0_solve_network_results_AR.BO.PE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Plotting PyPSA results\n",
    "\n",
    "### Jupyter scripts need to be improved\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the repository path:  C:\\Users\\Lenovo\\Desktop\\pypsa-africa\n",
      "Had to go 0 folder(s) up.\n"
     ]
    }
   ],
   "source": [
    "###### algunas cosas cambian si pongo como comentarios el import de geoviews y hvplot\n",
    "\n",
    "import logging\n",
    "import os\n",
    "\n",
    "import pypsa\n",
    "import yaml\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "#import geoviews as gv\n",
    "#import hvplot.pandas \n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import networkx as nx\n",
    "\n",
    "# plotting stuff\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use(\"bmh\")\n",
    "import seaborn as sns  ###\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "from scipy.sparse import csgraph\n",
    "from itertools import product\n",
    "\n",
    "from shapely.geometry import Point, LineString\n",
    "import shapely, shapely.prepared, shapely.wkt\n",
    "\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.max_colwidth\", 70)\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"../\")  # to import helpers\n",
    "from scripts._helpers import sets_path_to_root\n",
    "\n",
    "sets_path_to_root(\"pypsa-africa\")\n",
    "\n",
    "max_node_size = 1.0  # maximum size of a node for plotting purposes [used in plots]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# utility function for nice plotting\n",
    "\n",
    "\n",
    "def normalize_node_size(values, max_node_size=max_node_size):\n",
    "    return values / values.max() * max_node_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load all networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pypsa.io:Imported network elec_s_10_ec_lcopt_Co2L-3H.nc has buses, carriers, generators, global_constraints, lines, links, loads, storage_units, stores\n"
     ]
    }
   ],
   "source": [
    "# add_electricity_network = os.getcwd() + '/networks/elec.nc'\n",
    "# clustered_network = os.getcwd() + '/networks/elec_s_20.nc'\n",
    "solved_network = os.getcwd() + \"/results/networks/elec_s_10_ec_lcopt_Co2L-3H.nc\"\n",
    "\n",
    "# n_add_el = pypsa.Network(add_electricity_network)\n",
    "# n_cluster = pypsa.Network(clustered_network)\n",
    "n_solve = pypsa.Network(solved_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solved network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = n_solve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>sense</th>\n",
       "      <th>constant</th>\n",
       "      <th>mu</th>\n",
       "      <th>type</th>\n",
       "      <th>investment_period</th>\n",
       "      <th>carrier_attribute</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GlobalConstraint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO2Limit</th>\n",
       "      <td>&lt;=</td>\n",
       "      <td>77500000.0</td>\n",
       "      <td>1.599756e-09</td>\n",
       "      <td>primary_energy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>co2_emissions</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sense    constant            mu            type  \\\n",
       "GlobalConstraint                                                   \n",
       "CO2Limit            <=  77500000.0  1.599756e-09  primary_energy   \n",
       "\n",
       "                  investment_period carrier_attribute  \n",
       "GlobalConstraint                                       \n",
       "CO2Limit                        NaN     co2_emissions  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.global_constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.481127939015494"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################# to be defined\n",
    "\n",
    "# EURO/MWh\n",
    "n.objective / n.loads_t.p.sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial temporal resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.collections.PatchCollection at 0x18b4ce02530>,\n",
       " <matplotlib.collections.LineCollection at 0x18b4d091600>,\n",
       " <matplotlib.collections.LineCollection at 0x18b4d092530>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### augmented lines in green (can be disabled)\n",
    "\n",
    "n.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2920"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# temporal resolution - timesteps\n",
    "len(n.snapshots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component 'Bus' has 30 entries\n",
      "Component 'Carrier' has 14 entries\n",
      "Component 'GlobalConstraint' has 1 entries\n",
      "Component 'Line' has 10 entries\n",
      "Component 'LineType' has 32 entries\n",
      "Component 'TransformerType' has 14 entries\n",
      "Component 'Link' has 48 entries\n",
      "Component 'Load' has 10 entries\n",
      "Component 'Generator' has 63 entries\n",
      "Component 'StorageUnit' has 7 entries\n",
      "Component 'Store' has 20 entries\n"
     ]
    }
   ],
   "source": [
    "for c in n.iterate_components(list(n.components.keys())[2:]):\n",
    "    print(\"Component '{}' has {} entries\".format(c.name, len(c.df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store\n",
       "AR 0 H2         5.551115e-17\n",
       "AR 1 H2        -6.938894e-18\n",
       "AR 2 H2         6.938894e-18\n",
       "AR 3 H2        -6.938894e-18\n",
       "AR 4 H2         1.387779e-17\n",
       "AR 5 H2         0.000000e+00\n",
       "AR 6 H2        -6.938894e-18\n",
       "BO 0 H2         4.163336e-17\n",
       "PE 0 H2         0.000000e+00\n",
       "PE 1 H2         6.938894e-18\n",
       "AR 0 battery   -3.816392e-17\n",
       "AR 1 battery   -3.989864e-17\n",
       "AR 2 battery   -3.989864e-17\n",
       "AR 3 battery   -5.898060e-17\n",
       "AR 4 battery    1.127570e-17\n",
       "AR 5 battery    1.127570e-17\n",
       "AR 6 battery    3.556183e-17\n",
       "BO 0 battery   -1.110223e-16\n",
       "PE 0 battery   -2.775558e-17\n",
       "PE 1 battery   -1.110223e-16\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### que es stores_t??? losses?\n",
    "\n",
    "n.stores_t.p.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>p_nom_max</th>\n",
       "      <th>weight</th>\n",
       "      <th>p_nom</th>\n",
       "      <th>capital_cost</th>\n",
       "      <th>carrier</th>\n",
       "      <th>bus</th>\n",
       "      <th>p_nom_extendable</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>marginal_cost</th>\n",
       "      <th>control</th>\n",
       "      <th>p_nom_opt</th>\n",
       "      <th>sign</th>\n",
       "      <th>build_year</th>\n",
       "      <th>min_up_time</th>\n",
       "      <th>min_down_time</th>\n",
       "      <th>up_time_before</th>\n",
       "      <th>down_time_before</th>\n",
       "      <th>type</th>\n",
       "      <th>p_nom_min</th>\n",
       "      <th>p_min_pu</th>\n",
       "      <th>p_max_pu</th>\n",
       "      <th>p_set</th>\n",
       "      <th>q_set</th>\n",
       "      <th>lifetime</th>\n",
       "      <th>committable</th>\n",
       "      <th>start_up_cost</th>\n",
       "      <th>shut_down_cost</th>\n",
       "      <th>ramp_limit_up</th>\n",
       "      <th>ramp_limit_down</th>\n",
       "      <th>ramp_limit_start_up</th>\n",
       "      <th>ramp_limit_shut_down</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generator</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AR 0 CCGT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.926000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>CCGT</td>\n",
       "      <td>AR 0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.500</td>\n",
       "      <td>47.210081</td>\n",
       "      <td>Slack</td>\n",
       "      <td>4.926000e+02</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR 0 OCGT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.948495e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>OCGT</td>\n",
       "      <td>AR 0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.390</td>\n",
       "      <td>58.394020</td>\n",
       "      <td></td>\n",
       "      <td>1.948495e+03</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR 0 oil</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.508391e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>oil</td>\n",
       "      <td>AR 0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.393</td>\n",
       "      <td>130.236438</td>\n",
       "      <td></td>\n",
       "      <td>2.508391e+02</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR 0 onwind</th>\n",
       "      <td>1.926891e+06</td>\n",
       "      <td>835459.341409</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>109295.569252</td>\n",
       "      <td>onwind</td>\n",
       "      <td>AR 0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.024665</td>\n",
       "      <td></td>\n",
       "      <td>4.681251e+03</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR 0 ror</th>\n",
       "      <td>NaN</td>\n",
       "      <td>137.180000</td>\n",
       "      <td>1.371800e+02</td>\n",
       "      <td>270940.715283</td>\n",
       "      <td>ror</td>\n",
       "      <td>AR 0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.010811</td>\n",
       "      <td></td>\n",
       "      <td>1.371800e+02</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR 5 load</th>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>load</td>\n",
       "      <td>AR 5</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8000.009497</td>\n",
       "      <td>PQ</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR 6 load</th>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>load</td>\n",
       "      <td>AR 6</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8000.010701</td>\n",
       "      <td>PQ</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BO 0 load</th>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>load</td>\n",
       "      <td>BO 0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8000.010357</td>\n",
       "      <td>PQ</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE 0 load</th>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>load</td>\n",
       "      <td>PE 0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8000.010579</td>\n",
       "      <td>PQ</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE 1 load</th>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>load</td>\n",
       "      <td>PE 1</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8000.009500</td>\n",
       "      <td>PQ</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                p_nom_max         weight         p_nom   capital_cost carrier  \\\n",
       "Generator                                                                       \n",
       "AR 0 CCGT             NaN       0.000000  4.926000e+02       0.000000    CCGT   \n",
       "AR 0 OCGT             NaN       0.000000  1.948495e+03       0.000000    OCGT   \n",
       "AR 0 oil              NaN       0.000000  2.508391e+02       0.000000     oil   \n",
       "AR 0 onwind  1.926891e+06  835459.341409  0.000000e+00  109295.569252  onwind   \n",
       "AR 0 ror              NaN     137.180000  1.371800e+02  270940.715283     ror   \n",
       "...                   ...            ...           ...            ...     ...   \n",
       "AR 5 load             inf            NaN  1.000000e+09       0.000000    load   \n",
       "AR 6 load             inf            NaN  1.000000e+09       0.000000    load   \n",
       "BO 0 load             inf            NaN  1.000000e+09       0.000000    load   \n",
       "PE 0 load             inf            NaN  1.000000e+09       0.000000    load   \n",
       "PE 1 load             inf            NaN  1.000000e+09       0.000000    load   \n",
       "\n",
       "              bus  p_nom_extendable  efficiency  marginal_cost control  \\\n",
       "Generator                                                                \n",
       "AR 0 CCGT    AR 0             False       0.500      47.210081   Slack   \n",
       "AR 0 OCGT    AR 0             False       0.390      58.394020           \n",
       "AR 0 oil     AR 0             False       0.393     130.236438           \n",
       "AR 0 onwind  AR 0              True       1.000       0.024665           \n",
       "AR 0 ror     AR 0             False       0.900       0.010811           \n",
       "...           ...               ...         ...            ...     ...   \n",
       "AR 5 load    AR 5             False       1.000    8000.009497      PQ   \n",
       "AR 6 load    AR 6             False       1.000    8000.010701      PQ   \n",
       "BO 0 load    BO 0             False       1.000    8000.010357      PQ   \n",
       "PE 0 load    PE 0             False       1.000    8000.010579      PQ   \n",
       "PE 1 load    PE 1             False       1.000    8000.009500      PQ   \n",
       "\n",
       "                p_nom_opt   sign  build_year  min_up_time  min_down_time  \\\n",
       "Generator                                                                  \n",
       "AR 0 CCGT    4.926000e+02  1.000           0            0              0   \n",
       "AR 0 OCGT    1.948495e+03  1.000           0            0              0   \n",
       "AR 0 oil     2.508391e+02  1.000           0            0              0   \n",
       "AR 0 onwind  4.681251e+03  1.000           0            0              0   \n",
       "AR 0 ror     1.371800e+02  1.000           0            0              0   \n",
       "...                   ...    ...         ...          ...            ...   \n",
       "AR 5 load    1.000000e+09  0.001           0            0              0   \n",
       "AR 6 load    1.000000e+09  0.001           0            0              0   \n",
       "BO 0 load    1.000000e+09  0.001           0            0              0   \n",
       "PE 0 load    1.000000e+09  0.001           0            0              0   \n",
       "PE 1 load    1.000000e+09  0.001           0            0              0   \n",
       "\n",
