{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example - 11 - Neptune Aerocapture - Part 2a: Monte Carlo Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will use AMAT to perform Monte Carlo simulations to assess aerocapture vehicle performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reproduce the example Monte Carlo results from \"Girija, Saikia, Longuski et al. Feasibility and Performance Analysis of Neptune Aerocapture Using Heritage Blunt-Body Aeroshells, Journal of Spacecraft and Rockets, June, 2020, In press. DOI: 10.2514/1.A34719. Refer Section VIII A: Results for **prograde** entry with maximum range of FMINMAX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle\n",
    "\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.patches import Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a planet object\n",
    "planet=Planet(\"NEPTUNE\")\n",
    "\n",
    "# Load an nominal atmospheric profile with height, temp, pressure, density data\n",
    "planet.loadAtmosphereModel('../atmdata/Neptune/neptune-gram-avg.dat', 0 , 7 , 6 , 5 , \\\n",
    "                            heightInKmFlag=True)\n",
    "\n",
    "# Create a vehicle object\n",
    "vehicle=Vehicle('Trident', 1000.0, 200.0, 0.40, 3.1416, 0.0, 1.00, planet)\n",
    "\n",
    "# Set vehicle conditions at entry interface\n",
    "# The EFPA selection process is described in Sec. VII in the reference article.\n",
    "vehicle.setInitialState(1000.0, 0.0, 0.0, 28.00, 0.0,-13.85, 0.0, 0.0)\n",
    "vehicle.setSolverParams(1E-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the guidance parameters described in the paper.\n",
    "# See the function description for parameter details.\n",
    "\n",
    "# Set max roll rate constraint to 30 deg/s\n",
    "vehicle.setMaxRollRate(30.0)\n",
    "\n",
    "# Set Ghdot = 75\n",
    "# Set Gq = 3.0\n",
    "# Set v_switch_kms = 18.9 \n",
    "# Set low_Alt_km = 120\n",
    "# Set numPoints_lowAlt = 101\n",
    "# Set hdot_threshold = -500 m/s\n",
    "vehicle.setEquilibriumGlideParams(75.0, 3.0, 18.9, 120.0, 101, -500.0)\n",
    "\n",
    "# Set target orbit parameters\n",
    "# periapsis = 4000.0 km\n",
    "# apoapsis = 400,000 km\n",
    "# apoapsis tolerance = 10 km\n",
    "vehicle.setTargetOrbitParams(4000.0, 400.0E3, 10.0E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set path to atmfiles with randomly perturbed atmosphere files.\n",
    "\n",
    "atmfiles = ['../atmdata/Neptune/FMINMAX-10L.txt', \n",
    "            '../atmdata/Neptune/FMINMAX-08L.txt',\n",
    "            '../atmdata/Neptune/FMINMAX-06L.txt',  \n",
    "            '../atmdata/Neptune/FMINMAX-04L.txt',\n",
    "            '../atmdata/Neptune/FMINMAX-02L.txt',\n",
    "            '../atmdata/Neptune/FMINMAX+00L.txt',  \n",
    "            '../atmdata/Neptune/FMINMAX+02L.txt',\n",
    "            '../atmdata/Neptune/FMINMAX+04L.txt',\n",
    "            '../atmdata/Neptune/FMINMAX+06L.txt',\n",
    "            '../atmdata/Neptune/FMINMAX+08L.txt', \n",
    "            '../atmdata/Neptune/FMINMAX+10L.txt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up Monte Carlo simulation parameters\n",
    "\n",
    "# See function description for details.\n",
    "\n",
    "# NPOS = 1086\n",
    "# NMONTE = 200\n",
    "\n",
    "vehicle.setupMonteCarloSimulation(1086, 200, atmfiles, 0, 1, 2, 3, 4, True, \\\n",
    "                                 -13.85, 0.11, 0.40, 0.013, 0.5, 0.1, 2400.0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 1, PROF: ../atmdata/Neptune/FMINMAX-04L.txt, SAMPLE #: 22, EFPA: -13.97, SIGMA: -1.04, LD: 0.41, APO : 379646.10\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 2, PROF: ../atmdata/Neptune/FMINMAX+10L.txt, SAMPLE #: 194, EFPA: -13.86, SIGMA: 1.66, LD: 0.39, APO : 397722.98\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 3, PROF: ../atmdata/Neptune/FMINMAX+04L.txt, SAMPLE #: 188, EFPA: -13.89, SIGMA: -0.71, LD: 0.39, APO : 367641.10\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 4, PROF: ../atmdata/Neptune/FMINMAX+10L.txt, SAMPLE #: 125, EFPA: -13.79, SIGMA: -1.81, LD: 0.41, APO : 360589.69\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 5, PROF: ../atmdata/Neptune/FMINMAX-02L.txt, SAMPLE #: 172, EFPA: -13.83, SIGMA: 1.24, LD: 0.40, APO : 383384.65\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 6, PROF: ../atmdata/Neptune/FMINMAX+02L.txt, SAMPLE #: 29, EFPA: -13.77, SIGMA: -0.44, LD: 0.39, APO : 372647.16\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 7, PROF: ../atmdata/Neptune/FMINMAX-04L.txt, SAMPLE #: 63, EFPA: -13.71, SIGMA: 0.76, LD: 0.39, APO : 376441.97\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 8, PROF: ../atmdata/Neptune/FMINMAX+00L.txt, SAMPLE #: 162, EFPA: -13.67, SIGMA: -2.19, LD: 0.41, APO : 380977.85\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 9, PROF: ../atmdata/Neptune/FMINMAX+06L.txt, SAMPLE #: 60, EFPA: -13.73, SIGMA: 0.86, LD: 0.40, APO : 382393.74\n",
      "BATCH :../data/girijaSaikia2020a/MCBX, RUN #: 10, PROF: ../atmdata/Neptune/FMINMAX+00L.txt, SAMPLE #: 161, EFPA: -13.77, SIGMA: 0.79, LD: 0.40, APO : 368610.27\n"
     ]
    }
   ],
   "source": [
    "# Run the Monte Carlo simulation.\n",
    "\n",
    "# N = 10 shown here, run for a few thousand to be realistic. This will take several hours.\n",
    "\n",
    "vehicle.runMonteCarlo(10, '../data/girijaSaikia2020a/MCB1') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation statistics\n",
      "----------------------------------------------\n",
      "No. of cases escaped :3\n",
      "No. of cases with apo. alt > 800.0E3 km :0\n"
     ]
    }
   ],
   "source": [
    "# Post process Monte Carlo simulation data.\n",
    "\n",
    "\n",
    "peri  = np.loadtxt('../data/girijaSaikia2020a/MCB1/terminal_periapsis_arr.txt')\n",
    "apoa  = np.loadtxt('../data/girijaSaikia2020a/MCB1/terminal_apoapsis_arr.txt')\n",
    "\n",
    "peri_dv  = np.loadtxt('../data/girijaSaikia2020a/MCB1/periapsis_raise_DV_arr.txt')\n",
    "\n",
    "del_index1 = np.where(apoa < 0)\n",
    "del_index2 = np.where(apoa>800.0E3)\n",
    "\n",
    "del_index = np.concatenate((del_index1, del_index2), axis=1)\n",
    "\n",
    "print('Simulation statistics')\n",
    "print('----------------------------------------------')\n",
    "print(\"No. of cases escaped :\"+str(len(del_index1[0])))\n",
    "print(\"No. of cases with apo. alt > 800.0E3 km :\"+str(len(del_index2[0])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove escaped cases before plotting\n",
    "peri_new = np.delete(peri, del_index)\n",
    "apoa_new = np.delete(apoa, del_index)\n",
    "peri_dv_new  = np.delete(peri_dv, del_index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 234x234 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create apoapsis dispersion plot.\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([3.25,3.25])\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.plot(peri_new, apoa_new/1000.0, 'bo', markersize=3)\n",
