Warning, /analysis/UE_in_pp/plotting/jet_pt_unfolding.ipynb is written in an unsupported language. File is not indexed.
0001 {
0002 "cells": [
0003 {
0004 "cell_type": "code",
0005 "execution_count": 1,
0006 "id": "1a5e16da-687d-48dc-a489-c15628886f5a",
0007 "metadata": {},
0008 "outputs": [
0009 {
0010 "name": "stdout",
0011 "output_type": "stream",
0012 "text": [
0013 "Welcome to JupyROOT 6.26/06\n"
0014 ]
0015 }
0016 ],
0017 "source": [
0018 "import ROOT\n",
0019 "from ROOT import TCanvas, TFile, TProfile, TNtuple, TH1I, TH1F, TH2F, TH3F, TColor, TEfficiency, TProfile2D\n",
0020 "from ROOT import gROOT, gBenchmark, gRandom, gSystem\n",
0021 "import numpy as np\n",
0022 "import pdb"
0023 ]
0024 },
0025 {
0026 "cell_type": "code",
0027 "execution_count": 2,
0028 "id": "55d6089f-6f02-479d-8adc-f233fc982b0c",
0029 "metadata": {},
0030 "outputs": [
0031 {
0032 "name": "stdout",
0033 "output_type": "stream",
0034 "text": [
0035 "/direct/sphenix+u/egm2153/spring_2023\n"
0036 ]
0037 },
0038 {
0039 "data": {
0040 "text/plain": [
0041 "0"
0042 ]
0043 },
0044 "execution_count": 2,
0045 "metadata": {},
0046 "output_type": "execute_result"
0047 },
0048 {
0049 "name": "stdout",
0050 "output_type": "stream",
0051 "text": [
0052 "sPhenixStyle: Applying nominal settings.\n",
0053 "sPhenixStyle: ROOT6 mode\n"
0054 ]
0055 }
0056 ],
0057 "source": [
0058 "%cd /sphenix/u/egm2153/spring_2023\n",
0059 "gROOT.LoadMacro(\"sPhenixStyle.C\");\n",
0060 "gROOT.ProcessLine(\"SetsPhenixStyle()\")"
0061 ]
0062 },
0063 {
0064 "cell_type": "code",
0065 "execution_count": 3,
0066 "id": "2afc8a16-b226-4f2a-ac9d-9b437a0c2fe5",
0067 "metadata": {},
0068 "outputs": [
0069 {
0070 "name": "stdout",
0071 "output_type": "stream",
0072 "text": [
0073 "/gpfs/mnt/gpfs02/sphenix/user/egm2153/calib_study/JetValidation/analysis/mc_calibration\n"
0074 ]
0075 }
0076 ],
0077 "source": [
0078 "%cd /sphenix/user/egm2153/calib_study/JetValidation/analysis/mc_calibration"
0079 ]
0080 },
0081 {
0082 "cell_type": "code",
0083 "execution_count": 4,
0084 "id": "3e143fa2-828c-45e8-b0c0-f80aa1e97ac3",
0085 "metadata": {},
0086 "outputs": [
0087 {
0088 "name": "stdout",
0089 "output_type": "stream",
0090 "text": [
0091 "\n",
0092 "\u001b[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby\u001b[0m \n",
0093 " Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University\n",
0094 " All rights reserved, please read http://roofit.sourceforge.net/license.txt\n",
0095 "\n"
0096 ]
0097 }
0098 ],
0099 "source": [
0100 "f2 = ROOT.TFile.Open(\"jet_pt_unfolding.root\")\n",
0101 "h_truth = f2.Get(\"hTruthPT\")\n",
0102 "h_reco = f2.Get(\"hRecoPT\")\n",
0103 "h_meas = f2.Get(\"hMeasPT\")\n",
0104 "h_truth.SetDirectory(0)\n",
0105 "h_reco.SetDirectory(0)\n",
0106 "h_meas.SetDirectory(0)\n",
0107 "h1_truth = f2.Get(\"hTruthPTHalf\")\n",
0108 "h1_reco = f2.Get(\"hRecoPTHalf\")\n",
0109 "h1_meas = f2.Get(\"hMeasPTHalf\")\n",
0110 "h1_truth.SetDirectory(0)\n",
0111 "h1_reco.SetDirectory(0)\n",
0112 "h1_meas.SetDirectory(0)\n",
0113 "h_jes = f2.Get(\"jes_ratio\")\n",
0114 "h_jes.SetDirectory(0)\n",
0115 "h2_truth = f2.Get(\"hInverseTruthPT\")\n",
0116 "h2_reco = f2.Get(\"hInverseRecoPT\")\n",
0117 "h2_meas = f2.Get(\"hInverseMeasPT\")\n",
0118 "h2_truth.SetDirectory(0)\n",
0119 "h2_reco.SetDirectory(0)\n",
0120 "h2_meas.SetDirectory(0)\n",
0121 "h3_truth = f2.Get(\"hInverseTruthPTHalf\")\n",
0122 "h3_reco = f2.Get(\"hInverseRecoPTHalf\")\n",
0123 "h3_meas = f2.Get(\"hInverseMeasPTHalf\")\n",
0124 "h3_truth.SetDirectory(0)\n",
