OptInvest_R.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gekko import GEKKO\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "#%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set default setting for the figures.\n",
    "plt.style.use(\"default\") # style matplotlib\n",
    "plt.rc(\"axes\", labelsize = 14) # fontsize of the x and y labels\n",
    "plt.rc(\"axes\", titlesize = 15) # fontsize of the axes title\n",
    "plt.rc(\"xtick\", labelsize = 14) # fontsize of the tick labels\n",
    "plt.rc(\"ytick\", labelsize = 14) # fontsize of the tick labels\n",
    "plt.rc(\"legend\", fontsize = 14) # legend fontsize\n",
    "plt.rcParams['lines.linewidth'] = 3\n",
    "#sns.set(style=\"white\")\n",
    "#sns.set_palette('Dark2') #Set2 also nice for qualitative\n",
    "#paper_rc = {'lines.linewidth': 3, 'lines.markersize': 1}    \n",
    "#sns.set_context(\"paper\", rc = paper_rc) \n",
    "col_pal=sns.color_palette(\"colorblind\", 6).as_hex()\n",
    "sns.set_palette(col_pal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH_SAVE_FIGURES = \"./Figures/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# State and Control Variables Optimal Profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           12\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :           10\n",
      "   Residuals    :            9\n",
      " \n",
      " Number of state variables:           4200\n",
      " Number of total equations: -         4000\n",
      " Number of slack variables: -          200\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " solver            3  not supported\n",
      " using default solver: APOPT\n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.45387E+15  1.63750E+01\n",
      "    1  7.53407E+08  1.20690E+00\n",
      "    2  2.55941E+08  1.17734E+00\n",
      "    3  4.54187E+08  3.88392E-01\n",
      "    4  4.52005E+08  1.19363E-02\n",
      "    5  4.54352E+08  1.41806E+00\n",
      "    6  4.96506E+08  1.09537E+01\n",
      "    7  5.86495E+08  1.40687E+00\n",
      "    8  4.36422E+08  1.34314E+00\n",
      "    9  4.35197E+08  1.40196E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  4.42702E+08  1.99832E+00\n",
      "   11  4.28138E+08  9.27002E-01\n",
      "   12  4.27408E+08  1.26587E+00\n",
      "   13  4.26844E+08  1.18595E+00\n",
      "   14  4.26676E+08  1.37523E+00\n",
      "   15  4.26826E+08  1.15484E+00\n",
      "   16  4.26721E+08  1.35509E+00\n",
      "   17  4.26781E+08  1.03107E-02\n",
      "   18  4.26796E+08  2.85130E-02\n",
      "   19  4.26742E+08  3.86496E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  4.26726E+08  6.64124E-01\n",
      "   21  4.26723E+08  2.17034E-04\n",
      "   22  4.26723E+08  8.79822E-03\n",
      "   23  4.26723E+08  7.16346E-05\n",
      "   24  4.26723E+08  1.72733E-03\n",
      "   25  4.26723E+08  1.39347E-03\n",
      "   26  4.26723E+08  9.69230E-05\n",
      "   27  4.26723E+08  1.20324E-06\n",
      "   28  4.26723E+08  4.33125E-04\n",
      "   29  4.26723E+08  7.19143E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  4.26723E+08  6.74630E-05\n",
      "   31  4.26723E+08  6.97622E-07\n",
      "   32  4.26722E+08  1.44195E+00\n",
      "   33  4.26714E+08  6.57890E-03\n",
      "   34  4.26723E+08  6.57471E-03\n",
      "   35  4.26723E+08  5.23784E-06\n",
      "   36  4.26723E+08  1.20888E-05\n",
      "   37  4.26723E+08  3.64701E-04\n",
      "   38  4.26724E+08  2.65638E-02\n",
      "   39  4.26723E+08  2.03375E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  4.26722E+08  2.03084E-04\n",
      "   41  4.26723E+08  3.89089E-08\n",
      "   42  4.26722E+08  6.73548E-07\n",
      "   43  4.26723E+08  4.08180E-07\n",
      "   44  4.26723E+08  3.00041E-04\n",
      "   45  4.26723E+08  7.88696E-08\n",
      "   46  4.26785E+08  6.44804E-03\n",
      "   47  4.26723E+08  4.08122E-08\n",
      "   48  4.26723E+08  3.45578E-05\n",
      "   49  4.26723E+08  3.38426E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  4.26723E+08  1.35506E-02\n",
      "   51  4.26723E+08  2.47198E-08\n",
      "   52  4.26723E+08  4.27478E-02\n",
      "   53  4.26733E+08  2.34884E-08\n",
      "   54  4.26726E+08  3.20615E-02\n",
      "   55  4.26722E+08  1.62955E-04\n",
      "   56  4.26722E+08  1.15328E-05\n",
      "   57  4.26722E+08  2.50859E-05\n",
      "   58  4.26722E+08  9.20701E-05\n",
      "   59  4.26722E+08  1.98689E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  4.26723E+08  1.05242E-04\n",
      "   61  4.26791E+08  1.09123E-03\n",
      "   62  4.26722E+08  6.93352E-05\n",
      "   63  4.26722E+08  3.12743E-07\n",
      "   64  4.26723E+08  4.24467E-05\n",
      "   65  4.26744E+08  1.20228E-05\n",
      "   66  4.27107E+08  2.13054E+00\n",
      "   67  4.26723E+08  9.94937E-06\n",
      "   68  4.26891E+08  3.31116E-02\n",
      "   69  4.26806E+08  3.98286E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  4.26722E+08  4.02928E-03\n",
      "   71  4.26723E+08  7.44778E-10\n",
      "   72  4.26722E+08  2.16729E-04\n",
      "   73  4.26722E+08  2.73811E-07\n",
      "   74  4.26722E+08  6.75666E-05\n",
      "   75  4.26722E+08  7.44778E-10\n",
      "   76  4.26723E+08  8.84625E-06\n",
      "   77  4.26723E+08  7.37404E-10\n",
      "   78  4.26732E+08  1.78040E-03\n",
      "   79  4.26723E+08  7.46883E-10\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  4.26726E+08  2.25272E-04\n",
      "   81  4.26735E+08  5.95997E-06\n",
      "   82  4.26731E+08  1.33262E-02\n",
      "   83  4.26722E+08  7.20606E-04\n",
      "   84  4.26722E+08  1.40353E-05\n",
      "   85  4.26722E+08  1.65889E-06\n",
      "   86  4.26723E+08  1.42421E-04\n",
      "   87  4.26722E+08  2.00741E-05\n",
      "   88  4.26723E+08  2.00934E-05\n",
      "   89  4.26723E+08  7.23791E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  4.26723E+08  5.93911E-05\n",
      "   91  4.26723E+08  9.56002E-06\n",
      "   92  4.26724E+08  5.44627E-04\n",
      "   93  4.26724E+08  2.19381E-05\n",
      "   94  4.26723E+08  1.64105E-05\n",
      "   95  4.26723E+08  4.14627E-06\n",
      "   96  4.26723E+08  1.55347E-05\n",
      "   97  4.26729E+08  8.62231E-09\n",
      "   98  4.26727E+08  5.20729E-04\n",
      "   99  4.27403E+08  8.99838E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  100  4.26808E+08  2.94920E-02\n",
      "  101  4.26730E+08  3.66799E-04\n",
      "  102  4.26727E+08  1.56308E-03\n",
      "  103  4.26725E+08  1.85603E-04\n",
      "  104  4.26722E+08  1.85435E-04\n",
      "  105  4.26722E+08  6.50282E-07\n",
      "  106  4.26723E+08  4.91966E-04\n",
      "  107  4.26723E+08  2.62358E-04\n",
      "  108  4.26722E+08  8.64911E-06\n",
      "  109  4.26722E+08  2.64383E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  110  4.26722E+08  7.44604E-06\n",
      "  111  4.26722E+08  2.65685E-08\n",
      "  112  4.26723E+08  4.97967E-04\n",
      "  113  4.26722E+08  5.10401E-10\n",
      "  114  4.26722E+08  5.63925E-05\n",
      "  115  4.26722E+08  3.72584E-09\n",
      "  116  4.26723E+08  4.65516E-04\n",
      "  117  4.26723E+08  3.01409E-09\n",
      "  118  4.26727E+08  3.97847E-03\n",
      "  119  4.26723E+08  3.02864E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  120  4.26722E+08  1.52546E-05\n",
      "  121  4.26722E+08  1.37278E-06\n",
      "  122  4.26722E+08  2.41364E-08\n",
      "  123  4.26723E+08  1.99845E-08\n",
      "  124  4.26749E+08  2.50826E-03\n",
      "  125  4.66932E+08  3.42588E-03\n",
      "  126  4.68812E+08  1.71814E+00\n",
      "  127  4.44655E+08  8.93921E-01\n",
      "  128  4.77383E+08  1.45962E+00\n",
      "  129  4.26723E+08  1.68293E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  130  4.26798E+08  6.26541E-01\n",
      "  131  4.26870E+08  1.51444E-08\n",
      "  132  4.26995E+08  1.55537E+00\n",
      "  133  4.26737E+08  1.51414E-08\n",
      "  134  4.26755E+08  6.76443E-02\n",
      "  135  4.26814E+08  2.33241E-04\n",
      "  136  4.26802E+08  1.94359E-02\n",
      "  137  4.26722E+08  8.28779E-04\n",
      "  138  4.26723E+08  1.03746E-05\n",
      "  139  4.26723E+08  1.19413E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  140  4.26723E+08  2.51990E-06\n",
      "  141  4.26723E+08  1.89586E-07\n",
      "  142  4.26722E+08  2.99749E-06\n",
      "  143  4.26722E+08  1.19397E-09\n",
      "  144  4.26722E+08  1.12214E-05\n",
      "  145  4.26722E+08  2.57186E-09\n",
      "  146  4.26723E+08  3.42376E-05\n",
      "  147  4.26723E+08  2.48317E-09\n",
      "  148  4.26727E+08  3.40410E-03\n",
      "  149  4.26724E+08  3.65250E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  150  4.26722E+08  3.67610E-05\n",
      "  151  4.26723E+08  3.85511E-06\n",
      "  152  4.26723E+08  1.71101E-04\n",
      "  153  4.26723E+08  3.31590E-06\n",
      "  154  4.26722E+08  5.28863E-06\n",
      "  155  4.26724E+08  6.77340E-11\n",
      "  156  4.26732E+08  4.04372E-03\n",
      "  157  4.26722E+08  6.77333E-11\n",
      "  158  4.26722E+08  6.77333E-11\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  IPOPT (v3.12)\n",
      " Solution time  :    18.6563000000024      sec\n",
      " Objective      :    426722497.299648     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2133612.4865 Horizon: 80\n",
      "Max [CO2]: 448.1736406\n",
      "Average annual investment: 244.68752871249998\n",
      "Average share: 0.77950775545\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           12\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :           10\n",
      "   Residuals    :            9\n",
      " \n",
      " Number of state variables:           4200\n",
      " Number of total equations: -         4000\n",
      " Number of slack variables: -          200\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " solver            3  not supported\n",
      " using default solver: APOPT\n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  1.29743E+15  1.63750E+01\n",
      "    1  5.72966E+08  1.36031E+00\n",
      "    2  4.87092E+08  1.11414E+00\n",
      "    3  4.13749E+08  1.24179E+00\n",
      "    4  4.54593E+08  4.93562E+00\n",
      "    5  4.53678E+08  1.02905E-02\n",
      "    6  4.43215E+08  9.36838E-01\n",
      "    7  4.07686E+08  1.26705E+00\n",
      "    8  4.95044E+08  1.25077E+00\n",
      "    9  4.50924E+08  1.02393E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  4.50161E+08  1.50463E+00\n",
      "   11  4.87729E+08  1.32410E+00\n",
      "   12  4.29009E+08  1.59283E+00\n",
      "   13  4.31959E+08  1.28335E+00\n",
      "   14  4.30298E+08  1.53628E+00\n",
      "   15  4.28549E+08  1.40178E+00\n",
      "   16  4.27819E+08  1.33316E+00\n",
      "   17  4.28494E+08  1.15373E+00\n",
      "   18  4.28011E+08  1.24584E+00\n",
      "   19  4.27500E+08  8.83906E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  4.27088E+08  1.34067E+00\n",
      "   21  4.26799E+08  1.34215E+00\n",
      "   22  4.26768E+08  2.34749E+00\n",
      "   23  4.26733E+08  1.99645E-02\n",
      "   24  4.26725E+08  9.89124E-03\n",
      "   25  4.26725E+08  3.35412E-04\n",
      "   26  4.26727E+08  1.36796E+00\n",
      "   27  4.26725E+08  2.88576E-04\n",
      "   28  4.26723E+08  7.20106E-02\n",
      "   29  4.26722E+08  2.49302E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  4.26724E+08  8.92823E-01\n",
      "   31  4.26724E+08  2.61305E-05\n",
      "   32  4.26723E+08  3.52621E-04\n",
      "   33  4.26723E+08  5.21592E-08\n",
      "   34  4.26723E+08  5.38120E-02\n",
      "   35  4.26724E+08  1.18537E-05\n",
      "   36  4.26724E+08  1.28398E-01\n",
      "   37  4.26723E+08  5.31997E-08\n",
      "   38  4.26722E+08  1.45361E-02\n",
      "   39  4.26722E+08  2.09149E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  4.26723E+08  3.49527E-03\n",
      "   41  4.26723E+08  2.56345E-06\n",
      "   42  4.26723E+08  5.75582E-04\n",
      "   43  4.26722E+08  2.25382E-08\n",
      "   44  4.26720E+08  2.04735E-02\n",
      "   45  4.26723E+08  1.17210E-04\n",
      "   46  4.26723E+08  7.39568E-06\n",
      "   47  4.26723E+08  5.78100E-06\n",
      "   48  4.26723E+08  1.00063E-04\n",
      "   49  4.26723E+08  1.34303E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  4.26724E+08  4.50293E-03\n",
      "   51  4.26761E+08  1.08568E-03\n",
      "   52  4.26723E+08  2.68204E-03\n",
      "   53  4.26725E+08  1.21662E-08\n",
      "   54  4.26744E+08  1.36779E+00\n",
      "   55  4.26723E+08  1.21477E-08\n",
      "   56  4.26723E+08  9.50865E-03\n",
      "   57  4.27262E+08  5.33106E-04\n",
      "   58  4.26871E+08  5.61603E-01\n",
      "   59  4.26817E+08  2.31858E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  4.26722E+08  2.31674E-03\n",
      "   61  4.26722E+08  8.95426E-07\n",
      "   62  4.26722E+08  2.32444E-04\n",
      "   63  4.26723E+08  1.96937E-07\n",
      "   64  4.26723E+08  1.37471E-08\n",
      "   65  4.26723E+08  1.35357E-08\n",
      "   66  4.26723E+08  9.49258E-03\n",
      "   67  4.26723E+08  3.99200E-07\n",
      "   68  4.26723E+08  4.30780E-07\n",
      "   69  4.26723E+08  1.49928E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  4.26723E+08  4.07589E-06\n",
      "   71  4.26732E+08  1.14579E-08\n",
      "   72  4.28174E+08  5.14589E-02\n",
      "   73  4.29222E+08  2.83008E-04\n",
      "   74  5.58151E+08  1.49695E+00\n",
      "   75  4.30640E+08  1.14549E-08\n",
      "   76  5.82727E+08  9.70788E-01\n",
      "   77  5.86072E+08  1.28461E+00\n",
      "   78  4.68559E+08  5.43464E+00\n",
      "   79  4.33402E+08  1.27999E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  4.34698E+08  1.65295E+00\n",
      "   81  4.26722E+08  1.14549E-08\n",
      "   82  4.26723E+08  1.90071E-04\n",
      "   83  4.26791E+08  8.93059E-10\n",
      "   84  4.38904E+08  5.51253E-03\n",
      "   85  4.26722E+08  8.93302E-10\n",
      "   86  4.26722E+08  8.93302E-10\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  IPOPT (v3.12)\n",
      " Solution time  :    12.7148999999918      sec\n",
      " Objective      :    426722497.314858     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2133612.4866 Horizon: 80\n",
      "Max [CO2]: 448.17366056\n",
      "Average annual investment: 244.6875093375\n",
      "Average share: 0.747553628275\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           12\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :           10\n",
      "   Residuals    :            9\n",
      " \n",
      " Number of state variables:           4200\n",
      " Number of total equations: -         4000\n",
      " Number of slack variables: -          200\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " solver            3  not supported\n",
      " using default solver: APOPT\n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  1.02179E+14  1.63750E+01\n",
      "    1  4.54674E+08  3.44828E-01\n",
      "    2  4.52487E+08  2.24833E+00\n",
      "    3  4.52547E+08  7.13764E-03\n",
      "    4  4.64611E+08  1.59571E+00\n",
      "    5  4.50484E+08  5.47063E-02\n",
      "    6  4.46187E+08  2.69945E+00\n",
      "    7  4.45943E+08  5.63041E-03\n",
      "    8  4.45944E+08  4.85659E+00\n",
      "    9  4.44765E+08  3.03352E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  4.44662E+08  9.87130E+00\n",
      "   11  4.44363E+08  2.90258E-02\n",
      "   12  4.44341E+08  3.12151E+01\n",
      "   13  4.44319E+08  4.65404E-02\n",
      "   14  4.44311E+08  2.79377E+01\n",
      "   15  4.44266E+08  6.24347E-02\n",
      "   16  4.44241E+08  3.94254E+00\n",
      "   17  4.44294E+08  1.42372E-02\n",
      "   18  4.44298E+08  4.99232E+00\n",
      "   19  4.44354E+08  3.58232E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  4.44207E+08  1.55286E-01\n",
      "   21  4.44168E+08  1.39897E+00\n",
      "   22  4.44204E+08  6.18567E+01\n",
      "   23  4.44201E+08  7.78600E-01\n",
      "   24  4.44185E+08  2.42113E+00\n",
      "   25  4.44676E+08  1.06793E-02\n",
      "   26  4.44327E+08  2.50646E+00\n",
      "   27  4.44387E+08  1.05728E+00\n",
      "   28  4.44193E+08  1.18655E-01\n",
      "   29  4.44219E+08  4.19490E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  4.44175E+08  1.25323E+00\n",
      "   31  4.44166E+08  3.33430E-03\n",
      "   32  4.44173E+08  1.22707E+00\n",
      "   33  4.44168E+08  5.11972E-02\n",
      "   34  4.44171E+08  1.53154E-01\n",
      "   35  4.44168E+08  5.12023E-02\n",
      "   36  4.44168E+08  1.49809E-03\n",
      "   37  4.44168E+08  2.49741E-04\n",
      "   38  4.44168E+08  3.92090E-05\n",
      "   39  4.44168E+08  3.71471E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  4.44168E+08  2.09393E-04\n",
      "   41  4.44168E+08  8.22481E-06\n",
      "   42  4.44168E+08  1.93494E-03\n",
      "   43  4.44168E+08  1.98206E-06\n",
      "   44  4.44169E+08  8.38099E-03\n",
      "   45  4.44168E+08  4.49053E-07\n",
      "   46  4.44168E+08  2.34836E-04\n",
      "   47  4.44168E+08  8.06685E-07\n",
      "   48  4.44168E+08  6.37140E-04\n",
      "   49  4.44168E+08  4.92224E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  4.44168E+08  2.10643E-03\n",
      "   51  4.44168E+08  2.86097E-07\n",
      "   52  4.44168E+08  3.14719E-03\n",
      "   53  4.44168E+08  2.47368E-07\n",
      "   54  4.44168E+08  7.10676E-08\n",
      "   55  4.44168E+08  7.10676E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  IPOPT (v3.12)\n",
      " Solution time  :    9.19299999999930      sec\n",
      " Objective      :    444167872.421560     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2220839.3621 Horizon: 80\n",
      "Max [CO2]: 520.41634729\n",
      "Average annual investment: 87.58694389875\n",
      "Average share: 0.8217110277499999\n"
     ]
    }
   ],
   "source": [
    "# choose the values for the parameters ypu want to iterate on (either tau, SCC, Phi, rho )\n",
    "\n",
    "values=[700, 400, 100]\n",
    "\n",
    "dfs={}\n",
    "for i in range(len(values)):\n",
    "    # create GEKKO model\n",
    "    m = GEKKO(remote=False)\n",
    "    # time points\n",
    "    n=101\n",
    "    tf=80\n",
    "    # constants\n",
    "    E0,Co2o,ccl,cof,kf,kns,cf,c1,c2= 556,280,0.03,0.03,0.076,0.015,0.129,.1,.3\n",
    "    ncl,nof= 1./(19.46), 1./290.\n",
    "    phi=(1.-1.)*E0\n",
    "    Imax=1000.\n",
    "    kc=80.\n",
    "    rho=0.05\n",
    "    #assign valus to parameter\n",
    "    param=values[i]\n",
    "    #decide the parameter to iterate on \n",
    "    Imax=param\n",
    "    #phi=phi_r/100.*E0\n",
    "    m.time = np.linspace(0,tf,n)\n",
    "    # controls\n",
    "    u = m.MV(value=1,lb=0,ub=1)\n",
    "    I = m.MV(value=0,lb=0,ub=Imax)\n",
    "    u.STATUS = 1\n",
    "    u.DCOST = 0\n",
    "    I.STATUS = 1\n",
    "    I.DCOST = 0\n",
    "    #state variables\n",
    "    x1 = m.Var(value=419) # Co2\n",
    "    x2 = m.Var(value=32) # Kcl\n",
    "    x3 = m.Var(value=0) # Kof\n",
    "    t = m.Param(value=m.time)\n",
    "    e = m.Intermediate(m.exp(-rho*t))\n",
    "    # ode\n",
    "    m.Equation(x1.dt() == (kf*(E0 - x2) - x3)*cf - kns*(x1-Co2o))\n",
    "    m.Equation(x2.dt() == ncl * u * I -  ccl*x2)\n",
    "    m.Equation(x3.dt() == nof*(1-u)*I -  cof*x3)\n",
    "    m.Equation(E0-x2-phi>=0)\n",
    "    \n",
    "    \n",
    "    #objective\n",
    "    J = m.Var(value=0) # objective (profit)\n",
    "    D = m.Var(value=0) # objective (damage)\n",
    "    It = m.Var(value=0) # cumulative investment\n",
    "    ut = m.Var(value=0) # cumulative share\n",
    "    \n",
    "    Jf = m.FV() # final objective\n",
    "    Jf.STATUS = 1\n",
    "    m.Connection(Jf,J,pos2='end')\n",
    "    m.Equation(J.dt() == e* \n",
    "               ( kc/cf * (x1-Co2o) + u*I*(1 + c1*u*I) + (1-u)*I*(1+c2*(1-u)*I) ))\n",
    "    m.Equation(D.dt() == e* \n",
    "               ( kc/cf * (x1-Co2o)))\n",
    "    m.Equation(It.dt() == I)\n",
    "    m.Equation(ut.dt() == u)\n",
    "    m.Obj(Jf) # minimize cost\n",
    "    m.options.IMODE = 6  # optimal control\n",
    "    m.options.NODES = 3  # collocation nodes\n",
    "    m.options.SOLVER = 3 # solver (3 IPOPT)\n",
    "    m.options.MAX_ITER=2000\n",
    "    m.solve(disp=True) # Solve, True to see the output\n",
    "    \n",
    "    print('Optimal Cost: ' + str(Jf.value[0]), 'Horizon: ' + str(tf))\n",
    "    print('Max [CO2]: ' + str(np.max(np.array(x1.value))))\n",
    "    print('Average annual investment: ' + str(It.value[-1]/tf))\n",
    "    print('Average share: ' + str(ut.value[-1]/tf))\n",
    "\n",
    "    \n",
    "    df = pd.DataFrame(\n",
    "        {   '$\\tau$': m.time,\n",
    "            '[CO$_2$]': np.array(x1.value),\n",
    "            'EMI': kf*(E0 - np.array(x2.value) - np.array(x3.value)),\n",
    "            'E$_{CL}$': np.array(x2.value),\n",
    "            'NET': np.array(x3.value),\n",
    "            '$\\mathcal{C}$': np.array(J.value),\n",
    "            'Damage':np.array(D.value),\n",
    "            'Cumulative Investment':np.array(It.value),\n",
    "            'I': np.array(I.value),\n",
    "            'I$_{CL}$' : np.array(I.value)*np.array(u.value),\n",
    "            '$\\mu$': np.array(u.value)\n",
    "         \n",
    "        }\n",
    "    )\n",
    "    dfs[values[i]]=df\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Param</th>\n",
       "      <th colspan=\"10\" halign=\"left\">700</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">100</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th>$\\tau$</th>\n",
       "      <th>[CO$_2$]</th>\n",
       "      <th>EMI</th>\n",
       "      <th>E$_{CL}$</th>\n",
       "      <th>NET</th>\n",
       "      <th>$\\mathcal{C}$</th>\n",
       "      <th>Damage</th>\n",
       "      <th>Cumulative Investment</th>\n",
       "      <th>I</th>\n",
       "      <th>I$_{CL}$</th>\n",
       "      <th>...</th>\n",
       "      <th>[CO$_2$]</th>\n",
       "      <th>EMI</th>\n",
       "      <th>E$_{CL}$</th>\n",
       "      <th>NET</th>\n",
       "      <th>$\\mathcal{C}$</th>\n",
       "      <th>Damage</th>\n",
       "      <th>Cumulative Investment</th>\n",
       "      <th>I</th>\n",
       "      <th>I$_{CL}$</th>\n",
       "      <th>$\\mu$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8</td>\n",
       "      <td>421.458683</td>\n",
       "      <td>39.876262</td>\n",
       "      <td>31.310970</td>\n",
       "      <td>0.001376</td>\n",
       "      <td>69576.242534</td>\n",
       "      <td>69576.242534</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>372.765342</td>\n",
       "      <td>288.153139</td>\n",
       "      <td>...</td>\n",
       "      <td>421.437909</td>\n",
       "      <td>39.880113</td>\n",
       "      <td>31.261408</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>69568.419288</td>\n",
       "      <td>69568.419288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>83.576775</td>\n",
       "      <td>0.835768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.6</td>\n",
       "      <td>423.806734</td>\n",
       "      <td>39.025618</td>\n",
       "      <td>42.273057</td>\n",
       "      <td>0.231970</td>\n",
       "      <td>145892.712200</td>\n",
       "      <td>137573.231880</td>\n",
       "      <td>298.212273</td>\n",
       "      <td>372.309010</td>\n",
       "      <td>287.800567</td>\n",
       "      <td>...</td>\n",
       "      <td>423.829037</td>\n",
       "      <td>39.675043</td>\n",
       "      <td>33.914937</td>\n",
       "      <td>0.045026</td>\n",
       "      <td>138236.780730</td>\n",
       "      <td>137560.827370</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>83.562351</td>\n",
       "      <td>0.835624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.4</td>\n",
       "      <td>426.019033</td>\n",
       "      <td>38.196256</td>\n",
       "      <td>52.960867</td>\n",
       "      <td>0.456814</td>\n",
       "      <td>220241.575430</td>\n",
       "      <td>203948.047180</td>\n",
       "      <td>596.059482</td>\n",
       "      <td>371.831398</td>\n",
       "      <td>287.431548</td>\n",
       "      <td>...</td>\n",
       "      <td>426.166667</td>\n",
       "      <td>39.474877</td>\n",
       "      <td>36.504958</td>\n",
       "      <td>0.088763</td>\n",
       "      <td>205295.312070</td>\n",
       "      <td>203969.982900</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>83.559031</td>\n",
       "      <td>0.835590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.2</td>\n",
       "      <td>428.099898</td>\n",
       "      <td>37.387724</td>\n",
       "      <td>63.380235</td>\n",
       "      <td>0.676029</td>\n",
       "      <td>292600.184640</td>\n",
       "      <td>268664.580040</td>\n",
       "      <td>893.524600</td>\n",
       "      <td>371.329241</td>\n",
       "      <td>287.043621</td>\n",
       "      <td>...</td>\n",
       "      <td>428.452029</td>\n",
       "      <td>39.279468</td>\n",
       "      <td>39.033422</td>\n",
       "      <td>0.131471</td>\n",
       "      <td>270741.468530</td>\n",
       "      <td>268792.242060</td>\n",
       "      <td>240.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>83.546287</td>\n",
       "      <td>0.835463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>430.053539</td>\n",
       "      <td>36.599586</td>\n",
       "      <td>73.536759</td>\n",
       "      <td>0.889735</td>\n",
       "      <td>362954.710510</td>\n",
       "      <td>331696.155080</td>\n",
       "      <td>1190.587993</td>\n",
       "      <td>370.805533</td>\n",
       "      <td>286.638972</td>\n",
       "      <td>...</td>\n",
       "      <td>430.686326</td>\n",
       "      <td>39.088729</td>\n",
       "      <td>41.501410</td>\n",
       "      <td>0.173202</td>\n",
       "      <td>334576.708460</td>\n",
       "      <td>332028.107490</td>\n",
       "      <td>320.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>83.534391</td>\n",
       "      <td>0.835344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Param       700                                                             \\\n",
       "Variable $\\tau$    [CO$_2$]        EMI   E$_{CL}$       NET  $\\mathcal{C}$   \n",
       "1           0.8  421.458683  39.876262  31.310970  0.001376   69576.242534   \n",
       "2           1.6  423.806734  39.025618  42.273057  0.231970  145892.712200   \n",
       "3           2.4  426.019033  38.196256  52.960867  0.456814  220241.575430   \n",
       "4           3.2  428.099898  37.387724  63.380235  0.676029  292600.184640   \n",
       "5           4.0  430.053539  36.599586  73.536759  0.889735  362954.710510   \n",
       "\n",
       "Param                                                                  ...  \\\n",
       "Variable         Damage Cumulative Investment           I    I$_{CL}$  ...   \n",
       "1          69576.242534              0.000000  372.765342  288.153139  ...   \n",
       "2         137573.231880            298.212273  372.309010  287.800567  ...   \n",
       "3         203948.047180            596.059482  371.831398  287.431548  ...   \n",
       "4         268664.580040            893.524600  371.329241  287.043621  ...   \n",
       "5         331696.155080           1190.587993  370.805533  286.638972  ...   \n",
       "\n",
       "Param            100                                                 \\\n",
       "Variable    [CO$_2$]        EMI   E$_{CL}$       NET  $\\mathcal{C}$   \n",
       "1         421.437909  39.880113  31.261408  0.000267   69568.419288   \n",
       "2         423.829037  39.675043  33.914937  0.045026  138236.780730   \n",
       "3         426.166667  39.474877  36.504958  0.088763  205295.312070   \n",
       "4         428.452029  39.279468  39.033422  0.131471  270741.468530   \n",
       "5         430.686326  39.088729  41.501410  0.173202  334576.708460   \n",
       "\n",
       "Param                                                                      \n",
       "Variable         Damage Cumulative Investment      I   I$_{CL}$     $\\mu$  \n",
       "1          69568.419288                   0.0  100.0  83.576775  0.835768  \n",
       "2         137560.827370                  80.0  100.0  83.562351  0.835624  \n",
       "3         203969.982900                 160.0  100.0  83.559031  0.835590  \n",
       "4         268792.242060                 240.0  100.0  83.546287  0.835463  \n",
       "5         332028.107490                 320.0  100.0  83.534391  0.835344  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel=pd.concat(dfs,axis=1)\n",
    "panel=panel.drop(0)\n",
    "panel.columns.names=['Param', 'Variable']\n",
    "panel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#panel at 6 variables\n",
    "fig, axs = plt.subplots(nrows = 2, ncols = 3, figsize = (10, 5), sharex = True)\n",
    "plt.subplots_adjust(wspace = .35, hspace = .2)\n",
    "\n",
    "axs=axs.ravel()\n",
    "\n",
    "for i, value in enumerate(values):\n",
    "    j=i\n",
    "    i=0\n",
    "    \n",
    "    # Figure 1.\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"EMI\", ax=axs[i], legend=False)\n",
    "    axs[i].set(xlabel=\"Time (yr)\", ylabel=\"[CO$_2$] (ppm)\")\n",
    "    #axs[i].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "    # Figure 2.\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"E$_{CL}$\", ax=axs[i+1], legend=False)\n",
    "    axs[i+1].set(xlabel=\"Time (yr)\", ylabel=\"$E_{CL}$ (EJ)\")\n",
    "    #axs[i+1].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "    # Figure 3.\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"NET\", ax=axs[i+2], legend=False)\n",
    "    axs[i+2].set(xlabel=\"Time (yr)\", ylabel=\"NET (GtCO$_2$)\")\n",
    "    #axs[i+2].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "    # Figure 4.\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"$\\mathcal{C}$\", ax=axs[i+3], legend=False)\n",
    "    #panel[value].plot(x=\"Year\", y=\"Damage\", ax=axs[i+3], legend=False,  linestyle='dashed')\n",
    "    axs[i+3].set(xlabel=\"Time (yr)\", ylabel=\"$\\mathcal{C}$ (\\$ billion)\")\n",
    "    axs[i+3].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "    # Figure 5.\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"I\", ax=axs[i+4], c=col_pal[j], legend=False)\n",
    "    #panel[value].plot(x=\"Year\", y=\"I$_{CL}$\", ax=axs[i+4], c=sns.color_palette('Dark2')[j], linestyle='dashed', legend=False)\n",
    "    axs[i+4].set(xlabel=\"Time (yr)\", ylabel=\"$I$ ; $I_{CL}$ (\\$ billion)\")\n",
    "    axs[i+4].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "    # Figure 6.\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"$\\mu$\", ax=axs[i+5], legend=False)\n",
    "    axs[i+5].set(xlabel=\"Time (yr)\", ylabel=\"$\\mu$\")\n",
    "    axs[i+5].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "    \n",
    "    \n",
    "plt.legend(values, bbox_to_anchor = (0., -0.3), ncol = len(values), title=\"xx\");\n",
    "    \n",
    "#fig.savefig(PATH_SAVE_FIGURES + \"FIG3.png\", bbox_inches = \"tight\", dpi = 300) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aYHCzlrDTM1JovZfJ2o8//oivvvoKVlZWsLOzw+PHj5Gfnw8tLS0EBgaie/fuNfmTKkWTRxO1w7IsnOybw8m+OYBZAIC8/Bw8engeGU+vQCv7Jpy5iLdOhm3IFqJ12QUg5gJKYhbgPNxRYN4Xzq1HwsuzK1V2JAQvhhoHBgZi8eLFOHz4MGJjY2FpaYnp06djxYoVCAwMVHWIhBBCGonCciEuPYvFudRonEuJRnS+4rUGAMBZ3xQDmzXHwGbN0cPKFdp8jRrHsmDBAjRr1gzr1q1DeHg4NDQ0MGzYMHz33Xdo3bp1jbdbW/SkTMnormHdKi4pRGjoBaTFnId29nU4cxHQYMQKr/+cM0OKQS9YthyJ9u2G0GTWhNQzOkc2LnQ8CSHycBIOodnPcPa/IYnBz+NRzin+fU2bpwF/KxcMaOaBfrYecDcwf+uwxIZK0fMkJWVKRheo+pWXn4OQBwHIiguEaV4w7JlExdfl9PBU1x/GzcegU6eR0BLo1GGkhBCAzpGNDR1PQshLmaVFOJcajcDkKJxNjUZ6SfXm+2plZIV+ti+SsG6WTtCqxdOwhoSSMhWhC5RqJabEIOzevyhPOgtn4W3osYqNUS7gdBGn0wMmLceiU8eREAjoCRohdYHOkY0LHU9Cmi5OwuF+VgrOJEchIDkKtzISIYHiaYWZQBe9bdz+S8TcYaPTOCtpU1KmInSBajiEwlLcvR+A9MhjsMi9BFv2mULr5XL6eKrfD818JsLXpz94PNlyqoSQmqFzZONCx5OQpqWgvBRnU6JxOjkSAclR1XoaxmdYdLF0RF+bF0/DfExtwDKN/z1/SspUhC5QDRPHcXgccw+P7x+AXnrAi5L6CnjGWSLdfAS8un4IVyevOo6SkMaPzpGNCx1PQhq/hMJsnEiMwImkCFxJi6vWu2Gu+mboZ+uOfrYe6GHtAn0NrTqMtGGipExF6AKlHhKSHiP01l4Ikk/AVRIOlnn7fwbRaAWxywR06zkd+gqWXyWESKNzZONCx5OQxkcikSAkOxXHEsNwPDEcodmpCq+rw9dAT2tXDLBtjn62HnA1MKvDSNUDJWUqQhco9ZOcGof713dBK/kY3BDx1v5FnBai9frDqcNHaNPan0rsE1INdI5sXOh4EtI4iDgxgtKf4t+EMBxLDENiUa7C67obmP9Xrr5FoyrQoSyUlKkIXaDU29P4CIRe3wrj9GMKVXJ8InFFqfNkdO89k56eEaIAOkc2LnQ8CVFfZWIRLjyLwZH4RzieGI5MYZFC62mwPHS3dMYQu5YYZNeCnoa9BSVlKkIXqMaB4zg8DL+GJ7e3wTHvFIzZ/Cr7F3A6iDUcDk//ufBw9amnKAlRP3SObFzoeBKiXoRiEc6mPMah+Ic4kRSOvLJShdYzEehgULMWGGrfEv1sPGCg2fTeDaspRc+T/HqMiRC1wbIsvL384e3lD6GwFMFB+1AUsQMtRLfAYziZ/vpsMXwK9gEn9+EkrxOM2n6Gzn4jaWgjIYQQQlSqTCzC2dRoHHgaghOJEcgvVywRc9Y3xTB7Twy390RnC0fwWapGXZfoSZmS0V3Dxi05NQ53L2+AZdp+WLEZVfZNkDig2HU2evWbRRNTE/IfOkc2LnQ8CWmYRJwYF5/F4p+nIfg3IQy5ZYrN2+ptYoMRDq0wwt4LrYytwDBMHUfa+NHwRRWhC1TTIBKJEHz9IPJD/0RL0Y0qqzdmcsZItZ6EbgMXwMTIvB6jJKThoXNk40LHk5CGQyKR4MbzBOx7ch+H4h/ieWmhQuv5mTtgpIMXRjp6wVnftI6jbHooKVMRukA1PU/jI/Dg8jo4ZR+GEVv5JIqFnDZiTN+D34BFsLa0r8cICWk46BzZuNDxJET1onKfY0/cPex78gBPC7Pf2p8Bg66Wjhjt2BojHbzQTNeo7oNswigpUxG6QDVdhYX5uHJ+A/SfboYdk1Rpv1KJJiINRqPDoKVoZu1cjxESonp0jmxc6HgSohrpJQXY/+QB9sTdx72sZIXW6WLhiHec2mC0Y2vY6BjWcYTkJUrKVIQuUITjxAgKOoDCB+vQQvKg0n5CiQYi9Eei/cDlsLN1qccICVEdOkc2LnQ8Cak/QrEIJ5LCsSv2Ls4kP4ZYIlt47E3tzJrhPScfjHFsAzuaukclKClTEbpAkdc9eHgFCUE/wEt4sdL3zoQSDUQYvoPOQ76DlYVdPUdISP2ic2TjQseTkLolkUhwPysFO2JuY9+TB8hRoGCHh6E5xju3xVgnb7gZ0rvsqkYl8QlpAHxa+8OntT+i40Lw8MJKeBadggYjluojYMrhk78XWXsO47rp++gxbAUVBCGEEEKasKzSIuyJu4/tMbfxMOfZW/tbaevjPWcfTHBuCx9TW6qaqIYoKSOkHri7eMPd5RDiE6PwIHA5WhYegyYjkuqjwwjhk70Vidv+wTXbmeg3bDG0tXVVFDEhhBBC6hMn4XDxWSy2PL6FY4lhKOPEVfbX4WtghL0XJrr6ope1K80jpuZo+KKS0VAOooiklBjcDViKlgX/yiRnL6Vx5shvsRB9+s0Gj0cnWtI40DmycaHjSUjtpRXnY0fsHWyNvo0nBVlv7d/DygUTXdthtKMX9DW06iFCUhs0fJGQBszO1g12M/biaWIkQs4sQauiE+Az0i/sWrEZsHo8H5ceb4J+lx/Rsf1gFUVLCCGEEGWSSCS4+CwWfz6+gWMJYRC9pWiHg54xJrm2wyTXdjSXWCNFSRkhKuRk3wJOHx7E49gHeBj4NVqXX5Lp44JoIHgkjt3pA++hv8LBzkMFkRJCCCGktnKExdgZexd/Rt1AdH5GlX0FPD5GOnhhqlt79LJ2Bcuw9RQlUYUaJ2UXLlzAxYsXcf36dSQnJyMzMxM6OjowNzeHl5cX/P39MWTIEFhZWSkzXkIaJQ9XH3i4BuJByEWkXfkKzSWhMn1al51H8SFfHLX4AANGraT3zQghhBA1EZKVgg1R17E37j5KxOVV9m1lZIUZHh0xwcUXJgKdeoqQqFq13ikrLCzE//73P2zevBmJiYl4uaqWlhZMTExQUlKCvLw8cNyLR7B8Ph/Dhg3DvHnz0KVLl2oHl5ubiyVLluDOnTt4+vQpcnJyYGZmBg8PD8yZMwejRo2SqS6Tn5+PZcuW4fDhw0hLS4OVlRVGjx6NZcuWVTqOc+/evVi3bh3Cw8OhqakJPz8/rFixAu3atat2zDS+ntQWx3G4enUP2PtLYcfKnxAylbOCyPcH+PuPr+foCKkdOkc2LnQ8CalcOSfGvwlh+D0yCEHpT6vsq83TwLtObfChhx86mttT9cRGRNHzpMLPQTdt2gRXV1csXrwYRkZGWLlyJS5evIj8/HwUFxcjOTkZWVlZKC8vR1RUFHbu3ImxY8fi7Nmz6N69O0aNGoWnT6v+B/mmzMxMbNu2Dbq6uhgxYgQ+//xzDBw4EOHh4RgzZgxmzpwp1b+oqAj+/v749ddf4eHhgXnz5qFly5b49ddf4e/vj6KiIpl9fP/995gwYQLS09Mxa9YsvPvuuwgODkaXLl1w+fLlasVLiDKwLIsePSai08eRCLdbjHxO9omYDZsG+wdTcPyPPkhMiVFBlISQuuLo6AiGYeR+Zs2aJdM/Pz8f8+fPh4ODAwQCARwcHDB//nzk5+dXuo+9e/eiQ4cO0NXVhbGxMQYNGoS7d+/W5Z9FSJORVVqEHx5egMvB7/He5b+rTMhaGFpgXcfhSB67BNu6vYdOFg6UkDVRCj8p09DQwIQJE/Dll1/C09NT4R2UlJRg3759WL16NSZOnIglS5YovK5YLIZEIgGfLz3KsqCgAJ06dUJERATCwsIq4lm6dClWrFiBBQsW4Mcff6zo/7J9yZIlWL58eUV7TEwMWrZsCWdnZ9y+fRuGhoYAgPDwcHTo0AHW1taIioqS2X9V6K4hUbaMrFQEHf0CXgVHwGNkXwQu4rTwxH4eBg5fBA0NTRVESIji6Bz5do6OjsjNzcXcuXNllrVr1w5Dhgyp+L2oqAhdu3ZFSEgI+vbti7Zt2yI0NBRnzpyBt7c3goKCoKsrfWPn+++/x6JFi2Bvb48xY8agsLAQ+/fvR2lpKQIDA9GjRw+FY6XjScgrkbnpWB9+Dbvj7lU5RJHPsBjh0Aqzm3eGv5ULJWGNnKLnSYWTsri4OLi4uNQ4ILFYjOTkZDg4ONR4G6+bP38+fv31V/z7778YPnw4JBIJmjVrhvz8fKSlpUldhEpLS2FjYwMdHR0kJSVV/OP/5ptvsHr1auzcuROTJk2S2v7s2bOxadMmBAYGol+/fgrHRRcoUlcehQch9dwncEe43OWx8IBN/83wbNGpniMjRHF0jnw7R0dHAEB8fPxb+6r6ZiQdT9LUvayiuDbsCs6kRFXZ11rbAB96dMIMj46w0TGspwiJqil9+GJtEjIA4PF4SkvISktLcfHiRTAMg5YtWwJ4caFJTU1Fly5dZO4KamlpoXv37khJSUFsbGxF+8vhifKSrv79+wMArly5opSYCaktL8+u6PPJPUS5/YRsTvZk7orH0DjTE0f//gSlwmIVREgIqU8SiQRbtmyBnp6ezCiUhQsXwtjYGFu3bsXr9163b98OkUiERYsWVSRkAODp6YlJkyYhLi4OFy9erLe/gRB1Vc6JsSfuPtod/xX9Av+sMiHzM3fAHv8JePruIizx6UcJGZFLLUri5+bmYt26deA4Ds+fP8fp06eRlJSEpUuXws3NDcCLpAxAxe9ver3f6z/r6enJrRD55nYrIxQKIRQKK36vagw/IbXF47EYOPgzZHYai6uHP4FP8TGp5RqMGN5Zf+LmhjMw6rMF3l7+KoqUEFIbQqEQO3fuREpKCoyNjdG5c2e0adNGqs/Lm5H9+/ev9GbksWPHEBsbW3FNe9vNyE2bNuHKlSvVGiFCSFNSWC7EluhbWBd+FUlFuZX24zMs3nXyxqctu6K9uX39BUjUVq2SsrKyMvz777+4c+cOcnNzIRaLZfowDIOtW7fWZjfIzc2VGn6hoaGBn376CZ9//nlFW15eHgBI3fl73cvHhS/7vfzZwsJC4f7yrF69Wio2QuqDmakVRn14ELfunILw2icyVRodmASIzvfH0dAPMOidnyAQaKkoUkJITaSlpWHKlClSbQMGDMDff/8NMzMzAKq5GUk3IklTlVlahN8jg/B7RBByykoq7Wci0MFMDz981KIzPREj1VLjpCwhIQF9+/ZFXFwcqnotTRlJmaOjIyQSCcRiMZKSkrB//34sWrQI169fx4EDB6pViEPZFi5ciPnz51f8np+fDzs7O5XFQ5qWju0Ho6RVD5w5uACtsraC/1ohED7DwTvzTwRvOA/rQbvQwqO9CiMlhChq2rRp8Pf3h6enJwQCASIiIrB8+XIEBARg2LBhCA4OBsMwKrkZSTciSVOTXJSLtWFXsDn6JopFlRfvcDMww2ee3THZtR10+FR0i1RfjbOZefPmITY2FhMnTsS0adPQrFmzOk+OeDweHB0d8fXXX4PH42HBggXYvHkzZs+eXXFRquxi8vJu3usXr5cv3SnaXx6BQACBQFDtv4UQZdHW1sXISX/gUfg4PD/7AZyZOKnlTkwchKd74MSjLzFo5Lfg8XgqipQQoog33w/r2LEjTp48CX9/fwQFBeH06dMYPHiwSmKjG5GkqXhSkIUfH17Ezti7KOdkR4K95GfugC+8emKYfUuwjMKlGgiRUeN/PRcvXkTv3r2xc+dO+Pv7w8XFBQ4ODnI/deHlePeX4+PfNuxC3jAPNzc3FBYWIi0tTaH+hDRkXp5d0W3OA4SYzUa5RDrxEjDlaJX8PQL+6I7UtAQVRUgIqSmWZTF16lQAQHBwMACo5GakQCCAgYGB1IeQxuRx3nNMuboPzQ//iC3RtypNyIbYtcSVQXMQNOQTjHBoRQkZqbUaP9riOA4+Pj7KjKVaUlNTAaDi6ZybmxtsbGwQHByMoqIimZL4V69ehY2NDVxdXSva/f39cePGDZw9e1amJH5gYGBFn/qSlBKHOxd+Bk9gBA1tEwh0TKCjZwZdfVMYGpjD1NgKOjr6YFn6D5/Ip6mphZHvr0foozFIPT8NDox0AubJ3cGzve0Q0+5/8O8+TkVREkJq4uW7ZMXFL6qr1vRm5I0bN5CWlibzXhndjCRNWVTuc6wMPYf9T0IggfzXcngMi3HOPljg1ROexrLvZRJSGzVOyvz8/BAZGanMWGSEhITAyclJ5q5ddnY2vvnmGwDAwIEDAbx4d23GjBlYsWIFVqxYITVfy+rVq5GTk4NPPvlEaoK+qVOn4ueff8aqVaswfPhwqfladu3aBRcXF/Tq1atO/8bXpaZEwidb/vt3HIAMAEKJBgok+ihkDFHKGqKMbwyxpgkYLVO07z0PzWxqN3UBaRzaeHVDiWsIAvbNgU/+XqllJmweTO5PxpG4sxg8fiMVASFETdy6dQvAq3nMGsPNSEJU7XHec3wXUnUyJuDxMc2tA75o1QOO+ib1HCFpKhSePPpNDx48QLdu3bBjxw6MGTNG2XEBAObOnYstW7agZ8+ecHBwgK6uLhISEnDq1CkUFhZi9OjROHDgQMWTo6KiInTt2hUhISHo27cvfH19ERoaioCAAHh7eyMoKEimbPCqVauwePFi2NvbY8yYMSgqKsK+fftQUlKCwMBA9OzZs1ox12YizUsXd8Hx4YxqrfO60oHBcos5PE2MRMLhYSjmmaJMwwyclgV4utbQ0reGvlEzmJrZwdLSEQZ6xvQUrhEKvnEEGjfmwJzNklkWi+ZwHfUPnOxbqCAy0hTRZMNVi4iIgI2NDYyMjKTag4KC0LdvX0gkEkRHR8Pe/kWJ7epOHh0dHQ1PT0+aPJo0eXH5mfgu5Bz2PLkPrpKvwjp8Dczy6Iz5rfxhrUP/vknNKHqerHFStmLFCty5cwenT5+Gv78/fHx85I5DZxgG3377bU12gaCgIGzduhU3b95EamoqiouLYWJigrZt22LSpEl47733pJ58AS/G1i9fvhyHDh2qGJ4xZswYLF26tNJx8nv27MG6desQHh4OTU1N+Pn5YcWKFWjfvvrV6mpzgQo4+Suax35V7X2+pDvhCSzMm8m0374XCPNrQ9+6fjGnhWyYooBnAaHAChJta2jqN4Nv54mwtqqbdwNJ/XielYKb+ybCSxQksyyf00W2z3r07DlJzpqEKJc6f4m/cOECLl68iOvXryM5ORmZmZnQ0dGBubk5vLy84O/vjyFDhsgtN6+oZcuWYc2aNejduzccHR0hEAgQFhaGs2fPgmVZbNq0CTNmvLp5p+qbkep8PEnTlFSYi5Wh57A95g7EEk5uHz2+AB+37IK5nt1hrqVXzxGSxqbOkzJFn6gwDCN3/rLGqjYXqOAbR5D5YAv4ojxocvnQ5gqgiwLoM0XgMfJPHC+JJSwcPymUe3fzwoXtcH40s1qxvK6gzwW0btVNpj0nNxNXz/wMHRMnmFm4wbaZO8xMbOhpWwPFcRxO//s9XBNWQ8DIlvV9YDIdw8b/ptIpJkjjp25f4gsLC/G///0PmzdvRmJiYsUUMFpaWjAxMUFJSQny8vLAcS/O0Xw+H8OGDcO8efPQpUuXau/vypUr2LBhA+7fv4/09HSUlpbC0tISXbt2xbx589ChQweZdVR5M1LdjidpujJKC7E69AI2Pb4BoVgkt4++hgCftOiKuZ7dYaqlK7cPIdVV50nZlStXFO7blMan18UFSiwWI68gGzk56cjLS0dhfgZKijIhLMqAuCQLKM0EuDKMnHlE7von/10Nz/ilNd6/7sSnsDC1lWl/8PAyjC72k2or4rSQztiiQNMeYl0nCExcYWLpAbtmLWFr7UQJWwPwMOwa8s9OhC2bKrMskvVFu3EH5T5xJUQZ1OlL/KZNm7Bs2TI8f/4cbdq0wbvvvgs/Pz+0a9cOenqv7p5LJBLExMTg1q1bOHv2LI4dO4aioiIMHz4cv/zyC5ycnFT4V9QtdTqepGkqKC/Fr2FX8UvYFRSKhHL76PI18UnLrpjv6U/JGFG6Ok/KiHwN8QIV9zQMsVGXUVqQBnFRGtjS59Asz4CuOAOGkmwYsoWVrlsq0YTHZ/lyk6nz57bAJfwjheMolgiQhmYoFDiCM3BFpz5f0LBIFcnNz8KV3e+jddkFmWXpnDm0++5DG6/uKoiMNHYN8RxZGQ0NDUyYMAFffvklPD09FV6vpKQE+/btw+rVqzFx4kSZeccaE3U6nqRpKROL8Nfjm1gZeg4ZpUVy+2jx+JjTogu+9OpJwxRJnaGkTEXU8QJVXFKItPQEZGTEIy87CaX5KRAVJoNXnAowDIZ/dEbuescOLELr1J9qvF929CM42HnItOcX5OBp/CO4u7aFtjadJOsKx3E4dXQlPBJXQ4ORHmJcKtFEYvMf0H/gxyqKjjRW6nSOjIuLg4tLzSvaisViJCcn19l8nQ2BOh1P0jRIJBIceBqKb+8HIK5AtsAVAGiwPHzg3hEL2/SGjU7l8/IRogyKnidr/fLI9evXsWPHDoSEhFTszMfHB5MmTULXrl1ru3lSD3S09eDs6AlnR8XvBAOAtr41Itm2MBSlwoJ5Dv5b3nt7XbFEgOY2rnKXPQw9B9u77yPlPItU2CJb4A4Ye8HY1geubp1gbelAwyCVgGVZDB29BHfudYTkyhSp6oxaTBncH8/HkbQQDJu4id4zI01SbRIyAODxeI06ISOkoQlKf4oFd07gVkai3OUMGLzv0hZLffrBSd+0nqMjpGq1elL2xRdf4Ndff6148Zll2YqXnRmGwWeffYa1a9cqJ1I10VTvGpaXlyE5JRapz6KQ+zwaZTmx4BUlQL8sEZZIgTZTJtX/icQVvedFyN3W8YPfwivlR7nLACCbM8IzDXeUGXrB0LY9XN27wM7WjRK1Wkh7noSQfSPhIXkosyyM54duk4/AyIAuYKT2muo5srGi40kagrj8THx19xSOJjyqtM9QO0+s9B2AVsbW9RgZIfXwpGzXrl1Yu3YtmjdvjqVLl6JHjx6wtLTE8+fPcfnyZSxfvhzr16+Ht7e3zASVpPHR0NCEk2NLODm2lFnGcRxS054iKTEMWWkRKMuOBqtTeclocY78ZO0lEzYXJuLbQPZtIHsruEfAA84QzzRaoNzEF4Pf+QGamoJa/01NiZWFHXrOCsLpv6fDu/Cg1LJW4hsI2doJDiOPyT2+hDQ1HMchOTkZKSkpKC+XrWQKAN270zuZhNS1vLISrAo9j/9FBKGck1/p28/cAT+0H4Kulo234A5pHGr8pMzPzw+pqakICwuDvr6+zPL8/Hx4eXnB2toaN2/erHWg6oLuGtbesT/6onW54tU9X5fJmcB3bio9NauF08d/gkvcUmgy0iWDszlDoOde+Pr0VVFkpDFQ53OkRCLBDz/8gF9//RVZWfLfVXmpqUwFo87Hk6gvMcdha8xtfHsvAJlC+UU83AzM8L3vYIx0aCUzpy0h9anOn5SFhYXhgw8+kJuQAYCBgQFGjRqFLVu21HQXpIkaPucc8gtyER17F+mJ9yDMeASdokjYimOhx5ZUue4zTc9KE7KTR1ZCknoBnLkfmrn3hpdnV3qiJsegYV/i1p0W0Lw2GUZsQUW7CZuH0ssjcSFrPXr3ma7CCAlRjYULF2LNmjWwsLDA1KlTYW1tTe9bElLPrqbFYe6tYwjNlp3WBQBMBTpY6t0PHzb3gwbLq+foCKm5Wl1N3vaQje5MkJoy0DdCO58+gE+fijaxWIz4xEg8jb2B/NS7EOQ9gq0oSqqkP2fatvKNppxHK/F14Fkw8OxnRF/SQrxGG4jNu8C+eX94tepGX7D+07H9EDwxv4rEo8Nhz7x6YVqLKYPjo49wLPspho5ZQU8kSZOyY8cOeHh44M6dO1LzlBFC6l5yUS4W3DmJf56GyF2uwfLwSYuuWNSmD4wE2vUbHCFKUKvhiykpKYiIiJB7cSooKECrVq1o+CKpUxzHIT4xErGPr6Ew9Tac2k6ET5ueMv3EYjEerTevck62fE4XCQJfsDY90dx7GJwdKn/q1lTk5mchaOdweIpvyyx7YDAOwydvA49HdyKJ4tT5HKmnp4dZs2bh559/VnUoDYY6H0+iHsrEIqwLv4qVoedRJCqT22e4fSusaT8ErgZm9RwdIW+n6Hmyxt84Z82aheTkZPj5+eHw4cPIzMwEAGRmZuLQoUPo3LkzkpOTMXv27JrugpC3YlkWzo6e6Nd/FkZN3SY3IQOA6Nj7VSZkAGDAFsGr/Co8E5aDd8wXN9c54sjmcbh0aRcKCnPrIPqGz8jAFP1mXsADnREyy3zy9+HEn0NQUlpc/4ERogLe3t5ITZU/ZIoQonwXUmPgfewXLLx3Wm5C1srICmf7z8SR3lMoISNqr1Yl8efNm4f169dXDFN8vSS+RCLBJ598gvXr1ysnUjVBdw0bppzcTNy++Q+KkoJgWnhHakieIkJMZ2LkxN/qKLqGj+M4nDi4CK2f/SKzLIrxht+UABgZUsl88nbqfI48c+YMRo8ejWvXrqFt2yqGSjch6nw8ScOVWpyHz2+fwIFKhioaa2pjuU9/zGzuBz69N0YaOEXPk7VKygAgKCgI27dvR0hICPLz8ysmj548eTK6detWm02rJbpAqYf058l4FBqA/ITLMCu4ATsmucr+Wd1Pol3bfnKXcRzXZIY5Bgb8DseoBTKVGeMkbvCaEAhLi2YqioyoC3U/Rx46dAhz5szBsGHD0KZNm0r/hqYyFYy6H0/SsIg5DhujrmPx/QAUlAtlljNgMMO9I1b6DoSZlq4KIiSk+uotKSPS6AKlnpJT4xAechIliRfQrPgmTNncimW5nD68Pk2XWwQkJfUJkvd3RYphb1h7jkH7doPA52vUY+T1L/j6YRjenApdtlSqPZmzhd2YADjaN1dRZEQdqPM5UigUYsaMGdi7d29Foas3C1pJJBIwDEMl8QmppvuZyZh1/RDuZcm/SdrezA6/dRqJ9ub29RwZIbVT5yXxCWlMmtm4oJnNZwA+A8dxiIi6hbhHx6GZdgGlOjbwqaQqY8it/WjJZsKi4B/g5j8IvW6IRP0+sPIa91+C1vj+E+vSeTQeGlgg++xomLyWvDZjU5B+qCeEw07Dw9VHdQESUkfmz5+PPXv2oHXr1hgzZgyVxCdECYrKhVj24CzWRVwFJ+c5gbGmNlb5DsIM947gNZFRKaRpoidlSkZ3DRufqoYnnvytq9zKhMCLiayTjPrDse0keLfu2eiGOMbEhSLz2GBYsc+l2rM5IwgGHEOrln4qiow0ZOp8jrSwsICDgwNu3LhBydh/1Pl4EtULTHmM2dcPIaEwR+7yya7t8GP7ITDXoikoiPpS+pOyadOmgWEYfP/997C0tMS0adMUWo9hGGzdulXR3RDS4FSWTOXl58BNdB+oZDo+MzYbZvn7gMv7cOOiNZ5bjkKbLjPg7OhZh9HWHzeXNtB+7wpi/xkAByahot2EzUXemcF4UHYYPt7yq2ESoo5KS0vRs2dPSsgIqaWs0iJ8fvs4/o67J3d5C0ML/NF5NPytXOo5MkJUR+EnZSzLgmEYREZGwt3dXeG7/k1pbD1Adw2bmsjHd/H43j7opwfAmYlVaJ3HTGtwzuPRtcc0GOgb1W2A9SAzOw0PdvWFKx5LtRdy2hD2OFhpgRTSNKnzObJnz54wNjbGkSNHVB1Kg6HOx5OoxqH4UHxy4yiel8pOU6PJ8rC4TV986dUDmjy6+UEaB6UX+khIeHEn3NbWFnw+v+J3RTg4OCjcV93RBarpin36CGE3/4Z+2gk4M3Fv7f9A712MmrG7HiKre7n5Wbi5vT88JA+l2os4LRR3348O7QapKDLS0KjzOfLGjRvo3bs3Dhw4gCFDhqg6nAZBnY8nqV/pJQX4+MYRHEl4JHe5v5UzNnYeAw9Di3qOjJC6RdUXVYQuUAQAHsfcQ8StnTB/fgy27DO5fXJ6BKCtd+96jqzuFBXl4/LWgWjJ3ZFqL+a0UNh1Lzp2oC+xRL3PkStWrMDNmzcRGBiInj17wtvbW+7fwDAMvv32WxVEWP/U+XiS+iGRSPDP0xB8evMosoTFMssNNLSwpv0QzHDvKFPNlJDGgJIyFaELFHkdx3F4EHoBifd2wDH/DIzYAgBAgsQB3T97LHcYcGFhHmKfhsDby7++w621UmExzv01EK3EN6TaiyUCFHbZR4kZUetzJA3bl6XOx5PUvYzSQsy5cQSH4x/KXT7M3hO/dxoFW13Deo6MkPpTJ4U+aoIKfZCmjGVZ+Pr0ha9PXwiFpQgO2ouiyF2QWPWo9Ave1Yt/ocWTRTh7oSXQYhZ69JwKTU1BPUdeM1oCHfT9MADnNg9GK1FwRbsOIwSCxuMOewDt2w1QYYSE1NylS5dUHQIhauPfhDDMun4QGaVFMstMBTr4X6eRGOvkTU/HCPlPtQp91GgHTeiOIUB3DUntcByHS+tbSRUNyeRMkGo9EV36fw4zEysVRqc4obAUgVuGwKv8qlR7IaeNsp6H4OvTV0WREVWjc2TjQseTvCm/rBRzb/2LnbF35S4f7dgav3caCQtt/XqOjBDVUPqTsqdPnyolMEJI5UIeXpSp4mjGZsMsfT3Sd27CVf1haN17IVydWqkoQsUIBFoY8MEpnNk8WCox02NLUHDpHYTwj6nl8ExCXhKJRIiOjkZeXh4MDQ3h7u5OpfJJk3c1LQ5Tru2XO++YqUAHv/uNwrtO3vUfGCFqQOErSFOqoEiIquTnPkM5ZwoLNktmmQ4jhE/hQYj/PYx/BT3h2O3rBp3YaGoK0H/GSZzdPFBqKKM+W4zcc6MQoXkGLT3aqzBCQqovIyMD33zzDfbt24eSkpKKdm1tbYwfPx6rVq2Cubm5CiMkpP6ViUVY9uAs1jy6BAlkB2ANtfPEps6jYaVDT1MJqQwV+lAyGspBaqusTIirV3ZBFPGnTIn5N0Ww7WHWaWGDLqDxovjHALQS35Rqz+RMYDzyHFydvFQUGVEFdT5HpqSkoEuXLkhMTIS5uTl8fX1haWmJ9PR03Lt3DxkZGXBwcEBQUBBsbW1VHW69UOfjSZQjKvc5Jl7dg/tZKTLL9DUEWNdxBCa7tqN3x0iTpfTqi1ToQzF0gSLK9ODhFSQE/4yWpeehwVT+buZjxgv67b+BX6eRNX7/sy6VlBTh0uY+aMHdk2pP4yzQbOwl2Nm6qSgyUt/U+Rw5YcIE7Nu3D8uXL8eXX34JLS2timWlpaVYs2YNli1bhvHjx2P37sYxB+HbqPPxJLUjkUiwJfoW5t06hhJxucxyfytnbOv6Hhz1TVQQHSENh9KTMir0oRi6QJG6kJgSi7tnf4BbzkHosSVy+2Rwpmg9Jw5aAp16jk4xBYV5uL6lB9wRLtWeJLFD80lXYWHaNJ4sNHXqfI40NTVFx44dcfr06Ur7DBgwAHfu3EFWluwQ5MZInY8nqbkcYTE+DD4odyJoDZaHlW0HYH4rf7BMw7tJSEh9o0IfhDQi9rausJ+6Bdm5q3H1zE+wS90BEzZXqk+640cNNiEDAH09Q7SfchYPtvtLFTOxY5IQ+vcAdJh2FYYGxiqMkJCqlZWVoW3btlX28fX1RXBwcJV9CFFn19PjMeHKbiQW5cosa2Fogd3+E+BNN9kIqTYq9EGIGjExMseI99aguGQJLgT8CvP4TbBkM5DBmaLPwPmqDu+tTIzM4TXxLKL+9ocdk1TR7orHCN4xEL1mXmzQiSVp2nx9fREVFVVln6ioKPj6+tZTRITUH07CYc2jS1hyPxBiCSezfHbzzvip/VBo8zVUEB0h6o+eKxOihnS09TB01LfwmROHCKeVyGmxCNraunL7JqfG4d9NwxH3NFzu8vpmYdYMju+eQTonXaGuBXcfgVtHN6nhzkS9fPfddzh58iR27Nghd/m2bdtw+vRprFy5sn4DI6SOPS8pwKCzW7DoXoBMQmYi0MGRXlPwu98oSsgIqQWqvqhkNL6eNDRH/noHPsXHUCbhI9zgHXQb8QPMTa1VHRYexz5A0fG+MGbzpdofGIzDiCnbG2TBElJ76nyOXLFiBW7cuIGzZ8/Cw8MDXbp0gYWFBZ4/f47g4GA8fvwY/fr1g5+fn9R6DMPg22+/VVHUdUudjydRzNW0OEy4sgepxfkyy/ytnPF39wmw1TVUQWSEqIc6qb7IMAy+//57WFpaKlyNkaovEqI6UdF3wT/VFTzm1Z3NfE4X8c3moP/wb1Q+VDDk0RXwzw2FLlsq1f7Q+nMMH7taRVGRuqTO50gqeCVLnY8nqRon4fDTo8tYfD8A3BtfFVmGwbdt+mJRmz7g0Q00QqpUJ9UXGYZBZGQk3N3dFb44NeaLkTx0gSINyb8bh6CN8KzcZSmcDURtv0P37hNU+lQqKPgwLG6/L1PyP6b5r+g3YI6KoiJ1RZ3PkVeuXKnxuv7+DXei99pQ5+NJKpcjLMaUa/txMilCZpm1tgF2+49HD2tXFURGiPpRelKWkJAAALC1tQWfz6/4XRFNqUgIXaBIQ5KZnYarxxbDM3cvNBmR3D7hvA5wG7AeHm6qK04QGPA73B9LFyopk/CR0/kfdOo4VEVRkbpA58jGhY5n4/MgKwXvXtqFJwWy0zr0sXHH393HwUJbXwWREaKelJ6UEcXQBYo0RE8TIxF68gu0KTsnd3mZhI9wk4noO/pH6OsZ1W9w/zl2YDFap66RasvndCEYcg7N3dupJCaifI3lHCkSiRAdHY28vDwYGhrC3d0dfL7CBY0bjcZyPMkLO2Pu4KMbh1Eqlr6Jx4DBUp+++KY1DVckpLoUPU8q5b8skUiEiIgI3LhxAxERERCJ5N+RJ4SohpN9C4z46BSy/U8hBi1llmsyIvjkbEfEXy1x/txWcJxsueO6NnTMCjzQHyfVZsAWIefkCDxLU/zJPCF1KSMjAx988AGMjIzg5eWFrl27wsvLC0ZGRvjwww+RkZGh6hAJqbYysQgf3ziCaUH/yCRkZgJdnOn/Ab717kcJGSF1qFZPyjIyMvDNN99g3759KCkpqWjX1tbG+PHjsWrVKpibm1exhcaH7hqShk4sFuP82Y0wjlwFc1Z2eAoAhPH84DX8TzjaN6/X2EQiEU5v6g8v0TWp9hi0QNeZwdDR1qvXeIjyqfM5MiUlBV26dEFiYiLMzc3h6+sLS0tLpKen4969e8jIyICDgwOCgoJga9s0Js9V5+NJXnhWnI93L+3C9efxMsv8zB2wv+dENNM1qve4CGks6vxJWUpKCtq3b4+tW7dCV1cX/fv3x6RJk9C/f3/o6upiy5Yt6NChA1JSUmq6C0JIHeDxeOg/8GO0+DASD4ynokwiO+SqlfgGMuRcoOsan89H76lHEQvpZNANkTi3fUyTKhpEGp4FCxYgMTERy5cvR0JCAk6fPo3t27fj9OnTSEhIwLJly5CQkICvvvpKaftcs2YNGIYBwzC4efOmzPKXy6r6JCW9mqg9Pj6+yr779+9XWuyk4buVkYAOJ9bJTcjmtOiCiwNnU0JGSD2p8ZOyCRMmYN++fVi+fDm+/PJLaGlpVSwrLS3FmjVrsGzZMowfPx67d+9WWsANHd01JOrmccx9xAXMQQvuXkVbiM5wjPzwoMpiSn+ejLjdfrBm06XaQ0w/wMiJf6goKqIM6nyONDU1RceOHXH69OlK+wwYMAB37txBVpb8p9DVERkZCR8fH/D5fBQVFeHGjRvo1KmTVJ9ly5bJXTc2NhZ79uxBixYtEBHxqoJefHw8nJyc0KZNG4wYMUJmvTFjxqBVq1YKx6jOx7Op2xFzB7OvH0IZJ32zS4vHx8bOYzDJld7lJUQZFD1P1vit5DNnzmDAgAFyJ8TU0tLCkiVLcP36dQQEBNR0F4SQeuDh1hZuLsG4cO4vGIcvAQMJuo35TaUxWVo0Q/bgIyg41Rf6bHFFu3fWZgQGtED/gR+rMDrSVJWVlaFt27ZV9vH19UVwcHCt9yUWizF58mS0adMG7u7uld7crCwp++STTwAAM2bMkLvc29u70nVJ4ybixPjq7imsC78qs8xBzxiHe02Bj2nTGH5LSENS4+GLil6cysrKaroLpKSkYN26dejXrx/s7e2hqakJKysrjB49Grdu3ZK7Tn5+PubPnw8HBwcIBAI4ODhg/vz5yM+XnYn+pb1796JDhw7Q1dWFsbExBg0ahLt379Y4bkLUDcuy6Nt/FpynhQM998LMxEpuP47jEB0XUi8xtfBoj7wOf0EkkT5NOUR9hXsPztdLDIS8ztfXF1FRUVX2iYqKgq9v7aeX+PHHHxEaGopt27aBx+NVa93S0lLs2bMHmpqamDhxYq1jIY1HrrAEQ89vk5uQ9bRywe2hcykhI0RFapyU1cfF6bfffsO8efPw5MkT9O3bF59//jm6du2KY8eOoXPnzjhw4IBU/6KiIvj7++PXX3+Fh4cH5s2bh5YtW+LXX3+Fv78/ioqKZPbx/fffY8KECUhPT8esWbPw7rvvIjg4GF26dMHly5drHDsh6sjEyBy+Pn0rXR54ah2Y451w9O9PUFYmrPN4unV9F48dpZ/GC5hycJcmICklts73T8jrvvvuO5w8eRI7duyQu3zbtm04ffo0Vq5cWav9hIWFYfny5Vi8eDE8PT2rvf6RI0eQk5ODYcOGVVpsKzU1FRs3bsTq1auxc+dOJCcn1ypm0vDF5mei86n/4WzKY5lln7bshjP9P4SZlq4KIiOEALV4p+zatWvo27cvNm3ahClTpsgs37ZtG+bMmYNz586ha9euNQruyJEjMDc3R7du3WT23bt3b+jr6yM1NRUCgQAAsHTpUqxYsQILFizAjz/+WNH/ZfuSJUuwfPnyivaYmBi0bNkSzs7OuH37NgwNDQEA4eHh6NChA6ytrREVFVWtuWdofD1prJJSYpHzT7uK4YRPJG6wGbQDLTza1/m+j2x5Hz6F0jdh4uAOvw+uQ1eX/jtTJ+p8jlyxYgVu3LiBs2fPwsPDA126dIGFhQWeP3+O4OBgPH78GP369YOfn5/UegzDyB3qL49IJEKnTp0gEolw584daGhoYMqUKdi5c6fcd8rk6d27Ny5evIgzZ86gf//+UstevlP2Jj6fj08//RQ//fQT2GqUPVfn49mUXH4Wi3cu7UK2sFiqXZPlYUPn0Zjq1kFFkRHS+Cl98ugVK1bItClycercubPCF6Pq6N+/P86ePYs7d+6gXbt2kEgkaNasGfLz85GWlgZd3Vd3e0pLS2FjYwMdHR0kJSWBYRgAwDfffFNxl3DSpElS2589ezY2bdqEwMBA9OvXT+G46AJFGiOO43D6jx7wFEtXfxNKNBBt9yUGj/y22kOsqqOsTIjzm3pIFSMBgFBBfwybeaxaXyKJaqnzObKm/84YhlG4cuiKFSvw3Xff4datWxWvCFQnKXv69ClcXFxgZ2eHp0+fysT8/PlzbNiwASNHjoSzszNKS0tx8+ZNfPXVV4iMjMTXX3+N1atXV7p9oVAIofDVU/L8/HzY2dmp5fFsKrZF38bs64cgkkjPP2mhpYfDvaags6WjagIjpIlQeqGPql4IjoqKkjuUMTAwEGfPnq2TpExDQwMAKp5ixcTEIDU1taIk/+u0tLTQvXt3HDt2DLGxsXBzcwOAiuGJ8pKu/v37Y9OmTbhy5Uq1kjJCGiOO41Bu7IPyjDvQYF59uRQw5fBK/h4BG87D9509sLZyqJP9a2oK0OH9o4ja0Qm2bGpFexthIE4cWorh735XJ/sl5HWXLl2q0+2HhoZi5cqV+OKLL976znZltm3bBolEgqlTp8pNIi0sLKSu5/r6+hg6dCjat2+PVq1aYe3atViwYAGMjY3lbn/16tVSI05Iw8VJOCy+dwY/Proos6yNiQ3+7T0V9nryjzMhpP4pnJTV9cWoOhITE3H+/HlYWVnBy8sLwIukDEBFwvWml+0xMTFSP+vp6cHKSraowet9qiLvriEhjQ2fz8fI99cj5NEo5J2fDnsmUWq5p/g2nu31xWPf9ejhP6FOYjAzsYLZkEMoPNkbeuyryeo9U35C8HVvdOk8uk72S8hL/v7+dbr9yZMnw8XFpcZVETmOw44dO8CyLKZNm1atda2srDBo0CD8/fffuHPnTqU3IxcuXIj58+dX/P7ySRlpWEpE5Zh6bT8OxofKLBtq54nd/uOhpyFQQWSEkMoonJTV9cVIUeXl5Zg4cSKEQiHWrFlTMWQqLy8PACreC3vTy8eFL/u9/NnCwkLh/vLQXUPSlHh7+aPYNQRn9n0Cn/w9UsuM2XwYP5iKI3HnMHjCJggEWpVspeaau7fD5ba/Qy9kekUbj+Ggc/NDPLFpDmfH6hdFIKShCA198QX69Xk/X/fyXbWjR4/KnWPszJkzSE5ORv/+/WFvb1/t/ZuZmQEAiouLK+0jEAgq3uMmDVNmaRFGXtgud0LoL1r1wPe+g8CjId+ENDg1nqdMFTiOw7Rp03D16lV88MEHDaLUL901JE2NjrYeRk3bjus3hkDjxhyYsdlSy33y9+LaxrtwGr4fLk6KT0KrqB49JuJoygN4Z/xe0WbEFuDJ0TGw/PAWFf4gamv69Oly269evYqYmJiKaoqOjo5y+23duhVA5XOTvc3t27cBoNLtk4YvLj8Tg89tQUx+plQ7j2Hxh98ofODx9kIxhBDVUJukTCKR4IMPPsDu3bvx/vvvY9OmTVLLXz4hq+zJ1sthha8/SXv50p2i/eWhu4akqersNxoZ7n64uW8iWomuSS1zQTQKj3bBhdZr0buP/C+atTHsvZ9watNDeJW/mmvHmYnD2b8nYfiHR6jwB1GKIUOGYPny5TWa2qWkpAR//PEHdHV1MXv2bIXW2bJli9z2KVOmICYmBgsXLqy00EdGRgZOnDgBMzMzDBs2rNJ93L59Gz4+PhXvZb+0du1aBAcHo2XLlmjTpo1C8ZKG5U5GIoae34qMUunpfww0tHCg5yT0tXVXUWSEEEWoxTcXjuMwffp0bNu2DePGjasYM/+6t70DJu+dMzc3NxQWFiItLU2h/oQQaeamNhj80TmEOyyDUCL9JU+PLYFz2Gz8u/dzpe+Xx+PBf+JBJEqkh2h5l57GqWPfK31/pGlKSkpChw4d0Lt3b+zYsUOhd4bv3r2LuXPnwsHBAUuWLKkYEljXdu3ahfLyckyaNAmampqV9luwYAHs7OwwduxYfPHFF5gzZw7atm2Lzz//HMbGxvj7778rKhQT9XE6KRK9zmyUScia6Rji6qA5lJARogYa/JMyjuMwY8YMbN++HWPHjsXff/8tt/S2m5sbbGxsEBwcjKKiIpmS+FevXoWNjQ1cXV0r2v39/SvK+r9ZEj8wMLCiDyGkcizLYsjIb/Aowh/Pz0yEHftqElqRhIWNe6862a+hgTGshvyDwhO9pAp/uMd/jzv3O6B9W6qaSmonJCQE27dvx4oVKzBt2jTMmDEDzZs3R9u2bWFpaQljY2OUlJQgOzsbMTExuHv3LvLy8sCyLN59912sWrWq3oYCKjp08f3338fhw4dx/fp1ZGa+GOLm4OCAzz77DF988QWaNWtW57ES5doRcwcfBh+E+I2S962NrXGy7wzY6lY94ocQ0jDUePLo+vDyCdmOHTvwzjvvYO/evVVO5FzdyaOjo6Ph6elJk0cToiQFhbm4sHsS2pSeAQA8tP4cw8dWPueRMly4sA3Oj2ZJtT3nzOA08TYszOkLZkOjjudIjuNw6tQp7NixA1euXEF2drZMH5Zl0bp1a4wYMQIzZsyAjY2NCiKtf+p4PBsLiUSCHx9dxKJ7ATLL+ti442DPSTDQVH7BJUJI9Sh98mhVWLZsGZYvXw49PT189tlncpOjESNGwNvbGwBQVFSErl27IiQkBH379oWvry9CQ0MREBAAb29vBAUFycxhtmrVKixevBj29vYYM2YMioqKsG/fPpSUlCAwMBA9e/asVsx0gSJNHcdxOHPiJ5SnXMaQmafA49X9KOkjO2fCJ2e7VFsE2w79P7parZsqpO41hnNkZGQkkpOTkZWVBW1tbZibm8PT0/Ot7yA3Ro3heKojTsLhi9snsD7imsyyCS5tsaXLu9Dk0bmPkIagUSRlU6ZMwc6dO6vss337dkyZMqXi97y8PCxfvhyHDh1CWloarKysMGbMGCxdurTSC+aePXuwbt06hIeHQ1NTE35+flixYgXat29f7ZjpAkXICxzHVVlwo6xMCE1N5RTJEYlEOLuhK1pw96XaQ8xmY+T765WyD6IcdI5sXOh41r9yTozpQf9gT9x9mWVftuqB79sNAsuoRckAQpqERpGUqSO6QBHydif//QGC+D1o/s6/sLN1Uco2054nIWl3e6kS/ZyEQWr7v9Gt67tK2QepPTpHNi50POtXiagcYy/vwqmkSJllazsMw2ee3VUQFSGkKoqeJ5V6KyUzMxMLFizAkCFDMGvWLISFhSlz84SQRuDWnVNwf7ocrniMtH864+6D80rZrpWFHbhuWyGSvDqtsYwEurc/QnJqnFL2QQghqpJfVorB5zbLJGR8hsWu7uMpISNEzVU7KSssLISjoyM++ugjqfaMjAy0bdsWP//8M06fPo2//voLfn5+iIiIUFqwhBD1lpD0GPxrU6DBiAEAZmwO9C4Px9kzG5Sy/Y7tByPCVroEvzGbj0cHx6K8vEwp+yCEkPqWVVqEvoGbcCXtiVS7Nk8DR3tPxQSXtiqKjBCiLNVOyoKDg5GYmIiBAwdKta9duxbJyclYuXIl8vPzcfz4cYhEIixbtkxZsRJC1FxpaSFKoCPVJmDK4RY1F0f/ngOxWFzrfQwd8x0e8btKtTWXPMTJ/fNrvW1CCKlvz4rz0TNgA+5mJku1G2pqIbD/hxhk10JFkRFClKnaSVlcXBwYhoGPj49U+7///gsPDw9888030NPTw5AhQzBp0iTcunVLacESQtSbh5svmk+5hQjWV2aZd9ZmnPxzKEpKiuSsqTiWZdF5/F6kceZS7a0zNyMo+HCttk0IIfUpsTAHPU5vQHhuulS7hZYeLg38CF0snVQUGSFE2RSulzp16lQwDIPQ0FAAwOLFi6Uqq0VHR8PJyQnTpk2raIuMjERqaqpU27Zt25QRNyFETZmZWKHvrMs4uWs6fAoPSC1rXXYeV//sBp+JJ2BhalurfST03IHyy8MqhkqyjATat2bjmUs7WFs51OpvIISQuhaXn4m+gX8ioTBHqt1O1whn+8+Eu6F5JWsSQtSRwklZjx49AADFxcUICQlBly5doKmpCQB49OgRJBIJBgwYIFVGXkNDAw8ePIC/v79yoyaEqDVNTQFGzdiNE0ebo3n8SvAZrmKZO8IQvbML8keegKuTV4334evTF8diPkfr1DUVbSZsLu4fGIcBc66Bx+PV6m8ghJC68jjvOfqc2YTU4nypdld9M5wbMBP2esYqiowQUlcUTsomT54MABAIBDh48CBsbW0xaNAgAMBXX30FhmEwd+5cuLq6VqyTlZUFa2vrinUJIeR1Q0cuxtUgDxjfngE9tqSi3ZZNRfbRXrjfaz/aeveu+fbHrMCpP4LQSny9oq0ldxcnDy7C8Pd+qFXshBBSFyJy09D3zJ9IKymQam9pZImz/WfCWoemHiCkMar2PGUZGRlwcXGBjo4OvvzyS2RnZ2Pt2rXw8fHB9evXpfqOGjUKJSUlCAgIUGrQDRnN2UJI9T2KuIGiM2NgyWZItRdLBMhouwk9/CfUeNvPs1IQv7MdzNmsijaRhEWe/3G0a9uvxtslNaNO50hnZ+dab2Pu3Ln49NNPlRBNw6ROx1MdhOU8Q98zf+J5aaFUu7eJDc70/xDmWnoqiowQUlN1Nk+Zubk51q9fj5ycHHz55ZdYvXo1rKyssGXLFql+ycnJOHXqFIYPH1796AkhTYpXSz80G3cNTySuUu06jBDN7k/H6RO/1HjbFqa2EHf+E5yEqWjjMxzKLk9HTm5mjbdLGr/4+Hjk5ORAIpHU6JOQkIDc3FxV/xlETTzKfobeAZtkErL2ZnY4P2AWJWSENHIKD1983dSpU9G/f38EBwfD2NgYfn5+0NXVleqTl5eHv/76i5IyQohCmlk7Q396MIJ2DEFL7k5FO4/hIC4rqGLNt/PrNAxHY2fCO3NTRZs1m44r+6ZixOwTtdo2adzmzZuHJUuW1Gjd14thEVKVsJxn6HNmEzKF0tVnO5k74HS/GTDU1FZRZISQ+lLjK4aNjQ3eeecd9OnTRyYhAwBPT09MnjwZRkZGtYmPENKEGBoYo++sCwjVGlTRFmIyA0NHL6v1toeM/RmPGenCIW2EgQgM+L3W2yaEkJqqLCHrYuGIgH4fUEJGSBNRoydlhBBSVzQ1tTDswyM49vdHkAizMOJ95SRNGhqaaD5yD/IP+UGfffXlxyZyEeKa94CLUyul7Ic0HjExMTAxMVHZ+qTxe1nUI6NUOiHraumEk32nQ19DS0WREULqm8JPyoYMGYJ79+7VaCclJSX4+eefsXHjxhqtTwhpWliWxcjJmzBixn6lDgFztG+O9FY/SrXpsSV4cmwCysvLlLYf0ji4uLjA2Ljmpcdruz5p3KLzMuQW9ehq6YRTfWdQQkZIE6Pwt52kpCR06NABvXv3xo4dO5Cfn//Wde7evYu5c+fCwcEBS5YsgZmZWa2CJYQ0LSxb+Vxi4ZE3UVCYW+1t9u33IUK0h0q1uSISp/75strbIoSQmnhSkIU+ZzbJlL3vYuGIk32nQ09DoKLICCGqonBJfIlEgu3bt2PFihVITEwEy7Jo3rw52rZtC0tLSxgbG6OkpATZ2dmIiYnB3bt3kZeXB5Zl8e6772LVqlVwdHSs4z9H9ag8MCF1LzzyFsoDBiKddUCHKedgbFS9Gz4FhbkI/csbtmxqRZv4vzL5vlQmv041tnMkx3GIiIiAiYkJbGxspJaVl5fjxo0b6N69u4qiq3uN7XjWh8TCHPQI2ICEwhyp9k7mDjjT/wN6QkZII6PoebLa85RxHIdTp05hx44duHLlCrKzs2X6sCyL1q1bY8SIEZgxY4bMhaoxowsUIXXrSXwYso70gRn74twTB3e0mXgO5qbW1drOvQfnYXB5CPgMV9GWylmh1YxQGBrQkLO60pjOkQkJCRg0aBAiIyPBMAwGDx6M7du3w9TUFACQnp4OGxsbiMViFUdadxrT8awPacX56BGwATH50tNxtDNrhrP9Z1JRD0IaoTpLyt4UGRmJ5ORkZGVlQVtbG+bm5vD09IShoWFtNqu26AJFSN3hOA4X/ucNV0RJtcdLHNFiwgVYWdhVa3tH98yHd4Z0IZFQ7aEYMfNwrWMl8jWmc+TYsWORm5uLrVu3Ijc3F59//jkSEhJw8eJF2NjYID09HdbW1uA47u0bU1ON6XjWtazSIvQK2Iiw3DSp9jYmNjg/YBZMBDoqiowQUpfqLSkj0ugCRUjdinx8B4WnhsCMlR76kyixh/u4C7C2clB4W+XlZbj4R0e4I1yqPb7NFvTsOUkp8RJpjekcaWVlhcDAQLRp0wbAi2H+H374IS5evIhLly5BIBDQkzICAMgvK0XfwE24m5ks1d7SyBIXB86miaEJacQUPU/SzJaEELXSwqM9jIafRTpnLtVuzyQiZm9PpDx7qvC2NDQ04TZiN4o46Xc4DB58jvTnyZWsRcgLxcXFEAheFWRgGAabN29Gv3790L17d8TFxakwOtJQlIjKMeLCNpmEzEXfFIH9P6SEjBACgJIyQogacnNpA8sxF5DKWUm127HJeLKvF5JSYhXelrOjJxLdlkq1mbB5uHVgUqMedkZqz8PDA3fv3pVp37hxIwYNGoQhQ4aoICrSkJRzYoy9vAtX0p5ItdvpGuHcgJmw0Wmar3oQQmRRUkYIUUtO9s1hN/YCUjjpQkLN2BTE/9MXyamKP6UYMHgeHvG7SrV5iYJw5tSvSomVNE6jRo3C3r175S7bsGEDxo4dC3pDoOniJBymXfsHp5IipdottPRwtv9MOOjRxOKEkFfonTIlo/H1hNSv5NQ4PN3fB83YFOl2zhYu4y/CxspJoe2kP09G8m4fmLB5FW2FnDb0xtyEk30LpcbclNE5snGh4ymfRCLBvFvH8FtkkFS7oaYWLg38CG1Mmk5VakKaOnqnjBDSJDSzcYHL+EtIkkhXXmzGpiBuby88S4tXaDuWFs2Q7/OLVJseW4LIo5MbdaEGUn0pKSm4e/cu7t27h5SUlLevQJqcHx9dlEnItHkaONFnOiVkhBC5KCkjhKg9GytHuI67gCSumVR7MzYF0fv64HmGYkU7evachBDtoVJtLSQhOHV0hdJiJeqpsLAQy5Ytg729Pezt7dGxY0d06NCh4vfly5ejsLBQ1WGSBmBr9C0suhcg1cZnWBzqNRldLBV7ck8IaXooKSOENAo2Vo5wGXcRyZytVLs9k4igY18pvJ2e4/5CGmch1eaW9Asex9xTSpxE/cTFxcHX1xffffcdkpOTYW1tjQ4dOqB9+/awtrZGcnIyVqxYAV9fXzx9qnj1T9L4HE8Mx6zrh2Tat3d7DwOaNVdBRIQQdUFJGSGk0bC1doTTOOniH+G8Dhgw4U+Ft2FkYAp0/kOqTYspQ+KpKSgvL1NarEQ9CIVCDB48GDExMRg3bhwiIyORnJyMGzdu4ObNm0hOTkZkZCTGjx+PmJgYDBo0CEKhUNVhExW48Twe4y/vBvfGq/q/dhiO8S5tVRQVIURd1FtSlpCQgMmTJ9fX7gghTVQza2fYvXsWzzhLhPG7oPcHZ6GjXb15gPw6DccDg3FSba54jNOHFikzVKIGNm7ciOjoaCxduhS7d++Gh4eHTB8PDw/8/fffWL58OR4/foxNmzapIFKiStF5GRh+fhtKxOVS7V+37oVPPbupKCpCiDqpt+qL4eHhaN26daN/YZ4qURHSMCSnxsHc1BYCgdbbO8tRXFKIuxtbw4599T5amYQP0YCL8GzRSVlhNjnqdo7s1q0b0tPT8fjxYzAMU2VfiUSC5s2bw9zcHEFBQVX2bSzU7XjWhbTifHQ99TueFmZLtU92bYetXce+9d8NIaRxo+qLhJAmrZmNS40TMgDQ0daDbs8/IZa8Ok1qMiKknZmGsjIantZUREREoF+/fgp9sWYYBv369UNkZORb+5LGoahciGHnt8kkZP1tPfBnl3coISOEKExpSdnXX3+NAwcOIDo6WlmbJISQOlFUlI/jB79965N7X5++eGgiPezamYnFqQML6jI80oAUFRXB0NBQ4f4GBgYoKiqqw4hIQyHmOIy/sgf3sqSru/qaNsOBnpOgwfJUFBkhRB0pLSkLCAjAxIkT0aJFCxgYGKBbt2749NNPsWPHDoSGhqKsjF6QJ4SoXk5uJq5t6QWvlB9xbMcUcBxXZf9B7/2KBImDVJtnxl94GN40hqc1dRYWFoiNjVW4f1xcHMzNzeswItIQSCQSzLt9DCeTIqTanfRMcKLvdOhpCFQUGSFEXfGVtaGXiVdYWBgePHiA+/fv4+7du9i2bRtKSkroET4hROXSnichfE8/eDBxAACfgn9wbK8xRr7/v0rX0RLowKjPFojO9wefeZHAaTBiJJ+dAaFrSK2GSJKGz8/PDwEBAUhLS4OVlVWVfdPS0nDq1CkMHjy4nqIjqvK/iGv4IzJYqs1YUxun+s2Apba+iqIihKgzpT0py8rKwrFjx+Dh4YHp06fjjz/+wPXr11FQUIDw8HDs2rULn3/+ubJ2Rwgh1fYk7h6aIUGqzTtzE44fWlblet5e/ggzmyHV5sQ8wemDis9/RtTTrFmzUFhYiJEjRyIzM7PSfllZWRg5ciSKi4vx4Ycf1mOEpL6dSAzH57dPSLVpsjwc7T0VHoYWlaxFCCFVU1r1xa+//hrXrl3DtWvXwLLSuZ5YLAbHcdDQ0FDGrho0qkRFSMN27uxfcA7/BCwjfeqL9liL/gM/rnQ9obAU1ze0gSPzanLgcgkPpf3OobVn1zqLt7FRx3PkzJkzsXnzZpiammLmzJno1asX7OzsAABJSUm4cOECNm/ejMzMTEyfPh2bN29WccT1Rx2PZ208yEqB/+k/UCSSfiVjt/8EjHP2UVFUhJCGrN6rLx4/fhwLFy6USche6tSpE7Zs2aKs3RFCSI307fchopxXyLQ7RX2Ja9f2V7qeQKAF4z5/QfRaNUYNRoysszMgFJbWSaykYdiwYQM+//xz5OTkYPXq1ejbty+aN2+O5s2bo2/fvli9ejWys7Mxb948pc9RtmbNGjAMA4ZhcPPmTZnly5Ytq1j+5kdLq/KhtXv37kWHDh2gq6sLY2NjDBo0CHfv3lVq7I1NSlEehp/fJpOQfdd2ACVkhJBaU9o7ZfHx8WjTpo3cZTweD7NmzcLevXsxY8YMuX0IIaS+DB7+Ff7dl4U26esq2jQYMUzvzsA9XRP4tu0ndz1vL38cDZkB76y/KtocmScIOPQ1RkxYJ3cdov54PB5++uknzJw5E9u3b8eNGzeQlpYGALCyskLnzp0xefJkuLm5KXW/kZGRWLJkCXR1dd9a0XHy5MlwdHSUauPz5V/iv//+eyxatAj29vYVwzP379+PLl26IDAwED169FDSX9B4FIvKMPLCdqQU50m1T3TxxcLWvVUUFSGkMVHa8EVLS0ucOXMGPj7y7xZFRUWhR48eFReyxqqpDeUgRF1xHId/d0yDT/5eqfZcTh86Q8/Cw81X7nqVDWMsH3ARni386jTmxoDOkYoRi8Xw8/MDwzBwd3fH7t27cePGDXTqJD1x+bJly7B8+XJcunRJoWQqJiYGLVu2hLOzM27fvl1R7j88PBwdOnSAtbU1oqKiKk3o3tQUjqdEIsG4y7txMD5Uqr27pTPO9P8QAp7S7m8TQhqheh++2KVLF+zfX9XQHwEKCgqUtTtCCKkVlmUxfPJWhAgGSLUbsQXIPjEcKc+eyl1PINCCUW/ZYYxpgTNQTpNKEyX58ccfERoaim3btoHHU958V9u3b4dIJMKiRYuk5l/z9PTEpEmTEBcXh4sXLyptf43BytBzMgmZi74pDvWaTAkZIURplJaUzZ8/H7/++isOHDggd/ndu3fh5OSkrN0RQkit8Xg8DJ5+EOG8jlLtVuxzPN4/EDm58qvt+bT2xyPTqVJtzojB6cOL6yxWojqrVq3CN998g/Ly8kr7lJWV4ZtvvsEPP/xQ6/2FhYVh+fLlWLx4MTw9PRVa59q1a1izZg1++eUXnDp1CkKh/BsEly9fBgD06yc7RLd///4AgCtXrtQs8EboUHwolj04K9VmoKGFY32mwVRLV0VREUIaI6UlZV27dsWaNWswbtw4jBw5EufPn0d2djbKyspw4cIFfPnll3j33XeVtTtCCFEKTU0B/KeeQCw8pNqdmCe4sWtIpUU8Br7zMxIl9lJtzdP+QFT0nTqLldS/8+fPY8mSJTA1Na2ygrCmpibMzMywaNGiWj1pEolEmDJlClq0aIGvv/5a4fWWLFmCr776Cl988QWGDBkCJycnnDt3TqZfTEwM9PT05M659vKduJiYmBrH35iEZqdi6hvFf1iGwf6eE9HCyFJFURFCGiulJWUAMHfuXAQGBiIhIQH9+vWDubk5tLW10a9fP7i4uOCrr2hOH0JIw6OvZ4Q2EwKQwtlItbfg7uPUrmly19HW1oVej00QvzaMUZMRIen0DIhEojqNl9SfXbt2wdjYGB9/XPl0CS/NmTMHJiYm2L59e4339/3331cMW1RkGhlvb2/s3LkT8fHxKCkpQUxMDL777jvk5uZi2LBhCA2VHnaXl5cnNWzxdS/fdcjLy5O7HACEQiHy8/OlPo1RRmkhRl7YjmKR9NPRtR2Gob+tRyVrEUJIzSk1KQOAPn364P79+3j06BF27dqFP//8E0FBQbhw4QIEAkG1t7d7927MnDkT7dq1g0AgAMMw2LFjR6X98/PzMX/+fDg4OEAgEMDBwQHz58+v8sJBpYEJIRbmzWAz6hSyOaOKtmecJTx7VD7pva9PHzw0el+qzRWROH10eV2FSerZ9evX0adPH4WuXwKBAH369MH169drtK/Q0FCsXLkSX3zxBdq2bavQOiNGjMCkSZPg4OAALS0tuLq6YvHixVi/fj1KS0uxcuXKGsVSmdWrV8PQ0LDi83K+tsaknBNj7KVdSCjMkWqf7t4RH7egOQkJIXWjWm+oJicno1mzZgr19fT0VHgsfFUWL16MhIQEmJmZwdraGgkJCZX2LSoqgr+/P0JCQtC3b1+MGzcOoaGh+PXXX3Hp0iUEBQVBV1d6DDiVBiaEvOTs6ImQvgdRfG4o0phmaDk+ANZWDlWuM+C9dbi38QKasSkVbW7JvyL2yTtwdW5d1yGTOpaamgpnZ2eF+zs5OeHYsWM12tfkyZPh4uKCZcuW1Wj9N7f10UcfITg4WKr9ZQUweV7evKzsSRoALFy4EPPnz5dap7ElZp/fPo4raU+k2jpbOOK3TiPBMEy9xFBcUohnafHIykpEcWEWSotzUF6aB04khERcBglXDgYMwPLAsHywGjrQEOhDQ8sAOrpmMDK2hqnJi48yC8UQQupOtZIye3t7WFhYwMfHB76+vmjbti18fX3h4FD1l5ba2LJlC9zc3ODg4IAffvgBCxcurLTvmjVrEBISggULFuDHH3+saF+6dClWrFiBNWvWYPnyV3ewY2JisHTpUri7u0uVBv7000/RoUMHzJgxo1qlgQkh6s/byx93yw+hnWt7GBoYv7W/jrYeNLv9AS5oJFjmxQwjWkwZok5Mg9PHt+gLkZpjWbbKAh9vKi8vB8vWbBDKy6GGlU367Of3YsqFo0ePYsSIEVVuS1NTE/r6+iguLpZqd3Nzq5hn7c33yl6+S1bVfGsCgaBGo17Uxc6YO/gjUjqRtdUxxMGek5ReaZHjODxNiMDT2JsoSH8EpiAOesJ4mHLPYMQWgAVgXovtFwHIkfCRKTFDPs8KpVp2YAxdoG/eHDbNvODq3AYaGppK+msIIbVVrTNMq1atEBUVhcDAQAQGBlbcMTI2Nq5I0F7+b3XuLFalT58+CvWTSCTYsmUL9PT0sGTJEqllCxcuxG+//YatW7di2bJlFXG/rTTwpk2bcPHiRblVqgghjVe7SiaPrkzH9oNw5OG78Cn4p6KtueQhAo7/gCEjFyk7PFKPbGxsEBYWpnD/sLAw2Nra1mhf06dPl9t+9epVxMTEYNiwYTA3N5eZJFqemJgY5OTkoE2bNlLt/v7+uHHjBs6ePYtJkyZJLQsMDKzo0xTdy0zG7BuHpdoEPD6O9J4CK53az8FWXFKI0NALSIu7DM2sW7ATRUCfLYbLmx2V+GKJJiOCDZMGG0kaUBIClABIA/AIeCLhIwX2yNNuAb5FW9g4+aFlcz9oa1NVSUJUoVpJ2cOHDyEUChEaGor79+/j3r17uH//PsLCwnD+/HmcP3++IuExMDCoSNDWrFlTJ8G/LiYmBqmpqejfv7/MEEUtLS10794dx44dQ2xsbMVdwLeVBt60aROuXLlS50nZ9fR4/PM0pE73QQhRnrKyUmhqSj/NKHMfgv1hedBlimHP5WFY+WM4Pv0R8Ymj4WjfXEWRktrq1q0bdu/ejfj4+LcmQ/Hx8bh48aJMsqOoLVu2yG2fMmUKYmJisHDhQqnJowsKCvD06VO0bi09TDYnJ6ciwRs3bpzUsqlTp+Lnn3/GqlWrMHz4cKnJo3ft2gUXFxf06tWrRvGrs4zSQoy+uANCsXSRnk2dx6CdWc2HZz6Nj0DYvQNgUi/ARfQAVkwZKp5PKv2t/urRZERwwhOg9AmQeApIBBIv8xHPuKPIsB2MHbujjc9AmBjV5nkdIURR1X4WLxAI0KFDB3To0KGirby8HA8fPpRK1B49eoRLly7h8uXL9ZaUAZUPu3i91O/rPzeE0sAReen4PTKozvdDCKljWu4VP67T6oRZwrvocOwD2M+5UuMhbUS15syZg+3bt2PMmDE4c+YMzMzM5PbLysrCO++8A5FIhNmzZ9dLbFlZWWjTpg3atWsHLy8vWFhYICUlBQEBAcjKykLfvn0xb948qXXc3d2xbNkyLF68GK1bt8aYMWNQVFSEffv2oby8HJs3b25yQ/ZFnBjjL+9GUlGuVPvHLbpikmu7am8vIekxQq7vgO6zE3BBNFq9XFCD19GKJQLkSwxQwuhDyOpBzGpBwmhAwvABSMBIxGAkYvC4YmhyJRBIiqCPPBiwRdXfGV4kau6IAPIigNBdyA5hcY9xQ4FxZ1h7DEZbn34QCOQPryWE1I5SzrwaGhrw9fWFr68vPvjgAwAv5loJDw/HvXv3lLGLt3r54nJ1Sv3m5eXBwsJC4f7yCIVCqUk6G2t5YEJI9RQyAvys1QXWXAESjq3B/JGKzzlFGo62bdti7ty5WLduHVq2bIlZs2ahZ8+eFUWvUlJScOHCBfz111/IyMjA/PnzFa6cWFsmJiaYM2cObt68iRMnTiA3Nxe6urrw8vLC+++/jxkzZsh9p3HRokVwdHTEunXrsHHjRmhqaqJz585YsWIF2rdvXy+xNyRL7gfi4rNYqbZulk74ucNQhbdRUlKEa1f/RvnjXWguvofW/71fqogczgDP+G4Q6rpDw9gVRhYeMLdwhpWVExz0jGt0Q0coLMXzzGQ8f/4UOZnxKMp+AnH+E2gXP4WZOBFmbLZC2+ExHFzxGMh5DNzcjpjrWnii2Q58+4Hw6fDuWwshEUIUV2e3w/h8Ptq0aSMznr2xWb16tVTxEEIIed0zVh8rslPQMz4cPo61r0hL6t8vv/wCLS0t/PTTT1i1ahVWrVoltVwikYDH42HhwoVKL0EPADt27JA7FYyBgQF+//33Gm1zwoQJmDBhQi0jU3//JoThx0fSk33b6Bjgn56ToMG+vUhPalo8bp37GQ4Z++HG/ndTtoonYmIJi6eMGwoM28HQrivcW/jD29oZbZX8JF0g0IKdrSvsbF3lLs/Ieoa42LvISL4HUUYITErC0AxJFcWKKqPLlsJLFAQ8CULpk0U4ixYosR6EVh0mwMWpVZXrEkKq1mjGKLx8QladUr+1LQ0MKKc8cHNDC3zg3untHQkhKlNYlIuSJ8ehBaFUe5a2JxxdO1f8nlZSgBNJ4VJ9ClgB5pz7Fdem/UnVGNUQwzD4/vvvMX36dGzfvh3Xr19HWloaAMDKygpdunTBlClT4OIiU7KBNGAxeRmYem2/VJsGy8OBnpNgqa1f5brRcSEIP7ccLUsC4c2Iqnw/LJfTR7yuP/ScBqBt++FwNbZURvi1Ym5qDXPToUDHV08Dc/OzEPbwIjKeXoVW9i04ceEQMFVXHnVDJPAsEjj2Cy5IXFBgPRRtOk2Bk2PLuv4TCGl0Gk1S9rZ3wOS9c1bb0sCAcsoDd7V0QldLp1ptgxBS90IeuUHj/BDoMK8lZqWXEFPgin4DPqpoupeZjPePrUY0q13RdotvhGUn1uG7EZVPRk0aNhcXlzp5EkbqX4moHO9c2oX88lKp9l/aD4WfhWOl6z2OuYfI88vQqvQcvBmu0qdiBZwuYvX6wLLVe+jQfih81KD0vJGBKbp2fQfo+g6AF0MyQ0LPIy06EPrZV+GC6CrXd2bigLR1wL/rcA7NUWo3Bh27T4OFuWLz2xLS1DWaN8/d3NxgY2OD4OBgFBVJv+BaWlqKq1evwsbGBq6urx7lvyz7e/bsWZntNfXSwIQQWd5e/njuswFiifSp0yHyC9y+e7rid1+zZjgycB6MuRKpfhuzniAyVfrdFUJI/fvk5lE8ynkm1TbeuS0+atFFbv9naQk48te74J3sgjbCQPAYTm6/SMYH0R5r4To7CaM+PIgunUer7Vxg2tq68Os0HCMnbUCfuWHQGf8Ej91/xkPN3ijktKtc1xVRaJW0Erm73XDitx64cH4rhMLSKtchpKlrNEkZwzCYMWMGCgsLsWLFCqllq1evRk5ODmbMmFFRsh94URqYz+dj1apVUsMYm3ppYEJI5Xr2mIgIe+miHZqMCBpXJyL26aOKthY2rphlKv0EPIfVxuzTP4Koh/Hjx+PIkSMqW5/UjR0xd7A95rZUWysjK2zqPFrqOwLwYm6xf/fMR/ZeL/gU/ys3GSuVaOKBzgiUDAzGoM9uof/Aj6GjrVenf4MqWFo0w4BBn2L4RwFwn5OO5Ha78UB3JLI5o0rX0WDEaCW+Duew2Yj4wxZHtk5GeOTN+guaEDXCSCQSxUsEqcCWLVsQFPSiXPyjR49w//59dOnSpeKJ14gRIzBixAgAQFFREbp27YqQkBD07dsXvr6+CA0NRUBAALy9vREUFCQzh9mqVauwePFi2NvbS5UGLikpQWBgIHr27FmtePPz8yveVXtZwZEQ0rhIJBIc3ToJPoX/SLUnc7ZoOeUGTE1eDIcWi8Xoum0mbvONpPp9Z+mMbwZ9hKZInc6RLMti2bJlWLJkiUrWVwfqdDwB4FH2M/id/B9KxK/eldLjC3B72GfwMJSuxnz12j9g7yyALfvszc0AAIo4LUSbTUDXgYua9BA9kUiEO3dP49mjfXAqOAsjtuCt68SgBUTOE9G91wfQ06v63X1C1J2i58kG/05ZUFAQdu7cKdUWHByM4OBgAICjo2NFUqarq4vLly9j+fLlOHToEC5fvgwrKyvMmzcPS5culUnIgIZVGvhZcT5uZiQAABgwcoeqM8yr9td/BoBe1m7Q5mvIrJMrLMHtzMQX22RebfvlHcE3f67Y/3/bb2NiAz0N2ffmhGIRwnLSpLYh9b+v7Y9lGJn92+gYQFfOdjkJh7SSAqk4K9Z/I1aWYcBjWPAY9r8+AJ9lwTKN5iEwaYAYhsHQyVtxZlMCWolf3fVtxqbg1t/D0XvWFQgEWuDxePij71z0uvAnCthX/9bXPwvDuOdJcLKo+aS0pH6EhIRg165dqg6DKEFhuRBjL++SSsgA4M8uY6QSsmdpCbh9+EO0Lr8kdzxRsUSAx6bvo9vgJWhlal3XYTd4fD4ffp2GAZ2GoaxMiBs3jyAnfA/cSy9DmymTu44bIoEn3+BJ7CrEGg5Fi66foIVH05uOgZDXNfgnZeqmNncNjyeGY+SF7TXe99N3FsFez1im/cbzeHQ9VbOyyQBwZ+hctDWTvQsYm58Jj8M/1Hi7p/rOwIBmzWXa88tKYbxncY23u6PbOEx09ZW7zGTPYgjFIjBgKhK5lx8ew4Bl2Ipk72Uy+bJ9gVdPfOAhv0rmiPPbkVFaKL0tMGBZFux/++KxDFiw4DEMeP+181kWg+1aYpyzj9zt/hZxDSlFeeCxLPj/JZ/8/37ms2xFQsqr+Jn5bxkPrgam6Ggufw6ZqNznKCgvBZ/lQYNlwWd44LMsNFgWGiwPmiy/4ueX+6JEV1Zefg7ubfWDE/NEqj1EZziGz/inYn6h+YdWYn1BrlSf3lwBzk7/s75CbTDU6cnKy+P35pA2Rby8tNKTsoZjytV9+DtOeu7U2c0743e/URW/nzv7F8zCFsp92sNJGDzUHYYOQ9fA1pqKc71NXn4Orl/dAcTtQXPJw7f2j2R8IGg1E927v6+27+ERIk+dPykrKSnBnTt3AADdu3eXWX716lXw+Xx06tSpRhMfNkW1zY/lP1sD6irtrnW8lXzRkaC22618WYmoHGWcuEbbLSiv/CXlB1nJSC6ueqLxyjTTMaw0Kfs79h7uZSXXaLuTXdtVmpR9cec4ApKjqrU99r+Eb2CzFjjSe4rcPitDzuFYYhi0eRrQZHnQ4PGhyfKgyfIgqPj5RcKn+doyTZaPZrqGmOwm/05pbH4mkotyocXTgIDHh4DHg4Dly/yuwfJq9AW6pgwNjOH67gmk/9NNajJW7+JjOHFwEYaPXQ0A+GnkQlzdNhMPXhvGeIHVx7pzWzG37/R6i5dUz/btNb9J9pK3t3ftAyG1tjPmjkxC1tbUFr90GAYAyMpJR9C+qWhddl7u07FoeMKm/+8Y2VJ+IRAiy9DAGAOHzAMwD7FPw/Dw2kbYZR6udOLqFpIHwKNZuBf6LdJtJqJLv7kwM7GS25eQxqjGSVliYiJ69OgBlmUhEolklvfo0QMMw8DR0RELFizA1KlToalJdz6qUtvcqbLvorVPcipLnmqnsq/OXB0lp7XdNq+KJ0Xi2my3ipsWIon8Cl8KbbeKeMu56m+Xk0hQJhFDJKk8qY0ryML9rJRqbxsA2pvZVZqUbX58Ez+HXVZoOwIeH1o8PgQsHwIeH056JrhUyftbgSmPcTT+EbT+W0ebrwFdviZ0+Jr/JZGvEj5tvsaLPjwNaPM1oM3TgIuBGext3ZDVez9KLg6RGqrjnvobUlI/gK2NM3g8HtZ3n4kBwX+jmHl1Hvwp4S7ezRkCmwYwbxGRNXnyZFWHQJQgMjcdH9+ULriiryHA/h4TIeDxcff+WZRfnorWbIbMurmcPp65fYP+g+bSHIO14OrUCq5Of6C8/FcEBe1HUdgWtBDdkjtZtSWbAcu0tXi+83dc1RsCzx5fwsNN/ugXQhqTGidlOjo66N69e6Vf2Lt37w6JRIKIiAjMnj0by5cvR2pqao0DbQp0+ZpwMzCDRCI/kZLg1dMpiZwe/Eq+hGuyPFhrG1Ss8XI7Ekik9iW9fVQs51VyjBkAWjz+a9t78cX9ze1WptIne1Wu9XZVPSepzbbZKp7AiOsoeRLXIHl6iV9Fsieu4dNCANBgKv9iUiyS//6AIgS8yk9HpWLZGz+VEYpFEL7WX6uK7T7ISsbm6JpVAtPha6Bg4osnYT5teuBC5v/g/GgWgBdf5IJ91mGKuW1F/y4e7THpwQlsKimuaEvj6WLG0WU4PW1jjWIghFStRFSOcZd3o1gk/R7ZX13egaOuMY7t/xotn60Dn5U91z7U6Am/d7fCpwkX8VA2DQ1N9Ow5Ceg5CU/jI/Dg6m9wyDwEE1Z2pIk2UwafoiPAqSM4yesEk3bz0anjMBp9RRqtGidldnZ2uHz5cqXLX18WFhZWUUGRVK6vrTuiRn/99o7V1N7cHsnvKf+dBjdDcxRNevs7ZRKJ5FWyhlcJXGVJpJGmFjLHr6joy0EilfRVbBcSiDkJxBIOYglXsdxKW7/SWG4O+RQcXsXDSSQQSyTgJFzFzy/bKz6QQMxx8DKp/IXutR2Ho0hU9t82OHDci3hexvZy2/J+72bpXOl2h9p7oo2pDcQSCUScuGKd8td+rmjnXmxbJOEglkhgp2tU6XY1/nviI/pvW9WhwVaelL35An11aFaxXWE1krLqbLc6yd6btHjSRXV6956GYxnRMH52EBqD9uGLW4ex9EA0Znr44Zs2vaGnIcC6Md8iaPtshL02jDGQ0cWGi3/jo14TaxwLIUS+BXdOyMxHNtPDD/1M7XBqYz+0Fl2TuZOXy+kjw3MVhvb9kBKAOuTk2BJOjhtRKvwFVy5uA/v4L7hC/rB6T/FN4Na7uHTTBeIWH6NHrxnQ1JQtFEaIOqNCH0qmTi89EwK8SprLOTHKJWKUcxzKxCKUceL/kj4O5RLxi//lOBhqasHVwEzutq6kxSG+IBsl4nKIOA5l3IvtlInFEHIvnmCVv9FexokgFIvR2sQaK9oOkLvd+beOYXfcPQj/2051EkkfE1vcHT5P7rKv75zETwoOi3yTrY4hEsd+K9XGcRyKiwvww+PrWP3wQkV7W1NbXBr4EfQ0BLgYEYxhNw+ghHmV1NmIC3HvnZWwMJT//2tjQufIxqUhH89jCWEYdXGHVJuXsTX+btkD2afGwp5JlFknivFG63f/oUIeKnI/5AISb/yKlqUXoMFUfp5P48yRYf8Beg2YRyX1SYOn6HmSkjIla8gXKEIaC07CvUjQxCKUil8Ub3nxs6gi+SsVi1DGiaDD14S/lYvc7RxLCMOFZzEQikUoEZejRFSOIlEZikVlFdsUikUQcmKUiMpRIi5HqbgcpWIRXPXN8HiM7JPtonIhHA6sRE5ZiVT7MHtPHO41GSzD4oO932CbUHqo5yBJCU5M+0N5/yc1UHSObFwa6vFMLsqFz7G1yBa+Gi6szdPAn3Yt0erBpzBgi6T6cxIGDy1mY+jYn8HnN/jZghq95NQ43D7/M5wz/4EhW1hpv1xOH/EWE9Bl4FewMLWttB8hqkRJmYo01AsUIUR5JP89WdSU875aclEu5tw4gpNJETLLpto2x5Z+M1BWXgbfHR8h4rVhjIxEgg0uvvjQf3xdhq5ydI5sXBri8RRzHPoF/onLaXFS7R/rGWFO8iqZJzC5nD6K/Tajy2ul8UnDUFiYh8vn/oDR079gy1Zel6BEookog9FoN2Ax7G3d6jFCQt5O0fMkDZYmhJBqYhhGbkIGAM10jXCszzTcHz4fxpraUsu2p0Rh7sHvoKmhiZ87ToRA8uqdNgnDYGXMFWTkZdVp7IQ0dr+EXZZJyPxYDp+lrJBJyJ5KnGE06holZA2Unp4hhoz8Bp0+jcUTr02IQUu5/bSZMvgU7EPpgdY4+udoRMeF1G+ghCgBJWWEEFIH2pjY4GCvyTIFbTYWZOOPCzvRv7U/xgmkX1RP4elj2uHGO9EwIXXtbmYSvr1/RqrNTCLCHzl/yVTmfajRAx0+uAUnR/lf9EnDwefz0bv3NPT59D4yuv6LMF5nuf00GDG8S06Ad7wjjm0YiEcRwfUcKSE1R0kZIYTUkZ7WrvjMVLqctojhYXH8XQQ+uooN73yHFqJcqeUB0MJfV/bUY5SENA5F5UK8f2WP1PyOrESC34r+hQGk3+F8YDgJQ2YFUJEINcOyLDq0G4Shn1xGcf9rCNUaCJFE9qssy0jQuuwC9M72xPHfe+F+yAU5WyOkYalWUpacnFxXcRBCSKO0ZuinGAahVFs+K8AHt/YgITu5kmGM15CeKzuRLSGkcvNvH0dMfqZU2yzhHbQXv3oXiZMwCLP/FqOmbqHJoNWcZ4uOGDHrGDRGh+CB/liUSjTl9vMSBcH48kCc+l8X3L57up6jJERx1UrK7O3tYWVlhYEDB2Lx4sU4cuQIEhIS6io2QghpFP6ZuBYd33gilsLTxzsnvkdn17Z4T6D1xjI9TDuytB4jJES9nUgMx5boW1JtrUVp+ER4u+L3Mgkf8V4bMHTUt2+uTtSYo31zjJr+N0wmROGB8TQUctpy+7Xk7sA8aAQC1nfA9Rv/guNkJwwnRJWqVX2xdevWiIqKgkj04q4uw7wYoW1sbIy2bdvC19e34n+dnSufELcxa4iVqAghqpeem4HuB79BLF96uJS/OB8nJ6xHh92fIPK1aowAsM6uFT7pM6X+gqwHdI5sXBrC8cwoLUTroz/jeemr0uk6kjIcL9wHRy4PwIvqfFntt6Fb13dVEiOpP9m5Gbh6ajUc0nfCiC2otF80WkG33UJ07jyaJgkndarOSuILhUKEhobi/v37uHfvHu7fv4+wsDCUl5e/2OB/iZqBgUFFgrZmzZpa/CnqpSFcoAghDdPdJ6EYculPZLA6Uu3v8ziM9xmBUXcOo/S1SaUtxUW4OfJb2Jva1HeodYbOkY2Lqo+nRCLB6Is7cSwxTKp9VfEFjC0PBwAUctoo7rYXHdsPrvf4iOoUFObi4umfYJu8GaZsbqX9YtACgrZfo1vXsZSckTpRr/OUlZeX4+HDh1KJ2qNHjyAUCsEwDMTiymdlb2xUfYEihDRsB26dwLTw8yj5L/my4fKxuegEWI8lOJydhC2l0gUJenEFODf9T1WEWifoHNm4qPp4bo+5jRlBB6TaepU/wZ/FJ8HgRUJW1vMQfH361ntspGEoLinEhdNrYZGwERZs5VOOxMEdvDYL4O//PiVnRKlUPnm0SCRCeHg47t27h2nTptXFLhokVV+gCCEN3/enf8fStCdoJX6OP4tPwlxSjDIJH9mdD+Cjh//i4RvDGL+zdMY3gz5STbBKRufIxkWVxzOhMBtt/v0FBeWvCumYcMU4XbgHZpISFHNaKOq+Hx3aDarXuEjDVCosxvmA9TB98jus2MoLKT2RuIBpvQD+PSZRMRiiFCpPypoq+sJBCFHEkv2L8F7ar9DGq8qLuZw+nrTbiCmxQShmXlUSM+ZKcLX/Z2jZzF0VoSoVnSMbF1UdT07CoX/gX7j4LFaqfWPRSfQVPUGJRBN5fnvh12lYvcVE1INQWIoLgb/DMPZ/sGHTKu33VOIMrtUX6NlrKiVnpFYUPU/S81lCCFGBZe9+hyi9UVJtRmwBLO5+hclaelLtOaw2pgesaVJDwQmpysbI6zIJ2ZiycPQVPUG5hIfMdlspISNyCQRaGDTsC7T/OAaP3dYgmbOV28+JeQKX8I9w7X/NcTZwU0WRO0LqCiVlhBCiAizLYujkbQjndZJqt2aeYbiZE9q9UUL/Nt8IC47+UI8REtIwxeRl4Ku7p6TabLh8LCq5BgB42vwndO82VhWhETWiqSnAgMFz0emTx4j2WIskrpncfg5MAtwiP8X1/7kjMOB3lJeXye1HSG1RUkYIISqioaEJ/6nH8UTiAgAo5rSQ6LMFAYYWMLbxgRFXItV/c346LkfeVEWohDQIYo7DtKB/UCIul2r/ofg89FGGR7Zfof/Aj1UUHVFHGhqa6D/wY3T+NBoxLf6HRIm93H52bDLcH8/Hrd/cEXBiLcrKhHL7EVJTlJQRQogK6esZwWPsScSiOQp7H8NGToz1Eddw7vlTtNC1kOpbxGji46AtKBGWVLI1Qhq3/0Vcw/Xn8VJtE4Wh6CxOxgPDSRj2zneqCYyoPT6fj379Z6Hrp48R5/kH4iWOcvvZsqloHvc17v7uilP//oBSYXH9BkoaLSr0oWT0EjshpCY4jsPQ89twJiVKqt2NK0UMqyXV9g7LYf/ktfUZntLQObJxqc/jGZ2XAZ9jv6BU/OrdHgdxLk4U7kWcRmcMnhVIBRmI0ojFYly+vBN4+BOcmbhK+z3nzPDcYRZ6D5oPHW29SvuRposKfRBCiBphWRbzWnUHn5E+LceyWjARSz8ZOywGNl/dX5/hEaJSYo7DjKB/pBIyRiLBDyXnkSZxQq/JhykhI0rF4/HQu/c09PwsHAneWxEH+dVvLdhMtEpaiYiNzjj2z0Lk5efUc6SksaCkjBBCGog+Nu7Y3PVdqTYJgDyeNljJq8qLHMNiWfQVPHmeVM8REqIav0cGIfiNYYuTykLhWC6Ex9jj0NMzVE1gpNFjWRY9ekxEr08fIqntLsSgpdx+pmwuWj/7BU+3uODonrnIzK683D4h8lBSRgghDcgk13b4ru0AqTYxAD6kR5qn8XQx8dgKKpNPGr24/Ex880a1RTtxHj4puQ1+r91oZuOioshIU8KyLLp3fw99Pr2P1A77EMW0kdvPkC2Ed8YGpO9wx5Ht05Hy7Gk9R0rUFSVlhBDSwCxs3RsfukuXyi9j+NCQSCdgN/mG+PjgivoMjZB6JZFIMO3qPpRy0v/2fyg5j2cuS9DWu7eKIiNNFcuy6NJ5NAZ+dgfPuxxFBNtebj8dthQ+eX+jcL8njvz1LmLiQus5UqJuKCkjhJAGhmEYrG8/DB3fmKusnOGBlXBSbTuK83DwtvRTBEIai82PbyIoI0Gq7X1hKDS1OmLQsC9UFBUhL3RsPxiDPw1Gbo9APOJ3k9tHkxHBp/hfsMc74Ngf/fEg5FI9R0nUBSVlhBDSAGlqauLEez/B843EjHujEEgZw8eXD48jKSu1HqMjpO6lFOXh85tHpNpsuHyMLM3CoEnbVBQVIbJ8vHti2McXUNz/GkIF/SGWyH69ZhkJWpdfgtHl/ghY3wFXr+4Hx3FytkaaKkrKCCGkgTLVN8bRoYvhJM6vsl8STx/jjiyh98tIoyGRSDDp7BYUvzFrz8KiG/AetRdaAh0VRUZI5TxbdMSI2SfAjLiLB7qjUCbhy+3XXBICu/uTcG29O06f+IXmOiMAKCkjhJAGzcXKEQf7fAprcWGV/W7wjfDh/kX1FBUhdWvP41u4nPtMqm1UWQRatPoSjvbNVRQVIYpxcWqFUR/sh/64CDwwnooCTv5NBHsmES3iFiLsD0cc3TMPzzOS6zlS0pBQUkYIIQ2cj2Mr/N15Iky5N+6mvvEU4W9hGTZd3l2PkRGifDnCYnx6/R+pNjOuCEM0LNCz1yQVRUVI9dlYOWLU5D9hNy0WD60/RyZnIrefKZsL74w/kL3bA0f/egdhETfqOVLSEFBSRgghaqBnyy7403sYDDghAMCYK8G3pVfAvJaYiRkW38bdwJ0nISqKkijbmjVrwDAMGIbBzZs3pZaVl5fj8OHDmDJlClq0aAFdXV3o6+ujY8eO2LBhg9zhrPHx8RXbk/fZv1/1k5JPOfkb8hjpiaBnlkRj7IQNKoqIkNoxNjLD8LGr4TXnCSJdViNRYi+3n4Aph3fxMeie9UfA+g64cGEbysvL6jlaoiryB7sSQghpcEb6DkCxsARLIk5hQ/FJuHPZyGG08btWx4o+2aw23r/4B4JMf4C5oakKoyW1FRkZiSVLlkBXVxdFRUUyy+Pi4jBmzBjo6+ujV69eGDZsGPLy8nDixAnMmTMHZ86cwbFjx8AwjMy6bdq0wYgRI2TaW7VqVRd/isKOR9/ByfwMqTb/8niM7/cjvUdG1J6WQAeDhn4OsXgugoMPojBkHVpw9+X2bS4JAR7Nwr3Qb5Fm9R469ZkLKwu7+g2Y1CtGInlj/Auplfz8fBgaGiIvLw8GBgaqDocQ0gjdvHMa+kFjocMIIQaDGTrDcE3DQapPF1EeLk3bBB6PV8lWVIPOkYoRi8Xw8/MDwzBwd3fH7t27cePGDXTq9Gr+upSUFBw/fhyTJ0+Gjs6rhKWoqAg9evTA3bt3ceDAAbzzzjsVy+Lj4+Hk5ITJkydjx44dtY5TmcdTKBbBbcc8pLCCijZdSRnWG5hh6pgltQ2VkAbpYXgQ4q79gpYlgdBkRJX2K5PwESXoAYu2M9Gxw1CwLA12UxeKnifpiBJCiJrp1H4Q8jrtQqlEEzxI8GvJGTiIc6X6BPMNMWnPAtUESGrtxx9/RGhoKLZt21ZpYm1ra4vZs2dLJWQAoKuri/nz5wMArly5UuexKsusf3+WSsgAYFxZKiaPWqyiiAipe609u2LkrKMwGBeJELPZyOaM5PbTZERoXXYeVjffQfA6Fxz7ZyHSn1NhkMaEkjJCCFFDnf1GIrvTLpRINGEkEeKv4hPQ/+99s5f2ixl8ffRHFUVIaiosLAzLly/H4sWL4enpWaNtaGhoAAD4fPlvKaSmpmLjxo1YvXo1du7cieRk1X65u5UQgf156VJtLUUZWDTsR3oiQJoEaysHjHx/PTznxOOx+8+IReVVRpuxKWj97Bfk7XHD8T/64vKVPfTuWSNA75QRQoia6uI3CsFgILk5CSW2c9BVooGAgiypPr/mpMHm/DZ82meaiqIk1SESiSoKd3z99dc13s62bS8mV+7Xr5/c5efOncO5c+cqfufz+fj000/x008/VZkECYVCCIWvkv/8/Krn0FOERCLBrHO/oYynW9HGSjjMtm4LexuXWm+fEHWiJdDBgEGfAoM+xYOQS4i/9RtaFAdCwJTL9NVgxPAqvwI8uILQe/OQaDwErh2moVWLznQzQ4k4jsOdewFIfbAN/SfuhI62Xp3sh44YIYSosS5+I2E4NgRprfrJJGQAIGJ4WJwYgkN3TqsgOlJd33//fcWwxZdPu6rrr7/+QkBAAHr16oVBgwZJLdPR0cHSpUsREhKC/Px8PH/+HMePH4ebmxvWrl2LRYuqnutu9erVMDQ0rPjY2dW+8MCSU7/h4WsJGQAMFmVi1qBPar1tQtSZj3dPjJx5BGaT4/Co2UIkcf9v786jorjSNoA/3TTd7IuAbCIiKCIx4r4vxDUuuI0rGIm4ZRknGmM0kzEaTUwcncQzmcSJS3DGuAXROKMmagCNBreJIq5BRYkKEtmRnb7fH4b+bLuBbhSqpZ/fOZyk696qevsW9svbVXWrWbV9XeS56JC3BfaHXsDRtYHYs20hUm9easBoG5+Cwlz8d89KHFkbhKbHxyCk6D84Greh3vbHiT6eMt7ETkRSOJF5C8MPbUBuWbHedhd1MbZ2n4KBwb0aODJt/IysXlJSErp06YI333wTK1eu1CyPjIzE5s2bdSb60Gffvn0YM2YMvLy8kJiYCE9PT4P2nZGRgeeeew4FBQXIyMiAs7Oz3n76zpT5+PjU+Xjeun8Hnb/9ENlya80yd3UhEgbPQ2ufQKO3R9SYqdVqnD6zH3d/3oDA4sOwltV+yeI1tEGx9yg83y2cD143gFqtxvmLP+LGiX8ioGA/7OXazwe9hkAMeiPZqG1yog8iIjPSvakvEl58FR7W9nrbs+TWmHpiC45ePtnAkZGhpk2bBn9/fyxdurRO63///fcYN24c3N3dERcXZ3BBBgAeHh4YNmwYysrKcPr06Wr7qVQqODg4aP08iVf3ahdkADDDqTkLMiI95HI5unUdgTFz9sDj5VRcbLEMKWhb4zoBuIJ2dz6GiH0ehz99Dnu2vYWLl09CrVY3UNTPhvSMW9gbsxTxa4Ph+MMgdHgQo1OQAUAAruLsufh6iYH3lBERNRLtmnjiyLDXMPzgBlwruK/Tnim3xaTj0YhRKNCzVScJIqSaJCUlAQCsrKz0tvfo0QMAsHv3bp1njH333XcYM2YMXF1dER8fj5YtWxq9f1dXVwBAUZHuHyL1YfuJvTgI7dkWu1Rk473RnJyGqDZNnNwwYvRiAItxNeV/uPjTenhm/Qce8t+qXccfvwD3fgG+X4sTBzyQ4RgK19bD0Lnz8Hq7T8qU/ZaVjjM/bUPlzVi0qTiDdjI1oPtYR407ak9k+7yEXr41F8J1xaKMiKgRCXBwxbERr2P4gS/wv9x7Ou33LGwx/sh6bKkoRWhQTwkipOpERUXpXX706FGkpKQgLCwMbm5uaNGihVb7d999h9GjR6NJkyaIj49HQEBAnfZ/6tQpANDZfn0oKy/D+8l7oVY4aZZZiXJ81GumyT1bj8jUBbbqhMBWnVBZ+Q+c+d8B3En6N/wKDsNJXlDtOp7yDHgWbAP+tw03zyhxQ9EBle794Bc8FMFtejTaf4epNy/hws+7IL+9H60rz6KN7PczhtUUY2ohwyXLnnAKeQU9e4yr13HhPWW/O336NN577z0kJiairKwMwcHBeOONNzBlyhSjtsP7JYjIFOQU5mDUtrdw/JE/eh/loC7BF8GDMal7WIPGxc9I49V0T1lVQebs7IyEhAQEBtZ82d+pU6fQoUMHnUlE/va3v+HNN99E27ZtceHCBchkNXxd/Ii6Hs8/bv8LPi/Wvv9xsoXAlpfWGLwNIqpeWVkpTp76Fvcv7UCLwoQaC7TH5art8asyBKJpD/i0DkVwUC+oVPrP4Ju6nNz7SDp3ANk3foBL3jE0l6UZtF6G2g0ZTf+ADn1feeJ78Qz9nOSZMgAJCQkYMmQIlEolJk2aBEdHR8TGxiI8PBw3b97EO++8I3WIRERGcbZzxg8vf4FpWxdiZ4Uc4rE/svPlVoi8FIc7uRl4c+gsiaKkJ3HlyhWMHj0apaWl6N+/P7Zt26bTp0WLFoiMjNS8XrhwIa5cuYJ+/frBx8cHxcXFSExMxNmzZ+Hs7Ix///vfBhdkdZV86zK2PMgB5P//R55PZQH+MfGv9bpfInOiVKrQp/cEoPcElJeX4fSZ/ci4GAOv/Hi413CJIwA4yQvgVPEjcPdH4O4qpMZbIk3WCoUO7WHj3hE+LbuhlX8HKJWqGrfT0CorK5Gadgk3fjmGwts/wTH/ZzTHdfjK1PAFarw0EQCK1FZIsQmF63MR6N59TLXPeawvZn+mrKKiAm3atMHt27eRmJiIDh06AAAKCgrQo0cPXL16FZcuXUKrVq0M2h6/BSYiU/PRgc/xYfoVPJApddpkQo0IlGDjtL83yOUq/Iw0XnVnyhISEhAaGlrjuv369UNCQoLm9YYNG7Br1y5cuHAB9+8/vO/Q19cXQ4cOxYIFC9CsWfVTbutTl+M5aP0MxD12BveT5u0wd8A0o/ZNRMZTq9W4cPkn3DgXC+vf4uEvLsNCZvykH2VCgbvwQZ4qAMKxNWxdW8PNvQ18mgehiVPTen1OWkVFBe6kX8ed25eRnX4BZdlXYP3gCrwrr+mdnKMmxUKJa8oesAoYi569psDOzvGpx2vo56TZF2UHDx7EkCFD8PLLL2setlllx44dmDRpEhYvXowPP/zQoO3xDw4iMkWHLx7DnJ/+hVSF/s+lnmV3MdS5FeQyGWxc/XFXz9T6pSWFKCvKAQC42TqjmaMHAtxaILSt4fem8TOycanL8Vyy7x/4IuOSZtbFnpUF+HHGP+szTCKqRlbOPfx8+lsU3PoBTQtOwFue/sTbLBIqZAk3FCrcUWbpArXKFXIrF1hYOcHSyhFKKwcoFFawsFRCoVABv5cianUlysuKUVFejPKyBygrzkFlcQ7UpdmwKMmEsjwT9pW/wU32G5SyijrHl612RJpNL9j7j0D3HuPrpRB7FC9fNFDVN4iDBw/WaataduTIkYYMiYjoqRsY3Btn/UIwdcdi/EcogccuUftJ6YWfHjx4+KIwqfYNlhQDWXcx5NqPRhVlRO8Pfw1zcjIx+9sP8GOlGn8fPE/qkIjMlouzOwYNngXg4WXst369iisXDuHB7R/hUvg/g+/BepSNrBQ2stuA+jZQioc/+U8p4DqcgCsTClyXt0OZW2/4BL2IkOdD0ckEJzIx+6IsJSUFAPRenujs7AxXV1dNH330PUiTiMgU2dvYYc/Lf8c/4r7GP64fwlULF53ijKgheDk3xX8i1+J29j00a+IudThE9Dtfn0D4+gQCeB0AkJ37Gy5dPIr7N49DnnsebqVX4CHPlDbIWmSrHXHXMhiVrl3h0bIPnn8+FIHPwJT/Zl+U5eXlAQAcHfWfunRwcMDt27erXX/lypVYtmxZvcRGRFQfXnshHC/3GIW//ms6PlO4IltuI3VIZKZYkBGZtiZObujdaxzQa5xm2f3sDFy//j/8duc8yrIuQfXgBhwrbsNddg+KOtyfVldFQoUM+KDAqiXgGAgn744IaNUdHTz80Kke72mrL2ZflD2pxYsXY/78+ZrX+fn58PHxkTAiIqLa2Vjb4b3ZOxHy4w5surgdpy3dUQYLCABlsECRzLLG9VWiEkpUQik3vUtAiIio/rg28YBrk+FAl+Fay8vKSnEn4wYy791AXtZNFOffRWXRb5CV3oeiLBsK9QMo1YVQiWIoUA5LlEOBcgjIIH6fGrEMKpRBhXKZCqVyO5RbOKJS4QBh3RQKW09YO3iiiZs/mnm3ga+rF4KfweKrOmZflFWdIas6Y/a4qpvzqqNSqaBSmdaUoEREhhrVZyJG9ZkodRhERPSMUypV8GseBL/mQVKH8kxqPOVlHVXdS6bvvrGcnBzcv3/f4OnwiYiIiIiIjGX2RVm/fv0APJwa/3FVy6r6EBERERERPW1mX5QNGDAALVu2xNatW3Hu3DnN8oKCAixfvhwKhQKRkZGSxUdERERERI2b2d9TplAosGHDBgwZMgR9+vTB5MmT4eDggNjYWKSmpmLFihVo3bq11GESEREREVEjZfZFGQCEhobi2LFjeO+997Bz506UlZUhODgYy5cvR3h4uNThERERERFRI8ai7Hddu3bFgQMHpA6DiIiIiIjMDIuyp0wIAeDhVPpERKSt6rOx6rOSnm3MeURENTM077Eoe8oKCgoAgA+QJiKqQUFBQY3PgKRnA3MeEZFhast7MsGvK58qtVqNu3fvwt7eHjKZrNb++fn58PHxwa+//goHB4cGiPDZxbEyDsfLOBwvwz3JWAkhUFBQAC8vL8jlZj8B8DOPOa9+cbwMx7EyDsfLOA2R93im7CmTy+Vo1qyZ0es5ODjwH4WBOFbG4XgZh+NluLqOFc+QNR7MeQ2D42U4jpVxOF7Gqc+8x68piYiIiIiIJMSijIiIiIiISEIsyiSmUqnw3nvvQaVSSR2KyeNYGYfjZRyOl+E4VlRX/N0xDsfLcBwr43C8jNMQ48WJPoiIiIiIiCTEM2VEREREREQSYlFGREREREQkIRZlREREREREEmJRRkREREREJCEWZRI5ffo0hg0bBmdnZ9ja2qJr167YunWr1GFJ4s6dO/j0008xePBgNG/eHEqlEh4eHhg3bhxOnjypd538/HzMnz8fvr6+UKlU8PX1xfz585Gfn9/A0ZuGVatWQSaTQSaT4cSJE3r7cMyA3bt3Y9CgQXBxcYG1tTX8/PwwefJk/Prrr1r9zHmshBCIjY1FaGgoPD09YWNjg8DAQMyePRs3btzQ6W/OY0WGY87Txrz3ZJjzDMOcZxiTyXuCGlx8fLxQKpXCzs5OzJgxQ7z55pvCz89PABAffPCB1OE1uLffflsAEP7+/mL69Oli0aJFYty4ccLCwkLI5XKxY8cOrf6FhYUiJCREABCDBg0Sb7/9thg6dKgAIEJCQkRhYaFE70Qaly5dEiqVStja2goAIjExUaePuY+ZWq0Ws2bN0vyevfrqq+Ltt98WU6dOFc2bNxc//vijpq+5j9X8+fMFAOHp6SnmzJkjFi5cKIYMGSJkMpmwt7cXycnJmr7mPlZkGOY8Xcx7dcecVzvmPOOYSt5jUdbAysvLhb+/v1CpVOLnn3/WLM/PzxfBwcFCoVCIX375RcIIG96uXbvE0aNHdZYfPXpUWFpaiiZNmoiSkhLN8iVLlggAYuHChVr9q5YvWbKk3mM2FRUVFaJLly6ia9euIiIiotoEZe5jtnbtWgFAvPbaa6KiokKnvby8XPP/5jxW6enpQi6XixYtWoi8vDyttk8++UQAEC+//LJmmTmPFRmGOU8/5r26Yc4zDHOe4Uwp77Eoa2Dff/+9zgGusn37dgFALF68WILITNPgwYMFAHH69GkhxMNvf7y8vISdnZ3OtxHFxcXC2dlZeHt7C7VaLUW4De6DDz4QSqVSXLhwQUybNk1vgjL3MSsqKhJNmjQRLVu21EpE+pj7WCUmJgoAIjw8XKftl19+EQDE8OHDhRAcKzIMc57xmPeqx5xXO+Y845hS3uM9ZQ0sISEBADB48GCdtqplR44caciQTJqlpSUAQKFQAABSUlJw9+5d9OrVC7a2tlp9rays0LdvX9y5cwfXrl1r8Fgb2oULF7Bs2TK8++67CA4OrrafuY/ZoUOHkJ2djdGjR6OyshKxsbH46KOPsG7dOp33bO5j1apVKyiVShw/fhwFBQVabfv37wcAvPDCCwA4VmQY5jzjMe/px5xnGOY845hS3lPUaS2qs5SUFAAPfwke5+zsDFdXV00fc5eWlobDhw/Dw8MD7dq1A1Dz+D26PCUlpdo+jUFFRQUiIyMRFBSERYsW1djX3MfszJkzAB7+gdO+fXtcvXpV0yaXyzFv3jysXr0aAMfKxcUFH3zwAd566y0EBQUhLCwM9vb2SE5OxuHDhzFr1iz88Y9/BMCxIsMw5xmHeU8/5jzDMecZx5TyHouyBpaXlwcAcHR01Nvu4OCA27dvN2RIJqm8vBxTp05FaWkpVq1aBQsLCwCGjd+j/RqrDz/8EElJSTh58qTmW9XqmPuYZWZmAgDWrFmDjh074tSpUwgKCsLZs2