    "\n",
    "plt.xlabel('Periapsis altitude, km',fontsize=10)\n",
    "plt.ylabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=10)\n",
    "\n",
    "plt.axhline(y=350.0, linewidth=1, color='k', linestyle='dotted')\n",
    "plt.axhline(y=450.0, linewidth=1, color='k', linestyle='dotted')\n",
    "\n",
    "ax=plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=10)\n",
    "ax.tick_params(axis='y',labelsize=10)\n",
    "\n",
    "plt.savefig('../plots/girijaSaikia2020a-fig-15-N100.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/girijaSaikia2020a-fig-15-N100.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/girijaSaikia2020a-fig-15-N100.eps', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot only has 100 trajectories. Below is a similar plot with 5000 trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8d+TXAPv+iltcvjYAAHAgw07sCCil3Gs3aokMZDb77Kq/FE59jr9Of2/LtmtvaPlulx2WUspIPZL5a/zhzovQdZ2/wuBrOLXdruuCa54SWdW4tshuR1zgkwUAwO7s79bilRylnfvs7HZlKeWOVd1OTXmwYscMEmzv83HK0LPMDj5onlf6XEuJ/VnjpTtt26Z3S5jdolkbbd4AAJzabnPiXClD2CUBuX1Aq/NHLw4u5k+pfaV3xGF6iyYuObtMSq184jVMYsRP338AAHBM+89DeXZOu/LuydXJcOmTksTn+TsyU0Di1GA4NScpsXV86ewVmsIPO2Q7gduu37CrRGbDtBO4nTIYp+DE2X+ttfNrV7CMZPejEQAA3BVXpSxXtxp4L/aL6Rfyim3ruZ3cPLu2hQfD7vXcftSO7GHw1Ugp3Y4sn1hsPVtSP7VRZ1X+ArWOHAAAMGVJWBrt2c5ttxee9Kdzp6pk92eR21a9eyXMQlrr+GtuN/SmVHHMVk47LdPBrWutZ19Y+52KbNRZ1e4HGAAAKLNn5rZLis9YsepUldQanNvkyNGS8oDcx57uLUiRlVnjb6LzekZe3qyC7+MM6w4AAFay87glJl+a/NH3fdM0JxqiwRmrpEpgNZPUOPeMm6j+yrRte9IRS6ZorZcUpsffRKdxung9zvs4u/A4PMvF3iwAAB7Hnpk7ONCbSd7pK9kroztVJRvsQ8pU7bmZ7BoN2+ZZLM+jdSttIkNGOtn9Gu8CAACIYNySEitVlczq+z6euXfUpKm7UdMG7P84cCJ9gr33cS1KqcihEjnOy44xNnfezcUfGNlcfD/ZHJtjc2wuUWQ96ZgTp8R67ZROU2swb/V9P9t30F/V9aSMkw0AAHAEZO5szrDKVapKTD6eWpU/c0rbtoM30t+j8YfbC/4IkDjvzF4uf2kEAADEvpm7StrYuKXTqSqpMo/MbGrXWvttuguHNLkA5404WslNetB/8PcRAIBHsGfmPmPU2Kv3mxlX0Y5xR2u73bjd/Wgjoxc72vu4peIzQNnB9vr6av5xu93e3t7W3tzGz+7amyt+4LVfFjbH5tjcZpurUtJNH8oMa1SVpMvd3COHOVvF18H+qcFU1U8tGb8GcH6yOOPF5+l8+/bt/f3d/Pv9/f3p6Wnf/QEAPJrTZ+4t80r1qpJcTjWO/9wpDnbUbf921lZ8DNRaj41LrDjnRR7zNwAA29gzczdNszAxK6V2yb5CiLZtpwaU8Se1SRnspsDy537ekgzbVNx0iu+rsK9qpiZPTem16awn5YNwjTdrF1prP2TzegIAtrRzO3fbtsWx++DjURxB7mt7oiKH2akczfxBa2/XxO5x6+bfKYelsx4z4krw9TfjQvrXcj661U4JXp1y9gAAbGn/2pKyxloCd4q9fgTYhtNUbH5GMFISajHnVTXT1ow/cYzbna3z8ddjfjyxn0XTNG3bRp6L8yI4P7/QlCuiP3fw+gAANrN/5p76gT6CwG34jZpL+lmerhbcf7LBiRurP6+UiqaUq52p9WRNP0nD9qzIe8FpBACwmf0zt8iM3TsG7iGNE/KklOOfliSkxOpkp+EzvsLinTmIrusikdq88mtsVynlv9HjRv3BwqeqXCLr8Vc7tVjiGh7T7KeGpm4AwDaaHaczdNKzlHI2BfqB+4CToRQ8rxTO2JBTq3Vmz4ls3dnPgiPBXsOOB5LW2m/L3P2Kwn6/Ul6c4LMY/1Rttx5MyiU6U7oCAOJyv9PDEttuV+K0z3VdF1nYTyTx5fcSaef2iZ8to5Flgq25kYekLJm1k1PslRQ8/MKcY3Xv3XlQif0Zyg5+AMDjqPKdvnNtidbazm2RYUz8yc8P2MJdxu88Z+43A1aY8Tf8GuXIc3fytDMmhlmts8I1hvh4ZM58pTvuySNLzNxUdQMAtlDxIqCYs0t+u+xZWriN3HbuXCnNclk5r/jFfNh27shPE/6PEkc+Vi8sa9AemroBABH2V0bxSg7Rh9L5dnRauy/cwl0gsTrc+QEh4pFfzGLOTxNa66kfJeK/SGA9zlnldrtF/ktT9/a0FkqJpvl1U0rwWQFwZRUvApbwG6Wm7j9+q2FWO3d6g/Rs2bcv3tRXsMLIzi9Zz+kkvmWP9rIch3/k+59Kf4G99/qCum6Q8tdtJOUgxORt1XO8vT+H/zIBcBRVvtn3HLfE4Q+40bbt47Rw28/Ln1dl4SCDJoKY1Zp1miLv4nUaBxm3ZHvOGDJBtcarQQF/7CARGo3ead5+qGN4bUqJ4I8Hz8/ix4/5h0sp6n56tBZdF96l6tsCcD1Vxi05UOYWcwN7XThwn9TDZm7nMsZhLhc5VvfiV6MNwxAcwTNx/E1kiaTbLF23tNTE7IkQ4vVVvL/HliR2A4irkrn/Umln6ggOqWFQF4vjsIeXwdE4hSXxWZPssw1V3VXUGgOpbUXxhfxUK/uUvhdKEbsBrOtYmVtMxG7anwAkcs4ekVOH39SNhbIuRW838f27EGKyXVwp0bbCKc5v29hWtC4M/cRuAGs7xLglDmfMDQI3gHTO2aPiwojTOhCdpRRdJ7pOfBwqRggh3t9F14m2nWyT7vv7X+2bydzB74T0wN11wn+3+57MDWBFq2dupVSTz/nBN7IkP/EDsJmLdjMsz+zluinNN8tzbb+Q0yAtpRiG+5iASomXl8BDysp5TPJ23q6sFu6uu1d7O8k7Z1R3AMhzuNoSAFgoKz1TnV+LE6CdBFy9Wn42Yd9uk10nTZO2edvtHaOkH8B6jlhbAgA4u7b9NdnN9j8hSOkG7qkm7efnjXYJwIMjcwMzTH0UTaFAGVMK4tdzj0wVirnV4reCO7m/70XTiLZNGi8cAJZbvbakrTVw1E7rx4MbR9Hp+95M8773HgE7G8e9Hv8rEkYsmSrzsMfG/vZt+d7dBb8ZpJyvHhlrTgCgrtUzN7WSODXGbwZGwXGvmyZWOT1rDNy5g2qPbjfxj3+IP/8M3G/vlVLi9XV+bW3LFDkAVkFtCQBghmn9ncrEwcD96VPSms2oJk0TC9y3W2BoP3vrfuD296rvUy8MTNmJc6PtCMBCjFsCAJhRUMQXzMG+s/x6ZII4TeAAitHODQCI2b2J9/39KNHcTFcJAAVo5wYATJqaXXJ0kDS8GWaJB1CGzA0AmOTPLmnHzYqNvks6Yq7kdhNfvgQGE3y0ywwAVVBbAgCYFJ9dsqJ//KPkUZ8/CynXmtfm/f0euP31U2ECIBft3ACAJFK6WVNr0TR1Vp7Y59Lxxx9btDr/+HHEZngA50I7NwAAM/yRBwEgC5kbAFAuMqk7AGBE5gYAJPEbd7Wm4gIAkpC5AQCTnAkgnXpuZ1STS1qpgyaAR0PmBgCkMtMxmuQdHLr7ep6eAndG5qIHgCAyNwBgktaBfGmSd5VxA4OtyJ8+VVhzLW9vgZr19cZMBHBVZG4AQMyq+fLpKVCgUjZu4Ep+/HBr1m83xucGkI3MDQCIUWrdum2lTjb4CY3cAAqQuQEAM5QSwzBTxPw//2fJml9fhVInG/zkt9/23gMAJ0TmBgAk0VoMg+i6cLP0//t/Jet8f3+IjpgAQOYGAGQ4XbN0XYxYAqAMmRsAgFRa770HAM7pL3vvQDZldRdv21bRexwAsAnmxwFQ7HyZu7dK//q+l1KSvAFgS1I+aBH2jx977wGA0zp9bUnf9yZza37wA4BNMFgeAOQ6feY2SN4AsBmlHqgr4efPe+8BgEu4SOY2+r7vVp25AQAghHikpu5/+Ze99wDAJZyvnlt+bF3pH7OoEAB2ZZq6H+EETA03gCrOl7mD1SOmDyX5GwA2o7XQ+oEavAWDcwNY4HyZO4gybgDYnpkT/unpUWbJeagLDAB1XSRzAwD28v37o4TRthVS3qfF8YeopfEHQEQzDMPe+4CzUkqN9TxXPZCaprH/e9WnCSyk1EPUdhu322S7/u0mXl7uL4UpRCGIAxdgh4HiJHCpcUsAALvQ+oFqnSOFNO/vv649+l70vWiaQIs4gAdEbQkAoAKtH6u1O13fC6Vo8MYOnOu9yEGotRgHW25bLhRXsVvmtmdrZ/J2ADgm59w8Rupg4QSxewqxG1syAdr/JDaNkNKN1P5ndiyO4oita616bq11a/WpkVKOQ4s4fxp1XUfyPhfquYGrSo/OwS/mf/kX8X//b/WdOj1CzLWZCLP7W5wygqc5FFOW7DravIWoVM+9Sua2o5jNbMsJMTZi97mQuYHrKRhyOxglp8/0jyLY1ZJTyMVMtSj7zcm12Ov0N5H++X1+Tp3vidgtDpu5p5qxxc8pJOMz15BpToTMDVxM8Rw3fuymyEQI0XXu67mwqTuY8NaLd4ibPcgr/rIxFe6drax0rctX30Ezt71b4zztfs62m7Tt6EZT94mQuYGLWfKF/fwsXl8/3EPsHgb3RVgSwuKvJ4UrG0s8vKu8Lynhvm0nQ3miceihYLP9gx9dVTK3GKrqxl6v3prjGx3TuZSy7i5hPdIaG2zvfVnLqp8X4FCkHIRYepNy6LrK6zzvrevcV6D4Ky7lleT7czNZB7b9iVh7W2U3/8jxN2qYQ3rhMzqjKklgrcztR+fZVE2sOR0yN3AlufEx8Su864bn502T7sFvBcm47JXHeoKv/Hjz/1psl8A99Rz9Bz5O+K6SBCrPiTNmbr+ke6rIGwCwu/gPx1LmDXVnhsYzlBJPT4v27WLMRDnpL+ZUXUFwEqK+f/QagA34BbBddx8GxNysn/wnH2Izg2yOt/Ed1HrP6qzZWa76/t6RgEMu0Q7zUMZ7WAIAtuekhNstvNjzc+oK7dgNX9umJhU/dXWdGAahtRiGQLzz70FdzjviD+uhVOq0rEqJphFte5+11NzaVjSN+PatsENzroWJ2ewwsTsFc78DAFwvL+H7EwcXM8Y21wfvSTklJRz7UcZJeEq56+HV3ljw2tJ54/w3ZXYCqe/fF+9Zmio/Q3Gll6Jy5h7bsDtefgA4rbe3wJ1+tvj8+cN//VZw/6tgqgX9AfW9eHkRTeNWFNicF9AU+Tj8VlV+YajONEibm8055u0SkTjTtp1FyvsvG1llAbfbzPL+KPKGMwzRuA/m5uB3rRTbtXMrpaSUUsqpoQDNqHNUmGAvSqmmaZqmSRmtMmth4HScpq+pKuG//tV91Gyb69QX/GMyvxuMFQWzpbF0jNqe1qJpJvPxjx/3d82EcrtEJKLse8McG2Zz6Q2bb29C65nY/e2be49S7kfVJP6xZn0Y3HXyA8u8ip06jbI1RwY8wWFdadwS52JvPA6Dn5ephbGE/KnK2rquK1uheUiX2Rt/3FbuAw/FGZSgYLCR4Hr8e7jFb5HBFqeOr1ojEsJRa9iQ5+df6+y6wALmLYuPDlS2b+NoKrfbzGLm6AoOvXK7hV+f+B5eSZXkXP8VGuNI2aNO/Y31aC6cucdnlHInmbuYScYipCy/muA7tcLIOxXck8R31n7gqc9gC+PF+Go561m+5gvcci9gxuMoMUyTudcQDMfFt/T39OvX8Br8JTc7gDnwDpq5x+alrO+ey0S3h3KlzD1MJGk/c0+lc+RKKSRLv56JxPfZNyve/yR+KrMfe/arr4UJY+Rn7rrZ5UFuU2+Kfzz6y6CK4PsiZfl488HV+m9o5BrVWXjjo9FH5s5bScUdwqO5WOYOhunZe84es/aS3nMj5eo9fW3BhztvqNNYHn+LL/cpKPxKHt+lqfxHU3ckzQQrCsbjLvJqB19wzklT7GlrZl+lyDsS/GvKzaxh9s2KX6Oand94qimKmma/R5JWUnGHUJG0LP+12q5trZgRL5Y2hlCenr1n710+Jb9deTwyg83V6S3NfnR2ArT/8Kl4nVIxcpmqEltumIhP9m6fbzbLB+e