2221 " -.2, 1.9,100); \n",
2222 "\n",
2223 " TGraphErrors * grProD0_v2_ArAr_5year \n",
2224 " = GraphShiftCut(Significance2v2( gProD0_Significance_ArAr_5year, 0.0, ArAr_Psi2_resolution, 2),\n",
2225 " 0., 0,100);\n",
2226 " TGraphErrors * grNonProD0_v2_ArAr_5year \n",
2227 " = GraphShiftCut(\n",
2228 " Significance2v2( gNonProD0_Significance_ArAr_5year, 0, ArAr_Psi2_resolution, 5),\n",
2229 " -.4, 1.9,100);\n",
2230 " \n",
2231 " grProD0_v2_OO_5year->SetMarkerStyle(kOpenCircle);\n",
2232 " grNonProD0_v2_OO_5year->SetMarkerStyle(kOpenSquare);\n",
2233 " grProD0_v2_ArAr_5year->SetMarkerStyle(kFullCircle);\n",
2234 " grNonProD0_v2_ArAr_5year->SetMarkerStyle(kFullSquare);\n",
2235 " \n",
2236 " grProD0_v2_OO_5year->SetMarkerSize(2);\n",
2237 " grNonProD0_v2_OO_5year->SetMarkerSize(2);\n",
2238 " grProD0_v2_ArAr_5year->SetMarkerSize(2);\n",
2239 " grNonProD0_v2_ArAr_5year->SetMarkerSize(2);\n",
2240 " \n",
2241 " grProD0_v2_OO_5year->SetLineWidth(4);\n",
2242 " grNonProD0_v2_OO_5year->SetLineWidth(4);\n",
2243 " grProD0_v2_ArAr_5year->SetLineWidth(4);\n",
2244 " grNonProD0_v2_ArAr_5year->SetLineWidth(4);\n",
2245 " \n",
2246 " grProD0_v2_OO_5year->SetLineColorAlpha(kBlack, 1);\n",
2247 " grNonProD0_v2_OO_5year->SetLineColorAlpha(kBlue+2, 1);\n",
2248 " grProD0_v2_ArAr_5year->SetLineColorAlpha(kBlack, 1);\n",
2249 " grNonProD0_v2_ArAr_5year->SetLineColorAlpha(kBlue+2, 1);\n",
2250 " \n",
2251 " grProD0_v2_OO_5year->SetMarkerColorAlpha(kBlack, 1);\n",
2252 " grNonProD0_v2_OO_5year->SetMarkerColorAlpha(kBlue+2, 1);\n",
2253 " grProD0_v2_ArAr_5year->SetMarkerColorAlpha(kBlack, 1);\n",
2254 " grNonProD0_v2_ArAr_5year->SetMarkerColorAlpha(kBlue+2, 1);\n",
2255 " \n",
2256 " TCanvas *c1 = new TCanvas(\"D0_BUP2020OOArAr_v2\" + s_suffix,\n",
2257 " \"D0_BUP2020OOArArO_v2\" + s_suffix, 1100, 800);\n",
2258 " c1->Divide(1, 1);\n",
2259 " int idx = 1;\n",
2260 " TPad *p;\n",
2261 "\n",
2262 " p = (TPad *) c1->cd(idx++);\n",
2263 " c1->Update();\n",
2264 " p->DrawFrame(0, -0.25, 11, 0.25)->SetTitle(\";#it{p}_{T} [GeV];v_{2}\");\n",
2265 " \n",
2266 " grProD0_v2_OO_5year->DrawClone(\"p\");\n",
2267 " grNonProD0_v2_OO_5year->DrawClone(\"p\");\n",
2268 " \n",
2269 " grProD0_v2_ArAr_5year->DrawClone(\"p\");\n",
2270 " grNonProD0_v2_ArAr_5year->DrawClone(\"p\");\n",
2271 " \n",
2272 " TLegend *leg = new TLegend(.1, .8, .55, .9);\n",
2273 " leg->SetFillStyle(0);\n",
2274 " leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection, Years 1-5\", \"\");\n",
2275 "// leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. O+O, Res(#Psi_{2})=%.1f\", OO_rec_5year/1e9, OO_Psi2_resolution), \"\");\n",
2276 "// leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. Ar+Ar, Res(#Psi_{2})=%.1f\", ArAr_rec_5year/1e9, ArAr_Psi2_resolution), \"\");\n",
2277 " leg->Draw();\n",
2278 " \n",
2279 "// leg = new TLegend(.18, .2, .55, .4, \"O+O\");\n",
2280 "// leg->SetFillStyle(0);\n",
2281 "// leg->AddEntry(grProD0_v2_OO_5year, \" \", \"p\");\n",
2282 "// leg->AddEntry(grNonProD0_v2_OO_5year, \" \", \"p\");\n",
2283 "// leg->Draw();\n",
2284 " \n",
2285 "// leg = new TLegend(.28, .2, .55, .4, \"Ar+Ar\");\n",
2286 "// leg->SetFillStyle(0);\n",
2287 "// leg->AddEntry(grProD0_v2_ArAr_5year, \"Prompt #it{D}^{0}\", \"p\");\n",
2288 "// leg->AddEntry(grNonProD0_v2_ArAr_5year, \"#it{B}#rightarrow#it{D}^{0}\", \"p\");\n",
2289 "// leg->Draw();\n",
2290 "\n",
2291 " leg = new TLegend(.2, .2, 1, .4, \"Prompt #it{D}^{0}\");\n",
2292 " leg->SetFillStyle(0);\n",
2293 " leg->AddEntry(grProD0_v2_OO_5year, \" \", \"p\");\n",
2294 " leg->AddEntry( grProD0_v2_ArAr_5year, \" \", \"p\");\n",
2295 " leg->Draw();\n",
2296 " \n",
2297 " leg = new TLegend(.4, .2, .65, .4, \"#it{B}#rightarrow#it{D}^{0}\");\n",
2298 " leg->SetFillStyle(0);\n",
2299 " leg->AddEntry(grNonProD0_v2_OO_5year, Form(\"%.0f nb^{-1} str. O+O, Res(#Psi_{2})=%.1f\", OO_rec_5year/1e9, OO_Psi2_resolution), \"p\");\n",
2300 " leg->AddEntry(grNonProD0_v2_ArAr_5year, Form(\"%.0f nb^{-1} str. Ar+Ar, Res(#Psi_{2})=%.1f\", ArAr_rec_5year/1e9, ArAr_Psi2_resolution), \"p\");\n",
2301 " leg->Draw();\n",
2302 "\n",
2303 " c1->Draw();\n",
2304 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
2305 "}"
2306 ]
2307 },
2308 {
2309 "cell_type": "markdown",
2310 "metadata": {},
2311 "source": [
2312 "### p+Au"
2313 ]
2314 },
2315 {
2316 "cell_type": "code",
2317 "execution_count": 38,
2318 "metadata": {},
2319 "outputs": [
2320 {
2321 "data": {
2322 "image/png": 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\n",
2323 "text/plain": [
2324 "<IPython.core.display.Image object>"
2325 ]
2326 },
2327 "metadata": {},
2328 "output_type": "display_data"
2329 },
2330 {
2331 "name": "stdout",
2332 "output_type": "stream",
2333 "text": [
2334 "Save TH1 hframe\n",
2335 "Save TGraph Graph\n",
2336 "Save TGraph v2_D\n",
2337 "removed ‘fig_BUP2021/D0_BUP2020_pAu_C0_5_v2_3yr.svg’\n"
2338 ]
2339 },
2340 {
2341 "name": "stderr",
2342 "output_type": "stream",
2343 "text": [
2344 "Info in <TCanvas::Print>: png file fig_BUP2021/D0_BUP2020_pAu_C0_5_v2_3yr.png has been created\n",
2345 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2021/D0_BUP2020_pAu_C0_5_v2_3yr.root has been created\n",
2346 "Info in <TCanvas::Print>: eps file fig_BUP2021/D0_BUP2020_pAu_C0_5_v2_3yr.eps has been created\n",
2347 "Info in <TCanvas::Print>: SVG file fig_BUP2021/D0_BUP2020_pAu_C0_5_v2_3yr.svg has been created\n",
2348 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2021/D0_BUP2020_pAu_C0_5_v2_3yr.C has been generated\n"
2349 ]
2350 }
2351 ],
2352 "source": [
2353 "{\n",
2354 " TString s_suffix = \"_3yr\";\n",
2355 "\n",
2356 " TGraphErrors * grProD0_v2_pAu_C0_5_3year \n",
2357 " = GraphShiftCut(\n",
2358 " Significance2v2( gProD0_Significance_pAu_C0_5_3year, 0.0, pAu_Psi2_resolution, 2),\n",
2359 " 0.2, 0,100);\n",
2360 " TGraphErrors * grNonProD0_v2_pAu_C0_5_3year \n",
2361 " = GraphShiftCut(\n",
2362 " Significance2v2( gNonProD0_Significance_pAu_C0_5_3year, 0, pAu_Psi2_resolution, 10),\n",
2363 " -.2, 1.9,100); \n",
2364 "\n",
2365 " \n",
2366 " grProD0_v2_pAu_C0_5_3year->SetMarkerStyle(kFullCircle);\n",
2367 " grNonProD0_v2_pAu_C0_5_3year->SetMarkerStyle(kFullSquare);\n",
2368 " \n",
2369 " grProD0_v2_pAu_C0_5_3year->SetMarkerSize(2);\n",
2370 " grNonProD0_v2_pAu_C0_5_3year->SetMarkerSize(2);\n",
2371 " \n",
2372 " grProD0_v2_pAu_C0_5_3year->SetLineWidth(4);\n",
2373 " grNonProD0_v2_pAu_C0_5_3year->SetLineWidth(4);\n",
2374 " \n",
2375 " grProD0_v2_pAu_C0_5_3year->SetLineColorAlpha(kBlack, 1);\n",
2376 " grNonProD0_v2_pAu_C0_5_3year->SetLineColorAlpha(kBlue+2, 1);\n",
2377 " \n",
2378 " grProD0_v2_pAu_C0_5_3year->SetMarkerColorAlpha(kBlack, 1);\n",
2379 " grNonProD0_v2_pAu_C0_5_3year->SetMarkerColorAlpha(kBlue+2, 1);\n",
2380 " \n",
2381 " TCanvas *c1 = new TCanvas(\"D0_BUP2020_pAu_C0_5_v2\" + s_suffix,\n",
2382 " \"D0_BUP2020_pAu_C0_5_v2\" + s_suffix, 1100, 800);\n",
2383 " c1->Divide(1, 1);\n",
2384 " int idx = 1;\n",
2385 " TPad *p;\n",
2386 "\n",
2387 " p = (TPad *) c1->cd(idx++);\n",
2388 " c1->Update();\n",
2389 " p->DrawFrame(0, -0.15, 7.5, 0.4)->SetTitle(\";#it{p}_{T} [GeV];v_{2}\");\n",
2390 " (new TLine(0, -.0, 7.5, .0))->Draw();\n",
2391 " \n",
2392 " grProD0_v2_pAu_C0_5_3year->DrawClone(\"p\");\n",
2393 " // grNonProD0_v2_pAu_C0_5_3year->DrawClone(\"p\");\n",
2394 " \n",
2395 " \n",
2396 " //v2_B->DrawClone( );\n",
2397 " v2_D->DrawClone();\n",
2398 " \n",
2399 " TLegend *leg = new TLegend(.1, .75, .55, .9);\n",
2400 " leg->SetFillStyle(0);\n",
2401 " leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection, Years 1-3\", \"\");\n",
2402 " leg->AddEntry(\"\", Form(\"0-5%% p+Au, %.0f nb^{-1} trig., Res(#Psi_{2})=%.1f\", pAu_C0_5_trig_3year/1e9, pAu_Psi2_resolution), \"\");\n",
2403 "// leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. O+O, Res(#Psi_{2})=%.1f\", OO_rec_5year/1e9, OO_Psi2_resolution), \"\");\n",
2404 "// leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. Ar+Ar, Res(#Psi_{2})=%.1f\", ArAr_rec_5year/1e9, ArAr_Psi2_resolution), \"\");\n",
2405 " leg->Draw();\n",
2406 " \n",
2407 " leg = new TLegend(.2, .6, .7, .72);\n",
2408 " leg->SetFillStyle(0);\n",
2409 " leg->AddEntry(grProD0_v2_pAu_C0_5_3year, \"Prompt #it{D}^{0}\", \"lp\");\n",
2410 " leg->AddEntry(v2_D, \"#it{D}-meson (Au+Au)\", \"l\");\n",
2411 " //leg->AddEntry(v2_B, \"#it{B}-meson (m_{T} scaling)\", \"l\");\n",
2412 "// leg->AddEntry(RAA_pi, \"#pi\", \"l\");\n",
2413 " leg->Draw();\n",
2414 " \n",
2415 " c1->Draw();\n",
2416 " SaveCanvas(c1, \"fig_BUP2021/\" + TString(c1->GetName()), kTRUE);\n",
2417 "}"
2418 ]
2419 },
2420 {
2421 "cell_type": "code",
2422 "execution_count": 39,
2423 "metadata": {},
2424 "outputs": [
2425 {
2426 "data": {
2427 "image/png": 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e6RkyvfOeZe40XjdxZ+jeLRNnLT1jW/xW+KN1BcbLXHX5XWWq7Vx0jxk+gZEe8Nm/t8UfwGo8cbrnc7IjaLlX+eZdrXZGuuHVWneotrs/0MZ7GTlcKn04ObqpZb27iF+J66EV8+Q/mrOrd16BzWbzq1/9KrFWjswPTnqG2cRhBhdTmBJb6F3xYu/B0B4v/ghy8TATq+QfVPoQ4sL3rjvtzMK94tOav8eL5yWxnfSKiWs1LurFqr723avc+6Qcj8d0SAw80Qo+kos/gDXJudHfENik8wHCMolv3MwdzUqiI6XRaum26nPzrubX05ZNx8V+gHytTbVijEyt7pehQGUoG601ICcOk375y1+29pU+Hb3zOI+Pas6dH8K7uvtNBB6JAwkLDH2acpqtvS8mPsu9O7244tDvFIlj7H0rJxIY6kPOGcpycYGR4jOS6IRpyekV770P50QXF6+QoXUvJiG3BjXd7E4npTdJ706ddcDN7ndDfpjFH8CahNt9Ig7JWSaWGQitPrDprYF4yGzr9e53cI7W9m/bSG9puzMH3FCeoUBlKButOyAnbCp/5oBGb2CTXiXTtSerty0V3r0YKqSLHZbJ6SrJ2WDYb1ywiyuGOhkZ2OTcPYZuHTnhQU4NjNQb2aZXyVlsqIYvGtr4VFfgeHfaS+/nVBIazM297zAP8F73XsOzFEXRzEPaTBXaXaA1GWjONsuyDNtMLBbeHZk9NWeHw+FwOBRFEWZHzX80TY7WN3d3Atb0kJvWcyRaJ2KSRz0Mzd3cmjb6hz/8YfNKa/l4sdaDNVsTCXT1zgT9gx/84AFTPMeOx2P6g3PxkkifxIsf4e4G05+4siybCyMsH66rxIqtByDeLFwAiWNpPROmu+RzZ/Wt67o1genNP1vEwkFd9cHMmZQ5MTlyuEU0d7BlTZfcenZq80dzT57kWgVoCGxmJAQhh8Oh9yswp03T3WbzdZj+Ao6/MjO3PHNVVfU+cu50Op1Op+at/X4/9NCPq/ZVFEX6MZ3NMolmTbex1Xq2YOv0ffbZZzc8xCYRqHzyySc//OEPexfrlfNozmBoavwf/ehH0wY2Q23Wh13Vmb8jxAucTqec4j33IYaZFTjDwGaz2RyPx/Bp2u/3mQHnZoqSx0+nGXkGm8Cg2cjpdGqOqPmZpjGyqFcZ+kQP3VEbrecmhZOy3W7FNsBkntxjxLvCeenNmwrvDg01bgylwQxlTfROHxSvftuxPF3mT7PpKacSmoE3Q3tvLZxOurg4u3T63RzdUTqJXXzyySfdJ94kypOYOSAd83z88cc3HEtskmHTF7cQFkingOYPbrlhxrxmxTHThyQOs/etzIrtLVJ4MSex9qqjuEHm6TvflIba3ULOyb2thIktT5XWlXNSpipD5lEDD5P+7C/Cv7h4/+WRwvdWq9Mm/n2r+ZWutWLzOO1G69fisM3T6bTdbls/m8UZDr1bXq6yLFvT5vZq9Y10DX3pNk+LzyxMuhjd7p34n93n4dxgKA+t95Uf/vCHieW7A2Z6M82atVpJay0/+tGPEu8uzpo+Pq/ptjTUoKqq3W7X7SsOvzdd3EL6Eqqq6nw+7/f77uf3cDhst9v8O9IYvbN03PD1EY7iMcUGXsIDgyiyXGyI967Vmv/n2m1uks8BnPT4nqO3KdBbY92fbG/Y3chHc6ZnDrhtKqHW4Xdngk5PvHbDzAHd53Ke+zpwRs6N9pgem8x+kht6bK49mzev2FuM9FuZFRsWW1mPzbV7aXWn9F4qQxu/bae9vUMjez8mOSm9k7J0jbmYgXt4wA353vTYzE5d10P5Bs3df9ptNvb7feKnym2eGwr2MFVV1XV9zkgBSneeZBqZT9/aaRj9MmGRuhtJD9pJzBzQjXM2m83f/d3ftXpjmn6ebrSzpk6bcPE8vesmHt0xydZyFnv6UT9X3PV9Pp8f0AvRzCJwfjcem0PvR9Nztdvt5lAYYLOKhlyuZ0ZVJIVOhqLzCOqR2wybTY8SaSzuImk9baZ3mdanoPeX5u5b+VobSf8eebF7p/Vu6+k0mXI++EMh07UDbLq5avFYmu72bzic4DE9Ns+a7rnJpYwHzuUc79AyiRV735pkuudl9dhkliqcl6um4U4sk3MFJm5o5+me0TnJRjK7YsJFoscGZuIBN+R7MyvafN3jt65X+P2sm+B+8ajjX5pbPRu3/Qh91UbS3TvdsKcZSXWxDEVRhG6W1kaGAphPPvnk3/27f9e7qcSOugNsWq98+OGH8exnH3/8cauX5vHzPl8rnmRvaCrn+DK76rKpqmroEu0OAIu3PLTiVLO3X5yncXPTVI0zFyYfK8tyqKuqqqqwTP6Wx3SgxRNXzr9nLHM2zpz5xAGu8+zIirlb3EXSbWCF/q4m7Tv9KZjkA9LayK9//ev8Ak/yaM7Nuz+CDj2a82LJG3Ef0S9/+ct0FXVXby0w7cM6H9Njc373NKU30v35uXfFi71AQwtcXDG8m9NxlH4rcVDn5FSNY3psQsfyVD/kDxWy18X5Jy92iA3tZRMZemuoVBcvv6l6Py6W5NrtDJXn4hEBj7eCz+PiD4B7W9xVfu1EunErZA4zB7SioJt/CI+jl9ZGEslsvXFUvEBrRM0HH3wQv5s5N0B3FzdPIfCwwOb8brETl03+9uO1WjNYxKesm4CUWDFRkhtKGH+ainenkm+91VpxTGAzeZO396wlJCo/cdTxWq0dXRzif/F4W2e2dfkldn2tqWo+ceW0jmWqiaqB8aa66z7R4g+Ae1viVZ4fDKTbH7f99nntRlpFSr+bL45e0ru4uMfEu9///vfDW92JAYYeU9Nd8sMPP0yXasgjA5uch5z0NisT27+4wYtdJfkl6S6TU8KcT1NirSUGNudbz8vFVYa2kHO8Yya3zDdhzT+mwMCEJvnsP9fiD4B7W+hVfrHfpvcL9R4zB6Q3cu3MAfkSG0mXP11RrXfDzAHXxirpAud7ZGBzvhRRXMxB6n030f5LXzyJFXtLkngObLqEiU/TxUSjhQY252T1Dh114vJoTmWrJrslvLlIm4m6Pqat+QcUGJjQVJ/9J9qeR7SceAXb7YIvkmaW581mczqdwldsVVXGqjJGeDZruK7SF1WYQ3PooxRmZ2422GwqZ6qP1ooXS9KUPCx81XD21qepfCt/C1dppi54+s0nHHWjmWF5zCrxu7dNJ9A9EZt5TwzTvQ+7CcM8LbrJ11j8AXBvK7jK4bkuBjZ0NZWmxgAeZgVNPg/oBAAAFk9gA3BHY55e8rKaPKVrZzgE4MUJbADuaH2PsLy3ZnTNfr+f87gRAGbovWcXgAUIIwTSlp6XCROq63q328WvGC2dSR8XwLQyG3IrILDhMhELjFQUhf4HAJ4isyG3gvhn8bMfcG8rmCIDniLMhiykAWD+VtDkW/wBcG8ruMoBAEhbQZPP5AEAAMDiCWwAAIDFE9gAAACLJ7ABAAAWT2ADAAAsnsAGAABYPIENAACweAIbAABg8d57dgFYgO12m7PY0h/qBACwPpkNuRUQ2HCZiAUAYKEyG3IriH+kogEAAIsnsAEAABZPYAMAACyewAYAAFg8gQ0AALB4AhsABtV1Xdf1s0sBAJdtzeRL2nbrIoHXtd1ui6IQ2wCs3gqafHpsAOhXVdWziwAAuQQ2ALTVdV2W5eFweHZBACDXe88uAADzsoKHTwPwgvTY3FdVVWVZbrfbsizLspwkT72u62azYctVVaW3XFXVNsPQRnLW1RKC1di/VRTFs8sCwFiv05Bb/CCh2aqqqjeLY+Qw3LIsT6dT71uJLSfWih2Px7IsWy+uYCQZcJvmPmbyAIBXsIImn1S0u0gEEqfT6ebrJh1MN1vuDU5yoprVaM1Oe9Xo53jhpk/stnWHXLvNhRpTjdwgVLix/gC8tDNT2+/3oXqLorj4eqY4J2S/34fXj8djK12ku25465jUu9/FXSRxPbfqKu14PMYrXnWOWuum5RdpibpV8ewSXSEUPn+VkK9VFEVRFEOfo7u6X1U3n6Yb7lcALM6yvrJ7Lf4AZijRfo3bfFc1gC6uGC/Q3e+Yds/irvJWYJNf/lZ8eL/AZnHN/avcXP9zEAqfs3DipD84vLmtqnN+13hMYHM8HpshPXfdCwBpy/rK7iUVbWJxKkg3LaQsy6IomsSwiyP+Y/GSvYk98ZYPh0O867CuccBpUyXsddu7zSmo6zrexVSTScxNd2hZVVVLSZHKn914aBBdY7fb9SaFzsput4v/+cSBNFVVNR8NiYsAjCGwmdjFKKKqqqY9cVUzOjShuj+Hd7c8VKSXbTHkNKwnbNJ16zl+JYy/WuXAp7gahyLt2brqAxI+kkVRNLMUbjabuq7DZ3C3253nPQSzdTN52fsDAKthuueJhdbqUEsubj3c0JhOND60S1pCbJnzM3w4X/fu17p5YoNFiKuxt9twhpr507fbbX6o2foUh3+WZRkHMzM/v9W73EAAWDqBzb3ktBKmDWyGttbtsanfunbvi5MfpYRG7QOad6FU6zsFcWA/MoZ/gOZJULvdLj8DrREOs3eYTegJuXazAMAYApspZbbebugTCIOiEssM9Tm0xnU0LblG8zymmf+uPEZoW6ePMZy4RKbfzLUi1aYboTm/OY9wnaoM4e+m5jOb+DmR9nyi8dZk1ukFripw9xhve8JvfPabdR9fb62HCDcXYW9R47Fn3RpovRIOTf8SAD2eNm3BGsXN4sRiIfCYcK6hxLRpI6+ExV0k4Sw01XvVGWlmZ7rh7MT1f3HhsORU00CFvTcbHIqc7z3rVPfCzpwGMK7/oWXyqzdfa1qwCT+/YZmr6jze7FCM3bvTuNiJ4Dy/JMENs6KlZwjMvDuFxeKjbm35hsPhos3mx+G/Z5cFeLQV3Fr12DzB5L81xkOWL26/KIpmZtVW8zf99M/lyukfe2Qe2r3H2CTGihwOh8wDbH5uv/Zn/u4As3h3M+wYLN+VWcKL4+g276Z93lCwxJRrp9MpPdAu0Tn2gM/40BQmwW63u61OWnc5AOgS2NzFIydWbrUkzu/+CNqapep8PjdDpZvcpPO7v+4PNdS2k5r28C+6+FD2R+ahxY2ze1wk8ex54deL+BGup9Mpp/l+OBwylwyGErRCra5yFriLbjjq0+nUnMdWb098Eodig2Z3rQ6W+Eq79gNYVVVz08hcPp4sLu6ciTtb4kulVcKwSit4O51O4YPT/C5zw5OjANZn0S20ezDd84J1f9Y996V2NI2GRI57mIB4aFre3s0uRWghDR1db/fCGEP1HLdx7/rMkNYTVJq+l4tneaSh+DDuQ4gnEFuBm3tOcnQfg1PXdfjWSdRk99Jqfst4wFlojY2J3yrL8ng83jDTfeyJT9oBlq65e6zpO6gxbQttBbGNwGaRulkZ+/2+t7Wak1CU2WBarvBAld6jmzwP7WKL9q7ts6Ioeg8kPsv3eGLmUDW2stE0THMMncT9fn/x6uqt4Tiwud9ZCJvt7Y0so4cIj9w+QL7MX4FZB6lod5H+8h759dzKPWvSTqZqp66y6RAqp3t0j8xDC06n03a7vVNVJ66E+00znZ4obE3ZaJlVNzJIvvnjnLiMQ+7WA87C0C6a9NfbmhTLnbEQeKIQ1RRFEb4EV9AvwRCBzZSubc3c0PrZbretLPZJGqkrfrLK5t1stNZbk+ehbTab44DWhA03j6JOSxxIONjJm7bx2J50kVZ5gXWNPMybr8Z0dlz4+05noZsAeaeNA2QKQxabiePDoD63lLWSijale39O4t8Yuin4JAxlo91jPrShTYXXw3nc7XaP7BB/zAWT7m1YejbazD90zy1enGwWhvs3aXWNkRufoozAC+mdPahJzF5BBgG9BDb3khiscltjOo5q7tcaXmvrIeTvxeflKXlom83mfD7fabjLDTOtpcOM3ndbF0lc/osjQHyXPFF6sNkkWhMVbDab0+kUz/M2w1m/gbVqvsKGvuJXOagYqWgTu5jTFb+e/4lqTZCaX54whV+6/frIB7k8RRiPBooAACAASURBVG822j3y0DLNJ/dv16d5q/nRfejd4Nrpv1bTtE2cu6ef1rS7XvDNDNG9LYnD4XC/0WUALU3bpnvHa76C3YtWSWAzscRwjkZo1V3143rY2rVPbwh7SbQm0yO/V6MbSzwxnLvTHh/fHxJ/MTRPFxnSu8rqPfKRVhc98vJowpvWY5QadxpdBpBpxU0dpKJNrDWtaiuciB9mMjQ7c3i394N37acxpGA1D/Xrrl7XdXrk92q0stGelYfWeErDrnenvR2ATaZczrTU8WWc7ooJUxUvPRstJ6Er1NuDvz4zMyseWarwWYsnqV/6UCteRE738vjxY6Q1g/7Ty6TPwsUbNatyZmpxQ7n1APjweuvR4N0Fhla84bTGP5fG+23m6bp4MSzuIgkH1a3k+Ei7Dzu/uHpCfI5ylu890TeL9946nFg45IuHll8DVx3I0MJD5yK4tnpvlrOX+FNzcTuJ03HD3sOuWxWYcxYeUIf7t64tQ+ICeMx5J9hsfhz+e3ZZnm+TrZmk9NnlXaf8fu+hL/3uNpt76VXf8i9iBTdbqWjTq6oqfA6bnPJGPDjhqt8JRv6oEK/ePEEllCfOlzu/wPOq4my0J+ah3TX3L/ET4+SHHF9aV42cGUrUHLrUuwN7nujikd42jm4SifFOoQ7vlx13eMvvoLyaZkTiagYQjtHMIDJhVeR38jctnPwt62pbJYHNXdR1PZTg1PxC8ODynJO/eVws0jbPHQo+sXCrDY28q/LQmvzAkYkHrTmjWptqUonG7OJ0OqWzHDfTDd+/djtDtd2dpy5/L5OclGuFT1PvTm+7uqbSW6T8jMExQrUMBaK3jTDMEa4BMRX30DuAsPVoss1mI6rfbDbNb6bXTiqTo3sWmlPQOgvd1kj3pLzgaVpNQ+6yZ3YXvYBw4yuKovlMzqE8cZEurrK4iySdS9a6/rtnJLF6fPdsvXVDumB671dVe3fvrZzD+K38nLHMjLXMbSZWSWRpdmOD1gYTJ+UGmZtKFHhMeS6ueDEVLRQpXFqtsft3Tb1oVUt8ebeK0bryw1vdCymzMm+4FOk181S05gGLDzN0xbbEtynZTRPekM/ZObRDX3PNvaV7W2hef3qTbIamOnFPtPgD4N4Wd5WnA5vWTztXrT5tYNN7Sx0Z2Fz8ITzzSzdn4dvGbAxVfrrY8S+jrQ0+JbA5D0xnHLvhK/Pi3tOBTfeXy5YHNLlyumK6xehWZqi9zDMSFhPYjDTDwOaLL774r//1v37wwQfhLH/rW9/6m7/5my+++OLeu87/OE97I1q0aevhqu/E+B4Sr54/9oYVVItUNF5LnIfzlEyh0FF2p9Sp83Djcr/fT9j/fltaUVzncWHSxZ5h5npVVYlo9ng8PiV7u67roWrMmePurgVo9F6EMt3p9eWXX/785z9///33/+Ef/uE3v/lNeP23v/3tP/7jP77//vs/+9nPvvzyyyeWMGg92P5p5VidUJk5XzTd5wo056WZFTa81bw4q7n4mdJz4yrmz0Uyf3GPTXjl2pzD1gYbU5f08n5b2ZuZKzar3LNog0J6Z2MOuQ03V+OEulmv6ZoJV+x8qvE1zafH5g9/+MPf/M3f/Jt/828SDZivf/3rf/3Xf/2HP/zhTmUIO8q5IHsX7t5Lw487vduMb905n9/W9uOcz951497vxAetVeaLm43XCvUQhsQkyn9RvLXblo87uo/RZLBuMr1W0ORb/AFwbyu4ylevG9i8Gj/TwCTmE9j87Gc/S0c1Ibb56U9/eqcyjA9sQjO6tUzvNhOHOdSsj+/8Q10QzY7SExrFwhdKs9NEX3rvWl0Xqy4hUV3p5ePidQ9BVDNk5PmaA6loAMCM/P73v3/z5s3f//3fX1zyd7/73Xe/+93f//739y9UysX0s7quE1NOpd/dbDaHwyGdsVmW5dC0yM081EPTlA1NpNnYbrdDm71YpPHGTJ0fr1vXddNXEx60Jf11xQQ2wLI1X1FPGTEF3MMvfvGLv/zLv8xc+M2bN3/7t3971/JcFE9xnp5yPWRmxovFq7dmjAy9Da2BIrHT6dSEH/Hv1nE3RRPVtDpnWu92hdeHJrGMg6KyLFub7U4Bcq2rBti0tM5CWZbVWzeXh2WYvhOIdXGRzN8rp6INzeYJ3OB+qWjPbOj0uarMicylVgpWkZywvncL8TK9O0pMuZbeePxu9yY5NLNl64huLlLvwV4lUfgJVyE2yYl7rvcGPvIAC2ACIuDedrtdb6dBN00rcUcaGoUSd4z09vbEiWrNQ5wzN77f78PGuz0VOelYRVHcXKQJ3bB9yWYvS2ADAJAyNNQkln6g2cWmdiJLqiiKpgBVVfXGTumN3zy18ZgijXTDAJsxY3JYDWNsuGyb59nFfGnNDKFu5QCPd3FI+lBokRkShAAjJ77quvmrIbHiyCJddEOwpAM/4XUacnpsuOw8v/RoYmVZuqEDc3bV98gHH3zw29/+Nn/5b37zm1ctf62hJLFNdswwtNiY8fHP9bAp0fJrJk7qu0eRFi3zA7iC2EZgAwDMyLe//e0//uM//vnPf56z8Js3b772ta/dtTxlWT53GMlTeuOfG2uFjqAb8tBMffbKBDYAwIz8+Z//+fvvv5+58M9+9rPPP//8ruV5AL3uQzIDm3hC7fsVhvkzxgYAmJGvfvWrP/3pT7/+9a9fXPIb3/jGT37yk/woaKGeEvbcafBMjmunAajrOpRWd82LE9gAAPPyne985y/+4i/Ssc03vvGNP/uzP/vOd77zsFJNLrTanxhF3Oausda1Gw9PODWJDgIbAGBevvKVr3zve9/7T//pP202mzdv3rTebV75j//xP37ve9/7oz/6o4eXbjIzz7NKBBh3LdJVMwfEdSijD4ENADA7X/nKV968efPFF1/8yZ/8ybe+9a3w+je/+c2vfe1rn3/++Xe/+91FRzUtiSDnWXlWid3dtUj5MweUZRkWTj9HiBchsAEAZuqrX/3qf/7P//k3v/nN+Xw+Ho/n8/m3v/3tX/3VX61mXE2Ym/h0Ol18+OaD86yeXqTENNllWW632xDVJKbk5qUIbACABVhlyzXu8djtdvE/67qO2+5P6ZFIFynxxJibs8LiFXe7Xe9zJHe7XTwq6Xg8mjOAhsAGAOBp4ocnHg6HuPkeXn98j0QY3zJUpKIouuFEWCvEJNfu96qIqCiK8/m8yoiX2whsAACe6Xw+JwbK7/f7p/RIJEq13+97I5Dx5cwJbIqi2O/35/PZbAG0bOPfCaAr/+cW1xLAom23n4a/z+ePnliS11S/dTqdmoiiqqoHd0fUdd10yxRF0YQNdV034UpTqrIs09FLs3w4BLHHHLxOW05gwwXbrYsE4CUIbOgGNryOFTT5pKIBAACLJ7ABAAAWT2ADAAAsnsAGAABYPIENAACweO89uwAAAMxFM02zp16yRIuf1o17W8HcfwDkMN0zvLIVNPmkogEAAIsnsAEAABZPYAMAACyewGamqqoqy3K73ZZlWZZlXdfjt1nXdbPZsOWqqnK2vM0zvoQAAEzrdRpyix8ktD5VVR0Oh+7rRVGMCW/KsjydTr1vpbe8gpFkAOQweQC8shU0+RZ/ACuTCD8at52vnCj8eDz2zu24gqscgBwCG3hlK2jySUWbkaqqQlRTFMX5rf1+H5a5YV75eJX9fh82ezwem7nqG7vd7uaSAwDAcy0+MluT0K+y3++rqorfqus6BB5DXSu9Lq4YL9Dd72YV4TsAOfTYwCtbQZNPj81cxBFFN7ooyzL0rnTfTYgHz/SGQ/GWe8f2AADA/Als5iJEIHF6WCzEM+lBOC0hVonz2Ya2DAAACyWwmYsQrgyFGXF/yw3ToyWy124YtwMAALMisJmdnDBj2sBmkofkAADAEwlsZiEztBjKUksIc6Allgl9RDdsHwAA5kBgMwvX9plM2MdS1/XFLDgA4DXVdS2zg6UQ2CzJ5INh4rme77F9AGChqqrabre73W63222325yHfcNzCWzm5ZHJYFVVxVFNIl1tO6mHHBwAcLuqqpqJVYuiCI0TX+Jzo4XWIrB5Rc1vMPFTa9KDcM6Tuv/xAQCjNI2E/X7fpKKdz+cmvJHcMStaaC0Cm9dS13UrpNnv9+u4lAGASYQxt/Hg22akzVUP04MHE9jMS/p+MXL0Xiv3rCiK8/lswgAAINa0N4Ye7W0uAWZLYDML13bs3tARHHfUFEVxPB7dmACAruZn1m5jo8lG035gtt57dgHYbO6fsRoPCDsejxJkAYBrlWUpFY05E9jMTl3XQ4FHuJtcFZnEUY3hNABwg5zM7bIs5/PTYc7zZ9IFHnpLjw2zJbCZi6IomrhlKLCJ7yP59834RiyqAYDbxPPupJcpimIOTf+qqi72rsyqwDCeMTZzEWKVoVtnCFGuetZN2NrxeLy5bADwyq5q959Opzk8EiQ/Z+zaAs+nV+p1rOxpM/ejx2YuwpOwmr9bXd51XYc7VG9veLjLVFXVe8dxGwKA28SBTfeHwubd+Jt60/dV/iz7/b7VBghZanGBt9ttK7Ojm0KiY4e5m/bJPowRz6vYPF6mEd9DmzmaW+IFhla8+XpwkQC8iM3mx+G/Z5dlXkKuRO+3cGwmTay4AZC5WNyEaI43blHErx+Px7sUmmGPua5W0OSTijYjVVWFW+fhcAh9jvHDZ676scQvKwAwXv7kPUPPfnmwzAZAWZbnqNEcMkeaw+xuZGgaaJgJgc281HU9dE9sfiV6cHka2zxPKRsAK7PdfnrVf48s21La9CEmyRmXG/fbNCs2SXSn0ymObZoXrxroy0y8TkNOYDM7VVWdz+f9ft/cO4qi2O/36edpNr+4NOKM3mZTV+nd/ph1AWDRbpuV9LkBwFXPh4iXCQfb/My62+2qqqrrOowEnsnAIa7yOg05kwfMlBsHAMzBVXnd8TxAUxWgmRbots6im7uYmnjmdDodDod4htWl9FnxmgQ2AACD8tO64mBmqgCgrusmtMj/Qf22LqbuuvVbzT/95Mr8CWwAAAZlpnXFj22YMKunLMvmEd7d6ZiHXDXApru71j910bAgAhsAgMuaoSbdFzfvPhBm8rEKdV03A7vLsszJi2tNbgavQ2ADANzXXecuu2Hj5/NHmUvGgcTpdIoDmK6iKO70oIXz+bzdbk+nU2Zs07ghsBELsWhmRQMA6HdVoNIEHncqSTMp88Vd3DDAZsyYHJgVPTYAAP3i8SpDo+ebEfZNZ85Vg2GuUpbl8Xjc7Xbpfpsbuow8zpvVENgAAPSLZw4Y6s1oXo8nD6jr+h5dH2VZ7vf7w+GQiG1umDkgFHvoEeGwFHf5UYE1udMvTwDMTTxYJX8Uyl2LkeOuRQ2PY8/5KgwL7/f7pnund76BkUKs1Tukp1uGtLqud7td87ev+9kKp3Vzz9O0giafHhsAgB6T5Gil5xsYueVE11Bml1EIfm6YGxrmRmDDZfHvBAlLj/IBIDbmgTCNZmDMZAXabDabTehgOR6Prejl2mkAwtCgjedvrlpmQ24FBDZcJmIB4AXd/ECYePlpB9uErXWjms31XUwhRiqKwnxoK5bZkFtB/GO6ZwCAlJxG//2CmXizTQdLb1SzubKLKd6CidFYB4ENAEDbVWldcU7XneYWq6oqHdVs3p3DLb21ECNt3j4hB1ZAKhoAMCNPnJAtltmJ0cx7Fs9Udo/BKnVdN3lxiagmNrRMq7SbzWa/30tCYzUENgAAbXFgkzn2oHf+5UlK0gyGSQch8a7D4Jm0zDAJlkIqGgBA27XTNO/3+zuNVAlRTbov6Kq9F0VxPp9FNayMHhsAgNtlPgrzNk24kpPhlhPYNLOfmdmZtVr8E0a5txU8hhaAHNvtp+HvmQx0YbPZJJ7CyYuIkyHv1ypbQZNPKhoAwHyJaiCTwAYAAFg8gQ0AALB4Jg/gssxpLpeelwkAsD6ZDbkVENhwmYgFAGChMhtyK4h/pKIBAACLJ7ABAAAWT2ADAAAsnsAGAABYPIENAACweAIbAABg8QQ2AADA4glsAACAxRPYAAAAiyewAQAAFk9gc19VVZVlud1uy7Isy7Ku62m332z84marqtpmGNpOzrrb7XbaQwMAYLzXaci99+wCrFZVVYfDIfzzdDptNpvdblcUxYThTbPZi0bu8Xw+j1kdAIBnyWzIrSC2EdjcRVmWQyHH6XTabreThAplWWYumRn/AADAQglspldVVQgk4v6ZuA9nfFpaXdc3hCvH4zHxbn6kBAAAszJN1wGx0JG33++rqorfqut6t9s1fx+PxxsCiSYcimOnnE2FIt1wuqfqXwJg5rbbT8Pf5/NHTywJEIuTxO7XKltBk0+PzcTiSKYV1Ww2m7Isi6JoYpKqqq7qtImDoquEvRRFccPqAAAwf2ZFm9jFKCJEOw8b9xKKJNMMAIC10mMzsRCudLtrGnF0Udd1frBRlmVrhMxtHTgAALA+Apt7yYlYrgpsutsMWW0X99JaXR8OAAArIxVtSpljZh481iUOfpoHeu7eap7HNNS5BAAASyGwmdK1MzhP+KTOHLvdrreH53A4rOCRTAAAvDKpaE+QeHznAxRF0WSgtZ6Es4I5/gAAeFkCm7uYz8TKcadQ/LTQII6yqqrqTUubtj9H+AQAMJ6MmxapaOtXFEVRFPv9vjfzra7rEIYdDofeLZwndb8jBQB4HVpoLXpsVq4sy4sjeeq6DhH/tRO1AQDAHOixuYv0EJoHzxlwlTmXDdZqG3l2WQBgqQQ2U7q2r2M+fSMhG01gAw+WCGaGAh4hEAB0SUWb0nwCFWCemoCkm8p8Q3JzaybDONRZR6o0AFxFj829JLo+QqLaDAOhGRYJVqO3myUxZHNoTOdVcYskNwBehMBmYhdzuuLXHxBFhDZNOsdszrEWLE56zMwk3Sm90U7+zDbpJDdREABLJLCZWAgMhqZODg+KecyzbsJeeh9Q031LYAP385T5NMfP42luAwAWQWAzsThI6MYSdV3HT8Psrl6+NdUg/rCX0+k09BybEIPt9/tJdgqvo7fFP//HAiSS3OZcbABIENhML4QHh8Mhjl7qut7tds3fRVF0+0aasKcxVWBTlmXotNntdvFO67quqioUaZPs1QFe1lAUpCcHgFkxK9r0qqoKPTOHw6E3J+2RsyrHz988nU5DTRC/0UJa+OzEHxYfHACYCT02d1HX9VBaV1EUT0myTwzpuVikbZ47FByYr96eHPcEgLl5nYbc1s+NdxV6b5rcs8bTy7PZbEKRLqafbbcuEl5Ob+cMObYep7Nk2+2n4e/z+aMnlgSIPebWuoIm3+IPgHtbwVUO1xLYTG478GRSZkVgA/MksMlkjA3wuoa+KpZ+Z5+tFXxrAjBbxtgAcHe98cxqsroBmAM9NsBLMKfZ0w3Vtm4ceAUG4PEAemwAeI5EN46eHACupccGWLMwZt0PhPOUOC96cgC4isAGWC2/+i9RE8y0zp156gC4SCoasHKawks01MkmWAVgiB4bYLWENKvR242z8XgcACICGwCWITGvWuJdAF6EwIbLMnM/tCp4LsMwXtD5fA7n3WQDAL1eJ4lXYMNl2grAbA1lqQHQyGzIreBGKrABVkIE/sq6Z18PHsCrMSsaAACweHpsgKUyZJyExGzRrhmAVdJjAyzSClKBeRYXD8AqCWyABfPTO1dxwQCsmFQ0YJG0ULmNaQYA1kpgAyyApicAkCawAebOiAjuSrQMsA4CG2AZtD55JJ2EAIsjsAHmTssSALhIYMNlmYlAWp/AangMDrAar5PRLbDhMt/iPJgsIOZsu926MoEFybxlrSD+8RwbYF5WcGNlrcQzAHOmxwaYIy1I5inxGJzedwF4GIENMC+ahgDADQQ2wJMZUcOiuW4BZkJgAzyTETWskvw0gMcT2ADPp+XHipkkGuAxBDbAM2ntsUrxhd0ENiaJBrg30z0DD7XdbqWf8VLEMwCPoccGeBwhDa8pMUm0sAdgKnpsgEfTkgMAJiewWYyqqsqy3G63ZVmWZVnX9bTbbzbeu9ltnmnLwyqdz2dRDWzefhZaHwe3U+AeXqchJxVtAaqqOhwO4Z+n02mz2ex2u6IoJgxvms320hIFeCQzDQATyryfrCC2EdjMXVmWQyHH6XSa6suvLMvxG4EuE91CvuaTsoK2BcBTSEWbtaqqQlRTFEVIXdjv92GZ8TFJXdeJ7hq4mfYZ3KCVorayRBGA+xHYzFrIQNvv93HWWVVVx+Ox+ft0Ot2WkFbXdV3XZVnudruxBYVhumsAgAeQijZfVVX1/t0oy7Ioiqanpaqqq2Kbuq4FMzyAkAbG6/0cmS0aoEuPzXyFWKUoit4FQrQjkYyZkC0DD+YTBxDosZmvEK50u2sa8eiaJqksc8tlWYZMtoYOHMbTwIKHMc0AQJfAZgFyIparApvuNkNWG4wkMQYepvsYnKG3AF6BVLSZyhwzM5SlBo/n4ZsAwBPpsZmpayc6m/BJnQAskWkGgBenx2bZPFiT5zJbACyFjyqwenps5m4OyWbTfh361XA1tJNg/kwzACvmo90isOEyoQgJLg+YP9MMwCpN+/ldQZgksAFupD0EK2AQDrAaApu5S8/CbM4AAK4VxzAr+I0WoGHygJm6dlYAswgAcIPzW+++/P+YGgRYHIHNTAlUmCENHXg1PvLAgghsFiCRbxYS1QRC3Jv2DbySHxtyAyyOwGa+wkTPQ4FN/LrAhsfQ1oHX0UpR20aeWCqAIQKb+QqxyuFw6F2gqqrmjzk864bV68vCBwCYC4HNfIW4pfV3o67rkIfWfXez2ZRvmTkNgPHOkfCiPhxgPgQ2s7bf75s/DodDHL3Udb3b7Zq/i6Lo5qE1YU9DYMPNtFeATO4VwNMJbGatqqqQZnY4HMIPYyGq2XiUDXejmQJcJEkVmA+BzdzVdR36bVqKonjM18k2zwNKwuNpsgAXmWYA5ux1GnJbrZalqKqqSTBrcs8aD9jvdusiAXgJ2+2n4e/z+aMR2/m/LSTfIDRcFWM8pvZW0ORb/AFwbyu4ygHIMVVgM7z97Uaj9lUJbMYQ2GSSigb8X6vpjAZmy00GuBOBDfB/aG0Ad9X7Y7DfU4CpCGyAdyy9GxqYs6FZ1MQ2wHgCG+D/MG0r8HhuO8BU3nt2AQCAl9aNbUIHjrAHyCewgZdmkiJgzkQ4QD6BDbwuSe3APIUwxm0KyCewgVfnd1Bgti7ONPDIO5gnscDMCWzgdfliBgBWQ2DDZZmZAFrJADzArLpxYP5eJ6VTYMNlviEAABYqsyG3gvhHYAMvxBxowFoN3dnc9+B1eEAnvIoV/BIDcBs3QHgFAht4LX62BF7K0IAcoQ6sj1Q0eBVCGuA1JbLU3BhhTfTYAACvJdGNoycHlktgA2vmSxqg1/l8Ts83ACyOVDRYLd/NAPmaOKd15wz/lLQG8yewgZXzZQyQTzcOLJfABlZLSAMwUm83zsbjcWCWBDYAACnp8GYjwoF5ENhwWWb/u9v6HPgREeABzuez5DSW4nWuVYENl2klL8Xr3LkAni5nNI4vUOYg8zpcQSvCdM+wNr5HAWbI/Ptwb3psYD2ENABPl74Vb7db92q4Ez02AAB31xvPbN96fHlgffTYAAA8gr4auCuBDSyYOdAAFm2oGye9ANBLKhosldQFgJciaQ3S9NjAsvkxD2BlTD8AtxHYwFL5YgN4Kb1PBQ2v+FIAgQ0AwDKIXiBBYANLYrYAAGIXpx8YWgbWx+QB91VVVVmW2+22LMuyLOu6fspmq6raZhjaTs66hjM+gEoGAK71Og05PTb3UlXV4XAI/zydTpvNZrfbFUUxJrwZ2uwm+XvMyIDKLz1zIIsagEyJbwpzSb+gzBO9gthGj81dlGUZhx+x0+l083XTimpaEl0uTfzDcolqALirNf1sz8vSYzO9qqpCIBH3z8RhyQ1paXVdh9WLomiy0ZrXmx6bzWaz2+3SDd/j8Zh4t9kgsyWqAWAkXyWsmKnQpxd+7djv91VVxW/FQcjxeLwqkAib7U1mS+x0M+73/q358gFew3b7afj7fP7oiSWZpxdM4powX+AFa29Cj6m9FTT5pKJNLA4qugFGWZZFUQy9mxBHMr1dPfv9vvmjm6sWlg+7BgC4zcqGm7MmApuJXYwiQjxz1biXnHnPLq4r02xBfGEA8HTnt55dEMgisJlYCFeGIo04usgfZhOWDD0zOQuzUEIaAGbrHIlf143D0wls7iWneyQ/AgnxUmKzQ0lu3R6b+q3MvfMUfiEDYOlEOzySwGZKmaHCmLEuicBm6K045615rOfureZec9VoHx5Avz8AiyNvjacT2Ezphhmc77HZodE7u92u963D4eCnFABgcumkNZ05TEtg8wR3GsSfudmiKPb7/X6/b3UcubM8l5s7AIh2GMMDOu9iPhMrx709vQ/AKcsynvCgNy1t2vuLTuoud3AAXkdOS2AFz1R5AO2HFj0261cURdNL05vSVtd1CMO6z8BpnCd1vyNdOpUD3Jv8H2ZuqKnguu2lhdaix2blyrK8OESnrutwp6jr2uNuHm8ddxMAmETm12Ic5/gmZaPH5k7SD9+80zzLk2zWHNAAwNxc1auge+dlCWymdG1fR+by1272hhE+YRWBzWO44QLASFelUfnmfQUCmyk9IIlL4LECbqwAcD+JaMdX8LoJbO4lEYGERLUbAqHEZsNbY+IrA2weRjYwADxMek4CAc86CGwmdjGnK349P4rY7/fpzW6ieCmesjl8XNNdPWNiLa61mrlHAGBBbhilI+BZFoHNxEJgMDR1cog6rhoJEzY7NC1B7/Nn4r0MLdB6S2BzD+6MADBD1052LOCZOYHNxOIgoRtL1HXd268SlG+1OljieKM39ghxVOjbae3ldDoNPcdmaF0m4d4HAMtyc8Bz74KRJrCZXggPDodDHL3Udb3b7Zq/i6LoxidN2NPoBiHH47H543Q6tTYbf5BaI06EegAAIABJREFU8VJZlqHTZrfbxTut67qqqlCk7rpMSO4ZACzdDfOwCXgeaau9dQ9lWaYfZdNb7XHks9/vu2FGVVVDGW6JzW7yOg0S67pIANYh/UDD7fbT6N2PHlSm5fA4yDFesPaGDjm8nl8Pj6m9FTT59NjcRV3XQ2ldRVHcfNFUVZXIFkts9nw+J4b0XCzSNk/mUQAArN58undepyG3+Mhs5qqqahLMmtyzxrSbbf6Zudlmxc1mE4p0Mf1sBeH74zV3B/UGzI0emzFesM9hQmov7WL3Tu+7k5dh6adm8QfAva3gKn+wG7qYAR5DYDOGpvkYau82vYHNnSpzBU2+955dAFinpd8aAICna5oTmXliokeBDUzsNW8lAMBjXNvS6M0lWWWCicCG1+WHDQBgNW5rzKwgAy0Q2MBYpgoAAGart4lyVZLbUghsYJSV3REAgNexsp9lPccGJrCy+wIAwOLosYFRhDQAAHOgxwYAAFg8gQ1cZ7vdGlcDADA3UtG4LLMd/wpJWUIaAGBZXqf1IrDhsleIWK6iQuCJPIEK4CqZt8oVxD8CG7iCVhQAwDwZYwMAACyewAYuMFsAAMD8CWwgRUgDALAIAhu4zNAaAICZM3kApAhpAAAWQY8NAACweHps4B3NoBodNdyVJ7EAwOT02MD/ZaoAAICFEtjAZuMXdACAhZOKxmWZ/RgriAdWcAgPIxQEgEV4nYQUgQ2XvUKz9RWOEQB4QZmNnBXEPwIbXkj4xApjAABWxhgbAABg8fTY8EJ01AAArJXAhnWSdQYA8FKkogEAAIunx4Y1aPpn4s4ZHTUAAC9Fjw2Lt4LZCQEAGElgw0roogEAeGVS0VgYWWcAAHTpsXk5VVWVZbndbsuyLMuyruuLq2zz3L/sss4AAK4zn4bcvW392v06qqo6HA7d14uiSIQ32+2MLpJud834rTXmc4xLofbGUHtjqL0x0rW33X4avfvRg8q0HK69MdTeGI+pvVk1+W4jFe1VlGV5Op163zqdTjO8lHsfRDO3QgIAMBNS0V5CVVUhqimK4vzWfr8Py5Rl+ZzCAQDAaHpsXkLIQNvv91VVhdeb8Ta73W6z2ZxOp7qu5xPe3LVz5ve///0vfvGL+JUPPvjg29/+9p//+Z9/9atfvd9+10HtjaH2xlB7Y3RrL0486+q++8rJaa69MdTeGGrvKnps1q8VybTeLcuyKIqhdx/jkaPWvvzyy5///Ofvv//+P/zDP8Sv//a3v/3Hf/zH999//2c/+9mXX375gJIskdobQ+2NofbGGKo9crj2xlB7Y6i9G8xuZAWTC6NrhiYJqOu66bTZ9I8lvftF0juc5h6+/PLL//bf/tt//+///e///u+Hlvn617/+F3/xF9/73ve+8pWv3LUwi6P2xlB7Y6i9MZK19+OrNvWCPTauvTHU3hhPqb0Zjri+llS09Quja4Y6ZOL0swdkoz3xQTT/43/8j/Q9YrPZ/O53v9tsNv/6X//rN2/ePKZUS6H2xlB7Y6i9MXJqjyGuvTHU3hhq7zaLj8y4KKc/JCzTGoSzmTp8f1jnTNfvf//7999/P3/5L774QvZqoPbGUHtjqL0xLtWeHpsU194Yam+MZ9XeCnpsjLFZuZznb242mzDM5jGe8rH5xS9+8Zd/+ZeZC7958+Zv//Zv71qeZVF7Y6i9MdTeGFfVHi2uvTHU3hhq72aLj8xIix/KmTjXiXE4KwjfGx9++OFvfvOb/OW/9a1vXbX8uqm9MdTeGGpvjEu1p8cmxbU3htob41m1t4Im3+IPgLTMwCYstuLA5oZZ19Zx4JNQe2OovTHU3hiXak9gk+LaG0PtjfGs2ltBk08q2qsYk2y2ndSEB5Xvf//v/33DWv/rf/2vyUuyRGpvDLU3htob47bao+HaG0PtjXFb7S23hTYts6Jx2dLD981m8y//5b+8Ya1/9a/+1eQlWSK1N4baG0PtjXFb7dFw7Y2h9sa4rfam6rEZv5Hn0mPDq/jWt7511fLf/OY371SSJVJ7Y6i9MdTeGNfWHjHX3hhqbwy1dzOBzasIT7PplTl52qJ9+9vfvmqOkW9/+9t3Lc+yqL0x1N4Yam+Mq2qPFtfeGGpvDLV3s8UPEiKtruvdbtf8nTMr2r2fY/NE184K//nnn1+1/LqpvTHU3hhqbwzPsRnDtTeG2hvjWbW3giafHpuVK8vy2UWYi69+9as//elPv/71r19c8hvf+MZPfvITd9iY2htD7Y2h9sbIrz26XHtjqL0x1N7NTB7wQuq6HopzQqLaugOh73znO//0T/+02Wx+97vfDS3zjW9848/+7M++853vPLBcy6D2xlB7Y6i9MXJqjyGuvTHU3hhq70Zn1i5M9Lzf73sXOB6PiethZRfJH/7wh5/+9KebzebNmzetz0Lzyk9+8pN//ud/fnYxZ0rtjaH2xlB7YwzX3o+v+u/Zx/Ecrr0x1N4Yj6+9zfKbfFLR1i90woQndbaEQTVjnnWzFF/5ylfevHnzxRdf/Mmf/En8+je/+c2vfe1rn3/++Xe/+90/+qM/elbxZk7tjaH2xlB7YwzX3v/713/9/33++Z+ezx81/8Xvhhd7330drr0x1N4Yau8Gix8kRI4wMXl3boB4doHj8dhNRVvBSLIh8Xztaz3G+1F7Y6i9MdTeGOna224/jd590UgmwbU3htob4zG1t4Imnx6bl7Df75s/DodDHNjEUU1RFOseYAMAwIotPjIjU5jQecjQlbCC8H2IX4/GUHtjqL0x1N4YemzGcO2NofbG0GOTSY/Nq6jrOvTbtBRFkb6Ot3nuU3AAAG73Og25xUdmXKuqqrquT6dTk3vWSCy/gvB9iF+PxlB7Y6i9MdTeGHpsxnDtjaH2xtBjk2nxB8C9reAqH+ImO4baG0PtjaH2xhDYjOHaG0PtjSGwySQVDQAAWDyBDQAAsHgCGwAAYPEENgAAwOIJbAAAgMUT2AAAAIsnsAEAABZPYAMAACyewAYAAFi8955dABYgft5twtKfVgsAsD6ZDbkVENhwmYiFLlcFACxC5lf2CuIfqWgAAMDiCWwAAIDFk4oG8GgS+QBgcnpsAACAxRPYAAAAiyewAQAAFk9gAwAALJ7ABgAAWDyBDQAAsHgCGwAAYPE8x4bLttttzmIezQE8gFsNwFUyG3IrILDhMs0IAICFymzIrSD+kYoGAAAsnsAGAABYPIENAACweAIbAABg8QQ2AADA4glsAACAxTPdM6/LNNYAAKuhxwYAAFg8gQ0AALB4AhsAAGDxBDb3VVVVWZbb7bYsy7Is67p+ymarqtpmGNpOzrrb7XaSQwMAYEKv05AzecC9VFV1OBzCP0+n02az2e12RVGMCW+GNrtJDoUfGVAZZA8AsFCZDbkVxDZ6bO6iLMs4/IidTqebr5tWVNOS6HJp4h8AAFgrPTbTq6oqBBJx/0wcltyQllbXdVi9KIomG615vemx2Ww2u90uHZQfj8fEu80GAQBgcbayjCYXOmT2+31VVfFbcRByPB6vCiTCZnuT2RI7jd+94XRvty4SgJWIUwa69/bt9tPo3Y8eVKblSNceaWpvjMfU3gqafFLRJhYHFd0AoyzLoiiG3k2II5nerp79ft/80c1VC8uHXQMAwMoIbCZ2MYoI8cxV415y5j27uK5MMwAA1kpgM7EQrgxFGnF0kT/MJiwZemZyFgYAgBchsLmXnO6R/AgkxEuJzQ4luXV7bOq3MvcOAAAzJ7CZUmaoMGasSyKwGXorznlrHuu5e6t5HtNVo30AAGCGBDZTumEG53tsdmj0zm63633rcDis4JFMAAC8MoHNE9xpEH/mZoui2O/3+/2+1XEktgEAYLk8oPMu5jOxctzb0/sAnLIs4wkPetPSpo15lj5FOsByuQPDmvhVukWPzfoVRdH00vSmtNV1HcKw7jNwGudJ3e9IAQBehxZaix6btpvnCpvnU2LKsrx4RHVdh4i/rut5HggAACQIbNqqqrrq0ZmNVpZXegt3mmd5ks0KbAAAWCKBTdttzfpmrbIsh7K5xuzr2iLdMMKnKIomGPNwGwAAlkhg0zbmoS4P6OvQowIAAF0mD7iXRNdHSFS7IURJbDa8NSbyETUBALBEApuJhTSwoQgkfj0/itjv9+nNbqJ4Ke502r6VzjEbE2sBAMDTCWwmFgKDocE2Ieq4aiRM2OzQtARDGXRhL4kUu/gtgQ0AAEsksJlYHCR0Y4m6rnv7VYLyrVYHSxxv9MYeIY4KfTutvZxOp6Hn2AytCwAASyGwmV4IDw6HQxy91HW92+2av4ui6MYnTdjT6AYhx+Ox+eN0OrU2Gz93thUvlWUZOm12u12807quq6oKRequCwAAS7Fdx3NG56Ysy/SjbHqrPY589vt9N8yoqio9nfTQ2YwjnxvWdZEAvILt9tPw9/n80RNLMk/xl6lvxmupvTEeU3sraPLpsbmLuq6H0rqKorj5oqmqKpEtltjs+XxODOm5WKRtnsyjAADgYV6nIbf4yGzmqqpqEsya3LPGtJtt/pm52WbFzWYTinQx/WwF4TsAOfTYpOlzGEPtjaHHJtPiD4B7W8FVDkAOgU2apvkYam8MgU0mqWgAAMDiCWwAAIDFE9gAAACLJ7ABAAAWT2ADAAAsnsAGAABYPIENAACweAIbAABg8QQ2AADA4r337AKwAPHzbhOW/rRaAID1yWzIrYDAhstELAAAC5XZkFtB/CMVDQAAWDyBDQAAsHgCGwAAYPEENgAAwOIJbAAAgMUT2AAAAIsnsAEAABZPYAMAACyewAYAAFg8gQ0AALB47z27ACzAdrvNWex8Pt+7JAAAXCWzIbcCAhsuE7EAACxUZkNuBfGPVDQAAGDx9NgAAFwmfwFmTo8NAACweAIbAABg8QQ2AADA4hljA0ymruu6rtPLlGVZluUjSsMIzam8eLJaZ7yqqjuXa2Jxge9X+MzKBGCkrZFwpG23LhJylWV5Op1yliyK4mIIxBM1k35e/Oy3zvjxeFxQ272qqsPhEP653+/vF9tk1ufTbbefhr/P54+eWBLWJ55KeP6fhbl5TO2toMknFQ2YTGZU0yy5gvny16oJToqiuLhk64wvq8emFVrHQc7k9vv95m3FAnAni4/MuLcVhO88TIhV9vt9qw0XcpZaTWFX19zUdb3b7TYZpyYsGVvQCe2G1nftcWp2N/NOLT023I8emzH02GQyxgaYRnqsRWjMtVrDVVUt62f+1WvOTtPDkBZOXFEUIV5tBpPcq3DT6b3qqqq6X4bkfr8/HA673W7p7QaA2ZKKthhVVZVlud1umxGok3/7Nhvv3ew2z7TlYXEyr8myLOOG3V3zf7hWOIk5wUkIZqqqCnlrSwlTw4W33+9DFJefS3mDUDNGlwEP9joNOYHNAlRVtd1uD4dD86V7Op1Op9Nut5v2Z9HEN/o5z4SFYYlCcy1nbMbxeOyuyNOFxvfF20scwJRlGf5519hgKq3exfhY7no1Nh+NpcR+wGq8TkNOYDN3ZVkO/aQ94fDrRaSOMHOhRZtzOcXLXNWUrN8K/2w6GxvdJmPo6mx6O3PalGFm3qvW6t1XzqG1DuFif2xrhuV49fHduc1JzMlDi3s8Nu+e0HR1tc7gzcuMkdjyXQvfbPx0OgnmAe4iM4bjKeLmRVEUF1+/Tfzb+fF4bL276Iuke5175X4nNHEVpZff7/c37OX87gch1hQgvrBbEttPdDclyplYK/EJTZRwqBrDKk1hhvZ7220h1GfOwt1yhsKk937xLIRiXHVhXKV7TuNrKWfFoQXCCRqqhDEn6AE2mx+H/55dFtYmvkc9uyzL85jaW8GpWfwBrFv32zdIRyM5jsfj8XhsNYwENi/1yoTiCzJzlbD8VY28sFYrvN+8K93n0LvHdJgRNpso0gN2Fwc28VH31kB+rbYO5OKSvWFA5jVwcZmrApuiKIqiuOoeOFTO8GJiaxcLfzGwCWcqv8CPJLDhfuIb1LPLsjyPqb0VnJrFH8CKXfwFMfP30a5Ei2plgQ0PEy7X/KvxtiZ44ortBjOtwsSt//SW4822Pi+tz0i809aBxG/1frJydtdaq/vhzV/3ovwzOHTIOef0YvGuCmyGqjdh6M6Zc0e9WPiLgc1V3WKPJ7DhfsbcnXhM7a3g1BhjM18Xh2Iva7Qu6xZGXDxyvNb5fG4N7Wh1YrRGMrSGpsRvxSMrWpttpnEbmvUrHmrSeisxV1i8/d7d9S7ZlV73KvnzoSXm9Q7HO+fJ7uLJ3OLXw4Hf9Y4a9mIKAYDJCWzma+jbN7h5+HVZlsd33VxIaLkhsLktFupNNrv4oRj6mSA0xIc+DmFrQ63eoeeidAsWb2Rod+H1RCN7KN0uHOO1szI0f+QHNt3KnH9jPa6T1pHGhb/fgcQPdLrTLgBelsBmAXKafdd+R5bvypmfF4YkGosTrtLSu9Ztm8osTPpRLb0vNl0o5/N5aELhod3lTDI2beM7P7AJQWDiMay9785B/FDR7ru3xYS30dMOMDmBzUxlfq0KSJiDG1qB4xuO6fb3VR+NzCfw9O4xTr7KnxU6Z3ehQ+YxP+1ntrMvRmUzz0ZL94Q/JhsNgDt579kFoN+1TRlZDTxRZmAQaz0FZbPZVFU11BS+edxIpmu7j1pjdcLjpA6HQ3MI+/2+6QtNb2fMeKRn/ahxVb5W84ydexbnOq1+s/Rts/XgTgDmT2CzbGVZ+mWRp7vq0Zyb5Ojz58r8NLUWOx6Pu90ufiVEOEVRNE/t7N1OurrKtw/nndVnPC7MxT6ZaQObbhwSXhkKQlp7j7dwsfD5XXDXKopiVucUYDUENnM3h2Sz8IP0JO796ztPlNmK7c3FKstyKR2PrU9lM5CmruuqqloN1tPptNvtuvOzLdS1RzFhbFDXdSt6jDX13HqxW+0zCSdmUgxgBaZtoa2AwIbLhCIk3JDH1TvOYQ6BTXfK5nyh/PVb4TBPp1Pv0aU7NG5I8Lu3uHISsynWdR36Q+aTjZZZ+M1mE2Ik2WjAzE3bQltBmCSwuaObW2kzaQdAjmuv89BqLIpiJpd6SOmcJLKKR9eEgUOn06nbxH96IHetEKo1g4iGFgtJdJvNpqqqSUYM9j6iJ3Tj5ESkoUgXL7yQKnZDj9PizinAmghs7qibl5KjlT6R3oIvUZ7uqo6Fmx++dFeZo1lCGz0MmwkD0IemCmhWCVFTs0xVVU1zPL27Bz/z9OLAj6umDQhbu+EemL9KqJmrquhi4eOxi/PpcQLgIoHNHY15Okf8k+f99gXj5c8cELcXZ/VY2Fa4NXQgvZFG8+LhcMjPB8jZ3ePnV4i7rdJFyolgQ/CW2GCvO4W7raTHiwtfTKUbej3/vj2fDEOA1RDY3NGY5ohAhcUZumi7o+rTiUxPEXoYdrtdb4gSCtya8CD8PdTS7Q38Lu4uTti74jBGCD+mXDyQnDtbvIWhbLTeHSVmCBgjzkPLWX4oGy283lv4zDFm+QsDcBUP6FyAxE+Y106zC9OKL87dbrfts9vt4qjmeDzOcEB2fCDb7Tb+Z9OEvdis3+12rbfq6BE3m+E0vO7u4rUelrAXite7x6t6PBohhBhKLdvtdokDn9AN3V+tJ96Ev8Oxd4fflHmT74etzfBTALB0Apv5Cs2CoZbNtbNRweSuanYXRXE+n2d7rcbZcXGQFgdm3b6meK3D4dCK6HoXu7i7xFp31dxzelvn1/Z4bIbH5Fw88MmP+oaorNXj1Pt363Q39Xax8FflGANwFYHNfMU/DfYu0PswEHiknMCmKIr9ft886eXuBRqhLMt0q7S3r+niWs2KvWlLF3f34CAwkUwY/s7vZxjqoSrLcr/f965yp9A3jkvz1xrqcUqctfxT5qYNcA9bjyiZs5CY0Z3MtI4eVzfUbGr+SDz4PF44/NzYWni7dZHwWuq3TqdTMzXw0KRnvWuFV/JXbD7dze42eZ/ZO2nuOWOe55OvNfbmtqMOs9JNVapMrcKXZZlTY2FagtneVLfbT8Pf5/NHTywJ6xPnms72IzBbj6m9FTT5Fn8A6xZPzhM3NeKopveh5vECOW0UgQ2wiW4FPvX30FRv7017JgQ23I/AZgyBTSapaLNWVVXIWIjzueMs/Nl+QQKLE/968sxyrFQTNJo2AOBOBDZzV9d1Oh/9AWXoneqq6wElAe4qTmF9ZjnWKFTpbOfPANbqdRpyi+9yeh3hEeb5Sf+TWEG/JJAvJLL64E+raTQ8fk6Iq0hF436koo0hFS2TB3Quhh9QgQdoZi07HA5lWUpIm0pzA29+lnpyUQDWa/GRGfe2gvAduFbz66DP/lSWUp96bLgfPTZj6LHJZIwNAG3H43G/3+uxmUQzVPLBj1sFeEGLj8y4txWE7wDk0GPD/eixGUOPTSY9NgAAwOIJbAAAgMUT2AAAAIsnsAEAABZPYAMAACyewAYAAFi8955dABYgnmQwYelTBAIArE9mQ24FBDZcJmIBAFiozIbcCuIfqWgAAMDiCWwAAIDFE9gAAACLJ7ABAOD/b+/estXGASyA2mvVvGwymJ4GZhjdg4nNyOgPVRSVsWVxeRjB3l/JDeYKRX4c9ILqCTYAAED1BBsAAKB6gg0AAFA9wQYAAKieYAMAAFRPsAEAAKr3z94FoAJt25a87HK5PLskAADcpPBB7gMINmyTWAAAKlX4IPcB+cdQNAAAoHqCDQAAUD3BBgAAqJ5gAwAAVE+wgSf6gHl4O1J7d1KB91B791B791B791B7X06wAQAAqme5ZwAAnsvWEbyAHhsAAKB6gg0AAFA9wQYAAKieYPNcwzD0fd+2bd/3fd9P0/TY9w9vvvm2wzC0BcZxXDy85FjrkAAAvKHveZCzeMCzDMNwOp3iX8/nc9M0h8Oh67oHxpvwtpvu/I0m/AEAVKrwQe4Dso1g8xR9369FjvP53LbtQ6JC3/eFryzMPwAAUCnB5vGGYYhBIu2fSftw7h+WNk3TD+LK79+/M/96OBzuKNGqO4Nc1Yffr+qPr/bUXqWH3+/LP/6dqq693Su/9o+/bwWqvdp9++d/htiRdzweh2FI/2maphgexnEs73JJ36H5b3YqeatYpB/8d+9+mn3z4bsXoOrDdy+Aj1/v4bsXYK/D2/b/4p8vl/95fQEcvnsBfPz3PDwdJJb5Fbv/9+1Oj82DpUlmlmqapun7vuu6kEmGYbip0yYNRTeJSwJ0XfeDwwEA4P1ZFe3BYlZZSxEx7bxs3kv8RT/oIAIAgCrosXmwmCKuu2uCNF1M01QeNvq+ny3H/KQpMQAAUB3B5llKEstNweb6PeOots3fEl8f/hADkmgEAMBnMBTtkQrnzLx4rksafsKGnr/+CPsxrXUuAQBALQSbR7p1BecH7tRZ4tevX4s9PKfT6QO2ZAIA4JsZiraDzPadL9B1XRjSNtsJ5wPW+AMA4GsJNk/xPgsrp+sNpLuFRmnKGoZhcVja/f05d77DNx++ewGqPnz3Avj49R6+ewF2Ovx/9y6Aw/cvgI//5ofnX/PlY3AEm88XUlbf94uhJSxgELLN6XS6fo1uHICv9PMNOgF2IdjM/Xjey3vuEnM4HDaXPpumKeb7cRwtlQYAQHUEm7lhGH4wAWY2yiv/Di9eM+Am5/NZsAEAoDqCzdzPOl7CUX3fn06nZ/+uZ4hb4rxz6AIAgDWCzdw9m7q8T1ABAICvYh+bZ8l0fcSBam8YhN6wSAAAsEmwebC40PNasEl//oIU0f6Rrvt8LWat91moGgAAygk2DxazytpkmzjU7TURIv6WzOSfdPSdlQMAAKiRYPNgaUi4nq4zTVO6G+b14f0fj5rEfzwewx/O5/Nip804jjHzxBfHEvZ937btY4v0nUK/2d6lqMM0TbHtheZ3z8y3LxRqz8n7EKERaoF5Yc+ATapxU9hZLp68amxNYZNLuQxmpLfd6u8aFx4tjQfH4zH+PM0VXdddH5i+ID1wTeyNGcex5GWz3/v79+9Zkln8CKnFYrMp/s/uXZB3lx8wqfltWjtzSy4UXIsXz5IL8jfLn7lO4RKZOtT8ruWvdS6A5fInb42Vpsfm8YZhSAeAxW8L0lFer4zCsw12Ynl+/fqVjk+7XC7hD5lFq8PhzyzsZ/KtW4lpmvIjITW/vGEY8svNHw4HTbHcz/Y0+04Vf7n7HvJXv9Pp5MzlGTZvuzXeNQSbp5imKd/p8eLyXP7bbzOTFim9l6ffrqUfx8ppN/F4VCi9vM66OtPWq/ktmqYpppqu69Kv2dIv5G7aaOubpfXJphhsjsfjuE7+WZNe/eLJm176TqeT2ksNw5BpaVF8dOm6zr3jWua2G39eX9t7Uk8QwfF4DBemruvCFf8dypMWafaC2DCu/ylt6Lt/kPeXXlKdbpvSulpsXZsv+HLxAWhttE/m1Oba7MxVaXmx+e1dkCrlT970a52XF616qi5j864a/7WuQaT+s/krbeWLL9h8eOKSHfu7d9HeV0nTqvQi+xqbqW/z7Ca67t8WbPI0rXts1p7q/Zl40fNd2KLN2276dfZri3YXQ9H4K/Y2ro1bi0Mtja3isfKrBQbpEn8vKFJFSnbHMgyjUFy7MvRp710cPlzJDhBxqEVlI4J2FUeTGoS2ZnOz+Err7Z+9C8Ab2Xy4TFt5WJXy6WWqUFgzMf2J3YHKZRpVZlkLeJR4tob1T3ctS2Xs7/wD8ZqWaWza4Q+kJ/KuBXlfXdflvyWstOoEGxaUJBbBJkPN3GSzq/Cml32hvu/DmIFMw6v0FvVisQIL1y+mWWla8YcuhoVU1APFKOhELrF2d6j0riHY8K/CFrwZ8eFWfd9fCpYK9KiUsVkna/vwEsUFDI/HozZWLj0xF9ccN4ZqTSYTxp06X1uiDxEboQrMCJucNk1zPp+HYZh1DKYrQ1Z219h7kg/vonBusfUDfsDpdr9KZzHubrY6n9N2zWIDi1Vn8YCMwh5UE7ivzVrdWveCqrtJ4d6PFr9wAAAKcElEQVTlXK6aXFi8Ny7nW+ldw+IB3Mb3H7xeuomYcWglwldxYV/gdH8bX5yviQ3s8vJ9xj5MWHRh9mzUNM3hcND8ZtLhtcMwrM3GrHGTxB3FxT88rmyKw5iD0+kUbhlxYM7xeKzutDUUjTkPjryVvu/TTWOru8i+D7f5NbFmKhtx8R7SwcnjOM6aWTo47XA4yI2LzudzepULdRgX6Gv+jK0SbzbF5qeuCn3eLVWwAd7UbLy+VHOT+A1F+mx0Op08Wc7EqTXhW/O9i1Of2NIWT89QpfFEtupMxuIlLsyCaJrmdDppn3npWu2aWYnZ94ZhWtc0TbEma7xrCDbA20nHngXH49FNvVy4OcW/pvXZtm1dd6lnS5+5dy1IrTbrLf2GYhgG9bxmsWYul0vMNtczvEnFylFLJdJUk/a1xg7DSu8a5tgw95HrmlORvu/TVBNmLrpR3WM2kNpZHMVHRsvCPpXddTdlWmC+T4xoc8dJUoupJpqtVlrRLViw4V+3XghcOHi4MOU97Rm/XC7u5Q/R973Ho4ywr+5M7Gc4nU7xh7sWs1bqbVFaLfm9icMfxMKM+ORtplyJNKhk2l6szIruGoai8S83HvY1G362+B0Siwp3+IkDDyq6Rb3M5iNjfIFZIrxYmrFZkw533LUgdSjc8Dq2vYpCtWDDgsydW1cvT5Iu6OzJ+yax6qRB3pMzepGz9VEKH9O59nmN0FA0/tocqZL+/PNOBnYUm5NU8wOFY8xU7LX8Rm+xYtMNOl36Un3fhx2T8l+Te+7clDk91d6m2Pycnrf6vLuGYMNf8Yqw1usdrx0urzxWOs5n14JUafPMDWIlG6pRyEPSJm3vTnEOQ6ZmYt1qkGuMJblVbG/5MWbp/s7PLtKjCDb8lV5Yry+y6WZhbk48kDBzp/yZGxROU4ablExB1vYy0ufLxZM3rTF33kXGkvxAyVlZcmd5Q4IN/xG/PZrtBZZO7Lb1FY+V3pbaAprftfh1Wli/a7aJTbrWnCWDeKx0Ked827Om9qL0tptWoDO3kKF6PxPPx/P53Lbt9ZmbdtfUdNvNjzDmC21eGvYuYH1UXd6td6OwDDQzqu7h4qNkOseGayWnsDrM2KxAZ27G4lw4SpSk5eranh4b5qZpWmvroX2/uDxAicvW49HxeDTqj2fI3DWCcRwrGsryetM0ZU5eZ26eCTY/NgxDvh+1xrbXek5lzTAMYV5N6IW0OR1UIZy5TdOEk7f5s/vkvqXiG8zanhvHrdLbbuPM5VWmaQpfPcS21/d9pV9GCDYAAED1DEUDAACqJ9gAAADVE2wAAIDqCTYAAED1BBsAAKB6gg0AAFA9wQYAAKieYAMAAFRPsAEAAKon2AAAANUTbAAAgOoJNgAAQPUEGwAAoHqCDQAAUD3BBgAAqJ5gAwAAVE+wAQAAqifYAAAA1RNsAACA6gk2AABA9QQbAACgeoINAABQPcEGAAConmADAABUT7ABAACqJ9gAAADV+2fvAgAAwP6GYZj9pO/7vu/zh0zT1DTN+XyOP+y6bhiG/IE/Fgu5WbbMUdeftFn6+NVpL5fL3mUAAICdtW07+8nxeFx73O/7Pg0zizKH/1j6ewsf46dpOhwO4c/jOIZsc134DwgFhqIBAECpaZratt1MNU3TnE6nh/fbpEkpdBbddMiT+pHehB4bAAD422OTeTxOez+apum6bjaya5qmaZpOp1N61GOft2M5u64ryTbx9Ys9SCWfuhZ6bAAAYNss1YzjOE3TLCqEnHO5XLquiz987IC04/EY/lDSa5Qmn8/urmn02AAAQFPQd5FOwgmTVfJv+IP5MIViSTaLMQxD7D5aLIMeGwAA+CJpr0tJqmn+21vypDXHNt82pprYz/PBBBsAAN7C9Ef8a1jCq23btm37vi+cLv8MMSGEeTWFR8UBabNZNzPhk7Z/bH7SwtFoL0hWb8VQNAAA9hdnsIQ57mvrKT9jDeUgPyirfPTXTEwXi0fN5u3MZH5XSXk2x6E1hqIBAMBjpd0LmfWUT6fT6zsf7lkxuf/j+p/yqaZpmsPhsNZ1E/uCMrXxVePQGsEGAIB3EJ/gw+N413XjOF7+SB/N88O6nlq2dK2z+83WWIsfdhzHxdekYp5ZS4DfNg6tEWwAAHgH6QN62KEl7eUYhiF93H/xZJtYtgeumJy+1eVySf/a9306MGwxlswq5/oFTwpj7+yfvQsAAMC3S4PK2r6T6aP8LPa8iXzHyGxA2uZK0OM4hu6atdF3XdeFN1msrtiv9YYV9SSCDQAAO0sfzTO9MfFRfi+ZkDBNU36M3GIfS6Y7ZTPIDcMQks91nXzhOLTGUDQAAHZXOG4qPtzvte7zo35vfqm0KNbGZhfWLL184Ti0Ro8NAAC7i30Ohd0Lew2vygSbvu8XFx+bpum6RyX+JD+mbrN7am00Wuw7+p7umkawAQDgfeQTy14dNYVD4DZn+V8rHFm39sEzo9FKfvuHMRQNAIA9lceVZ6xOViL+ur1m+Kx93sXRaCUTeD6SYAMAwJ4KJ4Sk+WevYNPc3muUz0Lp9jUZmRFlcfxbHH72nePQGsEGAIB9FUaFHSfEp8FmbcfMRWsfLb8qwE0y6eWrxqE1gg0AAPsqHN+1b0dEujBAeQHWUlDh8m7DMMx2v8kbhuFrx6E1TdOubQkEAAAv0LZt/PPao2nf9yH/rG3f+cBirJUhLec4jpt5I5b5+pBpmmLmWXur9DX5J/ZhGK630CkpYVPwqSuixwYAgN3MUspiZ8gwDOn6yE8v04r00f9wOOT7bWap5vpfY4/K2vvEVLPZ9/KD1dg+kmADAMBurjdgSR/TwzYvsTtiHMcXFm1BWoDT6dS27TAM0zTFTxEK3LZt7F9a3NymSdLI+Xxu2zath2ma0t6hkiw3Cz9fOA6tMRQNAIAdxZ6NcRzz8/ILx1b9WOGgrHSEWN7xeAzpJbzzdflL3qrwU8/eqryuPmkommADAMBu0gfrtQf9582rWSvJ5osXp7Wk0mixFmyarWxzU5YrmaqUOeoDQoFgAwDAbq4frMPgrvP53HVd/8cuJdk0TVPokwmlbZqm7/sfLNo2e583/9RvS7ABAGAfsd/jNX0yeZ/0iF/ukz61xQMAANhHDDM/6J0YhqEtsMumN+xCjw0AAPu4p7sgv55yVN4X9El9F+U+6VP/s3cBAADgZtdxJTNHn0W7D/97LEPRAADYQXyqfuymK/enmo8fxhZ22mnbtnDd6loINgAA7OCeCTZwzVA0AAB2EPPMmwSb4/E4+8mbFOzhwoLa6U8+45NaPAAAgE8Q5th4uP1ahqIBAADVE2wAAIDqCTYAAED1BBsAAKB6gg0AAFA9wQYAAKieYAMAAFRPsAEAAKon2AAAANUTbAAAgOq1l8tl7zIAAADcRY8NAABQPcEGAAConmADAABUT7ABAACqJ9gAAADVE2wAAIDqCTYAAED1BBsAAKB6gg0AAFA9wQYAAKieYAMAAFRPsAEAAKon2AAAANUTbAAAgOoJNgAAQPUEGwAAoHqCDQAAUD3BBgAAqJ5gAwAAVE+wAQAAqifYAAAA1ft/NUvWSaTzwKwAAAAASUVORK5CYII=\n",
2428 "text/plain": [
2429 "<IPython.core.display.Image object>"
2430 ]
2431 },
2432 "metadata": {},
2433 "output_type": "display_data"
2434 },
2435 {
2436 "name": "stdout",
2437 "output_type": "stream",
2438 "text": [
2439 "Save TH1 hframe\n",
2440 "Save TGraph Graph\n",
2441 "Save TGraph Graph\n",
2442 "Save TGraph v2_D\n",
2443 "removed ‘fig_BUP2021/D0_BUP2020_pAu_v2_3yr.svg’\n"
2444 ]
2445 },
2446 {
2447 "name": "stderr",
2448 "output_type": "stream",
2449 "text": [
2450 "Info in <TCanvas::Print>: png file fig_BUP2021/D0_BUP2020_pAu_v2_3yr.png has been created\n",
2451 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2021/D0_BUP2020_pAu_v2_3yr.root has been created\n",
2452 "Info in <TCanvas::Print>: eps file fig_BUP2021/D0_BUP2020_pAu_v2_3yr.eps has been created\n",
2453 "Info in <TCanvas::Print>: SVG file fig_BUP2021/D0_BUP2020_pAu_v2_3yr.svg has been created\n",
2454 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2021/D0_BUP2020_pAu_v2_3yr.C has been generated\n"
2455 ]
2456 }
2457 ],
2458 "source": [
2459 "{\n",
2460 " TString s_suffix = \"_3yr\";\n",
2461 "\n",
2462 " TGraphErrors * grProD0_v2_pAu_3year \n",
2463 " = GraphShiftCut(\n",
2464 " Significance2v2( gProD0_Significance_pAu_3year, 0.0, pAu_Psi2_resolution, 2),\n",
2465 " 0.2, 0,100);\n",
2466 " TGraphErrors * grNonProD0_v2_pAu_3year \n",
2467 " = GraphShiftCut(\n",
2468 " Significance2v2( gNonProD0_Significance_pAu_3year, 0, pAu_Psi2_resolution, 10),\n",
2469 " -.2, 1.9,100); \n",
2470 "\n",
2471 " \n",
2472 " grProD0_v2_pAu_3year->SetMarkerStyle(kFullCircle);\n",
2473 " grNonProD0_v2_pAu_3year->SetMarkerStyle(kFullSquare);\n",
2474 " \n",
2475 " grProD0_v2_pAu_3year->SetMarkerSize(2);\n",
2476 " grNonProD0_v2_pAu_3year->SetMarkerSize(2);\n",
2477 " \n",
2478 " grProD0_v2_pAu_3year->SetLineWidth(4);\n",
2479 " grNonProD0_v2_pAu_3year->SetLineWidth(4);\n",
2480 " \n",
2481 " grProD0_v2_pAu_3year->SetLineColorAlpha(kBlack, 1);\n",
2482 " grNonProD0_v2_pAu_3year->SetLineColorAlpha(kBlue+2, 1);\n",
2483 " \n",
2484 " grProD0_v2_pAu_3year->SetMarkerColorAlpha(kBlack, 1);\n",
2485 " grNonProD0_v2_pAu_3year->SetMarkerColorAlpha(kBlue+2, 1);\n",
2486 " \n",
2487 " TCanvas *c1 = new TCanvas(\"D0_BUP2020_pAu_v2\" + s_suffix,\n",
2488 " \"D0_BUP2020_pAu_v2\" + s_suffix, 1100, 800);\n",
2489 " c1->Divide(1, 1);\n",
2490 " int idx = 1;\n",
2491 " TPad *p;\n",
2492 "\n",
2493 " p = (TPad *) c1->cd(idx++);\n",
2494 " c1->Update();\n",
2495 " p->DrawFrame(0, -0.15, 8, 0.35)->SetTitle(\";#it{p}_{T} [GeV];v_{2}\");\n",
2496 " \n",
2497 " grProD0_v2_pAu_3year->DrawClone(\"p\");\n",
2498 " grNonProD0_v2_pAu_3year->DrawClone(\"p\");\n",
2499 " \n",
2500 " \n",
2501 " //v2_B->DrawClone( );\n",
