{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 1.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Integrated Energy Grids**\n",
    "\n",
    "**Problem 1.4**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Select one country and one city within the country. Plot the duration curve of capacity factor (CF) time series for solar PV and wind power in both cases and discuss the results. You can download solar PV and wind time series from https://model.energy/.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we downloaded solar PV and wind power time series for Spain and Madrid from https://model.energy/ and saved it in the data folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the packages [pandas](https://pandas.org/) and [matplotlib.pyplot](https://matplotlib.org/stable/index.html) to plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as  plt\n",
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we retrieve the time series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>solar</th>\n",
       "      <th>onwind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     solar  onwind\n",
       "time                              \n",
       "2011-01-01 00:00:00    0.0   0.003\n",
       "2011-01-01 01:00:00    0.0   0.000\n",
       "2011-01-01 02:00:00    0.0   0.000\n",
       "2011-01-01 03:00:00    0.0   0.001\n",
       "2011-01-01 04:00:00    0.0   0.001"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CF_city=pd.read_csv('data/time-series-MADRID.csv',\n",
    "                  sep=',', index_col=0)\n",
    "\n",
    "CF_country=pd.read_csv('data/time-series-SPAIN.csv',\n",
    "                  sep=',', index_col=0)\n",
    "CF_city.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by plotting the duration curve for solar PV capacity factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CF solar PV')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GBuLi4kT9cXFxSE9Pt+o9li1bhn79+iEsLMzsmIKCAmi1WtHLJtzqGtrl53m9vNU2n0FEZANqtdpk/73DpMHBhnvCjx07hkOHDonGrV69GnXq1EF0dDQAQKFQwN3dHQqFQj8mJyfH5FWvzkC2Q695eXkoKSlBQID4Bv2AgACTv9GUp1ar8csvv2D16tUWxyUnJ2PWrFnVytUkj7KHDUrFh1839wae4DMqicgxPPzww2jSpAkGDx6MNm3aoLS0FAcPHsT7778PX19fTJ48WT82ODgYQ4YMwcyZMxEUFISVK1ciLS0N7777Lry9vQEAgwYNwrp16/DCCy/g8ccfR3Z2Nv773/8iKCgIJ0863wMiZL/qtexvJAAgCIJRnykrVqxA3bp1MXToUIvjkpKSkJhoOLeg1WoREmLiyrTKqtPC0M7ZBAw8Bmwoc1HRlZ1Aw27V/xwiomqaPn06fvjhB8ybNw9qtRoFBQUICgpCv379kJSUhIgIw20oHTt2xNNPP40ZM2bg5MmTCA4OxgcffIApU6boxzz99NPIzc3Fp59+iuXLl6NZs2Z4/fXXceHCBfvsmMhMIQiCLLs+hYWF8Pb2xrfffothw4bp+ydPnoyDBw9i61bzhy8FQUCrVq0waNAgzJs3r1Kfq9VqoVKpoNFoqj+t0ua+gG9zoH4noOW/gdXlCvyoYsDFse4HIiJj+fn5OHv2rP4K/NqqadOmiIqKws8//yx3KjZR0Z+rtfVAtnOU7u7uiImJQVpamqg/LS0N3bt3t7ju1q1bcerUKTzzzDP2TLFi0fOADu/oiiQADDkjXv617DvsRERUTbJe9ZqYmIilS5di+fLlyMzMxJQpU5CVlYWJEycC0B02NXXj6rJly9ClSxdERUVJnbJYvfaAZ9nbRMKBQPHFSdg7SdqciIjIpmTd5YmPj8fVq1cxe/ZsqNVqREVFITU1VX8Vq1qtRlZWlmgdjUaDtWvX4sMPPzT1lvJ78DfxIdiTnwBhI4FGD8iXExGRFc6dOyd3Cg5JtnOUcrHpOUoAuJ0F3MkGiu8AQQ/p+opuAd/WEY/jVbBEDovnKJ1TjT9H6TR+uw9Iux/Y8bihz80XGCC+DwmnPpM2LyIisgkWyuryaqL7WaQFCq8b+uu1B+p1NMR7npM0LSIisg0WyupSlLn9I/t78bIHN4njS5zejoiopuH9C9UVOgK4tlfXvrIdaD7esMyjAaD0Bkru6OI/HuG5SiIH5qxzldZWtvrzZKGsruD+wMFXde0zK4Cun4uXD8oEfigzF+3mvkDfzZKlR0QVUyqVUCgUuHTpktypkI0pFIpqPwiahbK6VOWehalOM1z9CgA+oboxmmO6+PLvwO3zgI/5idyJSFpubm5o3rw5SkpK5E6FbEypVMLNza1a78FCWV0KF/GE6FvijA+vPnIEWFPmdPAPTXkIlsjBuLm5Vfs/VHJOvJjHFsofSs3bJY4VCqDvFnHfpd/smxMREdkEC6Ut+IQa2gF9dZMPlBfQWxz/0d+uKRERkW2wUNrK4zeAPhuBB9OAwAdNj3n0nDje87y9syIiompiobQVd5XuIh5Lz9L0CQM8GhriU0uA0iL750ZERFXGQmkP+XlA8V3Ty4bniOONfLgzEZEjY6G0JUEAtH8DJxcB33ibHqNwATrONcTXMoDb2dLkR0RElcZCaWs/twaOvKVrZ31rekzEVHG8bahdUyIioqpjobQlhQLwKPMg5/2J5sf1XGeIr+/X7Y0SEZHDYaG0tYHHDe07F4BSMzN9hAwTx2n32y8nIiKqMhZKW/NsKI6PzzE/tum/DO28dCDnd/vkREREVcZCaQ9tkwztw9PNH1bt/pU4/r0vkLPJ9FgiIpIFC6U9RCaJ4z8GmB/78B5x/PtDwM1Tts+JiIiqhIXSHtzqAPU6GWL1b0CJmeeiNbgPuP8bcd9PLe2XGxERVQoLpb30zzC0fcIBzXHzY0NHAA/8IO77a5598iIiokrhY7bsRaHQHVa9c8H4CldTmgwRx/sTgTqtgcaP2Cc/IiKyCvco7anBfdYVyXsezRLHWwcCpcW2zYmIiCqFhVIq6jQgtQNQdNP8GJ8QYNBf4r71TeybFxERWcRCKYXC64BQCtw4DHzrZ3msX2sgbLQhzr8M3Dpr3/yIiMgsFkopaE8Aeyca4pOLLI/vsVoc/9jM9jkREZFVWCil4N8VuH3OEO99AcjbbXmdh3aI40u/2DwtIiKqGAulVIZfEccbu1oe37CHOP6DV78SEcmBhVIqnv5A71/FfYemW15nSLlzkyc/tW1ORERUIRZKKQU/DNRtZ4iPvW15vG9TwLWOId77b7ukRURE5rFQSq3/fnGcvd7y+PK3i2zhIVgiIimxUErNxRWoF61rd18NBD5oebx3MBDymCFW/wKcXma//IiISISFUg5x6f80BMCtgvsqAeD+b8Xx7meBuzk2T4uIiIyxUMpB6QGMLtVNhg4Ady4BN46YH69QAEPOiPt+6WC//IiISI+FUi4KBeDiptszvLwZSG0P5G43P943HOi6whDn5wLFd+yeJhFRbSd7oVy4cCHCw8Ph6emJmJgYbN9uoVgAKCgowLRp0xAWFgYPDw80b94cy5cvlyhbO1jfBNg5Vtfe9ABQWmJ+bLOnxHEq9yqJiOxN1kKZkpKChIQETJs2DQcOHEDPnj0xYMAAZGVlmV1n5MiR2Lx5M5YtW4YTJ05gzZo1aNOmjYRZ29iAA+J4Q4Tl8dHzDe1bp3TzyBIRkd0oBEEQ5PrwLl26IDo6GosWGeY+jYiIwNChQ5GcnGw0/tdff8WoUaNw5swZ1K9f36rPKCgoQEFBgT7WarUICQmBRqOBn58VF9JIYdd44Mznhnj4ZcCzkemxggCsKfP7Td32wCOH7JsfEZET0mq1UKlUFdYD2fYoCwsLkZGRgbi4OFF/XFwc0tPTTa7z448/IjY2FnPnzkXjxo3RqlUrTJ06FXfv3jX7OcnJyVCpVPpXSEiITb+HTXQtd+h4XYCuIJqiUABdytwecuOw5cO1RERULbIVyry8PJSUlCAgIEDUHxAQgJwc07c+nDlzBjt27MDRo0fx/fffY/78+fjuu+/wn//8x+znJCUlQaPR6F/Z2dk2/R4203OtON7cx/zY5uPF8e7xpscREVG1yX4xj0KhEMWCIBj13VNaWgqFQoFVq1ahc+fOeOSRR/DBBx9gxYoVZvcqPTw84OfnJ3o5pJDhuqtg78ndCty2UNRbJxjaZ7+0W1pERLWdbIXS398fSqXSaO8xNzfXaC/znqCgIDRu3BgqlUrfFxERAUEQcOHCBbvmK4l4w7lUqKIAzTHzY6PfF8fH3rFPTkREtZxshdLd3R0xMTFIS0sT9aelpaF79+4m1+nRowcuXbqEW7du6fv+/vtvuLi4oEmTJnbNVxIKBdA/A2j5AjDwCBDc38JYF6BOS0N8aBrPVRIR2YGsh14TExOxdOlSLF++HJmZmZgyZQqysrIwceJEALrzi2PHjtWPf+KJJ9CgQQM8/fTTOH78OLZt24ZXXnkF48ePh5eXl1xfw7bqRwOxHxku5iktMj92QLmrXfe9YL+8iIhqKVkLZXx8PObPn4/Zs2ejY8eO2LZtG1JTUxEWFgYAUKvVonsqfX19kZaWhhs3biA2NhZPPvkkBg8ejAULFsj1FexD4aLbuwSAU0vMP4fS1QsILzMJwakl9s+NiKiWkfU+SjlYe9+M7G6dBQ69AeRuA+5eAoblAF4mzt2WlgBfuxriBzcBgX2ly5OIqIZy+PsoqQLeIcD5r3VFEgC+DzQ9zkUJeDQwxL/3s39uRES1CAulo3JxBR5YL+7bMdL02PLnKs+ttktKRES1EQulI2vyKKBQGuKsb4FiE/eLejcGXNwNcfqT9s+NiKiWYKF0dKMKxfE33qbHDbskjq8dMD2OiIgqhYXS0SlcgE7lJhcw9XitsucpAeDXaPPzxRIRkdVYKGuCiERxHDXd9OQCg/4Wx7ywh4io2lgoa4rRZQpj6Ajd1a7l+bUE/MvManT5d+5VEhFVEwtlTaFwAUbcFD+42ZS4P8Xx0dl2S4mIqDZgoaxJ3HyBNpN1bUEA1Gmm9xjDDdP+4chMoOiW8RgiIrIKC2VNdOeSbrq6LXHAzjHGy7ssFcff1pEmLyIiJ8RCWRNd3gLs1U0cj3OrjJe7uAGtJ4v7Ti+3f15ERE6IhbImavqEOF4fajwmZr443v0MUFpst5SIiJwVC2VNpFAAQQMM8Z1s4Ope43HDc8Vx9lr75kVE5IRYKGuq3hvE8W+dgZJys/h4NgSCyjz8+c9RvF2EiKiSWChrKoUCGHhM3JfiYTyu6wpxvLm3vTIiInJKLJQ1maot0GSYuC93hzj2CgDqtCyzfJvuRUREVmGhrOkeWGdod/tKNzFBeYP+EsebegFCqX3zIiJyEiyUzmDIad1PpQfQsLvxcoULMOCguG/7Y3ZPi4jIGbBQOgPfZrq9xvqx5sfU66B7vuU9F9YDV/fZPTUiopqOhdJZ+LUGfMN17XNrgCKt8Zie68Txb/cZXylLREQiLJTOpPg2cOEn4Pwa4FuV8XKFC9B/v7gvxQMoyZcmPyKiGoiF0pnczgK2DQEu/qSLTZ2HrN8JaPovcV+Kl/1zIyKqoVgonYkqAvBtYYiz1wF5e4zHdf8KaNhD3Je3y765ERHVUCyUzmbw3+J4Yxeg8IbxuIfK3W+5sZvdUiIiqslYKJ2NQgH0+U3c910902MHHhfHh2faIyMiohqNhdIZBcUBnd4T95l6zJYqAvDwN8RHZ3EuWCKiclgonVXEy+J49zNAaYnxuGFqcbxjpP1yIiKqgVgondnj13U/w8fq9jCLTdxb6eIKBD5kiLO/43MriYjKYKF0Zu51gej5QONBuj1MdzPnKnv/Io63PGzvzIiIagwWSmfXZjIQOkLXFgSg4KrxGBcl0PFdQ3z5d6D4jjT5ERE5OBbK2kIQgJsngR+bA2dWGC9v+6o4PvO5JGkRETk6FsraIvM94OfWQJEG2PW06cdstZ5iaJ9fI11uREQOjIWytmgxQRz/3MZ4TJsEQ/vKn3ZNh4iopmChrC3c6wJhTxjimyd1U9yV5RMqjguu2T0tIiJHJ3uhXLhwIcLDw+Hp6YmYmBhs377d7Ng//vgDCoXC6PXXX39JmHEN1mOVON7+mPFFO56BhvaRmXZPiYjI0claKFNSUpCQkIBp06bhwIED6NmzJwYMGICsrCyL6504cQJqtVr/atmypUQZO4EBh8TxNz7i+yYjkwztW6elyYmIyIHJWig/+OADPPPMM3j22WcRERGB+fPnIyQkBIsWLbK4XqNGjRAYGKh/KZVKiTJ2AvXaAzELxH3bhhrazcucy7yUKklKRESOTLZCWVhYiIyMDMTFxYn64+LikJ6ebnHdTp06ISgoCH379sWWLVssji0oKIBWqxW9ar3WLwK+zf5pTwba/9ewzLXcsynvXJIuLyIiByRboczLy0NJSQkCAgJE/QEBAcjJyTG5TlBQEJYsWYK1a9di3bp1aN26Nfr27Ytt27aZ/Zzk5GSoVCr9KyQkxKbfo8Ya8s9h1aKbuoc5l+XX2tA+9IZ0OREROSBXuRNQKBSiWBAEo757WrdujdatDf+Jd+vWDdnZ2XjvvffwwAMPmFwnKSkJiYmJ+lir1bJY3jPyFnDngq5dcA3Y9x+g6+dA1EwgfbSu/+wXuj4zfyZERM5Otj1Kf39/KJVKo73H3Nxco71MS7p27YqTJ0+aXe7h4QE/Pz/Ri/7h6mPYeywtBM5/DXxbFwiLF48rfxsJEVEtIluhdHd3R0xMDNLS0kT9aWlp6N69u9Xvc+DAAQQFBdk6vdrlbg5w7G1du7QAKNIC9e8zLN/xuDx5ERE5AFkPvSYmJmLMmDGIjY1Ft27dsGTJEmRlZWHixIkAdIdNL168iC+//BIAMH/+fDRt2hSRkZEoLCzEypUrsXbtWqxdu1bOr1HzeQUCf39siL+rCwzPBdY1MvQV3QLcfCVPjYhIbrIWyvj4eFy9ehWzZ8+GWq1GVFQUUlNTERYWBgBQq9WieyoLCwsxdepUXLx4EV5eXoiMjMSGDRvwyCOPyPUVnEf3VUD6k4Y4b6d4+ZnPdVfLEhHVMgpBEAS5k5CSVquFSqWCRqPh+cryVpe7YKfdbODIW4b4iVr1V4WInJy19UD2KezIgQy/LI7rNBfHfEYlEdVCLJRk4NkI8C4zMXrZQ7EAcPB1afMhInIALJQk9tAOcdywl6H990fS5kJE5ABYKEnMJwRoNl63dzn8MtDvd/Fyrfl7VomInBELJRnrugzosxFw8wMU5f6KpEbKkxMRkUxYKMm0eh0ApafuEVwBfQz9pUVA8W358iIikhgLJZl37zmVl8s9oWXLAOlzISKSidWF8sqVK/bMgxxRfg6QMdm4/8p23bR3RES1gNWFsnHjxnj88cfxyy+/oJbNUVB7eTcBTi40veyXTqb7iYicjNWF8osvvoBWq8XgwYMREhKCN998E6dPn7ZnbuQIuq003Z+fYzg0S0TkxKwulKNHj8bGjRtx9uxZTJgwAatWrUKrVq3Qp08frFq1Cvn5+fbMk+QS/qT5ZYemSZcHEZFMKn0xT0hICGbMmIEzZ85g48aNaNy4MZ577jkEBQXhhRdesEeOJDdze5WZc6XNg4hIBtW66rVv375YuXIlvvzyS7i4uGDx4sW2yoscSdPR5pdlfStdHkREMqhyoTx37hxmzJiBpk2bIj4+HtHR0Vi1apUtcyNHoXABIs0cZt0xUtpciIgkVqlCmZ+fj6+++goPPvggWrRogRUrVuCpp57CqVOnkJaWhlGjRtkrT5Jb1D+P2/JpCjQZJl52MVXydIiIpGL1g5ufe+45fPPNN8jPz8ejjz6KDRs2IC4uDgqFouKVqeZTugPt/wsE9gP8u4qfXbl1IJ9VSUROy+pCuWvXLsyaNQtjxoxB/fr17ZkTOaqo6UDxXV17aDawPsSw7PphoF57efIiIrIjqwvl4cOHcfPmTezatQtFRUXo3Lkz/P397ZkbOSJXr38aCsAzUHc/JQBs6gmM0MiWFhGRvVhdKI8cOYL+/fsjJycHgiDAz88P3333Hfr162fP/MgRHZ4BXN5sKJIAUKQFCq8D7vXky4uIyA6svpjntddeQ2hoKLZv3459+/ahV69emDRpkj1zI0d1ciFw5U/j/kNvSp8LEZGdKQQrJ25t1KgRUlNTERsbCwC4evUqGjVqBI1GA19fX7smaUtarRYqlQoajQZ+fn5yp1MznVhgerJ0gBf1EFGNYW09sHqPMi8vD6Ghofq4QYMG8Pb25lNFaqOWFmZg0p6QLg8iIglYXSgVCgVu3rwJrVYLrVYLjUZj1KfVau2ZKzkKF1eg0/+ZXvZzG2lzISKyM6sPvbq4uBjdMykIgr7vXrukpMT2WdoQD73a0Goz99A26gX0+0PSVIiIKsvaemD1Va9btmypeBDVLj3XAtsfM+7P3QoU3QTc6kifExGRjVldKHv16mXPPKgmChluaCtcAKHUEH/fGBjJQ/FEVPNV6+khRBh5B1B6AwMzgc5LDP3FN4G8XfLlRURkIyyUVD2uXkD8bcC3OdBignjZpt6ypEREZEsslGQbLkrdz7jdhr7SAkCTKU8+REQ2wkJJtnNsDnBkprhvQ1vAuguriYgcUqUKZXFxMVxdXXH06FF75UM12emlgPoX4/5fY6XPhYjIRipVKF1dXREWFubw90qSTJqNM91/fT9QUihpKkREtlLpQ6/Tp09HUlISrl27Zo98qCaLnGZ+2U8tpcuDiMiGrL6P8p4FCxbg1KlTCA4ORlhYGHx8fETL9+/fb7PkqIZRKIDo+cD+BONld7KAy1uAgD5SZ0VEVC2VLpRDhw61QxrkNNpMNl0oAWDzg8DIW4Crj+nlREQOyOq5Xu1l4cKF+L//+z+o1WpERkZi/vz56NmzZ4Xr/fnnn+jVqxeioqJw8OBBqz+Pc71K4MyXwK6nzC8fXaKbyYeISEY2f8yWPaSkpCAhIQHTpk3DgQMH0LNnTwwYMABZWVkW19NoNBg7diz69u0rUaZUKc3GimPXcs8r/fU+6XIhIqqmSu9RlpSUYN68efjmm2+QlZWFwkLx1YyVucinS5cuiI6OxqJFi/R9ERERGDp0KJKTk82uN2rUKLRs2RJKpRLr16+3uEdZUFCAgoICfazVahESEsI9Snsr1ADrAoD+GUDdSOCHZsDts4bljxwG6raTLz8iqvXstkc5a9YsfPDBBxg5ciQ0Gg0SExMxfPhwuLi4YObMmVa/T2FhITIyMhAXFyfqj4uLQ3p6utn1Pv/8c5w+fRozZsyw6nOSk5OhUqn0r5CQEKtzpGpwVwGPngW8g3Vx/wzx8tT20udERFQFlS6Uq1atwmeffYapU6fC1dUVo0ePxtKlS/HWW29h1y7rJ8HOy8tDSUkJAgICRP0BAQHIyckxuc7Jkyfx+uuvY9WqVXB1te46pKSkJGg0Gv0rOzvb6hypmryCAPd6uvbZFcbLj74taTpERFVR6UKZk5ODdu10h8x8fX2h0WgAAIMGDcKGDRsqnYClh0GXVVJSgieeeAKzZs1Cq1atrH5/Dw8P+Pn5iV4ksbs5gIuHcf/h6cA13k5ERI6t0oWySZMmUKvVAIAWLVpg48aNAIC9e/fCw8PEf4Zm+Pv7Q6lUGu095ubmGu1lAsDNmzexb98+TJo0Ca6urnB1dcXs2bNx6NAhuLq64vfff6/sVyGpXEoF9v3H9LJfY6TNhYiokipdKIcNG4bNmzcDACZPnow333wTLVu2xNixYzF+/Hir38fd3R0xMTFIS0sT9aelpaF79+5G4/38/HDkyBEcPHhQ/5o4cSJat26NgwcPokuXLpX9KiSV5hX8vbhxTJo8iIiqoNITDsyZM0fffvzxx9GkSROkp6ejRYsWGDJkSKXeKzExEWPGjEFsbCy6deuGJUuWICsrCxMnTgSgO7948eJFfPnll3BxcUFUVJRo/UaNGsHT09OonxzQwGPAhkjTy3Y9BfTfJ20+RERWqnShLK9r167o2rVrldaNj4/H1atXMXv2bKjVakRFRSE1NRVhYWEAALVaXeE9lVRDqNoCLZ4DTi0xXnYtw7iPiMhBWHUf5Y8//mj1G1Z2r1JqnJlHRoIArDFztL/ta0DHOaaXERHZgbX1wKpC6eJi3alMhULh8I/gYqGUWUkBkOJpetkIDeDGPxMikoZNJxwoLS216uXoRZIcgNIDGJ6raw/6S7zsmPnZmIiI5MKZqUl6ng2BYTm6p4i0fd3Qf3KhfDkREZlRpUK5detWDB48GC1atEDLli0xZMgQbN++3da5kTPzCgC8mwDtykxFWKSVLx8iIjMqXShXrlyJfv36wdvbGy+99BImTZoELy8v9O3bF6tXr7ZHjuTMzpX7O5PLX7iIyLFU+ukhEREReO655zBlyhRR/wcffIDPPvsMmZmZNk3Q1ngxj4PZMgBQ/2qI3VTAiBuypUNEtYfdnh5y5swZDB482Kh/yJAhOHv2rIk1iCx4YL04LtIA+XmypEJEZEqlC2VISIh+CruyNm/ezEdYUeUpPYD4fHHf+iby5EJEZEKlZ+Z5+eWX8dJLL+HgwYPo3r07FAoFduzYgRUrVuDDDz+0R47k7JQegH8PIO9PXVxaAJTkA0oz91sSEUmo0ucoAeD777/H+++/rz8fGRERgVdeeQWPPvqozRO0NZ6jdFBFt4Bv6xhi3xbAkJPy5UNETs+mM/M4ExZKB7a63HNI4/N1e5tERHZgt4t5srOzceHCBX28Z88eJCQkYMkSE5NdE1VGoz6GtioKKOBFPUQkv0oXyieeeAJbtmwBAOTk5KBfv37Ys2cP3njjDcyePdvmCVItEjHV0L59HvBuLF8uRET/qHShPHr0KDp37gwA+Oabb9CuXTukp6dj9erVWLFiha3zo9oksJ+hXXxTvjyIiMqodKEsKiqCh4fuvNGmTZv0j9Vq06YN1Gq1bbOj2kXpLo5z/gBu83mkRCSvShfKyMhIfPrpp9i+fTvS0tLQv39/AMClS5fQoEEDmydItUxAX0N760DghzDgxlH58iGiWq/ShfLdd9/F4sWL0bt3b4wePRodOnQAoHu4871DskRVFjPP0C65o/uZ2k6eXIiIUIUJB3r37o28vDxotVrUq1dP3//cc8/B29vbpslRLVTXTFHUngT8WkqbCxERqviYLaVSKSqSANC0aVM0atTIJklRLadqa9z3cyvp8yAiAh/cTI6odYLp/qJbkqZBRASwUJIjCn7EdH/GS9LmQUQEFkpyROYmGjjzubR5EBGBhZIcVf0Y0/13LpjuJyKyE6sLZWhoKK5evaqPP/74Y2i1WrskRYQ2U4374u8CSi/pcyGiWs3qQnnhwgWUlJTo4zfeeAN5eZy0muwkZLhxn8IV8OCkFkQkrSofeq1lT+ciqSndAfcytyA1fhRQKOXLh4hqLZ6jJMf1YJqhffEHQKEABEF3nrLgmnx5EVGtUqmZeZYuXQpfX18AQHFxMVasWAF/f3/RmJde4iX8ZCN1O4rjrG+B3c8CRVrAtQ4wkufIicj+FIKVx1CbNm0KhUJhcYxCocCZM2dskpi9WPtEa3IQ39YFijS6dvv/AYenG5aNvAO48uIeIqoaa+uB1XuU586ds0VeRJXT/Fngr/d17bJFEgC2DREfniUisgOeoyTHFvGKOO4wx9DO2aQ7Z0lEZEdWF8rff/8dbdu2NXnvpEajQWRkJLZt22bT5IjgFSCOz68Rx9lrpcuFiGolqwvl/PnzMWHCBJPHcVUqFZ5//nnMmzfPxJpE1RSzwNC+cQhoMtQQ7xgheTpEVLtYXSgPHTqE/v37m10eFxeHjIwMmyRFJNJqkji+8IM41p6QLhciqnWsLpSXL1+Gm5ub2eWurq64cuVKpRNYuHAhwsPD4enpiZiYGGzfvt3s2B07dqBHjx5o0KABvLy80KZNG+7F1gYKBdBlWZmOcucl90yUNB0iql2sLpSNGzfGkSNHzC4/fPgwgoKCKvXhKSkpSEhIwLRp03DgwAH07NkTAwYMQFZWlsnxPj4+mDRpErZt24bMzExMnz4d06dPx5IlSyr1uVQDNR8vjjv+c1FPh2Qg9iPp8yGiWsPq+yhffPFF/PHHH9i7dy88PT1Fy+7evYvOnTujT58+WLBggZl3MNalSxdER0dj0aJF+r6IiAgMHToUycnJVr3H8OHD4ePjg6+++sqq8byPsgbbP9Vwq0idVsD93wB+rQGlp+X1iIhMsLYeWL1HOX36dFy7dg2tWrXC3Llz8cMPP+DHH3/Eu+++i9atW+PatWuYNm2a1QkWFhYiIyMDcXFxov64uDikp6db9R4