       "             up_time_before  down_time_before type  p_nom_min  p_min_pu  \\\n",
       "Generator                                                                 \n",
       "AR 0 CCGT                 1                 0             0.0       0.0   \n",
       "AR 0 OCGT                 1                 0             0.0       0.0   \n",
       "AR 0 oil                  1                 0             0.0       0.0   \n",
       "AR 0 onwind               1                 0             0.0       0.0   \n",
       "AR 0 ror                  1                 0             0.0       0.0   \n",
       "...                     ...               ...  ...        ...       ...   \n",
       "AR 5 load                 1                 0             0.0       0.0   \n",
       "AR 6 load                 1                 0             0.0       0.0   \n",
       "BO 0 load                 1                 0             0.0       0.0   \n",
       "PE 0 load                 1                 0             0.0       0.0   \n",
       "PE 1 load                 1                 0             0.0       0.0   \n",
       "\n",
       "             p_max_pu  p_set  q_set  lifetime  committable  start_up_cost  \\\n",
       "Generator                                                                   \n",
       "AR 0 CCGT         1.0    0.0    0.0       inf        False            0.0   \n",
       "AR 0 OCGT         1.0    0.0    0.0       inf        False            0.0   \n",
       "AR 0 oil          1.0    0.0    0.0       inf        False            0.0   \n",
       "AR 0 onwind       1.0    0.0    0.0       inf        False            0.0   \n",
       "AR 0 ror          1.0    0.0    0.0       inf        False            0.0   \n",
       "...               ...    ...    ...       ...          ...            ...   \n",
       "AR 5 load         1.0    0.0    0.0       inf        False            0.0   \n",
       "AR 6 load         1.0    0.0    0.0       inf        False            0.0   \n",
       "BO 0 load         1.0    0.0    0.0       inf        False            0.0   \n",
       "PE 0 load         1.0    0.0    0.0       inf        False            0.0   \n",
       "PE 1 load         1.0    0.0    0.0       inf        False            0.0   \n",
       "\n",
       "             shut_down_cost  ramp_limit_up  ramp_limit_down  \\\n",
       "Generator                                                     \n",
       "AR 0 CCGT               0.0            NaN              NaN   \n",
       "AR 0 OCGT               0.0            NaN              NaN   \n",
       "AR 0 oil                0.0            NaN              NaN   \n",
       "AR 0 onwind             0.0            NaN              NaN   \n",
       "AR 0 ror                0.0            NaN              NaN   \n",
       "...                     ...            ...              ...   \n",
       "AR 5 load               0.0            NaN              NaN   \n",
       "AR 6 load               0.0            NaN              NaN   \n",
       "BO 0 load               0.0            NaN              NaN   \n",
       "PE 0 load               0.0            NaN              NaN   \n",
       "PE 1 load               0.0            NaN              NaN   \n",
       "\n",
       "             ramp_limit_start_up  ramp_limit_shut_down  \n",
       "Generator                                               \n",
       "AR 0 CCGT                    1.0                   1.0  \n",
       "AR 0 OCGT                    1.0                   1.0  \n",
       "AR 0 oil                     1.0                   1.0  \n",
       "AR 0 onwind                  1.0                   1.0  \n",
       "AR 0 ror                     1.0                   1.0  \n",
       "...                          ...                   ...  \n",
       "AR 5 load                    1.0                   1.0  \n",
       "AR 6 load                    1.0                   1.0  \n",
       "BO 0 load                    1.0                   1.0  \n",
       "PE 0 load                    1.0                   1.0  \n",
       "PE 1 load                    1.0                   1.0  \n",
       "\n",
       "[63 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference between total load and generation\n",
    "# losses = n.stores_t\n",
    "n.generators_t.p.sum().sum() - n.loads_t.p.sum().sum()  # - substract storage losses\n",
    "\n",
    "\n",
    "n.generators\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total installed generator capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_nom_max</th>\n",
       "      <th>weight</th>\n",
       "      <th>p_nom</th>\n",
       "      <th>capital_cost</th>\n",
       "      <th>p_nom_extendable</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>marginal_cost</th>\n",
       "      <th>p_nom_opt</th>\n",
       "      <th>sign</th>\n",
       "      <th>build_year</th>\n",
       "      <th>min_up_time</th>\n",
       "      <th>min_down_time</th>\n",
       "      <th>up_time_before</th>\n",
       "      <th>down_time_before</th>\n",
       "      <th>p_nom_min</th>\n",
       "      <th>p_min_pu</th>\n",
       "      <th>p_max_pu</th>\n",
       "      <th>p_set</th>\n",
       "      <th>q_set</th>\n",
       "      <th>lifetime</th>\n",
       "      <th>committable</th>\n",
       "      <th>start_up_cost</th>\n",
       "      <th>shut_down_cost</th>\n",
       "      <th>ramp_limit_up</th>\n",
       "      <th>ramp_limit_down</th>\n",
       "      <th>ramp_limit_start_up</th>\n",
       "      <th>ramp_limit_shut_down</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CCGT</th>\n",
       "      <td>inf</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.046207e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>4.500</td>\n",
       "      <td>424.887180</td>\n",
       "      <td>1.046207e+04</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCGT</th>\n",
       "      <td>inf</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.127537e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.560</td>\n",
       "      <td>233.579890</td>\n",
       "      <td>7.127537e+03</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coal</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.359200e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>2.320</td>\n",
       "      <td>120.565553</td>\n",
       "      <td>2.359200e+03</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>load</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>10.000</td>\n",
       "      <td>80000.102646</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oil</th>\n",
       "      <td>inf</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.244730e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.144</td>\n",
       "      <td>1041.889377</td>\n",
       "      <td>1.244730e+04</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onwind</th>\n",
       "      <td>1.015664e+07</td>\n",
       "      <td>1.976444e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.092956e+06</td>\n",
       "      <td>10</td>\n",
       "      <td>10.000</td>\n",
       "      <td>0.247520</td>\n",
       "      <td>1.228460e+04</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ror</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.145570e+03</td>\n",
       "      <td>6.145570e+03</td>\n",
       "      <td>1.896585e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>6.300</td>\n",
       "      <td>0.072306</td>\n",
       "      <td>6.145570e+03</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solar</th>\n",
       "      <td>5.027147e+06</td>\n",
       "      <td>8.764364e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.506407e+05</td>\n",
       "      <td>10</td>\n",
       "      <td>10.000</td>\n",
       "      <td>0.199840</td>\n",
       "      <td>2.272041e+04</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            p_nom_max        weight         p_nom  capital_cost  \\\n",
       "carrier                                                           \n",
       "CCGT              inf  0.000000e+00  1.046207e+04  0.000000e+00   \n",
       "OCGT              inf  0.000000e+00  7.127537e+03  0.000000e+00   \n",
       "coal              NaN  0.000000e+00  2.359200e+03  0.000000e+00   \n",
       "load              NaN  0.000000e+00  1.000000e+10  0.000000e+00   \n",
       "oil               inf  0.000000e+00  1.244730e+04  0.000000e+00   \n",
       "onwind   1.015664e+07  1.976444e+06  0.000000e+00  1.092956e+06   \n",
       "ror               NaN  6.145570e+03  6.145570e+03  1.896585e+06   \n",
       "solar    5.027147e+06  8.764364e+05  0.000000e+00  5.506407e+05   \n",
       "\n",
       "         p_nom_extendable  efficiency  marginal_cost     p_nom_opt   sign  \\\n",
       "carrier                                                                     \n",
       "CCGT                    0       4.500     424.887180  1.046207e+04   9.00   \n",
       "OCGT                    0       1.560     233.579890  7.127537e+03   4.00   \n",
       "coal                    0       2.320     120.565553  2.359200e+03   5.00   \n",
       "load                    0      10.000   80000.102646  1.000000e+10   0.01   \n",
       "oil                     0       3.144    1041.889377  1.244730e+04   8.00   \n",
       "onwind                 10      10.000       0.247520  1.228460e+04  10.00   \n",
       "ror                     0       6.300       0.072306  6.145570e+03   7.00   \n",
       "solar                  10      10.000       0.199840  2.272041e+04  10.00   \n",
       "\n",
       "         build_year  min_up_time  min_down_time  up_time_before  \\\n",
       "carrier                                                           \n",
       "CCGT              0            0              0               9   \n",
       "OCGT              0            0              0               4   \n",
       "coal              0            0              0               5   \n",
       "load              0            0              0              10   \n",
       "oil               0            0              0               8   \n",
       "onwind            0            0              0              10   \n",
       "ror               0            0              0               7   \n",
       "solar             0            0              0              10   \n",
       "\n",
       "         down_time_before  p_nom_min  p_min_pu  p_max_pu  p_set  q_set  \\\n",
       "carrier                                                                  \n",
       "CCGT                    0        0.0       0.0       9.0    0.0    0.0   \n",
       "OCGT                    0        0.0       0.0       4.0    0.0    0.0   \n",
       "coal                    0        0.0       0.0       5.0    0.0    0.0   \n",
       "load                    0        0.0       0.0      10.0    0.0    0.0   \n",
       "oil                     0        0.0       0.0       8.0    0.0    0.0   \n",
       "onwind                  0        0.0       0.0      10.0    0.0    0.0   \n",
       "ror                     0        0.0       0.0       7.0    0.0    0.0   \n",
       "solar                   0        0.0       0.0      10.0    0.0    0.0   \n",
       "\n",
       "         lifetime  committable  start_up_cost  shut_down_cost  ramp_limit_up  \\\n",
       "carrier                                                                        \n",
       "CCGT          NaN            0            0.0             0.0            0.0   \n",
       "OCGT          NaN            0            0.0             0.0            0.0   \n",
       "coal          NaN            0            0.0             0.0            0.0   \n",
       "load          NaN            0            0.0             0.0            0.0   \n",
       "oil           NaN            0            0.0             0.0            0.0   \n",
       "onwind        NaN            0            0.0             0.0            0.0   \n",
       "ror           NaN            0            0.0             0.0            0.0   \n",
       "solar         NaN            0            0.0             0.0            0.0   \n",
       "\n",
       "         ramp_limit_down  ramp_limit_start_up  ramp_limit_shut_down  \n",
       "carrier                                                              \n",
       "CCGT                 0.0                  9.0                   9.0  \n",
       "OCGT                 0.0                  4.0                   4.0  \n",
       "coal                 0.0                  5.0                   5.0  \n",
       "load                 0.0                 10.0                  10.0  \n",
       "oil                  0.0                  8.0                   8.0  \n",
       "onwind               0.0                 10.0                  10.0  \n",
       "ror                  0.0                  7.0                   7.0  \n",
       "solar                0.0                 10.0                  10.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_cap = n.generators.groupby([\"carrier\"]).sum()\n",
    "gen_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Store</th>\n",
       "      <th>AR 0 H2</th>\n",
       "      <th>AR 1 H2</th>\n",
       "      <th>AR 2 H2</th>\n",
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       "      <th>PE 0 battery</th>\n",
       "      <th>PE 1 battery</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snapshot</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <td>0.005395</td>\n",
       "      <td>-0.000060</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000540</td>\n",
       "      <td>0.000571</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.004193</td>\n",
       "      <td>-0.000894</td>\n",
       "      <td>-0.000412</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>-0.000907</td>\n",
       "      <td>-0.000382</td>\n",
       "      <td>0.010541</td>\n",
       "      <td>0.001694</td>\n",
       "      <td>0.002168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 03:00:00</th>\n",
       "      <td>0.005233</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>-0.001431</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.001259</td>\n",
