{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2147f042",
   "metadata": {},
   "source": [
    "(cost-income-tutorial)=\n",
    "# Measure cash flows with `CostIncome`\n",
    "\n",
    "This notebook introduces the `CostIncome` class from CLIMADA, which is used to model the financial cash flows of adaptation or risk-reduction measures over time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a97e1043",
   "metadata": {},
   "source": [
    "## Quickstart\n",
    "\n",
    "A `CostIncome` object tracks:\n",
    "- **Initial (one-off) costs** — the upfront implementation cost\n",
    "- **Periodic costs** — recurring expenses (e.g., maintenance)\n",
    "- **Periodic income** — recurring revenues (e.g., insurance savings, avoided losses)\n",
    "- **Growth rates** — how costs and incomes evolve over time\n",
    "- **Custom cash flows** — arbitrary user-defined flows (layered on top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eca21610-32a8-477d-999e-54255557a3cc",
   "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>date</th>\n",
       "      <th>net</th>\n",
       "      <th>cost</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>-9705.636889</td>\n",
       "      <td>-9705.636889</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>2940.807977</td>\n",
       "      <td>-4901.346629</td>\n",
       "      <td>7842.154606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024</td>\n",
       "      <td>2970.216057</td>\n",
       "      <td>-4950.360095</td>\n",
       "      <td>7920.576152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2025</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>-5000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2026</td>\n",
       "      <td>3030.000000</td>\n",
       "      <td>-5050.000000</td>\n",
       "      <td>8080.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2027</td>\n",
       "      <td>3060.300000</td>\n",
       "      <td>-5100.500000</td>\n",
       "      <td>8160.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2028</td>\n",
       "      <td>3090.903000</td>\n",
       "      <td>-5151.505000</td>\n",
       "      <td>8242.408000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2029</td>\n",
       "      <td>3121.897135</td>\n",
       "      <td>-5203.161892</td>\n",
       "      <td>8325.059028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2030</td>\n",
       "      <td>3153.116107</td>\n",
       "      <td>-5255.193511</td>\n",
       "      <td>8408.309618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    date          net         cost       income\n",
       "0   2020     0.000000     0.000000     0.000000\n",
       "1   2021     0.000000     0.000000     0.000000\n",
       "2   2022 -9705.636889 -9705.636889     0.000000\n",
       "3   2023  2940.807977 -4901.346629  7842.154606\n",
       "4   2024  2970.216057 -4950.360095  7920.576152\n",
       "5   2025  3000.000000 -5000.000000  8000.000000\n",
       "6   2026  3030.000000 -5050.000000  8080.000000\n",
       "7   2027  3060.300000 -5100.500000  8160.800000\n",
       "8   2028  3090.903000 -5151.505000  8242.408000\n",
       "9   2029  3121.897135 -5203.161892  8325.059028\n",
       "10  2030  3153.116107 -5255.193511  8408.309618"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Axes: ylabel='Cash flow'>, <Axes: ylabel='Cumulative net'>)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RokUzt3QAAIfRvANnfl1sHjO8AAAAAOkOEIwYMULq168vBQsWlNGjR0tUVJSMGjVKzp8/Lzt37qRGAcBNXDy0VW5eCRHfnEFS+J7mzi4OAAAAnMwnvSe8/fbbUqhQIRk7dqw8/vjjUqVKFceUDADgUKe3fWPWxeo8Lt6+/tQ2AACAh0t3gEB7CWzYsEHWr18v7777rnh7e0vTpk2lWbNmZiFgAACuLybymoT8ucI8ZngBAAAAMhQgqFmzplleeeUVs717926ZPn262Y6Li5PY2FhqFgBc3LmdP0ls5HXJWais5KtQx9nFAQAAgDsGCGy9CLQHgS6bNm2S8PBwqVWrljz44IOZX0IAQKY7ve1bsy5Zv5NYLBZqGAAAAOkPEOisBVevXjW9CHRIwbPPPitNmjSRwMBAqhMA3MCNyyFy4cBG87hEg87OLg4AAADcNUAwb948AgIA4MbO/r5ExGqVfHfdL7kKlXF2cQAAAOCuAYI2bdrYH58+fdp0TS1RokRmlwsA4ABWq/V/wwvoPQAAAIB4vCSdNBHhhAkTJCgoSMqUKSOlS5eWvHnzyptvvmmeAwC4rvBT+yTizEHx8vGXYrXbOrs4AAAAcOceBK+//rp8+umnMnnyZGnUqJG5G7VlyxYZN26c3Lx5U9566y3HlBQAcMdsvQeK1GwufrnyUqMAAADIeIDgiy++kDlz5ki7du3s+zRhoQ4zGDBgAAECAHBRcbExt/IPmOEFXZxdHAAAALj7EINLly5J5cqVk+zXffocAMA1Xdi/QSLD/xG/3PmlUDWmpQUAAMAdBgi0t8CMGTOS7Nd9+hwAwLWHFxS//wnx8vF1dnEAAADg7kMMpk6dKo899pisXbtWGjRoYGYx2Lp1q5w6dUpWrlwpnmDmzJny9ttvS0hIiFSrVk2mT58ujRs3dnaxACBF0dfD5dyuVeYxsxcAAAAgU3oQNG3aVP7++2954okn5MqVK2ZYQYcOHeTQoUMe0UhetGiRDBo0yCRr3Llzp/nMrVu3lpMnTzq7aG5jyZIlprdJjhw5zFq3Qf3BsUL+/FHiom9K7qIVJagMvb0AAACQCT0IVPHixT02GeG0adOkb9++0q9fP7OtvQd+/vlnmTVrlkyaNMnZxXN5Ggzo2LGj6XmiM2Ds3bvXbL/77rvyyCOPOLt4Lk977gwdOjRJ/S1evNgE6oDbDS/Q3gN6/QAAAAAZChDs2bNH0uqee+6R7CoqKkp27NghI0eOTLC/RYsWZphFciIjI81iEx4eLp5s/Pjx9satsq210Yu0i19/Wp8TJkwgQIAUXb9wUi79vU3EYpES9QgkAQAA4A4CBLVq1UrQqEuJHhMbGyvZ1YULF8znK1KkSIL9un3u3Llkz9FeBdooTqxu3bri7e0t3bt3lxdffFEeeOAB+3P79++XwYMHm54JavTo0XLXXXdJnz59zHaFChVk+fLl0rZtWzl69KjZN3fuXDly5Ij85z//MdstW7aU9957T6pWrWp/3c2bN8tHH30kCxYsMNv6vnqcbcrKfPnyyZYtW6R3796yfft2e48JNWTIEHu5darLRo0ayeXLl82+ZcuWmbLqa6vUPpPe8U7pOtJ61GErMTExZjsoKMjU99WrV822v7+/5MmTx3wPNgUKFJDr16/LjRs3zHbOnDnNcbayeXl5Sf78+SUsLEyio6PNvsDAwATBGl9fX/NeOlwmLi7OXhca2NHXVjocQl/74sWL9vcuWLCgRERE2ANAuXPnNt+pvpfy8fGRvHnzZupnSu7fl9an1u+qVasy7XtyhWtvXb0K4s46/H3J4f+eUvqeEtfd9N2H5RcRaVQkv8w/OE5cXdVpR7n27kCjz5137elvxJyC4ta+ztndqf/nztlyq/7dVr/lXHsZxLXnvtee/kbM2XKrd7G7+v2eMU5ta7j7b1/vTfmddu2l53vS17wdi/V2rX4ROXHiRJorp0yZMpJdnT17VkqUKGF6C2iCRhsdbjFv3jw5ePBgmnoQlCpVyjQibQ1VT6I5BxIHCTSwpD1Pdu3a5dSyuQNPqr/zT7cVd1bk8+UuUXd6rTywdL0cDb8m0xvVlG4VS4mrc2bdKa496o/rz/1/+9wRv33UH9ef+yri5L9dsjxJoSYk1MasNv41+lGoUCHzOLklO9M7xnqHOHFvgdDQ0CS9Cmz0zq/WXfzFk40dO9beLV7ZeqbofqS//hT1h9TsvHDFBAdyeHtJm7LFqCwAAADcWYDgwIEDcu3aNfNYu8vbukd7Gj8/P6ldu7asWbMmwX7dbtiwodPK5U40kZ4m1NM73gEBAWatiQs1CIX01Z8OYVAasGvTpg3Vh2R9e/S0WbcuU1Ry+2YoLy0AAAA8RJpzEDz99NNmTITerXznnXfMeOvkvPHGG5Kd6ViSnj17Sp06dcwwg9mzZ5spDvv37+/sorlVI5eM+3def5qjoGzZsqYHy8KFC811CcQXFRsn3wefNY87VyhJ5QAAAODOAwSa7EC7Nv/444+ma/NPP/1kv3sZnz6X3QMEXbt2NYnqNGt8SEiIVK9eXVauXJnth1fA9WjiRE1cMmrUKJMM88knnzRJGQGbX86EyuXIaCmSw18aF3PzzHEAAABwjQBBpUqVzB1KpQ2Q//u//5PChQuLpxowYIBZAGd74YUXZPLkyWYY0A8//MBQDSQ7vOCJ8iXEh+ARAAAAbiPdtxt1GjhPDg4ArkSnTXzppZfM44kTJ952KlJ4jsuRUbL61HnzuAvDCwAAAJAG9EcG3NzAgQPNcIM//vhD1q5d6+ziwEUsCw6R6DirVMsXKFXze/bsKQAAAEgbAgSAm9NZDJ577jl7LwIg/vCCThVKUCEAAABIEwIEQDYwdOhQ8fX1lfXr18vWrVudXRw42bHwq/LHP5fFyyLSoTwBAgAAAKQNAQIgGyhVqpT06tXLPNYZDeDZvjt6xqybFi8kRXIGOLs4AAAAyO4BgqioKDl9+rScPHkywQLAOUaMGGFmGdHpSHfv3s3X4KE0kaxteEFnkhMCAADAkQGCw4cPS+PGjU1StDJlyki5cuXMUrZsWbMG4BwVK1aUzp07m8c69SE805YtW+TU1RuSy8dbWpUu6uziAAAAwI34pPeEPn36iI+Pj7lLWaxYMbFYLI4pGYB0GzVqlCxatEi++eYbmTBhggkawLPMmzfPrNuWLS45fbydXRwAAABk5wDBrl27ZMeOHVK5cmXHlAhAhtWsWVMee+wxWbFihUydOlU++eQTatOD3LhxwwSHFLMXAAAAwOFDDKpWrSoXLlxI9xsByBqvvfaaWX/xxRcmTwg8x/LlyyUsLExK5AqQhkULOLs4AAAAyI4BgvDwcPsyZcoUGT58uJlO7eLFiwme0wWAczVs2FCaNm0q0dHR8u677/J1eODwgk4VSooXw78AAADgiCEGefPmTZBrwGq1ysMPP5zgGN2nx8TGxqa3DAAc0Itgw4YNMnv2bPO4UKFC1HE2FxoaKj/99JN53Kl8SWcXBwAAANk1QLBu3TrHlwRApmnevLnUrl3b5Av54IMP5M0336R2s7mFCxeaAG3dunWlYt7czi4OAAAAsmuAQLsrA3Af2ptHew507NhRPvzwQ3n11VclMDDQ2cWCA3355Zdm3bNnT5E/V1PXAAAAcHySwlWrVsnmzZvt2x999JHUqlVLevToIZcvX05/CQA4RPv27c1sI5q0bubMmdRyNrZ//37TW0SnoO3WrZuziwMAAABPCRDonUhbMsK9e/fKkCFD5NFHH5Vjx46ZxwBcg5eXl4waNco8njZtmly/ft3ZRYKDkxPqbzH5JgAAAJBlAYLg4GAz1aFavHixtG3bViZOnGjuUNoSZAFwDd27d5cyZcrIP//8I5999pmziwMH0LwDX3311f+GFwAAAABZFSDw8/Oz34lcu3attGjRwjzOnz8/0xwCLsbX19dMS6qmTp0qUVFRzi4SMplOOXv69Gkz20ybNm2oXwAAAGRdgOCBBx4wQwk0K/rvv/8ujz32mNn/999/S8mSTK0FuJqnn35aihQpIqdOnZKvv/7a2cWBg4YXdO3aVQICAqhfAAAAZF2AYMaMGSYR1nfffSezZs2SEiVKmP06vKBVq1YZLwkAh8iRI4c9P8jkyZNNl3RkD9euXTO/xYrhBQAAAMiSaQ7jK126tPz4449J9r/33nt3XBgAjtG/f3+ZNGmSHDp0SJYuXSqdOnWiqrMB/S41SFC+fHlp2LChs4sDAAAAT+tBEN+NGzdM3oH4CwDXExgYKC+//LJ5rElFrVars4uETBxe0KtXL7FYLNQpAAAAsjZAoHerXnrpJSlcuLDkzp1b8uXLl2AB4JpeeeUVyZkzp+zcuVN+/vlnZxcHd+jMmTMmUax66qmnqE8AAABkfYBAM6L/8ssvZlpDf39/mTNnjowfP16KFy8uX3755Z2XCIBDFCxYUJ5//nl7LwK4N004GRcXJ40aNZIKFSo4uzgAAADwxADB8uXLTXBAxzBrssLGjRvL6NGjTYNj/vz5jiklgEwxdOhQM1Xppk2bzAL3pENEbAFZHV4AAAAAOCVAcOnSJSlXrpx9XLNu26Y/3LhxY6YUCoBj6Kwjffr0MY81aSHc0+7du+Wvv/4ywZ7OnTs7uzgAAADw1ACBZss+fvy4eVy1alX55ptv7D0L8ubNm/klBJCpdJiQl5eXmZpU8xHAfZMTtmvXjtwvAAAAcF6A4OmnnzZ3r9SoUaPsuQgGDx4sr776auaVDIBD6Hj1bt26mcf0InA/MTEx9uFcDC8AAABAZvJJ7wkaCLB58MEH5eDBg/LHH3+YRkfNmjUztXAAHGPkyJEmyd13330nhw4dkkqVKlHVbmLNmjVy/vx5k3SyVatWzi4OAAAAPLkHQWKlS5eWDh06EBwA3EiNGjVM93RNdjdlyhRnFwcZGF7QvXt38fX1pe4AAACQ9QECndpQcw6Eh4cneS4sLEyqVatGVnTAjegQIVuD8+TJk84uDtJAf3+XLl1qHjO8AAAAAE4LEEyfPl2effZZM3NBYkFBQWZ+9WnTpmV2+QA4SP369eWhhx4yY9rfeecd6tkN6JCQmzdvSuXKlaV27drOLg4AAAA8NUCgiQlTG+/aokUL2bFjhzhSdHS0jBgxwnSPzpUrlxQvXtzcRTt79myq582dO1csFkuSRf/Qjk8TLuoUjgEBAeaP78TzxGt37HHjxpn3zZEjhzRr1kz27dvnkM8KZIXXXnvNrD/55BMJDQ2l0t1keIH+7ulvGAAAAOCUAIEmxUptvKuPj4/8888/4kjXr1+XP//8U8aMGWPWS5Yskb///tuMpb4d7fkQEhKSYNFAgM2iRYtk0KBB8vrrr5up3xo3biytW7dO0PV66tSpppfEjBkzZPv27VK0aFFp3ry5REREOOwzA46kPQjuv/9+EyzTXkJwXSdOnJD169ebx08++aSziwMAAABPDhCUKFFC9u7dm+Lze/bskWLFiokj6VAGzeDdpUsXk3Vdu0h/+OGHpufC7cZQ6902bdDHX+LThn/fvn2lX79+UqVKFdNYKlWqlMyaNcvee0D3aQBBkzJWr15dvvjiCxO00GzwgDvSfxe2XgQfffSRXLlyxdlFQgq++uor++wxmhwWAAAAcFqA4NFHH5U33ngjSbd8dePGDRk7dqy0adNGspomSNRGTt68eVM97urVq1KmTBkpWbKkKaf2ErCJiooyQQYdJhGfbm/dutU8Dg4OlnPnziU4xt/fX5o2bWo/JjmRkZEmsVj8BXAlbdu2NUlG9drUYTZwPRqgjD+8AAAAAHBqgGD06NFy6dIlufvuu01X+x9++EGWLVtmpkjTu/n6nN5dz0oarND53Hv06JFs8kQbTeileQi0vAsWLDBDCxo1aiSHDx82z1+4cEFiY2OlSJEiCc7TbQ0KKNs6tWOSM2nSJNPzwbZorwTAlXh5edlnNHjvvfdMrxi4Fh3SdOjQIZP7pGPHjs4uDgAAADw9QKANYb1Trl3rtTHxxBNPSPv27U33ZN23ZcuWJI3nOzV//nzJnTu3fYmfNFATFnbr1k3i4uJue9dThyI89dRTUrNmTZNb4JtvvjGBDh2eEF/ipF961y7xvrQcE5/WlfZysC2nTp1K02cHslLXrl1Ngk4Nls2ZM4fKdzFffvmlWevvbp48eZxdHAAAAGRTPuk5WLvor1y5Ui5fvixHjhwxjeOKFStKvnz5HFI4TT5Yr169BHkQbMEBzUOg3f5/+eWXVHsPpHTHtG7duvYeBAULFhRvb+8kPQE0q7st6GHLWaDHxM+1EP+Y5OgwBF0AV6ZJRnWGkP79+8vbb79t1n5+fs4uFv4dArVw4UJTFwwvAAAAgEv0IIhPAwLawNbs544KDii