HDhxAeno6evXqZXZMQUEBtFqt6EU1VNmJ0m/+DdRtbyiSxXfkyYmInJ7VEw4EBAQgPT0d//73v5GUlKSfFF2hUODhhx/GwoULERAQUMG7GOTl5aGkpMRonYCAAOTk5Fhct0mTJrhy5QqKi4sxc+ZMPPvss2bHJicnY9asWVbnRQ7Mv5s4Pp4MRL6hu5fy+Fyg+BYQ/Z48uRGR06rUXK9hYWFITU3F9evXcerUKQiCgJYtW6JevXoVr2xG+WnxBEGocKq87du349atW9i1axdef/11tGjRAqNHjzY5NikpCYmJifpYq9UiJCSkyvmSjBQKoMVzwKl/zkkfmgaEPAb8GqsrkgDQJgHwbiJbikTkfCpVKO+pV68e7rvvvmp9sL+/P5RKpdHeY25uboV7puHh4QCAdu3a4fLly5g5c6bZQunh4QEPD49q5UoOJOZDQ6EEgMx5hiIJ6CZN7/Or9HkRkdOSbQo7d3d3xMTEIC1NPFdnWloaunfvbvX7CIKAgoICW6dHjkrpCTTobIhPLwa6rjDE6t90TxchIrKRKu1R2kpiYiLGjBmD2NhYdOvWDUuWLEFWVhYmTtTdF5eUlISLFy/iyy+/BAB88sknCA0NRZs2bQDo7qt877338OKLL8r2HUgG/bYDKWWOElz8Sbx8QyQwNFvanIjIaclaKOPj43H16lXMnj0barUaUVFRSE1NRVhYGABArVaL7qksLS1FUlISzp49C1dXVzRv3hxz5szB888/L9dXIDko3YG2rwPH/7mXMnst0G4mcGSmLr5zAVCnAUEPyZUhETkRq++jdBa8j9JJCKXAGqUhDh0NZJWbMP2JWvVXm4gqyeb3URI5FIWLeA7YrDXAkHIPDT+3WtqciMgpsVBSzRVTbhaoG0cBvwjAsxHQcS5QP0aevIjIqbBQUs2lUABeZeYX3jYEeHgX0HQM0Pol3fR2RETVxEJJNVv/co92O71cNzuPkvfOEpFtsFBSzeZV7ok1+6foprQDgNIS4MwK3YU/RERVxEJJNd/I2+L47BfA5a3AzjHArqeB7Y/JkxcROQUWSqr5XL2B4EcM8a6ngau7gfP/3C5yYb0saRGRc2ChJOfQZZk49m0pjg+8Jl0uRORUWCjJOXgF6p5Pec+O4UCjMs8pzZxrOHdJRFQJLJTkPB5YL46L88XxsXckS4WInAcLJTkP33CgYU9DfG03UC/aEB+ezr1KIqo0FkpyLv22iuPr+8XxTy2ky4WInAILJTkXhcK4WJZ16wyQnytdPkRU47FQkvNp9ADQJtG4P3I68GiWbi5YIiIrsVCSc4p+37iv5UTAJ0T6XIioRmOhJOc19KI4Vv+m+ymUApkfACX5xusQEZXDQknOyzsYcPUxxLuf0T2K6+9PgCNvAb91li83IqoxXOVOgMiuhpwD1jU0xKntDO0bR4CSQkDpLnlaRFRzcI+SnJunP9Ah2fzy3+6TLhciqpFYKMn5Rb4OeAaaXnbjMFBaJG0+RFSjsFBS7TDskvllv8ZKlwcR1TgslFQ7KBRA719ML7txGCi6JW0+RFRjsFBS7RHcH1B6m172bR1pcyGiGoOFkmoXS4dg71w0v4yIai0WSqpd3FW6Ke7KCnoE6PMb4NFAnpyIyKGxUFLt03eLOL59BgiKA5Se8uRDRA6NhZJqH4UL0CPFEGv/0k1rV1oMqDfymZVEJMJCSbVT2EhxvLkvkJ8DHEsG/oyXJycickicwo5qr8g3gGPv6Nq5fwDryzxZRBB0t5QQUa3HPUqqvdr/z/yy7+pLlwcROTQWSqq9FAqg6+emlxXdAHK3S5oOETkmFkqq3ZqNM79s0wO6i3yIqFZjoSRq+YKhXbe9eNmGttLmQkQOh4WSqNN7hvaNw0CjXoZYewK48JP0ORGRw2ChJHL1Ej+Gq+kY8fJLqdLmQ0QORfZCuXDhQoSHh8PT0xMxMTHYvt38BRTr1q3DQw89hIYNG8LPzw/dunXDb7/9JmG25LQa9jC09zxrmBP20Syg8yJ5ciIihyBroUxJSUFCQgKmTZuGAwcOoGfPnhgwYACysrJMjt+2bRseeughpKamIiMjA3369MHgwYNx4MABiTMnpxP7kTjO3QaMLgW8guXJh4gchkIQ5Juvq0uXLoiOjsaiRYbf2CMiIjB06FAkJydb9R6RkZGIj4/HW2+9ZXJ5QUEBCgoK9LFWq0VISAg0Gg38/Pyq9wXIuWyIBDTHdW3PRsDwy4Zldy7qZu6pHyNPbkRkc1qtFiqVqsJ6INseZWFhITIyMhAXFyfqj4uLQ3p6ulXvUVpaips3b6J+ffM3hycnJ0OlUulfISEhZsdSLdenzGH8/FxD+8THwO4JwK+xQN5u6fMiIlnJVijz8vJQUlKCgIAAUX9AQABycnKseo/3338ft2/fxsiRI82OSUpKgkaj0b+ys7OrlTc5Me8m4njfZN3P/ZMB9S+6dsZL0uZERLKT/WIeRbn5NAVBMOozZc2aNZg5cyZSUlLQqFEjs+M8PDzg5+cnehGZFfGqof33At3P/hmGvqt7OAkBUS0jW6H09/eHUqk02nvMzc012sssLyUlBc888wy++eYb9OvXz55pUm3TcY44vrwVqNcR8Gps6Ps+SNKUiEheshVKd3d3xMTEIC0tTdSflpaG7t27m11vzZo1GDduHFavXo2BAwfaO02qbRQKoF4nQ7y5t+5n8COGvvxc4MZRSdMiIvnIeug1MTERS5cuxfLly5GZmYkpU6YgKysLEydOBKA7vzh27Fj9+DVr1mDs2LF4//330bVrV+Tk5CAnJwcajUaur0DOqNsX4ri0GOj8qbhv62Dp8iEiWclaKOPj4zF//nzMnj0bHTt2xLZt25CamoqwsDAAgFqtFt1TuXjxYhQXF+M///kPgoKC9K/JkyfL9RXIGdVtJ45PLAAULsDDewx9t88BxXckTYuI5CHrfZRysPa+GarlDk0zPNTZvT7w+FVde3WZC83uWwS0nCh9bkRkEw5/HyWRQyt79WvhNeDe75Pt/6v7GfMhEPCg9HkRkeRc5U6AyCG5q8TxzZOAXysgajoQ8jjg11p34Q8ROT3uURKZExpvaB9/19BWtdEVyfw8oPiu9HkRkaRYKInMCStTKM8sN15+8UfgG2+gtEi6nIhIciyUROY0HiKO75aZHEOTCSj+OXPB51USOTUWSiJzXJSGYgjoJka/5+wXwK6ndO3tj0ubFxFJioWSyJIeXxval342tFs8Z2gLxcBtTrZP5KxYKIksCX1MHOds0v30bSbuTy03SQEROQ0WSqKK+LUxtP/+2NDutcHQFkoM91oSkVNhoSSqyAPrDe