       "      <td>-0.000072</td>\n",
       "      <td>-0.000236</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>-0.010170</td>\n",
       "      <td>0.002277</td>\n",
       "      <td>0.002775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 06:00:00</th>\n",
       "      <td>0.005013</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>-0.000919</td>\n",
       "      <td>-0.000566</td>\n",
       "      <td>-0.000332</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>-0.000033</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>-0.005628</td>\n",
       "      <td>-0.004629</td>\n",
       "      <td>-0.005129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 09:00:00</th>\n",
       "      <td>0.004793</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000654</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.001018</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.016397</td>\n",
       "      <td>0.004881</td>\n",
       "      <td>0.006728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 12:00:00</th>\n",
       "      <td>0.004309</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>-0.000035</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>-0.001095</td>\n",
       "      <td>-0.000784</td>\n",
       "      <td>-0.000383</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>-0.000179</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>-0.002586</td>\n",
       "      <td>-0.002841</td>\n",
       "      <td>0.001665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31 09:00:00</th>\n",
       "      <td>0.006276</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000302</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.016852</td>\n",
       "      <td>0.004870</td>\n",
       "      <td>0.006399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31 12:00:00</th>\n",
       "      <td>0.005921</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>-0.000175</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>-0.001183</td>\n",
       "      <td>-0.000704</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>-0.000249</td>\n",
       "      <td>0.002165</td>\n",
       "      <td>0.002256</td>\n",
       "      <td>-0.000226</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>-0.001124</td>\n",
       "      <td>-0.002504</td>\n",
       "      <td>-0.000414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31 15:00:00</th>\n",
       "      <td>-0.000963</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>-0.000186</td>\n",
       "      <td>-0.001714</td>\n",
       "      <td>-0.001381</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>-0.004692</td>\n",
       "      <td>-0.000583</td>\n",
       "      <td>-0.003082</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000776</td>\n",
       "      <td>-0.004427</td>\n",
       "      <td>-0.004916</td>\n",
       "      <td>-0.001054</td>\n",
       "      <td>-0.000158</td>\n",
       "      <td>-0.020486</td>\n",
       "      <td>-0.001774</td>\n",
       "      <td>-0.009099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31 18:00:00</th>\n",
       "      <td>-0.010351</td>\n",
       "      <td>-0.000192</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.000509</td>\n",
       "      <td>-0.000111</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>-0.000370</td>\n",
       "      <td>-0.000862</td>\n",
       "      <td>-0.000557</td>\n",
       "      <td>-0.000282</td>\n",
       "      <td>-0.005524</td>\n",
       "      <td>-0.001319</td>\n",
       "      <td>-0.000684</td>\n",
       "      <td>-0.000360</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>-0.000363</td>\n",
       "      <td>-0.001641</td>\n",
       "      <td>-0.000933</td>\n",
       "      <td>-0.002735</td>\n",
       "      <td>-0.000433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31 21:00:00</th>\n",
       "      <td>-0.010323</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>-0.005574</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>0.002107</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>0.002692</td>\n",
       "      <td>0.001501</td>\n",
       "      <td>0.010874</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.004007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2920 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Store                 AR 0 H2   AR 1 H2   AR 2 H2   AR 3 H2   AR 4 H2  \\\n",
       "snapshot                                                                \n",
       "2013-01-01 00:00:00  0.005395 -0.000060  0.000146  0.000328  0.000227   \n",
       "2013-01-01 03:00:00  0.005233  0.000046  0.000069  0.000364  0.000226   \n",
       "2013-01-01 06:00:00  0.005013  0.000121  0.000041  0.000405  0.000279   \n",
       "2013-01-01 09:00:00  0.004793  0.000097  0.000006  0.000441  0.000252   \n",
       "2013-01-01 12:00:00  0.004309  0.000128 -0.000035  0.000235  0.000237   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2013-12-31 09:00:00  0.006276  0.000241  0.000105  0.000456  0.000314   \n",
       "2013-12-31 12:00:00  0.005921  0.000127 -0.000175  0.000373  0.000391   \n",
       "2013-12-31 15:00:00 -0.000963  0.000079 -0.000186 -0.001714 -0.001381   \n",
       "2013-12-31 18:00:00 -0.010351 -0.000192 -0.000046 -0.000509 -0.000111   \n",
       "2013-12-31 21:00:00 -0.010323  0.000493  0.000698  0.000072  0.000274   \n",
       "\n",
       "Store                 AR 5 H2   AR 6 H2   BO 0 H2   PE 0 H2   PE 1 H2  \\\n",
       "snapshot                                                                \n",
       "2013-01-01 00:00:00  0.000346  0.000098  0.000540  0.000571  0.000860   \n",
       "2013-01-01 03:00:00  0.000374  0.000145 -0.001431  0.000605  0.000875   \n",
       "2013-01-01 06:00:00  0.000330  0.000146 -0.000919 -0.000566 -0.000332   \n",
       "2013-01-01 09:00:00  0.000297  0.000093  0.000491  0.000573  0.000887   \n",
       "2013-01-01 12:00:00  0.000233  0.000072 -0.001095 -0.000784 -0.000383   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2013-12-31 09:00:00  0.000302  0.000209  0.000403  0.000532  0.000846   \n",
       "2013-12-31 12:00:00  0.000107  0.000011 -0.001183 -0.000704 -0.000398   \n",
       "2013-12-31 15:00:00  0.000103 -0.000094 -0.004692 -0.000583 -0.003082   \n",
       "2013-12-31 18:00:00  0.000358 -0.000370 -0.000862 -0.000557 -0.000282   \n",
       "2013-12-31 21:00:00  0.001108  0.000404  0.000402  0.000552  0.000834   \n",
       "\n",
       "Store                AR 0 battery  AR 1 battery  AR 2 battery  AR 3 battery  \\\n",
       "snapshot                                                                      \n",
       "2013-01-01 00:00:00      0.004193     -0.000894     -0.000412      0.000649   \n",
       "2013-01-01 03:00:00      0.001259     -0.000072     -0.000236      0.000295   \n",
       "2013-01-01 06:00:00      0.000611      0.000181     -0.000033      0.000369   \n",
       "2013-01-01 09:00:00      0.000654      0.000014      0.000078      0.001018   \n",
       "2013-01-01 12:00:00      0.000476     -0.000179      0.000104      0.001320   \n",
       "...                           ...           ...           ...           ...   \n",
       "2013-12-31 09:00:00      0.002411     -0.000010     -0.000014      0.000631   \n",
       "2013-12-31 12:00:00      0.003746      0.000224     -0.000249      0.002165   \n",
       "2013-12-31 15:00:00      0.000519      0.000060     -0.000776     -0.004427   \n",
       "2013-12-31 18:00:00     -0.005524     -0.001319     -0.000684     -0.000360   \n",
       "2013-12-31 21:00:00     -0.005574      0.001584      0.002107      0.000608   \n",
       "\n",
       "Store                AR 4 battery  AR 5 battery  AR 6 battery  BO 0 battery  \\\n",
       "snapshot                                                                      \n",
       "2013-01-01 00:00:00      0.000284     -0.000907     -0.000382      0.010541   \n",
       "2013-01-01 03:00:00      0.000076     -0.000095      0.000068     -0.010170   \n",
       "2013-01-01 06:00:00      0.000309      0.000007      0.000169     -0.005628   \n",
       "2013-01-01 09:00:00      0.000368      0.000169      0.000192      0.016397   \n",
       "2013-01-01 12:00:00      0.000624      0.000307      0.000146     -0.002586   \n",
       "...                           ...           ...           ...           ...   \n",
       "2013-12-31 09:00:00      0.000427     -0.000094      0.000171      0.016852   \n",
       "2013-12-31 12:00:00      0.002256     -0.000226      0.000234     -0.001124   \n",
       "2013-12-31 15:00:00     -0.004916     -0.001054     -0.000158     -0.020486   \n",
       "2013-12-31 18:00:00      0.000205     -0.000363     -0.001641     -0.000933   \n",
       "2013-12-31 21:00:00      0.001170      0.002692      0.001501      0.010874   \n",
       "\n",
       "Store                PE 0 battery  PE 1 battery  \n",
       "snapshot                                         \n",
       "2013-01-01 00:00:00      0.001694      0.002168  \n",
       "2013-01-01 03:00:00      0.002277      0.002775  \n",
       "2013-01-01 06:00:00     -0.004629     -0.005129  \n",
       "2013-01-01 09:00:00      0.004881      0.006728  \n",
       "2013-01-01 12:00:00     -0.002841      0.001665  \n",
       "...                           ...           ...  \n",
       "2013-12-31 09:00:00      0.004870      0.006399  \n",
       "2013-12-31 12:00:00     -0.002504     -0.000414  \n",
       "2013-12-31 15:00:00     -0.001774     -0.009099  \n",
       "2013-12-31 18:00:00     -0.002735     -0.000433  \n",
       "2013-12-31 21:00:00      0.003003      0.004007  \n",
       "\n",
       "[2920 rows x 20 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.stores_t.p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='snapshot'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n.generators_t.p_max_pu.groupby(n.generators.carrier, axis=1).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If 0, all renewable capacity potential is used\n",
    "n.generators.p_nom_max.sum() - n.generators.p_nom_opt.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93050599.29425742"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.loads_t.p.sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67605782.39833555"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.generators_t.p.sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus   carrier\n",
       "AR 0  CCGT         0.000148\n",
       "      OCGT         0.000585\n",
       "      load       300.000000\n",
       "      oil          0.000075\n",
       "      onwind       0.000000\n",
       "                    ...    \n",
       "PE 1  load       300.000000\n",
       "      oil          0.000101\n",
       "      onwind       0.000000\n",
       "      ror          0.000134\n",
       "      solar        0.000000\n",
       "Name: p_nom, Length: 63, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### pq la multiplicacion?\n",
    "\n",
    "n.generators.iloc[:, :].groupby([\"bus\", \"carrier\"]).p_nom.sum() * 0.0000003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Colors not defined for all elements in the second MultiIndex level of bus_sizes, please make sure that all the elements are included in bus_colors or in n.carriers.color",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [21]\u001b[0m, in \u001b[0;36m<cell line: 15>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\n\u001b[0;32m      7\u001b[0m     figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m10\u001b[39m),\n\u001b[0;32m      8\u001b[0m     subplot_kw\u001b[38;5;241m=\u001b[39m{\n\u001b[0;32m      9\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprojection\u001b[39m\u001b[38;5;124m\"\u001b[39m: ccrs\u001b[38;5;241m.\u001b[39mPlateCarree()\n\u001b[0;32m     10\u001b[0m     },  \u001b[38;5;66;03m# PlateCarree(default), Mercator, Orthographic\u001b[39;00m\n\u001b[0;32m     11\u001b[0m )\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# loading = (n3.lines_t.p0.abs().mean().sort_index() / (n3.lines.s_nom_opt*n.lines.s_max_pu).sort_index()).fillna(0.)