{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial VI.1\n",
    "\n",
    "Consider a long-term multi-year investment problem where **CSP (Concentrated Solar Power)** has a learning curve such that\n",
    "\n",
    "$$LCOE = c_0 \\left(\\frac{x_t}{x_0}\\right)^{-\\gamma} + c_1$$\n",
    "\n",
    "where $c_0$ is cost at start, $c_1$ is material cost and $x_t$ is cumulative\n",
    "capacity in the investment interval $t$. Thus, $x_0$ is the initial cumulative CSP capacity.\n",
    "\n",
    "Additionally, there are **nuclear** and **coal** generators for which there is no potential for reducing their LCOE.\n",
    "\n",
    "We build an optimisation to minimise the cost of supplying a flat demand $d=100$ with the given technologies between 2020 and 2050, where a CO$_2$ budget cap is applied.\n",
    "\n",
    "> **Hint:** Problem formulation is to be found further along this notebook.\n",
    "\n",
    "**Task:** Explore different discount rates, learning rates, CO$_2$ budgets. For instance\n",
    "* No learning for CSP and no CO$_2$ budget would result in a coal-reliant system.\n",
    "* A CO$_2$ budget and no learning prefers a system built on nuclear.\n",
    "* A CO$_2$ budget and learning results in a system with CSP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyomo.environ import ConcreteModel, Var, Objective, NonNegativeReals, Constraint, Suffix, exp\n",
    "from pyomo.opt import SolverFactory\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('bmh')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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></th>\n",
       "      <th>coal</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>CSP</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>current LCOE</th>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>LCOE EUR/MWh_el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specific emissions</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>tCO2/MWh_el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential LCOE</th>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>EUR/MWh_el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current volume</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>GW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lifetime</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>existing age</th>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>existing capacity</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         coal    nuclear    CSP             unit\n",
       "current LCOE             50.0      100.0  150.0  LCOE EUR/MWh_el\n",
       "specific emissions        1.0        0.0    0.0      tCO2/MWh_el\n",
       "potential LCOE           50.0      100.0   35.0       EUR/MWh_el\n",
       "current volume      1000000.0  1000000.0  200.0               GW\n",
       "lifetime                 40.0       40.0   30.0            years\n",
       "existing age             20.0       30.0    0.0            years\n",
       "existing capacity        75.0       25.0    0.0               GW"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techs = [\"coal\",\"nuclear\",\"CSP\"]\n",
    "colors = [\"#707070\",\"#ff9000\",\"#f9d002\"]\n",
    "parameters = pd.DataFrame(data=[[50.,100.,150.,\"LCOE EUR/MWh_el\"],\n",
    "                                [1.,0.,0., \"tCO2/MWh_el\"],\n",
    "                                [50.,100.,35., \"EUR/MWh_el\"],\n",
    "                                [1e6,1e6,200,\"GW\"],\n",
    "                                [40,40,30,\"years\"],\n",
    "                                [20,30,0,\"years\"],\n",
    "                                [75,25,0,\"GW\"]],\n",
    "                          index=[\"current LCOE\",\"specific emissions\",\"potential LCOE\",\"current volume\",\"lifetime\",\"existing age\",\"existing capacity\"],\n",
    "                          columns=techs+[\"unit\"])\n",
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "#discount rate\n",
    "rate = 0.05\n",
    "\n",
    "#demand in GW\n",
    "demand = 100.\n",
    "\n",
    "#learning rate of CSP\n",
    "gamma_csp = 0.333\n",
    "\n",
    "# carbon budget in average tCO2/MWh_el\n",
    "co2_budget = 0.2\n",
    "\n",
    "# considered years\n",
    "years = list(range(2020,2070))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model\n",
    "> **Note:** We use [`pyomo`](https://pyomo.readthedocs.io/en/stable/) for building optimisation problems in python. This is also what `pypsa` uses under the hood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ConcreteModel(\"discounted total costs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$G_{t,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.generators = Var(techs, years, within=NonNegativeReals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.generators_dispatch = Var(techs, years, within=NonNegativeReals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Q_{t,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.generators_built = Var(techs, years, within=NonNegativeReals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$LCOE_{t,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.costs = Var(techs, years, within=NonNegativeReals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective is to minimise the system costs:\n",
    "\n",
    "$$\\min \\quad \\sum_{t\\in T, a\\in A} G_{t,a}\\cdot LCOE_{t,a} \\cdot \\frac{8760}{10^6\\cdot (1+r)^{t}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in billion EUR\n",
    "model.objective = Objective(expr=sum(model.generators[tech,year]*model.costs[tech,year]*8760/1e6/(1+rate)**(year-years[0])\n",
    "                                     for year in years\n",