2502 " v2_D->DrawClone();\n",
2503 " \n",
2504 " TLegend *leg = new TLegend(.1, .75, .55, .9);\n",
2505 " leg->SetFillStyle(0);\n",
2506 " leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection, Years 1-3\", \"\");\n",
2507 " leg->AddEntry(\"\", \"M.B. p+Au, 10 nb^{-1} str.\", \"\");\n",
2508 "// leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. O+O, Res(#Psi_{2})=%.1f\", OO_rec_5year/1e9, OO_Psi2_resolution), \"\");\n",
2509 "// leg->AddEntry(\"\", Form(\"%.0f nb^{-1} str. Ar+Ar, Res(#Psi_{2})=%.1f\", ArAr_rec_5year/1e9, ArAr_Psi2_resolution), \"\");\n",
2510 " leg->Draw();\n",
2511 " \n",
2512 " \n",
2513 " leg = new TLegend(.65, .65, .9, .8);\n",
2514 " leg->SetFillStyle(0);\n",
2515 " leg->AddEntry(grProD0_v2_pAu_3year, \"Prompt #it{D}^{0}\", \"lp\");\n",
2516 " leg->AddEntry(grNonProD0_v2_pAu_3year, \"#it{B}#rightarrow#it{D}^{0}\", \"lp\");\n",
2517 "// leg->AddEntry(v2_B, \"#it{B}-meson\", \"l\");\n",
2518 "// leg->AddEntry(v2_D, \"#it{D}-meson\", \"l\");\n",
2519 "// leg->AddEntry(RAA_pi, \"#pi\", \"l\");\n",
2520 " leg->Draw();\n",
2521 " \n",
2522 " leg = new TLegend(.2, .2, .7, .3);\n",
2523 " leg->SetFillStyle(0);\n",
2524 " leg->AddEntry(v2_D, \"#it{D}-meson (Au+Au)\", \"l\");\n",
2525 " //leg->AddEntry(v2_B, \"#it{B}-meson (m_{T} scaling)\", \"l\");\n",
2526 "// leg->AddEntry(RAA_pi, \"#pi\", \"l\");\n",
2527 " leg->Draw();\n",
2528 "\n",
2529 " c1->Draw();\n",
2530 " SaveCanvas(c1, \"fig_BUP2021/\" + TString(c1->GetName()), kTRUE);\n",
2531 "}"
2532 ]
2533 },
2534 {
2535 "cell_type": "markdown",
2536 "metadata": {},
2537 "source": [
2538 "# AN projection"
2539 ]
2540 },
2541 {
2542 "cell_type": "markdown",
2543 "metadata": {},
2544 "source": [
2545 "## Utilities\n"
2546 ]
2547 },
2548 {
2549 "cell_type": "code",
2550 "execution_count": 40,
2551 "metadata": {},
2552 "outputs": [],
2553 "source": [
2554 "%%cpp -d\n",
2555 "\n",
2556 "\n",
2557 "TGraphErrors *Significance2AN(\n",
2558 " const TGraph *ppSignificance,\n",
2559 " double AN_centroid,\n",
2560 " Double_t Res, \n",
2561 " const int n_rebin=1)\n",
2562 "{\n",
2563 " assert(ppSignificance); \n",
2564 " \n",
2565 " const int npoint = ppSignificance->GetN() ;\n",
2566 " assert(npoint%n_rebin == 0);\n",
2567 " \n",
2568 " TVectorD significance(npoint/n_rebin);\n",
2569 " TVectorD x_center(npoint/n_rebin);\n",
2570 " \n",
2571 " for (int i = 0; i<npoint/n_rebin; ++i)\n",
2572 " {\n",
2573 " significance[i] = 0;\n",
2574 " x_center[i] = 0;\n",
2575 " for (int j = 0; j<n_rebin; ++j)\n",
2576 " {\n",
2577 " \n",
2578 " significance[i] += pow(ppSignificance->GetY()[i*n_rebin + j],2);\n",
2579 " x_center[i] += ppSignificance->GetX()[i*n_rebin + j];\n",
2580 " \n",
2581 " }\n",
2582 " \n",
2583 " significance[i] = sqrt(significance[i]);\n",
2584 " x_center[i] /= n_rebin;\n",
2585 " }\n",
2586 " \n",
2587 " TVectorD y(npoint/n_rebin);\n",
2588 " TVectorD ex(npoint/n_rebin);\n",
2589 " TVectorD ey(npoint/n_rebin);\n",
2590 " \n",
2591 " for (int i = 0; i<npoint/n_rebin; ++i)\n",
2592 " {\n",
2593 " y[i] = AN_centroid; \n",
2594 " ey[i] = 1/sqrt(2)/significance[i]/Res; // AN error in max likelihood estimator \n",
2595 " } \n",
2596 " \n",
2597 " TGraphErrors * gr = new TGraphErrors(x_center, y, ex, ey);\n",
2598 " \n",
2599 " return gr;\n",
2600 "}"
2601 ]
2602 },
2603 {
2604 "cell_type": "markdown",
2605 "metadata": {},
2606 "source": [
2607 "## Model inputs"
2608 ]
2609 },
2610 {
2611 "cell_type": "code",
2612 "execution_count": 41,
2613 "metadata": {},
2614 "outputs": [],
2615 "source": [
2616 "%%cpp -d\n",
2617 "\n",
2618 "pair<TGraph *, TGraph *> getD0AN()\n",
2619 "{\n",
2620 " // DOI: 10.1103/PhysRevD.78.114013\n",
2621 " // Cite as: arXiv:0810.3333 [hep-ph]\n",
2622 " // Fig 6 D0\n",
2623 "\n",
2624 " const vector<double> pT_07_07 = {\n",
2625 "\n",
2626 " 1.02731939860653e+000,\n",
2627 " 1.06765676567657e+000,\n",
2628 " 1.10726072607261e+000,\n",
2629 " 1.14759809314265e+000,\n",
2630 " 1.18793546021269e+000,\n",
2631 " 1.22753942060873e+000,\n",
2632 " 1.26787678767877e+000,\n",
2633 " 1.30821415474881e+000,\n",
2634 " 1.34781811514485e+000,\n",
2635 " 1.38815548221489e+000,\n",
2636 " 1.42775944261093e+000,\n",
2637 " 1.46809680968097e+000,\n",
2638 " 1.50843417675101e+000,\n",
2639 " 1.54803813714705e+000,\n",
2640 " 1.58837550421709e+000,\n",
2641 " 1.62871287128713e+000,\n",
2642 " 1.66831683168317e+000,\n",
2643 " 1.70865419875321e+000,\n",
2644 " 1.74899156582325e+000,\n",
2645 " 1.78859552621929e+000,\n",
2646 " 1.82893289328933e+000,\n",
2647 " 1.86853685368537e+000,\n",
2648 " 1.90887422075541e+000,\n",
2649 " 1.94921158782545e+000,\n",
2650 " 1.98881554822149e+000,\n",
2651 " 2.02915291529153e+000,\n",
2652 " 2.06949028236157e+000,\n",
2653 " 2.10909424275761e+000,\n",
2654 " 2.14943160982765e+000,\n",
2655 " 2.18903557022369e+000,\n",
2656 " 2.22937293729373e+000,\n",
2657 " 2.26971030436377e+000,\n",
2658 " 2.30931426475981e+000,\n",
2659 " 2.34965163182985e+000,\n",
2660 " 2.38998899889989e+000,\n",
2661 " 2.42959295929593e+000,\n",
2662 " 2.46993032636597e+000,\n",
2663 " 2.51026769343601e+000,\n",
2664 " 2.54987165383205e+000,\n",
2665 " 2.59020902090209e+000,\n",
2666 " 2.62981298129813e+000,\n",
2667 " 2.67015034836817e+000,\n",
2668 " 2.71048771543821e+000,\n",
2669 " 2.75009167583425e+000,\n",
2670 " 2.79042904290429e+000,\n",
2671 " 2.83076640997433e+000,\n",
2672 " 2.87037037037037e+000,\n",
2673 " 2.91070773744041e+000,\n",
2674 " 2.95031169783645e+000,\n",
2675 " 2.99064906490649e+000,\n",
2676 " 3.03098643197653e+000,\n",
2677 " 3.07059039237257e+000,\n",
2678 " 3.11092775944261e+000,\n",
2679 " 3.15126512651265e+000,\n",
2680 " 3.19086908690869e+000,\n",
2681 " 3.23120645397873e+000,\n",
2682 " 3.27081041437477e+000,\n",
2683 " 3.31114778144481e+000,\n",
2684 " 3.35148514851485e+000,\n",
2685 " 3.39108910891089e+000,\n",
2686 " 3.43142647598093e+000,\n",
2687 " 3.47176384305097e+000,\n",
2688 " 3.51136780344701e+000,\n",
2689 " 3.55170517051705e+000,\n",
2690 " 3.59204253758709e+000,\n",
2691 " 3.63164649798313e+000,\n",
2692 " 3.67198386505317e+000,\n",
2693 " 3.71158782544921e+000,\n",
2694 " 3.75192519251925e+000,\n",
2695 " 3.79226255958929e+000,\n",
2696 " 3.83186651998533e+000,\n",
2697 " 3.87220388705537e+000,\n",
2698 " 3.91254125412541e+000,\n",
2699 " 3.95214521452145e+000,\n",
2700 " 3.99248258159149e+000\n",
2701 "\n",
2702 " };\n",
2703 "\n",
2704 " const vector<double> AN_07_07 = {\n",
2705 " -1.25647231389806e-002,\n",
2706 " -1.24874781595807e-002,\n",
2707 " -1.23709959231217e-002,\n",
2708 " -1.22872149960094e-002,\n",
2709 " -1.21968981211847e-002,\n",
2710 " -1.20804158847257e-002,\n",
2711 " -1.20031709053258e-002,\n",
2712 " -1.19063180827887e-002,\n",
2713 " -1.17898358463297e-002,\n",
2714 " -1.16733751806553e-002,\n",
2715 " -1.16091805258957e-002,\n",
2716 " -1.14992558079337e-002,\n",
2717 " -1.13827951422593e-002,\n",
2718 " -1.13186004874997e-002,\n",
2719 " -1.12086757695377e-002,\n",
2720 " -1.10922151038633e-002,\n",
2721 " -1.09822688151168e-002,\n",
2722 " -1.08984878880045e-002,\n",
2723 " -1.08016350654673e-002,\n",
2724 " -1.06851528290084e-002,\n",
2725 " -1.05686921633340e-002,\n",
2726 " -1.04522099268750e-002,\n",
2727 " -1.03618930520503e-002,\n",
2728 " -1.02258245432386e-002,\n",
2729 " -1.01550939407666e-002,\n",
2730 " -1.00451692228046e-002,\n",
2731 " -9.92870855713022e-003,\n",
2732 " -9.83837011152095e-003,\n",
2733 " -9.75458918440864e-003,\n",
2734 " -9.63810694794969e-003,\n",
2735 " -9.57393386397463e-003,\n",
2736 " -9.46400914601264e-003,\n",
2737 " -9.36059880497854e-003,\n",
2738 " -9.28988977329106e-003,\n",
2739 " -9.19303695075390e-003,\n",
2740 " -9.11577040056947e-003,\n",
2741 " -9.02545352574473e-003,\n",
2742 " -8.94167259863241e-003,\n",
2743 " -8.87747794387282e-003,\n",
2744 " -8.77408917362324e-003,\n",
2745 " -8.70989451886365e-003,\n",
2746 " -8.65225738260101e-003,\n",
2747 " -8.54886861235143e-003,\n",
2748 " -8.47813800987942e-003,\n",
2749 " -8.42050087361677e-003,\n",
2750 " -8.32364805107962e-003,\n",
2751 " -8.24638150089519e-003,\n",
2752 " -8.18874436463254e-003,\n",
2753 " -8.13108565758537e-003,\n",
2754 " -8.04076878276063e-003,\n",
2755 " -7.97005975107315e-003,\n",
2756 " -7.89932914860113e-003,\n",
2757 " -7.84169201233849e-003,\n",
2758 " -7.78405487607584e-003,\n",
2759 " -7.72639616902867e-003,\n",
2760 " -7.66875903276602e-003,\n",
2761 " -7.57842058715676e-003,\n",
2762 " -7.50117560775685e-003,\n",
2763 " -7.44353847149421e-003,\n",
2764 " -7.40548760758429e-003,\n",
2765 " -7.34785047132164e-003,\n",
2766 " -7.27060549192174e-003,\n",
2767 " -7.20641083716215e-003,\n",
2768 " -7.14877370089950e-003,\n",
2769 " -7.09113656463686e-003,\n",
2770 " -7.03347785758968e-003,\n",
2771 " -6.97584072132703e-003,\n",
2772 " -6.95086175284195e-003,\n",
2773 " -6.89976056429172e-003,\n",
2774 " -6.86173127116633e-003,\n",
2775 " -6.80407256411916e-003,\n",
2776 " -6.74643542785651e-003,\n",
2777 " -6.68879829159387e-003,\n",
2778 " -6.63113958454669e-003,\n",
2779 " -6.60618218684613e-003\n",
2780 "\n",
2781 " };\n",
2782 "\n",
2783 " const vector<double> pT_0_0 = {\n",
2784 "\n",
2785 " 1.02731939860653e+000,\n",
2786 " 1.10726072607261e+000,\n",
2787 " 1.18793546021269e+000,\n",
2788 " 1.26787678767877e+000,\n",
2789 " 1.34781811514485e+000,\n",
2790 " 1.42775944261093e+000,\n",
2791 " 1.54803813714705e+000,\n",
2792 " 1.62871287128713e+000,\n",
2793 " 1.70865419875321e+000,\n",
2794 " 1.78859552621929e+000,\n",
2795 " 1.86853685368537e+000,\n",
2796 " 1.94921158782545e+000,\n",
2797 " 2.02915291529153e+000,\n",
2798 " 2.10909424275761e+000,\n",
2799 " 2.18903557022369e+000,\n",
2800 " 2.22937293729373e+000,\n",
2801 " 2.30931426475981e+000,\n",
2802 " 2.38998899889989e+000,\n",
2803 " 2.46993032636597e+000,\n",
2804 " 2.54987165383205e+000,\n",
2805 " 2.62981298129813e+000,\n",
2806 " 2.71048771543821e+000,\n",
2807 " 2.79042904290429e+000,\n",
2808 " 2.87037037037037e+000,\n",
2809 " 2.99064906490649e+000,\n",
2810 " 3.07059039237257e+000,\n",
2811 " 3.15126512651265e+000,\n",
2812 " 3.23120645397873e+000,\n",
2813 " 3.31114778144481e+000,\n",
2814 " 3.39108910891089e+000,\n",
2815 " 3.47176384305097e+000,\n",
2816 " 3.55170517051705e+000,\n",
2817 " 3.63164649798313e+000,\n",
2818 " 3.71158782544921e+000,\n",
2819 " 3.83186651998533e+000,\n",
2820 " 3.91254125412541e+000,\n",
2821 " 3.99248258159149e+000\n",
2822 "\n",
2823 " };\n",
2824 "\n",
2825 " const vector<double> AN_0_0 = {\n",
2826 "\n",
2827 " -3.42500916758342e-004,\n",
2828 " -3.44852132272051e-004,\n",
2829 " -3.73368709419959e-004,\n",
2830 " -3.75719924933668e-004,\n",
2831 " -3.78071140447377e-004,\n",
2832 " -3.80422355961085e-004,\n",
2833 " -3.83959964623915e-004,\n",
2834 " -4.45156280333918e-004,\n",
2835 " -4.47507495847623e-004,\n",
2836 " -4.49858711361332e-004,\n",
2837 " -4.52209926875041e-004,\n",
2838 " -4.54582713173279e-004,\n",
2839 " -4.56933928686988e-004,\n",
2840 " -4.85428935050370e-004,\n",
2841 " -5.20459889126169e-004,\n",
2842 " -5.15110334562866e-004,\n",
2843 " -5.23997497788992e-004,\n",
2844 " -5.26370284087231e-004,\n",
2845 " -5.28721499600940e-004,\n",
2846 " -5.57216505964322e-004,\n",
2847 " -5.72639616902865e-004,\n",
2848 " -5.94620246338360e-004,\n",
2849 " -5.96971461852065e-004,\n",
2850 " -6.32002415927865e-004,\n",
2851 " -6.61683815440368e-004,\n",
2852 " -6.64035030954076e-004,\n",
2853 " -6.99087555814406e-004,\n",
2854 " -7.27582562177788e-004,\n",
2855 " -7.29933777691497e-004,\n",
2856 " -7.32284993205202e-004,\n",
2857 " -7.34657779503441e-004,\n",
2858 " -7.63152785866823e-004,\n",
2859 " -7.98183739942622e-004,\n",
2860 " -8.00534955456331e-004,\n",
2861 " -8.62896093530921e-004,\n",
2862 " -8.65268879829160e-004,\n",
2863 " -8.67620095342865e-004\n",
2864 "\n",
2865 " };\n",
2866 "\n",
2867 " const vector<double> pT_07_n07 = {\n",
2868 "\n",
2869 " 1.00751741840851e+000,\n",
2870 " 1.01778511184452e+000,\n",
2871 " 1.04785478547855e+000,\n",
2872 " 1.05738907224056e+000,\n",
2873 " 1.08745874587459e+000,\n",
2874 " 1.09772643931060e+000,\n",
2875 " 1.13733039970664e+000,\n",
2876 " 1.17766776677668e+000,\n",
2877 " 1.18793546021269e+000,\n",
2878 " 1.21800513384672e+000,\n",
2879 " 1.22753942060873e+000,\n",
2880 " 1.25760909424276e+000,\n",
2881 " 1.26787678767877e+000,\n",
2882 " 1.29794646131280e+000,\n",
2883 " 1.30821415474881e+000,\n",
2884 " 1.33828382838284e+000,\n",
2885 " 1.34781811514485e+000,\n",
2886 " 1.38815548221489e+000,\n",
2887 " 1.42775944261093e+000,\n",
2888 " 1.46809680968097e+000,\n",
2889 " 1.50843417675101e+000,\n",
2890 " 1.54803813714705e+000,\n",
2891 " 1.58837550421709e+000,\n",
2892 " 1.61844517785112e+000,\n",
2893 " 1.62871287128713e+000,\n",
2894 " 1.65878254492116e+000,\n",
2895 " 1.66831683168317e+000,\n",
2896 " 1.69838650531720e+000,\n",
2897 " 1.70865419875321e+000,\n",
2898 " 1.72845617895123e+000,\n",
2899 " 1.73872387238724e+000,\n",
2900 " 1.76879354602127e+000,\n",
2901 " 1.77906123945728e+000,\n",
2902 " 1.79886321965530e+000,\n",
2903 " 1.80839750641731e+000,\n",
2904 " 1.83846718005134e+000,\n",
2905 " 1.84873487348735e+000,\n",
2906 " 1.86853685368537e+000,\n",
2907 " 1.87880454712138e+000,\n",
2908 " 1.89860652731940e+000,\n",
2909 " 1.90887422075541e+000,\n",
2910 " 1.93894389438944e+000,\n",
2911 " 1.94921158782545e+000,\n",
2912 " 1.96901356802347e+000,\n",
2913 " 1.97928126145948e+000,\n",
2914 " 2.00935093509351e+000,\n",
2915 " 2.01888522185552e+000,\n",
2916 " 2.03942060872754e+000,\n",
2917 " 2.04895489548955e+000,\n",
2918 " 2.06949028236157e+000,\n",
2919 " 2.07902456912358e+000,\n",
2920 " 2.10909424275761e+000,\n",
2921 " 2.11936193619362e+000,\n",
2922 " 2.14943160982765e+000,\n",
2923 " 2.15896589658966e+000,\n",
2924 " 2.17950128346168e+000,\n",
2925 " 2.18903557022369e+000,\n",
2926 " 2.20957095709571e+000,\n",
2927 " 2.21910524385772e+000,\n",
2928 " 2.24917491749175e+000,\n",
2929 " 2.25944261092776e+000,\n",
2930 " 2.27924459112578e+000,\n",
2931 " 2.28951228456179e+000,\n",
2932 " 2.31958195819582e+000,\n",
2933 " 2.32984965163183e+000,\n",
2934 " 2.35991932526586e+000,\n",
2935 " 2.36945361202787e+000,\n",
2936 " 2.39952328566190e+000,\n",
2937 " 2.40979097909791e+000,\n",
2938 " 2.43986065273194e+000,\n",
2939 " 2.45012834616795e+000,\n",