9U3bXXXfZF+1eawsOaON+7dq1UqBAgXS/rgY2du3aZW/oa0NIpzXUBIjx6XbDhg3NY727qkGC+MfoH+4bNmywHwO4s969e5t/E6dPn7YnxIPz/fTTT3Lx4kXz+/Pwww87uzgAAADIxjIUIHCWmJgY6dSpk/zxxx9m+IHmDdA7+rpoYz0l48ePl59//lmOHTtmAgM6W4Gu9S6pzZAhQ0zX6s8++0wOHDgggwcPNjMj2I7RYQQ6DeLEiRNl6dKl8tdff0mfPn0kZ86cJgcC4O40N8fQoUPN48mTJ5t/X3A+W3JCndpQe3oAAAAAjuJWf23qnU1NNKhq1aqV4Ll169ZJs2bNzGNtuB8/ftw+Z7hO3fbcc8+ZQIImCrz33ntl48aNpgdE/DHYepduwoQJEhISYvIq6HAKHVZhM3z4cDNjw4ABA8wwCx3+sHr1asYEI9t4/vnn5a233jI9dBYvXmx668B5NPnr8uXLzWOGFwAAAMDR3KoHQdmyZc3wgOQWW3BAaXAg/rZmZj9x4oSZclBzBmhvggYNGiR5fW3467l6nE572KRJkwTPay+CcePGmQCCzqCgwws0kABkF5oMdODAgeax9pbRf1twHk2oqr2j7rnnHrMAAAAAjuRWAYK0iIiIkKNHj8qwYcOcXRTALb388suSK1cu2b17txn/DucPL6D3AAAAALJCtgsQaGJDnUpQ74QCSL/8+fPLCy+8YB7rcAN6ETiHzhSjU8vqrCvkOQEAAEBWyHYBAgB3TpN26uwe2kDdtGkTVerE3gPNmzdPMLUqAAAA4CgECAAkoQ3SZ555xp6LAFlLe20wvAAAAABZjQABgGS9+uqr4u3tbZJ6atJOZJ0tW7ZIcHCwGSrVvn17qh4AAABZggABgGSVL19eunfvbh5PmjSJWspCX375pVl36tRJcubMSd0DAAAgSxAgAJCikSNHmvWSJUvkwIED1FQW0ClUdXpDxewFAAAAyEoECACkqFq1aqaLu46JnzJlCjWVBZYvXy5hYWFSqlQpadq0KXUOAACALEOAAECqRo0aZdZfffWVHD9+nNrKouEFTz31lJniEAAAAMgq/PUJIFX333+/PPLIIxIbGyvvvPMOteVAoaGhsmrVKvO4Z8+e1DUAAACyFAECALf12muvmfWcOXPk3Llz1JiDLFy4UGJiYqRu3bpSpUoV6hkAAABZigABgNtq1qyZ1K9fXyIjI2X69OnUmIPMmzfPrOk9AAAAAGcgQADgtiwWi70XwcyZM+Xy5cvUWibbv3+//PHHH+Lj4yPdunWjfgEAAJDlCBAASJPHHntMatSoIREREfLRRx9Raw7qPdC6dWspVKgQ9QsAAIAsR4AAQNp+LLy87DMa6DCDq1evUnOZJC4uzswSoXr16kW9AgAAwCkIEABIs86dO0uFChXk4sWL8sknn1BzmWT9+vVy+vRpyZs3r7Rp04Z6BQAAgFMQIACQZjo+fsSIEeaxTnmoSQtx57788kuz7tKliwQEBFClAAAAcAoCBADSRbvAFy9eXM6ePWtv2CLjrl27JosXL7bXLQAAAOAsBAgApIu/v78MGzbMPJ4yZYrExMRQg3fg+++/N/kcypcvLw0bNqQuAQAA4DQECACk27PPPisFChSQo0ePyrfffksN3gFbL4yePXua6SQBAAAAZyFAACDdcufOLQMHDjSPJ06caLLwI/10mMbatWvtAQIAAADAmQgQAMiQl156yQQK/vrrL1mxYgW1mAFff/21Ca40atTIzA4BAAAAOBMBAgAZki9fPhkwYIB5/NZbb4nVaqUm72B4AQAAAOBsBAgAZNjgwYNN0sLffvtN1q9fT02mw+7du2Xv3r3i5+dnpjcEAAAAnI0AAYAMK1q0qPTt29eeiwDp7z3Qrl070xsDAAAAcDYCBADuyKuvvire3t4m2d7vv/9ObaaBTg2p+QcUwwsAAADgKggQALgjZcuWlSeffNI8njRpErWZBhpMOXfunBQsWFBatWpFnQEAAMAlECAAcMdGjhwpFotFvv/+e9m3bx81msbhBd27dzc5CAAAAABXQIAAwB2rUqWKPPHEE+bx5MmTqdFUhIeHm0CKYngBAAAAXAkBAgCZYtSoUWa9YMECOXbsGLWagsWLF8uNGzekcuXKUqdOHeoJAAAALoMAAYBMoY3dFi1aSGxsrLz99tvU6m2GF/Tq1csMywAAAABcBQECAJnmtddeM+vPPvtMQkJCqNlETpw4IevXrzePbYkdAQAAAFfhdgGCPn36mLtu8Zf69eunqVtv1apVxd/f36yXLl2a5JiZM2dKuXLlJCAgQGrXri2bNm1K8LzVapVx48ZJ8eLFJUeOHNKsWTMSsgHxNGnSRBo2bChRUVEybdo06iaR+fPnm/WDDz4opUuXpn4AAADgUtwuQKB0WjC9O2lbVq5cmerx27Ztk65du5qEYLt37zbrLl26yG+//WY/ZtGiRTJo0CB5/fXXZefOndK4cWNp3bq1nDx50n7M1KlTTaNnxowZsn37dilatKg0b95cIiIiHPp5AXehATv9N6RmzZolly5dcnaRXIYGGOMPLwAAAABcjY+4Ie0FoI3ztJo+fbppyNuSqOl6w4YNZr8mVFPa8O/bt6/069fPfs7PP/9sGjk6t7v+ca/7tPHToUMHc8wXX3whRYoUka+//lqef/55h3xWwN1oYK1mzZomGPfhhx/K2LFjnV0kl/DHH3/IoUOHTO+jjh07OvS9iny+3KGvDwAAgOzJLQMEOoa3cOHCkjdvXmnatKm89dZbZju1HgSDBw9OsK9ly5amwa+0O/SOHTvMXO7xacK1rVu3msfBwcFy7tw5sy9+oELfX49JKUAQGRlplvhTnAHZvReB5iLQXjvvv/++DBkyRPLkySOeztZ7QKeDpD5cGwEWAADgqXzc8e5k586dpUyZMqbRPmbMGHnooYdMA18b7MnRhr3e6Y9Pt3W/unDhgsm8ntoxtnVyx2jisZRo74Px48dn8NMC7knvkFesWFEOHz4ss2fPlqFDh4on0yCkrbeSDnECsjMCLAAAuC8fV0/oFf/O/E8//WTuStpUr17dTK2mwYIVK1bYu/4nJ/F0YjpkIPG+zDomPh3OoHdQ4/cgKFWqVIrHA9mBt7e36ZGjw3beffddefHFF03yT0+1atUquXjxohka9cgjjzi7OABcGAEWAIAzuXSAoF27dlKvXj37dokSJZIcU6xYMRMg0DuVKdE/ym09AGxCQ0PtvQEKFixoGjSpHWPLeaDH6Hsmd0xytFdDSj0bgOzsqaeeMvkHTp8+bfJ1eHKeDtvwAp3a0MfHpX92AcBtEVwBgDvn0n+p6jjd243V1btyp06dStBoT6xBgwayZs2aBHkIVq9ebaZjU35+fmZaQz1Gxwfb6Pbjjz9uHuv0hxok0H333nuvvduwJjucMmXKHX9WILvRf1evvvqqDBw40Pwb0d4Entg4vnz5sixffitpIMMLAACuigALAOVWf61fvXpVxo0bZ8Y3a0Dg+PHjJhma9gCI37BPTBsoOj+7NlK0wf/DDz/I2rVrZfPmzfZjdBiA/vGuQxY0oKDjpnWKw/79+5vndRiBToM4ceJEM7ZaF32cM2dO6dGjR5Z8fsDd6Kwgb775pskXolOJ6h10T/PNN9+YYOI999xjZncAAADZDwEW6i+78BI3osMA9u7daxr5d999t/Tu3dusdZaC+D0N+vTpI82aNbNva0+BhQsXyueff27+SJ87d65prMQfvqC5DXRWgwkTJkitWrVk48aNsnLlSjN8wWb48OEmSDBgwAATSDhz5ozpiUBGciB5GkCz9dzRhJ1xcXEeO7ygV69ezi4KAAAAkCqLVbPsZTMaHNBFexu4Gk1SGBQUJGFhYRIYGOjs4gAOd+XKFRNo02v/+++/tw/buZ3zT7cVd7+TcPToUbnrrrvEy8vL5GJIbSgUAAAA4Gxu1YMgLSIiIswf5cOGDXN2UQCISN68ec0sBkqH5WTDmGSK5s2bZ9bNmzcnOAAAAACXl+0CBNrdX5MW5s6d29lFAfAvHZqj0xz+/vvv8ssvv3hEvWggxBYgYHgBAAAA3EG2CxAAcD2FCxeWZ5991t6LwBNs3bpVjh07ZoKV7du3d3ZxAAAAgNsiQAAgS+iwH53mUHsQ/Prrrx6TnLBTp04mWSMAAADg6ggQAMgSpUuXNlOJ2mY0yM5uxsSamVIUwwsAAADgLggQAMgyI0aMEIvFIsuWLTNTlmZXa06fNzOVlCpVSpo2bers4gAAAABpQoAAQJapVKmS6XKvJk+enG1r/tujZ8z6qaeeMlMcAgAAAO6Av1wBZKlRo0aZ9cKFC82UpNnNhZuR8svpUPPYNqQCAAAAcAcECABkqXvvvVdat24tcXFxMnXq1GxX+98Hn5UYq1Xq1KkjVapUcXZxAAAAgDQjQAAgy7322mtmPXfuXDlz5lZ3/Ozi2yOnzZrkhAAAAHA3BAgAZLkHHnhAGjduLFFRUTJt2rRs8w38fSVCdl8MEx+LRbp16+bs4gAAAADpQoAAgFN7EXz88cdy8eLFbPEtfPdvcsKHShaWQoUKObs4AAAAQLoQIADgFC1btjT5CK5fvy4ffPCB238LcVarLD52a3hB5wolnF0cAAAAIN0IEABwCovFYu9FoAGCiIgIt/4mtp67KGeu3ZQgP19pXrKIs4sDAAAApBsBAgBO88QTT0ilSpXkypUrZqiBO/v26K3eA+3KFpMAH29nFwcAAABINwIEAJzG29tbRo4caR6/++67cuPGDbf8Nq7HxMqPx0PM484VSjq7OAAAAECGECAA4FRPPvmklC5dWs6fPy+ff/65W34bq06ek2sxsVImT06pWzifs4sDAAAAZAgBAgBO5evrK6+++qp5PHXqVImOjna7b+TbI7eGF3QqX8LkVgAAAADcEQECAE7Xt29fKVy4sJw4cUIWLFgg7uT89ZuyIeQf85jhBQAAAHBnBAgAOF2OHDlk8ODB5vGkSZMkLi5O3MWSY2ckzipmaEHZwFzOLg4AAACQYQQIALiEF154QYKCguTgwYPy/fffi7vNXkDvAQAAALg7AgQAXIIGB1566SXzeOLEiWK1WsXV7bsULvsvR4ifl5eZ3hAAAABwZwQIALiMgQMHmuEGO3bskA1nL4i79B5oXqqw5PX3c3ZxAAAAgDtCgACAyyhUqJA899xz5vH7ew6LK4uJizP5B1SXCiWdXRwAAADgjhEgAOBShg4daqY+3Hb+kvx+/pK4qk0hFyT0RqTk9/eTB0sUdnZxAAAAgDtGgACASylVqpT06tXLPP5g7xFx9eEF7csVFz9vfkoBAADg/virFoDLGTFihHhZRNaeDpW/LoaJq7kaHSM/nThnHne+i+EFAAAAyB4IEABwORUrVpS2ZYqbxx/uPSqu5sfjIXIjNk4qBuWWWgWCnF0cAAAAIFMQIADgkl655y6zXnb8rBwNuyquOLygU4USYrFYnF0cAAAAIFMQIADgkqrlD5RHShYWq4h89Jfr9CI4ffWGbDl30TzuWJ7hBQAAAMg+CBAAcFkD/+1FoHfsz1y7Ia7ANrVho6IFpGTuHM4uDgAAAJBpCBAAcFl1C+eXBkXyS3ScVT7+65iziyNWqzXe8AJ6DwAAACB7cbsAgY73TW55++23Uzxn7ty5yZ5z8+bNBMfNnDlTypUrJwEBAVK7dm3ZtGlTksbBuHHjpHjx4pIjRw5p1qyZ7Nu3z2GfFYD2IqhoquGrv0/IhZuRTq2SXRfD5HDYVcnh7SVtyhTl6wEAAEC24nYBgpCQkATLZ599Zhr7HTt2TPW8wMDAJOdqIMBm0aJFMmjQIHn99ddl586d0rhxY2ndurWcPHnSfszUqVNl2rRpMmPGDNm+fbsULVpUmjdvLhEREQ79zIAna1q8oNxTIMjMGjBnf7BTy/Ldv70HWpcpKnn8fJ1aFgAAAEA8PUCgjfL4yw8//CAPPviglC9fPtXzNIiQ+Nz4tOHft29f6devn1SpUkWmT58upUqVklmzZtl7D+g+DSB06NBBqlevLl988YVcv35dvv76a4d+ZsCT6b9dWy6Czw4cl/CoaKeUIyo2TpYeO2sedyI5IQAAALIhtwsQxHf+/HlZsWKFadjfztWrV6VMmTJSsmRJadOmjeklYBMVFSU7duyQFi1aJDhHt7du3WoeBwcHy7lz5xIc4+/vL02bNrUfk5zIyEgJDw9PsABIn9ali0rFoNwSHh0jXxw84ZTqW3cmVC5FRknhHP7SpHhBp5QBAAAAcCS3DhDoHfw8efKYO/qpqVy5sslDsGzZMlmwYIEZWtCoUSM5fPiwef7ChQsSGxsrRYoUSXCebmtQQNnWqR2TnEmTJklQUJB90V4JANLHy2KRl2vc6kXw8f5jcj0mNsur8Nujt2Yv6FC+hPh4ufVPJwAAAJAsl/4rd/78+ZI7d277kjhpoOYfePLJJxPkEkhO/fr15amnnpKaNWua3ALffPON3H333fLhhx8m6cocnw4rSLwvLcfEN2rUKAkLC7Mvp06duu3nBpDUE+WLS8lcOeTizShZcPh/uUGywpXIKFl96rx53JnZCwAAAJBN+YgLa9eundSrV8++XaJECftjDRYcOnTIJBdMLy8vL6lbt669B0HBggXF29s7SU+A0NBQe48BW84CPaZYsWLJHpMcHYagC4A74+vlJS/WqCCjfv1LZv51VHreXUb8vLMmxrn8eIhExcVJ1Xx5pFr+wCx5TwAAACCruXQPAh0+cNddd9kXnVrQ5tNPPzVTEWqvgPTSu/67du2yN/T9/PzMa61ZsybBcbrdsGFD81inP9QgQfxjNHfBhg0b7McAcKxud5WSQgH+cubaTVly7FaX/6zw7b+zF3Si9wAAAACyMZcOEKREE/19++23ZsaBtBg/frz8/PPPcuzYMRMY0KSGuu7fv7/9mCFDhsicOXPMsIUDBw7I4MGDzRSHtmN0GIFOgzhx4kRZunSp/PXXX9KnTx/JmTOn9OjRw2GfFcD/5PDxluerlTOPZ+w9IrFxVodXz/Hwa/J76GXxstzKPwAAAABkVy49xCAlCxcuNL0Aunfvnuzz2nA/fvy4rF+/3mxfuXJFnnvuOTM8QBMF3nvvvbJx40a5//777ed07dpVLl68KBMmTJCQkBAzjeHKlSvNzAc2w4cPlxs3bsiAAQPk8uXLZvjD6tWrTU8HAFmjd6Uy8uHeo3Ik/JqsPBkibcsWd+j7ffdvT4WmxQpJ0Zyp5zsBAAAA3JnFqi3tbKZZs2ZmGTdunLhi7wcNUmjCwsBAxjIDKTn/dNsUn5uy85C8t/uw1MgfKKvbNk41Ueid0J/H+kvWyYmI6/JR41rSMR1DDIp8vtwhZQIAAAAcxS2HGKQmIiJCjh49KsOGDXN2UQA4yLNVyklOH2/Zeylc1p35x2H1vD30sgkO5PLxllalbyUqBQAAALKrbBcg0O7+OpWgTosIIHvKH+AnPSvdGv7zwd4jDnuf7/5NTtimbDHJ5euWI7IAAAAAzw0QAPAM/auWEz8vL/n1/CX59fzFTH/9mzGx8sPxEPO4M7MXAAAAwAMQIADglorlyiFd77qVE+CDPZnfi2DN6fMSFhUtJXIFSMOiBTL99QEAAABXQ4AAgNt6sXoFM/3gL2f+kb0XwzL1tb87emv2go7lS4qXg5IgAgAAAK6EAAEAt1U2MJe0L1c803MRXLgZKf93OtQ87lShRKa9LgAAAODKCBAAcGsv17jLrH88HiJHwq5mymt+H3xWYqxWqVkgSO7OmydTXhMAAABwdQQIALi1KvkCpWWpImIVkRl7j2bq7AWd/81xAAAAAHgCAgQA3N4r//Yi0Ib96as37ui1Dl+5KrsuhImPxWIfvgAAAAB4AgIEANxe7cL55IFiBcywgFn77qwXwXfHbvUeeKhkYSkY4J9JJQQAAABcHwECANnCwBoVzXr+3yflnxuRGXqNOKv1f8MLSE4IAAAAD0OAAEC2oD0I7i2YV27Gxskn+4Mz9Bpbz12UM9duSqCvjzQvWSTTywgAAAC4MgIEALIFi8UiA++5lYvg84PHJSwyOt2v8d3RM2b9eLniEuDjnellBAAAAFwZAQIA2UaLUkWkUt48EhEdI3MPHU/XuddjYmX58bPmcecKzF4AAAAAz0OAAEC24WWxyCs1KpjH/90XbBr9abXq5Dm5FhMrZfLklLqF8zmwlAAAAIBrIkAAIFvR4QGlc+eUS5FRMv/vE2k+79t/kxN2Kl/CDFcAAAAAPA0BAgDZio+Xl7z0by+CmX8dk6jYuNuec/76Tdlw9h/zuBPDCwAAAOChCBAAyHa6VCgpRXL4S8j1m/ZpC1Oz5NgZibOKGVpQLjBXlpQRAAAAcDUECABkOzoDQf9q5c3jD/cekVht/adh9gKSEwIAAMCTESAAkC31qlRG8vr5SnDEdVl+IiTF4/ZdCpd9l8PFz8tL2pUtlqVlBAAAAFwJAQIA2VIuXx/pV7WcefzBnsNitVpTTU7YvFRhyevvl6VlBAAAAFwJAQIA2VbfKmUlp4+37L8cIWtPhyZ5PiYuzuQfsOUtAAAAADwZAQIA2VY+fz/pXamMefz+niNJehFsCrkgoTciJb+/rzxYorCTSgkAAAC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",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from climada.entity.measures.cost_income import CostIncome\n",
    "\n",
    "ci = CostIncome(\n",
    "    mkt_price_year=2025,\n",
    "    init_cost=10_000,  # 10 000 upfront\n",
    "    periodic_cost=5_000,  # 5 000 / year maintenance\n",
    "    periodic_income=8_000,  # 8 000 / year in avoided losses\n",
    "    cost_yearly_growth_rate=0.01,\n",
    "    income_yearly_growth_rate=0.01,\n",
    "    freq=\"Y\",  # annual cash flows\n",
    ")\n",
    "\n",
    "impl_date = \"2022-01-01\"\n",
    "start_date = \"2020-01-01\"\n",
    "end_date = \"2030-01-01\"\n",
    "\n",
    "\n",
    "display(ci.to_dataframe(impl_date, start_date, end_date))\n",
    "ci.plot_cash_flows(impl_date, start_date, end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75b7d54-6785-48af-9134-6dc4b564a76d",
   "metadata": {},
   "source": [
    "## Defining a `CostIncome`\n",
    "\n",
    "The simplest way to create a `CostIncome` is by passing keyword arguments directly.\n",
    "\n",
    "| Parameter | Meaning |\n",
    "|---|---|\n",
    "| `init_cost` | One-off implementation cost |\n",