    "                                     for tech in techs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a constraint such that demand is met by generator dispatch:\n",
    "\n",
    "$$\\forall a\\in A: \\quad d = \\sum_{t \\in T} G_{t,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "def balance_constraint(model, year):\n",
    "    return demand == sum(model.generators_dispatch[tech,year] for tech in techs)\n",
    "model.balance_constraint = Constraint(years, rule=balance_constraint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_constraint(model, tech, year):\n",
    "    return model.generators_dispatch[tech,year] <= model.generators[tech,year]\n",
    "model.generator_constraint = Constraint(techs, years, rule=generator_constraint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a constraint on carbon dioxide emissions:\n",
    "\n",
    "$$\\sum_{t\\in T, a\\in A} G_{t,a} \\cdot e_{t} \\leq \\hat{e} \\cdot |A| \\cdot d$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "def co2_constraint(model):\n",
    "    return co2_budget*len(years)*demand >= sum(model.generators_dispatch[tech,year]*parameters.at[\"specific emissions\",tech] for tech in techs for year in years)\n",
    "model.co2_constraint = Constraint(rule=co2_constraint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lcoe_constraint(model,tech,year):\n",
    "    if tech != \"CSP\":\n",
    "        return model.costs[tech,year] == parameters.at[\"current LCOE\",tech]\n",
    "    else:\n",
    "        return model.costs[tech,year] == parameters.at[\"potential LCOE\",tech] + (parameters.at[\"current LCOE\",tech]-parameters.at[\"potential LCOE\",tech])*(1+sum(model.generators[tech,yeart] for yeart in years if yeart < year))**(-gamma_csp)\n",
    "model.lcoe_constraint = Constraint(techs, years, rule=lcoe_constraint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_years(model,tech,year):\n",
    "    if year < years[0] + parameters.at[\"lifetime\",tech] - parameters.at[\"existing age\",tech]:\n",
    "        constant = parameters.at[\"existing capacity\",tech]\n",
    "    else:\n",
    "        constant = 0.\n",
    "    \n",
    "    return model.generators[tech,year] == constant + sum(model.generators_built[tech,yearb] for yearb in years if ((year>= yearb) and (year < yearb + parameters.at[\"lifetime\",tech])))\n",
    "model.build_years = Constraint(techs, years, rule=build_years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Hint:** You can print the model formulation with `model.pprint()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = SolverFactory(\"ipopt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = opt.solve(model,suffixes=[\"dual\"],keepfiles=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimised cost (in billion euros NPV):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1021.5906275691523\n"
     ]
    }
   ],
   "source": [
    "print(model.objective())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The unoptimized cost (where everything is supplied by coal) is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2190.0\n"
     ]
    }
   ],
   "source": [
    "print(8760*demand*parameters.at[\"current LCOE\",\"coal\"]*len(years)/1e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "dispatch = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        dispatch.at[year,tech] = model.generators_dispatch[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "dispatch.plot(kind=\"area\",stacked=True,color=colors,ax=ax)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"capacity [GW]\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "#fig.savefig(\"co2-0p2-learning.pdf\",transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the development of the technology mix of the optimal solution over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "capacities = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        capacities.at[year,tech] = model.generators[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "capacities.plot(kind=\"area\",stacked=True,color=colors,ax=ax)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"capacity [GW]\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(\"co2-0p2-learning.pdf\",transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "build_years = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        build_years.at[year,tech] = model.generators_built[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PfvazGB4e9m3crWhmZkbt1QS3fRrMrPgzkBamOR9kH/NBblRnY9tLLHKAMYDH1X2pAQrkekSyzyKa+V6oP9MYg6uvvhrveMc78MUvfhEA8B//8R+49dZbMTY2hvvvvx+RSATHjh1bs9j/tttuw8DAAG666SZ86lOfws033xzquJuN6j7e2/6Un/FlGK1MdT7IOuaD3KjOxnbPTgIAsgDiPgymealfYqH1UtP33XcfYrEYrr322tJtZ511Fjo6OjAyMoJIJP+f9pRTTql5PfcLL7ywdFlp2jq1p8GMgWxznRh3Gm+f2nyQCswHudGcDV+W33EJ34bUF8ha+yA/+eSTqL7CHwBceeWV+M53voN9+/bhQx/6EH74wx/WfPx3v/tdnHnmmUEPs+lNTEzYHoKL/PIIg+2cwuI6se3Smw/SgPkgN6qz4UNxKw43gW9EfYGsudVKLaeccgq+//3v48Mf/jAikQiuvPJK3HvvvaX73/KWt2Dfvn1YWFjAb//2b1scaXPo6uqyPYTaSj2Qt/G/GGeQt01tPkgF5oPcaM6GbLfHPsAZ5DroXL/QAM444wzcdtttNe9rb2/HZZddhssuuwxDQ0P49re/jde85jUAymuQqbmVT4FxBpmIiHzEJRahCGUGWUQSIvJ9EXlcRH4kIn9UuP2jInJMRB4r/HlT9WO19kHet28fVlZWcMstt5RuO3LkCB544AGMjY0BABzHwY9//GPs2rXL1jCbXiqVsj2E2owPM8jFnca0ZWrzQSowH+RGcza2f6EQthGtR1gzyCsALjXGpEQkBuB+EfnXwn2fMsb8L7cH1tMH2Ymd7s8oN/F8IoKvfOUr+OAHP4g///M/R3t7O3bv3o39+/fjQx/6EFZW8mtQX/nKV+LgwYO+jo/KtLbJ8+PNhzuNt09rPkgH5oPcqM6GHxeR4hrkDYVSIJt8v5Tix7FY4U9dU2P19EGup2dxEHbu3Ikvf/nL627/jd/4jZrf//jjjwc9pJYzOTmJU0891fYw1vNliQXyb4TCAnmr1OaDVGA+yI3abBjjzyY9M+fDYJpbaJv0RCQqIo8BOA7gTmPMQ4W7rheRH4rIl0SkL6zxUHMQrY3OfTp95ctmjBamNh+kAvNBbvRmIwuBs80OSQAcxZfSViK0TXrGmByAc0RkB4BbRWQvgM8CuAn52eSbAHwCwHWVjztx4gQuuugitLW1IZfL4cCBA7j22muxsrKCSCQCEUEulyvdD+R7J2ez2VIHjFwuh1gsVpqNjkajWF1dRTQahTEGjuMgFoshm81CROq+v62tDY7jrLk/EokgEomU7s/lcjDGrLm/eswb3b/V15TL5TA6OopYLIbe3l5MTU2ht7cXmUwG6XQaIyMjGB8fRzweR3d3N6anp9HX14d0Oo3l5eXS/YlEAslkErOzsxgYGMDCwgIymUzp/mQyiXg8jrm5OQwODmJubg7ZbLZ0f2dnJ6LRKObn5zE0NISZmRkYYzA0NISJiYnSbuFUKoXh4WFMTk5CRNDf34/JyUn09PQgl8thcXGx9JzF17S8vIwTJ06oe02DieNIAljKnoSIrGBscR8iksVQ8mGMLe7DjvansGqSSGX2YFfXHTiauhyx6AL625/AxNKFeEnP1xGNZPDisWfQP9Kv4jVt5/dkK3ttbW2YnZ1tqtfUjL8nW68pkUhgdHS0qV5TM/6ebLymrq4ujI6OqntNx8efxhkCGBPFs3NvxXDHg5hZ2Ytsrrt0LOmKj6JN0jixcgZ2dh7GZPp8OCaGnZ2HkXU60BP/GZYWJjE+PariNdnOnhuxcbUYEflDAIuVa49F5DQAtxtj9lZ+7z333GOq+w3Pz8+jp6cnhJE2tlb47zQ6Ooo9e/bYHsY60cVvIDH9bhi0Q7D1S0anR+6BE9+78TdSTVrzQTowH+RGazZkdQwdL54FgzYItt4KNNPzu8juuNHHkTWuI0eOPLJ///7zqm8Pq4vFUGHmGCKSBPA6AE+JyM6Kb7sKwBPVj63VB1lEkMnwMrxeMpmM4lNE/tH6AcCPXcYA2Ipnm7Tmg3RgPsiN2myUji3bO76LWdj+WJpcWEssdgK4RUSiyBfl/2SMuV1EviIi5yC/xOJnAN5Tz5N1dXUhlUphedmnIqQJiYjqRud+0doGsLzLeJtnaLjTeFvU5oNUYD7Ijdps+LUB3NHbxk6LsLpY/BDAuTVuv3qjx9YKqYigu7vbn8FRQ1tcXMTg4KDtYaxX+pTvbOtpxHAjxXaozQepwHyQG63ZEJ9mkOFwBnkj6i81XU8fZGpdIyMjtodQU7kP8vYKZOTYimc7tOaDdGA+yI3abPjRAxlcYlEP9QVyNpu1PQRSbHx83PYQaisUyMIZZKvU5oNUYD7IjdZs+HUFPOESiw2pL5BbYaMZbZ3WMwz+vYmxQN4OrfkgHZgPcqM2G75tAOf+lo2oL5BrdbEgKvLqYWiVXwUyT4Nti9p8kArMB7nRmg3fJl/YIWlD6gvkei41Ta1ramrK9hBq8u0KeNxIsS1a80E6MB/kRm02HJ86JPk1E93E1BfInEEmL1o/5fv25sN1YtuiNh+kAvNBbtRmo3Rs2W6BvPULWLUK9QWyjSv9UeNQe8EYrkFWQW0+SAXmg9xozYZfHZLE8Oz8RtQXyI6zzTZZ1NTSaZ3rqMS3VjycQd4OrfkgHZgPcqM2G6UCebsXMmGBvBH1BbLanaSkgt5eldxprIHafJAKzAe50ZoNKbUQ3SbOIG9IfYHMPsjkpfl7VSqdxWgQWvNBOjAf5EZtNnzrPqH0UtqKqC+QIxH1QySL4vG47SHU5tubGHcab4fafJAKzAe5UZsNFsihUV99skAmL93d3baHUJtfb2JG50aRRqE2H6QC80FutGZDfFq+JzCAYZHsRX31yT7I5GV6etr2EGrya2kEdxpvj9Z8kA7MB7lRmw3Hx30pvFiIJ/UFcltbm+0hkGJ9fX22h1Cbb288LJC3Q20+SAXmg9xozYavV8BjgexJfYHMNm/kRWUrHrMKQXa7bdxLz0VbpzIfpAbzQW7UZsPHK+DxctPeWCBTQ1teVriJrfSm48f/Xlwjth0q80FqMB/kRm02/Cxq/Vyu0YTUF8jsg0xeVPaqZIGshsp8kBrMB7nRmg2/LkLl93M1I/UFMvsgkxeNvSrLu4y33cq9sNOYyyy2SmM+SA/mg9yozYaPSyy4Btmb+gKZbd7ISyKRsD2E9UpvOtsvkNc+H22WynyQGswHudGaDT/XDYuT8u25mpH66pMFMnlJJpO2h7COlNZ1sUC2TWM+SA/mg9yozYafV1d15vx7riakvvpkH2TyMjs7a3sI6/l5CgzcabwdKvNBajAf5EZlNowDwYpvTyeGBbIX9QUy+yCTl4GBAdtDWMf3gtbPGYMWozEfpAfzQW5UZqNwbDE+lW7izPvyPM1KfYHMNm/kZWFhwfYQ1vN5DTJ3Gm+dynyQGswHuVGZDb/3tzgKX6MiLJCpoWUyGdtDWK/0JubLpUK4BnkbVOaD1GA+yI3GbJQ7JPk1g8xNel7UF8jsg0xeNPaq9HuJhfBT/pZpzAfpwXyQG5XZ8L1DEgtkL+oLZPZBJi8qe1WWPuX7dPaD68S2TGU+SA3mg9xozIb4vXyPxxZP6gtktnkjLxpb8ZTfxPwpkMWc8OV5WpHGfJAezAe5UZkNv5fbGZ6d9KK++hTx6VQCNaV4PG57COuV+iD7c5lofsrfOpX5IDWYD3KjMhs+dzQSZ9HX52s26gvkXM6fIoOa09ycwj6OhSUWvn204xrkLVOZD1KD+SA3GrPh+/4WbgD3pL5AZh9k8jI4OGh7COv4v0mPGym2SmM+SA/mg9yozIbvSyxYIHtRXyBzBpm8aPyU7/ubDgvkLVOZD1KD+SA3GrPh+4yv8e+qfM1IfYFsjE+9ZKkpaexy4v9pMH1v1I1CYz5ID+aD3KjMhs8dkgSrvjxPs1JfILMPMnnR3avSr+fjRoqtUpkPUoP5IDcas1G+qqpfF6FigexFfYGs8lMcqaG7V6VPz8edxlumMR+kB/NBblRmo3Rs8WvpKQtkL+oL5Gg0ansIpFhnZ6ftIaxXOg3mD+403jqV+SA1mA9yozIbpQ5JnEEOg/oCmciLyg9QpT7IPmGBvGUq80FqMB/kRmM2xOc+yL5d7bVJqS+Q2cWCvMzP67uIBnca66ExH6QH80FuVGbD+Dz5ghzARgiu1BfI3KRHXoaGhmwPYT3fl1hwHf5WqcwHqcF8kBuN2RC/jy2A78erZhJKgSwiCRH5vog8LiI/EpE/KtzeLyJ3isjThb/7qh+7uso1MuRuZmbG9hDW8X/NMP8f2CqN+SA9mA9yozIbQSy34xI+V2HNIK8AuNQYczaAcwC8QUQuAHAjgLuMMT8H4K7Cv4nqprJPNgtkNVTmg9RgPsiNymwEUMxyE7i7UApkk1e8HFis8McA+FUAtxRuvwXAldWP5aWmyYu602DGBNAHmevwt0pdPkgV5oPcaMxGIMWs7+uam0doa5BFJCoijwE4DuBOY8xDAIaNMWMAUPj7pOrHsQ8yeZmYmLA9hCorEBiY/Ooun7BA3ip9+SBNmA9yozIbnEEOVWjTs8aYHIBzRGQHgFtFZG89jztx4gQuuugitLW1IZfL4cCBAzh06BDGx8fR2dmJaDSK+fl5DA0NYWZmBsYYDA0NYWJiAl1dXQCAVCqF4eFhTE5OQkTQ39+PyclJ9PT0IJfLYXFxESMjIxgfH0csFkNvby+mpqbQ29uLTCaDdDpduj8ej6O7uxvT09Po6+tDOp3G8vJy6f5EIoFkMonZ2VkMDAxgYWEBmUymdH8ymUQ8Hsfc3BwGBwcxNzeHbDZbup+vaXOvKZ1O48SJE2pe00kDbegEYEwbJtPnYD5zOk7puhtji/sQkSyGkg9jbHEfdrQ/hVWTRCqzB7u67sDR1OWIRRfQ3/4EJpYuRH/iCSzn+nBy530QOBgd/Rni8faG/T3Zyp7jOJidnW2q19SMvydbrykajWJ0dLSpXlMz/p5svKZ4PI7R0VFVr+nlbfnZ3uNLv4i2yApmlvdiuONBzKzsRTbXXTqWdMVH0SZpnFg5Azs7D2MyfT4cE8POzsM4lroUPfFnsaP9ScSjKWSW5zA28ULD/p78yJ4bsbHORkT+EMAigIMALjHGjInITgD3GGNeXvm99913n9m7t65amlrQ9PQ0BgYGbA+jRFbH0PHiWTBo8/U694u7XgAiSd+er1VoywfpwnyQG43ZSB57JSK55319zvTQP8FJXurrczaaI0eOPLJ///7zqm8Pq4vFUGHmGCKSBPA6AE8BuA3AOwvf9k4A36x+LPsgk5dUKrXxN4WptJ7L5/+1eBpsS9Tlg1RhPsiNxmwEsRxCHIX9npUIa4nFTgC3iEgU+crhn4wxt4vIgwD+SUTeBeB5AG+rfiD7IJOX4eFh20NYI6j1XGLSfl1ctKVoywfpwnyQG5XZCGJDnTPn/3M2iVAKZGPMDwGcW+P2aQD7vR7LPsjkZXJyEqeeeqrtYZSVmq77uUkP3Gm8ReryQaowH+RGXTaMCeSiHpxBdqf+SnpEXkR8LkS3K6BCljuNt0ZdPkgV5oPc6MtGFoKczx2SADELvj5fM1FfILMPMnnp7++3PYQ1AitkHc4gb4W2fJAuzAe5UZeN0rHF5wKZM8iu1BfI7INMXiYnJ20PYa2ArmsvZjGQ52126vJBqjAf5EZbNsqTLz6XbY6+zYhaqC+Qo9Go7SGQYj09PbaHsIY4Qc0gcyPFVmjLB+nCfJAbddlwAppB5hILV+oLZCIv6toAlj7l+9xzggXylqjLB6nCfJAbddkIagM4l1i4Ul8gqwspqbK4qGzpQalAdnx9Wq4T2xp1+SBVmA9yoy0bEtgaZF2vUxP1BTL7IJOXkZER20NYQ4IqkHkabEu05YN0YT7IjbpsBNbqkxvA3agvkLlJj7yMj4/bHsJapdNgfs8gs0DeCnX5IFWYD3KjLRsS1AbwoPbNNAH1BbK+XoSkibYzDFL4lO97arnTeEu05YN0YT7IjbpsBNYLfyWg52186gtkdrEgL729vbaHsFZgl5rmDPJWqMsHqcJ8kBtt2Qisx77JBPO8TUB9gcxLTZOXqalHAx1wAAAgAElEQVQp20NYywnmNBi4xGJL1OWDVGE+yI26bATUIUkMayw36gtkziCTl1b5lM+dxlujLR+kC/NBbtRlwwmohShYILtRXyAb43cYqJlkMspODwV2Gow7jbdCXT5IFeaD3GjLRlAdkmDYSteN+gLZcXwOAzWVdFrXDtzAZpAD26DR3LTlg3RhPsiNumyUulj4XdByBtmN+gJZ3U5SUkVfr0ruNNZEXT5IFeaD3GjLRmAdkvyekW4i6gtk9kEmL/p6VXKnsSba8kG6MB/kRl02gjo7CQMY1lm1qC+QIxH1QySL4vG47SGsFVQzd+403hJ1+SBVmA9yoy4bAR1b8s+tbDmJEuqrTxbI5KW7u9v2ENYKbDMdC+StUJcPUoX5IDfashHkPhThJvCa1Fef7INMXqanp20PYY3ALtvJncZboi0fpAvzQW7UZcMJsIjl5aZrUl8gt7W12R4CKdbX12d7CGsFdhqMHxS3Ql0+SBXmg9xoy4ZwiUXo1BfIbPNGXlS14jGrEGR8b+Oex/8PtkJVPkgd5oPcqMtGoEsslL1WJVggU0NbXg7wU/Vmld5k/P/fijuNt0ZVPkgd5oPcqMtGkEVskMs3Gpj6Apl9kMmLql6VpVNgAf1vxU/5m6YqH6QO80FutGUj2E16C4E9dyNTXyCzDzJ50dSrsvwG5n8rdwAQZzGQ521mmvJB+jAf5EZdNgKdQT4R3HM3MPUFMtu8kZdEImF7CGUBF8icQd48VfkgdZgPcqMtG4HOIDtzgT13I1NffbJAJi/JZNL2EEqCnkFmgbx5mvJB+jAf5EZdNgLsYiHOfGDP3cjUV5/sg0xeZmdnbQ+hLOBektxpvHmq8kHqMB/kRlU2jBNomzeuQa5NfYHMPsjkZWBgwPYQSgIvYLnTeNM05YP0YT7IjapsFI4tJqizk04qmOdtcOoLZLZ5Iy8LC4o++ZY+4Qe0Sc/wNNhmqcoHqcN8kBtV2Qi6Q5Kj6LUqwgKZGlomk7E9hLLS9eyDuVQIuJFi01Tlg9RhPsiNpmxIgD3288/PArkW9QUy+yCTF029KgO9FCi4kWIrNOWD9GE+yI2qbAS9AZxLLGpSXyCzDzJ5UdWrsvQmFsxZDxbIm6cqH6QO80FuNGUj8B77hvtbalFfILPNG3nR1IpHgi6QeRps0zTlg/RhPsiNqmwEvAGcHZJqU199igR0SoGaQjwetz2EslKbt1xAz8/TYJulKh+kDvNBblRlI+Dle4E/f4NSXyDncgEVG9QU5uYUbVwrfAoP7CMddxpvmqp8kDrMB7nRlA0JusWnWQn2+RuU+gKZfZDJy+DgoO0hlAR9mopt3jZPUz5IH+aD3KjKRuDHFu71qkV9gcwZZPKi6VN+4KepnMVgn78JqcoHqcN8kBtN2Qi6QxLAKxbXor5ANiagnrLUFDR1OQl6J7AYFsibpSkfpA/zQW5UZSPgDeCB7ZtpcKEUyCJyqoj8m4g8KSI/EpH3Fm7/qIgcE5HHCn/eVP1Y9kEmLzp7VQaDO403T1U+SB3mg9xoyoYEfREqwwK5lrBmkFcB/I4x5hcAXADgkIicWbjvU8aYcwp/vl39QFWf4kgdXb0qudNYG035IH2YD3KjKhul9/6gClkWyLWEsgPOGDMGYKzw9YKIPAnglHoeG41GgxwaNbjOzk7bQygLeobX6Ln0aaNQlQ9Sh/kgN6qyUeqQFMwMssABjAOI+lW3oQr9v4aInAbgXAAPFW66XkR+KCJfEpG+sMdDjU3VB6iAW/Fwp/HmqcoHqcN8kBtN2RAnhOV1XMK3Tqg91ESkC8DXANxgjJkXkc8CuAn5hTU3AfgEgOsqHzM1NYWLLroIbW1tyOVyOHDgAA4dOoTx8XF0dnYiGo1ifn4eQ0NDmJmZgTEGQ0NDmJiYQFdXFwAglUpheHgYk5OTEBH09/djcnISPT09yOVyWFxcxMjICMbHxxGLxdDb24upqSn09vYik8kgnU6X7o/H4+ju7sb09DT6+vqQTqexvLxcuj+RSCCZTGJ2dhYDAwNYWFhAJpMp3Z9MJhGPxzE3N4fBwUHMzc0hm82W7udr2txreuGFFyAiKl7TaU4KUQCj829Esm0KADCfOR2ndN2NscV9iEgWQ8mHMba4Dzvan8KqSSKV2YNdXXfgaOpyxKIL6G9/AhNLF6I/8QSWc/1Yyp6MU7u+jfa2eazmIkilUg35e7KVvaWl/IeWZnpNzfh7svWaUqkU5ufnm+o1NePvycZrymQymJ+fV/GaEosz6AWQzXVgdOEt6Ii9iER0BjPLezHc8SBmVvYim+suHUu64qNokzROrJyBnZ2HMZk+H46JYWfnYRxLXYqe+LOl49PpvV+FiMHYsWfRO/Cyhvs9+ZE915o1rC4RIhIDcDuAO4wxn6xx/2kAbjfG7K28/YEHHjBnnnlm9bcTAQCWlpbQ0dFhexgAgOSxcxDJHQ3s+Q0SWNod3PM3I035IH2YD3KjKRvtU+9B29LXAv0ZSyc/CtN2aqA/Q6sjR448sn///vOqbw+ri4UA+CKAJyuLYxHZWfFtVwF4ovqxq6vsz0fuZmZmbA+hJPguE9xIsVma8kH6MB/kRlU2wlj+wCUW64S1xOIiAFcD+A8Reaxw2wcBvENEzkF+icXPALwnpPFQk1DVJ5vN3NVRlQ9Sh/kgN6qyEUIHI3EWg2oi17DC6mJxPwCpcde6tm7VeKlp8jI0NGR7CHnGAEFfKAQm369S9Gwe0U5NPkgl5oPcaMpG0BehAsAZ5Bpcq08Ruc7tviqrxpi/9Wk867APMnmZmJjAnj17bA8DwAoEBgYSWCseAPk3MekK7vmbjJ58kEbMB7lRlY0QildxUoH/jEbjNT371wDuq+M5zgcQWIGsqdUK6VPcuWpd6RRYBIGuFTZpAEpecwNQkw9SifkgN5qyEfhFqADAzAX/MxqMV4GcNsa8dqMnEJFZH8dD1JDKfSprrSTy8eeYNNeJERG1kjD6IDsskKt5dbF4ZZ3Pcb4fA3GTy3HnPrlLpZScFiqtEQu4MQzXiW2KmnyQSswHudGUjeA7JAHiLAT+MxqN69HcGPO0iJy00RMYY57xd0hrxWKxIJ+eGtzw8LDtIeSFcQoMgAR8tb5moyYfpBLzQW5UZYMFshUbTXeNi8hTIvLXIvJrIhJ6F2n2QSYvk5OTtocAoPITfrBLLDiDvDla8kE6MR/kRlU2wiiQDQvkahsVyLsAfBRAFsAHAPxMRH4qIreIyLtE5OeCHiCRl/w1aBQIqXDlTuPNUZMPUon5IDdqsmGyEORggp584bFlHc8mw8aYFwH8Y+EPRKQPwKsB7APwxwCGAATaZoJ9kMlLf3+/7SEACGeNGADuNN4kLfkgnZgPcqMmG2vOTga3RVuc+cCeu1HVvaNIRM4G8GsArgHwXwEcB/C5gMZVwj7I5EXNabCwCmTuNN4UNfkglZgPcqMlGxLaBnDOIFfznJ4Vkd8F8BoA5wF4BsD9AL4M4N3GmBPBD499kMlbT0+P7SEAAMQJZ5NexJkLssty09GSD9KJ+SA3arJR2gAecAtRLrFYZ6P1Cx8H8CSAjwC40xjzs8BHRLQJatoAlj7lB9ylmDuNN0VNPkgl5oPcqMlGaD322SGpWj2b9G4CcA6Ab4nI8yLy9yLy30Rkb/DDUxRSUmlxcdH2EPJKSyycQH8MP+Vvjpp8kErMB7nRko3wOiSFcxa0kWxnk95HRCRujBkIcoDsg0xeRkZGbA8BQOWlQIMtkLlObHO05IN0Yj7IjZpshLYBfCWcn9NAtrJJ7+rCn34APwloXCXcpEdexsfHbQ8hL7QZZO403gw1+SCVmA9yoyUbYXVIErDWqlbvJr2LACQAPATgMPLdK75nTPC/OTW9CEklLWcYiuu3Ak8rZ5A3RUs+SCfmg9yoyUZoM8i8KFu1jTbpvQ75gvj/BvB9Y0wm+CGtxS4W5KW3t9f2EPJCu9S0jnVxjUJNPkgl5oPcqMlGaFdPZYFcbaM1yG8IayBueKlp8jI1NYXOzk7bw6jYaRws7jTeHDX5IJWYD3KjJRvlJRYBd0gybIhQbaMlFj8P4DJjzF8W/v0dAPGKb/lvxphA1yFzBpm8aPmUH96V9LjTeDO05IN0Yj7IjZpsOCEVyOywv85Gm/RuBFC56PFVAP6u8OdHhfsDZUzQoaBGlsmEvuqnNu40VklNPkgl5oPcaMlGaB2SkANYb62x0RrkfQBuqPh3zhjzRQAQkW4AR4IaWJHjBB0KamTpdFjrs7xxp7FOWvJBOjEf5EZNNkrHlmBnePMbzDMA2gP9OY1koxnkk4wxlX2lril+YYxZADAcyKgqqNlJSiq1Xq9KrsnfDDX5IJWYD3KjJRuhdUgCAId7XCptVCAviMhpxX8YY75V/FpEXoq1yy8CwT7I5EVPr8qw1gazQN4MLfkgnZgPcqMmGyHuOwltL02D2KhA/hfkLzVdyx8V7g9UJFL3tUyoBcXj8Y2/KQycQVZJTT5IJeaD3KjJRphFK7skrbHRGuSPAPieiDwK4FYA4wB2ArgSQB+AC4IdHgtk8tbd3W17CHmhvbFwTf5mqMkHqcR8kBst2QhzVldMOvBeGY3Es/o0xowDOA/5meI3Anh/4e9vA/jFwv2BYh9k8jI9PW17CAAAccI6DcadxpuhJR+kE/NBbtRkI6Qe+wDCna1uABvNIMMYMwPgQ4U/oWtr23CI1ML6+vpsDyEvtC4WALCC/JXfaSNq8kEqMR/kRks2Ql0XzE16a7jOIIvI6+t5AhG5zL/hrMc2b+RFRSsek4MgE96pqTBnFBqcinyQWswHuVGTjTCXWDhzof2sRuC1xOL/1PkcX/VjIG5YIJOX5WUFV5YrvYGFs16eO43rpyIfpBbzQW7UZCPMq6eyQF7Da/1Cl4g8v8HjBQF3lWYfZPKiolflmgI5hA903GlcNxX5ILWYD3KjJRsS4vu9rLnsBXkVyK+t8zkCrQjYB5m8jI+PY8+ePVbHUJ7RDaWVO3cab4KGfJBezAe5UZONUJdYsECu5FogG2PuDXMgbtjmjbwkEgo2q5VOgYVTIHOncf1U5IPUYj7IjZZshHcRKkCcwK/91lDUV58skMlLMpm0PYSKU2AhFch8E6ubhnyQXswHuVGTjVC7WCyE97MagPrqk32Qycvs7KztIQCh9UDO42mw+qnIB6nFfJAbFdkwTrgzyIYFciX1BTL7IJOXgYEB20MIv6sEdxrXTUM+SC/mg9yoyEahODahnZ1kgVyprgJZRN4iIlYqVbZ5Iy8LCwr+hw65QOYMcv1U5IPUYj7IjYpshN5ClMv3KtX7X/0mAGMi8hci8stBDqgaC2TykslkbA8h/AKZp8HqpiIfpBbzQW40ZKO8vCKkk/1sIbpGXf/VjTFnA3gdgDSAr4nIT0TkQyJyWoBjA8A+yORNQ6/KsJdYcAa5fhryQXoxH+RGRTZC3gAuvErrGnV/LDHGPG6MeT+AUwEcAvA2AM+KyGER+a8iEshHHPZBJi/j4+O2h1AxgxzS2Q5nMZyf0wRU5IPUYj7IjYZshN1jH1gJ6ec0hk0VtSJyOoCPAPgsgETh678BcD08Lk0tIqeKyL+JyJMi8iMReW/h9n4RuVNEni783bdugGzzRh40tOKRkAtkXu2ofhryQXoxH+RGRTbCvMw0ABj7y0o0qXeT3iER+XcADwEYBnC1Meblxpg/NsZ8BcB+AK/3eIpVAL9jjPkFABcAOCQiZwK4EcBdxpifA3BX4d/VP3tTL4haSzwetz0EoHRaKhfSz+Ma5HqpyAepxXyQGxXZCH1/C9vqVqp3evaNAD4B4GRjzH83xvx75Z3GmCUAB9webIwZM8YcKXy9AOBJAKcA+FUAtxS+7RYAV1Y/NpcLqeighjQ3p6DlWeFNLKyPcmK4xKJeKvJBajEf5EZDNsQJe9McC+RK9RbI9xhj/tmYtfPvIvK+4tfGmO/W80SFjX3nojAbbYwZKzx+DMBJ1d/PPsjkZXBw0PYQQm3kDoA7jTdBQz5IL+aD3KjIRujHFhbIleqtPj8C4H/VuP1DAD5Z7w8TkS4AXwNwgzFmvp7lE5OTk7jooovQ1taGXC6HAwcO4NChQxgfH0dnZyei0Sjm5+cxNDSEmZkZGGMwNDSEiYkJdHV1AQBSqRSGh4cxOTkJEUF/fz8mJyfR09ODXC6HxcVFjIyMYHx8HLFYDL29vZiamkJvby8ymQzS6XTp/ng8ju7ubkxPT6Ovrw/pdBrLy8ul+xOJBJLJJGZnZzEwMICFhQVkMpnS/clkEvF4HHNzcxgcHMTc3Byy2Wzpfr6mzb2mn/70p9i1a5fV1/SSxAJiACaXfhFd8RdwLHUpeuLPAgDmM6fjlK67Mba4DxHJYij5MMYW92FH+1NYNUmkMnuwq+sOHE1djlh0Af3tT2Bi6UL0J57Acq4fS9mTS/f3xp/CUMdjSC8LFubnG+r3ZCt7KysrGBoaaqrX1Iy/J1uvaXl5GZFIpKleUzP+nmy8JsdxMDU1ZfU1ObNHcYoAi9lhjC++qnQs6Ii9iER0BjPLezHc8SBmVvYim+su3d8VH0WbpHFi5Qzs7DyMyfT5cEwMOzsP1zw+TSxegNN6bwPgYHR0tKF+T35kz7VmNcZ4FbSXFr68HcCvYO1Z5JcC+LAxZo97