0LPxjajctMlF58CyjIkywlIpIOCyVRReq0EscF1wztlv82tE8ukiYfIpIUCyVRRcrPwJNRZhJ+zzLPTj35iTT5EJGkWCiJrNHtS0P73EpDu80UQ9ujkXT5EJFkWCiJrBH2hDguuqn76eYHRM8HBp0ABh6RPC0isj8WSiJruCgBtzITpe80PFAcbSbrJkwnIqfEQklkrU5zDe0L642XlxbzFhEiJ8RCSWSt5hPEcfnZeIRS4NjbQEmhdDkRkd2xUBJZS6EQz/1adq9y93PAukbA4TeBy1skT42I7IeFkqgyYsvMzHPlT0Pboz5QpNG1T30qbU5EZFcslESV4d/N0M5KMbSbDDW0TZ2/JKIai4WSqDLqlpv8/OYp3c8GncX9Qqk0+RCR3bFQElWGQgH4hBniQ2/801/un9IN3lNJ5CxYKIkqq90sQzvrW0M7pMwDnC/9Il0+RGRXLJREldX0X+L41jndz5Ayz668vFmydIjIvlgoiSrLRQn4tjDEO8fofjbqaejT/iVtTkRkNyyURFUR86GhfWWHbvIB78aGvjsXOEsPkZNwrXgIERkJHiCOfwgFRpfqLvTxCQPqRQOlRYDSXZ78iMhmWCiJqkKhALqvAdJHG/rWhwBDTgPFtwCPBvLlRkQ2xUOvRFXVdJT4vsq7F4Hza1gkiZwMCyVRdTxyWBzvehoovqtrX/oVyForfU5EZFMslETVNUwtjn+NBY6/C/w1D9jxOJCfJ09eRGQTLJRE1eUVCLRNMsTa48DB14Gcjbp4XUN58iIim2ChJLKFju+IY6WnOD7/jXS5EJFNsVAS2UrHuYZ2Sb542Z/xhnOXRFSjyF4oFy5ciPDwcHh6eiImJgbbt283O1atVuOJJ55A69at4eLigoSEBOkSJapI21csL//GW5o8iMimZC2UKSkpSEhIwLRp03DgwAH07NkTAwYMQFZWlsnxBQUFaNiwIaZNm4YOHTpInC2RFYbliGOljzg+uUi6XIjIJhSCIN88W126dEF0dDQWLTL85xEREYGhQ4ciOTnZ4rq9e/dGx44dMX/+/Ep9plarhUqlgkajgZ+fX1XSJrJs51PA2S+N+32bAy7uwKDj0udEREasrQey7VEWFhYiIyMDcXFxov64uDikp6fb7HMKCgqg1WpFLyK76rrCdL9vc6DXj5wDlqiGka1Q5uXloaSkBAEBAaL+gIAA5OTkmFmr8pKTk6FSqfSvkJAQm703kUkKBfCoidMHJflAnRa65URUY8h+MY+i3H8agiAY9VVHUlISNBqN/pWdnW2z9yYyyycEiJ4v7rvxzyw+JfmAJlPylIioamQrlP7+/lAqlUZ7j7m5uUZ7mdXh4eEBPz8/0YtIEm0mAw3LPKOy6AaQ+T5QqAFSo4DDM2RLjYisJ1uhdHd3R0xMDNLS0kT9aWlp6N69u0xZEdnYQ9vE8YGpwPeBgFAKHJ0NaPiAZyJHJ+uh18TERCxduhTLly9HZmYmpkyZgqysLEycOBGA7rDp2LFjRescPHgQBw8exK1bt3DlyhUcPHgQx4/zKkJyYI9ZmOt1Q4R0eRBRlcj6PMr4+HhcvXoVs2fPhlqtRlRUFFJTUxEWFgZAN8FA+XsqO3XqpG9nZGRg9erVCAsLw7lz56RMnch6Hg2AftuBTT1NL8/bBfh3lTYnIrKarPdRyoH3UZJsVlu4SO2JWvXPkMghOPx9lES1Tp/fzC878ZF0eRBRpbBQEkklKM78soyXgLtq88uJSDYslERS6rLU/LK/P5YuDyKyGgslkZSaP2OiUwE8eg7o8LbU2RCRFVgoiaTWZ6M4Dh4AeDWRJxciqhALJZHUgh4CggcZ4kupQPE/k/VfOwAU3pAlLSIyjYWSSA69fhTHawOBrO+Aw9OB7+rJkxMRmcRCSSQHhQKI/cQQC4XAjhG6vUsA2PW0PHkRkREWSiK5tHpBHLuX2ZM8swIoKZQ0HSIyjYWSSE4xZSYaKLwuXnbwVWlzISKTWCiJ5NRigjhuMtzQPvGhtLkQkUkslERyUnqIY/Wv4vjUEulyISKTWCiJ5Db4lKFdckd8rnLP80BJgfQ5EZEeCyWR3Oo0F8flz1WeWyVdLkRkhIWSyBH02y6OgwYALu7A/d8AQf3lyYmIAMj84GYi+kej+8Wx+hdgVBGgUOruuSQi2XCPkshRDM8Vx8feZpEkcgAslESOwrMh4NfaEB+ZCZTkA/l5QEYCIJTKlRlRrcZDr0SOpN8OYF1DQ5zaAbj5t67t2wJoPUmevIhqMe5REjkST3+g3UxDfK9IAkDGi4AgSJ4SUW3HQknkaCKnm1+W9Y10eRARABZKIsfjogSCHzG97M9R0uZCRCyURA7pgR/NL8vdIV0eRMRCSeSQXJRAi+dNL9vUEzi5WNp8iGoxFkoiR3XfIvPL9k4Ebp+XLheiWoyFkshRKRRA1FvG/Z4BwIBDgHeI9DkR1UIslESOrP0soFW5eyfzLwOZ/wcoXICLPwMXN8iTG1EtwUJJ5OhiPwJGaMV951bqZuq5fgjYOgjIWitPbkS1AAslUU3gVgcYeVvc91NrID9H197xOFBaLH1eRLUACyVRTeHqDUTNMMS3TgF/f2yIv3YDbhyTPi8iJ8dCSVSTtJtheXlqFHBktjS5ENUSLJRENYlCAQw5bXnMkRnAagWQn2t5HBFZhYWSqKbxbWb87MrymjwK5O2WJh8iJ8dCSVQTeTYERpcAno1ML/dtATQZLG1ORE6KhZKoplK4AMMvA/d/a7zMxcPQzr+ie65l3i7pciNyIrIXyoULFyI8PByenp6IiYnB9u3bLY7funUrYmJi4OnpiWbNmuHTTz+VKFMiBxX6ONB1hbjv+DtAzu/AsWTg94eAG4eBjd2AFG9Ae0KWNIlqKlc5PzwlJQUJCQlYuHAhevTogcWLF2PAgAE4fvw4QkNDjcafPXsWjzzyCCZMmICVK1fizz//xAsvvICGDRvisccek+EbEDmIZk8BSk/xY7h+7wuoogDNUUNfyV3g5za6duOhgEcDoNl4wF1lGFNaZP08st6hgNLdEBdqgIIrFa+ncAV8m4r77qqB4tsmh4u4+Rkfcr55GoAVD7X2DATcfA1x8R3g7qWK1wN054YVZfYtCvKAwhsVr6f0BLybiPtuZwGlhRWv614f8KhviEuLgdvnrMlW95lKT0NcpLXuAi+Fi+67lnU3Byi+VfG6rr6AV6C479YZ3eQYFfFspPuzvackH7hzwfx4ryDdZ/kY1wpbUwiCfI9M79KlC6Kjo7FokWHy54iICAwdOhTJyclG41977TX8+OOPyMzM1PdNnDgRhw4dws6dO01+RkFBAQoKCvSxVqtFSEgINBoN/Pz8TK5DVGPlbAZ+7yd3FkTSaDIMeGBdlVfXarVQqVQV1gPZDr0WFhYiIyMDcXFxov64uDikp6ebXGfnzp1G4x9++GHs27cPRUVFJtdJTk6GSqXSv0JCOJE0ObHAvkDfLXJnQeRUZDv0mpeXh5KSEgQEBIj6AwICkJOTY3KdnJwck+OLi4uRl5eHoKAgo3WSkpKQmJioj+/tURI5rYDewKhi4NAbQOFV4+XFd4HSAkD7t+6nb3PAO1i8/No+6z6rfizg6mWI71765zBoBZSeQIP7xH2a40CBiXzL8woC6rQQ913507rDe6oIwMPfEBfeAG4cqXg9AGjYQ3zo9dZZy4cG73FTAfXai/uuHbDuUKZPU8CnzP9XpYXW3/ZTr6Nu6sN78i/r/swr4uIG+HcV92lPWHfY1rMR4Nda3Je3S3c4vyJ+rXRPxrmn6BZw/YD58apI3S+GEpD1HCUAKBQKUSwIglFfReNN9d/j4eEBDw8Pk8uInJaLEuj0rtxZEDkF2Q69+vv7Q6lUGu095ubmGu013hMYGGhyvKurKxo0aGC3XImIqPaSrVC6u7sjJiYGaWlpov60tDR0797d5DrdunUzGr9x40bExsbCzc3NbrkSEVHtJet9lImJiVi6dCmWL1+OzMxMTJkyBVlZWZg4cSIA3fnFsWPH6sdPnDgR58+fR2JiIjIzM7F8+XIsW7YMU6dOlesrEBGRk5P1HGV8fDyuXr2K2bNnQ61WIyoqCqmpqQgLCwMAqNVqZGVl6ceHh4cjNTUVU6ZMwSeffILg4GAsWLCA91ASEZHdyHofpRysvW+GiIicm8PfR0lERFQTsFASERFZwEJJRERkAQslERGRBSyUREREFrBQEhERWcBCSUREZAELJRERkQUslERERBbI/pgtqd2biEir1cqcCRERyeleHahogrpaVyhv3rwJAHx4MxERAdDVBZVKZXZ5rZvrtbS0FJcuXUKdOnUsPiC6IlqtFiEhIcjOzuacsVXA7Vd93IbVw+1XfTV9GwqCgJs3byI4OBguLubPRNa6PUoXFxc0adLEZu/n5+dXI/+COApuv+rjNqwebr/qq8nb0NKe5D28mIeIiMgCFkoiIiILWCiryMPDAzNmzICHh4fcqdRI3H7Vx21YPdx+1VdbtmGtu5iHiIioMrhHSUREZAELJRERkQUslERERBawUBIREVnAQlkFCxcuRHh4ODw9PRETE4Pt27fLnZIskpOTcd9996FOnTpo1KgRhg4dihMnTojGCIKAmTNnIjg4GF5eXujduzeOHTsmGlNQUIAXX3wR/v7+8PHxwZAhQ3DhwgXRmOvXr2PMmDFQqVRQqVQYM2YMbty4Ye+vKKnk5GQoFAokJCTo+7j9Knbx4kX861//QoMGDeDt7Y2OHTsiIyNDv5zb0Lzi4mJMnz4d4eHh8PLyQrNmzTB79myUlpbqx3D7ARCoUr7++mvBzc1N+Oyzz4Tjx48LkydPFnx8fITz58/LnZrkHn74YeHzzz8Xjh49Khw8eFAYOHCgEBoaKty6dUs/Zs6cOUKdOnWEtWvXCkeOHBHi4+OFoKAgQavV6sdMnDhRaNy4sZCWlibs379f6NOnj9ChQwehuLhYP6Z///5CVFSUkJ6eLqSnpwtRUVHCoEGDJP2+9rRnzx6hadOmQvv27YXJkyfr+7n9LLt27ZoQFhYmjBs3Tti9e7dw9uxZYdOmTcKpU6f0Y7gNzfvf//4nNGjQQPj555+Fs2fPCt9++63g6+srzJ8/Xz+G208QWCgrqXPnzsLEiRNFfW3atBFef/11mTJyHLm5uQIAYevWrYIgCEJpaakQGBgozJkzRz8mPz9fUKlUwqeffioIgiDcuHFDcHNzE77++mv9mIsXLwouLi7Cr7/+KgiCIBw/flwAIOzatUs/ZufOnQIA4a+//pLiq9nVzZs3hZYtWwppaWlCr1699IWS269ir732mnD//febXc5taNnAgQOF8ePHi/qGDx8u/Otf/xIEgdvvHh56rYTCwkJkZGQgLi5O1B8XF4f09HSZsnIcGo0GAFC/fn0AwNmzZ5GTkyPaXh4eHujVq5d+e2VkZKCoqEg0Jjg4GFFRUfoxO3fuhEqlQpcuXfRjunbtCpVK5RTb/T//+Q8GDhyIfv36ifq5/Sr2448/IjY2FiNGjECjRo3QqVMnfPbZZ/rl3IaW3X///di8eTP+/vtvAMChQ4ewY8cOPPLIIwC4/e6pdZOiV0deXh5KSkoQEBAg6g8ICEBOTo5MWTkGQRCQmJiI+++/H1FRUQCg3yamttf58+f1Y9zd3VGvXj2jMffWz8nJQaNGjYw+s1GjRjV+u3/99dfYv38/9u7da7SM269iZ86cwaJFi5CYmIg33ngDe/bswUsvvQQPDw+MHTuW27ACr732GjQaDdq0aQOlUomSkhK8/fbbGD16NAD+HbyHhbIKyj+eSxCEaj2yyxlMmjQJhw8fxo4dO4yWVWV7lR9janxN3+7Z2dmYPHkyNm7cCE9PT7PjuP3MKy0tRWxsLN555x0AQKdOnXDs2DEsWrQIY8eO1Y/jNjQtJSUFK1euxOrVqxEZGYmDBw8iISEBwcHBeOqpp/Tjavv246HXSvD394dSqTT6DSg3N9foN67a5MUXX8SPP/6ILVu2iB5hFhgYCAAWt1dgYCAKCwtx/fp1i2MuX75s9LlXrlyp0ds9IyMDubm5iImJgaurK1xdXbF161YsWLAArq6u+u/G7WdeUFAQ2rZtK+qLiIhAVlYWAP4drMgrr7yC119/HaNGjUK7du0wZswYTJkyBcnJyQC4/e5hoawEd3d3xMTEIC0tTdSflpaG7t27y5SVfARBwKRJk7Bu3Tr8/vvvCA8PFy0PDw9HYGCgaHsVFhZi69at+u0VExMDNzc30Ri1Wo2jR4/qx3Tr1g0ajQZ79uzRj9m9ezc0Gk2N3u59+/bFkSNHcPDgQf0rNjYWTz75JA4ePIhmzZpx+1WgR48eRrck/f333wgLCwPAv4MVuXPnjtEDi5VKpf72EG6/f8hwAVGNdu/2kGXLlgnHjx8XEhISBB8fH+HcuXNypya5f//734JKpRL++OMPQa1W61937tzRj5kzZ46gUqmEdevWCUeOHBFGjx5t8tLyJk2aCJs2bRL2798vPPjggyYvLW/fvr2wc+dOYefOnUK7du1qzKXllVH2qldB4ParyJ49ewRXV1fh7bffFk6ePCmsWrVK8Pb2FlauXKkfw21o3lNPPSU0btxYf3vIunXrBH9/f+HVV1/Vj+H24+0hVfLJJ58IYWFhgru7uxAdHa2