6RV75snfkKkdiNZEi6cj3YfBIzpqNdertTtmB3Q/LxBcqsb/BBZC5j6i4JNRYo7w1uLbcHZtd+cJVHUTusDk0cpdxXkb/wHZidLwOe3Zt45LB9diPnfrT1A5c9Ugom+Y9+JB4iyw358Bx/jpi7vdiKS9d8JzhvxcFadsvRHE+BYlZdoOD0PkRIKX9eR4QAAAgAElEQVSoaeoq8ZIi3xQZK6m4Q5hqb0t8ePXy1pS1LWmcu17mHrw4GLf3zp5SYp52Fptam/OpKTieI3uS3kB+mUZum0nezm/xWVEg+N5SZBLJMZFfCVJeNwK3I/1g8z/B/mub++YGPzL+eoInj9y6kU+fqh2EkVdvPMDi1VCXVOWrn9BQUzCWFQx9EJeywqw5iYrjwiUzd903Aj7nFU5ccralufhIjmwicoRfqetkrvTwEVnD7fbr9vlzeVw49c0/cOLVsfFX/sEOw3m5v6uMgoPlJd4+fRqen2MXS8GVO6eugl+Ein9Eihw2ueu8YsvDL4lfW3F/SYwXmLVkbhSlVP9xNHn7y975U9/3WuvI5rTWrTdxQmSFXdcxsctIa90484xNL7n2zlxe+hVOH5puwT5u5fR8W2uwM/ejHQmzc1YLIaQMzN6itfj6lTlxfvnyZWYB5zUcp0QR1vwj5jP0YMdgkuAcqF++/JrvyTmGnRd21u0WOJj//PM+z1FkeiN/Bsq2/TC/TMH8lEqJrsueMknK2JFj5jdN3JmuYx7KBBUvAnKNHfvie1i9598aIu3Ks3ueUpCa1XXPWdjfgWAPtoJnfcl27qF2kQ8c6S/jbHlJlXcksoapI/zyVSUpIg2B/lux8RgLZ7k584ww5F9Fs220S4b8My3Zf/vb/CamqqIrVljZH7eslunEU1dxQfzFJAawmZVU3KFEicPoTgWdY2YdZyezokBiQWriasuKZcuOoatm7iGhqnvvHTyxrE9H/KMRfEdyR+mJ7MzUEZ6+/+fl12sGh1cL1nz7y9SKFxe72ZmbIf/qOmy3gdw9jBS6OEMGGfEO0OZqITciRz7CBWs7qSoBYOvoUJy2HYdqW3KqTnMzd/rCKUumdylb3vnswpk7fqBeOGltIP2VjH+U/FbwslF6Iodx8OEXPuyN2UFLsk4VBO74bbxWefBAU90xf1fJKtAPLj8OKjLLH4dkIf8C+6EOTufrpnAlFXcoLqtXX4qDxO7ZL/4lqSK+cHAZ51WK73z6pmf3J/exxxc59vbetXNL6RnpL+kvHL/W9aVcpk5l6/Fsc/muk4kROf3sO5Uqbrf9A9BZbo8WbqrY/V1LfCunLnF504+mSgbYLj3Mfh1OiTzqCLHb35/qMXpq4dlC7awm9oLD6NqZe+rYu2TS2lL6MR9/5Qt+NJt67/zFph41/umSh0H1YtDgCj9/vj+WJvCs2wG+7k7jmI3c9i04ZE2kQAu7S/zamllJxR2K8L8dTZ5OD81T+XvV3U7ZK/+LOStz5z6d+JoXlpLPbj3y8NzHnoJ/vF31mW4p8coweNKIL+AvNruS4C45y4+nqWDL9+BVkGed2Q4ltwS2+gq5RW5EsURnOep4Q8+lSgzYIkCkTxSXIjdWrsd5XsEv5tk9zH0uzivp/DU3Qy98MS+fuRMTG3LNHnjBPD2buYNh1++0nbhX/haD91/m95BgH77IpO4poWH3WHOx2/iCO5W1pmNc7rtzSWcJ3I/8Hp1UVriaXEnFHZrifCctbwFa2Dpby9T366qZO/7cc1+Z3FqUyOayHngiTpDae3cuYmrG1ng52Wzmjsz6vuQ49zcX2YflG9qLP0pJ2TK23TPN9W65mfJUx+BS5wrcD/gGnVqVJLB15q71JbR7DIo8qfTMXdDMHE/Vzld+yhNZEhEeIXPfbrfxOd6c0XSxQGKn6ioftGHxWWiq66S/AwdpESjgRIGpthFnsYgzBqBL3jZOdXuNbnHe7gE4hSon9i3mobQnkKs1YZuUMjgv3Ta01vbW/UkfH9DTOLVX1MvLy9p7UtfLy8t//Md//Nd//deXL1/+/ve/M2HnaOFnWSnVdV3XdVMfZCll27ZrfLgKTh1j5pZSjk/cPhjGyVy11vZEsEqpKie9TQ68D/vZdSp4WXS7fXt///Upntqxt7eXHz++Vd09FOp78c///J9//es/vnx5fXp6XW9Db28vv//+Yh8e4w7cbq9rb/3//J//T4h/Xm/963l6en15+S2+zOvrNyHE+Nrebq9CiNlHIcXr64qHpaviRcCUNTa3bwtrvM2suJ07d9P+QxK3u/AhUzuDh5J1qET49damwsQ/Pp3SkayKkSXNz1NdJyObrn6CqvKWzXGa36Z0RYtxO86t8ri9Oe/4SpuWe7+kq74j8Rd2pZcUMcVn8i3auS9GKbVGyz3wmJRSU22lzofLWWyz3xzGz7uU8pF+6JBCBH8QSHwFHueFOh0lRCdE7i9I3c/Hip8/iXTWEdKlveMpm7Y3JKzNTT1KOj/RnFDkpZt9YcveTezjf2y8vVoJda/CEqeqpPpEPwBGThX1Lvtgh+yrX2A7zy74Le6c8a79glyYEuL3UItp8FNm2lmVFf7Mv/XPh8icSyw1sRXxs7laeWsz9wyhB14gcEdkXcngBLbI3M4vs8tXuGNTkxMC6u7Jqb/Rq/yAjhPZ4KCa7TVRFsTTH2VfY+9bSbXBG9p1btAJzVP9YRkp1dTatnxxUMTvgaOE0LNvuv+Qz5/dby4pRdeJYRBdJ/zPjZQ6dPgNCelZO90xrxG4pQx8wLsu/so71DmnBDiTOm/2BjtaZaCu0Y7jc5eN8su4JUCBlI+bc26JrK3sOI8/JPLXk340puagnhqBJMJe7PiTAnJzbibAlb1xfvjzRxSx/2TGFy9Y/2UGxgmekOKLMRz79nLDVdAW7dxKKfsbqO/7pmkKuvObuk+nqmTLMUOqV5VU33k6NeIanE/61IGdWOHt3J/4ubMf9SBjEwWfZd+LYClf+inwxw/xt7+V7xVqaZrUJf/3/xZNI378KNlK2wrz3a61UEooJbpOWGOuCiHu9zeNaFvR93kbatv7w7ccbWJV/ufOD0dd9+FOrd0P4H4DuSFHxYuAiPjUysEJk+37I4/dZv+N5a+2v8O5T8dZofPX3MEZFv5ocNLGPByB+Dg+ycj/yGcNSOKcRoKDoqTs3tSA3FObdjZa/JnaXeIgx7NPy2mHu932b03kxu2Yt9mPz9THLXeOKiyRFa4mV1Jxh+Kqdzfc/stsjX3OfUbx5Z14MTvl58L5O8jcKJb+kck9jMcHBleYuHspx3awai73M3hAsz/0pzyn805Qwo3bxjf/m98J01OfODL3lpaEpdF245aYKTBqrc2emeIyZgdjyX3KDKuCUzMf89meylprP2EHP02Jn4jErpPBqjmnJOZcYwtqnVRRMBYPRCgV6DwHwEdZyOPYdKxApdSweCIV0+51mcDtvBrx5+UkBr/GNPdlSSmZBXaR9TEPxm6baYROTMDjJ2v22j6y3TO2C6RfpKcsebZnD+wmfmai9ewydpgTx3wPaa0j0z77zBfbvt9hQ9oP4n73r8hut23rdM1MbxgLLimlHFcYf3lPFwhwJebTFDyGy45M86jlK7RjdErXSTPZu389fK4WbiGEUoH2tvGCwvlT3wul5lN114muoxkP+OB2E+/vH+5xPiOma+nUXw2tA4/C0VUqdFnKdHXy7b1fJXL7bCUWgCYOuZg+MuPCYu6Bem7gQvwaU/tUZMZ0c5ZJISUdKLld6vbpU/ljIwMdOt/V/l+dz6O/cqyqSnLmXapvYeYOLu/XoUb6ZqUsWWWYczI3cA2REZRtUwEiuMLLDJ/MjZt9G0qHBrc/L7OZO3ew/BP21j4ZMvdBFcRZ4bEf5ReMZo2eJj4O/uCPnhZP8IkbKng4gIOoOzYZg5Zwu+ptPOZzY/dsK7XwvkXTN3HOmoCTqZK5N+1DiSl+M7YZA8FwyrIL+nX1fd+2rVmbU0EuMovIAVzeVGFoSsGo1tSV4srGGX/M5PbOdD++T5/EMPzqJZn+AdE6afAfKemvfBpk7kNIH0gxcTCE2WEcRgRuAI7icRJyA/fz83xkAY6j7+8TYZrvYaXEy8uHBaR0g/Kff4qmuc+dqdTkByT4jT0buwnc50LmPoqUgRSzRh/zx1Lw10bgBpBuNov7C8QTw9OTG1mA4zPJe+rbOHh/399vucw07/7nSEp3QngcXzPUmF5xe03TmH+ccRDcWSYHmwqQMYUXP80xfNsrrDKWmT0q4kkPJADGz3PqXde5Ywb7bdj+h95ZiWmEc+4ELsMUjdhJ2hzzLy/zc0s5/E8cDqWxTmTFgWeH8bkxq+5VhFKKxmwAcVJ+iA5t++Fna3/0br/hzTnNmIc7JzNnK8Cp+dnaHPDfvuUVWUlJ4H4I1JYAAAIRoe9F09xvflCejRRmgdxqE+BEnMA9HttK5R3ndDt+EGRuAEBeSrjd5mtJTbvd5Ur/gEl2dE4cdURQVfJIyNwAACFyUsL7uzuAgy8YI/pevL2V7yFwWH59SMoHisD9UMjcAIA7M0hCuvgADoYTO3L7lgHHNzVm39SoI+Yh9rjdeARb9KGcHbQOAHAQSv2KAq+v4v19/iFtey81sYco6ft7K3jb0m8Sj8sMyw2IbTJ313U9Z1wAOA+thVJJgdtoWzEM7rAkJnYLIZ6fad7GlY1XmEAEtSUAgAC/qaTrxDDcp7wOjhUYHPyk7wncuL7ZpsVxKko8LDI3AMDlJwO79tQ06QVjN4WEeFjBPK3UrwE3zc38l/D9gMjcAIAZwSTt/JJu2vmI3YBhejhMtX+b8E05ykPZYR5KWWNGBArEAWA9zil2qk0uOK+kid1dF04b5hvg7Y2CE1yNfcCb3sOzTP9j2rwfxEbjltjz1Ldtu3wqcnuFAIBDGetW7ZP9ODMlbSa4vPSpJU3/YzyCHWpLGDcQAA7O+T2y+BdwrX/dhGDcQDyEYLuilPdb4vK4no0yd5V6EgDALqbqubMCNO0tuLYx6TifCzP9zXjxaUbVtHEh+iB2aOemFBsADs75ZXwcaXvkl6vGm1aUIljgIfg/CpXdg+th3BIAgEupQFOcGeDM3Pxy1XgBK4Ebl2dys/N7ztS1qHM/vwI9go0yt/54Bae5oAOAYwtm6HGMYYeUsZpUTvm4vKlsPXUtyofiAW03VmDdku6BXr4AsCYz5F/K8AtSzgQI2vBwbbfb5CdlaihA+k0+oO0yN23bAPby+vr69vY2/vfp6enl5WXH/TmLlNg9G7iBy3t/nxxp2xRlGVKKtiVtP66GBmMUU0qNPWI5kHBk9rEqhJBS0gqQJdgDMj1AJHagfH5mohycm/kmjM8gIqX48kV8/x54IA7LnhamOPDsMA8lAOBccq9QtL43kGc16f3+e95WgKNJOeCDl694BGRuAEAdJnCMkcL8Iz1PvL8nLRaccB44grIjM33SSpwamRsAsJRp2A4GDnPn7ZYaqeNM+fjLC1UouIj4mD+4EjI3AGARf34cX5XALX62CD491VkbsK/nZ7ogPxDmxAEALLLZL+OmRZApLXEZXD0+FNq5AQDlgj+LjzXcdcOx6ZRJ4AZwRrRzAwDKOQlYSjEMQuv7L+ZSBvpQFo/S0LYEbhzOp0/h+6UUXSeGQQzD5DHP8fxQaOcGABTyS1FTmqLbVrSt+Pq1WpF3Isb/xhr+/DNw5+324dNhrkIZn+TB0c4NACjkTOpuZsCebboz0/VtPxMogRub8a8nlar5mw/OiMwNAKgjq93aieZfv9bdF2BnflcH2rkfHJkbALCp19fAnUxCiYvp+w+x24xh7yCFPxTquQEA9dk/mjtN2sHmcL8vpikNb5qZrbTt5HQ8wL76XjTN5Myptxuz4TwW2rkBAIWmpvPoununsal+Y8Ha1pGZbNIMxR1ncnmwBdHZH3sEldttZrVAlnhZ9tQF4fZdGrAvMjcAoJyfNsZEa3Lz1K/nkV/VzZ9ShuIeQ3k8xJtcrvV94La3t5nVAonMgIBlc0kyA+WjIXMDAMr50fn9XbStaJrYGCam1HUqJXedaJrscpGs0ljGi8BCZuxtc2Ep8o8ojsAHROYGAJRbUpA6lZLT07bdUpi1J/RdQ7HbTQyDe7xpnRejaeR+QGRuAMAW/CpqpWbqsGfZD8/K3Au3i4cl5WRtUvqFHIffY9ozc6vF/XW11stXAgBYlZSTs04ujN3OcGxTWweqML17pygVm+Z9ZOaEwgPaM3P3fb8kMSulWn4dBIC9OSHjdrsPEmJupoeZM/y2/ZB498dZZji2p6fJipRgSGJsQWR5fk7tK2lG0Qke0lIGilLwOHauLen7vmkanV/WpJTqOWUCwAE4rR/v7/dEa4YKMa16zgnbecjy2tapKTAT07xpnpSSYQQR0HXi9TUjK5txcobhHr5Nb0vT4RKP7BD13G3bpjd4a62bpiFwA8BBBBuqTfOzGX7En+/GP+WvUeEarwRwmCuE79/r7wZObUkpiAnfNGzDOETmFsl1JtSTAMABZTXg+Wfx5Z0pffHA7Vwk2ON856Jp/MKC14dAmT0zt/x4zputMwnWkxDBAeAIpmpY/cXsEKN1uC18CVM1m3UZ0Pe/ls8tLn9/J3YDmLdn5tZaS+/cFqwzCdaTSCm7rmPcEgA4AvMzenxGd6cD2dTM8GXM+oNp20wjb/Zw3LTDTCNvJs7MzdBT1eQ4tazaJGDWX/bdvNZaa+20VZs6kzF8B5u3pZQFPS8BAKsyJ2a/McQ/YdcK3M/P4vV1cme67kMLuvm3lKJthZRu47r5b8UW965zdwAnwu/oqGvnzC2EUEoNw+AE677v+743reB+4KZ5GwCOLKVJJLeA24/Ixrdv4eX9wVJGppTFDFSyXiA2VTTmy0op8fpKc/iZMIo2qjtKH8pgnYlJ3vY91JMAwAVoHRjMZFZwmeAXQiRwj8Y277hPn+Z3zOeXrb+8lKwHazCXW5G/Erixhv3buUfBOhMb9SQAcGqm2EN49Rum2GM2JWstnp7mW4tTArfR96LrRNtOVoCYZGb/6fk5Nv+OvQ8O01sU+zJHmnl3nKPRvNfjX4HqDpS5hRBKqa7rgrGb5m0AOC+/tNoxW2pipnl3Arff2dFvQRcfG7Odv5rJBccKkJGdveys/OPHvR3Ufjq324cdm2pDXbWU5ZE9P4v//E/xxx+Tf316CoTpsfIH2MaxMneknbttW2I3AJzRbHfJvk8aKsQPrH7BhpPd7XbN4M6YUQLHhs8gJyvb5eDmv86VwNSTTWnL9zmBfhtNI4Zh8q+fPok//9xwbyZ8/iz+1//68P5qLb5+/fByRZ4FsLGj1HOLhPlusqarBAAcQeL4JGWx8u3NvcefZN5v3XTaoWeb2IP7b5J3sE294jfVMOwzNeZUVP30SXz9Kv7t3+psxfxikDggul9Y/49/uLM8KuVehpEacByHyNxa66kBAZ17zDCCVHUDwFmsOuDajx/i6Wnyr1PxN/c7JH2azPiIzgVzbZr6h9xpetbz3/8tfvut2tpMYo4P6z5yWtaP85oAifbP3KaexB+fZBgGrfUwDP50lW3bErsBBJkptIzZE4W54E9cGAX8yGtmrqkYmN7fRdMsmrskpd4jJXbPTqGSsqHn58Cdial0bbn7MLu8XROSe0GSeC3HGNs4jp0zd7CexBmfJH26SgDorK/ubu5rvOu68YI/XtuGMv74JObsXv3Fbtvy2J0YJZWavFowNRK5OzAM98oKczOTaDrz+4wv4BFi99S7dru5+2ZekFmmX6yhlPj6NWNnguv3e9CSFHAgw378JC2E6LouuHDwu9MM173xbmNkv4N77wtwFzylOGcb80uaf1bZe98vSIgPN5uU7l+X37outsWpTUtZ8tS6LvuBiRuNPAWz0eqvW8rN2WH/lZ99IvGVSzncbot2yX/3y95ZwFflm2LPdu5gPclU67WZrtKvM5ltxwLwUJyzROQU4fwp2AqAipwX+MuX+pswM7rbguNkVxmwzxQiFyvbB7PRusU5ieI/TfjN/FkNzKY36mw/Wmdwm74XTXMveVcqsIf8doVDOcpYgYnz3Uz1tgQAw9Rzj//t+z54btFaO2cSaks2MztW9xJ+bUbT3Gtapra7VyW/M+bGeKftCFeC/niLYnr8xPG/joIRD+3nbl6W4Bg4UwdS3QFkgOUOkbmzBt6ena5yY1prp7XsgLXmzk7SVwzXJqW083Swqdtv5D7ax/Z6Xl+3mIjRDBXn5DCTvIO2DLVOTjXzX0bGDj+CqY6h5oLBf52nvL1lzA8qhHh+DmzX9GRNeYlm+7MCO6hW6pJP/KwnKWN+CF6yhuLtGvEXNr3WvOAX7ay9jexh6WsQWPnCVQF1OUf788fBIJ69sSHoGVKRqTneq+xYhKq6EwuCN3hlgvvQdZOF2lMHprNw+lOevd1u97cv5TOR+C6bVaXv5PNz9su449uKR1CWwdyVVNyhXFUy3/aZOyscp+zeSpk7pdJ9YSdUMjcOy/lY3T7WgTr/3f40clU79vCzb0NavNvlbc96fSJ7GOwGuvzFv90qPyMnu1d8X4LbTbxUAHLlZrDwSiru0IPIzcdirgltjcyd1bW0OHaTuXFkG3wEYDtC2nZCW2SXdnzPE1+oePT0w6v9jKQcnp8XvXRZgtdakfg79QqQmHFYWRlsyiHquU/NH0rFXyarYH25YL27vZ/OTm68e8A2nKruyGIc/8tlleou4ZREx5mKXmeiyratOZNigWAltGO2HNk/Zs2YLePw58GGl3EB05309dXt1/j2Nrf3EzuT9Rmy98G5E7iyihcBD0IIYWpaIkOJ+03XkYY0e+Eqv3EHhyJ29tDZvbLt0s6Ng0s5B9LIvVytYuLn55k24E+f7pubHcjZiKxt93qi5fuW+8OCXzfiv3HxQmrgYVVJzkSltcwG3+CSyzO3PxpDymJlxxCZGweX0td57328gmBdQVmpyZAzkUrkdth6bsfY3zS926It90U2WxmilfcAfFUyN7Ula/EHCd5mu/7AhcHFlFLOL+9m7PN1dw7YlvMx9B1n1NFTc05vZvy7xNOJM2xzlQKD5+ek4eTMxOP7ljTkVmU4UspUbGbJDUZsBODbcx7Ky9tlWjsn3EditDNKNzN64pLiw2VynbmcP1VK8EV1hme83cQwiGG4D6c9qlJp/fvvqUua2H1qWh9i3hwAs2jnvhQnRmeFfmb3xCVFmrov0Mjt9EITIjBf4EF8+/ah7fn9PTwLox+XP30Sf/6Zty2nX6A5EZqzY9Y0LmcxPjVjnA/yAk8NuBIy94rsFLtNm3duW3XiwA7AqQWP87M3ck9NWGjy1o7Je+pCxj85BU9X/vTguYHbKVZxBgAxM8A7O7l7hUkVwVkbZ0/w/ssFYCVk7rUUf50787QvmUk+d453pRTTwuN6gk3dp27kjmepvhd9704qvplxu237YSf9HTb7WZ2T2pfMH349t9uHYp63N/Hjx357Azyaip06MfJbtbMWDiqY0nJ2+cRBTlI2l/VAYGP+PJR771G59KEqthkF0R8hZFRlBJIlw5VETmkPMlhHZPQSBi0B0lVJzrRzLxJsgfZ/xa7SPbHve6VUvNk7t1Dk1L+tA+ne3t7spu7zNnJrndE23LaiaObcPP5ZZCzVyJq/Zrm9mvaPLNiiP/Wm0JEeWBWZe5HZjCulXFIc4m+u73sp5QErQBKf4wH3HHtxyqgckQ9O/GCbOsb+9V//9d///d+FEF++fPn999+dlVTf3ErPTgh3c6Z6Wwj922+///77y/v7h3FAnp5eX15+W/vZ3W7f7O32vWiae2WwUy68kqlZG9/e3pT6OvGg2Lko8rJEXsz4mx45+623ufRCmufn37ruVYgzPTs2x+aWb+719dX/05cvX56cCWyrqNjw/oDir21iqYYzJ45tas1TM+cVbL3gIcE953hDrvjxEzkay46xS2xOTlWPWJvrvIKBbZ6dv91tbr87z85bIPhGfNhb/+Upe+/ib/rUozbYXMIMQfK8z47Nsbnqm4uLrDOO8blX1Pd90zQpLbsmYQ/DoD+aOp7O+8s4gAU+fPAnRsJuvRbcbYaiWFiX8CqEck51UqZUO7htUd75UnuvgBTisWpQlBLDIKQMvjhaiEYIBrACVkdtySJ+GvarTdq2jVeDzIZyk7+dnM0YI8CDS770bhcH4hS9EI0QXSTO3m7i/f0fQvxz6I8vQmjnN15zhhsGM06Lttb85kftkTNeilnTx+sQdw8f5FQ6Ps345KwAVkLmXiSYek1pkR2+TffHJRHZn6r9aONq7zLpJk4t/nNN5K9lB9sFNvf6+mTXRtuN3PYKncVut6cNn1339va7EO2PHx8y8biRvg8G7rupym+thVLdeOXw+vrt/f3X+r32iGDfzckrgWBTeuRlibyYxb9Asjk2x+b22lxiPXeV0NUsqWipbiyEt5/bofYwnVLKeYeWPxGnccJf4ewC8XXm9s60n+NJ3ybgRJxhuac6DiYuVrB1W+46U+ZncUydVJxVBRdL3FytFwfAtdlhqTjwHKWeW2ttBsIzQ3NEllRKNT8deag7rbVzxba8FGR2hTQ2A3DOoAtPPGaG9qa5T2Ez3pomY/rGrCEOR1Mn+JRVaT0/pSKBG8CW9s/cdtpOXH7899HqKxzO7x1VRumuu8IjX7QAcDgBse/DY2PX3WKoPPrXDiROMv/bb+H747G478OzSCauQWvRdeEFTO9MAjeALe2fudPT9shuzT1yatx933Jb1umUCRyckyDH9mZzM63RkeWzmMA9Kxj9Hf4E41KKYRBai2GYTMZC3DO9OTMFn2B8D81jx02YqG22u/fpGcDD2TlzB1NpfGhqwUh5yaq3rAPYV/DkN9Z7OJbUTiQG7nEHsjbktDGbZPz8PLnytg2kbTE5WmKA2QRRG8CO9szcWmu7hVtKaaZ6mW1ttZP6kctL1m429tefu0Xn9a+wTwDWZKYVTLSkdcLfythIHGyWjuyVE3M/f75P0u6UgxdM+kbzC4AT2TNz262wZsSM9GKMUwTEus3MziXKFPuViS9PJQmOTP2UvvwGvauP0Ic7JXabfLxkB/0albGR2GRlZx8iJxtnJK4//vjVMG8asMdG6CwLnyAAbGzPzG0nwuuVi/gReWHGdXobIxcAACAASURBVF6iqasOf+qcqRU6lwREcOSyA+isxIQ6rrP/6fiDFG1vnFbQN/YOXPKC+f0Ug30ZnR2Y2uLLy8zmTPh2Fvv8ebLI29SCc0QAOJf9+1AKIaSUS75Qt/wyNt/9KdUviRE5ZYXBXwCmrlKcJfu+D67cGT78FL8b4Nq01iZqB/9qwjdXhjbTO3Cc0/uwvQMTW1ScfpZ//ev9CZpn5zxHADid3eahPG+r1TiC+JhTTQ4ITugzijTkF6wwfpXizFhpJp9v29Y8xJ8mM757wAa01ikHYdu2Xded9+yxks0yqFNSEhlG0FZ2dhmbvUnYAK6Bud/LjbHVmf3Rl5gSElc4O1ukaRd3JoqP1HYTYrA7/3chc5XoXyK2bcukp9sLTuuYEriLTy1EbQAXc4jakoUO3kZbN9EmTs/uz4I5hcCNKoY5kePWOQLtHtVj7I4sj+qcc2pwIMJEZQ+k2A3A9eyWuRfWZdrtXlt+Ac+OHe4sOQxDfPfSC6nNWIrpr5ufVIIrJL7gUIJXlUqp9AF5sFzZWYGWaQCIOERtiennlx7+duxHNW7aTrRjAhhjwVg8nb7C4KDjToV3LqWUaV+068LNOtP3EFiVM8LP1M9Wpoel/d/rHcD2M4p85O2Tz3onQymzm6id8baFl91vN/H+nrQqM7clIR7Apcz+Irweu+HKNAkH/5T1QGwp/jbh8qocAM5PPZEl7cX8D37iacH/8ad4b8u24q+h67rg713x52gvZmYTq06I7Ju3wx/++vyct7app9V17pqFGKScXB4AFir+1rDtWc9tN2j1fZ8+fO+1B/YGHla81Gp5eYkzOopJqwXrqbgVs3Dw6dhnxchAin3ft227RoP31GTsEfGz+O+/560t+LIpFR4vxQzyfbkfPwBcx56Z26/RjI9UbSbLcEaVvt7vy8BJqY8SH7VZcbYzan7u3LcrbSXeajDG7tnGhTVaH5wBs1M4U046p3OnsMQMtj01uY8Qou8/rEFr0TQzFS+mKAUAjqhiw3uBlEamSLfFfXce1JY8uJQewLPVX/bCszUSkY9/vOrD2dXimrTlW5mtD/EXCG7Ur0ipXmhXUFvi14RMLTPubNfFVmU/J7+eJOVRALBc5NsnYyUVd6hM8W+7K5UwIh2Z+8Glj7oT+bRmncgiCyf2D/H/mmX5VvwXLfji+K9hSoIvfl5BfopNL8gen9NUUDbl1/5fnU2MTzq4Hikn18/3A4CKqpxp9x+fWylVELsZ5A44kamC4yVVyGU9QBJHuC/YmbKtTBXI+dG8+guYxZR5pFebjKUuUztoyq/9QpFv3yaXt5l54LW+zw/vf4esUKgPAIvsn7nFzyHt0se9HuYGvQawJVMAZtprTc2D/3EOFhyv0YVx5Pc7zBrhfputRMZGtP8bOT3af1o1gv/2m79pcbtNLj+epNPfZJOkI6saaf3hTqXcBzKGO4CjOUTmNrTWU1/Y8qchOpsdgC21bWtyttbamTnSjCHtfJC3vFR2Bg8R6/w4tnwriQsndpGsew3j5HynkVtK0bax8bbH1OsH4qnNJZ7dn5+FUqJpft3MJpwd5rsCwKEcYk4cW9aIBwB2FP+oms+yPZHNlvNHbhC4l28lvSB+l7NisPbDMM3bsxcCY2u0UmIYhHUsfGDiu1JCKXeLwdzsl7gE97PrGMMEwIEcqJ0bwL6aqLJ1xltel/xsFWn6dUqrV6pG22YrO1JqchS/9/dAzJXSXd5584NrM3d2XWAcwORLEgA4ATI3gBWtF0MvFnCPSevU4JtSGRJcW9/fb8Hll2DCNACHQuYGsK5aFRRljeLpc9wusc1WdpESu9NLsdNzsN1GHnlUZN8u+oYAOKvD1XMD2Et6ON5FegdBKWXbtmPxiQnEbdvWjcXbbOUItBYvL+GBAsdS7CA/K5vCbr9uO77O4Pq/fv01lMrTk9ubMzKgCgDsgswN4G6lQYHiXSellAV9K2cvD8zA/3Yg7vu+ek/KbbZyBK+vbg/I52d3pnc/SU+9DGZc7a4LF3D7h2FwPd+/i+/fJ3c4MqAKAOxi9doSM3DBeq733QZcyWyOt7tCxsN3bjT359uamppniW22cgT+0IFK3fNxcMiR+GWReewwiGG4D8s9DPcs7nPCPQCcEfXcAFY0WxDiXDZPXUU7KTYx1BK7K/ILRcxckma8Ef+CKP0FcCa4cShFozWAKyBzAyhh5r6ZXcYZUC+4mH1/3/fB1drN4Vl158TuWiJDB/pqTc4Tr/wGgBMhcwMo0fd927ZT9V2mqMypBpkaUdu530mrZnL1lPVMIXbXkjh0YK3JaLQOB+6//Y0ukgDOZ/XMbWZ0j/B/ejbTvJs5padmgxc/J6G42FcacDp93/sdLfzaaynlVN2IUsr5gI9pvmkaJ2FH1hNB7K7F9H2cSt6mMrtWL5upa6u//128vcV2AwCOKB6I1+Z8P5kYHeF8Mc8uj1XZb8fe+4KtZZ1nUj6qKRUjkfXYD59azL/CH6/tEy3fSsoaDHsNC3dpJVJ+uFUnxORNysG8qJEFAKCWxBNy3J61JVpruwWr67rZBiHTaj7+d6r0E8DaEouqzW9WKZ9