ebvmuWKF57nDGPPJwm0/AXCJMWZMRHYiP1P98srH3XPPPebss8+u50dQCxodHcWePXVFMDDtUwfRtnRraD/PiY4gfcoTof28RqYhH6QX80FuNGSjbeGLaJ/9AAziEAS/gc5AsLR7MvCfo82RI0ce2b9//3nVt280g/zFwt/tAL5UcbsBMA7gt+r54ZKfKv4igCeLxXHBbQDeCeDmwt/frH4s+yCTFx29KrnTWCsV+SC1mA9yoyIbYXdIggFMFhDWXcAGa5CNMS8xxrwEwN8Vvy78eakx5lXGmNvq/DkXAbgawKUi8ljhz5uQL4wvE5GnAVxW+Pca7INMXnT1qgzr53GdWL005IP0Yj7IjYZslI8tITYrCHvCR7G61iAbY67Zzg8xxtwP903++70eG41Gt/Ojqcl1dnbaHkLorXi407h+KvJBajEf5EZFNkodktyXwvpNzBIMukP7eZq5Fsgi8mShbzFE5AWg9m/IGLM7oLERbUjFB6iwL89p2PqwXiryQWoxH+RGQzbEsTCbG/qEj15eM8gHK77+taAH4oZ9kMnL/Pw8+vrWXYAxVGEvsQj1dFuD05AP0ov5IDcqsmGjpWfYEz6KuRbIhWURxa/vDWc463GTHnkZGhqyPQQLn7hZINdLRT5ILeaD3GjIRviTL/mfGd6CDt3qvdT010Xk1VW3vVpE/k8wwypbXeV6S6pNVl/A8vS/2h5G6BcKKe00pg3NzMzYHgIpxnxQTc4CVqa/br9jkI0Nc7wQVUm9V9J7DYDvVd32IIDX+jscovq1Tx/CqeY9kOxzdgdiY80WdxrXxavPOxHzQbXE5j+DXeYGtC0GPgfozcYMspMK/WdqVW+BvAygektnF4DAp7F4qWlyI6vPFf5+3t4gjLHyiVv4Kb8uGk6Tkl7MB9USWc1faS6y8n2r47DyPu+cCP9nKlVvgXwHgM+LSA8AFP7+CwDfCWpgReyDTDUZB5KbAgBIbsziQDIQGBjXLoYBcVgg12NiYsL2EEgx5oNqkdxk4W/LvZAtnCkUFsgl9RbIvwOgB8CMiBwHMAOgF8ANQQ2sSEOrFVLImYUU+gFHckftjaN0Cqze/5X8/rnkpaury/YQSDHmg2qR3PE1f1sbh5UlFvOh/0yt6r1QyCyAXxGRnQB2AXjBGGP/MjPUsoqf8AFAVl+0N45SS5xwZ5C505iIKBilGWTH8iZOGy3XDNcgF21q2ssYMwbgBwCOi0hERAKfNmMfZKpFnPIne8lZPE1q7BTIXGJRn1SKb/bkjvmgdcwyxMwBULBhzcoSi4XQf6ZW9bZ5O1lEbhWRaeSvc5ut+BMo9kGmWipPfVldJ2apQBbDN7F6DA8P2x4CKcZ8ULXi3pY8u92C7GzS44fGonpngD8PIANgP4AUgFcCuA3AbwY0rhL2QaZa1hTIFjcVlHsghz2DPBfuz2tQk5OTG38TtSzmg6qtWXfcgn2QxXANclG9PdReBWC3MWZRRIwx5nEReRfyvZH/JrjhEdW2Zg2yzdlUS+3WhAVyXURC/uBCDYX5oGprJl/g5NcBR5LhD8RkIViFQcjTL5xBLql3BjkHoDiVe0JEhgAsAjglkFFVYB9kqqVyDbLNi2bY2GUMcKdxvfr7+20PgRRjPqiaOJNV/561MxBLHZLYY7+s3v/yDwF4U+HrOwB8FcDXkd+wFyj2QaZaKmeQrZ4Gs7XEgjuN68JT6OSF+aBq1a3dxJm2Mw5rBTJbiBbVOz17Ncq/pRsA/C7yV9L7syAGVYl9kKmWNUsskMsXqpIIfxylNjzhNl3jTuP69PT02B4CKcZ8ULU1ky8AkLPU6s1WhySLZ2S1qbcP8omKr9MAbgpsRER1KLZ2MwYQASQ3C9O2M/yBlE5HhdyVmAVyXdgmkrwwH1StOINcPrZY6rNvqcc+C+Syetu8xUXkYyLytIgsFv6+SST4KTu+gdE6Jlc67ZV1ugHYOw1WfjMJeQaZO43rsri4aHsIpBjzQdWK+1uKxxZbV2oVay1E2TmsqN4lFp8F8HIA/wPAKIA9AH4f+U161wUztDz2QaZ1nBkIcjCIoi1SeBOxdBqs/CYW8gc57jSuy8jIiO0hkGLMB1UrziBHJT/5IatjdgZibS0wC+Sield/XwngzcaYfzXG/NgY86+F264Mbmh53KRH1cprxNqwlB0p3GbpNFjpTcwJ9cdyp3F9xsctXkSG1GM+qFrx+DKX+fnCv+1kxN5mORbIRfUWyOMAOqpuSwII/KMV+1RStfIuY4EpRDiSO2ZnLIU3sbBTyp3G9eEZKPLCfNAaThpiFmAgcEz+BHtxv0vobK0FNlzWWlTvEouvAPiOiHwGwFEApwI4BOBvReTS4jcZY+72e4DsYkHVKnsgx6P5/aOSa7HTYNxIUZfe3l7bQyDFmA+qVO6B3IaOtrHCbbb6INs6S8gCuajeAvk9hb8/WHX7b6J8uWkD4KV+DKoSLzVN1cozyA7mM6djKPmotdNg5Z3GITMrdn5ug5mamkJnZ6ftYZBSzAdVKi/fi2J6+Wx0xO4ELF2ptXyWMOQN4HAA4wASbv9ljept8/aSoAfihjPIVK38JpZFMnq8cJud02BiaSaXO43rwxlC8sJ8UKXK40h3fDR/m62zdY6dDkn5H7kMSPWq2taj/iOCMRbCQaoVC2SBwYrTV7jNVjN3W6fBWCDXI5OxeJVFUo/5oErlJRYG6dXBwpd2MiKWNoADsNhBQ5d6+yD3iMgnReQRERkVkeeLf4IeoONYCAepFqlYg7yULVwcxPppsLCxQK5HOs03enLHfFCl8vK9HBazp+ZvK16pNWy2WoiCm8CL6p1B/isArwTwMQD9AH4LwPMAPhXQuEq4y5jWqbgU6EnJhwBYPA3Gncaqsc8teWE+qFL57OQqdnV9t+L28Dfq2eqQBIAzyAX1FsivB/CfjDHfBJAr/P12AFcHNrIC9kGmapFceQb52OLr8l9Y2rRm75M2C+R6sM8teWE+qJJUHFuOpi4v3+FYWMJnsUgVwytMAvUXyBEAc4WvUyKyA/keyC8LZFSVPziifpk0hcnkgIrLSsci+aUV+dNgFtaKWXoTK+00Jk/xeNz2EEgx5oMqldcgA+3RclEsuela3x4sm7O4trozKVNv9fk4gNcUvr4PwF8if/np/y+IQVVigUyVxJmCwIEpNGDpaX+u4r7W+pTP02Ab6+7utj0EUoz5oEqVM8g97c9W3B7+mQab64DF4QwyUH+BfBDAzwpf/w8AaQA7AFwTwJjWYB9kqlTZpxIAJpfOL99poZOF2PykzQJ5Q9PTFmZ+qGEwH1SpskCuPLZEci+EPxirx5YT9n62IvX2QX6u4utJAO8ObERV2trqvZYJtYJygZzfujCQfLR8nzMTfsdIq+vE0jY6ZDaUvr4+20MgxZgPKnEWIWYRBgKBWXtsabEZZDjz9n62IvW2efu0iLyq6rZXicifBTOsMrZ5o0qVn/ABYCl7Svm+1ZAvN21yEGTsFamcQd4Q23iRF+aDiiovMw1UHVtyIR9bAHsdkgAIC2QA9S+xeAeAH1Td9giA/+LvcNZjgUyVxClfZhoA0qsnle6L5I6FO5hSgWpnnbxYu0hJ41hetneQIf2YDyoqn53Mv59XHluqJ2ZCYfXspJ3rCmhT75Hd1Pje6CYev2Xsg0yVym9U+fZ/u7ruqLjvxXAHY7lA5k7jjbHPLXlhPqiofGzJL99be2wJf626zQkQcVggA/Uf2e8D8D9FJAIAhb8/Wrg9UOyDTJUqLzMNrO1VGfY6sfLFSay0cudO4zqwzy15YT6oqFwgrz+2WLlSq8UlFnBS9n62IvXugHsvgNsBjInIKIDdyPdBviKogRWxzRtVqj7VlWw7XnHfRLiDKc0g2ymQudN4Y4lEwvYQSDHmg4rKa5DznbPWHFssFKt227xxBhmov4vFURF5JYBfAnAqgBcAfN+Y4K9UwAKZKlU2cgeAjlh53XHYp8HEdoHszG38PS0umUzaHgIpxnxQUXHyRQpXKa08tli5UqvNGWSuQQawicWTxhjHGPPvxph/Lvxdd3EsIl8SkeMi8kTFbR8VkWMi8ljhz5tqPZZ9kKlS9QzydPrc8j/C/p/a8hpg7jTe2OzsrO0hkGLMBxWVN+nlVR5bQr9Sq3EszyBziQUQ3u6i/w3gDTVu/5Qx5pzCn2/XeiD7IFOJWQWqeh0PdTxc+jrsNxSrfSrB02D1GBgYsD0EUoz5oKLqyZfKYwsQ8pVaC7PHxtb+FnZIAhBSgWyMOQxgS+limzcqyl9m2qByZdD8yukzEq0AACAASURBVOnlbwjzEz5gfQ0yW/FsbGGB/43IHfNBRdXL99YcWwAgF+LZhtLyCktLTG0u71DE9vTs9SJyDfI9ln/HGLMugdPT07jooovQ1taGXC6HAwcO4NChQxgfH0dnZyei0Sjm5+cxNDSEmZkZGGMwNDSEiYkJdHV1AQBSqRSGh4cxOTkJEUF/fz8mJyfR09ODXC6HxcVFjIyMYHx8HLFYDL29vZiamkJvby8ymQzS6XTp/ng8ju7ubkxPT6Ovrw/pdBrLy8ul+xOJBJLJJGZnZzEwMICFhQVkMpnS/clkEvF4HHNzcxgcHMTc3Byy2Wzpfr4m99eUW3wcLxUgk+vF+NIF6G9/AmOL+9AZO4a+9h9BZBUryymMT0yH8pq6nCkkAKw67TiWuhyOiWFn52EcS12KnvizAID5zOk4petujC3uQ0SyGEo+jLHFfdjR/hRWTRKpzB7s6roDR1OXIxZdQH/7E5hYuhD9iSewnOvHUvbk0v3t0Rn0tD+L1VwHBpL/gcVUCi8ujKr7PWnK3tLSEhKJRFO9pmb8Pdl6TalUCqOjo031mprx9xTGa0pmxwEBRuffhJzpwEquF8+c+M/Y3X074tEUjo/9GN3DLwnlNXW2z6ITwKqTxNLqMOZXTsdKrr90LOiIvYhEdAYzy3sx3PEgZlb2IpvrLt3fFR9Fm6RxYuUM7Ow8jMn0+XUdn4Y77keybRqrqw6mpqZU/p6CyJ4bMSac64CJyGkAbjfG7C38exjAFPI9VW4CsNMYc1314x544AFz5plnhjJG0i2avguJybfDIAFB/hPu8mofEm3lz1WLp/wYiJ7k9hS+akv9v2ifuQEG7RCEv4ljNXE5Vk76u9B/biNZWVlBe3u77WGQUswHAQCcFDqPngaDCKRwEarqY8ty/+eQ63prKMOR7NPoGLsQBnEIQj4zCsCJ9CG96+nQf64tR44ceWT//v3nVd9urUWEMWbCGJMrbPb7G+Q7ZKzDPshUVOtqRmt6VQKQXPjrxIpX9Qud4UaKjbDPLXlhPgio3KBXPqlefWyJ5I6GNx7bPfYNmyMAFgtkEdlZ8c+rADxR6/vY5o2KymvEcqXbOmIvVn1PeK3eyhsZ7BTIvFDIxtjGi7wwHwQA4hQnX8r1xrpjS5hXarW8AbzyGNvKQlmDLCL/AOASAIMichTAHwK4RETOQX6Jxc8AvMflsWEMkRpA+UIg5bMKieiMy/eEoNTmzc6bCXcabywej9seAinGfBCwvsUbYPnYYrtA5gwygJAKZGPMO2rc/MV6HpvL8ZMM5ZUvM102s7wX/YnyyQdZDe80WHGJhbWPcNxpvKG5uTns2LHD9jBIKeaDgNrL99YdW3LhLccRx/JFqDiDDMDiEot6sQ8yFdX6lD/c8eCaf0dyY2ENx3ofZCtXd2owg4ODtodAijEfBFQWyOWzk9XHlnD7IBePLeE0UVgvB4TUwEEz9QUyZ5CpqLxOrGxmZe/aG0IskK2fBrOwu7nRzM3xctzkjvkgoHxskYqZ03XHlhCvXGp78iU/b80GCeoL5LDa0JF+tWaQs7nuNf+OhLhOzPqbGNeJbYhdcMgL80FAfccWCXNJW+lnWax/uMdFf4Eci8VsD4E0MFlI1WWmAWBX1x1r/h1mFwv7M8gskDcyMjJiewikGPNBQO0CufrYEuqSNssdkoDKddCtS32BzE/4BACSmyp8tXZNenWvylY6DQbD5UcbYZ9b8sJ8EFBnj32sAiaceqR8bLH4Hs8ZZP0FcjQatT0EUkCc4tKJtXnoio+u/b4wi1brXSRYIG+ks7PT9hBIMeaDYExFj/2y6mMLAMCZXX9bEEodkuwtsbA+AaSA+gKZCKg8Bba27U2bVP1PHOZpMMf2J2zuNN4IP2CTF+aDYFIQk4apKofWHVsQ3pVaVSxvYIGsv0BmFwsCap8CA4ATK2es/T6shtbk3PYn7PxHBXay8DI/H96SG2o8zAeVjy1rl+9VH1uAEFu9aVjeYH0CyD71BTI36RFQOYO89gPTzs7D67855NNgVvFTvqehoSHbQyDFmA8qF8hry6Fax5awLhYSascM1zHww6P6Anl1lTv1CRVrxNZukphMn1/je8MpkG3PIANKTsUpNjMTYnN/ajjMB9Vafwy4HFtWjwU9nDwFxxbk2CNcfYFMBABS6G9cfeFNx6w/wyC5kFq9aXgT03AqTjH2UScvzAeVZ5DXZqHWsSWSezGEEUHFsYUzyA1QIPNS0wTU7lMJuJ0Gq71e2VfGKHkTsz8GzXgKnbwwH1Q+tqw9W11z+V5oSyzsv69LiC1TtVJfILMPMgHuRe+x1KXrv3f1haCHAyADgQOzbk47ZAreSDWbmAjvyorUeJgPKh5bpGp/S61jSySkAlnD+zpnkBugQGYbHgLc14n1xJ9d/725saCHU/EGZvl/IWfR7s9Xrqury/YQSDHmg8SpPflS89gS0pVaNcwgw0nZHoF16gtkIpgViHOi7pbpYew0Lm+OszuDLA43UhARbZXb8r2awlp24NjvYgFnwfYIrFNfILMPMpUvM71+08R85vQa3x/mDLLlJRYskD2lUpwFIXfMB7kt36t5bAlrZlfBDLKwQNZfILMPMhU7WNSK6yldd6+7LRLGabBSn0rLM8hcJ+ZpeHjY9hBIMeajxRnjOoNc69gS1pVaNSyxEMMPj+oLZPZBpvL64/XF6NjivvUPCOE0mCiZQeZOY2+Tk5s4fUoth/locWYegpV1l5kGah9bQrtSq4ICmS1EG6BAJvJaIxaR9V1OQvn0reTNQ7iRwpOI5SUwpBrz0drcLjMN1D62AACcE8ENCABMFoLVuvfcBIUXoWqAApl9kKn8JrZ+PfpQ8uH1DwjhNJgoWWIBngbz1N/fb3sIpBjz0drKky/rS6GaxxYA4gR89cXSscV2eaZgo6Bltn8DG2IfZCq/ia3PQs0lFsgCJuDNnRpOgQHcabwBnkInL8xHa/O6qFTtYwsguWALZCmdnbRcnpmM3Z+vgPoCmX2QqdinstZc7Y72p9Z/PxD4abDy6Se7J8K409hbT0+P7SGQYsxHayvvb1n/Pl7r2AJUbhoPiJb9LWGstVZOfYFM5PUpf9Ukaz8m8NNgSgpkLrHwxDaR5IX5aG3lY8v6s5Oux5bc0QBHhIoeyLbXx7NAVl8g8w2MvArkVGZP7ce0SIGsZbOgVouLvNIguWM+Wlv5MtPOuvtcjy2rLwY7ptJ7uu39LSyQ1RfI7INMXl0sdnXd4fIY96LaD+VOGXY/wInDAtnLyMiI7SGQYsxHa/M6TrgfWwK+UquW/S2Wj20aqC+QuUmvxZlliJmHcfk0fTR1ec3bZTXg02ClncbrZx7CFU7j+kY1Ph78ZcepcTEfra28Bnk912NLwAVyuUOSbSyQ1RfI7FPZ2sqzx7Xb/cWitTepyeqxgEZUeP7CaTDr6eROY088A0VemI/W5nV20u3YEnGmghpOnpIZZIFp+WUW6gtkdrFobV59KgGgv/0Jl8eFtdPYLu409tbb22t7CKQY89HCPC4zDbgfWwK/UqumfSVKjnO2qC+Qeanp1lYudGvP1U4sXejyuLGARlSg5jQY///wMjUV8GwPNTTmo4WZOQgyMKg9Ced6bAm4aNSzxAIskG0PYCOcQW5tXmvEAKA/4TKD7EwHMZyK51fyxsEZZE+cISQvzEfrKm/Qq11juB1bAr9Sa+nYYnt/S/AfBrRTXyAbY/uK5GRT+U2sdiG4nKt9qVhx5gIaUYGa02DcSOElk+EabXLHfLSu8rGldhnkdmwJ+kqt5aLUfoEMLRNBlqgvkB1HQUjImuIaMXEpkJeyJ9d+XMAFrJ7TYCyQvaTTrf0GT96Yj9a1UStQ12MLAAQ5AaOkhSgQ/HFUO/UFMncZt7aN3sTcelUGfhpMyakn7jT2xj635IX5aF3lDXq1z1K7HlsQ7BK+4gyy9Q5JgJrjnC3qC2T2QW5tG61BdutVmT8NFtzZB1VrszSNRRn2uSUvzEfrKh9batcY7seWgK/Uquj9XJzare5ahfoCORJRP0QK0EYzyO3R2m9U4Z0GU0DTWJSJx+O2h0CKMR+ty+sy04D7sSX/WO+Jm21Rs3wPwR5DG4D66pMFcmvbqEDuaX/W/bFBdrJQVJS2+joxL93d3baHQIoxH61rW8eW1Rf8Hk75uRW9n4s5YXsIVqmvPtkHuYU5SxCTcr3MNABMLp3vep84s0GMqvDcegrkVt9p7GV6Oth2f9TYmI/WtVGB7HlsCbLPvqL3c8lxiYVqbW21LzFMza+8Rsw9AwPJR90f3yKnwVSth1amr6/P9hBIMeajdW20v8Xz2LIaXIGsp0MSAMMCOXAi8iUROS4iT1Tc1i8id4rI04W/a75Tsc1b6yoXuO4Xi1nKnuL++NWjPo+owOQgWHHZ+2yBolNy2rCNF3lhPlqUcTacQPE8tjgBbu5UNOEhTsr2EKwKawb5fwN4Q9VtNwK4yxjzcwDuKvx7HRbIrWujU2AAkF49yePxx/wcTlnpDUzHCZhWfxPzsrysaDaG1GE+WpRzAoJV18tMAxsdWwK8RLmiAhktfmwJ5QhvjDkMoHpL6K8CuKXw9S0Arqz1WPZBbl3lAtl9rtazV2UuoE/5pVNgOgpkOK29kcIL+9ySF+ajNdWzfM+7D3Jw3R00LZkT09pdLGwu8B02xowBgDFmTERqflw7fvw4Dh48iLa2NuRyORw4cACHDh3C+Pg4Ojs7EY1GMT8/j6GhIczMzMAYg6GhIUxMTKCrqwsAkEqlMDw8jMnJSYgI+vv7MTk5iZ6eHuRyOSwuLmJkZATj4+OIxWLo7e3F1NQUent7kclkkE6nS/fH43F0d3djenoafX19SKfTWF5eLt2fSCSQTCYxOzuLgYEBLCwsIJPJlO5PJpOIx+OYm5vD4OAg5ubmkM1mS/fzNZVf067Ei2gHsJTtx0L2NKQye7Cr6w4cTV2OWHQB/e1P4MmZg3jZjn/Ecq4fS9mTsavrDsxnTsdJHT/A6spxjI6O+v6apsefwc8J4JgYfjr3VuzsPIzJ9PlwTAw7Ow/jWOpS9MTzO6DnM6fjlK67Mba4DxHJYij5MMYW92FH+1NYNcmar2li6UL0J55Y85qOpi5He3QGPe3PYnLpfAwkH0V7ZA4dsXHksrMYHR1l9mq8pqWlJYyMjDTVa2rG35Ot15RKpRCPx5vqNTXj78nv1xTL/hCnCZBe7cd85jS0SRonVs5Y816+kutFLLK45r18V9d3kWibwepqDktLS4G8png2hYgAJ1Zeiqn0L2Go42HMr5yOlVx/6VjQEXsRiegMZpb3YrjjQcys7EU21126vys+WvM11Xt8Gu64H93xo0gtZuGsrDR99tyIMeGspBSR0wDcbozZW/j3CWPMjor7Z40x69YhHz582Jx11lmhjJF0ic+8H7HUlz2/51jqUpzSdXfN+3JtP4/lk7/n+7gk+zQ6xi6EQRyCjO/Pv1mZnhuR3fG7toeh0sTEBIaHh20Pg5RiPlpTdPFrSEy/B0YSrpvivI4tBjEs7Q5mo17H0Z8P9kIkm5CLvQLLO2v/N2gmR44ceWT//v3nVd9u8xzxhIjsBIDC3zUXnLIPcuuqpwtFR8x9nXFQp8HKfSpVXAy05Xcae0kmk7aHQIoxH62ptHzP42qrXseWQK/UqmqJhZ6x2GCz+rwNwDsLX78TwDdrfRP7ILeuejbpTafPdX98UN0dFPWpBHg5UC+zs8H1wqbGx3y0po0uMw1scGwBgrnKnDG6ilJNY7EgrDZv/wDgQQAvF5GjIvIuADcDuExEngZwWeHf67APcuvaqE8lAAx1POx+p1nxcTSVz6ts53uL7zT2MjAwYHsIpBjz0ZrKl5l2X2LqeWwBglkGUTi2eF0cK1RBHUMbRCjVpzHmHS537d/osWzz1rrqmUGeXzkdvXG3S4JmAGMA8ffNRtsSCzHztoeg1sLCQmnzBlE15qM11bN8z/vYki+QDU73c1hVLURz/j73Fohxn2FvBeoX+LJAblFOCmIWN/wkvZLrd71PACCI4rH0JqbkUiGcQXaVydjfREl6MR+tqZ7JF69jS/45/L9SqyjrsQ+09hJXLb8FV+yD3JrKbz7eJzm8elXmn2fapxFVPKeyJRZiFm0PQS32uSUvzEdrEmfjAnnDY0sQV2otFcg6zk6yQFYum23tKf5WVV5/7H6lIwA4mrp8g+cJYBNO6U1Mx9kNVZs6lBkfD/CSsNTwmI8WZJy6roS34bEl96JfIyo/p7YC2dhf5mGT+gKZbd5aU72nrzpi3m9SwZwGK65B1lEgq9s0qAjbeJEX5qMFOTMQ5DwvMw3Uc2wJ4MOVuskOFsiqic8brKgxSG6i8JX3Ot9E1HsncTCnwYoFqZYCubV3GnuJx+O2h0CKMR+tp97lexseW3IBXChE2WSHwMlvdG9R6gvkXK61P8G0qvKb2P/f3r0HSXaW9x3/Pt1z3RntrnZ3tCsErLBwgoOAgJEJBuQyKgOuuGIMdgXHBOxKUpWEckFRYEwqVQFXYhkSY0ycxE7iSmQDoQxICi5MuBiDYmolVJK4CCECAtba1V5mZ3Zndq493f3kjz592dnZnnNOn+nz9jm/T9XUzvR097yz++x5n/Nenrf/GqjFjVt3eZ/sp8HadZD7lQgaprLvNO5naWlvDouRYlB8lE93/XH/wbfd+5bdl2kkZc2wKiQBwSXtwxR8gqw6yOXUvojZLlM8R/ed6P8+ezANFt6a33JvpOjnyJEjeTdBAqb4KJ84FSwgRt/SvJRFc64UWoUkCHDZx/AEnyBrBLmc4q4dXtzc7S5/L6bBQrtgKEG+Fo0QSj+Kj/LpJsj9l8jt2rfswUmtoVVIghAHhIYn+ATZS7z+pczi3uVvNa7b5X32slZlIEq+07gfVcGRfhQf5dPtE/r/2+/Wt+zJ3o8gR5CzvxEYFcEnyKqDXE5xE9tda1Xu6TRYKJQgX4vq3Eo/io/yaZcQ3W0PyW59S+ek1iyFViGJAAeEhij4BFl3+CXkHquQO8SoVbkXd7+BTYO1dhqHc0ENiercSj+Kj/KJOzu5a98CmZ/UaqFVSAJoagQ5WNVq/1qFUkC+gvk6HiM8ZydO7vJe2U+DBXlHHVjSHoqZmZm8myABU3yUT9zZyV37FsAa/UvBJebtCknhJMhlPqk1+ARZyidunUqAMdstWd2DabAQ76hDTNoDoBts6UfxUT