cxa9YsrFmzBv7+/njllVfMfqwAYMGCBfDy8sLs2bPxxRdfaJb37NkTERERmt83jhUZgjnPcMx71WPOMxxznvFMJe/x8kUyOWq1GtOnT8fRo0cxc+ZMTJ06VeqQTEpSUhJWrFiBBQsWoGPHjlKHY/LUajUAQKlUYs+ePejSpQvs7OzQp08fxMTEQC6XY82aNRJHaTpWrFiByMhILF68GL/++isKCwtx7NgxVFRUIDQ0FLGxsVKHSNToMO9VjznPOMx5xjOVvMeirIFVVdfVVdH5+fnVVuDmQAiBmTNnYsuWLYiIiMC6deu02g0Zv0f7NUbTpk2Dv78/li5dalB/cx+zqvfVuXNneHl5abUFBwejZcuWuH79OnJzc81+rOLi4vCXv/wFr7/+Ot555x00a9YMtra26NWrF/773//C2toa8+bNA8DfKzIMc17tmPdqxpxnHOY845hS3mNR1sAevd70cTk5Obh//36jvW63Nmq1GlFRUdi0aRMmT56M6OhoyOXav6I1jd+jyxvzGCYlJeHKlSuwsrLSPDxTJpNh8+bNAIAePXpAJpNhz549ADhmgYGBAAAnJye97VXLi4uLzX6s9u3bBwAIDQ3VaXNzc0O7du2Qlpam9TllrmNFhmHOqxnzXu2Y84zDnGccU8p7vKesgfXr1w8rV67EwYMHMWnSJK22gwcPavqYG7VajRkzZuCrr77CxIkT8e9//1tzPf2jWrVqBS8vLxw/fhwPHjzQmv2mpKQER48ehZeXFwICAhoy/AYVFRWld/nRo0eRkpKCsLAwuLm5oUWLFgA4ZlUftJcvX9ZpKy8vx7Vr12Braws3Nzd4eHiY9ViVlZUBAH777Te97VXLVSqV2f9ekWGY86rHvGcY5jzjMOcZx6TyXp0m0qc6Ky8vFy1bthQqlUqcPXtWs/zRB2levXpVugAlUFlZKSIjIwUAMX78+Fqfq2HuDzqsTnXPbBGCY1b13J/169drLX///fcFABEREaFZZs5jtW3bNgFABAcHi9zcXK226OhoAUB06tRJs8ycx4oMw5ynH/Pek2POqx5znuFMKe/JhBCibuUc1VV8fDyGDBkClUqFyZMnw8HBAbGxsUhNTcWKFSvw5z//WeoQG9TSpUuxbNky2NnZ4U9/+pPm2SyPGj16NEJCQgAADx48QO/evXHu3DkMGjQInTp1QlJSEg4cOICQkBAcO3ZM5/kR5iAyMhKbN29GYmIiunfvrtVm7mN2/fp19OzZE5mZmRg+fDjatGmDs2fPIi4uDr6+vjhx4gQ8PDwAmPdYVVZWYuDAgUhISICbmxvCwsLg7OyMpKQkHDp0CCqVCocPH0bv3r0BmPdYkeGY83Qx7z055rzqMecZzqTyXp3LOXoiJ0+eFEOHDhWOjo7C2tpadO7cWWzZskXqsCRR9W1XTT9fffWV1jq5ubli3rx5wsfHR1haWgofHx8xb948nW85zElN3xoKwTFLS0sTkZGRwsPDQ/P+X3vtNXHv3j2dvuY8ViUlJeLjjz8WHTt2FDY2NkKhUAhvb28xZcoUkZycrNPfnMeKDMecp41578kx59WMOc9wppL3eKaMiIiIiIhIQpx9kYiIiIiISEIsyoiIiIiIiCTEooyIiIiIiEhCLMqIiIiIiIgkxKKMiIiIiIhIQizKiIiIiIiIJMSijIiIiIiISEIsyoiIiIiIiCTEooyIiIiIiEhCLMqIGkD//v0hk8mkDsNghYWF8PT0xKuvvlpv+7h27RoUCgU+//zzetsHERE1POY8Xcx5VBsWZURGkslkRv08i1atWoXs7GwsXry43vYREBCA8PBwLF26FPn5+fW2HyIiqjvmvKeDOY9qIxNCCKmDIHqWLF26VGfZsmXL4OjoiDfeeENv/7S0NBQVFaFNmzb1H+ATys3NRbNmzfCHP/wB0dHR9bqvCxcuoF27dli+fDnefffdet0XEREZjznv6WHOo5qwKCN6CmQyGXx9fXHz5k2pQ3lif//73zF37lwcPnwYAwYMqPf9hYSEICcnB6mpqZDLefKeiMjUMefVHXMeVYe/DUQNQN/19dHR0ZDJZIiOjsZ//vMfdOvWDTY2NvD29sZf/vIXqNVqAMDXX3+NDh06wNraGs2bN8fq1av17kMIgU2bNqFXr15wcHCAjY0NOnfujE2bNhkVa3R0NFxcXBAaGqpZplar4efnBxcXF5SWlupdr2vXrlAqlcjMzNR5f/v27UOfPn1gb2+PFi1aaK03YcIEpKWl4YcffjAqTiIiMk3Mecx5ZDwWZUQS2717NyZMmICWLVtizpw5sLOzw4oVK7BkyRKsWbMGr776Ktq1a4dZs2ZBrVbjrbfewtdff621DSEEIiIiEBUVhfv372PKlCmYMWMGHjx4gKioKCxYsMCgWHJycnD27Fl07dpV6xs8uVyOmTNnIjs7G7t27dJZLzk5GadPn0ZYWBiaNm2q1fbNN99g9OjRcHV1xauvvophw4Zptffo0QMAEBcXZ1CMRET07GLOY86jaggiemIAhK+vb7Xt/fr1E4//c/vqq68EAGFpaSlOnTqlWZ6fny+aNm0qbGxshIeHh7h+/bqmLS0tTSiVSvH8889rbevLL78UAERUVJQoLy/XLC8tLRUjR44UAMSZM2dqfR/79u0TAMSf//xnnbb09HShUChEaGioTtvcuXMFAHHgwAGd9yeTycShQ4eq3Wd+fr4AIPr27VtrfEREJD3mPOY8evp4poxIYuHh4ejSpYvmtb29PUaMGIGioiK88soraNmypabNx8cHvXv3xsWLF1FRUaFZ/tlnn8HW1hafffYZFAqFZrlSqcQHH3wAANi2bVutsdy+fRsA4O7urtPm4eGBsLAwJCQk4Pr165rlpaWl2LJlC5o3b47BgwfrrDd69GgMHDiw2n3a29vDyspKs28iImq8mPOY80g/Re1diKg+dejQQWeZp6cngIc3BOtrq6ysxL179+Dt7Y2ioiIkJyfDy8sLH330kU7/8vJyAMCVK1dqjSUrKwsA4OzsrLd99uzZiI2NxcaNG/Hhhx8CeHgpSnZ2NubOnav3puWuXbvWut8mTZrg/v37tfYjIqJnG3Mecx7px6KMSGIODg46y6q++auprSrx5OTkQAiBO3fuYNmyZdXu58GDB7XGYm1tDQAoLi7W2z5o0CD4+fkhOjoay5cvh4WFBTZs2AC5XI7p06frXUffN5CPKy4uho2NTa39iIjo2cacx5xH+vHyRaJnXFUS69SpE4QQ1f7Ex8fXui03NzcAQHZ2tt52mUyGmTNnIj09Hfv27UNqairi4uIwdOhQ+Pj4VLtOTdRqNfLy8jT7JiIiqg5zHjVWLMqInnH29vYICgrC5cuXkZub+0TbateuHQAgJSWl2j7Tp0+HpaUlNmzYgE2bNkEIgRkzZtR5nykpKVCr1Zp9ExERVYc5jxorFmVEjcDcuXNRVFSEmTNn6r1kIzU11aCHfLZr1w5NmjTBqVOnqu3j7u6OsLAw7N+/H19++SU8PDwwcuTIOsd+8uRJAEC/fv3qvA0iIjIfzHnUGLEoI2oEZs+ejWnTpiEmJgatWrXCSy+9hEWLFuHll19Gjx494O/vjxMnTtS6HZlMhrCwMFy8eBHp6ek17q+yshKZmZmYNm2a1uxXxjp06BAsLCwwYsSIOm+DiIjMB3MeNUYsyogaAZlMhujoaOzYsQPBwcH473//i7/97W84dOgQrKyssHr16hqn6H3U7NmzoVara5xOeODAgfD29oZMJnuiyziKioqwZ88ejBw5El5eXnXeDhERmQ/mPGqMZEIIIXUQRGRaevbsiby8PFy4cEHvTct3796Fr68v+vTpg7i4uDrvZ9OmTYiKisKRI0fQt2/fJwmZiIioTpjzyBTwTBkR6Vi9ejUuXbqEb775Rm/7p59+ioqKCsyZM6fO+6ioqMCHH36IsLAwJiciIpIMcx6ZAj6njIh09OzZE+vWrdM8FwYA8vLy8MUXX+DWrVtYv349goODMW7cuDrv4/bt24iIiMDUqVOfRshERER1wpxHpoCXLxKRQW7evAk/Pz9YW1ujW7duWLduHQIDA6UOi4iI6KljzqOGxqKMiIiIiIhIQrynjIiIiIiISEIsyoiIiIiIiCTEooyIiIiIiEhCLMqIiIiIiIgkxKKMiIiIiIhIQizKiIiIiIiIJMSijIiIiIiISEIsyoiIiIiIiCTEooyIiIiIiEhCLMqIiIiIiIgkpJA6ACIiU1RZWYny8nKpwyAiM2JpaQkLCwupwyAiCbAoIyJ6hBACGRkZyM3NlToUIjJDTk5O8PDwgEwmkzoUImpALMqIiB5RVZA1bdoUNjY2/MOIiBqEEAJFRUXIzMwEAHh6ekocERE1JBZlRES/q6ys1BRkLi4uUodDRGbG2toaAJCZmYmmTZvyUkYiM8KJPoiIfld1D5mNjY3EkRCRuar6/OE9rUTmhUUZEdFjeMkiEUmFnz9E5olFGRERERERkYRYlBEREREREUmIRRkREUkiOjoaMpkMZ86ckToULe+//z7atm0LtVqttz0hIQGRkZEGb2/p0qWQyWS4f/9+rX2rxuTmzZvVLjOkT33YuHEjvL298eDBg3rbBxGRuWJRRkRE9Lu7d+9i1apVeP/99yGXN3yKHD58OBITE42eDr2u6xlj2rRpsLW1xapVq+ptH0RE5opFGRER0e/Wrl0LJycnjB07Vqdt06ZNaNu2LYYMGYLNmzfDyckJ3bp1w3fffffU9u/m5obu3btDpVI1yHrGUCgUmD17NtauXYuioqJ62w8RkTliUUZERASgrKwMGzduxJQpU3TOku3btw9RUVHo3LkzPv74YwwbNgybN2/GCy+8oHnYb21+/fVXjB07Fg4ODnB0dERERAR+++03rT51vQyxuvWOHTuGAQMGwN7eHjY2NujZsyf27dun1afq8sqLFy9i8uTJcHR0hLu7O6ZPn468vDytvuHh4cjPz8f27duNio+IiGrGooyIiAjAyZMnkZWVhdDQUJ22gwcPwsnJCZs3b0ZISAjc3NwwatQorFy5Ei+99JJB2x8zZgwCAgIQExODpUuXYs+ePRgyZEi9PY/qyJEjeOGFF5CXl4eNGzdi27ZtsLe3x8iRI7Fjxw6d/uPGjUPr1q2xa9cuLFq0CFu3bsW8efO0+nh4eKBNmzY6hR0RET0ZhdQBEBERmYLExEQAQMeOHXXavLy8UFBQgBMnTtR5+2PHjtXcjzV48GC4u7sjPDwcO3fuRHh4eJ23W51FixbB2dkZCQkJsLOzAwCMGDECISEhWLBgASZMmKD1TKyoqCi89dZbAICBAwfi2rVr2LRpEzZu3KjVr2PHjjh8+PBTj5eIyJzxTBkREREeTvIhk8ng6uqq0/b666+jb9++6NWrFyIiIpCYmIi1a9fixo0bBm//8cJrwoQJUCgUiI+Pf+LYH/fgwQOcPHkSf/jDHzQFGQBYWFhg6tSpuH37Nq5evaq1TlhYmNbr559/HiUlJTqXZzZt2hSZmZmoqKh46nETEZkrFmVERGQy1q5di/Hjx2PKlClwcHBAt27dkJGRgblz56JJkyZ47rnncOvWLQAP79F68cUX4ebmBicnJ8yaNUszjf1rr72GqKgoAIBarcaoUaMwd+7cGvddXFwMS0tLWFhY6LTZ2toiLi4Op0+fxrhx41BUVITVq1cjMDAQf/3rXw16bx4eHlqvFQoFXFxckJWVZdD6xsjJyYEQQu9sjF5eXgCgs18XFxet11WThhQXF2stt7KyghACJSUlTzNkIiKzxqKMiIhMxvnz53Hq1Cm8+eabyMzMRHl5OQYMGICwsDBkZmbCz88P0dHRAICCggIsXrwYd+/eRVJSEg4cOIAffvgBALB48WLs3LkTaWlpWLRoESorK/HJJ5/UuG9XV1eUlZXV+ByuTp06YcyYMRgwYABu3ryJSZMm4e2330Z6enqt7y0jI0PrdUVFBbKysnSKoafB2dkZcrlcb1x3794FAL1nBA2RnZ0NlUqldQaOiIieDIsyIiIyGefPn8eyZcvQqVMnWFlZwd/fH+PHj8fAgQOhUCjQpk0bVFZWAgDatm2Lvn37wtLSEr6+vujevTtycnIAAM2aNcNLL72EsLAwHDx4ENu3b9d7BuxRbdq0AQBcv35dp00IobPMwsICffv2hRBCZxZFfb7++mut1zt37kRFRQX69+9f67rGsrW1Rbdu3RAbG6t1pkutVmPLli1o1qwZWrduXadt37hxA23btn1aoRIREViUERGRiVCr1bh06RKGDh2qWfb468uXL2uKp61bt6Jbt25wdXWFk5MTdu/ejVatWmn6hoSEICkpCV988YVBZ3WqiiN9k3n86U9/wvz58xEXF4c7d+4gPz8fO3fuxPLlyxEQEICgoKBatx8bG4uFCxfi0KFD+PTTTzF79my0b98eEyZMqHXduli5cqVmNsmYmBjs3bsXw4YNw4ULF7B69WqtyTsMpVarcerUKb0zVBIRUd2xKCMiIpNw7do12NnZae69Ki0txbVr19CuXTtNn/Pnz+P555/HwYMHsXz5cqxfvx737t1DSkoKrKysEBwcDAA4deoUli1bhvHjx2Pz5s0G7d/Hxwd9+vTBt99+q9M2btw4pKenIyoqClFRUfj222/xxhtvoFevXvj+++9haWlZ6/ZjY2Nx5coVjB07FkuWLMHIkSNx8OBBKJVKg+IzVr9+/RAXFwdbW1tERkZi0qRJyMvLw969ezFx4sQ6bTMhIQF5eXn1MlskEZE5kwl912QQEZmhkpISpKamws/PD1ZWVlKHY3ZiYmLw5Zdf4uDBgwCAn3/+GZMmTcIvv/wCAMjNzYWHhwcKCwvxySefIC4uDjExMcjOzsaMGTOQnp6O8+fPIy0tDb1798a//vUvBAYGIjAwEMnJyfD19a01hl27dmHixIm4desWvL299fZJSEhAdHS05t42czJ16lTcuHEDx48flzqURoufQ0TmiWfKiIjIJCQnJ6N9+/aa10lJSVqvk5OTERQUBIVCgfDwcGRlZaFp06Z46aWX0LZtW7Rv3x4FBQUYMWIE3nvvPfTv3x+enp6IiIjAihUrDIph7Nix6NKlC1auXPnU39+z7vr169ixYwc+/vhjqUMhImp0eKaMiOh3/IaaAODChQvYu3cvFi1aBLlc97vLmzdv4ty5cxg9enTDByeh+Ph4pKSkYNasWVKH0qjxc4jIPLEoIyL6Hf8YIiKp8XOIyDzx8kUiIiIiIiIJsSgjIiIiIiKSEIsyIiIiIiIiCbEoIyIiIiIikhCLMiIiIiIiIgmxKCMiIiIiIpIQizIiIiIiIiIJsSgjIiIiIiKSEIsyIiIiIiIiCbEoIyJqhPr37w+ZTKb3Z/v27Zp++/fvR4cOHWBlZYWAgAB8/vnnere3evVqtGjRAlZWVujSpQsSEhIa6J00boWFhWjWrBlkMhnOnDmj1cZj0/CuXbuGOXPmICQkBAqFAs8995zefjw2RPS0sSgjImqEPv/8cyQmJmr9TJw4EQqFAgMHDgQAJCYmYtSoUejYsSMOHDiAyMhI/PGPf8SGDRu0trV69Wq88847eP3117F//34EBATgxRdfRHJyshRvrVFZvnw5KioqdJbz2Ejj4sWL2LdvHwICAtC2bVu9fXhsiKheCCIiEkIIUVxcLC5duiSKi4ulDqVe+Pn5iWHDhmleDx06VHTt2lWrz8yZM4Wnp6eorKwUQghRUlIiHB0dxVtvvaXpU1FRIYKCgsTEiRMbJvBG6vLly8LW1lasW7dOABCnT5/WtPHYSKNqbIUQYtq0aSI4OFinT30fm8b+OURE+vFMGRGRGfjpp5+QmpqK8PBwAEBpaSni4uIwadIkrX7h4eFIT0/H2bNnNevl5eVh8uTJmj4WFhaYOHEi9u/fDyFEw72JRmbu3LmYM2cOAgMDtZbz2EhHLq/5zyIeGyKqLwqpAyAiMlVqtUBWUZnUYWi42Cghl8vqtO7WrVthY2ODUaNGAQCuX7+OsrIyBAUFafWrumTr8uXL6NSpEy5fvgwAaNOmjU6/goIC3LlzB82aNatTTE9CCDXUxVkNvl995NYukMmM+44zJiYGSUlJiImJwc8//6zV9iwfG7VQI6u0qEH3WRMXlQ3kRh6bmjzLx4aITBuLMiKiamQVlcF96UGpw9C4t3Qw3OxURq9XUVGBb775BqNGjYKtrS0AICcnBwDg5OSk1dfZ2RkAkJ2dremnUqlgbW1dbT8p/rhUF2ch7UvvBt+vPs1n3YGFjZvB/YuKijB//nysXLkSDg4OOu3P8rHJKi2Cx7alDbrPmmRMXgo3K7untr1n+dgQkWnj5YtERI3coUOHkJmZiSlTpui0yWT6z7w9ulxfn6rLr6pbn6q3YsUKuLu7IzIyssZ+PDami8eGiJ42nikjImrktm7dChcXFwwZMkSzrOob+6pv/qtUva5qd3Z2RklJCUpKSmBlZaXpl5ubq9WPDHPr1i2sWbMGu3fvRn5+PoCH0+JX/bewsJDHxoTx2BBRfeGZMiKiRqy4uBjffvstxo8fD0tLS81yf39/KJVKzb0vVS5dugQAmntmqv6rr5+9vT28vU3jEsJnRWpqKsrKyjB8+HA4OzvD2dkZI0eOBACEhoZi4MCBPDYmjMeGiOoLz5QREVXDxUaJe0sHSx2GhouN0uh19u7di4KCAp1LF1UqFV544QXs3LkT8+bN0yzftm0bPD090aFDBwBAz5494ejoiB07dmiWVVZWYufOnRg2bJhkl2HJrV3QfNYdSfb9OLm1i8F9Q0JCEB8fr7Xs3LlzmDdvHtatW4cuXbo808fGRWWDjMlLG3y/1XFR2TzV7T3Lx4aITBuLMiKiasjlsjpNrGFKtm7diubNm6N37946bUuWLEHfvn0xc+ZMhIeH4/jx41i/fj3++c9/aqYGV6lUePfdd/HOO+/Azc0NHTt2xIYNG3Djxg1s3769od+OhkwmN2pyDVPh5OSE/v37623r1KkTOnbsCODZPTZymfypTqzR0IqKirB//34ADy81zc/PR0xMDACgX79+cHNze2aPDRGZOCkfkkZEZEoa20Nbs7OzhVKpFAsXLqy2z759+0T79u2FUqkULVu2FJ999plOH7VaLVatWiWaN28uVCqV6Ny5s4iLi6vP0M1KfHy8zsOjheCxkUJqaqoAoPcnPj5e068+j01j+xwiIsPIhOATDImIAKCkpASpqanw8/PTujmfiKih8HOIyDxxog8iIiIiIiIJsSgjIiIiIiKSEIsyIiIiIiIiCbEoIyIiIiIikhCLMiIiIiIiIgmxKCMiegwnpSUiqfDzh8g8sSgjIvqdpaUlgIcPkCUikkLV50/V5xERmQeF1AEQEZkKCwsLODk5ITMzEwBgY2MDmUwmcVREZA6EECgqKkJmZiacnJxgYWEhdUhE1ID48GgiokcIIZCRkYHc3FypQyEiM+Tk5AQPDw9+IURkZliUERHpUVlZifLycqnDICIzYmlpyTNkRGaKRRkREREREZGEONEHERERERGRhFiUERERERERSYhFGRERERERkYRYlBEREREREUmIRRkREREREZGEWJQRERERERFJiEUZERERERGRhP4PC8qp1ZXBciYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Panel at 2 for Investment and Co2\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 1, ncols = 2, figsize = (10, 3), sharex = True)\n",
    "plt.subplots_adjust(wspace = .35, hspace = .25)\n",
    "\n",
    "axs=axs.ravel()\n",
    "\n",
    "\n",
    "for i, value in enumerate(values):\n",
    "    j=i\n",
    "    i=0\n",
    "    \n",
    "    \n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"I\", ax=axs[i], c=col_pal[j], legend=False)\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"I$_{CL}$\", ax=axs[i], c=col_pal[j], linestyle='dashed', legend=False)\n",
    "    axs[i].set(xlabel=\"Time (yr)\", ylabel=\"$I$ ; $I_{CL}$ (\\$ billion)\")\n",
    "    axs[i].ticklabel_format(style = \"sci\", axis = \"y\")\n",
    "    #\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"[CO$_2$]\", ax=axs[i+1], legend=False)\n",
    "    axs[i+1].set(xlabel=\"Time (yr)\", ylabel=\"[CO$_2$] (ppm)\")\n",
    "    \n",
    "axs[0].annotate('a',xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "axs[1].annotate('b', xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "plt.legend(values, bbox_to_anchor = (0.28, -0.25), ncol = len(values), title=\" I$_{max}$ (\\$ billion)\", fontsize=11, title_fontsize=12);\n",
    "# (0.35, -0.15) $\\\\rho$, $\\Phi$ (% $E_0$), I$_{max}$ (\\$ billion),E$_{CL_{max}}$ (% E$_0$), SCC (\\$/tCO$_2$),$\\\\tau$ (yr)\n",
    "   \n",
    "#fig.savefig(PATH_SAVE_FIGURES + \"F7.pdf\", bbox_inches = \"tight\", dpi = 300) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Panel at 4 for Kcl and Kof\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 2, ncols = 2, figsize = (10, 7), sharex = True)\n",
    "plt.subplots_adjust(wspace = .25, hspace = .2)\n",
    "\n",
    "axs=axs.ravel()\n",
    "\n",
    "for i, value in enumerate(values):\n",
    "    j=i\n",
    "    i=0\n",
    "\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"I\", ax=axs[i], c=col_pal[j], legend=False)\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"I$_{CL}$\", ax=axs[i], c=col_pal[j], linestyle='dashed', legend=False)\n",
    "    axs[i].set(xlabel=\"Time (yr)\", ylabel=\"$I$ ; $I_{CL}$ (\\$ billion)\")\n",
    "    #axs[i].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "    #\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"[CO$_2$]\", ax=axs[i+1], legend=False)\n",
    "    axs[i+1].set(xlabel=\"Time (yr)\", ylabel=\"[CO$_2$] (ppm)\")\n",
    "    \n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"E$_{CL}$\", ax=axs[i+2], c=col_pal[j], legend=False)\n",
    "    axs[i+2].set(xlabel=\"Time (yr)\", ylabel=\"$E_{CL}$ (EJ)\")\n",
    "    #axs[i].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "    #\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"NET\", ax=axs[i+3], legend=False)\n",
    "    axs[i+3].set(xlabel=\"Time (yr)\", ylabel=\"NET (GtCO$_2$)\")\n",
    "    axs[i+3].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 3))\n",
    "    \n",
    "plt.legend(values, bbox_to_anchor = (0.56, -0.25), ncol = len(values), title=\"$I_{max}$ (\\$ billion) \");\n",
    "    \n",
    "#fig.savefig(PATH_SAVE_FIGURES + \"F6.png\", bbox_inches = \"tight\", dpi = 300) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot for damage vs abatement costs\n",
    "fig, axs = plt.subplots(nrows = 1, ncols = 4, figsize = (6, 3), sharex=True,sharey = True)\n",
    "plt.subplots_adjust(wspace = 0, hspace = .0)\n",
    "\n",
    "axs=axs.ravel()\n",
    "\n",
    "\n",
    "for i, value in enumerate(values):\n",
    "    \n",
    "    \n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"$\\mathcal{C}$\", ax=axs[i], legend=False)\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"Damage\", ax=axs[i], legend=False,linestyle='dashed')\n",
    "    axs[i].set( ylabel=\"$\\mathcal{C}$  (\\$ billion)\", xlabel='yr')\n",
    "    axs[i].set_ylim([0, 3000000])\n",
    "    axs[i].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (3, 1))\n",
    "    \n",
    "axs[3].annotate('a',xy=(0.8, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "plt.legend([\"Total\", \"Damage\"], bbox_to_anchor = (-0.1, -0.25), ncol = len(values),title=\"Cost\",fontsize=11, title_fontsize=12);\n",
    "\n",
    "    \n",
    "#fig.savefig(PATH_SAVE_FIGURES + \"F3a.pdf\", bbox_inches = \"tight\", dpi = 300) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saracerasoli/opt/anaconda3/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:677: UserWarning: The handle <matplotlib.lines.Line2D object at 0x7f7fc4699220> has a label of '_nolegend_' which cannot be automatically added to the legend.\n",
      "  ax.legend(handles, labels, loc=\"best\", title=title)\n",
      "/Users/saracerasoli/opt/anaconda3/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:677: UserWarning: The handle <matplotlib.lines.Line2D object at 0x7f7fc46af820> has a label of '_nolegend_' which cannot be automatically added to the legend.\n",
      "  ax.legend(handles, labels, loc=\"best\", title=title)\n",
      "/Users/saracerasoli/opt/anaconda3/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:677: UserWarning: The handle <matplotlib.lines.Line2D object at 0x7f7fb4648040> has a label of '_nolegend_' which cannot be automatically added to the legend.\n",
      "  ax.legend(handles, labels, loc=\"best\", title=title)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot for damage vs abatement costs, same plot\n",
    "fig, axs = plt.subplots(nrows = 1, ncols = 1, figsize = (3, 3), sharex = True, sharey = True)\n",
    "plt.subplots_adjust(wspace = .25, hspace = .0)\n",
    "\n",
    "#axs=axs.ravel()\n",
    "\n",
    "\n",
    "for i, value in enumerate(values):\n",
    "    \n",
    "    \n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"$\\mathcal{C}$\", ax=axs, legend=True,c=col_pal[i])\n",
    "    panel[value].plot(x=\"$\\tau$\", y=\"Damage\", ax=axs, label='_nolegend_',\n",
    "                      linestyle='dashed',c=col_pal[i])\n",
    "    axs.set(xlabel=\"Time (yr)\", ylabel=\"\")\n",
    "    axs.ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (3, 1))\n",
    "    axs.set_ylim([0, 3000000])\n",
    "    #axs.xaxis.set_visible(False)\n",
    "    #axs.yaxis.set_visible(False)\n",
    "    \n",
    "axs.annotate('b',xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "axs.legend(values, bbox_to_anchor = (1.1, -0.25), ncol = len(values), \n",
    "           title=\" SCC (\\$/tCO$_2$)\",handlelength=.5,labelspacing=.05,\n",
    "          fontsize=11, title_fontsize=12, markerscale=.4);    #SCC (\\$/tCO$_2$)  ; \n",
    "#E$_{CL_{max}}$ (% E$_0$)#\n",
    "\n",
    "#fig.savefig(PATH_SAVE_FIGURES + \"F3b.pdf\", bbox_inches = \"tight\", dpi = 300) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Annual Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  4.24757E+14  7.68750E+00\n",
      "    1  1.04161E+08  6.78192E+00\n",
      "    2  5.70693E+07  1.58488E+00\n",
      "    3  5.67755E+07  7.12214E-04\n",
      "    4  5.65926E+07  1.07646E+00\n",
      "    5  5.67444E+07  1.01824E+00\n",
      "    6  5.66692E+07  9.39677E-01\n",
      "    7  5.66299E+07  7.28268E-04\n",
      "    8  5.66218E+07  1.43134E+00\n",
      "    9  5.61711E+07  9.56963E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  5.62306E+07  1.03237E+00\n",
      "   11  5.66676E+07  1.71506E-01\n",
      "   12  5.66242E+07  2.29118E-03\n",
      "   13  5.66242E+07  5.42058E-07\n",
      "   14  5.66242E+07  1.80673E-04\n",
      "   15  5.66242E+07  2.46097E-06\n",
      "   16  5.66241E+07  3.24842E-03\n",
      "   17  5.66241E+07  1.19054E-05\n",
      "   18  5.66241E+07  1.36861E-03\n",
      "   19  5.66241E+07  5.89115E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  5.66241E+07  4.83784E-04\n",
      "   21  5.66241E+07  1.18260E-08\n",
      "   22  5.66241E+07  4.02233E-08\n",
      "   23  5.66241E+07  4.43453E-08\n",
      "   24  5.66241E+07  1.73496E-04\n",
      "   25  5.66241E+07  2.87949E-09\n",
      "   26  5.66241E+07  1.52583E-09\n",
      "   27  5.66241E+07  1.52583E-09\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :   0.786999999996624      sec\n",
      " Objective      :    56624105.9808902     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 566241.05981 Horizon: 10 Param: 50\n",
      "Cumulative investment: 118.96312923 Horizon: 10 Param: 50\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  6.76252E+14  1.20312E+01\n",
      "    1  1.07903E+08  1.08281E+01\n",
      "    2  5.72622E+07  1.46147E+00\n",
      "    3  5.68757E+07  1.12214E-03\n",
      "    4  5.65823E+07  1.27330E+00\n",
      "    5  5.66606E+07  1.19144E-01\n",
      "    6  5.66346E+07  2.39159E-01\n",
      "    7  5.66304E+07  2.25613E-04\n",
      "    8  5.66285E+07  7.86058E-01\n",
      "    9  5.66291E+07  1.36922E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  5.66311E+07  5.56117E+00\n",
      "   11  5.66272E+07  9.30273E-01\n",
      "   12  5.66257E+07  1.20979E+00\n",
      "   13  5.66256E+07  2.11212E-03\n",
      "   14  5.66247E+07  1.41220E+00\n",
      "   15  5.66246E+07  5.24213E-02\n",
      "   16  5.66242E+07  1.50064E-01\n",
      "   17  5.66241E+07  2.72308E-04\n",
      "   18  5.66241E+07  2.43834E-02\n",
      "   19  5.66241E+07  3.59438E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  5.66241E+07  3.44644E-06\n",
      "   21  5.66241E+07  3.23992E-07\n",
      "   22  5.66241E+07  3.23992E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :   0.848700000002282      sec\n",
      " Objective      :    56624105.9800766     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 566241.0598 Horizon: 10 Param: 25\n",
      "Cumulative investment: 118.96376858 Horizon: 10 Param: 25\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  8.96179E+14  1.63750E+01\n",
      "    1  8.70822E+07  1.55378E+01\n",
      "    2  5.76258E+07  1.00000E+00\n",
      "    3  5.67191E+07  2.23309E-01\n",
      "    4  5.66956E+07  9.12927E+00\n",
      "    5  5.66210E+07  4.29934E-02\n",
      "    6  5.66404E+07  2.42864E+00\n",
      "    7  5.66211E+07  9.00510E-03\n",
      "    8  5.66480E+07  3.27031E+00\n",
      "    9  5.66513E+07  1.14864E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  5.66261E+07  1.18923E+00\n",
      "   11  5.66250E+07  1.09441E-02\n",
      "   12  5.66243E+07  8.03685E-02\n",
      "   13  5.66242E+07  1.60292E-01\n",
      "   14  5.66242E+07  1.56048E-02\n",
      "   15  5.66242E+07  3.58038E-04\n",
      "   16  5.66241E+07  3.31868E-03\n",
      "   17  5.66241E+07  4.11008E-06\n",
      "   18  5.66241E+07  1.52847E-06\n",
      "   19  5.66241E+07  8.76875E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  5.66241E+07  8.76875E-09\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :   0.854500000001281      sec\n",
      " Objective      :    56624105.9799617     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 566241.0598 Horizon: 10 Param: 0\n",
      "Cumulative investment: 118.96261151 Horizon: 10 Param: 0\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  8.46170E+14  7.68750E+00\n",
      "    1  1.95280E+08  2.99201E+00\n",
      "    2  1.30900E+08  1.39924E+00\n",
      "    3  1.26685E+08  1.64574E-01\n",
      "    4  1.14310E+08  1.18632E+00\n",
      "    5  1.11814E+08  1.34676E+00\n",
      "    6  1.17514E+08  7.98596E-01\n",
      "    7  1.15764E+08  1.18226E+00\n",
      "    8  1.16342E+08  1.10273E-02\n",
      "    9  1.16335E+08  2.75834E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  1.16317E+08  1.15612E+00\n",
      "   11  1.16336E+08  3.04066E+00\n",
      "   12  1.16329E+08  1.51401E-01\n",
      "   13  1.16328E+08  1.16150E-05\n",
      "   14  1.16328E+08  2.65639E-04\n",
      "   15  1.16328E+08  7.53241E-08\n",
      "   16  1.16328E+08  2.88557E-06\n",
      "   17  1.16328E+08  3.23355E-06\n",
      "   18  1.16328E+08  1.00988E-04\n",
      "   19  1.16328E+08  1.15288E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.16328E+08  3.75898E-04\n",
      "   21  1.16328E+08  2.77446E-08\n",
      "   22  1.16328E+08  1.09013E+00\n",
      "   23  1.16327E+08  2.15499E-04\n",
      "   24  1.16328E+08  2.13086E-04\n",
      "   25  1.16328E+08  1.86665E-07\n",
      "   26  1.16328E+08  3.75530E-04\n",
      "   27  1.16328E+08  1.80730E-06\n",
      "   28  1.16328E+08  2.23278E-10\n",
      "   29  1.16328E+08  1.76585E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  1.16328E+08  1.81430E-08\n",
      "   31  1.16328E+08  2.23278E-10\n",
      "   32  1.16328E+08  1.90727E-03\n",
      "   33  1.16328E+08  1.01926E-06\n",
      "   34  1.16328E+08  9.64943E-07\n",
      "   35  1.16328E+08  9.64943E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    1.24199999999837      sec\n",
      " Objective      :    116327605.234848     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 1163276.0523 Horizon: 20 Param: 50\n",
      "Cumulative investment: 1049.8388875 Horizon: 20 Param: 50\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  1.28906E+15  1.20312E+01\n",
      "    1  2.40421E+08  5.22982E+00\n",
      "    2  1.20979E+08  1.57528E+00\n",
      "    3  1.19452E+08  2.66293E-01\n",
      "    4  1.22272E+08  3.70461E-01\n",
      "    5  1.19172E+08  1.09206E+00\n",
      "    6  1.23236E+08  1.20676E+00\n",
      "    7  1.18034E+08  2.07988E-01\n",
      "    8  1.18053E+08  8.66030E-01\n",
      "    9  1.16970E+08  3.86873E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  1.16488E+08  2.38771E-02\n",
      "   11  1.16453E+08  4.64983E-05\n",
      "   12  1.16425E+08  7.36695E-01\n",
      "   13  1.15254E+08  2.04848E-02\n",
      "   14  9.70125E+07  1.08298E+00\n",
      "   15  1.16832E+08  1.40180E+00\n",
      "   16  1.20309E+08  1.11597E+00\n",
      "   17  1.22772E+08  1.23667E+00\n",
      "   18  1.16707E+08  7.24843E+00\n",
      "   19  1.16399E+08  9.55940E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.16441E+08  1.58139E-01\n",
      "   21  1.16478E+08  4.05258E-02\n",
      "   22  1.16451E+08  4.96115E-01\n",
      "   23  1.16378E+08  5.71886E-04\n",
      "   24  1.16336E+08  6.48776E-01\n",
      "   25  1.16506E+08  2.14948E-02\n",
      "   26  1.16373E+08  1.06282E-02\n",
      "   27  1.16364E+08  3.95870E-03\n",
      "   28  1.16446E+08  7.86917E-02\n",
      "   29  1.18568E+08  3.01406E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  1.20572E+08  1.29843E+00\n",
      "   31  1.17468E+08  3.67385E-01\n",
      "   32  1.18902E+08  1.80617E+00\n",
      "   33  1.16352E+08  3.01406E-03\n",
      "   34  1.16310E+08  6.39188E-03\n",
      "   35  1.16412E+08  2.39018E-02\n",
      "   36  1.16380E+08  1.04220E-02\n",
      "   37  1.16328E+08  6.31430E-03\n",
      "   38  1.16328E+08  4.88084E-04\n",
      "   39  1.16328E+08  1.07964E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  1.16328E+08  4.28909E-08\n",
      "   41  1.16328E+08  4.28909E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    1.56549999999697      sec\n",
      " Objective      :    116327605.282073     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 1163276.0528 Horizon: 20 Param: 25\n",
      "Cumulative investment: 1049.8349665 Horizon: 20 Param: 25\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  1.50645E+15  1.63750E+01\n",
      "    1  2.30372E+08  1.47305E+01\n",
      "    2  1.37457E+08  1.14959E+00\n",
      "    3  1.25139E+08  2.49797E-01\n",
      "    4  1.16828E+08  3.45415E+00\n",
      "    5  1.15376E+08  1.25663E+00\n",
      "    6  1.02603E+08  1.39750E+00\n",
      "    7  1.17749E+08  1.01905E+00\n",
      "    8  1.20855E+08  1.37499E+00\n",
      "    9  1.19131E+08  1.14441E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  1.18823E+08  1.01115E+01\n",
      "   11  1.16447E+08  8.09490E-03\n",
      "   12  1.16235E+08  8.00251E-01\n",
      "   13  1.16698E+08  6.18947E-02\n",
      "   14  1.16379E+08  1.38941E+01\n",
      "   15  1.16611E+08  1.11066E+00\n",
      "   16  1.17001E+08  9.50092E-02\n",
      "   17  1.16962E+08  7.43037E-02\n",
      "   18  1.16303E+08  1.29614E-02\n",
      "   19  7.51781E+07  1.05427E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.19615E+08  1.41340E+00\n",
      "   21  1.31643E+08  9.40340E-01\n",
      "   22  1.17188E+08  9.74703E-01\n",
      "   23  1.16369E+08  1.12615E-02\n",
      "   24  1.16340E+08  2.87567E-02\n",
      "   25  1.17314E+08  7.83860E-05\n",
      "   26  1.17132E+08  2.58940E+00\n",
      "   27  1.16328E+08  1.49406E-02\n",
      "   28  1.16328E+08  2.43797E-03\n",
      "   29  1.16328E+08  5.38345E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  1.16328E+08  4.81365E-05\n",
      "   31  1.16328E+08  4.11654E-07\n",
      "   32  1.16328E+08  2.77801E-04\n",
      "   33  1.16328E+08  1.69734E-07\n",
      "   34  1.16328E+08  1.69734E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    1.33500000000640      sec\n",
      " Objective      :    116327604.923615     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 1163276.0492 Horizon: 20 Param: 0\n",
      "Cumulative investment: 1049.8140418 Horizon: 20 Param: 0\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  1.77928E+15  7.68750E+00\n",
      "    1  4.92101E+08  2.97307E+00\n",
      "    2  2.32352E+08  1.39036E+00\n",
      "    3  1.82334E+08  2.82915E-01\n",
      "    4  1.73049E+08  1.74187E+00\n",
      "    5  2.57801E+08  1.06218E+00\n",
      "    6  2.67413E+08  1.36936E+00\n",
      "    7  8.23172E+08  7.94442E-01\n",
      "    8  1.75690E+08  4.74502E-01\n",
      "    9  1.73709E+08  1.06761E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  1.71945E+08  1.40077E+00\n",
      "   11  1.76456E+08  7.31123E-01\n",
      "   12  1.80240E+08  1.21377E+01\n",
      "   13  1.78462E+08  1.16393E+00\n",
      "   14  1.41795E+09  1.84871E+00\n",
      "   15  2.48464E+08  1.40532E+00\n",
      "   16  2.49237E+08  1.93463E+00\n",
      "   17  1.87183E+08  3.21218E-01\n",
      "   18  1.74962E+08  1.00028E-01\n",
      "   19  1.75393E+08  6.82232E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.74900E+08  1.00000E+00\n",
      "   21  1.74934E+08  3.91248E-03\n",
      "   22  1.74894E+08  1.63852E+00\n",
      "   23  1.74872E+08  6.62791E-03\n",
      "   24  1.74843E+08  1.51138E-01\n",
      "   25  1.74840E+08  3.52968E-04\n",
      "   26  1.74844E+08  5.13240E-01\n",
      "   27  1.74841E+08  6.76056E-03\n",
      "   28  1.74843E+08  6.48399E-04\n",
      "   29  1.74839E+08  9.94065E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  1.74839E+08  4.52079E-04\n",
      "   31  1.74839E+08  3.39597E-06\n",
      "   32  1.74840E+08  5.04301E-03\n",
      "   33  1.75439E+08  1.46405E-06\n",
      "   34  1.74909E+08  2.19789E+00\n",
      "   35  1.74839E+08  1.46054E-06\n",
      "   36  1.74839E+08  7.20278E-04\n",
      "   37  1.74839E+08  1.40939E-05\n",
      "   38  1.74839E+08  5.55123E-05\n",
      "   39  1.74839E+08  1.77977E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  1.74839E+08  1.81922E-03\n",
      "   41  1.74839E+08  9.10714E-07\n",
      "   42  1.74839E+08  9.10714E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    1.96790000000328      sec\n",
      " Objective      :    174838812.126485     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 1748388.1213 Horizon: 30 Param: 50\n",
      "Cumulative investment: 3220.1206695 Horizon: 30 Param: 50\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.15337E+15  1.20312E+01\n",
      "    1  4.46005E+08  5.05580E+00\n",
      "    2  2.48865E+08  1.44801E+00\n",
      "    3  1.82456E+08  1.86875E+00\n",
      "    4  1.66266E+08  1.06795E+00\n",
      "    5  1.37901E+08  1.16624E+00\n",
      "    6  1.91969E+08  1.20348E+00\n",
      "    7  1.71435E+08  1.11086E-01\n",
      "    8  1.83557E+08  1.38089E+00\n",
      "    9  1.77428E+08  3.84566E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  1.73815E+08  1.42506E+00\n",
      "   11  1.74958E+08  4.68419E-02\n",
      "   12  1.74812E+08  7.58608E-01\n",
      "   13  1.74894E+08  9.41066E-04\n",
      "   14  1.74849E+08  1.87478E-03\n",
      "   15  1.74843E+08  5.98188E-04\n",
      "   16  1.74839E+08  1.90205E-01\n",
      "   17  1.74841E+08  7.00670E-05\n",
      "   18  1.74839E+08  5.26919E-03\n",
      "   19  1.74839E+08  2.89645E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.74839E+08  3.96983E-05\n",
      "   21  1.74841E+08  2.34815E-06\n",
      "   22  1.74840E+08  1.10809E-03\n",
      "   23  1.74839E+08  1.64262E-06\n",
      "   24  1.74839E+08  5.63126E-04\n",
      "   25  1.74839E+08  7.78565E-07\n",
      "   26  1.74839E+08  7.93555E-05\n",
      "   27  1.74839E+08  7.03107E-08\n",
      "   28  1.74839E+08  9.19032E-04\n",
      "   29  1.74839E+08  5.94581E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  1.74839E+08  5.90549E-05\n",
      "   31  1.74839E+08  7.43320E-10\n",
      "   32  1.74839E+08  9.09946E-10\n",
      "   33  1.74839E+08  7.89202E-10\n",
      "   34  1.74839E+08  7.33926E-04\n",
      "   35  1.74839E+08  4.58617E-10\n",
      "   36  1.74839E+08  3.28806E-05\n",
      "   37  1.74839E+08  1.05007E-08\n",
      "   38  1.74839E+08  9.27829E-03\n",
      "   39  1.74842E+08  1.13990E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  1.74839E+08  1.07862E-08\n",
      "   41  1.74839E+08  3.67540E-06\n",
      "   42  1.74839E+08  3.55511E-06\n",
      "   43  1.74839E+08  1.13457E-08\n",
      "   44  1.74839E+08  1.33810E-09\n",
      "   45  1.74839E+08  5.90523E-09\n",
      "   46  1.74852E+08  9.48514E-04\n",
      "   47  1.74839E+08  1.33810E-09\n",
      "   48  1.74839E+08  1.33810E-09\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    1.60340000000724      sec\n",
      " Objective      :    174838812.082382     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 1748388.1208 Horizon: 30 Param: 25\n",
      "Cumulative investment: 3220.0888473 Horizon: 30 Param: 25\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.40865E+15  1.63750E+01\n",
      "    1  4.14682E+08  7.19981E+00\n",
      "    2  2.22216E+08  1.79349E+00\n",
      "    3  1.59250E+08  5.82143E-01\n",
      "    4  2.04402E+08  1.42011E+00\n",
      "    5  1.69506E+08  1.31468E+00\n",
      "    6  1.87562E+08  1.04613E+00\n",
      "    7  1.79513E+08  1.40541E+00\n",
      "    8  1.76920E+08  1.38112E+00\n",
      "    9  1.77498E+08  1.00969E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  1.76049E+08  1.04108E+00\n",
      "   11  1.76170E+08  8.13669E-01\n",
      "   12  1.74929E+08  9.86951E+00\n",
      "   13  1.75277E+08  1.49358E-02\n",
      "   14  1.74800E+08  4.16262E-01\n",
      "   15  1.74863E+08  3.96729E-03\n",
      "   16  1.74857E+08  1.56495E-02\n",
      "   17  1.74847E+08  1.12905E-03\n",
      "   18  1.74839E+08  5.16680E-04\n",
      "   19  1.74839E+08  2.16508E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.74839E+08  2.60341E-04\n",
      "   21  1.74839E+08  1.33968E-05\n",
      "   22  1.74839E+08  4.32895E-04\n",
      "   23  1.74839E+08  1.20879E-07\n",
      "   24  1.74840E+08  4.24181E-03\n",
      "   25  1.74841E+08  8.75067E-01\n",
      "   26  1.74839E+08  1.20879E-07\n",
      "   27  1.74839E+08  1.20879E-07\n",
      " No feasible solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    1.27750000001106      sec\n",
      " Objective      :    174838811.260921     \n",
      " Unsuccessful with error code            0\n",
      " ---------------------------------------------------\n",
      " \n",
      " Creating file: infeasibilities.txt\n",
      " Use command apm_get(server,app,'infeasibilities.txt') to retrieve file\n",
      " @error: Solution Not Found\n",
      "\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           3420\n",
      " Number of total equations: -         3240\n",
      " Number of slack variables: -          180\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  3.76661E+15  1.63750E+01\n",
      "    1  6.99128E+08  7.08203E+00\n",
      "    2  3.40439E+08  1.16396E+00\n",
      "    3  3.33518E+08  7.12475E-02\n",
      "    4  3.04668E+08  7.83896E-01\n",
      "    5  2.95742E+08  5.64606E-01\n",
      "    6  3.51906E+08  1.40537E+00\n",
      "    7  4.29624E+08  3.42401E-01\n",
      "    8  3.98230E+08  1.39975E+00\n",
      "    9  5.45172E+08  1.27916E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.20309E+08  3.42401E-01\n",
      "   11  2.87362E+08  1.22574E+00\n",
      "   12  9.52458E+07  1.16744E+00\n",
      "   13 -3.90188E+09  2.66760E+01\n",
      "   14  1.06264E+10  3.18326E+01\n",
      "   15  1.17302E+09  2.22472E+01\n",
      "   16  2.00492E+09  7.35365E+00\n",
      "   17  2.78912E+09  5.47220E+00\n",
      "   18  1.23062E+09  4.76164E+00\n",
      "   19  3.95156E+08  3.01195E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.22851E+08  9.85414E+00\n",
      "   21  3.59708E+08  9.45380E-02\n",
      "   22  3.72121E+08  4.10812E+00\n",
      "   23  4.33578E+08  1.74637E+00\n",
      "   24  3.14055E+08  1.12314E+00\n",
      "   25  3.05708E+08  1.35560E+00\n",
      "   26  3.19234E+08  1.15542E+00\n",
      "   27  3.21332E+08  9.95654E-01\n",
      "   28  3.32888E+08  8.20129E-01\n",
      "   29  3.26537E+08  1.12740E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  9.38319E+09  1.19672E+00\n",
      "   31  3.27892E+08  1.87727E+00\n",
      "   32  3.29921E+08  1.09244E+00\n",
      "   33  3.34057E+08  9.11875E-01\n",
      "   34  3.14611E+08  1.87727E+00\n",
      "   35  3.15296E+08  4.37202E-04\n",
      "   36  3.20193E+08  1.25534E+01\n",
      "   37  3.31809E+08  1.18439E+00\n",
      "   38  3.17007E+08  1.55744E+00\n",
      "   39  3.17383E+08  1.36033E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.16046E+08  1.22262E+00\n",
      "   41  3.14157E+08  4.06241E-02\n",
      "   42  3.14064E+08  9.81002E-01\n",
      "   43  3.13982E+08  6.70657E-04\n",
      "   44  3.13978E+08  6.32981E-05\n",
      "   45  3.13978E+08  3.23222E-06\n",
      "   46  3.13978E+08  5.95021E-07\n",
      "   47  3.13978E+08  7.29964E-07\n",
      "   48  3.13978E+08  3.95096E-05\n",
      "   49  3.13978E+08  5.95021E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.13978E+08  3.33784E-02\n",
      "   51  3.13978E+08  8.86270E-05\n",
      "   52  3.13978E+08  7.57095E-05\n",
      "   53  3.13978E+08  2.12468E-09\n",
      "   54  3.13978E+08  9.10473E-01\n",
      "   55  3.13974E+08  2.78074E-04\n",
      "   56  3.13978E+08  2.78147E-04\n",
      "   57  3.13978E+08  3.76020E-10\n",
      "   58  3.13978E+08  2.81003E-09\n",
      "   59  3.13978E+08  5.76449E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  3.13978E+08  3.76020E-10\n",
      "   61  3.13978E+08  6.75702E-08\n",
      "   62  3.13978E+08  6.75702E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    7.45299999999406      sec\n",
      " Objective      :    313977619.197161     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 1744320.1067 Horizon: 30 Param: 0\n",
      "Cumulative investment: 3299.2943883 Horizon: 30 Param: 0\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.19912E+15  7.68750E+00\n",
      "    1  7.43102E+08  2.64346E+00\n",
      "    2  2.95490E+08  2.16519E+00\n",
      "    3  2.63762E+08  2.50722E-01\n",
      "    4  2.33238E+08  1.28279E+00\n",
      "    5  2.63005E+08  9.62445E-01\n",
      "    6  2.91030E+08  1.36291E+00\n",
      "    7  4.12081E+08  1.38306E+00\n",
      "    8  2.34099E+08  1.23355E+00\n",
      "    9  2.25574E+08  1.35163E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  2.25592E+08  1.27775E+00\n",
      "   11  2.38447E+08  1.26753E+00\n",
      "   12  2.47866E+08  1.48132E+00\n",
      "   13  4.63970E+08  1.40795E+00\n",
      "   14  2.49847E+08  1.80132E+00\n",
      "   15  2.35870E+08  4.97888E-02\n",
      "   16  2.36189E+08  7.78349E-02\n",
      "   17  2.50154E+08  1.18463E+00\n",
      "   18  2.60074E+08  2.64267E-01\n",
      "   19  2.30841E+08  2.07691E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  2.28322E+08  2.30623E-02\n",
      "   21  2.28023E+08  2.84865E-03\n",
      "   22  2.27957E+08  3.24431E-02\n",
      "   23  2.27944E+08  1.88367E-03\n",
      "   24  2.27933E+08  1.85548E-04\n",
      "   25  2.27933E+08  1.87497E-05\n",
      "   26  2.27933E+08  1.71573E-07\n",
      "   27  2.27933E+08  1.09923E-07\n",
      "   28  2.27933E+08  6.22454E-03\n",
      "   29  2.27933E+08  1.71573E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  2.27933E+08  1.71573E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    1.74610000000393      sec\n",
      " Objective      :    227932525.312781     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2279325.2531 Horizon: 40 Param: 50\n",
      "Cumulative investment: 6760.7938066 Horizon: 40 Param: 50\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  3.13183E+15  1.20312E+01\n",
      "    1  6.28502E+08  4.94619E+00\n",
      "    2  4.21382E+08  1.35818E+00\n",
      "    3  1.70949E+08  1.97717E+00\n",
      "    4  3.35189E+08  1.10308E+00\n",
      "    5  2.12498E+08  1.26924E+00\n",
      "    6  2.48856E+08  9.98690E-01\n",
      "    7  2.29166E+08  8.68478E-01\n",
      "    8  2.32495E+08  1.33481E+00\n",
      "    9  2.26915E+08  1.17677E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  2.36482E+08  1.18747E+00\n",
      "   11  2.33398E+08  8.13065E-02\n",
      "   12  2.28910E+08  1.32136E+00\n",
      "   13  2.28662E+08  2.21310E-03\n",
      "   14  2.28590E+08  2.13486E-02\n",
      "   15  2.27735E+08  1.60600E-03\n",
