atlas_main_double_lbt_0-10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88c93f3a",
   "metadata": {},
   "source": [
    "## Jet Analysis(Nuclear Modification Factor)( $R_{\\mathrm{AA}}$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06e4546",
   "metadata": {},
   "source": [
    "# Preparations\n",
    "Please set path of jet data files for both pp and PbPb.\n",
    "Also if you use jet cone size different from $R=0.4$, please change the value jetR below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dfb1c9a",
   "metadata": {},
   "source": [
    "# Loading all the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dcfa051a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set File Paths\n",
    "# Please set the path for pp jet data file\n",
    "#|0.2|1->0.3|2->0.4|3->0.6|4->0.8|5->1.0|\n",
    "file_pp = '../../data/music_cms/cms_pp_vir0.5_R0.2_trkpT4.0_maxT250_pt200-1001_m1-7_merged_315769.dat'\n",
    "file_pp1 = '../../data/music_cms/cms_pp_vir0.5_R0.3_trkpT4.0_maxT250_pt200-1001_m1-7_merged_338726.dat'\n",
    "file_pp2 = '../../data/music_cms/cms_pp_vir0.5_R0.4_trkpT4.0_maxT250_pt200-1001_m1-7_merged_352683.dat'\n",
    "file_pp3 = '../../data/music_cms/cms_pp_vir0.5_R0.6_trkpT4.0_maxT250_pt200-1001_m1-7_merged_369925.dat'\n",
    "file_pp4 = '../../data/music_cms/cms_pp_vir0.5_R0.8_trkpT4.0_maxT250_pt200-1001_m1-7_merged_380962.dat'\n",
    "file_pp5 = '../../data/music_cms/cms_pp_vir0.5_R1.0_trkpT4.0_maxT250_pt200-1001_m1-7_merged_389140.dat'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d70a0423",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please set the path for pbpb jet data file(LBT ONLY)######\n",
    "#|0.2|1->0.3|2->0.4|3->0.6|4->0.8|5->1.0|\n",
    "file_pbpb = '../../data/music_cms/LBT/R0.2/cms_music_0-10_R0.2_s1-7_35k.dat'\n",
    "file_pbpb1 ='../../data/music_cms/LBT/R0.3/cms_music_0-10_R0.3_s1-7_35k.dat'\n",
    "file_pbpb2 ='../../data/music_cms/LBT/R0.4/cms_music_0-10_R0.4_s1-7_35k.dat'\n",
    "file_pbpb3 ='../../data/music_cms/LBT/R0.6/cms_music_0-10_R0.6_s1-7_35k.dat'\n",
    "file_pbpb4 ='../../data/music_cms/LBT/R0.8/cms_music_0-10_R0.8_s1-7_35k.dat'\n",
    "file_pbpb5 ='../../data/music_cms/LBT/R1.0/cms_music_0-10_R1.0_s1-7_35k.dat'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fcee61ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preperations\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# pi and 2pi \n",
    "pi = 3.1415926\n",
    "twopi = 2.0*pi\n",
    "\n",
    "# define plot style\n",
    "width = 0.05\n",
    "plotMarkerSize = 8\n",
    "labelfontsize = 15\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.figsize'] = [7.5, 6.5]\n",
    "mpl.rcParams['lines.linewidth'] = 2\n",
    "mpl.rcParams['xtick.top'] = True\n",
    "mpl.rcParams['xtick.labelsize'] = 15\n",
    "mpl.rcParams['xtick.major.width'] = 1.0\n",
    "mpl.rcParams['xtick.minor.width'] = 0.8\n",
    "mpl.rcParams['xtick.minor.visible'] = True\n",
    "mpl.rcParams['xtick.direction'] = \"in\"\n",
    "mpl.rcParams['ytick.right'] = True\n",
    "mpl.rcParams['ytick.labelsize'] = 15\n",
    "mpl.rcParams['ytick.major.width'] = 1.0\n",
    "mpl.rcParams['ytick.minor.width'] = 0.8\n",
    "mpl.rcParams['ytick.minor.visible'] = True\n",
    "mpl.rcParams['ytick.direction'] = \"in\"\n",
    "mpl.rcParams['legend.fontsize'] = 15\n",
    "mpl.rcParams['legend.numpoints'] = 1\n",
    "mpl.rcParams['font.size'] = 15\n",
    "mpl.rcParams['savefig.format'] = \"pdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "707d8005",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ratio_error(v1,e1,v2,e2):\n",
    "  #v1, e1: numerator value and error\n",
    "  #v2, e2: denominator value and error  \n",
    "  error1 = e1/v2\n",
    "  error2 = (e2/v2)*(v1/v2)\n",
    "  error = np.sqrt(error1*error1+error2*error2)\n",
    "  return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe7826ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load files\n",
    "data_pp = np.loadtxt(file_pp, delimiter=',')\n",
    "data_pbpb = np.loadtxt(file_pbpb, delimiter=',')\n",
    "\n",
    "data_pp1 = np.loadtxt(file_pp1, delimiter=',')\n",
    "data_pbpb1 = np.loadtxt(file_pbpb1, delimiter=',')\n",
    "\n",
    "data_pp2 = np.loadtxt(file_pp2, delimiter=',')\n",
    "data_pbpb2 = np.loadtxt(file_pbpb2, delimiter=',')\n",
    "\n",
    "data_pp3 = np.loadtxt(file_pp3, delimiter=',')\n",
    "data_pbpb3 = np.loadtxt(file_pbpb3, delimiter=',')\n",
    "\n",
    "data_pp4 = np.loadtxt(file_pp4, delimiter=',')\n",
    "data_pbpb4 = np.loadtxt(file_pbpb4, delimiter=',')\n",
    "\n",
    "data_pp5 = np.loadtxt(file_pp5, delimiter=',')\n",
    "data_pbpb5 = np.loadtxt(file_pbpb5, delimiter=',')\n",
    "\n",
    "# Indices of the data array\n",
    "i_pp = data_pp[:,0] \n",
    "i_pbpb = data_pbpb[:,0] \n",
    "\n",
    "i_pp1 = data_pp1[:,0] \n",
    "i_pbpb1 = data_pbpb1[:,0]\n",
    "\n",
    "i_pp2 = data_pp2[:,0] \n",
    "i_pbpb2 = data_pbpb2[:,0]\n",
    "\n",
    "i_pp3 = data_pp3[:,0] \n",
    "i_pbpb3 = data_pbpb3[:,0]\n",
    "\n",
    "i_pp4 = data_pp4[:,0] \n",
    "i_pbpb4 = data_pbpb4[:,0]\n",
    "\n",
    "i_pp5 = data_pp5[:,0] \n",
    "i_pbpb5 = data_pbpb5[:,0]\n",
    "\n",
    "# Get Indices of jets in the data array\n",
    "jet_id_pp = np.where(i_pp < 0.1)\n",
    "jet_id_pbpb = np.where(i_pbpb < 0.1)\n",
    "\n",
    "jet_id_pp1 = np.where(i_pp1 < 0.1)\n",
    "jet_id_pbpb1 = np.where(i_pbpb1 < 0.1)\n",
    "\n",
    "jet_id_pp2 = np.where(i_pp2 < 0.1)\n",
    "jet_id_pbpb2 = np.where(i_pbpb2 < 0.1)\n",
    "\n",
    "jet_id_pp3 = np.where(i_pp3 < 0.1)\n",
    "jet_id_pbpb3 = np.where(i_pbpb3 < 0.1)\n",
    "\n",
    "jet_id_pp4 = np.where(i_pp4 < 0.1)\n",
    "jet_id_pbpb4 = np.where(i_pbpb4 < 0.1)\n",
    "\n",
    "jet_id_pp5 = np.where(i_pp5 < 0.1)\n",
    "jet_id_pbpb5 = np.where(i_pbpb5 < 0.1)\n",
    "\n",
    "# Extract jets\n",
    "jets_pp = data_pp[jet_id_pp]\n",
    "jets_pbpb = data_pbpb[jet_id_pbpb]\n",
    "\n",
    "jets_pp1 = data_pp1[jet_id_pp1]\n",
    "jets_pbpb1 = data_pbpb1[jet_id_pbpb1]\n",
    "\n",
    "jets_pp2 = data_pp2[jet_id_pp2]\n",
    "jets_pbpb2 = data_pbpb2[jet_id_pbpb2]\n",
    "\n",
    "jets_pp3 = data_pp3[jet_id_pp3]\n",
    "jets_pbpb3 = data_pbpb3[jet_id_pbpb3]\n",
    "\n",
    "jets_pp4 = data_pp4[jet_id_pp4]\n",
    "jets_pbpb4 = data_pbpb4[jet_id_pbpb4]\n",
    "\n",
    "jets_pp5 = data_pp5[jet_id_pp5]\n",
    "jets_pbpb5 = data_pbpb5[jet_id_pbpb5]\n",