5xR5B371vAmtkmyNYM8DreLO9G1uATZFWxKJ/uBWz38Ly0+Zz+74WDX86gVT3vGeBobZCj2gFYXs52ClSKRfFRMt7AmvHqF+/Wt8AebAIP8TquBDlc2qRXPu1NFHHcOHNfjPfbm2mwoITYpgDMzc3l3QQJmOKjZJoLGE08xuxkrL6lcTqLVnXfL8DruDXLexMZfIJcr6vGa9l0R5B3Xxoxv35bjPfLeBosxItYiUvx9LO4mPHNkRSK4qNcuiO+uy+tidW31LNNkEMc6LCMZ2BHSfAJspRPN0He/eao6bvPMGR+JGiAFzFCXLsWANVRl34UH+XSTZB3P8o5Vt/SzLgKSojL95pKkIOlo6bLp50g73bMNMSdBjs1cJs63INMkMu807gfTaFLP4qPcqnELB8KMfuWerYntQY5E9hcybsFuQk+QVYd5PJJsgb59Mord3+/xlODNGebrWgN2+4jEEO1FweiFMC5c+fyboIETPFRMjGPmYaYfUuCviqWEEeQtcQiXCrDUz5Jdgbvn3gixvtleJffGT0O7L9OiTdS9DM7O5t3EyRgio9yqXQS5N0H3uL1LdkOTIS4vwUtsRAJR9w6lbHfr57dOjHr1EAOawS5zDuNRUTiaA++7HbMdOz3y3pJRIB7SaxZ3uV7wSfIqoNcMu6Jpq2Wa7fs+pxMp8E6U2CBJcgZH3laFCsr5V0/J7tTfJSLJViDHKdvyfyk1hBHkEu8vyX4BFl1kEvGL2O+EeuYaYCbZr+463OseXHQVnV1LmCBJcglngbr5+jRo3k3QQKm+CiXJMv34vQtWZ/UGuISixAPxhqW4BNk1UEul+7yinjVS86s3r77e2Z4NLQFmiCXeadxP/PzGW+ikUJRfJRLkgQ5Vt+Cg2d47Q0wQYaMR8lHSPAJspRLkmOmASoWo8pJlnfAQV7ANIJ8LWaB3chIUBQfJeJ1aF6Ivfo4Vt8CWHMhfZt6+RZGPaPV0RnyWt4tyE3wCbLqIJdL0vXCc9MPxnhWdtNgwY4gl7gUTz+HDh3KuwkSMMVHeVhzIdqcFy+niNe3ZHhSa2cgJ6y0zLy8s/hh/UvsQHWQyyXJMdOQwzRYsCPI5d1I0Y+m0KUfxUd5dPuWeKVj4/QtrffNZgS5WxEjtLRMCXKwVAe5XLprxOL9pzw4+Xi8981oo153w0JgE2EhnsAUgP379+fdBAmY4qM8khwzDfH7Fhqn0zVou0ArJOHlrSQWfIIs5ZLkmGmAuk/HfN+M1ol1NvyFlSCXeadxPyoTKf0oPsojaX39uH1LpXEqTXOu1gx0+Z5GkMOlC1i5JL2IrdSOx3zfrDZStC9iYSXIIR5RGoLVVS09kWtTfJRHtwZyvJwift+SzXHl3SUWoSXIux/LXVTBJ8iqg1wuSTfpPX32s/Ge2MxmGqw7UhvaRUNr9Xdy7NixvJsgAVN8lEd3iUW8a2XcvsXqT6Vs0TaBDnK09vCUs38JPkHWJr1ySTqCfGrl1bGeV6lntU6sPYIc1sxGmXca93P2bHbHjEvxKD7Ko7t8L564fUtWJ7WGeEhIR8ht20PBJ8iqU1ki7okKuQOMV+OVN7NGNh1hexosvKhUgrwTzUBJP4qP8tizvqV5KU1zrhZwElrWKknBJ8iqYlEivoRRi33MNMChyUdjPc/qZ9K26kqBToOhEeQdHThwIO8mSMAUH+XRXYMcT+y+JavkMeAEOei27aHgE2QdNV0e3Tv8+IfDnFt7abz3zmgaLNwLRVhLPkJx4cKFvJsgAVN8lEfSEeS4fUtWgyZaYhGe4BNkjSCXR3c3cIIR5Km4d/kZ1UFuhnqhaGR2WmCRaIRQ+lF8lIRvYc2FRLWH4vYtmZ3U2ulbQtsAHnjyvoeCT5BdnX5pJL3DB9hoxDsq1poFP0kPUCWLq9VqtbybIAFTfJSDNdozBfFnJ2P3LTj44Msswq2QRE/9/3IJPkFuNgMMFtkT3WUQ8W+K1raeFu+Jvpm8QTsI+k465LblZH1dfydybYqPcuiuP44/Ix27bwGsuZiwRTvo1EEOL+cxX867CbmIfzu1R8zsh8BlWoso6+7+4t7va5dxeXRLvMUfCY1dB7k9DTZoVZSAk1BrruEVTRn3Up1b6UfxUQ5Jj5mGJH1LK0F2npmwVdveI+pbLLRDqACaS3m3IBehjCD/tLv/3e3JMagOcpm0L2KW4A46dq1Kmj136AMItYoFBJ2850V1bqUfxUc5pFm+F7dvAbB6Bie1Bty3WLOcI8ihJMjXVKkE30TJSJojOyer8ae2spgGsyyS7L2iBPkqExMTeTdBAqb4KIfu7GT8aj9J+pZMTmoN+PptJR1Bzn2JBa0Fp58zMwf+yN3/a+83FxcXednLXsbY2BiNRoPXve51vOUtb+Hs2bPMzMxQrVZZXl5mbm6OxcVF3J25uTnOnTvH7OwsACsrKxw9epT5+XnMjEOHDjE/P8/+/ftpNBqsrq5y7Ngxzp49y/j4OAcOHODChQscOHCAWq3G+vp65/sTExNcd911LCwscP3117O+vs7Gxkbn+1NTU0xPT3Px4kUOHz7M5cuXqdVqne9PT08zMTHB0tISR44cYWlpia2trc73y/w7za2fZszg9MpPs9Xcz9z0g5xZvZ2Dk49T92lWasd5+uxnObXyasarlzk0+ShLtVuY2biVjcYh1rae1vn+ZHWR/ZNPML92Gzfvv4exyiZnT3+Lg8duGOh3ek61dRE7v/4iKjS5tPkcbpy5j/n122j6ODfO3MfplVeyf+IJAJZrt3DT7Bc5s3o7FduK9TudW3sph6Ye7fs7HZ5+hLWtm1iv38CPHPg4FWtwceE0lZlnKPZ6fqdqtcrFixcL9TsV8d8pr9+pWq1y8uTJQv1ORfx3GvR3mms+yQSwWZ/l3PpPstW4rnNdnZ04yZitX3Utnxn/Id+79Ia+1/Kt5gwHJ7/L2tIPOLtwcqDf6fDGBQ4YnF19KQ2fZt/4aRbWX8jcvgdZ3ryFzcahTpv3jT/FVHWRxY1bObrvBIubt8b6nZL2T5OVRa6f+k7rGrr2VGFj71os7yoRZvY0d3/KzG4APg/8urvf1/7+l770JX/BC16QXwNlaKZPP49KI9mBHt+79AaeffBjsZ67MfdnNKZfmaZpHftOPTu7k5Mytj53N83p2/NuRlBOnjzJ8ePH826GBErxUQ6TF/4ZY2v3JHpNkr5la+YfUTv8oTRN65ic/xXG1uOvex6mrZk3Ujv8wbybsWcefvjhh+64446rlvjmvn7B3Z+K/jwP3AP8RO/3x8ZCGOSWPefeU4onvsPTj8R/cuOpxO9/lWDrIIN5OafB+rn++uvzboIETPFRDmnWICfpW6w++Fr2cGvsA1qDPHxmNmNm17U/B14FXFGdW2XeSqJ5CWMLT1CGB2Bt66bYz63UTyVt1ZW8ibEZ4h7jloYS5O1Uxkv6UXyUQ3cNcnxJ+pakx1jvKORNen457ybkIu/h2aPAPdYqvTUGfNTd/0/vE5Qgl8OVdSrjb6RYr98Q/2c0BrzL72yiqKBalaNhYyPcTkfyp/goh26N/fgS9S1ZnNQa8CY9moMfhDKKck2Q3f37QN8FxqqDXA7dO/xkkxqJalXWk61vvkroCXJJp8H6UZ1b6UfxUQJew5oXcZJUQU5aB3nwk1pDrpBkGZwUOIpyX4O8G9VBLoc0U2CQsFblgNNg3aNABzxsZI+UdRqsH9W5lX4UH8WX5phpSNa3flWkjAAAFCZJREFUQAYntQa9xCLg0e09FHyCrDrI5dDdRJFshe/0WPykd+BpsM5FIswEmQxGMYpmamoq7yZIwBQfxZfmmGlI1rfg0UmtAwg6CQ04ed9LwWefSpDLoXsRSzZjsG88foH2QafBulNgoSbIGkHebnp6Ou8mSMAUH8XXnZ1Mdt1O1LdkcVJr0AlyLe8W5CL47LNer+fdBBmCNMdMAyysvzDBswe8Cw78LlpLLK528WIGm2eksBQfxZemxBsk7VsGnKF0D3oE2bycS12DT5BVB7kc0q5Bntv3YPwnD3oXHHKdSrLZKFI0hw8fzrsJEjDFR/GlOWYaEvYtgDUXEj3/CtHgi4c6O0k5ByqDT5BV5q0c0ibIy5u3xP8ZNKGZfhos5F3GAJR0p3E/ly9rVF2uTfFRfN0R5GSjoEn6FgAai8me3+uKCkkB8mQ3F0UR6L9GlxLkckhTpxJgs3Eo4c8ZZBos9CUWYbcvD7VaOdfOSTyKj+Jr729JOjabuG8Z4KTW7rU71JRMCXKQVAe5BLyZep1YklqVANZMf5cf8hoxIPgEPg+qcyv9KD6KL+3sZNK+pdIY4KTW0DeAa4lFmFQHuQSaFzEaiY+ZhqS1KoHGIOvE2glyoLMankEtzoJRnVvpR/FRfGkHX5L2LYMcRBX64IvhpVxmEXyCrDJvxde9w0+eIO8bTzatZY0sLmKD1bvcK+blvMvvR2W8pB/FR/GlHUFO3rcMcLMV+CFUQNhl6PZI8NmnWcABI5norj9OHo5T1WRLJgabBmtfIEK9k1aCvN3ExETeTZCAKT4KzjcxX0pVHSJp35I2EQdGI/kchTZmLPgEudEINRmRrFRrX0v92sWNWxM9f5BpsHaZNwt0BBmNIF9laWkp7yZIwBQfxVapfT36LHm52MR9yyD7WwaorjQsoS8D2QvBJ8iqg1xs1fUvMn7p30VfJV/be3TfiUTPH2QaLPwqEbqZ3O7IkSN5N0ECpvgoLqs/yeT8r0ZfJJ8pSNy3DFKHXkssghR8gqwR5OKq1L7J5IVfizbozWAkL7m0uJnwLr9xLvHP6Ai9DrIS5KtohFD6UXwUVHOJqfk3UGmex20aS1EjPmnfMshJraHvb4HRGOXOWvAJsnu4ASPpWf00k/O/jPkqbjMY6Q652Gpcl+znDlAHOfQpprLuNO5HVXCkH8VHAfkmU/NvorL1HZyp1NftpH3LQCe1jkCCrBHkAKkOcgE1l1t3942zOOnu7tsS10H2AU7OCn6JBaW8iPWjOrfSj+KjYNyZXHgr1c2v4ExgA4zqJu5baHb2qSQWeglRwJrlO3Uy+ARZd/gF4zWm5n+Vyta3cSYxBkvoEtdBHiTJHYXkcxTaOESqcyv9KD6KZXzpTsbWPhHV1B9sNi1x30L6GUobgQQZX867BUMXfIJcrSavjSuBcmdi8e1UN++L7u4HP9hiduJkwjaknwYLfYkFjEYbh2lmZibvJkjAFB/FMbbyJ0wsfyAq6VbFBkyQE/ctgDVTHkTl7QpJ4SbI1izfev3gE2QpjvGl9zO++rFM7u7bxixZQmg00o8ij0LyWcKNFP3oBlv6UXwUQ3X9C0wsvhOgte44xYbv7ZL2LQA00pV6C79CkhLkIKmKRTGMrXyUieV/H93djw18d992afM5iV9jjZTTYGnXlw2RBV9pY7iWl8s3LSjxKT5GX6X2DSYv/JNONaTKgMv22tL1LclO3+sYhYENrUEOjzbpjb7q+l8xsfh2gNamvAyWVrTdOHNf4tcMOg0WtFFo4xDNzc3l3QQJmOJjtFn9VE81pH2pqyHtJE3fkvak1lFYGjdQnecRFXyCXK/rdLBRVqk9GtU6rkd399neKc+v35b8RYWeBivfXX4/i4vpT7eS4lN8jLB2rePGuajWcf59S+qTWkegb8GVIItkxupPRXf3K5nf3bc1PfkMgzXSXsRGYRpMU8a9VEdd+lF8jCivMTX/Zipbj+OWvtZxP+n6lpRVUUZiBLl8fUvwCbKOmh5RnVrHZ6Jax3