0125 "h3_reco.SetDirectory(0)\n",
0126 "h3_meas.SetDirectory(0)\n",
0127 "h_inverse_jes = f2.Get(\"inverse_jes_ratio\")\n",
0128 "h_inverse_jes.SetDirectory(0)\n",
0129 "f2.Close()"
0130 ]
0131 },
0132 {
0133 "cell_type": "code",
0134 "execution_count": 5,
0135 "id": "4359fb51-9b7b-432c-a1b0-4939664b6e0f",
0136 "metadata": {},
0137 "outputs": [],
0138 "source": [
0139 "direct = 'results_11_14'"
0140 ]
0141 },
0142 {
0143 "cell_type": "code",
0144 "execution_count": 6,
0145 "id": "6889abe6-1b4c-4b91-92ed-563ddc961852",
0146 "metadata": {},
0147 "outputs": [
0148 {
0149 "name": "stderr",
0150 "output_type": "stream",
0151 "text": [
0152 "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_14/h_full_unfold.png has been created\n"
0153 ]
0154 },
0155 {
0156 "data": {
0157 "image/png": 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\n",
0158 "text/plain": [
0159 "<IPython.core.display.Image object>"
0160 ]
0161 },
0162 "metadata": {},
0163 "output_type": "display_data"
0164 }
0165 ],
0166 "source": [
0167 "canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 800)\n",
0168 "pad1 = ROOT.TPad(\"pad1\", \"\", 0, 0.3, 1, 1.0)\n",
0169 "pad1.SetBottomMargin(0.02) # Adjust the margin for better separation\n",
0170 "pad1.Draw()\n",
0171 "pad1.cd()\n",
0172 "pad1.SetLogy(1) # Set logarithmic scale for the spectra plot\n",
0173 "\n",
0174 "h_truth.SetLineColor(1)\n",
0175 "h_reco.SetLineColor(2)\n",
0176 "h_meas.SetLineColor(4)\n",
0177 "h_truth.SetMarkerColor(1)\n",
0178 "h_reco.SetMarkerColor(2)\n",
0179 "h_meas.SetMarkerColor(4)\n",
0180 "h_truth.SetStats(0)\n",
0181 "h_meas.SetStats(0)\n",
0182 "h_reco.SetStats(0)\n",
0183 "h_truth.GetXaxis().SetLabelSize(0)\n",
0184 "h_meas.GetXaxis().SetLabelSize(0)\n",
0185 "h_reco.GetXaxis().SetLabelSize(0)\n",
0186 "\n",
0187 "h_truth.Draw()\n",
0188 "h_meas.Draw('same')\n",
0189 "h_reco.Draw('same')\n",
0190 "\n",
0191 "# Add legend\n",
0192 "leg = ROOT.TLegend(.28, .02, .5, .3)\n",
0193 "leg.AddEntry(\"\",\"Dijet Events |#Delta#phi| > 2.75\",\"\")\n",
0194 "leg.AddEntry(\"\",\"p_{T}^{lead} > 20 GeV, p_{T}^{sub} > 15 GeV\",\"\")\n",
0195 "leg.AddEntry(h_truth,\"Truth\",\"l\")\n",
0196 "leg.AddEntry(h_meas,\"Measured\",\"l\")\n",
0197 "leg.AddEntry(h_reco,\"Unfolded\",\"pe\")\n",
0198 "leg.Draw()\n",
0199 "leg.SetTextSize(0.035)\n",
0200 "\n",
0201 "canvas.cd()\n",
0202 "pad2 = ROOT.TPad(\"pad2\", \"\", 0, 0.05, 1, 0.3)\n",
0203 "pad2.SetTopMargin(0.02)\n",
0204 "pad2.SetBottomMargin(0.4)\n",
0205 "pad2.Draw()\n",
0206 "pad2.cd()\n",
0207 "ratio1 = h_reco.Clone(\"ratio1\")\n",
0208 "ratio1.Divide(h_truth)\n",
0209 "\n",
0210 "ratio1.GetYaxis().SetTitle(\"Unfolded/Truth\")\n",
0211 "ratio1.GetYaxis().SetNdivisions(208)\n",
0212 "ratio1.GetYaxis().SetRangeUser(0.9,1.1)\n",
0213 "ratio1.GetYaxis().SetTitleSize(20)\n",
0214 "ratio1.GetYaxis().SetTitleFont(43)\n",
0215 "ratio1.GetYaxis().SetTitleOffset(2)\n",
0216 "ratio1.GetYaxis().SetLabelFont(43)\n",
0217 "ratio1.GetYaxis().SetLabelSize(25)\n",
0218 "ratio1.GetXaxis().SetTitle(\"p_{T,lead} [GeV]\")\n",
0219 "ratio1.GetXaxis().SetTitleSize(20)\n",
0220 "ratio1.GetXaxis().SetTitleFont(43)\n",
0221 "ratio1.GetXaxis().SetTitleOffset(0)\n",
0222 "ratio1.GetXaxis().SetLabelFont(43)\n",
0223 "ratio1.GetXaxis().SetLabelSize(25)\n",
0224 "\n",
0225 "# Draw ratio plots\n",
0226 "ratio1.Draw(\"ep\")\n",
0227 "\n",
0228 "# Update canvas\n",
0229 "canvas.Update()\n",
0230 "canvas.Draw()\n",
0231 "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_full_unfold.png\")"
0232 ]
0233 },
0234 {