2940 " 2.46993032636597e+000,\n",
2941 " 2.48019801980198e+000,\n",
2942 " 2.51026769343601e+000,\n",
2943 " 2.51980198019802e+000,\n",
2944 " 2.54987165383205e+000,\n",
2945 " 2.57994132746608e+000,\n",
2946 " 2.59020902090209e+000,\n",
2947 " 2.62027869453612e+000,\n",
2948 " 2.62981298129813e+000,\n",
2949 " 2.65988265493216e+000,\n",
2950 " 2.68995232856619e+000,\n",
2951 " 2.70022002200220e+000,\n",
2952 " 2.73028969563623e+000,\n",
2953 " 2.74055738907224e+000,\n",
2954 " 2.77062706270627e+000,\n",
2955 " 2.78016134946828e+000,\n",
2956 " 2.80069673634030e+000,\n",
2957 " 2.81023102310231e+000,\n",
2958 " 2.84030069673634e+000,\n",
2959 " 2.85056839017235e+000,\n",
2960 " 2.88063806380638e+000,\n",
2961 " 2.89017235056839e+000,\n",
2962 " 2.92024202420242e+000,\n",
2963 " 2.93050971763843e+000,\n",
2964 " 2.96057939127246e+000,\n",
2965 " 2.97084708470847e+000,\n",
2966 " 3.00091675834250e+000,\n",
2967 " 3.01045104510451e+000,\n",
2968 " 3.04052071873854e+000,\n",
2969 " 3.05078841217455e+000,\n",
2970 " 3.08085808580858e+000,\n",
2971 " 3.11092775944261e+000,\n",
2972 " 3.12119545287862e+000,\n",
2973 " 3.15126512651265e+000,\n",
2974 " 3.16079941327466e+000,\n",
2975 " 3.19086908690869e+000,\n",
2976 " 3.20113678034470e+000,\n",
2977 " 3.23120645397873e+000,\n",
2978 " 3.24074074074074e+000,\n",
2979 " 3.27081041437477e+000,\n",
2980 " 3.28107810781078e+000,\n",
2981 " 3.31114778144481e+000,\n",
2982 " 3.32141547488082e+000,\n",
2983 " 3.35148514851485e+000,\n",
2984 " 3.36101943527686e+000,\n",
2985 " 3.39108910891089e+000,\n",
2986 " 3.40135680234690e+000,\n",
2987 " 3.43142647598093e+000,\n",
2988 " 3.47176384305097e+000,\n",
2989 " 3.48129812981298e+000,\n",
2990 " 3.51136780344701e+000,\n",
2991 " 3.52163549688302e+000,\n",
2992 " 3.55170517051705e+000,\n",
2993 " 3.56197286395306e+000,\n",
2994 " 3.59204253758709e+000,\n",
2995 " 3.60157682434910e+000,\n",
2996 " 3.63164649798313e+000,\n",
2997 " 3.64191419141914e+000,\n",
2998 " 3.67198386505317e+000,\n",
2999 " 3.68151815181518e+000,\n",
3000 " 3.71158782544921e+000,\n",
3001 " 3.72185551888522e+000,\n",
3002 " 3.75192519251925e+000,\n",
3003 " 3.76219288595526e+000,\n",
3004 " 3.79226255958929e+000,\n",
3005 " 3.80179684635130e+000,\n",
3006 " 3.83186651998533e+000,\n",
3007 " 3.84213421342134e+000,\n",
3008 " 3.87220388705537e+000,\n",
3009 " 3.88247158049138e+000,\n",
3010 " 3.91254125412541e+000,\n",
3011 " 3.92207554088742e+000,\n",
3012 " 3.95214521452145e+000,\n",
3013 " 3.96241290795746e+000};\n",
3014 "\n",
3015 " const vector<double> AN_07_n07 = {\n",
3016 "\n",
3017 " -2.49824413813930e-002,\n",
3018 " -2.49827433723765e-002,\n",
3019 " -2.49836277745422e-002,\n",
3020 " -2.49904441424535e-002,\n",
3021 " -2.48998252766453e-002,\n",
3022 " -2.50047024310274e-002,\n",
3023 " -2.49274358808430e-002,\n",
3024 " -2.49024784831424e-002,\n",
3025 " -2.49616040035376e-002,\n",
3026 " -2.48513772945922e-002,\n",
3027 " -2.48778015056408e-002,\n",
3028 " -2.48133264306823e-002,\n",
3029 " -2.47874846308160e-002,\n",
3030 " -2.47164736081451e-002,\n",
3031 " -2.47298474945534e-002,\n",
3032 " -2.46130848378956e-002,\n",
3033 " -2.46721887875062e-002,\n",
3034 " -2.45818719126815e-002,\n",
3035 " -2.44588537285101e-002,\n",
3036 " -2.43358571151233e-002,\n",
3037 " -2.42193964494489e-002,\n",
3038 " -2.40898423175651e-002,\n",
3039 " -2.39472378610410e-002,\n",
3040 " -2.37389719364093e-002,\n",
3041 " -2.38242412476542e-002,\n",
3042 " -2.36225112707349e-002,\n",
3043 " -2.36620073772083e-002,\n",
3044 " -2.34537414525766e-002,\n",
3045 " -2.35063310252594e-002,\n",
3046 " -2.32127957893829e-002,\n",
3047 " -2.32784572574905e-002,\n",
3048 " -2.30244396988719e-002,\n",
3049 " -2.30966371146919e-002,\n",
3050 " -2.28161737742402e-002,\n",
3051 " -2.28491339330011e-002,\n",
3052 " -2.26212601652322e-002,\n",
3053 " -2.26280981039280e-002,\n",
3054 " -2.23541707111888e-002,\n",
3055 " -2.24198321792964e-002,\n",
3056 " -2.21589766819819e-002,\n",
3057 " -2.21527427252529e-002,\n",
3058 " -2.19248689574840e-002,\n",
3059 " -2.19839944778792e-002,\n",
3060 " -2.17100670851399e-002,\n",
3061 " -2.17365128669730e-002,\n",
3062 " -2.15086390992040e-002,\n",
3063 " -2.15154554671153e-002,\n",
3064 " -2.12480855928730e-002,\n",
3065 " -2.12810457516340e-002,\n",
3066 " -2.10202118251041e-002,\n",
3067 " -2.10466360361526e-002,\n",
3068 " -2.08187622683837e-002,\n",
3069 " -2.08452080502168e-002,\n",
3070 " -2.06434780732975e-002,\n",
3071 " -2.06372225457840e-002,\n",
3072 " -2.03959964623913e-002,\n",
3073 " -2.04551004120020e-002,\n",
3074 " -2.01681226946224e-002,\n",
3075 " -2.02141547488082e-002,\n",
3076 " -1.99928169287517e-002,\n",
3077 " -2.00519424491469e-002,\n",
3078 " -1.98172307426821e-002,\n",
3079 " -1.98175327336655e-002,\n",
3080 " -1.96092668090338e-002,\n",
3081 " -1.96422485385793e-002,\n",
3082 " -1.94470545093725e-002,\n",
3083 " -1.94669427727086e-002,\n",
3084 " -1.92848206389266e-002,\n",
3085 " -1.92916585776225e-002,\n",
3086 " -1.91095364438405e-002,\n",
3087 " -1.91163743825363e-002,\n",
3088 " -1.88816626760715e-002,\n",
3089 " -1.89211803533295e-002,\n",
3090 " -1.87259863241226e-002,\n",
3091 " -1.87654824305960e-002,\n",
3092 " -1.85637524536767e-002,\n",
3093 " -1.83293427381954e-002,\n",
3094 " -1.83950042063030e-002,\n",
3095 " -1.81605944908216e-002,\n",
3096 " -1.82393062835695e-002,\n",
3097 " -1.80114325158006e-002,\n",
3098 " -1.78293103820186e-002,\n",
3099 " -1.78622921115641e-002,\n",
3100 " -1.76605621346448e-002,\n",
3101 " -1.77131517073276e-002,\n",
3102 " -1.75048857826959e-002,\n",
3103 " -1.75378459414569e-002,\n",
3104 " -1.73031558057767e-002,\n",
3105 " -1.73491878599625e-002,\n",
3106 " -1.71278500399060e-002,\n",
3107 " -1.71869755603011e-002,\n",
3108 " -1.70113893742315e-002,\n",
3109 " -1.70312776375677e-002,\n",
3110 " -1.68556914514981e-002,\n",
3111 " -1.68952091287560e-002,\n",
3112 " -1.66934791518368e-002,\n",
3113 " -1.67199249336698e-002,\n",
3114 " -1.65639465907375e-002,\n",
3115 " -1.65707629586488e-002,\n",
3116 " -1.64213205634289e-002,\n",
3117 " -1.64281585021247e-002,\n",
3118 " -1.62525723160551e-002,\n",
3119 " -1.60769861299855e-002,\n",
3120 " -1.61230397549559e-002,\n",
3121 " -1.59278457257490e-002,\n",
3122 " -1.59608058845100e-002,\n",
3123 " -1.57917556461529e-002,\n",
3124 " -1.58443452188356e-002,\n",
3125 " -1.56622230850536e-002,\n",
3126 " -1.56690394529649e-002,\n",
3127 " -1.55195970577450e-002,\n",
3128 " -1.55525787872905e-002,\n",
3129 " -1.53966004443582e-002,\n",
3130 " -1.54099743307664e-002,\n",
3131 " -1.52474600401217e-002,\n",
3132 " -1.52608123557454e-002,\n",
3133 " -1.51179059082379e-002,\n",
3134 " -1.52031752194827e-002,\n",
3135 " -1.49883733471386e-002,\n",
3136 " -1.48719126814642e-002,\n",
3137 " -1.49114087879376e-002,\n",
3138 " -1.47292866541556e-002,\n",
3139 " -1.47949481222632e-002,\n",
3140 " -1.45997540930564e-002,\n",
3141 " -1.46784874565888e-002,\n",
3142 " -1.44767574796695e-002,\n",
3143 " -1.45620052201299e-002,\n",
3144 " -1.43341314523609e-002,\n",
3145 " -1.44324726590306e-002,\n",
3146 " -1.42699583683859e-002,\n",
3147 " -1.43094544748593e-002,\n",
3148 " -1.41534761319269e-002,\n",
3149 " -1.41864578614724e-002,\n",
3150 " -1.40370154662525e-002,\n",
3151 " -1.40699971957980e-002,\n",
3152 " -1.39597704868526e-002,\n",
3153 " -1.39404430639142e-002,\n",
3154 " -1.38432882503937e-002,\n",
3155 " -1.38566621368019e-002,\n",
3156 " -1.37072197415820e-002,\n",
3157 " -1.37467374188399e-002,\n",
3158 " -1.36169028667573e-002,\n",
3159 " -1.36302551823810e-002,\n",
3160 " -1.35134925257232e-002,\n",
3161 " -1.35660820984059e-002};\n",
3162 "\n",
3163 " TGraph *g_07_07 = new TGraph(pT_07_07.size(), pT_07_07.data(), AN_07_07.data());\n",
3164 " TGraph *g_0_0 = new TGraph(pT_0_0.size(), pT_0_0.data(), AN_0_0.data());\n",
3165 " TGraph *g_07_n07 = new TGraph(pT_07_n07.size(), pT_07_n07.data(), AN_07_n07.data());\n",
3166 "\n",
3167 " vector<double> pt_0;\n",
3168 " vector<double> AN_0;\n",
3169 " vector<double> pt_07;\n",
3170 " vector<double> AN_07;\n",
3171 "\n",
3172 " for (double pt = 1; pt < 4; pt += .1)\n",
3173 " {\n",
3174 " pt_0.push_back(pt);\n",
3175 " pt_07.push_back(pt);\n",
3176 "\n",
3177 " AN_0.push_back(g_0_0->Eval(pt));\n",
3178 " AN_07.push_back(0.5 * (g_07_07->Eval(pt) + g_07_n07->Eval(pt)));\n",
3179 " }\n",
3180 "\n",
3181 " return make_pair(\n",
3182 " new TGraph(pt_0.size(), pt_0.data(), AN_0.data()),\n",
3183 " new TGraph(pt_07.size(), pt_07.data(), AN_07.data()));\n",
3184 "}"
3185 ]
3186 },
3187 {
3188 "cell_type": "markdown",
3189 "metadata": {},
3190 "source": [
3191 "## Projections"
3192 ]
3193 },
3194 {
3195 "cell_type": "code",
3196 "execution_count": 42,
3197 "metadata": {},
3198 "outputs": [
3199 {
3200 "data": {
3201 "image/png": 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\n",
3202 "text/plain": [
3203 "<IPython.core.display.Image object>"
3204 ]
3205 },
3206 "metadata": {},
3207 "output_type": "display_data"
3208 },
3209 {
3210 "name": "stdout",
3211 "output_type": "stream",
3212 "text": [
3213 "Save TH1 hframe\n",
3214 "Save TGraph Graph\n",
3215 "Save TGraph Graph\n",
3216 "Save TGraph Graph\n",
3217 "removed ‘fig_BUP2020/D0_BUP2020_AN_3yr.svg’\n"
3218 ]
3219 },
3220 {
3221 "name": "stderr",
3222 "output_type": "stream",
3223 "text": [
3224 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020_AN_3yr.png has been created\n",
3225 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020_AN_3yr.root has been created\n",
3226 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020_AN_3yr.eps has been created\n",
3227 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020_AN_3yr.svg has been created\n",
3228 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020_AN_3yr.C has been generated\n"
3229 ]
3230 }
3231 ],
3232 "source": [
3233 "{\n",
3234 "\n",
3235 " TGraphErrors * grProD0_AN_pp_3year \n",
3236 " = GraphShiftCut(\n",
3237 " Significance2AN( gProD0_Significance_pp_3year, 0, pp_beam_pol, 2),\n",
3238 " 0., 0,100);\n",
3239 " TGraphErrors * grProD0_AN_pp_5year \n",
3240 " = GraphShiftCut(\n",
3241 " Significance2AN( gProD0_Significance_pp_5year, 0, pp_beam_pol, 1),\n",
3242 " 0., 0,100);\n",
3243 " \n",
3244 " grProD0_AN_pp_3year->SetMarkerStyle(kFullCircle);\n",
3245 " grProD0_AN_pp_5year->SetMarkerStyle(kFullCircle);\n",
3246 " \n",
3247 " grProD0_AN_pp_3year->SetMarkerSize(2);\n",
3248 " grProD0_AN_pp_5year->SetMarkerSize(2);\n",
3249 " \n",
3250 " grProD0_AN_pp_3year->SetLineWidth(4);\n",
3251 " grProD0_AN_pp_5year->SetLineWidth(4);\n",
3252 " \n",
3253 " grProD0_AN_pp_3year->SetLineColorAlpha(kBlack, 1);\n",
3254 " grProD0_AN_pp_5year->SetLineColorAlpha(kBlack, 1);\n",
3255 " \n",
3256 " grProD0_AN_pp_3year->SetMarkerColorAlpha(kBlack, 1);\n",
3257 " grProD0_AN_pp_5year->SetMarkerColorAlpha(kBlack, 1);\n",
3258 " \n",
3259 " \n",
3260 " \n",
3261 " auto gs = getD0AN();\n",
3262 " auto g0 = gs.first;\n",
3263 " auto g07 = gs.second;\n",
3264 "\n",
3265 " assert(g0);\n",
3266 " assert(g07);\n",
3267 "\n",
3268 " \n",
3269 " TCanvas *c1 = new TCanvas(\"D0_BUP2020_AN_3yr\" ,\n",
3270 " \"D0_BUP2020OOArArO_AN_3yr\" , 1100, 800);\n",
3271 " c1->Divide(1, 1);\n",
3272 " int idx = 1;\n",
3273 " TPad *p;\n",
3274 "\n",
3275 " p = (TPad *) c1->cd(idx++);\n",
3276 " c1->Update();\n",
3277 " p->DrawFrame(0, -.025, 5, 0.035)->SetTitle(\";#it{p}_{T} [GeV];A_{N}\");\n",
3278 " (new TLine(0, -.0, 5, .0))->Draw();\n",
3279 " \n",
3280 " g0->Draw(\"l\");\n",
3281 " g0->SetLineColor(kCyan + 3);\n",
3282 " g0->SetLineWidth(5);\n",
3283 "\n",
3284 " g07->Draw(\"l\");\n",
3285 " g07->SetLineStyle(kDashed);\n",
3286 " g07->SetLineColor(kBlue+2);\n",
3287 " g07->SetLineWidth(5);\n",
3288 " \n",
3289 " \n",
3290 " grProD0_AN_pp_3year->DrawClone(\"p\");\n",
3291 "// grProD0_AN_pp_5year->DrawClone(\"p\");\n",
3292 " \n",
3293 " TLegend *leg = new TLegend(.0, .8, .83, .95);\n",
3294 " leg->SetFillStyle(0);\n",
3295 "// leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection\", \"\");\n",
3296 " leg->AddEntry(\"\", Form(\"#it{#bf{sPHENIX}} Projection, #it{p}^{#uparrow}+#it{p}#rightarrowD^{0}/#bar{D}^{0}+X, P=%.2f\", pp_beam_pol), \"\");\n",
3297 " leg->Draw();\n",
3298 " \n",
3299 " leg = new TLegend(.2, .58 ,.85, .83);\n",
3300 " leg->SetFillStyle(0);\n",
3301 " leg->AddEntry(grProD0_AN_pp_3year, Form(\"%.1f pb^{-1} str. #it{p}+#it{p}, Years 1-3\", pp_rec_3year/1e12), \"pl\");\n",
3302 "// leg->AddEntry(grProD0_AN_pp_5year, Form(\"%.0f pb^{-1} str. #it{p}+#it{p}, Years 1-5\", pp_rec_5year/1e12), \"lp\");\n",
3303 " leg->AddEntry(g0, \"Kang, PRD#bf{78}, #lambda_{f} = #lambda_{d} = 0\", \"l\");\n",
3304 " leg->AddEntry(g07, \"Kang, PRD#bf{78}, #lambda_{f} = -#lambda_{d} = 70 MeV\", \"l\");\n",
3305 " leg->Draw();\n",
3306 " \n",
3307 " c1->Draw();\n",
3308 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
3309 " \n",
3310 " \n",
3311 "}"
3312 ]
3313 },
3314 {
3315 "cell_type": "code",
3316 "execution_count": 43,
3317 "metadata": {},
3318 "outputs": [
3319 {
3320 "data": {