    "\n",
    "# Extract associated charged particles for pp\n",
    "assoc_pp= []\n",
    "for i in range(len(jet_id_pp[0])-1):\n",
    "  chunck = data_pp[jet_id_pp[0][i]+1:jet_id_pp[0][i+1]]\n",
    "  assoc_pp.append(chunck)\n",
    "chunck = data_pp[jet_id_pp[0][-1]+1:]\n",
    "assoc_pp.append(chunck)\n",
    "\n",
    "assoc_pp1= []\n",
    "for i in range(len(jet_id_pp1[0])-1):\n",
    "  chunck = data_pp1[jet_id_pp1[0][i]+1:jet_id_pp1[0][i+1]]\n",
    "  assoc_pp1.append(chunck)\n",
    "chunck = data_pp1[jet_id_pp1[0][-1]+1:]\n",
    "assoc_pp1.append(chunck)\n",
    "\n",
    "assoc_pp2= []\n",
    "for i in range(len(jet_id_pp2[0])-1):\n",
    "  chunck = data_pp2[jet_id_pp2[0][i]+1:jet_id_pp2[0][i+1]]\n",
    "  assoc_pp2.append(chunck)\n",
    "chunck = data_pp2[jet_id_pp2[0][-1]+1:]\n",
    "assoc_pp2.append(chunck)\n",
    "\n",
    "assoc_pp3= []\n",
    "for i in range(len(jet_id_pp3[0])-1):\n",
    "  chunck = data_pp3[jet_id_pp3[0][i]+1:jet_id_pp3[0][i+1]]\n",
    "  assoc_pp3.append(chunck)\n",
    "chunck = data_pp3[jet_id_pp3[0][-1]+1:]\n",
    "assoc_pp3.append(chunck)\n",
    "\n",
    "assoc_pp4= []\n",
    "for i in range(len(jet_id_pp4[0])-1):\n",
    "  chunck = data_pp4[jet_id_pp4[0][i]+1:jet_id_pp4[0][i+1]]\n",
    "  assoc_pp4.append(chunck)\n",
    "chunck = data_pp4[jet_id_pp4[0][-1]+1:]\n",
    "assoc_pp4.append(chunck)\n",
    "\n",
    "assoc_pp5= []\n",
    "for i in range(len(jet_id_pp5[0])-1):\n",
    "  chunck = data_pp5[jet_id_pp5[0][i]+1:jet_id_pp5[0][i+1]]\n",
    "  assoc_pp5.append(chunck)\n",
    "chunck = data_pp5[jet_id_pp5[0][-1]+1:]\n",
    "assoc_pp5.append(chunck)\n",
    "\n",
    "# Extract associated charged particles for pbpb\n",
    "assoc_pbpb= []\n",
    "for i in range(len(jet_id_pbpb[0])-1):\n",
    "  chunck = data_pbpb[jet_id_pbpb[0][i]+1:jet_id_pbpb[0][i+1]]\n",
    "  assoc_pbpb.append(chunck)\n",
    "chunck = data_pbpb[jet_id_pbpb[0][-1]+1:]\n",
    "assoc_pbpb.append(chunck)\n",
    "\n",
    "assoc_pbpb1= []\n",
    "for i in range(len(jet_id_pbpb1[0])-1):\n",
    "  chunck = data_pbpb1[jet_id_pbpb1[0][i]+1:jet_id_pbpb1[0][i+1]]\n",
    "  assoc_pbpb1.append(chunck)\n",
    "chunck = data_pbpb1[jet_id_pbpb1[0][-1]+1:]\n",
    "assoc_pbpb1.append(chunck)\n",
    "\n",
    "assoc_pbpb2= []\n",
    "for i in range(len(jet_id_pbpb2[0])-1):\n",
    "  chunck = data_pbpb2[jet_id_pbpb2[0][i]+1:jet_id_pbpb2[0][i+1]]\n",
    "  assoc_pbpb2.append(chunck)\n",
    "chunck = data_pbpb2[jet_id_pbpb2[0][-1]+1:]\n",
    "assoc_pbpb2.append(chunck)\n",
    "\n",
    "assoc_pbpb3= []\n",
    "for i in range(len(jet_id_pbpb3[0])-1):\n",
    "  chunck = data_pbpb3[jet_id_pbpb3[0][i]+1:jet_id_pbpb3[0][i+1]]\n",
    "  assoc_pbpb3.append(chunck)\n",
    "chunck = data_pbpb3[jet_id_pbpb3[0][-1]+1:]\n",
    "assoc_pbpb3.append(chunck)\n",
    "\n",
    "assoc_pbpb4= []\n",
    "for i in range(len(jet_id_pbpb4[0])-1):\n",
    "  chunck = data_pbpb4[jet_id_pbpb4[0][i]+1:jet_id_pbpb4[0][i+1]]\n",
    "  assoc_pbpb4.append(chunck)\n",
    "chunck = data_pbpb4[jet_id_pbpb4[0][-1]+1:]\n",
    "assoc_pbpb4.append(chunck)\n",
    "\n",
    "assoc_pbpb5= []\n",
    "for i in range(len(jet_id_pbpb5[0])-1):\n",
    "  chunck = data_pbpb5[jet_id_pbpb5[0][i]+1:jet_id_pbpb5[0][i+1]]\n",
    "  assoc_pbpb5.append(chunck)\n",
    "chunck = data_pbpb5[jet_id_pbpb5[0][-1]+1:]\n",
    "assoc_pbpb5.append(chunck)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38aafd6",
   "metadata": {},
   "source": [
    "## Jet-$R_{\\mathrm{AA}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "760626b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R=0.3/0.2= [1.08107391 1.06643159 1.09826343 1.0993677  1.04522903]\n",
      "R=0.4/0.2= [1.13168569 1.10707858 1.15661189 1.1279165  1.09701365]\n",
      "R=0.6/0.2= [1.18848962 1.16407164 1.20533627 1.15082099 1.16323418]\n",
      "R=0.8/0.2= [1.2180547  1.19135318 1.22732049 1.16635575 1.1792821 ]\n",
      "R=1.0/0.2= [1.22978061 1.20244544 1.24267477 1.18146972 1.1779314 ]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAHBCAYAAADjMSi8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABJa0lEQVR4nO3dfZyVdZ3/8dcHGIUZBQ43Oo3lLSSGJS1n86YbS0YUk7QCMTPXLKfd1twY6WaTm5Hx529RBGuzXQ62ZWZlkOkPA8NBc1PRZdjSEpDwJpGBGOEgyjACw+f3x3VmHIZr7q9zM+e8n4/H9Zg51833+py5hjkfvrfm7oiIiIi01S/bAYiIiEhuUpIgIiIioZQkiIiISCglCSIiIhJKSYKIiIiEUpIgIiIioXIuSTCzqWb2/8xsi5m9ZWZrzexzXbjuSDO73cy2m9keM/uNmZ2YgZBFRETyUs4lCUAl8BYwHfgU8BjwMzP7WifXfQ+4GpgBTAFGAI+Y2cD0hSoiIpK/LNcmUzKzEe7+ept9PwPOdveT2rnm3cArwDXu/pPUvuOAl4Gvuvtd6Y1aREQk/+RcTULbBCHlD0BZB5dNTH29v1U5W4AngEnRRSciIlI4ci5JaMfZwMYOjo8BXnP3t9rsX586JiIiIt00INsBdMbMJgCXAtd0cFoM2BWyP5k6dpgjjjjC9+/f3/L6Xe96F2VlHVVWSC6or69n5MiR2Q6jTyrEn12+vOe+8j5yLc5sx5Op+9fV1bF169bml/vd/Yioys7pJCE1OuFnwIPu/uMoyy4qKmLfvn1RFikZEI/Hqa2tzXYYfVIh/uzy5T33lfeRa3FmO55s3N/MiqIsL2ebG8xsGLAC+Cvw+U5OTwJDQvbHUscOM2LEiF7FJ9lRUVGR7RD6rEL82eXLe+4r7yPX4sx2PNm+fxRybnQDgJkVAzXAsQSjGrZ3cv41QAIY4u57Wu1fBexy98+2vSYej3suZbwiIiK9ZWa4u0VVXs7VJJjZAGAJMBq4sLMEIWVl6uunW5VTBnyUoDZCRESkEGzt/JSuy8U+CT8ALgL+BRhuZsNbHfuDu7+dqiHA3Sekvr5mZj8E7jAzA+qBKoKmip9mMngREZEsqouysFxMEprnPPhuyLGTCCZN6h9y7HpgD7AAKAYeBz7n7o1piFFERCTv5VyS4O4nduGcj4fse5tgSufK6KMSEREpPDmXJOSK3bt3s337dlrPpSCZV1RUxDHHHMPgwYOzHYqISMFRkhBi9+7d/O1vf+O4445j0KBBBN0cJNPcnb1797JlyxYAJQoiIhmWc6MbcsH27ds57rjjKC4uVoKQRWZGcXExxx13HNu3d2WQi4iIRElJQoj9+/czaNCgbIchKYMGDVKzj4hIFihJaIdqEHKHnoWISHYoScgEs2ATERHpQ5QkiIiISCglCWmWTCZZDFQDixcvJpkMXW8qLaqqqjCzlq20tJSLL76Y5557rtdlr1u3jgkTJlBcXExZWRmzZ8+mqampy9cfPHiQeDyOmfHQQw/1Oh4REYlewSYJ9fX1xONxEolEWsp3d6pnzeLksjJWAW8DNdOnc3JZGdWzZpGphbWGDBnC6tWrWb16NXfccQcbN27k/PPPZ+fOnT0uM5lMUl5ejpnx4IMPMnv2bG6//XbmzJnT5TLuuusuXnvttR7HICIi70gkEsTjcYBolzh294Lcxo8f7+1Zt25du8e6au7Mmf53xcW+BdxbbVvA/6642OfOnNnre3Rmzpw5Pnz48EP2rV692gG/9957e1zuLbfc4kOHDvU33nijZd+8efN80KBBh+xrz86dO33EiBF+1113OeDLli3r9JoonomISL4Daj3Cz8qCrUlIp2QyyYL581nW0EBZm2NlwLKGBhbMn8+uXbsyHtsZZ5wBwObNm3tcxooVK7jgggsOmdzo8ssvZ+/evTz++OOdXj9r1iw+/OEPM2HChB7HICIi6ackIUqpUQxLhw1jYmPjYQlCszJgYmMjS2KxjI98ePXVVwE46aSTWvYdOHCg081bNY9s2LCBMWPGHFLu8ccfT3FxMRs2bOjw/s899xz/9V//xfz58yN8VyIikg5KEtJgGzC6k3NGpc7LhOYP+hdffJHrrruOcePGcckll7QcLyoq6nS7++67W85PJpMMHTr0sPvEYrFOO2Z+7Wtf47rrrmPUqFGRvT8REUkPrd0QpdT/tksXL6Zm+nTYs6fdUzeVlFC+cCFce21aQ9qxYwdFRUUtr4cPH86aNWs48sgjW/atWbOm03Ja1zz01C9+8QteeOEFli1b1uuyREQk/ZQkpMGUKVP45vXXUwehTQ5bgJVNTSyaOjXtsQwZMoSamhqampp49tlnmTFjBldccQVPPvkk/foFFUnjxo3rtJz+/fu3fB+LxXjjjTcOOyeZTBKLxUKv379/P9/4xjf41re+xcGDB9m1axe7d+8GYM+ePbz55pscffTRPXiHIiKSLmpuSINYLEbljBlMLi6mrs2xOuBTxcVUzpgRWmUftQEDBhCPxznzzDOpqKjgzjvv5Omnn2bJkiUt53S3uWHMmDGH9T3YvHkzDQ0Nh/VVaLZnzx5ee+01KisricVixGKxlk6Ul19+OR/84AfT8O5FRKQ3VJOQJjPnzgVg7Pz5TGxsZBRBE8PKpiYqKytbjmfalVdeybx585g3bx7Tpk0Dut/cMGnSJG677bZD/vd/3333MWjQIM4999zQ64866igee+yxQ/Zt27aNz33uc9xyyy2cd955PX1LIiKSLlGOp+xLW7rnSWi2c+dOT4DPBU8kEp5MJiMruzNh8yS4u997770OeE1NTY/K3blzp5eWlnp5ebk/8sgjvmjRIi8pKfEbb7zxkPNOOeUUv+aaa9ot5+WXX9Y8CSIiEULzJPQtsViMa4FZwLXXXpuRJobOTJs2jdGjR3Prrbf26PpYLMaqVatoampi8uTJzJkzh+nTp3PTTTcdct6BAwe6NVWziIjkFnPPzPTAuSYej3ttbW3osfXr13PaaadFd7PmeRAK9GcdhcifiYhIHjKzte4ej6o89UnIBCUHIiLSB6m5QUREREIpSRAREZFQShJEREQkVMEmCfX19cTjcRKJRLZDERER6ZVEIkE8HgcYEWW5BdtxceTIkbQ3ukFERKQvqaiooKKiAjN7PcpyC7YmQURERDqmJCEDzN6ZKkFERKSvUJIgIiIioZQk5LGqqirMrGUrLS3l4osv5rnnnut12evWrWPChAkUFxdTVlbG7NmzuzwF8/3338/f//3fM2jQIIYPH86FF17Inj17eh2TiIhES0lCmiWTSWAxUM3ixYtTrzNnyJAhrF69mtWrV3PHHXewceNGzj//fHbu3NnjMpPJJOXl5ZgZDz74ILNnz+b2229nzpw5nV571113ccUVVzBp0iRWrFjBXXfdxejRozlw4ECP4xERkTSJcrWovrSlexXIgwcP+syZc33gwKEO0xxu9JKSy3zgwKE+c+ZcP3jwYK/v0ZmwVSBXr17tgN977709LveWW27xoUOH+htvvNGyb968eT5o0KBD9rVVX1/vRx11lCcSiW7fU6tAioh0Dq0C2TfMnn0zCxY8QGPj88AvgJvZs+c+GhufZ8GCB5g9++asxHXGGWcAsHnz5h6XsWLFCi644AIGDx7csu/yyy9n7969PP744+1e98tf/hKAf/iHf+jxvUVEJHOUJKRBMplk/vwFNDQsA8raHC2joWEZ8+cvYNeuXRmP7dVXXwXgpJNOatl34MCBTjdvtUjVhg0bGDNmzCHlHn/88RQXF7Nhw4Z27/3MM89w6qmn8sMf/pB3v/vdFBUVceaZZ/LUU09F/C5FRCQKShIi1DzUcdiwpTQ2TuTwBKFZGY2NE4nFlmRkeGTzB/2LL77Iddddx7hx47jkkktajhcVFXW63X333S3nJ5NJhg4deth9YrFYh30utm3bxgsvvMDNN9/MvHnzWLZsGSUlJVx44YX87W9/i/Q9i4hI7xXsjIvptQ0Y3ck5o1LnpdeOHTsoKipqeT18+HDWrFnDkUce2bJvzZo1nZbTuuahp9ydt956iyVLlnDhhRcCcM4553DCCSfw/e9/n+rq6l7fQ0REoqMkIULNNfKLF5cyfXoNHY3qKynZxMKF5Vx7bXpjGjJkCDU1NTQ1NfHss88yY8YMrrjiCp588kn69QsqksaNG9dpOf3792/5PhaL8cYbbxx2TjKZJBaLtVtGLBbDzPj4xz/esm/w4MGMHz+edevWdf1NiYhIRqi5IQ2mTJlCU9NKoK6dM7bQ1LSSqVOnpj2WAQMGEI/HOfPMM6moqODOO+/k6aefZsmSJS3ndLe5YcyYMYf1Pdi8eTMNDQ2H9VVo7bTTTmvpMduau7ckLCIikjv0lzkNYrEYM2ZUUlw8mcMThTqKiz/FjBmVoe366XbllVcyduxY5s2b17JvzZo1nW6TJ09uOX/SpEn89re/5c0332zZd9999zFo0CDOPffcdu998cUXA/DYY4+17HvjjTdYu3Zty6gLERHJIVGOp+xLW2bnSbjM4Ts5MU+Cu/u9997rgNfU1PSo3J07d3ppaamXl5f7I4884osWLfKSkhK/8cYbDznvlFNO8WuuueaQfZdccomXlpb6j3/8Y3/ooYf8Yx/7mI8YMcJ37tzZ4T01T4KISOfQPAnRqK+vJx6Pk0gk0lK+mVFdPYu6upeAcmAgCxeWs3Xry1RXz8KyuOLTtGnTGD16NLfeemuPro/FYqxatYqmpiYmT57MnDlzmD59OjfddNMh5x04cOCwqZp/+tOfcumll1JZWcmUKVMoKiri0Ucf7bAvg4iIdCyRSBCPxwFGRFmueZv24UIRj8e9trY29Nj69es57bTTIrtXcz5QoD/qSET9TERE8pGZrXX3eFTlaXRDBig5EBGRvqhgmxtERESkY0oSREREJJSSBBEREQmlJEFERERCKUkQERGRUEoSREREJJSSBBEREQmVc0mCmY0ys0Vm9pyZNZnZ77p4XdzMVprZztRWY2ZnpjncLrGbDLspezMsioiI9ETOJQnAWOAi4AVgY1cuMLP3ADUEk0N9IbUNAB4xsxPSFKeIiEhey8UkYZm7v8fdpwLPd/GaTwJHA59299+4+2+ATwMlBAlH1iSTSVgLPA6LFy8OXmdIVVUVZtaylZaWcvHFF/Pcc8/1uux169YxYcIEiouLKSsrY/bs2Yet0xCmtraWiRMnMmzYMIYNG0Z5eTnPPPNMr+MREZHo5VyS4O4He3BZEXAA2NNq31upfVmp53d3ZlXNouz4MngpiGT6D6ZTdnwZs6pmkak1M4YMGcLq1atZvXo1d9xxBxs3buT8889n586dPS4zmUxSXl6OmfHggw8ye/Zsbr/9dubMmdPhdZs3b6a8vJwDBw5wzz33cM8993DgwAHOP/98/vrXv/Y4HhERSY98WbvhV8Bc4HYz+z+pfbOBJLAkGwHNvmk2C360gMaKRhgc7NvDHtgNC360AIDqquq0xzFgwADOOussAM466yxOPPFEzj77bB5++GGuuOKKHpX5n//5n+zdu5f777+fwYMHc/7557N7926qqqr45je/yeDBg0Ov+81vfsObb77Jr3/9a4YMGQLAOeecw4gRI1i+fDn/9E//1LM3KSIiaZFzNQk94e51wCeAzwJ/S22fAS5w9/qwa+rq6g6piq+qqoosnmQyyfzb59MwpaElQWgxGBqmNDD/9vns2rUrsnt21RlnnAEE/6vvqRUrVnDBBRcckgxcfvnl7N27l8cff7zd6/bv38+AAQMoKSlp2XfUUUcxYMCAjNWsiIjkm9ZNy0BZlGXnRZJgZu8iqDFYC0xKbWuB35jZ8WHXlJWV4e4tWxRJQvMohmH/MIzGExoPTxCaDYbGExqJXRXL+MiHV199FYCTTjqpZd+BAwc63Vp/iG/YsIExY8YcUu7xxx9PcXExGzZsaPfen/3sZykuLuaGG25g+/btbN++nenTpxOLxZg6dWrE71REpDBUVVW1fJYBdVGWnRdJAvANgn4JU9z9YXd/mKBWoQmYkfFo3gKGdXLOsNR5GdD8Qf/iiy9y3XXXMW7cOC655JKW40VFRZ1ud999d8v5yWSSoUOHHnafWCzWYcfMsrIyHnvsMX71q19x7LHHcuyxx3L//ffz29/+lpEjR0b6nkVEpPfypU/CGOB5d9/fvMPd95nZ88ApmQrC5wT/2168eDHTfzA96IPQjpI3S1j41YVce+21aY1px44dFBUVtbwePnw4a9as4cgjj2zZt2bNmk7LaV3z0FNbt25l6tSpjB8/nrvuuguAO++8k09+8pM89dRTHH98aKWPiIhkSb4kCX8FLjKzI9x9H4CZHQmcDizLdDBTpkzh+srrYTfhTQ67oWlTU0aq2IcMGUJNTQ1NTU08++yzzJgxgyuuuIInn3ySfv2CiqRx48Z1Wk7//v1bvo/FYrzxxhuHnZNMJonFYu2Wcdttt7F//36WLl3akricd955jB49mvnz5/O9732vm+9ORETSKeeaG8ys2MymmNkU4DhgZPNrMytOnbPJzH7Y6rK7CDpr/NrMPmlmFwMPAO8CEhl+C8RiMWbcMIPipcVBotDabiheWsyMG2aEVtlHbcCAAcTjcc4880wqKiq48847efrpp1my5J1BH91tbhgzZsxhfQ82b95MQ0PDYX0VWtuwYQNjx449pGbjiCOOYOzYsbz44osRvmsREYlCLtYkHMPhwxabX58EvEIQd8t/bd19rZldCMwB7knt/hNwvrs/m9Zo2zF3zlwA5t8+P+jEOCxoYmja1ETlDZUtxzPtyiuvZN68ecybN49p06YB3W9umDRpErfddhtvvvkmRx99NAD33XcfgwYN4txzz223jBNOOIHly5ezb98+jjjiCADefvtt/vznPzN58uTevC0REUmH1j38C2kbP368t2fdunXtHuuunTt3OpNxPoEnEglPJpORld2ZOXPm+PDhww/bf++99zrgNTU1PSp3586dXlpa6uXl5f7II4/4okWLvKSkxG+88cZDzjvllFP8mmuuaXldW1vrAwYM8IsuusgfeughX7ZsmV944YU+YMAA/+Mf/9jhPaN8JiIi+Qqo9Qg/K3OuuSHfxGIxGA+cC9dee21Gmhg6M23aNEaPHs2tt97ao+tjsRirVq2iqamJyZMnM2fOHKZPn85NN910yHkHDhw4ZKrm8ePH8/DDD/Pmm2/yhS98gauuuoqGhgYeeeSRlvkbREQkd5gX6CQ28Xjca2trQ4+tX7+e0047LbJ7Nc+D0Dz6Qbov6mciIpKPzGytu8ejKi8X+yTkHSUHIiLSF6m5QUREREIpSRAREZFQShJEREQklJIEERERCaUkQUREREIVbJJQX19PPB4nkcj4rM0iIiKRSiQSxONxgBFRlluwQyBHjhxJe/MkRM1SXzUQUkRE0qGiooKKigrM7PUoyy3YmgQRERHpmJKEPFZVVYWZtWylpaVcfPHFPPfcc70qd9OmTXzlK1/hAx/4AP379+fjH/94l6994403+OIXv0gsFmPIkCF8/vOfZ8eOHb2KR0RE0qNgmxsyJZlMwtKlsG0bi0tLmTJlSrCeQ4YMGTKEhx9+GIBXXnmF2bNnc/7557N+/XqGDRvWozKff/55li9fzllnncX+/fu7de1ll13Gxo0bueuuu+jXrx/f+ta3uPTSS/n973/fo1hERCR9VJOQJu7OrOpqyk4+GVatgrffZnpNDWUnn8ys6moytWbGgAEDOOusszjrrLO4/PLL+clPfsL27dtbEoeemDx5Mps3b2bJkiWMHTu2y9etXr2alStXcvfdd/PZz36WT3/60/z0pz/liSeeoKampsfxiIhIeqgmIU1m33wzCx54gMbnn4eyMgD2ANTVsWDyZACqZ83KeFzNqy1u3ry5x2X069ez3HLFihUce+yxfOxjH2vZ96EPfYiTTjqJFStWUF5e3uOYREQkeqpJSINkMsn8BQtoWLasJUFoUVZGw7JlzF+wgF27dmU8tldffRWAk046qWXfgQMHOt2iqPnYsGEDY8aMOWz/aaedxoYNG3pdvoiIREtJQoQstQ1bupTGiRMPTxCalZXROHEisSVLWq5Jp+YP+hdffJHrrruOcePGcckll7QcLyoq6nS7++67ex1HMplk6NChh+2PxWJB3w0REckpam5Ih23bYPTojs8ZNSo4L8127NhBUVFRy+vhw4ezZs0ajjzyyJZ9a9as6bSc1jUPIiJSGJQkRKi5Qn5xaSnTa2qCPgjtKNm0iYXl5Vyb5piGDBlCTU0NTU1NPPvss8yYMYMrrriCJ598sqVvwbhx4zotp3///r2OJRaLUV9ff9j+ZDKZ0REfIiLSNWpuSIMpU6bQtHIl1NWFn7BlC00rVzJ16tS0xzJgwADi8ThnnnkmFRUV3HnnnTz99NMsWbKk5ZxMNTeMGTMmtO9Be30VREQku1STkAaxWIwZlZUsmDz58M6LdXUUf+pTVFZWhrbPp9uVV17JvHnzmDdvHtOmTQMy19wwadIkqqureeKJJ/jIRz4CQG1tLS+99BKTJk3qdfkiIhItJQlpMnfmTADmjx0bdGIcNYqSTZtoWrmSysrKluOZZmZ85zvf4fOf/zyrVq1iwoQJzYuCdFlDQwPLly8HYMuWLezevZulS5cCcNFFF1FcXAzAqFGjOPfcc/nhD38IwNlnn83EiRO56qqrmD9/fstkSh/5yEc0/FFEJAepuSFNzIzqWbOoe+klKC+HgQNZWF7O1pdfpnrWLMzSPaahfdOmTWP06NHceuutPbp++/btTJ06lalTp/L000+zbt26ltfbt29vOe/AgQM0NTUdcu19993HueeeyzXXXMNVV13F+PHj+fWvf92r9yMiIulhmZr5L9fE43FvbxXI9evXc9ppp0V2L60C2XtRPxMRkXxkZmvdvXvVwx0o2JqE+vp64vE4iUQi7fdylCCIiEj6JBKJ5qbjEVGWW7B9EkaOHEl7NQkiIiJ9SUVFBRUVFZjZ61GWW7A1CSIiItIxJQkiIiISSklCOwq1Q2cu0rMQEckOJQkhioqK2Lt3b7bDkJS9e/cesv6EiIhkhpKEEMcccwxbtmyhoaFB/4vNInenoaGBLVu2cMwxx2Q7HBGRglOwoxs6MnjwYADq6urYv39/lqMpbEVFRRx77LEtz0RERDJHSUI7Bg8erA8mEREpaGpuEBERkVBKEkRERCSUkgQREREJpSRBREREQilJEBERkVBKEkRERCSUkgQREREJVbBJQn19PfF4nEQike1QREREeiWRSBCPxwFGRFmuFeq0w/F43Gtra7MdhoiISGTMbK27x6Mqr2BrEkRERKRjShJEREQklJIEERERCaUkQUREREIpSRAREZFQShJEREQklJIEERERCaUkQURERELlXJJgZqPMbJGZPWdmTWb2u25c+xkzW2Nme81sh5k9bGYlaQxXREQkb+VckgCMBS4CXgA2dvUiM/sy8DNgBTAJ+DLwF2BAGmIUERHJe7n4AbrM3R8EMLOldGEeajMbASwEvubui1sd+nV6QhQREcl/OVeT4O4He3DZZamvd0cZi4iISCHLuSShh84kaJ74kpm9Zmb7zewZMzsn24GJiIj0VfmSJJQCpwIzgW8Bk4E9wMNmdmzYBXV1dZhZy1ZVVZWxYEVERKJSVVXV8lkGlEVZdk4vFd3cJ8HdP97JeSuB84FJ7v5wat9g4K/A9919VttrtFS0iIjkGy0VHS4JOPC75h3uvhtYC7wvSzGJiIj0afmSJKwHLLW1ZkBPOkKKiIgUvHxJEh5Kff1E8w4zGwKMB57NSkQiIiJ9XM7Nk2BmxQSTKQEcBww2symp18vdvcHMNgGPu/uXANy91sweBH5oZt8GXge+CewH7szsOxAREckPOZckAMcAS9rsa359EvAKQdz925xzJXAbsAAoBp4EznP3ZNoiFRERyWM5lyS4+ysc3reg7Tknhux7C/in1CYiIiK9lC99EkRERCRiShJEREQklJIEERERCaUkQUREREIpSRAREZFQShJEREQklJIEERERCaUkQUREREIVbJJQX19PPB4nkUhkOxQREZFeSSQSxONxgBFRlmvuHmV5fUY8Hvfa2tpshyEiIhIZM1vr7vGoyivYmgQRERHpmJIEERERCaUkQUREREIpSRAREZFQShJEREQklJIEERERCaUkQUREREIpSRAREZFQShJEREQkVKdJgpl92syuN7NT2+y/Ln1hiYiISLZ1mCSY2b8B/wKMAh4xs6+3OnxNGuMSERGRLBvQyfFPAh909wNmdhOwxMyOc/dvAJb+8ERERCRbOmtu6OfuBwDcfQdwIXCimf2wC9eKiIhIH9bZB/1WM/u75hfuvg+YBjhwejoDExERkezqLEm4GqhrvcPdD7r7l4GPpiuoTKivrycej5NIJLIdioiISK8kEgni8TjAiCjLNXfv2YVmf+fu/xtlMJkUj8e9trY222GIiIhExszWuns8qvJ606+gOqogREREJPf0OElw909GGYiIiIjkls7mSSgxs7lm9ryZvZHanjezajM7OlNBioiISOZ1VpNwL3Ak8Gng2NT2aeCI1DERERHJU51NpnSau1/aZt9G4Ftm9kJ6QhIREZFc0FlNwltmdkHbnWZ2IbAnPSGJiIhILuisJuEq4D/N7C7gtdS+dwOvAP+QxrhEREQkyzpMEtz9eeCjZnYMQXJgwGvu/rdMBCciIiLZ01lNAgDuvh3YnuZYREREJIdokSYREREJpSRBREREQnU5STCz21t/FRERkfzWnZqET6S+npeOQERERCS3qLlBREREQhVsklBfX088HieRSGQ7FBERkV5JJBLE43GAEVGWa+7etRPN/tfd/87M/uDuH+zWTczMu3qjDInH415bW5vtMERERCJjZmvdPR5VeWmtSTCzsWZ2K/BqOu8jIiIi0etOktA8mVKHsy2a2TFmNt3M/gCsBXYBE3oWnoiIiGRLl5MEd7+w9de2zOxyM1sO/C9wPHAtsNXdb3H3jVEEKyIiIpnTpWmZu+hnwO+Aj7j7KwBmllP9EERERKTrouyT8EGC5oXfm9mjZvZFoH+E5YuIiEgGRZYkuPuz7v4N4ATg3wj6IcTMbKmZTY3qPiIiIpIZPUoSzGySmT0RdszdD7r7Sne/EngXsAz4x17EKCIiIlnQYZJgZh82s/Vm9paZ/cjMTjWz/wH+HfiPLpR/IjAWeG/vQxUREZFM6qwm4bvAQiAObACeAR4FTnP3e8Mu0BBIERGR/NBZklDk7gl33+Du84Dd7v5td9/f9sSohkCa2SgzW2Rmz5lZk5n9rhvvBzPrZ2a1ZuZmdnF3rhUREZF3dDYEsqnN69c7ODeqIZBjgYuAp4GiHlz/ZeDdPbhOREREWumsJmFc6n/zTWbW1Or1wdTr1qIaArnM3d/j7lOB57tzoZnFgP8D3NiD+4qIiEgrHSYJ7t7P3fu32vq1/trm3EiGQLr7wZ68kZRq4ElgVS/KEBEREaKdcRFo+ZBfCaw0sxJgKlABLIn6Xq2Z2QeAa4APpPM+IiIihSKtq0C6+x53/7G7n5/O+6T8O/B9d9/UlZPr6uows5atqqoqvdGJiIikQVVVVctnGVAWZdldShLMbJiZ/czM6s1sm5klUu3/rc/5kJnNNrOnogywi/FdDpwK3NzVa8rKynD3lk1JgoiI9EVVVVUtn2VAXZRld7UmYSFwGfAUwQyK5wAPpeZE+J6ZbQNWA7OB3vQp6DYzKwJuA+YB/cxsKDA4dbjEzI7OZDwiIiL5oqt9Ei4Avu3u8wEsqNNIAGuA9wAPEwyBXOHuO9IRaAdKCIY8Lkhtrf0CeBEYleGYRERE+ryuJgnHAC1rNbi7m1k18CVgtrt3uZo/Dd4CPtFmXynwc+A7BDNEioiISDd1Z3RD23kRtqS+PhJRLACYWTHBZEoAxwGDzWxK6vVyd28ws03A4+7+JXc/QDCJU+syTkx9+yd3fybK+ERERApFd5KEO8xsLbA+tb2Y2r8v4piO4fDhks2vTwJeIYi7JxM1iYiISBd1NUn4LnA6QefFY4DW0y1/PzWi4Q/AH4ENvZkQKTWls3Vyzom9LUNEREQ61qUkwd2nN39vZscA709tH0h9/SowKHVKA3BUtGGKiIhIpnV7xkV3304w7XHL1Mep0Q6jCZKG0yOLTkRERLKmS0mCmW0FfpPaHnH3t1of92AGh42pbWnUQYqIiEjmdXUypX8hSCj+E3jdzB4xs6+b2ej0hSYiIiLZ1KUkwd1/6e5XE8w/cC7BzItXAhvMbKOZLTSz8tTshyIiIpIHurXAkweecfc57h4nmMfg34DjgfuBHWZ2v5ldk4ZYRUREJIN6tQqku29z9/9y988Cw4HPAH8FvhlFcCIiIpI9kS0V7e773b3G3ae7+5jWx1KjH3JKfX098XicRCKR7VBERER6JZFIEI/HAUZEWa6llpZMCzMbC/wD8Dl3f0/abtQD8Xjca2trsx2GiIhIZMxsbao7QCQiq0lollo+erqZ/QFYC+wCJkR9HxEREUmvyJIEM7vczJYD/0vQkfFaYKu73+LuG6O6j4iIiGRGt2dc7MDPCFZj/Ehq7QTMLH1tGSIikpfspqAbm8/RR0i2RZkkfJBg7oTfm9lfgHvQSo0iItJdSg5yRpSjG551928AJxDMnTABiJnZUjObGtV9REREJDN6lCSY2SQzeyLsmLsfdPeV7n4l8C5gGfCPvYhRREREsqDDJMHMPmxm683sLTP7kZmdamb/A/w78B9dKP9EYCzw3t6HKiIiIpnUWU3Cd4GFQBzYADwDPAqc5u73hl2gIZAiIiL5obMkocjdE+6+wd3nAbvd/dvuvr/tiRoCKSIikl86G93Q1Ob16x2cqyGQIiIieaSzJGGcmbVOFCz12ggWhWw9xFFDIEVERPJIh80N7t7P3fu32vq1/trmXA2BFBERySORr90QMgTyIaAi6vuIiIhIekWeJLTm7nvc/cfufn467yMiIiLR61KSYGbDzOxnZlZvZtvMLGFmsTbnfMjMZpvZU+kJNVr19fXE43ESiUS2QxEREemVRCJBPB4HGBFluebe+QAEM7sb+DzwG2A7cDbwBvBpYCZwGTAScOBpd/9IlEGmQzwe99ra2myHISIibVjqq4bHdZ+ZrXX3eFTldXWBpwuAb7v7/FQQBiSANcB7gIcJhkCucPcdUQUnIiIi2dPVPgnHAC1rNXhQ/VBNkCDMdveL3P2nShBERETyR3c6LradWGlL6usjEcUiIiIiOaSrzQ0Ad5jZWmB9ansxtX9f5FGJiIhI1nU1SfgucDpBB8VjOLQ/yfdTIxr+APwR2ODuB6MMUkRERDKvS0mCu09v/t7MjgHen9o+kPr6VWBQ6pQG4KhowxQREZFM605zAwDuvh1YldqAltEOowmShtMji06kj9IQLhHJB91OEsKkRjtsTG1LoyhTREREsiut0zKLiIhI36UkQUREREIpSRAREZFQkfRJEJF3JJNJWLoUtm1jcWkpU6ZMIRaLdX5hHlNHTpG+STUJIhFxd2ZVV1N28smwahW8/TbTa2ooO/lkZlVX05XF1EREcolqEkQiMvvmm1nwwAM0Pv88lJUBsAegro4FkycDUD1rVvYCFBHppi4tFZ2PTjjhBB85ciQVFRVUVFRkOxzp45LJJGUnn3xIgnCIujoGjh3L1pdfZujQoRmPL9vU3CDdod+X7kskEiQSCdauXftXdz8xqnILNkmIx+NeW1ub8fsmk0mWLl3Ktm3bKFV7dbdZ56dkx+LFUFMD993X/jnTpkF5OVx7bebiaiWb/9L1R1+6Q78vPWdma909HlV56pOQIa3bqytWrWK22qvzy7ZtMHp0x+eMGhWcJyLSR6hPQoaovToauZpKLS4tZXpNTfBM21GyaRMLy8vJTj2CiEj3qbkhA9Renf+SySTHHH88B154IfwZb9nCgDFjqN+8uSCfsaqPpTv0+9Jzam6IyFqCX8RMbMOWLqVx4sTwDw+AsjIaJ04ktmRJWuOQ9HJvggvOg7q6Qw/U1cGFE4LjIiJ9iJobMmHbtqA9uiOnnKL26i6wm3I03VkLnAAc+xKcegqUnwdj3g8b/gQ1j8KZTTQd3E/sqhiMz06IPkf/LxOR7inYmoTxBFVZmdgSpaUUrVvXYTxF69eTKC1NaxySRm8Bw4GP7od/boSm5fDMvODrPzcG+4enzhMR6SNUk5AB5eXl7L/++qDauZ326v0PP8zEO+7IeGxRS3dbYq7+b3jx4sVM/8F09rAHBhFaW1DyZgkLv7qQa7M0BFJEpLsKtuOilZnzlQzdbC2wtgiGnwwPP3poolBXBxeeBztegvH701oVnYkP2ELtcJRMJik7vozGikYYHHLCbhiYGMjWzVvVcVGkE/p96bmoOy6qJiET3gJO3g9Htt9ezZD9fb4qupAXNorFYsyonMG8xfPY/7n9hyYKu6Ho50XMqJxRkAmCiPRdOZckmNko4BvA2cBY4Pfu/vFOrvl74KvAR4EyYDPwM2CeuzeGXTO+bDy1czIzBLKlKrp8D8T3w7rl8MxyOAr4Z2AQlDzQd6ui3Z3ZN9/M/AUL4IILYNQoptfUcP03v8mMykrmzpyJWY52OIyQNxXhO4fD95Jw0gA4pgG2F8PLB/D+MbypKNshioh0S841N5jZJcD3gaeB04G/dSFJmA98CLgH+AvwAaAaqHH3z4Zdk/F5EvK4KnpWdTULHniAhmXLDmtKKZ48mcpLL837iaKSySRlZSfT2Pg8QaeEpcA2oBSYCjQwcOBYtm4tzLkwVH0s3aHfl56LurkhF5OEfu5+MPX9UmBEF5KEEe7+ept9FcAi4ER3/2vbazK9dsOsqlks+NECGqY0HFYVXby0mMovVlJdVZ2xeKKS6YmicrdCYjFQA3SwdgPTgHLI0pyL2fynrj/60h36fem5vO+T0JwgdPOa10N2/yH1tQw4LEnItLlz5gIw//b5HDzxIPti+zgieQT9XulH5Q2VLcfTKS2fr0uXQhcnisrWwkaZsQ3oZO0GRqXOExHpG/J5noSzgYPAi9kOpEXTEfj+I9j35w/A7z8GG9+P7z8Cmo7IdmQ9t20bjDql43NOPjmyiaLcc3NLJEopKflLh7GXlGwikSjNWowiIt2Vl0mCmZUCM4F73H172Dl1dXWYWctWVVWV1phmz76ZBQse4O231wNrgMfZt6+Wt99ez4IFDzB79s1pvT+kZ4KmhUcfDc8+1/GN//Qn7hg8OK8nhJoyZQpNTSuBunbO2EJT00qmTp2aybByQjKZDJbSrq5m8eLFwWsRiUxVVVXLZxlB7Xlkcq5PQmtd7ZPQ5pojCBqH3w2Md/fQv0gZ77jY0qkt7PnVZaRTW1qmNF4N/PdA2PRiuxNFMXoUnNsIZ/X+drk6mRLArFnVLFjwAA0Nyzj0OddRXDyZyspLqa7O7w6crbUe9dKYGvVS8pe/0LRyZUGNepHuSSaTDEsNpU4U2FDqKOR9n4TesOAvzk8Ihk5+uL0EAWDt2kx2glsKTKT9BK+MxsaJxGJLSGuntqo0lLkPKGsKJoQKmyhq0oTg+NtpuHeOmTt3JgDz54+lsXEiMIqSkk00Na2ksrKy5Xih0PLo0h3uzuybZnPb/NtgmEO/fVx38Ai+Nv1rfGPGN5g7Z66SyizIq5oEM/suUAGc7+5PdHxu3CFToxuqCT4lO2pSuBEYCKTvj2Y6HvXixYv5+p1fp2HEfnimf+hEUcWvF3HHP9/RJ+eA6IlkMsmwYcEQyESilKlTpxbcsEctjy7dNXPOTObduZADbx+E8yfAaR+Adc9CzaMMOLIf3/rn6dx8U/qbZfu6vB8C2Vp3kgQz+1eCT+HL3P1XnZ2fyeaGxYsXM316DXv2tD88rqRkGgsXlve5D9JD5oAoAtYRzBx5FPA+YH/fngOip5r/w5NL/7wy+n+wxYuhpgbu62BI6LRpUF6e1lEvOfTjlw4kk0lGvuc4mk46Hn4bUiN5wXn0f/lVXn+trqD+jvRE3jc3mFkxcFHq5XHAYDObknq93N0bzGwT8Li7fyl1zRXALcCPgS1m1rr1+0V3r89M9OGmTJnC9dd/k6BTW1iTQ3OntkUZjqz3YrEYM26Y8c4cEK3XnmieA+KGSv3DLjTbtsHoToaEjhqV9uXRc3ZpcTnUauDgwMMTBAhe//ZRmkadQuxzsUj6NknX5VySABwDLGmzr/n1ScArBHH3b3V8Yurr1amttS8SJA9ZE4vFmDGjkgULJrfTqe1TVFb23Q/S1nNANL6nEYbDEbsyOweEdC6T/6teXFrK9JqaoA9CO0o2bWJheXlap5ayHO7oKq1ccgmUH+hwvhXKzwMbAHMezGxsfU1VtIlxziUJ7v4KndSMuvuJbV5fzeHJQU5p3amtf/+JNDSMorg4jzq1peaAYONoaBoKRzTg9pe+PQeE9NiUKVP4amVlh8ujv718OVMX9b3aM0mT08/o+Pj73g/r12cmFmmRc0lCvjIzqqtnUVl5HUuXLmXbtm2UlpYzdeqiPluD0OzQOSCCD4R9+wDqWLAg1Yu9gIb+JZNJEgxjG7B4caJgh3C5N8EF54W3MV84ITie7hjSfgeJwh2f+ATTax7p+KQ//4k7zp/Iv2QmpD4r6ga2nO64mE6ZXrshX+XKHBC5wN25efZsFsyfzwWNjYwC/lJSwsqmJipnzGDm3OwO4cpo+/xagrlO31XU7qgXtu6HUzi0H0vEcnleDXnHyy+/zMnvex+82MF8K6NG8cqGDZxwwgmZD7APyfuOi5I+6fl8yuwcELmc0948ezYPLFjA842N7/w09uyhDpi8YAEQrJhZEN4ChgMf3d/u8uisSp2XRhpW31fUQNF74bwL4NHfHl7zNOFCaHovJ564kmwtkFaoVJNQQNLzBzOzc0Dk6q9rMpnk5LKyQxOEVuqAsQMH8vLWwhgKunjxYqb/YDp7Lm2/62LJAyUs/OrCtA77VZLQV1QDjVA0EPovgPM+Ae8/Ff70Ajz6GDRVwv69BNll4TRd9ky0NQl5uXaDhMuHhY0wy8lt6bBhTGwnQYCgnmViYyNLYrHsxZlBU6ZM4e0Nb8Pudk7YDW9veDvta1lke+Evbd35O7IJ9s+Cxpdg+SSYVxx8bXwZ9s+ipOTFrC6Q1le2qClJkF7RwkYBLRR9OD/YH+4edHiisBu4e1BwXIS2f0diBE0Ks1Jfh1Iof0dykZIE6ZXmOSCKiydzeKIQzAExY0aEc0BkO01vZytNJPhLSUmHoW8qKaE0kchajJmstBg2bClN+z4FyRvgewPh3hJ4xIKv3xsIyRto2vcpYrEl+VJ5Ir2Q8b8j0nXuXpDb8ccf7+PHj/dFixa59M7Bgwd95sy5PnDgUIfLHL7jJSWX+cCBQ33mzLl+8ODBbIeYdjt37vShAwf6lnY+ol8DHzpwoCeTyazFmNmsZK7DjanvdzokUvsSDsnU/u+k9qUvDuk7Dvk7UnKZY4X3d6Q3Fi1a5OPHj3fgFY/ws1IdFzMsmUy2michv5ZBLfSFjapnzeKBBQtY1tDQZk5NmFxczKWVlQUzuiGf1yuR9Gq7VHSh/R3prYJa4CmdMp0kuOf2GPqo5OLCRpnS+hlPTD3jTXn4jLui8/kztjBw4OkFMX+GdF/zv5IC/DPSa1EnCeqTkCGtx9D/gmDA4H179vB8YyMPLFjAzbNnZzvESDRX9BYiM2NWdTUv1dVRTjDos3zhQl7eupVZ1dUFkyBAcxvzdIqKLiCsjbmo6EJmzJiuBEEkx2kypQxIJpMsmD8/dAx9GbCsoYGx8+fztRtu0B/NPBCLxd6Z7qWAq9KLfB/D/SWSnMIAzqOB91PMnzjAo8S8H0W+L9shikgnCre5wcwz1diwGKgB2m+dhWlAOWmeS6xAn3VWFHK7C4dOLjWIYF7ObUApMBVooLAml5LuUXNDz2la5j5IY+glJ2SwuaPtZN1tk9+hvDO5lBJjkdxVuH0Sxo8vvDH0maAB6oISY5F8UbhJQgZNmTKFlU1NHcxJCCubmjSbWD7JxR6cGUqKCy4xFsljShIyIBaLUTljBpOLi0P6ecOnioupnDGjz7fNJpNJFhMs1bJ48WKSyWS2Q5IsUWIskh+UJGTIzLlzubSykrEDBzKtpIQbzZhWUsLYgQO5tLKSmXPnZjvEHnN3qmfN4uSyMlYRrAlZM306J5eVUT1rFoXaObaQFUpiLJLvCnd0Q47MuJgPs4lppkEJ03pyqXP69+f9DQ28XFxckJNLSfdodEPPacbFiGQrScg3rYe6hc2rV4eGuhU6TbMr3dH29yWfpq7PBCUJESnIJCEN/2vL+BwQBfr72tfpf4bSmdY1Tx/u358PNDTwomqeuk3zJEhO0VA3EYlC66nrW2ol9+wJmi0XLABQs2UWqONiIcmHoW7SN91kwSYSonnq+rb9muCdqesXzJ/Prl27shBdYSvY5oYTTjjBR44cSUVFBRUVFdkOp8/qrE/CFuB09UkoeJZKEHxOhv/eqHq6T8iZqevzgMFf3f3EqMor2OaGkSNHUnB9EtKgZahbO6MbPlVcTGVlpRIEEWmXmi0j9XqUhRVskiDRaZ7jYez8+UxsbGQUQRPDyqYmKvv4HBDSxxVoTWlfU7p4MTXTp8OePe2es6mkhPKFCwt6ZdUuibj2rGCbGwpydEOaJZNJlg4bFqz2l0hoqJu0KPBFMaUTaraMTtSjG9RxUSITS63oNwu49tpr9Y9ZRLpEM3TmLjU3SLT0X0UR6YHWzZaHzdCpZsusUXODiKSdmhukqzRDZ+9oMiUREclbsVispXOiuihmn5IEEUk718TMIn2SOi6KiIhIKCUJIiIiEkpJgoiIiIRSkiAiIiKhlCSIiIhIKCUJIiIiEkpDIEUk/TSLkkifVLA1CfX19cTjcRKJRLZDERER6ZVEIkE8HgcYEWW5mpZZRERyiqbe6jmtAikiIiIZoSRBREREQilJEBERkVBKEkRERCSUkgQREREJpSRBREREQilJEBERkVBKEkREJLcY70yWIFmlJEFERERC5VySYGajzGyRmT1nZk1m9rsuXjfEzH5kZkkze8PM7jWz4WkOV0REJG/l4gJPY4GLgKeBom5c90vgvcCXgYPAPOAB4KMRxyciIlIQcjFJWObuDwKY2VK6sFiFmZ0NTATOdff/Tu3bAjxjZuXuXpPOgEVERPJRzjU3uPvBHlw2Cfhbc4KQKud/gJdTx0REpI9wDFfPxZyQizUJPTEG2BCyf33q2GFeeAE+/vFD9112GXz1q9DQABdddPg1V18dbK+/DlOmHH78n/4Jpk2DzZvhC184/PgNN8DkycG9v/KVw4/PnAnl5fDHP8LXv3748VtugXPOgaeegu985/Djd9wB48ZBTQ3cfPPhxxctglNPhWXL4PbbDz9+zz3wnvfAfffBf/zH4ceXLoURI+DHPw62tpYvh+Ji+MEP4Je/PPz4734XfJ0/Hx566NBjgwbBihXB99XVsGrVoceHD4df/Sr4/l//FVavPvT4u98NP/1p8P3Xvx78DFt773uheVXwigrYuPHQ4+PGBT8/gCuvhNdeO/T42WfD//2/wfef/Szs2HHo8QkTYNas4PtJk2Dv3kOPX3wxzJgRfN/29w70u6ffveB7/e41730sFbB+93rzuxeFfEkSYsCukP1J4OSwC/bt28fjj/+u5fUJJ5wInBh5YCIiIulUVVXFd787FBgHUBZl2eaeuyt2N/dJcPePd3LeI8Aed7+0zf6fAie7+zltr4nH415bWxthtCIiEglLNTXk8OdTrjKzte4ej6q8nOuT0ENJYEjI/ljqmIiIiHRTviQJGwjve9BeXwURERHpRL4kCSuAUjP7SPMOM4sT9EdYkbWoRERE+rCc67hoZsUEkykBHAcMNrPmPrXL3b3BzDYBj7v7lwDcfbWZrQR+YmYzeGcypSc0R4KIiEjP5FySABwDLGmzr/n1ScArBHH3b3PONGAh8F8ENSQPAdenLUoREZE8l3NJgru/Qifrf7n7iSH7dgFfTG0iIiLSS/nSJ0FEREQipiRBREREQilJEBERkVBKEkRERCSUkgQREREJpSRBREREQilJEBERkVAFmyTU19cTj8dJNC/0LiIi0kclEgni8TjAiCjLzemlotNJS0WLiOQoLRXdY1EvFZ1zMy6KiEiBU3KQMwq2uUFEREQ6piRBREREQilJEBERkVBKEkRERCSUkgQREREJpSRBREREQilJEBERkVBKEkRERCSUkgQREREJpSRBREREQilJEBERkVBKEkRERCSUkgQREREJVbBJQn19PfF4nEQike1QREREeiWRSBCPxwFGRFmueYEuyRmPx722tjbbYYiIiETGzNa6ezyq8gq2JkFEREQ6piRBREREQilJEBERkVBKEkRERCSUkgQREREJpSRBREREQilJEBERkVBKEkRERCSUkgQREREJpSRBREREQilJEBERkVBKEkRERCSUkgQREREJpSRBREREQhVsklBfX088HieRSGQ7FBERkV5JJBLE43GAEVGWa+4eZXl9Rjwe99ra2myHISIiEhkzW+vu8ajKK9iaBBEREemYkgQREREJpSRBREREQilJEBERkVBKEkRERCSUkgQREREJpSRBREREQilJEBERkVBKEkRERCRUTiYJZvY+M1tlZg1mVmdmc82sfxeui5vZSjPbmdpqzOzMTMQsIiKSb3IuSTCzGFADOHAJMBe4Abipk+vek7puAPCF1DYAeMTMTkhnzCIiIvloQLYDCPGPwCDgM+6+m+BDfjBQZWa3pvaF+SRwNPBpd38DwMyeAl4HLgL+I/2hi4iIZFVZlIXlXE0CMAn4bZtk4BcEicO5HVxXBBwA9rTa91Zqn7U9ub6+vveRSsZp1c6eK8SfXb68577yPnItzmzHk6X7vyvKwnIxSRgDbGi9w91fBRpSx9rzq9Q5t5vZMWZ2DLAQSAJL2p78+uuvRxawZE62/9H3ZYX4s8uX99xX3keuxZnteLJ9/yjkYnNDDNgVsj+ZOhbK3evM7BPAQ8D1qd1bgQvc/bBqg4aGhn1mdkSrXVuBup4GLRkzwsyU4fVMIf7s8uU995X3kWtxZjueTN2/jHdqEPZHWXAuJgk9YmbvIqgxWAt8ObX7n4HfmNk5qdqIFu5+ZIZDFBER6VNyMUlIAkNC9sdSx9rzDYJ+CVPcfT+AmT0K/AWYwTu1CyIiItIFudgnYQNt+h6khjcW06avQhtjgOebEwQAd98HPA+ckoY4RURE8louJgkrgAvM7OhW+6YBe4HHO7jur8DprfsZmNmRwOnAK2mIU0REJK/lYpLwn8DbwP1mVm5mFUAVsKD1sEgz22RmP2x13V0EnTd+bWafNLOLgQcIOnOkvYupmb0nNUvkejN73sxuNbPDhl5K7tGz6zn97PomPbfCZWaPm9mzZvacmS1NzUPUrpxLEtw9CUwA+gPLCGZaXAjMaXPqgNQ5zdetBS4kmFDpHuAnBE0U57v7s+mPnAPAt9z9NOCDwJnAZzJwX+k9Pbue08+ub9JzK1yfcvcz3P0DwKsE/fnalXNJgplNBf4NOBVoIhiWuM7dm1qf5+4nuvvVbfatcvePufuw1Hauu/+ug3uNMrNFqYyqycw6OrfD9STcfau716a+3wc8B7ynu++/LzOzKWb2lJntMLNGM3vBzGa2GWoa1b3y9tmZ2XFm9paZuZkdlYby8/Znlw1mNsDMvm1mfzGzt83sNTNbmIb76LkVqK4++86eO0CrGYn7ASUESyC0KxdHN1QCLwPTeWdK5Z+Z2Qh3//eI7zU2Vf7TBCMjQtk760msI1hP4hTgdoIka2bI+cOBS4GJEceb64YDjwK3Ecx18SGCpqJS4LqI75XPz+42gtlCS9JUfj7/7LLhx