0235 "cell_type": "code",
0236 "execution_count": 7,
0237 "id": "ac01feba-c7a8-4911-b1bd-72ac15f42083",
0238 "metadata": {},
0239 "outputs": [
0240 {
0241 "name": "stderr",
0242 "output_type": "stream",
0243 "text": [
0244 "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0245 "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_14/h_half_unfold.png has been created\n"
0246 ]
0247 },
0248 {
0249 "data": {
0250 "image/png": 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\n",
0251 "text/plain": [
0252 "<IPython.core.display.Image object>"
0253 ]
0254 },
0255 "metadata": {},
0256 "output_type": "display_data"
0257 }
0258 ],
0259 "source": [
0260 "canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 800)\n",
0261 "pad1 = ROOT.TPad(\"pad1\", \"\", 0, 0.3, 1, 1.0)\n",
0262 "pad1.SetBottomMargin(0.02) # Adjust the margin for better separation\n",
0263 "pad1.Draw()\n",
0264 "pad1.cd()\n",
0265 "pad1.SetLogy(1) # Set logarithmic scale for the spectra plot\n",
0266 "\n",
0267 "h1_truth.SetLineColor(1)\n",
0268 "h1_reco.SetLineColor(2)\n",
0269 "h1_meas.SetLineColor(4)\n",
0270 "h1_truth.SetMarkerColor(1)\n",
0271 "h1_reco.SetMarkerColor(2)\n",
0272 "h1_meas.SetMarkerColor(4)\n",
0273 "h1_truth.SetStats(0)\n",
0274 "h1_meas.SetStats(0)\n",
0275 "h1_reco.SetStats(0)\n",
0276 "h1_truth.GetXaxis().SetLabelSize(0)\n",
0277 "h1_meas.GetXaxis().SetLabelSize(0)\n",
0278 "h1_reco.GetXaxis().SetLabelSize(0)\n",
0279 "\n",
0280 "h1_truth.Draw()\n",
0281 "h1_meas.Draw('same')\n",
0282 "h1_reco.Draw('same')\n",
0283 "\n",
0284 "# Add legend\n",
0285 "leg = ROOT.TLegend(.28, .02, .5, .3)\n",
0286 "leg.AddEntry(\"\",\"Dijet Events |#Delta#phi| > 2.75\",\"\")\n",
0287 "leg.AddEntry(\"\",\"p_{T}^{lead} > 20 GeV, p_{T}^{sub} > 15 GeV\",\"\")\n",
0288 "leg.AddEntry(h_truth,\"Truth\",\"l\")\n",
0289 "leg.AddEntry(h_meas,\"Measured\",\"l\")\n",
0290 "leg.AddEntry(h_reco,\"Unfolded\",\"pe\")\n",
0291 "leg.Draw()\n",
0292 "leg.SetTextSize(0.04)\n",
0293 "\n",
0294 "canvas.cd()\n",
0295 "pad2 = ROOT.TPad(\"pad2\", \"\", 0, 0.05, 1, 0.3)\n",
0296 "pad2.SetTopMargin(0.02)\n",
0297 "pad2.SetBottomMargin(0.4)\n",
0298 "pad2.Draw()\n",
0299 "pad2.cd()\n",
0300 "ratio1 = h1_reco.Clone(\"ratio1\")\n",
0301 "ratio1.Divide(h1_truth)\n",
0302 "\n",
0303 "ratio1.GetYaxis().SetTitle(\"Unfolded/Truth\")\n",
0304 "ratio1.GetYaxis().SetNdivisions(204)\n",
0305 "ratio1.GetYaxis().SetRangeUser(0.9,1.1)\n",
0306 "ratio1.GetYaxis().SetTitleSize(20)\n",
0307 "ratio1.GetYaxis().SetTitleFont(43)\n",
0308 "ratio1.GetYaxis().SetTitleOffset(2)\n",
0309 "ratio1.GetYaxis().SetLabelFont(43)\n",
0310 "ratio1.GetYaxis().SetLabelSize(25)\n",
0311 "ratio1.GetXaxis().SetTitle(\"p_{T,lead} [GeV]\")\n",
0312 "ratio1.GetXaxis().SetTitleSize(20)\n",
0313 "ratio1.GetXaxis().SetTitleFont(43)\n",
0314 "ratio1.GetXaxis().SetTitleOffset(0)\n",
0315 "ratio1.GetXaxis().SetLabelFont(43)\n",
0316 "ratio1.GetXaxis().SetLabelSize(25)\n",
0317 "\n",
0318 "# Draw ratio plots\n",
0319 "ratio1.Draw(\"ep\")\n",
0320 "\n",
0321 "# Update canvas\n",
0322 "canvas.Update()\n",
0323 "canvas.Draw()\n",
0324 "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_half_unfold.png\")"
0325 ]
0326 },
0327 {
0328 "cell_type": "code",
0329 "execution_count": 8,
0330 "id": "1bc42416-ec5e-4a38-bfe7-e4b40dc2d0bf",
0331 "metadata": {},
0332 "outputs": [
0333 {
0334 "name": "stderr",
0335 "output_type": "stream",
0336 "text": [
0337 "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0338 "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_14/dijet_leadjet_jes.png has been created\n"
