notebooks/330_chapter7_fig7.18.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Fig 7.18 from Tables 7.14 and 7.15\n",
    "\n",
    "Theme Song: It's Not True Rufus, Don't Listen To The Hat<br>\n",
    "Artist: TTNG<br>\n",
    "Album: This Town Needs Guns<br>\n",
    "Released: 2008\n",
    "\n",
    "Piers 5 March \n",
    "Post-edit by Chris 9 March"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "import matplotlib.pyplot as pl\n",
    "import pandas as pd\n",
    "from matplotlib import gridspec, rc\n",
    "from matplotlib.lines import Line2D\n",
    "from netCDF4 import Dataset\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pl.rcParams.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib inline\n",
    "\n",
    "pl.rcParams['figure.figsize'] = (18/2.54, 6/2.54)\n",
    "pl.rcParams['font.size'] = 9\n",
    "pl.rcParams['figure.dpi'] = 150\n",
    "pl.rcParams['font.family'] = 'Arial'\n",
    "pl.rcParams['xtick.direction'] = 'out'\n",
    "pl.rcParams['xtick.minor.visible'] = True\n",
    "pl.rcParams['ytick.major.left'] = False\n",
    "pl.rcParams['ytick.major.right'] = True\n",
    "pl.rcParams['ytick.labelright'] = True\n",
    "pl.rcParams['ytick.major.size'] = 0\n",
    "pl.rcParams['ytick.major.pad'] = -56\n",
    "pl.rcParams['xtick.top'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from the FGD-approved \"dark\" palette\n",
    "colors_bar = {\n",
    "    'red1': '#dd512e',\n",
    "    'blue1': '#2134db',\n",
    "    'cyan': '#35a5c5',\n",
    "    'red2': '#aa1818',\n",
    "    'blue2': '#082e72',\n",
    "    'orange': '#ec9c2e',\n",
    "    'green': '#327f51',\n",
    "    'purple': '#8036a8',\n",
    "    'grey': '#c0c0c0',\n",
    "    'aerosol': '#66665f'\n",
    "}\n",
    "\n",
    "# as above with 30% added to lightness\n",
    "colors_bar2 = {\n",
    "    'red': '#f2bdb0',\n",
    "    'blue1': '#a2aaf1',\n",
    "    'cyan': '#aadbe9',\n",
    "    'red2': '#eb7070',\n",
    "    'blue2': '#246ff0',\n",
    "    'orange': '#f8deb9',\n",
    "    'green': '#7ecd9e',\n",
    "    'purple': '#b174d2',\n",
    "    'grey': '#e0e0e0',\n",
    "    'aerosol': '#9b9b92'\n",
    "}"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equilibrium Climate Sensitivity (ECS)\tCentral value\tLikely \tVery likely \tExtremely likely\n",
    "Process understanding (7.5.1)\t3.4 °C\t2.5–5.1 °C\t2.1–7.7 °C\n",
    "\n",
    "Warming over instrumental record (7.5.2)\t\n",
    "2.5–3.5 °C\t\n",
    "> 2.2 °C\t \n",
    "> 1.8 °C\t> 1.6 °C\n",
    "Paleoclimates (7.5.3)\t3.3-3.4 °C\t< 4.5 °C\t> 1.5 °C\t< 8 °C\n",
    "Emergent constraints (7.5.4)\t2.4–3.3 °C\t\t1.5–5.0 °C\t\n",
    "Combined assessment\t3 °C\t2.5–4.0 °C\t2.0–5.0 °C\t\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecs_process=[3.4,3.4,2.5,5.1,2.1,7.7,-999,-999]\n",
    "ecs_hist=[2.5,3.5,2.2,-999,1.8,-999,1.6,-999]\n",
    "ecs_paleo=[3.3,3.4,-999,4.5,1.5,-999,-999,8]\n",
    "ecs_ec=[2.4,3.3,-999,-999,1.5,5,-999,-999]\n",
    "ecs_combined=[3,3,2.5,4.0,2,5,-999,-999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#THese data from Sophie \n",
    " #from  Schlund et al. Earth Syst. Dynam., 11, 1233–1258, 2020 https://doi.org/10.5194/esd-11-1233-2020\n",
    "\n",
    "cecs=pd.read_excel('../data_input/Schlund_et_al_2020/ecs_for_faq.xlsx')\n",
    "cmip6_ecs=np.array(cecs.ECS)\n",
    "ctcr=pd.read_excel('../data_input/Schlund_et_al_2020/tcr_for_faq.xlsx')\n",
    "cmip6_tcr=np.array(ctcr.TCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ne=len(cmip6_ecs)\n",
    "nt=len(cmip6_tcr)\n",
    "ycmip6e=np.zeros(ne)\n",
    "ycmip6t=np.zeros(nt)\n",
    "ycmip6e[:]=1\n",
    "ycmip6t[:]=1\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transient Climate Response (TCR)\tCentral value\tLikely range\tVery likely range\n",
    "Process understanding (7.5.1)\t2.0 °C \t1.5–2.5 °C\t1.3–2.9 °C\n",
    "update: process assessment TCR likely range changed to 1.6-2.7, v.likely to 1.3-3.1\n",
    "\n",
    "Warming over instrumental record (7.5.2)\t1.9 °C\t1.5–2.3 °C\t1.3–2.7 °C\n",
    "Emergent constraints (7.5.4)\t1.7 °C\t\t1.1–2.3 °C\n",
    "Combined assessment\t1.8 °C\t1.4–2.2 °C\t1.2–2.4 °C\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcr_process=[2,2,1.6,2.7,1.3,3.1]\n",
    "tcr_hist=[1.9,1.9,1.5,2.3,1.3,2.7]\n",
    "tcr_ec=[1.7,1.7,-999,-999,1.1,2.3]\n",
    "tcr_combined=[1.8,1.8,1.4,2.2,1.2,2.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBsAAAFoCAYAAAAWzPMMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde5xN9f7H8ddiZlzmxrjNuMwgt1C6/GKi4650F3I9DodIFyJUikyJjiiVU8rpCCdCqEgUle7SheR+ZzCGaBgzMoPv74+9Zzf32Xtm7dkz4/18PNZjz9p7re/6rO/67rU+893rYhljEBERERERERGxSylfByAiIiIiIiIiJYs6G0RERERERETEVupsEBERERERERFbqbNBRERERERERGylzgYRERERERERsZU6G0RERERERETEVupsEBERERERERFbqbNBRERERERERGylzgYRERERERERsZU6G0RERERERETEVupsEBERERERERFbqbNBRERERERERGylzgYRERERERERsZU6G0RERERERETEVupsEBERERERERFbqbNBRERERERERGylzgY3WJb1b8uy5ro5bYxlWcaN4Rovx7zOsqyE/I7bscziwBv1UIBYQizLquKLZftCdnWduQ7ysz1ymqco168d6+3NeAp52cGWZcValnVLDp+3syzrV8uytliW1c+N8spYljXYsqwvLcuKtyzrvGVZe5379RrZTO/2/l4kO+5+f91tax7kFcayrNp2rIPdCmufZue+qyjlB87lF9ljmNgnu+18ueamygdKBj9fB1BMPAPstSzrHWPMGjfnmQxsz+XzgwUPK1eTgIoF+NwbyywOfLIOlmX9DVgC9ATWFfbyfSRDXedQB/nZHlnmKcr1a+N6ezOewvQ8sMMY80kOn78A3AIkAOsty1pqjEnObkLLsqKApcD1wMfANCARuA64D+hpWVY7Y8yWdLPlZ38vkh/utrVlwJ5045WB6cDXwKxM056wN0TbeH2fVgj7rst5vyyFIJftfLnmpsoHSgB1NrjBGHPCsqzXgdcty2pojLnkxmxrjDHrvBxajvL6UnjjS1MSvog+XIfrgao+WrZPZFPXWeogP9sjh3mKcv3ast428lldWZbVDHgA+Fsukxkcx67SOM7OMzmUFQC8DzQB7jbGLM/0+X9wJE+rLcu6whhzHvK9vxfxmLttzRizGdicNu48e2E6sM8Y805hxFpQhbRP8+q+63LdL0uhynY7X465qfKBkkOXUbhvFnAF0MXXgYiIlFCjgC3GmO9ymeZRHL9OfA08Z4w5l8N09wPXAtMyJxYAxpgfgVeBGkDXTB9rfy+FRW1NRCQr5QMlhTHmsh6AfsBXOE7BSQFicTSsStlMuwH4Mo/yYnD0rLX1MI47gO+BJByXWIwCRjvLquCcZh2QkM28Lzunq53uvQzTujsOtAF+Av4E9jvXJyDT8tYB63F8yf9wztcnr2W4Ee83QFtnPZwDDgNPAhYwENgBJAMbgU5u1OlVwHvAcRynSm0AumWzLnnVS4HiyquNAXOc9ZE2HEg373XAcmc9nwN+BHq72aaCgddxnH57HjjqXFZkpuncWka67d4S+BJHWz0JzAOq5mO5rrrOqQ4yTfOa87NG2cT2sbN+y2azDbOUjeOUxGy/p8BsHO2/Qi51626d5VoX7qy3XW0RN/Z1OcXj7jq7u+1zqNNqznme8mTfmUt5653rUDOXaSrk9Dlu7O81aMhuwIPjqXN6j9oaUNvZtufkEUOW47TzM7fyHmzc5+ewT7Pt2JPbviuXOso1R8gccw7jXs0P8lo3d+vQk/bhbrkebPefnHX0s7Osvc46Kp1NXNcDHzqX+yfwK/AgYHnaLt2NsSD1aGd95bGd12Fj26OQ2p279Z/NfMoHStDg8wB8uvIw1tn43sdxqs5DwArne6uzmf554CK5/xMS45z/bhzXVWY3BGeapydwybljeBSYCJx2fjkLs7MhFcfOfZ6zLpY7y12YaXnrgLPOncZwHP+41cprGW7EGw+cAaY6t8dG53QrcexEH3fWT1piUC2X7XADjgPQ78CzzvK+dZb3dw/rJd9x4UYbA24E/ud8bxLQxfl+Wxw78Z3OZTyC48BqgCfdaN8f4zjQ/AsYhKP9JuFoVwGeLiNTXbwBDOavg9FqT5abua5zqYP00zR3fh6TKa4qONrurBy2YZaygQbO8VmZyiqL47v3Xi716kmd5VoX7qy3HW0RN/d1ucTj1jq7u+1zqNf+zvJutGHfbjnbxMEClJHn/l6DhuwGPDieOqf3qK3hfmdDdsdpt/MebNznZ96nubtPcTeOnPZdudRPnjlCNjFnN+7V/CC3dfOkDt1tH56U68F2j3dON9+5jiudZc3NFFMnHP/4xgETnHF94Zz2LU/bpQcx5rse7ayvnLaz3W2PQmx37tR/DnWqfKAEDT4PwGcr7ri+5ySOeytk/my9s1H5Z3q/t7Px35lLuTFk7AnMbvggUxxHcexcQ9K9f70zBkPhdTYY4JlMZS9wvh+dzbR3Zpo212W4Ea8BBqV77yrne+eBK9K9P9j5fvdctsM3OP5pjEr3XlnnznFPPurF47g8aWPACNL90o7jEqddwDagfLr5LGAxjh1nVC7rX8VZ3r8zvf8gjl8Kmnq6jHR1MTBTmaud70e4s9xc6jpDHeQwzXZgZ6ayH3bO1yqndpdD2d87t49/uvfudU53Vw716naduVsX7qx3QdoiHu7rMsfj7jp7su1zqNvZODpdA3Obzp0BxzWmBvi+AGXkub/XoCG7AQ+Op873PWpruN/ZkKHMfOwL0soo8D4//T7N3X2KJ3E4x7PsS3OpnzxzBIpIfpDdunlah+60D0/K9XC7GxynuKefbrHz/evT1cd+4BQQnmm57zmn7ehhe7A9D8qmHu2ur2zbsF1trzDbnbvrnEO9Kh8oQcNle88GY8xFoDqOL5+L8/Eup3F8ocplmm2v8/UKNxYxGkcvbXbD0+mm+z8cB+y3jDFn0sX3M44ewcJ0AcedXdN70fl6VzbTf+6FGN5L93fa0zw2GmP2pnt/n/O1enYFOLdhS2CpMcb11A9jzJ/A7Ti2gdfjymcbS9MMqI/jdMLylmVVtiyrMlAJxw7dz7kuOTnjHHpaljXQsqxKzpheN8Y0M4677eZnGQZYlOm9jc7Xym4utyDmAg0sy7ou3Xt9gb3GmG/zUVYYcGu69/6O45euVTnM40mdeasuPGqLBWyH4P46F3R9rwCOGWOS8pjOHRecrwW5CbIn+3uRzDw5nnqzrbmO0/ncF3hjn++NY4/bvJAjFHZ+AAXPEdJkzuO8sb8/D0zJtJy070badfDX4ehE+48x5ljaRMbxn94E52i3dPO70x68lQelV1jHx5wU1XwACrbOygdKkMu2s8EpBWhlWdZ/Lcv61rKsozhON7rZ+Xnm+jntfHXnebM/G2PW5jBsTjddlPN1dzZlbHV3RWxyKJsvdtrjtupmev+sTTuB9JIzdbik7SDiM0130fmaU/uNwtHLujPzB8aYPcaY/YUYl6dtLE195+sTOB5lln5IO7hEZTNfWozncfRmlwH+Cxy3LOsHy7LGp3uWcH6Wkd12P+98Le3mcgviHRy93b0BLMu6AojGcaqfpxbhiL2Ps6y0joeFxpjUHOZxu868VBf5bYv5bYfg5jrbsL5pyU6BGWNO4TiFPbwAxXiyvxfJzJPjqbfaWnb7a0/3Bd7Y59t+7MlhOTmxM0fwRX4ABcwRnLKrU2/s7w8ZYxIzvbfL+Zr2XajjfM3ukfG7cPzDWCfde3m2By/mQekV1vExO0U2H3DGU5B1Vj5Qglzuj75cjKN372ccN/+Yj+MmJ8Nx/MKZWdoBzRuPPrGyee9PN+f19ECbk+zWKy2uC5nev5h5Qg/kFG9O/+AZD8tPa9eezpeTgsTlaRtLk7aznwKszWGaQ7kt2Biz2LKs1ThuPtoZ6Ijj2tTHLMtqk89l5Nn281quMeaXvMrIpezDlmV9jqOn/DEcZzUYHNdFe1rWH5ZlrQDutCwrCMclFP55lOVRnXmhLvLbFvPbDsGDdS7g+l7CUf92+QboaFlWpDEm2++KZVl1cJzaPtsY859MH3tzfy8lnyfHU2+1teyO057uC7yxz/fKsccDduYIvsgPwIYcgezbhzf29ynZlJHW5tO+C9nlwKT7rBR/dSaAm+3BS3lQeoV1fMxOkc4HoEDrrHygBLlsOxssy7oJx5dtpjHmwUyfVcthtrRTs+JsDCWtd7dRNp/VyzR+Acc1hZkVpLcuveqWZZVx9kamaeh83ZPdDHnwdrw5STstsn7mDyzL6ge0B0Y5ezu9Jp9tLM0B52uKMSbDDt2yrFo4HuFzNpdlBwJX47ghzgIcO1Asy+rt/PshIG1nmq9lFGC5gzwpMxtzcZzJ0ALoBXyTj7NV0pfVHbgFuAfYYRyPQMrJAedrnnVWSHWRpwK2Q3BznW1Y32POsuzyHo7EZiiOO3RnZwCOM2Pey+Yzb+zv5fLhyfG0UNqaDfuC7MrMz/f+gPPVtmOPh9zKEby4/ELbL+cjNLfK9XC7R1mW5Zful3f467uQdlZv2jH8ymxiaojjn9xYT1akkPKgA+7Mf7nlA87xgqyz8oES5HK+jCLtVJjf0r9pWdb/4XhcFWTtjIl0vh6wMY5fcXQ43Oe87iktjigyXp8GjhtJlrEsq0m66aoCHWyKpTzwj3RlWzjuNAuwLB/leTvebBlj4nD02HazLCsi3bIDcKxPB293NDh50sYyn+b2E44D6wPOOkub1wJm4LheLq09ZqcO8B2OU93S+875esGGZeR3uTnJ6/KYNMtw3Fl5LI7EZK4bceVU9mocpw/2xnGH5bwux/CkztytC3fXO7883dflty0WZNuDY79a0bKs4Dymc9dsHDexGmlZ1h2ZP3T+qvUEjrt2Z/4VA7yzv5fLhyfH08Jqa/nJe/KSn++9N4494Oa+tIjkCIW1X/aUN/b3QcCQTGU9juNX4qXOt39xLnewZVnhmaYd7xz90MN1KYw8yO76Kin5ABQsJziA8oES47I9swHHKTV/AJMsx41RjgLX4Hg2bdppMqE4bhaX5kYcp3F96Ub5nSzLqpnL55uNMZuNMcayrCHAJ8BPlmXNxHHK2PBs5nkHx7NxP7As61UcN3F5AMc/SxXdiCkvZ4DplmU1wvHLyz04bpQ0PdN9Jtzl7XhzMxz4DPjZsqzXcWzrvkBjHOtVGDxpY2nX2D1gWVa4MWaBZVkPAB8AmyzLetM5TVcc22SOMWZ9Tgs2xmyxLGs58LBlWSE4HukVguOA/yfwhjHmQkGWkd/l5jJ7ljrIYRnJlmUtAf6J4/FL2fVCu1W2sw7mAyNxnHr4Th7r53adeVAXbq13AXi6r8t3WyzAtgdYg+NXjmjn3wXi3FZdcOxbl1uWtRLHPuEijn15TxzPGL8nm+uJwbP9vUhmnhxPC6ut5SfvyVV+9vneOPY4ebIv9XWOUGj7ZU94sm082O4XgZcsy2qK415kd+G4P8AUY8z2bJa70bKsN3A8meIeoB3wP2OMR8eFwsiDvFBfJSYfKGA+qHygJDFF4JEYvhqA5jie4ZvgHH7F0bN1N45/PB7INP0WYFUeZcY4581riMk0XwscdwVOxvHlfxrHteOGdM91xfHl24aj0e/B8XzbtMfa1E433To8f/TlFhw3yNvsLH8XMDybdcwwb27v5zfedO8b0j0q1PleW+f7I/LYFtfheHbwaRyJ39c4H52U33ryNC532xiOjpgPcPzzfAoo63z/RhzPTE5wto3fnHXo50b7DgIm4rgJVrKz3I+A5pmmc2sZudRFjHNdrvFwuZnrOksd5LLMNs5lvutmO8y2fp2fNXOW9YUH+w536yzPunB3vQvSFt1thwVti+5u+xzqtAKOa1Cfy2taTwYcHZtP4PhF5nf+2he9jPMRaTnMl+f+XoOG7AY8OJ46p/eoreH+oy+z2194si/IqYwYPNzn57BPs/vYk+N+Poc6yjVHyLzcvMbTvW9rfpDburlbh+62D0+2jSfbHUeHwRYc/2T+BgzOZbkfO7dJEo4zUO4HLE/bg7sxFqQevVBfOW1n29peYbY7d+s/m/VQPlCCBstZiZIHy7KuwfFYnXuMMR8U0jLnAP2BisaYhMJYpsjlxrKsZsAm4J/GmDk+DueyZlnWO0AroK7x4cHJF/t7uTyprUlJZlnWOhz//FfwdSxSvCgfKDku53s2eGogjl/oPb1mTESKtmE4fkVx53IM8a7ncTw2y6v3dXGD9vdSWNTWRESyUj5QQlzO92xwm/MGQoOAHr7sXRMR+1iWtRCohuMUw2dM1md2SyEzxmy1LGs2jsvIcnqslldpfy+FRW1NRCR7ygdKDp3Z4J4JwDJjzEpfByIitokCbgDeAib5OBb5yyigrmVZt/to+drfS2FRWxMRyZnygRJA92wQEREREREREVvpzAYRERERERERsZU6G0RERERERETEVupsEBERERERERFbqbNBRERERERERGylzgYRERERERERsZU6G0RERERERETEVn75ndGyrGNAeSDWvnBERETERrWAZGNMuC+DUM4gIiJS5NmeM1jGmPzNaFlnAgLKBNete4VdsYjkyBjDzp3badjwSizL8nU4IiLFwr59e0lJOZ9ojAnxZRzKGcQXlDuIiLjPGzlDQTobtjZq1Ljxhg1b7YpFJEepqalUqhTAyZMp+Pv7+zocEZFioXnzJuzYsW2bMaaJL+NQziC+oNxBRMR93sgZdM8GEREREREREbGVOhtERERERERExFbqbBARERERERERW6mzQURERERERERspc4GEREREREREbGVOhtERERERERExFbqbBARERERERERW6mzQURERERERERspc4GESmyQkIsQkIsX4chIiIiYhvlNnK5UGeDiIiIiIiIiNhKnQ0iIiIiIiIiYit1NhRTBw8e8HUIIiIiUowodxARkcLk1c6GkBCL8PBAIiKCqF49mMjIMAYN6ktCQoI3F1vkTJ4cw9ChA2wr7/jxeFq0aGJbeWluu60t8+fPAaB58yZ8993Xti9DREQkN8odHJQ7iIhIcef1Mxt++GErcXFnOXo0kS1bDhAfH8fIkQ94e7El2rlz50hOTvbqMjZs2ErLln/z6jLEu5KSklw3WExKSvJ1OCLFir4/vqXcwX7KHaQo0z5XvEHtyvcK9TKKkJAQ7rqrG9u2/QZA06a1GTZsMJGRYbzyylROnz7NsGGDqVu3Ko0a1SQmZiypqamA4yA5YsRQIiPDuOKKakyaNMFV7uLFC7j++kZERobRt29XTpw4DsCxY3HcdVdHIiMrcs019Zkx40UALl68yPDhQ6hduzKNGtXkwQcHcv78+SzxZv5VYf78Odx2W1vXZw8+OJDbb29H9erBdOzYkn379gKOhj1oUF9q1AihZctm7N2721VGamoqzzzzJFdeWYsGDaozfvxjrnW87ba2DB06gDp1qjBmzHC2b99K+/bR1KpVgebNm7Bo0XwAbr21NQAREUHExR1lz57ddO16K/XrRxAREUSvXneTmJjoKnPSpAlcf30jatWqQP/+PVzrunv3Ljp1akVERBB9+3bN8CVs2rQ2X3+9zrndLF5//RXq14+gXr1wXnjhOdd0X331Bc2bNyEyMoyhQwfQqVMr13wiIiIFpdxBuYOIiBRPhdrZcOjQQRYvns9NN7V1vXf6dAK7d8cxcOBQHnlkCCdOHGfjxt2sW/cj69Z9xrRpkwF47rnx7N69k40bd/Ptt5tYtOgdVqx4n/Xrv+WJJ0Ywe/a77Np1lFq1orj//v4AvPDCczRt2oyDB0+xaNFypk59joMHD7B8+TJ27NjGtm2H+OGHrWzZ8ivLli32eH2WLl1ITMy/2LMnnooVw3j55SkAPPPMk/z++wm2bz/MrFn/Y+3a1a55XnllKl9/vY4vv/yJ77/fzI8/ruff/37J9fmePbvYsuUA48c/x/jxj3HvvX2IjU1g+vSZjB79EMnJyaxa9RUAcXFniYiozvDhg7nxxpvYufMImzfvY+/e3bz33gJXmcuXL2Xlyi9Yv34LGzZ8z4cfLgWgf/97+dvf2nHo0B/cfXd3Nm78Kcd1/fnnDWzevI/Zs9/l+edjOHLkMKdOneLvf+/K2LEx7N0bT/36Dfnhh+88rkcREZGcKHdQ7iAiIsWTn7cX0LLl1ZQqVQpjDMHBIXTocAsxMc+7Pr/zzq6UKVOGS5cusXz5Mn74YSuhoaGEhoYybtxERo16iLFjJ/D++4uZOXMOlSpVAmDp0lWEhoYyadLTDBgwmGbNrgXg6acnUaNGCMePxxMaGspnn33CRx99QLt2nThw4CSlSpViz55d7N69k0WL3qFz5ztYt+5HSpXyvN/lb39rxw03tADgjju6uA7SK1Ys48035xESEkLTplfTp09//vjjFAALFsxl2rR/U7VqNQDGjBnH448/wsiRjwNw6613EhgYCEBISCirVi2nXr0GtG7djoMHT2Ub55tvzqNatXDOnTvH0aNHCAurRHz8Mdfnffr0Jzw8AoBWrdqwb98e9u3by65dO/jiiw34+/vTo0cfZsyYluO6PvjgCMqVK0fr1u2oVi2cAwf2cfDgAa68sin33HMvAI8++gSzZv3b43qUoscY4/XTbT1xuZ/6Vr58eSxLz+SWy4dyB+UOInZTbuM7ymMuX17vbPjuu81ERdXO8fNq1cIBx68UFy5cIDIyyvVZZGQUR48eBhw3NqpevYbrs/r1GwBw+HAsCxf+j//85zXXZ/7+/hw6dJAnnpjAxYsXeeqp0Rw9eph77+3Diy++RocONxMT8zyzZ7/Jo48+SIsWLZk5cw516tT1aN0qV67i+tvPz49Lly65Yo2I+CvWWrWiXAnDkSOx9OvXzXXgN8Zk+PKl1QfAiy++xrPPPsnDDw/izJnTDBw4lGefnZIlju3bt9K9+2388ccprrrqGs6cOY0xJtc4T5yIJyysEmXKlMkQpyfrGhd3hOrVa7retyyLGjVq5ViG+M6bb87g0UefAKBfv+7UqhXF5MmOU4Mff3wE8fFxzJmzCIBXX53GBx8s4aeffvBZvJlFRAT5OgSfmj//fe68swuJiYncfXcn+ve/j/797wOgS5dbuOGGaJ566hkAHnpoEACvvfZfACZNmsCPP67ngw8+AWDu3LeYO/ctPvxwDcHBwaxZs5rnn49h5sy3adjwSrZt28LDD9/HuHETad++EwkJCXTt2pmBA+/n73//JwB33dWRVq3a8Pjj4wEYOnQAAQEBvPrqLACefXYcv/76C0uXfgzAf//7BvPnz+Gjjz6nfPnyrF69khdemMibb86jfv0G/PbbrzzyyP1MmDCZNm3ac/LkSe6993buu+9B+vT5BwC3396Odu06MXr0kwAMHtyPoKBgpk9/HYCYmLFs27aFxYtXADBr1mssXPg/Vq36kjJlyrBy5Ye8+OLzvPXWfOrWvYJNm37h0Ucf5Nlnp3DTTW04ceI4PXvexf33D6Nnz75e25biHuUOyh3k8uVpzrJixfusWfMtAEuWLOT1119mwYL3CQ+P4Pvvv+Gpp0bzwguvcuWVTYpUPlGUYvG2uLizBAYG+iRnEd/y+aMv0w6WVapUJSAggEOHDro+O3BgP1WqVAWgevUaxMUddX22cuWHrF69kmrVwhk7NobY2ATX8M03G7nmmuvYuvU3hg0bxebNe/nmm438/PMG5s37L/v37yM6uhVffvkjO3ceITy8OuPHj8kSW6lSpbhw4YJr/NSpk26tU7Vq4Rw+fMg1Hh8fl+GzDz9c64p1+/bDfP31xiz1AbBt229MnDiVnTuPsGLF5yxa9A6ffvpxhmWlpKTQv/+9TJ78Ert2HWXp0o+pXTvvxKdq1XBOnvydc+fOud47diwulzmyql69JkeOxLrGjTHExR3xqAwRERFPKXdQ7iAiIkWf189scFfp0qXp1q0X48eP4c0353HuXDKTJ0+gW7deAHTr1otp0yZx9dXXcu5cMuPGjWHy5Bfp0aMvw4bdx6233kn9+g15440ZTJ36HFu3HuS116YTEBDAyy+/QdWq4ZQqVYqKFcP48svP+M9/XuODDz6lYsUwypYti79/cJaY6tatx9tvv8nJk7+TmprKggVzqVgxLM91uffePkyZ8izXXHM9x48fY/78OXTs2Nn5WV+efz6GWbP+R7ly5Rg+fAiJiWdYsmRllnJiYsbSunV7nnwyxnUqY8WKYa5fFM6cOYNlWfz5558EBQVhjGH16o9Yu3Y1V16Z++Ot6tSpS7Nm1/Hss0/xzDP/4pNPVvLTTz8waNDQPNcvza233snjjw9n+fJl3HbbXbz55r85cuSw2/NL4bn//mGuv//3vyUZPpsy5eUM48OHj2bYsFFF4lTDtF7/uLizPo7Et8qXLw9AcHAwn3++PsNnab3/adJ+HUiT9utBmvRnRQB06tSZTp06u8YbN26aYRkVKlTIsszly9dmGH/jjTkZxp9++rkM44MGDc2wb+nc+XY6d77dNX7VVc0yLKNSpUpZlrly5RcZxv/zn/9lGE9/ij3AkCEPMWTIQ67x22+/m9tvv9s1fs0112VYRpUqVbMsU4o+5Q7KHaTk8TRnGT58tGu8e/dedO/eyzV+4403ufbtjo6topFPOG7UWjRiKQxpeYwvchbxrSLT2QDwwguv8sQTI7juugZcunSJXr36MW7cRAAee2w8Tz01mv/7v0ZYlsVDD43k1lvvBODxx5+md+8uxMfH0aDBlSxe/BHly5dn8uQXefDBgdSrV43Spf3o3fsf3Htvb4wxbNmymebNm5CScp6bbmqbpbED3H13N1av/ohrr21A1arV6Nnz73zxxZo81+PJJ59h1KiHuOqq2lSrFkHnzne4fuV47LFxPPvsU7Rq1YykpLPcdFNbZs58O9tyXnvtv847blekfPlAHnpoJNHRrTDG0LFjZxo2rM7nn//AlCmv0KfPPaSmptK06dX07TuAnTu35xnn3LmLGTq0P5GRFWne/Ebatu2Q5zzpVahQgdmzFzJ69MM8/PAg7rqrG5GRUQQEBHhUjhQ9lmW5rv8tCopSLJpWH/4AACAASURBVCJStCh3yEi5g0j2lNuIFD4r/fV5Hs1oWVsbNWrceMOGrTaHJMXFiRPHOXr0iOsGWwD16oXz8cfraNCgka3LSk1NpVKlAE6eTMHf39/WskuqpKSkDGcGFMeDWkiI49TgM2fyt58Sya+S8P0BaN68CTt2bNtmjMn9J2svU84gaZQ7SHZKyj7XXSEhlnKbQnC5tauC8kbO4PN7NkjxlZyczO23t2X79q0YY5gz5z+UKVOGevUa+Do0ERERKYKUO4iIXD6K1GUUUrxERdXm+een06PHHZw8+TuNG1/FggUf5OtRYGK/wMBA9ZqL5JO+PyLeodxBsqN9rniD2pXvqbNBCqRfv4H06zfQ12GIiIhIMaHcQUTk8qBuZBERERERERGxlTobRERERERERMRWuoxCRIosXWcnIiIiJY3yG7lc6MwGEREREREREbGVOhtERERERERExFbqbBARERERERERW6mzQURERERERERspc4GEREREREREbGVOhtERERERERExFbqbBARERERERERW6mzQURERERERERspc4GEREREREREbGVX0FmLlUKgoPtCkUkZ6mpjtfgYPD3920sIiLFRaki9JOCcgYpbModRETc542coQilISIiIiIiIiJSEqizQURERERERERspc4GEREREREREbGVOhtERERERERExFbqbBARERERERERW6mzQURERERERERspc4GEREREREREbGVOhtERERERERExFbqbBARERERERERW/n5OgARySQpCYKCHH+fPQuBgT4LZcuWLT5bdk6aNm3q6xBEREREcmRZFsYYX4ch4nM6s0FEREREREREbKXOBhERERERERGxlTobRERERERERMRWRb6z4cCBA74OIUdFOTYREZHLVVE+Phfl2EREROzk1c6G2rVrs27dunzPv3z5cv75z3/aF5CN3I3twIEDWJZVCBF5bt26ddSuXdvXYYgHkpKSsCwLy7JISkrydTgiIrZT7qDcQS4/ym9KHm1TgSJ+ZsOpU6eK7J1ci3JsIiIil6uifHwuyrGJiIjYrdA6G9q2bcuECRNo1KgRFSpUoEePHpw/fx6AJUuWUL9+fcLCwmjVqhW//PIL27dvZ+jQoXz99dc0a9aMAwcOEBERQffu3QkLC+Pbb7/FsqwMpyOm/zXEsixmzpxJeHg4VapUYd68eTz11FNUrFiROnXq8NVXX7nmmz59OnXr1qVq1aoMGjSIs2fPAhATE8PAgQNp164dwcHBtGzZkr1792aJDeDjjz+mefPmVKxYkcqVK/PEE0/kWSe1a9dm8ODBhIWFMXXqVFJTU3nyySepVasW1atX57HHHiM1NRWAc+fOMXToUMLCwqhWrRoTJkxwlTN16lSioqKoXLkyPXr0ID4+3hV/ly5dqFu3Ltdeey0AkyZNokqVKtSoUYMVK1bkc2uKiIh4n3KHrJQ7iIhIcVGoZzYsXbqUL774gi1btvD999+zdOlSLl68yMCBA1myZAknT57k1ltv5fHHH+fKK6/kjTfe4G9/+xu//vorAMeOHaNVq1YcPnyYFi1a5Lm8H374gUOHDjFlyhQGDhyIv78/x48fp3fv3owfPx6ABQsWMHPmTNasWcO+fftITExkzJgxrjIWLlzIv/71L+Lj4wkLC2PKlClZYjt79iy9evVi6tSp/PHHH3z66ae89NJL7Ny5M88YExISiIuLY+jQoUydOpV169bx008/sXnzZtavX89LL70EwPjx49m5cye7d+9m06ZNvPPOO7z//vvMnDmTWbNmsXbtWmJjY6lQoQK9e/d2lb9u3To+++wzvvzyS9f033//PZs3b+bbb7/1aPtJ8WaMISkpyaNBRMTXlDtkpdxBJKv85DneGgCfx5A26Gwq8aVC7Wzo378/ERER1KxZkzZt2rBnzx5Kly5NcHAws2bN4qeffmLs2LGsWbMmxzJ69epF+fLl8fPzy3N5w4YNIyAggDZt2nDx4kVGjhyJv78/HTt2JDY2FoC5c+cyZswYrrjiCoKCgnjuueeYO3eu64vZrl07WrRoQfny5enSpQt79uzJspxy5crx66+/0qZNG06ePEliYiJBQUEcO3Yszxi7du1KmTJlCA4OZu7cuTzzzDNUq1aNypUrM27cOObMmQPA4sWLGT9+PJUqVSIiIoJVq1bRsmVL5s+fz5gxY6hfvz7lypVj+vTpfPXVVxw+fBiAFi1aUKdOHUJCQli2bBmDBg2iXr16VKpUiSeffDLP+KTomjFjhuvv7t27M2rUKNf4iBEj6Nmzp2t82rRp3HjjjQQFBXk0iIj4mnKHrJQ7SEnmaX7TqlUrAJKTkz3Oc7w1AD6PIW1ITk4mOTmZ6Oho3njjDVfd3XbbbYwbN841PmTIEAYMGOAanzhxIh07dnSNv/3220RHR5OQkADAmjVriI6OZsuWLQBs376d6OhoVq9eDUBiYmKB24IUf4Xa2VClShXX335+fly6dAmA1atXExcXR4cOHYiMjGTevHk5lhEeHu728sLCwgAoXbo0AKGhoQCUKlXKtezY2FhGjhxJhQoVqFChAs2bN+fSpUscP34815jTK126NEuWLKFGjRpER0fz5ptvYoxxqycx/frExsbSrVs3Vyz33nsvcXFxAMTHx1OjRg3XtA0aNKBatWqcOHGCqKgo1/uBgYFUrlzZlTCkLz9zGennExERKYqUO+S+PsodRESkqMq7i9/LkpKSOHnyJMuWLSMlJYWlS5fSr18/7rrrrmynT393ZsuyuHDhAuA4deqPP/7IcdqchIeHExMTQ48ePQBISUnhwIEDVK1a1e11+O6773jxxRf58ccfqVWrFsYYV7KSl/QxhoeHs3DhQpo3bw44egR///13AGrUqMHRo0dp2LAhAB9++CF+fn7UrFmTgwcPuso4e/Ysv//+uyv+zOUfOnTINZ6WjEjxNGzYMNffS5YsyfDZyy+/nGF89OjRjBo1iuTkZI+WsX///vwHKCLiJcodlDtIyeVpfjN69GgAypcv77p3iq8FBQUVmVjKly+PZVmsX78+w/sff/xxhvFZs2ZlGB8/frzr0jGAf/7znxmeptOpUyc6derkGr/yyiszLCM4ONiW+KV48/nTKC5cuMAdd9zBJ598QkBAAJUrV6Z8+fKULVuWMmXKcObMmRznrVevHosXL8YYw+uvv56v03X69u3LCy+8QGxsrOsmS127ds1zvvSxJSQk4OfnR9myZTl//jzPPvssCQkJrhs0eRJLTEwMJ0+eJDk5mSFDhvDwww8DjlNAJ02aREJCAkePHmXMmDFcunSJv//970ydOpXdu3dz7tw5Hn30Ua677jrq1q2bpfzevXvz1ltvsXXrVhISEnj++ec9ik+KN8uyCAwM9GgQESmKlDtkjEW5g0j+8hxvDYDPY0gbiupjdOXy4PPOhtDQUBYsWMCIESMICgpi+PDhLF68mLJly9KmTRvOnDmT4yl7r7zyCvPmzaNixYr89ttvrmu2PDFw4EC6d+9O69atqVy5Mhs3bmTZsmV5fjHTx9a5c2duvvlm6tWrR2RkJHv27KF9+/Zs377do1jGjRtH48aNadasGdWrV+fcuXO8/fbbgKN3sWHDhjRq1Ijrr7+e++67jzvvvJMBAwYwcOBAOnXqRLVq1Th+/Djvv/9+tuXfeuutjB07lvbt29OwYUOio6M9ik9ERKQoUO7wF+UOIiJSVFn5vUOpZVlbGzdu3Hjr1q02hySSVWpqKgEBAaSkpODv7+/rcLwrKQnSbs549iykO8MgKSnJdeOhs2fPev3sg7Sb/hQlTZs29XUIIsVGkyZN2LZt2zZjTBNfxqGcQXzhssodirnCzm+8zbKsy/4pECVtm14OvJEz+PyeDSLivsDAwMv+4CUiIiIli/KbkkfbVKAIXEYhIiIiIiIiIiWLOhtERERERERExFbqbBARERERERERW6mzQURERERERERspRtEihQ1gYFQRG6ooyc/iIiIiHhGN0YUcdCZDSIiIiIiIiJiK3U2iIiIiIiIiIit1NkgIiIiIiIiIrZSZ4OIiIiIiIiI2EqdDSIiIiIiIiJiK3U2iIiIiIiIiIit1NkgIiIiIiIiIrZSZ4OIiIiIiIiI2EqdDSIiIiIiIiJiK7+CzHzpEiQm2hWKSM5SUx2viYng7+/bWEREiotLl3wdwV+UM0hhU+4gIuI+b+QMOrNBRERERERERGylzgYRERERERERsZU6G0RERERERETEVupsEBERERERERFbqbNBRERERERERGylzgYRERERERERsZU6G0RERERERETEVupsEBERERERERFb+fk6ABGRzA4e3OL6OyqqqQ8jEXFDUhLBEUEAJMadhcBAHwckIiIi4ns6s0FEREREREREbKXOBhERERERERGxlTobRERERERERMRW6mzwgkOHDvo6BBERESlGlDuIiEhJ49XOhpAQi/DwQCIigqhePZjIyDAGDepLQkJCnvM2bVqbr79e55W40pcdERFEbOwh28p+440ZTJ48wbbypORLSkoiJMQiJMQiKSnJ1+EUOaoTkcuLcgcREZGSwetnNvzww1bi4s5y9GgiW7YcID4+jpEjH/D2Yt0WF3eWWrUibSvv1KmTtpUlIiJyOVLuICIiUvwV6mUUISEh3HVXN7Zt+w2APXt207XrrdSvH0FERBC9et1NYmJilvn2799Hly63EBlZkejoq1i79hPXZxs2rKdjx5bUrBlKq1bX8Nlnn7o++/XXjXTocCMREUHceOPVfPfd19nEZHHw4AEOHjxAo0Y1+de/niUyMoxGjWryyScfM2zYYGrUCKFFi6bs2LENgBMnjvOPf9xL48aRVK1ajttua8vRo0dYs2Y1L744mcWL59Ojx50AbNr0C506taJWrQq0a9eCjRt/BuDixYsMHz6E2rUr06hRTR58cCDnz5+3r7JFRERKAOUOyh1ERKR4KtTOhkOHDrJ48XxuuqktAMOHD+bGG29i584jbN68j717d/PeewsyzHPhwgV69LiD1q3bsXfvcV544VUGDuxNbOwhjh+P5557buaf/7yfAwdOEhPzL/r168aePbs5f/48PXveSffuvYmNTWDMmHH07duVlJSUHOM7evQIKSnn2b//BP36DaJXr7to3vxG9u//nauvvpbp06cAMH78Y4SFVeLXX/eyZ088lmUxa9a/6dSpM6NGPUmPHn1ZvHgFp0+fpmvXztx334Ps3/87w4aN4t57bycxMZHly5exY8c2tm07xA8/bGXLll9Ztmyx1+pepLhKTk4iKanoD8YYX1eVSImk3EG5g4iIFE9e72xo2fJqatWqQM2aodxyy000atSYmJjnAXjzzXk88sgYzp07x9GjRwgLq0R8/LEM8//8848kJp7h0UefwN/fn9at29GxY2eWLHmXVatWcOWVTenbtz9+fn506tSZW265g2XLFrF+/beULl2aBx4Yjp+fH1279mDRohVYlpVrvA899CilS5emVavWBAcH06/fQAICAmjduh1HjsQC8Mwz/+K556Zx8eJFjhyJpWLFsCxxA3z66cdccUV9evbs64qhdu26fPrpx4SEhLJ7904WLXqH5OQk1q37kd69+9lU61JcvfnmDNff/fp158knR7nGH398BAMG9HSNv/rqNDp1auUaX7JkIe3bR3PsWBwA33//De3bR/PTTxsAOHjwAO3bR7N8+TLAkYy3bx/NzJmvusro0+cexo9/zDU+evQwBg7s4xqfPn0KnTu3do0vWjSf9u2jOXHiOADffPMl7dtHs2nTLwDs27eX9u2jWbnyQwDOnz9P+/bRzJr1mquMHj3uJCZmrGt85MgHM9TJFVdUIyIiqMgPycnJeWxdEXGXcgflDiIiUvz5eXsB3323maio2tl+tn37Vrp3v40//jjFVVddw5kzp7P8OnjkSCzx8ceoVauC670LFy4QFlaJS5cuUatWVIbpIyOjOHr0MMePxxMeXj3DZ82bR+cZb1hYGAClS5cmJCTU9X6pUqW4dOkSAIcPxzJ69MMcPLifJk2u4s8//yQoKDhLWUeOxPLLLz9miD01NZUjR2Lp1q0nMTHPM3v2mzz66IO0aNGSmTPnUKdO3TxjFBERKcmUOyh3EBGR4s/rnQ05SUlJoX//e3nnnWV06HAzAL17d8kyXbVq4dSr14Aff9zmeu/w4ViCgoJZtWoFq1atyDD9wYP7qVevIdWr1+DYsaMZPnvuuacZPDjjr6aZ5fXrBcB99/XlkUfGMGDAYMDxi/Pp01nvkl21ajht23Zk6dKPXe/t37+PKlWqsn//PqKjW9G//30cPx7PY489wvjxY3jnnaV5Ll9KrvvvH+b6+3//W5LhsylTXs4wPnz4aIYPH+0a7969F92793KN33jjTXz++XrXeFRU7Qzjfn5+GcYBFix4P8P4tGkzMoyPHPk4I0c+7hrv2bMvPXv2dY3fdFObDGXWrXtFhvEyZcpkWebixRm/w9Onv87Bg1tc43v3xlO+fCBFXfny5X0dgkiJp9xBuYOIiBQfhXrPhvTOnz/v7NUPwhjDqlUrWLt2NRcupGaY7oYboklJSeHtt2dx8eJFdu7cTtu2N/DFF2u4+ebb2LVrBwsWzOPChQusWbOaVatWcPfd3fi//2tBqVKleOutmVy8eJEPPljCvHlvUbFiWIFjP306wfXPzw8/fMeiRe+QmuqIu0yZMiQmngHglltuZ+PGn1i16iOMMaxf/y0tW17Njh3b+PLLzxg4sDcnThynYsUwypYta0tsIiVN+fKBBAYW/cGdfzZEpGCUOyh3EBGR4sNnnQ3BwcFMmfIKffrcQ1RUJWbMeJG+fQewc+f2DNMFBATw3nsf8cEH71GnTmXuvrsTDz00knvuuZdKlSrx3nsreeut14mMrMhTT41i9ux3ueqqZpQpU4aFC5fz3nsLiIoK46WXnufddz8kICCgwLFPnz6TZ54ZS82aoTz22CP06zeQXbsccd9yy+388MN3dO7cmkqVKrFw4XKmTZtErVoVGDLkH0yb9m/+7/+a06/fQG688W80b96E2rUr8ccfp5gwYXKBYxMRESmplDsodxARkeLDyu8d1C3L2tqoUePGGzZstTkkkaxSU1OpVCmAkydT8Pf393U4tkpKSiIiIghwPLs9MLDoXzLgbekvo6hcuY7qRIq2pCSCnd/hxLizUITaa/PmTdixY9s2Y0wTX8ahnEF8oSTnDiIidvNGzuCzezaIiENgYCBnzuixiTlRR4OIiIiISPHjs8soRERERERERKRkUmeDiIiIiIiIiNhKnQ0iIiIiIiIiYit1NoiIiIiIiIiIrXSDSBEpcqKimvo6BBH3BQaSqJu8ioiIiGSgMxtERERERERExFbqbBARERERERERW6mzQURERERERERspc4GEREREREREbGVOhtERERERERExFbqbBARERERERERW6mzQURERERERERspc4GEREREREREbGVOhtERERERERExFZ+BZm5VCkIDrYrFJGcpaY6XoODwd/ft7GIiBQXpYrQTwrKGaSwKXcQEXGfN3KGIpSGiIiIiIiIiEhJoM4GEREREREREbGVOhtERERERERExFbqbBARERERERERW6mzQURERERERERspc4GEREREREREbGVOhtERERERERExFbqbBARERERERERW6mzQURERERERERs5efrAESk6NiyZYuvQwCgadOmvg5BRERELkOWZQFgjPFxJCLFn85sEBERERERERFbqbNBRERERERERGylzgY3HThwwNchYIzh0KFDXl1GUVhPERGRkqAoHFOVO4iIiK94tbPBsiwCAwMJCgrKMMyaNcubi7VdfHw8TZo08XUYjB49mtmzZ3s836FDhwgKCspzuqKyniIicvlS7mAv5Q4iIuIrXr9B5NatW6ldu7a3F+NV586dIzk52ddhcPLkSYKDgz2eLzIykrNnz+Y5XVFZz8tNUlKSK6E7e/YsgYGBPo5IfEHtQOQvyh3so9xBCoOOYUWLtocUFT69jKJ27dpMnz6dqKgoQkNDmTJlCq+99hrVqlUjIiKCJUuWuKZdsGABjRo1IiwsjK5du3L8+HEAYmJi6NKlC3Xr1uXaa68FYMaMGURERFCzZk2mTp2Kn99ffSpr1qzhmmuuoUKFCnTs2JG9e/cCsG7dOqKjoxk8eDAhISE0aNCAlStXAtC6dWsAgoKCOHr0aJb1ePfdd2nQoAEhISHcfPPNHDlyBID169fTsmVLQkNDueaaa/j0009d81iWxSuvvEJERATh4eE899xzrs/Gjx9PeHg44eHhdO3alVOnTjFr1izmz5/P5MmTefjhh5kzZw7t27fn6quvplatWiQnJzN37lyaNWtGSEgIERERvPrqq4Dj9Ma0O+t6sp5Lliyhfv36hIWF0apVK3755Zd8b2sRERE7KHdQ7iAiIsWEMSZfA7C1cePGJjeA2b9/f46fR0VFmU6dOpnExETz2WefmVKlSplBgwaZ8+fPm5kzZ5rIyEhjjDHffPONqVKlivnll1/MuXPnzIgRI0znzp2NMcZMmDDBhIaGmn379pnTp0+btWvXmmrVqpnffvvNJCYmmrvvvts4VtOYffv2mZCQELN27VqTkpJiXnzxRdO0aVNz8eJF88UXXxjAzJgxw6SkpJiJEyea+vXrG2OM2b9/v6uMzDZt2mSCg4PN119/bS5cuGCGDRtm7rrrLnPs2DETHBxs5syZY1JTU82qVatMUFCQ2bVrl6tu+vTpY5KTk83nn39uSpcubWJjY82GDRtM3bp1zalTp8y5c+fM7bffbiZOnGiMMaZ///5mwoQJxhhj3n77bVO6dGmzYcMGc/r0abNnzx4TGhpqtmzZYowxZsWKFSYgIMCcOXMmQ/zurueFCxdMcHCw2bRpk7l06ZKZOHGi6dixY67b25tSUlIMYFJSUnwWg7ecPXvWAAYwZ8+e9Wksv/32W5EYLkdFqR1IydG4cWMDbDX5PNbbNbiTM6RR7qDcwS4lOXcoakrSMSxtPYqzkrQ9pPB4I2fw+pkNV199NRUqVMgwnDp1yvX5/fffT1BQEG3atOHSpUsMGzaMgIAAbr75ZmJjYwGYN28egwcP5tprr6Vs2bJMmjSJNWvWEB8fD0CLFi2oU6cOISEhzJ8/n8GDB9O0aVOCgoKYMmWKa1kLFy7kjjvuoEOHDvj7+/Poo49y6tQpfvzxRwACAwN5+OGH8ff3p1evXuzZsyfP9Vu6dCldunThpptuonTp0kycOJGJEyeyYsUKmjZtSv/+/fHz86Nz587ccccdLFq0yDXviBEjKFeuHO3atSM8PJx9+/YRHBxMfHw8s2fP5ujRoyxfvpxx48Zlu+wrrriCG264gZCQEGrVqsXmzZtp0qQJx44dw8/Pj5SUlAx1ncad9SxdujTBwcHMmjWLn376ibFjx7JmzZo860NERKSglDsodxARkeLP650NmzdvJiEhIcMQFhbm+jzt79KlSwMQGhrqCKxUqbRfQ4iNjWX69OmuhKN69er4+/tz8OBBAMLDw13lHTlyhJo1a7rGo6KiXH/HxsaydOnSDMnLH3/84bpLc+XKlV3T+vn5uZafm/j4eGrUqOEaDw0N5eqrr+bEiRMZlp0Wy+HDh13jVapUybC8S5cu0ahRI9555x0+/PBDGjZsyHXXXcfPP/+c7bLTr7efnx8zZsygatWqdOjQgcWLFwNkuw7urufq1auJi4ujQ4cOREZGMm/evNyqQmwwY8YM19/du3dn1KhRrvERI0bQs2dP1/i0adNo1aqVa3zhwoVER0cTFxcHwDfffEN0dDQbNmwAHKfERkdHs2zZMgAuXLhAdHS065TZoqRfv36uvydPnky7du1c4/PmzSM6OpqTJ08C8PnnnxMdHc2vv/4KwK5du4iOjnad4pucnEx0dDRvvPGGq4zbbrstQyI+ZMgQBgwY4BqfOHEiHTt2dI2//fbbREdHk5CQADhOqY6OjmbLli0AbN++nejoaFavXg1AYmIi0dHRvPXWW64ybrnlFiZMmOAaHzRoEIMGDXKNpz8dWuRyp9zhL8odpLjxdS5zzz338Nhjj7nGhw0bRp8+fVzjU6ZMcV3+AzB//nyio6Ndl1mlSbsEaO/evURHR/Phhx8CcP78eaKjo3nttddc0955552MHTvWNf7ggw/6PJcRKQp8/ujLtOsBcxMeHk5MTEyGpGPjxo1cd911WcqoWbOm61cNIMMBOjw8nIEDB2YoZ9OmTdx55535jr9GjRoZrsWMj49n/Pjx1KxZ05XQpNm/fz9Vq1bNtby4uDiioqL46quvOHHiBO3atWPIkCHZTpt+vRcuXMjq1avZsWMHW7du5aWXXsr3OoHjxjInT55k2bJl/P7770ybNs1VdyIiIr6k3CEj5Q4iIlIk5ff6C2y6Z8MXX3yR7fTprwFcu3atqV27ttm2bZu5ePGiefnll03lypVNUlKSmTBhgunfv7+rjM8//9yEh4ebLVu2mKSkJNOjRw9XOdu3bzeVK1c233//vbl06ZJZunSpKVu2rImNjTVffPGFiYqKcpWTfvlHjx41gDl9+nSWddi6dasJDQ0169evN6mpqeaRRx4xXbt2Nb///rupWLGimTt3ruu6y3LlyplNmzZlWzdpdfHpp5+a6tWrm3379pmLFy+acePGua53HDJkiBk5cqQxxnHdZZs2bVzzv/baa+a6664ziYmJJjEx0QwZMsQAZteuXVmuu3RnPRMSEkxgYKBZvXq1McaYTz/91AQHB5tz587luD29qSRfd1mUrqvz9b0adM+GotEOpOQoqfdsUO6QsS6UO2SvJOcORU1JOoalrUdxVpK2hxSeYnnPhiZNmmR5VvbQoUM9KqNDhw48/fTTdOnShQoVKrBgwQI++ugjypcvn2Xadu3aMXLkSNq2bUv96Ib6mgAAIABJREFU+vWpW7cu/v7+ADRq1Ig5c+Zw//33ExoayoQJE1i6dGmGUyezEx4eTufOnalevTpbt27N8Fnjxo3573//y4ABA6hUqRL79u3jjTfeoFKlSqxcuZLXX3+dihUrMmrUKN59912aNWuW67I6derEAw88QKtWrQgJCeHrr792nTLVrVs3Zs+ezT/+8Y8s8/Xv35+aNWtSvXp1GjRoQLly5WjcuDHbt2/PdXk5refhw4dZsGABI0aMICgoiOHDh7N48WLKli3rdnkiIiL5odxBuYOIiBR/lnHj2sJsZ7SsrY0bN26c+QDqazt27KBcuXKuax537NhB27ZtOXbsmI8jk4JITU0lICCAlJQUVwJYUhSlZyGn3YPA15o2berrEApdUWoHUnI0adKEbdu2bTPGNPFlHEU1Z0ij3KFkKsm5Q1FTko5haZca5fd/pKKgJG0PKTzeyBl8fs8Gu/3444+u50v/+eefvPDCC3Tq1MnXYYmIiEgRpdxBRETEfiWus6Fv3760atWKRo0aER4eztmzZ3n55Zd9HZZIjgIDA13XNann+fKldiDiO8odRApGx7CiRdtDigo/Xwdgt1KlSvHqq68Wycf5iYiISNGj3EFERMR+Je7MBhERERERERHxLXU2iIiIiIiIiIitStxlFCKSf5fjUyBERERE0hTnp1CIFDU6s0FEREREREREbKXOBhERERERERGxlTobRERERERERMRW6mwQEREREREREVups0FEREREREREbKXOBhERERERERGxlTobRERERERERMRW6mwQEREREREREVups0FEREREREREbOVXkJkvXbhE4tFEu2IRyVFqaioAiUcT8ff393E0IiLFw6ULl3wdgotyBilsyh1ERNznjZxBZzaIiIiIiIiIiK3U2SAiIiIiIiIitlJng4iIiIiIiIjYSp0NIiIiIiIiImIrdTaIiIiIiIiIiK3U2SAiIiIiIiIitlJng4iIiIiIiIjYSp0NIiIiIiIiImIrdTaIiIiIiIiIiK3U2SAiXhdSI8TXIYiIiIgUWEiNEOU1Im5SZ4OIiIiIiIiI2EqdDSIiIiIiIiJiK3U2iIiIiIiIiIit1NkghWrPvj2+DkFERESKEeUOIiLFk9c7G5Z8uITWnVtT9f/Zu+/wKKr1gePfd5PQifRO6DUgFwErRWyI2FAUlSIWBMWuXL1er4WLXhG7gg34IU2ago2mgjRpQUSMkNBChyRSYgiQZPf8/pjCbgoE3WQ38n6eZ5/dnZmdeXd2ds47Z86caVSNZu2aMfChgezeszvoy1n641Kia0czbMSwPMe//PrLRNeOZtGSRUFd7o5dO4iuHc3gRwcHdb5/RwMGD2DIE0NCHcbf1tGMo26nRUczjoY6HHWG9PdT6iTNHZRDc4e/By3jwov+HqqoRBbmzEePGc3Tzz9Ng/oNuG/AfezYuYNpn09j5ZqVrFy4krJlyhbm4gN0uaQLkRGRNGzQsMiWqQJ9/tXnXNjhwlCHoZRSKoxp7qD8ae6glFLFV6G1bPgj/Q+GvzqcqlWqsnTeUob/ZzgTP57IA/c+QEydGJJ2JAGQnJLMgPsHULtZbZq1a8bQ/wwl41gGcPKMwphPxtDqglbEtIxhzCdjGPXxKGJiY2jerjlzF8wNWO7uPbu56sarqNW0FrfddRupv6cCsHj5YoaPHM627dsA67Y1A+4fwCP/fISaTWpy8RUX8/OGn935vPfhe7S6oBU1m9Tkrgfu4kjaEXfc6+++TtPzmtKkbRNmzJpxyvXw2juv0fS8plRpUIXzOp3HnAVzANizdw8397uZOs3rUKd5HW7udzMHkg8A8Num37j6pqup2aQmMbEx3D3kbrfWMf1oOg//82Hqt6pPozaNePn1lzHGALB85XI6X92Z6o2q0/Dchjz57JN4vV4AvpzzJR0u7UDVhlVp1q4ZI94c4cYYXTuaAYMHcPeQu6nWqBpX3nAlP63/iW49u1GtUTXuuOcOjh8/DkBKagr97+tPneZ1aNmhJWM+GePO55pe19C8XXM+Hv8xDc9tSIv2LZgyY4o7DmDlmpW0uqDVKdeZUkqps5PmDhbNHTR3UEqpv4NCq2xYvXY16UfT6X5ld6LLn7wX7SsvvsI3M78htkUsxhhuHXArs7+ezY3X3siF7S/kw3Ef8uCTDwbM65U3X+Haq68lOzubJ599kg/GfkDvm3qTnJrMk88+GTDttM+n0bhhYy7vcjlzFszh0acfzTfGr+Z+xd79e7my65X8uvFXnhv+HADTZ03nmWHP0KhBI/rf3p9v5n/DI089AsCcBXN48ZUXKVumLLfdfBsfjf8o3/kvX7mcYSOG0bRRUx667yEOHjrIE888AcAL/3uBH5b+wJ133MktPW/h24Xf8q8X/wXAY/96jPiN8Qy6exBXXHoFM2fP5LW3XwPgmRefYfzk8Vx79bV0urgTr7zxCuMmjgPg3gfvJSU1hQcHPUjbNm356P8+YsKnE8g4lsE9D94DwCP3P0JMnRheeu0lvl/8vRvrrK9nkZ6ezsUXXMyquFVcft3l1K1Tl9gWsXw972tmfjETgIEPD2TOt3O4/Zbbad6sOY8/8zjfLvrWnU9yajLjJo2j1429OJBygCeeeYITJ07Q77Z+ANStXZeHBj+U7zpT4cEYw9GMo0F7AEGdXzAfTsKtlAo9zR00d9DcQeUn2LnJX8lpIDzyGs1hVLgrtMsonLMCVSpXyXea1XGr+ennn7irz128/erbAPzR9w9mzp7J/57/nzvds0OfZUCfAezbv49ZX8/ijf+9wZVdr2T12tWs37A+YJ7Xdb+O0W+MBqBTt07MWTCH7OzsPJdfuVJlpoydQmRkJPVi65G0KwmAydMnU6JECUYMG0FUVBSpB1OZ9dUs0v5I48s5XwIw8aOJtGrZiovOv4jb7rotz/lf2OFCVi1cRbWq1di8dTOLli1y4y1RogQiQsUKFbnp+pt48L4HiakTY42LKoHH46FqlarccuMtPPPEMzSo1wCfz8fUz6bSvm17HhvyGADrflnHpOmTuKf/PUSViMLn9VG7Zm1u7Xkrr7/0OvXq1uPEiRNEeCKIioyiYf2G3NrzVsqVLUetmrXcWGvXrM3U/5tK4pZEzu96Pm3PbcuYd8cw77t53HrnrezZu4d9+/excPFCrr/megbfPRiv10uXVV2YPG0yV3a9EoDs7GzGjRpHi2Yt2LlrJ3MWzCElNYXbe93OoEcGUbtWbQbdNSjfbUIFx4fjPuTxBx8HoN/AftStU5eXn38ZgKeee4oDyQcY/8F4AN754B2+mvsV335hJX4zv5jJux+8y7pf1gU1pppNagZ1fsESvyqeunXqsnjZYl585UXeHvE2rWNbs3nrZgY9Moh/PvpPrr7iajKOZXDtLdfS59Y+3NPfSsBv7nczbVq14bmnrIONh//5MJmZmXzw1gcAjHhzBMtXLufLadZ+Y9K0SYybOI7PJ39OhXMqsHDJQoa/Opz3XnuPejH1QrMClAojmjto7qC5w9/fn81RMo5lhFUuEQ6xTPhoAjf2uPFP5ShKFYVCa9lQ4ZwKgNV0zt+OXTvYs3cPADv37ASgZYuW7vjWLVu70znq1qkLQOnSpQGoH1MfgLJlyuLz+QLm36BeA/d140aNyc7O5uChg3nGWLd2XaKiohARypcrj89rzWvP3j1kZmZaBWfHtsyYNYPs7Gy2bt9KcmoyAI0aNAKgScMm+a6DjGMZjHhrBK0uaMVTzz/FocOH3KaJw/49jGuvvpaRb4+ka4+uXHXjVcyYbTWrfPvVt+lwXgeee+k5LrriInre0ZOFSxaS+nsqx48fJ25dHG07tqVtx7ZsT9pOQmICAOPfH0/dOnV5/JnH6XBpB26/+3bW/bKOUqVKMWnMJCIjIxn86GDad2nPgPsHsC1pmxtrrZq18Hg8J9dxvZPrGMDr87J7r9U515dzvqRtx7a079KeoxlH2bR5U8D3bljfurb1nOhz3M8qpZRSp6O5g+YOmjsopdTfR6G1bDi/3fmUKlWKud/OJe2PNLc55LPDnuXLuV8y97O5xNS2auM3btrofu7Xjb8CgQW/RwLrRDye/OtIfkv4zX29bfs2SpQoke8ZkoiICPe1iLivq1Wtxo5dO/jwrQ/xeDzs2LWDihUqElMnhlo1rBr9Ldu20Dq2NYlbE/ON5fV3X+fzLz/n6+lf0/mSzvQe0JvtSdutz2/dwg3X3MCHb33Imp/W8OjTj/LIU49w8/U3s3vPbu69817GfzCelWtWct/D9/HEv5/gpyU/ERkZSctmLXnyYasJaMLmBOrXq8+xY8c4dPgQzzzxDLEtYlm2Yhl3PXAXz7/0PJPGTCI7O5u3RrxF7Zq1mbNgDo889Qivv/s6o14flec6zWsdV6taDYCunbtyV5+7MMaQsCWBNrFt8lyv/uvUoc29isagu0+eAZr48cSAcSOGjQh4//Dgh3l48MPu+1439OLm6292r38OhppNarJv876gzS+YypQuA0CXjl1Y+PVCd3iTRk0C3pcpXSbgPcBnEz8LeP/Oq+8EvH/qsacC3vft3Ze+vfu67y/rfBmXdb4MQHuDVgrNHUBzB80d/v7+bI5SpnSZsMglnBYN4RCLk8P8mRxFqaJQaJUNFStU5J+P/JNhI4bR+erOXNf9OhK3JDL327m0a9vO7Vm4VYtWjJ8ynmxvNulH0/l24bfc3ut2qlap+qeWu2jJIu564C6Mz7Dul3XcdvNtp0ww8nJrz1tZtmIZk6ZPomWzlnwy5ROiy0dze6/b6XVDLyZ8OoF+9/Xjuu7XMXP2zNPO77V3XuOzLz5j3nfzAMjMzOSNUW8w99u59Lm1D1WrVGX/gf1Ur1qdkiVL8tTzT7EpcRP33nkvHvFw+MhhmjdtTmRkJD2v68lnX3zGwiUL8fl8TPh0Ar1u7MX13a/nvofv49ixY9zd726OpB0hKyuL2rVqc/ToUfoP6k90+Wj69e7H1u1bAahdq/YZrZd6detxQfsLWLl6Jc2aNGPfvn18MecLnnjwCbpf1f20ny9frjyJWxIZPnI4zw599oyWrYqWiAS9x/ei7EFeKVU8ae5wkuYOFs0dlKMwcpO/IpxiUSpcFdplFABPPvwko14fRenSpXl/7Pus+2Udd/W5i5kTZuLxePB4PHw++XNuvPZGZn89mxWrV/DAvQ/w9oi3//Qyhz4ylOTkZBYsXMD111yfq4a0IO68406eeuwpNsRv4KPxH9GyeUtmTJhBVFQUl3a6lBHDRnDs+DFmzJrBsGfzvjc3wAP3PkDHizqyKm4VP67+kV439gLg5w0/M/qN0fS6sRdzFsxhzCdjaB3bmiljrd6XPx33KVdcegVTZ05lyowpXN7lct5/830AXhv+GjffcDOff/k5s7+ezc033Myb/3uT0qVL8/nkz/nHuf9g3MRxfDX3K3rd2IuXnnuJWjVrMX38dGrXqs3oMaP5cfWP3NP/Hh4f8vgZr5txo8fR+ZLOTJgygSU/LmHggIH864l/Feiz9997P5mZmUyfNf2Ml6uUUursoLmD5g7+NHdQSqniS/5s0zQRiW/etHnL1YtWBzkkpXLLysqicv3K/J70O1FRUaEOJywdzTga0LQvnGrco2tHk7YnLdRhhLVw/v1U8XV+1/PZlLjpN2NMbCjj0JxBhYLmDuHj71TGRde2Lu8qznnN3+n3UMFTGDlDoV1GoZQqWmXLlC3WBd/ZTn8/pZRSf1daxoUX/T1UUSnUyyiUUkoppZRSSil19tHKBqWUUkoppZRSSgWVVjYopZRSSimllFIqqLSyQSmllFJKKaWUUkGlHUQqpQqddkKklFJKqb8DzWmUKjht2aCUUkoppZRSSqmg0soGpZRSSimllFJKBZVWNiillFJKKaWUUiqotLJBKaWUUkoppZRSQaWVDUoppZRSSimllAoqrWxQSimllFJKKaVUUGllg1JKKaWUUkoppYJKKxuUUkoppZRSSikVVJF/5cPGGLKysoIVi1L5crYz3d6UUqrgjDGhDsGlOYMqapo7KKVUwRVGziB/dqYiEg+0DG44SimllAqy34wxsaEMQHMGpZRSqlgIas7wl1o2tGjRgvXr1wcrFqXylZWVRdmyZTl69ChRUVGhDkcppYqFNm3asHHjxlCHAWjOoIqe5g5KKVVwhZEz/KXKBhHRnbcqUlFRUbrNKaVUAYlIqENwac6gQkVzB6WUOr3CyBm0g0illFJKKaWUUkoFlVY2KKWUUkoppZRSKqi0skEppZRSSimllFJBpZUNSimllFJKKaWUCiqtbFBKKaWUUkoppVRQaWWDUkoppZRSSimlgkorG5RSSimllFJKKRVUWtmglFJKKaWUUkqpoNLKBqWUUkoppZRSSgWVVjYopZRSSimllFIqqLSyQSmllFJKKaWUUkFV6JUNs2fP5pJLLqFChQpUr16dvn37snfvXgCSkpIQEc4777xcnxs/fjwiwvjx4wF44YUXGDBgAAA//PADHo+HcuXKuY86derw3HPPYYwBIDU1lZ49exIdHU1MTAyffPJJnvGNHz+eiIiIgHk5j6NHjwKwfPlyOnXqRPny5alQoQI9evQgPj7enYeIUK1aNbxeb8C8f/jhB0SEF1544a+sQqVIP5FNXNLBXK/9LdmcwtLEFNJPZDNpRRLpJ7JzzWPMsm0sTUwhLukgB9KOE5d00J1f+olsxi7dxtil20g/kc22lHQemvIT6SeyWZqYwpc/7+Glr39jaWIKB9KOszQxhYem/MTq7alMWpHEks0pPPPZL0xfs5OxS7YyetFmnv/yV2u+S7by5c97GLt0G2OWWcuYvmYn90+M40DacQDikg6yIH4/Y5dsZVtKOmOXbuP52b8yfc1OJq1Icr/H0sQU5sfv54UvfuXN7xLc+R1IO+5+l6WJKYxZto358fvdcc46c7776EVbGLtka651kH4imwXx+3np698Ys2ybG5//epy0IolRi7YwP35/rt/G+ey2lHRe+OJXliZav8uSzSnu8p35TlqRFDAPZ/lLNqfk+Rs7n3HWlTNP/9he+vo3d13ktR04y8n5WqlwormD5g7q7OKUbzn5l7GTViS5ZbL/cP/y08kRnLLSv4xzysUlm1MYbZfhzvAF9mecXApO5lUH0o7z0te/ufmJE0dc0kF3Gc6wbSnp3D8pLt9cC+D5L39lxJzf+PLnPUxakcTSxBR3+c73cGIZu2SrOw8nFv/l+ucPOWPJmfc48885vf+6ybm+/PMb//hyrvucv0POGPL7zXL+RqfLS/LLgf1pbhNeIgtz5u+99x4vv/wyn3zyCV27duXo0aM8/fTTdO3alV9++cWdbseOHWzZsoXGjRu7w6ZNm0a5cuXynXdMTAxJSUnu+y1bttCxY0dq167NoEGD6N+/P/Xq1SM1NZX4+Hg6d+5Mx44dadSoUa55derUiR9++CHP5Rw+fJgePXowduxYbrzxRrKyshgxYgRXXnkl27Zto1SpUgB4vV4WLVrEFVdcUeDvoFRBbdqXxt3j1/DeHedRpkSE+7pz06oALElM4f5JazHADf+oxaerd3E828e9nRq685i6eifDv9lIlAeiIiN4pntzXp2fwMOXN+Gd7zfT49yafLp6FwAGWJt0kLnxB9hxMIMtyelkZvvI9hnGr0ji+Wtb8vxX8Xh9sC0lnfh9fxAh4DUwLW4XXnMy9nU7D/PL7iNEeoRs38kRHgGfgSyfof9F9Rk8MY5sH2R6fcTtOMTc+AMA7nyPHM+mde1zGDxpLSeyvAHLACgZ6aF5zWj6j11FlteQ5TNEeMDrg50HM5i1bg+DujTiw8Vb6dm2Np+s2GF9LkIYenVz3vl+M+MGdABgyJSfyLIXUCrCQ9+L6udajwClIj082a0Z73y/2f1tnM/+kJjM5uSjTFy5g0iPEBnh4YGujXnnu0ROeA0rt/7OdxuTA+bhLP/+iWsxxvBBv/bubwzwbfx+nv0iniiP4PEIkR7h/b7t3Gle+uY3Pl29i0yvj7qVyjD8m40B28GSxBQenPIT4wZ0ICPT675uX7/SmW6SShUazR00d1BnH6d8O3I8myFdT/6nnfznqtgazFy7G4C+F9V3hzvlulN+Dpq0lmyvj0yvoWSkh8n3XuCWcU757eQVpSI9fNS/PWVKRPDw1HUcz/IRFSFERXgY0rUxoxdtwQAXN6rMdxuTAdz8ZHNyOjPW7gZjuKFtbTd/Wr4llbnxB5j/6wFKlcida51btwLLNqcCuHlRVIQgCJleH8ezfQBWnE4Oc+iYtSzgme7NeXnuJjCGCfdcAMDd49fkyuU2J6fnmffE70vjm1/2udM7OYCzbvxzBv/cM/HAH+74f9StkGvd++cUP+86zPBvNroxOPlqzt8sZwz+OUp+eYl/TP75kaMg81BFq9BaNqSnp/P000/z8ccfc+WVVxIZGck555zDe++9R7t27di6das7bc+ePZkxY4b7/vfffychISHPsxb5ady4MZ06dWLDhg3s3r2bpUuX8sYbb1CiRAnatm3LqlWrqFat2hl/j8TERACuv/56IiIiKFWqFP/5z3/o0aMHBw+erFnL+R2ys7OZM2cOV111lTtsyZIlnHvuuVSoUIHzzjuPBQsWnHE86uzUvn4l3rvjPB6c8hMZmV739ZLEFHfH+n7fdgzp2phPV++iV7s6vPP9ZpY4tfOJKbzz/WZ6tatDlg+8Xh8xlcvy3h3n8c73m7kqtgafrt5FlEcoGelh5PwEFiemEFsrml92H6FBlbJEeASALK/h0zW78PqsioAtKUfx2AV3bK1oIu3pAAT4ZfcRYmtFEyGB38lnoGOTKny3MZl7xq/BayDSA0O7NWNhwskz9h4RojzCyPkJDJwQxzWta+aqaIiKEGIqlyUj06qEyPIZK5nwQa92dZi1bg9XxdZg5PwEroqtwax1e+jVrg4AJ7yGkfM28d4d59G+fiUyMr1gTi7gyPGTteNLElMYOW8TYFVuPGQXlIO6NOLBKT+xavtBoiI8RAhsTj5KvUpl8Brru3ZvXZOR8xM44TXu9/afh7P89vUr8X7fdiDC4Ilx7m8IEFO5LFEeIctnuK5NLd7v287dDkYt2uL+9jPidvHGt4kMtSsx/LeT9+44z00KnGUqFS40d9DcQZ2d+l5Un6HdmjFyfgKjFm1xh7evX8mtaOjVro5b+d++fiUGdWnEyPkJDOrSyC0/P+jbDifdOJHtY9V26/+2JDGFNxYkuBUNvdrV4aP+7d286qHLmgBWjnNevYqMnJ/AA10bM6RrY7eiAWDhpmSGdmvGjLW78Xp9Vk60ehdDuzUjpnJZlm/9ndha0fiAE1negFyrbqUyLNucSoRYuY6TF2V5DcZYlSMj521i5PwEt6KhY5MqbqXGB33bEVO5rJWjiJCR6XXzQ/9c7lR5z6erd3FVbI2AvMPJEf1zBmcdv3fHeQyetJY3FiS44zMyvQHr3j+nyMj0uvNyKjucPMX/N8srhoLkJf75sH9+5PzGmtuEn0KrbPjxxx/x+Xx069YtYHhERARTpkyhZcuW7rDevXszffp09/1nn33GTTfdhEiOo5N8+Hw+Vq1axcKFC7n00ktZv349TZs25b///S81a9akefPmJCQkUL58+TP+Hueeey5VqlTh4osv5q233mLdunUAfPzxx9SqVSvgO8yaNcttDrlw4UI6dOgQsMz777+f//73vxw+fJjHHnuMIUOGnHE86uzVuWlVdwcLuAXA4IlxvHeHlVx/uHgrQ7s1Y0H8fncHP2rRFh6c8hODujRiQfx+q4CL8DB4YhwAg7o0Yuba3UR5hKgI4bo2tTiRbRWg21PSia0VTfzeNLJ9PoZ2a4ZHIH5vGrG1onn8qmacyPbhsysarOmsA3XBaiEhWNNn+QJrCKI8cFHDyu7Bs89nnckHK0EoGemxK0cMPvvg/0S2z401wt57RXgEDzBwQhwDP1ljz1vcZGJB/H43UenYpAoz1+7mqtgaLIjfH1DhsGHPEZYkpjDwkzVk2ZUUJSPETXyccSe8hqHdmvFx//Z8uHirW0PvFOrXtK5JyagIIgR2HMwgtlY0WT7jnpGJEFi2OZWSkR4evrwJHy7emquGvnPTqnyQo8JhSWIKgyetJSpC6NWuDjPX7mbDniO8d8d5DJwQx8j5ViJwfZtaIALG0Lr2OdZ2MjGOwZPWutuJUxjndVZAqVDS3EFzB3X2GtK1ca4Kh1GLtrgVDQvi9wecRHFyng8Xbw048IyI8FAy4uSJiidnrGfwxDiyvCYgNwDcMnLUoi0M7daMKI+wbHMqHZtUYdTCzbz1nVVxGOWx5nfCa9ieehSMwYd1cqOkXWvg5Fq7DmbQsUkVvAbuGb8agHPrViB+bxoeoGRUhBtnlJ3LZPkMHRpU4oTXcCLb51Y0LNucitfrc/OjB6f8xAf92vOB38mGzk2rurlcQfKemWt3M6hLIzo3rRpwgD6ka+O8D+Ttyg03p5i0llELrQqFUQs355lfOPNy8iQnH/1w8dZTxlCQvMQ/H/bfHjS3CU+FdhnF77//TsWKFYmMPP0iLrvsMvbs2eM2h5w6dSqvvvoqcXFx+X5m586dVKhQAQBjDDVq1GDo0KH06tWLSZMmsX79erp3705SUhIrVqzguuuuY+3atTRt2jTXvJYtW+bOy3H11VczdepUSpUqxapVq3jzzTcZO3Ysjz/+OFWrVuXZZ5/loYcecqdv0qQJderUcZtDTp06ldtuu42vv/7aneacc85hypQpVKxYkdtvv52+ffuedt0o5c9/B9u7Q123AFi6OYVpa3a5O9nWtc/hwSk/cUnjKoycn0D3VtUDDmqdyxHu/WQNiNWaIUKgS7OqzFy7m/b1KhK34xAG2Jyc7taw/9/y7fgMboXDxn1pgFUIb05Od1s4VI8uyYG0E26FA1hn9533UR7BACPnJxAVEzZ3AAAgAElEQVRpF+BZPsPI+ZvYsCeNod2aud/BicVfts9gsBKGOb/sJdNryPZaTQ8jPYYSkRE82rUxHy7eyiWNK1vfqX5Flm1OpX29Csxcu5vurWq4lS/vfJfIyPkJ7qUdQ7s1Y0jXxlzfphYDP1mT5zjA/S0uaVzFXW/WvKuzOCGFLK8hfm9aQOxOq4zLmlfLs6LB/7f+oG+7gN8pUnAvrWhQpSwj5yfQuna0WzlzOCPTSkT6tgM4uZ3YlQ85txOlwo3mDpo7qLObU76OnJ/AvF/3uTnBkK6NWdKmlluu5ZXzOMOdMnDwxDi8xqrsd/KPvObllJGHMzKJivRAto9lm1Pdcr99vYr8ti8NjKFN3XOYuXY3DSqXYfvvGZSM9HBZ82p55lpPzljPzLW76T/OqnCIEKuioUtTJzerweLEFDxeH9nGOhHhxBkhsHr7QUpGCBERnjzLb/98cNqaXW7Fy8mcpIKd91TMlfd8uHgrhzMyc80zZ545bc2ugIoO/9zzcEbmKfML/3n556Oni6Eg8opTc5vwVGgtG6pXr87BgwfJzs7dQUdqamrA+4iICG666SamT5/O/v372bt3L+3btz/l/GNiYjh8+DCHDx/myJEjJCQk8PTTTwNQokQJIiIieOGFFyhZsiSXXnopXbt2Zf78+XnOq2PHju68nMfUqVPd8ZUrV2b48OFs2LCB5ORkhg0bxlNPPcW8efMC5nPrrbcyffp0MjMz+e677+jRo0fA+GnTphEVFUXPnj2pVq0aI0eOPOV3VCovnZtWpXeHuny8dDt9LqxHnwti+Hjpdnp3qBuwg+/doS5zf91P69rRzP31QK7xfS6IIdNryMz20f+ievS5sB5zfz1A69rRxO04ROva0WT7rPF3X9KAKuVKkJqeSZVyJbinYwPAKoRb145mwCX1ybRbOFQpV4IDaSeoUq4EJkfshpPTOy0gsn2GAZfUp3XtaDbsSaN17WiGdG3sfoe4HYeoUq5EnvN57ZY29LmwXkBfENk+6HNBDEO6NrbXgf2dkg7Z3+2wvU7207tDXYZ0bUz/i+sHfB8n2enctGq+4/Jaz856m/vrAfpcWI8Bl9R3py0ddXJ367/8UxWMOX+nPhfWc6cf0rVxwDrrf1G9gO0gYDu5IIY+F9bLtZ0oFW40d9DcQamc5Zt/meyUa3nlPDnLwD4X1sNJD5y8Ia95+ZeRfS6IcctuJ6eJ23HInSZux2GqlCvB9t8z3LI3v1zrtVvaBOQvd3dsQJ8LYty8ZO6v++lzQQz9L66P1w7UYC3Ta7Dys4vr55nn5fW9T+Y9+wPynZO5yf6A6fLLCfJbn3nlnqfLL/LLR08XQ0Hktz2o8FJolQ0XXXQRkZGRua4t9Hq9dOjQIVcPz71792bmzJl89tln9OrV6y8tu2nTpmRnZ5ORkRGwXGNyHvqc3ogRI7j22mvd91WqVGHQoEH06NGDDRs2BEx76623Mnv2bObNm0fnzp0pXbq0Oy47O5uEhAQmTpxISkoK06ZN4/nnn881D6VOZ0miVXs8sFMDJq/cweRVOxnYqQHT1uwKaE42bc0uureqwYY9aXRvVT3X+MmrdlIiQigR6WHCih1MXrmD7q2qs2FPGu3rVWTDnjQiPdb4ccu3uxUNqemZjF22HbBaOGzYk8b45UmUiPTgEUhNz6R6dElS0zPJ2ZhZODm907dDpEcYvzzJTSo27ElzL1uYtmYX7etVJDU9M8/5PDljPZPtDhgdkR6YvGonoxZtsdeB/Z3qV7S/WwV7ndRg2ppdjFq0hQk/JgV8H6f55pLElHzH5bWenfXWvVV1Jq/cwfjlSe60x7J87mv/5ee85jDnb+3/O01eucOdftSiLQHrbMKKHQHbQcB2smonk1fuyLWdKBVuNHfQ3EGpnOWbf5nslGt55Tw5y8DJK3fgpAdO3pDXvPzLyMmrdrplt5PTtK9X0Z2mfb0KpKZn0qByGbfszS/XenLG+oD8Zdyy7UxetdPNS7q3qsHkVTuZ8GOS2y+WYC0zQrDysx+T8szz8vreJ/OeGgH5zsncpEbAdPnlBPmtz7xyz9PlF/nlo6eLoSDy2x5UeCm0yobSpUszbNgw7rvvPhYuXIjP5yM5OZkBAwZQunRpbrnlloDpu3Tpwr59+3j33Xe57bbb/tKy27RpQ2xsLMOGDSMrK4uFCxeyePHigIK/oK6//np++OEHRo8eTUZGBpmZmXz//fcsXrw4oAMngIYNG1K/fn2eeuqpXN8hIiKCu+66izFjxiAi1KhRA4/Hk6sJplKn4n9NWqcmVd3ma52anGxO5t9Hw/ItqQzt1ozlW34PuGZu8KS1YAxj7uzAI5c3cftoWJyQQq92dYjbcYgoj1AiQmhSrZzVGaQH7rqkgdusMLZWNE9c1QywrjVsUq0cPmM1/fO/hMKpBvDIyUsqsnwGwer7QOz3UR5haLfm7vWaAyfEcVVsDeJ2HMrVuWSk3WfDzLW7OeG1OoMsGWldoxnh8ZCV7XM7IFq+5XfrOyUdomOTKsTtOEyvdnVYviXV7WfB6Ydh/F3nu/00PDljfUAfDf7jnMoQ//XsrLde7eqwODHVWqc+Q2yt6IDYne+ycFNyQMdJef3W/r/TmP7t3T4cnpyx3u2jYWi35pSM9HAi20eFMiVy9eXRqUlVt8mj/3aihbIKR5o7aO6gzm6jFm1xy7evHurk5gRPzljv5j//7tEyV87jP3zwpLVW/0teg8/uo6F0lCegD4eAXMouIyuUKUGWXXZ3bFKFUpEeoiKEuB2HrOFe4+YQB9KOExUhnMj2sXDjgVy5Vt+xq+w+pmDC3ee7fTgcz/SyONHKzRYnppCV7eWE1+C1l+nkSV4D59t9OHi9vlzlt38++O8eLQM6XjyZkxy2855DufKeQV0aBazHvPo+cNenX79P/rlnhTIlTplf5MyTnHV0uhgKIq84NbcJT4VW2QDw2GOP8eKLL/L4449TsWJFzj33XDweD9999x1lypQJDMTjoVevXkRGRtKqVau/tFwRYe7cuSQmJlK9enUGDhzIhAkTaNiwYZ7TL126NM97ZS9dupQWLVowb948PvvsM2rVqkWlSpX417/+xYQJE2jTpk2uefXu3ZsDBw7k6txKRJg2bRrvv/8+0dHRXHfddYwePZq6dev+pe+qzh7+O1bAvTb/g37t3U4j/Qsb57pB/056nB28f2dDTmc9WT5Dltfw1fq9lLQL2AZVy7mdQUZ6rLtU+HcG+caCBEraLRpOTmcdTTsVDQZr+ihPYI1Blg9WbPvdrWjweMTttNI5eHY6g/TYHb75dxppd9GA12d10vRx//Z8fGcHe95WBUTOTpGWbU7N1XkSWLe/bF37HDo3rcrHd3Ygyq8iw7m+0xnnVDgMnBDndg7p3/nmnA373Ftz1qtUhvi9aUR5xO2M0mvfheNEti/gThY5z1Y4FQ1OHw1OHw5ZXuN2AuVcp/px//ZuQvbl+r1u4b9hz5FcnUkBWiirsKa5Q2BMmjuos4V/RYNzucOQro3dDgWviq0RcOmE/x0R/JvQe70+Tti3wB7arRmv3dKGD/q1JypCAnIDONnh4pCujRk5P8GtaFi2OZUhlzXh0Sus/lqyfMbtDLJBlbIgVufUTqeRgJtr+d91YuyA8wH4ZdfhgLtUOHE6jR6jPMIau4+GkpEeIjy4HVX6d+qd8+Df6WDRv+PF0+U9vdrVcTvV9O/7wL/iJuCSBLtywc0p+rZjyGVNGDk/gSGXNckzv/A/8eWfj/p3ZJlXDAXJS/LqDPJM56GKjvyZ5oEAIhLfsmXLlvHx8UEOSancsrKyKFGiBJmZmURFRYU6nCIXl3TQva9wmRIRue4xvCQxhfsnrcUAN/yjFp+u3sWzPVq490oGGLN0G8O/2UiUB6Iic9/72bk3M8CzPVqwNukgc+MPcG6dc9iSnE5mts+9H/Tz17bk+a/i8fogtmZ54vf94d5Oynl2nFvnHH7ZfcS9n7TDaSFxRYtq9L+oPoMnxpHtg0yvj+6x1ZkbfwA4OT+n08jBk9a6B/P+ht8QS/Oa0fQfu4osO8lwOra886J6ed5vGqyKhqFXN3fv9Qxw+8crybIXMPyGWPdWW/7rEax7dD9p3wrK+W2czzapVpbNyUeJEKslRmSEhwe6Nuad7xI54TVc0aIa321MDpiHs/w7x63G+FU0OCatSOLZL+LdyplIj/B+33buNP/6/Bc+Xb2LOy+qR91KZRj+zcaA7cD//tP+98TWW0T9fcXGxvLbb7/9ZoyJDWUcmjOoUDjbc4fixCnf/Csa4GT+4xwoO2WyM9wp153ys/+41WR7fWR6rVtJTr73AreMc8pvJ68oFenho/7tKVMigr5jV3E8y0dUhBAV4WFI18aMXrQFA1zcqLJ7+0snP7nzonruLSlvaFubT1fvYvgNsSzfksrc+AN4gFIlcuda59a1Om0E3LwoKkIQhEyvj2d7tACw4vTLYWbYJ0ee6d6cl+duAmOYcM8FANw9fk2uXC6/vOf28+vyzS/73OmdHMBZN/45g3/umXjgD3f8P+pWyLXu/XOKn3cdZvg3G90YnHw152+WMwb/HCW/vMQ/prz6aCjIPFT+CiNn0MoGVSyc7QlD+olsNu1Lo339SgGv/S3ZnIIYaFuvIrN/2s2N59WhXMnIgHlMXbOTZtXKU7pEBHUrlWHXwQya14xm0740mteMZtrqnQD0Pj+G5LTjvPltIv+7+VzW7TjEoYxMNuw+QuemVWlaozyJ+/9getwu+l0UQ+L+dGKqlGXeL/v4R0wF/jiWxQmvjwN/nGBot+ZMW7WDqtGlSPnjBEZADJQvFcmiTcm8cEMrqkeXIi7pIAePZrLr96N0bVGdRZuS2fl7BrG1o8nM9rkH/EsTU8jI8rJiSyrnlI2ifKkoxMC1bWpRtmQkm/alcSzTS0LyH9StWIbdBzO4tk0tdh3MoH39SsQlHaRupTJ8tnY3JSOENjEVA9YBwI9bUlmz/SDVK5TiunNrUT26VMB6nP3Tbo4cz6ZxtXJc0rhKwG/jfPb2C2KY8GMSl7eoDoAR6NzEKmx/3n2Y686txbfx+6kaXcqdh7P8n3YeokxURK7f+EDacb6N30/zmtEcPJpJqRIRdG5SNSC2t79N5N7ODSlbMjLP7SAu6SDNa0ZTrmRkwGv196SVDepsdrbnDsWJU775V+5DYP4zaUUSV8bWoHp0qYDhTlkGsG7HITKyvKSkHad5zeiAMs4pv2OqlOXX3UdoVK0c3WJruGV3ctpxYqqURQx0alrVzaua1ijPmCXbqBFdkq4tqvPjllSujK3BroNW/y7Na0Yz+6fdXBlbg6Mnshk5fxN3nF8vz1yrff1KPP/lr5SJ9NCi1jmkHcuiXuWyHMvykpx2nBvPs1pBOnFu3pfGtf+oza6DGRzL9NK0RvmA5QJu/uA8O7HkzHu+jd/PjefVyTV9uZKR7rrxzxly5p7OeGeZ/uveP6dw4vePIef8nGn9YwBOm5fklwP709zmz9PKBnXW0oRBKaXOnFY2qLOZ5g5KKVVwhZEzFGqfDUoppZRSSimllDr7aGWDUkoppZRSSimlgkorG5RSSimllFJKKRVUWtmglFJKKaWUUkqpoNLKBqWUUkoppZRSSgWVVjYopZRSSimllFIqqLSyQSmllFJKKaWUUkGllQ1KKaWUUkoppZQKKq1sUEoppZRSSimlVFBpZYNSSimllFJKKaWCSisblFJKKaWUUkopFVRa2aCUUkoppZRSSqmgivwLn627detWYmNjgxaMUvkxxgDQpk0bRCTE0SilVPGwdetWgLqhjgPNGVQIaO6glFIFVxg5gzg74jP+oMh+oAywK5gBKZWPRvbz1pBGEUhjKrhwjEtjKhiNqWDCMSaA5oDPGBMVyiA0Z1AhEq7/y+JK12fw6LoMHl2XwRP0nOFPt2wwxtQIVhBKnY6IxAMYY8LmtJjGVHDhGJfGVDAaU8GEY0xwMq5Q05xBhUK4/i+LK12fwaPrMnh0XQZPYeQM2meDUkoppZRSSimlgkorG5RSSimllFJKKRVUWtmglFJKKaWUUkqpoNLKBqWUUkoppZRSSgWVVjYopZRSSimllFIqqP70rS+VUkoppZRSSiml8qItG5RSSimllFJKKRVUWtmglFJKKaWUUkqpoNLKBqWUUkoppZRSSgWVVjYopZRSSimllFIqqLSyQSmllFJKKaWUUkGllQ1KKaWUUkoppZQKKq1sUEoppZRSSimlVFBpZYMKeyJSU0Q+EZF9IpIuIgtF5PxQxwUgIreKiBGRCmEQS2sRmSUiv4vICRHZKCKPi0jI/uci0lJEvhCRZBE5IiJzRaR9qOLJSUQqicgeEfkhxHGssLejnI/0EMdVQkT+IyKbReSYiCSIyL9EJCoEsdTPZx35P8YXdVx2bE1E5HMROSQix0VktYj0CEUsfjG1FZE5IvKHvU/4QkTOC2VMShWlcM4diqtwynmKq3DM1YqrcM8xi6tg58aRwZiJUoVFRKKBhUAt4C0gFXgQWCwiFxtj1oUwtvbAx6Favj8RaQgsB44D7wApwHXA60AT4P4QxNQEWAEctePIBIYAy0WkszFmVVHHlIfRWNvW5lAFICICtAK+B8bnGJ1V5AHZ7LhmAtcC44A1wBXAy0AMRb9NpQD98hn3DNACmF104VhEpBbwI1Aaax91ABgIfCUiNxtjZoUgpgux9pte4F0gGbgbWCYi1xpjFhZ1TEoVpXDOHYqrcMp5iqtwzNWKq2KSYxZXwc2NjTH60EfYPoD/AAbo7DesKtYO+vsQxtUHSLdjM0CFEK+nmViFV5Mcw6fZ8bUIQUzTgRNAM79hNe31Ni8Mtq3bgGz78UMI42hg/0ZPhHqd5IjrDjuuZ3IMn2oPrxPqGO14etnxvB6i5b9sL/9mv2HnYFU6JIQopp/s7bq937CywBZgG1Ai1L+bPvRRmI9wzR2K6yPccp7i+gjHXK24PsI9xyyuj8LIjbXJjgp3/YD1xpglzgBjTAowAehqn1UsUiIyF5gExAPzi3r5OdlnoLsCS40xOWshP7WfLynaqADrrPx0Y0yCM8AYsw/YBLQJQTwuEakJjAJewyqYQqm1/fxbSKPIbSCwDxiZY/gIYBhQqsgjysE+ezkaSAKeDVEYjeznec4AY8wRYBnQVESKdD2JSAzQFvjcGBPnF9NRrFYODYDLizImpUIg7HKH4irccp7iKoxzteIqbHPM4qqwcmOtbFBhy74msAmwOo/RawABQnFtVhPgSaxCYX8Ilh/AWFWR7YDBeYyuaj97iy4iizGmjzEmoNm7iJTDOjjbUdTx5DAW2As8F+I44GRlQzy46yikRCQSuBj4zhiTZQ8rKyIeY8w6Y8zzxpgtoY0SgKewtvHHjTHHQhRDov3cPMfwhsAhY8zxIo6njv38Sx7jnAS3XRHFolSRC+PcobgKq5ynuArXXK24CvMcs7gqlNxYKxtUOKttP+/OY9xe+7lBEcXiL9YY87oxJjsEy86TMSbJGLPVf5iIRAAPYTXNWxaSwE7GUllEugJzgPLAiyGMZRBW3wN3GmMyQxWHn9ZYzdWGisgh4A8RSRGRF+3fMBQaACWA7SIyUES2YdVyp4nIaBEpHaK4XCJSEXgUWG1C0C+Cn7exKor+T0QuEZFGIvIG8A/gfyGI56j9fE4e4yrbzzWLKBalQiFcc4fiKuxynuIq3HO14iqccsziqjBzY+0gUoWzaPv5aB7jMuznskUUi8sYc6Kol/knjcQ6kJ2SR5O9oraIk2fw3wO+C0UQdudMrwEvGWN+CkUMeWiNtS9ugnXGIwKrv4TnsM6O59cpYmGqaD/3Bmpg9UuwBeiO1YFVA/t1KN0LlAFeDWUQxphUEXkOq3NP/0TxHWNMzktQisJvwGGgp4g8m2N/dZP9HPJLYJQqRGGZOxRXxSjnKa7CKVcrrsIixyyuCjs31soGFc6kAON8RRFIcSMiw4DHgASs3nlD7SWs6+uuwYqnqYh0N8YU2e9n31bqE6xm7y8V1XIL4H2sDvve8Bs2RURmAn1FZLQxZkURx1TSfm4KXGyMWWm//1xEjgMPikg3Y0wor98dDGwHQtmqAREZilXh8QtWK4c/gOuBh0WktDHmvqKMxxiTJSKvAK8AX4jIf+yY7gU625PpGUr1d6a5gyoWwjBXK65CnmMWV0WRG2tlgwpnTuckZfIY5wxLK6JYigV7p/EO1s52C3CFMeZwaKMCY8w0++Xn9qUCTwI3ULQHik8AF2F1jlfB6qsJsJLPKBGpAmQYYzLy+XyhMMa8l8+oUcDNWPEWdWWDc0ZwvV9Fg2Ms1i3kLidEnYWJSBusVh+vhDKZsDuofAGrL4QL/fqNmCEi+7AujfnCGPNNEYf2KlAOq0+Lbvaw9Vi9TC8ADhZxPEoVJc0dVFgL11ytuAqTHLO4KvTcWPtsUOEsyX6uncc4pyfpvK7JPCuJSAms2xIOATZg3fIrHNeP0+tyUXdS1wPrEoUfsG5/5jyisTpDTAH+WcQxnUqy/Vw+BMt2tpsDeYxzhkXnMa6oXGc/Tw9hDGC1/CgDTMujg8ox9vMVRRuS1RGZMeY/QHWgE9DSGPMPrFuuAWzN98NKFX9J9rPmDirsFKNcrbgKVY5ZXBV6bqwtG1TYMsakichm8t5hdLCf1xRhSGHL7mBoKtAT67rx60JZS273Br4aWGSMGZRjtHPwXKQtCLBqbyvmMXw21sHXE8C2ogxIRBoBXwJfGmP+lWN0C/u5SGMCMMYki8gOIFZExO5F2+Hc6jGUvT13AlKNMetCGANY9/gGq6DOyRlW5JX6ItIb604YCwjsR8LpZ2NJ7k8p9feguYMKV+GWqxVXYZpjFleFnhtrywYV7qYC7UWkkzNARKpidZr3vX1PXQXPYBVei4FuoS687OUfB24TkRhnuN10cChWr8tfFXFMa40x3+V8YF2/fsh+X9QH9tuxbnl1j71dA2Df7eEZrMLy8yKOyTER6zaKA5wB9n3Cn8C63jlUcQGcB8SFcPmOeGAXVt8aOQtr5/rbUHRU9TAwRkTcZuQi4nRAOscYk5jvJ5X6e9DcQYWjsMrViqtwzDGLq6LIjbVlgwp3rwH9ga9E5HXgENYtgkpj7VDOevb1VP/C2rl+Ddzkd82VI84Ys6mIQxuEVaAuE5FRWAVDb6xrw/5rjNlQxPGEHWOMT0Tuw7qucKWIvG+PuhOIBe4yxqSEKLxXsDo6/EhE2mEdWF8PXI3VV0JCKIKyD+qrcLKpdMjYv98grNYpa0XkI6xrwa/Bapo42xgTioTnJax9wXciMhHrNpiPYrXEeCQE8ShV1DR3UGEljHO14kpzzGJCKxtUWLObQ3bCujXQE1gdlqwB+oZBE+pwcSFWAgXWesrLY0CRFmDGmBUicilWB3r/wWpW/gtwuzFmalHGEs6MMbNFpAdWEjIMq9XAWqB7KO/2YIw5KiKdgeeBW7HuZrAFGGyM+TBUcWG1BAHr9o4hZ4yZa++jnsf6DUthrad/Am+GKKY5InI98CxWZ5FpWJ15vmCM2R6KmJQqSpo7qDAUlrlacaU5ZvEhgZfiKqWUUkoppZRSSv012meDUkoppZRSSimlgkorG5RSSimllFJKKRVUWtmglFJKKaWUUkqpoNLKBqWUUkoppZRSSgWVVjYopZRSSimllFIqqLSyQSmllFJKKaWUUkGllQ1KKaWUUkoppZQKKq1sUEoppZRSSimlVFBpZYNSSimllFJKKaWCSisblFJKKaWUUkopFVRa2aCUUkoppZRSSqmg0soGpZRSSimllFJKBZVWNiillFJKKaWUUiqotLJBKaWUUkoppZRSQaWVDUoppZRSSimllAoqrWxQSimllFJKKaVUUEWGOgCllFJKKaWUUjmICFDGfpeBMSaU4Sh1prRlg1JKKaWUUkqFnzJAuv0oc5pplQo7RdKy4ddffy2SWrjWrVsDsGHDhkJfVqtWraTQF6KUUkoppZRSShVD2rJBKaWUUkoppZRSQaWVDUoppZRSSimllAoqrWw4i4hIvSDPr34w51fU81dKKaX7WqVU6IlIlIjUCnUcquCCfVyhCq44ldta2RBmRKS+iBgRSbcfR0UkTkQu/IvzfQh4MUhhIiJtgW/93seLSKcgzv964P+CNb+zgbPthDoOpRyn2iZFpJOIxNuvB4jID8Ga99+d//oSkWdE5IMz/HyMiKTbr6sD8cGP8uwkImXtMtyISNlQxxMMhf2dRGSRiLyUx/BmInJMRCoVwjJ/EJEB9ms3f7G/Y/0/O6+zjbO+/PcpZ/j5uSLSx347FbgquBGq07F/w6N+xx3O477TfC6oxxVnSkTGi8gLZzC9mzOEIv/wjzfHdl/Qz/cRkbn262J1jKS3vgxTxphyACLiAe4FZopIPWOM90/OsnLQgrOcA0Q5b4wxsUGefyVAO+FU6m/KGLMUCPZ+46xjjHn5T3xmJ1DOflsa7eFchdY4rIOWf+cYficwyxhzsDAXXgj5y1knxz7lTD7X3e9tsPNUVXCxxpikM/xMsf29Qp1/5NjuC/qZycBk+22xOkbSlg1hzhjjA6YDtYEqACJSTUSmi0iqiGwWkX7O9CLygIjstMctEJEGInI18AzQR0S+yrkMu+nayyKyS0T2isirIhJlj+ssIr+IyGER+UlErrLPbMwFYkTksD1dkohcar82InK/iOwXkRQR6S8iL4nIIRHZLiKd7enKishYe9gxuwVHaxFpAXwAdBKR9fa0DUVkvj2PDSLSLa/1Zdccfmp/jy9EJEJERtrr6aiIbPSLc4A9zed2Le56ETnfHid2zL+LyDYReVpEkvyWc4eIbBKRg/bnq+UTT3P7dzhsT9/Xb5wRkfft73Rbjs+tEZE7/N7fLCLr7NfXiMhq+3OpIvJKHssNqKkVkUv/TPxKFZac26Tf8FgROSAiPe33p/3vi3Vm8Wo0giUAAA7LSURBVCG/911EZGMe0/0t9g855vGCiIz3+36viLWvPioiM0Skqz3vIyIywp7O/6zMEntYumgTZhUaM4HKInKRM0CsEy39gDH2+8fs/1qyWHmDc0Im5396u4hc5zefO0Vk3qkWLn75i9+wSBH5WkSm2fuJfPMkv88MEJG1OYZtkxwtU+19388issQuw+ufqly39wWPiMg+sfKqZ/3GdRWrZcZBe10s99uHFTRvKmF/n732/D8SkfJ5rd9TrEP/s8aXisgKe/+VJiKJYp1JnmXvZ1aISA172h/s9fYS0An4QET+mf+vpYqSiJwrIsdFpL39/n7797wGv+OKfLbpfLc/KeBxgj1tnv99v/GX2uMi/IYtklOXm0Wef+TxmQH26yT7O+4Qq5x+SkSG2HHsE5Fe9nQD7M/lOkYKd1rZEObsP8/dwE/GmAP24ElAClYFRC/gVRG5QKzmsC8DlwDVge3AM8aYefbwycaY63IuAxgKXAq0B84FLgQet8e9D/zHGFMBeBMYZYw5CnQHdtrD83IBEAM8hXXWIguoBnwK/NdvudWwahcrAgl2vBuBwcBSY0wbEYkEvgYW2dM/DHwqIjGnWHYrrESlH9AVOB+IBr4E/uc37XXAeKwa2pXAcHv43cBNQFt7fjc4HxCRS4C3gNuBWsAO4JOcQYhISWABsNiOuz/wtoh08ZvsOFDDjsvfFOBWv/e9gSn2TnYqMNQYUxGryeHjItIsn3WRS0HjV7mJpezf+BHSmnKxrv+cCzxmjJl1Bv/9acDNfu9vtYfl5e+wfziVfljlQj3gMuBVoKP9eExEGuSYvjNYremMMXvPYDlKBYUx5hhWuebfrPgyIBNYJFbF+/3AlUBDoDww0m9a///0dHLvC6aeYUiClbf4gL52i9JT5UmO2UCs2JdhiEgHQIwxK/NYRhusnKohkMrpy/Xz7WlvB14QkTpiXV7yOfACVs6XAFxsL/tM8qb/Yu0H2gHNsfZb7/iN91+/BXUh8DNQAfgR+B4YDVTF2q/d7z+xMebfwFJgsDHm1TNYjipExphfsMqQD0SkIVb5OMAYM4fcxxX+2/RuTr/9nfY4oQD/fbAqzLOxKquwj4XaAblOrp5KEeUf+emOdSzUE2u9tgXqYrX4et1/wpzHSGe4nJDQyoYwJdaZrsPAMawN7QN7eE2s5HioMeaEMWY91nU7A4AMoAQwEKvAuN8YM7AAi7sTeN4Yc8AYk4qVUA+wxx0B7hCrlvFToGkBv8K7xphMrEQ6AnjTGJMFfIf1BwJ4FyvBzsLa4RzGSqxz6gBEG2NeMcZkGWMWAfOwCt28fGuMOWiMScMqiK8B0uxl/JFjGRuNMV8aY04AM4DG9vA+wGvGmJ3GmBSswtzRH/jYGLPOGHMcq+nnlfYOzl8nrP/Yy8aYTGPMauAjAgvs6fbvmJHjs1PteZYXkTJYO6JPsbaHNsaYxSJSGWvHm57PestPQeNXuZXBWt9/10com9NXwjr4nmCMmWIPK+h/fyZwkVitvjxYFQH5HWD8HfYPp/K5MWabvS//DZhkjEk1xmwA9nNy/6sK34POCxGZKSKv+71/S0Sm+b1/UkSW+72/TURW2mU+ItLRfu+0rqlvv7/Jfh9pv3/Ybx6zRORVv/fvisgUv/dPicgSv/d97HlUs993EZGVWAcRhW0scKud4IP1PxpnjDFYOcpIY8xWY0w68Cxwp1/lqP9/eipwvVgtESpg/c9mnWEsI7EO2m+x8xY4dZ4EgDHmMDCfkwcet5D/QcdxYLYdc0HK9beMMcfsfeB+rAOva4FfjTEz7DhfAZwKwzPJm/oAzxlj9tnf4QmsvM85U+y/fgvqD+Aju3XuUiDeGPOtXbG0FN0PhRunBbP/w+krZTjW5XZLsX7TH/OZh/82XZDtryDHCaf77zstwGdw8n93MzDHPjFaUEWVf+TnQ/v7LcbKC5x1s4C/wX9F+2wIU06LAfsPdQkwW0R+x6otjAD2+v3XIoBFxpg/5OQlE0OBPSLyoN2y4VTqAp+JiM9+L4DTxLY3Vk3mLHv4K1i1nKfjXGPptb/PEfu9j5OVXBWxKlHaAhuxKkvyOrNaF6hhV744IoHf81n2fr/XJYEPgS7AFqyC2H8ZKX6vs/1iq421rh07csTTT0SG+A3LwjqTeMBvWFVgl50s+c+nbT6xuowx+0RkBdYZ00ysli27AUSkl4g8irW+1tjf50zOSBc0fqWKUmuss189ReRFO+ko0H/fGJMiIouBG4FEINkYsymf5RT7/cNp+F/f7sWqMHb473+VChvGmNUikoxVMbcUq+xraY+uC7wpIv5nND1YZxvB739ijFknIilYJ2VqYeVG/v+BgqiL9f/sACzzG5ZfnuRvGlYl0+tYlQ035bOMZOe/b4zxFqBcz2tfFLAfMsYYEdnlF29B86aqBO7DdmCduKpqv/8z+6FD/9/evYfIdVcBHP8e0YJFVLRVaoxY8BVJQiNYQkERS63QVmKxWqgigog2SHyh9UksbSC0BAoapUhBTdokaoXahyIWG9um/lGKJhGqwUcT21DExJZG8+rxj3OnezOd2Z3Jzs7ubL8fWMjd3bn3N9n53fv7nfs757bObZ6HFr6V2admQ2Yei4gt1B33W3v9TuPJ1t98kM/fIPOEmfp+x3ZgRxNs/TBw0zTt7GVc449+/t3s62Qzt2v/X0xMbYZ+DDYscE3HvT8q//4iauL/DPCqJppHVO7bySYKeTwz39/cDV9LFROZqYjLQeDK5s4aUbl6ZzV3GN5K3WkL4ELgjmiqoc7U9AF+ZzOwC3hfZj7bXGjX9GnfnzOzM/AgIpZSkfOZjn091Wlfm5nHI2INtcR5Jgc4NZr4+q72rM/WUr+IeBs1WenexxsiIlon4HOBJ/u0tdut1EDlBLWqgYi4gLrr8M7M3N8Eo3oVz3qWU/t3+zMwaPv1fEc4jSJYE2SYO+ij9iBwMfAwFSzdwPR9vzuFaxt1R+MvTH9XYbGcH/p5QT6hY4H6Tucfmfmh9g8y83Nd2zcCN7a2t9H6HGfm/dTS9M7237u2T7S3m+99sGv7s13bG4GNre12ATIy8z5gdYzvqRq3UHcNz6aWCP+z+X6nT+2AqjEAvJGpvtL9md9OBSuWMPwdRoCPAh+glo6vaiYePcdJPV57B3BzVG73scx8pM8xnmvzENf1bgeAS1r7Ceo9w3DjpgNUMPTRZvtcKkB6qLutQ/A8tEg0q6u+RI1DvxsR7+rMP7q0/+aDfP4G+YzM1Pc7dlFBi4upNKe7B9h327jGH/0s6v5iZHECRMRKKlfwoayKv48A344q6rMEuJdaznc28KuIeEez7PYwUxeso1ROci9bqRzAVzdBipupQdJJKkXjk1RHOEhNYg83+zuztczudLwSONIEGt5O3Q3oFFxqt/ch4IyI+FRUoaZlVOT/ogGP8V8qGLMUuKZ1jOlsofKblzZBnG+2frYV+ExELIuIF0XEOmp52Rld+/g9NXn7WrOk83wqxWXQXK6fUXnW76WWiHXezwngf1E5399qvtf9nvYBqyLiTRHxCk7Njxy0/eqS5ZlF/DUnF7yo/OL2V6/Jy/Gs3Oi1wNej8kOH6fs/p5Y+X8LgfWySzw+jchQgIvpdH6Rx+TGVMngVlVbRsRX4ctPfXkJNBG6fZj/bqfPAaobM224cp2oWBDXJ6rSh1zjpFFlLoe+hUjGGOQ8Ncl3v9guqRsTlzc2hdUwFPoc5d24Bro2Ic6JST26glsMfHbD9ozLdOFXzZzM1Bv049bnspIdN9/eazbi9baC+34xddlArr+88jc/ufIw/ZmOi+orBhgUqpp5z+zRVoOSmzOwUGbuSWl74OBV4uAfYlJmPUgWLbo963vGngc4TDe4CLohWfmbLdVRu7x+afb4U+ETTeT9CTVSfoi5sV2fmfuCP1FK7Q7MYpH6Ryr16mjqR/Qh4c3PRvA94eUT8Iytv6VJqSeK/gF9TuV0/6bPftvVUYaX/UEVebqdWbcy02uOHVG7WbirS+TCVzkBm/ga4lioGdZj6P740u/Kqm3ZfRgULOgWgvpKZdw3Q7s6Sst9RQabOsq1fUjlc+4DHqBzye4FlXa99kBqs7aKKNN3Z+tlA7ZdGaH/XV99CY5n5AJUDuXmYvp+Zh6glz4cy868Dtms9E3p+GKGD1Ht5PCJ8BKDmTVb9k51UjYh2kOAW6pywk+orq4DL+wVHM3MvdfdxZzP5P522HKcmVd+IKqrac5zU5+XbqGXZg97hHOi63qONh6nx4EYqzWI5NS47NuS4aQPwAHUe+1vz+4PU+xq124ANEXHdjL+pUdvbmnd0vr4fEVdQk+ivNn3iauD6qCKofecVsxy3tw3T94ftd88z5vHHbDw3RxrDsWYt5uhG1in27NkzluUhK1asAGD37t1zfqzly5dPfA6N+ouI84AnsnkCSFQtjPWZuXr6V0qaLxGxGdiXmZvm+DieH9RTs2qnM8F9WQ5XpGxBmsT3FBF3Az/IzOlWQMzVsc9vjr1yjo/zGmBJtlI1IuIg8J4cPmdcC1VX/2MC+t98iCrCvBc4J6cKu47z+GMZf0wiazZIvV1G5apeQfWTdVRUU9IC0wy638LU4yjnmucH9dRMxBfVzYhJek8R8TrqHHAeded1nMd+MfUksGsYz+OkzwR+29R8+BOV8nqUKlInvWA0qdhrgdvGHWiYh/HHxDGNQuptE7UEej+1rPAxaqmhpIXnQmop8g2Z+cQYjuf5QVqYPkYVV/7CPNQcCCr18Szge3N9sKwioZ+n0iSfotI61mTv4n3SYvZT4N1U0edxG/f4Y+KYRnGaTKOQJEmSNGdMo9CEc2WDJEmSJEkaqbGsbJAkSZIkDcGVDZpwBhskSZIkaaGJCKoYKMARnLhpwhhskCRJkiRJI2XNBkmSJEmSNFIGGyRJkiRJ0kgZbJAkSZIkSSNlsEGSJEmSJI2UwQZJkiRJkjRSBhskSZIkSdJIGWyQJEmSJEkjZbBBkiRJkiSNlMEGSZIkSZI0UgYbJEmSJEnSSBlskCRJkiRJI2WwQZIkSZIkjdT/AZi7j5PkbeW5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1062.99x354.331 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pl.subplots(1,2)\n",
    "fig.tight_layout()\n",
    "\n",
    "#striped background\n",
    "rectangle = pl.Rectangle((-10,5.5 ), 20, 1, fc='blue',alpha=0.05)\n",
    "ax[0].add_patch(rectangle)\n",
    "rectangle = pl.Rectangle((-10,3.5 ), 20, 1, fc='blue',alpha=0.05)\n",
    "ax[0].add_patch(rectangle)\n",
    "rectangle = pl.Rectangle((-10,1.5 ), 20, 1, fc='purple',alpha=0.1)\n",
    "ax[0].add_patch(rectangle)\n",
    "#dotted_line = pl.Line2D((0,10), (1.5,1.5), lw=1.,  ls='-', color='black')\n",
    "#ax[0].add_line(dotted_line)\n",
    "#dotted_line = pl.Line2D((0,10), (2.5,2.5), lw=1.,  ls='-', color='black')\n",
    "#ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((0,0), (0,10), lw=0.5,  ls='-', color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "ax[0].axhline(1.5, color='k', lw=0.5, ls='-')\n",
    "\n",
    "#dotted_line = pl.Line2D((ecs[5],ecs[6]), (6, 6), lw=1.,  ls='-.', marker=\"|\",  markersize=10,  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "width=0.05\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle((ecs_process[0]-width/2,6-depth/2 ), width, depth, fc='black')\n",
    "ax[0].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((ecs_process[4],ecs_process[5]), (6, 6), lw=0.75,  ls=':',  markersize=5,marker='|', markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((ecs_process[2],ecs_process[3]), (6, 6), lw=1.,  ls='-',  markersize=10,  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "\n",
    "width=ecs_hist[1]-ecs_hist[0]\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle(((ecs_hist[0]+ecs_hist[1])/2-width/2,5-depth/2 ), width, depth, fc='lightgray', zorder=5)\n",
    "ax[0].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((ecs_hist[4],(ecs_hist[0]+ecs_hist[1])/2), (5, 5), lw=0.75,  ls=':',  markersize=5,marker='|',  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((ecs_hist[2],(ecs_hist[0]+ecs_hist[1])/2), (5, 5), lw=1.,  ls='-',  markersize=3, markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((ecs_hist[6],999), (5, 5), lw=0.,  ls='-',  markersize=10, marker='|',  markerfacecolor='red', markeredgecolor='red', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "\n",
    "width=ecs_paleo[1]-ecs_paleo[0]\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle(((ecs_paleo[0]+ecs_paleo[1])/2-width/2,4-depth/2 ), width, depth, fc='lightgray', zorder=5)\n",
    "ax[0].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((ecs_paleo[4],(ecs_paleo[0]+ecs_paleo[1])/2), (4, 4), lw=0.75,  ls=':',  markersize=5,marker='|',  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D(((ecs_paleo[0]+ecs_paleo[1])/2,ecs_paleo[3]), (4, 4), lw=1.,  ls='-',  markersize=5, markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((-999,ecs_paleo[7]), (4, 4), lw=0.,  ls='-',  markersize=10, marker='|',  markerfacecolor='red', markeredgecolor='red', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "\n",
    "width=ecs_ec[1]-ecs_ec[0]\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle(((ecs_ec[0]+ecs_ec[1])/2-width/2,3-depth/2 ), width, depth, fc='lightgray', zorder=5)\n",