      "   16  2.29127E+08  2.33701E+00\n",
      "   17  2.28179E+08  7.78340E-03\n",
      "   18  2.27805E+08  3.48129E+00\n",
      "   19  2.29112E+08  4.12176E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  2.28709E+08  8.07123E+00\n",
      "   21  2.27937E+08  1.04089E-02\n",
      "   22  2.27934E+08  7.17707E-05\n",
      "   23  2.27933E+08  2.30402E-05\n",
      "   24  2.27933E+08  3.26333E-05\n",
      "   25  2.27933E+08  3.43531E-07\n",
      "   26  2.27933E+08  2.13919E-04\n",
      "   27  2.27933E+08  1.08591E-07\n",
      "   28  2.27933E+08  5.41589E-05\n",
      "   29  2.27933E+08  1.37052E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  2.27938E+08  1.93561E-03\n",
      "   31  2.27933E+08  4.50048E-08\n",
      "   32  2.27933E+08  1.27776E-03\n",
      "   33  2.27933E+08  2.96604E-06\n",
      "   34  2.27933E+08  5.50090E-04\n",
      "   35  2.27933E+08  3.83607E-08\n",
      "   36  2.27933E+08  9.18004E-05\n",
      "   37  2.27933E+08  1.60092E-08\n",
      "   38  2.27933E+08  1.98707E-03\n",
      "   39  2.27933E+08  1.20819E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  2.27933E+08  1.86381E-04\n",
      "   41  2.27933E+08  2.15021E-07\n",
      "   42  2.27933E+08  6.31830E-05\n",
      "   43  2.27933E+08  4.04030E-09\n",
      "   44  2.27932E+08  3.79698E-02\n",
      "   45  2.27933E+08  6.80796E-06\n",
      "   46  2.27933E+08  1.38713E-04\n",
      "   47  2.27933E+08  1.50418E-07\n",
      "   48  2.27933E+08  1.40452E-04\n",
      "   49  2.27933E+08  5.32754E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  2.27933E+08  9.84385E-05\n",
      "   51  2.27933E+08  5.80261E-08\n",
      "   52  2.27933E+08  1.59914E-03\n",
      "   53  2.27935E+08  7.07013E-05\n",
      "   54  2.27933E+08  5.92037E-05\n",
      "   55  2.27933E+08  5.19833E-09\n",
      "   56  2.28750E+08  9.38433E-01\n",
      "   57  2.27943E+08  5.19190E-09\n",
      "   58  2.32677E+08  1.06921E+00\n",
      "   59  2.28067E+08  9.02406E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  2.27947E+08  5.13652E-02\n",
      "   61  2.27946E+08  4.96399E-05\n",
      "   62  2.27972E+08  8.95904E-01\n",
      "   63  2.27933E+08  5.19037E-09\n",
      "   64  2.27933E+08  1.26036E-03\n",
      "   65  2.27932E+08  1.41131E-06\n",
      "   66  2.27933E+08  8.74828E-04\n",
      "   67  2.27933E+08  9.36330E-07\n",
      "   68  2.27933E+08  3.07450E-04\n",
      "   69  2.27933E+08  2.93666E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  2.27933E+08  1.86746E-04\n",
      "   71  2.27933E+08  2.62033E-07\n",
      "   72  2.27933E+08  9.28151E-04\n",
      "   73  2.27933E+08  2.93666E-08\n",
      "   74  2.27934E+08  2.47603E-03\n",
      "   75  2.27993E+08  2.85213E-08\n",
      "   76  2.61020E+08  1.87262E+00\n",
      "   77  2.31107E+08  1.34342E+00\n",
      "   78  2.29067E+08  7.16977E-01\n",
      "   79  2.27935E+08  2.45219E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  2.27934E+08  3.89763E-01\n",
      "   81  2.27933E+08  8.77000E-05\n",
      "   82  2.27933E+08  2.39697E-02\n",
      "   83  2.27933E+08  6.32225E-06\n",
      "   84  2.27933E+08  6.32264E-06\n",
      "   85  2.27933E+08  9.58078E-07\n",
      "   86  2.27933E+08  4.92514E-05\n",
      "   87  2.27938E+08  1.14652E-08\n",
      "   88  2.27933E+08  9.63297E-04\n",
      "   89  2.27940E+08  1.14575E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  2.27933E+08  1.27348E-02\n",
      "   91  2.27935E+08  5.74861E-04\n",
      "   92  2.28099E+08  2.38872E-02\n",
      "   93  2.28078E+08  8.85579E-04\n",
      "   94  2.27933E+08  8.81298E-04\n",
      "   95  2.27933E+08  3.16112E-07\n",
      "   96  2.27933E+08  4.66639E-05\n",
      "   97  2.27944E+08  1.19033E+00\n",
      "   98  2.27933E+08  3.80936E-03\n",
      "   99  2.27942E+08  1.18910E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  100  2.27939E+08  1.77844E-03\n",
      "  101  2.28329E+08  1.18848E+00\n",
      "  102  2.27979E+08  7.16703E-01\n",
      "  103  2.27973E+08  2.72421E-04\n",
      "  104  2.27936E+08  1.97020E-03\n",
      "  105  2.27936E+08  7.62323E-05\n",
      "  106  2.27933E+08  4.97533E-05\n",
      "  107  2.27933E+08  1.12641E-08\n",
      "  108  2.27933E+08  5.94125E-04\n",
      "  109  2.27934E+08  5.92310E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  110  2.27943E+08  9.28410E-04\n",
      "  111  2.28765E+08  1.18579E+00\n",
      "  112  2.28402E+08  1.18578E+00\n",
      "  113  2.28294E+08  4.30344E-03\n",
      "  114  2.27933E+08  4.30162E-03\n",
      "  115  2.27933E+08  1.13013E-05\n",
      "  116  2.27933E+08  3.30233E-05\n",
      "  117  2.27932E+08  6.02446E-05\n",
      "  118  2.27933E+08  5.97201E-05\n",
      "  119  2.27933E+08  1.80076E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  120  2.27933E+08  5.45116E-06\n",
      "  121  2.27933E+08  5.21670E-08\n",
      "  122  2.27933E+08  3.72569E-05\n",
      "  123  2.27933E+08  6.38983E-08\n",
      "  124  2.27933E+08  6.40219E-08\n",
      "  125  2.27933E+08  4.94035E-08\n",
      "  126  2.27933E+08  5.73064E-05\n",
      "  127  2.27945E+08  4.42870E-08\n",
      "  128  2.28044E+08  3.21462E-03\n",
      "  129  2.28033E+08  8.45285E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  130  2.27953E+08  6.36108E-04\n",
      "  131  2.27933E+08  1.71858E-04\n",
      "  132  2.27933E+08  7.29902E-07\n",
      "  133  2.27933E+08  1.32871E-07\n",
      "  134  2.27933E+08  1.33994E-05\n",
      "  135  2.28185E+08  1.14284E+00\n",
      "  136  2.27933E+08  2.58600E-03\n",
      "  137  2.27936E+08  4.40759E-08\n",
      "  138  2.27933E+08  9.18663E-04\n",
      "  139  2.27933E+08  4.27531E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  140  2.27933E+08  3.91737E-03\n",
      "  141  2.27933E+08  2.85840E-06\n",
      "  142  2.27933E+08  2.79198E-06\n",
      "  143  2.27933E+08  2.03449E-09\n",
      "  144  2.27933E+08  3.70812E-05\n",
      "  145  2.27933E+08  5.77583E-08\n",
      "  146  2.27933E+08  1.10713E-04\n",
      "  147  2.27933E+08  2.03449E-09\n",
      "  148  2.27933E+08  2.03449E-09\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    4.72190000000410      sec\n",
      " Objective      :    227932526.267553     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2279325.2627 Horizon: 40 Param: 25\n",
      "Cumulative investment: 6760.9252081 Horizon: 40 Param: 25\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.92811E+15  1.63750E+01\n",
      "    1  6.09084E+08  6.93834E+00\n",
      "    2  3.62804E+08  1.32133E+00\n",
      "    3  1.81786E+08  1.10047E+00\n",
      "    4  5.02453E+08  1.72700E+00\n",
      "    5  4.68467E+08  1.26981E+00\n",
      "    6  2.50492E+08  1.13041E+00\n",
      "    7  2.37358E+08  1.26265E-01\n",
      "    8  2.31937E+08  1.24389E+00\n",
      "    9  2.31040E+08  1.33994E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  2.30302E+08  1.33753E+00\n",
      "   11  2.30602E+08  1.25678E+00\n",
      "   12  2.31169E+08  1.39820E+00\n",
      "   13  2.31465E+08  1.29849E+00\n",
      "   14  2.30945E+08  1.47746E+00\n",
      "   15  2.28069E+08  1.00317E+00\n",
      "   16  2.27858E+08  9.37991E-01\n",
      "   17  2.28025E+08  7.26174E-03\n",
      "   18  2.27934E+08  5.83472E-03\n",
      "   19  2.27932E+08  2.60197E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  2.27933E+08  2.98668E-05\n",
      "   21  2.27934E+08  4.84304E-06\n",
      "   22  2.27934E+08  7.23788E-03\n",
      "   23  2.27933E+08  1.67295E-05\n",
      "   24  2.27933E+08  2.39492E-03\n",
      "   25  2.27933E+08  1.72336E-04\n",
      "   26  2.27933E+08  6.00080E-03\n",
      "   27  2.27933E+08  2.43022E-07\n",
      "   28  2.27933E+08  1.60248E-05\n",
      "   29  2.27933E+08  1.35994E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  2.27933E+08  9.55693E-05\n",
      "   31  2.27933E+08  1.37228E-08\n",
      "   32  2.27933E+08  4.48162E-04\n",
      "   33  2.27933E+08  5.17359E-04\n",
      "   34  2.27927E+08  1.84913E-01\n",
      "   35  2.27933E+08  2.88570E-04\n",
      "   36  2.27933E+08  1.13866E-02\n",
      "   37  2.27932E+08  3.68430E-06\n",
      "   38  2.27933E+08  6.44086E-05\n",
      "   39  2.27933E+08  2.16784E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  2.27933E+08  2.46995E-06\n",
      "   41  2.27933E+08  1.86085E-06\n",
      "   42  2.27933E+08  1.41039E-04\n",
      "   43  2.27933E+08  2.02124E-04\n",
      "   44  2.27933E+08  5.85581E-05\n",
      "   45  2.27933E+08  1.83874E-07\n",
      "   46  2.27940E+08  2.15847E-03\n",
      "   47  2.31807E+08  1.35959E+00\n",
      "   48  2.27933E+08  2.25525E-02\n",
      "   49  2.27935E+08  1.00509E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  2.28203E+08  7.20004E-01\n",
      "   51  2.28143E+08  7.91142E-03\n",
      "   52  2.27933E+08  7.91457E-03\n",
      "   53  2.27933E+08  1.22155E-08\n",
      "   54  2.27933E+08  6.81885E-04\n",
      "   55  2.27933E+08  3.41691E-07\n",
      "   56  2.27933E+08  2.12449E-04\n",
      "   57  2.27933E+08  7.91132E-09\n",
      "   58  2.27934E+08  2.43760E-03\n",
      "   59  2.27934E+08  1.52314E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  2.27933E+08  2.96987E-03\n",
      "   61  2.27933E+08  2.68372E-06\n",
      "   62  2.27933E+08  5.35655E-04\n",
      "   63  2.27933E+08  7.81344E-09\n",
      "   64  2.27934E+08  2.18796E-03\n",
      "   65  2.27968E+08  2.47473E-06\n",
      "   66  2.32197E+08  1.41245E+00\n",
      "   67  2.27991E+08  1.01491E-02\n",
      "   68  2.28181E+08  1.56480E+00\n",
      "   69  2.27940E+08  1.27329E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  2.27939E+08  4.56247E-02\n",
      "   71  2.27933E+08  9.34077E-05\n",
      "   72  2.27933E+08  1.73623E-05\n",
      "   73  2.27933E+08  3.52747E-09\n",
      "   74  2.27933E+08  3.08983E-05\n",
      "   75  2.27933E+08  1.96463E-08\n",
      "   76  2.27935E+08  9.54705E-05\n",
      "   77  2.27933E+08  3.52624E-09\n",
      "   78  2.27933E+08  3.42591E-05\n",
      "   79  2.27937E+08  1.50202E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  2.32634E+08  1.17500E+00\n",
      "   81  2.30069E+08  1.50323E-09\n",
      "   82  2.94419E+08  1.32511E+00\n",
      "   83  2.28114E+08  1.02642E-06\n",
      "   84  2.28097E+08  1.32487E-01\n",
      "   85  2.27966E+08  6.76362E-04\n",
      "   86  2.27934E+08  5.34783E-04\n",
      "   87  2.27934E+08  1.29359E-05\n",
      "   88  2.27933E+08  3.23331E-03\n",
      "   89  2.27933E+08  2.77410E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  2.27933E+08  2.77240E-05\n",
      "   91  2.27933E+08  1.46948E-07\n",
      "   92  2.27933E+08  2.94484E-04\n",
      "   93  2.27933E+08  4.05165E-09\n",
      "   94  2.27973E+08  7.20810E-01\n",
      "   95  2.27934E+08  4.04018E-09\n",
      "   96  2.27933E+08  2.81817E-04\n",
      "   97  2.27933E+08  1.48857E-06\n",
      "   98  2.27933E+08  1.37805E-04\n",
      "   99  2.27933E+08  4.03838E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  100  2.27938E+08  9.52550E-02\n",
      "  101  2.27933E+08  3.94590E-09\n",
      "  102  2.27933E+08  2.81627E-04\n",
      "  103  2.27933E+08  3.95587E-09\n",
      "  104  2.27933E+08  9.50831E-02\n",
      "  105  2.27947E+08  7.17929E-01\n",
      "  106  2.27940E+08  7.98403E-02\n",
      "  107  2.27933E+08  1.92758E-03\n",
      "  108  2.27933E+08  1.40228E-05\n",
      "  109  2.27933E+08  5.84709E-12\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  110  2.27933E+08  5.84709E-12\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    3.76840000000084      sec\n",
      " Objective      :    227932526.283330     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2279325.2628 Horizon: 40 Param: 0\n",
      "Cumulative investment: 6760.9668905 Horizon: 40 Param: 0\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  3.13104E+15  7.68750E+00\n",
      "    1  7.03897E+15  5.23388E+00\n",
      "    2  2.44142E+09  4.32397E+00\n",
      "    3  4.56478E+08  4.37351E+00\n",
      "    4  5.50408E+08  1.47179E+00\n",
      "    5  5.03332E+08  8.02632E-01\n",
      "    6  3.53105E+08  1.39618E+00\n",
      "    7  4.83018E+08  1.38203E+00\n",
      "    8  2.67688E+08  1.38227E+00\n",
      "    9 -2.77832E+07  4.14897E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  7.11686E+08  2.78965E+00\n",
      "   11  7.63335E+08  1.59463E+00\n",
      "   12  2.30194E+09  1.24601E+00\n",
      "   13  1.24278E+09  9.31013E-01\n",
      "   14  1.19245E+09  1.36534E+00\n",
      "   15  4.49240E+08  8.56128E-01\n",
      "   16  3.43981E+08  2.27525E+00\n",
      "   17  3.23428E+08  3.53537E-01\n",
      "   18  3.11727E+08  9.68591E-02\n",
      "   19  3.07227E+08  1.90392E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.02278E+08  2.24617E-01\n",
      "   21  3.37176E+08  7.43269E-01\n",
      "   22  3.09699E+08  3.70738E-01\n",
      "   23  3.09196E+08  4.63308E-03\n",
      "   24  3.09073E+08  4.75878E-02\n",
      "   25  3.08933E+08  1.35708E-03\n",
      "   26  3.08878E+08  4.62415E-04\n",
      "   27  3.08878E+08  2.19667E-06\n",
      "   28  3.08878E+08  5.77358E-08\n",
      "   29  3.08878E+08  5.88551E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.08878E+08  8.61839E-05\n",
      "   31  3.08878E+08  5.77358E-08\n",
      "   32  3.08878E+08  5.77358E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    4.08190000000468      sec\n",
      " Objective      :    308877722.823254     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3088777.2282 Horizon: 60 Param: 50\n",
      "Cumulative investment: 14866.901489 Horizon: 60 Param: 50\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  3.65681E+15  1.20312E+01\n",
      "    1  1.13215E+09  4.26224E+00\n",
      "    2  5.61963E+08  1.38776E+00\n",
      "    3  3.06694E+08  1.70633E+00\n",
      "    4  2.46236E+08  1.75273E+00\n",
      "    5  1.22774E+09  1.18650E+00\n",
      "    6  3.20993E+09  1.36908E+00\n",
      "    7  2.43413E+09  3.52708E+00\n",
      "    8  7.57067E+08  2.75983E+00\n",
      "    9  4.02538E+08  9.11577E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  2.94189E+08  1.27179E+00\n",
      "   11  2.90978E+08  1.44628E+00\n",
      "   12 -4.55584E+08  1.40043E+00\n",
      "   13  5.70966E+08  3.03984E+00\n",
      "   14  7.13276E+08  1.21914E+00\n",
      "   15  3.98643E+09  1.26347E+00\n",
      "   16  3.24733E+09  1.71065E+00\n",
      "   17  4.66128E+08  9.64662E-01\n",
      "   18  8.53933E+09  1.19290E+00\n",
      "   19  6.17388E+08  1.15855E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  9.02693E+08  1.68485E+00\n",
      "   21  1.07157E+09  1.36149E+00\n",
      "   22  3.58205E+08  1.36068E+00\n",
      "   23  3.25962E+08  2.96108E-01\n",
      "   24  4.11425E+08  6.37752E+00\n",
      "   25  3.83397E+08  4.33697E-01\n",
      "   26  3.09273E+08  1.91968E+00\n",
      "   27  3.07169E+08  3.59467E-02\n",
      "   28  3.06562E+08  3.76113E-01\n",
      "   29  3.06327E+08  1.07637E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.07514E+08  3.05791E-01\n",
      "   31  3.06782E+08  7.96532E-02\n",
      "   32  3.06680E+08  4.53203E-03\n",
      "   33  3.06600E+08  3.28324E-03\n",
      "   34  3.06594E+08  8.32348E-03\n",
      "   35  3.06586E+08  2.02401E-04\n",
      "   36  3.06584E+08  3.55358E-04\n",
      "   37  3.06584E+08  4.61535E-06\n",
      "   38  3.06584E+08  1.15911E-06\n",
      "   39  3.06584E+08  1.10250E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.06584E+08  7.10996E-05\n",
      "   41  3.06584E+08  5.38095E-07\n",
      "   42  3.06584E+08  1.86054E-07\n",
      "   43  3.06584E+08  1.78270E-07\n",
      "   44  3.06585E+08  1.97180E-03\n",
      "   45  3.06593E+08  3.22062E-06\n",
      "   46  3.06584E+08  1.40474E-04\n",
      "   47  3.06584E+08  1.68573E-07\n",
      "   48  3.06584E+08  1.25166E-07\n",
      "   49  3.06584E+08  7.89546E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.06584E+08  7.89546E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    3.25159999998868      sec\n",
      " Objective      :    306583833.380550     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3065838.3338 Horizon: 60 Param: 25\n",
      "Cumulative investment: 17763.114151 Horizon: 60 Param: 25\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  3.98115E+15  1.63750E+01\n",
      "    1  1.00798E+09  3.30328E+00\n",
      "    2  6.94763E+08  1.44626E+00\n",
      "    3  2.45315E+08  8.86634E-01\n",
      "    4  4.04784E+08  1.67852E+00\n",
      "    5  3.18434E+08  5.41208E-02\n",
      "    6  2.42762E+08  1.33880E+00\n",
      "    7  4.96987E+08  1.53842E+00\n",
      "    8  5.16874E+08  1.43152E+00\n",
      "    9  3.76617E+08  1.04489E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  4.80033E+09  1.40099E+00\n",
      "   11  7.63331E+08  1.38698E+00\n",
      "   12  6.82220E+09  1.40982E+00\n",
      "   13  5.95352E+08  1.21205E+00\n",
      "   14  3.14338E+08  9.03358E-01\n",
      "   15  2.97508E+08  8.66023E-01\n",
      "   16  3.67281E+08  1.11153E+00\n",
      "   17  3.14000E+08  1.61524E+00\n",
      "   18  2.99830E+08  7.13500E-01\n",
      "   19  3.06761E+08  2.26568E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.06489E+08  4.84514E-03\n",
      "   21  3.06652E+08  3.57233E-03\n",
      "   22  3.06639E+08  2.76271E-03\n",
      "   23  3.06585E+08  2.07872E-04\n",
      "   24  3.06584E+08  1.95906E-04\n",
      "   25  3.06584E+08  1.78445E-07\n",
      "   26  3.06584E+08  6.18510E-04\n",
      "   27  3.06584E+08  8.39594E-08\n",
      "   28  3.06584E+08  2.74924E-03\n",
      "   29  3.06584E+08  2.93867E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.06584E+08  3.37079E-02\n",
      "   31  3.06525E+08  2.35957E-03\n",
      "   32  3.06584E+08  2.37171E-03\n",
      "   33  3.06584E+08  1.58429E-05\n",
      "   34  3.06584E+08  3.37335E-04\n",
      "   35  3.06584E+08  2.74238E-07\n",
      "   36  3.06584E+08  2.89992E-04\n",
      "   37  3.06585E+08  4.75305E-07\n",
      "   38  3.06584E+08  2.89343E-03\n",
      "   39  3.06584E+08  2.13691E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.06584E+08  2.14005E-06\n",
      "   41  3.06584E+08  6.96581E-06\n",
      "   42  3.06584E+08  4.71582E-05\n",
      "   43  3.06584E+08  1.15950E-07\n",
      "   44  3.06584E+08  3.10368E-04\n",
      "   45  3.06586E+08  6.25427E-08\n",
      "   46  3.06584E+08  2.95039E-04\n",
      "   47  3.06584E+08  6.34186E-08\n",
      "   48  3.06600E+08  1.15945E-03\n",
      "   49  3.07429E+08  9.78695E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.06589E+08  3.92831E-04\n",
      "   51  3.06588E+08  2.84807E-05\n",
      "   52  3.06584E+08  2.81803E-05\n",
      "   53  3.06584E+08  1.37617E-10\n",
      "   54  3.06584E+08  6.25775E-05\n",
      "   55  3.06584E+08  4.40518E-08\n",
      "   56  3.06584E+08  1.34008E-08\n",
      "   57  3.06584E+08  1.37617E-10\n",
      "   58  3.06584E+08  4.35478E-09\n",
      "   59  3.06584E+08  4.35478E-09\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    2.38130000000820      sec\n",
      " Objective      :    306583834.535163     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3065838.3454 Horizon: 60 Param: 0\n",
      "Cumulative investment: 17763.128089 Horizon: 60 Param: 0\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  1.27248E+15  7.68750E+00\n",
      "    1  2.38648E+08  9.02470E-01\n",
      "    2  7.75198E+08  4.29175E+00\n",
      "    3  5.79423E+08  3.68473E+00\n",
      "    4  4.60499E+08  1.21384E+00\n",
      "    5  3.16682E+08  2.10965E+00\n",
      "    6  4.98241E+08  1.37665E+00\n",
      "    7  4.08116E+08  1.39995E+00\n",
      "    8  3.95296E+08  1.34894E+00\n",
      "    9  3.85692E+08  1.28540E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  4.38899E+08  1.34620E+00\n",
      "   11  3.62007E+08  7.18466E-01\n",
      "   12  4.22438E+08  1.47213E+00\n",
      "   13  3.80786E+08  1.18770E+00\n",
      "   14  3.99161E+08  1.38429E+00\n",
      "   15  4.07154E+08  8.86703E-01\n",
      "   16  3.80309E+08  1.07439E+00\n",
      "   17  3.60364E+08  1.09518E+00\n",
      "   18  3.98312E+08  1.67627E+00\n",
      "   19  3.79009E+08  1.11090E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.91307E+08  1.32862E+00\n",
      "   21  4.03977E+08  9.97084E-01\n",
      "   22  3.87535E+08  1.68309E+00\n",
      "   23  3.97251E+08  1.08620E+00\n",
      "   24  4.54557E+08  1.69248E+00\n",
      "   25  3.63454E+08  1.05551E+00\n",
      "   26  4.02984E+08  1.94080E+00\n",
      "   27  3.72569E+08  1.07520E+00\n",
      "   28  3.65482E+08  3.73617E-01\n",
      "   29  3.67737E+08  1.08261E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.66656E+08  1.26882E-01\n",
      "   31  3.66492E+08  2.12918E-03\n",
      "   32  3.66444E+08  2.44614E-04\n",
      "   33  3.66439E+08  1.10814E-04\n",
      "   34  3.66439E+08  5.76738E-04\n",
      "   35  3.66439E+08  1.66700E-06\n",
      "   36  3.66438E+08  1.33099E-03\n",
      "   37  3.66438E+08  3.12758E-05\n",
      "   38  3.66438E+08  3.40285E-05\n",
      "   39  3.66438E+08  2.26834E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.66438E+08  1.87137E-07\n",
      "   41  3.66438E+08  1.18043E-07\n",
      "   42  3.66438E+08  1.22666E-07\n",
      "   43  3.66438E+08  2.26834E-07\n",
      "   44  3.66438E+08  3.63500E-04\n",
      "   45  3.66438E+08  3.37942E-06\n",
      "   46  3.66438E+08  3.33700E-06\n",
      "   47  3.66438E+08  2.50493E-08\n",
      "   48  3.66438E+08  2.50493E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    4.76129999999830      sec\n",
      " Objective      :    366438096.020524     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3664380.9602 Horizon: 80 Param: 50\n",
      "Cumulative investment: 21643.130175 Horizon: 80 Param: 50\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.17666E+15  1.20312E+01\n",
      "    1 -6.70220E+07  2.19183E+00\n",
      "    2  2.92142E+09  9.26733E+00\n",
      "    3  1.01412E+09  6.19965E+00\n",
      "    4  7.72378E+08  1.32708E+00\n",
      "    5  6.66693E+08  1.41065E+00\n",
      "    6  3.20229E+08  1.34996E+00\n",
      "    7  1.30068E+08  1.44078E+00\n",
      "    8  8.13383E+08  1.50262E+00\n",
      "    9  7.98320E+08  1.34874E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  7.03619E+08  1.41249E+00\n",
      "   11  4.22244E+08  1.34578E+00\n",
      "   12  3.72064E+08  1.02775E+00\n",
      "   13  3.56000E+08  7.40452E-01\n",
      "   14  3.49247E+08  9.27089E-01\n",
      "   15  3.53649E+08  1.87307E+01\n",
      "   16  3.51584E+08  1.82230E+00\n",
      "   17  3.50529E+08  1.31883E+00\n",
      "   18  3.49598E+08  5.41006E-01\n",
      "   19  3.49522E+08  5.08006E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.49449E+08  1.45254E-03\n",
      "   21  3.49420E+08  1.13168E-04\n",
      "   22  3.49410E+08  1.52964E-04\n",
      "   23  3.49410E+08  1.05981E-06\n",
      "   24  3.49410E+08  1.78845E-03\n",
      "   25  3.49409E+08  1.29200E-06\n",
      "   26  3.49410E+08  7.28314E-05\n",
      "   27  3.49410E+08  8.44419E-06\n",
      "   28  3.49410E+08  2.27228E-03\n",
      "   29  3.49409E+08  1.69865E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.49409E+08  2.31094E-03\n",
      "   31  3.49409E+08  1.43571E-07\n",
      "   32  3.49409E+08  2.70991E-07\n",
      "   33  3.49409E+08  3.03149E-07\n",
      "   34  3.49409E+08  1.43571E-07\n",
      "   35  3.49409E+08  1.12734E-07\n",
      "   36  3.49409E+08  1.68577E-02\n",
      "   37  3.49409E+08  2.95020E-06\n",
      "   38  3.49409E+08  5.11891E-05\n",
      "   39  3.49409E+08  1.35983E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.49409E+08  5.92839E-07\n",
      "   41  3.49409E+08  5.92333E-07\n",
      "   42  3.49409E+08  5.92333E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    3.93369999999413      sec\n",
      " Objective      :    349409342.624006     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3494093.4262 Horizon: 80 Param: 25\n",
      "Cumulative investment: 27435.095239 Horizon: 80 Param: 25\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.66084E+15  1.63750E+01\n",
      "    1  3.98526E+08  1.21093E+00\n",
      "    2  6.41511E+08  2.02355E+00\n",
      "    3  4.37919E+08  4.53615E-02\n",
      "    4  2.84966E+08  1.39768E+00\n",
      "    5  1.95372E+08  5.62744E-01\n",
      "    6  1.02856E+09  1.43780E+00\n",
      "    7  9.22521E+08  1.66129E+00\n",
      "    8  9.35785E+08  1.10718E+00\n",
      "    9  2.70009E+09  1.26088E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  9.21215E+08  1.90970E+00\n",
      "   11  4.57883E+09  1.09978E+00\n",
      "   12  4.66534E+09  3.42630E+00\n",
      "   13  5.42537E+08  2.68448E+00\n",
      "   14  6.11695E+08  9.85637E-01\n",
      "   15  8.64349E+08  8.75421E-01\n",
      "   16  1.92919E+09  1.60177E+00\n",
      "   17  4.08255E+08  8.45195E-01\n",
      "   18  2.88521E+08  1.07141E+00\n",
      "   19  5.64339E+08  1.09249E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  4.91054E+08  9.54482E-01\n",
      "   21  3.75033E+08  1.09385E+00\n",
      "   22  3.68756E+08  1.34173E+00\n",
      "   23  3.60168E+08  9.96586E-01\n",
      "   24  3.43936E+08  1.34390E+00\n",
      "   25  3.43425E+08  1.00499E+00\n",
      "   26  3.43116E+08  3.30401E-03\n",
      "   27  3.43322E+08  8.27067E-03\n",
      "   28  3.43231E+08  9.96192E-01\n",
      "   29  3.43292E+08  1.48801E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.43262E+08  1.11661E-04\n",
      "   31  3.43262E+08  6.93762E-07\n",
      "   32  3.43262E+08  1.38821E-03\n",
      "   33  3.43264E+08  9.13296E-07\n",
      "   34  3.43262E+08  4.59955E-04\n",
      "   35  3.43262E+08  1.38614E-05\n",
      "   36  3.43262E+08  1.08572E-05\n",
      "   37  3.43262E+08  1.01684E-07\n",
      "   38  3.43262E+08  9.13321E-04\n",
      "   39  3.43262E+08  8.68508E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.43262E+08  5.53914E-05\n",
      "   41  3.43262E+08  1.01684E-07\n",
      "   42  3.43262E+08  4.07932E-08\n",
      "   43  3.43262E+08  1.56947E-07\n",
      "   44  3.43262E+08  1.96558E-08\n",
      "   45  3.43262E+08  1.31978E-08\n",
      "   46  3.43262E+08  3.54517E-03\n",
      "   47  3.43262E+08  2.34007E-06\n",
      "   48  3.43262E+08  2.35310E-06\n",
      "   49  3.43262E+08  9.43871E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.43262E+08  1.28547E-04\n",
      "   51  3.43262E+08  9.60484E-07\n",
      "   52  3.43262E+08  2.86821E-05\n",
      "   53  3.43262E+08  5.70518E-09\n",
      "   54  3.43262E+08  2.44675E-03\n",
      "   55  3.43262E+08  6.29627E-09\n",
      "   56  3.43262E+08  4.55779E-07\n",
      "   57  3.43263E+08  4.54169E-07\n",
      "   58  3.43290E+08  5.40034E-03\n",
      "   59  3.43543E+08  4.33507E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  3.43512E+08  2.73703E-02\n",
      "   61  3.43270E+08  1.35360E-04\n",
      "   62  3.43269E+08  4.29141E-03\n",
      "   63  3.43262E+08  2.19875E-05\n",
      "   64  3.43262E+08  2.59469E-04\n",
      "   65  3.43262E+08  1.58706E-07\n",
      "   66  3.43263E+08  5.39301E-03\n",
      "   67  3.43262E+08  5.82112E-09\n",
      "   68  3.43262E+08  5.66791E-09\n",
      "   69  3.43262E+08  1.04181E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  3.43262E+08  1.02533E-06\n",
      "   71  3.43262E+08  4.87319E-10\n",
      "   72  3.43262E+08  1.80281E-04\n",
      "   73  3.43262E+08  9.03933E-07\n",
      "   74  3.43262E+08  9.03933E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    3.67379999998957      sec\n",
      " Objective      :    343261925.817319     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3432619.2582 Horizon: 80 Param: 0\n",
      "Cumulative investment: 32231.616876 Horizon: 80 Param: 0\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  1.32345E+15  7.68750E+00\n",
      "    1  2.43252E+09  5.85403E-01\n",
      "    2  2.15051E+09  1.25215E+00\n",
      "    3  1.51984E+09  1.26261E+00\n",
      "    4  3.32485E+09  1.37303E+00\n",
      "    5  5.37028E+08  5.85403E-01\n",
      "    6  3.31898E+08  3.52202E+01\n",
      "    7  2.56866E+08  1.35578E+00\n",
      "    8  7.60109E+08  1.47929E+00\n",
      "    9  1.36189E+09  1.02063E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  2.04457E+09  1.34365E+00\n",
      "   11  1.49870E+09  1.19260E+00\n",
      "   12  3.44987E+10  1.38779E+00\n",
      "   13  1.26490E+09  1.62371E+00\n",
      "   14  1.29405E+09  2.17412E+00\n",
      "   15  4.83925E+08  8.84233E-01\n",
      "   16  9.01956E+08  1.27980E+00\n",
      "   17  9.37849E+08  1.35482E+00\n",
      "   18  8.34309E+08  2.25234E+01\n",
      "   19  6.71045E+08  1.33525E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  8.01140E+08  1.94006E+00\n",
      "   21  8.73350E+08  1.25441E+00\n",
      "   22  1.33179E+09  2.44185E+00\n",
      "   23  4.63474E+08  6.70924E-01\n",
      "   24  5.14732E+08  1.68058E+00\n",
      "   25  4.33965E+08  1.12421E+00\n",
      "   26  4.13624E+08  4.67140E-01\n",
      "   27  4.10319E+08  2.98287E-01\n",
      "   28  4.08909E+08  4.81516E-02\n",
      "   29  4.07935E+08  2.80198E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  4.07295E+08  1.40432E-02\n",
      "   31  4.07266E+08  2.02225E-04\n",
      "   32  4.07264E+08  5.93265E-05\n",
      "   33  4.07262E+08  5.31767E-05\n",
      "   34  4.07261E+08  1.85840E-03\n",
      "   35  4.07261E+08  3.25475E-06\n",
      "   36  4.07261E+08  5.18763E-07\n",
      "   37  4.07260E+08  8.04441E-07\n",
      "   38  4.07261E+08  8.83830E-05\n",
      "   39  4.07261E+08  3.31508E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  4.07260E+08  3.39837E-06\n",
      "   41  4.07260E+08  4.31451E-08\n",
      "   42  4.07260E+08  5.96499E-08\n",
      "   43  4.07260E+08  6.61614E-08\n",
      "   44  4.07261E+08  5.81720E-08\n",
      "   45  4.07260E+08  4.31451E-08\n",
      "   46  4.07260E+08  4.31451E-08\n",
      " No feasible solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    5.51720000000205      sec\n",
      " Objective      :    407260333.969848     \n",
      " Unsuccessful with error code            0\n",
      " ---------------------------------------------------\n",
      " \n",
      " Creating file: infeasibilities.txt\n",
      " Use command apm_get(server,app,'infeasibilities.txt') to retrieve file\n",
      " @error: Solution Not Found\n",
      "\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           3420\n",
      " Number of total equations: -         3240\n",
      " Number of slack variables: -          180\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  7.20356E+15  7.68750E+00\n",
      "    1  1.98934E+10  3.50837E+00\n",
      "    2  1.36605E+10  1.86449E+01\n",
      "    3  5.00617E+09  2.19871E+01\n",
      "    4  4.85999E+09  5.40897E+00\n",
      "    5  2.24395E+09  3.50837E+00\n",
      "    6  8.51185E+08  4.81084E+00\n",
      "    7  6.38522E+08  1.69689E+00\n",
      "    8  3.05833E+08  1.33422E+00\n",
      "    9  1.96536E+09  1.40499E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  5.68805E+08  1.12216E+00\n",
      "   11  1.64483E+09  1.36140E+00\n",
      "   12  1.60302E+09  1.83323E+00\n",
      "   13  2.18419E+09  1.52778E+00\n",
      "   14  4.37912E+09  1.68175E+00\n",
      "   15  8.52476E+08  2.06931E+00\n",
      "   16  9.19159E+08  1.98065E+00\n",
      "   17  9.65818E+08  1.43088E+00\n",
      "   18  7.66732E+08  2.07284E+00\n",
      "   19  1.05768E+09  1.46731E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.55718E+09  3.29752E-01\n",
      "   21  1.29041E+09  1.17374E+00\n",
      "   22  9.23735E+08  1.61380E+00\n",
      "   23  7.73577E+08  1.61600E-01\n",
      "   24  7.48073E+08  1.93262E+00\n",
      "   25  7.17973E+08  1.31716E+00\n",
      "   26  6.87818E+08  1.26971E+00\n",
      "   27  8.20757E+08  1.71080E+00\n",
      "   28  8.20463E+08  1.39494E+00\n",
      "   29  7.47962E+08  1.08081E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  8.15028E+08  2.28331E+00\n",
      "   31  7.43409E+08  1.02632E+00\n",
      "   32  7.19470E+08  1.32811E+00\n",
      "   33  7.26150E+08  1.33556E+00\n",
      "   34  7.23510E+08  1.46692E+00\n",
      "   35  7.22883E+08  3.23733E-03\n",
      "   36  7.22751E+08  5.10112E-02\n",
      "   37  7.22695E+08  1.09955E-01\n",
      "   38  7.22715E+08  2.18027E-01\n",
      "   39  7.22689E+08  1.22625E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  7.22682E+08  1.23637E-02\n",
      "   41  7.22680E+08  4.60464E-05\n",
      "   42  7.22686E+08  1.92785E-03\n",
      "   43  7.22687E+08  1.73148E-05\n",
      "   44  7.22685E+08  7.63761E-05\n",
      "   45  7.22679E+08  5.09803E-05\n",
      "   46  7.22679E+08  1.60652E-06\n",
      "   47  7.22679E+08  5.71748E-07\n",
      "   48  7.22704E+08  9.15545E-01\n",
      "   49  7.25097E+08  5.88162E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  7.22679E+08  1.61063E-03\n",
      "   51  7.22678E+08  2.91732E-07\n",
      "   52  7.22678E+08  1.20505E-05\n",
      "   53  7.22679E+08  1.15062E-05\n",
      "   54  7.22682E+08  4.78332E-07\n",
      "   55  7.24681E+08  8.01868E-01\n",
      "   56  7.26558E+08  1.18595E+00\n",
      "   57  7.27069E+08  6.02803E-02\n",
      "   58  7.23249E+08  4.50820E-02\n",
      "   59  7.22741E+08  7.83732E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  7.22688E+08  1.18329E-03\n",
      "   61  7.22680E+08  1.37010E-04\n",
      "   62  7.22679E+08  3.79106E-05\n",
      "   63  7.22736E+08  3.99362E-07\n",
      "   64  7.22679E+08  4.68117E-07\n",
      "   65  7.22682E+08  3.75011E-07\n",
      "   66  7.22686E+08  1.41641E-02\n",
      "   67  7.22721E+08  3.67639E-07\n",
      "   68  7.22840E+08  1.46529E+00\n",
      "   69  7.22933E+08  9.02235E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  7.22698E+08  1.24388E+00\n",
      "   71  7.22695E+08  4.77692E-04\n",
      "   72  7.22679E+08  4.75486E-04\n",
      "   73  7.22679E+08  5.77810E-07\n",
      "   74  7.22679E+08  7.87794E-04\n",
      "   75  7.22678E+08  8.63877E-08\n",
      "   76  7.22678E+08  2.51682E-04\n",
      "   77  7.22678E+08  2.00294E-08\n",
      "   78  7.22679E+08  2.57215E-07\n",
      "   79  7.22679E+08  2.03520E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  7.22678E+08  2.00294E-08\n",
      "   81  7.22678E+08  1.80819E-08\n",
      "   82  7.22678E+08  9.41919E-06\n",
      "   83  7.22678E+08  9.38593E-06\n",
      "   84  7.22678E+08  7.42442E-05\n",
      "   85  7.22678E+08  1.13261E-07\n",
      "   86  7.22678E+08  7.62581E-08\n",
      "   87  7.22678E+08  2.97722E-08\n",
      "   88  7.22679E+08  1.99357E-03\n",
      "   89  7.22683E+08  2.52839E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  7.22678E+08  2.97722E-08\n",
      "   91  7.22678E+08  5.63580E-07\n",
      "   92  7.22679E+08  5.42508E-07\n",
      "   93  7.22679E+08  5.42508E-07\n",
      " No feasible solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    23.6695999999938      sec\n",
      " Objective      :    722678461.133108     \n",
      " Unsuccessful with error code            0\n",
      " ---------------------------------------------------\n",
      " \n",
      " Creating file: infeasibilities.txt\n",
      " Use command apm_get(server,app,'infeasibilities.txt') to retrieve file\n",
      " @error: Solution Not Found\n",
      "\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           4940\n",
      " Number of total equations: -         4680\n",
      " Number of slack variables: -          260\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  1.09236E+16  7.68750E+00\n",
      "    1  3.30633E+08  3.79482E+00\n",
      "    2  9.43810E+09  1.65998E+01\n",
      "    3  2.87345E+09  1.15199E+01\n",
      "    4  1.01846E+09  1.78552E+00\n",
      "    5  1.82813E+09  2.64396E+00\n",
      "    6  1.91021E+09  1.41081E+00\n",
      "    7  9.62613E+08  1.31823E+00\n",
      "    8  2.00210E+09  1.16836E+00\n",
      "    9  2.71998E+09  1.83841E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  8.66666E+09  1.50011E+00\n",
      "   11  1.13200E+09  1.08697E+00\n",
      "   12  1.03139E+09  8.14883E-01\n",
      "   13  9.88759E+08  1.34818E+00\n",
      "   14  8.90498E+08  1.97440E+00\n",
      "   15  1.32385E+09  1.35551E+00\n",
      "   16  1.80363E+09  1.35012E+00\n",
      "   17  1.54629E+09  1.26682E+00\n",
      "   18  4.80796E+10  1.69658E+00\n",
      "   19  1.32505E+09  1.66086E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.11529E+09  8.15889E+00\n",
      "   21  1.42515E+09  1.31237E+00\n",
      "   22  1.74084E+09  5.90894E+00\n",
      "   23  1.10486E+09  6.61727E+00\n",
      "   24  1.16901E+09  8.47446E-01\n",
      "   25  1.08630E+09  1.42355E+00\n",
      "   26  1.05125E+09  1.65867E+00\n",
      "   27  1.04130E+09  4.40779E-02\n",
      "   28  1.04049E+09  1.47904E-01\n",
      "   29  1.03918E+09  1.12640E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  1.03821E+09  7.38311E-03\n",
      "   31  1.03809E+09  2.94859E-04\n",
      "   32  1.03841E+09  2.75832E-02\n",
      "   33  1.03828E+09  1.17246E-02\n",
      "   34  1.03814E+09  4.46123E-02\n",
      "   35  1.03811E+09  2.47051E-04\n",
      "   36  1.03811E+09  2.71350E-02\n",
      "   37  1.03812E+09  1.45010E-04\n",
      "   38  1.03811E+09  1.46241E-04\n",
      "   39  1.03811E+09  2.06003E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  1.03811E+09  6.68228E-03\n",
      "   41  1.03811E+09  2.57189E-06\n",
      "   42  1.03811E+09  2.37468E-02\n",
      "   43  1.03811E+09  1.21268E-06\n",
      "   44  1.03811E+09  2.42073E-04\n",
      "   45  1.03811E+09  7.74601E-06\n",
      "   46  1.03811E+09  2.52738E-06\n",
      "   47  1.03811E+09  3.01175E-06\n",
      "   48  1.03811E+09  3.48622E-02\n",
      "   49  1.03811E+09  3.69370E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  1.03811E+09  3.55610E-05\n",
      "   51  1.03812E+09  6.88511E-06\n",
      "   52  1.03811E+09  5.68993E-05\n",
      "   53  1.03811E+09  5.72233E-05\n",
      "   54  1.03811E+09  8.87707E-08\n",
      "   55  1.03811E+09  7.73944E-06\n",
      "   56  1.03811E+09  6.37291E-04\n",
      "   57  1.03811E+09  6.99741E-06\n",
      "   58  1.03811E+09  9.38224E-03\n",
      "   59  1.03811E+09  1.08304E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  1.03811E+09  6.20536E-06\n",
      "   61  1.03811E+09  6.25797E-06\n",
      "   62  1.03811E+09  4.48460E-07\n",
      "   63  1.03811E+09  3.19819E-07\n",
      "   64  1.03841E+09  1.15125E+00\n",
      "   65  1.03838E+09  1.62024E-03\n",
      "   66  1.03817E+09  4.35105E-03\n",
      "   67  1.03811E+09  3.76344E-04\n",
      "   68  1.03812E+09  6.17561E-04\n",
      "   69  1.03811E+09  3.15316E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  1.03811E+09  7.44640E-08\n",
      "   71  1.03811E+09  1.13067E-07\n",
      "   72  1.03811E+09  7.32755E-03\n",