\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m \u001b[43mn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     16\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmargin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     17\u001b[0m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     18\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgeomap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m#'10m' leads to a beautiful high resolution map\u001b[39;49;00m\n\u001b[0;32m     19\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprojection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     20\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbus_colors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcadetblue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     21\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbus_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     22\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbus_sizes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize_node_size\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     23\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerators\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbus\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcarrier\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mp_nom_opt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     24\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     25\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbus_cmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     26\u001b[0m \u001b[43m    \u001b[49m\u001b[43mline_colors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrosybrown\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     27\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlink_colors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdarkseagreen\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# loading\u001b[39;49;00m\n\u001b[0;32m     28\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtransformer_colors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43morange\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     29\u001b[0m \u001b[43m    \u001b[49m\u001b[43mline_widths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlines\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms_nom_opt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3e2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     30\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlink_widths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     31\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtransformer_widths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     32\u001b[0m \u001b[43m    \u001b[49m\u001b[43mline_cmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     33\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlink_cmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# plt.cm.virdis (colordmap for line loading)\u001b[39;49;00m\n\u001b[0;32m     34\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtransformer_cmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     35\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     36\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbranch_components\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     37\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlayouter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     38\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGenerator Map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     39\u001b[0m \u001b[43m    \u001b[49m\u001b[43mboundaries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     40\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgeometry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     41\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjitter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     42\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolor_geomap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     43\u001b[0m \u001b[43m)\u001b[49m  \u001b[38;5;66;03m# None or True\u001b[39;00m\n\u001b[0;32m     45\u001b[0m ax\u001b[38;5;241m.\u001b[39maxis(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moff\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\pypsa-africa\\lib\\site-packages\\pypsa\\plot.py:223\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(n, margin, ax, geomap, projection, bus_colors, bus_alpha, bus_sizes, bus_cmap, line_colors, link_colors, transformer_colors, line_widths, link_widths, transformer_widths, line_cmap, link_cmap, transformer_cmap, flow, branch_components, layouter, title, boundaries, geometry, jitter, color_geomap)\u001b[0m\n\u001b[0;32m    221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bus_colors, pd\u001b[38;5;241m.\u001b[39mSeries):\n\u001b[0;32m    222\u001b[0m     bus_colors \u001b[38;5;241m=\u001b[39m n\u001b[38;5;241m.\u001b[39mcarriers\u001b[38;5;241m.\u001b[39mcolor\u001b[38;5;241m.\u001b[39mdropna()\n\u001b[1;32m--> 223\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m bus_sizes\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mlevels[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39misin(bus_colors\u001b[38;5;241m.\u001b[39mindex)\u001b[38;5;241m.\u001b[39mall(), (\n\u001b[0;32m    224\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColors not defined for all elements in the second MultiIndex \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    225\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlevel of bus_sizes, please make sure that all the elements are \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    226\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mincluded in bus_colors or in n.carriers.color\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    227\u001b[0m )\n\u001b[0;32m    229\u001b[0m bus_sizes \u001b[38;5;241m=\u001b[39m bus_sizes\u001b[38;5;241m.\u001b[39msort_index(level\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, sort_remaining\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m geomap:\n",
      "\u001b[1;31mAssertionError\u001b[0m: Colors not defined for all elements in the second MultiIndex level of bus_sizes, please make sure that all the elements are included in bus_colors or in n.carriers.color"
     ]
    },
    {
     "data": {
      "image/png": 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w4MG4dOkShgwZ0uH3AwMDERgYiMTERGzbtg0+Pj6wWCwYP348goKCJP1ZbOngwYMOe36XOB6771Vi5YzdXwCZOA7GWI/+JoMGDcL69euRkpLS6fM8PT3xzDPPSFGiLFpbW+Hn5yd3GcRJ2H3oLYpWmOjClEuYOnUqDhw4IHcZNiWKotMuJEPsz+6BOmDocOt3f/5blDc027tpIrGgoCC0tLSgpcV1tw8fN24cdu/eLXcZxEnYPVATBqUoJi1ZgZ/8+h0cyTPQLalObvbs2S4dOEFBQZJPFyOuS5ar7Z4+vlj82pvYuT8H76/fJprpFIDTUqlUCAkJQXFxsdyl2Iwoig82QCSkM7JNX2KMYXLGCsZ13vjFP9ZQT9WJjR8/HsePH5e7DJuZOXMm1q1bh9bWVrlLIQ5O9vmgA4YOF4wWEQcu5NO4yomlpaXh/PnzcpdhE2q1GqtXr8auXbvkLoU4ONkDFQDGz1vMtmRlC9RLdV79+/fHjRs3XPZ8o1KphFqtlrsM4uAcIlAZY1AqlbzR7JpvRncxY8YM7NmzR+4ybKatrQ0fffQRysrK5C6FOCiHCFQACAyP4HnXb8pdBukDPz8/iKKIxsZGuUuxiczMTLzwwgs4duyY3KUQB+UwgTpw2Cjh0NFj1EV1crNmzXLpXipjDCEhIaipqZG7FOKAHCZQvXz90NjURPejOjmFQoGYmBjcvOm6o40RI0Zg48aNLtsTJ73nMIEKADU1NazJZJG7DNJHI0eOxJkzZ+Quw2Z0Oh1eeOEFnDlzBhs3bnTpObikZxwqUEdOny1+66dvo8VCI39nN3r0aJw8eVLuMmxGrVZj6tSpWLp0KS5fvoxr167JXRJxAA4VqFH9BghDxk20/m3DFtFKU6icWmxsLIqLi2GxuP6IY86cObh79y6ys7NddtoY6R6HClQA6D8kTWFiKvz0j//gLWbaIM2Zufp9/g9LT0/H6NGj8dlnn6G6ulrucohMHC5QAWDI+IlCXOpw/p3//QXq20xyl0N6ycvLC0qlErW1tXKXYhf+/v5YtWoVDh8+LHcpRCYOGagAEBGXIIycMZev2bqTxlBObMaMGdi3b5/cZdiNIAjw9vZ2mw8R8ii7r9jfExFxCWzn4QPMZBWhVjhs9pNOCIKAxMREXL16FQMGDJC7HLuYOnUqsrOzYbVawRiDVqtFZGQkYmJioNPp0NraipycHFitVnDOMXr0aAQHB8tdNpGAQwcqAEQmJIq7TpzDogkjKVGdlMlkgre3t9xl2I0gCI9sYNja2oqSkhIcP34cbW1tUCqVmDRpErRaLURRxLFjx5CTk4NFixZBpVLJWDnpK4cP1KETJgtb/v4+po1Kg5fa4cslHSguLsbo0aPlLkM2Wq0WiYmJSExMfOJ7giAgPT0dbW1t2LRpE9LS0h7pydP+a86FdbbC0yZDmUPMXbp75xYvK7jEf/DyaoFeXM6loqICFy9exIwZM+QuxSmcO3cOd+7cgVKphNFohFqtho+PDyZNmiR3aeQenU731BByii5fWHQsM5w7jX3n88QZw1No6O8k9u/fD845pk+fLncpTmP48OEYPnw4zGbzg+H/li1b5C2KdJvThNPERZnsiy1bhdpWmkblDPbu3Yt+/fph+vTpNGTthYfPpaalpWHLli20+6oTcJpAZYxhaPoUnpVzjGb7OzhRFNHS0oK4uDi5S3EJsbGxmDJlCj755BMYjUa5yyGdcJpABYC4gcns9PnzCpE+qR2axWKBTqeTuwyX4uvrixUrVmDr1q04fvw4DAYD9VgdkFMFKmMMfkGh4uHLX9JkfwemUChgtdJAQmpqtRrLly9HfHw8OOfYsmULsrOzaUdWB+IUF6UeNmb2fOHzv7+P4frvw1tDc/YckUKhoEVCbCg8PBzh4eEYOHAgWlpacOjQIRiNRgQFBSE8PBx1dXVISUmhOa0ycKoeKtDeS01fuJS/9ce/cYtIQx7i3nQ6HWbPno1FixYhKSkJbW1tCA0NxWeffSZ3aW7J6QIVAILCIlhwbIL4jy27RDqPREi7gIAADBo0CJGRkRgyZAjy8/PlLsntOGWgAkDyqHGK60VF7NiVa5SohDwmLS0NJSUlKCoqkrsUt+K0gQoAUzNXsXVbtjG66k/Ik2bMmIFLly6htLRU7lLchlMHKmMMKrWa17aZ5S6FPMRoNNIFEQexcOFCXLhwAZcuXerysa2trbh+/TpKSkrsUJlrcrqr/I/Tenrxitp6Fqil5c8cxdmzZzF06FC5yyD3zJ07F5cvX8aOHTsgCO19qPtLC97/j3MODw8PREREoLa2FqdPn0ZcXBzS0tJkrt65OH2gtjQ1CrEhgXKXQe7hnKO4uBjjx4+XuxTykJSUFKSkpHT78SNHjsSNGzfwxRdfICUlBXq93obVuQ6nHvIDgMDAdUqn/zFcxpkzZzBmzBi5yyAS6NevHzIzM2E0GrF+/XrU19fLXZLDc+okKi++jcS4OLoi5UBSU1O7db6OOI8hQ4YgMzMTx44dw65du2A20zWLp3HaQM09fEA8vTcLC6ZNdtqfwRVpNBpaXcoF3d+FID09HZs2bZK7HIfltGF09eJ54T+++hqiAn3lLoU8xtfXlzapc1FeXl5ITk5GQUGB3KU4JKcN1MWvvoE/frSGhvsOaMSIETh+/LjcZRAbGTx4MK5fvy53GQ7JaQNVo9WBKZQ0tnRAWq0WKpUKFRUVcpdCiF05baAC7etuWmmBFIc0Y8YM6qW6qOLiYnh5ecldhkNy6kCNShxg/cn7f+OtZlp709GIooimpia5yyA2cOHCBUyZMkXuMhySUwdq8qhxioTUEXj3489o8U0Hcu3aNWzZsgVz5syRuxRiA4Ig0KLWT+HUgQoAkQmJrLikTKhqpj+wo7h27RqWLl2KwEC6g80VzZgxAxs3bpS7DIfk9IEKAJMWZ+LtP/yZFpx2AGazGXV1dXKXQWxIpVIhPj6eLjp2wCUC1cc/ENH6QeIHm7No6C+j5uZmrF27FhkZGXKXQmxsxIgROH/+vNxlOByXCFQASBo5RnHlyy+F8sYWuUtxS5xzbNq0CatWrYKHh4fc5RAbU6vVsFgscpfhcFwmUAFgwrzFeP+jTzhNpbK/LVu2YP78+VAoFHKXQuzE09MTZWVlcpfhUFwqUH0Dg+AdHM5//N5feTNNpbIbzjmUSiX8/f3lLoXY0dixY2nfqse4VKACwJDxE4WBoybgWz99G4bSStAmfrbFOUdWVhYGDx4sdynEzjQaDYxGo9xlOBSnX2C6IyFR0WzBS6/jr5+s5f4+3vj688+wAJ1G7rJcUnZ2NkaPHo2goCC5SyF2RquKPcnleqj3qdQazHzmeZYwYgx+/Ot3sfPkeToHYAOiKFKYEnKPywbqfYGh4WzBy1/FjuzdipLaRrnLcTl0pde90Sm1R7l8oN43Y8Vz+OUf/wIzzQCQ1PDhw5GTkyN3GYQ4BNbZJ8wmQ5lLpc+1i+esZdcNwve/+grz9VDLXY7LOHHiBLy8vHq0CRxxPqIo4p133sHAgQMBtN/T7+fnh7Fjx8pcmX3pdLqnnjx2q0AFgOryMn5w41r29n9+DyHeOrnLcRn79u1DYmIi4uLi5C6F2ND169dx584dt15tqrNAdZsh/32BoeFsxsrn8fsPP+Z0/kc648ePx4ULF+Qug9hYYmIi/Pz8kJubK3cpDsntAhUAfAMCwRUqHLiQT/f+S4Bzjg0bNmD+/Plyl0LsIC0tDTU1Nbh586bcpTgctwxUAEhfuJRt3pHF9l/Ip1tV+yg7Oxvz58+HUumS05pJB6ZNm4bz58/TZoyPcdtAZYxh7vOvsiNnc/m33v4tzl6/xUU6BdBj1dXVUKlUCAgIkLsUYmcZGRnYsWMHRJEGeve53UWpjljMZpzcvZ231Nfj5dUrWXJUKN0F0k0bNmxAZmYm/b7c1KlTp9CvXz+3urmjs4tSNEYDoFSpMGF+BjMZ27Bm0xbOzSa8/vwqlhAcQEHRieLiYsTFxdHvyI0FBwejsrLSrQK1MxSoD1FrPDAlYyVrbW7Gn9Z8zrUqFb75ygssyJPW9+wIYwyC4LZnjQiAoKAgXLx4Ue4yHAa9Gzqg9fTEjOXPsSGTZ7Afvv0rHM27SlOsOhAZGYni4mK5yyAy8vb2RmMj3dJ9HwVqJ3z8A5Hx+r9j+76DePvvH/PbNQ2gC1ePUqlUtISbG6PTPY+iIX8XGGOYsnQlqy4vw3sffcJNxjaWMihJfH7+LEGtoM+jyZMn4+DBg5g9e7bcpRCZ0OjtXygRuikwNBzTlq1mc559BW0qHb791q9RUkdDHZ1OR3u0E3IPBWovJKYMFaYuW43/+d3vsefsZdHdP6GHDh1KtyISAgrUXtN5eWPxq2/i8Jlz7NcfrxONVved3BwXF4dbt27JXQYhsqNA7QPGGCbMX8J8ImLx7bd+jfUHjor1RotbnlNKSEjAiRMn5C6DEFnRnVIS4Zzj6oVz4s38y4wBbGjKYOvsSRMUwZ4aCG5yJfTLL79EYWEhLJb2D5XRo0cjLCxM7rKIjW3fvh3x8fHQ6XTQarXw8PCATqeDWq12yVkAtB6qnXHOceeagX957hSUAsM3X32JRfh5yV2W3W3ZsgVjx45FaGio3KUQGzIajSgrK0NLSwva2trQ2tqKlpYWmM3mJx7LOe8wZO9//X4ePfwYURRhNptRXV2NV1991XY/SDfRrad2xhhDzICBLGbAQDQ31OPn7/8FUZHh4gtLlwih3lqX/NR+GOccBw4cAOecFk1xAxqNxi4Li+/cudPmbfQVBaqNefr4Yt4Lr6G6vIz94k9/42qFgFWZGSwlLhIKFw3WTZs2YdKkSXR/N5GUM3REKFDtJDA0nM1e/RKMba3YsGuX+GFNtfDK6pUYEhfpFC+UnvDw8KAwJZIqKiqCt7e33GV0iQLVzjQeWqQvyBAsZjM+276Jf2oyYf6s6WzYwP7wVAouEa7uOMuB2I7RaMTJkyexcuVKuUvpEgWqTJQqFaYsWcGMba04euq4uCkrW2AcSOqfKC6cMUUI8/GUu8ReOXPmDNRq2lGWSGffvn3IyMiQu4xuoUCVmcZDi2GTpj2YD1xaVMj+990/YsSwNOvz86YrnOU8q9lsxoYNGzB+/HiMHDlS7nKIi3GWD2ma2O9gIuIS2IKXXkd5QzP70/qtorOsbrVlyxZkZGQgNjZW7lIIkQ0FqoMamj5FqG8z4/efbRLNDr6J4NGjR5GWlgYPD1qIm0hPrVY7zQI8FKgObMTUmYLCx5//4Dd/4HWtJrnL6dDVq1fBGENiYqLcpRAXNWjQIOTn58tdRrdQoDq4AUOHK4ZPnY0f//YP+NlfPxJLHWjJwLKyMly/fh3jx4+XuxTiwiIjI1FSUiJ3Gd1CgeoEAkLD2LwXXkP/MROF//7Nuzh345bs5wBOnz6NnJwczJkzR+5SiBtwlr3LnKNKAgDwDQjEole/js+2bMdP//whv1PbaPc5n6IoYuPGjfD398fKlStdYt4sIVKhxVGcVF1VJc7s38VFs4XNmDpZnDo8VfBQ2v7z8fDhwxg4cCBCQkJs3hYh9x0/fhwRERF2WTOgK7TalAsTRRF5J4+JN65cEN548QWkJkTbtL2qqirs3r0bmZmZ0Gg0Nm2LkIfl5OSgsbERs2fPhlIp3xR6ClQ3YLVYsO0ff8Z3vv46BoQH27Qtk8mEnTt3YsmSJTZth5DHNTc3IysrC4MGDUJycrIsNXQWqHQO1UUolErMfeFVrNmwyeZ7sajVaqe5c4W4Fk9PTyxbtgwmkwlbt26F1WqVu6RH0K2nLkTjoUWr0WTzD8mmpibk5+dj3rx5tm6KkA6lpaUhMTER27Ztg0qlQkNDA2bOnCn7KmcUqC7GyjlMVhFqhW1ytaioCKdPn8agQYNgMpmop0pk4+3t/eC0E+cc27Ztw6JFi2StiYb8LsY3MEjMPn2BSzmdymQyYfPmzcjKykJJSQmWL1+OadOmYf369bRUH3EIjDGHmKsqfwVEUqNnzBXOXsrDT//yf7zZbJHkmBcuXMCYMWMwd+7cB3dFeXh4YP78+di4caMkbRDSVxqNBo2N8t5JSIHqYhhjGDd3EYtPHYlv/fTnOH31Zp97qwMGDMDVq1ef+Lqfnx+ioqJw9+7dPh2fEClMmTIFOTk5stZAgeqiQqNj2KJXv44vsnbjx+/9jd+uru/1sfz8/FBf3/Hzhw8fjtzc3F4fmxCpqFQqWK1WiKLNJ7o8FQWqC1MoFJi69Bk2dOps9vP3/sSzT1/s9StNpVI99etNTU24detWr+skRCrjxo3DsWPHZGufAtUNePn6YdErb7C9h4+wrcfO9mrR6o72WL9v2bJlqKqqwqZNm5CXl9eXUgnpk+DgYFRXV8vWPgWqG5mx4jl2/sqX+OmfPuSNxqcH5ONu3LiByMjITh8zfPhwzJs3DwUFBX0tk5A+SUhIwLVr12RpmwLVzYyds0DoN2w0vvO/v8ApQ2GXF6xaW1tx7tw5DB8+vMtjnz59GpMnT5aoUkJ6Z8iQITh9+nSnoypboUB1QyFR0Wzxa29iY9Ye/PrjdaLR2vGp1bNnz2Lnzp3d3nFy9OjROHXqlJSlEtIrcXFx2L59u93bpTul3JQgCJia+Qy7ZSjg3/vFO/zZeTNYbcVdWCwWWK1WcM6RnJyMESNGdPuYarWaJvoT2YmiiOrqalm2nqZAdXOx+iRBo9Xy99d8jvd/9v/gre3bRnuCIEAURYe4a4W4pzNnzmDUqFGytE2veoKwmDgWGhUjNpj7Pn8vOTkZV65ckaAqQnqnoqICYWFhsrRNgUoAAAFhESi8fafPx4mNjcXt27clqIiQnhNFUdZteShQCQAgOCJKuFFY1OcuKu0xReR05syZHp33lxoFKgEAqD080GY0SnIsOW/9I+6tvLxctuE+QIFK7mGMQao1/wRBgMUizUpXhHSX3MN9gAKV3MPuXZ2XwqhRo3DmzBlJjkVId3X3BhRbokAlANrv96+srpLkWMHBwaipqZHkWIR0V0lJCcLDw2WtgQKVAGjfj6qlzSjZ64Em+BN7S0hIQHZ2tqw1UKCSByxi59uK98T9u1UIsZeIiAg0NzfLWgMFKnmAATCJ0gTqggULkJOTgxs3bkhyPEI6U1NTgwMHDmD27Nmy1kGBSv6Fcd5qkebCFGMMGRkZqKyslH1bCuL6Dh06hMzMTHh5eclaBwUqecBD5yVW1PV+q5SOjBkzBqGhodi/f7+kxyXkvpqaGvj6+jrE+hG0OAp5ID45RTh68rQ4YNEcSV+ZAwcOhFKpxBdffIH58+fDw6NvC7AQx8E5R0lJCfLy8mCxWCAIwv05zU887mlzRJuamjBt2jQEBgb2qoZDhw5h8eLFvXqu1ChQyQOR8Yls7+fHwBfOlnyCdGJiImJiYrB161aMHTsWUVFRkh6f2EdFRQUuXbqEtra2B1+LjIzE1KlToVare3VMzjn27t0Ls9mM2bNnQ6FQdPu5NTU18PPzc4jeKUCBSh7CGIMIzhpNVvhopH9pqNVqpKen48KFCxSoTqCurg4XL15EU1PTgx5ncHAwxo0bB51OJ1k7jDHMnDkTTU1N2LJlC2JiYjBy5MhuPTcnJweLFi2SrJa+okAlj/ANCLIW3a1UDImVfoJ0W1sbDhw4gFWrVkl+bNI3zc3NuHjxImpqah6MTnx8fJCWlgYfHx+71ODl5YWlS5fi5s2bWL9+PcaOHYvo6OinPr6mpgY+Pj4O0zsFKFDJY/yCQ4Xi0lLYIlA3bdqEZcuWSX5c0jPV1dUwGAyora0F0D7k1ul0SE1N7fV5TCnFx8cjPj4ex48fx5kzZzBnzhxotdonHudI507vo0Alj0hITmE5277gc8YMY1KeR92zZw+mTp0KlUol2TFJz2zZsgVKpRKBgYEYOHAgAgIC5C6pU+PGjYPZbMauXbvg5eWFKVOmPOg919bWOlzvFKBAJY/ReGhhtoqszcqhVUoTqCaTCbW1tbIuq+buGhsb4enpiRkzZshdSo+oVCosXLgQ5eXl2LBhA1JSUpCUlIQ9e/Y45GjHseKdOAQmMG6ScE1TtVqNsLAw5OXlSXZM0jNHjhzBpEmT5C6j10JDQ7F8+XKYTCasX78e6enpWLt2rcMtE0mBSp7AwCD12iaTJk3CzZs30djYKO2BSbeYzeZeT2tyJKmpqZg7dy4KCgqwePFifPbZZ2hqapK7rAcoUEmHbLFWVEpKCm7dumWDI5PO5OfnY+DAgXKXIRkvLy+0tbVBp9Nh9erV2LFjB8rKyuQuCwAFKrETi8WCU6dOQa/Xy12K27l27ZrL/t5LS0uh0+lQWloqdykA6KIUsYO1a9fCz88P6enpdJXfzm7fvo2goCC5y7CJXbt2ISQkBPPmzevR3VW2RD1UYnMKhQKzZ89GRESE3KW4Fc45jh07hvHjx8tdiqREUUReXh7Cw8MxfPhwhwlTgAKVdICDQ8o7+dVqNa3gL4OsrCzMmjVL7jIkJwgCBg0ahKFDh8pdyhMoUMmTOGNapXQvjSFDhuD06dOSHY90LT8/H2FhYQ4/eb+3OOdoaWmRu4wnUKCSJ3BwqAXp+qiHDx9GWlqaZMcjnWtpaUFBQYHsO4Da0ty5c7FlyxaHG/lQoJInqBjjUt52qtFoXGIOpLPYunWrQ63AZAtKpRJTp07FoUOH5C7lERSo5BGNdbUIDPCX7jYpAOnp6Vi3bp3DTG1xZQcPHkR6ejqUStefwBMWFuZww34KVPKI5oZ6BAb4Sbq6dGRkJFasWIEbN25g27ZtECW8rZU8qqGhwa3WmqUhP3FoVosFKqVthufp6elQq9UOc1eLK3K3eb6iKOL27dtyl/EABSp5BGMMFtFqk2MXFhbCz88PkZGRNjk+cT8LFizAsWPHHGbUQ4FKHhESFYPrNwptcuyioiKkpqba5NjEPTHGoNVqJd8DrbcoUMkjlCoV2swWwRbnpvz8/FBTUyP5cYl78/T0RH29tNuf9xYFKulQs0X6IdSQIUNw9uxZbNy4ESaTSfLjE/c0YcIE7N27V+4yAFCgkg5oPb3EkupayY+rVCqxaNEizJ8/Hzt27JD8+MQ9abXaDveckgMFKnlC/OAhwokz5212ll+j0SAxMRGXL1+2VRNuyWq1Otw0IntxlL2lHKMK4lAi4vqhwHDVpmf5hwwZAoPBgLa2Nls241bOnTuHYcOGyV2GLBzlFBIFKnmCIAgwiSITbdzbWbhwIbZv327TNtzJ3bt3ER4u/fbfjs5kMjnMEn4UqKRDCoUCJqttA1WtViM5ORlXrlyxaTvuwF2H+gBw8eJFjBgxQu4yAFCgkqfgnHOLHd6kCQkJuHv3rs3bcXX5+fkYNGiQ3GXIIjAw0GFeQxSo5GmYwg6TpUVRdJhJ2c7s+vXrSExMlLsMWZw/f95hFpumQCUdEkURKgnXRH0arVYLo9Fo83Zcnbt+KHHOoVarHebnp0AlTzCbTNCqlFxph0BljLn1+T8p3L59G9HR0XKXIQvGmMNckAIoUEkHCq9c4mNG2u8kv6OtaelsLl686DBDXndHgUqeUFlyh+v7xdttDJWWlobz58/bqzmXwzl3mCGvHBxlpSmAApV0oLW5kQX5+9utvX79+tntKm1BQQF27NiBiooKu7Rna9XV1S67EV93ONqHCQUqeQIXuaS7nnarTTudRzUYDJg7dy7Onz+P3Nxcu7RpS6dOncKYMWPkLkM2ZrPZobZ7oUAlT+IcWoXjfOpLpbKyEsHBwRAEAbNnz4ZCocDmzZthtdpmQW17sFgsDhUo9tbU1ORQG0C671+CdKiuqhJRYaEiY8yuH7ZJSUnIzs7G7NmzbdbGiRMnMG/evAf/HjJkCPr164f169dj8uTJdrlt02QyYc+ePQ/m31qt1k4D4fHhrNlsxsKFCwEAzc3NDrPKklyOHTv2