    "ax[0].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((ecs_ec[4],ecs_ec[5]), (3, 3), lw=0.75,  ls=':',  markersize=5,marker='|',  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((ecs_ec[2],ecs_ec[3]), (3, 3), lw=1.,  ls='-',  markersize=3, markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "\n",
    "width=0.05\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle(((ecs_combined[0]+ecs_combined[1])/2-width/2,2-depth/2 ), width, depth, fc='black')\n",
    "ax[0].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((ecs_combined[4],ecs_combined[5]), (2, 2), lw=0.75,  ls=':',  markersize=5,marker='|',  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((ecs_combined[2],ecs_combined[3]), (2, 2), lw=1.,  ls='-',  markersize=3, markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[0].add_line(dotted_line)\n",
    "\n",
    "ax[0].text(-4, 6,'Process understanding', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[0].text(-4, 5,'Instrumental record', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[0].text(-4, 4,'Paleoclimates', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[0].text(-4, 3,'Emergent constraints', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[0].text(-4, 2,'Combined assessment', fontsize=6,verticalalignment='center',horizontalalignment='left',weight='bold')\n",
    "ax[0].text(-4, 1,'CMIP6 ESMs', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "\n",
    "ax[0].scatter(cmip6_ecs,ycmip6e,s=20,lw=0.5,alpha=1,marker='x')\n",
    "ax[0].set_xticks([0,1,2,3,4,5,6,7,8,9])\n",
    "ax[0].tick_params(axis='x', which='minor', bottom=False)\n",
    "ax[0].set_xticklabels(['0','1','2','3','4','5','6','7','8','9'],fontsize=9)\n",
    "ax[0].set_xlim([-4.2, 9])\n",
    "#ax[0].set_xlabel('ECS ($^circ$)')\n",
    "ax[0].set_ylim([0.5, 6.5])\n",
    "ax[0].set_title('a) Equilibrium climate sensitivity estimates ($^\\circ$C)',fontsize=9)\n",
    "ax[0].tick_params(axis='y',labelright=False)\n",
    "\n",
    "\n",
    "# #TCR\n",
    "\n",
    "#striped background\n",
    "rectangle = pl.Rectangle((-10,5.5 ), 20, 1, fc='blue',alpha=0.05)\n",
    "ax[1].add_patch(rectangle)\n",
    "rectangle = pl.Rectangle((-10,3.5 ), 20, 1, fc='blue',alpha=0.05)\n",
    "ax[1].add_patch(rectangle)\n",
    "rectangle = pl.Rectangle((-10,1.5 ), 20, 1, fc='purple',alpha=0.1)\n",
    "ax[1].add_patch(rectangle)\n",
    "#dotted_line = pl.Line2D((0,10), (1.5,1.5), lw=1.,  ls='-', color='black')\n",
    "#ax[0].add_line(dotted_line)\n",
    "#dotted_line = pl.Line2D((0,10), (2.5,2.5), lw=1.,  ls='-', color='black')\n",
    "#ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((0,0), (0,10), lw=0.5,  ls='-', color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "ax[1].axhline(1.5, color='k', lw=0.5, ls='-')\n",
    "\n",
    "\n",
    "#dotted_line = pl.Line2D((ecs[5],ecs[6]), (6, 6), lw=1.,  ls='-.', marker=\"|\",  markersize=10,  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "width=0.02\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle((tcr_process[0]-width/2,6-depth/2 ), width, depth, fc='black')\n",
    "ax[1].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((tcr_process[4],tcr_process[5]), (6, 6), lw=0.75,  ls=':',  markersize=5,marker='|', markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((tcr_process[2],tcr_process[3]), (6, 6), lw=1.,  ls='-',  markersize=10,  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "\n",
    "width=0.02\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle(((tcr_hist[0]+tcr_hist[1])/2-width/2,5-depth/2 ), width, depth, fc='black')\n",
    "ax[1].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((tcr_hist[4],tcr_hist[5]), (5, 5), lw=0.75,  ls=':',  markersize=5,marker='|',  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((tcr_hist[2],tcr_hist[3]), (5, 5), lw=1.,  ls='-',  markersize=5, markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "\n",
    "width=0.02\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle(((tcr_ec[0]+tcr_ec[1])/2-width/2,3-depth/2 ), width, depth, fc='black')\n",
    "ax[1].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((tcr_ec[4],tcr_ec[5]), (3, 3), lw=0.75,  ls=':',  markersize=5,marker='|',  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((tcr_ec[2],tcr_ec[3]), (3, 3), lw=1.,  ls='-',  markersize=3, markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "\n",
    "width=0.02\n",
    "depth =0.5\n",
    "rectangle = pl.Rectangle(((tcr_combined[0]+tcr_combined[1])/2-width/2,2-depth/2 ), width, depth, fc='black')\n",
    "ax[1].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((tcr_combined[4],tcr_combined[5]), (2, 2), lw=0.75,  ls=':',  markersize=5,marker='|',  markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((tcr_combined[2],tcr_combined[3]), (2, 2), lw=1.,  ls='-',  markersize=3, markerfacecolor='black', markeredgecolor='black', alpha=1,color='black')\n",
    "ax[1].add_line(dotted_line)\n",
    "\n",
    "ax[1].text(-1.8, 6,'Process understanding', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[1].text(-1.8, 5,'Instrumental record', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[1].text(-1.8, 4,'Paleoclimates', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[1].text(-1.8, 3,'Emergent constraints', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[1].text(-1.8, 2,'Combined assessment', fontsize=6,verticalalignment='center',horizontalalignment='left',weight='bold')\n",
    "ax[1].text(-1.8, 1,'CMIP6 ESMs', fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "\n",
    "ax[1].scatter(cmip6_tcr,ycmip6t,s=20,lw=0.5,alpha=1,marker='x')\n",
    "ax[1].set_xticks([0,1,2,3,4])\n",
    "ax[1].tick_params(axis='x', which='minor', bottom=False)\n",
    "ax[1].set_xticklabels(['0','1','2','3','4'],fontsize=9)\n",
    "ax[1].set_xlim([-1.9, 4])\n",
    "#ax[1].set_xlabel('TCR ')\n",
    "ax[1].set_ylim([0.5, 6.5])\n",
    "ax[1].set_title('b) Transient climate response estimates ($^\\circ$C)',fontsize=9)\n",
    "ax[1].tick_params(axis='y',labelright=False)\n",
    "\n",
    "# #key\n",
    "rectangle = pl.Rectangle((0.03, 0.01), 0.05, 0.05, transform=fig.transFigure, fc='lightgray',clip_on=False)\n",
    "ax[0].add_patch(rectangle)\n",
    "rectangle = pl.Rectangle((0.054, 0.01), 0.002, 0.05, transform=fig.transFigure, fc='black',clip_on=False)\n",
    "ax[0].add_patch(rectangle)\n",
    "dotted_line = pl.Line2D((0.32, 0.37), (0.035, 0.035), transform=fig.transFigure, lw=1.,  ls='-',  markersize=3, markerfacecolor='black', markeredgecolor='black', alpha=1,color='black',clip_on=False)\n",
    "ax[0].add_line(dotted_line)\n",
    "dotted_line = pl.Line2D((0.57, 0.62), (0.035, 0.035), transform=fig.transFigure, lw=0.75,  ls=':',  markersize=5,marker='|', markerfacecolor='black', markeredgecolor='black', alpha=1,color='black',clip_on=False)\n",
    "ax[1].add_line(dotted_line)\n",
    "#dotted_line = pl.Line2D((0.78, 0.83), (0.035, 0.035), transform=fig.transFigure, lw=0.,  ls=':',  markersize=10,marker='|', markerfacecolor='red', markeredgecolor='red', alpha=1,clip_on=False)\n",
    "dotted_line = pl.Line2D((0.83, 0.83), (0.01, 0.06), transform=fig.transFigure, color='red', lw=1, ls='-',clip_on=False)\n",
    "ax[1].add_line(dotted_line)\n",
    "\n",
    "ax[0].text(0.10,0.035,'Best estimate range or value', transform=fig.transFigure, fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[0].text(0.39,0.035,'Likely range or limit', transform=fig.transFigure, fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[1].text(0.64,0.035,'Very likely range or limit', transform=fig.transFigure, fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "ax[1].text(0.85,0.035,'Extremely likely limit', transform=fig.transFigure, fontsize=6,verticalalignment='center',horizontalalignment='left')\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig('../figures/fig7.18.pdf')\n",
    "fig.savefig('../figures/fig7.18.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}