      "   73  1.03811E+09  9.98781E-08\n",
      "   74  1.03820E+09  1.16233E+00\n",
      "   75  1.03841E+09  1.16117E+00\n",
      "   76  1.03836E+09  6.95068E-03\n",
      "   77  1.03811E+09  2.31471E-03\n",
      "   78  1.03812E+09  1.38792E-03\n",
      "   79  1.03812E+09  7.83749E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  1.03811E+09  5.89129E-06\n",
      "   81  1.03811E+09  5.59268E-06\n",
      "   82  1.03811E+09  2.38587E-06\n",
      "   83  1.03811E+09  2.13746E-06\n",
      "   84  1.03812E+09  2.25233E-04\n",
      "   85  1.03830E+09  2.12329E-06\n",
      "   86  1.03819E+09  5.28620E-02\n",
      "   87  1.03812E+09  2.12057E-06\n",
      "   88  1.03814E+09  1.00529E+00\n",
      "   89  1.03814E+09  1.06583E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  1.03811E+09  5.28091E-02\n",
      "   91  1.03811E+09  2.18809E-05\n",
      "   92  1.03811E+09  6.65325E-06\n",
      "   93  1.03811E+09  1.89253E-06\n",
      "   94  1.03811E+09  2.31497E-04\n",
      "   95  1.03811E+09  7.69541E-08\n",
      "   96  1.03811E+09  1.05989E-04\n",
      "   97  1.03811E+09  8.86795E-09\n",
      "   98  1.03811E+09  1.48409E-05\n",
      "   99  1.03811E+09  3.45390E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  100  1.03811E+09  5.44150E-07\n",
      "  101  1.03816E+09  4.08047E-07\n",
      "  102  1.04479E+09  1.08931E+00\n",
      "  103  1.03823E+09  1.25962E+00\n",
      "  104  1.03821E+09  2.28557E-04\n",
      "  105  1.03811E+09  2.28332E-04\n",
      "  106  1.03811E+09  2.00301E-07\n",
      "  107  1.03811E+09  6.71906E-06\n",
      "  108  1.03811E+09  3.24559E-04\n",
      "  109  1.03811E+09  2.00301E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  110  1.03811E+09  1.72683E-04\n",
      "  111  1.03811E+09  3.21030E-07\n",
      "  112  1.03811E+09  1.22390E-07\n",
      "  113  1.03811E+09  9.17010E-08\n",
      "  114  1.03816E+09  1.04316E+00\n",
      "  115  1.03814E+09  9.07140E-08\n",
      "  116  1.03813E+09  3.68084E-03\n",
      "  117  1.03811E+09  2.62403E-04\n",
      "  118  1.03811E+09  8.24703E-07\n",
      "  119  1.03811E+09  8.01673E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  120  1.03811E+09  9.07140E-08\n",
      "  121  1.03816E+09  8.98098E-08\n",
      "  122  1.03817E+09  1.71609E-03\n",
      "  123  1.08298E+09  1.75400E+00\n",
      "  124  1.03936E+09  1.24920E+00\n",
      "  125  1.03872E+09  4.99461E-03\n",
      "  126  1.03922E+09  1.30627E+00\n",
      "  127  1.03895E+09  2.03284E-02\n",
      "  128  1.03811E+09  1.95222E-02\n",
      "  129  1.03815E+09  1.76173E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  130  1.03814E+09  8.24016E-06\n",
      "  131  1.03811E+09  2.53822E-06\n",
      "  132  1.03813E+09  6.45077E-08\n",
      "  133  1.03813E+09  2.56813E-06\n",
      "  134  1.03818E+09  1.93715E-06\n",
      "  135  1.03811E+09  6.45077E-08\n",
      "  136  1.03811E+09  6.45077E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    42.0828000000038      sec\n",
      " Objective      :    1038106945.40164     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3992719.0208 Horizon: 100 Param: 50\n",
      "Cumulative investment: 29562.348846 Horizon: 100 Param: 50\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.15224E+15  1.20312E+01\n",
      "    1  8.79604E+07  1.41899E+00\n",
      "    2  1.47560E+09  6.69227E+00\n",
      "    3  7.44364E+08  5.76353E+00\n",
      "    4  4.74892E+08  1.55732E+00\n",
      "    5  5.58846E+08  1.52205E+00\n",
      "    6  8.26317E+08  1.30368E+00\n",
      "    7  4.81145E+08  1.35024E+00\n",
      "    8  4.25846E+08  1.86950E+00\n",
      "    9  3.80634E+08  2.02809E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.71908E+08  7.11778E+00\n",
      "   11  4.34775E+08  1.40919E+00\n",
      "   12  3.94865E+08  1.38057E+00\n",
      "   13  4.12344E+08  7.03890E-02\n",
      "   14  7.57341E+08  1.28260E+00\n",
      "   15  3.88691E+08  1.26814E+00\n",
      "   16  3.75592E+08  3.66956E-01\n",
      "   17  3.74176E+08  3.72135E-01\n",
      "   18  3.75601E+08  7.29188E-02\n",
      "   19  3.74835E+08  3.28157E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.74506E+08  4.89821E-02\n",
      "   21  3.74389E+08  6.49076E-04\n",
      "   22  3.74388E+08  4.67275E-04\n",
      "   23  3.74388E+08  3.48314E-06\n",
      "   24  3.74388E+08  2.89161E-03\n",
      "   25  3.74388E+08  3.77578E-06\n",
      "   26  3.74388E+08  5.25240E-03\n",
      "   27  3.74388E+08  1.75549E-06\n",
      "   28  3.74388E+08  1.12122E-06\n",
      "   29  3.74388E+08  5.28932E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.74388E+08  1.54772E-05\n",
      "   31  3.74388E+08  9.20571E-04\n",
      "   32  3.74390E+08  2.33811E-03\n",
      "   33  3.74389E+08  5.08589E-05\n",
      "   34  3.74388E+08  4.12851E-04\n",
      "   35  3.74388E+08  3.05642E-06\n",
      "   36  3.74388E+08  1.64914E-04\n",
      "   37  3.74389E+08  4.20591E-07\n",
      "   38  3.74388E+08  6.05801E-02\n",
      "   39  3.74387E+08  7.50235E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.74388E+08  7.05617E-05\n",
      "   41  3.74388E+08  2.67112E-09\n",
      "   42  3.74388E+08  1.97280E-04\n",
      "   43  3.74388E+08  7.22991E-08\n",
      "   44  3.74388E+08  7.22991E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    3.44160000000556      sec\n",
      " Objective      :    374387732.658293     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3743877.3266 Horizon: 100 Param: 25\n",
      "Cumulative investment: 36540.112691 Horizon: 100 Param: 25\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           1900\n",
      " Number of total equations: -         1800\n",
      " Number of slack variables: -          100\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  2.45423E+15  1.63750E+01\n",
      "    1 -3.08250E+08  2.61570E+00\n",
      "    2  5.42279E+09  1.07331E+01\n",
      "    3  2.98088E+09  6.62326E+00\n",
      "    4  9.09937E+08  1.44074E+00\n",
      "    5  1.84161E+09  1.50223E+00\n",
      "    6  1.27128E+08  1.40713E+00\n",
      "    7 -2.95807E+08  1.10008E+01\n",
      "    8  8.33156E+08  5.38815E+00\n",
      "    9  2.42885E+08  4.22983E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.22631E+09  4.37275E+00\n",
      "   11  2.85414E+09  1.61105E+00\n",
      "   12  7.37760E+09  2.50935E+00\n",
      "   13  5.31482E+09  1.74596E+00\n",
      "   14  1.78197E+09  2.13932E+00\n",
      "   15  2.39265E+08  1.24729E+00\n",
      "   16  1.41334E+09  1.39262E+00\n",
      "   17  3.84006E+08  9.85640E-01\n",
      "   18  1.86313E+09  1.50485E+00\n",
      "   19  1.22266E+09  1.33772E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  1.08950E+09  1.25137E+00\n",
      "   21  9.59249E+08  1.27451E+00\n",
      "   22  5.02306E+08  1.64337E+00\n",
      "   23  4.18709E+08  1.35544E+00\n",
      "   24  3.50986E+08  1.30786E+00\n",
      "   25  3.09147E+08  1.29258E+00\n",
      "   26  4.01906E+08  1.21899E+00\n",
      "   27  3.53105E+08  7.65436E-02\n",
      "   28  3.62356E+08  1.41491E+00\n",
      "   29  3.55951E+08  1.75086E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.52475E+08  2.21527E+00\n",
      "   31  3.50380E+08  1.49614E+00\n",
      "   32  3.55028E+08  3.15884E-01\n",
      "   33  3.54436E+08  4.31316E+00\n",
      "   34  3.54511E+08  3.93600E-03\n",
      "   35  3.54490E+08  3.63696E-05\n",
      "   36  3.54490E+08  1.59199E-06\n",
      "   37  3.54490E+08  1.35423E-07\n",
      "   38  3.54490E+08  1.53172E-07\n",
      "   39  3.54490E+08  7.37104E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.54490E+08  1.26284E-03\n",
      "   41  3.54490E+08  8.50930E-08\n",
      "   42  3.54518E+08  1.45053E+00\n",
      "   43  3.54490E+08  3.48470E-08\n",
      "   44  3.54490E+08  3.48470E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    4.26900000000023      sec\n",
      " Objective      :    354489887.915361     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 3544898.8792 Horizon: 100 Param: 0\n",
      "Cumulative investment: 43083.86647 Horizon: 100 Param: 0\n"
     ]
    }
   ],
   "source": [
    "#choose the values for the parameters to iterate on\n",
    "\n",
    "values=[50, 25, 0]\n",
    "tfs=[10, 20, 30,40,60, 80, 100]\n",
    "\n",
    "df = pd.DataFrame(index = tfs, columns = values)\n",
    "df.columns.name = \"values\"\n",
    "df.index.name = \"tfs\"\n",
    "\n",
    "#df for CO2 max\n",
    "dfC = pd.DataFrame(index = tfs, columns = values)\n",
    "dfC.columns.name = \"values\"\n",
    "dfC.index.name = \"tfs\"\n",
    " \n",
    "    \n",
    "dfs={}\n",
    "for j in range(len(tfs)):\n",
    "    for i in range(len(values)):\n",
    "        # time points\n",
    "        n=51\n",
    "        J0 = np.nan\n",
    "        counter=0\n",
    "        while J0 is np.nan and counter<=3:\n",
    "            print(\"ciao\")\n",
    "            try:\n",
    "                # create GEKKO model\n",
    "                m = GEKKO(remote=False)\n",
    "                # constants\n",
    "                E0,Co2o,ccl,cof,kf,kns,cf,c1,c2= 556,280,0.03,0.03,0.076,0.015,0.129,.1,.3\n",
    "                ncl,nof= 1./(19.46), 1./290.\n",
    "                phi=(1.-1.)*E0\n",
    "                Imax=10000.\n",
    "                kc=50.\n",
    "                rho=0.01\n",
    "                #assign valus to parameter\n",
    "                param=values[i]\n",
    "                #decide the parameter to iterate on \n",
    "                phi_r=param\n",
    "                phi=phi_r/100.*E0\n",
    "                tf=tfs[j]\n",
    "                m.time = np.linspace(0,tf,n)\n",
    "                # controls\n",
    "                u = m.MV(value=1,lb=0,ub=1)\n",
    "                I = m.MV(value=0,lb=0, ub=Imax)\n",
    "                u.STATUS = 1\n",
    "                u.DCOST = 0\n",
    "                I.STATUS = 1\n",
    "                I.DCOST = 0\n",
    "                #state variables\n",
    "                x1 = m.Var(value=419) # Co2\n",
    "                x2 = m.Var(value=32) # Kcl\n",
    "                x3 = m.Var(value=0) # Kof\n",
    "                t = m.Param(value=m.time)\n",
    "                e = m.Intermediate(m.exp(-rho*t))\n",
    "                # ode\n",
    "                m.Equation(x1.dt() == (kf*(E0 - x2) - x3)*cf - kns*(x1-Co2o))\n",
    "                m.Equation(x2.dt() == ncl * u * I -  ccl*x2)\n",
    "                m.Equation(x3.dt() == nof*(1-u)*I -  cof*x3)\n",
    "                m.Equation(E0-x2-phi>=0)\n",
    "                \n",
    "                #objective\n",
    "                J = m.Var(value=0) # objective (profit)\n",
    "                D = m.Var(value=0) # objective (damage)\n",
    "                It = m.Var(value=0) #cumulative investment\n",
    "                Jf = m.FV() # final objective\n",
    "                Jf.STATUS = 1\n",
    "                m.Connection(Jf,J,pos2='end')\n",
    "                m.Equation(J.dt() == e* \n",
    "                           ( kc/cf * (x1-Co2o) + u*I*(1 + c1*u*I) + (1-u)*I*(1+c2*(1-u)*I) ))\n",
    "                m.Equation(D.dt() == e* \n",
    "                           ( kc/cf * (x1-Co2o) ))\n",
    "                m.Equation(It.dt() == I)\n",
    "                m.Obj(Jf) # minimize cost\n",
    "                m.options.IMODE = 6  # optimal control\n",
    "                m.options.NODES = 3  # collocation nodes\n",
    "                m.options.SOLVER = 1 # solver (3 IPOPT)\n",
    "                m.options.MAX_ITER=2000\n",
    "                m.solve(disp=True) # Solve, True to see the output\n",
    "                J0=Jf.value[0]\n",
    "                I0=It.value[-1]           \n",
    "            except:\n",
    "                n=n+40\n",
    "                counter+=1\n",
    "                \n",
    "        print('Optimal Cost: ' + str(J0), 'Horizon: ' + str(tf), 'Param: ' + str(param))\n",
    "        print('Cumulative investment: ' + str(I0), 'Horizon: ' + str(tf), 'Param: ' + str(param))\n",
    "        #print('Cumulative investment: ' + str(It.value))\n",
    "                           \n",
    "        #print('Damage: ' + str(D[-1]), 'Horizon: ' + str(tf), 'Param: ' + str(param))\n",
    "        \n",
    "\n",
    "        #store result\n",
    "        df.iloc[j,i] =J0/tf  #divide by the horizon to have the annual cost\n",
    "        dfs[values[i]]=df\n",
    "        dfC.iloc[j,i] = np.max(np.array(x1.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>values</th>\n",
       "      <th>50</th>\n",
       "      <th>25</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tfs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>447.368633</td>\n",
       "      <td>447.368636</td>\n",
       "      <td>447.368635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>467.766498</td>\n",
       "      <td>467.76651</td>\n",
       "      <td>467.766633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>474.925132</td>\n",
       "      <td>474.925406</td>\n",
       "      <td>474.334724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>466.690597</td>\n",
       "      <td>466.689477</td>\n",
       "      <td>466.68933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>439.706178</td>\n",
       "      <td>437.574016</td>\n",
       "      <td>437.574019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "values          50          25          0 \n",
       "tfs                                       \n",
       "10      447.368633  447.368636  447.368635\n",
       "20      467.766498   467.76651  467.766633\n",
       "30      474.925132  474.925406  474.334724\n",
       "40      466.690597  466.689477   466.68933\n",
       "60      439.706178  437.574016  437.574019"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df for CO2 conc max for each simulation\n",
    "dfC.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.9, 0.9, 'b')"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot CO2 max for different planning horizons and chosen parameter (choose param in legend title)\n",
    "#sns.set_palette('RdGy') \n",
    "\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (10, 3), sharex = True)\n",
    "plt.subplots_adjust(wspace = .35, hspace = .25)\n",
    "for i, value in enumerate(values):\n",
    "    df[value].plot(ax = ax[0], style = \".-\", legend = False)\n",
    "    ax[0].set(xlabel = \"$\\\\tau $ (yr)\", ylabel = \"Avg Ann Cost ($ billion / yr)\")\n",
    "    ax[0].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (3, 3))\n",
    "    \n",
    "    \n",
    "    dfC[value].plot(ax = ax[1], style = \".-\", legend = False)\n",
    "    ax[1].set(xlabel = \"$\\\\tau$ (yr)\", ylabel = \"Max [CO$_2$] (ppm)\")\n",
    "    ax[1].ticklabel_format(style = \"sci\", axis = \"y\")\n",
    "    #ax[1].set_ylim([425, 500])\n",
    "    #ax.xaxis.set_visible(False)\n",
    "    #ax.yaxis.set_visible(False)\n",
    "    \n",
    "plt.legend(values, bbox_to_anchor = (.3, -0.2), ncol = len(values),  \n",
    "           title=\"$I_{max}$ (\\$ billion) \",fontsize=11, title_fontsize=12); #$\\Phi$ (% E$_0$)\n",
    "ax[0].annotate('a',xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax[1].annotate('b', xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "#fig.savefig(PATH_SAVE_FIGURES + \"F8.pdf\", bbox_inches = \"tight\", dpi = 300) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pA3=df\n",
    "#pB3=dfC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the annual cost for different planning horizons and chosen parameter (choose param in legend title)\n",
    "#sns.set_palette('RdGy') \n",
    "\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (7, 5), sharex = True)\n",
    "\n",
    "for i, value in enumerate(values):\n",
    "    df[value].plot(ax = ax, style = \".-\", legend = False)\n",
    "    ax.set(xlabel = \"$\\\\tau $ (yr)\", ylabel = \"Average Annual Cost ($ billion / yr)\")\n",
    "    ax.ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (3, 3))\n",
    "    #ax.set_ylim([35*10**3, 140*10**3])\n",
    "    #ax.xaxis.set_visible(False)\n",
    "    #ax.yaxis.set_visible(False)\n",
    "    \n",
    "plt.legend(values, bbox_to_anchor = (1.05, -0.12), ncol = len(values),  title=\"$\\Phi$ (% E$_0$) \",fontsize=11, title_fontsize=12);  #$\\Phi$ (% E$_0$)    SCC (\\$ / tCO$_2$)\n",
    "#fig.savefig(PATH_SAVE_FIGURES + \"F4a.png\", bbox_inches = \"tight\", dpi = 300) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### combined panels of figure 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annotate here the plot for each SCC scenario and combine as subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.legend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "fig, ax = plt.subplots(nrows = 2, ncols = 1, figsize = (10, 8), sharex = True)\n",
    "plt.subplots_adjust(wspace = .35, hspace = 0.)\n",
    "plt.rcParams['lines.linewidth'] = 3\n",
    "\n",
    "for i, value in enumerate(values):\n",
    "    k=i\n",
    "    pA1[value].plot(ax = ax[0], style = \"-.\", legend = False, c=col_pal[k], label=value)\n",
    "    pB1[value].plot(ax = ax[1], style = \"-.\", legend = False,c=col_pal[k], label=value)\n",
    "    \n",
    "    pA2[value].plot(ax = ax[0], style = \"-\", legend = False, c=col_pal[k],  linewidth=2.5)\n",
    "    pB2[value].plot(ax = ax[1], style = \"-\", legend = False, c=col_pal[k],  linewidth=2.5)\n",
    "    \n",
    "    pA3[value].plot(ax = ax[0], style = \"--\", legend = False, c=col_pal[k],  linewidth=2.5)\n",
    "    pB3[value].plot(ax = ax[1], style = \"--\", legend = False, c=col_pal[k],  linewidth=2.5)\n",
    "\n",
    "ax[0].set(xlabel = \"$\\\\tau $ (yr)\", ylabel = \"Avg Ann Cost ($ billion / yr)\")\n",
    "ax[0].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (3, 3))\n",
    "ax[1].set(xlabel = \"$\\\\tau$ (yr)\", ylabel = \"Max [CO$_2$] (ppm)\")\n",
    "ax[1].ticklabel_format(style = \"sci\", axis = \"y\")\n",
    "\n",
    "ax[1].legend(values, bbox_to_anchor = (.67, -0.2), ncol = len(values),\n",
    "           title=\"$\\Phi$ (% E$_0$) \",fontsize=11, title_fontsize=12); \n",
    "ax[0].annotate('SCC=100 \\$/tCO$_2$',xy=(0.05, 0.85), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax[0].annotate(\"SCC=80 \\$/tCO$_2$\",xy=(0.05, 0.6), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax[0].annotate(\"SCC=50 \\$/tCO$_2$\",xy=(0.05, 0.2), xycoords=\"axes fraction\", fontsize=13)\n",
    "\n",
    "one = mpatches.Patch(facecolor=col_pal[0], label=values[0], linewidth = 4)\n",
    "two = mpatches.Patch(facecolor=col_pal[1], label = values[1], linewidth = 9)\n",
    "three = mpatches.Patch(facecolor=col_pal[2], label = values[2], linewidth = 1)\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "myHandle = [Line2D([], [], color=col_pal[0], lw = 3), Line2D([], [], color=col_pal[1], lw = 3),Line2D([], [], color=col_pal[2], lw = 3)]\n",
    "\n",
    "\n",
    "legend = plt.legend(handles=myHandle,  labels=[values[0], values[1], values[2]], bbox_to_anchor = (.67, -0.2),\n",
    "                    ncol = len(values), fontsize = 13, title=\"$\\Phi$ (% E$_0$) \", title_fontsize=12, fancybox=False)\n",
    "\n",
    "ax[0].annotate('a',xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax[1].annotate('b', xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "fig.savefig(PATH_SAVE_FIGURES + \"F4.pdf\", bbox_inches = \"tight\", dpi = 300) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uncertainty of NET "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           3040\n",
      " Number of total equations: -         2880\n",
      " Number of slack variables: -          160\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  6.62532E+14  1.63750E+01\n",
      "    1  1.30247E+09  1.25059E+00\n",
      "    2  2.93735E+09  1.10950E+00\n",
      "    3  5.35018E+08  8.65088E-01\n",
      "    4  4.01244E+08  4.70987E+01\n",
      "    5  3.65985E+08  1.36160E+00\n",
      "    6  3.52518E+08  1.39363E+00\n",
      "    7  3.48771E+08  1.01622E+00\n",
      "    8  3.46863E+08  1.28027E+00\n",
      "    9  3.47484E+08  2.10250E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.47194E+08  7.93260E-01\n",
      "   11  3.47180E+08  2.71827E-04\n",
      "   12  3.47184E+08  2.72415E-01\n",
      "   13  3.47181E+08  1.58880E-04\n",
      "   14  3.47187E+08  1.73432E-03\n",
      "   15  3.47186E+08  1.37398E-05\n",
      "   16  3.47182E+08  9.34432E-02\n",
      "   17  3.47182E+08  2.23712E-05\n",
      "   18  3.47182E+08  3.17071E-03\n",
      "   19  3.47183E+08  4.89193E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.47183E+08  1.31118E-02\n",
      "   21  3.47182E+08  5.92221E-05\n",
      "   22  3.47182E+08  1.09824E-02\n",
      "   23  3.47182E+08  2.59375E-06\n",
      "   24  3.47182E+08  6.10613E-04\n",
      "   25  3.47182E+08  2.82865E-07\n",
      "   26  3.47182E+08  4.73044E-04\n",
      "   27  3.47183E+08  7.38739E-07\n",
      "   28  3.47181E+08  2.01830E+00\n",
      "   29  3.47147E+08  1.45609E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.47182E+08  2.35845E-03\n",
      "   31  3.47182E+08  1.05720E-05\n",
      "   32  3.47182E+08  3.81407E-01\n",
      "   33  3.47182E+08  3.10489E-05\n",
      "   34  3.47182E+08  4.09565E-05\n",
      "   35  3.47182E+08  6.17193E-10\n",
      "   36  3.47182E+08  1.92635E-03\n",
      "   37  3.47182E+08  3.73326E-07\n",
      "   38  3.47182E+08  7.49735E-05\n",
      "   39  3.47182E+08  1.08040E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.47182E+08  2.33933E-04\n",
      "   41  3.47182E+08  2.83122E-08\n",
      "   42  3.47182E+08  1.11338E-04\n",
      "   43  3.47182E+08  2.47132E-04\n",
      "   44  3.47182E+08  2.69416E-04\n",
      "   45  3.47182E+08  3.15187E-08\n",
      "   46  3.47182E+08  8.99127E-04\n",
      "   47  3.47182E+08  6.09893E-08\n",
      "   48  3.47184E+08  6.64390E-03\n",
      "   49  3.47237E+08  6.20257E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.47186E+08  1.16848E-03\n",
      "   51  3.47220E+08  2.88885E-04\n",
      "   52  3.47212E+08  1.41848E-01\n",
      "   53  3.47182E+08  1.42749E-03\n",
      "   54  3.47182E+08  8.14736E-05\n",
      "   55  3.47182E+08  4.17924E-05\n",
      "   56  3.47182E+08  4.15160E-05\n",
      "   57  3.47182E+08  1.81012E-09\n",
      "   58  3.47182E+08  4.25472E-04\n",
      "   59  3.47182E+08  2.23224E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  3.47182E+08  3.79848E-05\n",
      "   61  3.47182E+08  3.07961E-07\n",
      "   62  3.47182E+08  8.25344E-05\n",
      "   63  3.47182E+08  7.51765E-08\n",
      "   64  3.47182E+08  3.01237E-04\n",
      "   65  3.47185E+08  4.35875E-04\n",
      "   66  3.47228E+08  1.17216E+00\n",
      "   67  3.47223E+08  9.68529E-04\n",
      "   68  3.47182E+08  9.67878E-04\n",
      "   69  3.47182E+08  9.93874E-10\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  3.47182E+08  4.23104E-03\n",
      "   71  3.47182E+08  1.56277E-06\n",
      "   72  3.47182E+08  7.63438E-05\n",
      "   73  3.47182E+08  9.93874E-10\n",
      "   74  3.47182E+08  9.41513E-05\n",
      "   75  3.47182E+08  5.71794E-07\n",
      "   76  3.47182E+08  2.33947E-04\n",
      "   77  3.47182E+08  4.65980E-08\n",
      "   78  3.47182E+08  4.50098E-03\n",
      "   79  3.47182E+08  3.20461E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  3.47182E+08  2.17809E-04\n",
      "   81  3.47182E+08  2.42743E-08\n",
      "   82  3.47182E+08  1.83339E-03\n",
      "   83  3.47182E+08  2.39440E-08\n",
      "   84  3.47182E+08  1.94716E-03\n",
      "   85  3.47182E+08  4.07415E-06\n",
      "   86  3.47182E+08  3.79317E-04\n",
      "   87  3.47182E+08  1.34156E-08\n",
      "   88  3.47182E+08  2.41787E-04\n",
      "   89  3.47182E+08  2.58879E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  3.47182E+08  6.81084E-03\n",
      "   91  3.47182E+08  8.00563E-01\n",
      "   92  3.47182E+08  2.68810E-04\n",
      "   93  3.47182E+08  2.06535E-05\n",
      "   94  3.47183E+08  7.26459E-01\n",
      "   95  3.47203E+08  7.95230E-01\n",
      "   96  3.47199E+08  2.11395E-02\n",
      "   97  3.47182E+08  6.37667E-05\n",
      "   98  3.47182E+08  4.77565E-04\n",
      "   99  3.47182E+08  1.82586E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  100  3.47182E+08  2.06128E-05\n",
      "  101  3.47182E+08  7.94926E-01\n",
      "  102  3.47182E+08  4.70318E-03\n",
      "  103  3.47188E+08  1.30142E+00\n",
      "  104  3.47205E+08  8.08379E-01\n",
      "  105  3.47325E+08  1.30015E+00\n",
      "  106  3.47444E+08  8.86627E-01\n",
      "  107  3.47363E+08  4.11642E-03\n",
      "  108  3.47182E+08  4.09888E-03\n",
      "  109  3.47182E+08  8.86991E-10\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  110  3.47182E+08  9.54560E-04\n",
      "  111  3.47182E+08  1.57835E-07\n",
      "  112  3.47182E+08  6.63249E-03\n",
      "  113  3.47182E+08  1.12265E-06\n",
      "  114  3.47195E+08  5.95231E-03\n",
      "  115  3.47182E+08  4.62283E-08\n",
      "  116  3.47183E+08  8.73711E-03\n",
      "  117  3.47182E+08  4.39209E-08\n",
      "  118  3.47182E+08  8.69083E-03\n",
      "  119  3.47182E+08  4.32189E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  120  3.47182E+08  1.98264E-04\n",
      "  121  3.47182E+08  2.40840E-05\n",
      "  122  3.47182E+08  1.90588E-02\n",
      "  123  3.47182E+08  2.81013E-05\n",
      "  124  3.47182E+08  2.68288E-04\n",
      "  125  3.47182E+08  4.33135E-08\n",
      "  126  3.47183E+08  1.65782E-03\n",
      "  127  3.47339E+08  2.40391E-05\n",
      "  128  3.47198E+08  4.68949E-03\n",
      "  129  3.47185E+08  1.56076E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  130  3.47697E+08  9.54567E-01\n",
      "  131  3.47220E+08  1.95916E-04\n",
      "  132  3.47213E+08  1.09235E-02\n",
      "  133  3.47182E+08  9.10706E-05\n",
      "  134  3.47182E+08  3.87049E-05\n",
      "  135  3.47182E+08  4.87961E-07\n",
      "  136  3.47182E+08  7.51312E-06\n",
      "  137  3.47182E+08  1.09604E-08\n",
      "  138  3.47182E+08  4.04561E-04\n",
      "  139  3.47182E+08  3.87572E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  140  3.47182E+08  1.51832E-05\n",
      "  141  3.47182E+08  5.26622E-08\n",
      "  142  3.47182E+08  2.03345E-04\n",
      "  143  3.47182E+08  4.96045E-07\n",
      "  144  3.47182E+08  2.61632E-04\n",
      "  145  3.47182E+08  5.27833E-08\n",
      "  146  3.47182E+08  6.98679E-03\n",
      "  147  3.47182E+08  4.67570E-08\n",
      "  148  3.47182E+08  6.91159E-04\n",
      "  149  3.47182E+08  4.34758E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  150  3.47182E+08  9.92163E-05\n",
      "  151  3.47182E+08  1.91297E-09\n",
      "  152  3.47182E+08  6.78869E-03\n",
      "  153  3.47182E+08  1.15497E-07\n",
      "  154  3.47182E+08  8.90890E-05\n",
      "  155  3.47400E+08  1.88814E-09\n",
      "  156  3.47204E+08  1.32323E+00\n",
      "  157  3.47183E+08  1.88765E-09\n",
      "  158  3.47420E+08  7.27060E-01\n",
      "  159  3.47258E+08  1.88542E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  160  3.47182E+08  1.32106E-03\n",
      "  161  3.47182E+08  6.67324E-04\n",
      "  162  3.47303E+08  4.56497E-03\n",
      "  163  3.47182E+08  1.88292E-09\n",
      "  164  3.47182E+08  4.50941E-04\n",
      "  165  3.47182E+08  2.52317E-07\n",
      "  166  3.47182E+08  6.39481E-05\n",
      "  167  3.47183E+08  4.62457E-07\n",
      "  168  3.47221E+08  6.89937E-03\n",
      "  169  3.47188E+08  2.00997E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  170  3.47187E+08  5.35944E-03\n",
      "  171  3.47182E+08  8.64898E-05\n",
      "  172  3.47182E+08  1.27582E-02\n",
      "  173  3.47182E+08  1.66647E-05\n",
      "  174  3.47182E+08  4.52481E-05\n",
      "  175  3.47182E+08  4.14472E-07\n",
      "  176  3.47182E+08  5.21348E-04\n",
      "  177  3.47182E+08  2.69420E-05\n",
      "  178  3.47182E+08  1.65473E-03\n",
      "  179  3.47182E+08  1.01832E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  180  3.47209E+08  1.02224E+00\n",
      "  181  3.47182E+08  1.02062E-08\n",
      "  182  3.47190E+08  8.56920E-01\n",
      "  183  3.47365E+08  1.01769E-08\n",
      "  184  3.48266E+08  1.56570E+00\n",
      "  185  3.47192E+08  1.02404E-08\n",
      "  186  3.47189E+08  1.32094E-02\n",
      "  187  3.47182E+08  2.75418E-04\n",
      "  188  3.47182E+08  1.38508E-03\n",
      "  189  3.47182E+08  2.88450E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  190  3.47182E+08  5.17661E-05\n",
      "  191  3.47182E+08  8.84859E-09\n",
      "  192  3.47182E+08  6.27841E-04\n",
      "  193  3.47280E+08  1.12643E-03\n",
      "  194  3.47222E+08  9.75266E-01\n",
      "  195  3.47204E+08  1.36826E-04\n",
      "  196  3.48776E+08  1.64665E+00\n",
      "  197  3.54387E+08  1.16670E+00\n",
      "  198  3.47795E+08  1.09103E+00\n",
      "  199  3.47185E+08  8.08246E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  200  3.47188E+08  1.30330E+00\n",
      "  201  3.47182E+08  7.87257E-09\n",
      "  202  3.47206E+08  1.18317E+00\n",
      "  203  3.47285E+08  7.70230E-09\n",
      "  204  3.47677E+08  8.16468E-01\n",
      "  205  3.47419E+08  2.90609E-04\n",
      "  206  3.47205E+08  6.25128E-01\n",
      "  207  3.47195E+08  3.19999E-05\n",
      "  208  3.47193E+08  1.38938E-02\n",
      "  209  3.47182E+08  4.06653E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  210  3.47182E+08  7.35101E-04\n",
      "  211  3.47182E+08  3.25344E-06\n",
      "  212  3.47182E+08  4.86781E-05\n",
      "  213  3.47182E+08  3.74555E-09\n",
      "  214  3.47182E+08  1.05484E-04\n",
      "  215  3.47182E+08  3.26108E-06\n",
      "  216  3.47182E+08  3.38936E-05\n",
      "  217  3.47182E+08  9.03058E-08\n",
      "  218  3.47182E+08  5.51137E-03\n",
      "  219  3.47182E+08  5.78734E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  220  3.47182E+08  1.81072E-04\n",
      "  221  3.47207E+08  3.91809E-08\n",
      "  222  3.47547E+08  1.28733E+00\n",
      "  223  3.47510E+08  3.83062E-03\n",
      "  224  3.47247E+08  2.88538E-03\n",
      "  225  3.47182E+08  7.72418E-04\n",
      "  226  3.47182E+08  1.91460E-02\n",
      "  227  3.47182E+08  6.66733E-08\n",
      "  228  3.47182E+08  1.78308E-04\n",
      "  229  3.47246E+08  2.96429E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  230  3.49941E+08  2.10227E+00\n",
      "  231  3.48631E+08  1.10277E+00\n",
      "  232  3.48647E+08  1.01497E+00\n",
      "  233  3.47182E+08  2.52873E-07\n",
      "  234  3.47181E+08  3.13421E-01\n",
      "  235  3.47182E+08  1.42274E-05\n",
      "  236  3.47182E+08  5.29693E-03\n",
      "  237  3.47184E+08  1.34804E-07\n",
      "  238  3.47183E+08  3.30189E-02\n",
      "  239  3.47182E+08  1.30883E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  240  3.47182E+08  3.72086E-03\n",
      "  241  3.47182E+08  1.25936E-06\n",
      "  242  3.47182E+08  2.15442E-03\n",
      "  243  3.47182E+08  1.91228E-07\n",
      "  244  3.47182E+08  5.82200E-04\n",
      "  245  3.47182E+08  1.15542E-06\n",
      "  246  3.47182E+08  3.24070E-07\n",
      "  247  3.47182E+08  1.84686E-07\n",
      "  248  3.47182E+08  1.84686E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    18.8341999999975      sec\n",
      " Objective      :    347181709.935149     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2169885.6871 Horizon: 80 Param: 0.3\n",
      "Cumulative investment: 17584.876614 Horizon: 80 Param: 0.3\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           3040\n",
      " Number of total equations: -         2880\n",
      " Number of slack variables: -          160\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  6.62532E+14  1.63750E+01\n",
      "    1  1.70191E+08  2.63441E-01\n",
      "    2  1.10759E+09  1.12486E+00\n",
      "    3  3.48634E+08  5.92873E-01\n",
      "    4  3.41845E+08  2.47780E+00\n",
      "    5  3.47951E+08  1.25183E+00\n",
      "    6  3.47197E+08  7.96972E+00\n",
      "    7  3.51146E+08  1.35060E+00\n",
      "    8  3.54371E+08  1.36588E+00\n",
      "    9  3.45372E+08  4.77747E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.43808E+08  2.44446E-02\n",
      "   11  3.43776E+08  6.00403E-04\n",
      "   12  3.43812E+08  2.19085E-03\n",
      "   13  3.43797E+08  2.04653E-04\n",
      "   14  3.43796E+08  3.75720E-02\n",
      "   15  3.43797E+08  1.04504E-04\n",
      "   16  3.43797E+08  6.01618E-05\n",
      "   17  3.43797E+08  3.89250E-07\n",
      "   18  3.43799E+08  6.37828E-01\n",
      "   19  3.49071E+08  1.96563E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.43797E+08  4.76301E-04\n",
      "   21  3.43800E+08  1.23249E+00\n",
      "   22  3.43808E+08  6.89061E-01\n",
      "   23  3.45865E+08  1.23130E+00\n",
      "   24  3.43833E+08  8.25662E-01\n",
      "   25  3.43798E+08  5.77073E-05\n",
      "   26  3.43797E+08  1.64056E-04\n",
      "   27  3.43797E+08  3.15593E-07\n",
      "   28  3.43797E+08  2.95744E-04\n",
      "   29  3.43797E+08  9.37076E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.43797E+08  5.29822E-05\n",
      "   31  3.43797E+08  5.73256E-08\n",
      "   32  3.43797E+08  5.89237E-05\n",
      "   33  3.43794E+08  4.51498E-05\n",
      "   34  3.43797E+08  2.60381E-04\n",
      "   35  3.43797E+08  8.06600E-05\n",
      "   36  3.43797E+08  3.36397E-02\n",
      "   37  3.44205E+08  1.30129E-04\n",
      "   38  4.88570E+08  1.51136E+00\n",
      "   39  4.71603E+08  8.79247E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  4.20861E+08  1.37758E+00\n",
      "   41  5.06933E+08  1.32059E+00\n",
      "   42  3.43796E+08  1.40971E+00\n",
      "   43  3.42782E+08  1.36308E+00\n",
      "   44  3.44056E+08  1.31237E-01\n",
      "   45  3.46126E+08  5.09020E-07\n",
      "   46  3.45684E+08  1.02681E+00\n",
      "   47  3.45378E+08  9.18398E-01\n",
      "   48  3.45136E+08  8.19280E-01\n",
      "   49  3.46510E+08  1.35444E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.44007E+08  1.81206E+01\n",
      "   51  3.43797E+08  1.76307E-03\n",
      "   52  3.43797E+08  3.13186E-04\n",
      "   53  3.43797E+08  5.65553E-06\n",
      "   54  3.43797E+08  5.14139E-02\n",
      "   55  3.43797E+08  5.23574E-07\n",
      "   56  3.43797E+08  5.36136E-05\n",
      "   57  3.43797E+08  2.56697E-07\n",
      "   58  3.43797E+08  2.68144E-01\n",
      "   59  3.43797E+08  6.52721E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  3.43797E+08  3.15703E-04\n",
      "   61  3.43797E+08  3.27486E-08\n",
      "   62  3.43797E+08  8.89018E-01\n",
      "   63  3.43804E+08  1.40438E-04\n",
      "   64  3.44088E+08  1.00055E+00\n",
      "   65  3.43802E+08  7.65162E-01\n",
      "   66  3.43801E+08  2.33083E-02\n",
      "   67  3.43797E+08  5.64780E-05\n",
      "   68  3.43797E+08  3.46287E-05\n",
      "   69  3.43797E+08  1.31978E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  3.43797E+08  1.36930E-04\n",
      "   71  3.43797E+08  6.01198E-06\n",
      "   72  3.43797E+08  3.43243E-05\n",
      "   73  3.43796E+08  2.02629E-06\n",
      "   74  3.43797E+08  3.83652E-02\n",
      "   75  3.43797E+08  9.66297E-07\n",
      "   76  3.43797E+08  6.91813E-05\n",
      "   77  3.43797E+08  6.35062E-08\n",
      "   78  3.43797E+08  3.96421E-03\n",
      "   79  3.43797E+08  1.82684E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  3.43797E+08  2.24290E-03\n",
      "   81  3.43797E+08  4.55098E-08\n",
      "   82  3.43797E+08  1.72087E-04\n",
      "   83  3.43871E+08  1.25647E+00\n",
      "   84  3.43811E+08  9.85539E-01\n",
      "   85  3.43802E+08  1.36666E-04\n",
      "   86  3.43797E+08  2.12174E-03\n",
      "   87  3.43797E+08  9.83436E-05\n",
      "   88  3.43797E+08  2.11727E-03\n",
      "   89  3.43797E+08  8.10421E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  3.43797E+08  1.57919E-04\n",
      "   91  3.43797E+08  5.28475E-08\n",
      "   92  3.43864E+08  1.22747E+00\n",
      "   93  3.43799E+08  5.66081E-08\n",
      "   94  3.43996E+08  1.24680E+00\n",
      "   95  3.43798E+08  5.77499E-08\n",
      "   96  3.44132E+08  1.36942E+00\n",
      "   97  3.43798E+08  5.78457E-08\n",
      "   98  3.43797E+08  9.41889E-03\n",
      "   99  3.43797E+08  2.39218E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  100  3.43797E+08  1.22337E-04\n",
      "  101  3.43797E+08  5.27294E-05\n",
      "  102  3.43797E+08  4.72838E-05\n",
      "  103  3.43797E+08  1.93040E-08\n",
      "  104  3.43797E+08  1.75036E-06\n",
      "  105  3.43797E+08  4.89288E-08\n",
      "  106  3.43797E+08  1.16533E-04\n",
      "  107  3.43797E+08  1.93040E-08\n",
      "  108  3.43797E+08  1.32228E-03\n",
      "  109  3.43798E+08  2.31987E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  110  3.43800E+08  1.36506E-02\n",
      "  111  3.43797E+08  2.21030E-07\n",
      "  112  3.43797E+08  8.80386E-06\n",
      "  113  3.43797E+08  3.96746E-07\n",
      "  114  3.43797E+08  4.39240E-03\n",
      "  115  3.43797E+08  2.62547E-05\n",
      "  116  3.43797E+08  3.17270E-03\n",
      "  117  3.43797E+08  3.29946E-09\n",
      "  118  3.43797E+08  4.04718E-04\n",
      "  119  3.43797E+08  4.76813E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  120  3.43798E+08  1.27613E+00\n",
      "  121  3.43797E+08  4.64833E-08\n",
      "  122  3.43797E+08  6.88047E-05\n",
      "  123  3.43797E+08  4.21317E-08\n",
      "  124  3.43797E+08  3.90297E-04\n",
      "  125  3.43797E+08  2.13625E-06\n",
      "  126  3.43797E+08  2.46660E-04\n",
      "  127  3.43797E+08  4.48801E-08\n",
      "  128  3.43797E+08  2.27751E-03\n",
      "  129  3.43797E+08  1.95702E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  130  3.43797E+08  5.58569E-02\n",
      "  131  3.43797E+08  1.43024E-08\n",
      "  132  3.43849E+08  1.48491E-01\n",
      "  133  3.45183E+08  1.12389E+00\n",
      "  134  3.44062E+08  1.12385E+00\n",
      "  135  3.43849E+08  4.93969E-04\n",
      "  136  3.43842E+08  1.84750E+00\n",
      "  137  3.43797E+08  1.05557E-04\n",
      "  138  3.43797E+08  1.12002E-04\n",
      "  139  3.43797E+08  6.34972E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  140  3.43797E+08  1.96802E-03\n",
      "  141  3.43797E+08  7.80601E-06\n",
      "  142  3.43797E+08  6.36379E-06\n",
      "  143  3.43797E+08  7.40461E-09\n",
      "  144  3.43797E+08  2.24195E-04\n",
      "  145  3.43797E+08  3.32660E-07\n",
      "  146  3.43797E+08  1.68749E-04\n",
      "  147  3.43797E+08  4.93996E-07\n",
      "  148  3.43802E+08  7.41028E-01\n",
      "  149  3.43802E+08  2.48716E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  150  3.43798E+08  1.96802E-03\n",
      "  151  3.43797E+08  5.41668E-06\n",
      "  152  3.43797E+08  1.42466E-04\n",
      "  153  3.43797E+08  3.07286E-07\n",
      "  154  3.43797E+08  2.55863E-01\n",
      "  155  3.43797E+08  2.84711E-05\n",
      "  156  3.44914E+08  1.21958E+00\n",
      "  157  3.43873E+08  3.23081E-05\n",
      "  158  3.43801E+08  6.75450E-01\n",
      "  159  3.43801E+08  9.28697E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  160  3.43799E+08  1.68190E-02\n",