0339 ]
0340 },
0341 {
0342 "data": {
0343 "image/png": 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9+/fb29sNSwUAdYQfzuzy8vL+/v5wOMi3h8Ph4uLi/v7+xYsX2xYMACx6Mw6ht+dTSI9QTiCA2Za7CfPMD0sh9gA4i2ZPAEBwqPmd0DmamzoNAJzXyvPgE34nkHMAsILOm+1yiUizJwAgOIQfACA4hB8AIDiEHwAgOIQfACA4hB8AIDiEHwAgOIzzO4FB7gCwAga5u4WcA4AVMMgdAIBlEX4AgOAQfgCA4BB+AIDgEH4AgOAQfgCA4BB+AIDgMM7vBAa5A8AKGOTuFnLOHfK3wRUBdolB7gAALIvw+02WZVmWbV0KNB2Pxw8fPsjXz58/f//+/fF43LZIALxG+P0mz3NjzNalwG+Ox+Pbt29//Pgh3/78+fPXr19v3rwh/wDMRvj9j9Z66yKgw+fPn1++fPnlyxe75O7u7tWrV3/99deGpQLgtYjuA1mWGWPKspRv4zi2lb8o4vysZInH2lw7wHfL3YSp+al68gEAQkD4KWNMVVVVVRVFsXVZwlX1e/bsWecmh8NhYCuqfQAGMM4Prru6uvr169fd3V194c3NzcXFxVZFAuA7an4nRKNtXdLdur29fXh4uL6+tktubm6+f/9+e3v7xD1z4YBNuHBfJfxOGG5Yo5FtBZeXl/f394fDQb49HA4XFxf39/cvXrzYtmAA5nHhvkpvxv8xxiRJQm9PZ519ejPmSwMcR29PQJ3xk+CaU8bQuAo4iPBDcJgyBgDhh+AwZQwAhjpgn6a2NMpQik+fPg2sw9NBYDeo+QEAgkP4YZ8GOk8zZQwAmj3/R2vdvsF1tp5xH/TaalPGHI/Hz58/y9fPnz+/urq6vb29vLw8728B9mHlTtHU/E6gBrA/y00ZU0efUmCSlW+2hB+Cs86UMfQpBVzGDCZDmOFl3ybN8LLhQHX+EyJYzPACAMDZEH7AKMMdQc/Yp5RnzMAK6O0JLILXEAIuo+aHcC1ar1qnTymAeQg/YBG8hhBwGb0Zh/R18OOkYTzeGgiMsfL9lmd+J3DPAoAVdN5slxtiRLMnANfxQmCcHeEHAAgO4QcACA7P/IBl8dgYcBA1PwBAcAg/ICD0HAEE4QcA+8Hnm5EIPwBAcOjwckLnZyi6MADAea1cYSX8TiDnAGAFzPAC4PyOx+OHDx/k6+fPn79///54PG5bJGBDhB+wf8fj8e3btz9+/JBvf/78+evXrzdv3pB/CBbhB+zf58+fX758+eXLF7vk7u7u1atXf/3114alGoMKKxbCK42GRBHnBxvzotv6Qn8mUmGtx/b19fXDw8P9/f3l5eUSv3EHdvYKreVuwtT8ADjK3wor3EfNZgg1P+zD8+fPf/782V5+OBw6l0/iRd10pB38vVPzG4mhDsD+XV1d/fr16+7urr7w5ubm4uJiqyIB26LZ84Soy9aFAqa5vb19eHi4vr62S25ubr5//357e/v0nVeLefbsWedvPBwOC/3Gp58NzLbyzdaDmp8xxhijlNJaa62nbqWUyrKsvULfruxWgr8H7MDl5eX9/b19VHY4HC4uLu7v71+8eLFtwYZRYQ1K5812ufxz/ZlW+8jHFFhrXZZlfUmapo0I7Dun9f3zzA8749cDoePx+ObNm1evXtn8kwqr+7G9Ib8u8UmB9vaUylkcx0VRFEURx7Ea8UEgiqKyLO1WaZoqpfI8r1fp5Gu7Tt1SBwNgIqmwHg4H+daXCiv8sFDT+dPZHKovlCVFUfRtJVHX2Ep2FcdxY7U0TYfL4PL5AWZw/K++j6fF3sTOztVyx+JuzU9aKW2YCfm28xmekCpdowInNch6Q6h9iHiesgIAvOJu+ElWNfJJYq/xPK+9VZs0mTY6s8jOGwsBALvnbviJqZUzCbk+NudsskZRlCSJ9KkdqFACAPbE9fCbSsKyEWPGmM4aofSLSdNUIjPPcxpCsW/2OQoQOHe78vd12D3ZkVdWiONYItAYk+e5/MgOeJB1iqKop1174aQhJs6eScB3O+u+vygvzpULt9a91fzUY2+XsiyTJEmSJM/zOI4bzaHy+bdRz+vsTTO+79DSxwUAAzx6/ZML99Udhp/WuqoqGeGXpmlRFPZR33CrZrtTKAB4gfcVT+V6s2ejZVLNrdSP2coYkyRJfTVmeAFc4EVT3rY+fPjw48eP+uuflFLX19eHw+Hjx49blerpFrwJj69+rqxzHLosrA9Xb5CJYNqj4OsH2zfCvT0W3uXzA4RjnZvVIndYPNlCl9vpmk374167Otho0mzX3tTjVJ+2t0vnOp07p+YHuGCdmh8vbHHTUtd9oVA9C+ml0pjbs1Fmu86MrZRS8lDQziPTqFM6fn4AQKz/+qd1LHcTdr1m034/Q6PAskIcx/WJWk5u1blO55sfHD8/AKCUev/+fd/rnz59+rRVqZ5uuZuwBzf3J77Pb2Crk+sQfgC8sNfXPwUdfhsa884/AHDB8Xj866+/pJ53OByurq5ub289Sr6V77eE3xBqfgD8srNhIYG+zBYAgCUQfgCA4BB+AIDgEH4AgOAQfgCA4BB+AIDgEH4AgOAQfgCA4Px76wK4rnPSgd0MIAUAR6z8Vg3C7wRyDgBW0HmzXS4RafYEAASH8AMABIfwAwAEh/ADAASH8AMABIfwAwAEh/ADAASHcX4nMMgdAFbAIHe3kHMAsAIGuQMAsCxqfgCwHzRWjUTNDwAQHMIPABAcwg8AEBzCDwAQHMIPABAcenuewCB3AFgBg9zdQs4BwApWHuS+k/AzxhhjlFJaa6311K2UUlmWLVEwAICDoh3UbNofDcYclNa6LMv6kjRNGxEYRXs4PwDgqeVuwt53eJF6XhzHRVEURRHHsRpRU46iqCxLu1WapkqpPM9tRRAAsGN+12yMMUmSqN+repJ8RVH0tX9mWZbneWMr2VUcx/X8o+YHABui5tdNWiml3mbJtwPP8CTeiqKoL5SkbDSEAgB2ye/wk6xq1PAk9gZirO9H0mRKyycA7J7f4SfGd+8UEnJ9CD8A2L09hN9UEpaNdlFjDG2eABCIEMPPtotqrWWoX5Zl0nGmLRpt1WMAAG+5cF/dySD3qYqiSJKkLEubedIW2q780dsTAM5r/H2VGV7OTGtdVVVjXhhpDp36BBEA4B2/x7H1DemT5VMPrb0V4/wAYEOM8+smQ/oa/TPlkd5Al05jjDztW7RsAABneV+z6ayuqd+rgzbnZEnnvDAy1Wdjek9qfgCwIWp+vaSGZ/tt2sCrN4RKZ06balrr9lbS1YV3OwBACLzv8GKjqz5W4eQnhXlbAQD2YSfNek98n1/fVjR7AsCGlrsJc3Mf0jfEhJMGAOe18v3W+2bPpZFzALCCzpvtcoPcve/wAgDAVIQfACA4hB8AIDiEHwAgOIQfACA4hB8AIDiEHwAgOIzzO6FzlAmD/wDgvBZ9b3sb4XcCOQcAK2CQOwAAyyL8AADBIfwAAMEh/AAAwSH8AADBIfwAAMEh/AAAwSH8AADBYZD7CczwAgArYIYXt5BzALACZngBAGBZhB8AIDiEHwAgOIQfACA4hB8AIDiEHwAgOIQfACA4jPM7gUHuALACBrk3GWOMMUoprbXWeupWSqksy9or9O3KbiXIOQBYwcqD3CPHb+7tIx9TYK11WZb1JXEcN1Kt75zW9x9Frp8fANix5W7CTtf8pHIWx7FU3bIsK8vy5LmwyVcUhSxJkqQsyyzLbBVQgtDuGQAQFHdrNsaYJElUqyqmlCqKYqD9U9apb9XeVZZleZ6naTocftT8AGBDy92E3e3tKbGUpml9oXw7kFi2Sldf2E5K+xDxycUEAPjH3fCTpstGPknsNZ7n9W17kuy88SwQALB77oafmFo501pLtc9uaIyRhtB6JdImaxRFSZJEURRFEc//ACAQ7j7Taj+6G15e1+7t2Xi8Z7t6xnGstTbGyPqNTqE88wOADS13E3b35j47/Gz3FqVUHMc2BevdZDo7zrQXThpi4uyZBACnuHBrdb3ZcyqbfEVRVFVljKmqSho8bSIqpaqqqqqq0aba2ZumGm35gwOAPXDhvrq38LN9ROvBlmWZPAgcfqonm4zsLAMA8Jfr4Te1K2ZnH1FFx04AQI274SeNkI24kqpbYxhf3cCP1GMEZlk20LdzeA8AgB1wt8OL6urb0u6TYtPRBlue530zecqGnXPHdO6c3p4AsKEFb8LjHzyuTyphcRwXRVEUha2Tda5jl9hDS9O0KAo7vK++jt3VwDqyq0UPEAAwYLmbsOs1m/aIvUaBZYVGVW/MWx1OjgVU1PwAYFMhjvOzZr/PT/4d2Orkngk/ANhQ0OG3IcIPADYU6Pv8XNA5EwGJCADntdxL2zsRfieQcwCwgs6b7XKJ6O44PwAAFkL4AQCCQ/gBAIJD+AEAgkP4AQCCQ/gBAIJD+AEAgsM4vxMY5A4AK2CQu1vIOQBYAYPcAQBYFuEHAAgO4QcACA7hBwAIDuEHAAgO4QcACA7hBwAIDuP8TmCQOwCsgEHubiHnAGAFDHIHAGBZhB8AIDiEHwAgOIQfACA4hB8AIDiEHwAgOIRfEFYeQLOc3RyI2tGxcCCu2c2BLIrwAwAEh0HuJzDDCwCsgBle5jDGGGOUUlprrfXIrb59+/bPP//I1x8/fuxch5wDgBWsPMNLtIObe/vsjDmoP/74wyafeP369d9//93Y8w7Oj+JAnLSbY+FAXLObA1FLHov3z/yknhfHcVEURVHEcaxGfFiwyff1kVLqn3/+ef/+/YwyjP9sMnLNs+9wvPOWcDcHssQOz/t7x68Z2oFMWvO8O9zN/y73D2QGvz8gGGOSJFG/V/XktBZFMdD+KevUt/r27dt//vOf9q7GnJ/xn01C2+FuDiTAHe7mQALc4W4OZNKaU/ld88uyTCmVpml9oXwrP+r07ds3pdTr16/rC//8888FCggAcJHfNb++Sl67YlfXWcnr3Go3H4v4BMoOffm97NDf37vhDmfwu+YnxnfvFH/++adU+/744w9Z8u3bN0m+d+/enbt0AADn7GSow1R///239HmpP3d99+5d34AHAMCuVD7rO4SThybdO0X94d/Xr1/b+wEAbOXMsfFoD8/82ocw8pnf169fbT+X9+/ff/r0aWArAMBu7OGZ31QScu/evav38Pz48aNUAecN9QMAeGQP4ScTm40nw9sbQx3sksa0LwCA/fE7/GRIXyP8ZISfTPXSqR17438KANgBv5/5qZ7Beer3wX82HWWJPN7rnMlT/f4g0G7eOWReaz0wlH4TdoJv1T/Mf8w6mxsupEdXRNXKPzDruhcXZfhA/LooQsrc2XTkxRWxOg/ElyvSdwmyLGv8NzvzRVmoI81qpIbXmNuzcVx2HbvEHv67d+++fv1qh/e9fv26/SsaM8hY9R1uriiKdgnTNG2s1q4QF0WxQXH7jTkQL65I1VPO9gl3/6KMORBfLkqdlLC93P0r0tB5IL5ckb5WusZf/dkvivfj/IwxWuuyLGWST1Gdqs5WVSXj/KTzi3j9+vW//vWvzl+hlErTdOpo+jXJ4cdxLB+IjDF5nud5rmofkbIsK8tSKSUBI+skSXLydK1pzIF4cUWk5KpWzs4T7v5FGXkgXlyUur65ld2/Ig19B+LLFamf7bp6sRe5KE9JTncURZGmaZqmkz4LyPscpPInZ6PzM5H7J0o+4jUKb/8z2SXybf0UyYbtCuJWJh3I2oWbSD6odn56rS90/6JMOpCVyzZbvVbU+JH7V6Tu5IFsUqpJxpRziYviwalZVKMq7Wn4tf9n1JfL1xIh7QN06ujGHEj7Wzd1FrKR7h5dlMbC9scUp8o8zJ72dpm9uCLWwIFUrpa5re/Gay10Ufzu7fl0xpj6+R2z/sIlmkP+9w90plCPbYaON4CMOZAxC10gTRHD63hxUcYcSIOzF0VI03pfJwvl/BWxBg6kwdkrIgVrd06sW+qizI7Nnen7cNEXio4/AK9arYWSK329LVw+nMaB7OCK2KJ6elHaB+LRRamf2/Y90KMrMnwgvlwR24RQL2TjPsq0HI4AAAdmSURBVLzQRQm95jdJHMdpmsoZT5LE2Q9TqvaaX/uZXR4Xe6d9IHW+XJEoiqIokgOpD8Lx7qL0HUidyxdF+k0M9AHx5YqcPJA6l6+IFEZOu21jKMuy3otnqYsyLzP3p6/m19kFw96IVyveJLZ4wx0rRGd3Bkd0HoiPV6T+F9d+TubRRek7EC8uSvtvvF08L67ImAPx4opUg92pbOEXuigOnYVt9YVfn77rsa16W0ejbB6151SDB9LHzSvS0Pir9uui1LXHznZy6qL0RV19iRdXZMyBDG/ryIEMqB/RQhfF+3F+W4njuCxLGWW4dVn+S8Y7KqXSNHVqBoep5h2Ig1ekzRgTRZEvbWsDRh6IOxfFznrY/h9l+1NsXsgxnngg7lyRMRYtJ+E3k71BO8KWp/q9baqxQvs/k2zlzl/CyQM5uaEj5E40HN5eXJQxB9LHtYtSlmW7PDKEXz3GhvtXRI04kL4NXbsiYyx1UWZXS3emr9lTatbt9Z1qBuls32/wYgDTmAPx4opU49607MVFGXMgXlyUokUOQb6267h/RcYciBdXpOof5LfCn4krl3Nzw+HXWF605hzZ1sjCtFdzbeqKMQfixRWpeh6ujBkb7uZFGT4QXy5KQ2fx3L8ibe0y+3JFNvwzcegsbGugw4uc9/hx7mzbY8qpT08D7Gr1ktcPZMOSN4w8EPevSFW70cise0Vt1vV6Od2/KCMPxIuL0tB5qt2/Im2dJfTiigz876qvtsRFcfqKrmkg/Iqu4aLu/AfqLF5nZlRuT1c//kAcvyJW59jEdjldvihizIH4clHq2n8gwv0r0tB5IL5cka3+TLx/n99q7KukfOkVNsCv7m19fLkiI8vp/kUZcyC+XJQx3L8iY/hyRdb/MyH8AADBYXozAEBwCD8AQHAIPwBAcAg/AEBwCD8AQHAIPwBAcAg/AEBwCD8AQHAIPwBAcAg/AEBwCD8AQHAIPwBAcAg/AEBwCD8AQHAIPwBAcAg/AFiJvK/16evg6Qg/AJhgRjjZTZIkGd7cGJMkyZxi/U5rLS89Rx/CDwAmyLJsUq5MXd9Bxpj91UcJPwAYyxhTlqUNA/nXZls9IezXdpP6j0YmYt+a2aPO5eODKsuy6JHWuvFTrbX8KEmSJEnk63lBLrsa2NauYIyJamb8rrEqAMA4aZrKnTNN06Io7I20KIo0TeM4tmvKQruO/Ei+EHZhnaxf/1125aIo7J5lef0eXv9FtoQjDyeOYzmcRjHsDuXQ7J5nBIfdYd8K9Z+maWrP89RfNB7hBwATSBhUjzd0mzGd4ddYXg+8es5Z9YX1wJMwaG9l15Fwqu9kfPjVY8/udiCbO390kv2U0P5RZzQuHX40ewLAfFObAW3rYruZsa7eamqMqW8lwSAtonb9siztt8N7HlnCOI7bzaf2tzcacseQimPn6ZKFtra3DsIPAP7LPkiTvJEnYeftrjIymezTRMu2Op58fqZqjZ8zlGWpBkNdUqodflmW2ceEnc8j7c6n/sYl/HvNXwYALpPOKUqpPM9lSVmWZVnmeW4b4tahtc7zvN2DRrLTFsaWUz0GtnxdluW8+p8kUBzHA5t39rVpdE7J87xx0uwO6+VUj8f1lLSeh5ofAPwmz/PG0zs1rsZmqzWNWlFndWeY/Lp6S2Z7/F/9t8RxbIPwKcMS6hE7nm0ptSdNwqyxH6kyNoJTvn1iU+0M1PwAoKmeH1VVRVFUDzCJmcb9OsuyPM+lAlTvGyl1OK311EwqiiJJEhtp0iskyzIZdWB/S5IkVVXZEQKy8tkrUgPP6mx1uVFPbZw09XiKGgs3afNUiqEOAPBIMqPdT7Ix2KCzy6L9UfunnQtHOrnDxhCFSb+o3duzcaTWQHzITtonrXO5bFgvvOrpO7p0QlHzA4DfdA73lv6N8qOBNrrOHz2lTe/kDvu+forGYzn1WO+06m2wdrx/Y5POxt40TfM8tyPxt2rzVDR7AsBJ7e4ne2Vjvr18eMORzzUbLZ+btXnS4QUATprXDcRHcozDSdb5IaCvubUdbNKyaqeIW7+fpyD8AOA37Zt7aOGnBg+20e9U1uw8aZ0TjUoc2vESm836vdzjRADwS2PCTHFyXkp/dU5vZo+3c4K0zhk+O8/PwEkbk0FLn3NqfgDQZN8wIEML1OqTb21Iay0HKyM37HmQ2VvKsmyfClliV5ZxDqr/pNkE3arNU6k9fpYBgHlsR//GfXLMJNE+6pvYuvr9xQ51dh7tRny0c25g8mu784GBGUsnVFStO2cPADhL+jpWjzNHS/f9HT/qk76XRVH0HWP9NbZjTsUZT5rUHZdLKIY6AECHvjt4Z1/HvQbk1Bjz6LMC4QcAY0nVsL18oPIEN9HhBQDGMsbIEyP7dlnhdfLJZKGOHEJjktLlUPMDgEDVp9t2JPxUrQvookWiwwsATCZdRbh/+otmTwBAcAg/AEBwCD8AQHAIPwBAcAg/AEBwCD8AQHAY6gAACA41PwBAcAg/AEBwCD8AQHAIPwBAcAg/AEBwCD8AQHD+H6UcCqbChM3qAAAAAElFTkSuQmCC\n",
0344 "text/plain": [
0345 "<IPython.core.display.Image object>"
0346 ]
0347 },
0348 "metadata": {},
0349 "output_type": "display_data"
0350 }
0351 ],
0352 "source": [
0353 "canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 500)\n",
0354 "h_jes.GetYaxis().SetTitle(\"p_{T}^{lead}/p_{T}^{truth lead}\")\n",
0355 "h_jes.GetXaxis().SetTitle(\"p_{T}^{truth lead} [GeV]\")\n",
0356 "h_jes.GetYaxis().SetRangeUser(0.8,1.2)\n",
0357 "h_jes.GetXaxis().SetRangeUser(15,45)\n",
0358 "h_jes.Draw()\n",
0359 "canvas.Draw()\n",
0360 "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/dijet_leadjet_jes.png\")"
0361 ]
0362 },
0363 {
0364 "cell_type": "code",
0365 "execution_count": 15,
0366 "id": "d4073912-59b3-40ee-bec0-3375465d035d",
0367 "metadata": {},
0368 "outputs": [
0369 {
0370 "name": "stderr",
0371 "output_type": "stream",
0372 "text": [
0373 "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0374 "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_14/h_inverse_full_unfold.png has been created\n"
0375 ]
0376 },
0377 {
0378 "data": {
0379 "image/png": 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\n",
0380 "text/plain": [
0381 "<IPython.core.display.Image object>"
0382 ]
0383 },
0384 "metadata": {},
0385 "output_type": "display_data"
0386 }
0387 ],
0388 "source": [
0389 "canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 800)\n",
0390 "pad1 = ROOT.TPad(\"pad1\", \"\", 0, 0.3, 1, 1.0)\n",
0391 "pad1.SetBottomMargin(0.02) # Adjust the margin for better separation\n",
0392 "pad1.Draw()\n",
0393 "pad1.cd()\n",
0394 "pad1.SetLogy(1) # Set logarithmic scale for the spectra plot\n",
0395 "\n",
0396 "h2_truth.SetLineColor(1)\n",
0397 "h2_reco.SetLineColor(2)\n",
0398 "h2_meas.SetLineColor(4)\n",
0399 "h2_truth.SetMarkerColor(1)\n",
0400 "h2_reco.SetMarkerColor(2)\n",
0401 "h2_meas.SetMarkerColor(4)\n",