uTXA5tGsx9JO7yzcu3kaIfTaFLP4qPEdSpdfzXrWpIe3RdTrV8L+VJraOwxEIjyAFSHeQR5FtMXfg1KluPZVLruJ/TK69M/Jp0I8hb0SYQS/Ha4SnjXX4/584NcLS4FJ7iY/SML/0OY2sfz7Qa0k5S9S1pT2odgQTZmtnPAIcu+ARZZXhGTLvW8caXccYz3ZC3k/0TTyR+TappsM4FLPD/MlqDfIXZ2dm8myABU3yMlrGVP2Vi+Xczq3XcT5q+hZQntY7CCHIZKyRp/YJkp7nOxNKdjK/+r+juPsyi52mmwboXsNBHkJUgi0jBuFNd+zgTi+9ofcl05hu+s5B6uccILN8biTZmLPDhMNVBHgleY+zy/2D6zG2MX/7Pmdc67me5dkvi11iaKhbN0UiQy7hOrJ+VFf19yLUpPgLnTnX9C0ydvYOphX/ZU+t475PjNH1L6pNaR6EOsu/tbHCIgh9BVh3kgHmDsdVPML70fiqN1rGczhRQ3/OlFW03zX4x+YvSTINpBHkkHT16NO8mSMAUH+GqbHyFiaU7qW7eD4AzjlOlsgfVkHaSpm/pnNRqU8leNwpLLCjffrDgR5BVBzlA3qS69immz7yCycW3UGmcxJmM1hxvYAzv3+zM6u2JX5NmGqz7mrAT5LRr4Ipqfn4+7yZIwBQf4alsPszU+V9k+vzPU928H2eMJtMYW1QY3jR/mr4FUp7UOgrLF7x8uVjwI8gSEHeqG19g/NJvU936ZushJgEf2ojxdhVLcVebZqpoRDYolHGncT9mgd/QSK4UH+Gw2mNMLN3J2PpnAHDGcBun4utDHXRpS9W3ADQXgRvjP9+3MLZwQh9+UYIcHNVBDkNl46+j6a4HAHAmgAo2xDv6ncxNP5j4Na1psBrYRPzXjMoSixGYqhumQ4cO5d0ECZjiI3+29QQTS++junYPhuNUgCmMNSzHUcs0fQuANRZIdPxMZ/S4Qqgb24FSjiAHv8RCdZDz1Zruej3T519LdfMBnHGaTGHUck+OYYBpsGbCjXqdi1jgJ28pQb6CptClH8VHfqx+iomFtzF95icZW7sbMNxmMJpYABUq0i+xSFhGdFRKiIacvO+R4IdnVQd5+Kx+hsrmCcbW7rnGdFc4Ny0HJx9P98LGIlSPxX76yIzMlnCncT/79+/PuwkSMMXHkDWXqW4+QHX984ytfBijhrcTY18FD2eJWNq+pdI4lah+06jMTkKjdaJsiZYlBZ8gyx5zx+rfo7p5P5XN+6lu3N+pSAFE9Ywnc5/uupa6T6d6nTUXk40Fd8rwhD2CnMdavZCpTKT0o/jYW9Y41+1XNk9Q2foWFl1DHWjaDJXAEuO21H1L0pNaR6SEaKt1m0CyCh2jLPgEWRewjHmDytajVDbup7p5gurmA1jzymlGpwo22dk8QADTXdeyUjsO+04kfp3VE17ERmaJhRLkXqurqxw5ciTvZkigFB8ZcsfqP+gOtmyeoFL/wZVPwXCmgSbGZmvUOFCp+5aESyxGZwSZVjJfVYIcDNVBTskdfAmrn6XSeIpK7WvRReur2LbDJJxxWqFQw2hEm9jCTYp7PX32s6leV2mcTjkNFvo6LCXIvY4di7+MRspH8ZFScxVrnMUaZ6lsPdZKijdOUGmev+JprcGWCfBGtG/FgdFYrpa2b0l8UuuoLN+j1Q861+fdjKEJPkHWJr0dNNc7FydrnGldpKI/r/i4xn88ZwKsCr6B4dEo8Wj+PZ9aeTXPPvixxK+zxlPJXtD5uwx8RkMjyFc4e/Ysx48fz7sZEijFxza+hTXOX9G3tD+vNM5i9egxX9755YyBjYP3DraMTgLYK33fspDs+aP09zMiA2dZyT1BNrPXAL8PVIH/7u6/0/v9S5cu5dKuofIGNBexxgWseQFrLER/9n493/2zGe/vpLV+eIzu1M0GBhi14FcKxPXJTz/Ku34l+evSToOFPwkW+gj3cN1777289a1vzbsZEqjCx0d7JrHR7k8WsMZ89OeFnn5mARoXWv1LjM7BMWCcVuUFAzaj6hP1wtykp+1bEh/WNAqHhERaI8jlkWuCbGZV4D8BPwOcAh40s0+5+2Pt54xcguwOvoZ1Et5FaC5gjcXWY53PF7oXpuZirItS50dccXGCVlK0ddV7GA2CH/Ec0Cf//PGUCXJRp8HKt9O4n7vvvrvYCZAMZOTiwzc7/UdrUGVhh35lodXntAdXEiy7cqJRYKq0El8H6lFf0tXqa2rZ/V4BSt23JE14R2lUdmT6wWyYe373A2b2UuA97v7q6Ot3A7j7ne3nPPCX/8VfxFtyamFMFWAcfLz1J9UUycmWQw0sWu1gtejP3sd7v59h88ukeRjqz6tAzbGdZwl35LPAlFF9vEk1YZnLYandblAxbMELM0MgUmoVYKynbxlLceWv+5V9yk59S+/junak4mOw9fIKNB1LUmZ/CnzWqJxxxr4T5l/+1gsMv96wJR/V1Zh93cfnH7rjjjtevP3xvJdY3AQ82fP1KeAlvU+YXz3mH134g85V4chBOHIwwPSwHn3EucHa7f9AFZiOPqSvC5c8eTws0up4ktRlXwM2gBuijxBdojvjGeB/kTykig8pjZGIj/ZEYJyByd36ltYhdWWq1JXa0PqWGnCR1r/J85P9uKFxWm10wj/PJIVKbeWFOz2ed4K8U/Rd8V/85/7BLxTwn0NEREREQpV38nkKeEbP108HEpYXEBERERHJTt4J8oPAj5rZs8xsAngD8Kmc2yQiIiIiJTb0BNnMnmFmf2Vm3wa+DnwO+CzwHWAGuNfMPm9m10fP/xkze8jMvhn9+cqe9/rx6PHvmdmHzLR1f9T1xoeZfcvM3ho9fiiKi+9ui4+fMLOvRR9fN7Nf6HkvxUfBJI2Pntc908xWzOwdPY8pPgomxfXjZjNb77mG/GHPeyk+CiTNtcPMnm9mJ6Lnf9PMpqLHFRtl4O5D/QBuBF4UfX4d8P+AvwO8H/jN6PHfBN4Xff5C4GnR57cCp3ve66vAS2mtZf4M8LPD/n30kXt87APGel57vudrxUfBPpLGR8/rPgl8HHhHz2OKj4J9pLh+3Aw8eo33UnwU6CNFbIwB3wBeEH19GKgqNsrzMfQRZHc/4+4PR59fBr5Nq5rFzwN3RU+7C3ht9JxH3L29LvlbwJSZTZrZjcB+dz/hrYj9k/ZrZHSliI81905l+imiTZ6Kj2JKGh8AZvZa4Pu0rh/txxQfBZQmPnai+CieFLHxKuAb7v716DUL7t5QbJRHrmuQzexmWiPEDwBH3f0MtAKZnYtpvR54xN03aQX2qZ7vnYoek4KIGx9m9hIz+xbwTeCfRwmz4qPg4sSHmc0A7wLeu+3lio+CS9C/PMvMHjGzL5vZK6LHFB8FFjM2/hbgZvZZM3vYzH4jelyxURK5lXkzs1la055vc/fl3ZbwmNlzgffRuquDGCXiZHQliQ93fwB4rpn9GHCXmX0GxUehJYiP9wK/5+4r256j+CiwBPFxBnimuy+Y2Y/T2gPzXBQfhZUgNsaAlwO30aqE/5dm9hCw0xFTio0CyiVBNrNxWgH6EXe/O3r4nJnd6O5noimM8z3PfzpwD/Amd38ievgUrbJwbSoRVxBJ46PN3b9tZqu01qorPgoqYXy8BPhFM3s/cBBomtlG9HrFRwEliY9oNnIz+vwhM3uC1sihrh8FlPDacQr4srtfiF77F8CLgA+j2CiFPKpYGPDHwLfd/QM93/oU8Obo8zcD/zt6/kHg08C73f0r7SdHUyGXzezvRe/5pvZrZHSliI9nmdlY9Plx4G8DP1R8FFPS+HD3V7j7ze5+M/BB4Lfd/Q8UH8WU4voxZ2bV6PMfAX4U+L7io3iSxgat6lrPN7N9UR/zU8Bjio3ysNYa8yH+QLOXA/+X1nrRZvTwv6K1FujPgGcCfwP8krsvmtm/Bt4NfLfnbV7l7ufN7MXA/6R1KPNngF/3Yf9CkqkU8fGPae083oqe/1vufm/0XoqPgkkaH9te+x5gxd3/Q/S14qNgUlw/Xg/8FlCndaDzv3H3P4/eS/FRIGmuHWb2Rlr5hwN/4e6/ET2u2CiBoSfIIiIiIiIhy/skPRERERGRoChBFhERERHpoQRZRERERKSHEmQRERERkR5KkEVEREREeihBFhERERHpoQRZRERERKSHEmQRkRJrn0QpIiJdSpBFRAJlZu80s09ue+w/mtkHzeyAmf2xmZ0xs9Nm9m97jk2+xcy+aGYLZnbBzD5iZgd73uOHZvYuM/sGsKokWUTkSkqQRUTC9WHgNe3kNkpk/yHwp8BdtI5IfjbwQuBVwD+NXmfAncDTgB8DngG8Z9t7/zLw94GD7l7f099CRGTEKEEWEQmUu58B7gN+KXroNcAF4BTws8Db3H3V3c8Dvwe8IXrd99z98+6+6e7zwAeAn9r29h9y9yfdfX0Yv4uIyCjRtJqISNjuAv4F8N+AN9IaPT4OjANnzKz9vArwJICZ3QB8CHgFcF30vYvb3vfJvW64iMio0giyiEjY7gWeb2a3Aj8HfIRWcrsJHHH3g9HHfnd/bvSaOwEHnu/u+2kl1rbtfX04zRcRGT1KkEVEAubuG8AngI8CX3X3v4mWXnwO+F0z229mlWhjXnsZxXXACnDJzG4C3plL40VERpQSZBGR8N0FPI/W8oq2NwETwGO0lk98Argx+t57gRcBS8CngbuH1lIRkQIwd82yiYiEzMyeCTwOHHP35bzbIyJSdBpBFhEJmJlVgLcDH1NyLCIyHKpiISISKDObAc4BJ2mVeBMRkSHQEgsRERERkR5aYiEiIiIi0kMJsoiIiIhIDyXIIiIiIiI9lCCLiIiIiPRQgiwiIiIi0kMJsoiIiIhIj/8Ptp+/s68Lr30AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "build_years.plot(kind=\"area\",stacked=True,color=colors,ax=ax)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"capacity [GW]\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "#fig.savefig(\"co2-0p2-learning.pdf\",transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the development of the costs of the technology over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "costs = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        costs.at[year,tech] = model.costs[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "costs.plot(color=colors,ax=ax,linewidth=3)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"LCOE [EUR/MWh]\")\n",
    "ax.set_ylim([0,160])\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(\"lcoe-learning.pdf\",transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}