      "  161  3.43797E+08  4.32522E-05\n",
      "  162  3.43797E+08  8.80942E-05\n",
      "  163  3.43797E+08  2.33719E-05\n",
      "  164  3.43797E+08  9.30130E-04\n",
      "  165  3.43797E+08  1.38204E-07\n",
      "  166  3.43797E+08  1.38819E-03\n",
      "  167  3.43928E+08  8.29288E-01\n",
      "  168  3.43920E+08  8.41920E-01\n",
      "  169  3.44394E+08  8.29257E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  170  3.45845E+08  1.21512E+00\n",
      "  171  3.45576E+08  9.71091E-03\n",
      "  172  3.43797E+08  9.69357E-03\n",
      "  173  3.43797E+08  6.76835E-05\n",
      "  174  3.43797E+08  7.73137E-03\n",
      "  175  3.43797E+08  1.64161E-06\n",
      "  176  3.43797E+08  8.52698E-05\n",
      "  177  3.43809E+08  1.23506E-07\n",
      "  178  3.43834E+08  6.96061E-02\n",
      "  179  3.44049E+08  7.66808E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  180  3.43819E+08  1.28338E+00\n",
      "  181  3.43804E+08  1.23295E-07\n",
      "  182  3.43797E+08  8.97687E-04\n",
      "  183  3.43797E+08  5.48228E-05\n",
      "  184  3.43797E+08  8.05871E-06\n",
      "  185  3.43797E+08  1.05638E-09\n",
      "  186  3.43797E+08  2.63234E-04\n",
      "  187  3.43797E+08  3.35817E-08\n",
      "  188  3.43797E+08  4.54885E-04\n",
      "  189  3.43797E+08  2.18513E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  190  3.43797E+08  1.66546E-03\n",
      "  191  3.43797E+08  5.59371E-07\n",
      "  192  3.43797E+08  1.84710E-04\n",
      "  193  3.43797E+08  2.18513E-08\n",
      "  194  3.43796E+08  1.55616E-02\n",
      "  195  3.44107E+08  1.47523E-05\n",
      "  196  4.04852E+08  1.51051E+00\n",
      "  197  3.50262E+08  1.47446E-05\n",
      "  198  3.51931E+08  1.51049E+00\n",
      "  199  3.43842E+08  1.21637E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  200  3.43836E+08  1.15769E+00\n",
      "  201  3.43797E+08  1.10843E-03\n",
      "  202  3.43797E+08  9.81496E-02\n",
      "  203  3.43797E+08  7.62801E-07\n",
      "  204  3.43797E+08  1.71645E-07\n",
      "  205  3.43797E+08  1.71645E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    15.4648999999918      sec\n",
      " Objective      :    343796520.653143     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2148728.2541 Horizon: 80 Param: 0.3\n",
      "Cumulative investment: 19343.309911 Horizon: 80 Param: 0.3\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           3040\n",
      " Number of total equations: -         2880\n",
      " Number of slack variables: -          160\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  6.62532E+14  1.63750E+01\n",
      "    1  1.29486E+08  2.63441E-01\n",
      "    2  1.20756E+09  1.12486E+00\n",
      "    3  3.39742E+08  1.09244E+00\n",
      "    4  3.36032E+08  2.60467E+00\n",
      "    5  3.46307E+08  1.25724E+00\n",
      "    6  3.40358E+08  1.46090E+00\n",
      "    7  3.43432E+08  1.12081E+00\n",
      "    8  3.47161E+08  1.20657E+00\n",
      "    9  3.54296E+08  1.21051E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.37302E+08  1.08909E+00\n",
      "   11  3.37143E+08  2.39206E+00\n",
      "   12  3.37113E+08  2.48170E-02\n",
      "   13  3.37166E+08  3.41392E-04\n",
      "   14  3.37158E+08  9.39489E-01\n",
      "   15  3.37123E+08  2.60584E-04\n",
      "   16  3.37120E+08  4.39509E-04\n",
      "   17  3.37120E+08  5.77535E-06\n",
      "   18  3.37120E+08  6.95789E-06\n",
      "   19  3.37120E+08  4.16576E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.37120E+08  1.00204E-03\n",
      "   21  3.37113E+08  2.56501E-04\n",
      "   22  3.37120E+08  1.46336E-04\n",
      "   23  3.37120E+08  2.70732E-05\n",
      "   24  3.37120E+08  4.14678E-04\n",
      "   25  3.37121E+08  1.89965E-05\n",
      "   26  3.37120E+08  7.69797E-05\n",
      "   27  3.37120E+08  6.15211E-08\n",
      "   28  3.37119E+08  9.94996E-03\n",
      "   29  3.37120E+08  4.29914E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.37120E+08  4.11035E-05\n",
      "   31  3.37120E+08  3.21070E-04\n",
      "   32  3.37119E+08  3.44774E-02\n",
      "   33  3.37120E+08  1.06237E-02\n",
      "   34  3.37120E+08  8.66317E-04\n",
      "   35  3.37120E+08  3.25482E-04\n",
      "   36  3.37120E+08  1.05119E-03\n",
      "   37  3.37120E+08  9.39343E-06\n",
      "   38  3.37125E+08  1.16226E-03\n",
      "   39  3.37324E+08  8.37957E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.37302E+08  6.37245E+00\n",
      "   41  3.37156E+08  3.40810E-03\n",
      "   42  3.37128E+08  9.66428E-01\n",
      "   43  3.37128E+08  6.50716E-05\n",
      "   44  3.37121E+08  1.86695E-04\n",
      "   45  3.37485E+08  8.43723E-01\n",
      "   46  3.37141E+08  1.28399E+00\n",
      "   47  3.37776E+08  8.50205E-04\n",
      "   48  3.50610E+08  1.30776E+00\n",
      "   49  3.37122E+08  1.27997E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.39498E+08  7.59736E-01\n",
      "   51  3.38960E+08  1.25415E-02\n",
      "   52  3.37120E+08  1.22853E-02\n",
      "   53  3.37120E+08  8.92934E-06\n",
      "   54  3.37120E+08  1.04652E-03\n",
      "   55  3.37120E+08  9.69407E-05\n",
      "   56  3.37120E+08  2.86706E-04\n",
      "   57  3.37120E+08  7.67068E-01\n",
      "   58  3.37188E+08  1.46737E+00\n",
      "   59  3.37123E+08  1.79711E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  3.37639E+08  9.49408E-01\n",
      "   61  3.37127E+08  7.55618E-01\n",
      "   62  3.37157E+08  1.46558E+00\n",
      "   63  3.39734E+08  1.19934E+00\n",
      "   64  3.39165E+08  1.30433E+00\n",
      "   65  3.37226E+08  6.24385E-01\n",
      "   66  3.37123E+08  1.10369E-01\n",
      "   67  3.37120E+08  1.87012E-01\n",
      "   68  3.37120E+08  2.33713E-03\n",
      "   69  3.37120E+08  7.14251E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  3.37120E+08  1.94833E-04\n",
      "   71  3.37120E+08  1.29576E-03\n",
      "   72  3.37131E+08  2.05106E+00\n",
      "   73  3.37129E+08  1.06071E-02\n",
      "   74  3.37120E+08  2.56457E-04\n",
      "   75  3.37120E+08  3.66413E-08\n",
      "   76  3.37120E+08  5.02769E-05\n",
      "   77  3.37120E+08  1.14957E-04\n",
      "   78  3.37121E+08  2.26422E-03\n",
      "   79  3.37120E+08  3.31793E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  3.37120E+08  1.09606E-04\n",
      "   81  3.37120E+08  2.31891E-05\n",
      "   82  3.37120E+08  6.03371E-02\n",
      "   83  3.37119E+08  3.67145E-05\n",
      "   84  3.37120E+08  1.13316E-04\n",
      "   85  3.37120E+08  2.80026E-08\n",
      "   86  3.37547E+08  8.53852E-01\n",
      "   87  3.37173E+08  1.02596E-04\n",
      "   88  3.37164E+08  1.70402E+00\n",
      "   89  3.37159E+08  4.41475E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  3.37120E+08  4.40867E-04\n",
      "   91  3.37120E+08  6.37590E-12\n",
      "   92  3.37120E+08  1.43833E-04\n",
      "   93  3.37120E+08  3.80151E-08\n",
      "   94  3.37120E+08  1.34623E-04\n",
      "   95  3.37120E+08  3.78127E-09\n",
      "   96  3.37120E+08  6.36549E-05\n",
      "   97  3.37120E+08  4.80778E-09\n",
      "   98  3.37119E+08  1.36932E-02\n",
      "   99  3.37120E+08  5.68782E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  100  3.37120E+08  2.86195E-03\n",
      "  101  3.37120E+08  5.89947E-07\n",
      "  102  3.37120E+08  8.50328E-04\n",
      "  103  3.37120E+08  1.07691E-05\n",
      "  104  3.37120E+08  4.26901E-05\n",
      "  105  3.37120E+08  8.38578E-08\n",
      "  106  3.37120E+08  6.68237E-08\n",
      "  107  3.37145E+08  1.16098E-04\n",
      "  108  3.37120E+08  2.84251E-04\n",
      "  109  3.37120E+08  8.49478E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  110  3.37120E+08  1.16408E-04\n",
      "  111  3.37125E+08  6.20830E-08\n",
      "  112  3.37136E+08  8.02967E-03\n",
      "  113  3.37135E+08  2.89521E-04\n",
      "  114  3.37779E+08  1.77464E+00\n",
      "  115  3.37659E+08  7.64183E-03\n",
      "  116  3.37120E+08  4.41751E-03\n",
      "  117  3.37391E+08  3.00928E-05\n",
      "  118  3.41810E+08  1.35112E+00\n",
      "  119  3.41449E+08  5.49972E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  120  3.37919E+08  6.55949E+00\n",
      "  121  3.37238E+08  1.95339E-01\n",
      "  122  3.37171E+08  1.72178E+00\n",
      "  123  3.37130E+08  2.39067E-02\n",
      "  124  3.37121E+08  4.35629E-02\n",
      "  125  3.37120E+08  7.25422E-03\n",
      "  126  3.37120E+08  1.17920E-03\n",
      "  127  3.37120E+08  7.34781E-05\n",
      "  128  3.37120E+08  1.21318E-05\n",
      "  129  3.37120E+08  7.19633E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  130  3.37169E+08  4.81004E-04\n",
      "  131  3.38035E+08  6.97717E-09\n",
      "  132  3.37173E+08  1.52361E-02\n",
      "  133  3.37812E+08  6.98003E-09\n",
      "  134  3.37148E+08  5.21199E-01\n",
      "  135  3.37142E+08  5.22871E-04\n",
      "  136  3.37120E+08  5.21201E-04\n",
      "  137  3.37120E+08  5.19932E-07\n",
      "  138  3.37120E+08  1.89608E-05\n",
      "  139  3.37120E+08  7.71023E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  140  3.37120E+08  5.35677E-04\n",
      "  141  3.37120E+08  6.55212E-08\n",
      "  142  3.37122E+08  1.21392E+00\n",
      "  143  3.37132E+08  3.71695E-05\n",
      "  144  3.37128E+08  9.27297E-01\n",
      "  145  3.37127E+08  5.33353E-05\n",
      "  146  3.37120E+08  5.08924E-05\n",
      "  147  3.37120E+08  8.67897E-08\n",
      "  148  3.37134E+08  1.76844E-02\n",
      "  149  3.39835E+08  1.36461E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  150  3.38934E+08  1.35112E+00\n",
      "  151  3.39518E+08  8.69084E-01\n",
      "  152  3.37120E+08  8.40090E-05\n",
      "  153  3.37121E+08  4.92436E-08\n",
      "  154  3.37194E+08  1.03132E+00\n",
      "  155  3.37161E+08  9.73604E-01\n",
      "  156  3.38089E+08  9.36809E-01\n",
      "  157  3.53991E+08  1.25045E+00\n",
      "  158  3.37478E+08  1.16781E+00\n",
      "  159  3.37392E+08  1.46629E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  160  3.37120E+08  1.46752E-02\n",
      "  161  3.37121E+08  3.95690E-06\n",
      "  162  3.37366E+08  7.16226E-01\n",
      "  163  3.37305E+08  6.05675E-03\n",
      "  164  3.37120E+08  6.00410E-03\n",
      "  165  3.37120E+08  1.58744E-08\n",
      "  166  3.37120E+08  2.76055E-05\n",
      "  167  3.37209E+08  9.71465E-09\n",
      "  168  3.37143E+08  3.91412E-03\n",
      "  169  3.37127E+08  8.63723E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  170  3.37120E+08  8.83958E-05\n",
      "  171  3.37121E+08  1.21151E-02\n",
      "  172  3.37121E+08  1.45917E-03\n",
      "  173  3.37121E+08  7.38957E-05\n",
      "  174  3.37120E+08  7.38988E-05\n",
      "  175  3.37120E+08  1.41120E-06\n",
      "  176  3.37120E+08  6.20047E-03\n",
      "  177  3.37120E+08  8.33095E-06\n",
      "  178  3.37120E+08  1.21206E-05\n",
      "  179  3.37120E+08  5.48665E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  180  3.37120E+08  4.20454E-04\n",
      "  181  3.37120E+08  1.05976E-07\n",
      "  182  3.37120E+08  1.45197E-05\n",
      "  183  3.37120E+08  1.70243E-10\n",
      "  184  3.37120E+08  2.28304E-04\n",
      "  185  3.37120E+08  2.11949E-08\n",
      "  186  3.37120E+08  1.71761E-05\n",
      "  187  3.37120E+08  2.08613E-08\n",
      "  188  3.37120E+08  6.00600E-05\n",
      "  189  3.37120E+08  2.09362E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  190  3.37120E+08  1.97523E-04\n",
      "  191  3.37120E+08  2.09725E-06\n",
      "  192  3.37120E+08  2.39154E-04\n",
      "  193  3.37120E+08  1.44060E-05\n",
      "  194  3.37120E+08  1.43983E-05\n",
      "  195  3.37120E+08  2.94440E-06\n",
      "  196  3.37120E+08  6.23470E-03\n",
      "  197  3.37120E+08  1.27024E-08\n",
      "  198  3.37120E+08  3.69444E-04\n",
      "  199  3.37120E+08  1.76701E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  200  3.37120E+08  7.34540E-04\n",
      "  201  3.37120E+08  1.27040E-06\n",
      "  202  3.37123E+08  2.19326E-03\n",
      "  203  3.37148E+08  7.13670E-01\n",
      "  204  3.37145E+08  2.08574E-02\n",
      "  205  3.37120E+08  9.82236E-05\n",
      "  206  3.37120E+08  1.74351E-04\n",
      "  207  3.37120E+08  7.24540E-08\n",
      "  208  3.37120E+08  4.42274E-02\n",
      "  209  3.37120E+08  4.40265E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  210  3.37117E+08  5.73309E-03\n",
      "  211  3.37120E+08  2.99523E-04\n",
      "  212  3.37120E+08  8.21550E-01\n",
      "  213  3.37120E+08  7.82727E-07\n",
      "  214  3.37120E+08  8.32450E-05\n",
      "  215  3.37130E+08  8.59463E-07\n",
      "  216  3.37129E+08  1.05136E-01\n",
      "  217  3.37122E+08  2.82174E-04\n",
      "  218  3.37120E+08  1.62286E-04\n",
      "  219  3.37120E+08  7.80076E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  220  3.37120E+08  1.93546E-03\n",
      "  221  3.37120E+08  7.16678E-05\n",
      "  222  3.37120E+08  1.09162E-02\n",
      "  223  3.37120E+08  2.26002E-07\n",
      "  224  3.37120E+08  6.37397E-04\n",
      "  225  3.37125E+08  1.12627E-04\n",
      "  226  3.37123E+08  1.31316E-03\n",
      "  227  3.37120E+08  1.14117E-07\n",
      "  228  3.37125E+08  1.55951E-02\n",
      "  229  3.37642E+08  1.12114E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  230  3.37555E+08  4.54211E-02\n",
      "  231  3.37120E+08  1.79794E-03\n",
      "  232  3.37120E+08  3.26524E-03\n",
      "  233  3.37120E+08  1.26412E-07\n",
      "  234  3.37120E+08  8.76766E-05\n",
      "  235  3.37120E+08  5.09157E-10\n",
      "  236  3.37120E+08  1.58295E-04\n",
      "  237  3.37120E+08  3.11918E-05\n",
      "  238  3.37120E+08  3.26524E-03\n",
      "  239  3.37120E+08  1.95713E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  240  3.37123E+08  1.09782E+00\n",
      "  241  3.37120E+08  5.77862E-05\n",
      "  242  3.37120E+08  9.22394E-03\n",
      "  243  3.37120E+08  1.68120E-05\n",
      "  244  3.37120E+08  3.72533E-04\n",
      "  245  3.37120E+08  6.94192E-08\n",
      "  246  3.37120E+08  1.14609E-04\n",
      "  247  3.37120E+08  1.25310E-07\n",
      "  248  3.37120E+08  2.36745E-05\n",
      "  249  3.37126E+08  6.84572E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  250  3.38490E+08  1.36484E+00\n",
      "  251  3.37150E+08  2.62226E-05\n",
      "  252  3.37126E+08  4.83262E-03\n",
      "  253  3.37120E+08  4.88053E-06\n",
      "  254  3.37120E+08  3.11853E-03\n",
      "  255  3.37120E+08  3.44095E-06\n",
      "  256  3.37120E+08  8.54198E-05\n",
      "  257  3.37120E+08  2.16906E-07\n",
      "  258  3.37120E+08  1.06207E-04\n",
      "  259  3.37120E+08  9.66685E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  260  3.37120E+08  3.88959E-05\n",
      "  261  3.37120E+08  2.16906E-07\n",
      "  262  3.37120E+08  1.09503E-04\n",
      "  263  3.38420E+08  1.40915E+00\n",
      "  264  3.37120E+08  1.95035E-07\n",
      "  265  3.37120E+08  1.95035E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    19.2667999999976      sec\n",
      " Objective      :    337119659.884917     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2106997.8743 Horizon: 80 Param: 0.3\n",
      "Cumulative investment: 21541.905529 Horizon: 80 Param: 0.3\n",
      "ciao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           3040\n",
      " Number of total equations: -         2880\n",
      " Number of slack variables: -          160\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  6.62532E+14  1.63750E+01\n",
      "    1  1.06249E+09  1.25059E+00\n",
      "    2  2.15428E+09  1.19355E+00\n",
      "    3  4.87779E+08  8.42373E-01\n",
      "    4  3.79455E+08  1.22634E+00\n",
      "    5  3.59957E+08  1.35849E+00\n",
      "    6  3.52464E+08  1.39466E+00\n",
      "    7  3.52920E+08  2.44908E+00\n",
      "    8  3.50849E+08  1.17560E+01\n",
      "    9  3.46692E+08  1.71377E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.46566E+08  4.23123E+00\n",
      "   11  3.46240E+08  1.03158E+00\n",
      "   12  3.43922E+08  1.92910E+00\n",
      "   13  3.54107E+08  1.02813E+00\n",
      "   14  3.55732E+08  1.35004E+00\n",
      "   15  3.48453E+08  1.78619E-01\n",
      "   16  3.48924E+08  1.35936E+00\n",
      "   17  3.46783E+08  1.27780E-01\n",
      "   18  3.46787E+08  2.19363E-02\n",
      "   19  3.46279E+08  2.68324E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.46188E+08  9.27979E-01\n",
      "   21  3.45477E+08  1.16001E+00\n",
      "   22  3.46476E+08  7.25924E-01\n",
      "   23  3.47817E+08  9.85907E-04\n",
      "   24  3.46273E+08  3.27685E-02\n",
      "   25  3.46241E+08  1.05018E+00\n",
      "   26  3.46118E+08  2.15340E+00\n",
      "   27  3.46272E+08  8.47391E-02\n",
      "   28  3.46271E+08  1.19378E+00\n",
      "   29  3.46270E+08  2.58363E-03\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.46273E+08  2.60907E-01\n",
      "   31  3.46272E+08  1.47098E-05\n",
      "   32  3.46272E+08  6.83003E-04\n",
      "   33  3.46272E+08  4.24251E-06\n",
      "   34  3.46272E+08  9.72468E-03\n",
      "   35  3.46272E+08  4.39240E-07\n",
      "   36  3.46272E+08  4.84525E-03\n",
      "   37  3.46272E+08  4.13449E-06\n",
      "   38  3.46272E+08  8.52333E-03\n",
      "   39  3.46272E+08  4.39240E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.46272E+08  1.87848E-02\n",
      "   41  3.46272E+08  3.97318E-04\n",
      "   42  3.46272E+08  8.02342E-05\n",
      "   43  3.46272E+08  3.76284E-07\n",
      "   44  3.46272E+08  7.10104E-04\n",
      "   45  3.46272E+08  7.34048E-04\n",
      "   46  3.46272E+08  5.98544E-04\n",
      "   47  3.46272E+08  1.01856E-06\n",
      "   48  3.46272E+08  6.75677E-01\n",
      "   49  3.46272E+08  2.67855E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.46272E+08  4.71613E-04\n",
      "   51  3.46272E+08  9.64638E-06\n",
      "   52  3.46272E+08  4.40871E-03\n",
      "   53  3.46272E+08  1.76429E-07\n",
      "   54  3.46272E+08  4.40871E-03\n",
      "   55  3.46277E+08  1.11390E+00\n",
      "   56  3.46272E+08  9.30236E-01\n",
      "   57  3.46272E+08  3.48307E-07\n",
      "   58  3.46272E+08  5.51288E-05\n",
      "   59  3.46272E+08  1.44323E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  3.46272E+08  5.32354E-05\n",
      "   61  3.46272E+08  1.26815E-07\n",
      "   62  3.46272E+08  1.03561E-01\n",
      "   63  3.46272E+08  1.78278E-07\n",
      "   64  3.46272E+08  2.35815E-02\n",
      "   65  3.46272E+08  2.85998E-08\n",
      "   66  3.46272E+08  2.64518E-04\n",
      "   67  3.46273E+08  1.13745E+00\n",
      "   68  3.46272E+08  1.38243E-03\n",
      "   69  3.46272E+08  4.42391E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  3.46272E+08  1.05952E-03\n",
      "   71  3.46272E+08  3.47096E-07\n",
      "   72  3.46272E+08  6.30100E-03\n",
      "   73  3.46272E+08  1.35049E-07\n",
      "   74  3.46272E+08  2.04422E-04\n",
      "   75  3.46272E+08  7.45763E-08\n",
      "   76  3.46272E+08  9.20512E-08\n",
      "   77  3.46272E+08  6.97493E-08\n",
      "   78  3.46272E+08  7.45763E-08\n",
      "   79  3.46272E+08  7.45763E-08\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    7.74819999998726      sec\n",
      " Objective      :    346271795.937065     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2164198.7246 Horizon: 80 Param: 0.2\n",
      "Cumulative investment: 18840.908975 Horizon: 80 Param: 0.2\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           3040\n",
      " Number of total equations: -         2880\n",
      " Number of slack variables: -          160\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  6.62532E+14  1.63750E+01\n",
      "    1  1.70161E+08  2.63441E-01\n",
      "    2  9.97490E+08  1.12486E+00\n",
      "    3  3.43743E+08  3.74518E-01\n",
      "    4  3.40683E+08  1.37225E+00\n",
      "    5  3.42358E+08  1.96972E-02\n",
      "    6  3.41306E+08  2.58553E-02\n",
      "    7  3.41205E+08  8.22300E-03\n",
      "    8  3.41194E+08  5.07077E-03\n",
      "    9  3.41194E+08  1.43162E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.41194E+08  1.72149E-04\n",
      "   11  3.41194E+08  5.05318E-05\n",
      "   12  3.41194E+08  1.19104E-03\n",
      "   13  3.41194E+08  4.79633E-05\n",
      "   14  3.41194E+08  5.05318E-05\n",
      "   15  3.41194E+08  3.36861E-07\n",
      "   16  3.41194E+08  4.97441E-03\n",
      "   17  3.41194E+08  3.94851E-06\n",
      "   18  3.41194E+08  7.79792E-05\n",
      "   19  3.41194E+08  8.16392E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.41196E+08  1.30562E+00\n",
      "   21  3.41196E+08  1.83358E-04\n",
      "   22  3.41194E+08  2.79478E-04\n",
      "   23  3.41194E+08  3.94483E-07\n",
      "   24  3.41194E+08  3.24401E-04\n",
      "   25  3.41194E+08  1.48627E-05\n",
      "   26  3.41194E+08  1.24763E-04\n",
      "   27  3.41194E+08  3.42177E-07\n",
      "   28  3.41194E+08  2.68419E-05\n",
      "   29  3.41194E+08  1.36051E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.41194E+08  1.11593E-05\n",
      "   31  3.41194E+08  1.36747E-05\n",
      "   32  3.41194E+08  5.16826E-05\n",
      "   33  3.41194E+08  1.82951E-07\n",
      "   34  3.41194E+08  1.46866E-04\n",
      "   35  3.41194E+08  3.73423E-08\n",
      "   36  3.41194E+08  1.29842E-03\n",
      "   37  3.41194E+08  4.75051E-07\n",
      "   38  3.41194E+08  1.02432E-02\n",
      "   39  3.41194E+08  3.63944E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.41194E+08  1.35057E-02\n",
      "   41  3.41194E+08  9.35335E-08\n",
      "   42  3.41196E+08  6.90528E-02\n",
      "   43  3.41201E+08  1.91338E-04\n",
      "   44  3.41204E+08  1.20556E+00\n",
      "   45  3.41195E+08  2.26935E-04\n",
      "   46  3.41194E+08  6.86823E-02\n",
      "   47  3.41195E+08  7.46071E-01\n",
      "   48  3.41195E+08  2.35118E-03\n",
      "   49  3.41194E+08  3.84212E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.41194E+08  1.03202E-04\n",
      "   51  3.41194E+08  1.96458E-08\n",
      "   52  3.41194E+08  7.38732E-04\n",
      "   53  3.41194E+08  7.59584E-08\n",
      "   54  3.41194E+08  1.03202E-04\n",
      "   55  3.41194E+08  1.18435E-09\n",
      "   56  3.41200E+08  1.23841E-03\n",
      "   57  3.41226E+08  2.67385E-04\n",
      "   58  3.43175E+08  1.39775E+00\n",
      "   59  3.41498E+08  1.39773E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  3.41464E+08  1.95959E-01\n",
      "   61  3.41194E+08  5.44390E-04\n",
      "   62  3.41194E+08  1.94747E-05\n",
      "   63  3.41194E+08  4.19598E-06\n",
      "   64  3.41194E+08  3.76854E-05\n",
      "   65  3.41194E+08  2.71764E-08\n",
      "   66  3.41194E+08  6.67921E-04\n",
      "   67  3.41194E+08  9.79631E-09\n",
      "   68  3.41194E+08  2.02599E-05\n",
      "   69  3.41194E+08  7.17104E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  3.41255E+08  9.06026E-01\n",
      "   71  3.41194E+08  1.38991E-10\n",
      "   72  3.41194E+08  1.38991E-10\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    7.48819999999250      sec\n",
      " Objective      :    341194012.435818     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2132462.5777 Horizon: 80 Param: 0.2\n",
      "Cumulative investment: 21478.428081 Horizon: 80 Param: 0.2\n",
      "ciao\n",
      " ----------------------------------------------------------------\n",
      " APMonitor, Version 1.0.1\n",
      " APMonitor Optimization Suite\n",
      " ----------------------------------------------------------------\n",
      " \n",
      " \n",
      " --------- APM Model Size ------------\n",
      " Each time step contains\n",
      "   Objects      :            0\n",
      "   Constants    :            0\n",
      "   Variables    :           11\n",
      "   Intermediates:            1\n",
      "   Connections  :            1\n",
      "   Equations    :            9\n",
      "   Residuals    :            8\n",
      " \n",
      " Number of state variables:           3040\n",
      " Number of total equations: -         2880\n",
      " Number of slack variables: -          160\n",
      " ---------------------------------------\n",
      " Degrees of freedom       :              0\n",
      " \n",
      " ----------------------------------------------\n",
      " Dynamic Control with APOPT Solver\n",
      " ----------------------------------------------\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "    0  6.62532E+14  1.63750E+01\n",
      "    1  1.29456E+08  2.63441E-01\n",
      "    2  1.04009E+09  1.12486E+00\n",
      "    3  3.32278E+08  1.09244E+00\n",
      "    4  3.29076E+08  1.21565E+00\n",
      "    5  3.24579E+08  7.86565E-01\n",
      "    6  3.39479E+08  1.39552E+00\n",
      "    7  4.95546E+08  1.29549E+00\n",
      "    8  6.64190E+08  1.47279E+00\n",
      "    9  3.45495E+08  9.17776E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   10  3.51041E+08  1.33741E+00\n",
      "   11  3.37683E+08  4.65867E-01\n",
      "   12  3.26606E+08  1.38791E+00\n",
      "   13  3.14490E+08  1.11828E+00\n",
      "   14  4.09809E+08  1.37728E+00\n",
      "   15  3.79296E+08  7.21745E-01\n",
      "   16  3.51781E+08  1.18454E+00\n",
      "   17  3.40890E+08  1.28720E+00\n",
      "   18  3.32437E+08  2.89130E-01\n",
      "   19  3.34394E+08  1.19601E-02\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   20  3.32428E+08  7.43307E-01\n",
      "   21  3.31486E+08  2.50651E-02\n",
      "   22  3.33459E+08  9.85359E-01\n",
      "   23  3.34687E+08  9.74878E-01\n",
      "   24  3.34002E+08  2.38864E+00\n",
      "   25  3.31260E+08  3.68152E-03\n",
      "   26  3.31171E+08  5.05733E-04\n",
      "   27  3.31240E+08  1.14618E+00\n",
      "   28  3.31185E+08  3.82801E-02\n",
      "   29  3.31188E+08  2.19529E-01\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   30  3.31180E+08  2.06127E-04\n",
      "   31  3.31179E+08  1.64797E-05\n",
      "   32  3.31179E+08  2.84097E-05\n",
      "   33  3.31179E+08  6.73807E-08\n",
      "   34  3.31179E+08  4.52734E-03\n",
      "   35  3.31179E+08  4.43482E-08\n",
      "   36  3.31179E+08  9.05468E-03\n",
      "   37  3.31179E+08  6.50444E-08\n",
      "   38  3.31178E+08  7.40408E-03\n",
      "   39  3.31179E+08  2.31369E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   40  3.31179E+08  8.25739E-04\n",
      "   41  3.31179E+08  8.14834E-07\n",
      "   42  3.31179E+08  4.75624E-02\n",
      "   43  3.31179E+08  1.73739E-07\n",
      "   44  3.31181E+08  7.20136E-04\n",
      "   45  3.31181E+08  3.71773E-05\n",
      "   46  3.31179E+08  1.77603E-04\n",
      "   47  3.31179E+08  3.06720E-06\n",
      "   48  3.31179E+08  2.75994E-02\n",
      "   49  3.31179E+08  8.52097E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   50  3.31179E+08  4.43099E-03\n",
      "   51  3.31215E+08  9.97195E-05\n",
      "   52  3.34047E+08  2.14495E+00\n",
      "   53  3.31184E+08  6.47521E-08\n",
      "   54  3.31185E+08  4.39325E-03\n",
      "   55  3.31183E+08  1.08712E+00\n",
      "   56  3.31182E+08  7.02127E-03\n",
      "   57  3.31179E+08  7.99879E-06\n",
      "   58  3.31179E+08  1.91722E-03\n",
      "   59  3.31179E+08  6.44724E-08\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   60  3.31181E+08  4.94821E-02\n",
      "   61  3.31180E+08  6.07140E-08\n",
      "   62  3.31191E+08  1.24940E+00\n",
      "   63  3.31185E+08  1.00195E-04\n",
      "   64  3.31182E+08  1.34501E+00\n",
      "   65  3.31180E+08  1.58703E-05\n",
      "   66  3.31180E+08  2.49461E-03\n",
      "   67  3.31179E+08  8.24180E-06\n",
      "   68  3.31179E+08  2.09861E-04\n",
      "   69  3.31179E+08  5.24523E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   70  3.31179E+08  7.34762E-02\n",
      "   71  3.31179E+08  7.40933E-07\n",
      "   72  3.31179E+08  4.16820E-04\n",
      "   73  3.31179E+08  5.01926E-07\n",
      "   74  3.31179E+08  9.19272E-05\n",
      "   75  3.31181E+08  6.67010E-07\n",
      "   76  3.31182E+08  1.11178E+00\n",
      "   77  3.31229E+08  1.11312E+00\n",
      "   78  3.31186E+08  1.30213E-02\n",
      "   79  3.33578E+08  1.34112E+00\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   80  3.32397E+08  1.35159E+00\n",
      "   81  3.32171E+08  3.75311E-02\n",
      "   82  3.31179E+08  3.74410E-02\n",
      "   83  3.31179E+08  1.37108E-05\n",
      "   84  3.31179E+08  1.01223E-04\n",
      "   85  3.31179E+08  1.36178E-07\n",
      "   86  3.31179E+08  3.82669E-05\n",
      "   87  3.31179E+08  4.78757E-09\n",
      "   88  3.31179E+08  8.42112E-03\n",
      "   89  3.31179E+08  8.29314E-09\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "   90  3.31178E+08  3.56028E-04\n",
      "   91  3.31179E+08  1.86782E-05\n",
      "   92  3.31179E+08  3.26519E-04\n",
      "   93  3.31179E+08  1.28279E-07\n",
      "   94  3.31179E+08  3.27231E-04\n",
      "   95  3.31183E+08  3.83056E-05\n",
      "   96  3.31182E+08  3.84414E-03\n",
      "   97  3.31179E+08  2.61683E-05\n",
      "   98  3.31179E+08  4.32324E-04\n",
      "   99  3.31179E+08  2.80780E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  100  3.31179E+08  8.94782E-05\n",
      "  101  3.31179E+08  3.57122E-08\n",
      "  102  3.31179E+08  1.20238E-02\n",
      "  103  3.31179E+08  2.90296E-08\n",
      "  104  3.31181E+08  1.06092E+00\n",
      "  105  3.31179E+08  3.98204E-08\n",
      "  106  3.31181E+08  8.67558E-01\n",
      "  107  3.31206E+08  8.34682E-01\n",
      "  108  3.31270E+08  1.17066E+00\n",
      "  109  3.31189E+08  1.88276E-04\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  110  3.31180E+08  5.08990E-02\n",
      "  111  3.31179E+08  5.93325E-04\n",
      "  112  3.31179E+08  2.49092E-05\n",
      "  113  3.31179E+08  2.84874E-08\n",
      "  114  3.31179E+08  1.89290E-05\n",
      "  115  3.31179E+08  1.02547E-08\n",
      "  116  3.31179E+08  1.72877E-04\n",
      "  117  3.31242E+08  7.98196E-09\n",
      "  118  3.31202E+08  1.23086E+00\n",
      "  119  3.31180E+08  2.96753E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  120  3.31241E+08  1.23074E+00\n",
      "  121  3.31179E+08  1.63388E-05\n",
      "  122  3.31186E+08  7.79038E-01\n",
      "  123  3.31182E+08  6.09521E-04\n",
      "  124  3.31179E+08  6.08673E-04\n",
      "  125  3.31179E+08  1.66652E-08\n",
      "  126  3.31203E+08  1.37962E-02\n",
      "  127  3.31190E+08  1.78823E-02\n",
      "  128  3.31179E+08  3.57530E-02\n",
      "  129  3.31179E+08  4.19278E-06\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  130  3.31179E+08  4.78935E-05\n",
      "  131  3.31179E+08  1.30102E-08\n",
      "  132  3.31179E+08  8.07175E-06\n",
      "  133  3.31179E+08  1.17104E-08\n",
      "  134  3.31179E+08  7.60920E-06\n",
      "  135  3.31179E+08  1.93176E-11\n",
      "  136  3.31179E+08  5.00583E-04\n",
      "  137  3.31179E+08  4.69019E-07\n",
      "  138  3.31179E+08  5.55408E-04\n",
      "  139  3.31179E+08  3.77973E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  140  3.31179E+08  4.32198E-03\n",
      "  141  3.31179E+08  9.85907E-08\n",
      "  142  3.31179E+08  2.01874E-04\n",
      "  143  3.31179E+08  1.47894E-07\n",
      "  144  3.31179E+08  3.23655E-05\n",
      "  145  3.31179E+08  8.85676E-08\n",
      "  146  3.31179E+08  1.99188E-04\n",
      "  147  3.31179E+08  2.13133E-05\n",
      "  148  3.31179E+08  4.10335E-04\n",
      "  149  3.31179E+08  5.07550E-05\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  150  3.31188E+08  2.76126E-03\n",
      "  151  3.31185E+08  4.29217E-04\n",
      "  152  3.31179E+08  4.17309E-04\n",
      "  153  3.31179E+08  2.88789E-07\n",
      "  154  3.31179E+08  1.95774E-04\n",
      "  155  3.31179E+08  1.00340E-07\n",
      "  156  3.31179E+08  2.10729E-05\n",
      "  157  3.31179E+08  2.88789E-07\n",
      "  158  3.31179E+08  1.51767E-04\n",
      "  159  3.31197E+08  2.74766E-07\n",
      " \n",
      " Iter    Objective  Convergence\n",
      "  160  3.31184E+08  9.81168E-04\n",
      "  161  3.31182E+08  2.66086E-04\n",
      "  162  3.31201E+08  2.66072E-04\n",
      "  163  3.34566E+08  2.74703E-07\n",
      "  164  3.31179E+08  2.74703E-07\n",
      "  165  3.31179E+08  2.74703E-07\n",
      " Successful solution\n",
      " \n",
      " ---------------------------------------------------\n",
      " Solver         :  APOPT (v1.0)\n",
      " Solution time  :    13.4083000000101      sec\n",
      " Objective      :    331178721.267856     \n",
      " Successful solution\n",
      " ---------------------------------------------------\n",
      " \n",
      "\n",
      "Optimal Cost: 2069867.0079 Horizon: 80 Param: 0.2\n",
      "Cumulative investment: 24775.823233 Horizon: 80 Param: 0.2\n"
     ]
    }
   ],
   "source": [
    "#choose the values for the parameters to iterate on\n",
    "\n",
    "values=[0.3, 0.2]\n",
    "eta=[500, 300, 200]\n",
    " \n",
    "    \n",
    "dfs={}\n",
    "for j in range(len(values)):\n",
    "    for i in range(len(eta)):\n",
    "        # time points\n",
    "        n=81\n",
    "        J0 = np.nan\n",
    "        counter=0\n",
    "        while J0 is np.nan and counter<=3:\n",
    "            print(\"ciao\")\n",
    "            try:\n",
    "                # create GEKKO model\n",
    "                m = GEKKO(remote=False)\n",
    "                # constants\n",
    "                E0,Co2o,ccl,cof,kf,kns,cf,c1,c2= 556,280,0.03,0.03,0.076,0.015,0.129,.1,.3\n",
    "                ncl,nof= 1./(19.46), 1./290.\n",
    "                \n",
    "                nof=1./eta[i]\n",
    "                phi=(1.-1.)*E0\n",
    "                Imax=700.\n",
    "                kc=80.\n",
    "                rho=0.05\n",
    "                #assign valus to parameter\n",
    "                param=values[j]\n",
    "                #decide the parameter to iterate on \n",
    "                c2=param\n",
    "                #phi=phi_r/100.*E0\n",
    "                tf=80\n",
    "                m.time = np.linspace(0,tf,n)\n",
    "                # controls\n",
    "                u = m.MV(value=1,lb=0,ub=1)\n",
    "                I = m.MV(value=0,lb=0, ub=Imax)\n",
    "                u.STATUS = 1\n",
    "                u.DCOST = 0\n",
    "                I.STATUS = 1\n",
    "                I.DCOST = 0\n",
    "                #state variables\n",
    "                x1 = m.Var(value=419) # Co2\n",
    "                x2 = m.Var(value=32) # Kcl\n",
    "                x3 = m.Var(value=0) # Kof\n",
    "                t = m.Param(value=m.time)\n",
    "                e = m.Intermediate(m.exp(-rho*t))\n",
    "                # ode\n",
    "                m.Equation(x1.dt() == (kf*(E0 - x2) - x3)*cf - kns*(x1-Co2o))\n",
    "                m.Equation(x2.dt() == ncl * u * I -  ccl*x2)\n",
    "                m.Equation(x3.dt() == nof*(1-u)*I -  cof*x3)\n",
    "                m.Equation(E0-x2-phi>=0)\n",
    "                \n",
    "                #objective\n",
    "                J = m.Var(value=0) # objective (profit)\n",
    "                D = m.Var(value=0) # objective (damage)\n",
    "                It = m.Var(value=0) #cumulative investment\n",
    "                Jf = m.FV() # final objective\n",
    "                Jf.STATUS = 1\n",
    "                m.Connection(Jf,J,pos2='end')\n",
    "                m.Equation(J.dt() == e* \n",
    "                           ( kc/cf * (x1-Co2o) + u*I*(1 + c1*u*I) + (1-u)*I*(1+c2*(1-u)*I) ))\n",
    "                m.Equation(D.dt() == e* \n",
    "                           ( kc/cf * (x1-Co2o) ))\n",
    "                m.Equation(It.dt() == I)\n",
    "                m.Obj(Jf) # minimize cost\n",
    "                m.options.IMODE = 6  # optimal control\n",
    "                m.options.NODES = 3  # collocation nodes\n",
    "                m.options.SOLVER = 1 # solver (3 IPOPT)\n",
    "                m.options.MAX_ITER=2000\n",
    "                m.solve(disp=True) # Solve, True to see the output\n",
    "                J0=Jf.value[0]\n",
    "                I0=It.value[-1]           \n",
    "            except:\n",
    "                #n=n+40\n",
    "                counter+=1\n",
    "                \n",
    "                \n",
    "            print('Optimal Cost: ' + str(J0), 'Horizon: ' + str(tf), 'Param: ' + str(param))\n",
    "            print('Cumulative investment: ' + str(I0), 'Horizon: ' + str(tf), 'Param: ' + str(param))\n",
    "            #print('Cumulative investment: ' + str(It.value))\n",
    "\n",
    "            #print('Damage: ' + str(D[-1]), 'Horizon: ' + str(tf), 'Param: ' + str(param))\n",
    "            df = pd.DataFrame(\n",
    "            {   '$\\tau$': m.time,\n",
    "                '[CO$_2$]': np.array(x1.value),\n",
    "                'EMI': kf*(E0 - np.array(x2.value) - np.array(x3.value)),\n",
    "                'E$_{CL}$': np.array(x2.value),\n",
    "                'NET': np.array(x3.value),\n",
    "                '$\\mathcal{C}$': np.array(J.value),\n",
    "                'Damage':np.array(D.value),\n",
    "                'Cumulative Investment':np.array(It.value),\n",
    "                'I': np.array(I.value),\n",
    "                'I$_{CL}$' : np.array(I.value)*np.array(u.value),\n",
    "                '$\\mu$': np.array(u.value)\n",
    "             }\n",
    "               )\n",
    "            dfs[values[j], eta[i]]=df\n",
    "    \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Param</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0.3</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0.2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NET</th>\n",
       "      <th colspan=\"10\" halign=\"left\">500</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">200</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th>$\\tau$</th>\n",
       "      <th>[CO$_2$]</th>\n",
       "      <th>EMI</th>\n",
       "      <th>E$_{CL}$</th>\n",
       "      <th>NET</th>\n",
       "      <th>$\\mathcal{C}$</th>\n",
       "      <th>Damage</th>\n",
       "      <th>Cumulative Investment</th>\n",
       "      <th>I</th>\n",
       "      <th>I$_{CL}$</th>\n",
       "      <th>...</th>\n",
       "      <th>[CO$_2$]</th>\n",
       "      <th>EMI</th>\n",
       "      <th>E$_{CL}$</th>\n",
       "      <th>NET</th>\n",
       "      <th>$\\mathcal{C}$</th>\n",
       "      <th>Damage</th>\n",
       "      <th>Cumulative Investment</th>\n",
       "      <th>I</th>\n",
       "      <th>I$_{CL}$</th>\n",
       "      <th>$\\mu$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>422.072221</td>\n",
       "      <td>39.887572</td>\n",
       "      <td>31.162812</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>87163.152601</td>\n",
       "      <td>87163.152601</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>336.318374</td>\n",
       "      <td>287.973399</td>\n",
       "      <td>...</td>\n",
       "      <td>422.098052</td>\n",
       "      <td>39.887111</td>\n",
       "      <td>31.162811</td>\n",
       "      <td>0.006785</td>\n",
       "      <td>87175.348485</td>\n",
       "      <td>87175.348485</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>473.013098</td>\n",
       "      <td>287.972411</td>\n",