TrXW8w2XieiJo7a7LDDMy3upyGsudYpKx++ZUkckVX28A59YMme1VFdn9q7JGtLXDupTykl9mp2C/gzseSNhX5JNb9tn0w/clJ5rB+Jb2/b1KxJ4Kx+k9KWVGZ8r0eTEBIIXdrag48OyZuccnUJCbSXtHwLsAoEBkNJJxEko/c7dt0tTxBG4A1VXJ3MxDCQBI5fzeEJn4PbKGSIu1Geo7WGSklOi6wOyVtudnAjeAg9qtnrviL8X86AwAqzLT1ozT34w3U1SdHnMTx9v2m7T7/r4P8YFTvn1L3RMA2BjjcwMAYrQWbRvLyomt3VkT3LStG6zHrZjkHYzdtMAAOKzdMrc9Yok/lG/xqgAAFZnAPavv5/Ouf6aXUgyDGIZw0/XbW2ANTXO/+WurNfklAKzhEH0oRWY3SmeQQXrv7YU+lMDlfZwDdIY/Ic7439dX8f4+v7CTpNOHK6HrJID1VOlDuWdtiT2+b/pI207gThwkGACQy2+6NjNNTrVMjy3Nfv23E7ilDKzc/xJInG2ewA3g+PbM3E5NiBmCN5K8tdZKKf9Ra+0fADw2v9XZhGkhfnVndJY3C8Trv8X0nDV+wva34j+EwA3g+PasLREfixNsfuv11GJMiLMjakuAa3MKS4Kfcqcg5HZzm7SDpoKyszZ7iybrj3813xJ8AwDYwBXG5zZN136eTulVSeAGgPU4tR9TNR5Ok3ZK4BZC9L14eRGvrxn7w/kewKntP1ag1rqgJpvADQBHUDw8348f4uXlwz1aZwwmCADnsn/mFkJorbuuS0zeUsqu6wjcAHAES07GP378iuz+oIT0kAdwJTvXc/vGSSXt8pIxjpt+ljvsFkKo5wauraCeu8DtJl5ewuNtc74HcARXqOf20YANAAfhDI9txiqxpQfu52fxn/8p/vgj8Kf39/B0OQRuAFdyuMwNADgmMw3kWPKR3rz9/Cx+/MjYEMP/AbgeMjcAIMwMtu1k64JKEgI3AJC5AQCTZme3GeU2Zk+tpG7g9qfUIdAD2AWZGwAgxM94aidsU0bSde79PrPMcmYkkyqx2H86himPaVvqxQFsas+xApsaGMYEAJYLTtje96Lv70P4dV148D4pxTDcH15F31dIw/H5582TosEbwJYOMT43AGAvWoummWnG7nvRdUJrMQz38G2i9jD8Sq5KVRtRu+8XBeLE0VSI3QC2ROYGgIeWWBMyNj+b2o9gWtW6WuwurlTJmsyyVts8AMwicwPA45pqEg5G55Tm51qxu3ieHT+sSym6TgzDvXneQX0igG3smbmHZGZmeGdy+K7rhmFgDh0AKOZE2zGemjISP6GmND8Xn5WdzRWkYb+R25TEmFWZ5nlnKwsn0QSAROdo51ZKaa211iZ8mzvbtiVwA0Ax/ww6xtPxv07IPnhCdfY2OJkl3xsAdnGOzD0y4duO3fvuDwCcl59QfX7PyJTMWquqe6Gpr4jlDeoAkOtkmduwozZjBQJAFYkNwFXG4fb5MX15owrfDwCO45SZ287Z/cF/6QSAa+l70TT32zh/jVJCKfHyIp6eJkc1ifvyxS1cWZ6YKSMBcBy7Ze6FpdjyIL9cAsBVFGRcM7mMGd6778WPH+L9/X5PlttNfP/+4Z4q5/ipJnkn3BPNAWxgt8xdsSaE8hIAWC74q2HWcNfF3t/de8oKS5z0HJzSkm8MALv4y947AADYh5mB0mamTB9TaeKEjtUFxxtJf6y9z07s9p8OP5oC2MZZM7ddxs3oJQBQxk+o5r/O/Y5Pn8Sff664S0uKPdrW3fPIE1m4LQBId/o+lP5/AQCJpqaNjDdvFwTu52dxu4nbTXz6FFtseQhWKmNkFVpsAGzmZJlba62UYqwSAKhlm9xpule+v4fzupmV3cx/uVxK7DYzbtJiA2Aze9aW5LZPB6M2A5gAwBJKiWHYrXTbqF7gEX9SlJQA2F4zDMNu284dTcojpWT69x3ZvznseCABqMLM9L5L8l71/GHPYM83BoACdmQtDjxn7UMphJBS0nsSAGox89rYFjeMpNJ6xTIP/3kBwPbOmrmP08Ktte4+Vg62bbu8W6e9huUrdHbyIC8dgIdyu4m3N6GUeH11B+SmtBrA5e2ZuctKsask2iXGrU915TT3m2b43F014dhZ85IVBrucmp9IjnPdAuARvLzcSzv8GXD6ft2mbgDY3Z713CeVVYaelWtThmRJX6HWerb2pizHj6jnBq4teLaLD91djH6NAA6rSj33ycYKPJ2+75umSUnJiWMg9n2fEpFTArdZW9u2tHYD8E2daRisFQAKnLWe+zicCplgbu66Lh6U/cBtr9YvNTHjlE+tLRi4Iyuc3T0AD8if0BEAUG5AJiGElFJK2XVdcIGu6/xS9amFzfKzC/srjOyhs7CUcnaL/jIp7A0VPBzAwQmx1k3KoevcewDgmKok5z1rS9RPBbUNSx670DAMWutIS7PZKyf4+jF36k/BVmd/hVNb11rbzdjB+m+llLNRpvYEsA0zAaQ9ZnYB0+GyaX7dGBAQwNHVuwbINu5DQSPrksduJvGltpeJP52UJf2sP7W29CVT1pD7WADHJ2W1Vm2H08gtxJB+Borv1YG/EwCcVZXkTB/KFaUMhphVSB2pyZ66P172bf830hIP4DGV/Y74t7+J2+3DPX1/b4o27dNKCafLiZRJrdRai6aZqTLvexq8ARwRmftYskplgiUo9n+zRkCnvASAL/di/PlZ/P3v4uXFjd1CiL6/98v0TzaJcwon7gyxG8ABkblX5NRVzy4zazaR57ZVl01LBOBxKJUXu3/8uKdqf+KbKYmN3EqFW7iDpzEzyQ4AHAeZey0Fo+/lJuDZvJ7bwZQRAwH4TOyOnJ8+fdpiN5wTnumLOQxCazEMgd2jXA7AoZwycx9/Dhd/vO1a+1y3ZTpl3hwAMKXYJnmPJyEpxfOzEEL8+eeilZtq7/g50m8QcOaK9wdCoVwOwKGcb04crfVxevsFG4b95uepHa7erpxbk03DNoB0/nh8FU8hXZextmDjg1LuvPROLgeAHW2UueMTm5sJ0svWvG9L7WzGlVK2bfsI0TbxHRy8IRQBnFSwwFpK0bb3Jud4+bXzV9PxMfEXwcTFsnI8gAdUnD8LnK+d23HkOBucjyaibs0M/SMBbEnKX1HYnJj977KvX8Vvv93/bapB7ORNNQiACztlPffoOEUmQab9/vjV5wCQxdRsBBuq7WV8v//+69/BVu3E82ViYws9VgAcx1nbuQ9Ss+G3JfvVJm3b5jZ4nxHN6sCDmBqzT/zsDWkqOhKbRJwK7MRqkOAOaO3ev/dXBICjS0wvVeYw2ShzB6uuxyeQFdcOFV6DO2N6edpvT9/3SqnZPdda734VUexQ7wuAlUQC92is5/a9vrrN22ZqyVn+YmYyy/GU6e8Y7QAAZiWmlypl39v1ofTTpP0ErpTY1E9O7J59YNd1FTM380oCqCslcBtTRR3v7x/awkXO9PJOi/g4n6Vz/+w+AMAuzl3PfWRaa6f93r+uqD7oCgUeAFbiV24sMTZRp/fK0XpyykmfP7elqUEfbxdq5wFwDntm7uGnKzVy25xI7ff4LGjVrtt6fd5SFgAb88OxmRBndLvlrbDvxbdvbmKON0QkNlPYI6gIIZQSTSPa9t40bm5tK5qGgm8A26Gde0VrJ9rZVu3ci5mrXvwAWM4Jx10nnp4+3PP+nr3O798//NdvnHYoFZ7m3VmJE7gjLRVZg4IDwBJk7gOZbcPOzcQHH0sRwHmZqFq3nC2xGXuchd5mJqUfhozAbZg2bwBYG5l7RSkRebbm2+ZkaL8cPDeU2ymfWnAAicazRcW0OtvIbTOJfxh+3bR2m6vTe3wKRhUEsL7VM7dSqrHY5RZNDUeuSE5pZp6t+Y4IPnc7OscbzqkkAbCQUuLr1wrrcQpClgv2+DQN4cHqlL6nwgTAumjnXovW2om8wYyrlHJS8lQUdlY41SzthPjINYmT74ngABLZ5zZ7aklfSsfK6oFbhHp82mUnpo3cOYlSiwdgVWTuPKZlfTaeKqWc7JteuRFs6tZaOyucGmfQCdlTId4ZPpzCEgBxzkliPNPE6zdSOlaucb3v9/j0OSdRpjQAsCoyd7a+79u2NeHbnuvHzCJpammCM8BPrdDJxH3fOzUzwQQfacB2AnTbtvZ1gtlPZw+rjxQO4NrONeJH8Hx54MpEABfUDMOw6gbMROjjf03+M/+uUoptr3ADZZN/zk4w6TdjR0gpUxra00fyLp7/0t7K2gcSgN3VmPzYtUZhifB2der85PSz5DQGIMiOf8WBZ/XMfTEFmTsx0SbG7pTAbSTG7iUTzpO5gYeideFAJVPTswvxaxL4uhIzd+JiAB5clcxNbUkeKWVi6bNZchiGxESrlBqGIb7y9MAtvF8YgmtbErgBPBqlCjsaRi7/V+q5OFV9bnPOpvRqAbCqv+y9AydjV0WPiXZs6x0Tc3HFi1m/eaxZ7bjOgnFFTI4fd9Ve4cY1OQCuwcTurjtZj0MzFKB9zitusweAMtSWoBy1JcDDMpNBihrDfax08vArAe2WbH+3OYcBmFKltmTPdu6xnbWgzXXJYwEACyn1q9l4jb6Vy/lF5JHLAwpLAKxtz8xt9/DLzc1LHgsAqKjrFtVp2Kfwtq3WpdIUk6Q0w680dgoA2KjnBgAssrAfpB2Lzb/HVmcnMUuZF8pTYjeBG8A2GLcEAFAusS1ZCCGl6Drx/Dy/ZN/fb/79uQ3hpu48WDpi9ofADWAbtHMDAAqlB24hxNub6Drx48fSjeZOgTmWno/9PsU6E84DQASZGwBQQutA4L7dxPt7ePnlaXtUNvO83e8TADZ2ytqSgpGqAQB1+WXcwyC+fy9f4e0WvlPKQHGIGXIbAM7ifJl7dnpFAMAGnEZuc2IunqtSiHAD+fu7aNsPZSHOFgHgFDaaE8eePKUuZi/fEXPiAI/MGZbbPgcUjNgdKUoZhxbxJ4/kxANgA1XmxDlfO7eDwA0AD0IpJq8BcFbnztwUmQDAARUk46lGbsOMtN00FaaaB4BdnDVzSympKgGAHTnB2plOMujz55INvb6Ktg2n7YLRSwBgFxuNFdiGTsBjKbDMaRJh0BIAOCAzkIhJ3qYIxE/Jf/wRfmzXia6bbMOONIGbqXO6jkEAARzdRplbKeW3SdsF6SRpADgXrd2+km1bUlUi5aLRTsx2id0ADo45cQAAGexo6w82UlZvbUpECkY7GZG5ARzcnpmb0eUA4ODGIPv2Jn7/faanYxkT05cEbvGxsgUADoh2bgBAgFJrDRLy+fNkYXc6vwScpm4AR3bWcUsAACsxZR7rjcr3xx/i+XnRGky8djrnM4wggCMjcwMAfvHnelwo2Kvyxw/RdSUdLm+3X+3ZtGoDOBEyNwDgl6zxQ263yT9JKbpODIPQWgxDIF533a8/RdbjeH//sIdMSwngLFbP3EqpZk1MiwMAtWidV6HhJOCRlG6PRq3dJccNta3bNTOepO09jOytUvcbQ9ECOALauQEAdwWDZP/2W2pjs5koZ3ajJq/H1xmsLTEPMcXoph7d3NpWNA3hG8DOyNwAgLuCbog/fgSybGK6NaHZ2Wj6Y/299TtWjkz45pdRAHthrEAAQJKx7dkJuxWD7LiJ+BQ5U9cGs9cMfU+DN4B9rJ6527od4DdfPwBgZNKqk4aD7c0puTa+TNdljKDy/JzaSM/sOQB20TAZJIoppfqf33IcSMAFzE4Gacbpm13M1GQ7/FEIzWnDWZt9Lkmcl+frV/H9e2AfRv5KOGMBSNdY56niwEM9NwBgkjN5TdeJl5f5R/klHH6l9VQvSftR/mgnvq4Tv//urtkMRDje/JVQXgJgY2RuAMCdn4OdhN334sePpFXZA4YEZ7UcI7iz0bZ1w/rUcIRSimEItIX7fSX9IVMKRmgBgCXoQwkAuGtbN7/6NRu5gsUhUv6Kxf5G27a8MsResy3eKRMA1kbmBgDcKSVuN3eGmnTBumqfU+1tGqGdYB0p467SRF0wKiIALEFtCQDgl7e3wgd2nfjtt/A0785iwe6ViRPrTDVj52LSeAAbO1DmVkoVTBTP3O8AUFdBHv369UOtiNPzUghxu4nn53v5dVBK7A4Oh+KYasDmuwLAvg6RuU3U7vu+59c+ANhbwbQH37//mo+9bQP9LN/fxY8fM8HXDDASTN6mu+RU4HYe4m9Fa4pJAOxs/8xtj/EMADipvhcvLzPR1owhGGFGGByGe42KidqRtG04Fwl9fx8vRYj79Df+VcTUCs3y5sZ4ggAq2rkPJYEbAI6muJNiyjCC6bOvp0feqV6YUwOVBJvS/TEHzX9TCloAYNae7dxaaydwSym7rpPW6XAYhmEYzJ3y42my67phGDTnQgDYnGmETlnMN9vaXSC9HsbP0GYYwan2H5Pd+aoBsNCembv72JbSdZ3WOtgnUimltdZa2w9p25bADQCbud3uUzzOFnsI8WuxYQhUaVf/gXNq6hxHMHCn5HVnph4AyLVn5rYbuaWUKSOQKKXsae6J3QCwmbe3e7nzrK77sFiwmKRpPtyW108rNTNSYXCYwvQG8oKupQAw2r8PpZEVne3W7o4JfAGgqpRwGRkJZGoI7XgtipkrfnnNiWlZN6Uv4800uvsrn9rPqcoZBhwEUGy3PpR2yJaZg8EqpaSUppmcLpgAUNdssvS7G6bw53j3pfewjEtcg7M/fuWJ80z5wgFQbLd27sT2aUpHAGB7wZaQsQ6kIHomlk2LdXpYBgWnw/TvcV4KvpQAlDlKbYnNztkp0ZxcDgB1pU/Gnih4Lp8q4ej7LaKts0uJz5d6RgBldsvcbb3eKJR0A0B1WgemcE8RTMx+07jp0TgObOL/