8B5BLWeGwg+bN+XhvvouRWuTp99d567mS0H/p6gY/8NHd7Z3XNqA0aE7PsZ8HIH13wQeHc7xy4C+rdzrF+r75cCv2vnvH8lGH45uNW+bxLM8Di4zblHAo8BN2T7Z5kLG/B/CBIG07Pr0s/rY8BOgmG7DhzVwbn62WV5I2ji3A+8rxvX6Llp6+7vWafPvjvPPXWsP3Ar8M2O7p1zzQ3uHjY71R/oeNGKKoKFoEa03mlmVxH0azivnXsd7GJYXVpPIlWtcy/wB3e/vYtl57sdQEfNDVXo2bWO4d8JVj7tyixtVehnl23XAI+6+7puXFOFnpt0QxeffbfWPfKgCf9u4KqOCs25JKEdZwMbOzh+NUE2/3BzT00zuwT4L+A77v5IL+/f1fUkFgFv0ln1TZ4zs/5mVmxmHyGYxOo/PJW6hrgaPbtm/0jwP7s7u3j+1ehnl21nAhvN7PtmtjvVFny/mXX0n5qr0XOT6HX63M0sZmbHtjrls8CfOyo055MEM5tA0F7WbqbrwYiICwhmZfx/ZnYRcB9wu7vPiyCMTteTMLMPA18C4sAfzOyPZlaoszzuSW2/J5jbot3es3p2gVS7cDVQ6a0mBOuIfnY5oZTgQ38ccDnwRWA8wVDs0CGFem6SJl1Z9ygGPJTqAPkngnmE/qWjQnOx42ILMzuRoD/Cg+7+447OdfetZlYOPAn8BrjL3b+V9iDfuf+ThCxJXaDOIRh++iFgNvB94KvtnaxnBwR9N5529+XduUg/u6yz1HaJu+8AMLOtBMnxecCqsIv03CQb3P0lgg6LXZazSYKZDSOYffGvwOe7eNkggvbvg8BRZtavG+14HenpehIFyd3/N/XtExasgHa3md3u7i92cFnBPjszG0vQtv0xMxua2l2c+jrEzJrcfW8HRRTszy4HJIGXmhOElCeAfQQjHEKThBQ9N4lSWp57TjY3mFkxwZLPRwAXu3tDF645EVhJMETkXOBiut6225merich0JwwnNTeCXp2jCYY1rSa4B9zknfe/2sEnRlD6WeXdesJ/x+5EXz4h9JzkzRIy3PPuZoEMxtAsOTzaOAcd9/ehWuOBR4BXgSmuvveVEeg5WaWdPfv9DKsFcA3zOxod38zta8r60kIfDj19eWwg3p2QPA/z0+02Xch8C2CIXEvhV2kn11OeAi4KTWPS/OIlI8RJH2hM73quUmapOe5p3NsZ082gnUWnKBX/FlttiPbueYhoJbDxwBfQtCL+IJ2risGpqS21QQTSzS/Lm51XgzYSvAPuxyoIJjs5uZs/7xyaQMeJhjfP4lgYpabUj+nX3RwjZ5d+Pu7ms7nSdDPLvvPaTDB1LargcnAFcBm4BE9N20R/p51+uzT9dyz/uZDfhivpP44hm0ntnPNewmZhCl17Bzan8jnxK7ei6B98VGCrGwrQU/00ElPCnVL/Uz+nPrF3EXQ1PA1oKiDa/Tswt/f1XSeJOhnlwMbMApYTjCiJ0kwA2NMz01bVFtXn306nrulChYRERE5RE52XBQREZHsU5IgIiIioZQkiIiISCglCSIiIhJKSYKIiIiEUpIgIiIioZQkiIiISCglCSIiIhJKSYKIiIiEUpIgUuDM7I9dOKcq/ZGISK7RtMwi0ikzc3cPWxJZRPKYahJECpyZeavvP2pmvzeztWb232b2PjNbmDr2RzP7ffYiFZFMU02CSIFrriUws2EEa9Jf6O5JM/swMN/dz1ZNgkhhUk2CiDQ7GxgNPJbqp3AncFx3CjCz35mZp7avtzl2qZmtNLMdZrbPzLaY2VIzu7Cb91hmZn/q4Pj3zWyXmR1pZlWt4lnanfuIiJIEEXlHP+Apdx/Xaju+B+U8RpBw/KJ5R6rJ4lfAFuDLQDnwbWAQsMLMTulG+T8HTjez97U9YGb9gSnA/e7+NnBXKpY/9OB9iBQ8JQki0uwpYLyZfRDAzPqZ2d+ljr1pZoO7WM5Od3/a3belyrkE+DrwJXf/orv/2t3/293vcfdPAp8C9nYjzgeBBuBzIcc+ARxLkEjg7q+5+9PA7m6ULyIpShJE8piZ7TSzajO7N1XNv9PMFpnZkW3PdfcdwFTgTjN7Fnge+HTq8HeB/+lhx8WvA2vc/cdhB919mbvXtYn7o2b2uJk1pOJebGZHp87fAywDpoUUdzmwHXi0B3GKSBtKEkTylJkdD8SA6cBOggTgDqAC+OfUOSNTxwBw9yfc/Rx3P8PdT3P3Wan9s9x9jLt/tJsxDCCo7l/ZjWs+DNQA2wiaDr4OXAT8qNVpPwdGm9n4VtcVAZ8BfunuTd2JU0TCDch2ACKSNmekvn7f3b+d+v5RM/sYcKGZ/QZYDtySxhiGA0cCm1vvNDMD+rfa1eTvDLX6N4K+EdNanb8FWGVmp7v7nwlGYewiqDlYmzrtAoKk6OdpeB8iBUk1CSL56wyCtvvb2uzfCAxz9xfc/RR3vz0DsbQda30DsL/V1lyzUUxQ8/BLMxvQvAFPpM4bD+Du+4D7gctSCQcEzQ9/BVan+b2IFAwlCSL56wzgv1N9DVorBepCzk+HHcDbwLvb7L8H+PvU1lqMoIbhBxyaRLwNFAHvaXXuz4HjgbPNbCBwCfCLVjUSItJLam4QyV9nAId0NEx1/jsPWNhqzYZhwFHAq6nX33L330YRgLsfMLPVwERgdqv9fwP+loqp9SW7CGodqgiaQtpqndw8lirjcuBdwNGoqUEkUkoSRPKQmZUAp3B4jcH1qa/fdfebUudeDXzc3a9OUzh3AA+Y2Rfc/Z6OTnT3PWb2NHCqu8/t5NwmM/slQYfM44D17v5sVEGLiJIEkXz1foLmxFPMbDZBjcJEgr4AV7r7rkwF4u4PmtkdwI/N7BMEwxdfJ+jUODF12lutLvkmQSfFg8BS4E2CZoVPAje6+8ZW5/4c+BrBUM056XwfIoVISYJIfjqDYAKhTxIMHbwReAGY6u4PZjoYd59uZv8NfBX4IUHTQD1BJ8OL3H1Fq3OfSI3AuImg70J/gg6JD5Nqomh17mozewU4ETU1iEROSYJIfjoD+JO7P0dqREAGWWpEQuthjbj7r4Ffd6UAd38G6NKaDu5+UgeB9COoUdHiVCI9oNENIvnpDCBb7fOfIRiR8C9Zun9rswli+Vi2AxHpi1STIJJnUvMGvB/4SRZu/xWCpgR4Z7RENiWAh1Lf7+zoRBE5nJIEkTyTquLv6mJMUd/7hWzctz2pNSEyNSeESN4xzTsiIiIiYdQnQUREREIpSRAREZFQShJEREQklJIEERERCaUkQUREREIpSRAREZFQShJEREQklJIEERERCfX/Ab4NN8dTBgJcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 540x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare arrays of Jet-pT\n",
    "jet_pt_pp_in = jets_pp[:,1]\n",
    "jet_pt_pbpb_in = jets_pbpb[:,1]\n",
    "\n",
    "jet_pt_pp1_in = jets_pp1[:,1]\n",
    "jet_pt_pbpb1_in = jets_pbpb1[:,1]\n",
    "\n",
    "jet_pt_pp2_in = jets_pp2[:,1]\n",
    "jet_pt_pbpb2_in = jets_pbpb2[:,1]\n",
    "\n",
    "jet_pt_pp3_in = jets_pp3[:,1]\n",
    "jet_pt_pbpb3_in = jets_pbpb3[:,1]\n",
    "\n",
    "jet_pt_pp4_in = jets_pp4[:,1]\n",
    "jet_pt_pbpb4_in = jets_pbpb4[:,1]\n",
    "\n",
    "jet_pt_pp5_in = jets_pp5[:,1]\n",
    "jet_pt_pbpb5_in = jets_pbpb5[:,1]\n",
    "\n",
    "\n",
    "bins_11 = [100,200,300,400,500,1000]\n",
    "pt_min = 100\n",
    "pt_max = 1000\n",
    "\n",
    "# Fill Histogram\n",
    "n_pp, pt = np.histogram(jet_pt_pp_in, bins= bins_11 )\n",
    "n_pbpb, pt = np.histogram(jet_pt_pbpb_in, bins= bins_11 )\n",
    "\n",
    "n_pp1, pt = np.histogram(jet_pt_pp1_in, bins= bins_11 )\n",
    "n_pbpb1, pt = np.histogram(jet_pt_pbpb1_in, bins= bins_11 )\n",
    "\n",
    "n_pp2, pt = np.histogram(jet_pt_pp2_in, bins= bins_11 )\n",
    "n_pbpb2, pt = np.histogram(jet_pt_pbpb2_in, bins= bins_11 )\n",
    "\n",
    "n_pp3, pt = np.histogram(jet_pt_pp3_in, bins= bins_11 )\n",
    "n_pbpb3, pt = np.histogram(jet_pt_pbpb3_in, bins= bins_11 )\n",
    "\n",
    "n_pp4, pt = np.histogram(jet_pt_pp4_in, bins= bins_11 )\n",
    "n_pbpb4, pt = np.histogram(jet_pt_pbpb4_in, bins= bins_11 )\n",
    "\n",
    "n_pp5, pt = np.histogram(jet_pt_pp5_in, bins= bins_11 )\n",
    "n_pbpb5, pt = np.histogram(jet_pt_pbpb5_in, bins= bins_11 )\n",
    "\n",
    "# Statistical Errors\n",
    "err_n_pp = np.sqrt(n_pp)\n",
    "err_n_pbpb = np.sqrt(n_pbpb)\n",
    "\n",
    "err_n_pp1 = np.sqrt(n_pp1)\n",
    "err_n_pbpb1 = np.sqrt(n_pbpb1)\n",
    "\n",
    "err_n_pp2 = np.sqrt(n_pp2)\n",
    "err_n_pbpb2 = np.sqrt(n_pbpb2)\n",
    "\n",
    "err_n_pp3 = np.sqrt(n_pp3)\n",
    "err_n_pbpb3 = np.sqrt(n_pbpb3)\n",
    "\n",
    "err_n_pp4 = np.sqrt(n_pp4)\n",
    "err_n_pbpb4 = np.sqrt(n_pbpb4)\n",
    "\n",
    "err_n_pp5 = np.sqrt(n_pp5)\n",
    "err_n_pbpb5 = np.sqrt(n_pbpb5)\n",
    "\n",
    "# bin width\n",
    "dpt = (pt[1:]-pt[:-1])\n",
    "# bin center\n",
    "pt = pt[0:-1] + 0.5*dpt\n",
    "\n",
    "\n",
    "n_ev_pp = 315769\n",
    "n_ev_pp1 = 338726 \n",
    "n_ev_pp2 = 352683 \n",
    "n_ev_pp3 = 369925\n",
    "n_ev_pp4 = 380962 \n",
    "n_ev_pp5 = 389140\n",
    "\n",
    "\n",
    "n_ev_pbpb = 35000\n",
    "sigma = 172.5\n",
    "d_eta = 2.8*2\n",
    "\n",
    "# Jet Spectrum\n",
    "dn_dpt_pp = (n_pp*sigma)/n_ev_pp/dpt/d_eta\n",
    "dn_dpt_pbpb = (n_pbpb*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "dn_dpt_pp1 = (n_pp1*sigma)/n_ev_pp1/dpt/d_eta\n",
    "dn_dpt_pbpb1 = (n_pbpb1*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "dn_dpt_pp2 = (n_pp2*sigma)/n_ev_pp2/dpt/d_eta\n",
    "dn_dpt_pbpb2 = (n_pbpb2*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "dn_dpt_pp3 = (n_pp3*sigma)/n_ev_pp3/dpt/d_eta\n",