0402 "h2_truth.SetStats(0)\n",
0403 "h2_meas.SetStats(0)\n",
0404 "h2_reco.SetStats(0)\n",
0405 "h2_truth.GetXaxis().SetLabelSize(0)\n",
0406 "h2_meas.GetXaxis().SetLabelSize(0)\n",
0407 "h2_reco.GetXaxis().SetLabelSize(0)\n",
0408 "\n",
0409 "h2_meas.Draw()\n",
0410 "h2_truth.Draw('same')\n",
0411 "h2_reco.Draw('same')\n",
0412 "\n",
0413 "# Add legend\n",
0414 "leg = ROOT.TLegend(.28, .02, .5, .3)\n",
0415 "leg.AddEntry(\"\",\"Dijet Events |#Delta#phi| > 2.75\",\"\")\n",
0416 "leg.AddEntry(\"\",\"p_{T}^{lead} > 20 GeV, p_{T}^{sub} > 15 GeV\",\"\")\n",
0417 "leg.AddEntry(h2_truth,\"Measured\",\"l\")\n",
0418 "leg.AddEntry(h2_meas,\"Truth\",\"l\")\n",
0419 "leg.AddEntry(h2_reco,\"Inverse Unfolded\",\"pe\")\n",
0420 "leg.Draw()\n",
0421 "leg.SetTextSize(0.035)\n",
0422 "\n",
0423 "canvas.cd()\n",
0424 "pad2 = ROOT.TPad(\"pad2\", \"\", 0, 0.05, 1, 0.3)\n",
0425 "pad2.SetTopMargin(0.02)\n",
0426 "pad2.SetBottomMargin(0.4)\n",
0427 "pad2.Draw()\n",
0428 "pad2.cd()\n",
0429 "ratio1 = h2_reco.Clone(\"ratio1\")\n",
0430 "ratio1.Divide(h2_truth)\n",
0431 "\n",
0432 "ratio1.GetYaxis().SetTitle(\"Unfolded/Measured\")\n",
0433 "ratio1.GetYaxis().SetNdivisions(208)\n",
0434 "ratio1.GetYaxis().SetRangeUser(0.9,1.1)\n",
0435 "ratio1.GetYaxis().SetTitleSize(20)\n",
0436 "ratio1.GetYaxis().SetTitleFont(43)\n",
0437 "ratio1.GetYaxis().SetTitleOffset(2)\n",
0438 "ratio1.GetYaxis().SetLabelFont(43)\n",
0439 "ratio1.GetYaxis().SetLabelSize(25)\n",
0440 "ratio1.GetXaxis().SetTitle(\"p_{T,lead} [GeV]\")\n",
0441 "ratio1.GetXaxis().SetTitleSize(20)\n",
0442 "ratio1.GetXaxis().SetTitleFont(43)\n",
0443 "ratio1.GetXaxis().SetTitleOffset(0)\n",
0444 "ratio1.GetXaxis().SetLabelFont(43)\n",
0445 "ratio1.GetXaxis().SetLabelSize(25)\n",
0446 "\n",
0447 "# Draw ratio plots\n",
0448 "ratio1.Draw(\"ep\")\n",
0449 "\n",
0450 "# Update canvas\n",
0451 "canvas.Update()\n",
0452 "canvas.Draw()\n",
0453 "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_inverse_full_unfold.png\")"
0454 ]
0455 },
0456 {
0457 "cell_type": "code",
0458 "execution_count": 14,
0459 "id": "520befe9-1bc7-460f-b526-9c39e51bdb7e",
0460 "metadata": {},
0461 "outputs": [
0462 {
0463 "name": "stderr",
0464 "output_type": "stream",
0465 "text": [
0466 "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0467 "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_14/h_inverse_half_unfold.png has been created\n"
0468 ]
0469 },
0470 {
0471 "data": {
0472 "image/png": 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\n",
0473 "text/plain": [
0474 "<IPython.core.display.Image object>"
0475 ]
0476 },
0477 "metadata": {},
0478 "output_type": "display_data"
0479 }
0480 ],
0481 "source": [
0482 "canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 800)\n",
0483 "pad1 = ROOT.TPad(\"pad1\", \"\", 0, 0.3, 1, 1.0)\n",
0484 "pad1.SetBottomMargin(0.02) # Adjust the margin for better separation\n",
0485 "pad1.Draw()\n",
0486 "pad1.cd()\n",
0487 "pad1.SetLogy(1) # Set logarithmic scale for the spectra plot\n",
0488 "\n",
0489 "h3_truth.SetLineColor(1)\n",
0490 "h3_reco.SetLineColor(2)\n",
0491 "h3_meas.SetLineColor(4)\n",
0492 "h3_truth.SetMarkerColor(1)\n",
0493 "h3_reco.SetMarkerColor(2)\n",
0494 "h3_meas.SetMarkerColor(4)\n",
0495 "h3_truth.SetStats(0)\n",
0496 "h3_meas.SetStats(0)\n",
0497 "h3_reco.SetStats(0)\n",
0498 "h3_truth.GetXaxis().SetLabelSize(0)\n",
0499 "h3_meas.GetXaxis().SetLabelSize(0)\n",
0500 "h3_reco.GetXaxis().SetLabelSize(0)\n",
0501 "\n",
0502 "h3_meas.Draw()\n",
0503 "h3_truth.Draw('same')\n",
0504 "h3_reco.Draw('same')\n",