1UxMY3eyXLX9e+pLiVYdAFXslrlTRikBAOzo9VXcbiUPdHpDvrwElnEGNnFid9/fb8494wST1U213vBlBaCKI9aW2Pq+D5aO2F0nqS0BgJV8/174QJOPzS04P6WTZc1ckolrJgcDOJ09M/c4XIk/9og9kokzCPfUvDkAgOoS55oRIrsQZcn8jlVit7Pp4Dr9EVpo5AFQ5ijt3E6Mdqq927ZVP7Vt60yms9EuAsAJ2SXRdm101hr8iSRtZgDs19eSbpf2/I5vbxkPrNLP0p8dc6waN7P/+IMJAkCZPTN3pBulGYHbvqf/KX0lAPDItJ4c1y+3jdmMme0nb1Pt3ff3Lo9lo52MsdsvQZFSDMP95q95ef95/wtkrBoPjibOFw6AYntmbrtt26/b1lrPtmEnzhgPAI9GqfmAmDvv45i8R+/v93+MUVWImQnY0/fEmard2bSoMUNNeuWM8Ca0B4AsO9eWDBY/Pcdjd9d19J4EAF9kYnZHboXGbJQvrrR2dvh2C1dXO98Jy0NwYuwmcANYaLe53xNprbXW/gjcpG0AmBKMxSatBuslhiFptYlTvvd94QiDtuDwgisxsbvrws9OyuwfBADAd/TMLYQwXSf33gsAOAf/fCnlh8ZsPzqbopG49LZzYdWcFJtqTQ+WWS83dp10Xj2adwDUcpRxSwAAa3ACtyid190vwIh3cPQfnlXnPVXvsfa8w2ZqzPEGALU0Q+JvioBHKTWOJMOBBByE6cg4mvpoJi42tbxf36z1ZOO0vXBigcrULs3uBgBU11innuLAQzs3AFxWfFDtdE6Lr5RJHRxHJhmbmykfl1J8/jyzUafixQyY7S8DAKdwgnpuAEB1WVXRiRUdU8XW9p3m31KKf/mXDwNyf/ok/u3f3CX7/leO99fMDDUAToTMvZQzrIqZMnPH/QlydpJRX4AHYYYC9M9J6ecAMyr262vSwuknPz9A//nnfcATp//l1LWBX6cOAEdG5i5hIqw/KaYQwtxphhVPjLZ2VXSi9Fqi4MpNWZKUkvANXI+UH3JqsOLZabcONhhHCq+nIvjyBof0AU+YEhLAuVDPnUcp1TRN27bxlGymqW+aZsdQq7Vumiayn33fK6WI3cDFOGHUTFIzftBNs7dzYvDza7yn4/u7eHnZrZnZjJdyvB8UASCGcUvyNE6f+QRd18WrTdZo59Zat8mtQLN7OIVxS4BjSh8bRISKNMqGFsna6KzPn8Vf/+o2ezM9DYBdVBm3hNqSCuwJ6v303LZtcagtEwzckZ3cePcArC1r4hjnbJEVnZ+e7hNGLkzbfhn3H3/8+jel2wCuYECO8XWTUkopg8tIrzRyakl/+fiSiZwd8NfZeWMQlG3X3tDy3QZQUdcNQszfus59oL/MeHq43ZLWmXu73QYpS3YVALZRJTlTz51NStl1ndZ6qhLaGSREhBq/16O1tjcX7CiplNpxDwFsQCnRdTPjc/tV0cHeluMp5O0tb3i+2y1pk8p5SgAAIABJREFUsff3pGbytqW1G8CJkbnzmLQ9W4mhlHIamzcr3nDC9FRVt7+HdKYELsZ0nTTJe/y4m3/bSToiONnk83PqDry8VJ6qnbFKAJwXmTtPenRO78JYl9NiHdlhJ2T7BScALsAkb63FMIhhuP976sTgtDcHF3t9DcduE+WdtSl1n3WyjP9A+p4AOCky91p26ZXoxGi/sjyC8hIAiZ6ePvx3TPNTk+8U/4qmNZNNArgIMvel5LZVZ4VyADAiV+jOSWUswk452QSbz528TuMAgJMic6+luJ3b1IuPlpRZ5z6WEQOBB+ck4+ApIX5e8avqzIjaKbPH//gR3hkaBwBcAONzbySxvNtMYOncY/6xxlTtsxNqAnhkfe9WjGjtpmo/pjuTz4vSxml6TAK4khO0c5t238Zy/BnL/QH7ljchp0zVnhugadgGYPNPMKaVerwFm7H9lSxvmZaS7pIALmXPzK21tpP01DJ+W2zf923bHjkvOq3atcYwOfITb9LsvZsAZvhxue9/3fyFp+pPlsRue+JJranhBrCWLdPLnpnb7vA31ZkvklZNu2/93VrM2ausRm75UXCZvu9TmvnpHwmgQHpcjk/JPg4NnssJ3E7PcE5sAE7qKPXcwWw9G1UPWIuslHL2Kj0fTy3pr7NtW2cmUgCoxdRwx8+v8cBtmHIUYUVnU6li/htZ/3ju95ehyBvASTU7Rje7rT64G05j/thwOzu3+V78cNx1XZXGeFNjY98TfOL2K5b4yhQ8ZOQ/31lcKgBnMZWMpbxH5yqb+O03d7iSiJSgDwDpCupGipPMIdq5g1UQTlS1w6sdQI/T1L1e4BY/p2q313+cJ24QpoGLmWqlrr4JfyyUIAI3gOoS00uVku5DZO5ZTkn0Acu4Vw3chulymr780UI5gJMaw/d66++6mdhN4AZwdrv1oVxYq32oDoIbBG7DedZ+HcihXhYASKSUGIZw/0gpRdcRuAGc3jnaueNFxmYA7812xrFZ4PYt39ABfzEAUMwZ5aN6KcjazJl+vVIWANjRQTN3VhbcLOP6dgzcKXKvRo7TGxVAluAwI+Ye0+XR7w152GqNtUtZAGAXu9WWpE8Tc9h6iYMHbvFxBHQAVxUf16/vRduGE3nTkG4BYCOHqOf2S7ez+v/VmuUxyxECt98sndtQ7Yy6WGGfAGxrdiDtuL4ndgPAFvach9Jmh0Unuc7myO2blrXWTuDOmmyy1kaD7OgcX55KEuDsFgZug9gNABvYM3Pb6bBtW/VTSuPrjgPhJU5PU52/0ZTFIlcCTuUJERw4F63DE9YMgxgGcbuFHxU8c/T9QWu7AeAy9szcTsjrf7Lv/P/bu4N0R5H2TKCon39ge9geVnkfVfsA9uGNlPcB7CNrDz105dA9tHtGD+IWSUYAChAIJJ3z3EGmLgqQbgi9Cn1ETNaNjO/45IqI3QP37XarqmrDxCxzFTXRll3XTTae+dkGuKz0eo3xnHrfv0/cJVw32ffT9wXgOCfPWxKtrZj+dnKY9sRLAyeTbmZVyVywHj5pDME3bNm2bXik6VO0XMoSPat1XZdlGb5JGJrN+WwDvJCm+VEiMvcpfnjdp8vQWEQL4FC305fsTi9GDBbGj8fLMT75ysVHFv+cfKo3NJgzsj73rE7a/ByO93J6R4JPE508xi/BhTrvhc0uO3sgwLnGaW1z4Dn/Gsow7FqW5TDKW5Zl0zRzsTJKh5eam+8JMktZ2rbNLBe52vyGAADv5xJr4oRLJzM3frNL/Zara6Ith/qQHG3bptXnjzQIAMA2l8jcL2T3CorhI8TkhOVRhfdaVVX1fR/VhYc2pW14dWX5U2VIVakMAbiu8+u5eV3queFEadH2cBll2xaTX3GNK7bTbbyIASbtUs9tnBvgJbVtfBllXU9Pvz3eYLhvFLjNFwpwKJkb4FVF5SXFvSn/FiYFffJ8oePStrq2ECbw/g6ft6SqqtvIuIb4tgdFycDHatt9xqfL8kmpt22Lqiput6LrfvzUdXG7id3Amzt/rkAANns8dj9tWu6qKup6diS+61wGCrwzmRvgtbVt0TTTybssi99+W7rvMwP33WlRw5g3wFtSzw3w8qrqqzZjXKExnqKkaeIlJ4v5JeL3OqTBt2/F9+8r7mi0G3g/h2fuaE2W8X8zF0pc1T7AJ5tMq0Mif84BRPl+Ull+DWmnG3fdV9k3wDsxPzfbmZ8b3kMIuI/PHzI3L3hkmEd8fADRMLyhbuA6dpmfWz03wIcKU4gMs4gM84dsy7uZgXtSdMe7w+QAL0fmBvg4YT2dyWgbwvfaAe9VgTvdsqosygO8OZkb4LPk5OMwc1++polvKcuiLGfXk1euDXwamRvgg+QPSK+K3dGQeSjIXqhRUT0CfJoXyNxt26aLWbaurwFYLx2QLv4ek06rO8IUIndF0Ty6AnKuaCSa1lAKB97bmZm7bdtxkp7bpq7r7ueTcdd1dV1b9R1glTTahvKPMCYdpvmLTGb03UV7UdsNvJ8zM3czOsvOzdW9MP1213ViN8BdYX7u2y2uKkmn5KuqOP5uGH6O2pwbKf/27ceBRXux7gLwfq5SWzKZre9G6s6XkQDzhvlJ5qYoSQPx41OIpGfuycHy79+nD6wsXWEJvKEzM/c4MU/G6yhSl38b32ioG2BSzuWSdX3s6jNhRclVBSqu1gHe0uFrv+eYLCyJwnTTNMMtocg7/NtQN0Aqf36Sup5YGHLtvsaX5IQJT0LaXnWGtvwk8MauUluyrCzLcQQ3tg2wbHJo+Zdfpjd+fBnIaOSk62bX3FloQeAG3thpmfvBWu25ay4BSAeYw/wkv/++dJfoH6tsjsu//VY0zdfcKQBv7DXGuZdn4zZXN8CCYQh5+WQ5WQ2SP76xbVbBP/90xSTwES6auVdVjzTPmT8W4EVEuTlnhZquK+p6ohokf0wj81rJ336buCPA2zstcy9MvB1RRgKwl7Y9cOh6eZ7BUN/y7Zslb4BPdIl67rR0e9VsJPnxHYDM2P3gZCbBb78VZRlXbEdj52afAj7BVWpLxjXZUWHJ3XJt05gALEhPosuxO6TkbWfWKEB/+/ZVJp7uAuCjnDk/d1mWw3h2XddDDcl4kHuusMS03ACpySmx//3fv3614JdfvmY1cVE6wBHOzNxt295G6yhMxujJupHxyLdqb4BicRGc79+Lui5++6349u3rljSa//77Pmm7LH9qeXKQu1BPAnyek2tLlhNztBTOwEQlAGM5q07++Wdxu339TEbeI8r0Js/WhtKBD3Ry5m7bdi52l2U5V8k9HhF3ASXw4fKXeV/QdV+LRz4YiNMlLaMoX1XxNr6tBD7Bre/7s4+haNs2DF2HMF2WZV3Xc1dGVlU1ztxXOP6PNf5b+EPAWSZruMuy+Ouv4s8/tzT44Iwlc8dTzJSUOHkAFzeuhd4ceC6RuXlRMjecLh3kHladDL/9448tyfuR2L1q3H18tADXtEvmvspcgQBsEBVMRxG2qopv377mLZlUltOlHWuLVcK1kqFYXOAGSMncAO9jMu/+8Ufxyy/xjWFVyLYt2rbo+4nknT/OHUq0105FInADH0XmBngfmUH5l1/ivLtqTfhojxvS9nhZSoBPcOb83HOGSyrHFq6qBGDZ9+8//fc//mNim7YtRiWLWUk6P3Ab1QY+3IUyd4jacwtMDlOaFBmrwQN8pqrKirZzIxjRijZ3G5nceLKRrptdHwfgE1yitiQsSFnX9d0V3buu67rudruJ3QDF1HzY6dnxoKSbnrCb5qtopO8nVsOxmhnwyc7P3FVVbVjXRqkJQDGVp+v6x41haDl/6Dp/yzTZR9MLVlUcsq33DnyykzN3tMDNKl3Xid0A6bWPYUXJMG1f/jLv0Y1lWVTV108qnaMw3ayq4mNzzgY+1pmZu23bNHCXZdk0TZ9omiZdJb7rOkUmwIfbMOXI5JLs0fk4rAY/rAl/uy0l5scXnwd4b2dm7mhykrIs+75v23Zy9LqqqrZt+76Pknc6wwnAawkVIOFn2zDCttgdknT4yfnGMdzFQAfABmdm7vEgd1mWmSPWbduOY/fm0hSA04W1G0MFSPip6zsjynOWY3dZLq1GuUpdTxdzA7DgtnnV+B32/cDi9bssfM+DxuX4/gqwStver8eIrkrMbzlKwENEztlppqaJm0qPNt2d8wTwinaJnaeNc48LSNJC7bvGd3ElJfBaMrPv5IjyXaFAZfwz/lXTrKhCKcuvn1Q6sB0dbfoYt61zCfAezp8rEOCjrBps3v3axJDI7ybvYXn28NP38fZdN9HCUBgTCmbS3wJ8rNMy94Y5ueeYugR4IekI8TCcPJmDj/gmLyTvvv/6SQ8pPa1O1otPHvDkVTa//WaiQOCjnbb2u4IQ4DNFkbQs44AbTdv3hAvF08m2o+OZPIxwyy+/FN+/39/Fn39un5UF4A2cWVsy1GRvmHtkuMuGWnCAs6TrzkyOKN+95Tna9msawYWTdE7gDtJJwQE+x5mZe1xesmrY2xg58N6iwYRtM/GNp/3ecNasqv0rsMVu4GOdVltSFEVVVWVZhhHrsJB7TmX2eH66/Fm9AS7ooBNYuqhkURS3W1GWRV1npd62faimZfyZIV3eMnwYAPgoJ89bMl7gpuu62+1WVdXkMHZYn/J2uwncwNvYPXqGODsXl8OaO+lOo1Np2GyzUDAz/KSD9BbQAT7QmePc4wnGByFST/4q3XJ5M6EceA+ZCThzFsJQ4BGdHctyn4s1f/ll4pLQaAEdywcDH8j83ADPk44opyMD6UB15nB4/vhxut+1A9u//TZ9+++/T9xYVRbEAT6dzA3wVFH6DMUeIVWHf6STCeaYrMAuy6/lbybXrxkLo9GZyrL49m36V8awASbJ3ABPlY4oh8n45mblyyyRS+fYDgtJDkvBp5E6Gj7PjN3D/IaGrgHynVnPffTU2jsudQmwl7S+ecGqcpGxtP1Q4LE8Dr18bNG0J6uGtI1/Ax/uzMz9Hhc4tm3bjN4V67reZfrwcSOPtxkd5Hs88/C6MmN302yc2KQsp+8Y1ri5e2xzxoF74SySTgWYrgQE8HF61muaZnmQvizLsiz3bbksy6Zp1ja4cJzbjnCu8Qebgg/UNH1Z9kUx8bPh1Zl592hHk3KOam6baMvJx/jwuQfgqXZJzreoIZaNV+TJ0TRN/hB1TuP5EyC2bXu3uqYsy0cG0ccHrCPBZtFLcNsXUeMB7Mkl5fM3W5hzsCyLf/u34j//c3utyMKxAVzTeHLqzYFH5l4nZ+LwSGbszk/zObE7J3APVn0wGJO54Tqik9NkXUo0KcpC/F1YWOdBmwtmAM6yS+Y+s577bYxLLNLcXNf13VCbBu6FNruuC6tyzrU2GbgXGtycuYHriK6PTKPtquXc6/qQzC1wA59rlzKXzzE8bwsV22kJ9XLldJNMTJDWbadtLjQYbZzuPd3jttpu9dxwHU0zXVcdKqony683NLjws1CeroYbeGm7JGdRaZ0i71rGNNQubBxF5LnG7ybpyV1nbratD8nccCnL1zWmEfmu5RgdJfvoXuMfgJf2bpk7jBwXKz0++cYq+TOHZEbk/uc/5PLDydkyM8Gv2jKnhbX3BY6QGbij88fc7CLhrBCNlP/yy48NorQN8JZ2ydyXqOdeOxnIifLrnuu6znlQqwqpy7Ic2pxrPLp9uex7fE2Aqm54A01TNM2dUuzxpZNhfcrJ7cOimGFj5waAB52/9vsLBe5VtuXXVavVpLuI7r7qe4O3/CvApwkrvc+99MuyaJqfAvfdayXDovQAPOjkce53Ddz5Vj38aGQ6lVZpLxsPnANvI6TqMIYd3TiWPaHoV5QHYLMzx7nbtk0nyIsWYgwVMOHGyfrjyy5jvmGce205+924vPbJUVsC7yQE5eEn/W2qLL9+Il2nvATgIWdm7mhQtmmauWmnq6pq27Zt2/Fd6rq+bOBO5a9Qs2zDZaYL9joq4OVEn9nLsuj7HwG9aeLk7SsxgEecmbnHw7RlWWYu1ji+evSysTsawp97dLuPK68tFDGwDZ8pPXFGt1TVisoTAO46/xrKYFV0Ho92r61gfo5o/PgThpNvec4+TKAoiiI6cU5+f1ZV8e0+pANv5pnp5bTMPQ7Za+slqqoa7nLBSwCjwePMIfxiffn1sn2rUIA3dsnvCwHeymmZO3N8+pqlIwvSmVhe7iEAn8ZZCuBoV6ktGRuH1Jxofp1Qmwbua5a+AIw5UQEc7bTMvWOJ80Vy7WTgXnWR4nU+PGywYfVU4CzRCbjrpq+qjGr3XvkUBTDhmenltMz9ZjNmPB64i70/PFyw0h24iPT6yLr+ms+7qoqqKn79Nc7lrhABeMQVa0vGuq6bHP0dB8rTh4c3B+7d5zNx3SSQKT39dN3XUvBdV3z/fn97APKdmbkX5h4ZZ8doEu65dXPO8sgI94YHsu/o9aWeSeCZ0qHuBf/7f5soEOAh/zj7AL6ElSaH/9Z1PQ6XdV3PBfRzR3Z3KSnZ7O5jX/v55PRvDIBnCpUkOR/k/+//LX79tfjrrzutjevjnE4Axs7M3FGwHgszcI9/O7flicvN7B64745hr83Ez/wAALy379+L338vvn2b+NVkcL/dirL8KhMH4MzaknEcTOu227a9O46bv9zM7vYK3NFjXE7V0UWW6eeNtaE8WqB+1X2BV5fOTLLszz8nWlgYKQ8F4jI3QHH6NZTjeVjSwLocu5umOasWom3btMRlW/qPcvOqqUsm9zh+xpYHzlWSwIfbMFXSr7/++Hfbfl1zuazrxG6A4nb9KZPbtk2T6Il5sW3bKCiXZfnI8URD5nPj5dF+53aauVm63w09YdzC9TsSELndttxreK1n1oIHTSN5A6/qNjpdbg48V7mGckFVVdcpSt49cKcmM3e637lC9ui+oWgnbTAK3ApLgFUmA3co4A6DJNFvZW7gw73AOPel3JJxofy0uhDNJ5sdtk9rx5eD/uT2dV2H5B2+N9ilGN04N7y0R8a5o8xdlvFEJaHyZEzsBl7ULuPcMvc6aTjOt/BUp8PYC3JG1tPYvWDz9CYyN7y0yfPZkJ7D3H/piSS81qP7Tp4A7uZygJewS+a++jqUH6KqqsyrJzNLWXJmfQnMJwgMmuZHLA5Lwf/yy8Rm0Ulo7mRj6UqAwRXrueci4HvPs1FVVZi8ZWF8elXteNu2y8Pn44IT4AP90z8V/+//LW3QthOLwBfZE544uwAMLpS5J+uMx2632+kx8egKihCpwwMMT8UwXL3hI0fI8cPEL+MGpW3g3/6t+D//56db6vqnQevJ83HmFIEAjF2invtu2o6cnrwJ1HPDS1s12d8glGWr5wY+x5vUc4f6h/zAXRRF13V1Xb93qQnANYVTb1TDnY6BpItcKu8GPtnJtSWr5uuI1HXt+j+ASJhvZOwJC7B3XXG7fY1kT054UpbKu4GPdnJtyeS81JN1I3P1J0oaTqS2BC5lbna/4u/VaqIzazqF9l3j+hDrUAIf4uVrS6JgXZZl0zSTiyaGjdu27fs+mgLPODdAURRVtXRpY9cVdR2XU1fV7DR/c4aMvipwG+QGOL+eOwiz4OUE6PyZpwE+RGYCTmP3X3+t2EuIzm27OnC7+gbgzMw9LhRZVdU93njVxZcA7ye9WnHBELurqrjdij//zL3jUKudP1FgWf60yA7AJ7vEOHdZlqtKRKqqMtQNEKQr1ISw2/dF00xUj4TS6lXjFcNYdeZqOMETrt0EeBWnZe4d67CVdAOfLErPYWg5nBdDKUgUu7tuReD+7bcfY9WTSb0sv35SqwI6wHu70DqUAJ8jnUJkl3Ymr1bMqcAOoTnd5tdffzSYTv+XXpQ53kbpH8DgtHHuzdNyH9oUwKFC/L3dvgabh5/bbZ8yjA2nw7Is+v6rVrvvJwbF5z4PpLenY+q+hgQILlFbsuE6yPFd1JYAL2H5AsSQvB+84nDt6TAdq05zcygRSQfUAch3iWsoi5W5WcgGXk7mAjTpdH6rrD07Tu7rke8OzVICMOnMzD2ee6TruswkPV77MGoE4JpWrfh4RLnc3OKUk6KT8apvImVugElnZu6oDrvrutvttpC8q6qKAnfaCMAFTU7nFwqpJyf9yB+ujjJu103c95mV4uYqAZh027xq/C7SDD2IRsHntmkNqpxn/Oc7tyPBxd1uP/03zJA9ls4rkv+SmpvCL1gepZ7cSzQqP9R8R49ishY8iuZODMAbuI1Of5sDz8lzBYb13icj9d0LKwVu4CVkTucXJdphju270vsW2QUhYQLvyNxYdVnGUwFW1dfCN21bNM3EZIIABOdfQ9m27YaabIEbeFFzRRqTs4Vk2lzREdWiTM7kPRxwetLtuqKui9ttej4WpX8Ag/Mzd1EUbds2TZOZvMuybJpG4AZe1BETL1XVQ7H7dvv6SaNzNCqfv5fJ4XyAj3WVdSjD9ZHFaB7AyclJ6ro2USDw0ibLOXZptu/vrDcZFYfkSFeabJr7A9hpqTfAh7tK5h4YwAY+0EJFxyrhDJq2Nr5qs22LP/4o/vzzfmuTo9ohdqfV20FZflV4AzB28rwlvDTzlkCmuRk/FoalH3lJ3Z0mJd0mkjlQHTUrbQNv6R3mLQH4BOmMH7//fmekeXMJSs40KekhDTcWa6KzLyYBMl0xc4dLKqMbVXIDryu9NvFuaUeYUeS4UBvNMKgCG+BQF8rcIWrPTcsdbg8XU6r5Bl5L5qWHkUNjt/MowDNdZa7A2+1W1/XddXC6rgtLxIvdwGvZNp1f1x0Vji3SDvBM52fuqqrq9dfnKzUBXk5VFf/+77O/Lcui7yei8NpwHGX0ydTetvtMkwJAppMz99zC7zm6rhO7gdfyn//5039/+aUoy6Jpir7/MZNJFLI3nCOjFcaiayLbdiJhO5sCHOrMzN22bRq4wzKTfWJyocqu6xSZAC8kOuf99dfXcutjj8ffNFJHi01G8lYBBmC7MzN3NDlJWZZ937dtOzl6XVVV27Z930fJO53hBODVPRiCq2pFC2YsAXiCMzN3tLp75oh127bj2L25NAXgjbVtVuwWuAGe4/xrKINVJSLqSYAXFeXguZPZLoMJd2O3wA3wNKdl7nEBSVqofdf4Lq6kBF7U5Gwh6UKSm7Vt0fdFWf7USPjvcNUmAE9woTVxAN5etPpjURS324/x5rYtmmb/Wfxka4DTnTbOvWFO7jlKTYAXko5bD5OKpEvEl6VZ/ADewSVqSwA+R+bVjYGlagDew5nXUA412RvmHhnusqEWHOBcmbG7aQxyA7yJMzP3uLxk1bC3MXLg1S3H7nCNo1MdwNu49X1/4u7Ha79nTtG94S4cZPy3OLcjwetKg7WzGsCl3EYXv28OPCdn7uLn3Fb8XSuSJum2bZum2baMDgeRuQGAt7dL5j5zrsBbNGNWURR/F2pP/irdcnkzoRwAgCu4yjqUAADwrmRuAAA4lswNAADHOrOe++iptXdc6hIAADY7M3O7wBEAgE+gtgQAAI4lcwMAwLHOrC15XFVVdV1bCh5gTnSCVNMHcIqXzNzRmpQyN0CkbYumKUZL93653YqyLOp6Ys15AI7zYpk7WigegFRVTaTtQdcVXVc0jdgN8DyvUc/dtm1VVbfbTeAGWLYcuAd1rc4E4HkuPc4d1ZAAsKxtswJ3UNdF3x95NAD87aKZO5Ro303bZVla+AZg0DTxLUP1dqgkiU6rVWW0G+AZrpW5Mwe2Q9R26SRAJDp9jou2Q7aOKk98jwjwHFfJ3DkXR4a14q1eCTApGogoy4mrJNs2jt3hFgAOdXLmzqwhCaRtgHyZlXcmMAF4gnMyd34NiQsodxGe8PF/TzwY4DkkaYDreHbmzqwhGcq1b7fbU45royHLRg+qLMv8XLth0vE+e66BycbDs7rqIIGX4/pIgOt4UubOGdh+lXLtV5nBsG3b5Ulduq6rqsrVqPBporOXyZ8AnuAZmfvuOO5rDbi+R+AOuq7ruq5pGrEb3kDbFuOvBrtuYqg7fa179QM8wZnXUJry7yCTgTt8jRBEnxlkbniy6AUX5s/eRVnGUwGOW06HC0YnBgAOdE7mfq2B7btCnN1l8HuXZ6b5eVWMtM0olIcik3f6i8BlTS7MHm7ZZf6Quo7bXz4zed0DPMf/OmWvIeS97thq+be+7/u+v1Rabdt2nP4nQ3xVVVEuv361DLy6UPix8FLbZbS7qiaWopyTvyUADzoncxd/VxLfbreXG2FtR84+lglRmJ6r6q6qqvz5S+VrPhx4D22bdaliVAqyTWbsNi03wDM9I3PXdV3O1wx2XVfX9SuG72uKRqwXvkyInu3GkBccJv/ltVfs7vvZWu2yLPpe4AZ4qmdk7hCm+75vmuZu+H7pmpPTRTF64dlOKS+Bg6Q13GX5FXybZiIZ7/VabNsfuwg/TVP0vRpugBM89RrKIU8vzB4Ybr/dbqvyIsHasWorfcLzleWP1FtVX9P5RZUnOy5nE3YBwLlOW/u9uLe4zMdmwWid9kemU1xbq6O8B44QnczSF1lVxXP8AfBmzpyfezzsXWSE7E9YNDFcWhrdEv5xxASLdV1/7GcbOMXcF3jpcjYAvJPT5i0ZC3OA9H2/XE/y4QXfObNorw3Qn/lMAgA82Znj3Kn8mpOh4PujaiHCKPg1VxTKjO8XPHK4INezADzBMwcfr5W5g8yak3D7m5UgRyP9k4+967q2be/2kidfhKpGBXJ0XdG2Exc1+s4J4PmemV6umLkHOcPe72F5zD6d5qWu677vn3FkwMOi6yPDNdLjkN22cQF3zgI6ALyQS2fuYBj2ftfwfXecPtS7RytKvtkAP7yxuv4pUndd0XU/VY+ks3dfc9g7PSonIYBML5C5BznTe7+rsFT7+FFf7RkwnzrMmZwKcOEVfMFB7nRZn+B2+2m6cYDXkpledgldr5S5B+Oak7OP5Xnatr2N5xK758mh3KA7LAhRz6DXAAAV1ElEQVQ13DkvyqsNcrdt0TRLRx4Wq6/rax02QI7M9LIqgM25xFyB24Tiio+KetGnsfSxG2yGy2rb+7ORXHDMOCqMmdR1xScNgABs8cKZm8eH+c3PDc8Uxownk3dZFk1zucCdf4YIo90AzJG539naLwE+6ksDOEVVFW1b9H1Rll8/TVP0/fQEgudKZ1MpiqIsi77/Ov5ImAYRgEkvWc9NpqZpjGTDNV0/nkZfpEV1L+HfUYlj01zukwPARRjnfmHpsPTagerxdZZqwYGxnCnDo1x+semUAC5E5n4ZbdvmTEUyjs7L26skATLNzaZiVBsgk8z9MqI1ceaGpdOlc+YajC7BFMEBAA4ic5/sdrvdXVGybds0Otczy2ZEW3ZdN9l4tK6QwhJggaIRgAe5hnKLnAsT023mgnXXdSH+DsE3WvQnLREpy3LhGKIVK+u6Lsuyrutwl9Bs1OZcggc+VrR25uTMKtEtPrwDzLn1fX/2MbyebcsRTT7VG5oqy/JuHUg0jL1s8/Qm473oSPBm0oUzo2lJ2ja+sPKCa/oAPG6c1jYHHpl7ixMzd07gDjJj9yPzCcrc8N7S89N4JDs9wTgNAG9pl8ytnvtk+YXUZVk2TZN/peNQmrLcoAm8gTmTC98MP3c3BmBgnPsqxtl3GDyOKry3ierCQ5tDefcjjHPD20srTCapKgHemNoSTiZzwye4G7sFbuC9qS0B4HBtWzTNdOlIWRZNI3AD3GeuQADuqKqvGUtC/i6Koq4tQgmwgswNQK4hfAOwitoSAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcKx/nH0ATKuqavh3Xdfj/27Qtm3TNOP/PtLa4K+//tqlHXhceI389ddfv/766149HLbRG7kOvfE6bn3fn30ML2zIsl3XjW8vy3Jbzw4NRq0NbW4I31VVTbb2yEEOfv311+/fv4d/60ic63a7Df/WGzmX3sh16I272OVplLlXW4jFg21xdiEfb2i5bdu6ru+29sgguszNdXhf4Tr0Rq5Db9zFLk+jeu7V7gbubXICd1EUXdflROScwB1aq+val00AAIdSz30JaeAuy3L4d/Srruvatl1I3pOBe6HBpmkerBcHAGCBzL2DEGc3D363bXs3BEehvK7rha82xpdLFlPlKFEoD2PnRrsBAA6itmSL8m993/d9/2BajSLy5Khz27bjgeri54lNoi3H6Xyy/ruqqminR1TLAAAQGOdebffx4CgiL4TpcQn/nChMz1V1V1VVluV418v1KgAAbGac+2SrYu5CTfbc7ctl3+P/RmEdAIC9yNzXsmoQfbIEZfzfqBxlmfISAICDyNwnW5V07ybytWPVq0I5AADbyNwXsjYB383ra0vP1XMDABxB5n4x+45M56ybAwDAg8xbcqbdx5XX1mQ/eAD/8z//M/z7119/zbnL77///sgeIYdvbLgOvZHr0BtT3759e9q+ZG62++///u/h39+/f8+5iys1eQLdjOvQG7kOvfFcaksuZN+Zv10fCQBwETI32/3rv/7r2YcAAPAC1JawXV3XTdP813/9V5Gdv9VzAwAXkVnPHaLOv/zLv/zzP//z5n3J3Bfycquv//HHH3/88cfZRwEAcHVqSy5k39XXXSoBAHARMveZdp8e23WTAAAXJHOfaUMlyb6j169VygIA8KJk7hd2d1R77eSD+05WCABAIHNfyN0x7LWZeN8CcQAAtpG5TxaNVS+n6ihDp+Xga0P5OOWrBQcAOIjMfbIoN68amZ6sxh5H5+WBc5UkAADPIXOfrKqqKCXPReG2bXOGpaMQv3CVZJTvRXAAgIPI3JczOdTdtm0UpufmGYxC9lyIr6pKYQkAwHPc+r4/+xhez+Tg8d0IuzCQfLvdolvKshy2j/Jx9NvJw0u3r+s6HHbbtk3TRBs0TWPeQACAg8jcW6QROcfCU50OYy9YDtxBGrsXCNwAAIeSubfYPXMX2bE7J3AHmbH7yYE7jLKP//tgg+ODH8by+UB6Atexb2/c/bTJNUV/6F3OY7t3Hr3xIT3rHfdUL9dVl2W56jiXZ0Epy7JpmoUjCfbqMAsPbe3jCg9trsHlx8Wb0RPYLHSetP9sOCONG9yxN+572uSCFvrM8Ife9rfevfPojY+Tua9o/DYwZN/NrUXvK+Hfk2f/ybefyLYDWG6zWPmGlHPFp1PAJ9ATWOtuxNncZ/btjbufNrmatVMX5P+td+88euNeZG5+yHnZr21z1YzjOa/Y/POUsPXe9AQ2OOhz2r69cffTJheU/yde9bfevfPojTtSz80POXXqqzrMZJH6wqo9d6vVJ6dkmWutcHno+9IT2CbnQpf8y2bm2nykN+5+2uSaJt9w765q9+TOozfu7OzQz4Wk3ePBeu7o7ukAT/oBemEQKN04/Ui9YwE6l6UnsNlk9fbdM9WC3XvjvqdNLivqgZPbrL3YYPfOozfuy/sQPwwv/vF7Rv5bRSR6Kc69DtNX7FyD0Yt/7jusR94+eQl6ApuVI+MbN3eVfXvj7qdNLqvIq4HO/1vv3nn0xt15arhj82sp860of8ucF//aLXlFegL7eiRz79sbdz9tclmbpw14WufRG3dn7XeOEpV5LZegjf87ecXGqmLcuyVxvC49gevYvTfue9rkyvI7T+aSebt3Hr1xdzI3h4hegasmRcrJRmuvcMrfmNeiJ3AdD/bGo0+bvKicE9funUdvPILMzSHWfsy9+3pe9Rp23fQb0xO4jn174+6nTT7H7p1HbzyCzM0zrI0+yx/r1762feZ+V3oC17F7b9z3tMnr2vCX3b3z6I27kLm5hMx6tXw+cxPoCVzHvr1x99MmL2GXv/vunUdvzCFzc4i1A4rLn4l9YibQE7iO3XvjvqdN3kPbtuOOUZbl5N99986jNx5B5gYAuKJo/Nhw8kuTuTnc7l/uuzCOQE/gOvbtjWqiKJLB47lB7sjunUdv3IvMDQBwLVVVRQUeRhlencx9dVVV3Xal6AoAriwN3BaaeQMyN6/HZ30CPYHr0BvZy2TgNl72BmRuDrf7pMg+7hPoCVzHvr3RXPIf6/HAvXvn0Rv3InNzCNPQcgQ9geuwqgC72xy4XTf5Ev5x9gFwx4d8X7n7/Nw+l78lPYHrOL03KjZ4M88sKdm9Wb0xh3FunmHtJwezbnEEPYHruNsbzz1t8mT7Bu7dO4/euAuZm2d4cqWjV/uH0BO4jt17o8sVPsfuI9y7dx69cRcyN4dY++4SrW2bbhDduNx+dHZQBPxO9ASuY9/euPtpk5ewS+DevfPojUeQuTnK+FW3PAKU89qO3p9WfeZWZ/ZO9ASuY/feuO9pk+tr2zb6Q2cuNpnavfPojfvrYdHmDhO9/ZRlObdl9Jk4c7OmaR7cLy9KT2BfUY9a1VX27Y27nza5svRD2iOnqd07j964O08Nd0RnhEfuO/lulP9ul7NlegqbewvkdekJ7GvHzP14b9z3tMll7Ru4g907j964r1ufPKd8rLZt07NA+rVXtEFd13NfhKVlamVZDtuH3a2qY7vdbtEtZVkO32pN7s53Xm9JT2CzyTPMI+Wt+/bG3U+bXNNkt8m871z/2b3z6I07Ozv0cyHbrntY/lC7qs27I5GryiV92n5jegKb5fecsYUGd++N+542uab1ffCHhWZ37zx6445kbn44InPnN5v5Ws18exOz3p6ewDY53Sa13ObuvXHf0yYXtK0f5vTG3TuP3rgXmZsfDsrc/b03pLIs175Wlw9VzPocegJrbTjLFXnfCe/bG3c/bXIp2/phZm/cvfPojbtQz83zDPXiofwrvD8tlIPfFe44bq0wadFH0hO4jn174+6nTT7H7p1Hb3yQzA0AAMeyJg4AABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjcAABxL5gYAgGPJ3AAAcCyZGwAAjiVzAwDAsWRuAAA4lswNAADHkrkBAOBYMjfw/m4jZx/LOd74GTjiob3f01VV1fCIqqo6+3BWG/9FXvH4oSiKf5x9AMBF5aSNsiyLoqjr2rsgACwwzg1s13Vd13Uhc7dte/bhwITxEO9e49ZHtAm8N5kb2MGQvM8+EAC4Ipkb2E3XdWI3AKRkbuC+siz7RFmWoZ57rOu6U45w2fiwzz6Wc3gGVvF0AbuTuYGN2rZt27Zpmuh2Q90AEJG5gYdUVRWNdl9zqBsATmSuQOBRbduunbphGCAPAX1I7XcnP4lG1oft0wbH13SOh95X7WLt4a09yPw2J1sYH9tyU9uegbV72WA4sGhfu1ySO9nC5I3R7haeriPabNs2amHchTKfirDNuGs9/hyO29yx2aNFhzc+4F1em4+/kPlQaY0mQP9zGetkPffcxssnlrQEPLK8r+jufd83TTPZ5ridnGObLE+fbLZpmuVnI/8gcx7yqsMrimLy8HKegcf3kmn52Vj7zCw8tJxdTO7oyW2mxs/P3Sdh+ckMf6lVDd79A+W8Cva18MRG0iMfH+qDr83ljZ/8nPByZG5gWv6bXD/1TpZKK78X3ufm3r2iHS20uTZzZx7bsN/8ZyPzgS+0mZlQg8k/1niD4/aSad8dLTy0zXt5cpvLT9HaiJmKkuLjDea0s6/M/S4H7nSDnNfmsLtVG0NKPTewg7s13G3b1nWd31rmxvlt7quu6/yvkh98LG3bPqFE/jl72cAElAuqqsr5q+W/TDIbDC71pwn1OeODD/F3+Qhznpmu68L14vkb5xwwH0jmBg43+XYVDQilA1Sr3s5DUURoKr90Ya6pdMg5bXBD3I9aznzI0Y4mD+/Bh/y0vUTGf7WFvWzOdpPP8+T4U35OOqLNbdJ8vNDBcpL03QYn/zSPPYh9hDNMFLhXPf/jR5o+zKZpxi+Q8caTI+ubHgQfYPJMATA+Uaz9Mnp5g/yvhnP2VWSUUeac9DYUamcWwCxsnL49Lx/83SNsmmZbPfcue8k0GbXTXWT2mbsPLScfX6HNufbnHvu2DjbXYLrl3J84/0+zo4U9pke+qvhk7oU/d5VIzsaPP17ekp4BTMt8D7v7hrf2HXp547v1mnfbvLvxspwwNPm+Ptfg8vOz13v5ciPXTAzRMzO32d1t3jJz53+OzYykmZ8nJze++3AeN3f8qwJ3v+YEMvlxJbNZF1MySW0JsFEooExLLKJboreuu1/4rvpCvCzLEytK879YX57Fb20NwIeI/rLXKR2+oPwONicqzFh+tnPmT3yC9PzTNM3akpK5g09vX3gao50qL2GSzA3c13XdLREVUAbpe1j0Xn53X2ddFplpQ5HuIzXQ0bNxULh5zl422Ld8/J2selndfU2ls4PfPYDT/zRp9XnTNGu77vIzEz1GF0fyIGviALtZe93SpPTtf+F99KB3wfEeHx94vpt4okWFqqoaHlf6ASbcctDCNIfuZVK0Rolh/iNc50PUXnYJ3MWuz0xZlnovy2RuYB+T73lRaAvj5Y83e5CQ/3Z/43zw+KP38vDv2+02DMLtkoyfs5exVdPSMbZhWHqVtS/SJ/8d08lVrr80JhQyN/CI8t5a0C9U17hqBvFnSiceDoZbhmT8SPZ6zl4GAveTLY/Cvu7fYpfv1uA5ZG7gvrd/Y1sI3Gnd6vMDylwgHoRfhUvKNg/4PWcvxWLgvsKzzQsJa9AY5OYlyNzA82y47uo5Y8/pijALsXLtN++7CJ95wiHNxdCu67que6Qa5wl7mVx4Ze4TnSz1fKdfHLlWXdfPrECDzWRu4HmuOVgevVtfeVB/OLDh0sM0Gdd13ScLplxnL7tc+sZaq74uuGz/D8KngvEjErt5CeYKBA50kXl8V1kOHBd5CGFuk7Zt++w15C+4l7vzQH9sYcnyA9/9ZfVyM8S3bRt1yLquL/5RAWRugB+O+GL9biR6cBqKaLq9gxyxl48NSU/+5LahRx10JDsSu3k5MjdwrGgI7dXfFDeMAj5h4PA5Ge7JSfEiXyk839oHfreD3f2wtLx27GWlnwNNGsiVydzAsV7r7Xw5vjx4beLcr9Yu1fk2llc72uuzyhGX4T7Y5vJDy3ngUT958JmM1off5bNxmE5kcNzyVdEpZVjRCa5G5gaOlb6d57wjDm/YRx7atLmdPjKl9Ny33ukcheky7DnPw4OB5jl7Sc09n/vWsUSPa5dOtaHNzIXEMw8vXW1qroNlPpNpP8z5c1dVNTeNT1heanDch22xm5fRA0wZnyjKsnykqcm327Ism6ZJtxxHk3S/UXDZ8FhyDi/ab3RUdw9gbuOo2XSz5ceb84zNHdXyb/faS46onXR3mc9e5kNLtwlNRaJjOKLNyW42bLPQx+Ye+N3+s0uDkxuH25efovQPPfdM5rjb1N1X8eRRLe9088YPPljelcwNTNvx/bKfid13PSdzp80uH9KOrUV2aSpNzHefgb32kmNVT8jJMcsPLfMBRo0f0WbOXVY98HMbHMtp6ujM3efFbpmbE6ktAZ4hfP972WLlzKqJbVN3Z6bMXb58vzv73i4e2UtUa7Rgx2qEdI6LU9rM7Dz5DzzzGPKP84gn6mkUmXBxMjfwJOE6qsx39PDl9TMnOenvDfJtPp40CqQt930/GQ7qus5/xpqm2XaEz9nLIKfIePclTkLf2zdQbmhzuZuFp3fVA7/7mlr79wp/nVX9IbPZ/GPYTOzmym79Y8uVAWyQLnA4fo8/cT7B6MCGo1p1SNHVlsNpdrLx/JaH6JA+aTtOkfacvUzuK+zoE6Z7m+wJDz7wcN99n8lHOsP42sonf36Ga5K5AXY2l7nhQ0QT8ngJQKG2BADY17jA43ULxGFfMjcAcBRVJRDI3ADAniav04AP94+zDwAAeCsKuCFlnBsAAI4lcwMAwLFkbgAAOJb5uQEA4FjGuQEA4FgyNwAAHEvmBgCAY8ncAABwLJkbAACOJXMDAMCxZG4AADiWzA0AAMeSuQEA4FgyNwAAHEvmBgCAY8ncAABwLJkbAACOJXMDAMCxZG4AADiWzA0AAMeSuQEA4FgyNwAAHEvmBgCAY8ncAABwrP8PS8q4kdFSF2AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='../plots/prograde-higher-res.png', width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 234x234 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of periapse raise manuever DV\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([3.25,3.25])\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.hist(peri_dv_new, bins=100, color='xkcd:periwinkle')\n",
    "plt.xlabel('Periapse raise '+r'$\\Delta V$'+', m/s', fontsize=10)\n",
    "plt.ylabel('Number of cases', fontsize=10)\n",
    "ax=plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=10)\n",
    "ax.tick_params(axis='y',labelsize=10)\n",
    "\n",
    "plt.savefig('../plots/girijaSaikia2020a-prm-histogram.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/girijaSaikia2020a-prm-histogram.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/girijaSaikia2020a-prm-histogram.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='../plots/girijaSaikia2020a-PRM-higher-res.png', width=600)"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}