    "| `periodic_cost` | Recurring cost each period |\n",
    "| `periodic_income` | Recurring income each period |\n",
    "| `mkt_price_year` | Reference year for cost/income growth rates |\n",
    "| `cost_yearly_growth_rate` | Growth rate for costs |\n",
    "| `income_yearly_growth_rate` | Growth rate for costs |\n",
    "| `freq` | Period frequency (e.g. `'Y'` for yearly, `'M'` for monthly, `'Q'` for quaterly) <br> see more [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases))\n",
    "\n",
    "```{note} **Sign convention**\n",
    "`CostIncome` stores costs as negative numbers internally.\n",
    "```\n",
    "\n",
    "```{note}\n",
    "Financial values in `CostIncome` are currently unitless (no currency is specified)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41147b9d-9fef-4e58-9070-b384a5377f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from climada.entity.measures.cost_income import CostIncome\n",
    "\n",
    "ci = CostIncome(\n",
    "    mkt_price_year=2025,\n",
    "    init_cost=10_000,  # 10 000 upfront\n",
    "    periodic_cost=5_000,  # 5 000 / year maintenance\n",
    "    periodic_income=8_000,  # 8 000 / year in avoided losses\n",
    "    cost_yearly_growth_rate=0.01,\n",
    "    income_yearly_growth_rate=0.01,\n",
    "    freq=\"Y\",  # annual cash flows\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9a8398",
   "metadata": {},
   "source": [
    "## Calculating cash flows\n",
    "\n",
    "Three methods are available to calculate cash flows:\n",
    "\n",
    "| Method | Returns |\n",
    "|---|---|\n",
    "| `calc_cash_flows(impl_date, start_date, end_date)` | Three `np.ndarray`: net, costs, incomes |\n",
    "| `calc_total(impl_date, start_date, end_date)` | Three scalars: summed net, cost, income |\n",
    "| `to_dataframe(impl_date, start_date, end_date)` | A tidy `pd.DataFrame` |\n",
    "\n",
    "The `impl_date` is when the measure is *deployed*. Cash flows before this date are zero; the initial cost lands on `impl_date`; periodic flows start the following period.\n",
    "\n",
    "```{note}\n",
    "Dates should follow the standard format \"yyyy-mm-dd\" or be an integer representing the year.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25bf1e1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Period  | Net       | Cost      | Income\n",
      "---------------------------------------------\n",
      "2020  |         0 |         0 |      0\n",
      "2021  |         0 |         0 |      0\n",
      "2022  |     -9706 |     -9706 |      0\n",
      "2023  |      2941 |     -4901 |   7842\n",
      "2024  |      2970 |     -4950 |   7921\n",
      "2025  |      3000 |     -5000 |   8000\n",
      "2026  |      3030 |     -5050 |   8080\n",
      "2027  |      3060 |     -5100 |   8161\n",
      "2028  |      3091 |     -5152 |   8242\n",
      "2029  |      3122 |     -5203 |   8325\n",
      "2030  |      3153 |     -5255 |   8408\n",
      "---------------------------------------------\n",
      "Total net    :      14662\n",
      "Total cost   :     -50318\n",
      "Total income :      64979\n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "impl_date = \"2022-01-01\"\n",
    "start_date = \"2020-01-01\"\n",
    "end_date = \"2030-01-01\"\n",
    "\n",
    "net, costs, incomes = ci.calc_cash_flows(impl_date, start_date, end_date)\n",
    "total_net, total_cost, total_income = ci.calc_total(impl_date, start_date, end_date)\n",
    "\n",
    "print(\"Period  | Net       | Cost      | Income\")\n",
    "print(\"-\" * 45)\n",
    "periods = pd.period_range(start=start_date, end=end_date, freq=\"Y\")\n",
    "for p, n, c, i in zip(periods, net, costs, incomes):\n",
    "    print(f\"{p}  | {n:>9.0f} | {c:>9.0f} | {i:>6.0f}\")\n",
    "print(\"-\" * 45)\n",
    "print(f\"Total net    : {total_net:>10.0f}\")\n",
    "print(f\"Total cost   : {total_cost:>10.0f}\")\n",
    "print(f\"Total income : {total_income:>10.0f}\")\n",
    "print(\"-\" * 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f3fd0c35",
   "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>date</th>\n",
       "      <th>net</th>\n",
       "      <th>cost</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>-9705.636889</td>\n",
       "      <td>-9705.636889</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>2940.807977</td>\n",
       "      <td>-4901.346629</td>\n",
       "      <td>7842.154606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024</td>\n",
       "      <td>2970.216057</td>\n",
       "      <td>-4950.360095</td>\n",
       "      <td>7920.576152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2025</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>-5000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2026</td>\n",
       "      <td>3030.000000</td>\n",
       "      <td>-5050.000000</td>\n",
       "      <td>8080.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2027</td>\n",
       "      <td>3060.300000</td>\n",
       "      <td>-5100.500000</td>\n",
       "      <td>8160.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2028</td>\n",
       "      <td>3090.903000</td>\n",
       "      <td>-5151.505000</td>\n",
       "      <td>8242.408000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2029</td>\n",
       "      <td>3121.897135</td>\n",
       "      <td>-5203.161892</td>\n",
       "      <td>8325.059028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2030</td>\n",
       "      <td>3153.116107</td>\n",
       "      <td>-5255.193511</td>\n",
       "      <td>8408.309618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    date          net         cost       income\n",
       "0   2020     0.000000     0.000000     0.000000\n",
       "1   2021     0.000000     0.000000     0.000000\n",
       "2   2022 -9705.636889 -9705.636889     0.000000\n",
       "3   2023  2940.807977 -4901.346629  7842.154606\n",
       "4   2024  2970.216057 -4950.360095  7920.576152\n",
       "5   2025  3000.000000 -5000.000000  8000.000000\n",
       "6   2026  3030.000000 -5050.000000  8080.000000\n",
       "7   2027  3060.300000 -5100.500000  8160.800000\n",
       "8   2028  3090.903000 -5151.505000  8242.408000\n",
       "9   2029  3121.897135 -5203.161892  8325.059028\n",
       "10  2030  3153.116107 -5255.193511  8408.309618"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci.to_dataframe(impl_date, start_date, end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612c0b66",
   "metadata": {},
   "source": [
    "## Visualising cash flows\n",
    "\n",
    "`plot_cash_flows` draws a bar chart of the cash flows. The top panel of the plot shows costs, incomes and net values for each period, while the bottom panel shows the cumulated net value.\n",
    "\n",
    "Figure size and title can be customized directly in the method. The method returns a tuple with the two matplotlib axes objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "315fc0ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: ylabel='Cash flow'>, <Axes: ylabel='Cumulative net'>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ci.plot_cash_flows(\n",
    "    impl_date,\n",
    "    start_date,\n",
    "    end_date,\n",
    "    figsize=(16, 7),\n",
    "    title=\"Custom title for cash flow plot\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb73614",
   "metadata": {},
   "source": [
    "## Growth rates\n",
    "\n",
    "Costs and incomes can grow year-over-year using compound-interest factors anchored to the `mkt_price_year` attribute.\n",
    "\n",
    "$$\\text{value}(t) = \\text{base} \\times (1 + r)^{\\frac{t - t_0}{365}}$$\n",
    "\n",
    "Pass `cost_yearly_growth_rate` and / or `income_yearly_growth_rate` (as decimals, e.g. `0.02` for 2 %)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b69f1ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: ylabel='Cash flow'>, <Axes: ylabel='Cumulative net'>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ci_growth = CostIncome(\n",
    "    mkt_price_year=2025,\n",
    "    init_cost=10_000,\n",
    "    periodic_cost=5_000,\n",
    "    periodic_income=8_000,\n",
    "    cost_yearly_growth_rate=0.15,\n",
    "    income_yearly_growth_rate=0.10,\n",
    "    freq=\"Y\",\n",
    ")\n",
    "\n",
    "ci_growth.plot_cash_flows(impl_date, start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "067c07aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   No growth  With growth\n",
      "Net                    14662        17138\n",
      "Cost                  -50318       -58474\n",
      "Income                 64979        75612\n"
     ]
    }
   ],
   "source": [
    "# Compare totals with and without growth\n",
    "no_growth = ci.calc_total(impl_date, start_date, end_date)\n",
    "with_growth = ci_growth.calc_total(impl_date, start_date, end_date)\n",
    "\n",
    "labels = [\"Net\", \"Cost\", \"Income\"]\n",
    "print(f\"{'':15s} {'No growth':>12s} {'With growth':>12s}\")\n",
    "for label, ng, wg in zip(labels, no_growth, with_growth):\n",
    "    print(f\"{label:15s} {ng:>12.0f} {wg:>12.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1408d2db",
   "metadata": {},
   "source": [
    "## Custom cash flows\n",
    "\n",
    "For irregular or one-off flows, pass a `pd.DataFrame` with columns `date`, `cost`, and/or `income`.\n",
    "\n",
    "These are **added on top** of any periodic amounts; dates not present in the DataFrame simply contribute zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c469839c-e2e2-43e9-ac33-93de7f04a6d9",
   "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>date</th>\n",
       "      <th>net</th>\n",
       "      <th>cost</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>-10000.0</td>\n",