yN9UbhSo5BGFVy6Jk0YMs/vIJSEhARqNBkePHsWECRMkP35dXR0UCsUTU2w8PT3xzDPPICsrC3l5eQgPD0dNTQ0aGxsBtAeaIAgQBOHB83U6Hfr164fAwMAe1cA5x9q1a7FixQpoNJpe/Ry5ubm4evUqBgwYgOPHj2P8+PG9Oo6rEAQBzc3N8Pb2Rnl5OUpKSpCWltbr86pmsxm7d++G2WzGzJkz4enp2aPnU6CSR1SVFrOkzAWytB0ZGSnp1f6WlhYcOXIERqMRHh4emDNnzlMfO3fuXDQ1NaGyshKxsbHw8vJ6MEdWFEVYrdYH/7W0tMBgMKC2thaiKMLLywujRo3q8s139OhRzJgxo9dhCrTPiFi/fj3CwsLQ2toKnU7X62O5gpkzZ+LgwYMwmUzw8fFBfHw8NmzYgMTERCQkJMDX17dH4bp161bMmzcPKpUK27ZtQ0ZGRo/qoUAlj2KMeankmygtxbqWly5dwu3bt6FWqzFx4sRuD4u9vLzg5eX1yNfuTxx/uGfr5eWFkJCQB/9uaGjA0aNHYbFYnjr8FEURFRUVSE9P78VP9KjMzExs2rTJYdYAlZNKpcLMmTMf+Vp0dDRu3ryJy5cvo6Gh4ZHbm+/PCkhNTX1wM0RDQwOOHTuG+vp6jBgx4sHrJSUlBRs2bEBmZma3Q5kClTyG2eWWU1sqLCzE4sWL7daej48PZs2ahevXr+PcuXMYPnz4E4/Zt28fpk2bJkl7giAgMzMT//znP9Ha2ur251E7Eh8fj/j4+A6/J4oiLl68iEuXLgFoP+0zffr0J7bg7t+/P4KDg7Fjxw4oFApwzsE5x/Lly5/aLgUqeYTA5L8v3Gw293p/+bKyMoSGhkpcUfckJibiiy++wLBhwx75Hba1taG1tRV+fn6Strdq1Srs3LkTvr6+mDx5sux/N2chCALS0tKQlpbW5WP9/PywYEH3T4HRmIE8QsEg6431EydOxKFDh3r8PM45cnJyZJ+XOWbMGJw7d+6Rr2VlZXV6/ra3VCoVFi9ejMGDB2PDhg0oKiqSvA3SMxSo5AHOueyB6u3t3eNtUW7evIkvvvgCAwcOxOLFi2XtqUVFRaGkpOTBv2/fvo2IiAibzpUMDg7G8uXLcenSJYdZF9RdUaCSB6rKShAZGSl3GT1ePSg3NxfLli2Tbaj/OKVSCbPZDAA4c+aM3XrMCxcuRE5OjsPsUe+OKFDJA1+eOy1OmTBe9tdET25DraiokPzcZF+lp6fj0KFD4JzDw8PDrm0vX74ce/bssWub5F9kf/MQx9HS2CDEhwbJXUa3tba2Yt++fZgyZYrcpTzCx8cHzc3NuH79Ovr162fXthljSEpKQl5enl3bJe0oUMkDjIHbe1GUjnRnOba2tjZs2LABy5cvd8ir26mpqfjkk08wYMAAu7c9ePBgXLlyhRbulgHr7Je+yVBGfxE3UVJ4XTRXluKrmQtkT9Tr16/j7NmzCAsLg6+vL5RKJe7evQuTyfRgYrZarUZ1dTUmTZrksFsn19TUyLa0Xnl5OS5fvozp06fL0r4r0+l0T/0Ep3moBABw4chB4Wff/YbcZQBon8+ZmJiIxsZG1NfXw2KxIDExscMJ7Fu3bsWwYcMccgsQOdcpDQ0NRX5+PoqLixEVFSVbHe5G9t4IkZextQVHtm/iI4cPs/pqejeZ3la8vb0RFRWFuLi4p94NtGjRol7NW3UHU6ZMweHDh516eUJnQ4Hqxi4cOSie2L6RTx07Es/OnuI4O531kK+vr9wlOKxFixbh888/d6htQlwZBaobq60sx9deeoFNSxvMBAe8sNMdLS0tdp+a5Ew8PT2xaNEifPrpp7BYLHKX4/IoUN2YsbVFCPR27uXfbLV+qivx9vZGZmYmvvjiCxQXF8tdjkujQHVjgkIFTweYJtUXRqPR7dcE7Q6tVouVK1eisLAQmzdvRnV1tdwluSS6yu/GLGYTjCKHh5PuH0XD/Z6bOHEiOOfYuXMnBg8ejLi4OLlLcinO3T0hfZI0Yoz4l3WbRWedAH706FG33wKkNxhjmDdvHvLy8nDkyBG6AUBCFKhuLD4pWWhoM+F7v3mPVzS2yF1Oj7W1tdFwv5cYY5g/fz769euHTZs24dSpU3KX5BIoUN3cqBlzhdFzFrH3P1rjVPNqaLgvjYiICCxduhTh4eH45JNPaKWqPqJAJWioqeZREfIv29cTdHVfWjExMVi1ahX27duH0tJSuctxWhSoBGf2Z7MV82c51WuBhvvSEwQBS5cuxfHjx+UuxWk51ZuISO/L82fEyRMmiI5222lnaLhvW6NGjcKGDRtw/fp1uUtxOhSobu7OtQI2edxop3od0HDftmJiYrBs2TI0NjZi06ZNyMnJgclkkrssp0DzUN0c52B+Hs7TOwXaF5am4b7t3d8ZtLa2Fvv374fZbIZGo8GYMWNo/YSnoEB1ewwqwXkm9ldXV9Ob2c78/f0f7Nra2tqK06dPo6mp6cH3GWMQRRFxcXEYPHiwXGU6BApUNycIDCYrh4fSsUP1ypUruHr1KrRaLWbMmCF3OW5Lq9Vi0qRJHX7v2rVr2Lp1K5RKJaZPnw6NRmPn6uRHgermRFGEKO/O0U/V3NyMnJwcGI1GJCUlYcmSJXKXRDrRv39/9O/fH0ajEbt27UJYWJjddnx1FBSoBFqFY12TunTpEgoLC6HVajFt2jS37Ok4M41Gg8WLF6OoqAjr1q3DxIkTHXabGqlRoLoxURShYowzB9jl7n5v1GQyISUlBYsXL5a7JNJHcXFxiI2NRU5ODi5evIhZs2Y55IaKUqJAdWN1lRWIigjnAGR7lV+9ehX5+fnw8PCg3qgLYoxh8uTJqKysxOeff44ZM2YgODhY7rJsxrHGesSu6qoqEBkeJttrwGAw4O7du1i8eDFmz55NYerCgoOD8cwzz+DgwYOoqKiQuxyboUB1Y/XVVWJYSIjd2+WcY8+ePSgvL8fEiRPt3j6RB2MMy5Ytw8WLF7Fjxw6X3JKFhvxurL66EuFh9g/U3bt3IzU11W0uVJB/YYxhxowZaG5uxrZt2+Dj44OpU6dCEFyjb0eB6sZMxjZ4e3ratc09e/YgKiqKwtTNeXp6IiMjA7W1tdi6dSu0Wi2mT58OpdK5I8m5qyd9otFo0dDUhCAPP7u0ZzKZYDQa3f5uGvIv/v7+WLJkCRobG7Fz506o1WpMmzYNarVa7tJ6hQLVjYXExKHg2g2eEDTcLlf5b926hQEDBtijKeJkvL29sWjRIrS0tGD37t3gnCM6OhqpqalOdTrAeSolkktMGSocO2m/rS8CAgJQVlZmt/aI89HpdFiwYAEWLlyIwMBAZGVlYePGjaiqqpK7tG6hHqobU6nVaDGZmVnkdlkgJTAwEBqNBjk5OU+9H5yQ+2JiYhATEwPOObZs2YJRo0ZBrVZDqVTC399f7vI6RIHq5rQ6nVhUVSP0Dwm0S3tjx47Ftm3b7NIWcQ2MMSxZsgRHjhwB0L6zQHV1NTjnGD9+PIKCgmSu8F9YZ1vIbjKUOeaqGUQy1XdLUWXIE7/x7DK7nf65evUqqqqqMG7cOHs1SVyQKIo4duwYysvLMXfuXLutkavT6Z46nKNzqG4uMCwCRSUlgr32Zq+rq8OXX35Je8GTPhMEAenp6Vi0aBGys7MdYssWGvITMEEJe93Qv2fPHixbtszlF8kg9qNSqZCRkYGsrCx4eHggKipKtlqoh+rmOOeAaOWCHQKuqakJgYGBFKbEJubOnYtTp06hsrJSthooUN3cLUMBT00eZJfx96lTp9xuwWFiXxkZGdi9e7ds6wRQoLoxk7EN5w/tZRkzJtvlddDa2gpPO9/qStwLYwxLly7Fnj17ZGmfAtVNiaKInR/9Hd//9zfgrbHPrqd0IYrYg1arhSiKsrRNgeqmju3cIr7w7DO8X6j95vB5eHg8slsmIbZw5coVhIaGytI2Baqbaq6vE0YNSLDr1aH09HQcPXrUnk0SN8I5R3Z2Nurr6zFy5EhZaqBpU26ovqYaIUGBolJgdv1A9fDwgNFotGeTxI0cPHgQaWlpsvVOAeqhuqVLRw+Ji+fNkeVvr9Pp0NraKkfTxIVxzlFdXS1rmAIUqG7JNygExSWlslwhGjlyJM6ePStH08SF7d+/H+np6XKXQYHqjpJHjxN27N7LrDJcdffz80N9fb3d2yWuy2g0orW1FWFhYXKXQoHqjhQKBVQeHryklq64E+d34sQJjB49Wu4yAFCgui/OuVbrIUvTOp0Ozc3NsrRNXAfnHLt374ZSqUSIDLv3doQC1V0xxnRKef78kZGRKCoqkqVt4hqampqwZs0ajBw5EhMmTJC7nAcoUN0U52AeCvv/+VtbW3H69GkkJSXZvW3iOg4cOIDVq1cjICBA7lIeQYHqtjjMon0vSnHOsXHjRjzzzDNOtfEacSzFxcXQarVQKBRyl/IEelW7KYvJBHvf7Xz8+HFMmzbN6fdeJ/LKzc3FjBkz5C6jQxSobsjY2gJfby9u73OodXV1CA8Pt2ubxPU4Ys/0PgpUN1RefAcDB/S3+yRUWm2KSMGRX0cUqG6oquSO2C8u1u5/+8GDB+PEiRP2bpYQu6FAdUPlxbeYPi7a7u3GxcWhoqICLS0tdm+buAbOOaqqquQu46koUN2M2WSCWqmEn50WlX7cvHnzsHHjRocethHHlZeX51DzTh9Hgepmcg/tE5fMmyvbRnlKpRJz5szB9u3bZWmfOK/y8nJcvnwZCQkJcpfyVBSobqamslwYobfvwtKPCwoKQmJiIs6cOSNnGcTJHDx4EKtWrXLoXXMpUN2MQqmEWoY7pB43aNAgVFRUyF0GcQLV1dXYsGGDU9xdRzOs3UhrUxO8PDQiHOCD1Gq1yrbVL3EuBw4cQGZmpkP3TO+jQHUj5w7tFZcvmCdrmB45cgR1dXXgnGPatGlylkKcwOnTpzFw4ECnCFOAAtWtNNXVCoOi7XenUnNzM7KysqDRaB4s1zdp0iRERETYrQbivA4dOoSAgACkpKTIXUq3UaC6ibaWFvj7eNt1Y74jR45g0aJFUKvV9mqSuJD6+npMnjxZ7jJ6hALVTdy9UwR9/0S7jpva2tooTEmvMcZQVlaG8vJylJeXP3HOnTEGQRAQFhaGmJgY+Pv72+zUgNVqRWVlJYKDgzt9HAWqm2htauQJkSF2C9TCwkLExcXZqznigtLT01FYWIjQ0FAkJSVBo9E88Rir1YqysjJ8+eWXqKmpAdA+13nEiBEICgrqcw0tLS3YuXMnPD09ERISgtzcXCxduvSpj6dAdRMqtYYZjSa7tNXQ0IBjx47hueees0t7xDX5+/tj+PDhnT5GoVAgKioKUVFRD75mMplw5syZB+tGKBQKREZGol+/fvDy8kJzczPOnTuHlpYWJCUlITY2tsNjm81mfPHFF1i9enW3V7iiQHUTCqUSRpN9AvXgwYNYvXq1Xdoi5HFqtRrjx49/8G+r1YqSkhKcOXMGzc3N8PDwwIgRI+Dj44MzZ87g0qVLj5wqUCqVmDVrFrKysrBs2bIeLRdIgeom7t4uEsdMnWTzC1L379GnFfmJo1AoFIiJiUFMTMwT3+tot9SKigps3LgRgwYNglar7VFbFKhuoLG2BpV3bglJsbafrnTixAmMGjXK5u0QYishISHIzMzs1XOpG+HiWpuasHfdGvzv974Ne2zKV15eTqvyE7dFgeriTuzezr/3jTcR6Olh87aKi4sfuThAiLuhQHVxprY2lhDsb5e2cnNzMWLECLu0RYgjokB1Yc0N9Qjy9xUFO9wH3dTUhLa2Nqe555oQW6CLUi7syqnj4uwpk23+oXnnzh2cPHkSGRkZtm6KEIdGgerCaivLheT4J6eKSNpGbS1Onz6NZcuW2bQdQpwBDfldGBMU0Clt9yfmnCMrK4t6poTcQz1UFyYw2Oyc5o0bN3D+/HnMmTOHzpsScg8FqgtTMHAAkqcd5xynT5/GM888I/WhCXFqNOR3URazGRqVSpT6uJxzbNiwgVbbJ6QD1EN1Uaf3ZvEZU6W/wp+dnY2pU6dKsjQaIa6GeqguqLGuFqaWJoxPHiD5cN9qtVKYErd14MCBTr9PgeqCLh49JD6/PJNJPaG/paUFSiUNaoh7qqys7HIpPwpUF9TcUC/EhUrfi9y7dy+dOyVuqbGxEbt370Z6enqnj6PuhoupLi/jnjodtArp5zKpVCqoVCqpD0uIQzt//jzu3LmDZ555pst1fqmH6kIa62pxaNM69r2vvMhsMTf0/uLRhLiLM2fOgDGGRYsWdWvlfuqhuojC/MvW67lnhbd++F3mo5G+F2mxWGgVfuJWbt++jaamJkyZMqXbz6F3iIswnD+jePu7/85CvHU2Of758+eRlpZmk2MT4ojOnDnTozAFKFBdhkKpgtKGt4BWVFQgLCzMZscnxJEUFhZ2uAdVVyhQXQRj4K0WyW+MIsTt1NbWIjc3FyNHjuzxc+kcqosQBIE3Gk3MW92zXRq7Iooitm3bhkGDBkl6XEIcDecc2dnZ4Jz3egU16qG6iLC4BH741FlJu6icc6xduxbTpk3DgAEDpDw0IQ5nx44dGDFiBObOndvrFdQoUF1E8sixihOnz7BThkLJ5jbt2bMHs2fPhre3t1SHJMQh1dfXQ6PRIDg4uE/HoUB1IbNWv8Q++OcaVtNqkuR4RqMRAQEBkhyLEEfFOce2bdswffr0Ph+LAtWFCIKAoelT+N6jJyQZ+tO8U+IOtm7divnz50vyeqd3jIuJG5jM8vLzJRn2q9VqtLW1SXEoQhzSnj17MGTIEPj7S7PVOgWqi1EolbBYrZJMSE1JScHly5elOBQhDicnJwcxMTFISEiQ7JgUqC5I5NJsexIeHo7y8nIpDkWIw7i/uWRgYCAGDhwo6bFpHqqL4ZxL+ilJC6IQV1JfX48dO3Zg+vTpCA0Nlfz4FKguprmhHv5+fiKArpfG6Qba0ZS4AovFgh07dkCn02HlypXdWjmqNyhQXUzV3VLExcZI1kkVBAGccwpW4tQ+//xzZGZmQquV9k7Cx9E5VBdTXVYixkVHSZZ+NOQnzq6srAwDBw60eZgCFKgup7qslPWLiZL0mNQ7Jc7KbDZjz549dlt6kob8LkbknPl7SLfAtNVqlexYhNhTaWkpDhw4gBUrVthtc0kKVBfSWFcLLw+NKDAm2cgjOjoan376KVatWkU9VeI0Dh48CAB49tlnH/l6UVERLly48GBvNKvVCl9fX0yaNEmSdilQXcTlE0fFssJr7Idf/4qkp3HS0tJw7do1tLS0wNPTU8pDE2IT27dvh7e3NyZPnvzI1znnOHv2LDIzMx/5+u3bt7F27VosXbq0z5tQUqA6OWNbK/at+wQjhw/j3/reNwSlIG0vsrW1FT4+PhSmxGkwxtDa2oqsrCwAeDBLpbKyEvPnz3/i8TExMQgJCcG6deswZ84cBAYG9r7tzq7ibjKU0SVeB7fhj7/DD7/1b0gMDZLsmOXl5QgODkZFRQWys7OxevVq2j6aOIWTJ08iODgY/fr16/FzOefYvn07Bg4c2On6vzqd7qm9FuqhOjGrxYLw0FAxMTRI0mH+3r174ePjg7CwMKxcuZLClDiFgoICtLW19SpMgfae7cKFC3Hs2DFUVVVh3LhxPT4GTZtyYtV3yxATHS3pMS0WC7y9vbFw4UKMGjUKHh4ekh6fEFu4cuUKqqqqnjhv2hvjx49HYGAgNm/eDFHs2UqYFKhOrPzOLbF/v3hJ/4YHDx7E1KlTpTwkITZ37do1pKenS3Y8vV6PqVOn4tNPP0V9fX23n0eB6sSqSu8gIS5O0mM2NzfTlifE6djijj5fX1+sXr0aBw4cwK1bt7r1HApUJ8a5yJUSLvJw6dIlpKSkSHY8QuxFrVbDZJJm65+HCYKAJUuW4PTp02hoaOj68ZJXQOxGo/Vidd34I3dXUVFRr0/oEyKnyMhIVFZW2uz4S5cuxdatW7vcwYIC1YklDB4inDgjzdbRnHNaCIU4LW9v7271IHtLEASsXLkSu3fv7vxxNquA2FxYTBwM129IMpO/oKAAgwYNkuJQhNiV2WxGTk4O4iS+nvA4lUrV5ZoAFKhOjDEGKxizStCzjI2NxY0bNySoihD7On78OObNm2eX5fm6QoHq5KRaB6WsrAw6nU6SYxFiT+PGjcP58+ft0lZ1dXWn36c7pZxcX/eQys3Nxa1bt6BQKLBgwQLJ6iLEXuy5o0RXc7QpUJ2cKFph5YCyl6+nkpISLF68WNKaCLGnkydPYsSIEXZpKyqq88Xbacjv5FQqFS9vbOnVc3t6Wx0hjoZzjvLycgQFSbc4UF9QoDq5xCHDcOjEqV4l4/Xr19G/f3+pSyLEbvbs2SPZ4tBSoEB1ctH99ezC5bxeDfiLi4sRLfHiKoTYS1FREXx8fBASEiJ3KQ9QoDo5QRBgFjkzWXveSW1tbaUr+8RpnT9/HmPGjJG7jEdQoLoAhULgDaaeb6ZHd0YRZzZhwgTs2rVL7jIeQYHqAgJDI/i1ots9fl5ERASuXbtmg4oIsb2QkBCH6xRQoLqAqP4DhUtX8ns85h82bBguX75si5IIsTmr1UqBSqQXFB6Botu3e3VhKj4+HkVFRRJXRIjt7d+/H+PHj5e7jEdQoLoAQRBg4bxXgZqWloajR4/SnFTiNDjnOHz4MDw9PeHv7y93OY+gO6VcBEfvb71bsGABduzYgaamJixduhQajUbCygiR1pEjRxAfH++QU/6oh+oq+nAqydfXFwsXLsSUKVNw+vRp6WoixAaCg4PR3NwsdxkdokB1EWIfT87n5ubi+PHjDndOipDH9e/f32HP+1OgupC+XPEsLS1FRkYGBIFeEsSxXb58GcnJyXKX0SF697gIzkVY+9BJjYuLowWmiVOIjIzEzZs35S6jQxSoLsLHz188mX+t15E6aNAgHD9+3OHm9RHyuJCQEFRVVcldRocoUF3EqJnzhHWbtzCL2LtAZIxh1qxZ2LNnj8SVESI9Rz015ZhVkR5TKBRQKFW8pq33e5OHhoZ2uU0uIXIrKytDcHCw3GV0iALVlQgC8/dQ9+kQycnJ1EslDotzjh/87n0MH+1Yq0zdR4HqQlQC4yqhb3vrJCYmwmrt+cpVhNhDi0XE5RNHceRivkOe7KdAdSUS7YBKt6ESRxadOABHjh+nQCW2Y2xtgaKPvdP7qIdKHJVWKcDbLwCVlVUOmV0OWRTpuZO7d/BnMzMkSdTAwEBUVlZKcShCJCUwhuCwcJSXlaA3u1TYGgWqCxBFES0NDWxQdJgkxxszZgx27tyJ1tZWSY5HiJQ0KiX30HriUmHPF1W3NQpUF3A+Z5+4eP4cLjBphvwKhQLPPvsstm3bJsnxCJGSv5+fGJnQD7v27Xe4LioFqpMzm0woKyoUJqQkSZOm91RUVCA8PFzKQxIiDQZWfucW4mLjHO7CFK2H6sSqykp4zpYN7Ltvfg19nS71uJMnTyIjI0PSYxLSW1VNrbhSWAQICpw5eUIQFEpkzpiskLuux1GgOrGDm9axd376X/DRqCQ/Ni0yTRyFySrihz//FVpbWnEj7wKCI6Lwy7d+DK3K4fKUAtWZ+QYEQqWwzYvKaDTa5LiE9FS90YKG2jqo1Cr86p3fIyUxAX4e0ncipECB6sS0nl7i7aoaQR8WJPmxx44dizVr1iAwMBAmkwkjR45EZGSk5O0Q0pWCG4XQeXrygJBQlj5YL3c5naJAdWJBkdHs5u07sEWghoeH49lnn4XFYoFKpcL27dspUIksTpw5KwZFRgkaBhEOfiHdoYsjnWOMQbTY7q4mxhhUqvahFd09ReTAOcfdyiqhvqqKjx01UtorrzZAgerEYgcksX2Hj/LeroHaXVVVVfDz87NpG4R0pMUior6mBmZTG0YOGew6gWo2GdHc2GDLWkgPab28YBGtqDNabNrOyZMnkZ6ebtM2COnIjZJyGFtbRb+gUOav7dvSlPbQrXOopUWF4sk9OwWNhxYWsxmLXvmaresi3aRQqpiP2vbTRxQ2mk1ASGf2HT4iRiUOECwmo+RzrW2hW4F64/IF5uXjC08vHzE2aRAD4Pg/mRvIP3NCjIqIgFph2/0gHHW7CeL6yisqmGdQCJqrKzmcIHe6FajpCx6sYkTvLAeRf+aEaKqrwX+8+Az9TYjL6peQwPMMV5lG6RwjJHozOqGK4ju8vqyY/ceLzwhKJxgGEdJbK+bOFCpL7qC6upqJTrAjLwWqEzqfsw+vP7eKUZgSV+frocKIUaPFooIrKK1vlrucLlGgOpmqu6U80M8X4d5auUshxC6WL5grxCUl439+/TtUNrXIXU6nKFCdzMnsHezNF1YzJtHap4Q4ughfL/j4+WPWqhfxo5//BtXNjrvVOQWqEyktKhT7JcSLzjAfjxCpKAQGjUrJPX18MWPl8/jBW7/E7ao6ucvqEAWqE7l0LIc9t2ShLH8z7gQXBIjrUjBws8kEbz9/zH/xK3jrD+/j8OUvRUd7XVKgOhFBIXBux5H+rVu3kJWVherqavs1SshDWs1W/Pqjz0UTFwSVun1kptHqsPjVN7Fj3yH2wfbdotXGt173BAWqE+EiZ0o7nTu9efMmrl69itmzZ+O5554DnbMlcsi/XYKK2jo2ffnqR77OGMPUzGdYdVMbPtiyy2H2lqJAdSqc+Wnss+Jibm4upk+fTndJEVmlxEXB2NLy1E/zoelThHzDl+xmZe2Dr1lEjmazFY1mK5rMVjSbrWixiGi1iLD1XFZaD9VJWMxmeGm1ImPM5gl3584dhISEUK+UyE6tEBAdGSHevXOLhUXHdviCnLR4OXv73ffgHxDIrSJnHABj4A+/fDkHOOdM4CL/wde/woI8bTPtkHV2UneTocxxTk64ubt3bsF89474WsY8mwaq0WjEpk2b8Mwzz9iyGUK6rclkwbf++y0sfOUN3D+P2lsNtdXYv+EzLM9YIk4dOkjoTadBp9M99Uk0nnMSpYXXxQGJCTb/e2VnZ2Pp0qW2boaQbvNSK/HdN7+GHf/3V4hi306X+vgHYvFrb+LgyTPs7Q/W8BaztAunU6A6ibKiQmFE0gCbtlFfXw+NRgN1H3sBhEhtQEQIXnr2Gb75r39A2a2bfRo5M8YwYf4SFq4fzL/79m/w/97/m7jv3GVe22bu8zlWGvI7gfrqKlw7dUT8r9dftOkH4Pbt2zFv3jy6EEUcVnWLEZ9vzxKvXi8UgsIjxWGTpwtKVd92QDW2tsCQe068e6uQcc6Zj6dOfGVlphAT5N/h4zsb8lOgOoF969bwf3/pORYT6GvTdrZt24aFCxfatA1CpGAWOU7lX+NbsnbByoFhk6cjOCJKkquorU1NyFrzAf7f9/8DET46CPfOs1pEjstFxRg/WP/Udugqv4OzWq2wms2IDvCxaTtms5l6psRpqASGCYMHsPHJ/VHe2IrPtu4QT+7eyQaPmSDGJyX36YWs9fLCzGdewDt//5CLVpFplAoOcN5qNAs+/oHi+E62sqYeqoMrzL/MAwUrXzFjkk3T7sSJE0hMTERwcLAtmyHEZlrNVvzflp1i/pcGIX3hUvgGBNqknQx9OF3ld1Y3r1zi40eNsPnfqbq6msKUODWtSoE3li0UfvzNr+Piwd385O4ddr/XnwLVgTXV16G5oV4I9dbJXQohTiPUW4effeOrbPSQZGz5+/torK2xW9t0DtVBVZYW82M7N7P//d63bbrbY3l5OXJycjBs2DCbtUGIvQmMYc6oocKIpAH45V8+4LFJg8X+qcNtvjEV9VAdUH11FU5lb8dvf/xDBHvZrnfa2NiIffv2ITMzE4mJiTZrhxC5BHvr8Ivv/Bu7VZAn1FSU23z8T4HqgA5uWov/+e63mKfKth+o3t7e0Gg0KCgosGk7hMhJKTD81799lR3ctJZZLRabtkWB6mCuXcoVJ4wbaw3QaezSXmZmJgoLC+3SFiFy8fNQI2PhAhScO2XTpf4oUB1Ia3Mz8k4eFZZOn2zXTcgtNv7UJsQRxERGoKG6yqZtUKA6CM45dn3yD/zkW/8OrdK+f5bAwEDU1NjvSigh9mblHNv37BfD4/rZ9DwqBaqDuFmQh/FjRvNIG98R1RG1Wg2rVdpVdwhxFJxz/HXTTpFpPRE/aLBNR38UqA4iMiERN4tuyXJnWlJSEvLz8+VomhCb+2zvYWtFTR1SJ0y2ed5RoDoItcYDtfUNsvw9fH190djYKEfThNiU0Sri5JmzijGzF9jlvUWB6iCObNvI58yYLstmY6Io0jbRxCW1WkSo1Cq7vbgpUB3AxWM5YnR4GJ81cogsf4/CwkL069dPjqYJsammNhMEhZIC1V1cu5RrZcYWfCVjbq/2t5FCcHAwqqurZWmbEFsK8NTAYjLZLecoUGVWmHdR8ebq5YJCxh1GNRoNjEajbO0TYis6pQIeahVvbWqyS3sUqDITGINWIV+YNjU1Yfv27RgxYoRsNRBiS197fjU7tHW9XYb9FKgyaqyrRXCAn1WOoT7nHJ999hnOnj2LSZMmwc/Pz+41EGIPsUF+GD50qLj/i8+4rS++0vJ9MjLknhGnjh9v19tMgfbFpLOzszFnzhwEBATYu3lC7O65WZMVhyPDxc/+/j6b+9wr0Ghts4obBaqMqstK2eB+GXZv9/Dhw1i1ahXkughGiL0xxjApZaAQF/Z1vPvBR1CoVDxp5FjGOYfJ2IboRD1UEmyfTkN+mXDOYTGbmZfKPn8CURRx8eJFHDx4EFqtlsKUuKXYYH/87gffxL+99BxjdRWiR2u96M8s2L/hU0nOBVAPVSbHs7byzEXzYY+5Um1tbdiwYQPS09ORkJAAb29vWzdJiMNijCE20BcvL5rzoDdz/uIlSY5NgSqDpvo6WFpbkN7J/t5S+fLLL3H16lUsX74cGo191lglxNloVCputVqZQtG3Sxo05JfB2QN7+EvPZDLBDsPuGzduYOHChRSmhHQiODhYkrVSKVDtqKG2Gjs++hsC/Xx4v5Agm7dntVrpDihCuiEyPEyoqez7nlM05LejKyePWV97brUiNS7CLh9ku3btwpIlS+zRFCFOLSQ4CNfzDBxAn4aN1EO1o8a6WiEuPMQubZnNZgCgC1CEdIPFbIFSperzcShQ7Ykxm+9k+lBTEEVZVgMkxKlwznH45Ckenajvcx5SoNqRwBiUgn3mfyqVdDaHkO64drcK9U3N8A3s+3UNClQ74ZxDwWDXVZx9fHxQV1dnzyYJcSotZit++6e/YurSlZL0dChQ7eRG3kVx6JAUuwbqgAEDUFhYaM8mCXEaIuf49T8+5qNnzhNVammmFVKgSsxqteL03l3iLUMBNxnbAACNtTXIzdkvzJ803q6/70uXLiEpKcmeTRLiNLYfOysytQePiEuQ7H1JJ9okdnb/bnFQYrxgaWsTz2Vv5S2tbSzQz1f8n+//h8Jbbb9fN+ccLS0t0Gq1dmuTEGdxq7IW2fv2CYteeUPS41KgSqym4q6w+KWVUAlMwKzJ979s9yX6iouLERMTY+9mCXF4RquIX77/Z8xa9ZLkx6Yhv8REqxUmq/zTlaKjo3H79m25yyDEoXDO8c7Ha8XU9Kmih076NVEpUCWWOCRN3JFzzCp3HQAgCAJtD03IPZxzfL7viLXVLCJWn2ST7KNAlVhVaTGLjrTPraVdUSqVtO4pIWgP0zXZB6351wuFsXMW2Oz9SedQJdZQU8XGDJR/j3vOOd0pRQjap0d9uGOPeKeskk2Yv8SmPQwKVImp1Gpuj0Wju6OvazsS4uxEzvG3zbvEiroG2LJnep9DDE1didlsgaG0Qu4ywBij86fE7X24fY9Y09yKUTPm2CXrKFAlNm3ZKvb7v34AowNc6bdYLLBaHeL6GCF2V1Bcjrz8AjZs8nS75RwFqsTUGg8MHjdR/Mv6LbIn6pQpU7B//365yyDE7kxWEe998CGmLVtl19NvFKg2kDAoRbhReFNoNFlkrcPX1xfNzc3USyVu51Z1LXwDgiS7R7+7KFBtJDF1mJh99KTsvdRp06ZRL5W4naMnz4iJqcPsnm8UqDbiGxgk1NXVy10GfHx8YLHI21MmxJ7KG5px5OhRISLe/tMXadqUjRRfN4hThg91iA8sutpP3IHJKmLHsdNi9p69wrwXviLLTS0UqDZSVVrCUlbQBnmE2JrJKmL70VPi/kM5QnxSCpZ85d9ku0OQAtV2mJfKITqo1EMlLunhIE0YlIIFL30VjDFZ33QUqDYgiiK0KoUo9x/3vsjISBQXFyMqKkruUgjpM6NVxI4jJ8X9OYeFhOQhDhGk91Gg2oAgCBB53/b3ltKQIUOwZs0aPP/88xAEh3jdEdJtnHPUGS04cjaXHz15Cm0mM4sZMBALX/4a4GAX1ilQbcRosTCRcwgOcFu/QqFARkYG1qxZg+eee45ClTg8k1XEzYpq7Nx7QLxTViYAjMcMSOKTlq4SFO07+jrki5gC1UaYIPC7ja0swkf6RWx7w8fHBwEBAbScH3FInHPUmyw4dv4SP3L8BNrMFqb19BJTxqYLg6bMBgB27z+HRoFqI2kTp+HzLdvE7zy/0iE+SU0mEywWCwUqkR3nHC0WEZUNzcjNy+MXL18R65ubFeDgUQMG8glLVgpKlQpw0F5oZyhQbSQ4Ioqd3pfN2iwiPJTyvC445zhx4gRqa2vR0NCApUuXylIHsQ/OucN9YFpFjgaTBTdKSnEu95JYWHSLmTlnAKBSq8Wo/gNZ2sz5Co1WBzhJL7QzFKg2FDtgoLjn1DksHD/Srona2tqKnJwctLS0YMKECQgJCbFn80QGVc1t+Mmv34WHpxdUCsa/89pLLMTbfqebOOcwWjlarSIqautw9cZNfvLMWd5sNAmCwLhfYAiPTUoWJo9Kfzj0na4H2hUKVBtKHj1e2LXmH5g/boTdLk7l5ubizp07mDlzJjw8POzSJpFfQWERD4mK5mNmzReaG+rZj37+a7z5+lfQPzIUWgXrc8+Vcw6TyNFqEVFZ14AbRbdw42aRtaSslJmtnFnv9ToZA/fQeYkhUTHC6PkZglrjAbhAz7O7KFBtiDEGnbcPLyipYMlRoTZvr7CwEK2trVi4cKHN2yKOZWyynq3dvJWJoghPH18sfOUNrN26RTS2tQoAoFYKYkxUFJL1A4T+CfHgACoqq1FZXQUA8NJ5wsvLC546D9TU1aHsbgUvLi0TS8vKmMliZZaHAlPjoRODI6NZSL+Birgxkx7fGYJBhm3THQUFqo2NnjmPffrFJv72N79m80/ogoICzJs3z9bNEAekFBhCg4JEY2uroPX0hEqtxqQlyx8MqUVRFKrvluJEwXVx+76DYIzBy9cf3gEBjDEGY0sLb2tp5sbWVnj6+jK/oBDBL36AInZUOu5NU7rPrQOzKxSoNqb19ERTSwuraTEiQGfbtRk1Gg3a2tpoqO+m6puaBa2nJwDgZkEejmVtQ3S//uKwydMFbz9/BEdEITgi6mnnLd1iSG5rLndS2BENTZ/KP9ueZfO1UQMDA1FTU2PrZogDEjmH2Wx+8O9Y/SBoPb0QE58g7F//CRrramWszn1QoNpBRFwCu36zyC6/a1oIxT0JjCE1JVnM/vT/+N3bRVwQBGS8/m+ob2wUzRYLaivK5S7RLdCQ306USiWHjYdVJSUlSE1NtWUTxIG9OH+WUDpxAjZszxIvHs3BjJXPsaETJgtDJ0yWuzS3QT1UOxBFEWqFYPOuo8Viofv03ZhCYIj284KXjy8rL7nNTuzeQcMVO6N3nx0UXrkkDk5OtumL+/z589Q7JQCAeRPHMW8/fyQOHkqBamcUqHZgyD0rLJoywaZTTYqKihAfH2/LJoiTiPT3xuplS1F+55bcpbgdOodqYzXldxEVHip6qqS/ob+yshInTpyAIAjw8fGR+vDEiRWXlom+gUE0FcrOKFBt7Mz+bP69r71mk5HA0aNHsWjRIjDW91sLiWvJ/9KAMQsy6UVhZzTktyFjWyvUSgXCvLU2OX5qaioKCgooTMkjOOdoNZnvL4FH7IgC1UZEUcTuzz7iz2ZmMFsFXnx8PIqKimxybOKcOOcorKyDyWSSuxS3REN+G7hlKBDPH94nvPrcaqTERtisHcYYWltbbXZ84lwqGpvxyz99wAWlEpMXL6NhiwwoUCWW9fEHGJDYD+/8+IfQqRQ2f1FrtbY5nUCcC+ccv/vgn3zsvMXMy9dP7nLcFgWqxHRajfjmisV2O5Xy8P3bxH01mqxoMxopTGVGgSohY2sL1Ha6EPDll1/i8uXLGDp0qF3aI47t2IXLPCpRz0HXRWRFgSqh0/uyxRUL5tvlBV1QUIBly5bZoyniBA4cPoLJmaspTGVGfwAJNdbVCMkxtrsI9TCj0QhRtPmKgMQJWEQOCwd7bCFoIgMKVInU11QjOjxcVAj2ubg6Z84cfPLJJ7BYLHZpjziuZosIgTG6b98BUKBKpDDvojh25HC7/T6VSiXa2tpoviGB2SrSaMVBUKBKpLLkDhuUmGC39rZt24aXX34ZOp39tgomjinAQwmLxcJocXH5UaBKRBStzFdjn3NYRUVFGDhwIJR0zoygfbX+pQvmisd2bqFElRkFqgQqS4sRHx0tKux0T/358+eRlpZml7aIc5iUMlBQM47yO7cpVGVEgSqBS0cP8Yy5s+zyu7xy5Qqte0qewBjDd155jp3I3ka3nMqIxowSMJtNLNLPyy5tFRUVYd68eXZpi3SsyWTBmm1Z4rXCmywiNJT/27PLBY1C/r6JgglQa2gLcTlRoPZR+Z3bPDEulguM2eUdZTKZIIoi7R0lk33nLosbt20X0iZNw7Th49lPnl/KoqKjMWpIMkJ9vaFTCrIsp8g5x8YDR3h0/4F0t5SMOr0yuMlQRudjurDr0w/59772Gov0tU8PtaGhAceOHcOcOXPs0h75l9K6JvzvO3/AwlfeeBCaxtYWFN+4xouvX+VtLc0CA/jA/ol8zpSJQoSvF2w9L5lzjtKGFvzfui+4UQTGz1tMQ34by9CHP/V3TD3UPqgovsODAwIR4eNptzZ9fHxA02Pkse/YSXHY5Bns4QVuNVod+g1OZf0Gp97/GistKsQf/m+NaDaZhMjQEDFj3hwhNsgPSgnDtdUi4tC5i+LuA4eYoFAgbdI0BEdEUZjKjAK1D24Z8vnsKekCrZjvHmpraxEaEdflHzsiLoFFxCUwAKitrBD+vnaD2NLYKAxOGigunztT8PPo3QI6Iue4WVmHzzZtESura4SofgMwc9WLjE7/OA4K1D4IjooWCotu85H94+2aqBTg8rBYLVyh6Nnmtf7BIZi8ZIUAAHeuG/CT3/0BOg8PvipjMRsUE9Fpr9UscjSaLCgqvYtzFy6Jlwu+FDw8PfnIqbOEkf4BAJ0rdTgUqH0QGZ+InI2fYfn0iXYNORryyyMuJlq4W1rMvf0DevXHjk7UC9GJehhbW9i6rGyxualRUAgKMHDg/uuHA7z9fwDOAYjcNyCYRw8YKMwZMR4220+HSIICtQ9UajW4oMCdmgbEBPrKXQ6xsZGpqez9jz8V+yUP6VOoabQ6pC/I6G7vkt37jzgBGjL00ZiZ89hH6zfSyhRuINzPC6bWVnrPkKeiF0cfefsHoK6pWaBhuOtTCQxBAf5iY12t3KUQB0WBKgHRakFtm7R7O3HOIT4lpCm85bN0/lwhN2c/jUhIhyhQJTBi2mz+41/+lpfUNXUZdiLn+HxPjvXbv/w9vvvrP/BDF/P5488ROccP//gh3lu/jZLTwfQPD0ZLYwO9b0iH6IUhgaCwCDZ56TPst3/7EM9/5z9xs/LpQ8K1ew9brxaXspmrXsTkZc+yfcdP4w9rN4sW8V/ZeelmMc7n7Mew5IFPXIzgnMNoNNrmByFdUgoMOo1KpAWdSUfoKr9EvP38ERASJoYEBrIwf58Or8peLLzDT507L8x59mUGAAqFAhPmL2EFZ06K3377N3zyhHEoLr3LD+7bK4THxmOYPvGJY5SWliIyMtLGPw3pjK+vL29raYbOy1vuUoiDoR6qhAounBVmTJnIONp7kpxzmEWOVouIK8XleP+DD9nMlc8/EbYD0kYodL7+7J8ffcROnjwhDBszjv/hrf+Gp+rJSeSRkZG4ffu2PX4c8hS+Pr6suaFe7jKIA6LFUXrp1pf51rzTxxXhMXFi0sixgtbTE411tcg7cURsqK0RmKAAGLs3gZBDpdbwsXMWMI2H9pHjNNbVYu/aj/G1V15GYlQ4PJQCVF3c852fnw9BEDBw4ECb/Xzk6f7r938RR8/PEFRqjdylEBnQ4ig24OnrK3j7+iE9bbBwYH+WWNvYJAiCwAWFAv7BoWL/ocOZf3DIw7/4Ds+H7lv3Cd764X8g2Kv7e0MNGjQIW7dupUCVgcg5WowmClPSIQrUXlIoVczP00NMH6wX0gfrBZFzNJpF1mIys9t3K7B242Y+a9WLnR5DFEVYLCZYxfY3qlnkqGszI1inhtDFHYYKhQIWi4X2lbKzJrMItN8ZSncvkSfQu7GXLh09JL68YumDc9ACY/BVK+CrViAkIRprTEZmtVig6CTwFAoF5jz7Cn7+p79yQaFkXBRRVHAFf//T+wjWqTttf+TIkTh37hxGjx4t3Q9FuqQWGNpaWihMSYcoUHuptaWJhfj5dPg9hcAwe9pUfvXieZ40fFSnF/68fP0wZ/XLD96guYcPiIbCm0LwYH2n7VutVjQ3N/emdNIHHkoB06ZMtp49sIeNmDqzVxd1zSYjqsvvIiAklLYscTEUqL1ws+CKGBIcwnzUT1/KbdjgQWzXu+8jafioHh07ZsBA4ULeFXHCYP0jb1aTyYS1a9fCy8sLSqUSgYGBmDJlSu9+ANIni9JHK6qqa8TtH/0V+rSRYmLKUKGrNUkrSopx5dRR0djaKnhpPcS4mGjsO7Bb6DdkmFWfNqJnawISh0WB2guXj+cI7/74+50u2RfmrYVSqehyaNjcUI/D2zdyBRjMFgubvuJZXLhT/MTzNmzYgBUrVkCjoYshclMKDK9nzBVq5kzHoVNncXD9Gm40mVny6PFcpVaziPh+Dx57LGsrLp88hoRBKVi9fJmQGh0CpdC+/5hl0Vz85+/eFyLiEuDdvr4pcXI0D7UXvL28ubqTXS4bTRZ8/zfvcd+gkC6nnR3dsZl/9cUX2O9+8E325isvYN+6T2AWOTNZ/3Unzt27dxEdHU1h6mACtGpkTB4n/Pq7/85+/I034NHWyHd8/AEs5n+t65A8ahymZKzgAaFh4saNG8SHF5RWCgzffO1ldiJ7O01PdBEUqD1kNhmhVik7fQO0WTmsAMbNWdhlD3XUjLn4dMMXXOQc+vBgpKWmiDcL8lBS2/jgMWFhYaioqOh78cRmgr20SIyPEwaPGseVqn9tceIXFIyYRD1jgsBiYmKfeF64jw6cc7rI5SJoyN9DV04dF6dNmdTpG0CnFNDa1MSaG+rh6dP5wtP+wSFM4+UrbjhwVFwxLV3x3LyZwoW8fPg9Ni/Vy8sLJpMJanXnV/+JfCJDgtBcW4W969aAc0AQGARw7uvlJaalDhFmjEp7ogPDGIOHSiGCOjcugQK1hwRBAaPR9MTXRc6RW3gb67ds52aryILCIkWVRtPlm+TSsRyxtqpS+EfWFsyaMBZ+GiV+/4NvPnJ+1mKxoLy8nPaSclCcc5wyFPLs/QdEpVIpAOBzZ03H6EEDmEf7PtKdXnSiv6vroEDtoeQx44W969fwWSNTH/n6z/6+hpusIhs1ZxHz9PYButHj4Jyj+PpV4Vc/+DZEfA3ae+dlH3+D7d27F8uWLYNK1bvdMoltcM5R3WLEu//4J+cKFUbPWqhQazxgMZtx5OhB8bP1X7Bvvv4akqJCOz2O1UpDfldBgdpDgiBAITzZ4Zg0ZiTfsHUH89B5dvtYF4/liPNmzYCHsvM5N0qlErRVsOMwixy/++fnYnlllcBFkY+cMRcBIaEPQlGpUmHElJnC0AlT8M6f/4zf//RH0Co7/vtxzmGmc6gugwK1hypLSxAXG/PErYeThiQJPl6e/K8f/pkljxpn7Z86rMu5hVWlxUhdOLvLpIyNjcXt27cxYMCAPlROpLJ+b45V6ePPZk6fB3RyC6pSpUJodKx4raRcGBIb3uFjblbW4t4K4xSqLoC6PT1UVJAnjkwb0uHvLS0hhv3+//0QzeWlQmH+5S5XIFZrPFBZU9dlm35+fqir6/pxxD5CgwJx+2qBsH/Dp7y+uqrTxw6bPF340z/+gWazFUB7j7TZbEVBaSX+tnGH+LPfvoNJi5ZRmLoIWr6vh3Z/9hH/8TfeYAEeTz+fabSK+PbPfoWpy1bj3vnUjh/X1oqsjz/AOz/5IXQdrH1638mTJ9GvXz8EBwf3qXYiHZFz3Kqux9ot28Wy8gohODJaTJs4VVB2cJ67ouQOP5G9nfl4eXGz2D6813p5i/1Thwmh0bF0UcrJdLZ8HwVqD3DOkf3JP/DOD7/d5WpQd+qa8Nbvfo/Fr73Z6eOunDnB9aGBmD9+5FMPuHPnTsydO5feeA7KaBVxpuA6z9q7D61mC2MAZ4zxfkPSkDAohUaBLobWQ5VI/tmT1mkT05nAWKdvEpFzfLZlu5g6YTLQyWmV5sYGgIOV3i0X0ck5NEEQ0NLSAk/P7l/wIvajUQiYMHgAmzB4AETOUd1mYZ9m7WNFX+YjYVCK3OURO6JPz27inOPahXOK6WNGPPV3JnKOoqo6/PgPf+VWpRrxSYM7DdOs//srAlVMnD8lvdO/w/Tp0/HFF1+gtLS0Lz8CsREr5yhtaMGnuw+K3//Ne/zt9/4MLiisUzNWyF0asTMa8nfToS3r+ayJ4/mU1EEdhl95Ywv+53d/gM7bh4+ZNZ91dYfUwc3r+CsrMtiA0KButS+KInJzc1FaWgrGGBQKBWbPnk2nAWRkFjl2Hj0tHjhyVFBrNDwxdThi9UmM/iaujYb8fcQ5R0N1FUtPSXrqL9ICBp23jzht2eoue/3V5WW8taGBJQQHdrsGQRAwfPhwDB8+HABQUlKCM2fOYNSoni0PSKRRVt+EX7z/Fx6ZOBBznnsFlKIEoEDtFsYY4gYOFr/51q+FqRPT+bQxI5n3vbVQ71+cam1tQ2e9/fuuX75gvXn5gvDWd78BZReb8T2NxWLBlStXEBUV1avnk765eLOYv//Bh2z26peYp3fHW4YT90RD/h7gnOPL82fE24Z8xhljAhPAOAcTgLbWNkxbthraLi4cZX/6f/jVd/8dnS3/15nKykps374dixYtQmBg93u4RBq3Kmvxs3ffw+LX3oRCQetCuyMa8kuEMYak4aOEnq7C/zClwLha0cuuKdpDPTExkcJUBtXNbfjZO3/A/JdepzAlHaKr/I+pq6pEbaXt1h5tbW1jVU2tvX5+SEgI6urqcPfuXQmrIl2pbm7Dj37xG8xc+Tw0Hlq5yyEOigL1ISd27xAvHDmIQ1vW43zO/i5vHe2N9IVL8e6HH/PunG99mgULFuDChQswGAwSVkaepsVsxX/+4jeYkrmKtiohnaJAfUjq+EmCxWwSY/rrxcQhQ23yu/ELCkZ9YxNrtfY+UBljmD17Nq5duyZhZeRpjl3MQ8LgVNE3gE6zkM7ROdSH6Ly8MX35szb9kOGcQ6VUcq2ib9NsCgsLodPpun4g6bMvr14TY/QdL4hDyMMoUHuhtqIc5cW3ucVs4lUld1BfWyuMnjlXDIuO7fJNd8uQz4enDuF9nbdYWVlJ06bspKKyCvFjaKhPukafur1waPN61FbcZQmREULykFTBPyAAe9Z+3Onvsrq8DEe2bxJP7t7JZk8c1+ff++jRo3HhwgVYrda+Hop0IW3oEFaYf5mmEJIu0TzUXjAZ22DIPSeWFV1nGg8txs5ZyBQKJRTKp3f4N//9fXzzq19BbEggdE9Zvb2nbt++jdLSUowZM0aS45GOlTe14Zd//hufufIFmsRPaB6q1NQaD6SMGS+kjBnf7ef4+PjypIhgSd+Qx48fx4oVtACHrQXpNBBF2qaEdI2G/HZgtVp7fZvp0xw4cAAjRoygxVHsoM1qhbGlWe4yiBOgHmovld26Kebm7BeUajWCwiPFYZOmPfXDqfj6VR4TEwPOuWQB2NLSgsTEREmORTr35083iKNnzgOoA0K6QC+QXuCc49SeHcJb3/0GfvMfb8La2sRO790lWszmDh8fmZDIbpXexQ9+90eed7sMVrFvp6bNZjOUnZyvJdIKDwvjLU2NNBQgXaJA7YXGuhr0i4sXvdVKCIzhh688x9KHD2Gf/vatDh+vVKkweclyNmZeBvt8+y7xm2//BocuFXCxl3dLVVVVISQkpC8/AumBKePHKK5fPC93GcQJUDenmzjnaGlqhKe3D7Se3mhobHzwPaXAMCEliW0Kj+zwuTcLroh5x3OYWqtjAFj/tJH8wMmz2JG9h7/2/GoWGxwAjx6sPuXl5YVLly7By8sLwcHB8PPzo3OpNrT70GGx/9Dh9AsmXaJA7UJjbQ1O7t7BRauF+fv6iDX1jQK4iGczlzCRc1wtq8K6LdvEyqoqIWnUOCuAJ5Yhult0A6+/+AIbHBuOeqOF7T5yXLxRVSFAocQHn38Bi8mIuNhY8RsrF3crVb29vbF48WJUVlaioKAA9fX1j6zFyjmH1WrFlClT4O3tLd0vww1VN7fh9LnzwqJX3pC7FOIEKFC7cGjLenz7ja+yhEAfMMaEhy4ssffWbhZLKqrY6BlzBa2XFyxms+LxC0+iKKLsVpEQFxECgTH4e6iwcsYkYfn0iSgoLsdv3/8jlnzl33F0y7oe9YD8/Pzg5+eH/v37d/h9URTx+eefY/Xq1X358d0W5xzHrlzjH69dx6ZlruLoZBNFQu6jQO2E1WpFQ00NdB6aByH5cFheu3FDmLX6ZSgUCuxa8w/oPDTcAoZpy1YzAKivqca+9Z/g9Ref4z5q5SNvSIEx9AsPhk7nyZsbG5hGpRLRQe+2t0wmE7RaWmauN+paTfjtB//kXKHE4tfehCBIPOeNuCy6KPUUtRXl+PCtH+O1V1/mIZ6aDh/z4splfMvf3kNbSws0KiV/+1tvMG42PRh+H9j4OX7xo+9jRP/4J96QIuf45Qcf86GTpvGCsyfFOTOnS/q3yMrKwoIFC6Q8pFs4e7VQ/P7bv0LS2ImYsCCDCQK9RUj30avlKfxDQpEyepx47mLeU2+RGdYvlr3x6ss4vTeLi4IC+87nibHRUby2ohwAoPP04h1NkbKIHH/dlCUqPDx5YFikUFF8Wxg5IKFPvSCz2YympiYA7cNVpVIJlUrVl0O6Hc45Ptu8nS185Q0EhIZRr5T0mFsP+W/kXbQWX78qTFyU2eHiT2NmzRfOHtgjbjly2poxcXSHw/HU+Cj8s7EB01c8xzZ9/HcWFxfPA9AeoiOmzcIfPvwn/883XmMeCobS+mZsyd4jGq4XCjEDB7GWpkYc27aBf++N11lXd1IdPXoU9fX1j3yNMfbgZgGFQgGVSoWmpiYIgkC7ofZCs0VEQ30do+1NSG+5daAW5l8S/IPD2PXLF3j/IWkdJtqIqTOF7R/+BaFBgXxcUr8ngldgDEvmz8GRowfFUdPn8KKCPBYY2r54QmBoOAuJ72/9r9+9rxBFEUqlkiePTRfCzJzf/vIKe2HFMj4sMZYJ3ZjyZDKZkJ6eDh8fHyl+dNIBT6WAWTNmWPet/0SYvvxZ6qGSHnPr1aaMba04nrVVHD9vsaDWeDz1caIoYsdHf+WvP/8sS41/cg3SNouI7//695jz7MtPfI9zjtqKchTmXxary0qY1WJhs6dPFWeOHCr05P5+s9mM7OzsB5vDiaIItVqNoUOH0iT/Hmg2W1FwuxjBAYHw0XlAoxCgUTDcX+9b5Bzf/tUfMHvVi/IWShwWrTb1FBoPLaZkrOzyPLIgCJjz7CvsH5/+E+/853fw+KalGgWD8JRh4sa//B6pg5PFqSPThOR+S+CtEiAw1uNz1yqV6omLTG1tbbh48SJ27dqF5557DnQB5emqmlvxj3UbxeKyciEwLFxsbWzgFotZABjjHGAMEADOweEXGMRB1xdIL7h1D7WnLhw5KEYF+vHVMycpHh/6v7duiyj4BvJ+yUMeJGtdVSVu5Z4Uv/+ybbdVaWhowN69e7F06VJbNuOUTFYRH23dJV68nCeMm7eI3z8dQ0hvddZDpU/hbmptbka/lKFCnuGq8O6nX4j1xkcXQvlK5kLBcP7MI93Ui0cPikvmzbb579jHxwfDhg3Dnj17bN2UU7CIHA0mKwx3q/Afb/+GGzWeWPDyV0FhSmzNrYf83bV37Rp4aTW8tr6BzX3+VVZ26yb+590/8t9+/5sP3qBtFhHCY/fj3719S7iQ9yXKK4MRFRqKuCBfm91zHx8fDwDYsGEDli1bZpM2HFlZfRM+XLdRrKlvEO7d1sTVHlo+KWOl4OnjS0FK7IICtQsHN67l6WNGiYsmjFR8+9fvAQDCY+NZwamjvNUiggM4lX8V6zZuwvDJ0x+5l3/26pdQVV6Gm3kGsTxrD9NpNfiPV19kflq1TWqNj49HQUGBTY7tyK7frcQvfv9HzFj5vPDQVs8MdLsosTMK1C5YLGY2e9wIhVnkePhaklKj5f/5zh8ZYwKsZiOfveol5qHTPTLk9/LzhyH3rFhTXiZAEFBVVYXv/M/bSB40SHw+Y6EQ5Pn0mQW9UV1djcrKSkmP6ehuVtTgl+/9GYtefQMqdcd3tBFiLxSoXTAZjeAcaLRY710Pbu/1sPaVUiDAys1WkZmMrfDQ6R48z3DhnNVw7pRiZWYGG5W0DCqB4cqtUqzZuBnnzp4RTp04gc//+Dt0Zw5qd9y4cQNffvklnn/+eUmO5wxuVtTg7Xffw4KXv0phShwCBWonrl3KFZMH6qFVCoJVFNFYV8c457h68bw4ID6WPT9nKhhj7L//9A8OgH36u58jPDqai6KIQQMGsHd//H2oH5pjNSQuEr/69tdR0dyGqrqGPo9HRVFEUVERbt++DYPBgNdff72PR3Qetypr8fa772H+S6+jsznEhNgTBepT5J06JjZW3GU/ev1FBgBeaiVWZi4Vj+zNQuKQNKHm2hWRMcaqWoyorq1FWdFNMWPxIiyZNPb+eYEO85IxhlAvLUK9+r4S1GeffYa0tDQMHz4ckydP7vPxnEVNixE/e+cPmP/iV6DxoBW1iOOgaVMdqCor4Q13S9lPvvoSU9+7ci9yDg6wxrpaFhgWgcLbdwQA2Hv0pDhw+GjcMlzBqLRUu/0+m5ubERERgeTkZLdaRFrkHL/889/59OXPQqPVdf0EQuyIeqgdaKyrRXVlOf7zd++JaampSIiNEbZkZXOVzptPzVwlVN0t5RqNBgDYuQsX2LSVLzBPH1+89c4fMGTwYPGlxXMFTQ+2NOmNq1evQq/X27QNR7TlyGmrd3AY8w0Moiv4xOFQD/UxZpMJ0f0HsklLn2GTlz8vVJrBduQcg9rHj7W2NGHvujW4evo4fvTGa6zFIsIicsY5h9loZGNnL4Do5Ydf/v2fNr/DLCQkBBUVFbZuxqHcqKjG3gMHFCOmzKDXLXFInd56SgghpPvok54QQiRCgUoIIRKhQCWEEIlQoBJCiEQoUAkhRCIUqIQQIpH/D/tFI+8gAoyCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######################################################## se importaron librerias nuevamente por los errores en celdas anteriores\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "########################################################\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    figsize=(10, 10),\n",
    "    subplot_kw={\n",
    "        \"projection\": ccrs.PlateCarree()\n",
    "    },  # PlateCarree(default), Mercator, Orthographic\n",
    ")\n",
    "\n",
    "# loading = (n3.lines_t.p0.abs().mean().sort_index() / (n3.lines.s_nom_opt*n.lines.s_max_pu).sort_index()).fillna(0.)\n",
    "\n",
    "n.plot(\n",
    "    margin=0.1,\n",
    "    ax=None,\n",
    "    geomap=True,  #'10m' leads to a beautiful high resolution map\n",
    "    projection=None,\n",
    "    bus_colors=\"cadetblue\",\n",
    "    bus_alpha=1,\n",
    "    bus_sizes=normalize_node_size(\n",
    "        n.generators.iloc[:, :].groupby([\"bus\", \"carrier\"]).p_nom_opt.sum()\n",
    "    ),\n",
    "    bus_cmap=None,\n",
    "    line_colors=\"rosybrown\",\n",
    "    link_colors=\"darkseagreen\",  # loading\n",
    "    transformer_colors=\"orange\",\n",
    "    line_widths=n.lines.s_nom_opt / 3e2,\n",
    "    link_widths=1.5,\n",
    "    transformer_widths=1.5,\n",
    "    line_cmap=None,\n",
    "    link_cmap=None,  # plt.cm.virdis (colordmap for line loading)\n",
    "    transformer_cmap=None,\n",
    "    flow=None,\n",
    "    branch_components=None,\n",
    "    layouter=None,\n",
    "    title=\"Generator Map\",\n",
    "    boundaries=None,\n",
    "    geometry=False,\n",
    "    jitter=None,\n",
    "    color_geomap=True,\n",
    ")  # None or True\n",
    "\n",
    "ax.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(n.generators.iloc[:, :].groupby([\"bus\"]).p_nom_opt.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='snapshot'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n.loads_t.p_set.loc[\"2013-01-01\":\"2013-01-27\",\"BO 0\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(n.loads_t.p_set.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus\n",
       "AR 0    3.516827e+06\n",
       "AR 1    1.133223e+07\n",
       "AR 2    2.188431e+07\n",
       "AR 3    1.137545e+07\n",
       "AR 4    6.449651e+06\n",
       "AR 5    6.788410e+06\n",
       "AR 6    5.754304e+06\n",
       "BO 0    3.779364e+06\n",
       "PE 0    1.571429e+07\n",
       "PE 1    6.455749e+06\n",
       "Name: load, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = n.loads_t.p_set.sum().rename(\"load\").rename_axis(\"bus\")\n",
    "a.rename(\"load\", inplace=True)\n",
    "a.rename_axis(\"bus\", inplace=True)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus\n",
       "AR 0    3.516827e+06\n",
       "AR 1    1.133223e+07\n",
       "AR 2    2.188431e+07\n",
       "AR 3    1.137545e+07\n",
       "AR 4    6.449651e+06\n",
       "AR 5    6.788410e+06\n",
       "AR 6    5.754304e+06\n",
       "BO 0    3.779364e+06\n",
       "PE 0    1.571429e+07\n",