    "dn_dpt_pbpb3 = (n_pbpb3*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "dn_dpt_pp4 = (n_pp4*sigma)/n_ev_pp4/dpt/d_eta\n",
    "dn_dpt_pbpb4 = (n_pbpb4*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "dn_dpt_pp5 = (n_pp5*sigma)/n_ev_pp5/dpt/d_eta\n",
    "dn_dpt_pbpb5 = (n_pbpb5*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "# Errors \n",
    "err_dn_dpt_pp = (err_n_pp*sigma)/n_ev_pp/dpt/d_eta\n",
    "err_dn_dpt_pbpb = (err_n_pbpb*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "err_dn_dpt_pp1 = (err_n_pp1*sigma)/n_ev_pp1/dpt/d_eta\n",
    "err_dn_dpt_pbpb1 = (err_n_pbpb1*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "err_dn_dpt_pp2 = (err_n_pp2*sigma)/n_ev_pp2/dpt/d_eta\n",
    "err_dn_dpt_pbpb2 = (err_n_pbpb2*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "err_dn_dpt_pp3 = (err_n_pp3*sigma)/n_ev_pp3/dpt/d_eta\n",
    "err_dn_dpt_pbpb3 = (err_n_pbpb3*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "err_dn_dpt_pp4 = (err_n_pp4*sigma)/n_ev_pp4/dpt/d_eta\n",
    "err_dn_dpt_pbpb4 = (err_n_pbpb4*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "err_dn_dpt_pp5 = (err_n_pp5*sigma)/n_ev_pp5/dpt/d_eta\n",
    "err_dn_dpt_pbpb5 = (err_n_pbpb5*sigma)/n_ev_pbpb/dpt/d_eta\n",
    "\n",
    "# Generate Plots\n",
    "fig = plt.figure()\n",
    "\n",
    "# Calculate RAA and error\n",
    "raa =  dn_dpt_pbpb/dn_dpt_pp\n",
    "error_raa = ratio_error(dn_dpt_pbpb,err_dn_dpt_pbpb,dn_dpt_pp,err_dn_dpt_pp)\n",
    "\n",
    "raa1 =  dn_dpt_pbpb1/dn_dpt_pp1\n",
    "error_raa1 = ratio_error(dn_dpt_pbpb1,err_dn_dpt_pbpb1,dn_dpt_pp1,err_dn_dpt_pp1)\n",
    "\n",
    "raa2 =  dn_dpt_pbpb2/dn_dpt_pp2\n",
    "error_raa2 = ratio_error(dn_dpt_pbpb2,err_dn_dpt_pbpb2,dn_dpt_pp2,err_dn_dpt_pp2)\n",
    "\n",
    "raa3 =  dn_dpt_pbpb3/dn_dpt_pp3\n",
    "error_raa3 = ratio_error(dn_dpt_pbpb3,err_dn_dpt_pbpb3,dn_dpt_pp3,err_dn_dpt_pp3)\n",
    "\n",
    "raa4 =  dn_dpt_pbpb4/dn_dpt_pp4\n",
    "error_raa4 = ratio_error(dn_dpt_pbpb4,err_dn_dpt_pbpb4,dn_dpt_pp4,err_dn_dpt_pp4)\n",
    "\n",
    "raa5 =  dn_dpt_pbpb5/dn_dpt_pp5\n",
    "error_raa5 = ratio_error(dn_dpt_pbpb5,err_dn_dpt_pbpb5,dn_dpt_pp5,err_dn_dpt_pp5)\n",
    "\n",
    "err_1 = ratio_error(raa1,error_raa1,raa,error_raa)\n",
    "\n",
    "err_2 = ratio_error(raa2,error_raa2,raa,error_raa)\n",
    "\n",
    "err_3 = ratio_error(raa3,error_raa3,raa,error_raa)\n",
    "\n",
    "err_4 = ratio_error(raa4,error_raa4,raa,error_raa)\n",
    "\n",
    "err_5 = ratio_error(raa5,error_raa5,raa,error_raa)\n",
    "\n",
    "\n",
    "##########\n",
    "\n",
    "plt.errorbar(pt, raa2/raa, ms=9,mfc='red',mec='black',fmt='o', label=r\"R=0.4\",\n",
    "             xerr=0.5*dpt, yerr=err_2, color='red')\n",
    "\n",
    "plt.errorbar(pt, raa3/raa, ms=9,mfc='blue',mec='black',fmt='o', label=r\"R=0.6\",\n",
    "             xerr=0.5*dpt, yerr=err_3, color='blue')\n",
    "\n",
    "plt.errorbar(pt, raa4/raa, ms=9,mfc='green',mec='black',fmt='o', label=r\"R=0.8\",\n",
    "             xerr=0.5*dpt, yerr=err_4, color='green')\n",
    "\n",
    "plt.errorbar(pt, raa5/raa, ms=9,mfc='cyan',mec='black',fmt='o', label=r\"R=1.0\",\n",
    "             xerr=0.5*dpt, yerr=err_5, color='cyan')\n",
    "\n",
    "\n",
    "\n",
    "#axes setting\n",
    "plt.legend(loc='upper left')\n",
    "plt.xscale('log')\n",
    "plt.xlabel(r\"$p^{\\mathrm{jet}}_{\\mathrm{T}}$ [GeV]\")\n",
    "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n",
    "plt.xlim(pt_min,pt_max)\n",
    "plt.ylim(0.8,2.0)\n",
    "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5) \n",
    "#plt.text(pt_min+5,-0.05, r\"PbPb(0-10%)\"r\"$\\sqrt{s_{\\mathrm{NN}}}=5.02 TeV$\")\n",
    "#plt.text(pt_min+5,-0.15,r'$anti-k_{\\mathrm{t}},R=0.4,(|y_{\\mathrm{jet}}|<2)$')\n",
    "#plt.text(pt_min+5,-0.25,r\"$JS(MATTER+LBT),Q_{\\mathrm{sw}}=2GeV$\")\n",
    "\n",
    "# save plot to the Desktop\n",
    "plt.tight_layout()\n",
    "plt.savefig('cms_double_pT_0-10')\n",
    "print(\"R=0.3/0.2=\", raa1/raa)\n",
    "print(\"R=0.4/0.2=\",raa2/raa)\n",
    "print(\"R=0.6/0.2=\",raa3/raa)\n",
    "print(\"R=0.8/0.2=\",raa4/raa)\n",
    "print(\"R=1.0/0.2=\",raa5/raa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a64d3017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 540x468 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R03 = raa1/raa\n",
    "R04 = raa2/raa\n",
    "R06 = raa3/raa\n",
    "R08 = raa4/raa\n",
    "R10 = raa5/raa\n",
    "\n",
    "R_bins = [0.3,0.4,0.6,0.8,1.0]\n",
    "\n",
    "R_100_200 = [R03[0], R04[0], R06[0], R08[0], R10[0]]\n",
    "R_200_300 = [R03[1], R04[1], R06[1], R08[1], R10[1]]\n",
    "R_300_400 = [R03[2], R04[2], R06[2], R08[2], R10[2]]\n",
    "R_400_500 = [R03[3], R04[3], R06[3], R08[3], R10[3]]\n",
    "R_500_1000 = [R03[4], R04[4], R06[4], R08[4], R10[4]]\n",
    "\n",
    "err_JS_100_200 = [err_1[0],err_2[0],err_3[0],err_4[0],err_5[0]]\n",
    "err_JS_200_300 = [err_1[1],err_2[1],err_3[1],err_4[1],err_5[1]]\n",
    "err_JS_300_400 = [err_1[2],err_2[2],err_3[2],err_4[2],err_5[2]]\n",
    "err_JS_400_500 = [err_1[3],err_2[3],err_3[3],err_4[3],err_5[3]]\n",
    "err_JS_500_1000 = [err_1[4],err_2[4],err_3[4],err_4[4],err_5[4]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc25de68",
   "metadata": {},
   "source": [
    "# ATLAS-jetR-100-200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b5b8a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = [7, 6]\n",
    "\n",
    "R_bins = [0.3,0.4,0.6,0.8,1.0]\n",
    "\n",
    "R_100_200 = [R03[0], R04[0], R06[0], R08[0], R10[0]]\n",
    "err_JS_100_200 = [err_1[0],err_2[0],err_3[0],err_4[0],err_5[0]]\n",
    "\n",
    "#input the arrays obtained from loading martini and ads individually\n",
    "R_300_400_M = [1.087421457060924, 1.1371201834649416, 1.1923907263828304, 1.2017355836199048, 1.2122445381874856]\n",
    "err_JS_300_400_M = [0.05722454528682157, 0.058917362987257485, 0.06069459864960222, 0.060693234914417186, 0.06080044276875342]\n",
    "\n",
    "\n",
    "plt.errorbar(R_bins, R_100_200,ms=7,mfc='magenta',mec='black', fmt='s', label=\"MATTER+LBT\",xerr=None, yerr=err_JS_100_200, color='magenta')\n",
    "\n",
    "plt.errorbar(R_bins, R_100_200_M,ms=7,mfc='Red',mec='black', fmt='s', label=\"MATTER+MARTINI\",xerr=None, yerr=err_JS_100_200_M, color='red')\n",
    "\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel(r\"$jet-R$\")\n",
    "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n",
    "plt.ylim(0.7,1.6)\n",
    "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5)\n",