       "      <td>-10000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024</td>\n",
       "      <td>-7000.0</td>\n",
       "      <td>-15000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2025</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2026</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2027</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2028</td>\n",
       "      <td>-17000.0</td>\n",
       "      <td>-25000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2029</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2030</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    date      net     cost   income\n",
       "0   2020      0.0      0.0      0.0\n",
       "1   2021      0.0      0.0      0.0\n",
       "2   2022 -10000.0 -10000.0      0.0\n",
       "3   2023   3000.0  -5000.0   8000.0\n",
       "4   2024  -7000.0 -15000.0   8000.0\n",
       "5   2025   3000.0  -5000.0   8000.0\n",
       "6   2026  18000.0  -5000.0  23000.0\n",
       "7   2027   3000.0  -5000.0   8000.0\n",
       "8   2028 -17000.0 -25000.0   8000.0\n",
       "9   2029   3000.0  -5000.0   8000.0\n",
       "10  2030   3000.0  -5000.0   8000.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custom_flows = pd.DataFrame(\n",
    "    {\n",
    "        \"date\": [\"2024-01-01\", \"2026-01-01\", \"2028-01-01\"],\n",
    "        \"cost\": [10_000, 0, 20_000],  # extra one-off costs\n",
    "        \"income\": [0, 15_000, 0],  # extra one-off income\n",
    "    }\n",
    ")\n",
    "\n",
    "ci_custom = CostIncome(\n",
    "    mkt_price_year=2025,\n",
    "    init_cost=10_000,\n",
    "    periodic_cost=5_000,\n",
    "    periodic_income=8_000,\n",
    "    custom_cash_flows=custom_flows,\n",
    "    freq=\"Y\",\n",
    ")\n",
    "ci_custom.to_dataframe(impl_date, start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11f21296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: ylabel='Cash flow'>, <Axes: ylabel='Cumulative net'>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ci_custom.plot_cash_flows(impl_date, start_date, end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f011b6",
   "metadata": {},
   "source": [
    "## Sub-annual frequencies\n",
    "\n",
    "`freq` accepts any [pandas-compatible period aliases string](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases).\n",
    "\n",
    "Common options:\n",
    "\n",
    "| `freq` | Meaning |\n",
    "|---|---|\n",
    "| `\"Y\"` | Annual |\n",
    "| `\"Q\"` | Quarterly |\n",
    "| `\"M\"` | Monthly |\n",
    "| `\"7D\"` | Every 7 days |\n",
    "\n",
    "```{note}\n",
    "The periodic amounts are interpreted as **per-period** values. A periodic cost of 500 with a monthly frequency (\"M\"), means a cost of 500 per month. The growth rates however are always considered to be yearly.\n",
    "```\n",
    "\n",
    "```{note}\n",
    "The implementation, starting and ending dates are coerced to the period frequency. For instance `\"2022-01-05\"` with monthly frequency is considered as `\"2022-01\"`.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "277e6948",
   "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>date</th>\n",
       "      <th>net</th>\n",
       "      <th>cost</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-03</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-05</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-06</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-07</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-08</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-09</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-10</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022-11</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022-12</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date     net    cost  income\n",
       "0   2022-01 -5000.0 -5000.0     0.0\n",
       "1   2022-02   200.0  -500.0   700.0\n",
       "2   2022-03   200.0  -500.0   700.0\n",
       "3   2022-04   200.0  -500.0   700.0\n",
       "4   2022-05   200.0  -500.0   700.0\n",
       "5   2022-06   200.0  -500.0   700.0\n",
       "6   2022-07   200.0  -500.0   700.0\n",
       "7   2022-08   200.0  -500.0   700.0\n",
       "8   2022-09   200.0  -500.0   700.0\n",
       "9   2022-10   200.0  -500.0   700.0\n",
       "10  2022-11   200.0  -500.0   700.0\n",
       "11  2022-12   200.0  -500.0   700.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci_monthly = CostIncome(\n",
    "    mkt_price_year=2022,\n",
    "    init_cost=5_000,\n",
    "    periodic_cost=500,\n",
    "    periodic_income=700,\n",
    "    freq=\"M\",\n",
    ")\n",
    "\n",
    "df_monthly = ci_monthly.to_dataframe(\n",
    "    impl_date=\"2022-01-05\",\n",
    "    start_date=\"2022-01\",\n",
    "    end_date=\"2022-12\",\n",
    ")\n",
    "df_monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "03a80747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: ylabel='Cash flow'>, <Axes: ylabel='Cumulative net'>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ci_monthly.plot_cash_flows(\n",
    "    \"2022-01\",\n",
    "    \"2022-01\",\n",
    "    \"2022-12\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10191dfd",
   "metadata": {},
   "source": [
    "## Combining multiple CostIncome objects\n",
    "\n",
    "`CostIncome.comb_cost_income()` aggregates a list of `CostIncome` objects into a single one by summing costs and incomes.\n",
    "\n",
    "```{warning}\n",
    "All objects must share the same `mkt_price_year`, `cost_growth_rate`, and `income_growth_rate`.\n",
    "```\n",
    "\n",
    "```{note}\n",
    "`custom_cash_flows` are merged by summing them together.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39c99bd5-3a54-4d5c-bf0d-ffbb713ce1a8",
   "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>cost</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-2000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01</th>\n",
       "      <td>-10000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028-01-01</th>\n",
       "      <td>-20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029-01-01</th>\n",
       "      <td>-10000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cost  income\n",
       "date                     \n",
       "2022-01-01  -2000       0\n",
       "2023-01-01      0       0\n",
       "2024-01-01 -10000       0\n",
       "2025-01-01      0       0\n",
       "2026-01-01      0   20000\n",
       "2027-01-01      0       0\n",
       "2028-01-01 -20000       0\n",
       "2029-01-01 -10000       0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M81rKr7MIjBkzxoQI27dvl06dOkm6dOmkXbt2ib7ua6+9ZgIBPZDfvXu3uV66dKl5LSvtdzB9+nT57LPPZNeuXSZM0CkOu3btek/vDQAAkNonXKwNCu82vWFS5KvRUh7oPNkMNzi88kvZNmsAww0AeK1Vq1aZ6e51xrv77rtPKlasaKa715PDKaXHkps3bxZP4FYVBNYpBsPC4qbd//vf/8zBuOrfv78JDLp16yYXLlww0xPqwb8GCla67MGDB00VgtJKgTlz5siQIUNk6NChUrRoUZk7d65Z134qxHPnzsmoUaPkxIkTpjHiokWLpFChQrZlkvLeAAAArnTtzEGJOHdE/ALSSLYStRzymvlqtDLBw+bPusvhP2bqv7tS/plxVBIA8Co//vijtG3bVt544w2ZOXOmGYquJ5jHjRtnjhHtjw29lZ9Ff9t7GQ0Y9DJixAhxNzrNoc5moA0LM2XK5OrNAQAAbqTZ9FP3/BoHl/1Ptn81ULKVfFBq9Z0njnR03bey+bMepr9BodCOUq5d3JBg4QuJ92UCAHceYqCHxUWLFpXnn3/enFhOyF9//WWq03fs2CF58+Y1J581UFB///23OZG8c+dOM4Rdh7kvXLhQqlevLhs2bJC0adOKv7+/DBo0yFzclVtVEDhCeHi47N+/36Q/AAAAvju94b31H0hI/ppPmmutJDi04gvTuNCEBHeZTQoA3N3evXvlwIEDtgP++C5evChNmjSR4cOHm2Hoa9askUcffVQKFiwoDz74oJmFr1mzZubx69evy/r16816f/75p/kdqY9XqlRJ3J1b9SBwBC3316kEM2TI4OpNAQAASFWxN67fnMFA+w+UqeuU99CQoNJz75tw4NDyz2X7V6+bM28A4MnOnDljrvPly5fg8z/99JPkyJHDNNVPkyaNhIaGml50n3/+uXleHzt06JAcP37czGJXt65zfgc7m9cFBAAAAL7q4oG/5UbkFUmTIatkLlDeae+Tv9ZTUqnTe3YhwUBCAgAeTfsNqGPHjklCjh49KoULF47z2P33328eV9rsPjIy0syOV6pUKZk8ebJ4IgICAAAAL3Fm580GzTlK13V6A8H8tVtLxU7v3goJZsj22YMICQB4rBIlSpgAQJvbJyR//vymEb49HZKgjyvtX/DFF1+YWfh0dry+ffvKxo0bzXOeNAyLgAAAAMBLnNlh7T9w79MbJkWB2m2k4rOTboYEy/4nPXr0ICQA4JH0IP6DDz4wMxbotc5wp/bs2SOdO3eWhx56SE6fPi1TpkyRGzduyMqVK+Wrr76Sjh07muU0HDh16pR5HZ0iURsSBgbebPmXK1cu0yfPE3jlLAbujFkMAACAM2YxiL56URb3KmNmGKg//m9JmzVvqu3oI6tny5bPe2sbcDM+97333vOoM2YAYLVq1Sp58803Zd26dea+NiHs0KGDmb1g8+bN0rNnT9ssBoMHD5b27dub5TQoWLx4sVy5csUEAr1795ZXXnnFPKcVBdrc8Nq1azJgwAB5/fXX3XaHExCkMgICAADgjIDgxMYfZeNHL0iGPCUkbNQfqb6TD6/6SrZ90cdUEGglwbvvvktIAAAehiEGAAAAXsCZ0xsmRcGH2pmzZOr999+XXr16MdwAADwMAQEAAICH07P2Z3Ysc+r0hknx/PPP20ICHWagJbaMZgUAz0FAAAAA4OGunj4gEeeOil9AGslWopZLt0WbeX3yySfmtg4z6NPn5rADAID7IyAAAADwcGdvTW+YtVh1CQxO7+rNkRdeeEGmTZtmbk+aNMlM90VIAADuj4AAAADAw/03vaFr+g8k5MUXX5SPP/7Y3J44caL069ePkAAA3BwBAQAAgAeLvXFdzv2z2tzOUSZU3MlLL70kH330kbn9zjvvSP/+/QkJAMCNERAAAAB4sIsH/pYbkVckKENWyVSgnLibLl26yNSpU83tt99+28wBznADAHBPBAQAAAAe7Myt/gPZy4SKn797/mvXtWtXmTJlirn91ltvyeuvv05IAABuyD3/igAAACB5/QfcbHhBfC+//LJ8+OGH5vaECRNk4MCBhAQA4GYICAAAADxU9NULcvHgZlsFgbvr1q2bTJ482dweP368DBo0iJAAANwIAQEAAICHOrt7lYglVjLkKSFps+QRT/DKK6/YQoJx48bJ4MGDCQkAwE0QEAAAAHios244vWFSQ4IPPvjA3B47dqwMGTKEkAAA3AABAQAAgAfSmQCsDQrdvf9AQl599VV5//33ze0xY8bI0KFDCQkAwMUICAAAADzQ1dMHJOLcUfEPDJKsJWqKJ+revbu899575vbo0aNl2LBhhAQA4EIEBAAAAB7o7K3qgSzFqktgcHrxVD169JB3333X3H7zzTdl+PDhhAQA4CIEBAAAAB7IU6Y3TIrXXntNJk2aZG6/8cYbMmLECFdvEgD4JAICAAAADxN74/rNGQy8JCBQPXv2lIkTJ5rbo0aNIiQAABcgIAAAAPAwF/7dKDFRVyUoQ1bJVKCceItevXrJO++8Y26PHDnSXAAAqYeAAAAAwMOc3XlzeEH2MqHi5+9d/8717t1b3n77bXNbhxpoNQEAIHV4118UAAAAH3DmVkDgLcML4uvTp4+89dZb5rY2LdS+BAAA5yMgAAAA8CDRVy/IxYObbRUE3qpv374yYcIEc1unP9QZDgAAzkVAAAAA4EFMc0JLrGTMW1LSZskj3qxfv34yfvx4c3vo0KEyevRoV28SAHg1twsI/vjjD2nWrJnkzZtX/Pz85Pvvv4/zvMViMePR9Pm0adNKWFiY7Nix466vO2/ePClTpowEBweb6/nz59+2zJQpU6RIkSISEhIiVapUkZUrVzrkvQEAABzl7K3pDbOXCfOJndq/f38ZN26cuT1kyBAZM2aMqzcJALyW2wUEV69elYoVK8rkyZMTfF5LzXQKHH1+w4YNkjt3bmnYsKGEh4cn+ppr166VNm3aSIcOHWTLli3munXr1rJ+/XrbMnPnzjXT6wwePFg2bdokderUkaZNm8rhw4fv6b0BAAAcRU9WnNm53NzOUaauz+zYAQMGyNixY81t/V/NehsA4Fh+Fv1L46a0gkDP9Ldo0cLc103Vs/d6IK9/KFRUVJTkypXLlJ916dIlwdfRcODy5cvy888/2x5r0qSJZMmSRWbPnm3u16hRQypXrixTp061LVO6dGnz3vpHKKXvHZ9uR+bMmeXSpUuSKVOme9g7AADA2zSbfuqOz185uV+WD31Q/AODpNG7uyQwOL24k4Uv5HLq6+v/ZIMGDbLdfv311536fgDga9yuguBODhw4ICdPnpRGjRrZHtMhA6GhobJmzZo7VhDYr6MaN25sWyc6Olo2btx42zJ637pMSt9bQwQNBewvAAAAKWGtHshSrLrbhQOpYeDAgbY+BHrb2sQQAOCDAYEeoCs9a29P71ufS2y9O61z9uxZiYmJueMyKX1vTbe1YsB6KVCgQBK/WgAAgLjO7vzDq6c3TAqtILDOaKBVndbpEAEAPhYQ2A89sKfl//EfS8k6jlrGnqbbOpzAejly5MgdtxMAACAhsTeu35zBQAOCsr7RoDAx2ofgjTfesDUxfPvtt129SQDgFTwqINCmgCr+GfvTp0/fdmY//np3Wid79uwSEBBwx2VS+t46DEF7DdhfAAAAkuvCvxslJuqqBGXMJpnyl/X5HagzGowaNco2HeI777zj8/sEAHwqINApCPVAfcmSJbbHtH/AihUrpHbt2omuV6tWrTjrqMWLF9vWCQoKMtMaxl9G71uXSel7AwAAOMLZnbemNywdKn7+HvUvnNMMHTpURo4caW737dvXzDYFAEi5QHEzV65ckX379tnua3PAzZs3S9asWaVgwYJmFgGd/7Z48eLmorfTpUsn7dq1S/Q1X3vtNalbt66ZbeDxxx+XH374QZYuXSqrVt0s01O9e/c20x9WrVrVBArTpk0zUxx27drVPK/DCFLy3gAAAI7gi9MbJsWwYcPMkM8RI0ZInz59zP9svXr1cvVmAYBHcruA4K+//pJ69erFOXBXzz77rMyYMcOMM4uIiJBu3brJhQsXzPSEWg2QMWNG2zqdOnWSgwcPyvLlN/+Q6hn+OXPmmFI0TZqLFi0qc+fONevaT4V47tw5U6p24sQJKVeunCxatEgKFSpkWyYp7w0AAOBo0VcvyMWDm83t7D7coDAxw4cPNyGBVhNY/3ckJACA5POz6G9TLxMWFmYumiS7G53mUGcz0IaF9CMAAAD2mk0/leAOOf7XAvn745ckY96SEjry5lADd7TwhcT7MqVWUGDtSzBp0iRT/QkA8OAKgnsVHh4u+/fvlx9//NHVmwIAAODQ6Q2zl/Ht2QvuRk8O6bkvneFAKwh0uIEONQUA+GhAoOX+TCUIAAC8hR7w2voPlGV4wd2qLywFuknxR6/I3p9uVhB8su6KFKn/grg7V1dfJFa94inYf+w/X/78ORItcAEAANzY1VP/SsS5o+IfGCTZitd09ea4Pa0aKPF4fyn2yM3hBTvmDJEDv0139WYBgEcgIAAAAHBj1uqBrMVrSEBwOldvjseEBCVbDJBij7z2X0jw+6eu3iwAcHsEBAAAAG7s7M6bTQmzl2Z6w+SHBK9LsaY9zP0dswfLwWWfOeV7BADegoAAAADATcXeiJazu1eb2znK0qAwRSFBy4FStGl3c3/7V4Pk4LL/Ofz7BADegoAAAADATV34d6PERF2VoIzZJFP+sq7eHI8NCUq1HCRFm7xq7m//aqAcXD7D1ZsFAG6JgAAAAMDdpzcsHSp+/vzbdk8hQavBcn/jbub+9lmvy6Hlnzvs+wQA3oK/NAAAAG6K6Q0dGxKUfmKo3N/oZXN/26wBcmjFFw58BwDwfAQEAAAAbij66gW5eHCzuZ2jTKirN8d7QoInh8n9jbqa+9u+7C+H/pjp6s0CALdBQAAAAOCGzu5aKWKxSMZ8pSTkvtyu3hwvCwmGy/0Nu5j722b2IyQAgFsICAAAANzQGdv0hlQPOCUkeGqEFGlgHxJ86fD3AQBPQ0AAAADgZiwWi5zdsdzczlGWgMBZIUGZ1hoSvGTub5vZVw6vnOWU9wIAT0FAAAAA4GauntovEeePiX9gkGQrXtPVm+PlIcFIKVL/RXN/6xd95PDKr1y9WQDgMgQEAAAAbjq8IGvxGhIQnM7Vm+P9IUGbUf+FBDP7yOFVhAQAfBMBAQAAgJs5a+0/wOwFqRoSFK7/gmkMqZUER1bPTp03BwA3QkAAAADgRmJvRMvZ3avN7Rxlwly9OT4VEpRt84YUfrizCQm2fN5bjqye4+rNAoBURUAAAADgRi78u1Fioq5KUMbskil/GVdvju+FBE+/KYXrPX8rJOglR9bMdfVmAUCqCUy9twIAAMDdnNlhnd6wrvj5cy7HJSFB29FiEYscWvY/2fK/1+Sf78dLdPg5SZ+7qJRo1kfyVH401bcLQPKd+Psn2bPwHbl6cj8/v0nEXx0AAAA3cmYn0xu6Q0hQru0YyVG2nrkfeeG4xN6IkvBju2Tj1M7moAOAe9OfU/15DT+6i5/fZCAgAAAAcBPRV87LpUNbzO0cNCh0eUgQeelk3ActFn3CnJEE4N608ucmy60rfn6TgiEGAAAAbuLsrpXmn9iM+UpJyH25Xb05Pu/qyX9v3wcWiylXBuCedDjQ/iUfyZUTe25/kp/fuyIgAAAAcBNndv1hrqkecA/ac0CHFZgzj3aCMmV32TYBSLwC69/FH8mB3z81jV4T5OcnGXIXYxfeAUMMAAAA3IDFYpGzO272H8jO9IZuQRsSWsuS7UWePy7H1n/nsu0C8J/oqxdk9/yx8tvr1WTfz++bcCBTgXJStMmrNxew/vzqtcUixfXnGomiggAAAMAN7NmzRyLOHxP/wGDJVryGqzcHIma2giovfxqnC3pwxuxydtcfsunTmwcf+Wq0Yl8BLgoG/l38sRz8fbrciLxiHsuUv6yUaN5XclVqYvqI3FfkgXizGPSVPJUf4ft1BwQEAAAAbmDx4sXmOmvxGhIQnM7VmwO7kMB+WkNLbKxs/bKfHFk561ZI4Cf5arRkfwGpJPrqRTmw9GM58Nt0uRERbh7LmL+MOfjPrcGA3fSw8X9+cXcEBAAAAG4UEGQvU9fVm4I70IOPCu3fMrdvhgSvmNLlfNVbsN8ApwcD0+TAb5/ECwb6SO5KTeMEA0g5AgIAAAAXi46OlmXLlpnbOeg/4DkhgcUiR1Z9JZumdzPDm/NWIyQAHO36tUvyrwYDSzUYuGwey5iv9M1g4IFHCAYcjIAAAADAxdauXStXr16VoIzZJVP+Mq7eHCQ1JOjw9s2QYPVs2TT9FTPcIG+1x9l/gIOCAQ0FNBz4LxgoZZoM5nngUYIBJ6EOIwWmTJkiRYoUkZCQEKlSpYqsXLnS8d8ZAADgM5YsWWKb3pAyWQ8LCTq+IwUefFossTGmkuD4XwtcvVmAR7t+7bJpLPj7wOqyZ+HbJhzImLekVO4yTeoO+13yVmnG70knIiBIprlz50rPnj1l8ODBsmnTJqlTp440bdpUDh8+7JzvkBf67rvvpGLFipI2bVpzrffB/oP742cXcH7/AQ0I4IkhwUTJX7vNzZDgk5cJCYCUBgM/TpTfB1aTPQveMhUEGfKUkMovfSx1hy+TvFWbEwykAj+LTrqLJKtRo4ZUrlxZpk6danusdOnS0qJFCxk7duxd1798+bJkzpxZLl26JJkyZfLJA4wnnnjCTDuiHz3r9TfffCMtW9IB+G7mz58vTz311G37b968edKqFdMsIfV/dvnsAffu3LlzkiNHDvMz1eCtLRJyXy6P3a0LX3DdtjebfkpcScOBLTN6ydG1X4uff4A88NJH5kynJ+w7d9h/94r957n7T4+PqnccI/8u+ViuX7toHsuQp7jpMZCnimeEAgtd/PPrSPQgSGYDoY0bN8rrr78e5/FGjRrJmjVrElwnKirKXOx/AHzZyJEjbQcWynqtB71Iuvj77/nnn5fly5dL3rx5zSVPnjy22/fdd5/Z58DdREZGyokTJ8zl+PHj5mK9ba30sf/s6edq1KhRhFPAPfrtt9/Mz1T58uU9OhzwdRoKVOw0SX9DytG138imaV3F7yU/yVPlMVdvGuCW9Ljogw8+kHfeeUcuXLhgCwaKP9ZH8lbVYQQBrt5En0RAkAxnz56VmJgYyZUr7h9vvX/y5MkE19GqAj0ojq9atWoSEBAgbdu2lVdeeUUeeugh23M7d+6UXr16ya+//mruDxkyRIoVKyadOnUy94sWLSoLFy6UZs2ayf79+81jM2bMkH379smbb75p7jdu3FgmTZokZcr81+ho1apV8uGHH8rs2bPNfX1fXa558+bmfpYsWWT16tXy7LPPyoYNG8xjEydONNe9e/e2bffnn38uDz74oO0HecGCBWZb9bXVnb6mbdu22Q4w4DhakaK/YBOiB3GBgYFSoUIFiY2NlYMHD5r7Ojymffv20rdvX3Pf399fdu3a5TafvWU1inr0R6TVnvNO/3lK6vdJv+/jxo2TLVu2yCeffCI3btyQggULmu/Rt99+a+7rRX+/JYf+LOvP9MyZM20VVKn1NSk+e+752ZueXTzabw3bpvpn74cffjCPFS9eXKasfkE82gsLff6zF1PcIq+dziff7j8mWz5+UbqFVZZHC+W56677Kl3qf/bsf0dMX33zZ99jufCzp9+n6R7+s/tnhaGp9tlr166dLF26VM6fP2/736N45gzSu2JxaV44rwRc/1Fk7Y/iSZ5dmdXl/+8l5fukr3k3DDFIBj2Lli9fPlMtUKtWLdvjo0ePNv8g7969O0kVBAUKFPDZIQbacyB+SKAHsGXLlpUVK1a4dNs8Qd26dc0Pdvz9p5UCHTp0uO3Mr/7iTSrtCWFfeWB/2/6+fm5ToyLh1HNJL8t0R7n+tzBVqpoSO+Nvf1vLl5MqODj4tu+/3tbmrEePHk0w4AsKCpIXX3xRBg4caH5Hejo+e+y/1KQ/U4UKFZIjR47IL7/8IpXmTBZPlhq/+zzhZzcm1iKvrd5sQoJAPz+ZFlZZHrlLSODKfedu+y8l2H/uv//Cw8Nl8uTJ8vbbb9v+Ry1ZsqQMGzZMwn79SgL8PbfiNZeLf34diQqCZMiePbs56x+/WuD06dO3VRXY/7OtF9w0fPjwBMcxa5ly1qxZ2U13ofspof2n1QMJ9XCwlownduBova1pZkREhPz777/mcifp0qVLNDywv50xY0aGNqTQ9evXze8Z6/cose+fVjUllR7EJ/a9sr+t6X5CAVCJEiUS/OxpDxatPNEEXKsTXnrpJTMMyxuCAiA1/PPPPyYc0P8VtLIr3MMDAtykBzrvPVhJRxvIt/8ek5eW/52kkADwRhoM6P8JGgxYT1ro/xUaDDz99NPm+OrUkptVB3A9AoJk0H+wdVpDnYrI/mBM7z/+OHPeJoU20tOmZnqgq/8UaWqooQENCp2z/3QqTp2SUy93ouFAUoKEixcvyrVr10wZk7WUKTHp06dPcpDgSwf+p06duuuB/5kzZ5L8mmnSpLnjgb/1vgZw91L5kdhnTxu0Llu2zNzW0kI9M2AfFOj7A7j79IYaDmgAG87O8q6Q4KFKmhHIvFshwSdhVaRpodyu3jQgVVy5csUEA2+99ZYtGNChVBoMaEm9BgNwPwQEyaRjSbSUu2rVqmaYwbRp08wUh127dnXOd8gL6YEGHffda//p8IL777/fXO5Ew4GkBAk6hObq1atmDJRe7iRDhgwJHuCm//eY5EoXIrnSBkvudCGSPo37/rq6ERsrZyKi5GRElJy6FimnrkXJlWHDEjzwT2oPDj3w1/1wt5AlW7ZsqVapkdhn7+GHH5Z69erJ77//boICHV+oVS36+7FLly4mKNBtBZD49Iba8BjeGRK8b0ICi3z373F5cflGmV6vijQpSEgA7w4GdGiiBgPWakcNBoYOHWqCAe19BffFdyeZ2rRpYxIwPYum//SXK1dOFi1aZMYPAt5Oz25pQxS93ImGA0kJErQnh/4R2bNnj7ncSYY0gZI7bbDkTBciudMFS660ISZA0MfMdboQyZk2+K5Bwk+HTsg7m/fI/ktXpWjm9NKnUolEm0fpgf/ZyGg5aQ76I+WUhgC3bmsYcFqvr0XJ2cgoc4YojnXbEnxN/aOY1AN/bRzpKTSkqF+/vgkLtCO7BgXar+X999+3BQUDBgwgKADi9RHRChxFQOADIYFFZP6BWyFBWBVpTEgAL6P//2kwMGHCBFswoE31NBjQxoQEA56BgCAFunXrZi4AEh9eoH8Q9HInGg4kFiQc/P3Xmwfi1yLl6o0YuXL9huzTy+Wrd3zNjBok3AoL9DqXXZhw4PIVGb9pj+j5dj2g33UhXDov2ygdSxSUHGmDbQHA6VvXeuAfm8RJNwL8/Ey1g7XqochjrRI88NdeJp504J+SoKBBgwYmLNAOxRoUrF27Vt577z35+OOPTbWVBgW5c3P2DNCfDf2HWvsY6RSH8F6B/v7yQZ2bww2+P3BcXli+UT6tV1UaFWBaS3g+/T02depUEwxYh0nqySQNBp555hmCAQ9DQADAZXR4gZac6eVO3ZQ1HLCGBdaD9//K+f87s3/tRoyEX78h4ZeuyN5LVxJ9X0u86y/2HL7jgb+GDfZBw38VDMGS+9Zj2UKCxN+u1D/XlCniyzQoaNiwoQkLdIy1BgXr1q2Td999Vz766CN5+eWXTVCQWINXwJeGF+jPiTcHh/gvJJhcp5K5rSFB52V/ERLAo+nQU2swoE3blQ5X1WBAp9KmYsAzERAAcPvpYvQQ8s6DGv7rkpvQcAbr/ZUrVybYA0D/Mddp+hJq8GedvQQpDwq0dFrDAj0Y0qBg/fr1Zk5eDQq0Gqtfv34EBfBJ9B/w3ZBA/xT9cJCQAJ4bDOjf8PHjx8cJBoYMGWKCAe2jBM9FQADAa+iMCNpdXy8JqVixomzbti1OSKAHsFraq3/o4Dy6nxs3bmzCgl9//VVGjBhhgoJ33nnHjFd85ZVXTFCQM2dOvg3wCdrPaOPGjea2BmjwrZDgw7o3GxcuOHhCXlimww2qSHtXbxiQhGBAhwtqMKCzMimdKUuDAW3iTjDgHahnA+Az9Oy1hgPWrv96rff1caQO3edNmjQxY6+1wWv16tXNNJs6N7L+k9G/f3/b2QjAm2kzT/39owEls3z4Zkgwpe4D0qxwHomOjTX9cPR3IuCO9O+0DhHUvgI6o5uGA4ULF5bp06ebqY+ff/55wgEvQkAAwGfoFH3z5s2TChUqSEhIiLn+7rvvpGXLlq7eNJ8MCpo2bWr6Evz0009SrVo1c2ZCp0TSoED7E1gbHQHeiOEFsIYEjxW6GRLo3yJCArhbMKBNhnX4QK9eveTkyZNm5rZPPvnEzD7VuXNnggEvlOyAQDtR6pRVd5uSDADcNSTYvHmz+aOn14QDrg8KHnnkETPc4Mcff5SqVauaoEAbHmlQ8Prrr9umSgK8hVYOEBBApfH3l6mhD8ijhXKbaS/1b9LPP//MzoFLRUZGmmmKtWKgZ8+etmDAegz4wgsvEAx4Mf+UdB2fOHGilCpVyjTwatu2rRm7u3v3budsIQDAJ4KCRx99VP78809ZuHChVKlSxUybpOMctYxx4MCBBAXwGlqSe+TIEQkODpY6deq4enPgBiHBR6GV5YknnrCFBL/88ourNws+Ggx88MEHJhh47bXXTIPnggULmr4DGgxoQ+egoCBXbybcLSDQD4iGAdoVXIOCzJkzm9KTsmXLMoYOAHDPQcFjjz0mGzZskAULFkjlypVNUDBu3DhTUTBo0CDT3A3wZNbqAQ0H0qZN6+rNgZuEBLNnzzZVblFRUdKiRQvT0BVIDZE3YmTy5MkmGOjRo4c5zitQoIA5Cbx371556aWXCAZ8iP+9dAvPkiWLudx3331mnsvcuXM7dusAAD4bFDRr1kz++usv+eGHH+SBBx6QK1euyNixY01FweDBgwkK4LEYXoCEaAf4OXPmmAoCDQkef/xxQgI4VVRMjHy266DU+m6ZdO/e3RYMTJ061QQDXbp0IRjwQckOCLRxVM2aNc3c4DqlhZZCaemndrPctGmTc7YSAOCzQUHz5s3NdHDff/+9VKpUyQQFY8aMsU2tdP78eVdvJpBk+n/T8uXLzW2d9hNIKCTQCgJrSGANlABHBgP/231Qas1bJoPWb5cT1yIlf/78ZtphDQa6du1qhkDBNyU7INAO0wcOHDDTgn3xxRdmDmv9502rCAAAcFZQoP8o//333zJ//nypWLGihIeHy+jRo01FwdChQwkK4BF0ik8dNpMrVy4zxSEQn47xnjt3rvmdZw0JlixZwo6CQ4KBGbeCgYHrtsvxa5GSJ12IjK1ZTvbt2ycvv/wywQCSHxBolYCWdmojqbp165phBW3atDGlKLt27WKXAgCcGhTomTUNCnSKSp2qUoOCN99801QUDBs2TC5cuMB3AG7Leja4YcOG4u/PbNNIPCT4+uuvTTigjeP0ZNzSpUvZXUiR6JhY+Xz3Ian93TJ53S4YGFOjnKx7op48V6owwQBskv2XSc/aaPMK/cdM56jWBirp0qUzj5UrVy65LwcAQLLpgZWO09XQet68eSYouHz5srzxxhumokCr3AgK4I7oP4DkhgTaj0VDAr0mJEByg4Ev/jlkegwMWLdNjl2NlNzpgmV0jbKytlU9eb50YQkOCGCnIg4/i07Gm0z6D5mOn9PLypUrzT9lOi60Xr16ZggCEqf7Smd+uHTpkmTKlIldBQAOEBsba4YejBw5UrZt22Ye09+1On+zXpIzDO7Uc808+nuS638LXfr+7L/EnT17VnLmzCn6r5c2A8uTJ08qfmfgqXSYwZNPPik//vijhISEmOv69es7/H342fWe/afBwNx9R+Tdrfvk2NUI81iutMHSvUIxaV+8oIQEBrjd3w54cAWBzlpQvXp1mTVrlhQvXtz0IdAGUdppmnAAAOCqigKdQ3zz5s3yzTffmIo2DWI1MNCKAr2+ePEi3xy41G+//WbCAe09QDiApNJmcd9++608+uijtkqC33//nR2I21yPjZUv99wcStBvrVYMRJhg4M3qZWX9Ew/LC6WLJBgOAPcUEMycOdNMLaWBwNtvv23mq+ZMOADAXYICPdO2ZcsWU5pbtmxZExSMGDHC9CgYNWqUuQ+4AsMLcC8hgQ6n0pAgIiLC/P9NSAD7YGDWnsMmGOi7ZpscvRohOdMGyxvVy8g6DQbKEAzAiQGBfSBw9OhROXbsWHJfAgAApwcFTz31lGzdutV0Ay9TpoypINDeBFpRoL0KCAqQmrRygIAAjggJHnnkEVtIsGzZMnaqjwcDX90KBvqs2SpHrkRIjpBgGVWtjKkYeLHM/ZKWigE4OyDQcZ56BkbHdhYqVEgKFixoxnbqP1v6HAAA7hQUtG7d2vQl0LnFS5cubYICne1AKwp09gPtDQM42z///GNOrOhBXp06ddjhuKeQoGnTpiYk0IoC7QkGHwwG9h6WB79bLr3tgoGRGgw8+bC8VJZgAKkYEOgUh5MnT5Zx48aZZoU61dSYMWPkgw8+MPNQAwDgjkGBTsmrQcHs2bNNUKCzHOjfLa0oGD16NEEBnMpaPaBTRKdNm5a9jRTTRoU6m1iTJk1sIcGKFSvYo17qp0Mn5OEfVkihLxZJvR9WyMB12+Sh+cul9+qtcvjKNckeEiQjbgUDXcreL+moGEBqBwSff/65TJ8+XV5++WUzrZROe9itWzf55JNPZMaMGfe6PQAAOE1AQIA8/fTTJij46quvpFSpUiYoGDJkiKko0MD7yvUbfAfgcAwvgKNDAp25RUOCa9eumWEHhATeGQ50XrZRdl0Il6jYWHP9v92H5FD4zWBgeNXS8ueT9aUrwQBcGRDojAX6D1V8+pg+BwCAJwQFbdu2le3bt5tZeUqWLGn+hmmVXLVvf5P3t+4jKIBDp6mzjhVv1KgRexYODQkaN25sCwn++OMP9q4XeXvzHnMdf056nZlAewy8XK4oFQNwfUCgFQM6xCA+fUyfAwDAk4KCdu3ayY4dO+TLL7+UEiVKyIWo6zLm791S/dvf5AOCAjjA2rVrzQFcrly5zBSHgCNDgu+//94ET9aQYOXKlexgD3cjNla+3ndUdl8IT/D5i9HXJX2awFTfLviGZAcEEyZMkM8++8x0hO7cubO88MIL5rYOL3jrrbecs5UAADg5KHjmmWdk586dMrlOJbk/U3o5H3VdRluDgm375CpDD3CPwwsaNmwofn5+7Ec4JSTQz9fVq1dNA0NCAs8OBurMXy49Vm2+rXJA6W+QYpkyuGDr4CuSHRCEhobKnj17pGXLlqYTtJZktmrVynTnpSsvAMDTg4Ini+aXP1qEygf2QcHG3VLt299lMkEBUoD+A3A2bXz5ww8/xAkJVq1axY730GDgQPg1yRocJE8WzWeet8aKeq2hQZ9KxV26vfBuKapNyZs3r+n4DACANwr095eniuaXlkXyynf/HpdJW/aYf9je3Lhbpmz/V14pX1Q6lSxEiSfu6uzZs2bGJ9WgQQP2GJweEjRv3lyWLl1qQoJffvlFHnzwQfa6GwcD8w/o35i98u/lq+YxDQa6lbtfnitV2PyNaVowt7yzeY/sv3xVimZKL30rlZBHCuVx9abD1wOCrVu3JvkFdWYDAAC8JShoXSy/tLo/r8z795j5J+5g+DV5469dMmX7fnmlXFF5lqAAd/Dbb7+JxWIx/x/lycM/9Ui9kEA/ezrLwa+//iq1a9dm13tYMGD1aKE85gK4VUBQqVIlM2ZO/8DdiS4TExPjqG0DAMBtgoI2xQrIE/fnk2/3H5NJW/eaaaZG2QcFpQrTTRq3YXgBUlu6dOlkwYIF0qxZM/n9999tIUGtWrX4ZrhYcoIBwFWS9Ck8cOCA87cEAAAPCAqeLl5Anih6Myh491ZQMPKvXfLh9v3yavli0rFkIYICGHpihYAArgoJFi5caAsJdCpEQgLXBgPf3woGdKiAyhqcxkxT+DzBADyxSaE2JMyUKZMUKlRIPv/8c8mRI4e5ndAFAABvl8bfX9oWLyCrWobJxAcrSMEM6eRsZLSM2LBTanz7u3y841+5doOKOl+3e/duOXr0qAQHB8tDDz3k6s2Bj4YE9erVk/DwcBMSrFu3ztWb5XPBwLf7j0ro9yvk1ZWbTTigwcDgKqVkw5P1pXv5YlQNwDMDgl27dpmOqGrkyJFy5coVp2zMd999Z355Zc+e3QxX2Lx5823LREVFSffu3c0y6dOnN2Os9I/v3UyZMkWKFClipoKpUqXKbdO/aMo/YsQI04BRx2+FhYWZebEd8d4AAO8NCtoVLyirW4XJxNoVpECGtHImMkqGb9gpNef9LtN2/CsRBAU+y1o9ULduXfO/BeCqkED/r7WGBOvXr+cb4WQEA/CJHgTPPfecSb/1QPrtt9+WDBkSnn9z2LBhKd4YDSG00+pTTz0lL774YoLL9OzZ0/yimzNnjmTLlk369Okjjz32mGzcuNFMT5WQuXPnmvU0JNDX//jjj01nV53vumDBgmaZCRMmyMSJE2XGjBlSokQJefPNN81UMTp9Y8aMGVP83gAAHwkKShSUp4rlN1NVaRnp0asRMmzDTpm8fb90L19UsocEywfb9sn+S1elaOb00qdSCRpPeTmGF8Ad6EmtH3/80fzPunz5cmnUqJH5bNaoUcPVm+Z1YmItMv/AsduHEpQtKs+VLiwZ6DEAD+BnuVvnQRFzkDx8+HDZv3+/maqnTJkyEhh4e7agZ/2tU/nci4MHD5qz/Zs2bTLhhNWlS5fM8IaZM2dKmzZtzGPHjx+XAgUKyKJFi0wqmhD9BVi5cmWZOnWq7bHSpUtLixYtZOzYsSb00MoBDQAGDBhgqxbIlSuXjB8/Xrp06ZLi947v8uXLkjlzZvN6OmwDAOBeTj3X7J5fIzomVr7ef0Te3bLPBAXxWeey/rReFYeHBLn+t1A8ff+5kqP2n/4fkTVrVrl27Zps2bKFWZ7gcnoi7tFHH5UVK1aY/0GXLFki1atXtz3Pz27KaZP2j+tVlYkeHAy4+m8HPGyIQcmSJc1Z8w0bNpiDaZ02RQ/e418cEQ7ciZ6pv379ukk+rfTAvly5crJmzZoE14mOjjbr2a+j9L51HW3CePLkyTjL6HjB0NBQ2zIpeW/rPwgaCthfAADeLSjAX9qXKCRrWtWTt2qVl0A/jQT+o+GAPqJzW8M7rV271oQDerKhfPnyrt4cwFQS/PTTT2bIi/4/qv/T6v/2uLdgYNasWVK2bFl55VaPgSzaY6ByKflTewxUKOYR4QBgL9mf2NjYWHEVPYgPCgqSLFmyxHlc//jqcwk5e/as+eHVZRJbx3qd0DKHDh1K8XsrrVDQvg0AAM/g6LMofUVkSEiI3IiKui0k+DfiOmdtfGB4gVZYAu4UEjzyyCOmH5cOp9VKgmrVqrl60zyKHlvoydM33njDVFqrLLcqBp73kIoB4J4qCJxB0zbtY2C9xG8amBxa1XC3P77xn09onaQsk9z3HjhwoBlOYL0cOXLkjq8HAPA+WomX0N8cfRzeif4DcFf6f7cOj61Tp47531RDgr/++svVm+UxwcBXX31lKgbat29vwgEdSjRmzBgzK0EPKgbgBVwWEOgMADpLgfVStWrVu66TO3duM2TgwoULcR4/ffr0bWf/rXTGAW0gGP8sv/06+rrqbssk972tQxV0nJf9BQDgW7SPT/xAWe/r4/A+Z86csQ27bNCggas3B0g0JNAG5NaQYMvZi+ypJAQDzzzzTJxgQHun6QlBqgbgLVwWEOjMAMWKFbNdkjL9j05PmCZNGlMKZXXixAnZvn271K5dO8F1dFiArme/jtL71nW0IaIGAPbLaBigTVysy6TkvQEAUK1atZJ58+aZRnX6t8Q6/djDDz/MDvJC2qtJAyD9fltPQgDuGhLoDF8XL16U1ovXExIkMRgYPXq06WGmwYB1tjPAW7gsIEjI+fPnTTWBTj+o9IdQ71vP7Gv3/86dO5vpBa2NErW8R5v/3Cmh7927t0yfPl0+++wz2bVrl/Tq1UsOHz4sXbt2Nc/rGR2dwUBTwPnz55uD/k6dOpl/3tq1a3dP7w0AgDUk0L9pERERZjYgbWD3/vvvs3O8EMML4Cn04Pbnn382IcGl6OvSZvF62Xrukvg6DQZmz55tmpEnFAwMGjSIqmB4rRR30NAz7FpeH79pYcGCBVO8MQsWLJDnnnvOdv/pp58211qCOWLECHN70qRJZorF1q1bm3+y6tevLzNmzDDDCKzCwsKkcOHC5nGl0xKeO3dORo0aZc766w+7JqaFChWyrdO/f3/zet26dTPDCHRqRP0Db58KJuW9AQC4E/2bMWzYMPM3buLEidKjRw8TQsM7aOWAtdow/gxKgDuHBPWLFZINpy9I61/XydeNa0qFbJl9Mhj4+uuvzTHD7t27zWPaoLxv377y6quvEgrAJ/hZ9C9ZMuzdu1eef/7526b2s46t1B8sV9NwQAMFrQJwNzqtjP4jqOO96EcAAL5J/1ZqBZpWtWkX7CFDhoi38PW51PV7qhUiISEhpjIyKUMoAXew75km0nbJn/LXmQtyX1Aa+aZxTSnvQSHBvfzsJhYMaOVw9+7dk/Q/u6//7oMPDzHQg25/f3/58ccfZePGjaYJj1605N7akMeV9Idak9COHTu6elMAALhjFYHSKgINjeFdwwt0rnnCAXiSjEFpZHbD6lI1Rxa5GH1dWi9eJ9u9fLiBdbpCDWx1WLEeR2gw8Oabb5rmg4MHD+aEHnxOsocY6PhJDQZKlSol7ki3a9u2ba7eDAAA7uipp54yZ6v0jPMHH3zgVVUEvoz+A/CGkODpJetl45mL8tTidfJNo5pSzoMqCZIaDHzzzTe238EpqRgAvFWyKwi0bO7s2bPO2RoAAHwEVQTeJyoqSpYvX25u038Anh0S1JDK2e+TC1HXTUiw4/xl8baKgbZt25pwQIMBHeqlzQepGACSGBDouHnrZfz48aahn/4B1MZ/9s/pBQAAJL2KoHTp0qY5rlYRwLNpfyadnUKnNtSGyICnyhSURuY0sgsJfl3r0SFBQsHAfffdZwsGtIKLZrFAMgIC/QHSdE0vDRs2lHXr1pkO/jlz5rQ9bl0GAAAkDVUE3jm8QP9X0sbNgDeEBA9kv0/O3woJdnpYSKDBwNy5c6VChQq3BQPaY4BgAEhhD4Jly5YlZTEAAJCCKoKRI0ea5lj0IvBsTG8IrwwJGtaQNkvWyeazl+TJX9fJvCY1pXSWTG4fDHz77bemx8DOnTvNYxoMWHsMUC0AOHCaQ9wbpjkEAMQ3e/Zs00FbK/H0rJYnN8jy1am+zpw5I7ly5TLTPp84ccIMMwC85Wf3UtR1W0iQNTjILUMC/dmNjY21NR+0DwZ69+4tPXr0cGow4Ku/++B9kt2k8JdffpFVq1bZ7n/44YdSqVIl84+NjqEEAADJ07p1azMLD70IPNdvv/1mwoGKFSsSDsDrZA5OI3Mb1pSK2TLL+ahoeeKXdbLrgvsMN4i1WOTrr782PQaefvppEw5oMKBBgYauQ4cOpWoAcFZA0K9fP1szQp1OUBO5Rx55RP79919zGwAApLwXwTvvvEPTXw/E9IbwiZCgUQ2pcCsk0OEGrg4JNBj44cBxCfthhbRp04ZgAHBFQKCdPnWqQzVv3jxp1qyZjBkzRqZMmSI///yzI7YJAACfQxWB59LKAQIC+IL7goPk61shwbnImyHB7gvhLgsG6v3wh3RZ8bfsuXjFVAhoPxc9VqFiAEjFgCAoKMhM4aOWLl1qm+c3a9asnPEAACCFqCLwXNoZ/dixYxISEiIPPfSQqzcHSOWQYK38czE81YKBBQf/Cwb0fTOlCZR+lUqYoQRaiaVDCwCkYkCgf/h0KIFOD/Lnn3/Ko48+ah7fs2eP5M+f/x42BQAA30YVgWeyVg/UrVvXhASAL4QEOtygfNZMcjZSexI4NySwDwZeWh43GNjwZH3pU6kEwQDgqoBg8uTJEhgYaKYOmTp1quTLl888rsMLmjRp4qjtAgDAJ6sItDRWTZw4kco8D8H0hvBFWbSSoHFNKXcrJHjyl3Wyx8EhQWLBQF+7YEB7IwBwHKY5TGVMcwgAuNv83WXLlpV//vlHRo8eLYMGDfKoHeZrU31FRUWZYZY6/HLr1q2mizrgiVL6s3s+MlpaL14n289flhwhwWYKxBL3ZbznYODHQyfknc17bZUJGgy8VPZ+ebF0kQRDAabpA1xUQWAvIiLCHPDaXwAAQMrRi8CzrFmzxoQDuXPnlnLlyrl6c4BUlzVEexLUlLJZMsmZyCgzBeLei1dSHAwsPHhcHk6kYkCvqRgA3CwguHr1qrz66quSM2dOyZAhg2TJkiXOBQAA3BudrqtkyZJy/vx5M7QP7st+9gI/Pz9Xbw7gupCgcU0pkyXjzZDg17XJCgnsg4EXl/8tuy+GS8Y0gdKnYnGCAcDdA4L+/fvL77//bqY1DA4OlunTp5spRfLmzStffPGFc7YSAAAfQhWB52B6Q+CmbCFB8k3jWiYkOB1xMyTYd+nKPQQDD0u/B0pSMQC4e0CwcOFCEw48+eSTpllhnTp1ZMiQITJmzBiZNWuWc7YSAAAfQxWB+ztz5oz8/fff5naDBg1cvTmA24QEpa0hwS8JhwSmx8DBE1J/QeLBgM6UACD1BSZ3BS13LFKkiLmdKVMmc986/eHLL7/s+C0EAMCHZzRo3769vPPOO9K9e3fJmPHeGn/BsZYuXWquK1asKLly5WL3AraQoKY89es62XUhXB79aZVkDwmWo1ci5P7M6SUsbw5ZfvyMeU5pMPBimSLyUpkihAKAJ1YQ3H///XLw4EFzu0yZMvL111/bKgvuu+8+x28hAAA+6umnn5YSJUrQi8BNMbwASJgGAhoS5EsXIpeib8j+y1clKjbWhAJTd/xrrjUY6H2rYqA/FQOA5wYEzz33nGzZssXcHjhwoK0XQa9evaRfv37O2EYAAMTXexG8/fbbEh7u2DnGkXIWi0WWLFlia1AI4PaQIH2ahIuVc4QEEQwA3hIQaBDQo0cPc7tevXqye/dumT17thmD99prrzljGwEA8FlUEbinXbt2ybFjxyQkJMQMswRwu0Ph1xLcLZev32A4AeAtAUF8BQsWlFatWpnxdwAAwLGoInDv4QWhoaEmJABwu6KZ00v8yT/1frFMGdhdgKcHBDq1ofYcuHz58m3PXbp0ScqWLSsrV6509PYBAODzqCJwP/QfAO6uT6USYrkVCsita73fp1Jxdh/g6QHBu+++Ky+++KKZuSC+zJkzS5cuXWTixImO3j4AAHyedUYDRS8C14uKipLly5eb2w0bNnT15gBu69FCeeTTelXMtIfBAf7m+rN6VeSRQnlcvWkA7jUg0MaETZo0SfR5bdCzcePGpL4cAABIYRXBhx9+yL5zodWrV0tERITkzp1bypUrx/cCuEtI8PvjoXKowyPmmnAA8JKA4NSpU5ImTZpEnw8MDJQzZ844arsAAEC8v7NUEbjf8AI/v/gjrAEA8IGAIF++fLJt27ZEn9+6davkyUO5EAAAzq4iOHfuHFUELsT0hgAA8fWA4JFHHjFzMUdGRt72nJbZDR8+XB577DFHbx8AALiFKgLX02pJndpZNWjQwNWbAwCAawKCIUOGmHGPeuZiwoQJ8sMPP8iCBQtk/PjxUrJkSfPc4MGDU7wh169flwEDBkj58uUlffr0kjdvXunYsaMcP378tsZA3bt3l+zZs5vlmjdvLkePHr3r60+ZMkWKFClipiKqUqXKbTMuWCwWGTFihHnftGnTSlhYmOzYscMh7w0AgCOrCIoXL04VgYssXbrUXFeqVEly5crlqs0AAMC1AYH+EVyzZo1pxjNw4EBp2bKltGjRQgYNGmQe04Y99/KH8tq1ayaR1/GVev3dd9/Jnj17zEG4vZ49e8r8+fNlzpw5smrVKrly5YqpXIiJiUn0tefOnWvW0wBj06ZNUqdOHWnatKkcPnzYtoyGHjoLw+TJk2XDhg2m8ZB2Jg4PD7+n9wYAwJlVBPq3CKmH6Q0BAN7Mz6KnzpPpwoULsm/fPnPWXc9iZMmSxSkbpwfq1atXl0OHDknBggXl0qVLkiNHDpk5c6a0adPGLKMVBgUKFJBFixZJ48aNE3ydGjVqSOXKlWXq1Km2x0qXLm0CjrFjx5qvQysHNADQKgZrtYAGHlohoVM4pvS947t8+bKZFlJfL6EpIwEAuJsbN25ImTJlZO/evTJu3Djb3y53cOq5ZuLJcv1vYaLP6f8L+fPnN3//tQ8BQwzgTbz5ZxeAEyoI7GkgUK1aNXPw7qxwQOlBtHYHvu+++8x9nUZRhyJo12ArPbDXCgatbkhIdHS0Wc9+HaX3rescOHBATp48GWeZ4OBgCQ0NtS2Tkve2Bg0aCthfAABwVBXBW2+9RRVBKtm5c6cJB3S44kMPPZRabwsAgHsHBKlBmyG+/vrr0q5dO9uZdj2IDwoKui2U0DP9+lxCzp49a4YAxB/+YL+O9fpuyyT3vZVWKGjFgPWiFQcAANyrtm3b0ovARcML9ASChgQAAHgblwUEs2bNkgwZMtgu9k0D9Uy9NmGKjY01zQXvRkv+7jYPcfznE1onKcsk9721X4NWQlgvR44cuePrAQCQFFQRpD6mNwQAeDuXBQTafHDz5s22S9WqVW3hQOvWrU3Zv/4hth+nr40DdciA9kCwd/r06UQbJOqMAwEBAbed5bdfR19X3W2Z5L63daiCfg32FwAAHIEqgtSjQwaXL19ubscftggAgLdwWUCQMWNGKVasmO2iUwtawwFtuqTTCGXLli3OOjo9YZo0aWwJvjpx4oRs375dateuneD76LAAXc9+HaX3revo9IcaANgvo2HAihUrbMuk5L0BAHB2FYFOQ6zoReBcOltTRESE5MmTR8qWLevkdwMAwMd7EGhH5ieffFL++usvM/xA+wboGX296MG60jH8nTt3lj59+shvv/1mpixs3769lC9f/o6dhHv37i3Tp0+Xzz77THbt2iW9evUyUxx27drVPK9DBHQGgzFjxphpDPWgv1OnTpIuXTrTA+Fe3hsAAGfSv1MatJ87dy5Jw/Jw79Mb3m34IQAAnipQ3MTRo0dlwYIF5nalSpXiPLds2TIJCwsztydNmmTOmGilgSb59evXlxkzZphhBFa6bOHChc3jSqcl1H+cRo0aZc7668wDOjVhoUKFbOv079/fvF63bt3MMAKdGlH/GdBKB6ukvDcAAK7oRfDss8+aKgL9O6a9feCcgKBhw4bsWgCA1/KzaJc9L6PhwIgRI0wVgLvRaQ61GkEbFtKPAADgqCq80qVLy759+2T8+PEm9HYVb5xL3b7fkFY23qn3EOCpvPFnF4AHDzFwlN27d5uz/h07dnT1pgAAkCqY0cC5tC+StcKRcAAA4M28LiAoVaqUbNu2Tfz9ve5LAwDgrr0Izp49Sy8CB2N6QwCAr+AoGgAAL8CMBs6hIzHtGxQCAODNCAgAAPASzzzzjBQtWpQqAgfauXOnHD9+3EzH/OCDDzrypQEAcDsEBAAAeGkvgqtXr7p6kzyetXogNDRUQkJCXL05AAA4FQEBAABehCoCx2J6QwCALyEgAADAS6sIJkyYQBXBPYiMjJQVK1aY2/QfAAD4AgICAAC8DFUEjrF69WqJiIiQPHnySNmyZR30qgAAuC8CAgAAvHhGA6oIHDO9oZ+fn4O+OwAAuC8CAgAAvFD79u2Z0eAeMb0hAMDXEBAAAODlVQTMaJB8p0+flk2bNpnbDRo0cPB3BwAA90RAAACAl1cRnDlzRqZOnerqzfEoS5cuNdcPPPCA5MyZ09WbAwBAqiAgAADAS9GLIOUYXgAA8EWBrt4AAADg3CqCN998U/bv32+qCPr27cvuvguLxWILCBo2bMj+gk/I9b+Frt4EAG6ACgIAALy8imDw4MHmNjMaJM2OHTvkxIkTkjZtWnnwwQed+v0BAMCdEBAAAOADVQT3338/vQiSOb1haGiohISEOPNbAwCAWyEgAADAy6VJk4YZDZKB/gMAAF9FQAAAgA9VEej0fR999JGrN8dtRUZGyooVK8ztRo0auXpzAABIVQQEAAD4WBUBvQgSt3r1aomIiJC8efNKmTJlUu37AwCAOyAgAADAR1BFkLzhBX5+fk7/ngAA4E4ICAAA8KEqAmY0uDOmNwQA+DICAgAAfEiHDh2kSJEi9CJIwJmIKNm8ebO53aBBg9T+1gAA4HIEBAAA+HAvgmvXrrl6k9zGyhNnzfUDDzwgOXPmdPXmAACQ6ggIAADwMVQRJGz5sTPmmtkLAAC+ioAAAAAfriIYP348VQQiYrFYZMVxAgIAgG8jIAAAwAdRRRDX7ovhcioiStKmTSsPPvigi74rAAC4FgEBAAA+PqMBVQQiK47f7D8QFhYmwcHBLv7uAADgGgQEAAD4qI4dO0rhwoWZ0UADglv9Bxo2bOjqbwsAAC5DQAAAgI+iF8FNkTdiZO2pc+Y2DQoBAL7MrQKCESNGSKlSpSR9+vSSJUsWMwfx+vXr4ywTFRUl3bt3l+zZs5vlmjdvLkePHr3ra0+ZMsXM+xwSEiJVqlSRlStX3tacSN8/b968Zvyhlhju2LHDIe8NAIAnVBF8/PHH4os2nL4gkTGxkjtdsJQpU8bVmwMAgMu4VUBQokQJmTx5smzbtk1WrVpl/mHRJP/MmZtlf6pnz54yf/58mTNnjlnmypUr8thjj0lMTEyirzt37lyzno613LRpk9SpU0eaNm0qhw8fti2jc0FPnDjRvP+GDRskd+7cpswwPDz8nt4bAAB3RhWByPJbsxeE5s0hfn5+rv6WAADgMn4WPXXupi5fviyZM2eWpUuXSv369eXSpUuSI0cOmTlzprRp08Ysc/z4cSlQoIAsWrRIGjdunODr1KhRQypXrixTp061PVa6dGlp0aKFjB071lQPaOWABgADBgywVQvkypXLNG7q0qVLit87sa9JXy9TpkwO2EsAANyb69evm5D+4MGDJizv1atXil/r1HPNPO7b0WDBH7L9/GWZWvcB6brib1dvDgAALuNWFQT2oqOjZdq0aeZgumLFiuaxjRs3mn9i7McH6oF9uXLlZM2aNYm+jq4Xf0yh3reuc+DAATl58mScZbSDcWhoqG2ZlLy3NWjQUMD+AgCAO/HlGQ3ORESZcEDVyZvd1ZsDAIBLuV1A8OOPP0qGDBlMr4BJkybJkiVLzJh/pQfxQUFBpj+BPT3Tr88l5OzZs2YIgC6T2DrW67stk9z3VlqhoCGH9aIVBwAAuGsvglOnTvlUL4I/bg0vqJAts2QPYXpDAIBvc1lAMGvWLBMEWC/WpoH16tWTzZs3m7PyTZo0kdatW5vGSXeiQwTuNmYw/vMJrZOUZZL73gMHDjTDCayXI0eO3PH1AABwBQ3BfbGKYPnxs+a6bh6qBwAAcFlAoDMAaBBgvVStWtU8rrMDFCtWTGrWrCmffvqpBAYGmmuljQN1yMCFCxfivJYGCPHP/ltp9UFAQMBtZ/nt19HXVXdbJrnvbR2qoL0G7C8AALh7FYEO8/N2GvJbKwjC8uVw9eYAAOC7AUHGjBlNEGC96NSCif3x1nH8Sqcn1HGSOuzA6sSJE7J9+3apXbt2omdEdD37dZTet66j0x9qAGC/jIYBK1assC2TkvcGAMCTqwgiIiLEm+2+GC6nIqIkbYC/VMsZdwghAAC+yG16EFy9elUGDRok69atk0OHDsnff/8tL7zwghw9elSeeuops4yO4e/cubP06dNHfvvtNzNlYfv27aV8+fLSoEGDRF+7d+/eMn36dPnss89k165dpjuzTnHYtWtX87wOEdAZDMaMGWOmMdSD/k6dOkm6dOmkXbt29/TeAAB4YhWBVtV5ey+CFbeGF9TKnU2CAwJcvTkAALhcoLgJHQawe/du+fzzz01jwWzZskm1atVMb4KyZcvaltPGhTrsQHsT6JkNnf5wxowZZn2rsLAw88+NPq50WsJz587JqFGjzFl/nXlApyYsVKiQbZ3+/fub1+vWrZsZRqBTIy5evNhUOiTnvQEA8PQqAg3sX3rpJdtUv4lV+Xm6FcduDS/Iy/ACAACUn0Vr+L2MhgMjRowwVQDuRqc51GoEbVhIPwIAgDvSYXYlSpQwFX0ajmuVXVKdeq6ZeILIGzFSavavEhkTKytahErJ+26eEMj1v4Wu3jQAAFzGbYYYOIpWIehZfy2RBAAAyecLvQj+PH3ehAN50oVIicwZXL05AAC4Ba8LCEqVKiXbtm0Tf3+v+9IAAEg1zz77rBmKp70IvHFGg+W3Zi+omzf7Xac0BgDAV3AUDQAA7lhFMG7cOK+rIlhx7GaDQvoPAADwHwICAADgU1UEZyKiZMeFy+Z2nbzZXb05AAC4DQICAABwxxkNvK2K4I9bwwsqZMss2UOCXb05AAC4DQICAACQKJ0RqGDBgl5VRbD8+M3hBaFUDwAAEAcBAQAA8JleBDq784pbFQSheXO4enMAAHArBAQAAMBnqgh2XwyX0xFRkjYwQKrlzOLqzQEAwK0QEAAAgCRXEYwfP96jqwiWH7tZPVArV1YJDghw9eYAAOBWCAgAAECSqwhOnDghn3zyicfusRW3+g8wvSEAALcjIAAAAD4xo0HkjRhZd+qcuR2aj/4DAADER0AAAACS5LnnnpMCBQp4bBXBn6fPS2RMrORJFyIlMmdw9eYAAOB2CAgAAIBPzGiw3G72Aj8/P1dvDgAAboeAAAAA+EQVwYpj1v4D2V29KQAAuCUCAgAAkOIqgsjISI/Ye6evRcqOC5dF6wbq5KX/AAAACSEgAAAAXl9F8MeJm9UD5bNllmwhQa7eHAAA3BIBAQAASPGMBmPHjvWIKoL/+g8wvAAAgMQQEAAAAK+uIrBYLPLH8bO2BoUAACBhBAQAACDZgoODPaaKYPfFcDkdESVpAwOkWs4srt4cAADcFgEBAADw6iqC5cduDi+onTubBAcEuHpzAABwWwQEAADgnqsI3HlGgxW3hhcwvSEAAHdGQAAAAO65iuD48eMyffp0t9uTETdiZN2pc+Y2/QcAALgzAgIAAHBPVQQDBw78rxfBjRi32pt/nj4vkTGxkiddiBTPnMHVmwMAgFsjIAAAAPfk+eefl/z585sqgll7D7tl/wGtHvDz83P15gAA4NYICAAAgMN6EXywbZ9bVRGsOH4zIKD/AAAAd0dAAAAAHFZFcPJalNtUEZy+Fik7L4SL1g3UyZvD1ZsDAIDbIyAAAABeWUXwx4mbsxeUz5ZZsoUEuXpzAABwewQEAADAYVUEedOFmCqCr/YecfleXW4bXkD1AAAASUFAAAAAHFZF0L1CMbeoIrBYLLLi2M0KgtC82V22HQAAeBK3DQi6dOliug2/++67cR6PioqS7t27S/bs2SV9+vTSvHlzOXr06F1fb8qUKVKkSBEJCQmRKlWqyMqVK2/7R2LEiBGSN29eSZs2rYSFhcmOHTsc8t4AAPiKdsULmCkFT1yLdGkVwa4L4XImMkrSBQZI1ZxZXLYdAAB4ErcMCL7//ntZv369OViPr2fPnjJ//nyZM2eOrFq1Sq5cuSKPPfaYxMQkfpZi7ty5Zr3BgwfLpk2bpE6dOtK0aVM5fPi/JkoTJkyQiRMnyuTJk2XDhg2SO3duadiwoYSHh9/TewMA4EuCAwKkhxtUEViHF9TKnc1sEwAA8MCA4NixY/Lqq6/KrFmzJE2aNHGeu3Tpknz66afyzjvvSIMGDeSBBx6QL7/8UrZt2yZLly5N9DX1wL9z587ywgsvSOnSpU1VQoECBWTq1Km26gF9TAOEVq1aSbly5eTzzz+Xa9euyVdffXVP7w0AgK9xhyoCpjcEAMDDA4LY2Fjp0KGD9OvXT8qWLXvb8xs3bpTr169Lo0aNbI9plYEe0K9ZsybB14yOjjbr2a+j9L51nQMHDsjJkyfjLKPjKENDQ23LpOS9rcMSLl++HOcCAIAvVRFEpXKlXcSNGFl/6ry5HUqDQgAAPDMgGD9+vAQGBkqPHj0SfF4P4oOCgiRLlrhjCXPlymWeS8jZs2fNEABdJrF1rNd3Wya5763Gjh0rmTNntl20cgEAAG/nyiqCP0+fl8iYWDOjQvHMGVL1vQEA8GQuCwh0CEGGDBlslxUrVsh7770nM2bMMM0Jk0OHCNxtnfjPJ7ROUpZJ7nsPHDjQDE+wXo4ccf20TwAApEYVQffyN6sI3t+aulUEy4/d7D8Qmi9Hsv+nAADAl7ksINAZADZv3my7aJn+6dOnpWDBgqaKQC+HDh2SPn36SOHChc062jhQhwxcuHAhzmvpevHP/lvpjAMBAQG3neW3X0dfV91tmeS+t3WoQqZMmeJcAADwBa6qIrD2H2B4AQAAHhIQZMyYUYoVK2a7vPTSS7J169Y4oYGO8dd+BL/++qtZR6cn1MaFS5Yssb3OiRMnZPv27VK7du0E30eHBeh69usovW9dR6c/1ADAfhkNA7SqwbpMSt4bAABfFhKY+lUEp65Fys4L4aJ1A3XyZHf6+wEA4E0CxU1ky5bNXOzpAbkeuJcsWdLc1zH8OhuBVhXoslmzZpW+fftK+fLlzcwCiendu7dpfli1alWpVauWTJs2zUxx2LVrV/O8lh/qFIZjxoyR4sWLm4veTpcunbRr1+6e3hsAAF+vItBGhdYqgudK3awKdJY/Tpw11+WzZZZsIUFOfS8AALyN2wQESTVp0iQz/KB169YSEREh9evXN30LdBiBVVhYmBmWoI+rNm3ayLlz52TUqFHmrL/OPLBo0SIpVKiQbZ3+/fub1+vWrZsZRlCjRg1ZvHixqXRIznsDAIDbqwgGrd8uH2zdZwID7U/gLCtu9R8IY/YCAACSzc+iXfa8jIYDI0aMkE6dOom70WkOtRpBGxbSjwAA4G1OPdfstscib8RIre+WmSqCcTXLSScnVRHovzQV5i6VM5FRMq9xTXkwBUMMcv1voVO2DQAAT+BW0xw6wu7du81Z/44dO7p6UwAAwK0qglfLF3V6L4JdF8JNOJAuMECq5ow7LTEAAPDBgKBUqVKybds28ff3ui8NAACP9UzxgpI7XbAcvxYps500o8HyW7MX1M6dzanDGAAA8FYcRQMAAK+Y0cA6vSH9BwAASBkCAgAA4PFVBBE3YmTdyfPmdigNCgEASBECAgAA4PFVBOtPnZeo2FjJmy5EimVO77DXBQDAlxAQAAAAl1QRzNl71OH9B0Lz5RA/Pz+HvS4AAL6EgAAAAKTyjAa3qgi27XVYFcEf1oCA4QUAAKQYAQEAAEhV7YsXlFxpg+XYVcdUEZy6Fik7L4SL1g3UyZPdIdsIAIAvIiAAAACp34ugguOqCP44cdZcV8iWWbKFBDlkGwEA8EUEBAAAwKOrCFYcuzW9Yb4cDto6AAB8EwEBAADw2CqCWItFVhy/WUFA/wEAAO4NAQEAAPDYKoJdF8LlTGSUpAsMkKo5sjh8GwEA8CUEBAAAwHVVBHYzGkTHxKZ4esPaubNJUAD/1gAAcC/4SwoAAFzmmRJ2VQT7jqR4esMwpjcEAOCeERAAAACXSWtXRfDe1uRVEUTciJF1J8+b2/QfAADg3hEQAAAAj6wiWH/qvETFxkq+9CFSLHN6p24jAAC+gIAAAAB4ZBWBtf+AVg/4+fk5dRsBAPAFBAQAAMAjqwhW2AUEAADg3hEQAAAAt6gieLV8UXP7va377lpFcOpapJniUOsG6uTJnkpbCQCAdyMgAAAAbqF9iUKS01QRRMjcu1QRrDh+1lxXyJZZsoYEpdIWAgDg3QgIAACAG/UiuFlF8O5dqghs0xvmY3gBAACOQkAAAAA8qoog1mKxVRDQfwAAAMchIAAAAB5VRaC9B85ERkm6wACpmiOLC7YSAADvREAAAAA8qorAOr3hg7mzSVAA/8oAAOAo/FUFAADuN6NBucRnNFhx7Nb0hvQfAADAoQgIAACA2+lQ8mYVwdGrEfL1/v+qCK7diJH1p86b26F5aFAIAIAjERAAAAC3riJ4d8t/VQTrT52TqNhYyZc+RIplTu/irQQAwLsQEAAAAI+pIrCfvcDPz8/FWwgAgHchIAAAAB5TRbDiVoNCpjcEAMDLA4JOnTqZswH2l5o1a8ZZJioqSrp37y7Zs2eX9OnTS/PmzeXo0aN3fe0pU6ZIkSJFJCQkRKpUqSIrV66M87zFYpERI0ZI3rx5JW3atBIWFiY7duxwyHsDAIB7ryL4YNs+M8Wh1g3UyZOdXQoAgDcHBKpJkyZy4sQJ22XRokVxnu/Zs6fMnz9f5syZI6tWrZIrV67IY489JjExMYm+5ty5c816gwcPlk2bNkmdOnWkadOmcvjwYdsyEyZMkIkTJ8rkyZNlw4YNkjt3bmnYsKGEh4ff03sDAIB7qyJ45VYVwVub95jr4AB/WXvqHLsVAAAH87PoqXM3qiC4ePGifP/99wk+f+nSJcmRI4fMnDlT2rRpYx47fvy4FChQwAQJjRs3TnC9GjVqSOXKlWXq1Km2x0qXLi0tWrSQsWPHmuoBrRzQAGDAgAG2aoFcuXLJ+PHjpUuXLil+7/guX74smTNnNq+XKVOmZO8jAADc2annmjn8Nb/795h0+2PTbY9/Wq+KPFooj0PfK9f/Fjr09QAA8CRuV0GwfPlyyZkzp5QoUUJefPFFOX36tO25jRs3yvXr16VRo0a2x/TAvly5crJmzZoEXy86OtqsZ7+O0vvWdQ4cOCAnT56Ms0xwcLCEhobalknJe1uDBg0F7C8AACDpJm/bd9tjOszgnVsVBQAAwDECxY1o2f9TTz0lhQoVMgftQ4cOlYcfftgcnOsBux7EBwUFSZYsWeKsp2f69bmEnD171gwB0GUSW8d6ndAyhw4dsi2T3PdWWqEwcuTIZO0HAAA8lTPOwP+bNu1tj2n5478R1znjDwCAN1QQzJo1SzJkyGC7aNNALd1/9NFHzVn5Zs2ayc8//yx79uyRn3766Y6vpUME7jbVUfznE1onKcsk970HDhxohhNYL0eO3JymCQAAJI1WFSb0N7tkyZLsQgAAvCEg0BkANm/ebLtUrVr1tmXy5Mljqgn27t1r7mvjQB0ycOHChTjL6TCE+Gf/rXTGgYCAgNvO8tuvo6+r7rZMct9baeWD9hqwvwAAgKQbPnx4nEBer/W+Pg4AALwgIMiYMaMUK1bMdtGpBeM7d+6cOeOuQYHS6QnTpEkjS5YssS2jMx1s375dateuneD76LAAXc9+HaX3revo9IcaANgvo2HAihUrbMuk5L0BAMC9a9WqlcybN08qVKhgpivW6++++05atmzJ7gUAwBt7EOiUgSNGjJAnnnjCBAIHDx6UQYMGmQoA6z8A2v2/c+fO0qdPH8mWLZtkzZpV+vbtK+XLl5cGDRok+tq9e/eWDh06mCqFWrVqybRp08wUh127drWdidAZDMaMGSPFixc3F72dLl06adeu3T29NwAAcExIoBcAAOADAYEOA9i2bZt88cUXZqpDDQnq1asnc+fONdUGVpMmTZLAwEBp3bq1RERESP369WXGjBlmfauwsDApXLiweVxpbwOtRhg1apQ56689DnRqQh2+YNW/f3/zet26dTPDCHRqxMWLFyf7vQEAAAAA8ER+Fh3E52U0HNBqhE6dOom70WkOtRpBGxbSjwAAAAAAIL7eg8BZdu/ebc76d+zY0dWbAgAAAACAx/DKCgJ3RgUBAAAAAMAdeV0FAQAAAAAASD4CAgAAAAAAQEAAAAAAAAAICAAAAAAAAAEBAAAAAAAgIAAAAAAAAEbgzSukFuuskjrdIQAAAAAAqSVjxozi5+eX6PMEBKksPDzcXBcoUCC13xoAAAAA4MMuXbokmTJlSvR5P4v1lDZSRWxsrBw/fvyuyQ0AAAAAAI50t+NQAgIAAAAAACD+7AMAAAAAAEBAAAAAAAAACAgAAAAAAAABAQAAAAA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+/fRT07CwV69eztlKAAAAeD3L9esisbGu3gzP5O8vfmnSuHorAHg4P4vFYrmXF9C+A2vWrDHVBM2bN3fclnmpy5cvS+bMmc3QjEyZMrl6cwAAsImNuCZRWzeIf0h68aMqEC4IB2LOnxG/kBD2fUr2X2SkBGTNQUgAxP/ZiIqS2MirElyhmvinTcf+cXQFQXw1a9Y0FwAAACDFYmNNOBBUqoL4BTFsNTks0VESvXsr1RcAUicgWLBgQZJf0FFVBF26dJFp06bJpEmTpGfPnrbHo6KizAwKs2fPloiICKlfv75MmTLF9Ea4E13mrbfeMv0TypYtK++++67UqVPH9rwWUugUjvqeFy5cMEMoPvzwQ7Psvb43AAAAkkbDAc7yJQ+DMgCkakDQokWLJL2Yn5+fxMTE3Os2yffffy/r16+XvHnz3vachgULFy6UOXPmSLZs2aRPnz5mBoWNGzdKQEBAgq83d+5cs54ezD/44IPy8ccfS9OmTWXnzp1SsGBBs8yECRNk4sSJMmPGDClRooS8+eab0rBhQ/nnn38kY8aMKX5vAAAAAAB8ogeBox07dsycvf/111/l0UcfNQfl1goCHbefI0cOmTlzprRp08Y8dvz4cSlQoIAsWrRIGjdunOBr6utVrlxZpk6danusdOnSJvgYO3asqR7QMELfZ8CAAbZqgVy5csn48eNNNUNK3zs+ehAAANwVPQjuHU327mHf3YgWy43rjBNOAX52gTv8bqEHQer2IHCk2NhY6dChg/Tr1y9Oab+Vnqm/fv26NGrUyPaYHtiXK1fONEpM6CA9OjrarPf666/HeVxfQ9dRBw4ckJMnT8Z5XZ2yMTQ01CyjAUFK3tsaNOjFPiAAAADehyZ7984vOK34UZUJAJ4TEIwaNeqOzw8bNizFG6Nn6wMDA6VHjx4JPq8H8UFBQZIlS5Y4j+uZfn0uIWfPnjXDHnSZxNaxXie0zKFDh1L83korFLS3AQAA8HI02btnGg7QoBAAPCggmD9/fpz7elZdz8DrgX3RokWTHBDMmjXLnJm3+umnn+S9996Tv//+2/QySA4dInC3deI/n9A6SVkmue89cOBA6d27d5wKAh2WAAAAvBNN9gAAPhMQbNq06bbH9KC3U6dO0rJly2TNdqC9Aay++eYbOX36tK1poNIz/9oIUGccOHjwoOTOndsMGdBZBuzP5Ot6tWvXTvB9smfPbhoIxj/Lr+tYKwb0dZUukydPnkSXSe57W4cq6AUAAAAAAHfm74gXyZQpkxl6MHTo0CSvozMDFCtWzHZ56aWXZOvWrbJ582bbRcf4az8CbVioqlSpImnSpJElS5bYXkenLdy+fXuiB+k6LEDXs19H6X3rOkWKFDEBgP0yGgasWLHCtkxK3hsAAAAAAJ9rUnjx4kXT6T+ldNpAvdjTA3I9cC9ZsqS5nzlzZuncubOpKtBls2bNKn379pXy5ctLgwYNEn1tLfHX5odVq1aVWrVqybRp0+Tw4cPStWtX87wOEdAZDMaMGSPFixc3F72dLl06adeu3T29NwAAAAAAXhkQvP/++7eNwdcz6Tr9X5MmTcTZJk2aZPodtG7dWiIiIqR+/foyY8YMM4zAKiwsTAoXLmweVzot4blz50yVg26rzjygUxMWKlTItk7//v3N63Xr1s0MI9DhD4sXLzaVDsl5bwAAAAAAPJGfRY/wk0HL8e35+/tLjhw55OGHHzYN+ewPqF1Fw4ERI0aYvgjuRvs1aDWCVlvo0AwAANwFc6nfG+bahqvwswskjt/NTq4g0BkL3Nnu3btNSNGxY0dXbwoAAAAAAL7Xg8BdlCpVSrZt2+bqzQAAAAAAwLsDgsjISPnggw9k2bJlZoq/2NjYOM///fffjtw+AAAAAADgjgHB888/b6b6e/LJJ6V69epmBgAAAAAAAOBjAcFPP/1kZgB48MEHnbNFAAAAAAAg1fknd4V8+fK5xUwFAAAAAADAhQHBO++8IwMGDJBDhw45cDMAAAAAAIBHDTGoWrWqaVR4//33S7p06SRNmjRxnj9//rwjtw8AAAAAALhjQNC2bVs5duyYjBkzRnLlykWTQgAAAAAAfDEgWLNmjaxdu1YqVqzonC0CAAAAAADu34OgVKlSEhER4ZytAQAAAAAAnhEQjBs3Tvr06SPLly+Xc+fOyeXLl+NcAAAAAACADwwxaNKkibmuX79+nMctFovpRxATE+O4rQMAAAAAAO4ZECxbtsw5WwIAAAAAADwnIAgNDXXOlgAAAABIMcuNaPZeSvn7i1+86dsBX5TsgOCPP/644/N169a9l+0BAAAAkAx+AQHiF5xWLFERYrlxnX2XApbISAnImoOQAD4v2QFBWFjYbY9p7wErehAAAAAAqccvKFiCy1QUC73AUsQSHSXRu7eKxMY6+lsDeH9AcOHChTj3r1+/Lps2bZKhQ4fK6NGjHbltAAAAAJIYEvx3yg7JQSwA3ENAkDlz5tsea9iwoQQHB0uvXr1k48aNyX1JAAAAAADgYv6OeqEcOXLIP//846iXAwAAAAAA7lxBsHXr1jj3LRaLnDhxQsaNGycVK1Z05LYBAAAAAAB3DQgqVapkmhJqMGCvZs2a8tlnnzly2wAAAAAAbs5y/brbNnlk+k8nBwQHDhyIc9/f398MLwgJCUnuSwEAAAAAPDwciDl/Rvzc+HhQpwHV6UDhhICgUKFC4ky7du2SAQMGyIoVKyQ2NlbKli0rX3/9tRQsWNA8HxUVJX379pXZs2dLRESE1K9fX6ZMmSL58+e/4+vqMm+99ZYZDqGv+e6770qdOnVsz2tFxMiRI2XatGlmpoYaNWrIhx9+aJa1Sul7AwAAAIBXio014UBQqQpmNg13pOGAu26bxzYp/P3336VMmTJy+fLl2567dOmSOZBeuXLlPW3M/v375aGHHpJSpUrJ8uXLZcuWLWb6RPvqhJ49e8r8+fNlzpw5smrVKrly5Yo89thjEnOHeV/nzp1r1hs8eLCZklGDgaZNm8rhw4dty0yYMEEmTpwokydPlg0bNkju3LnN7Azh4eH39N4AAAAA4O30ANw/bTq3vBAOJJ2fJX4zgUQ0b95c6tWrZ6YyTMj7778vy5YtMwfQKfX0009LmjRpZObMmQk+r0GEDmfQ59u0aWMeO378uBQoUEAWLVokjRs3TnA9rQaoXLmyTJ061fZY6dKlpUWLFjJ27FhTPZA3b14TAGj1grVaIFeuXDJ+/Hjp0qVLit87Pg1YdKpIfb1MmTIlex8BAOAssRHXJGrrBvEPSS9+wZxpSS5LVJTERl6V4ArVzD+kADwDv/vuDb/7fLSCQM/mN2nSJNHnGzVqJBs3bkzxhuhwgp9++klKlChhDrZz5sxpDuy///572zL6+tevXzfvZaUH9uXKlZM1a9Yk+LrR0dFmPft1rNtrXUf7Kpw8eTLOMsHBwRIaGmpbJiXvbQ0aNBSwvwAAAABwv2Z2erDLJZn74Ea0q791cEUPglOnTpmz+4m+UGCgnDlzJsUbcvr0aVOyr9Mlvvnmm+bM/S+//CKtWrUylQl6sK4H8UFBQZIlS5Y46+qZfn0uIWfPnjVDAHSZxNaxXie0zKFDh2zLJPe9lVYoaG8DAAAAAG46Pj04rViiIsRy47qrN8cj0QTQBwOCfPnyybZt26RYsWIJPr9161bJkydPkt941qxZpnTfSqsH1OOPP24bxqBTKurZ+Y8++sgEBInRIQI69eKdxH8+oXWSskxy33vgwIHSu3dv232tINBhCQAAAABcT8enB5epKBb6iqV8H9IE0PcCgkceeUSGDRtmmvvFn9JQO/oPHz7cNOxLKu1poEMIrHR8v1YhaCNEe9orQBsCKm0cqEMGdJYB+zP5Wn1Qu3btBN8ne/bsEhAQcNtZfl3HWjGgr6t0GfuQI/4yyX1v61AFvQAA4CkoF2W/Ab4YEtz5tCDgG5IcEAwZMkS+++470yPg1VdflZIlS5oz5zotoU4HqGX8OktAUmXMmNFc7FWrVk3++eefOI/t2bPHNrVilSpVzDCHJUuWSOvWrc1jOm3h9u3bzSwECdFhAbqertOyZUvb43pfqxVUkSJFTACgjz3wwAPmMQ0DdKpFHeqQ0vcGAMCTUGbrgH3IXNsAAF8ICPRMupb7v/zyy6Zs3jr5gYYE2lRwypQpt43hT65+/fqZGQLq1q1rZkzQHgQLFy40Ux4q7f7fuXNn6dOnj2TLlk2yZs0qffv2lfLly0uDBg0SfV0t8e/QoYNUrVpVatWqJdOmTTNTHHbt2tX2NegMBmPGjJHixYubi95Oly6dtGvX7p7eGwAAT0GZrQP2IWW2AABfCAiUnsnXKf20zH7fvn0mJNCD6fiN+1JKz/BrvwFt7NejRw9TpTBv3jx56KGHbMtMmjTJDEXQs/g6tKF+/foyY8YMM4zAKiwsTAoXLmweVxo6nDt3TkaNGmXO+uvMA/p1WCsTVP/+/c3rdevWzXx9Ovxh8eLFcaockvLeAAB4MspsAQDwXX4WaymAF9FwYMSIEdKpUydxN9qkUKsRLl26JJkyZXL15gAAAAAAkPwKAk+we/duc9a/Y8eO4o6seYwGBQAAAAAApBY9Vr7TLHxeWUHgzo4ePco0hwAAAACAVHe3SnYCglQWGxsrx48fv2ty4+20gqJAgQJy5MgRhlqAzx98Br/7wOcPvojffeDz5z7udhzqdUMM3J2/v7/kz5/f1ZvhNjS9ohcD+PzB1/C7D3z+4Iv43Qc+f+7P39UbAAAAAAAAXI+AAAAAAAAAEBDANYKDg2X48OHmGuDzB1/B7z7w+YMv4ncf+Px5DpoUAgAAAAAAKggAAAAAAAABAQAAAAAAICAAAAAAAAAEBAAAAAAAwGCaQwAAAAAAQEAA57BYLOxauASfPQAAAN/A/32ORwUBHO706dMSHh5uu88PLlLLpUuXJCYmhs8eXGLfvn2yZMkS9j5S3Z49e6Rr166ycuVK9j5S3ZEjR2Tjxo1y/Phx9j5SFccczkFAAIe5ceOGdO7cWapXry4NGjSQZ555Rs6ePSt+fn7sZTjV9evX5ZVXXpFHHnnEXN544w0TFPDZQ2rZunWrlChRQtq2bSuHDh1ixyNVxMbGSq9evaRSpUpy9erVOOE8kBp/e7t06SKVK1eW559/XipWrCirV69mx8PpOOZwLgICOOwHtVOnTrJz5075/PPPzT/J+g9zq1atZNeuXexlOI2esS1Tpozs2LFD+vXrJwUKFJBZs2bJiBEjzPNUsCA1REdHS+PGjSVNmjQyYcIEdjpSxc8//ywbNmww1zNnzjQBqRW/++BMV65ckSeffFL27t0rixcvlq+//toEBUOHDuXzB6fimMP5CAjgECdOnJA///zTnMUNDQ01ZzT0wO3ff/+VqVOnyqlTp9jTcLjLly+bf0r0wEw/by1atDCft6efftr803zt2jWqCJAq/v77b8mSJYsJp6ZNm2Z+HwLONn36dFM9oH93V6xYYQ7OZsyYIYcPH+Z3H5xKTwjpCSD9zD3wwANSsmRJeeqppyRjxoymsoUKPjgLxxzOR0AAhzh37pwcPXpUatasae5HRUVJ7ty5ZeDAgSZZ/uOPP9jTcDgdRvDQQw/JCy+8YM7c6hmzoKAgiYyMlIiICEmXLh1n0eA09mdog4ODpVChQvLwww9LtWrVZOTIkbYQC3DGZ0+HE+gwvvr168ubb75pgtFt27bJsGHDzOdw4cKF7Hg4dXiB9l3R331KP4sffvih5M2bVz777DPzNxhwBo45nI+AAMmmZ8c++eSTOAf9xYsXN4HAl19+efOD5X/zo6UVBZoma/mjhgaAIz57eqZM6Rnbjh07mjNoSs9aWJsV3n///eY2ZzHgrM+ffrasnzmtINCSW6VVBL/88os0bdrUVLfs3r2bbwIc/tnTv616kKZVBNqk8LvvvpNvv/3W9MAoWrSoOUjjswdn/d/34IMPSlhYmDz33HPmd12uXLnM/4Ea0uvJoWeffdYEVsC90CF7gwYNMtWhVlqtop83jjmcyAIk0VdffWXJmTOnpVatWpZKlSpZcuTIYRk9erR57tKlS5b+/ftbSpQoYTl16pR5LCIiwlx//vnnlvvuu892H3DkZ+/GjRu25WJjY811jRo1LNOnT4/zGODIz9+YMWPMc1FRUeb66aeftixdutTc/uSTTyxp06a1pEmTxvLtt9+y4+GUz5769NNPLX5+fuZv7+nTp22P//HHH5Y8efJY1qxZw96HU/72qitXrlj27t1rqV27tuXtt9+2Pb5p0ybL/fffb/n666/Z+0iRWbNmmc+bfvaeeuop8zf1pZdeMs9dvnyZYw4nIyBAkn9QK1asaPnoo4/M/WPHjlkmT55sSZ8+vQkH1JIlSyzVqlWzdOvWLc6B2bJly8wfmC1btrC34dDPnv6RiO/AgQPmj8ru3bttj+3fv99cx8TE8B2AUz5/zz77rKVDhw7md6B+/t544w1LlixZ4vzTDDj67+727dstYWFhljJlylhOnDhhW1cD+QwZMli++eYbdjqc+rvv77//tpQsWdIEVNb/+zS45/cfUkpDdg2kPv74Y3P/+vXr5vOoIUF4eLh5jGMO52KIAe5WYWKutYyxRo0appxb6RgzLevOly+faVSjdCx4u3btzCwG8+fPN+sonfJGu8yXL1+evQ2HfvYSmiFDS7t1JgMtQdu0aZNZV3tjaNdb69AXwJGfPx1rq70GFi1aZKZ51c/dkCFDZMCAAWZmjYMHD7LD4ZTffaVKlZKePXvK/v375aOPPpJjx46ZxxcsWGD+5tatW5c9D6f+7dVePzqTwZEjR2xD+rT/RZEiRUwvDCCprEP29HPXt29fMzuaCgwMlPPnz8uLL74oISEh5rHatWubYw5tysoxhxM4OYCAh9q4caPlwoULtvsXL16MU8qtNm/ebMmdO7fl/PnztsesZT8ZM2a0hIaG2sqCPvzwQ/M85d5w1mfP+tnq3r275cknn7T06tXL4u/vb+ncubMlMjKSHQ+n/u77888/LTt27IiznH7uJkyYQOUKnPrZU++//74lb9685kxuy5YtzVle+1JwwFmfv3Pnzlnatm1rSZcunaVr166Wjh07mv8Bhw0bxv98SPLvPv282bM/Xnj99dctgYGBllKlSpnKgpkzZ5qqgujoaEvfvn055nCCQGeEDvBc8+bNM2cjtCutpsfaZEYbDWozEGu6Zz0L+/vvv5tGSNooTucA18Y02jRp/Pjxpov39u3bzfSG2rxLz3IoGsbBWZ89a8M4PXOmU3zptF/aIEmrVwBn/e7T5qu6jv7Oi08f1woCwJl/d1X37t1N9Yr+vdUzuePGjZMSJUqw4+H0z1/WrFnl008/NZV7p0+fNsv99ddffP6Q7M+eVgy8/PLLptml9bM3Z84cWbduncyePds8rjOj6TKZM2eWZs2ayVtvvWUqDjjmcDBnpA7wTBs2bDDp3Lvvvmv6BUyZMsWMpX355ZdNQmwdw62pndKzFK+88oqLtxrewFGfPU2gx44da/n1119T/WuA5+J3H/jswRc5+nefdTnAEZ89pf1W4n+uChYsaBk5ciQ72YkYkAvbeDNNfHWaLp2ypkKFCiahGz58uBlPO2XKFLOMpsh60XW2bt1qprZROsVS27ZtzZkLwFWfPU2UX3/9dWnUqBHfBKT65w9w1e8+wB0+fzpWHHDUZ09pZbL1c6Xrak8f7XuRPXt2drQTERDAVvZ/4MABUxJm/wtey32qVKkiP//8s+zYsePmh8bfXzZs2GB+QCtXrmzKg/SH+9y5c5IzZ072KFzy2cuRIwd7HsnC7z64Cp89uBKfP3jKZ89+aPLFixfNkAId/vL444+7YOt9BwGBD1qyZIn06NFD3nvvPfnzzz9tjz/44IOyZs0aOXnypLkfExMj6dOnNz+E+gOq436stFu3jvfRTvH6ejpTgT6v44gAV3z2rJ1tAX73wd3wdxd8/uCL7vV3n/a6mDt3runnU7ZsWdm4caNMnTrVzKYB5yEg8CEnTpwwDT3at29vpgvRpjJaim39gdXbhQsXNk0G7VO7hg0bmjO3+/bts71WmjRpTHmPTi+iKZ8mfgCfPbgjfveBzx58Eb/74OmfPR1WoCeA/vnnHxMyaMPC0qVL8411Nmc2OID7uHr1quXZZ5+1tGnTxvLvv//aHq9WrZqlU6dO5vb/27t7lUaDKAzAs5slGEhtKVhYCqlTBBs7O8HCkCaIraV1bsALSKld+lyAYG5BC9HeXIBYJO4ysySw3e4i+fhynqfxB8SBvJziMGdOXmdze3tbVsPNZrM//r7f7/88Ojpa/zyfzzd4eupM9pA/IlL7kD8i+uralx/KZLPcIAgiz2zn6/95vmd/fz8tFovy+5OTk/T09FS+bzQa6ezsrFzvubi4SPf396Vzl6//PD8/ly7ginlvZI86UPuQPSJS+9iW7K3WbLI533KXYIP/jwrlHaN5NCDLH3u+zjMYDFKr1Urj8Xj9u4+Pj/JK7ePjY+p0OmXee29vL00mk7LnFmSPOlH7kD0iUvuQPf6HBkFwvV4vDYfD0uXLDYLPz8/S1Xt7eyvrbPKL8XlG6Pz8vOqjsmVkD/kjIrUP+SMita8+NAgCe319Td1uN02n0/Ujg/m10GazWfXR2HKyh/wRkdqH/BGR2lcvhjoCWk2VPDw8pHa7vW4OjEajdHV1lebzecUnZFvJHvJHRGof8kdEal89/aj6AGzeapVIXjVyenpadpReXl6m9/f3dHd3l3Z3d30syB5bR+1D9ohI7UP2+BdGDILKDxEeHh6ml5eXMlKQbw9cX19XfSwCkD3kj4jUPuSPiNS++tEgCOz4+DgdHBykm5ubtLOzU/VxCET2kD8iUvuQPyJS++pFgyCw5XJZNhaA7BGJ2ofsEZHah+zxNzQIAAAAAFsMAAAAAA0CAAAAQIMAAAAA0CAAAAAAiu+/vwAAAACRaRAAAAAAGgQAAACABgEAAACgQQAAAABoEAAAAAAp+wVUgg0TRmIvFAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "custom_flows_a = pd.DataFrame(\n",
    "    {\n",
    "        \"date\": [\"2024\", \"2026\", \"2028\"],\n",
    "        \"cost\": [10_000, 0, 20_000],  # extra one-off costs\n",
    "        \"income\": [0, 15_000, 0],  # extra one-off income\n",
    "    }\n",
    ")\n",
    "\n",
    "custom_flows_b = pd.DataFrame(\n",
    "    {\n",
    "        \"date\": [\"2022\", \"2026\", \"2029\"],\n",
    "        \"cost\": [2_000, 0, 10_000],  # extra one-off costs\n",
    "        \"income\": [0, 5_000, 0],  # extra one-off income\n",
    "    }\n",
    ")\n",
    "\n",
    "ci_a = CostIncome(\n",
    "    mkt_price_year=2020,\n",
    "    init_cost=30_000,\n",
    "    periodic_cost=2_000,\n",
    "    periodic_income=4_000,\n",
    "    custom_cash_flows=custom_flows_a,\n",
    "    freq=\"Y\",\n",
    ")\n",
    "\n",
    "ci_b = CostIncome(\n",
    "    mkt_price_year=2020,\n",
    "    init_cost=20_000,\n",
    "    periodic_cost=3_000,\n",
    "    periodic_income=4_000,\n",
    "    custom_cash_flows=custom_flows_b,\n",
    "    freq=\"Y\",\n",
    ")\n",
    "\n",
    "ci_combined = CostIncome.comb_cost_income([ci_a, ci_b])\n",
    "ci_combined.plot_cash_flows(\n",
    "    \"2025\",\n",
    "    \"2020\",\n",
    "    \"2030\",\n",
    ")\n",
    "\n",
    "ci_combined.custom_cash_flows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a15baaf",
   "metadata": {},
   "source": [
    "## Loading from dict / YAML\n",
    "\n",
    "### From a Python dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eeac2088",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CostIncome(\n",
      "  mkt_price_year          = 2020\n",
      "  freq                    = 'Y'\n",
      "  init_cost               = -50,000.00\n",
      "  periodic_cost           = -5,000.00\n",
      "  periodic_income         = 8,000.00\n",
      "  cost_yearly_growth_rate = 2.00%\n",
      "  income_yearly_growth_rate = 3.00%\n",
      "  custom_cash_flows       = None\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "config_dict = {\n",
    "    \"mkt_price_year\": 2020,\n",
    "    \"init_cost\": 50_000,\n",
    "    \"periodic_cost\": 5_000,\n",
    "    \"periodic_income\": 8_000,\n",
    "    \"cost_yearly_growth_rate\": 0.02,\n",
    "    \"income_yearly_growth_rate\": 0.03,\n",
    "    \"freq\": \"Y\",\n",
    "}\n",
    "\n",
    "ci_from_dict = CostIncome.from_dict(config_dict)\n",
    "print(ci_from_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b2a868e",
   "metadata": {},
   "source": [
    "### From a YAML file\n",
    "\n",
    "Create a YAML file structured as follows, then load it with `CostIncome.from_yaml(<path to file>)`.\n",
    "\n",
    "```yaml\n",
    "# measure_cost.yaml\n",
    "cost_income:\n",
    "  mkt_price_year: 2020\n",
    "  init_cost: 50000\n",
    "  periodic_cost: 5000\n",
    "  periodic_income: 8000\n",
    "  cost_yearly_growth_rate: 0.02\n",
    "  income_yearly_growth_rate: 0.03\n",
    "  freq: \"Y\"\n",
    "  # Optional custom flows:\n",
    "  # custom_cash_flows:\n",
    "  #   - date: \"2024-01-01\"\n",
    "  #     cost: 10000\n",
    "  #     income: 0\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "```python\n",
    "# Inside Notebook or script\n",
    "ci_from_yaml = CostIncome.from_yaml(\"measure_cost.yaml\")\n",
    "```"
   ]
  }
 ],
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