3321 "image/png": 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FLdkcyr0GYxlV3E6VqIepv5daW43lq/auTV+ZJmpTp+hJDA8XRqRkRAotsxjZZ6CyX0ETJ0fq1d67pQnLtIWhCWufx1lTOI7WkmYid+1e7+wskINYEhYkPe2tjaj9m36GpifJxP1Y2W8y4zjekF3qIaYti4ul86gOzYX6fGHQikfF7y0oiOgzsEKWG03vDCHZKUvlpvMuadqpy0QovVvuXGtttLuelll7xUtb16W1rb2XiZ3F7dlx0e5a1rkL9RP1OGgxj/0QffWrX7Wci855BZJR06BpHH9HLWFJYz4CKlNrT3sj7S10ruhy7Xeu2PuspDebllXcM2XPgpr4znVn7ZPTe0Idb7wjdupYQ5qewtEsO20nb7FAwtIPLUh6Orc2rvqbtExPj8vuBnVk9bV3x+t6xPNcxweFE5uA0LaB4xN9BlZIvSC157Ly897fNntQZNpsZ4m03AVcnhRGUcotDSmCdpS046DdK7MsK1rat9TedgB32qa0GMOR1vxiClRMv3DagBw1TPrqV7+q7ct+Otqv8kx8RDVN6yJqa+/XEnhYMiIXMNWTLCFW5+fyQ8svcedOe1c09VO15LHzK5c6iqmC0hsH2tMzRe99Vdt752GcNbDxksIp5tj1xNQ6FpXF0tO5tdGbShR+w1T7vhxviR6T5BjYmO5RjluWp0PQMuV4mmiucWQ/lVG4rV3oMZGYE0ZM6LHf79WX/koj5iQR866IrZk223RVryvl1cimFZNtzRDSfrlynudqBtVZVtrn4nA4uMzf4nFiGW3Wl6GzxwjatGmmKc4ee+yx3/zN35SfX7x4UaxomRJNzGw2kffJnV2OUpZlltNkeiiolZzOZbQi5D5vj2VmJHmBd27QtGKe56aLehB5oCzPSsuyFDcT08Gf2G45gmW+dVWWZeMuq+mCp1D8BBwOh4jmXouXdoQH3Rwmcik/VVW1J6QeuqN2jrxf+GqqOtOs1mpcppuTN8n1zE2HmRDYzEKd7FJjr2nZiQvSVInpjGqS0zq95R0pWZaJuVnHpWqdRAiXZZmcatb91TQutDiwfa+0t6RrJ1H7VfBSvzEFKlr885nPfKYzsFEX0wIbbSKBts6ZoD/96U8vMMWzqv1zqOktEvaTKGuivXWX3hlRZXpkYCM+keXKsmKe56ZrfxCXyVV7w7nlbyPux1aerPkTdYvgKZQ/ASuJbUSpnjgocbW0n9r9fi/m9Q6UnH9iqnuUZTn0F6f31jqd3H5n5USr1WjPMduY4vmoENjMpTwlWm9EDdt+g3OpoLQ3mxiu/PaW5YsUxF3MJUmxMIWRou1FfFUUhemlH4P21fnzoH0iYkXTFtr3Vu23UPuZeemll0a8xMYSqDz22GOf+cxnOhfr5PJqTsn0NpjHH3/cb2Bj+olarDy710TlAqJm2bvlUG0LguMBXElgI4+V44PYzhTOWtf3ksKJZIO/99hmRGrF3mcqKmF/zjr3vtvtvDx6sCvMb/+M8Se+92LJ81w+WupttJHVgxgPBYYisJnXTI2e4za7jRjGxN4qlZy24bQfNTlWItXGn7Ypr+bs5ffVnEmS7Pd7GdgkXY9O7Y+WLYGNfUW/jTbTC7NpCzO9GE7buAtZAV3PlRuwT5edY6PZCJZ11Xdu9iZmUApFs79D6gYL3m4jys9KirSvkyuWl5eGeOzi3qTgxbhD6qUk2O8JMxU22TO2dzHxx1YbCaELOcAHMYiokGjT5jqWee3bcQMrtY1oL6XR2Kdoa88cMCI97Vdz9qZB/ee4mQPsLTmj86Ly8sKK3i3IBeyFQZ2Q1L79ET+oYsVs+DSpLtns/MrxwHYmSX5oOWK+yoDKvTw4nlP7Lhyp5WF0CufuMzNoggrLMRl0MJu++dAWTs/Ek2tKtlzGZVbSiYYeK7/mm+7Zkcve5z4FGzPiol4bWmywHfIxpDp/Q5v68Kz9JGnEQ6/ermUae/OOZRC/u96NaFMITJ854NOf/rS2ZNM0WZZpH7744oveZxHwznHwzIjHkEOH2AUcGWKyquYax+Er6tFbuKFgdArzW+c78ULelyaOkXAZ+mUyRz+0KenxRRt+Jv5Q+0gv0yFtYStpdrMIeO0jFAIbbJD8nTONvZHaY2NG7G5iVU/bqdpJzFeS2huxd2+zzBzwyCOPtJd/8cUXH3/8cfUTMePzo48+qk0k8Pjjj68/sHG0nn5iflPiOI9T8FwnzvGeY3TRyTT0UYwlM43ca+/arjOFfo+w3Nr0kd9T+mrO0Q9tdHomnlxJHX+vNc+qfaTD9gA0GZEk00C1hTv3uqScfmjHKGyDEdZv5YVEe9tM5zJame/sQtP+yp29a5mmPfrFnlrt7TSOXC5zUyXP1LFKaL+as/3WmkcffdS0uikxjpbpiub4cga5jMubMV02KPpSZsr7eV3ya1rGsmLnVy7dRUz7CtUVTW7TftGZztT0XffeMQKmUGU/QXLvLjdAl3cWmYjy0/syk8XSY0/AiLfodGZt1g5pg45VpxHRfuebqU0JGHHTdnzvjcuN2nvZ2LwNHKvoM4C5rbyUazfcznur5aZs+cpvGqT2cyP12/YAm8fcqPGP6dWcGtNrQNUBNu3c9Wb/wQcfVL999NFHtQXUsGeoZQKbxqGiaflN7fxcfrjtF3SGCmxGpNnLrt0Dm+VTKMkzazo7LoVTUCvBI2rSYvXe2+xi6bEnwHGDLpGVSzEYZ3r2JwY2vfeNEcXb8T7fm3cvvxfHZgPHKvoMYG7uN7sgyWvflIuikKNvOzupW3I3Lg3aRgbNHKDdkbVB/+7U3wxtI1qgYkm5oMZIvTMHtFfXFmi357SXcbdYYNNbWek88pbt9/78mxboXVF+69JwZP/Kkqnm1oOv7WtKYCNeJS6Y1u2kXd2dlRvHxrcRLDtdSQq1NLicGkt2tCcy42Iwx2wulh57AlxCBTUZjk2dnUmVl8DQh2sue5+bJQ2Wm4ZgynjvHdjlwnFs+TkSibPQKZ0q+gxgbisv5UM7zqp3z3bYMyIBvV3LNNrCWhQ0evCrGr1oG7F0ZuuMo9QFtBE1DzzwgPptexq0F154oTfLpsVcLBbYNLcm21Js3LevrqXWALTZ/NqVA8uKlpSMSKF6NWlVDe0rbcUpgc3orkTta99yVGfqAjS0eXbJFDZKCXGchczRuGqiYz+0xdLTm4DeUKE3XFFZzrW6naHxyegVPTJFd73Hx5Jx7eZmuQO7tIEHPDjRSdZd5XMRfQYwt/WXcvdgQPs58fLMb+hGtCTZv3WnRi/2XfTu0fLtI488Ir9y72PWXlLrruZuycDGZU4q+4gFy1cmvT//7ilpL+OSQperybLWkoGNutOhB2c6sWV7hSlsCh0T2Qy87Uzs8uSS02XS05sAL/0MXZaPPbBpHO4bnWvZM+7y1NIxYveUy6OwgcMVfQYwtyhKee8d0NT6736LNBm0kaEzB7izbMSefvuB0r6VMwcMjVXsCXa3ZGDT9EUUvSMWOr+11AAca8mOKbGM5rKn0HI1meL2IIGNupbpTM3XBcXllIVNod8WkizLJlad3U/xMunpTYB7c5xjSkwtGBsIbBrzfcNSAnszPu4O3E7P1Lwdkw0crrSZUJfCMUjTaAqJeH1NkiSHw0HWlsqyXMO8tIiXfAe8LFf2QiXmik2SxHTh1KfEBsWmXCaW1VbsTYlIuVx40Lyu2tWUn3LfwiDijT2DbjXt4yxfYLVAgl3exxI2hYnzzN3LEJl1nEA5rDletuOy091uVzjPMb1Oc5TwoXdgTBFRlc8k+gxgbhso5cCSegMbtImD5n7E5CuqsoHvPF3M+lOIVREFJvbABrHbQJXvXaETAADAMOt5O6rJ+lOIFaK0ABNFH5lhbhsI34HFiP4k4m8uHEeiH9qgZ9XrbxZbfwqxHrJ9j9KCsDZQ5aPFBgC8kVXz0TN3H5sRUQ2wJXVd7/d7Mcg+dFqA6N0WOgEAED21oUagS4mjEeNP5CqrjR7Xn0KsR57nhDSAL7TYAIBnWZbR/jCf9Q9fWX8KAWCTaLEBgKnyPB80cTOmkNHCasOG9acQADYp+kFCmNsGRpIBAADAbgNVPrqiAQAAAIgegQ0AAACA6BHYAAAAAIgegQ0AAACA6BHYAAAAAIgegQ0AAACA6BHYAAAAAIgeL+hEvzRNXRaLfe5zAACA7XGsyG0AgQ36EbEAAABEyrEit4H4h65oAHCM6rqu6zp0KgAA8CblYTzs0pRCAmxQmqZZlhHbAACEDVT5aLEBgKNTlmXoJAAA4BmBDQAckbqu8zzf7/ehEwIAgGdMHgAAx2IDA0MBADAhsAGAY1EUhfijruvD4RA2MQAA+EVgAwDHQg6tKcuSwAYAsDEENgCwUmocEjIdWDG1bLiXE3Wyb0oXgM0gsAEQt7quRc3scDhkWZbn+fSKWn1K3awwccuDyCH+VD1hovUqdCwqu91u6CqbV9f1brfzOwd6WZbyBIW6jQBHJfr5qjG3DUxqjq0SFZHOr4qiGF1dy/Pc1E1r4Re/yLH+g65BLYWdtaiyLPf7/dzZkcEhVedZqXNCuBQVtYRXVUU9WxCHxddFYbmNJBx2rNUGqny02ACIkiWqSU7bOkbUp+3zhh0OhzRNV14p0Q5LwLdwypE8PKWeVVVV8qTneW4/3WoLj2hDmDl1cfA7nUbv9IO73W7ltxEgUrzHBkCU1Op7VVVN0zRNU1VVlmXiw/1+P7RCr9YziqJoTqmbTVqRw9oUt6K1ZPPyPJfl83A42Iu9WnpDRbwr5PGiVq849TbSNI2cltDvHgH8kwawopBghWQ1Lssyy7eDSm9VVXItGSmZFtDqKzOZ70YtKlidR88jeSI6jyf8cvllVyvWnJSmaaqqUo+Jl4vCfpfovc8AAW2gykeLDRCHa9euhU7CishOI52PnMc9h1bX6uwioj4Xl8P6gZVQK+imZjpZbumElqZpmqa73c7vtazeRjrPgnrYaTEDvCOwAdbr+vXrr7766le+8pWnnnrqL//yL5966qkvf/nLr7766vXr10MnLSRZXVBbZjRFUWRZNmh4iazfaE9wO3ftTp1gTW4kz3NRrxo0h1td13JFse7yFSOReJEM8Udn+kWW1fhTOwjaJzJr3mvbnTtSj/9Mx3D5/aonorOyri7Qu/d2gh0Lqlq85YqW3ZlKQufutGsnyCVgJ9NjvztpCwPwJnSTEdaOQhLEjRs3Ll++fP78+YsXL165cuXk5OStt946OTm5cuXKxYsXz58/f/ny5Rs3boROZhjy9uW3I4fjZofePGX9prm1F4qmc6fqvkz1pHE9Z0Z0RbMkvp3+3sXUxGtbHpGd3jSLTkGmY+i9Y2Hw/XaeXPf9up9oleWJQGd61H2JJKlb0Ja3xAnJqDJT3creu9Wd3I7lIMtszt0XFBhq3NW0KtFnAHPbQCmPzo0bNy5dunThwoVr167dvHlT+/bmzZvXrl27cOHCpUuXjjO2mVKbmbhZteLouFl1nImlZtZZE1IrefZ1h2Z2aGBjr7PKDLZTblpG5qt9WIbmxSXZvenvreuLBkDHWNrjfocyjaJxHHjWW0pNaXYpIZ27ltu0jHXpLf/2TLmQGZ8ebIhIybKAS/ADBDH9Ugou+gxgbhso5dG5fPnyhQsXfvCDH1iW+eEPf3jhwoXLly8vlqqV6AwtRE2iKIq5B+OOeKzbrpOpidTqkfZ2D8sz7Lkf/ao7MqW/nQbL5AHtiqmo2vo9g9rB1xKv1aTtmxpUE/W43xHaG3cfsG4qjb2taqbMNn0zFsi11JKgxgbq6pZJxiYGCR4DG8cdzXHqgYk2UCyjzwDmljgLndKNODk5OX/+/LVr13qXvHbt2vnz509OThZI1Xpo1QLTA+Y5Ipxx0xlpddzOZeS3WqVKXbH3Mfl8QZ29Kmb51jGwma8q2buXoTV+x9qzx/2O0J6+z/FQq8WpcwFTYO++oqVZ0nR47e0bvrqQLRDYVLfOGk9zDZaUOAud0qmizwDmtoFSHpdLly5dvHix3QOt7ebNm1VVXc9gEmMAACAASURBVLp0aYFUrYfaPd3e+8VvvWH0OBCXqowpNnDZo1xgvgpZ75AAdRxR5+f2wGaGJDdNX2tSOyX2AuNeqPzudxytNc/xUMvFLLFW56Z6+1ZZilDvsbKnyldAMl9g03mbIqrBOs13Q17Mbe3rDdiwp59+OnQSejRNkyTJ3/3d37kv/Nd//dfzpmmac+fOzbHZw+HQfoG6OhOXmBjKy+spy7JUp5lqhjz90rbT+bk6FVhZlu3FLCFcURQibR7fm25i2sWUyZ0cx2aMoE3CZlosyzK/hy7UfrU0pGkq/pZ7sR/q3unOBZlsMX1Ze93RTFdH5x6lPM9HX48BHflc28B8mO4ZQMTE49W6rkU8UNe1WsuZ/oaKsizTNPUS1dgHQNtngLWEZ14iNzu1EuZ9gt35anguE++qCfCVr1D71bR7adqLyqzJdrkSTSVBfr7f7xco7d6J918J8sPdbkdsA8yBwAZYkXGV5hgfWPrSWbtSD8jompB45q1WyLThCkMtUI+ZqYqsvpn0cDjsdjuX95O4b3ziFkxkS4VjGfCVklD7bW9WrUn3TnemtdiYqC+csWxKPGuQ75yZkA/9/TwBX+Ik7gmdLGuJpAqN0jPtcDjEGKcBK0dXNGBF0jQdUXWeWG+Il6WupnZfGbFlre/ZoBd9jpPn+WI9ykYQ9VT1mIiugOKToijWXEWzRw7tM9tbZXfcxdD9epfnuSxO7uHT6BIoCskcBbhpGjUvyWmAnZx2Q11z8dOIpIoLJ9I2KGDVlh/Wg7hQSBb2/PPPX7lyxXHhK1euPP/887OmZ20cJ0vtnabJQr1Dur+6xMTxnRWd+XLMglxs7tmuG+vrStp7d5k8YKZ0uk+qq6Xf5UUuGnW4+ej9zmHQVeDyrhhTrhvD4xgxw4eYu7lzrWZISdBmFdMMOC6GjZtSqCWg09DdLXD2gRGmX0rB0WIDrMuZM2euXr36gQ984F3v6ukp+rOf/ew73/nOmTNnlklYXGQDyFBq81dVVYv1g3cc4RCcnNug/Xh+t9stecTsHI+n44j59e/XoxHtb+pVk2WZ6ISmLuCleUrtfia6QarFb1xz9+gEmMgF1nZagSPBGBtgXc6ePfu3f/u3b7zxRu+Sb7zxxre+9a2zZ88ukKr1mLW6oNbPmqbxu69luh7NvQt1X2LMgNpGsZ5+NY5Hux2HiFm22sS3nc1ujtOg2fcb3OjCo2a5qqrOucu8kzOFqK1Sy4+6adudclx+DWkGtoTABliX22+/Pc/zl19++eTkxLLYycnJK6+8kmXZ7bffvlja1sY+FkL84V5xVCvlczz6tY89kO1LnZXCsLOilac6v83zXMY26xkg5JgSedh9HcZQ+51OnXzMsphojemcRUDOut42pfpuL35lWcprfFVBgmNiaNgBPJutkxs2gkKyvBs3bly6dOnChQvXrl1rv6nz5s2b165d+4u/+Iu/+Zu/+elPfxokhWH1vi+yGfX2Q7mK317vLi/oNI2FcLlXj8jpUL1HxjSwJOAYG5dDN+il9Y4H2ft+pxg60szlEmhv0OV6tGTZnkKXMUuOw9jsfL2g0+Xkug/EAha2gTIZfQYwtw2U8hjduHHj8uXL58+fr6rqypUr169ff+utt65fv37lypWqqs6fP//Nb37zOKMawV6bt79qXY731WpvM1U1tCajziqj/NYyIHtETn3p3YupMjc9sDGdLDttAoDOQzeu0u8+A8S4/cr8eglTh+Zx3InuraaryRgxeYD9eDaeHkn4CmzUo2FKj+VoAGE53ivWLPoMYG4bKOXxOjk5uXTp0vPPP3/+1PPPP3/p0qWTk5PQSQtMqyrJCkRvzVJdQP12xFxYjklt94VT96tNMqatq62o5dSlLcgL9eBoMYaWDK0mZ3mU7ngYx2WwPXWbdq4tabanxJ6M6ftVj7NLquxGzA2oJl470aavLMnunMfMtEdTkizlXEuVYx47+QpsmtbzCPVY2a93ILgNFMvoM4C5baCUb8Pf//3fh07CuvQOnumsoJgCG8ssxiZD09mbYFOzRm/aFnju6zJUqZ2MdsplHh0Po1xsUGCjthQNPeb2lNiTMX2/loM5wojAxiXCbx+E3iKqLaBuwSWFvUmyHNKhGZ9+5B2fkjDRM1bIfiVGgckDgDj86q/+augkrEtd15badlEUqxpJnCRJWZaWGo99ouTGHFosk1P70TYlI9TAaPWtlKZjLuqvflMYar8eWVIuVFXVHsdflqUpthFNfNoqQ6didwkU13NIxcR69mVWlWBgS2af9x2xW+DlAMAU8o0WoubdfodGWPKN6bIqIxIsF3BPsHh1TJIkIrNBXrguEy/TIJiWl2mWqy9wduS03fLepRaS3jR3Erm2r+Vrv2maZlkWNjKvTw1KvD2/8u9xRVcmSd3giFO5GPUYJqezxq05wcAGqnzRZwBz20ApBwJqBzaYVVmWokFg4djA437XENgAOEIbqPLRFQ0AsB2hXv3ua78uTUMAgE7RR2aY2wbCdyAgWmwW1u4PFtF+67oWL63nrgtgeRuo8tFiAwDAKoioZsTk4wCAJEluC50AREA+ibSLPcoHEDvZH8xlfuoV7pe7KIA5OFbkNoDABv34rQUQhdgH2ADAHBwrchuIfwhsAGBG1HSXpE4ofAz7BQCooh8khLltYCQZAAAA7DZQ5WPyAAAAAADRI7ABAAAAED0CGwAAAADRI7CZV1mWeZ6naZrneZ7ncuacUJsVK86RJAAAACCg6AcJrVZZlvv9vv15lmVTYgnTZpO+ufwsKyZJUhRFWZadX21gJBkAAADsNlDliz4D65Tn+eFwsCww7rDbg5MkSaqq6pxstHfFxBxxbaCUA8dJXNFMQAwAcLGBKh9d0fwry1JGNVmWNaeKopDLjKhq1HUtg5Msy6qqEputqkous9vt7CsmSSJX1NY9HA6mRhsAMdrtdlzUAIDjEX1ktkLyva3t/l11XcvYw9S60rvZzqYVy07V98h2nm77AhsI34EjJNppJ/Z9BQAcjw1U+Wix8UwNKtrPSvM8z7LM9K2FWjXprKbI5iBLlzO1fUalFmLqQEDs6rrO87y39ykAABtDYOOZDAxkAKOR8Yx9EI5psyamMEld0aWBiMAGiFqaprvdbtDtBQCAbSCw8UzWJ0yRhhpduEcRckl1oE7vwolDoOXyLYBYFKe4qAEAx4bAZi5+m0dkvGTZrL2TGzMjAcegPMUlDwA4NreFTsCmOAYqWZaN7ihiqax0zjEtqji9m3UJnAB4Jy9Ppi+DiX3opkld1/InidIF4EgQ2Pg0dICK4/JDNzs0alKDGQIbxKU+dTgcRKPloMaKsizluvmp+VLbJof4U/WEiSii8p+ORUWd/Z/SJYiJSb1MFejydrhk+PSnACYisAmg9/Wdozc7Yh4kNTGmadOAFVInTxdESRYf9tYntHrJ4XA4HA7ik/XXRbRqWcDUymYBqs7zqetaTsq/3+9dDrVaJLixSx5LqftzyZXfTICNIbCZRRTDdrV6YVEUpvuv+qKb6WKfIh1r0I5qNLvdrv1OJ8n+cGG32628lGp5D/iyGvk+4uUbu45KVVXypOd5bj/daguPaIqcOXVx0Bq+gG3wW0PbAAKbI6VV7CxVwIRQBOujxeSy9KoBz36/76xtq4VfXVdtw+mtO4alzY5IzXXzxDvQRLk9HA72dgD16lhzMV6Y/VHIUI49Hbg2MTe/NbQNhEkENken3TN4/R1vAJUahGulN8/zpmnkrVkMoVHXVZ/aauuKwTmi9tNbdwyLfl9HSO2QZmlU1K6OBRK2cqK35Hzvq13tXQI4Tkz3PAt7e/dMj9B6Nyt+F9X7e1EUTdNwX0Zc1LczdZZeWZ/rnCdQ/NHZ91I8F9f2AqyE2lJnCm7lHZ5OaGmaivfVeo9qHF8QB2B5BDY+Df0VcVx+6GY7b7VlWWq9d5qm4blvROgdLvXOTm65ZHpfoVuWZZZlQ+sr6tS6yemIYVGvyvO83XBkIRqO1HUHpWQ6kQCRBvFHZxrkZHTqP7WDoH4ij4n32nbnjtRjON+zpIX3q56Izsq6uoDLoy4twY6FTS2iLiXcVBI6d6eV/5V3CgWwOg28cjmwss4kootBm62qyrSMfJiXZZnpq85ve3c9aHl49Prrr3/2s5994IEH5Om7//77n3zyyddffz100kLqvYLUHjidnw+9CuzkZkV6TEFRZ2rVdGojZ1SWC99EbG1QTu09l7Q09C6mHmpty0Pz4pLmEQc/6v12nlz3/bqfaJWliHamR92XSJLlx8j+NKHnuHSpbiW3P/3yl7nwfn6BsMZda6tCi81cLA+ZprwN0/5UrHOzavfioih4+hWFd95557nnnvvQhz70zW9+82tf+5r8/OWXX758+fKHPvShZ5999p133gmYwjUwFebez+fropOmqaltTUxmYFrR/lqM3W4395WrNep6TEPdN4XdFGqS7Ae/tzliUPuAx/0Okue5rFWLkWDqV/Jvy07VsTqddrudqS3F3qfrcDjYt2zZgn2iwmTUmOb8VnPM9SyPebvFEkAYoSOrrRn9LNnO0hojmTYrPx/3bIlCsry33377ySef/OhHP2q5cu+5554nnnji7bffDp3YMCylWr3EtG87L8+qqoqiKIpiRKtIe4+d21cfRWtJap9ZNRnaA/JxyXMk95JlmZoGexOBzFr76LWzNvE4d9Ie82uJH3QATSVq7v2O0N64eprsR9hU2Hpb1UyZbW7Nr6kkaFeBaEVpr64df8tXQ3lssFWLfWdDEy05iNQc96uFRZ+BtVHvwp0LjGsN7w2HTPvtTU+vDZTy6Dz77LP2qEa45557nnnmmdCJDUOrhIl6UlEUaiXD0lFH1Kh81Ui0xHRWK9V9dSbJdE+wxGke2e8wlm8dAxu/ff8G7WVojd/xIHvc7wjtUuF4qEf/Qrmv2D6A6rHqPLz2B4K+upB5DGwSNxP3AixvA+U2+gyskOUO3vtTl52y1BI6b8qmnY4Yz9Pe8rgVMc7rr7/u+KspHO14G/uAEPuAFvtQgaFl3t6m0bn39oeWnZqCIo9624RlGkyf2wObGZLcNNOOvGkxl1ul3/2Oo8XwjodaLmaJtTo31ftTYilCvcfKnipfAcl8gU2WZaI1sv2sZOKOgIVtoNAyxsY/eX/XelerHc07J+IUUwwJ7a666gy22mbVzsdaN2LZa3m/36cO6CIc3Je+9KVPfepTjgs//PDDX/ziF2dNz2pNKavqlLidrT2jX1Jm6cdvn0jaMnJam2psXMIcmcY51HUtfjNGbLM3jBzN8ch4n5M31H5NaVBfOOu4imW4V2dBlQVgykgV07r2S0O8nKppmhX+PIkYqa7rsizF1HDNrTHnkc+4DQQQIJg6Ar2/Z51r9fY5GfGYeWh56HzsOvVwrMnQA4Lp5jiP2uPqzuCkvWstYe3SPq5Dv+PAuc7FHHc3IlWDaE0Q7l2nXFpsvHfEau/dsf+VfTH3I+x3v6O1Gy3tyzumZ9yUX2r5MX3Vu8f5SnjjteVH9q0wLbNAKyswhw2U2OgzsFqmIMRyK3TpTG+JbTqXNy1sQmAD77yfRLXS0C6xljHQ6uem2vaIGon7jCDtxXrTo6Vqvmpf+3GMjBhd1rIENjMluHE+eo41dfcj7He/U9ivBdPCWR+5mGlTYuh/Z+erEYFN03VnHhRgu3AJbCwdXIfuzv28AOsx6x17GdFnYOXkfd+lijBus97v/poNlHKV6UcL85nvJLqME+gc0OLy5N6+fZX7k+D2MXE8SgtUkRvrcxPTflcS2NgXa4eFVRd5Eju/9bJf7wZNDzO0a1y7PFeGScDsaw09XO0NejmAoQKbmdrrgDmMKOprc5v7PQ4jzPTW8OVfRg6shFr4Lf3Xi6IQA2lMvfZ9p2sLylPtl43s9/v9fl9V1XoOnfuIC+3VYfb36hwOh/a3WZbJ3Y3eb+w6B56J8aIigxPfViTeA1OWpTbQS4w73e/3zSJPpnyNjMqyzP5mHgBzYPIAHJfQjxL63X///YNydN9994VOcg+/Z1BWK+31D1mVVOsWchXHiubQwcpT6jH2fS05bLosS3Hi2o/nF3hPqDvHkuA4Yn79+/XIsQFEewmp/FvtJiBCEV8ZFC9IFVtu93MbPaXH0AR0GrEp/+kD0IfABliXhx56aNCsaA899NCs6VmtKfUGx2qK36rJlNAlyLN/WctU++esp7nY8SS24xA5y1ZnBN5Z6R8xMZ1j/LOk0YVHzXJVVXVdL1AO5SRjane79cTVvWRSiXCAJRHYAOvy8Y9//LnnnnNc+Nlnn/34xz8+a3pWa0ScICvl81WPLFuWe++s6QaJtSTZCc20U3W6+TkSMIJjSmS3Ol8hWaj9TicLT7uroUq0wIiwVnyiBmmmEjjlmrIXv7Is5SWzhsDG8e0IG+uICERjSI8SHCMKyfKeeeaZe+65p/fivffeez/3uc+FTuzSHKcgk8toI3fl56aJAQaNxm4nyTJQWC5jekGnx4nahupNg+mwB5w8wOWHzHFeZnWDvd20vO93iqHFtfdEN10nzmWeaEuW7Sl0uaK9TMPga7pnx1nj5i7/wBw2UGKjzwDmtoFSHp233377iSeesMc2995775/8yZ+89dZboRMbgDwIpoqFWtvTKnD2IMFlynX7WqYVTfvVTqt94wu8EMZ0vZsqc9MDGznF8KDcuRzzcZV++3mfvl+ZXy8zfQ3N47gT3VtNV5MxYlY0+/Fs3OKxXr4CG5e7hOVoAGvmeCdZs+gzgLklzkKndFPefvvtZ555JkmShx9+WDvO4pPPfe5zxxnVNK3KpVrX0b7qrFV0riteyjGuPLeniDVVCts1ofZ1pGbHXl/0qDK/oFObQsAUKLZreI5H0nRk7NozU6ur29NsT4k9GdP36/eEjmhgNBU2y2VlSXbnBNCmPZqSpG5BO/6V+bVUQ/kKbJrWC4K93EaAWSXOQqd0qugzgLltoJTH6/XXX3/yySfVedLuu+++J5544sqVK6GTFpjljStqhaNzXcurKsbd2dUK09Akya96czTqOA3gMsa9nf52smUlzzHl6hEYkdres+n+jN8lGdP3azmYI0zsOWnSPgi95VNboLO/pSVVvUmyHNKhGfdy5BdIMLA8+3UahegzgLltoJRvA7+RGnv9bFCHItWISo9WYTJFCJ1Jkt825rrjYr1Z7LFNZ/rbR3KZwEbduOlsDj1uLsmYvl+/p3VEYNP0XTsuw8+0/IpVtM/lii4p9Big9u7C1wVluV7ogYZIDbqTrFPa8C52WKUphQTrJV6jkSTJ4XAQ9Yw8zx3nodLWzU+NSIN4NaF8jWN7y+5TY4lZbicmaQqRgMT5sMjMytUXSLB8pYm8O00/biLX9rV87TdN00x56WcQ8g0tgxJvz6/8e1CZbydJ3eDyl8AgQ68XYM02UOWLPgOY2wZKOTCrdmCDWZVlKSYsXviAe9zvGgIbANBsoMrHe2wAADEJ9epDX/t1aRoCAIwQfWSGuW0gfAdmRYvNwtr9wSLarywt3FcBrM0Gqny02AAAsBAR1bhMTQYAGOq20AkAAMCVbBNzmZ96hfuN/WkoAKwZLTYAgGjEPsAGADAfWmwAYCo503TohGyfOqHwMewXAOAu+kFCmNsGRpIBAADAbgNVPrqiAQAAAIgegQ0AAACA6BHYAAAAAIgegQ0AAACA6BHYAAAAAIgegQ0AAACA6BHYAAAAAIgegQ0AAACA6N0WOgGIQJqmLovF/lInAACA7XGsyG0AgQ36EbEAAABEyrEit4H4h65oAAAAAKJHYAMAAAAgegQ2AAAAAKLHGBsAkanruq5r8XdZloOWz/M8z/OZErZaLkepdzH1MNq5HOTeJIktLHay1KzNt9M8zw+HQ5ZlZVnGXg63lBcA29EAVhQSrE1VVe53sKIouOMN+lGoqqpzI9qR7JVlmWlTg5JUFMX0TJk2VVVVlmVDEz+Otgvv21/SlvLSqaqqoihE2ciyrCiK7eURaEvi/5WMPgOY2wZKOTbGPbBRlzzmkjw9AGiGBzb2Ku/07YxIjJav3hxlWebh6Bv25XHjC9tSXjp1xrrbyybQlsT/Q0lXNADbVNf1breT/2yYtTxJRMch7UPRC2u/38tPxN+W3mJaxKhuR9vUbrezH/miKLSOTGI7dV0fDgd1O1VVeezyVJalms5Oh8Mhz3PHDnh2+/0+yzKZtd1udzgc6rqOsRPXlvLSZpnu9nA4pGnKnQRYtcCBFVaPQoK1cWmxoa1GJY+DqVuXYD9o6nP63j2qC3c+55bf2nv4WHoSjvi9k9nvDMyyLOt8VO+lD5KWeJEA++lYrS3lRaOWNzVHps+BjXG5va9c9BnA3DZQyrExvYENUY3GvU6mVuC0Cv2gwKa5NfCwfNsbNqhnU6trZn1MIYr2lb2LmpcOSO2NxFs4t5QXjeVKGVr+gRhtoHhHnwHMbQOlHBtjD2yIatrcAxvLwkMrdvbl3QOb5tY4xGXXglYS1Oz0xi1a5OO+U3diF3NseXnbyEtvCR90HQEx2sCFzHtsAGzHuHE1YnhAnudpmqZpKv62jKyoT8lPxIy3cnXHSag7V6kVLolfLY8jLrRD7biWWhI6BxcJnel038toYhcL7GgB28iLHHNlmlVCft47OgtAKAQ2ADZiRFRTlmWapmL0sxyqLv7e7XamYcRlWe52O7Gvuq7TNN3v9+rq+/0+TVNLZCJ32l5F5GK328Ue2PhNv2xCcdysdu6GJmaxgx/7WVZtJi+mCC32yA04BgQ2ALZgXFTT++TVMkWSoO7U8SvLNi1bi46s5pomzx1EtquoU6WZaBXQ9lQB6iP5zjKg1tG9pN+0C5fsrN8G8rLa0BfAIAQ2AKI3rgearNG238aoVnwtj2lFiKIN0rCvq0Y16k5l5TtsLxe1X9aUvmTqZM1ennO7J6aua/UYtqeTbidJa17L81yto89Uhd1Sd6YN5IXABtgGAhsAcdOiGve11L+1um9ZljI+sddgiqLQFrCsq9anm6bRogjHeGw+WoV+dGAjuuqJv7Ms8zLYRt2I/YxoQYsprNKacUTPQ0E9CJ0TQ0+npir2KvKW8pL0NdDN1HwHwBcCGwAR64xqXGrSsgZmGijsWB3vrDeb+k25j06eyX6/z7sMrdCnVmo2F67paq/1tBxPl0jS7ytBVWoTR+zBwJbyAiB2t4VOALCo9Pd+L3QSjk7z1FPzbVxGNaIKK+pYLu9BL8tyehep0UGRZXTy3L16XAZCeKnQF0Wx/GBrLcq1JMCloW+3280R22ip8h4MjNjglNa5ibseZL6s0RUN2AYCGwDRk3VoGRXsdruJPbu8V8oda0JZlgUchJ1lma8a236/X3jSam1fluaazqhGLK+1+cwR26iDu5IZxtxro4xcjI5C586LZsmsAYgRgQ369U4MJQQfIYDjpFZcqqqSFVb7u2g0YklREXevnA2q7/qdJWyczpH0wqC8WC52cQxl09mgs+DIlFTH0TVJq2FHq/tqYc/0IFllH9wVly3