       "PE 1    6.455749e+06\n",
       "Name: load, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.loads_t.p_set.sum().rename(\"load\").rename_axis(\"bus\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus\n",
       "AR 0    7.0\n",
       "AR 1    6.0\n",
       "AR 2    7.0\n",
       "AR 3    6.0\n",
       "AR 4    8.0\n",
       "AR 5    7.0\n",
       "AR 6    6.0\n",
       "BO 0    3.0\n",
       "PE 0    6.0\n",
       "PE 1    7.0\n",
       "Name: p_max_pu, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.generators.iloc[:, :].groupby([\"bus\"]).p_nom_opt.sum()\n",
    "\n",
    "###### potencia maxima?\n",
    "n.generators.iloc[:, :].groupby([\"bus\"]).p_max_pu.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(10, 10),\n",
    "    subplot_kw={\n",
    "        \"projection\": ccrs.PlateCarree()\n",
    "    },  # PlateCarree(default), Mercator, Orthographic\n",
    ")\n",
    "\n",
    "# loading = (n3.lines_t.p0.abs().mean().sort_index() / (n3.lines.s_nom_opt*n.lines.s_max_pu).sort_index()).fillna(0.)\n",
    "\n",
    "n.plot(\n",
    "    margin=0.1,\n",
    "    ax=None,\n",
    "    geomap=True,  #'10m' leads to a beautiful high resolution map\n",
    "    projection=None,\n",
    "    bus_colors=\"cadetblue\",\n",
    "    bus_alpha=1,\n",
    "    bus_sizes=normalize_node_size(\n",
    "        n.loads_t.p_set.sum().rename(\"load\").rename_axis(\"bus\")\n",
    "    ),  # n.loads_t.p_set.sum().rename(\"load\").rename_axis(\"bus\")*.0000003,\n",
    "    bus_cmap=None,\n",
    "    line_colors=\"rosybrown\",\n",
    "    link_colors=\"darkseagreen\",  # loading\n",
    "    transformer_colors=\"orange\",\n",
    "    line_widths=n.lines.s_nom_opt / 3e2,\n",
    "    link_widths=1.5,\n",
    "    transformer_widths=1.5,\n",
    "    line_cmap=None,\n",
    "    link_cmap=None,  # plt.cm.virdis (colordmap for line loading)\n",
    "    transformer_cmap=None,\n",
    "    flow=None,\n",
    "    branch_components=None,\n",
    "    layouter=None,\n",
    "    title=\"Generator Map\",\n",
    "    boundaries=None,\n",
    "    geometry=False,\n",
    "    jitter=None,\n",
    "    color_geomap=True,\n",
    ")  # None or True\n",
    "\n",
    "ax.axis(\"off\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus   carrier\n",
       "AR 0  CCGT       4.926000e+02\n",
       "      OCGT       1.948495e+03\n",
       "      load       1.000000e+09\n",
       "      oil        2.508391e+02\n",
       "      onwind     4.681251e+03\n",
       "                     ...     \n",
       "AR 5  AC load    6.788410e+06\n",
       "AR 6  AC load    5.754304e+06\n",
       "BO 0  AC load    3.779364e+06\n",
       "PE 0  AC load    1.571429e+07\n",
       "PE 1  AC load    6.455749e+06\n",
       "Length: 73, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# backup\n",
    "gen_serie = n.generators.iloc[:, :].groupby([\"bus\", \"carrier\"]).p_nom_opt.sum()\n",
    "load_serie = n.loads_t.p_set.sum().rename(\"load\").rename_axis(\"bus\")\n",
    "\n",
    "load_serie = pd.Series(\n",
    "    load_serie.values,\n",
    "    index=pd.MultiIndex.from_tuples([(id, \"AC load\") for id in load_serie.index]),\n",
    ")\n",
    "\n",
    "tot_serie = pd.concat([gen_serie, load_serie])  # .groupby(['bus', 'carrier']).sum()\n",
    "\n",
    "pd.concat([gen_serie, load_serie])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of Average Marginal Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "now = n.snapshots[4]  ##one specific time\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
    "fig.set_size_inches(6, 4)\n",
    "\n",
    "n.plot(ax=ax, line_widths=pd.Series(0.5, n.lines.index))\n",
    "plt.hexbin(\n",
    "    n.buses.x,\n",
    "    n.buses.y,\n",
    "    gridsize=20,\n",
    "    # C=n.buses_t.marginal_price.loc[now], #at one time (previous set up by scigrid)\n",
    "    C=n.buses_t.marginal_price.sum()\n",
    "    / len(n.snapshots[:]),  # Marginal price averaged over the whole year (My version)\n",
    "    # - shows in which regions electricity will be cheap and where el. intensive companies should be placed.\n",
    "    cmap=plt.cm.jet,\n",
    ")\n",
    "\n",
    "# for some reason the colorbar only works with graphs plt.plot\n",
    "# and must be attached plt.colorbar\n",
    "\n",
    "cb = plt.colorbar()\n",
    "cb.set_label(\"Locational Marginal Price (EUR/MWh)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='snapshot'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y+C6u1FipNsjF6NwysH/hcbH69MZrZGDPsFSq460zYPq+LzT2K8jYE0aQsZebGXvUsldiKClGoVgXfaddpmneBNwLvNeyrOORn2eB3wVeZVnWl0zT/DHgD4Fv7ve5JyYK/e/xDubk5TKLK3XmKzY3HJvquE0ylSCZ0JmZHmZmooDr+QyN5FhuNCWJU5dKNGyPG45OMDU11PF5cgXR2nhhvsLV+4e59qoJAK46OIrrnUZLJZgay635b6jUbFzPZ3qi0PW1e5FOGSQSBvJPKtccUsFFamoiHz5vOmWQSBrrfh2F4p++9Czlms3rX37tVu/KmrmS877f4uldwMeBd1mW9bG2h28GqpZlfSn4/reBn1vLTszPl9adQe4kFhaFHe8zzy5y/f7Ob9pSsUbS0JmdXUEL9OWTzy5wblb8biqh89SziwAMZwxmZ1c6Po/v+yQMDcf1OXZgONwumxArOp88MYd21dia/4a5YqCyuW7X1+6F7/lUqjYXIjNdn3xmDoBGzWk+rw+VSmPdr6NQ/NmnLIrlOi80J8NFbzuBqamh2PNe17XYhLif4ulh4B7gLR2COsDTwGHTNM3g+9cD9/d63r2IHMwcBscONBw39IAZCuSUlYodFk6P7BtCDlU6MJHv+jyapoVyzA2RAD49mgVgdmltZRDf9/noJ5/goeMiABeupHhqiKJo1H3y8qLYn1VSzB644Cs2h1LV5uJChWrd5dSFvZUc9JOxvwfIAB9qxm7uBr4F0QnzgGmabwP+3DRNDbgMfM8m7OuOR3Z4zMVo7LbjkQ7scIfClsUGiyt1NA2umh7i6bNFJkcyZNPxb99QLsXich0zYsI1PpzG0LUwkPZLpe7wbw9fYDJwnbzirhjXp1huMD2W5fJitXNgNzpbDygU/XDiXDH8+qunFrj24MgW7s1g6RnYLct6J/DODg/dHdnmk8AnN3C/diUyY48rntbtSMaea83Yh/MpxkeEOdjBye7ZumTfeI5kQm9ZTGToOhMjGS7HZOye7/Pw03PcdmwydIFcCewJ5EUpf4UrTxuOS7HU4Jaj40FgF3bC6bauGFU8VayXp88VMXSN6bEsj59a5HV3XbPVuzQwdo7otAuQssJssdp1SHW0jXEoyIpXqg2WSg1GC+nQ9fHgVO+C89u+8Qbe9cZbV/18ejTLbEzG/pWnZvlfH380XBEKtBiKwZVl7AlDp1JzqNsuh4NhHGeCGkIucheSCBYyKRTr4cS5IkdmCtx2bJIT54pd5wfvRlRgHyByHFzD9lipdvZZFwM0RNaaCYy2Vio2iyt1xgrp0Kf94FTvjD2dEkZi7UyNZbm8VOXeL5zid+/9Kg88ebnl8afOiFvY+chCpvaReleascvnnhjJkM8kWC43ODSVD/3k5Xa9NPbNnO2q2Ll4vs8zF5Y5emCEfeM5YWFR2fxB79sFFdgHSFRWmFvqLMc0IlKMpmkM5ZKslBvMFqtMjGS47vAIb/raY9xxfed2yX6YHs1SrTt8/F+f4b7HL3H3Xz/essLz6XNLAC2DOlYiH4pUUo8di9cLw9BCs7ORQjos8r7s1gMtA0B6LVA6O1vih3/131qGfysUIEYqNmyP0UIqMtxl79z9qcA+QKInVrfOGDuSsYMooJ6fL1NvuEyPZTF0nW984ZGeQy7ikJ0xmZTB6196DZ7vs1wWgbtuuzx7SbQhRj1lloOMPZ00rqgjBprTkQxdY/94jpG8mNX64pv2rdouTmO/OF/B8/01d/godj/yTs/Q9bDN0dlD9Ro1QWmAtGTsXQqoDcdtmSU6nEvxxGnRty4D8pWyb0IsTHrV8w5zICjCLpcbjA2lOXl+OfxQLC63ZuzZdIIj04Vwleh6uWrfEOfnKnz3N5qMD2d45R2HuNNsrLpg9LLtLQVyVq3ReRiJYu8i18XoWnQalwrsik3A8Xw0hCVvN72vYbd6wAzlkuEJOT22MYF9/0Se93zHbVx/eJTTF0V/71KpzlUMcTxoETt6YLhNimkwnEvy1m8wQwfK9fKaF17Fa154Vfj9826Y7rhdL3dHWaeo1ZXOrmhFfmZ0XYtk7HtHilGBfYC4rodh6IF23PkkE66NUSlG6M8aMDmyMYEd4MarhXfMSCFwgQy6Xh59Zp4j0wUOTRV46PhsuP1yucFQPhVm+IOgl7ujvDhWVcauaMMLpRhtT2bse0pjf9Ca3dLbdtfzMQwtttvDbpNi5CKlseF0rE3vehnJNwP7crnBibNFbrtukvGhNMsVO8zOV6p22H45KMQCpe4fRinFVFXGrmhDnjYiY5cD1PdOxr5nAnux3OA3/+pRvvDYxS3bB8f1SAQZRKeTzPN8HNdvK56KwLtR+no7ycAeuFiq8/DTc/jAHddPhW2H0spgpdwIu1cGRa8FSqHGXlcZu6IVeaen6xpGMJt3L1lA75nALu1wr3R60JUgMnadhKF3rNDLsXgtGnuQJW+Uvt6J4XyKYrnBV47PMTGc4fB0gbFhEdgXV+p4vi8y9tyAA3svKaYq5KMrLeYqdh9RKSZsd1RdMbsP+UbLYRVbgesKx8VuUkw4ZCO5OmOf2qSMHWC0IIZvnJ8r85Jb9qFpGmNDwhNmcaVOuWrj+01ZaFAk9PiMfUVp7IouhMVTTSMRFE9VH/suRN6GLZW3LmN3PC8o5ugdK/ThkI1Ixj49niWXTnDdodFN26+RfIrTF1eo2y7XB68jV7gurtRZDgLo8KAzdiN+5Wm5pqSYvcjlpSo//Kv/yonzxa7beC1dMSpj37XIN3p5izN2sWCicxtfc5B1ax/7b/zoyzd1v0YKqXA607HAAS+bNkinDBZWapSCxUnDA87Y5Z2N7/stK1JB1Ctk0bSqpJg9xYmzRap1ly8+folrD3R2bHQ7SDF7ySl0z2Ts8jZsqbx1gd1xPSHFdGl3lMvsU1ewqnQ9jORFdj42lGY80NY1TWN8KM3iSj1shRzaguIpiA/pwnKtxdOmFPHaqaqMfU8hLSQeenquq5meH+2KkedRj4y91MW/aSeydwJ78KaulBtbprW5nsjYRVdMf8XTQSB72a89ONKSGY8FgV0Ovx4PdPdBkYjcQv/zg2f5rb9+LLzzkj3smZShiqd7jPNBYJ8r1rr6BEUz9vA8ipH1LsyXeef//GyLh/tOZu8E9uBN9SH0RRn4PsiMvYsdrSyebka/ehyyl/26tkEEMrDPF2tk04mOTpGbSaiNeh7zyzV8v3nxk9nV5EhWWQrsMc7NlTh2SJyrDz8913GbFo09zNi7J3TPXirh+/FDcHYSey6wAxS3qIAqMvageNohe7C3SIq5Zv8wzzOnuNNsdYwcG8qwVKoLZ8nhwWbrQEv/sWxTrQfHSAb2qdEMtbob1gi64Xrenpiru9up2y5zSzVuunqc0UKKSwudDeDCPnatWTyNMwGTg2e2e5Lg+T6feuBMmAR2Yw8F9uaBWNqiAqoT9LF3K55ulRSTTSf4oTfcwnhb8B4fSuP78Mz5ZSaG011+e/OIdjNIQzLZObQSBvYsPr192T/yd0/w4b/76ubtrGIgXJyv4CMmiCViTOLWaikgJ3htd3//0xdX+NN/Os5jJ+djt9tzXTFAyxDlQeK6Xjg8I654mhxwxt4Nufq0VLWZGNmKjF18IG3XC1fAysAuO3XkDNZaw42dAXt+royua10fV+wMzs0JS+kDk/nA/bNzsHb9tZmAyYli271eI+9Uiz0WWu6djN2NSjFbk7G7rk9C14UHSoeMXY7uuhKv9Y0kOs1oKwK7XFiyuFwLP8BSilmp2qRTRriAq1dnTLlmq373XcClhSqaJlZix7l/yrxJj648jcnYLy3tjMAu124s9gjseyZjb9HYt1SKERlEp0KOHD9XyG6Pt6UlsG+Jxi4+kNGClszYF1fqjA+lyabFRbDX6tNy1WkZlK3YmTQcl2RC2HIYutY1C49KMbom/nWTbeoNN4wJtW0+F7VSE+f5Yo8Ytj0iyACItj8tbaEUIzW/TtnDSsUmn0mEt45bTSGbDFfJbklgD6ST6IQkeVezsFxjfDhDJiVO4bhMy3Y86rbbtedZsXOQi/yAeCkmYikgtu18lwyt59d2L56Wg8DeK4ZtjwgyAOTVerSQ6jpIerNxXF9kGkb3jH3QDopxyEVKsDVSjPwARz94shtgfrnOxHA61NXjZBZ5+9pwvD3lF7IbcYLOMoi3nPAiGjvQde0INDtidE3b9kNbytX+pJg9FNjFmzqcT4cHZ/D74IULJjplD8uVwXue92JsKE3C0LbkgiO7GWaXWqUY23FZLjeCjD2QYmI+kNH3u5eG6vs+1rOLKrvfpsi1IEDwOep8oZYX8OZ83e7DbS4HhdP9E7nwjnC7IqWYRo/93HOBfSSfCl0Bt2IfQtveDifZSqUx8GX7vTg4lefgVCG8pR0koRRTrIYBvG67YU/7xHAmzNjjNHZ5+wq9i6xPnyvy3//PV3h6l6xA3G3I1dsQL8X4keIpxA9GX640SBgaY8PpHSDF9Be79o7GHrypI4UU5ZqN5/sDD1aO65MIqvSdTrKVyuA9z3vx5lce27Lp7nKBUrHU4Op9Q5y6uELD9lgIiqnRjD1Wiolm7D1uteeXxXNv1epkRTxyChkQWzwNa2paRIrpkrHbtkc6aZBJGsxv85WnlZrTV9dcX4HdNM33A28Kvr3Xsqwfb3vcBH4bGAMuAt9hWdbimvZ4k/EiGbvviwNUGLDs4Xpey2i8qGuh5/mUq/bAHRR7kUwYJLfo8m9E+s6vOTDMqcBaeH5ZZuxpEoZOMqHHSiylSJbTq3tGBvTtnrntVZygAQEIFij1p7EbMd7+9aDTJpNK7IB2R4dD0/meqkNPKcY0zVcBrwZuB24D7jRN8w2RxzXgb4BfsizrVuArwE+uf9c3h1CKKTQX3Qx8HyLF0+g+yf3xYdtl7FtJwmiens89OkHC0GnYLgtBVi2HgWRTRqx1b7kalWLiP7jLZTWVaTsjP0MQL69Eu+Agfn6u7XikksaOMJSr1G1mxnI9/aT6ycUuAO+2LKsBYJrmE8CRyON3AGXLsv4++P4XgNE17/EmI2/DpOFVqWLD+OBe3/f9iFdMc6m8HG+6HPSwD3pK0XYmmrHfcGSMdFKnYXuUqjYj+VR4cmdSib66YqB3Ji7fB2UFPHju+ewzVOsu//FV13Xdxm3riunVx661ZOydt23YLqmETjplUGs4Hf3/twvlmkM+k+T26yZjt+sZ2C3Lelx+bZrmdQhJ5q7IJseAi6Zp/i4iq38CeMd6dnozaWbsQWAfcMYeZhCGHq6odDyPpKfz/t//UjghSWXsTaSWCpBOGaSSBnXbZXGl1uJrk0kbsYE4WjztlZHJjF2N2xs8Dx2f68vMzQi7Ynr3sffTGtmIZOy+38zgtxuO61FvuOSzCb71ZUdjt+1bPTVN8ybgXuC9lmUdb3uOVwAvtyzrAdM0fw74EPC2fp97YqLQ76brJp1JomlwzWGRpmsJnampoU1/XYnMKEeGMmSCTo7R0TwJQ+PcbDlsubrq4OhA92s74wYXwBffsp+pqSFymQSaoVOs2BzZNxQep+FCGsen63FzPJ/xYTHX1Ugascc3lHT0wZ4fCphfqZPPJmOPu27oZIL3Jp9P4dP5fc8FCdL01BC5TJJsOoFudH5PfSCfTTE1ngcgP5RldGjwpnfttO+r9LiamSz0jJn9Fk/vAj4OvMuyrI+1PXwROG5Z1gPB938K/GU/zyuZny9tuqVqqVRH1zTqFXFwzl9eYXZ2ZVNfM0pFzuesNkIji0uXl8PH5Vg8p24PdL+2Mwbwzjc+lxuvHmN2dgVD11heqTFfrGEeHg2PU0LTWFipdT1u80vVcGD37Hw59vguFMUFdrFYVe/DAKnUbMpVGw1ij3u1ZpNOGszOrmA3HGzb7bj98oqowywulCknDTzPp1rt/NkqB9KeHdylnbuwhF3Ldd2HB61ZKjWbl916YI1/Zf9MTQ2t2teLC8KB0nNc5udLscG9n+LpYeAe4C0dgjrAvwNTpmneGnz/OuDBfnZ+kLiuaJMS7orawKUY2TIobXtB3C5GF0RoMPBOne3OrccmSQaFiFTSoFJ3qNadllpEJh1f9CrXbArZZKChdt/O9/1wcPd2X4G425B+QL2ksqilQLe5BhCxFIj2sXeTYgKNPWyd7bEPn/7yWf7h/jOx22wGsm0338fAm34y9vcAGeBDoqsRgLuBbwHeF8gvbwA+bJpmHjgLvHXtu725OMGqT03TKGST4Wi1QdHU2CPDdV0vtOoFyGeTylo2hnRCD7OWaC0ik0rEL1CqOhycLIjumRgtvtZwwzsnVTwdLDKwO64XzAbunHO2FE9j3R3bLQX0roVz2/FIJoy+fIdA1Oe24vyQtaJcpnfy10/x9J3AOzs8dHdkmy8CL+h7D7cAL7JirZBNbUHGLgJGQm8WT13XbznZVEdMPKmkEa46jfb7i4DdO2PPphOxbZGyIwZU8XTQRB0867YbH9iNSEE0ZoGSRsQErIuNB4iMPZ3sP2MvVe1waf8gkXJuPxn7nrIUkFf6QjaxhV0xrf7Q0l/8Bc+Z5sU37RvoPu000kkjnD7fkrGnEziuF2bb//KVc1wKMnvH9agFnQRiAUr3D6TsiClkk0qKGTBzxYjDYsyxj2bzCV3Hh471Oc/zW+5+uzmqguiKSSaM0Na5l19MqWpTt92BG8qtJWPfU4FdvtGF3OAzdplZJAKvGJBSjDiJXvviq3ntS64e6D7tNKItaENtGTuIHvVLCxX+6B8sPvWA0EClLikydiM2aMjAPj2W7StjPztb4szl0tr/EMUqokv54zzRhcbezNiBjgHW8/2WdRBGYD/diYbtkYpm7DEyS92OynWDvfirjL0D0RNiKJscuBFYWDzVo8N1vTA7SKkhED1JJZunazRjD617Gy5fOS6m1suAK6dljeRTPbV4WTidGcv29aH9P596ij/51FNr/CsUnZhdqoUJT9xdlXRIhaatcyeJpT1j76bHO66H5/tB8bS3xh6tzVX6NOTaKKoNUeTtJlNF2TuB3fPCpfz5bDI0Ahvc60eH6zYtBeRJtF3G4W1n5DHSNY1cJGtpWvc6fPn4LCACu+f7kcCeJpsyYrMxmbFPjWbDIl4cyxV7Uyygz8+V+cvPnNgz1sG+7zO/XOXAhGgxjAus0iEViB15F5VeobsJmGxekAuUIP6OIXqnP+iMvVZ3wn3sxZ4J7F7kjZZGYIPsjHEiUkwzY/dDKSajAntPpBQzlEu2OHPKBV+XF6ucOFtkciRDreEyV6yFI8+GCyky6UTsh7FYqlPIJsOW015FtHLVprLB3RGe5/MzH/kin7jvdHgHsduxHY9q3WVmXAT2esxxlw6pQPh/pwKq57XaAhi63jGzt53gjjnIhBOGFnvHEDWU24qMPRMzsD3Kngns0Sv4aGArMMgReZ0z9qYUk0zumbdi3aQDb5j27qFscAv98NNz+MBrXiisjM5cWqFYFu/xSC5FNm1QDbxAOjFXrDE5EvF4jwnavu9Tqtob7gL5bw+fD7/u1vGx25BTseQwl55STNgV012Kac/Yu/Wx151mxg70dHiM3qFVtiBjl+d6L/ZMNIkWT0cDh8eBBvZoxh4xAavbLqmkviWDLHYazYy91U9HDrQ+Myt09TvMaTQNnr1UolhukEmJjodsKoHvNwNJOzKwS601LrCLrgifWt3dUEnvs49cCL/u1sWx25DFSHnBjpViojNP9Zji6aquGL3jdrascYWBvbXAXqra4f4BLbW5Sn0LMvadLsVcWqhw4nxxwxYCuF7zFq4Z2OMnfW8kTrTdMVo8bbhKX++TdESKiSID8YX5Crl0gpF8iv0Tec5cLrFcboSOnnFdD57vB4E9G14oYotoQebmEy8dgLgI/MW/PN1zO9/3ubhQDrse9kxgd2VgF+9Tt+MUdUiFeI19dVdM5+KpvMi3OIUGdwy+7/PffveL/P2Xng23j2bs1QH3stcaTng32YttGdhXKg1+6sP38fN/+CC//4knNuQ53YhBv3R4XOoxEHYjcd3OxdO6rQJ7v8iumPaMXQZs2/FC18cj0wWevbxCsRQJ7OEYvdWBY7ncwHE9JvrM2Fs93uM/4MfPLPHJLz6LdWYpdrtiuUG17nJwSniA9Cre7hZk1pzPJNC0zu8PNAN4IuLuCGvoigmG20SRNS4p82Uj9hRLpQbFUqMlTpSqdtjvvtH1lV7U6js8Yy9VbXxfeKecmS1vyHNG3+iEoTOUSw5YYw+G67YUTz3qtheeKIp4Ul0y9nTKQH6EJ0dEYD88U2Bhuc6F+TLDwR1ati1gO64XdsLIlY9To5kwY49rjWyZytTjAy5v33ttd2FeLKo6HAT2bsvlW3+nvCWrIDcSmbGnEnLYRee/J2p9Lf7vLsWs0tg7DLeBSMYenFvZdCJ8n+Qit+jg6FIw5SybNgYe2KsNZ2cXT2VB8cBknrml6oas8Iq2SYGQY3pJMRtZ9ZYf0oTK2NeNPE7DbRm7rmlkgmA8EWTsh6dFcFyu2GHGHkoswQfy0w+e5ac/fB+u54UrHydGss1+5jibgjW0vfU7vOPivEhiDk4L+9hOA88lvu/zN587yc985Iv87b+fjH3e7Y5sOUwmdNJJo6sUE73rFf/rLT+P4nmtM42bHTSdM/ZUKMU0p3FJX6J6RGMvVW0K2VTLBWBQ1BpuuBivF9szsAcH9shMAdfzN2TArNN2BR8tpFkMMvbPP3qBX/mzh1q2P3GuyDt+/bNcWqxc8WtD87baMJoTlBzXVxr7GpBtiNEhGxIZjCdkxj7d9LJuauytUsy5uTLlmsNKxWZuSZxjk8P9Zewtgb3fqUw9trswXyGdMsK7jriM/eSFFe753El8f/D91BuNzNh7zR0N73rDO+/mne/qbdukmEgnWpRGW1dMNt2cxnWxS8ZeyCbJpRMDvVOSlhk7WoqRGbv8cF5arMZt3hfeqsCeCqWY+x6/yOMnF1pOkGcvl/B9WFzeGLmm2e6oRzINT2Xsa+DAZJ6f/u47ufno6pmG8oSXgX0knwprKU2NvWk9AISGYsVSg7lijaGcsPZNJ4W0ExcwS7X+NfaVcr9STJn94zmSRvP86Mb8cjPZ2eltkbYTDexGV6+WqPU1RLtiOhdP2zX26HNI2jP2bGR1cjcpppBNkBtwxi4vdjtcihFv9JEZcTstD/CV0K65jRTSYcHs6fNi4EVUM5N3Cb0MgfqluUApYtsrpRilsffNtQdGOraGym6B8eHm5JsjQWIgA3xTYxfv6aIM7OU688UqkyNZADRNI5PuMUe1uhaNPcjYa/Hn0oWFCvsncs0Lf0xXjKwNZFLGju+ekYFdeqJ3O+4y206EXTHdj1N7Ipfo0kHTnrFn0gYN28P1PC4GCWWjixQzSI1dHpMd3ccupZip0SzplLEhGbvrei1X8LGCWH361VML4etFP6wyI9qowN6asTdXzNUDy1DFlSG1x8mITCMTg5F8UDxtz9iD93ip1GC2WAuzfbltr4Uqsi2xt8Yuzqu4QGA7LgvLdWbGcy0X/m4Uy3U0TSQocdvtBOTqz95STLNlGNYoxYQdNG1STIeMHUTX09xStWUb23GpNVwKuSS5zGClGCkf7gopJp0ymBnNrkvnlsUlqZO5ET92aPay3//E5fBn0aHHmxXYE4YY9iHbr+oNd1sOzt1pZNIJ0e2UbxZWn3vtBPvGc0yPiUxc2jlU6y7VuhMGkMWVOvPFGtOj2fB3s72Gd9QcxoczaPRue5MZe9yKSrmKcSibbLnwd2O53GAolyJp6Dteimn2kgddMV0+c83iqd7yf7