/HaK2AWDy9fnnn+vHlJaWCjNmzBACAwMFDw8P4YEHHhCOHDkiep+7d+8KkyZNEurXry94eXkJgwYNErKyskRjrl69Kjz55JNCnTp1hDp16ghPPvmkcP36dQm+pbTKF0puv4r99NNPQlRUlODh4SG0adNGWLJkiWg5t6F5Wq1WmDx5shAaGip4enoKzZo1E6ZNmyYUFBTox3D7CQIfs0VERGQBz1ESERFZwEJJRERkAQslERGRBSyUREREFrBQEhERWcBCSUREZAELJRERkQUslERERBawUBLVML1790ZCQoLcaRDVGiyUREREFrBQElGFCgsL5U6BSDYslEQ1UGlpKV599VXUr18fgYGBmDlzpn5ZVlYWHn30Ufj6+sLPzw8jR44UPQtw3LhxGDp0qOj9EhIS0Lt3b33cu3dvTJo0CYmJifD398dDDz0EAJg5cyZCQ0Ph4eGB4OBgvPTSS/b8mkQOgYWSqAb64osv4OPjg927d2Pu3LmYPXs20tLSIAgChg4dimvXrmHr1q1IS0vD6dOnER8fX6XPcHV1xZ9//onFixfju+++w7x587B48WKcPHkS69evR7t27ezw7Ygci6vcCRBR5bVv3x4zZswAALRs2RIff/wxNm/eDAA4fPgwzp49i5CQEADAV199hcjISOzduxf33Xef1Z/RokULzJ07Vx+npqYiMDAQ/fr1g5ubG0JDQ9G5c2cbfisix8Q9SqIaqH379qI4KCgIubm5yMzMREhIiL5IAkDbtm1Rt25dZGZmVuozYmNjRfGIESNw9+5dNGvWDBMmTMD333+P4uLiqn8JohqChZKoBnJzcxPFCoUCpaWlEAQBCoXCaHzZfhcXF5R/DG1RUZHROj4+PqI4JCQEJ06cwCeffAIvLy+88MILeOCBB0yuS+RMWCiJnEjbtm2RlZWF7Oxsfd/x48eh0WgQEREBAGjYsCHUarVovYMHD1r1/l5eXhgyZAgWLFiAP/74Azt37sSRI0dslj+RI2KhJHIi/fr1Q/v27fHkk09i//792LNnD8aOHYtevXrpD6U++OCD2LdvH7788kucPHkSM2bMwNGjRyt87xUrVmDZsmU4evQozpw5g6+++gpeXl4ICwuz99cikhULJZETUSgUWL9+PerVq4cHHngA/fr1Q7NmzZCSkqIf8/DDD+PNN9/Eq6++ivvuuw83b97E2LFjK3zvunXr4rPPPkOPHj3Qvn17bN68GT/99BMaNGhgz69EJDuFUP5kBREREelxj5KIiMgCFkoiIiILWCiJiIgsYKEkIiKygIWSiIjIAhZKIiIiC1goiYiILGChJCIisoCFkoiIyAIWSiIiIgtYKImIiCz4f58h+sUswYEoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "ax0 = plt.subplot(gs[0,0])\n",
    "ax0.plot(CF_city['solar'].sort_values(ascending=False).values, \n",
    "             color='orange',\n",
    "             linewidth=2,\n",
    "             label='Madrid')\n",
    "\n",
    "ax0.plot(CF_country['solar'].sort_values(ascending=False).values, \n",
    "             color='orange',\n",
    "             linewidth=2,\n",
    "             linestyle='--',\n",
    "             label='Spain')\n",
    "ax0.legend(fancybox=False, fontsize=12, loc='best', facecolor='white', ncol=1, frameon=True)\n",
    "ax0.set_xlabel('hours')\n",
    "ax0.set_ylabel('CF solar PV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot now the duration curve for onshore wind capacity factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CF onshore wind')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "ax0 = plt.subplot(gs[0,0])\n",
    "ax0.plot(CF_city['onwind'].sort_values(ascending=False).values, \n",
    "             color='blue',\n",
    "             linewidth=2,\n",
    "             label='Madrid')\n",
    "\n",
    "ax0.plot(CF_country['onwind'].sort_values(ascending=False).values, \n",
    "             color='blue',\n",
    "             linewidth=2,\n",
    "             linestyle='--',\n",
    "             label='Spain')\n",
    "ax0.legend(fancybox=False, fontsize=12, loc='best', facecolor='white', ncol=1, frameon=True)\n",
    "ax0.set_xlabel('hours')\n",
    "ax0.set_ylabel('CF onshore wind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The wind capacity factor duration curve for the country shows less extreme maximum and minimum values since wind power gets smoothed by regional integration. This strategy is more effective for wind than for solar due to the shorter correlation length of wind. "
   ]
  },
  {
   "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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}