lZaijyiw2wLEitwGMsUE/x7e9hk4mjpFWJc3zXD6nFx3S7KvXp2/JFK+OUV9HMx97lWjuClPnGBuPOxUvG1XPgpfNukRHjqNrtICn/e7OPM+14NPjI391WoXk9HT7jf3Kshz6ru5xGVwgL5r5skaggm1zvF5CJ9MDAhsAEWvXWsqylLVS+yAK7S2ZQpZlRVFUVTXTdFhHQj0vXqKC3upyOzhx3FRn8mYaOqJuR+xigWBgJlvKi8oeikf9rh7gGNAVDcdl1oHsWIm6rmWzu6krlFrxFc/starwfPUz+5ZjrxdKfgcL9U4opw29mPgAXlvdV0ZkvCRnwY43GNhSXpIkyfN80OgdWniAdSKwAbBBRVHIYR6dE6DJquryY4t56DuUWld2qVBOH8U0U+3c9NJSv0VimVnRlsmLZr6sDT0CBDbASg3tropjQyHB2qidxCyLqbXbqqqGbkEuk2WZacvaZlWd23dJtlymKArLYoN42abaVOKyvDxKnTs1nZpOvb9ZWr9B+za1sKd9fptbM2taZii5X21r4vN2EW2nxzEZI96GNLRgTMlLmzh9LiVh1qz1ruJ45wHitYGyzRgbANukPtw1DbaxPNqftRnH8h6b+XYaysTWD3UyH9PAp0G7aLcwaKu35xT28nje1MQhNq59qJZY2UtqPW19o/PSSc5J6Cl1I8moyZQSma+5X6QLYDQCGwCbpdaDO2MGU02xLMs5KpGyPiTe8dJewN7LX85gFrwK6GJ6MFCWpRrVWOaq1g7I0HnndrudmMxNDLVqV8S1mfeEQVGotoV2etT4SsuOKIqiQcNxX0OfcS6Wl4lmzZpcsnNCRXUi+E0+gAA2Yug9AseGQoK1GdQhxNQhTX5o6Wlm2svormjNrRVTtcdLuy1C6w+jLjC049DoFVVDu6LZl+/9bbIfDfvWetM2aMo77SzLzwf1TzNtrb1NWbq0b933NbcpeekkiorHvpejqYVWzd2Uqw+IyKpuNePQYgNgy0wd0tQXraS3Es9l1SpOmqa+xgo3Ss1vv9/LnYq0icmmvewoOPWITXzCXVWV32fk6vuO7CzNRO4sTRxyL+KPw+HQLn6r4iUvdV2XZbnChkc1d7vdTrs8k663HgFYFQIbABunPm2VVTH1JZKdq6jvw0m8Dm9oDO0VWZatsKo3mlrrHTSRrpRlmXhqbg8ttIPmOCVaWZa97Ta+Aqreuaq1wiZrz3mey854ooY9PTETjc6LIKZiFy/DFZHD2qYXa7rabIWNXaHAJqWmn1hASFMKCbZMPDk+HA6iNqO90EY+Vx46pqKXeGit7ki+DESOqWjX+eq63u12y09RvWH1qSRJRDGQo2gsa6Vp6r2aK5Kh7lp8ImIJEUjEct7beRFkbCayo0a8qyrVslTIIrGetAHz2UCVL/oMYG4bKOVARGTN6QgnFgAAIABJREFUrzOwKctyv993foUlzRHYWPaVmBv6IiJKb3JrXmQkv6rABjhOG6jy0RUNABYiu+y7LNwZutATZg1kI17gdMRGRDVaD0CKNACPCGwAYCGy776pTiw/7+zlLyehpkodkOgNmMTTK2xt2qXXcVgUAPQisAGAhagvymhX72SfnKTrMbYYayGmGJ41kbATUc2gCaORnBbpzhiGQB2AL7eFTgAAHAsxy7B8i7ypT1rnfFN5nhPSrAFnAQBWixYbAFhO7yzD3t/ZAqyBaJbpnDadYTYAfKHFBgAWJdpe5BTDYlZc8Tl9crB5ssBLHl8SBeDIEdgAQACEMTg2WZYdDgf5biiB9kkAHhHYoJ/j7LR0PQcAmNR1naapGF1WFIV46yXNNcACHCtyG8AYG/Rr3IROJgBg1eQAs/1+v9vtDodDlmVMMQfM7XgqctG/YRRz28BraAEA6yEHmNEhE1iVDVT5os8A5raBUg4AAAC7DVT56IoGAAAAIHoENgAAAACiR2ADAAAAIHoENgAAAACiR2CztLIs8zxP01TMBqO+pyzsZsUbBpigBgAAADGKfvaDiJRlud/v259nWTYlvDFtNhn4xsw8z8WL0rS1NjBFBgAAAOw2UOWLPgOxkGGDybgTYYlqhKqqHBth5FtpCWwAAACOzQaqfHRFW0JZljKqybJMvuG1KAq5zIg+YHVdy6hGvLxZbFZ9i/Nut3PZlIxqAAAAgBhFH5lFQYYNRVGUZal+Vde1jD3cW1e0zXZ2ZrPsVN27Gh0JtNhg5eRry5MkMZVt0/LH+aZzl6PUu5h6GO1cDnJvksQWFjtZatbm26lous+yTIyKnGkvwR1JNsPSriDHazxpXcjuK7okZtANdtxamNUWqnwNZqY2y3QukGWZ+FZtzOmlNsuM2K/caW+RoJBgbXoLv0q9EI62MA/6UZBtvxrtSPZSm5GnJKkoiumZMm2qqqrOm6E98eNou/C+/ZXYXjaHlvzEfJ8pikKUtyzLsiwzlW0Xjnts0wr86ASoeus5beptfAOFZDN8FYmAos/A+vXGLYNqaZLLfcSygPsdeQOlHBvjfsmoSx5zSbZc750661sjqneJucoyfTsjEqPlqzdHgx422bX35XHj67HJbHoJbLR7kWpctX70RnqTOsKIKGVELIQFbOB03Db0csVQcnSNqcFXbYGt69qxQVY2Jbvcc9ub1W6y7Q5pQOzUfp7J2Pk5Nkb0DtI+FDcT9Q4g/rb0Uemspcmbkrqp3W5nP/JFUWh3J7Gduq7VCVd2u93Qzrp2vTOvJElyOBx8Tcq/3+9ln2FRMg+Hg/sNPxZHks2htHuRxkvZLsvSpaB66XjWpibeMSXy6hv3xAQwCh1ZbZ/LoZbLuDdMy1UsT0fcO7lZHoFTSLA2Li02tNWoHO8w9oM26AmrunDn/cflDtZYexL2/bh1kNnvDMxE76D25176yWiJFwmY0hNpnbaazcqBLKvtAt9ZnCbeo9oF1WWtdgkful+TQfcH+qGtlsciEUr0GVg5xz4z8l4zIrCxLGO5z7qncwOlHBvTe1kR1Wjkoei9w6i1E63CMbTriP0UuFdr1LOppl8MV7DT6nByX9pX9i5qXjpTmSq707e8KkeSzU6m0q4WNstaQ8M/tXw6XkpyLXuSRnO/qOmHtlobOCPRZ2DlHK/eofMHOMZL7vcOAhtExF7+iWraBlWeTAsPrYvYl3evAzV9VUMTrSSo2VE/77zrzjHAunMXc2x5VY4qm53l2V7Uxw2ybW69Ti0lWSWXnCmusF9TnUtuoDVvYzZwtfIem1WYqf/xkXdrxhEaN65GjAHI8zxN0zRNxd+WbuL1KfmJmNZWru44CXXnKrXCJfGr5fH+M252WrUkdA4uEjrTOdNQhPYuFthRWMeQTTkerD1gTM14Z0nTBtmO2Lvcgv0l4Or2HU+Heo9yubPJeMmekmWmVsfRIrBZiGV65fVLvQqdG2zWiKimLMs0TcUQZ/ljLP7e7Xam4lqW5W63E/uq6zpN0/1+r66+3+/TNLVUU+RO26uIXOx2u9gDG7/pl7dQx81q525oYhY7+LGfZUfbzqa87Zhm5kisdYChZbtNRhT2LYgbjkttRNzW1HtU4nBnU7NvfzAk/yawmY4amobABv38thKGzg22aVxU0zsvVu+N3j7Z0dBtWrYWHZcqnTv3x9JJq37ZnipA7YrTWQbUutdMj6XELlyyE7XNZ1OWzM4ZKXqnRVW3MDqwkVuw7EV9G6Z9a/Y53JIksTx2kReLJSXMh+YXNTQNgQ2A6I3rgSZ/X9tvY1R/cS2/0CJE0TqU29dVoxp1p7JWFHbidbXSM+VhqjpZs5duSO6J0Savb/cOaidJewid57laEZ+pteFIZtjfdjZlOc+ybPT1MihoH70F935o6r208x6VmB/ByI2PeAABeEFgsxD3Lqcebbv1HxB6ny+a1lL/bveMd+zdURSFtoBlXfWHvGkaLYoI/rRMq9CPrqiJrnri7ykVPpX7UASttmSqPGnP10XPQ0E9CJb3Kk7h2GkndpvPpsxg8Dq6vOeYUuLYD0290Oz3qN4hQ50pCX6gsHkENvMa+ovuuPzQzUY9wgew6IxqXC4QWc0ydYdwvMo6f6dND1B7+2DM3Tdjv9/nXYZW6O29tNVsLlydrW99rafleLpEkn5fCapS2zE2WeMXtp3NQc01cw8mUWcfsXzbmwx5+ZjuAPJz0+Na+5Ah+qFhbreFTsDGLTAwrv2wGRZp+rT4o2nO8cmSn8xERjXiZ1L8arq87Lwsy+nPDkcHRaZduwz7mcili4iXCn1RFMs/ndWiXEsCXBr6vLwSvk1Llfca/4gNzvEjMnc2NcvnWpafiVnze/A7L3DHfmjyW0uolud5lmViL523Wdlga7/V0HSDmRDYLMdS05LX/4gbnGWz8l5G5INtk3VoGRXsdruJPbu8/+461n5kpSGILMt8VUD3+72Y5M3L1lxo+7I8Eu6MasTyWpvPHLGNOrgrmWFgvTbKyMUcUejc2dQsnGu/c2N4URSFOALtpzaO/dCG1hk6qx9abzQ1JWrg5LJ9YAQCm9nZn20kY6c+lLcwS73B7+BdYJ3U2klVVbLCmlvfRaMRS4qKuHsNbFB9dw01oc6R9MKgvFiCRnEMZdPZoLPgyJRUx9E1SathR6vgamHP9CBZZR/ctXKWFkXtEEWdTRe+mmu8bEGQZ8c0tG/QDByDOvRqZP1kv9+rV5YsPNsrD1gPApvZyfG42hUujXuGkee5rDp0LkAwg2OgVUnzPJe/qS4d0uq6Lsty4RYSe5K0EfwL793L9sUu7DeooVwqf46ja7R7Y/vdnWpnG7mKrzuqOq1Ccnq6/Vb9PaZ2tAWyqVky1yNCkSWjO+2ic+yHpq448bKV9ROT4OUTG8bkAbPrbIeV6r5JUeXoXu1Oqt4iO2+XDNHDMWhfNWVZymcE9kEU7TfQJUmSZVlRFFVVzTQd1pGw3/dG6K1KtoMTx025zN3k65m6uh2xC5HOWAbWi5CvTfuViT2bvWTxWNvPa+fcaO7v5RzKpflXpoR+aFiI3zf7oJN67yuKQn6u1py0V2G0F1BXtH+rVchcUqiuon1FIcHaWIqrZL+ymqZRf1zb77FR99Legly3vVY7AfITeR8wJUnbePuSH81yG3Gn3sdclrdnRG7Kcgw7lzdlIbmVS8LsCXDfoDu5a7UM9BaJ6Gw+m+6lwqWcO94ZHLesbUpuXLtwOrMw9Kq0aN8uPG4c8/F1rwuIrmhLKMtStszs9/veF107UnvdmDbbhH4zBhCE2iGts4+KbKhZfvKuDb+FfSZDByJOfyQ8U9uCqX3eb5EIPivaMtnULJbrQS0Psk+jqAZ0LjPHND/yULv3Q0schgS7U4djaVtjgA3mFTqyOiKmNmvLQxp7i419s4NOLi02iIhLi01z64N57Rmhyxa8t9iYPjStSItNe2HTrrVmavs2tfpo5x1Yu696aWrobMeQn7eLaDs9jskY0T/KY2Gbks02cWZdCsliuZaru6RqUPPyoKYM01paE03nuWgM9yLHtqOiKEQXRHsK1TvAuFYpLM9SUGPBGJvllGXZnN4REqUrv+U5U57n8lSZHre0Nyv71binTd3RoEwBq6VeWabBNpZnrrM241gu5/l2GsrE1o80TeXfpoFPg3bRbkbQVm9PHOzlGbOpHUNsXPtQLbG9U8WsyuhsdpLTFXpKnU8upUJdpjMX46ZFtZBHWJ2vyHHLcvn2RaHa7/eHw6G3NMqdipnftV0Ac1k6kkJsKCRYG8cWm8ZtHFrniurTX48tNupmO9dVb87tx8lyrPbQTuqWbbob2mJjf0ZrPw7tPdoTrwWovWlr/xSKJ03yCZFG25ccMd+7o868aF/J0igPhfhES20UIxOmZNOyQY8NShO533wkUxOWMPratFxB7TJsWd2yrn2/Lm0v9ksJK7SBcxR9BjC3DZRybMyguoWpQ5rl57m3WjA6sGnMoUu7LUKr6Lh0TO1NTJDApnP5do3Hzp5y+/lqGzTlXefg7MTTUO/2NmXp0r5131dAU7LZaW2BjT1KMTGVmd4owmWb7UPt0pHSsl9T4R8xL5FLt0+sSiy3Ggu6ogHYMlOHNPnbfzgc0luJLhZq5SBNU18DXhul3rDf7+VORdqy1sy58eqc8nWcqqr89mARM6+4LGl5pak77VVLnXsRf8gePjEWAy/ZFK+WWmffs2Tsa69lSKDdbeTW/E4u7z71eSc1MbvdTrtHtZdxTwn90LCE0JEV1o5CgrUZ2hvENK+6pe4onlOaOjhNabHRvlWJtJmmZ42uxaa5NZuWr0wcO91pdSz3p8K9lbPeZgfHHbkcf7WwyS279Claj9HZFNqnQ8b562mxGX0iLIVtXCdD++ruPcqGpnZogmMpvRA2cJrShsHisEpTCgm2TM7GLqoCZVlqTQ3i4XGe534fN4on0+qOxH7FO9qTJKmqqv2ota7r3W63/BTVG1afSpJEFAP5WmTLWmmaZlnmt2FBJEPdtfhEzBwgqvgbOO/tbApylgiRU3X+hs0UeK0xSrvbrI28R7lfF4jdBqp80WcAc9tAKQciIqt3nYGNeDtE51dY0hyBjWVfydZfSiZfe6JmUwb5mwlsgJXbQJWPF3SinzrdqkXsFwMwN3kpuVwsnaHLasceHBXZiBc4HRsiohqtB1Rd146/PgDsjudSYvIA9HPs1xg6mcDayY7vpjqx/Lxz0uGyLMUDbKrUAYnegMkmeoWtTbtgW940BcDd8VTkCGwAYCHq++/adTjZ8SbpapkRYy3EeOtZEwk7EdX4ncbqyInS3hnDEMMDGISuaACwEDHLsHyLvKlvQOd0bXmeE9KsAWcBAFaLFhsAWE5Zlr1TqdLHCUdFNMvI5koVg8oADEKLDQAsSrS9yCmGxdS34nM63uCYyWtB6ox2AMCEwAYAAiCMAaQsyw6HQ/s1L8ESBCBOBDYAACAkMbOzGHhWFIV4Hy7NNVjS008/Lf8+d+5cwJRgCsbYAACAwOTYs/1+v9vtxNvumX0OwCC02AAAsF5HMg+bNvZM9tU8kuwD8ILABgAArAJjzwBMQVc0AAAAANEjsAEAAAAQPQIbAAAAANEjsAEAAAAQPQIbAAAAANEjsAEAAAAQPQIbAAAAANEjsAEAAAAQPQIbAAAAANG7LXQCEIE0TV0Wa5pm7pQAAABgEMeK3AYQ2KAfEQsAAECkHCtyG4h/6IoGAAAAIHoENgAAAACiR2ADAAAAIHoENgAAAACiR2ADAAAAIHrMigZ0e/rpp+Xf586dC5gSAAAA9KLFBgAAAED0CGwAAAAARI/ABgAAAED0CGwAAAAARI/ABgAAAED0CGwAAAAARI/ABgAAAED0CGwAAAAARI8XdAIAAPBeZiB6tNgAAAAAiB6BDQAAAIDoEdgAAAAAiB5jbNAvTVOXxZqmmTslAAAAGMSxIrcBBDboR8QCAAAQKceK3AbiH7qiAQAAAIgegQ0AAACA6BHYAAAAAIgegQ0AAACA6BHYAAAAAIges6IBAPBPnn76afn3uXPnAqYEwJFQpyNjKtopaLEBAAAAED0CGwAAAADRoysaAOjojAQAQHRosQEAAAAQPQIbAAAAANEjsAEAAAAQPcbYYL2Y/RAAAACOCGwAAAAQGE8zMR1d0QAAAABEj8AGAAAAQPQIbAAAAABEj8AGAAAAQPSYPAD91PF8Fgz1AwAAWBvHitwGENigHxELAABzY1owzMSxOG0g/qErGgAAAIDo0WIDrBSP7gAAANzRYgMAAAAgerTYAACAVaClGsAUBDYAAKwFNXsAGI2uaAAAAACiR2BzdMqyzPM8TdM8z/M8r+s6dIoAXaoInRYsjbMPABiHrmhHpCzL/X4v/3k4HJIk2e12WZYR3gDrQWckAABGILA5Fnmei0im7XA4pGlK/QkAAADxoivaUSjLUkY1WZY1p4qikMvkeR4mcetGvHfMOPvHjLN/tDj1x4yzHzsCm6Mge6AVRaH2OivLsqoq8ffhcFhPh7SrV6/+6Z/+qfrJAw888NnPfvbq1asL7P369euvvvqqiP2SJGma5stf/vKrr756/fr1BfaehM5+WMHzHvbsB89+WMGzz9kPiNs+Z1/95HjO/pGfev8abJ3aLNO5QJZl4lu1MUdauJC8/fbbzz77bJIkn/rUp7SyKj555pln3n777Zn2fuPGjcuXL58/f/7ixYtXrlw5OTl56623Tk5Orly5cvHixfPnz1++fPnGjRsz7b0Jnf2wguc97NkPnv2wgmefsx8Qt33O/nGe/RWe+iT+uCD6DKCXPW5pmkY22nQW6CVL+dtvv/3kk09+9KMfTczuueeeJ554Yo5L/caNG5cuXbpw4cK1a9du3rypfXvz5s1r165duHDh0qVLM93mwmY/rOB5D3v2g2c/rODZ5+wHxG2fs3+cZ3+dpz4hsMH6ySukqqoRyyxZyp999ln7RS4v9Weeecb73i9fvnzhwoUf/OAHlmV++MMfXrhw4fLly9733oTOfljB8x727AfPfljBs8/ZD4jbPmf/OM/+Ok99QmCD9ZOXh8syRVG0v5oxcYrXX3+99wpXvf766x73fnJycv78+WvXrvUuee3atfPnz5+cnHjcexM6+2EFz3vYsx88+2EFzz5nPyBu+5z94zz7qz31SfyBDZMHbJzjfACyu1pAX/rSl9rdTE0efvjhL37xix73/tprr334wx++8847e5e88847P/KRj7z22mse956Ezn5YwfMe9uwHz35YwbPP2Q+I2z5n33HhjZ39Iz/18wodWWFevTMHCJZxOIsVkgceeGBQ0b3//vs97v3LX/7ylStXHBe+cuXK888/73HvTejshxU872HPfvDshxU8+5z9gLjtc/aP8+yv9tQn8ccFtNggSdbxEpuvfe1rg5Z/+eWXPe79u9/97i/90i85Lvz+97//H/7hHzzuPQmd/bCC5z3s2Q+e/bCCZ5+zHxC3/UHLc/Y97p0Lf6sIbI7FlM5m6SICJuw973lP0zR33nmn4/K//Mu/3DTNe97zHl8JSINmP7iweQ9+9o/51Kehs8/ZDytg9oOf+pSzf8Rnf7WnfkTC1ua20AlABJpFXsQ74orymLCnnnrqjTfeuP32210W/tGPfvT5z3/+H//xH33tPQmdfZP0935v7l0kSZI8/fTgVc6d87Xzt5IkSdNf/Lf/9h///b93Wd772V/nqV9M8OyHvfaDZz8sbvtDV+Hs+9o7F36nDcQ2BDZYi/vvv39QY+t9993nce933XXX97//fcd73Pe+97277rrL496T0NkP7K67kkGt/A7DPQf59o9//K9+8Rcdo7h73/vej915p8+Qb3j2Fwo4Z9A89ZT2SfCSH/baD579sLjtc/bdl9/S2T/yUz8rAptjcTgcLN86Tp42q4ceeujs2bPPPfecy8IPP/zwhz/8YY97P3PmzNWrVz/wgQ+86109/TN/9rOffec73zlz5ozHvSehsx/YmTPJ7bcnf/u3Tgt/+MPJe9/rd///78nJr7/vff/3D3+Y9D6sappff9/7vnFy4nP3obO/pI6Q7J13kg9/2D37r7zzjt+47n+58867/ut/fe7qVZez/79/8INvvPXW//of/+O4fbXjuqO+8ENnn9t+WMd89o/81M8q3VKzJtrqut7tduJvy7nO81xEPkVRlGWpfpWmCxWSq1evfuhDH3Jf/sqVK4OWt7t+/foXvvCF3/7t3/6VX/kV+5Lf/e53v/KVr3ziE59wfNLjKGz2TRZqGfjRj5IvfGHA8h//eOL14N/x7nf/yT33/J/f+tZr//2/25c8+8//+f/xkY88+o1vfP+nP/W2+9DZDyx09jn7y2gHdUno+x63/bCO+eyv9tQvVuWbDy02G7eG6c4cffCDH3zmmWf+/M///Bvf+IZ9yXvvvfcP/uAP/F7kt99+e57nL7/88sc+9rH3mp+In5ycvPLKK1mW+f15S0JnP7B/+S+TLEu+8Y3kzTd7lrzjjuSee7xX7N786U+fu3r1E2fOnH/tte+9845psff//M9/4syZ/3D1qs96bRI++4GFzj5nfxnGpyRDsv9rn/2s31T9T3fc8fdf+lLvqf/9s2dfeuON3/93/0580hmkjXDUt/3Q2Q/7o3/kp35WBDZHpK5rU5wjO6qFDYQ++clPfve7302SxHKp33vvvb/zO7/zyU9+0vve77777p/85CcvvPDC/ffff+edd2rN0z/72c/eeOONV1555dd+7dfuvvtu73tPQme/k6/f717vvPPOn/3Zn33hC1/ozfsf/uEf/sIv/IKv/crK1tfffPO973737589+4X/9t9e+/GP9V5JTXP2X/yLT5w581/efPPrvTWwEe6+O/nJT5IksVXv7rgjOXs2mafsBRY6+5z9kIJmf9yp99mUffNm8s/+WXLHHfbs/z+/8Av/5pVX/s1//s8j9rDYbXyEY/7RX+Ev/jZE3+SEXpZuZoK9u9rC7ZLvvPPO5z//+d/93d99+OGHn332WfUr8cnnPve5T37ykx6rtqqbN29++9vfruv6Ix/5yAc+8IH3v//9P//zP//OO+9873vf+853vvOtb30ry7K77777ttvmeiIQNvthBc972LMfPPvzcaoF3ryZfPvbyeHQMd5GfJJlyd13Jz/3czMl8l1J8j/eccenPvjB//T97/9fP/zh//eTn/zjzZvv+bmf+1e/+Iu//r73/c+/9Ev/4erVr7/55s2Zdh86+4EFzX7gU5/Mnv01BzZJ6Fsft33NBrqiRZ8B9CrLcr/fi787T7eMfLIsa88iEKSUX7169Ytf/OIf/dEfyU/uu+++hx566OMf//gCDbLXr19/7bXXvv71r8tP7rrrrjNnzpw9e9Z7D7ROYbMfVvC8hz37wbMfVvDsz3H2Bzzd/9GPktdeS5S9J3femZw5k5w9G28PtAGCZv+Od7/7f7jjjv/tV39VfvLtH//4Gycn/+XNNz13PjSZLfsrD2yEY/7RD37fUxHYIA5yYvJ2o43aXFNVVbsrWsBSrs6nvnwann76abHfNE3P+Xtriruw2Q8reN7Dnv3g2Q8rePY5+76M6bKlvtVq8YP/1L/+18npqf+9v/mbhfeeJLNkP4rARjjmH/2VXPgbCGwYY3MUiqIQjTbi/zK2UaOaLMsimmlgMRt4WRVG4+wfM87+dCOq1KlSs1++Ri6qtuLUa3uP9+VRGIQLP3YENkehLMu6rkV/s/1+L3umqdbwKhsASfzP6YHtWSbKChvXARvQ81oibEZd10VRdH6VZRkVKaxKowidFiyNsw8AGIfA5oiUZdk0TVEUWZYlSZJlWVEUVVXRVgMAAIDY0RXt6HTO+AwAWAPaqQBgNAIbAACwCsR1AKagKxoAAACA6BHYAAAAAIgeXdGAlaJLBgAAgDsCGwAAgPB4ngVMRGCDfo4v4uWODAAAsDaOFbkNILBBPyIWAACASDlW5DYQ/zB5AAAAAIDoEdgAAAAAiB6BDQAAAIDoMcYGAAAAgTGgF9PRYgMAAAAgerTYYL14eAMAAABHtNgAAAAAiB6BDQAAAIDo0RUNAHTnzp0LnQQAADAMLTYAAAAAokdgAwAAACB6dEUDAOCf0BERwMKYBtYXWmwAAAAARI/ABgAAAED0CGwAAAAARI/ABgAAAED0CGwAAAAARI/ABgAAAED0mO4Z/dI0dVmMyQoBAADWxrEitwEENuhHxAIAABApx4rcBuIfuqIBAAAAiB6BDQAAAIDo0RUNAAAgOXfuXOgkAJiEFhsAAAAA0SOwAQAAABA9AhsAAAAA0SOwAQAAABA9AhsAAAAA0SOwAQAAABA9AhsAAAAA0SOwAQAAABA9AhsAAAAA0bstdAKAleIV1AAAABGhxQYAAABA9AhsAAAAAESPwAYAAABA9AhsAAAAAESPyQPQL01Tl8Wappk7JQAAABjEsSK3AQQ26EfEAgAAECnHitwG4h+6ogEAAACIHoENAAAAgOgR2AAAAACIHoENAAAAgOgR2AAAAACIHoENAAAAgOgR2AAAAACIHoENAAAAgOgR2AAAAACIHoENAAAAgOgR2AAAAACIHoENAAAAgOilTdOETgNWLU0pJAAAABu3gSofLTYAAAAAokdgAwAAACB6BDYAAAAAokdgAwAAACB6BDYAAAAAondb6AQgAmmauiwW+0waAAAA2+NYkdsAAhv0I2IBAACIlGNFbgPxD13RAAAAAESPwAYAAABA9AhsAAAAAESPwAYAAABA9AhsAAAAAESPwAYAAABA9AhsllaWZZ7naZrmeZ7neV3XK9lsXddidS/pAQAAAJaU8oqSxZRlud/v259nWTYlvDFtNhn4/pk8zw+HQ3utNKWQAAAAbNwGqnzRZyAWMmwwGXciLFGNUFWVYyOMfCsTgQ0AAMCx2UCVj65oSyjLUkY1WZY1p4pwK307AAAaIklEQVSikMuM6ANW17WMarIsq6pKbLaqKrnMbrdz2dQG3jULAACAYxZ9ZBYFGTYURVGWpfpVXdcy9nBvXdE229mZzbJTde9qdCTQYgMAAHBsNlDliz4D66f2Fus82rKX2qDBNmpE1LlZ+34tXeMIbAAAAI7NBqp8dEWbnYxVsizrXEA2p9gH4Zg2a2JqpRmxLwAAAGDlbgudgO2TIYQp0lC7n9V17dgbTQY26kAdy8LaZtVxOMmtw3UAAACA6ETf5LR+ptnGOpexjIcxrWIZmePeyc3SsW0D7ZIAAACw20CVj65o83IcM2PqpebC0sLD2zYBAABwJAhs5jX0zZuOyw/dLCNqAAAAsG0ENqswU9MKLTYAAAA4EkwesJApnc2C8/v6zti7bwIAsFpp+rT2SdOcC5ISLIAXrGsIbNCPUAQAgFVpBzAJMczx8VtD20CYRGDTb+iAFomeYAAAYLTO6CUhgAEMCGz6lWU5YvC9NsOyfQujYye7mTYLAAA8ovkF8ILApt+4hhexVp7ng1586bivoUmKeoQPAAAbQPMLMDcCm36Ob8zstEBvtLqu6fMGAMBKhG1+IUzCMSOwWY4lApEd1UaEKJbNyq5oRD4AAHgUvPmFAAZoI7CZXZZlIm4xRSDqSBj3CKQoCtHJzTKQRsZLUxqdAAA4WjS/ABEhsJldnuciwNjv950Bhvxw0EgYOXrHNC0BwQwAAC5ofgG2gcBmdmVZyvkDyrLU4o26ru3tKrINpyxLtT1H+7vdbiN3WhTFyKQDALAtAd9fSfQCzO1doRNwFGRooTXa1HW92+3E31mWtfuhibBHaIcuVVWJPw6Hg7ZZ9RVLNN0AAJCYW2b8appz7f8W2C+AlJfKL0N2SDPpPBFq5FMURTtEUZuD3Ddr35G2SppSSAAA0bD0KzMNmBmxypQUAuu0gSofXdEWUte1KQjRXuU5iAh1TLFN7KUTAACT/7+9O0xSFdnWAAodZ15Qk3nTEEcmNTLfD+7JphEQFYSdrBU3bpy2lMptFshnJsl3IocMA4GET2bhNE3TTTDr5p511t1s8XBBzicyiO8ABGX4Bb4mg1O+8AWwtQz+ygE4vtHL+jcNNiIN9GVwymcqGgCws69d1v+F3wLsRbABAL7hO7eLkV7gtAQbAGBNLusHdiHYAAAvM/wCHI1gAwDMGb2sf9PfKM8AbxBseK4syyVPi76SBgCPXNYP0S08kcuAYMNzEgtA3swrg4wtPJHLIP8INgBwIl+YVybAALsQbADgLFacVya9AEcj2ABAVjadVybPAIcl2ABAVOaVASSCDQCEZF4ZQJ9gAwCHNhpgzCsDGBBsAOAotp5aJskAGRNsAOAQ1ppaJr0A5yTYAMBXbTS1TJ4BTk6wAYDvccU/wEYEGwBY34rDMgIMwBKCDQAckTwD8BLBBgDeNDWvzMgMwPcJNgDw3EYLMQszAGsRbADgCVf8AxyfYAMARWFeGUBwgg0Ap7PRvDIAdiTYAHAu5pUBZEmw4bmyLJc87X6/b90SgOVWvJPMFhsB+I6FJ3IZEGx4TmIBwllxWKaQZIDIFp7IZZB/BBsAAltxWEZ6AQhNsAHg1OQZgDwINgAEYGQGgHmCDQBH9/kFM8IMQPYEGwCOwrAMAG8TbAAITIABoCPYAPBt291hBoDTEmwA+CoXzACwBcEGgE24YAaAbxJsADgKAQaAtwk2ALxval6ZiALAlwk2ALzp86tlChEIgJUINgAsskqMKSQZALYh2ADw3BupRoAB4Jv+2bsBAAAAnzJiA8C/Plmj2RANADsSbAD4n1fnm0kyAByHYANwOp/fOlOkAeBoBBueK8tyydPu9/vWLQH2IskABLXwRC4Dgg3PSSwQ1OcjMwBEt/BELoP8I9gA5Onz286IQAAEItgAxLbisIwkA0Bcgg3A6QgwAORHsAEIYGpemYgCAB3BBuDoPr9aphCBAMidYANwLKvEmEKSAeBkBBuAA3kj1QgwAFAUxT97NwAAAOBTRmwAvu3DlQAM0QDAI8EGYEOPGeaNWCLJAMBTgg3AVj5fBkCkAYCFBBuAj6x7hxlJBgDeI9gALLXKvDIAYAuCDcAia91ephCHAGADgg3Av9adV7bFRgCAUYINcFJfmFcmyQDA1wg2wBmZVwYAmRFsgGyZVwYA5yHY8FxZlkuedr/ft24JjBoNMOaVAUCx+EQuA4INz0ksHMGmwy8bbQ0AdrfwRC6D/CPYAMfyneGXL28fANiaYAPs4DvDLzMkGQDIjGADbGiX4Zfv/yIAYHeCDfCC0Xu/GH4BAHYn2AAjXgow3yG9AAAzBBvgWAQYAOAN/+zdgMw1TVPXdVmWdV3Xdd227b6b7V64RZPgJff7/43+b+92AQBRlW5RspGmaa7X6+PjVVV9kiWmNls8W6R85oVFUVwul6ZpRn9Ulv5Izmj5VLQjXGMDAHwog1O+8AUcU13Xv7+/M094722fDydFUdxut7qu33hhMZ24Mvgr5w1Lgo3cAgDZyOCUz1S09TVNk1JNVVX3vy6XS3rOaPyY17ZtCidVVd1ut26zt9stPefn52f+hUVRpBcOXvv7+zs1aMMJjU4SM20MADis8MnsgMqy7P7xOL+rbduUPaZGV55udnRoZeaXph8VEyNF80/IIL4DADAvg1M+IzYr64eKxwGQuq6rqpr66Yx+khmdMJaGg2amnPXHZ/r6f8TWEgAAICLBZmUpGKQAM5DyzPxFOFObnTIVk/ovXDJAJNgAABCRYLOyFFemkkY/XSxPEemZ/Qt1nj65WBC0lvwUAAAOTrDZyrrDIykvzWx2fpLbG8sVAABAFILNmhYGlU+GR2byydRCz90CaPOX9CwJTgAAcFiCzZpevUBl4fNf3exLV+8U/w0zgg0AABEJNjvYKDy8t9n+vUSnlk0DAICD+7N3A/IU4lr8/k11iqK4XC5T0ah/o5vPRV8iHQDgCNY9Q8uAYHNS/YGaYuy2nn2iCADA0ax7hpZBTBJsht6+kUuUq1OaphncxPN2u0VpPAAAjBJshpqmefXi+6IoqqrqJ6L5LWx0E8ynmx3MPSueDdQAAEAUgs3Q25fgd/8/GAxZ5Xe92qTRK3wGAzUiDQAAORFshj453f/ChK62bd/4Lf1UMxhcAgCADFjueSsz4eGTu2HObDb9aLDZtm1TqrlcLlINAAD5EWxWlqaBTeWH/uPLg83lcpnfbNHLS4NBp3RdjelnAADkSrBZWcoqUxfbpGjx0r1u0manliWYSiz9x6UaAAByJdisbD5ItG07Na7Sqf8ajMz0x3ZGx3n6k80Gv3H0cQAAyIlgs74UIa7Xaz+99FdbrqrqMZ90safzOOXsdrt1//j9/R1stn9DpUFeSjnqer2WC7gCBwCAiASb9TVNk6aZ9eNE/x4yb+SHuq77kWl0s+vegBYAAKIQbDbRtu3U1K+qqt6OH03TzMwok2oAADit0tnwppqm6SaYdXPPOututvvP7W6hU5b+SAAAMpfBKV/4AthaBn/lAADMy+CUz1Q0AAAgPMEGAAAIT7ABAADCE2wAAIDwBBsAACC8P3s3gADKslzytOgraQAA5GfhiVwGBBuek1gAAIJaeCKXQf4xFQ0AAAhPsAEAAMITbAAAgPAEGwAAIDzBBgAACE+wAQAAwhNsAACA8AQbAAAgPMEGAAAIT7ABAADCE2xgTlmWezdhT8rfuwl7Uv7eTdjNmWsvlK98IhNsAACA8AQbAAAgPMEGAAAIT7ABAADCE2wAAIDwBBsAACA8wQYAAAjvz94NIICFy7rf7/etWwIAwEvOc38eIzY8d18mPf+N/eewL3nDYWtR/qsvecNha1H+qy95wxcadubav/aSNxy2FuW/+pI3HLaW/ktePZGLS7ABAADCE2wAAIDwBBsAACA8wQYAAAhPsAEAAMITbAAAgPAEGwAAIDzBBgAACK/M43Y8bOc8d6sFADiz6LlAsAEAAMIzFQ0AAAhPsAEAAMITbAAAgPAEGwAAIDzBBgAACE+wAQAAwhNsAACA8AQbJjVNU9d1WZZ1Xdd13bbt3i361LoVlQvUdb1O0/fWlbN3K9axSi0Z937btmlP6apommbvRn1k9Ypy7f3uXcrpsL96Rbl2/aOuluj7/sCHReXX+23bLikq2J/BHR5cLpfRv5aqqvZu2pu2qOg8u9jtdlPLQJa9n96cbHb/jSrKr/enjpCd2+22dwNftlFF+XX9qKqqukIul8vebVnN50Xl1/vzR8gk1sHfiA1DdV1fr9fRH/3+/pYBv7bfoqLoX2S+JNi3NbNWqSXL3m/b9ufnZ+YJ4Xb/jSrKr/ebppk6QnZ+fn5iHQQ2qii/rh/VNM3v7+/erVjZ50Vl2ftZFvVn7wZwLP2dv6qq9Eff/5yINT9ho4rS86uqivWR/6qcPuTWqiXL3u9ngMvlkurq5nGl9y3Q7r9RRZn1ftu26UjYVZTm0vST4fV6jVLsdhVl1vWj+u9eNlYpKsveT0VdLpeZSXSx5tdFGjLjC9IfxuNYbX/IMtDMhI0qSoPagd6Kl9xut8e5HHs36k2r15Jf7/ffn9Ginj7haLarKLPeT+VMzTaZOYQe03YVZdb1owbHySidPm+VorLs/VTU3g1ZU1bF8KH+J/3oE55+YBzNdhVleYzrzMxN37tpL9uolvx6f8mOkN66ELv/dhVl1vvpTZgq5+lR9Gi2qyizrn+UCkwyCDZrFZVl78fatRfKqhg+9PRUoD/E8d2mvWm7imK9Dy8RbJ6K+4ZMSRXNfGbHOsHdrqJAb8JTSw6AsQ77m1YU6H14Q3pbqqpKu0b0YLNiUVn2fpZFWTyAf6VJ51PzR/vzLEPMs8+voi9omub2X3u36H051fId+Uyz/iu/imAL6eqjnD4KsyxqdY+DWqFZPIARSz7v27YNdFrwhYrScTPQ2zIlgxKS79QSuvf7F8V+/rQj+HJFcXu/rusu7c+0PNYZ4Zcritv1A6n9OX37s3VR0Xt/dEeIXlRR5DX8xCcWjs4HWt5+u4r6o9ujR8zu8c9LOIicDhef13K23k8C7fsL2feXSKVl0+/vVZRx1z/O0cpgKtq6RWXZ+/03ZHTadojLKR/lcKbCKhZONw+0fsB2Fc3f+i2J+5EwsORtjOLzWs7W+51YF1os8V5F5+n9wVqCxz/gP/VhRbl2/eiOED3YrF5Ulr2/cKQ6XGAzFY3X1HWdzV1NOqtUlI56bdumrXUL52ez4D1Tsu/9/t0/jj8PbYkVK8qs90fvatq/A1g421WUR9enN+f+sCxyXF8oKo/e76uqqpt+1q+oKIqfn5/b7RZpZtreyYqjSHvp/PdYC592BNtV1D8TevwyYzBO/U7TD0YtfWfr/X69x9/rl/ikoux7f3SaTazvoQfWqijLrp+ajRl6xGaLorLs/X6bH4saDFLt0cA3WRUNXlbXdVVV3Zzax68x0tWrnaBf3jDlPL3fNE1Zlumru9Bf23c+r+gMvV/9lR65Xq9lWe7YpA+tUlF+Xd80TbcvVFUVosFLbFRUfr1f9PaL+/3+WFTTNP1sE+ngv3ey4iiM2KxbUf9DdMXN7iKbQu7fqiV67z9+yR3xi9u+b1YUvff7gn4VPWPTimJ1/UxT447Y7FhUrN5fKFV0/FO+xIgNQ/MXnERK7X/tUlGU72zYQujer+u6f01C93mmouVCv1cDg6+iIx7/BzatKFDXpwGrnNZ33reoQL2/XEqDgS6uLu8ZXS7GJ/pXWM78VaRL7S+Xy8F3490rSgfZ6HtZNoUUX6wl4ps2uMw6g7lne1UUsfdnBDrsL+Swn9o5unhG/yw2PeH4R4Pdi4rS+8stPI86FKui8T+RlrxYJr+KYDuDDBBsGZwx+VW0uoU340sx4PintvlVtLWn38SnJwS6K3eWRbGQYMOImV09HQ5iHQu+X5HPyzOL2Pv95Y8jtv/RXhUFevfSW5RN6tu3okBdz+qy7P2IRbnGhn89HZztPx7iU3CLirqFlcqynH9+xMMBT+Xa+6mcbFLNFhXl1/sLJ+SoKL+un7/8enTF5ON/6G9UVH69XxRFXdddUfNTMVNRke5g9sJCA+Suv7Tf6BPSX3aU9TG2qGjhDcvTc8KtKvMop8PFh7Xk2vs5dXFni4ry6/2nR8hOes7x70G+UUX5df28uKuizXi7qCx7P799P8nnY4xVzOyZ/X070p/4BxWlVd4HP326quPCQ0YU56zlPL2/8GM7kM8rOk/vPz0bC7eI7YcVnafrZ5w22Jyq91ODp77YDbfvdyK1lS/o75z9nb9/ohBluKbzdkX9JwyOg4OlJPtHwMGP8vhUiHhom7KwllP1/uAO008d/wjwYUWn6v3+ucvgfO6EFZ2q62ecM9icrff7x8n5PSXQd9l3wYZHT2dS7t3Al71X0cwx7r7szCnQAW5e3K5/tLCWU/X+q5Onjx9sPqzoVL1/X7aE6/E7ve/tis7W9VMEm5P0/pJDZbiiLB7AUNu2Uztw90nw5fZ8bouKmqaZvwXY7XbL44YPPNL7Z5Zf79+fnd9cLpdYF0ZvVFF+Xc9yWfb+zNlRJ2JRbtDJpKZp2rb9/f2tqqr+a+9GfWSLitq27Xb7brPdb4n+RrGQ3j+z/Hq/O0IWKnomv65nuSx7f7CnhD7lE2wAAIDwTEUDAADCE2wAAIDwBBsAACA8wQYAAAhPsAEAAMITbAAAgPAEGwAAIDzBBgAACE+wAQAAwhNsAACA8AQbAAAgPMEGAAAIT7ABAADCE2wAAIDwBBsAACA8wQYAAAhPsAEAAMITbAAAgPAEGwAAIDzBBgAACE+wAQAAwhNsAACA8AQbAAAgPMEGAAAIT7ABAADCE2wAAIDw/uzdAAAA2F/TNINH6rqu63r+JW3bFkXx+/ubHqyqqmma+Re+LTXyadtmXvVYaTFWfjjl/X7fuw0AALCzsiwHj1wul6nT/bqu+2Fm1MzL39b/vQtP49u2/fn56f59u926bPPY+AxCgaloAACwVNu2ZVk+TTVFUVyv19XHbfpJqRsseuklG40jHYQRGwAA+HfEZub0uD/6URRFVVWDmV1t27Zte71e+69a93w7tbOqqiXZJj1/dARpSdVRGLEBAIDnBqnmdru1bTuICl3Oud/vVVWlB9edkHa5XLp/LBk16iefvIdrCiM2AABQLBi76F+E012sMr/BN66HWSi15GkzmqZJw0ejbTBiAwAAJ9IfdVmSaor/jpZstObY082mVJPGeTIm2AAAcAjtX+k/uyW8yrIsy7Ku64WXy28hJYTuupqFr0oT0gZX3Qx0lZZ/Pa104Wy0LySrQzEVDQCA/aUrWLpr3KfWU95iDeXO/KSs5bO/BlK6GH3V4LqdgZnftaQ9T+ehFaaiAQDAuvrDCzPrKV+v1+8PPnyyYnL91+OP5lNNURQ/Pz9TQzdpLGjm3TjVPLRCsAEA4AjSGXx3Ol5V1e12u//VPzWfn9a1adv6a519brDGWir2druNPqcv5ZmpBHi2eWiFYAMAwBH0T9C7O7T0Rzmapumf7n/5YpvUthVXTO5v6n6/9/+zruv+xLDRWDJ4cx6fsFEYO7I/ezcAAICz6weVqftO9k/lB7HnIOYHRgYT0p6uBH273brhmqnZd1VVdRsZfbvSuNYB36iNCDYAAOysf2o+MxqTTuX3MhMS2radnyM3OsYyM5zyNMg1TdMln8f35ITz0ApT0QAA2N3CeVPp5H6vdZ/X+r3zS6Ul6d14OoQ1SC8nnIdWGLEBAGB3acxh4fDCXtOrZoJNXdeji4+1bfs4opIemZ9T93R4amo2Who7Os9wTSHYAABwHPOJZa+BmoVT4J5e5f9o4cy6qcJnZqMt+e2ZMRUNAIA9LY8rW6xOtkT6dXtd4TNV7+hstCUX8GRJsAEAYE8LLwjp55+9gk3x+qjRfBbq375mxsyMsjT/LU0/O+c8tEKwAQBgXwujwo4XxPeDzdQdM0dNlTa/KsBLZtLLqeahFYINAAD7Wji/a9+BiP7CAMsbMJWCFi7v1jTN4O4385qmOe08tKIoyqlbAgEAwBeUZZn+PXVqWtd1l3+mbt+5YjOm2tBv5+12e5o3UpsfX9K2bco8U5vqP2f+jL1pmsdb6CxpYbGg6kCM2AAAsJtBShkdDGmapr8+8uZtmtA/9f/5+ZkftxmkmsefphGVqe2kVPN07OWN1diyJNgAALCbxxuw9E/Tu9u8pOGI2+32xaaN6Dfger2WZdk0Tdu2qYquwWVZpvGl0ZvbFL008vv7W5Zl/31o27Y/OrQkyw3CzwnnoRWmogEAsKM0snG73eavy184t+ptCydl9WeIzbtcLl166bb82P4lm1pY9WBTy9+rnKaiCTYAAOymf2I9daK/3XU1Uy15+uTRy1r6+tFiKtgUz7LNS1luyaVKM6/KIBQINgAA7ObxxLqb3PX7+1tVVf3XLi15qm3bbkyma21RFHVdv7Fo22A7B6/6sAQbAAD2kcY9vjMmMy+nU/zlcqra4gEAAOwjhZk3RieapikX2OWmN+zCiA0AAPv4ZLhgfj3lZPlYUE5jF8vlVPWfvRsAAAAve4wrM9foM2r36X/rMhUNAIAdpLPqdW+68nmqyX4aW3ennbIsF65bHYVgAwDADj65wAYemYoGAMAOUp45SLC5XC6DRw7SsNV1C2r3H8mjUosHAACQg+4aGye3p2UqGgAAEJ5gAwAAhCfYAAAA4Qk2AABAeIINAAAQnmADAACEJ9gAAADhCTYAAEB4gg0AABCeYAMAAIRX3u/3vdsAAADwESM2AABAeIINAAAQnmADAACEJ9gAAADhCTYAAEB4gg0AABCeYAMAAIQn2AAAAOEJNgAAQHiCDQAAEJ5gAwAAhCfYAAAA4Qk2AABAeIINAAAQnmADAACEJ9gAAADhCTYAAEB4gg0AABCeYAMAAIQn2AAAAOEJNgAAQHj/Dx8gKQiwMzzVAAAAAElFTkSuQmCC\n",
3322 "text/plain": [
3323 "<IPython.core.display.Image object>"
3324 ]
3325 },
3326 "metadata": {},
3327 "output_type": "display_data"
3328 },
3329 {
3330 "name": "stdout",
3331 "output_type": "stream",
3332 "text": [
3333 "Save TH1 hframe\n",
3334 "Save TGraph Graph\n",
3335 "Save TGraph Graph\n",
3336 "Save TGraph Graph\n",
3337 "Save TGraph Graph\n",
3338 "removed ‘fig_BUP2020/D0_BUP2020_AN_5yr_comparison.svg’\n"
3339 ]
3340 },
3341 {
3342 "name": "stderr",
3343 "output_type": "stream",
3344 "text": [
3345 "Info in <TCanvas::Print>: png file fig_BUP2020/D0_BUP2020_AN_5yr_comparison.png has been created\n",
3346 "Info in <TCanvas::SaveAs>: ROOT file fig_BUP2020/D0_BUP2020_AN_5yr_comparison.root has been created\n",
3347 "Info in <TCanvas::Print>: eps file fig_BUP2020/D0_BUP2020_AN_5yr_comparison.eps has been created\n",
3348 "Info in <TCanvas::Print>: SVG file fig_BUP2020/D0_BUP2020_AN_5yr_comparison.svg has been created\n",
3349 "Info in <TCanvas::SaveSource>: C++ Macro file: fig_BUP2020/D0_BUP2020_AN_5yr_comparison.C has been generated\n"
3350 ]
3351 }
3352 ],
3353 "source": [
3354 "{\n",
3355 "\n",
3356 " TGraphErrors * grProD0_AN_pp_3year \n",
3357 " = GraphShiftCut(\n",
3358 " Significance2AN( gProD0_Significance_pp_3year, 0, pp_beam_pol, 2),\n",
3359 " 0., 0,100);\n",
3360 " TGraphErrors * grProD0_AN_pp_5year \n",
3361 " = GraphShiftCut(\n",
3362 " Significance2AN( gProD0_Significance_pp_5year, 0, pp_beam_pol, 1),\n",
3363 " 0., 0,100);\n",
3364 " \n",
3365 " grProD0_AN_pp_3year->SetMarkerStyle(kOpenCircle);\n",
3366 " grProD0_AN_pp_5year->SetMarkerStyle(kFullCircle);\n",
3367 " \n",
3368 " grProD0_AN_pp_3year->SetMarkerSize(2);\n",
3369 " grProD0_AN_pp_5year->SetMarkerSize(2);\n",
3370 " \n",
3371 " grProD0_AN_pp_3year->SetLineWidth(4);\n",
3372 " grProD0_AN_pp_5year->SetLineWidth(4);\n",
3373 " \n",
3374 " grProD0_AN_pp_3year->SetLineColorAlpha(kGray+1, 1);\n",
3375 " grProD0_AN_pp_5year->SetLineColorAlpha(kBlack, 1);\n",
3376 " \n",
3377 " grProD0_AN_pp_3year->SetMarkerColorAlpha(kGray+1, 1);\n",
3378 " grProD0_AN_pp_5year->SetMarkerColorAlpha(kBlack, 1);\n",
3379 " \n",
3380 " \n",
3381 " \n",
3382 " auto gs = getD0AN();\n",
3383 " auto g0 = gs.first;\n",
3384 " auto g07 = gs.second;\n",
3385 "\n",
3386 " assert(g0);\n",
3387 " assert(g07);\n",
3388 "\n",
3389 " \n",
3390 " TCanvas *c1 = new TCanvas(\"D0_BUP2020_AN_5yr_comparison\" ,\n",
3391 " \"D0_BUP2020OOArArO_AN_5yr_comparison\" , 1100, 800);\n",
3392 " c1->Divide(1, 1);\n",
3393 " int idx = 1;\n",
3394 " TPad *p;\n",
3395 "\n",
3396 " p = (TPad *) c1->cd(idx++);\n",
3397 " c1->Update();\n",
3398 "\n",
3399 " p->DrawFrame(0, -.025, 5, 0.035)->SetTitle(\";#it{p}_{T} [GeV];A_{N}\");\n",
3400 " (new TLine(0, -.0, 5, .0))->Draw();\n",
3401 " \n",
3402 " g0->Draw(\"l\");\n",
3403 " g0->SetLineColor(kCyan + 3);\n",
3404 " g0->SetLineWidth(5);\n",
3405 "\n",
3406 " g07->Draw(\"l\");\n",
3407 " g07->SetLineStyle(kDashed);\n",
3408 " g07->SetLineColor(kBlue+2);\n",
3409 " g07->SetLineWidth(5);\n",
3410 " \n",
3411 " \n",
3412 " grProD0_AN_pp_3year->DrawClone(\"p\");\n",
3413 " grProD0_AN_pp_5year->DrawClone(\"p\");\n",
3414 " \n",
3415 " TLegend * leg = new TLegend(.0, .8, .83, .95);\n",
3416 " leg->SetFillStyle(0);\n",
3417 "// leg->AddEntry(\"\", \"#it{#bf{sPHENIX}} Projection\", \"\");\n",
3418 " leg->AddEntry(\"\", Form(\"#it{#bf{sPHENIX}} Projection, #it{p}^{#uparrow}+#it{p}#rightarrowD^{0}/#bar{D}^{0}+X, P=%.2f\", pp_beam_pol), \"\");\n",
3419 " leg->Draw();\n",
3420 " \n",
3421 " leg = new TLegend(.2, .58 ,.85, .83);\n",
3422 " leg->SetFillStyle(0);\n",
3423 " leg->AddEntry(grProD0_AN_pp_3year, Form(\"%.1f pb^{-1} str. #it{p}+#it{p}, Years 1-3\", pp_rec_3year/1e12), \"pl\");\n",
3424 " leg->AddEntry(grProD0_AN_pp_5year, Form(\"%.0f pb^{-1} str. #it{p}+#it{p}, Years 1-5\", pp_rec_5year/1e12), \"lp\");\n",
3425 " leg->AddEntry(g0, \"Kang, PRD#bf{78}, #lambda_{f} = #lambda_{d} = 0\", \"l\");\n",
3426 " leg->AddEntry(g07, \"Kang, PRD#bf{78}, #lambda_{f} = -#lambda_{d} = 70 MeV\", \"l\");\n",
3427 " leg->Draw();\n",
3428 " \n",
3429 " c1->Draw();\n",
3430 " SaveCanvas(c1, \"fig_BUP2020/\" + TString(c1->GetName()), kTRUE);\n",
3431 "}"
3432 ]
3433 },
3434 {
3435 "cell_type": "markdown",
3436 "metadata": {},
3437 "source": [
3438 "# Post ops"
3439 ]
3440 },
3441 {
3442 "cell_type": "code",
3443 "execution_count": 45,
3444 "metadata": {},
3445 "outputs": [
3446 {
3447 "name": "stdout",
3448 "output_type": "stream",
3449 "text": [
3450 "(int) 256\n"
3451 ]
3452 },
3453 {
3454 "name": "stderr",
3455 "output_type": "stream",
3456 "text": [
3457 "Traceback (most recent call last):\n",
3458 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/bin/jupyter-nbconvert\", line 5, in <module>\n",
3459 " from nbconvert.nbconvertapp import main\n",
3460 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/site-packages/nbconvert/nbconvertapp.py\", line 140, in <module>\n",
3461 " class NbConvertApp(JupyterApp):\n",
3462 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/site-packages/nbconvert/nbconvertapp.py\", line 225, in NbConvertApp\n",
3463 " \"\"\".format(formats=get_export_names()))\n",
3464 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/site-packages/nbconvert/exporters/base.py\", line 141, in get_export_names\n",
3465 " e = get_exporter(exporter_name)(config=config)\n",
3466 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/site-packages/nbconvert/exporters/base.py\", line 102, in get_exporter\n",
3467 " if getattr(exporter(config=config), 'enabled', True):\n",
3468 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/site-packages/nbconvert/exporters/templateexporter.py\", line 325, in __init__\n",
3469 " super().__init__(config=config, **kw)\n",
3470 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/site-packages/nbconvert/exporters/exporter.py\", line 114, in __init__\n",
3471 " self._init_preprocessors()\n",
3472 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/site-packages/nbconvert/exporters/templateexporter.py\", line 491, in _init_preprocessors\n",
3473 " conf = self._get_conf()\n",
3474 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/site-packages/nbconvert/exporters/templateexporter.py\", line 509, in _get_conf\n",
3475 " if conf_path.exists():\n",
3476 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/pathlib.py\", line 1370, in exists\n",
3477 " self.stat()\n",
3478 " File \"/cvmfs/sphenix.sdcc.bnl.gov/gcc-8.3/opt/sphenix/core/stow/Python-3.8.0/lib/python3.8/pathlib.py\", line 1176, in stat\n",
3479 " return self._accessor.stat(self)\n",
3480 "PermissionError: [Errno 13] Permission denied: '/u0b/software/jupyter/nbconvert/templates/conf.json'\n"
3481 ]
3482 }
3483 ],
3484 "source": [
3485 "gSystem->Exec(\"jupyter nbconvert --to html D0_BUP2020.ipynb\")"
3486 ]
3487 },
3488 {
3489 "cell_type": "code",
3490 "execution_count": null,
3491 "metadata": {},
3492 "outputs": [],
3493 "source": []
3494 }
3495 ],
3496 "metadata": {
3497 "kernelspec": {
3498 "display_name": "sPHENIX ROOT C++",
3499 "language": "c++",
3500 "name": "sphenix-root"
3501 },
3502 "language_info": {
3503 "codemirror_mode": "text/x-c++src",
3504 "file_extension": ".C",
3505 "mimetype": " text/x-c++src",
3506 "name": "c++"
3507 }
3508 },
3509 "nbformat": 4,
3510 "nbformat_minor": 4
3511 }