    "plt.text(0.7,1.5,r\"$100<p^{\\mathrm{jet}}_{\\mathrm{T}}<200$ [GeV]\")\n",
    "\n",
    "# save plot to the Desktop\n",
    "plt.tight_layout()\n",
    "plt.savefig('atlas_double_100-200')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b23d662d",
   "metadata": {},
   "source": [
    "# ATLAS_200-300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2908934f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = [7, 6]\n",
    "\n",
    "R_bins = [0.3,0.4,0.6,0.8,1.0]\n",
    "\n",
    "R_300_400 = [R03[1], R04[1], R06[1], R08[1], R10[1]]\n",
    "err_JS_300_400 = [err_1[1],err_2[1],err_3[1],err_4[1],err_5[1]]\n",
    "\n",
    "#input the arrays obtained from loading martini and ads individually\n",
    "R_300_400_M = [1.087421457060924, 1.1371201834649416, 1.1923907263828304, 1.2017355836199048, 1.2122445381874856]\n",
    "err_JS_300_400_M = [0.05722454528682157, 0.058917362987257485, 0.06069459864960222, 0.060693234914417186, 0.06080044276875342]\n",
    "\n",
    "\n",
    "plt.errorbar(R_bins, R_200_300,ms=7,mfc='magenta',mec='black', fmt='s', label=\"MATTER+LBT\",xerr=None, yerr=err_JS_200_300, color='magenta')\n",
    "\n",
    "plt.errorbar(R_bins, R_200_300_M,ms=7,mfc='Red',mec='black', fmt='s', label=\"MATTER+MARTINI\",xerr=None, yerr=err_JS_200_300_M, color='red')\n",
    "\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel(r\"$jet-R$\")\n",
    "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n",
    "plt.ylim(0.7,1.6)\n",
    "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5)\n",
    "plt.text(0.7,1.5,r\"$200<p^{\\mathrm{jet}}_{\\mathrm{T}}<300$ [GeV]\")\n",
    "\n",
    "# save plot to the Desktop\n",
    "plt.tight_layout()\n",
    "plt.savefig('atlas_double_200-300')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0735cd7",
   "metadata": {},
   "source": [
    "# ATLAS_300-400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d3f26e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.figsize'] = [7, 6]\n",
    "\n",
    "R_bins = [0.3,0.4,0.6,0.8,1.0]\n",
    "\n",
    "R_300_400 = [R03[2], R04[2], R06[2], R08[2], R10[2]]\n",
    "err_JS_300_400 = [err_1[2],err_2[2],err_3[2],err_4[2],err_5[2]]\n",
    "\n",
    "#input the arrays obtained from loading martini and ads individually\n",
    "R_300_400_M = [1.087421457060924, 1.1371201834649416, 1.1923907263828304, 1.2017355836199048, 1.2122445381874856]\n",
    "err_JS_300_400_M = [0.05722454528682157, 0.058917362987257485, 0.06069459864960222, 0.060693234914417186, 0.06080044276875342]\n",
    "\n",
    "\n",
    "plt.errorbar(R_bins, R_300_400,ms=7,mfc='magenta',mec='black', fmt='s', label=\"MATTER+LBT\",xerr=None, yerr=err_JS_300_400, color='magenta')\n",
    "\n",
    "plt.errorbar(R_bins, R_300_400_M,ms=7,mfc='Red',mec='black', fmt='s', label=\"MATTER+MARTINI\",xerr=None, yerr=err_JS_300_400_M, color='red')\n",
    "\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel(r\"$jet-R$\")\n",
    "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n",
    "plt.ylim(0.7,1.6)\n",
    "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5)\n",
    "plt.text(0.7,1.5,r\"$300<p^{\\mathrm{jet}}_{\\mathrm{T}}<400$ [GeV]\")\n",
    "\n",
    "# save plot to the Desktop\n",
    "plt.tight_layout()\n",
    "plt.savefig('atlas_double_300-400')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11bd7797",
   "metadata": {},
   "source": [
    "# ATLAS_400-500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6dd67559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_400_500 = [R03[3], R04[3], R06[3], R08[3], R10[3]]\n",
    "err_JS_400_500 = [err_1[3],err_2[3],err_3[3],err_4[3],err_5[3]]\n",
    "\n",
    "#input the arrays obtained from loading martini \n",
    "R_400_500_M = [1.107902948996912, 1.1417333072053066, 1.1667852266008045, 1.2077353007835328, 1.2923555587444864]\n",
    "err_JS_400_500_M = [0.15511130554633093, 0.15791887747022149, 0.15901250213085974, 0.16216272340527998, 0.17006620500934602]\n",
    "\n",
    "\n",
    "\n",
    "plt.errorbar(R_bins, R_400_500,ms=7,mfc='magenta',mec='black', fmt='s', label=\"MATTER+LBT\",xerr=None, yerr=err_JS_400_500, color='magenta')\n",
    "\n",
    "plt.errorbar(R_bins, R_400_500_M,ms=7,mfc='Red',mec='black', fmt='s', label=\"MATTER+MARTINI\",xerr=None, yerr=err_JS_400_500_M, color='red')\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel(r\"$jet-R$\")\n",
    "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n",
    "plt.ylim(0.7,1.6)\n",
    "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5)\n",
    "plt.text(0.7,1.5,r\"$400<p^{\\mathrm{jet}}_{\\mathrm{T}}<500$ [GeV]\")\n",
    "\n",
    "# save plot to the Desktop\n",
    "plt.tight_layout()\n",
    "plt.savefig('atlas_double_400-500')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd7f866",
   "metadata": {},
   "source": [
    "# ATLAS_500-1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "214e2c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_500_1000 = [R03[4], R04[4], R06[4], R08[4], R10[4]]\n",
    "err_JS_500_1000 = [err_1[4],err_2[4],err_3[4],err_4[4],err_5[4]]\n",
    "\n",
    "#input the arrays obtained from loading martini and ads individually\n",
    "R_500_1000_M = [1.0985570406473855, 1.1472664622184126, 1.3028718885511639, 1.3537250450673393, 1.296983750176387]\n",
    "err_JS_500_1000_M = [0.25694207865218277, 0.26393293540568064, 0.2890884673570552, 0.295898961361187, 0.2834177949359136]\n",
    "\n",
    "\n",
    "#####\n",
    "\n",
    "\n",
    "plt.errorbar(R_bins, R_500_1000,ms=7,mfc='magenta',mec='blue', fmt='s', label=\"MATTER+LBT\",xerr=None, yerr=err_JS_500_1000, color='magenta')\n",
    "\n",
    "plt.errorbar(R_bins, R_500_1000_M,ms=7,mfc='Red',mec='black', fmt='s', label=\"MATTER+MARTINI\",xerr=None, yerr=err_JS_500_1000_M, color='red')\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel(r\"$jet-R$\")\n",
    "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n",
    "plt.ylim(0.7,1.8)\n",
    "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5)\n",
    "plt.text(0.7,1.7,r\"$500<p^{\\mathrm{jet}}_{\\mathrm{T}}<1000$ [GeV]\")\n",
    "\n",
    "# save plot to the Desktop\n",
    "plt.tight_layout()\n",
    "plt.savefig('atlas_double_500-1000')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc2071ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "009adc34a3a8452e02639bd458ec0b5a4261dfbc6c9492726e6a0ec46c9767d3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}