       "      <td>0.608804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>424.990614</td>\n",
       "      <td>38.842420</td>\n",
       "      <td>44.819593</td>\n",
       "      <td>0.095938</td>\n",
       "      <td>180722.906560</td>\n",
       "      <td>171847.596240</td>\n",
       "      <td>336.318374</td>\n",
       "      <td>335.772332</td>\n",
       "      <td>287.503953</td>\n",
       "      <td>...</td>\n",
       "      <td>424.962423</td>\n",
       "      <td>38.779946</td>\n",
       "      <td>44.819549</td>\n",
       "      <td>0.918004</td>\n",
       "      <td>186719.045300</td>\n",
       "      <td>171866.705810</td>\n",
       "      <td>473.013098</td>\n",
       "      <td>472.268591</td>\n",
       "      <td>287.518275</td>\n",
       "      <td>0.608802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>427.722769</td>\n",
       "      <td>37.829972</td>\n",
       "      <td>58.049005</td>\n",
       "      <td>0.188201</td>\n",
       "      <td>271277.610310</td>\n",
       "      <td>253986.791300</td>\n",
       "      <td>672.090706</td>\n",
       "      <td>335.225268</td>\n",
       "      <td>287.033253</td>\n",
       "      <td>...</td>\n",
       "      <td>427.538519</td>\n",
       "      <td>37.707358</td>\n",
       "      <td>58.049685</td>\n",
       "      <td>1.800863</td>\n",
       "      <td>282889.769180</td>\n",
       "      <td>253953.200700</td>\n",
       "      <td>945.281689</td>\n",
       "      <td>471.487166</td>\n",
       "      <td>287.041894</td>\n",
       "      <td>0.608801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>430.275926</td>\n",
       "      <td>36.849269</td>\n",
       "      <td>70.863606</td>\n",
       "      <td>0.277587</td>\n",
       "      <td>358800.364610</td>\n",
       "      <td>333530.115530</td>\n",
       "      <td>1007.315974</td>\n",
       "      <td>334.635885</td>\n",
       "      <td>286.539229</td>\n",
       "      <td>...</td>\n",
       "      <td>429.838308</td>\n",
       "      <td>36.668417</td>\n",
       "      <td>70.864698</td>\n",
       "      <td>2.656126</td>\n",
       "      <td>375627.100320</td>\n",
       "      <td>333336.816550</td>\n",
       "      <td>1416.768855</td>\n",
       "      <td>470.654158</td>\n",
       "      <td>286.533917</td>\n",
       "      <td>0.608799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>432.657096</td>\n",
       "      <td>35.899466</td>\n",
       "      <td>83.274468</td>\n",
       "      <td>0.364143</td>\n",
       "      <td>443280.889240</td>\n",
       "      <td>410446.308670</td>\n",
       "      <td>1341.951859</td>\n",
       "      <td>334.013417</td>\n",
       "      <td>286.004719</td>\n",
       "      <td>...</td>\n",
       "      <td>431.873376</td>\n",
       "      <td>35.662257</td>\n",
       "      <td>83.275258</td>\n",
       "      <td>3.484513</td>\n",
       "      <td>464897.197310</td>\n",
       "      <td>409948.619400</td>\n",
       "      <td>1887.423013</td>\n",
       "      <td>469.773737</td>\n",
       "      <td>285.997153</td>\n",
       "      <td>0.608798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Param       0.3                                                             \\\n",
       "NET         500                                                              \n",
       "Variable $\\tau$    [CO$_2$]        EMI   E$_{CL}$       NET  $\\mathcal{C}$   \n",
       "1           1.0  422.072221  39.887572  31.162812  0.000709   87163.152601   \n",
       "2           2.0  424.990614  38.842420  44.819593  0.095938  180722.906560   \n",
       "3           3.0  427.722769  37.829972  58.049005  0.188201  271277.610310   \n",
       "4           4.0  430.275926  36.849269  70.863606  0.277587  358800.364610   \n",
       "5           5.0  432.657096  35.899466  83.274468  0.364143  443280.889240   \n",
       "\n",
       "Param                                                                  ...  \\\n",
       "NET                                                                    ...   \n",
       "Variable         Damage Cumulative Investment           I    I$_{CL}$  ...   \n",
       "1          87163.152601              0.000000  336.318374  287.973399  ...   \n",
       "2         171847.596240            336.318374  335.772332  287.503953  ...   \n",
       "3         253986.791300            672.090706  335.225268  287.033253  ...   \n",
       "4         333530.115530           1007.315974  334.635885  286.539229  ...   \n",
       "5         410446.308670           1341.951859  334.013417  286.004719  ...   \n",
       "\n",
       "Param            0.2                                                 \\\n",
       "NET              200                                                  \n",
       "Variable    [CO$_2$]        EMI   E$_{CL}$       NET  $\\mathcal{C}$   \n",
       "1         422.098052  39.887111  31.162811  0.006785   87175.348485   \n",
       "2         424.962423  38.779946  44.819549  0.918004  186719.045300   \n",
       "3         427.538519  37.707358  58.049685  1.800863  282889.769180   \n",
       "4         429.838308  36.668417  70.864698  2.656126  375627.100320   \n",
       "5         431.873376  35.662257  83.275258  3.484513  464897.197310   \n",
       "\n",
       "Param                                                                  \\\n",
       "NET                                                                     \n",
       "Variable         Damage Cumulative Investment           I    I$_{CL}$   \n",
       "1          87175.348485              0.000000  473.013098  287.972411   \n",
       "2         171866.705810            473.013098  472.268591  287.518275   \n",
       "3         253953.200700            945.281689  471.487166  287.041894   \n",
       "4         333336.816550           1416.768855  470.654158  286.533917   \n",
       "5         409948.619400           1887.423013  469.773737  285.997153   \n",
       "\n",
       "Param               \n",
       "NET                 \n",
       "Variable     $\\mu$  \n",
       "1         0.608804  \n",
       "2         0.608802  \n",
       "3         0.608801  \n",
       "4         0.608799  \n",
       "5         0.608798  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel=pd.concat(dfs,axis=1)\n",
    "panel=panel.drop(0)\n",
    "panel.columns.names=['Param', 'NET', 'Variable']\n",
    "panel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jYTZIRBkloowyUUaJWLOMoZ5Fiywbqwo3WwbBxP6g+g4LNi+SrwjZsQt51wfIvz8fiiOP7oCWNBgteSRyTIdQiCRgMiiYDqfmT7CZaZdgY3QHFb+qElA1cp1+tuWV8/efdrG/1IvVqDCwVSxjOiUyumMCUaYm8aeu08Ds3bsXoMZaZRs2bGhAb3ROh6NF2VVXXcV7771Xsa/saLp06UJGRgYrVqzA5XJVSZe/bNkyMjIy6Ny5c0O6r6NTK4LOQ5RvepvyTbNQnQdrPE8y2jG3Go21zRlYWo1ENjW/cg6SrGBK6o0pqTcxfW5H9RTiPbAYz/6F+HJ/AS0Ydn6gZAfFyx6keMVj2DpfSkzf2zGnD29Rq2hbt26tVpht3boVgMzMzIZ2qYJG/7S2YcMGnn76ae6//34GDBhwUtdcfPHFYa87d+7MY489RmpqKrfddhtPP/00H3/8cZ34p2+CPjk0TTBvWz6v/7yXVko+l0av4IboXxBSGsQNh/YPoMkKGKIIShZyXILdBUH2lAbILveS41TJcQbJc6k4fBrqaVTfizHLJFhkEqwKaXaFdLuBNJtCRrSB9rEGWkUbMCp1f4OSZQXkI3vEYsCaghqTiSqCIcEW9CC7s5HLd6JkT0fyFYaSjiQOQksbjZTQLyzRiNWkVIRCJtktdE+2MTGo4Q9quPwqWwvK+WpzDjMXZ6EJ6JUWzZiOCYzvlERStKXOP59O0+NIOuC8vKq1ebZt28bXX3/d0C7pnAKapnHLLbfwzjvvcMUVVzBnzpxqRRmEMm5OmTKFGTNmMGPGjLAC08899xwlJSX85S9/aVEPaTpNAyEE3v0Lcfz+f7h3f1NjUgzJaMfcehy2DueFBEcL21ulWJOwZV6BLfMKNF8Znn0/4N79Df78teEnqn5c2z/Etf1DTKmDiO1/F7Yul7WI39err75aJXrO7/fz1ltvAdVvbWooGn2B6X79+uHz+Vi/fn3Y/rDJkycze/bsauuY1YTf78dms5GcnEx2dnaFPTk5Ga/XW21M6ebNm+nVqxdXXHEFH3300Qnb0AtthuP0Bfnvyr18s+kAl8etYxQ/ICtRqCmjMCb2IoiBPS4Tm4tlNhb42ZjvY1dJEN9JKC+rQcJukrEYJIwymA2hVPihFPagCfAGBe6Ahjsg8ARP7quuSNA6xkCHOCOZCUa6JIT+bR9nrPfVNgBNU0NCTfMh+UuRS7ciO7Yhu/aCIQqROBAt/QykxIFhQq06PH4VX1DFEwyyu9jNllwnG3PL8QQ0uqbYGNchkTMzk0iNiUy9Dp3I8sknn3DFFVcQHx/PokWL6NevHxDa+HzZZZexc+dOvF4vN910E++8806dtavfJ+uWI7U77XY7d999d7UZhC+++OKK/nW5XIwaNYr169czYcIEBg4cyIYNG5g/fz79+vVj+fLlVWqcHQ+9P3XqEy3oxbVtLmXr/kGgeGv1J0ky5oyR2DpfiqX1WCRFL0NzLEHnIdyH9+BpNRTKVuytiR1wN9G9pyAbT/4e0FQ4MuFkNBp58cUXueuuu5AkCY/Hw+233867775LmzZt2LZtG1FRdZdsrTb3yEYvzE521u7zzz+vslJWHQkJCWiaRmlpaYVtxIgRrFy5kpycnCr7zL744gsuueQSHn30UZ555pkTvr8+QIUocft5ZeluNu7ewq0x82krdhBIHA7JQznoi+KXPCOrczVW5/hw+LQq15sUaB9rpFO8kdbRBtKjFdJsBlLtCgkWmVizgtlQO5GkaoIyn0axV6PEo1LoVslzhVbicpwqBxxBDjiCNQo4syLRPclIz2QTvZLN9E010TbGUO8zy5qmhsIfg26kgBO5bBty6SZk1x4w2hFJQ9EyzkKK710R+lgT/mBor5o/qLKr2MPmvHI25ZTj9AfJTLYzvmMiZ2UmkaILtRZBMBhk9OjR/PLLLyiKQmZmJoqisHnzZtLS0rjzzjt57LHHdGHWyDkyUXk8Zs2axeTJkytel5WVMX36dD755BNyc3NJS0vj8ssvZ9q0abVO/KH3p059oHqLQ/uiNryO5imo9hwlui1RXa7A1umCFrtfqrYITcWb/RPuHR/jPbiMY/ejAcjWJGL7301M39uRzc0nEdCR57UXXniBhx56iLS0NNq0acP27dtxOBxYLBa+//57xowZU6ftNithNmXKlGrty5YtIysriwsvvJDk5GT+/Oc/V8wG1kRWVhaZmZn07duX9evXV9gfeeQRnn/+eWbPns2NN94Yds0dd9zB66+/zvfff39SS5stfYAqcft5fuFOCg78wh+ivibOGMCbdh5bg+1ZnG1g4f4gB8vDww+sBoleySZ6pZjonWKmW2JIjCkRSGChCUG+S2VvWZBdJQGyigNkFfvZURzAHaj6p5JolemXaqZ/mplB6WZ6JJkaxG9N9YWEmt+BXLoZuXRjKKmINRUtdTRaxjnI9lYnfJ+jhdreUi+/ZzvYmOvA5dfolmLnjE6JnNlFD31szjgcDh577DE+/fRTCgoKSE1NZeLEicyYMaOilosuzHSOh96fOnVJ0JWLY90rOH7/LyLgrHqCpGBpPR57t2swpQ3Rw25Pg2D5fpxb5+De+Xm1iVNkUywxA+8ltv9d9Vo2oKE48l0RQjB37lxeeeUVNm/ejNFoZOzYsTz11FP06dOnztttVsKsJmoKZSwvL2fPnj1VfrElJSVcdNFF/PTTTzz//PM89NBDFcd27NhBz5496dixY1iB6c2bNzNkyBDS09NPusB0Sx2g3P4gryzdzb7tC7g56jMs9nQ2287nu9xY5u/VKPJUfs1MCvRPNTOslYVhrSz0SDZh0NxInjwkTy74S5EC5RB0IAWcoZS3Qj38o4FsCtUnk00IxQqmWIQxFkxxCHMCwpoGhrpdgteEYH9ZkE0FfjYdDrncUujHf0yIu90kMTDNzNBWFka2ttAp3tgwK2pHQh89OSglW5BL1iMFHYjYHmjpE5AyxoayWZ6Ao4XaziIPm/IcbMwpxxvU6JUWzRmdEjmjSxJxUY0rpbBO06Kl3iebK3p/6tQFQVcOZb/OpHzjWwi1al1FyRRNVJcriO5+HUpUagQ8bL5ofgeuHZ/g3PIumrewynHZmkzckIeJ7n0rskGfqK0tLVqY7d27lw4dOjBo0CB69+5NSkoKhw4dYv78+RQVFTFhwgS++eYbTKbw+ONnnnmGxx57jLZt23L55Zfjcrn44IMP8Hg8fP/994wfP/6k/GppA5SmCd5evY+Vv87n5qhPUKM68IX/POYdNLGjpPKrFWOSGNvOysTWAUZE78Hq2YNUvgu5fBeS6wBSsG5rRghDNMKahohqhbB3QLO3P1z8uQModXNT8auCzQV+fsv1sTbXx9ocL+X+8D+nVJvCiNYWRrWxMKK1lRizXMO71R0hoeaBgBfZmYVS8jty2VYwWNFSx6C1Ogc59uRSYR8Rau5AkF2FHjbllbMxx0FQE/RrFcuELsmM75KE1dj0Ug7rRI6Wdp9s7uj9qXM6qJ4iyta8iGPDa9Wu2ii2DGw9JmPrfDGyse72/ehURQS9uHZ+Tvmmt9DcuVWOK9FtSBgxA1u3a5Ck+n+eaS60aGHmcDh49NFH+eWXX9i3bx+lpaXYbDZ69+7N9ddfz5QpU2rMVvX+++9XLGuaTCaGDx/OjBkzGDx48En71ZIGqNX7S/jXN/P5g3E2pabOzC0/k3kHjHgPryIZZbi0nYtrUrbQlc0YSjeGEljUQIWYMieAMRphjAaDPVSgWVJAUgAJRABJ9YN2JJyvDClQFlpp8xUhBWquJSckBWHvgIjtihbTDS2+NyKmK8inn6BU1QTbigKszvay8qCXNTm+sCQmigQD0s2MbWtlfDsr7eMaJvORpgUg6EHyFSMXb0ApWY/kK0DEdkfLmICUcWZoBfIkOCLUXP4gOwpdbMwpZ3NeOTISg9rEcVaXZEZ3SsBs0IWaTs20pPtkS0DvT51TQQu4KFv7MmXrXkb4q07OGmLaE93ndqztJyLVwRitc/IINYB715c4fn+t2kQh5tTBJIx9EUtG9aVWdMJpEcKssdISBqhCl48nv1jBSMf/4ZFieKPsPFYVhG6aEhoXJe3iptQNdFd/xejcWeV6zdYWEd0FLboTIroTwt6ubsMPgy4kT27ox7UfybkX2bkXybkbyV9a5XShWNBieyIS+qIlDUGL633SQuV4eIMa63J9rDjgZdl+D7tLw2uJdIwzcFaHKM5ob6VXsqlB4uQrwh4DbmTnLpTi35AdW8EYjZY2Hq31+Se1N+0I/qCK26/i8gfYVuDi95xythe4MCoSQ1rHc0aXJEZ2SMBk0GfWmiNCiFP63raE+2RLQu9PndogNBXnlncpWfkkqiunynFDbCdi+v0ZS9sz9VWZCCNUH67tH+HY+F+Er6TKcVvmFSSMfgFDdNWaYDqV6MIsgjTnAUoIwdy1e9n78z9Ilgp4w3kxa4tCYYHdjXv5c9pKxhmWYQnkV16DhIjriZY4AC2+L1p8HzDFReoDgK8glNWwbBtS6Rbk0o1VVtiEbEZL6B8SaSmjEPb2J8x2eDLsLwuwbL+Xpfs9rM72EjwqGWW6XeHsjlGc3SGKPqkm5AbazFyxmuYtQCn6DbnkN6SgE5E4AC3jPKTUIVCLgfFIev5yf4CteaHU/NsLXJgNMkNaxzO+SyKjOiTqQq0J4wuqfLkxh/d/O8SYjon8ZUQHTKbarZA25/tkS0TvT52TxbN/IUVL7ydQtLnKMSW6LTH9/oK1/Tm6IGtkaAEXzs2zKN/8Dhyz/08y2ogb+hix/e9qETXQTgVdmEWQ5jpAHSj1MPPjj+nr+oL/Oi9hTWkcUZKHS2w/cXvCD2RoeyrOFQYbWsoo1JSRlMYMY58nigK3SoE7lKK+xKvhCwp8qsAXFGiASZYwKWBUQrXJEqxKRRHodLtCmxgDUcZ6uFELLbSiVrIBuWgtctGvSL7isFO0qFZoKaNC2Q4TBtZJ2KPDp7Fsv4dFez0s2+8JS9GfZlM4p1MU53WOomdSw6ykwdF709wojq2h34dzD9hao2Wcg5Z+FrKldmlzjwg1hy/A1nwnm3OdbCtwYlJkBraKZVznJMZ0StT3qDVyhBCs3l/K7F8PsK3AyYh28YxsH4fNbKBvRiyx1tqtMDfX+2RLRe9PnRMRKNtL8U8P4t75RZVjsjUZe78/Y+90kR6y2MhRXbmU/fYPPLu/rnLMmNCdxDP+jbX16Ah41rjRhVkEaW4DlBCC937ZRs7Kmfzk6cX8ki60VXK5KXo+V9kXY8EdOk82cihuIr8aJ7Le24GdpRq7SoIUuNUTtHDyJEXJtI0JFXzulmiie1Lo/5a6XH0RAsm5C7lwNXL+SuTitUhaoPKwMRY1dQxa2hloyUNBPv3ZIW9QY8UBL9/vdrN4nycsLX/bGAPndY5iUhcbHRpoT9oRjqTkl137kYt+QyndALIplECkzfnIMZ1q/Z5HhJrTH2B7gYtNueVszXciBPROj2F0h3jGdUokOVqvo9YY2JJbzntrD7JyXzHdkm2M7phAm7goLIpMtFmhwO3XhZmO3p86NaIFvZSteZGyX1+skmlRMkRh63UL0T1uRDbo9/ymhL9wI6WrnyVQuLHKseheU4gf9SyKJa7hHWuk6MIsgjSnAarE7eeZD96nNG8T75eOobWczZ+iP+VC23JkNMq1KJaIs1jEufzqSCXH1fBfJUWC7kkmBqabGZhmZmC6mThLHa6+BN2HRdpylLylYXvUhDEGNW08WsZEtMT+h5OTnB6+oOCnAx7m73SzZL8H71EraT2TTVzQJYpzO9lIimrYFaYjIY/4SlCK16MUr0PyFyES+qFlnIuUNhLk2vt0RKi5A0H2lLjZkutkU24oPX+bOCvD28Uzun0CPTNi9Fo1DYAQgt8OlfG/9dms3l9C2zgrYzsl0iHeitWoYDUasB4Vtphb7tGFmY7enzrV4tm/iMJFfyZYeuxecwlrl0uJ7XcXijUxIr7pnD5CaLizPjucvKUs7JhiSydx/D+xdb4oQt41LnRhFkGaywC1ZPshvvjyJb4q64eiurg75n+ca12JU1j5xj2K79RzWO1sR0DU7mFZkSAxSiHKIGExyJgNEopERVijXxWU+jQcPu3Eb1YNEiEBM7qNhdFtQ0k16qzgsxYMhTzmLELJXYTkq6z1IczJqK0moraehIjuWCfNuQIai/d6mLfTxfIDXo4keFQkGNXGwkWZdsa1s2I2NKxgqUwg4kJ2bEcpXotcngXWNLSMs9EyJiJb40/pvY9kfQyoGtnlPrbmlbMl30l2mRer0UD/jBiGt09geLtYfVWtjnD7gyzcUcBXW/LYWeiiU2IUI9sn0D7eitkgYzEq2M3Vr9bqwkwH9P7UCUd1F1D804M4t75f5ZgxqQ9xQx/HlNg9Ap7p1AeqtwTHupdx7/ysyjFb5hUkjv9nixfgujCLIE19gNI0wcwvv2PZhl9Z7e7IXTEfcVXUAtb4u/Ox60y+947AJ44fA25WJHokm+ieZKJnspluSWbaxBhIsxtIilJOKrGFL6hR6FHJc6rsLQ2wu9TP7pIA2wp9/J7vw+k/ua9tgkXmzA5RnN3RyuB0C0aljkSMUJGLfkPO/g4lZ1FYHTYttgdq60morc4Fo71OmivyqHy3y83XWS425vsr7DEmiXM727ikq63BMjsey5GQR8l9CKV4A0rJbyAEImUEWpvzkOJ6nnLyFFVV8QQ1/EENl19lR6GTHQUuthc48QQ04q1GBraJY1DrOIa1iyfWqm88PhGaJtiQ4+CHbfks212MKgS90+wMahNLeowVkyxVWRmrCV2Y6YDenzohhBC4tn9A0ZKpaN7wvdqyJYHoAfdh63ShHv3QTPHlrg5l2izfH2ZXotJImvA6UR3Oi5BnkUcXZhGkKQ9QJW4/U//vVRbkxXOe5SemRH/Jj54hvOm8kL3BjBqvsxokxrSLYmy7KEa0ttI/zYy5HrPuaUKwpzTAhlwfq7M9LN/vZn2eLyzLYXXEmmXO6mDlokwbA9LMdTc4qH7kghUoB79Fzl+OJEL76oRsRs2YgNr2EkRc7zrJ7AiwuyTAVztcfJ3lItdVuYevS4KRi7vauLCLjQRrZJJpVCQQ8ZWilPyOUvIbkicHojuHVtPSxiObT0+sHr2qVuwJsKPAyZ5iDzsLXXiDGrEWI73SounfKpYBrWJon2hr0Q8CAVXjt0NlLN1ZyIq9JZT7g3SMj6JvRgxdU2xYDQomRSbKpGCqZf05XZjpgN6fOhB05VC08E7cu7855oiENfNy4gbci2zSvxvNHRH04tjwGs4t74AIzzFg7zmZxDF/Qza3vO+BLswiSFMdoFbtzuGJ92ZT5FN4NPYdVvl68Y7zfAq06kPSEq0yV3QSXNSmjP7R+RiCZYiAE83vRARcIREiG5FkI5JiQjbHo0SlIFuTUKLSUGypdZoO1+XX+OWQh4V7XHy3y8WWAv9xz28drXBhpo2Lu9ppFV2HWaB8xSiHvkM58EUoo+FhtOjOqO0uD62iGaLqpClVE6zO9vH5dicL9rjxH74HGmQ4s30Ul3WzMby1pcFS71eHFnCD6kV27kUu/g2ldBMYzGgpI9FanYsc1/W021BVFb8KfjUk1sq8of1q+0o87C5yU+jyo8gS6TEWeqVF0z0lmp6pNjok2pHrKsy1kRBUNbIKXfy6v4Rf9pWws8iNJEGnhCh6pEbTOSkKu8mAoRarYsdDF2Y6oPdnS0YIgXPb+xQvmYrmKw07ZojtRNyI6ZiT+0XEN53I4S/cRMmKRwmW7Q6zG2I7knLue5jTBkfIs8igC7MI0hQHqP8sWMmri9dxmfVHBPBq+eWUaOG+x8sOBpq3c2nKbkZE7SDGt6fKZs/aIBmsGOI6Y4zrjDE+E1PKAIzxXessVe4BR4Dvdrr4Yns5S/a6K/ZnVfGD0H6tK3vYGdPWiqGuHtSFQCr5HcOBz5Gzf0TSfCGzwRYKc2x3BcLerm7aAsp8GvN3uvhsu4vNR4nSdLvCpd3sXNrVRpo9smmIK2qm+UqQSzaGVtO8eRDdCS39rNBqWi3T8R+PIytrqibwqyqF7iB7i11kO3wcKPVQ4PQjSRJRRoX2CVF0ToyiS5KN9olWOiTaG3UKf09AZWehi+355WzLd7E5t5witx8EZMSa6ZJko1NiFCl2M0ZFwqwomA3KaQuxY9GFmQ7o/dlSUT1FFC78E+6dn4cfkAzYe99KTJ/bkOogc7FO00QEvTjW/wvnlneBox7CZAPxw6YRO+h+pFNIGtYU0YVZBGlKA5QQgj++9Snbd29mQtSvvFl+IfvV9CNH6WI4wDnWVZxrW0U3w57jvlddIBltmJL7YU4bgqX1OAyxHeskBK3IrfJ1lpOPtzhYtMdNTV/4NJvC1T3tXNHdXreZHQMOlAPfoOz7BNl9oMKspoxC7XA1WuKQOgtzBNhW5OezbU6+3uHCcXgvnizB6DYWrugeEqB1lhDlFKlMIOJG9hxALl6PUroZEIikwWhpZyClDDmlTI/H4+jVNVUTBDQNT0Ajt9xLbrmPvHI/eU4fBU4/flXDoMgoEqTYzWTEWEi1m0iJtpAcZSI1xkSizUysxYjdrJz2d1UIgduvUuIJUOIJkFPmIcfhI9fpI9fhZW+JF3cgiBChBDCtYq20ijWTHmOhVayZWIsJCYFZUTAoElaDjKLUw6CnBeHQAuQ9H1Ia3Z+OI+8hwV678NSmdJ/UOTF6f7Y83Hu/p3DBbaiunDC7Ib4bCaOexRifGSHPdBobvry1lKx4FNV5KMxuaT2W5InvYLC3ipBnDYcuzCJIUxmgyr0BLnxpFp38v7I50JFV/l4AJMqlXG37kYujltLRmF3r95WMdmRTNABCDSA0PyLogaNqgZ0sSnQbLK3HY217FqaU/nUi0g6VB/hwk4M5Gx1sLaw+3NFikLgo08YNvaPrtnaY0JALV6Hs/Qg5fwXSYYmo2Tuidrg2FOao1G7l4Xh4gxoLdnv4ZJuTNTm+CnuaTeGybnYu624j1dY4inlWpOMPlKOUbUcu/R25fDeYYtBSRiHSxyPFda9TAXssR0SbKjSCqiCoaahCoGlQ7PFT6Pbj8ARx+IKUeYOUeQM4fSpuv4pPDW1wlCDse3rkf0dMR+624vCxIzdfIQQSEmajjM2kYDMqxEcZibcaibOG/k20GbEaDUgIFEnCIMsYFAmTItd6b1itEQJKNiDt+hC5dBNqwgCCSSMp0KLpndmfhPjkWr1dU7lP6pwcen+2HLSgl5Llj+BY/5/wA5KCvc/txPS+VS8SrVMFzV9O6S9P4dk7L8wuW5NJmTgba7uzIuRZw6ALswjSFAaorHwHf/jn67RX9vGJ6wz8GBlo2sYN9vmcY12FSQrWfLEkY0zsiTltMKaE7hhiO2KM64TB3grJFF3tvjEhBJqvBNWVg+rMIeDYS6BoC77CTQSKNqF5CqtpKBzF3pqojpOI6nghhpi2p/PxK3z65ZCXN9aV8unWcnw1xDqe1d7KlP4x9E4xn3abRyO59qPs/QjlwFdIqifkkzmRYPurUNtdBsa6/e7sLgnw6TYnX+xwUeoNiQhZgnHtrFzZ3c7INpHdi3YsFZke/aXIxRtRyjYjebLBkhzan5Y2Fjk2s16FWnWoqooqQNVCAk7VBEKEEtJoh2+l6uF/JU7smzgszZTDn0OWJGRJQpJAkSUUSUaRQytk9bL6dVznBJRtRdr9MXLRatTojqiJIxD2dgjFBEY7RaWF9MocqAuzFo7eny2DQMkO8uddh79gQ5hdiWlPwugX9RT4OsdFCIFn91eUrnoGEXQfdUQibuijxA19rNmGNurCLII09gFqyY4cnnr3dQ4GU8gKtmWoeRNTYz5gkHlbjdcY4jKJ6nwxUW3PwJw2uGJFrC4QQhB07MF7cDne7OV4DixBdew97jWm1EHYu12Lpc0ZdTIzV+RWeff3Ml5bU8J+R/WidERrC7f2j2FIhuW02wsjUI6y/wsMez9E8uYDIBQratuLCXa4DqypddqcLyhYsMfNx1vDV9FaRytc3t3Opd3sJEYoo2NNVIQ9aj4kTy5y6VYUx5bQ78ucgJY0DJE6Eim+d52HPrY4hIZUtBb2folcugEtqg1q0nA0eweEbAKTHVmpnKQoKM7RhZmO3p8tgPKtcyha9JdQcq+jiOp6DXED70My1PHYqNNsCTr2U7zsfgLFW8LsljbjSTn3PZSolAh5Vn/owiyCNOYBataKLcyZ/z9WePvS3bSHqTEfMMrye7XnGmLaY+sxGXvmJZgSGm4WTAhBoGQb7l3f4Nr1Ff7c1VDDrjAlKhVb5pVEZV6FYok77baDmuCr7U7++Wsxvxz0VnvO4HQzdw6KZXBdCzQtiJz9A4Y9c5AdWQAIyYDa6lzUTjci7O3rtj1gV0mAj7Y4+WqHs2IvmkGGCR2iuLqHnYHpdVhSoA4JE2q+IuTSrciO7cju/WCIQsT1RksaipQ6HPT0zCfGVwLZC5Gzf0Dy5KJGd0ZNGIywtUXIRjBYkY3VZxLVhZkO6P3ZnNGCHooW341z8zthdtkcR/yo57G0GhUZx3SaNEL1UbbmRVzbPwyzK/bWpE76X7PL2qgLswjSWAeoRz9ZzLy1v3FQTeGR2He51LakmrMkrO3PIabfn7G2O6tO09mfKsHygzi3fYBz6xwCxVurPUcyWLFlXom9x011NtOy6pCHF38u5pssZ7XHh2SY+cvgOAak1W2II0IgF/6CsnM2SvHakAkJLf1Mgp0mI2JPP738sXgCGt/tdvO/Lc6w4tWd4o1c1cPOhV1sRJsj/12oiTChFnAhO3chObKQy7OQVF8o/DGhHyJpEFJivzorV9BkCTgReSuR85YhlW5EyBa0hH6ocf3AZEcoFjBEha2M1YQuzHRA78/mSqB0F/nfXl0ldNGUOpiE0TNRomr3d6+jcyzuPfMpXTktPLRRMZE47h/E9L4lco7VMbowiyCNbYASQjD5zS+Zn1XGOMtvPBz7LvHKMWJDkrFlXkX88CcwxnWKjKMnQAiBP/83yje+gXPb3FBCkWORjdg6X4K9960YbOlVj58Cm/J9vLiyiI+3lKNV85cytq2Fe4fG0SWh7pJ2HEEq2Yhh1zsoecsqbGrKKIKdb0HE96rz9gC2FPj535Zyvt3pxhMMfWCrQeK8zlFc3SOaHsl1/znrA00LhMRa0IfkL0J27ER27gnVlhMqmOMRcT3R4vogEnoj2zIafL9agyAEuA5A/iqkwl+QyneBZESN7YYW2wtha4OQpJBYVazItQwF1YXZiVm4cCGLFi3i559/5uDBgxQWFhIVFUVycjK9e/dm7NixTJo0ibS0tEi7esq0pP5sKbh3f0PBd39ACyuLI2PvewcxvW9rtnuBdBqeQNkeipfcQ7BsV5jd3utmksb9A8lQxxPgEUAXZhGkMQ1QQggmvfwBW/McPJPwfwwzb65yTlSni4kfOb1BwxVPF9VbgnPzO5RteK36/WiyCXu3a7D3moJiqb5Adm3JKvLz7Ioi/rfZUUWgScAFmTb+MiiWjLosVn3k/R07MeyaFaqHRihxh5o8jGCXWxHxfeq8PYByn8bXWS7+t8XJzpLKjJq9U0xc1cPOuZ2isBga7yrasWiaCiIYEmtqAClQily+B8m1D9l9AMlfGtqfZk1DRHdGRGci4rohxXSAplKHJ+gCx04o2YJU8juSIwtEEGFORovughadibAkI2QZFEsoRPE0P5suzKrH6XTyz3/+kzfeeIP9+/dzZJi1WCwkJCTg8XgoKytD00J/zwaDgQsvvJB7772XkSNHRtL1U6K592dLQgiN0l+eonTVM2F22ZJAwpi/YU4bEiHPdJozWsBN6c+P49n3fZjdnDGC1EkfNfl9Zw0mzFrCTGBtaSwDlD+gMvqFOST4tvN8/KvEyO6w48aEbiSd9X9YMoZHyMPTR2hBXDs+ofTXFwgUVRWdktGGvecfsPe4CdlgrZM2txf6eHp5EZ9sKa+y882kwI29Y7i1fwx2U92LFsm5D2XXOyiH5iMJFQA1aWhIoCX0rfP2ICTu1+b4+HCLkwV73ARDz5HEmGUuzrRxVQ877euypEADo6m+UCkHLQCqH8lbiOzJDiUZ8RxC8haETpQkMCchbK0RURkIWxuwtkKypYMpvmGSjqh+8OYjeXIQzn1Ijr1Irr3gyQWhImQjwtoKEZWBZuuAsKYjJBESYbIZFHOtV8ROhC7MqvL666/z5JNPkp+fT9++fbnyyisZPnw4gwYNwn5UvTchBFlZWaxatYoffviBL7/8EpfLxUUXXcRLL71Ehw4dIvgpakdz7s+WhOYvp+C7ybh3fx1mNyb1JXHcy03+4VincSOEwLllNo51fwehVdiV6DakXvAp5pR+kXPuNKlXYdbSZgJrS2MYoByeACOfnc1V5i+4zv5D+EHFQtzQvxI3cCqS0nQfqI9GCA337m8pXf0s/ry1VY4rUWnEDJyKtf25dZbM4vc8L48vKeT7Xa4qxxIsMn8eHMtl3ewY6qGQs+Q+iLLzHZSD3xwl0IYRzLwNEd+7zts7QqFb5fPtTj7a4iTbqVbYh7Uyc2WPaM5oZ8WoNP1wQE1TAQ3UwOFVtgCSCEKgFMlTiOQvQfIXI/kKkQJlSEHn4cJkEiCBJIPRDsYYhGI9vDoVBbIJJAUhKSAZAAFCRdICIAKgeiHoQgq6Q6tfAUdocBKCI5XPhDkBYYpHmJNCK2HmFDDHgawgUEAxht67HkRYdejCrCpGo5HrrruOBx54gJ49e570dR6Phw8++IDnnnuOG264gSeeeKIevaxbmnN/thQCpbvI+/oyAkXhmfKiul1H3KD7kZpK5IBOk8eb8wvFS6ci/I4Km2SwknzO29i6XBZBz06dehNmLXEmsLZEeoAqcvo474XXmRb9b7qb9oUdM6UOJuW89zDGdmxwvxoCIQTunZ9T8vMTBEp2VDluShlA7OCHMSX2qLM2l+1z89jiAlZnV83imJlg5JGR8XWfYv8wkvsQys5Z4QIteTjBzD8i4k7+gbC2qJpg+QEv/9tSzrL93oqVw6QomUu72rmiu71eQjobCxVhkUf+FSpoGqCGKpdpAdD8SEHP4dBJH5I4vConNEKCTDss5OSQkENBKOZQ2KRiQRgOCzpJCok5gArhJYeuU4ynHYp4uujCrCq7du2iU6dT36urqioHDx6kXbt2dehV/dKc+7Ml4DmwhPxvrkLzlVQaZROxw6dj73RBxPzSabkEy/dTtOgvVfadxY94itjBDzbKjNHHo96EWUucCawtkRygchxebvjbSzwb+w+SldIKu0AidtD9JIyYXid1vxo7Qgvi3DqHkp+nobpywg9KMrau1xDT7y/IJnv1b1Db9oTg063l/HVxAfvKqtZBm9gxivuGxdWbWJHch1Cy3kY59G2lQEsZHRJo9ZDF8Wiyy4N8vNXJp9ucFHlCq+QSMLqthSu62xnT1lovq4ZNjdAqXMWrY45Whr02xCpXXaILMx3Q+7MpU775HQoX/gm0yrFLtqaQeMa/MCXW3wSfjs6J0PxOSpY/jPfgkjC7vfsNJJ35apNKClJvwqwlzgTWlkgNULuLXDz6r2lMi30di1SZ8lw1J9Pq/Pewtj2jwXxpLGh+J6VrZlK29uXQysVRyFGpxA15BEubM+ts5sUb1Hh1TSnPryjC4Qt/+LYYJP7YP4bJfWMw1VO4n+Q6iCHrTeRD8yuThKSdGRJo0fW7Su1XBYv3efhoSzm/HKr8XafaFC7tauOy7nbS7c1/UqCloQszHdD7sykihEbJ8r9StvalMLsxqQ+J4/+JYk2KkGc6OpUIoeFY9w+cm98Ks5tbjSJ10sco1sQIeVY79KyMESQSA9SeIhf/fPVP3BX9QZhdjetN+8u/xmDPaBA/GiuBsj0U//QQ7p1fVDlmaXMGcUMfr9N6LPmuIE8sKWT2hrIqCULaxxp4bFQCw1vXT3gjgOTciyHrDeTsBUgIBDJaq3MJZt6KiGpVb+0eYV9ZgI+3Ovliu4sSb0ggyhKMamPhsm52xrZtHnvRdHRhdqpomsaWLVtISEggIyP8/hwIBFi5ciVjxoyJkHe1p6X3Z1NDC3oo+O6mKmOipf35JIx8CklpGmVRdFoOrqzPKP1lRmj7wGEMcZ1Ju/jrRlvm6Wh0YRZBGnqAOlDi5r+v/oGbbZ+H2UXr82h/0fvIRlu9+9BUcO/5jqLFdxE8JsW+ZIohbsgjWDtMqtO45bU5Xu77IY9fDlXdfzaxYxQPj4gn2VZ/oWuSYyeGHf+HkrcEACEZUNtcRLDLLWCp/8KgflWwcI+bj7c6WZVduYqWFCVzcaadS7vZaBerbyhvyujCrPbs27eP8847j61btyJJEueffz6zZs0iMTE085uXl0dGRgaqqp7gnRoPLbk/mxqqp4i8ry7Fl7MyzG7v92diev+xye3d0Wk5+HJXU7TknrCkILI1mdQLP8eS3rjLODS4MNM0jYMHD3Lo0CECgUC15zSl2b/ToSEHqJwyD2+9egPXWb8Ks8u9/kLbM19EkppOnamGQgu4KV31NGXrXgmLqQewtB5P3LAn6nT1TBOC9zc6eGRRAYXu8Actu0ni3iFxXNnDjlyPg6FUuhnD9tdQClcBIGQzavurCHa6EUyx9dbu0ewrC/DpNhdfbK/ciwYwKN3Mpd1snN0hCqtR/742NXRhVnuuuuoqSktLeeuttygtLeW+++5j3759LFq0iIyMDPLy8khPT6/IbNwUaMn92ZQIlO0l74tJ4cmxZBOxI5/G3uG8yDmmo3OSBMr2ULTwDlTnwQqbZLCSPPFdbJ0viqBnx6fBhJkQgueff56XX36ZoqKi457blGb/ToeGGqAKnF5m/+dqLjPPC7Mbhs6kzfB76q3d5oK/4HcKfpiCv2B9mF02xxE3fDrWtmfWaXslHpVpSwt5Y11plfDGvikmnhyTQGZi/YaPSEVrMW5/FbnkdwCEwUaw4w2oHa4JpXNvAAKqYMk+D59td7L8gLeiWLfNKDGxUxSXdLXTL9Wkz9o2EXRhVnvS0tL4/vvv6ds3VHtQCMFtt93GokWLWLx4MWazWV8x06lzfPnryfviQlR3boVNMsWQeOarmJP7Rc4xHZ1aonqKKFr8FwKFvx9llUgc/woxfe+ImF/Ho8GE2cMPP8zMmTNJSUlh0qRJpKenYzBUv8F/2rRpp9pMk6IhBiinN8Db/76SCwzfVtg0IWEY8TLthv6pXtpsjgg1QOmamZSuejaUyvwoojpfRuzgh5CNdStY1mR7+Mt3efyWG56MxCDDLf1iuH1AbL0lBwFACOT8FRi2v4pcnhUymRIIdr4Zte0l0IB7C3KdQb7c4eLz7S4OOCpXL9vHGrgo08akLrZmnXa/OaALs9oTExPD6tWr6datW5j9jjvuYP78+cydO5fRo0frwkynzvAc/Im8ry4JCwFTbOkkTngDY0z7yDmmo3OKaEEPJT89hPfAojB77OCHiB8xo9FN7jaYMEtLSyM+Pp5ff/01rI5ZS6a+B6igqvHvV2/jIvXdCpsqZJQR/6LD0FvrvL2WQGj17Bb8BRvC7Ep0WxJGz8SU1KtO2wtqgtfWlPDk0kJcgfA/v45xBqaPTWRAWj2ngRUacvYCDDteR3aHQgKENZ1A5h/RWk0EqeHStmtCsDbHx+fbXfyw240nWPk7GZJh5sIuNiZ0jMJu0kMdGxu6MKs9gwcP5u677+b666+vcuxPf/oTH374IWVlZbow06kT3Lu/If/baxFq5V5nQ3w3ks56Xc+8qNOkEZpK2ZqZuLa9H2a397iRpDNfQ1Iazx722twjT+tJx+l0cv755+uirIEQQvDf2Y9XEWVi5Gu6KDsNTMl9yLh6BbED7yNUhSuEWr6fgu+ux7nlPeoyR45BlvjLkAR+u60D53cJT86yuzTIjV/m8czyYlyBetxjIslorc7BP/ZjAr0eQZiTkTw5mDY8iWnZtci5S6GB8gLJksTgDAvPjk9k6Q2teHpsAkMyQsJ0dbaPx5YWM+bdQ9y3oJBFe934VT1fkU7T5dJLL2Xu3LnVHnv11Ve56qqr6vR+o9NycW59n7yvrwgTZaa0ISRPfFcXZTpNHklWiB38MDEDpobZnVveJe+rS9ECrgh5dnqc1orZqFGjaNu2bY2DTEukPmcO53z2b4bvuw9Zquyy8v5/p8/YP9dpOy0Zz4GlFPxwM2r5gTC7pfV44kc+jWyu22QZQgg+2+bknu/zKDgmOUjraIXpYxMZ1qr+UutXoHpR9n6EYddspEAo3EWL602g252IxIH13341ZJcH+SbLxVdZLvaUVoY6xphlJnSwMrFTFEMyLHoB6wiir5jpgN6fjRHHhtcoWnx3mM3S5iwSxszU0+HrNDvcu7+mZMXjYen0zWlDSL3oy0ZR66zBQhm/++47LrvsMn766ScGDBhwqm/TrKivAWr+ks/o8Nv1mKXKL11Ox/sZceGzddaGTgjVW0rhwttxZ30WZlei0kgY+3dMyX3qvM1ij8pDC/N573dHlWNXdrdz37C4hgnlC5Rj2PUeyp65SFpoH5yaPIxg1zsRsd1OcHH9IIRga2GAb3a6mLfTHSZgEywyEzpGcXbHKAalm3WR1sDowkwH9P5sbJSt/TvFPz0cZrN2uoT44U8iyQ0Xpq6j05B4s1dQvOReRNBdYTPGdyXtkm8xxLSNoGcNnC7/k08+4c477+TCCy+kb9++NTZ44403nk4zTYb6GKA27thI4JtxxMvlFbY9Kdcz/pq3Gt0Gx+aCEILyjW9QtPQ+UI9K1CEbiB30MLauV9XL737Bbhd/mpcblgwDIN2u8PS4Blo9A/AWYsh6E+XAF0giJITU9AkEu96OsEXuBqdqgjU5Pr7b5eaHPW5KvZXhnvEWmTPaW5nQIYqhrSz1m0RFB9CFWW04dOgQOTk5SJJEWloarVrVf7H3hqIl9mdjpWTVs5SufDLMZus5mdgB9+nPCzrNHn/RZooW3oHmLa6wKfbWpF3yDabEHhHzq8GEmc/nY8qUKcydO7ciJv7YP3whBJIkNamNzKdDXQ9QRWXF/PbmUDop+ypsO6LO5uxbv9LrlDUAvoINFMy7lkBJVpjd2nESccOmIRusdd5muU/j0UX5vPFbWZVj1/S0c+/QOGwNVPNLch3EsON1lOzvARCSgtrmQoJdpoAlpUF8qImAKliV7eWH3W4W7vWEiTSbUWJ0Gyvj21sZ09ZKjFn/W6lLSr0qP+z28F1WCZ/dNJj05NRaXd9SHuSdTid/+9vfePvttzl06FDYsVatWnHLLbdw3333Nfl92i2lPxszQghKfn6Csl9fCLPb+95JbCNNIa6jUx8EHfso/PE2VGflPVe2JJB60VcRK0TdYMLszjvv5LXXXqNPnz5cfvnlx02Xf9NNN51qM02KuhyggqrKF/85h4HasgrbLqk74+9YhWJqoJUTHTR/OYUL/ogr65MwuyGuC4nj/4khuk29tLt4r4s75uWxtzQ8lX+bGAPPjEtgYHrDfQckx45Qker85cCRItVXEOx0E5jiGsyPmghqgjXZPn7Y42bRXk9YuKMiQf80M2PaWhnb1kKneKM+c3wKeIMay/Z7+SbLxdL9HoKHdfDsyzK5YXjXWr1XS3iQ37VrF+eddx47d+5ECEFGRgZt2rRBCMHBgwfJzs5GkiQ6d+7Md999R4cOHSLt8inTEvqzMSOEoGT5o5StfSnMHj1gKjG9bo6QVzo6kUN151P44x8JllZOqktGG6kXfFLndWpPhgYTZikpKbRr146VK1fWKMhaGnU5QH383j0MKnq14nWBlkDnG1eTkBTZWNmWiBACx2//onj5w6BVhhlKphgSxryEJWN4vbTr9Gs8uqiA/64rDbNLwOS+0fxlUBxmQ8OJDKl4PcZt/0EuWQ8cKVJ9/eEi1bbjX9xAaEKwKd/Por0eFu51s7s0PCw0za4wsrWFEa0tDGtlIc6i77moCVdAY/n+0Krk0v2esFIGmQlGzmit8udx/ejRtnZhec39Qd7n89G3b1927NjBtddey+OPP07XruHidfv27Tz99NO8//77dO3alfXr12M213OZjHqiufdnY0YIQcmKv1K25m9h9ujBjxDT/boIeaWjE3k0v4OiRX/Gn7+u0qiYSJn4LrYulzaoLw0mzGJiYrj99tuZOXPmqb5Fs6OuBqjFS96n/fo/VLz2CSPahG/p0WtcHXipc6p4D60gf961qK6cSqMkEzPwPuzdb6y3lZiFe1zc/m3VvWed4408f0Yi3ZMaMMuWEMgFP2PY/h9kx5Ei1fEEO09GbXsZKI3r4fKAI8iy/R6W7fewOtuL/6ioagnommhkSIaFIRlmBqVbiG7hYY/Z5aHf19L9HlYd8uE7qjxBul3hvM42JnWOIjPRpO8xq4FXXnmFqVOnMm3aNKZNm3bcc5966immTZvGyy+/zN13333ccxsrzb0/Gys1hS/GDH2C6K5XRsgrHZ3Ggxb0ULz0PnyHKiPPkGSSznyV6AZcTW4wYTZ+/Hji4+P57LPPTnxyC6EuBqiD2bvI/3BIWLKPnZkvMuG8pjloNzeCrlzyv7kKX87KMLu14wWhrFf1JEwcPpX7F+Tz7jGZGw0y3Dkwllv6xaA0ZFZCoSHn/Ihh++vI7lB5AWFJIdhlCmrrC0BufKvonoDG2lwfPx/08vNBL1nF4WGisgRdEoz0TzUzIN1M/1Qz6XalWYc+Onwaa3K8rDrk5ZdDPnaWVA2dndDBytkdo+iVbAr7XejCrHpGjx5NXl4e27dvP+F3RwhBt27dSE5OZvny5Q3kYd3S3PuzMSKEoPSX6ZSuCs/MHDN0GtFdr4iQVzo6jQ+hBShZ8RiePd+G2RNGP0/swKk1XFW3NJgwW7lyJWeeeSYfffQRkyZNOtW3aVac7gAVDAb54d/D6c6GCts625VcduucunRT5zQRQR9FS+6hfNNbYXZTcl8Sxv2jXot3fpvl5PZvc6vUPeuXauL5M5JoE9PAgkgLohz8GkPWm0je/JApqjXBzNvQMs4GqfGGCha4VFbnePk128fqbC/7yoJVzkmwyPRKMdE72Uz3JCPdkkyk2ZqmWBNCcKhcZX2ej/V5Pjbk+dhWFEA7ahSQJeiXamZMWwtj21rpklDznjxdmFVPYmIi11xzDf/+979P6vy//OUvzJ07l6Kionr2rH5o7v3ZGCld/RwlP4evxsYMfZzorldFyCMdncaLEBplvz6Pa1t43eXYwQ8SP+Kpeh/Pa3OPPK0nuAULFjBu3Dguuugixo8fT79+/aptUJIkHn/88dNpqsXwzUcP0fcoUZaldWLSjW9G0COd6pAMZpLOeg1TSn+KltxTse/MX7CBgnnXkHjGvzHG1y4hwslyfhc7625tz13f5fH5dmeFfX2en0s+zuHhEfFc1s3WcMJBNqC2vQS11Xko+z/DsHMWsvsgpvVPoO2cRTDzdrS0cdAIs4gm2xTO72zj/M6h/XEFLpV1eT5+yw39bCvyU+wNJb1Ytt9bcV2MWaZropHO8UY6xh3+iTeQHNV4BJs7oLGvLMiukgDbCv1sKwqwtcgflr3yCO1jDQxtZQn9ZJj1fXenicvlIjb25IvRx8TE4HK56tEjneZE2bp/VhVlQx7TRZmOTg1Ikkzs4EeQzXGUb6jM3VD260w0bymJ4//RaGr8ndaKmSyf3IOWni7/5Fiz9jtil12MQQo9OLk0C5ZLVtC5Q+/6cFWnjvAc/In8b65E81bOdksGK/GjX8TaZly9tSuEYO4mB/f+kI/DF/6wfUZ7K9PHJJBgjcCNJuhG2fs/DLveQwqGwnG1mK4EM/+IljIKGolwORl8QcH2Ij8bC/xsyg+tLu0uDVRkJDwWi0GiVbSB1tEKraINpNgUUmwKqVEGkqJkYi0KsWb5tGusqZqg1KdR7FEp8mjkuYJkl6vkOEP/7ikLkOus/p5rkKF7kom+KWb6pZron2YmzX5qc3T6iln1tG3bluHDh/O///3vpM6/+uqrWbFiBQcOHKhnz+qH5t6fjQnHxrcoWhie/l5P9KGjc/I4t75XZV+mLfNKks95G0mpn/36DRbKuHTp0pM+d+zYsafaTJPiVAeo4tJ8tr/dnzS5oMK2pdNMzr/gnnrwUqeuCZTuIu+rSwgUb6s0SjKxgx7CXs8D5r6yAFO+zuGn/Z4we6JV5plxiYxuW/e11k6KQDmGPXNRds9FUt0AaLE9QiGOySOalEA7Gr8q2FUSYHuRn92lQXaXhMTaAUcwLCTweNiMEtEmGatRIsoQ+tcggyyF/pUAVYTKAKga+FSBKyBwBTTcfo1yv+Bkmoq3yLSPM9It0Ui3RBPdk0x0jjfWWSZPXZhVz1VXXcX8+fPZsWMHaWlpxz03NzeXLl26cP755/Phhx82kId1S3Pvz8aCc9tcCr77Axz11x/d/x5iek+JnFM6Ok0Q164vKf35CRCVE5jW9hNJOf9DZGNUnbfXYMJMpyqnMkBpmsY3r02kd2BJhW2N8WyuuPObevJSpz7QfGXkz7sOz74fwuy27tcTO/CBel0m14TgH6tKmLa0EL8a/id9TU879w+Lw2KIUCihvxTDrvdQ9n2EpIbCAbW43gQzb0VLGtZkBdqxBFRBjjPIwfIgBx2hlas8t0qBSyXPFaTIo+HwaSclqE4GCYizyMRbZFJtBjKiFdLsBjLsCu1ijXSIM9R7SKIuzKpn8eLFnHnmmQwdOpSvv/6apKTq95wWFRUxadIkVq9ezYIFCzjjjDMa2NO6obn3Z2PAvftb8r6+POxB0tb7NuL63xVBr1omQU1Q7FEp9WqU+zXKfRrlfhVPQOBTBf7DP8dO1BllMBlkTApYFBm7ScZukog2y8SYFZKsClFGqdGEwzd3PPsXUrzsftAqE16ZM0aSdtEXyOaTD0U/GSIizILBIDt27KCsrIzY2FgyMzNbZG2zUxVm3379dzrumk6U7OOAmk6/2zYQEx1Xv87q1DlCC1K89H4cR8UwA1hajyd+9Av1MhNzNL/neZn8ZQ5bCv1h9k7xRl5o6LT6x+IrxrDrXZR9nyBpPgC0uF4Eu9yKljy82Qi046FqgnK/RqlXwxnQ8AQE7oDAE9QIaqCK0AqZJkCRwSiHVtBMikSUUcZmlLAZZaLNIUFmaMgsnNWgC7Oa+eMf/8gbb7xBYmIif/zjHznjjDNo0yZUjP7AgQMsXLiQN954g8LCQm655RbeeOONCHt86rSE/owk3kMryP3sXIRauc81qvv1xA16SH+Ir0OEEBR5VPaWBthXFmR/WeBweHjoJzTBpla7T7eusBgkEq0KqTaF9GgDGXYD6dEG2sYa6RBnpH2ckXS7AVnv9zrBl7OKosV/QQTdFTZTcl/SLv4GxZZaZ+00qDArKCjg0Ucf5YMPPsDjqQylslqtXHvttTzzzDMkJ9du0G7KnM4AtXn7GrLnTSbpzFfp32dMPXmoU99UFKNe9gBHh5wYE3uReMZ/UKyJ9dq+N6jx+OJC/vVrSZjdKMM9Q+O4sXd0ZG/q3kIMu99D2ffpUQKtJ8HOtzS5PWgtHV2Y1Yyqqjz88MO8/PLLVDfMCiGQZZm7776bmTNnoiiNY+P5qdAS+jNS+At+J+fjM9H8ZRU2a6eLGySTXHMloAp2FvvZVOBje5GfHUV+sopDP05/4w8iMysSnROMdEs0k5looluSid4pof9HerKuKeIv3Ejhj7cjjvobM8R2Iu2SbzDGdaqTNhpMmB06dIiRI0eyf/9+kpOTGThwIKmpqeTl5bF27VoKCgpo164dy5cvp1WrVqfaTJPidAcoTdNOOqmKTuPGtfNLCr67ERGsnLBQotuQdObrGGLa1Xv7P+5xcevXOeQckwRieCsLz45PIMUW4RXt6gRaTGZIoDXSLI464ejC7MTs3LmTWbNmsXLlSnJzcwFIS0tjxIgR3HTTTXTp0iXCHp4+Lak/G5JA2R5yPhqH6sqpsJlbjydx3CuNJoNcY8fl19iQ5+W3XB/rcr38nhcSY8eG/DcHzIpEj2QTfVMtDMqwMDjDQs9ksy7WToJA6S4KF9yK5smvsMlRKaRd/DXmlP6n/f4NJsyuu+46PvjgA6ZPn84DDzyAxWKpOOb1epk5cyZPPvkk1157LXPmtIw6XPoApXM0vtw15H51MZr7qD92czyJZ/wHU3Kfem+/0B3kjnl5fL3DGWaPs8jMGJPAmR3qN7TypPAVYdg9NxTieCRJiL0DwU43oWWc0ygLVeuE0IWZDuj9WR+o7gKyPxpLsHRnhc2UMpCkCf9FUswR9KzxIkQoMdPKgx5WHfKw6pCXzQW+k07KVB3RJomkKIWUKIXkKJkEq0K8RSHucIZdm0nCbgz9azm8f8wkSxgViaP1kACCWmi17sgetCMJnZx+rSLEvdQbyrZb6FbJc6nku1WKPae+N9lqkBiYbmFEGyuj20YxtJWFGLMu6qsj6DxE0Y9/JOjYW2GTjHZSL/gEa9vT2//bYMIsMTGRoUOHMm/evBrPmThxIr/++muTLZxZW/QBSudYAmW7yf18UtgAKykW4sf+DWvrcfXevhCCt9eX8cCP+bgD4X/uV3S38+DwOKKMjWB1yl+KYc+HKHv/hxQMCUlhTSfY8XrUNheCYjnBG+g0NLowq5lnnnkGl8vF9OnTMRqN1Z7j9/t58skniYmJ4eGHH25gD+uOltCfDYkWcJP76Tn4cldV2AzxXUk+ZzayyR5BzxoXQgh2FPlZtt/Dsn1uftrvJtdVu9JMEpBuV2gfa6BjvIG2sSZaRRtoFa2QEW3A1gjGRr8qyK1ILKWy3xFgT2mA3SUBDpartRKesgQD0iyM7xDFme1tDG9twRypxGCNENVbTNHCPxEo2lRplI0kT/jvaWXYbrAC036/nwEDBhz3nIEDB7JixYrTaUZHp0ljjO1IxlXLyPvqUnw5vwAgVC/Fi+8mbvh0bJ0vrtf2JUnilv5xjG4bxeQvs1mX66s49vFWJ2tyvLx4ZlJkE4MAmOIIdr2dYMfrUfZ9gmHPB0ieHIybX8SQ9SbB9legtrsCTHGR9VNH5wT8+OOPPPHEE8ycObNGUQZgMplISkrigQceYMiQIU02K6NO3SE0lYL5N4SJMsXeiqSz/quLMkJRIIv2uPlxj4uFe9wcKg+e9LXxFvlw6RAjmYlmuiQY6RhXd+VD6guTItE21kjb2Kr3Er8q2FMaYHtRgG1FfrYV+tlcWPNeOU3AmhxvaNz/uRirQWJsuyjO62xjYmd7tW20JBRLAklnv03x0nvxZR/WLlqAgu//QLD8ALGD6z/hzmmtmI0bN46kpCQ++eSTGs+57LLLKCoqYsmSJafaTJNCnznUqQkt4Kbguxtx7/oqzB4z4F7sPW9ukI3cflUwY1khL60sDguNMMhw75A4buwT4cQgR6N6UQ58jbJ7DrInGwChWFDbXITa4WpEVOsIO6ijr5hVz4033si8efM4dOgQZvPxw858Ph+tW7dm4sSJvPfeew3kYd3S3PuzoRBCULTkHso3vFZhk0yxpJz3AYaYthH0LHIIIdhU4GNelov5O52sOuQ9qbA+iyLRM9lI/zQTfVIs9Eg2kWZTWkTCFE0I9pUF2ZjvZ0NeaG9dVnHwpH5vPZNNXJAZzSXd7PRJMbeI31d1CDVAycrH8ewOL1sV3WsKiWf8E6mWWywaLJTxp59+YsKECbz++utMnjy5yvG3336bO++8kwULFjBq1KhTbaZJoQ9QOsdDaCpFi/5C+aY3w+y27jcQO+gBpAZKeLFkr5tbvs6pMts4orWFZ8clkmxrRDHoWhA5dxGGXe8iO7YDIJDR0sYS7HAdIr6PnskxQujCrHo6d+7MoEGDTrpg9DXXXMPq1avZtWtXPXtWPzT3/mwoytb+neKfjgppVcwknz2rQfYjNyZUTbDyoIcvtzv5ckc5+8tOvCoWa5YZkGZiWCsrA9LMZCYa9aQXR1Hm0/gt18eabC8rD3nZVhQ44TUd4oxc0s3OFT1i6Jfa8kSaEILy9f+ifON/w+zW9ueSct77tVrBbjBhNmPGDFauXMkPP/xA165dGTlyJCkpKeTn57NixQq2b9/O2WefzfDhw8MblSQef/zxU222UaMPUDonQghB6S8zKF31TJjd2vEC4kfMQJIbJpSgyK1y5/xcvtgenhgk3iLz1NgExrdvBIlBjkYI5KJfUXa/h1LwS4VZi+1BsMPVaOlnQQP97nRC6MKseqKiorjnnnt49tlnT+r8Rx99lFdeeQW3233ikxshzb0/GwLXzi/I/+YqKkusyMSNexlb2zMj6VaDoQnB8v0ePt7i4MvtTvLdx98rZlZgYJqZ0W2tDGtloXOCsfFEezQBij0qvxzysvyAl2X7PZScoDZblwQjV/WM4eqeMXROiPC2hwbGteNjSlc9HVbc3d5zMskT/nucq8JpMGF2qmndJUlCVWu3QbOpoA9QOieLY8PrFC2+m6NrnVlajyNhzN+QDA2T6EIIwawNZdy/oGpikKt72HlgeByWRrgxWCrfhbLnA5RD85G0UDFtYU5AbXspwbaXgSUpwh62DHRhVj12u5077riDF1988aTOf+CBB3jttddwOp0nPrkR0tz7s77x5a0j5+PxYaVVYob8lehu10TQq/pHCMH6PB8fbHLwyZZysp3HXxlrG6Mwtq2Vce2iGJBuxqToQqwu0IRgU4Gfpfs8LNzrJqv4+P0wrLWFG3rHcnn3aGItjSi6ph7xHlxG8bL7EEEPir0NGdeswGBLO+nrG0yYLV269FQvZezYsad8bWNGH6B0aoNrxyfkf3cTaJVhBaaUgSSe8W9kU3SD+bGjyM+NX2SzPs8XZu8Ub+TFMxPpmthIZ8h8xSj7v8Cw7xMkXwEAQlLQUsehtrsULXGwHuZYj+jCrHoyMzPp1KkT8+fPP6nzzz33XHbv3s327dvr2bP6obn3Z30SLD9A9oejwmqVRXW/gfjBD0XQq/rlUHmADzc5eH+jgy2F/hrPk4A+KSYmdLAwvr2N9nF6RERDsK8swI97PPyw28WmgppDHi0GiUu6RXNLv1hGtrE2+1BHf9EWipc9QPLZb2NtM7pW1zaYMNOpij5A6dQW974F5H99BSJYGcZkTOhO4ln/h2JJaDA/fEGNJ5cW8vKqkjC7UYapQ+O4vncjSgxyLEf2oe39CLlkQ6XZ1ha17SWorc4Dc8P9LlsKujCrnltuuYU5c+awfft22rdvf9xz9+7dS9euXbnxxht54403GsbBOqa592d9ofmd5Hw0Dn/h7xU2c6sxJI7/V7MrIB1QBfN2Opm1vowfdrtqTPEuAQPSTJzX2caZ7aMa137nFsj+sgDzd7n5NsvFrtKaV9K6J5mY0j+O63rHENeMV9H8zlwsCZkY7Ce/WgYREmbBYJAdO3ZQVlZGbGwsmZmZGAwtrzCsPkDpnArenFXkfXEhmq9SFBliO5B01hsotVgurwsW7nEx5esccpzh4cYjW1t4ZnwiyVGN+6YrObJQ9n0aCnM8XLA6tIo2FrXNhWjJw0Bq3J+hqaALs+pZt24dgwYNYsCAAXz33XckJVUfWltUVMTEiRNZt24dv/766wnLzzRWmnt/1gdCaOR/fQXu3V9X2AzxmSRPnINsbGT7e0+DvaUB3vytlDm/lx23xlivZCOTOkcxsZNdF2ONECEE24oCfLHdxbc7XTXuSYsySlzfO5Y7BsbRPbn5FUIPuPIxx3du3MKsoKCARx99lA8++ACPpzI+2mq1cu211/LMM8+QnFy7Qbspow9QOqeKv2gzuZ+dj+rKrrAptgySJrzZ4KmSC91B7piXx9c7qiYGeXpcIuPaWRvUn1Mi6EI59D3KgS+Ry7ZUmIU5GbXVRNTW5yGiO0fQwaaPLsxqZurUqbzyyiskJSVx++23M378eFq3DpV4OHToEAsXLuS///0vBQUFTJ06lb/97W8R9vjUaQn9WdeU/DyN0tXPVbyWrUmknP8/lKjUCHpVN2hC8ONuN6+vLWH+TleNadrT7QoXZUZxYaaddi28flZTwq8Kftrv4dNtTpbtr7l8wRnto7hrSDzndLI1mzDHRi/MDh06xMiRI9m/fz/JyckMHDiQ1NRU8vLyWLt2LQUFBbRr147ly5fTqlWrU22mSaEPUDqnQ6BsN7mfnkvQsafCJluTSDrrDYzxXRrUFyEEb/5WxoM/5uMJVk0Mcv+wOKzGxpcYpDokRxbKgS9RDn2HFCirsGsxmagZE1EzJoC1YVcmmwO6MKsZIQR//etfefHFF9G0qrPLQggUReHBBx/k6aefbtIPLi2hP+sS5/aPKJh/faVBMZM88V1MiT0j51Qd4PRrvPd7Gf9ZU8LO4ur3JpkUmNDeyhU9ohmYbm684fE6J0V2eZBPtjn5bJuTAnf1q2g9kkzcMyyBq3pEY26EycRqQ6MXZtdddx0ffPAB06dP54EHHsBiqcwk5/V6mTlzJk8++STXXnstc+bMOdVmmhT6AKVzugSdh8j97FwCxdsqbJIplqQJ/43IwL2t0MdNX+aw4ZjEIB3jDMw8M4nuSY00MUh1qH7kghUoB+ch5y9HEpUx81p8X9T0Cahp48Da9Get6x0hKMleS7e+F+rC7Djs2rWLWbNm8fPPP5ObmwtAWloaI0eOZPLkyXTq1CnCHp4+Lak/Txdf/m/kfDQuLANj7KgXsHc8P4JenR4HHAFeW1PC2+vLKK0hxK1zvIEru9uYlBlNrLlpP5zrVCWgChbt9TBnUznrcn3VnpNuV7h7aAJT+sdhNzXN70CjF2aJiYkMHTqUefPm1XjOxIkT+fXXXykqKjrVZpoU+gClUxeonkJyP5+EP39dhU0y2kk881XMKQ2/D8UX1Ji2tJBXjkkMYpDhniFx3NSnEScGqQl/KUrOjyjZC5CKf0M6KiBDi+uJmjoOLW0cwtZOz+x4FJJzL3L2ApTsH5Bde4m+dCVJbQfW6j30+2TzQu/Pk0N153No7jBU58EKm63XLcQNuDeCXp06Wwp8vPRLMf/b7CBYjR4zyHBWeyvX9Y6hf6qpSa8K65w8Wwr8zNlUzrc7XdV+LxKsMncOiudPg+KJtzat/YSNXphFR0dz99138/TTT9d4zl//+lf++c9/Ul5efqrNNCn0AUqnrtB8ZeR+eTG+7BUVNslgJWH8v7CkD4uITzUlBhmSYebZ8Ymk25towh9vAUrOApScRUglv4eLtKjWaCkjQz8JA0Bpfhuaj4sQSOU7kfOWouQuRnbsqDwkGbGNfZXUfjfV6i1b0n1y//79xMXFHfdzlpeXU1JSQtu2DbuXtK5oSf15qgg1QO5n5+I9tKzCZm41msQz/oMkNa3Vg1WHPMz8uYhvs1zVHo+3yFzT08ZVPWJIauTJonTqj3xXkPc3OflwSzlOf1WpEW2S+dOgOO4emkBCExFojV6YjRs3jqSkJD755JMaz7nssssoKipiyZIlp9pMk0IfoHTqEi3gIu/ry/HuX1hplE0kjnsZS+vI1AIscqv8aV4uXx6TGCTGJPHE6ATO7WyLiF91hrcQJW8Zct5i5KK1SEfVmBOyGS2hH1rSYLSkIYiYzOaZ4THoQS5eh1zwC3LeMmRPZUIaISloycNR0yeQb+pKzx6j9VDG46AoCk8++SSPP/54jee88MILPProo6hqzVnrGjMtqT9PlaKlD+D47R8Vrw0x7Uk+70Nkkz2CXtWO5fvdPLeiiIV73NUe7xxv4KY+diZ1idaLP+tU4PJrfLzNyewNDvKr2YcWYw6toN01pPGvoDWEMDut6e2nnnqKCRMm8M477zB58uQqx99++23mzZvHggULTqcZHZ0Wi2y0kXrh5xTMuwb37m9DRs1P0ZK7SRjzN6xtz2pwnxKjFD68LIN3NpRx/4J8XIHQ3I7DL7h/YRFL9nn466gEYprqPgJLEmq7S1HbXQpBN3LhauT85Sj5PyP5ClAKV6EUrgJAGGxo8X3Q4vuhJfRDxHYHQxPIWHksqg+pbAty8frQ5y3ZUFWQJg0JlRxIGwumuJC9OKeGN9Q5ghCCE81/6uVEmzfObR+GiTLJYCPhjH83GVH20343Ty0rZNl+T7XHB6WZuHVALCNbW/RwRZ0q2Ewyk/vEcG3PaL7Y7uTN9Q4OlVdOQjl8Gs+tKOLVNSXcOyyBPw+Ob7J70OqC0xJmixcvZvz48dxyyy3MnDmTkSNHkpKSQn5+PitWrGD79u2cffbZLFq0iEWLFlVcJ0nScWcPdXR0KpENFlLO/4iC7yfj2vFxyKgFKV56H/GjniOqw3kN7pMkSfyhXxxj2kbxh69yWJ3trTj2zU43a3J9PDc+kSEZluO8SxPAEIWWFtprFhQCybknJFwKVyMXr0MKulAKVqIUrARAICOiO6DFdkfEdkeL7oyI7gSm2Ah/kKMQKpJzH5JjO3LZNuTSTUhlW8OEGICwpqEmDUVLGY2WNKRpCs4mwsGDB4mOjo60Gzr1gL/gdwp//GOYLXbUsxhj2kfGoVrwa7aH6UsL+bGGFbIz2lm4bUAsvVNaWHi3zilhUiSu7BHNpd3sfJPl4vV1ZRxwVAq0Mp/Gk0sLefXXEh4emcgt/WObfBbHU+G0Qhll+dR+YZIkNdmQjROhh3To1BdCUylccBvOre8dZZWIG/EUts4XR8otAqrg+RVFPL+iCPWou4kE3NQnmrsGx2E2NMNZVC2IVL4Lufg35JINyMUbkHwF1Z4qzMlo0R0QUW0QttYIWxuENR1hSQFjbN0nFxEa+EuQPLlI7mwk1z5k5z4k1z4k5x4k1Vv1EnNCaOUvcSBa0lCEre0J/dLT5VfPjBkzKv7/5JNPMm7cOMaNG1flPFVVOXjwIB9++CFDhw4Nm8BsSjT3/jxVVG8p2R8MJ1i2q8Jm630bcf3viqBXJ2Zzvo9pSwv5JstZ5ZgEnN3Byh2DYumS0IQy8uo0OoKa4OssF6+vLeNgeVVN0C7WyIxxSVzRo/EkF2v0e8yWLl16qpcydmxk9sfUN/oApVOfCKFRtPDPlG96M8weN2watswrIuRViFWHPPzhqxx2l4SvvHRJMPLc+MSmlVb/VPEWIJdtQS7dGlqRKt+F5Dl+uJ+QzQhLEpjiEcZYMMUgjDGgWBCKGWQLyEZCj0RHLgqC6g0JLNWLFCgHf2moRpu/BMlbgKT5a25TsSJiuqDFdEOL646I74eIalVrgagLs+o5etJSkqQThipmZGTw+eefM3jw4Pp2rV5o7v15KgghyP/mCty7vqqwmTNGkHjm64022ccBR4CnlhUyZ6MD7ZivrARM7GTlTwPj6BivF4PWqTsCquCLHS5eXVtKvqvqHrQBaWaeOSOZ8e0jv3+90QsznaroA5ROfSOEoHjpfTjW/zvMHjvkUezdro2QVyGcfo0Hf8zn7fVlYXaDDH8eFMsf+sZgkBvHzFeDEXAiOXcfXrE6gOQ+EPrXm4fkL623ZgUSWJIRllSEvS2arT3CfvjH1qZOkpbowqx6jkxaCiE444wzmDx5MjfdVDVzpaIoJCQk0K1bt1OOQGkMNPf+PBXK1r1C8bIHK14rtgxSJn2MbG5EYc2HKfOqzPy5mH//WoJPrfpIeFZ7C38ZEk9nXZDp1CPeoMaHW5y8sc5Bqa+qQDu3s43nz0ima1LkQmd1YdYE0QconYZACEHJ8kcpW/tSmD120IPYe9wYIa8qmZfl5I55ueS5wsMT+qaYeHZ8Iu3j9AEeCCXd8BWAtxDJX4oUcIC/LLQCph1eDVN9oPkJWzGTFITBCooFZDPCGA2mOIQxFmGKBUtKKExSrt/yBbowOzHTp09n/PjxjBkzJtKu1BstqT9PBm/2z+R8chZohwvYy0aSz52LKbF7ZB07hqAmePu3UmYsK6LQUzWUbEQrM1OHxbeMaAedRoPTr/HWegfv/l6O95iJAkWC2wbE8djoJBIjUIpBF2ZNEH2A0mkohBCUrnyS0tXPhdljBtxLdK9bIuRVJQWuIH+en1clrb7FIHHvkDiu7WVvNHHjOqeGLswaB3PmzOGnn35i7dq1bNy4Eb/fz6xZs6rNlgyh3/+TTz7Jp59+Sm5uLmlpaVx22WU8+eSTp9Qfen9WoroLODR3CKrzUIUtZujjRHe9KoJeVeXHPS4eXJDPlsKqIc89kozcPyyeoa2aePImnSZNnivIf9aU8fl2V5XQ2jiLzGOjkvjjwDiMDViaodGly580aRLTp09n4MCBtXIIwOPx8J///AebzcYdd9xR6+t1dHTCkSSJ+BHTQTFRunJ6hd2x7mXQgkT3+eNxrq5/km0GPrwsgzkbHdy3IB/H4dAEb1Dw3M8l/LjXzVNjE2kT00SLUuvoHIfi4mK8Xi/p6ekVKcR//vlnli9fXuXcPn36MHHixFNu67HHHmPfvn0kJSWRnp7Ovn37ajzX5XIxduxY1q9fz4QJE7jmmmvYsGEDL7/8MosXL2b58uXYbJHfy9EUEUKj4LvJYaLM0v5c7JlXRtCrcPaWBnjox/wqE2YArewK9w2L5+yOVj3tvU7ESbUZmDE2kRt6RzNzZSk/H6xMWlXq1bj/x3zeWl/KixNSOKtD87ln1eqJ6MCBAwwZMoRx48Zxww03cOmll55Q+a1Zs4Y5c+Ywd+5cnE4ns2fPPi2HdXR0wokf+lck2UjJiscqbI71/0IIlZi+f4qgZyHxeEOfWMa1j+KP3+SyaG9l2uVfs31c8nEO9w2L46oe+uqZTvOhuLiYjh07MnDgQBYurCwOv2DBAmbMmFElGUhsbCy7du0iISHhlNp788036dKlC+3ateP555/nkUceqfHcmTNnsn79eh588EFeeOGFCvu0adOYMWMGM2fOZPr06TVer1MzZWtewrO/sm6rIaYD8cOnNwqR4wlo/P2XYl5cWYw3GP79sxklbh8QzfW9Y/XC0DqNji4JJv57XjI/HfDy4soSdpcGK45tLfQz6YODXJBpZ+ZZyXSIa/pht7USZuvXr2fWrFnMmDGDm2++mSlTptCtWzcGDBhAamoq8fHxeDweiouLycrKYs2aNZSVlSHLMldeeSXPPPMM7du3r6ePoqPTcokb/CCSYgrbbF6+4VUQGtF974z4g0GbGCPfXtOaN9aV8vCiAtyHi1J7goKnl5fww+7Q6llrffVMpxnw3nvvUV5ezsyZM6s9PmvWrIr/l5SUMHXqVObMmcNdd51aGvWzzjq5QvNCCN58803sdjtPPPFE2LFHHnmEf/3rX7z11ls8+eSTEb9nNDW82b9Q8vNRv1PFQsK4V5CNUZFz6jALdru467s89pSGZ8yVJbisq427hsSRYG34/To6OieLJEmMaWtleCsLH2118q9fSyn3V04wfL3DyYLdLu4fnsB9wxKwGptuMqVaPQVJksTNN9/M5MmT+fbbb3nnnXdYunQpc+bMqXKuLMv06dOHiy++mClTppCRkVFnTuvo6FQldsA9IBkoXjq1wlb+++sILUBM/3si/qAlSRK3DYznzI42/vhNLssPeCqOrc72cfHHOdw7NI5reuqrZzpNm3nz5tG9e/caw/6PzdA4d+5cvv3221MWZidLVlYW2dnZnHPOOVXCFS0WC2PGjOHLL79k586ddOnSpV59aU6o3hIK5t8AojKBRsyQRzHGdYqgV5BdHuTBH/P5ZGt5lWN9U0w8PjpBT+yh06QwKhLX9YrmvE5R/GtNGR9vdVbsP/MGBU//VMT7Gx28NCGF87rYI+vsKXJK09OyLHPBBRdwwQUXALB161YOHjxIUVERVquV5ORkevbsSWxs40sLq6PTnInt/2ckWaFo8d0VNuemt0BoxAyYGnFxBtAp3sQP17fhtTWlPLa4AE+wcvXs2RUlfLfLzVNjE/TMjTpNlo0bNzJp0qSTPr9fv35888039ehRiKysLIAaRdcRe1ZW1nGFmc/nw+fzVbx2OBx16GXTQghB4Y9/JFheua/P0v5c7J0viZhPmhC8ua6Ux5YUVuztPUKiVea+obFcmGlvFOOBjs6pEG9VeGJ0Ald0t/PsihLW5Vbej/aUBrj040NM6mLnbxNSmtyzRJ3EDXXv3p3u3RtXGlgdnZZKTN87QDJQtOjOCptz86yQOBt4f6MYjGVJ4s7B8UzsZOO2b3NZcdTq2bpcH5d8ksOfBsYyuU9Mg2Zc0tGpC4qKikhJSaliHzNmDI8++mgVe0pKCsXFxfXuV1lZqL5gTZOmR/aMHzmvJp577jl9H9phyn//L+6dX1S8VuxtiB8euVDQ7YU+7piXx88HPWF2Cbi6h427h8QTbW66YV46OkfTPcnEuxem8E2Wm7/9UkKhp3Ii4pssJwv3uHh4ZCL3DI3HbGga3/um4aWOjk6tiOlzK0ln/R9H175ybplN2ZqZVRIPRJJOCSYWXN+Gf5yTgs1Y6atfhVdWl3HV57lszPcd5x10dBofFosFt9tdxT5+/HieeuqpKna3243J1HRCyh555BHKysoqfg4cOBBplyKCv3grxcseqDTIRhLGvoRsbPgMcQFV8MKKIga/ta+KKOuWaOTDS1N5bHSiLsp0mh2SJHFBpo1vr87gxt7RHD2X6wkKpi0tZPCb+1iyt+o9uTGi/4Xq6DRTonv9gaQJ/+Voceba+h5lvz7XqMSZLEn8cWA8a2/twJkdwjfKby8KcO0XebzwcwmugFbDO+joNC5at27Nxo0bT/r833//nVatWtWjRyGOrJTVtCJ2JCTxRNsQzGYzMTExYT8tDRH0UTDvBoRamcI7esC9mBJ7NLgvm/N9jJ29j2lLC/EfVZDXokg8NDyWjy5No1eyucH90tFpSOwmmYdGxPPJZWn0Twv/vu8o9jNx7gFu+TqHfFewhndoHDRJYTZz5kwkSUKSJH755Zdqz3E4HEydOpV27dphNptp164dU6dOPW4s/Ny5cxkyZAg2m434+HjOO+881qxZU18fQ0en3onueRNJZ79BmDjbNpey1c82KnEG0D7OyDdXt+aNSWnEWypvTZqAdzeWc+FHOWHp9nV0GiujR49m2bJl7N2794Tn7t69m6VLlzJmzJh69+voPWTVcaI9aDqVFP/8BP7C3ytem9KHE939hgb1IagJXvy5iOGz9oXtsQEYlmHmq6vSubFPLIqsh4PrtBwyE0PhjU+PSyDBEi5z3t/ooO//7eGt30rRGtkz0BGanDDbunUrTzzxxHELYB4poPnyyy/TtWtX7r33Xnr06MHLL7/M2LFjcblcVa559tlnue6668jLy+P222/nyiuvZMWKFYwcOZIlS5bU4yfS0alfonvcSPI5b4NU+efu2v4BZaueRojGtQp1pO7Z+ts6cEWP6LBjuU6Vv3xfyF++LyDH2bhnvHRaNnfccQeBQICrr76akpKSGs8rLi7m6quvRtM0br/99nr3q0uXLmRkZLBixYoq46DX62XZsmVkZGTQuXPnevelKePZvxDHupcrXsvmOBJGPdeg+8p2Fvs54939PL4kfJUs2iTx9Ng43pyUQqtovfyITstEliQu6Wrn66vSuaJ7eHbGEq/GnfPzOOO9/WxqhFslmpQwU1WVm266ib59+3LJJTVnPDq6gOYPP/zA888/z/z583niiSdYv359ldoyWVlZTJs2jczMTH7//Xdeeukl/u///o+ff/4Zg8HAlClTCAb1B0Gdpou9+3UknzMrXJzt+F+jFGcAqXYD712cwedXtqLNMbXNFu31cMH/cnhzvSPsgURHp7HQt29fpk6dyurVq+nRowfTp09n6dKlZGVlkZWVxZIlS5g2bRo9e/ZkzZo13H333fTv37/e/ZIkiSlTpuB0OpkxY0bYseeee46SkhKmTJnSKBIENVZUTxEF398cZosb+TSKNalB2hdC8Ma6Uoa8tZfV2d6wY6PaWPjqynQu6Raj96GODhBnUXhyTALvX5RKZkJ4dsZfDnoZ9vZe/rqoAJe/8TwHSaKxxTMdh2effZbp06ezbt06XnzxRWbPns3KlSsZNmxYxTlCCFq3bo3D4SA3NzdsZc3r9ZKRkUFUVBQHDhyouHE9+uijPPfcc8yePZsbb7wxrM077riD119/ne+//56zzz77hD46HA5iY2MpKytrkXH3Oo0b5/b/UfDdTXCUGIvqcjlxw55AkhrnPI3Lr/H0T4X8c3UJx+qwjnEG/joqgWGtLJFxroVTUJxDr8yBJMQn1+q6lnCfFELw6KOP8re//Q1NqzroCyGQZZmpU6fy/PPPI8un/vf35ptvsnz5ciCUqn/dunWMHDmyYuXr4osv5uKLLwZCESWjRo1i/fr1TJgwgYEDB7Jhwwbmz59Pv379WL58+XEjUqqjJfQnhPosf941uLM+q7BFZV5J/LAnjnNV3ZHnDHLHvFzm7Qxf7bQbJR4eEcvFXaN1QaajUwNBTTBnYzn/XlNWUabnCG1jDfzjnFTO7Xz82mcBVz7m+M4Y7Gm1ars298jG+SRWDZs2bWL69Ok89thj9OzZs8bzjhTQHDlyZI0FNA8dOsTOnTsr7EdCFasTXueccw4AS5curYNPoaMTWexdryJ54rsgKRU2d9YnlK6c3ihXzgBsJpnnzkxh5c3tGHqMANtdGuSWb/K5b0Eh2eX6qrZO40GSJJ577jm2bt3Kww8/zLhx4+jWrRtdu3ZlzJgxPPLII2zZsoWZM2eeligDWL58ObNnz2b27NmsW7cOgBUrVlTY1q9fX3GuzWZjyZIl3HvvvWzbto2XXnqJTZs2ce+997JkyZJai7KWhGv7h2GiTIlpT+ygB45zRd3x/S4ng9/cW0WUDU4386W+Sqajc0IMssTkvjF8dWU649tZw47tLwtyyUeHuObTQxwqD0TIwxBNIgA5GAwyefJkunfvzsMPP3zcc0+lgGZWVhZ2u520tKoK+ESbpfVCmzpNDXvXK5Ekmfz5N4BQAXDv/BSEStyIGY125axPqoXFN7blnQ1lPL64gKKj6pV8t9vNkv0ebukbwx/6RmM1Ns7PoNPy6Ny5M88880y9tvHOO+/wzjvvnPT5sbGx/P3vf+fvf/97/TnVzAiWH6Ro8d2VBkkhYfRMZIO15ovqAF9Q44klhfxjdfheRZMM9w6N5freMci6IGt4tCCoXtB8SKoPND9ogdCYKtTQ8WORDaFJUckAsgEhm0Exg2IJ/XvUhKlO/ZERbeDfE5NZuMfNMytKyHOpFcc+3+5kwR4Xj49O4k+D4iNSR7VWwqxjx46n3eA999zDXXfdVatrnn32WTZs2MCqVaswGo9fwftUCmiWlZVVWwy0pvOPRi+0qdMUsWVeTookkz//+ooBxL3rCwDihk9HkhvnACFLEjf3i+OizGieWFLA2+vLOBKQ4A0K/rO2jM+2O5k6NI5zO0XpM8g6OjqnjRCCggW3oflKK2z2Pn+s99T4O4v93PBFNr8dk3GxS7yBv52VROeEplP7rtEiBATKkHyFSN4i8Bcj+UuQfKXgL0EKOCDgQAqUQ6AcSXVD0IOk1X3SCKFYwWBDGKLAGI0wxoAxBmGKQ5hiwZSIMCcgTPEISwpYkkA+/jOxTs2c2SGKYa0s/HtNGXM2laMdfphw+gUPLSzg3d/LeOWcVEa3jTr+G9UxtRJme/fuJTY2lri4uFNqbP/+/ZSWltbqmg0bNvD0009z//33M2DAgFNqtz555JFHmDp1asVrh8NBmzZtIuiRjs7JYetyKSnS++TPuy5MnAmhET/iqUYrzgASoxT+c14aN/eP474f8vjlUOUm+BynygMLi3hvYzkPDo+vUs9ER0dHpzaU//5/ePf/WPHamNCDmN631WubH29xcMe8XJz+8L0w1/a08cDwBEwRmMlvkmhBJG8ukvsQkjsbyZ0Teu3JBU8ekq8ASTu90LWKlS/ZEFoNkxSEJBO+W0hDElporBVqaHVN8yFp/oozJNUDqgepFppPmBIQlhREVDrCmo6wZiCiMhC2NoioVrpwOwG2w7XPLsy08eSyYjYVVPbH5gI/E+Yc4JpeMTwzPpmMBspyWutW7r33Xp544tQ2up5KHP1NN91Ep06dePLJJ0/q/FMpoHlkQ97Jnn80ZrMZs1l/8NNpmtg6X0LKeR+QP++aCnHm2f0VoBE/4plGLc4ABqZbWHRjWz7c7ODRRQXkOitDEn7P93P9l3mc0zGKu4fE0i5WH6B06p9JkyYxffp0Bg4cWOtrPR4P//nPf7DZbNxxxx314J1ObQmU7qT4p6O2UChm4ke/gCTXz0OaN6jx4I8F/HddaZg9zizx9Nh4xnc4fnKCFokQ4C9GLt+D5NyD5Npf+ePJQRLqid/CGIswh1akMMWHVqXMcWCMRRijQytXBjsYbaBEhVa1FCvIJjidyAyhhcIgg26koBuCLgi6kILlSP7Dq3X+stDqnb8YyVeM5CsCXyGSFgjZ/MXg2Fb1rZER1jSErS0iugPC3h7N3gER3QmMzTdJz6nQPcnE3ItT+WSbk1dWl+LwVU6IfLDJwdc7ynl4ZCK39xDU9xN/o99jtmHDBiCUuKM6hg8fDsDnn3/OxRdffEoFNLt06cLKlSvJzc2tss9ML7ip09yxdb6IlPM/JP/ba0KzeIBn9zcgBPEjn6m3B5C6QpYkru0Vy4WZ0cz8uYh/ri7Be1TGpe93u1m4181l3ezcMTCW5KjGLTZ1mjYHDhxgyJAhjBs3jhtuuIFLL730hFm41qxZw5w5c5g7dy5Op5PZs2c3kLc6x0MIjYIFtyGClYXtowfcizG2Q720t7vEz7WfZbM+L3zJZFCaiRfPSiLF1rjvxQ1C0I1UvhPZsROpPCv0r3N3KOSwBoRsDq0iWTMqV5YsqQhrKlhSEObE0IpXJJDkw3vMLCFReMTnE10nBPhLkbz5h1cEc0Mi1JMdWh10HURS3UiebPBkQ+Ev4ZdbUtCiuyBiOqPFdkPEdAutsLXg8H9FlriqRzQTOkTx8qpSPttemWjH6Rc8triQWetkXjq/lAsH1C4rY22o1V95VlYWCQkJJz6xDq+/5ZZbqrUvW7aMrKwsLrzwQpKTk2nfvj1QtYDmsenyqyugOXbsWFauXMkPP/xQJV3+999/X3GOjk5zxdbpQlIn/Y+8b66qFGd7vgWhET/quUYvzgDsJpkZ45KZ0j+OaUsL+WBT5UAd1OB/W5x8tcPFDb2jmdw3hlizniBEp+5Zv349s2bNYsaMGdx8881MmTKFbt26MWDAAFJTU4mPj8fj8VBcXExWVhZr1qyhrKwMWZa58soreeaZZyrGM53I4tjwGr5Dyytem1IHEd3t2npp67udTiZ/lUOptzKpkQTc2j+aPw+KQ5Fb4ANz0IVctg2pdAuyYztS2XYk1z6kamSLQEJEtULY24d+bG3RbG0RtrZgTgyr4dkskCQwxyPM8YjYrlWPCwG+IiT3AWTnvtBKonMPsnMPkicXyZuP4s2HghWVlxjsIZEW1xMtrjdaXM/QPrYWRoJV4alxiVzWzc6zK0rYXFgZ3rirTOPiuVk8nCt49rzu9dJ+k6pjdjSTJ0+uto4ZwLRp05gxYwYPPvggL7zwQhX7E088EZawY8eOHfTs2ZOOHTuyevXqirDFzZs3M2TIENLT09m2bRsGw4kfTltKPRed5ol7zzzyvr4yFFpxGGu7c4gf/TxSE4tVX5Pt4ZFFBfy031PlmN0kcVPvGG7oHU20LtBOGb2OWc1omsa3337LO++8w9KlSykuLq5yjizL9OnTh4svvpgpU6aQkZERAU9Pn+bYn4HSXRyaM7BitUxSLKRc9CUGe6s6bUcTgmeXF/HMT0VhciPOLDPzzERGtqnfrI+NBqEhOfcil/yOVLIRuXRTSExUJ8LMSWgxXRDRXdCiOyFiOocEmKLXszwpAk6k8l3I5VlIjh3IZduRyneG7Xc7grCmo8X3RUvoh5bQF2Hv2PxE7nFQNcHn2138Y3UpxYcnTSRgzb1j6N+q+i1O1VGbe2SzFGanUkDzmWee4bHHHqNt27ZcfvnluFwuPvjgAzweD99//z3jx48/Kb+a4wCl07Jw7/mOvG+uALUynMbSdgIJY2Y2OXEmhOCH3S4eX1zI7/lVd1THmGVu6hPNtT2jidEFWq3RhdnJs3XrVg4ePEhRURFWq5Xk5GR69uxZ4/7lpkRz608hNHI/mYD30E8VtpghfyW62zV12k6pV+UPX+Uw/5jaZH1SjLxydjKpzTl0UQuEVsKKfwv9lGxECpZXOU1YUtHieqDFdkfEdEWL7RpaAdOpW7RgSKyVbUUq3RQSxuW7qwhjYYxFS+iPljgQLXEQIrplCDWHT+PVtWXM3VTONb0TePfGkbW7PpLCTNM0tmzZQkJCQpXZv0AgwMqVKxkzZsxpt3M8YQah5B/Tp0/nk08+qdg7dvnllzNt2rQaB8L333+fV155hc2bN2MymRg+fDgzZsxg8ODBJ+1XcxugdFom7r0/kPf1ZeHirM0ZJIx5CUlpWuIMQrPSH20pZ8bSQnaXVs3AZTdJXNszmht6R5Ng1fegnSy6MNOB5tefjvWvUrTknorXppSBJJ0zq05rPG4v9HHZJ4fYWRx+P7qmp40Hm2PWRS2IVLYVufBX5KJfQ0LsmJTzQrGgxfZExPdGi++FFtsyQ+kaDQEnctlm5OINSMXrQ2JNDY9AEaYEtKTBaElDUZOHgqX60lPNhbV7D9C3Yzd6d+p84pOPImLCbN++fZx33nls3boVSZI4//zzmTVrFomJodmNvLw8MjIyUNUTZ8hpqjS3AUqn5eLZ9yN5X12KUCtT0VtajyNh7N+RlKZZPyeoCeZsdPDc8kL2lVUtAGo1SFzazcaNvWNoHdOMZ6vrCF2Y6UDz6s9A2V4OvdcvPITxwi8wRLeuszbmZYX2kzl8lfvJzAo8OSaeCzOj66ydiCJEaH9TwS+hn+J1SMHwlUFhigutviT0Q0voj4juEko5r9M4OSKui9aGfkrWIx31fACgRXdCSx6JljICLb5vs+vPouKDdGjbk1bp7Wt1XcSE2VVXXUVpaSlvvfUWpaWl3Hfffezbt49FixaRkZFBXl4e6enpaJp24jdrojSnAUpHx3NgMXlfXowIVs6SmVuNJnHcK0iRymJVB/hVwbu/l/Hiz0XVCjRZggkdopjcN5o+KU33c9Y3ujDTgebTn0II8j6fhGf/ggpb9OBHiOl+XZ29/4sri5m2pDAsQCzDrvDvicl0TWyaE14VqF7kwjXI+cuRC1Yie7LDDgtjTEUInJY48PB+pWa2MtiS0AJIJb+jFK5GLlyFVLoVicrne2GwoSUPR00di5Yyolmk6G9ywiwtLY3vv/+evn37AqGb0G233caiRYtYvHgxZrNZXzHT0WlieA4sJe/Li8JSRpszRpI47h9Ihqa92TqgCj7c7GDmz0VkFVdfZLRPiolre0ZzTqeo5hdedJrowkwHmk9/lm+dQ+H3N1e8Nib3I3niu3USwugLatw+Ly8sWyzAoHQz/zg7iThLEw2h9hag5C1DzluGXLQ2LDxRSIbQaljSULTkoYiYTJCa6OfUOTH+UuSCVSgFPyMXrETyl1QcEpISWh1NG4+aOhasqRF09NRpcsIsJiaG1atX061btzD7HXfcwfz585k7dy6jR4/WhZmOThPDc/An8r68EBGoDEUxpw8jYfy/kA1NP2uYqgk+31bOy6tKWJvjrfacBIvM5d3tXNrNThs9zBHQhZlOiObQn6o7n4Pv9kHzHs6eKRtJueCzOqlZVuAKcuWn2aw8GL4/55qeNh4ekYChiaXCl8p3I+cuRslbily2NeyYsKahHgllSxwEhqgIeakTUYSGVLolJNrzlyGX7wo7rMX1Qk07Ay39TERU08lG2xDCrE6fLrp27cqaNWuqCLPXXnuNP/3pT0yaNKkum9PR0WkgrK1Hk3bJPHK/mITwhzJn+XJ+oWjhHSSe8SqysWkPvooscXmPGC7rHs3yAx5eWVXMt1nh+yGKvRr//c3Bf39zMKyVmcu62TmzfRRmQ9N6qNLR0alK0dL7KkUZYO99W52Isq0FPi75+BB7j0o6pEjwxKg4Lu/RRESsEEiOHSi5i5BzFiG79lYeQkLE9UJNHYOWOgph76SHJ+qAJCPiexGM7wXd/oTkPoicuxQldzFSye/IhzM/su2faHH/3959x0lVnY8f/9x7p8/2xhZgqQIiShFUlCiKJajEntgLdo1RY9QUe40x0fxSNGrU5KvGWFCjiL0LiAURRNoKbO99+tx7fn/M7iwrC+wuZbY879drXzv33Dtzz96d3TPPPec8ZyJm3hzMvDng3n0LN/cXu7TH7J577uHjjz/m9ddf73L/ZZddxj/+8Q+ZYyZEPxWsWEbVS8dihZviZY7sKWQe8RC6IymBNdv11tWF+ceXDfz7m2Zawl3/z0p2aBw1ysOxY7xMz3eiD7IPJNJjJqD//z79G1+n6pUT4tu21NHkHPfCTmegfX+Tj5++WN4pyUeKQ+PPR2czI7/vDwPXWr7HKH8LveJtdF9xvFxpNqysGVi5h2HmzJLMiaJngrUYVR+gV7yLXvdVp3lpVsYUzPxjMPMOB0da4uq4Df1uKKPo/w2UEDsSqvqKygU/xgp1jB+3Z00i64iH0Z39f02mH2oJWfxnVRMPf9XI6pqtF+BsN8RrcNQoD0eOdDN5iBOjnw1P6gmlFKtrI/zvu2pmTxjN+TPH7/hJWxjo/ycNw+DWW2/lpptuSnRV9oj+/Pu0wq2U/t9+mC0lbSU62XOfxpE1aade9+mVTVy6sJLIFvd0hqcYPDw3h8LUPrzkSKASo/wtjLJF6C0b4sVKd8YSOeQdjpUzC+wD60acSJBgLUbl+xgVb6PXL48XK83AyjkYs2AuVs4h0EeSjfWboYxlZWVUVFSgaRq5ubkUFBTsipcVQvRBziFTyT35TSpfmosVqAUgUruS2rcvJHPOoxiutMRWcBdLdupcPC2di6am8Xl5kCdXNPHct820Rjrf06rymfzfyhb+b2ULmW6d2YVuDi10c0C+C6+j/y/AaVqKr6tCvL0xwDsb/VS0xuYKlwSrexyYDXRKKeSeZ//QsPSOLYIy8Iw/faeCMqUU93xax+0f1XUq3z/Xwf87JofUvriQfdSHUfEueunrsbT2bTkjlWaLBWP5R2IN+RHYvAmuqBhwXFmYI07FHHFq202BtzHK30RvXotR9RFG1UcoWzJm/hzMoceh0iYN+KGyvQ7MWltbuf/++3n88ccpKyvrtK+goID58+fzy1/+kqQkuasixEDjzJlM3slvUbngx5j+KgAi9d9R+9b5ZB35KIZ74A1t0TSNGQVuZhS4uW9ODi+vbeHZVc28t8mP9YPP4HUBixfW+HhhjQ+bDlNznRw8zM2MPCcTshzY+0l2xypflE9LgnxaEmRJWZCm0NZDOj/Z1ES9P0yGp5+n+haDTqhmBc3L/198W/fkkDrlF71+vail+PkbVTzxdVOn8uPGuLnjsKy+ldVVWeh1X2KUvoZe+V6n9aisjKmYBT+ODScbACnORT/hzsUcfTbm6LNjw2jLXscoW4QWrMZW/BK24pewvCMwhx2HWTAXXD0bQt9f9CowKyoqYu7cuWzYsAGlFPn5+QwbNgylFKWlpZSWlnL77bfzzDPP8MYbbzBy5M5PoBVC9C2OrH3IPeVtKl88GtNXAUC0cT21b55P1lGPYXj6Zzrc7khy6Jw1KZWzJqVS2RrlhdUtvPBdM0vLts7oGLVgWXmIZeWxNNJum8aUXCdTcp1MynawT7aDdHfiU0hbSrGpMcryqhBfVYT4qjJEcfPWa7z90N45XkoaAxKYiX5FWSZ1714BqiNLdNoBN/U6kVEgYnH2yxW8tr61U/nFU5K5anoaWl+5yx+sxih5DaPklU7rjFneQsyhx2EWHCMJGETCqeRRRMdfSXTc5W03EBaiV76L7tuEvuav2Nb8PTbUcdhPsHIOHlALWff4JwmFQhx77LGsX7+eM844g5tuuolx48Z1Ombt2rXceeedPP3008ydO5evv/4ap7NvjA8VQuw6jozx5J36LhUvHh0fDhRt3kjNG+eSddTj2JL6Txrc3spNsnHljHSunJFORWuU19a18tKaFj4u9neaX9IuEFUsLg2yuLQjiMtPMhif5WB0mp3R6XZGpdsZmmIjxaHt8g90Simq/Sabm6Jsboqyti7Md7Vh1tZFCES7N/xuRr6LE8YlcdyIEBNGTcFwD7y5hTurz3wQF11qWfkYocpl8W3n0MNwD5vdq9dqCJic/HwZi7dIh9+nMi8qE716CUbxi+jVi+PJFpTNi5l/NObQ41FpEwf8EDHRD2k6VtZ0rKzpELkOo+JdjNJX0RtWYFR/jFH9McqZGbupMOwElHdoomu803qc/OPBBx/k2muv5ZZbbuGWW27Z7rF33HEHt9xyCw888AC/+EXvhwf0J/15ErQQvRVp2kTlgmOINn0fLzM8uWQd9U9sKYUJrFnitIYtPtjk563vfbxV1Mqmph33Pv2Q166Rn2wjL8kg3WWQ7tJJc+mkOHWchobbpuO0aehabA6YAkwL/FGL1rDCF7ZoDlvU+k2qfSY1fpOKVrPbAVi7VKfOYYUejhrtZe7YJPKSYvf0Iv5KXJn79TgwG+j/J3VdJy0tjbS0tG4/R9M0ioqKdnxgH9Tffp9RXyVl/5oUzy6r2dzk/OR/2Lx5PX6tspYIx/+nlNW1HYmBXIbGH+dkcNiIBM/JCtVhlPwPW/FLaIGKeLGVMYXosBOw8g4Ho+9nhxTih7TWTRgl/8MoXYgW7ljmwsyagTn8RKwhh4K+65Ps9MmsjLNmzaKqqoq1a9fu8I6gUorx48eTnZ3NJ5980pPT9Fv9rYESYleJtpZR+eLRRBrWxct0VyZZRz6GPX1sAmvWN2xuivBxsZ9PigN8XOynqCGy4yclSLJD44ACN4cMc3PoCC/T811dLoIrgVnXdL13CR7661Iy/e33Wb3obHxr/xvfTp72K1Imntvj1ymqDzP3P6Vsbur4W05xaDw8N4f9hiRulJDWsArbpv+iV7yDpmI3hJQ9JdarMPxEVNKIhNVNiF3KiqJXfYRR8jJ6zdKOxDWODMxh8zCHn4Dy7LqEhH0yK+Pq1as5/fTTuzVMQ9M0jjrqKJ555pmenkYI0c/YkgrIO+VdKl76MZHaVQBYwTpq3jyPrDn/wJG1T4JrmFiFqXYK2+alQWz40/LKIF9WBPm6MsS3tSGK6sNdDn/cndw2jX2yneyX62RqnotpeS72yR7Y6f73hFtvvZWbb7450dUQPxAo+aBTUGZL34vkCWf2+HVWVYc47j8lVPo65qgN8eo8duwQRqUnIB2+FUGveAfbxmfRm1Z3FKftQ7TwFKy8I6R3TAw8ug0r73CsvMPR/OUYJa9glLyCFqrDVvQkRtG/YplFC0+OzUXTEj+fe0d6HJj5fD5SU7t/dzQlJQWfz9fT0wgh+iHDO4S8k9+m8uXjCFd9CYAKN1H79nwyD38I55CpCa5h35HuNjh8pJfDR3YMd4qYiqKGMOvqwhQ3RdncFGFzU5iylij1AYu6gNlpsdruSHJo5Hhs5CXbyPUaFKTYGJvhZHS6nTEZDoam2AbdwthicFJmhLr3t5xWoZF+4K1oPUwc8Hl5gHnPltIQ7PhbHJFq45/H5ZCbtIeTEISbMIpfwrbpObRQDQBKt2PlH0V0xE9RqRP2bH2ESBDlySc67jKiYy+K9aIVL8Co/QyjZjFGzWKUO5fo8JMwh80DZ2aiq7tNPf4PkpOTw4YNG3Z8YJuioiKyswdmSkshxNYMdyZ5J71J5Ss/IVT+KQAq4qPunYvJOOzPuAoOTnAN+y67oTE+y8n4rG0PgwqbipaQRSBqEYwqAlELpUDXNAwNdA28jtg8tCSHLkGXEG2av/4rkfrv4tvu0SfgyN63R6/x4WY/Jz9fSmu4YxbIhEw7jx6bs0ezq2r+cozvn8Yo/V881b1yZhItPBVz+IngzNhjdRGiT9miFy3qK8HY/CJG6WtogUrsa/+Obd0jWHlHEC08BZW+X59LetPjwOyggw5i0aJFVFZWkpu7/ZSqlZWVLFy4kGOPPbbXFRRC9D+6M4XcE1+j6tVTCRa/A4Ayg9S9fwUZs+7DXXhUgmvYfzkMjUyPAfT9IRlC9BXR1jIalt4R39YcKaROu7ZHr/HORh+nPl/WKXnO1FwHD/04h6Q9tIi81rQW2/f/1zZ/LDaM0koZS3TkmVh5R4Ihy1YI0U55hxHd+2qi4y5Fr3gX2+YX0BtXYZS/iVH+JlbyGMzCkzELftxnFlDvcWB26aWX8vzzz3PiiSfy6quvkpXV9UKydXV1nHjiifj9fi6++OKdrqgQon/R7V5y571E9aIz8Rf9L1ZoRan/6DrSDroN75gTE1tBIcSgUf/RDahIxxpjyVN+geFK7/bzF21o5WcvlhMyO4KyQ4Y6+fPR2bhsuz8o0+q/xrbhcYyaJfEyM+tAzNFnYWXO6HN3/fszpRQhU9EaVvgjFoGoIhBRBKKx8lD7d1MRtRQRUxG1YguMW4BlgdlFXj1D09D12FIKhqZhN8Cua9gNDYeu4bRpuGxaLOOuXcNj1/HYNDx2jSSH3mUCJtFNhgtr6LGEhx6L1rQGY/MLGGVvoLdsQF/1e2zf/SW2qHrhSaiUvRJa1R4HZrNnz+aiiy7i0UcfZcKECVxyySUcfvjhDBs2DICSkhLeffddHn30UWpra5k/fz6HH374Lq+4EKLv02xOco59ltq3L6L1u6djhcqicfFNqHArSXufndgKCrEb9NfsigNVoOQDfOuei2/bM/Ymaewp3X7+K2tbOOul8k6JeY4Y4eL+Odk4jN34YVkp9Npl2DY8jl7/VawIHSv/SKKjzkaljtvBC4iwqagPmNQFLOoDJvVBk4agRWPAojFk0Rg0aQlbNIdiXy1hhS9iEe2Df8JuWyxAS3ZopLoMUp16/CvDrZPhji2pkuk2yPYYZLoNnDYJ5n5IpY4nuu/viE64CqP0NYzNC9B9m7EVv4it+EWstEmYw0/AzDsSbO49Xr8ep8sHME2TG2+8kQceeICunq6UQtd1fvGLX3DfffdhGINnyE1/SxssxJ6glEX9B9fSvOLvncqTJ11C8uQrZTHefkrS5Xdt6tSpXHbZZVx00UXxsjfffJM333yTP/3pT1sdf9ttt3HHHXcQjfZ8rbu+oC//PpUVpezp6UTqvm0r0cie+wyOrEndev5La2JB2RYdZRwzys3vj8jafT0YSqHXfoZt3SPojStjRZotlu5+9LkDYhHdnaWUoilkUd5iUumLUuUzqWo1qfKb1LSt2Vjt73mypB9yt/VYeex6vDfLZdNwGLEvux6bG2zTY481wNA1NDp3YipF2zqTCktBVMWSPUXaetzCJvHeuKCpCEQs/NFYj13Y3EbluiHFoZHttTHEazDEa5DjNchLiq2Nmd/23W3fM8Nw+yyl0Ou+jC3AXvl+fIhwbAH2YzCH/wSVMh40rW+mywcwDIM//OEPXHLJJTzxxBMsWbKEyspKAHJzc5k5cybnnnsuY8fK2kVCCNA0nYzDHkB3ptK47J54ecvKf2CFGkid8Vs0ffDcwBED29dff01FRUWnsqVLl/LnP/+5y8AM6PImp9h5Ld88skVQ1pbwYyeCsuPHerjrsMzds5xEVwGZ7sQcfgLRUWeDe8iuP2cf5o9YlDRHKW6KUtYSpbQlSmlz7HFFq9lprt/22HTIcBnxXqU0l066Sydti16nFKdOikMn2amT5NBIsuu47VqfSJ4UMWO9eC1hResWvXuNIYumtl6/hoBFQzDWM1gXMKkLmIRNaA4rmsOR7a6bmenWGZZioyDZxtAUG4WpNgpT7AxPtZHu0gf+jVNNw8raHytr/9ii7KWvYRS/jO4v7ehFSxmLOXQeuqdnyYJ6Y6fyuo4ZM4a77rprV9VFCDGAaZpG+szb0F3p1H90fbzct+45rFAT6Yfci2YkYP0fIcSAZAbqaFhyW3xbs3tJnXZNt5778poWzn65c1B24l4ebj8sc7d8WNfql2Nf+xB6/XKgLSArPInoqHPA1fVc/oEgYipKW6JsbIywsTHKpsYIm5uiFDdHqfHvuKso062TmxRbCqS9RyjHGxvKl+MxyPLEgq/+HFzYDY00wyCtB8vQKaVoDitq2noQq3xRqn0mlT6TytYo5a0m5S1RfBHVFsyF+boqvNXrpDg0RqbZY1/pdkal2RiTbh+4y6w4MzFHn4s56uxYL1rJy+iVH6A3r0df/UdyNRtm6OeQ9/vdVoU9vOCGEGKwS516Nborg9q3L4G2IQOBzW9ihZvJOOxBdHvfyIwkhOjfGpbcihVqiG8n7Xs5hmvHaeRfWdvCWS+Xd5pntLuCMq3pO2xr/45RsxQApTswh59EdPS5Ayogi1qKzU1RNtRHWF8fpqgxQlFDbK3G7c3nSnPFenOGJse+Ctoe5ycb5HptModqGzRNI9WpkerUGbONBc/bg7ey5iglLVHKmmMBcXFThM3NUSpbTZrDihXVYVZUdw7aXDaNUWk2xmY42CvTzri275l7cMmI3UrTsbKmY2VNj60VWP4mRsn/0JvXwm4eStyrwOyuu+7C5/Nx2223Ybd3/QsPh8PceuutpKSkcOONN+5UJYUQA0vy3uegO9Opfv0MMEMAhCqWUPvW+WQe/ncM98D5QCKE2PNCNStoWflofNtIGUHyhDN2+LzX17dy5ku7PyjTfCWxgKyibTkRzcAc9hOiYy7o90MWG4Mma2ojrKkPs7Yuwtq6MN83RDolT9mS26YxIs3GiFQ7I9NsFKbaGZFmY1iKnVTnIJ//tBvFg7dsB3tnb73MQiBiUdwcZVNjlO8bI3zfGBsSubExQjCqWF0bYXVt5yGSOR6DCVl2xmc5mJDpYGK2g7wko1/3WOJIxRxxGuaI02gu+YiCgiN36+l6HJi988473Hzzzdx3333bDMoAHA4HWVlZ/OpXv2LGjBmSmVEI0Yl39PHknriQqv+dhAo3AxCpW03NG2eTNecf2JKHJ7iGQoj+SClF/YfXgeqIBNJm/BpN3/5Q6Xc2+vjZgt0clIXqsK1/DKP4JTRlotCwCo4hOvbifpnUoyFgsqomzLe1Yb6rCbO6Nkx5a9dDEN02jbEZdsZm2BmVbmd0mp3R6XZyk4yBOSyun3PbdcZlOhiX2Tloi1qKspYo6+sjrKuPsK4uFoCXNEep9ptUF5t8WByMH5/h0pmYHQvSJuU42TfHQUY/7VmLeEeh2XowprQXehyY/fvf/yY9PZ0rr7xyh8deccUV3HPPPTzxxBMSmAkhtuIe+iPyTn2XqpeOx/THEgiZLSXULDqLzCMewpE5McE1FEL0N/6ilwmWfhjfdhbMwpV/8Haf83Gxn1OfLyO8xaSy48fuwqDMDGJ8/wy2on+hmf5YUfZMouOvSPi6Sd0ViFisrg3zTXWYldVhVtWEKGvpOggblmJjXKadcZkOxmfaGZvhoCBZArCBwKZrFKbaKUy1M2dkR7kvYrG2LsJ3tWHW1oX5tibMhoYI9UGLj0uCfFzSEawNTTaYlONkSq6T/YY4GJfhwL47l57oR3ocmC1evJg5c+bgdDp3eKzT6WTOnDksXry4V5UTQgx8zuz9yPvph1S+dCzRxg0AWMF6at88j4xD/4SrYFaCayhEzz311FMsXbo0vr1hQ+y9PXfu3K2Obd8ndp6Khqj/+DcdBbqdtOm/3u5zlpUFOPG50k5Z/o4Z5eauXRGUKQu97A3sa/+GFqwGwEqdQHT8VbEscH2UUoqKVpPlVSG+rgzxdVWItXWRTslQ2o1ItTGxbTjc3lkOxmc6SJEhiIOO164zNdfJ1NyO+CAUVaytD7OqOsyqmjArq0N83xiltMWktMXPoqLYTQqXTWNSjiP+/P1ynCQP0vdQjwOz8vJyRo0a1e3jR44cySuvvNLT0wghBhF76kjyT/uQyld+QrjqCwBUNEDde1eSduAteMeelOAaCtEzGzZs6DLgeuONN7o8vl/PwehDmr95iGhTUXzbO/4MbCnbHha9sjrIvP+W0hruiDhmF7q49/CsnU6JrzWsxL76j+iNsXT9yp1LZNwVWPlHgda3PnRaSrGhPsKXlSG+rAixvDJEpW/r3rBsj8G+OQ4m5cSGpU3McgzaD9Bix5w2jX1znOyb0xGstYQsVtWE+boqxIqqECuqwzSHLD4vD/F5eWzOuQaMz7IzLdfF9Hwn03KdpPfT4Y891ePATNd1IpFtr4fwQ5FIBF2XP1ohxPYZnmzyTn6L6tfPJLBpUaxQmTQuuRnTX0nyvpfJh1fRL2zcuDHRVRiUzEAtjUs7lvDRnKmk7HvJNo8vqg9z/H9KaQx2TCqbWeDkT0dm79ywqmA19jV/wyh7HYgtVBsdfR7myJ+BsXvnp3SXpRTr6yNtH4aDfF4RoukHizHbdBif6WDyECdTch3sm+Ps/4kcRMIlO3UOGurioKGxvwVLKTY2RvmqMsRXlbGbAiXNUb6rjfBdbYSnVrUAMDbDzox8JzPyXeyf5yTNNTADtR4HZvn5+axatarbx69atYqCgoKenkYIMQjpjiSGzHuR2veupHXV4/HylhV/x2wtI+3AW2WtM9HnFRYWJroKg1Lj0juxwk3x7eR9r0B3pHR5bFlLhLn/Ke3UKzQ118FfjsnG0dugzIpgbHwG2/p/opkBFBrm0OOJjrusT6S+L26KsLQsyNKyEMvKgzQEOwdibpvG5CFOpubFeij2zXHgtsuNdbF76ZrG6PRYIphTJyQBUO2L8mVFiM8rYj24GxoirK+PfT29qjXeo3ZgvosDClxMzXPiHSDv1R4HZrNmzeKpp55i06ZNjBgxYrvHbtq0iffee49zzjmnt/UTQgwymm4j64iHsCUPo3GLxWH9Ra8Q9VWQeegD6M7UBNZQCNHXhOvX0PzNP+LbRsoIksad1uWxdX6T4/5TyuamjtE/EzLtPPTjHFy23n2402s/w7bqD+i+zQBYaZOITLwOlbZ3r15vV2gOWXxWFmRxaZDFpQFKf5Cow23TmJrrZHpbL8TeWZKAQfQNOV4bPx5j48djYuua1gVMvqiI9e4uKw9R1BCJ96g98U0LNh32y3Fy4FAXBxW4mJTjwLaTQ5ETpceB2RVXXMETTzzBKaecwhtvvEFWVtd3gerq6jj11FOJRqNcdtllO11RIcTgoWka6Qf8FlvSMGrfvRSsKADhymXUvHEWmYf/HVvysATXUoiuXX755T1+jqZp/O1vf9sNtRkc6j++Mb5gPUDa/tej6Vt/xPGFLU56vpTvajsWzC1MtfHosTkkOXoRlAVrsK/+U8d6ZI4MIhOuwiqYC3t4yJ+lFGvrInxcHODjkiArqkKdknXYdJg8xMkBBS4OyHcyKcfZ+95BIfagTLfB0aM8HD3KA0CN32RZeZDPyoIsLQtS1mLG5kdWhvjbF00kOTRm5MeCtIOHuRieYus3Q3A1pVQXOXa279prr+XBBx8kKyuLSy+9lNmzZzN0aGz9jbKyMt59910eeeQRampquPbaa7n//vt3ecX7qubmZlJTU2lqaiIlpeshFEKI7guUvE/1q6d1GqKkuzLIOOxBnDlTE1gzEfFX4srcD8Pdsx7Mgf5/sifzqrf8sGCaXace7+sS/fsMlHxA5YtHxbcduTPIOvKfW30Qi5iK014sY9EGX7ws16vz9Am55Cb18D61MjE2vYBt3UNoUR8KHXPEqUT3ugTsyTvz4/SIL2KxtDTIh8UBPiwOUOvvPDxxZJqNg4e6mDnUzf75A2e4lxBbKmmOsqQ02DZUN7jVfMmCZIOZQ93MHOriwAJXr7OG1tWXMnL4RAryRvToeT35H9mrwEwpxW9/+1v+8Ic/YFlbL+WulMIwDK6//nruvPPOfhOl7gqJbqCEGIjC9d9R9fJPiDZv6ijU7aQfdCue0T9JWL0GOwnMuvbhhx/u+CCguLiY22+/naKiIjRNk8CsF5SyKH/2YMJVX7aVaOQc/wL29HE/OE5xycJK/v1Nc7wszanz9AlDGJHWs3mrWtMa7CvvRm/6DgArbR8i+9yISh23g2fuGtW+KO9vDvDepgCflQWJbPExzG3TOLDAxazhLmYNc5Of3OOBUUL0a6al+K4uzOKSIIvLgiyvDHVaNF7XYN8cBwcPdXPwMBcTs7s/7LHPBmbtioqKeOKJJ1i8eDGVlbHFYXNzczn44IM577zzGD16dG9fut8a6B84hEgU01dF1asnE6pc1qk8aZ/5pEz5BVofSz89GEhg1jsNDQ3cfffd/O1vfyMYDHLQQQfx+9//nkMOOSTRVeuVRP4+W9c+R82is+Lb7pHHkzHrnq2Ou/mDGu5bXN9xnE3jyXk57JO94zVZ48wgtnWPYGx8Bk2ZKFsS0fFXYA4/EbTdmyFuY2OEdzb6eWdjgFU14U77hqXYOHS4i0ML3eyf55LhiUJswRex+LIixKclsbmW3zdGO+1PcWgcUBDrVT54mIuC7dzM6POBmdjaYP/AIcTuZEUD1L59Mb61/+1U7ho6m/RD7kF3JCWoZoOTBGY9EwwGefDBB7nvvvtobGxk/Pjx3H333ZxwwgmJrtpOSdTvU5lhSv+9L9Gm72MFuoPcE1/H8OZ2Ou6RLxu46s3q+Lahwd+PyeSQ4d5un0uv/RzbyrvR/aUAmHlziOz9y92WbVEpxbr6CG9+HwvGiho6L1O0X46D2SPcHD7Cw6i0/jN/RohEK2+J8mlbQpylpUGaw53DoOEpNg4scHHg0NhczC3T8u+JwEz6uIUQ/YZuc5N9zL+xZ4zvlLExWPo+NYvOJHP2X7a7mKwQiaCU4p///Ce33XYbZWVl5Ofnc99993HBBRfIOp87ofmbRzqCMsA74cytgrKF61u5+q3qTmV3Hpbe/aAs0ortuwexlbwCgHLlENnnBqwhP9q5yndBta0t9sb3ft4s8rOpqePOvk2HA/JdzBnpZvYID9megbmGkxC7W36yjVMnJHHqhCSiluLbmjCflgZZUhpLmFPcHKW4uZXnvutIy39AvosZ+S5GunZ/X1aPeszOOOMMTjnlFE466aRenWxnn98fDNY7wULsaa3rnqf2zfkoMxgv0xwpZPzoD7jyD05gzQYP6THbsZdffpnf/OY3rF27lpSUFG644QauvvpqXK6+sdDwrpCI36cVaqLkifFYwToANEcquSe9ge7oSLzxZUWQI58qxh/p+Jhz9YwULpqS1q1z6NWLsa+8Cy0YC+yihacQHXcF2Hdtz/zmpgivb/CzqMjfqWfMYcAhw9wcNdLDoYXuXicsEEJ0T2vY4vPy2Fp/S8uCbPhBT7WhwdUH5fCHkw7o0evuth6zZ599lvHjx/c6sNrZ5wshRLukvU7FnjqaqtdOxWwpAUCFm6l79zJSplxN0sTzZXiPSJhPPvmEG264gaVLl+JwOLjmmmv47W9/S3p6eqKrNiA0fnF/PCgDSJp0caegbFNjhJOeK+0UlJ02wcuFk7txEyHSim31A9hK/weA5RlKZN+bUJm7Lgtsrd/kjSI/r673dZozZtdh1nA3R4/ycFihu3cp/IUQvZLk0Jk9wsPsEW1p+X0myypiafmXlYcoaY4yLNWxW+vQ46GMX3/9Nf/+9793R12EEKJHnEOmUnD6EqoW/oxQ2SexQmXR/NWfCNd+Q/rMO2Xemdjj5s2bx8KFC9F1nXPPPZfbb789vqSM2HlRXwXNy/9ffNvw5pM8/vT4dmPQ5IT/llLl68hyecgwF789JGOHN2v02s+xr7gNLViFQsMc8VOi4y4Hm3un6x2MWry7KcCr63wsLg3G1xgzNDiwwMWPx3iYM8JDsvSMCdEnZHsNjh3j5di2ha6/LSlhn1G7d1RAjwOzl19+mVdeeaXHJ5IcI0KI3cHw5JB30hvUffhLWr75R7w8WPwONY1FZBz2IPa0wZchViTOa6+9hqZpDB8+nMrKSi6++OIdPkfTNBYuXLgHatf/NX52DyoaiG8nTb4KzYjdxY6YijMWlLOmrqMXanymnQeOzNp+SmwziG3N37BtehYAy1NAZL9bUBlTdqquSimWV4V5eW0rb37vp3WLRAP75jg4bqyXY0Z7yHTLnDEh+rpcr0ayc/f+rfYoMHviiSd2+oSTJ0/e6dcQQogtaYaDrMP/gjNnKrXvXwVmCIBo80ZqXv8ZaQfdhmfk3ATXUgwmSik2btzIxo0bu3W8DLvtnkhjES2rHotv29L2wjsq9retlOKat6p4b5M/vj/Xq/Pw3Bw821lYWWtag335Tei+TQBEh59EdMIvwObpdT1r/CavrPPx0prWTkk88pMM5u3lZd5eXgpTe7Z+mhBi4OtRYHbuuefurnoIIcROS97nfBzZ+1L12k8xW4oBUNEADR9fT6jqc9L2vwHNNnCSLoi+qbvBmOi5hqW3g9UR6KROvTq+huHfPm/kseVN8X0em8bDc3O2ncFQWRjfP4Vt7UNoKopyZhHZ93dYOb1LHmRaik9Kgjz/XSsfFQfiQxXdNo2jR3n4yTgv++c50SUI37OUAisEZhAt6gczCGYQFfGBFUIzgx37rXDsxp4ZRrMioMKx95uKgBVFU2bbttn2ZcW+MGPnof1323mUmNa2rdBB0wAddKPtuw00A6UZbY/tse+6HaU7QbOB4UQZLjAcYLhij3U3GC40uwdleGLDbQ0P6Pa2c4j+SNLlCyEGFOeQaRScsZTqRecQLH4nXu5f9zyRmm/IOPRP2FIKE1hDMdAVFsr7a3cI167Et+bZ+LY9az+cBbMAWLShlevf7UiLr2tw/5wMxmZsY6J+oBL7itsw6r4AwMydTWTSb8CR1uN6VbZGWbDGx4trWqncYl7b5CEOThqfxDGjPHgliUfvKBULpCJNaJEWVKgBLdIM0Ra0SAtauBmirWjRVoi0oqkoKFBaLBCKfVMowwlGLJBRugt0J9hcscDHcIIeC3yULQV0BxgOLN0RO64tWFK6EXuN9gAKHQxbWyCkx7668/PQFswpE6LhjiDPisbqbwZiwZ8VBiuMZoZjAWK0LXCMNKC1B5RmCMxA2+O27yociwu19vCwLUgz3Ch7MtiTUbZklD0JHKkoexrKkYbmTAd7KhguCewSSAIzIcSAY7izyD3hVRqX3U3j0jtpb54iDWupfu1U0g74HZ7R8xJbSSFEjzQsvpkteyJSp/0STdNYXRPinJcrsLbopLj+wFQOLex6rTK98gPs39yBFmlGGW6iE6/DHHp8jz6MWkqxtCzIs9+28v7mQPzcaS6dn+zl5eTxSYxOl6GKW1EWRJrRQnWoYB1auB4tFPsi3IAWaUIzg/H+JU0RC6raggnsKSh7Uiyw8AzHSk0GeyrKkQo2L9jdQOy663YH0NZbqumxgKr9MXRstx9jdN7WAN1I7Nw/yzS32DJj16WtTNG2T7V/tzq22x9jYkXagj+zFUJNaOEGtHBTLKANN6H5KmLXPdIc+91YwbbXB01pKLsH5chAOdJRzgyUKxucWeDKBmdmW5AqdhW5mkKIAUnTDdIPvAlX/sHUvHEupr8KABX10/DpbwiWf0raAb/rlGJbCNE3BcuX4P++IzmKM38mziFTqQ+YnPJCGS1hK77vp3t7OXvfLtLimyFs3/0/bJufA8BK3ZvIlDtQ3u4vSt8SsnhpbSvPrm5l8xZzx6bnOTl17yTmjPDgtA3C3gYrCqE6tFAt+CvRglVowVq0cC2EGtGUBSjQtLbemjRwZqAcaShXDlbKOHBmopxZ4PAC9o7Aqj2oigdXHYGUhpHw4Gl36vyzGZ2+dfs1uiiztgzuTDP2PT40sz2wM7HCIYg2ogVq0UI1EKxBby1Bq1uBFq5DizSBFQE0lKaBPQ3lzkE5s1DuIViuPDRPXqwnWnrhukUCMyHEgOYefjj5Z35OzaJzCJZ+EC8PbFxIuGYF6bPuxZk9OWH1E0LsWKy3rEPK1GuJWoozXyrn+y0WgZ2R7+Q3B2ds9XytdRP25b9Bb14PQHTU2UTHXRYbhtYNRQ0RnlnVwivrfASisf4cr13jJ3t5+enEZMYM5N4xZUKwFi1QCYFytEAlWqAiFniZYWIBl4Fypsd6Vpw5KFc2VuoElDMHPDmgu7cOtHR7bHsQBFh9jb5l7+B2Lrv+g/w3lhlG/TCQsyLEgrgghGvQWkvRgtVo/kps9d+gBWvQos1twzhBOVJQrlwsdy7KU4By56N582NDKIUEZkKIgc/mzSX3pEU0ffEHGpbcFh/6YbaWUvvGOSTvcyHJ+16GZgzgD1dC9FOBkvcJln4Y33YNPwpHxnh++VYV72+RgXFostFlWny97E3sK+9CMwMoRzqR/W7Fypm5w/MqpVhcGuRf37TwaWkwXj463c6Z+yRx3Fgv3u1ke+w3lAXBajR/OfhK0Pxl6IFytHBj7MO0psWGsbmGxL6SRmNlz0J58sDuQbd7AWOLuVYGGA40DAwJtgYU3XBsM5CLBXEjIWcGlmmizHBHAGdFQFlYYT9E6tBaNqEFKtGb16FVfYwerI4lYEFD2bwody6WZygqaRjKPTTW6zZIhkwOjp9SCDHoabpB2owbcQ2bTc2ic4g2t2XOUxYtKx8hWPoR6Yfcgz19bGIrKoSIU0rRsPjWLUp0Uqb8nCdXNPG3LxrjpR6bxt9/nEOaa4tPjWYI2+oHsBW/GNvM3J/I5Ntjc2O2IxRVvLbBx7+/aWFDW2+cBswe4ebMfZI5IN/Z/5Y3MMNo/jKUrxjdV4zmK471aljRWJIIR2asF8NTgJUxDdN7AnhyQXOg292xgEu3S9AlukU32hKl/LDcAzACsqd1Ct4sK9LR8+avQmvZjOYvwWhYhVb2VmyIrDJRmo5yZaM8w1BJhVieQrSkYbEELgOEBGZCiEHFlXcABWcuo/b9q/Ct+U+8PNKwhuqFp5Ey+UqS9j4XbZDcnROiLwtsfotQxZL4tmvkXJa35nLVGyXxMg247/D0Tsk2NH8p9i9/jd68BoWGOeYContdtEXCh601Bk3+u7qVp1e1UBeIzVnz2DVOGpfEWZOSGZbSx/8nKAstUIlq2YTu24jWGuuV0JQFuoHlyosNHfMMxcqaifLkgz35B4GXA80wMIxtZLMUYhfZdvCWB1mTsUwTqz0jZduQSSvcGrup0LoZzbcZW+2X6IHytt42HcuVjfIOjwVt3hFoScO7PVy5r+jj/2WEEGLX052p5BzzL3yj51H77pVYwbrYDitC81cPENj0Jukzb8eeMT6xFRViEFNK0bDk1o4CzSCy16Wc/lw5YbMjBePPp6cwe2RSfFuv/hT78pvQoi0oeyqRybdvd+hiRWuUJ1e08OKa1vj8sVyvwdmTkjl5fBLJzj42XNGKoPmKobkIrbUIvWVjLF08xBJpeIaivMOxMqfHEpvYkmLBl26PfUmPl+gHOgK3juBN9+RA2qhYbxuxHrf23jYr7EMLlKI1fx8L2mo+Rw9WgBVFaQbKkxfL5Jk8AuUtRPPkd2+Jgz1MAjMhxKDlHXsyzvyDqX3nUgIbX4+XR+pXU73wZyTvcwHJ+16CNoCGSQjRXwQ2LiRc9WV82zlqHqe/rVPWEoqXHTnCzcVT2jIwKgtj/T+xrX8UDYWVtg/hqfeAO7fL19/YGOGfXzfz6nof0bakjntl2LlgvxSOGe3BbiR4uKIVRfOXQtN6tNYN6C1FaNEAaAaWpwDlHYGVPhVz+CngzorN9dLsW/R8OQZt8BW1LIIRi7BpEYq2fZkWgXCUcNQiYloEo4qIZRG1FBHTIhSNfY9airBpYSqFabV9KUXUjH23FFiWwiL2WKnY8glKtS3moFTXldI0dC3Ww6u1Pda3+G7osceGrmHXdQxdw6bHyh2Gjt3Qsekadl3DadNjZTYNl6HjtOm4HTYcbY9dNh2nzcBl1wfkguax5CWxYbXxMg+QNhIrZ+bWQVuoBc23Ea15A3rjGvTyt9GCNYBC6U6UJx/LOxyVNAKSRsSGOycoaJPATAgxqNm8uQyZ9xKtq/+Puo+uQ4UaYztUlJaVj+Df9AZpB/wOV/6OkwUIIXYNpaxYop52up2HWk7j4+JAvGhUmo27Ds+MzfeKtGD/+maM6k8AiA4/meje13b64NZuTV2YR75q5q3v/fFV0abnO7lwcgoHD3UlZv5YuBG9eR00fofesh4t1AAaWO4ClHdkWwB2KrhzYgFYW+9XLPjqv8MOI6ZFa9jEF4riC5s0BcL4wiYt4SitIRN/2KQ1EsUfMvFHTPwRi2DEJGR2LI/wwzhIoeLBjNOm4zR0HDYdh6FhN2LbdkPHbrQHPG3lNp0kp4GtLSCy29oCIy323aZp6PrWAVUs14yGw9ax/llXb6H2wC0cNVGoeEBntX1vDwAjVqysPRCMmrFAMWLG9vmjJs2hCGEzFliGtghAO74rQqZFOGp1uj6q7R3fHqzZdA233cDrMPDYDTwOgySHQZLTjtdhkOI0SHHaSHLaSHU7SHLGjuvLcyy7DtpyIH00ljkbZYYxVbgtIUkEK9iI1rIRrXUjRv0KtNKFaKE6YkGbHeXOiw3/9Q7FiCYBE3dr/SUwE0IMepqmkTzxHNwjjqTuvavwF70S32e2FFP3zsW4C48mdfoNGJ6cBNZUiMHBX/QK4ZoV8e2yzOO566uOD1pJdo2/HpON166jtWzE/uV16L5ilO4gus+NmMOO3+o1V9WEePjLZt7f3BHczS50c9GUFPYbsod6xZVCC5RD42r0xu/QWr9Hs0IoewrKOworZQyRvCPBm9cWgLn6dACmlMIXNmkMRGgMRmn0h2gIRGkMRGgKRmgKRmkJxb6Cbd2SWwYKug4eu0GS0xb77ogFB16HQZLDRnZSLBjw2g08dp0UlwO33SDJoaNpsYAKwGGPBUXxJPBtvZ1G2ypeW3YcdloZbIsdncu3/ln3VO+j2WlR6fayLR5v41izy2OttjLV6ZhwJPbIF47SHIzij5g0ByP4wiatYZOWkElrKEplS5CWUJTWUJTWcCxQDkQ6guJ2DptGitNOqstGistGmttOmstOuttOhsdGptdFmtuG05bYHtyuh0fmQcaEznPa2oO2kA+tNTZfU28txhlRwBG7tY4SmAkhRBubN48hxz+Pb/2L1L5/NVbbotQAgc1vEiz7mOR9LyFpwtloffBDkhADgVIWjUvv7NjWHZy9em6nY+6dnU5hqh296iPsX9+MFvWh3LmEp/0Bldp5buiqmhB/+6KJj4pjKe814JjRHi6eksJembvx71gpNH8ZNKxCb/oWvXVTLLOcKwcreS/M7JmosfPBnhqbA2Y4Y1kQDUfC1vQKRkzq/BHq/WFqW0PU+sPU+6PU+8M0BMI0BaOYVqzfpT3A8jgMktt6VlJddlLdNtI9dkZmukly2khzGqR5nKQ6jXivksNuYNA5gDKMLQMrY4vHey4o6gu6+lm3/ePv+utimmY8uDPbt+P7YsGeaapOQV5rKEqtL0y9P0xjMEpzKEpDIEJxY6AtaI/QHIwSaQsQFQpd0/A6DDLcsQAu0+sgw+NgSJKD7CQXGZ5Yr92e6p3rOmgD0sfEM0j6WnzgzNqt9ZDATAghfsA79mRcw46gYckttKx4mLaZA6ion+avHsC37nlSp/0S1/A5fXpIhxD9kb/of4RrV8a3XwkfzeZQenz7kinJzB7hxVj/OLZ1D8fmk2VMITz1XnB2LC69uibM375s4oO2HjJdg+PGeLhoSiqjdseC0MFatIaV6I0r0Zo3oKkIyjUEK2U8Zt6RRJPHxrIgOrxguPZYL1ggYlLrC1PTGqKiOUitL0KNL0StL0xDIBL/sAzgsOmkuW2kuexkeGJfY7I8ZHpTyXDZyPQ68ToM7IbeKbjaMrBqD6rag4nBFFQNBIZh/CAQ7Nnvr70XLxbEdQR2PwzqQuFY8FbVEqbGF6QuEKG6NcR31S3U+yM0BiL4IxYasUDOpmukux1keu1keR1ke53kJDnIS3GR7XXicey+91l70KY5tN3+fpbATAghumC40sia/WeS9z6H2veu7JSEwGwtpf7Da3AM2Z/UqdfgyN4vgTUVYuBQStH42V3x7Qh2fl89L759YIGTK6e6sH/9O4zytwCIFp5CdO9fxheg3dAQ4S+fN/LOxs4B2aXTUilM3UUBmRlCa16LVv81euMqtHALypGGlTIOM2smasxFuz0Ii5gWNb4wVS0hypoCVDaHqPKFqG4J0xqOxuY0KYXTbpDZFmRleR1kJznZO9dLlsdOTpILr8PA67LvMMiSAEt0R/v7pOPt0r33TThsbjeQaw2GKW8KUdUapMYXodYXYWO9j+rWMPWBCMG2IA7AbdfJ9jrI8joYkuxiSJKDIckOclPcpDhtffqGqgRmQgixHc4h08j/6Se0fPskDYtvxgrUxPeFq76gZtGZuIYfQcrkq7CnjU5gTYXo//zfv9ppbtkzLUdSbcV6wXI8OvcfouNaeil602qUZhDd5wbM4ScCUNoc5W9fNvHqOh+K2JDFuWM8XDYtlZFpOxmQBWtjQVj98tiQRM3ASh6NlTqRyIRjwJMfH46o2dw7HYSZlqLGF6K8OURJg5+K5hDlzUGqW8OE2xJfGDpkemIfPnOTnQxPdzN9eCr5yW6yvDYcNgOH3cDxg2DL0dYjIoGW6Esc8R6vbbwvU92MGxJ72D7cMtxFEBeImDQFIpQ3h6hsCVDREubbqhY++D5MjS9Ma8iM98I5bTpZHgeZXgfZXgd5yS5ykuzkJLvJ8Njjcxj3JAnMhBBiBzTdIGXSfJL2OoXGZffQtPwvscnBbYLF7xIseR/3yGNJnnQR9tRRCaytEP3TD3vLwsrGI60nAGDT4c8z/eR+eQlasDq2Ptm032NlTqMuYPLwl008911rPO39nJFufr5/KmMyehEgKYXm2wx1X2HUf4UWrEM5UrHSJmHmH000bQK6M7UtMYcLm2PrRXJ3JGJaVLaEKG7wUdoUoqQxQHlzkMZAND7/JsNjZ0iSk/xUF3vleJk9JoOCFHcsGUZbD1c8yEICLjF4tA+3dGynN24yHQFce09c+Ae9cPWtISpaQlQ2B6nxh9nY4Ofz0jB1/tgwX9NSaG39cB6HwdgsDzfN2Wu3/mwSmAkhRDfpzlQyZt1L8qQLaVh8K751z3XsVBaB718l8P1ruEccTfKki7Gn795/4EIMJIGNrxOuXh7f/q9vDpVmJgDX713D9PVXo5lBLO8IItMfoNWez7++aOKJb5rxR2LzpGYOdXHV9FQm5fQgy6Ky0Fq+R6v7HL1+eWxYoicPM20/ImMuguTh6PaUHvWGKaVobEt+sLHeT0ljkOJGP3W+SHy+THaSk9xkJ0NTXRw6OoOCFCd5yS4cNgO33cBoS+fukOGEQvRK5/lyW//tFKS6mdT2uKuhlO09cqFwtC2BiYlD5pgJIUTfYk8bQ87cpwjt/0saPr2JwOa3ttirCGx6g8CmN3AWzCJpwjk48w7s02PahUg0pRQNn3VkYgwrG/9oiQ1RPDqnhvMbLkPTFGbWgQQm382CIvjrF+XUBWJdZBOzHVx7QBoHFri6c7JYCuzaz9DrvkSLtGJ5C7EyJhOZ8EvwFMTnhumGe5sZEpVSNAQibKr3U1TnY2N9gOLGAP5wFAWkOO3kpzoZnuZmSkEKP9k7m2Fp7njg1d7D5TAMHBJ0CZFQOxxKuYdIYCaEEL3kzJlC7omvESj5kMbP7iRY+mGn/aGyjwmVfYwtbSxJe5+De8Qx6LaeD3sSYqALbH6rU4Kd532HU2FmMczZwu9t16BriuiwE/kg9efc90orRQ2xocTDUmxcPSOVo0d5tn/zI1CJVr0Uo+4ztFADyjMUM2Mqkb1/tcNALBAx2VTv5/s6Hxvq/Gys99McjA05THXZGZrqYkSGm8PHZjAqLYkMrw2vy46jrcerfZihYzdmjRNCDAwSmAkhxE5yDzsU97BDCZZ9SuOye37QgwbRxvU0Lr6Jps/vwzPqeLx7nYw9fVyCaitE39O47N7444gyeLjlRGyayV9SbydZD1A5/HJ+VTyXxUsaAEh16lw+LZXT9k7CYXQRkEV9aDXL0GuWxBaedmZiZUwjstdlkDQC3ZG8VSDWGIhQVN3C2upW1tf6KG0KYloWDkOnINXFiHQP04am8tP9cslNdnUKvqTXSwixK0hgJoQQu4ir4GByT3yNUNWXNH31IL51L4Ay4/tVpAXf2mfwrX0Ge+Y+eEYei3vEMRie7ATWWojECpR+TKj80/j2S/5DKTdzuCn1n+zjKuVZ52+4eck0TBXCrsNZ+yRz0dRUUp1bZExTFlrzerSqjzEaVoBmYKbvhznsJ7FkHY5UsHnQDTe+qGJ9TSvfVTewrqa0LQBTJLtsFKa5GZ3l4aR9hzAq3Uua2x4fdugwpOdLCLF7aUoptePDRHc1NzeTmppKU1MTKSkpia6OECKBoi0lNH/9d5pX/RMVatzGURqO3Om4C4/CNfRQbN68PVnFXlFKEW1cj2/TItxDDiB5n3N69Hz5P9k3fP7559xyyy0sWbKEcDjMxIkTufrqqznjjDN69Do7+/usXHAsgeK3ATCVzlFVf2asrYQHs//OZfW/5iPfeADmjHDzywPTGN6+FlmkBa16CUb1J2jBaqykkVhZB2BlzUD3ZBHBzaYWxZqaIKurWtnU4CcctXA7DEZleBiT5WVctocR6V5SXDaSXDbp/RJC7HI9+R8pPWZCCLGb2JKHkTHrHtIOvAnf+hdoWfU4ofLFPzhKEa5cRrhyGU2f3Yk9fTyuoYfizD8IR9YkNKMH2eV2IyvURKhyGcGyTwiVf4LprwIgWremx4GZSLwPPviAo48+GofDwc9+9jNSU1NZsGABZ555Jps2beI3v/nNHqlHqPKLeFAG8HrgIMLKznVpz/KTijvZEB3GXhl2bpyZzgH5TjTfZrSiDzFqv4j1imVMIzL6PJqdI1nXZGNlTZQ160PU+yvRNY3haS7GZHk5ZnwWe2UlxXvA3A5DAjAhRJ8jPWa7mNwJFkJsT7juW1q+/Re+tc9h+sq3f7DuwJG9L84h+2PP3AdH5gR0d85uz/CoLJNoawmRutWEq78iVPUl0cYNQBfNhW6n8NLK2JydbpL/k4kVjUYZP348paWlLFmyhClTpgDQ0tLCQQcdxNq1a1m9ejVjx47t1uvtzO9z44snQsnC+PaxlfczP+lV7m8+k4A9m19MT+bUId/jqHoPvXkdypNPQ8pBfBWdzKpmL2vqo/ij4HXYGJvlZXxOEhOHeBmW5sHrsuOWIYhCiASTHjMhhOijHJkTyfzRfWTMupdg2af41v4X34YFWIHarQ+2woSrviBc9UW8SHdlYE8fhy2lEFvycIyU4di8eeiuTHRnGprevX/ryoxghRox/ZVEW0oxW8uItpQQaVxPtHE9Khro1usYSQVEmzbiyN63W8eLxHvvvfcoKiri/PPPjwdlAMnJydx000387Gc/44knnuDuu+/erfXwV6/sFJS9HZjORMdG7miazzHDo1yT8S9SGksobR7HYnMmHzWfSaDeRqrbwbghaexXmMzp073kp7qlF0wIMSBIYCaEEAmgaTruobNwD51F5uH/j1DlF/g3voa/aCGRupXbfJ4VrCdUsYRQxZKuXjUWnNm9aIYDzXChGXaUZYIVQVlRlBnECjWiIr7eVVx34MqfiWfUcbhHHI09fS9Zo62f+eCDDwA46qijttrXXvbhhx9utW9XW/zazYzeYvtV/8GUqkLuzXuORmsId1YcS8hVwPghSUzKz+COvHTyUjqCMLf0ggkhBhgJzIQQIsE0TceVNwNX3gwyZt6O6asiWPYxgdKPCJZ+SKT+u26+ksIKNUCoYdfVzXDhGDINZ/4huIcegqvgEHS7d5e9vtjz1q9fD9DlUMX09HSysrLix3QlFAoRCoXi283NzT2uw5JVXzKiaRG0xfRLghPx24cwc2g2oYLrOWhYJidnZJDs9kgQJoQYNCQwE0KIPsbwDsG71yl49zoFACvUTLj2G0LVywlXLydcv4Zo4wasbWZ67B3dk4M9bSz27P1w5UzGkbUvjqx90AzHLj2PSKympiYAUlNTu9yfkpJCaWnpNp9/zz33cNttt+1UHepWPEmuZsW3G4bP54k5p+B2ppLkduCWIEwIMQhJYCaEEH2c7kzBVRDrrdqSGawn2lhE1FeB6a/G9FdhBWoxwz6UGURF/SgzjKbb0HQ76A40mwubOxPdlYnhzsLw5GBLHYktZQS63ZOgn1D0J7/+9a+59tpr49vNzc0MGzasR69x7Bl/5uMP96fl83tJdru5/PQrZEisEGLQk8BMCCH6KcOVgZGbQd9IqC/6i/aesvaesx9qzyC2LU6nE6dz5951mqbzo8POpWLKaXhpkaBMCCEAPdEVEEIIIcSe0z63rKt5ZA0NDdTW1nY7Vf7Oykt1k5Kas0fOJYQQfZ0EZkIIIcQgcuihhwLw1ltvbbWvvaz9GCGEEHuOBGZCCCHEIHLEEUcwatQonnnmGb7++ut4eUtLC3fccQc2m43zzjsvYfUTQojBSuaYCSGEEIOIzWbjscce4+ijj2bWrFmcfvrppKSksGDBAjZu3Midd97JXnvtlehqCiHEoCOBmRBCCDHIzJ49m08++YRbbrmF5557jnA4zMSJE7njjjs488wzE109IYQYlCQw28WUUkDvFtwUQojBoP3/Y/v/S5EYM2bMYNGiRTv9OtLuCSHEtvWkzZPAbBdraWkB6PGaLkIIMdi0tLRsNy276B+k3RNCiB3rTpunKblluUtZlkV5eTnJycndWpelfWHOkpISUlJS9kAN+y+5Vj0j16tn5Hr1zM5cL6UULS0t5Ofno+uSg6q/60m7J39nPSPXq2fkevWMXK/u21NtnvSY7WK6rjN06NAePy8lJUX+KLpJrlXPyPXqGblePdPb6yU9ZQNHb9o9+TvrGblePSPXq2fkenXf7m7z5FalEEIIIYQQQiSYBGZCCCGEEEIIkWASmCWY0+nklltuwel0JroqfZ5cq56R69Uzcr16Rq6X6A153/SMXK+ekevVM3K9um9PXStJ/iGEEEIIIYQQCSY9ZkIIIYQQQgiRYBKYCSGEEEIIIUSCSWAmhBBCCCGEEAkmgZkQQgghhBBCJJgEZgny+eefM3fuXNLT0/F6vcyYMYNnnnkm0dVKiLKyMh588EGOOuoohg8fjsPhIDc3l5NPPpnPPvusy+c0Nzdz7bXXUlhYiNPppLCwkGuvvZbm5uY9XPu+4b777kPTNDRNY+nSpV0eI9cMXnrpJY488kgyMzNxu92MHDmS008/nZKSkk7HDfZrpZRiwYIFzJ49m7y8PDweD+PGjeOSSy7h+++/3+r4wX69xI5Jm9eZtHs7R9q87pN2b8f6VJunxB73/vvvK4fDoZKSktSFF16ofvnLX6qRI0cqQN11112Jrt4ed8MNNyhAjR49Wl1wwQXqxhtvVCeffLIyDEPpuq7++9//djq+tbVVTZ48WQHqyCOPVDfccIM65phjFKAmT56sWltbE/STJMbq1auV0+lUXq9XAWrJkiVbHTPYr5llWeriiy+Ov88uv/xydcMNN6izzz5bDR8+XH388cfxYwf7tVJKqWuvvVYBKi8vT1166aXq+uuvV0cffbTSNE0lJyerlStXxo+V6yV2RNq8rUm713vS5nWPtHvd15faPAnM9rBIJKJGjx6tnE6n+uqrr+Llzc3NauLEicpms6l169YlsIZ73osvvqg++uijrco/+ugjZbfbVUZGhgoGg/Hym2++WQHq+uuv73R8e/nNN9+82+vcV0SjUTV9+nQ1Y8YMddZZZ22zkRrs1+zPf/6zAtQVV1yhotHoVvsjkUj88WC/VhUVFUrXdTVixAjV1NTUad8DDzygAHX++efHywb79RLbJ21e16Td6x1p87pP2r3u6WttngRme9ibb7651S+53bPPPqsA9etf/zoBNeubjjrqKAWozz//XCkVuwOUn5+vkpKStrorEQgEVHp6uiooKFCWZSWiunvcXXfdpRwOh1q1apU699xzu2ykBvs18/v9KiMjQ40aNapTQ9SVwX6tlFJqyZIlClBnnnnmVvvWrVunAHXssccqpeR6iR2TNq/npN3bNmnzukfave7ra22ezDHbwz744AMAjjrqqK32tZd9+OGHe7JKfZrdbgfAZrMBsH79esrLyzn44IPxer2djnW5XPzoRz+irKyMDRs27PG67mmrVq3itttu43e/+x0TJ07c5nGD/Zq9/fbb1NfXc8IJJ2CaJgsWLODee+/l4Ycf3upnHuzXCmDs2LE4HA4+/fRTWlpaOu17/fXXATj88MMBuV5ix6TN6zlp97ombV73SbvXfX2tzbP1+pmiV9avXw/E3gg/lJ6eTlZWVvyYwa64uJh33nmH3NxcJk2aBGz/+m1Zvn79+m0eMxBEo1HOO+88JkyYwI033rjdYwf7Nfviiy+A2Iec/fbbj7Vr18b36brONddcw/333w/ItQLIzMzkrrvu4le/+hUTJkxg3rx5JCcns3LlSt555x0uvvhifv7znwNyvcSOSZvXM9LudU3avJ6Rdq/7+lqbJ4HZHtbU1ARAampql/tTUlIoLS3dk1XqkyKRCGeffTahUIj77rsPwzCA7l2/LY8bqO6++25WrFjBZ599Fr+7ui2D/ZpVV1cD8Mc//pGpU6eybNkyJkyYwPLly7n44ov54x//yOjRo7nssssG/bVqd91115Gfn88ll1zCQw89FC+fOXMmZ511Vvw9J9dL7Ii0ed0n7d62SZvXM9Lu9UxfavNkKKPocyzL4oILLuCjjz7ioosu4uyzz050lfqUFStWcOedd3LdddcxderURFenz7MsCwCHw8HLL7/M9OnTSUpKYtasWbzwwgvous4f//jHBNeyb7nzzjs577zz+PWvf01JSQmtra188sknRKNRZs+ezYIFCxJdRSEGFGn3tk3avJ6Tdq9n+lKbJ4HZHtYeZW8rmm5ubt5mJD4YKKW46KKLeOqppzjrrLN4+OGHO+3vzvXb8riB6Nxzz2X06NHceuut3Tp+sF+z9p9r//33Jz8/v9O+iRMnMmrUKIqKimhsbBz01wrgvffe46abbuLKK6/kN7/5DUOHDsXr9XLwwQfz2muv4Xa7ueaaawB5b4kdkzZvx6Td2z5p83pO2r3u62ttngRme9iW409/qKGhgdra2gE5hrc7LMti/vz5PP7445x++uk8+eST6Hrnt+j2rt+W5QP5Gq5YsYI1a9bgcrniC2xqmsa//vUvAA466CA0TePll18G5JqNGzcOgLS0tC73t5cHAoFBf60AFi5cCMDs2bO32pednc2kSZMoLi7u9L9qMF8vsX3S5m2ftHs7Jm1ez0m71319rc2TOWZ72KGHHso999zDW2+9xc9+9rNO+9566634MYONZVlceOGFPPHEE/z0pz/l//7v/+Lj67c0duxY8vPz+fTTT/H5fJ2y4gSDQT766CPy8/MZM2bMnqz+HjV//vwuyz/66CPWr1/PvHnzyM7OZsSIEYBcs/Z/tt99991W+yKRCBs2bMDr9ZKdnU1ubu6gvlYA4XAYgJqami73t5c7nc5B/94SOyZt3rZJu9c90ub1nLR73dfn2rxeJ9oXvRKJRNSoUaOU0+lUy5cvj5dvudjm2rVrE1fBBDBNU5133nkKUKeeeuoO19wYzAshbs+21nRRSq5Z+7pAjz76aKfy22+/XQHqrLPOipcN9mv1n//8RwFq4sSJqrGxsdO+J598UgFq2rRp8bLBfr3E9kmb1zVp93aetHnbJ+1e9/S1Nk9TSqneh3WiN95//32OPvponE4np59+OikpKSxYsICNGzdy55138tvf/jbRVdyjbr31Vm677TaSkpL4xS9+EV+7ZUsnnHACkydPBsDn83HIIYfw9ddfc+SRRzJt2jRWrFjBokWLmDx5Mp988slW60sMBueddx7/+te/WLJkCQceeGCnfYP9mhUVFTFz5kyqq6s59thjGT9+PMuXL+e9996jsLCQpUuXkpubC8i1Mk2TOXPm8MEHH5Cdnc28efNIT09nxYoVvP322zidTt555x0OOeQQQK6X2DFp87Ym7d7OkzZv+6Td654+1+btVFgneu2zzz5TxxxzjEpNTVVut1vtv//+6qmnnkp0tRKi/a7X9r6eeOKJTs9pbGxU11xzjRo2bJiy2+1q2LBh6pprrtnqbsdgsr27h0rJNSsuLlbnnXeeys3Njf/8V1xxhaqqqtrq2MF+rYLBoPr973+vpk6dqjwej7LZbKqgoECdccYZauXKlVsdP9ivl9gxafM6k3Zv50mbt2PS7nVPX2rzpMdMCCGEEEIIIRJMsjIKIYQQQgghRIJJYCaEEEIIIYQQCSaBmRBCCCGEEEIkmARmQgghhBBCCJFgEpgJIYQQQgghRIJJYCaEEEIIIYQQCSaBmRBCCCGEEEIkmARmQgghhBBCCJFgEpgJIYQQQgghRIJJYCbEHnLYYYehaVqiq9Ftra2t5OXlcfnll++2c2zYsAGbzcbf//733XYOIYQQe560eVuTNk/siARmQvSCpmk9+uqP7rvvPurr6/n1r3+9284xZswYzjzzTG699Vaam5t323mEEEL0nrR5u4a0eWJHNKWUSnQlhOhvbr311q3KbrvtNlJTU7n66qu7PL64uBi/38/48eN3fwV3UmNjI0OHDuWUU07hySef3K3nWrVqFZMmTeKOO+7gd7/73W49lxBCiJ6TNm/XkTZPbI8EZkLsIpqmUVhYyKZNmxJdlZ32l7/8hauuuop33nmHI444Yrefb/LkyTQ0NLBx40Z0XTryhRCir5M2r/ekzRPbIu8GIfaQrsbbP/nkk2iaxpNPPsmrr77KAQccgMfjoaCggJtuugnLsgB4+umnmTJlCm63m+HDh3P//fd3eQ6lFI8//jgHH3wwKSkpeDwe9t9/fx5//PEe1fXJJ58kMzOT2bNnx8ssy2LkyJFkZmYSCoW6fN6MGTNwOBxUV1dv9fMtXLiQWbNmkZyczIgRIzo977TTTqO4uJh33323R/UUQgjRN0mbJ22e6DkJzIToA1566SVOO+00Ro0axaWXXkpSUhJ33nknN998M3/84x+5/PLLmTRpEhdffDGWZfGrX/2Kp59+utNrKKU466yzmD9/PrW1tZxxxhlceOGF+Hw+5s+fz3XXXdetujQ0NLB8+XJmzJjR6U6erutcdNFF1NfX8+KLL271vJUrV/L5558zb948cnJyOu17/vnnOeGEE8jKyuLyyy9n7ty5nfYfdNBBALz33nvdqqMQQoj+S9o8afPENighxC4BqMLCwm3uP/TQQ9UP/+SeeOIJBSi73a6WLVsWL29ublY5OTnK4/Go3NxcVVRUFN9XXFysHA6H2nfffTu91iOPPKIANX/+fBWJROLloVBIHX/88QpQX3zxxQ5/joULFypA/fa3v91qX0VFhbLZbGr27Nlb7bvqqqsUoBYtWrTVz6dpmnr77be3ec7m5mYFqB/96Ec7rJ8QQojEkzZP2jyx60mPmRB9wJlnnsn06dPj28nJyRx33HH4/X4uu+wyRo0aFd83bNgwDjnkEL799lui0Wi8/K9//Ster5e//vWv2Gy2eLnD4eCuu+4C4D//+c8O61JaWgrAkCFDttqXm5vLvHnz+OCDDygqKoqXh0IhnnrqKYYPH85RRx211fNOOOEE5syZs81zJicn43K54ucWQggxcEmbJ22e6Jptx4cIIXa3KVOmbFWWl5cHxCYJd7XPNE2qqqooKCjA7/ezcuVK8vPzuffee7c6PhKJALBmzZod1qWurg6A9PT0LvdfcsklLFiwgH/+85/cfffdQGxYSn19PVdddVWXE5lnzJixw/NmZGRQW1u7w+OEEEL0b9LmSZsnuiaBmRB9QEpKylZl7XcAt7evvfFpaGhAKUVZWRm33XbbNs/j8/l2WBe32w1AIBDocv+RRx7JyJEjefLJJ7njjjswDIPHHnsMXde54IILunxOV3cifygQCODxeHZ4nBBCiP5N2jxp80TXZCijEANAe0M2bdo0lFLb/Hr//fd3+FrZ2dkA1NfXd7lf0zQuuugiKioqWLhwIRs3buS9997jmGOOYdiwYdt8zvZYlkVTU1P83EIIIcS2SJsnBioJzIQYAJKTk5kwYQLfffcdjY2NO/VakyZNAmD9+vXbPOaCCy7Abrfz2GOP8fjjj6OU4sILL+z1OdevX49lWfFzCyGEENsibZ4YqCQwE2KAuOqqq/D7/Vx00UVdDt/YuHFjtxYCnTRpEhkZGSxbtmybxwwZMoR58+bx+uuv88gjj5Cbm8vxxx/f67p/9tlnABx66KG9fg0hhBCDh7R5YiCSwEyIAeKSSy7h3HPP5YUXXmDs2LGcc8453HjjjZx//vkcdNBBjB49mqVLl+7wdTRNY968eXz77bdUVFRs93ymaVJdXc25557bKStWT7399tsYhsFxxx3X69cQQggxeEibJwYiCcyEGCA0TePJJ5/kv//9LxMnTuS1117jT3/6E2+//TYul4v7779/u+l7t3TJJZdgWdZ2Uw3PmTOHgoICNE3bqSEdfr+fl19+meOPP578/Pxev44QQojBQ9o8MRBpSimV6EoIIfqemTNn0tTUxKpVq7qcyFxeXk5hYSGzZs3ivffe6/V5Hn/8cebPn8+HH37Ij370o52pshBCCNEr0uaJvkB6zIQQXbr//vtZvXo1zz//fJf7H3zwQaLRKJdeemmvzxGNRrn77ruZN2+eNFBCCCESRto80RfIOmZCiC7NnDmThx9+OL5uDEBTUxMPPfQQmzdv5tFHH2XixImcfPLJvT5HaWkpZ511FmefffauqLIQQgjRK9Lmib5AhjIKIbpt06ZNjBw5ErfbzQEHHMDDDz/MuHHjEl0tIYQQYpeTNk/saRKYCSGEEEIIIUSCyRwzIYQQQgghhEgwCcyEEEIIIYQQIsEkMBNCCCGEEEKIBJPATAghhBBCCCESTAIzIYQQQgghhEgwCcyEEEIIIYQQIsEkMBNCCCGEEEKIBJPATAghhBBCCCES7P8DxpB02TiyPKkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Panel at 4 for Kcl and Kof\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 1, ncols = 2, figsize = (10, 2.5), sharex = True)\n",
    "plt.subplots_adjust(wspace = .35, hspace = .2)\n",
    "\n",
    "axs=axs.ravel()\n",
    "j=0\n",
    "k=0\n",
    "for j, etaa in enumerate(eta):\n",
    "    for i, value in enumerate(values):\n",
    "        k=i\n",
    "        i=0\n",
    "        #panel[value][etaa].plot(x=\"$\\tau$\", y=\"I\", ax=axs[i], c=col_pal[k], linewidth=j+.5,legend=False)\n",
    "        #panel[value][etaa].plot(x=\"$\\tau$\", y=\"I$_{CL}$\", ax=axs[i], c=col_pal[j],linewidth=j, linestyle='dashed', legend=False)\n",
    "        #axs[i].set(xlabel=\"Time (yr)\", ylabel=\"$I$ ; $I_{CL}$ (\\$ billion)\")\n",
    "        #axs[i].fill_between(panel[value][200]['$\\tau$'], panel[value][200]['I'], panel[value][500]['I'], color=col_pal[k], alpha=0.3)        \n",
    "\n",
    "        #axs[i].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "        \n",
    "        panel[value][etaa].plot(x=\"$\\tau$\", y=\"[CO$_2$]\", ax=axs[i],  c=col_pal[k], linewidth=j+.5,legend=False)\n",
    "        axs[i].set(xlabel=\"Time (yr)\", ylabel=\"[CO$_2$] (ppm)\")\n",
    "        axs[i].fill_between(panel[value][200]['$\\tau$'], panel[value][200]['[CO$_2$]'], panel[value][500]['[CO$_2$]'], color=col_pal[k], alpha=0.05)        \n",
    "        #axs[i].set_ylim([365, 550])\n",
    "        #panel[value][etaa].plot(x=\"$\\tau$\", y=\"E$_{CL}$\", ax=axs[i+2], c=col_pal[k], linewidth=j+.5, legend=False)\n",
    "        #axs[i+2].set(xlabel=\"Time (yr)\", ylabel=\"$E_{CL}$ (EJ)\")\n",
    "        #axs[i].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 0))\n",
    "        #\n",
    "        panel[value][etaa].plot(x=\"$\\tau$\", y=\"NET\", ax=axs[i+1], c=col_pal[k], linewidth=j+.5,legend=False)\n",
    "        axs[i+1].set(xlabel=\"Time (yr)\", ylabel=\"NET (GtCO$_2$)\")\n",
    "        axs[i+1].ticklabel_format(style = \"sci\", axis = \"y\", scilimits = (0, 3))\n",
    "        axs[i+1].fill_between(panel[value][200]['$\\tau$'], panel[value][200]['NET'], panel[value][500]['NET'], color=col_pal[k], alpha=0.05)        \n",
    "        #axs[i+1].set_ylim([0, 21])\n",
    "#plt.legend(values, bbox_to_anchor = (0.35, -0.15), ncol = len(values), title=\"$I_{max}$ (\\$ billion) \");\n",
    "axs[0].annotate('a',xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "axs[1].annotate('b', xy=(0.9, 0.9), xycoords=\"axes fraction\", fontsize=16)\n",
    "\n",
    "fig.savefig(PATH_SAVE_FIGURES + \"F6.pdf\", bbox_inches = \"tight\", dpi = 300) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (7, 5), sharex = True)\n",
    "\n",
    "for i, value in enumerate(values):\n",
    "    df.plot(ax = ax, style = \"-\", legend = False)\n",
    "    ax.set(xlabel = \"$\\\\tau$ (yr)\", ylabel = \"Max [CO$_2$] (ppm)\")\n",
    "    ax.ticklabel_format(style = \"sci\", axis = \"y\")\n",
    "    #ax.set_ylim([425, 480])\n",
    "    #ax.xaxis.set_visible(False)\n",
    "    #ax.yaxis.set_visible(False)\n",
    "    \n",
    "#plt.legend(values, bbox_to_anchor = (1.05, -0.12), ncol = len(values),  title=\" $\\Phi$ (% E$_0$)\");  #$\\Phi$ (% E$_0$)  ; SCC (\\$ / tCO$_2$) 1.075\n",
    "#fig.savefig(PATH_SAVE_FIGURES + \"F4d.png\", bbox_inches = \"tight\", dpi = 300) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}