0505 "\n",
0506 "# Add legend\n",
0507 "leg = ROOT.TLegend(.28, .02, .5, .3)\n",
0508 "leg.AddEntry(\"\",\"Dijet Events |#Delta#phi| > 2.75\",\"\")\n",
0509 "leg.AddEntry(\"\",\"p_{T}^{lead} > 20 GeV, p_{T}^{sub} > 15 GeV\",\"\")\n",
0510 "leg.AddEntry(h3_truth,\"Measured\",\"l\")\n",
0511 "leg.AddEntry(h3_meas,\"Truth\",\"l\")\n",
0512 "leg.AddEntry(h3_reco,\"Inverse Unfolded\",\"pe\")\n",
0513 "leg.Draw()\n",
0514 "leg.SetTextSize(0.04)\n",
0515 "\n",
0516 "canvas.cd()\n",
0517 "pad2 = ROOT.TPad(\"pad2\", \"\", 0, 0.05, 1, 0.3)\n",
0518 "pad2.SetTopMargin(0.02)\n",
0519 "pad2.SetBottomMargin(0.4)\n",
0520 "pad2.Draw()\n",
0521 "pad2.cd()\n",
0522 "ratio1 = h3_reco.Clone(\"ratio1\")\n",
0523 "ratio1.Divide(h3_truth)\n",
0524 "\n",
0525 "ratio1.GetYaxis().SetTitle(\"Unfolded/Measured\")\n",
0526 "ratio1.GetYaxis().SetNdivisions(204)\n",
0527 "ratio1.GetYaxis().SetRangeUser(0.9,1.1)\n",
0528 "ratio1.GetYaxis().SetTitleSize(20)\n",
0529 "ratio1.GetYaxis().SetTitleFont(43)\n",
0530 "ratio1.GetYaxis().SetTitleOffset(2)\n",
0531 "ratio1.GetYaxis().SetLabelFont(43)\n",
0532 "ratio1.GetYaxis().SetLabelSize(25)\n",
0533 "ratio1.GetXaxis().SetTitle(\"p_{T,lead} [GeV]\")\n",
0534 "ratio1.GetXaxis().SetTitleSize(20)\n",
0535 "ratio1.GetXaxis().SetTitleFont(43)\n",
0536 "ratio1.GetXaxis().SetTitleOffset(0)\n",
0537 "ratio1.GetXaxis().SetLabelFont(43)\n",
0538 "ratio1.GetXaxis().SetLabelSize(25)\n",
0539 "\n",
0540 "# Draw ratio plots\n",
0541 "ratio1.Draw(\"ep\")\n",
0542 "\n",
0543 "# Update canvas\n",
0544 "canvas.Update()\n",
0545 "canvas.Draw()\n",
0546 "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_inverse_half_unfold.png\")"
0547 ]
0548 },
0549 {
0550 "cell_type": "code",
0551 "execution_count": 13,
0552 "id": "2a5e4522-3834-40fc-a7c0-a6caea45402b",
0553 "metadata": {},
0554 "outputs": [
0555 {
0556 "name": "stderr",
0557 "output_type": "stream",
0558 "text": [
0559 "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0560 "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_14/dijet_leadjet_inverse_jes.png has been created\n"
0561 ]
0562 },
0563 {
0564 "data": {
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\n",
0566 "text/plain": [
0567 "<IPython.core.display.Image object>"
0568 ]
0569 },
0570 "metadata": {},
0571 "output_type": "display_data"
0572 }
0573 ],
0574 "source": [
0575 "canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 500)\n",
0576 "h_inverse_jes.GetYaxis().SetTitle(\"p_{T}^{truth lead}/p_{T}^{lead}\")\n",
0577 "h_inverse_jes.GetXaxis().SetTitle(\"p_{T}^{lead} [GeV]\")\n",
0578 "h_inverse_jes.GetYaxis().SetRangeUser(0.8,1.2)\n",
0579 "h_inverse_jes.GetXaxis().SetRangeUser(15,45)\n",
0580 "h_inverse_jes.Draw()\n",
0581 "canvas.Draw()\n",
0582 "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/dijet_leadjet_inverse_jes.png\")"
0583 ]
0584 },
0585 {
0586 "cell_type": "code",
0587 "execution_count": null,
0588 "id": "0ba5d6d0-4008-4ea6-8e53-808824c37d88",
0589 "metadata": {},
0590 "outputs": [],
0591 "source": []
0592 }
0593 ],
0594 "metadata": {
0595 "kernelspec": {
0596 "display_name": "Python (sPHENIX)",
0597 "language": "python",
0598 "name": "sphenix-env"
0599 },
0600 "language_info": {
0601 "codemirror_mode": {
0602 "name": "ipython",
0603 "version": 3
0604 },
0605 "file_extension": ".py",
0606 "mimetype": "text/x-python",
0607 "name": "python",
0608 "nbconvert_exporter": "python",
0609 "pygments_lexer": "ipython3",
0610 "version": "3.10.8"
0611 }
0612 },
0613 "nbformat": 4,
0614 "nbformat_minor": 5
0615 }