fiqRG1FOiSsdvtfeyRxW5uUICVxV3ZbDFaGHzxVJ47O66PfalUD2+1wt7SpMH0eI7LC2vP2Kt1h3s+d5IHLRG4owb9QDis9sGnZsOfRQ1+pBQTnXB0JUSLpyCCjO2IjL3fq7CiO0emC5iHW2Wa6w6N8gvf/6Lww6BpWjBswwn1dYCTF5ZxPT+8AEAwbi/mgyuLaL0kG+iv3bEefHBTSaOla6oby2Wb4VxKLLzZ8Rl7pCsmZnxh+BmKTFCCLiZg/uqVpyAulg3bDd0/G7ZHKqGHvjIyY3/2kpg3OjOepe60SndjQ2mRsde721NsNPKucEdl7OWqzU/e/QU+/+hFQBQKNMTt0cxYlrlibc3DrmUWIE+ads3tqpkhXvrc/diOx/WHR8P9AHECycJqo4+M/QuPX+RBazZ2G5lVRCe61G0X31fOjhvB6+66hnd/x+09t5NLxhcjxcfjZ4sAHTL2+KlM+WySXDp+3F694YZ3fXGZfS1yq23ESAySYrnBSD4pXAt3eMbutBVPe/Wxh4M2Yo5TpwVKYluff37wLB/86P1Uag4Np/WOWSYBF4M1BdOj2UjGHgT2QppcOonv9zaK2yjkMdlRxdPjZ4s0nGYvsfBPMdACe1bP92k4azuAMqDLVqr24qmua3zvNz2HX33HS/mBb7kJaEoxiyv1cFJPP1LMX3/uJP9w/7Ox2zieF8owIE5K+XpKihkccsm4zNinR7NhYG7J2HvMRy1VbQqZhHCMjPlwr0TG7cX1xcvgkU4ZESkmvng6nE91dS3cSTRaAnuChu11TOTaNfa4InN7IhfN7p85L+7QLi9VaNheKMNAU4qRDRuTo9mwBiAz9tEgY4fNnY3rel74mvICsqP62J86uwQ0A2sjUlBMBRPquxk3daM9YxcG/av/3EI2yUg+hUYzY59vm78Yh+t5zBdrPdsio+ZFICr6cgGUytgHRzbIsBdX6mjAkX2icyZh6KE8ByIz6paN+b5PueqQl3NUYz7cK8E5NTaUjs/YbfFYOiLFdCuK+r7PckUG9t0hxSQMDV3Tws9Cp8+d2y7FGN1rEasWKEWmlp0OZJbLi9XVGXsgxVxcqJBNGwxlkziuHwbZVEInl06QD8bTbebAni88don/53e+QL3hhudYZid1xchbYWmqIzN2aBY1+pFEosirrON0ztij6Lq4MygHgTbaI1zvobHPL9dxPZ+lUj3W5c91/fDWEcTJKS9kKrAPDjnQemG5xnA+xUTQHjk9lm0JBNlU94BdDRwd85lk7HbQbEsUU5m6a7KyMyudNHoWT2sNMZ5tOJ/q6lq4k2g47qriZaeLatT6Goi9ALrtJmDB16WqHdpHzC5VQ41dIqUYsWI5Hcahhi3k2dGhNJqmhbYWK9XNMxJcWKnRsD3KNTsS2HdQxl4MtKtyGNibK6zkQbfXmrHbbRm721o8bSefSYavLwP7SD7V05Hv8mKz6ybu6h0d6QXipAwzdlU8HRiyT3pxpc7YUDpshYzq69Bsd5QX62rdCeUBuZJ0OJ8M7wC6IbedHsvhen5X3VxmqJlU7+KpnAo1nEt1Hfm2k3CCYdKw2s+nZTu3VWOPk6w62faCKJRLLi9WsR23ZeRiNHCO5FPhYw1HZOxjQTedDOybOaxHnhPVhmizTKeMlr8pjm0R2LOpBPsncmGgi67GjF4x10JUY5d2n3Ge5/lsIpRizs+VGS2kKGSTPe8UZiM99osr3a0PnDavGsPQwj5YlbEPDpmxzy/XGB/OhENXptoCu7zlrTdcag2Hn7j7C/zj/WJAtszCh/MpsQKxjxmZM4F+3204Q6eMvdtFYDkyx9UwtB3fx95wvHC1bdyQk6aRXmsfezcTsFZLAfG1DOxToxlml6rUHS+Ue6HVUG6kkGpKwbYbJgPQdBjdTClGnhO1ukOt0f9YPNgmgf193/M8Dk4Vmhl7pLdbZuzrLp46XlgIjc3YgzmotuPyyIl5bjk6QSpphK1O3bjUEti76+xR22AQlqNKihk8sig6u1RlZjwb2g1EC6fQXMxUrTs8cmKeUtXm6XNCMlyOZMyZHhp7sdwgldDDdRPdtq1F1m70ytijFxaRse9sKcZ2mgXMOJ+etfax69pqKebkhWWG8ymOHRzl8lIVu02KiRrKCSlGPFa3XZZKjbAOkwsshjdTipEZey3I2PtddQrbJLAbuk4+01yiG83YQ419zcXTYLWY660yD+pEIZOkXHV47JkFag2X5z9nmnRSp9FTiqmG5lSxgb1Dxi5RfeyDI5tKYDsejuszM5bj0HSBQ1N5brhqrHW7dHNi/QNPirUQZ4MJTU0pRixUEc/X+fycXaoyOZrtOW5PZmeppLjd1ujsMw5EBnRLjX3jMvatGKBtO83g2jxOvTX28Dj1NWhDD5/36n1DTI1mWFyuc2GhzFibqZy8WxsppMIEc3GljuN6oRSjaxqFbHJzpZjgnKjWHapr8GKHbRLYgWCJro3vt/qnyANrr1OKcRyvZTl/N4TGbnP/k5fJZxLccGRMZOw9Xnd2qcqxgyMYuhYb2J224qn0u0gl9VXZomLziPYB7xvPMZRL8cH//EIOTuZbtws+3MVSnUdOzJMwNGYXq9QbLsvlBhpCZ5XtZ90C9uxSlenRbMsdQCfaRySKhUfdpRgNKOTEKtWNGshxebHCD/yPz3A2WLwzKOxI8TTXdgE8ca7IR/7uq3i+3+K3JDG6rLxtl2ISka9vOzbJ9FgWHyHxft0dB1t+V15cRvKpcACHbH+Mdk4N5VKbKsXUQo3dobqG6UnQZ2A3TfP9pmk+Hvz7/2K2+2bTNNdlDp1LJ3Bcn4bT6p+yXikm2u7YfqXvRD4rvB8eenqOO66fImEE3tAxGrvn+1wObulHC2kWYloe26UYmUFce2AkvPVWbD7RPuCZ8Vz37YJA/NDT8zQcj5ffegAfOD9fZrkiFicZuh6bifvB+TE91l/GnolIciJgdc6eKzXxITd0XWy3QRn7hfkKjuuHNhyDIirFtE+vetCa5d8fu0ipandM0LqtvO3Wxw5w+3WTTI+K9/62Y5PhxCqJPEeiGbuUXGXGDsL+IbpOYaORakGt4VKrb7DGbprmq4BXA7cDtwF3mqb5hg7bzQD/A+gePWOQfaGVmhP0sYs3OLne4mlo3OM1tbkeXTE+hDIM0DOwr1TEoNvJkSxjQ+nYiUyO57cEcLlw6rpDI33/TYorRwbYbNpguG0SU8t2bUvLX3TjPoBwjupwOLyjVToo12yeCkbgFcsNGrbHVIsU06V42ubymYjpdqk1nFAHjtturUhLjUEPkGhEumIybXc2s8GixXI0sLfd+fbTFROVQUcKaQ7PFLjj+in+w8uPrvrdTJixN9sdZwNDMOkUCuKOaaW6+Rl7re5Qqopkol/6SRUvAO+2LKthWZYNPAEc6bDdR4Cf7fuV25AruSo1m3rDI50Su5YO2x3XWDx1m10x8oSIaxXKZ8XrSxkGRGCP64qR+5RK6IwNpVv8R9ppz9hPXRAB47rAzkAxGGTWMz2WC1cBd9wuCDDn5sokDJ2jB4ZJJXXOzgaBPbgotGfin7r/DL/8p1/BdlwuB1neTB8Ze61t4IoR059ea7hhZruRXjFbFdhtt5mx65oWFLjFZ0sG1FLVXrVACcRx6jRpqr0LTr7vL7/1ACA+2z/yH27h0HRh1e+2Zuxiv2Tv+3DEZG6zpRgpA1cbLqWqE9by+qGnaGNZ1uPya9M0rwPeBNwV3cY0zf8KfBm4r+9XbkNm7MVyA8/3I8XTda48taNSTO/iqXx9KcMApFJ6bMYezSDGhtI8fGIO3/c7Bgy3LWOXeuHR/cNr+bMUV4jMxvbFyDDQlARKVZuZsSy6rnFwMs+52TLLlQZXBytW27s4Li9VcT2fYrkRBvapsf409mjGLrTz7hm7/HwIS4GN0dhlYB/0LM9o8RRa547KyValqr2qjx3o2sffLsVk0wn+xw+9pEUj70YmnQiLo/K555aqZIIhLJKhbJJy1V51d7BRSGO4YknMjdjQwC4xTfMm4F7gvZZlHY/8/Gbg24CvAw71/coRJiYKHAimzDiBkjM+lmNqaqjp/JZKMDU11PU52kkEH0zX8xkJ9LTRkVzX53gOGrqu8c0vuzbcZnw0h+P6jI/nW27lJLXg8zQ6kqPhiYvJyFi+Y/uipmtkM8nwub/rG2/goeOzHDk0tmpbxeZRCT6oRw+Nxp5PY5FguW8yz9TUEDdcPcFnvnwW8JmZLIjzU3r/pMR7K73XjVSSUsNF1zVuuDao2aQMMPSOr+v6MJRPh4+lUwaJlNF12+FCiqmpIYaG0riez+RkIfYOpB+cID5qRufXvRJ+4y8e4uajE7zizsOrHnM9n6FC828v5FJ4GmTzTRsGPWGQCQLbzMxwyzqXTsfJ86EQeU6g77/pxc89QCqVYGZ6mHxBaOgNx2N/cB5I9k0X8IFMPs1IofcFY63IZHY5uODumxrq+2/oK7CbpnkX8HHgXZZlfazt4W8H9gMPACnggGman7Us62V97QEwP1+iURMH8PR50Sts1x1mZ1fwfXFLtVisMju70u9TUlwODMUaLnNzospfKde7PkcK+M0ffTnppBFuYwcn1bkLxY4V6ejz1oKDf+nScsdta3WHbOS5X3nbAV5524E1/U2KK0dzXMaG0hyeyPU89qmETsPxGMokmZ1d4chUPswkkxrMzq5QDVoPL8+VmJ1d4cKcGEh96uwip84tMTGcZnFB/CyTMlhYqnR83XKlQS7VPD/woVxudNx2pdIgm8oyO7tCI1jUd+nycmzXVz/MBUXT+S77uF4atss/3neauYUKNx0Zxfd9fvaj9/PKOw7x8lsPUG+4uI4bvmYqobG0XOPJE3Phc1y4XAobKBYXSuHfqtH5OLmeR61mr+vveM6hEZ5zaITZ2ZWWFe/5TKLl+bRACTh1ZpFS1eYT953mHd92yxW/DwCTk4XQNO580GbrR46RrmtMTKyWkST9FE8PA/cAb+kQ1LEs6/2WZV1vWdZtwDcB59cS1CVSClkKdGp5RdY0jWRS72kp0H6L24isPG2fbt6N9kxb3hp3k2Oi3TbdjPzDbV1fdb9sA7LpBL/yw3et6lvvhJRt5Lg988ho+Njq4qnTYvcspZjpsabkk0snwpWnvu/z83/0APd9tWlVHe16iNPOa/Woxi7Nra5cZ1/ZJI39wnwFH5iLTKx69lKJZ4KRlFGvGGj69Eh9XexbY5X1NXQ+Tp7v4/u9P+/9IBxZxdcjuVTLY83Vpw2ePL0YLGTbmGNnO15oZyFjYiG7se2O7wEywIdM03wo+PeDpml+wjTN5619lzsj+1fluLKo3phK6LFFzMdPLfCOX/ts6NsCTfMvp892x06kI6vOOuFEljjHmf6LbeO9ahTbD1lEkyPzRgvp0BpgOPhQJxM6CUOj2nBa7J6Xy42wh12SSTWHcqxUbE6cW+bpwABPaOzND24iRjuPdsX0Y/HbL9JSo9fgkLVyfl7csUjX1AvB9yuVBr7vt7Q7gtTY3TCwp1MG5aq9yvoapMYujtOff/ppnji1EHr6bITurWla2BkTLZyC0NhB6P9SMpK2KFdK1ARNvrMbXTx9J/DODg/d3WHbU8DVfb96BF3XyKaNcJFPNHtOJYzY4unnH7mA5/vMFmthhiS392kG5rUG1tBCtNsMxsgS516OfKIrRmXsOwmZsU9EViaaR0a5tFht+ZDLQBS1ez4/V6Zcc1oWn0WHcsguC2kP0D77Ni5jr9vNnvem/cCVF1BXwuLpxg6POB/IU6WqTb3hht+vBC2Mvt9skoCmtfJssUY+k2B0KE2p6pBJJVZ9hhKGjuMJG+O//9KzVOoO1x4ULcQbVc9MJ3TqDXd1YI/4xUjfp40qPHe6uG50u+PAyKUTYctgS2BP6l0De8N2+crTQouLDpeNLmiSV7+1BtZ0jx76Fimmx2T59tF8iu1PmLFHAvvt102RSupMjUYHX4tMXAbrVEIPfWVapjJFJtvLoTLLFdFV0bC9lnM+0cUDxnGFHYKUbaKTga4E4THfvxRz6uJy+Df24txsOfx6brnGhWA6USlYBwKEJmAgjdqEFDM1mqWQSYYLlNrvumXGLtuHF1ea9tkblUjJjH2kLbBLaSSasVc3aMC17LKSA9PF6+3UwB68gUBL9pKMkWIeOTEfZtTlyG1Q1IJAZuxrvTWTb2g3I7Co21xvjd1rWdas2P5I/Xws0iJ367FJfuNdLw+zNRCacKXuhHbPR2aGwlXI0Yw92sYnt12pNCKWvc0PsRFkou00R+gFGnsoAXbP2H3fj50VAGLhlDx344Z4S/7iX07wfz71VM/tQGTs8hjOF6stUowM7FHr3GwwRencbImZ8RyFoK2wU3IkvXJOXRR6/eJKfUOlGLFvnaWYZMIgldRFYK9tbKuojGmy2yZqDtcP2yqwR1d1tWbsRtfi6VdPLYQnRfRqaUeyHXlbs3aNPcjYe0gxut6Hxu76HVsmFduXXCYReHK3FtXbP2BSOpgv1hgtpEJNHlrtgKVlMLRKMc3h7W1STIcsXJ7LYcbeI6EA+N17n+C3/uqx2L+1FASmQjbZV8a+XGm0DH/vRt0WWvktRycAobNLKaZcc8LVle0ZO4gi66GpPPlsMuhj91Yde9nvLzP2pVJ93TW1bsge+/bADoQXnabGvkEZe/B88i6hkOk/W4dtFtjf9LXHwlvcbOQWRLSddQ6u1YbLaCHdMpEIWuWTphSz1ow9vngqM4NEPxp7zAQnxfbktS+5mh98/U09t5Ma+1yxysRIJvwwjg2laR+UXG+4eJ4f6vHlmkNJ2je3WAp0NrcKM/Yg+IUae4wU8/TZIseD8ZPdkC6FU8Hw5l6LnkpVu+Xz1gnH9fibz5/EB266ZhxD1zh9qcRyxWYyuPjJoeKd5o4CHJwsCBfFqh2Ml+wgxXgeJ4OMvVS1w2O02Rk7EMpEG62xy4xdzgtYiwwD2yywH5oq8P63vYCfeMvtYfsjiOJpN3fHRmDxK90hJbbjhi5wtSstnvbT7thDY++UbSi2NzNjOcwjvdsipcQyV6wxOZINA0D78I6wNbLR1ONByBNA6I8EMmB1yNjD7L5dY+/8+XBcj7lijeWK3SJVtiOzb5lYdZv3CkLaKVXEuLY4CeifHjjLJ+97lhffNMPt100yMZzhkaA3/frASkPW1KLF01xkHcihqbxYAer5lGv2qsCeMHTmizWKpQZXzYjFO7KzbqPyKJngtbc7giholmrNwL5RraJhxh5IMWtpdYRtFthB3P62f5iSCZ16TPE0ldTJRUbbgeiKyQUXh1pDSjFrLJ6GfexdPjQRjV1mB52mq8sJTipj351kUwlKNZv55RrTozHDO6TFb81hrlgNs9bZJdni2yrFdArW8lxuFk/jE4r5Yi3U1y/Od3dtLAUDI+TFKC5A1RpNPT5OelhYFl0t3/e6m0gYOhMjGZZKDSZHMjzPnA63gfaMXQSxdMpgfCQT+jgVy41VcqZhNO/U7zSngKbMtVEZezohrAQ6jbAsZJOsVJozSTdKipHvsxzQspaOGNiGgb0TqaTe1QRMjrbKr8rYvfDKX1/nrVnPjD2y8ClO65Q/S6iumF1JNmNQb7j4vgjmYWDvkrHLIcrXBD5Bsl8705Kxd7bjlasRZWBP9NDYL0XWdsTZ8UopZrqPwF6OaOtxckzDcVukqOmxLIau8YOvv5nJ4M5AFpmjXjHyc3toMo+uaQxlxfEsluqrGhBksjQ1muGO60Vgl4XpjUqkxobS7Jvo7C1UyCZZWK6FvebrlWIqNTtsfYXmHdN6pZi15fdbRCphdG05bNguhUwSw9BappnYjhc6RsrAvtauFEPX0DWta0dO1B86EaOxN83CdsR1VLFGoiPLZsZyYYZ5cKp1eIesG50JBllcs3+Y+5+83LIQR5Lo0sferrH3qu1IH3GNHoG9ZqNpMB7cRcQF9qhVbTmmgNpoGzv3rS87ytfcdoCr9w2HU6A6ZuzBcZDHTwa1YrnR0qEEosAKcNct+yOdNxubsb/xFdd2bSfNZxMtj603Y//jf3yKc3NlfvZ7XwA0i+Rh8XQ3ZuzJmOJpw/ZIJXXymWR4UH3fp+G4YWBfb/FU0zTSMQ6PUXfHuFvifi0NFDuTqDfQ1FiW/RN5PvA9z+e2Y5Ot2wUXgDOB98fRAyJjPx14vkc/vIbReeXpKilGWgp0ydgvLwhXwn0TubB/HOCRE3P86p8/HEqHwoo4FZlg1F1jb83Yuwd2MRWqebEayae4et9w8LeK15lflhp7MxQN5cQs16uCbYfy4rgIm4DWkCUdNF9y8z6y6QTZtBFKMRvZx57LdM6B27tVKvX1rTxdWK5x5nIpvCjVGi6GroXnxFqlmJ2RsSdF8bSTJW7DcUklDJIJPbwNkqvZZAG2ts4+dvnaXTP2iD+0fO5OgV1q8ap4ujuRXRzpVHN4x5GZ1S580rpXZuyHpvIkDJ2F5Tr7J3It2Wi3ARJhz3tb8bRbV8ylxQozYznGh9MtGfs9nz3JqYsrXFqssH8iT7HUYCSY4QrxveytGXuMFBPUvzoh5xzLfYquCyhkk3zge54fTriaGs2GnTHtydEPveFmTl1YZnJESEijhXQoxehX6HbZD9GAW8gm152xy9j1+KkFXn7rgXBi0vhwhumxbCjb9cuOiDSphI6P6AX/0hOXuPuvmz25MmMXXTGOyNYD2UZmH2HxdB2BNR0z97S1KyZGY1cZ+65Gnmczo9lY61y53fm5Mrl0glwmyUiQjT732omWbQ1D6zhAotZw0TUtzHB7WQoII7Is+8ZzXF6s4Hk+Jy8sc+qiuEt49pK4yBTLDYYLqZ4DQYCW/vVSXMYe1L+6MZRL4bgexw6NrJJYDk4Vwr9N1zRuCAzY2jvbrtk/zNfe0XQLHx9KR6SYri+9YUTvsiZGMqs09n56/aEZ2B97Zh4QF9Z0yiCbTvBLP/Bijh1c26S1HRPYQWTnn3/0Ivc/eTms9MsCTT6TxPN9ag03LLTK26elFaHDZTtUtXuRThpdvWK8iMYeWzx1m90zit2HDIZTPYaSS13ccf2wI0Zmqrde2yrbGHrnmae1YFq9vIDEZeyy1XFmPMuByTyO63NpscJnvnKOVFKsvXj2sgjwxbLI2HM9BoKAKLTKMzleY3c7ziaQFIK7m5fesr/rNpLnBG6ccXOFAcaHMxu+QCmOaGCfHMmEiyR93+eezz7Df/31z2I9u9jzeWSm/9VTi8JyuBF/7HqxMwJ7xLPlxLkivk/QheCHBRoZxMu1pv+ElGIWVkTbVfsKwn6Im3vqtGjs3Zd2u5GFTIrdh9TO29sb20klmgvZ5OrU4UD+ONY2+1YWT/02K4CosyM0k4VO2f1c0Oo4M5bjcDAC7tlLJR55Zp7br5vi4GSeZy+V8Hyf5XKDkXyahCH2MU5jL9VscpkEuXQivismuJvuxnAuRSqhh62PcUib5V6DtvdPNAvWmzHVqJ32wN5wxOKuf3v4PH/z+VNAs0unGzKQT49mqdQdLsxVqNWdKwrsO0Jjl7edpy+utCzdlR8SkbEnwp/LWzgZ7H2fvkZidSKVjCmeRjT2XpkTqIx9tzJcEMW+wx3mZ0bRNI1sOkGpaoeB/bUvuZqVcqPjUnmQIxWb503NXj0bFTqfd9LGenpMZOyGrvHwiTmKpQbHDo6QNHQePjEX+rCM5FPhPvbK2Au5FJ7nxRZPZf2rG9/84qt4yc37uhYmo8hRhtHFSx23i7QlGgPW2McDs7hK3WkpVMddJKOPX3d4hMtLVc7PlylV7Y4De/plRwR2mWl/9fRC+LNK3Qnbw0TGLg5wueaEb370JBhb5+iqdNJgudz55JUDczWtR1dMRLJR7D6Gcyl+4ftf1OIR041MyqBUtcNiXzfttKmd+0RjY3SQNTRbeDudd5cW5DDtHAlD5+BkngeevAyIjhzP9/ncoxdCnV16NeV6BfaqTSGbwHX9HsXT+Iz9mv3DXNNbhQHERfGD//kFLcNIOrE/Mst2EBl7Lp1AQ8hsMnuv1hxqDZd8RtzR9Optl48f3T/Mvz96kXOzZU5fWOauPiSqbuyMwB5k7E+cbmpVlZodZumppBEG8UrNDjOcTMpA1zQ83193xp6O64qJuM3J/zutPI22RSp2J+32Ad2Q5+lkj4tAS396JEOvNZzWSUsxfeyXFitk0wZDkU6dZy+XSBg6h6cL4Xn9yAlRsJM905mIb3wnSlWbieEMtuPGZ+xt7Y5XyqGp+DsiIFz4BINJpHRdI5dJkEklmjGo7lBrOOSzSWzH62nlKx8fKaSZGs3yleNz1BouR2Z6/71d92vdvzlAZGA/N1sOs4pK3QklEtHH3pRipHd7MqGHMs7oOjP2VIzGHjUlim13dGW7owrse51sv4G9rT/ddlz+9J+OcynoS++2XZTLi1WmR3NhofVwECiu2ic6To7MDKFp8HAwz0B63PSTseezCfKBs2EnPN+n4XhXpBOvh2gw1wYU3QrZJLlMInxvKzWHejDqMOrB3w35eC6d4MBknrPBOoerOrTM9suOCOzJyMnx+ruuAYIAHrQhphNGixTT9Hg2wsDe3k7VL3HFU9fzwsAeu/LUVVKMQtB/YG+t2Tz89DyfeuAM1brT8oGPq+1cWqwwM968k5C/d3T/SLgvBycLXA5Wvo7k0+HP46Yolao2Q9kU+TZ/pijStC9Oitks5DEZVHvxSCHNcC4Z1gpExi6mXPUV2INjmMsk2D8ppKSEoXNgMh/3a7HsiEgjF2Psn8jxvBtEBb1Sd8LVqKmkQSZtoGli5ZfdIWNft8YerDxt704AIbs0T6LuGntzgZLK2Pc6cnVkroe/drvE8pXjs+QzCe5+z9fwLS+9Jtyu6RUjtvvcIxe4uFAJWx2jw7Svmhni2MERnn9Dswvl2oNi4UsyoYcLqDKpRLj2A0TTgpRc6g0X2/GCjD1BuWZ3HOIhh9PEFU83C9md1GO2yIbxttfcwHd9g9kiB9caLpl0glwm/u4HmqtVs+kEB4KunqsPDF/RgsYdEdgPTuV522tu4Ge++3nhyVeNZOyppI6uaWH7lew7TyWN0MB/dGi15WY/pBIGvt/ZFlUMqA4WUfS1QGlHHG7FJvJ1dx7iO7/++p7bJSISi+N6PPz0PLcdm1x1DoUJhSu2+/1PPMGnHzzLXLGG7xMO3waxMvan3npnS2vltQfE17IjBlqlmGrd4ef/6EH+NmjdWyqLPvLRQpp8Jonvd+55b0Rk0kFza2DlMIiVpyA6dmbGcm0ZuxNKMb0CezWSscss/do1LkhqZ0cUTzVN4+W3Hgi/T6cMkbHbrVmB9IuRLo/5TOLKM/bQ4dFr8YyGVo1duTsq+uHogeHQIyaOaMZ+/MwSlbrDbddNrdpO1zU0TVwAylUbH9E3fSno954Z6+xKKJEZe3R6WTZjUK2Lu9QnTi/iuGL9CEAxMN0aKaTCRoFq3WmZnwDNQTeD1tgB3vg113LrtRNctW/9GvV6SCdFs0a17oRtqb5Py5DzTkipJpsSgX16NMvznjNzRfuyIwJ7O7m0sA8Ib/eCrCCbEbeGpZqDhri1SSRENj/UwSS/H2RLZcN2oc2IJ6qxi7bHzguUnEi/u0LRD9F2x6+eXsTQNW6+ZrzjtkYwbWk5cDedX66F5ljT4/HdOjPjOfKZRKivgwgwXrD477GTosX49KUSjuuxVAoy9nw6tBCuddDjG1soxei61teAlI1G07TQ2kS2pWpBoI+jUnPIpg10XSOtG/zSD76YqakhZmdX1r0vOzOwZxJBxt6aFeSDg1oJVsZJT42RQmrdPa1x4/Fcr3WOaTcPbWXbq1grzclIPmcvl9g3kes46AGC+aiez0pFZNPzxVqz1bGHK6CuaXzf624Kfb+hWeCt1B0ee2Y+bCA4N1tuydhlkO9kGNbYwuLpVhKVgzMpA8PQehZPq3Wn58KrtbIjj7rI2O2W7heAXCjFNG8NC5nkqoEHayFu2Eb7DEajiyNf2O6oMnZFn0TNvc7OlmJ7uKUT5EqQsZdrDs9eLjE9los1JZM899qJFjdKaVlw+tIKc8UaX3v7QQCeubDMUrke2slK7xu5ctL3mxYITY198Bn7VpLNJFgKxv1Jjd124mfIVuoO2fTabHl7sXMDe0Rjlzq6nKIkvSwA3voNJm9/7Y3rfi15YnYyAmsfd9dtTqXK2BVrRZ5XpYrN/HKdQ1PdW98MQ8fxvDBjBzh5frmlcLoWZPb41LNLALzophmGcklOnl9maaXBSCGwHgjuIGQHzX2PX+LHfuPzOK4XOqJGB23sBXLpBAsrQlPPpIyWRUvdqNScvmwV1sKOPOpSx6q3BfacXMJbc0IPh7GhdF9LvbsRLZ6243ley2pSo8vUm7ArRhVPFX0izxU5hCMuYzfaMnYQycR0j8JpN6QUc26uDAgPlGv2D3PywjLFcr2l3x2ag2xOnC9SLDdYqdhh0rUVxdOtJJdOsBhm7M3VqHGrTyubIMX09Wymab4feFPw7b2WZf142+OvB34WMYHrJPA9lmX19qpcJ7l0kmpdrDBNBcVREF0xruezuFLvuQCkX+SJ2clWwPX8FqMhXdfwOrk7KilGsUakFCN903sGds9jpSrG28n+7fVm7NKt8vxcmWRCrOo+un+YR0/Mtyx1lytgZXFQdn8slxuRxoa9FdizmeaovKi9cq+M/fD0gDN20zRfBbwauB24DbjTNM03RB4fBn4L+GbLsm4FHgE+sKF72UY2KJ7WG61eFPKqt7hSX9V+tV7SMcVTp6142m3qjZJiFGtFSjGnL66QTScYH+7erpsw9LB4Oj2WC393ZvzKMvb55RpjhTSapnHNgeGwlXIkaB2WZmRhYA/saVeqjT1dPJWkU0ZLb3s3hMY+eCnmAvBuy7IalmXZwBPAkcjjSeCHLcs6F3z/SNvjG04uncD3YbnSaDlxojrVRmlWsRn7quJp564Y1e6oWCsyCSiWGxyayscWQQ1DwwmkmJFcMrTP6OUP341okJHmeVdHesJlB42ua6STRijFhIG9vIelmEjcyaSa/jHdpBjP96ltQmDv+WyWZT0uvzZN8zqEJHNX5PF54K+Cx7PATwL/ay07MTGxNhezmeC2tFwT1eSpKXHSHQhsSgGmJwrhz6+E/JCQdBKp5Krn03SNbLb582TSIJE0Vm2XzogPwr6Z4b66FBSK5owi+Ka7rok9l9OpBEZCp9pwODQ9RDIlVjsePTK+rvMtmpzsm8wzNTXEFMLr/OJ8hUP7RsL9yWcToOtkC5mwO8bTNRKpBLqu7blzfjoSyw7sGw5bVI1UouN7WKmJRWVT4/lVj19J/Or7MmGa5k3AvcB7Lcs63uHxEUSAf9iyrD9Yy07Mz5c62t12ww2q8LOLVfLZRNjIb0cnhLvuFTX4S+R+LSxVVj1fveHgOl7k5z6Vqr1qu+WVGoauMTdXuuL9UewNisVmknLL1WPx57LvU63aLC7XufbACLdcM87MaOaKzrdMSmTiuaQRvvaR6QIX5ysYfvOcTyUMFotVrBOz4e9euCwWM6US+p475z2nmZlXSjW8QK66PFfq+B7KQqtrOy2P91qgpOtabELcb/H0LuDjwLssy/pYh8f3A/8AfBr40X6e80oIZ5mW6oxFtMd8ixSzMRq7rmskjM5TlDr3sXcajeepjhjFmhgfyvC1tx/klXce6ul5YugatuNRrtoMZZO8+vmHr/j1s+kEtYbb4op6dP8wX3ricosFdiZlUG04LePflisNErq25wqnIBo7JJlUYE5I9xmyslU0Ou5wI+gZ2E3TPAzcA7zZsqxPd3jcAP4W+HPLsv7fDd27LkijHNfzSSeiGnvzoBayG6dZpZN6R43diwzagDiN3VfzThVrQtc13voNZl/bGrpGsdzAh3CoxpWSDdr2ooH9xTfvo9pwW0YAyguA7IgZLaRYKTfIZZJ7rocdCE0KQRRPdU0jE1igdELKV9GpWBtBP8/2HiADfMg0wxPtbuBbgPcBh4E7gIRpmm8MHn/Asqy3b+ieRhgtpLlqZojTl1Y6dsXAxmXsIN6gzpYCXt8LlFTGrtgsDEMPb+nX64nUjgxQ0cljQ7kUr49YBoPISleWGswVa6QSYvzeStUmkdD3XOEUmnEnmdBD581czEQqmbFne4z8Wyv9FE/fCbyzw0N3B/8/wBYsdHrutROrAruua2TTwpkuv4EruYRXRhfb3n4Cu+tdkbeyQhFHQtfCzpSNzNihtyuqsKV1mV+uMTGSYSif4tJikVwmsedaHaGZXEanXGXTya7tjvJ92+iMfcce+ecemwCaq04lUuPaqD52EIssurc7RkzADK2Lu6OvWh0Vm0Z0fcS+dfaut5NNiSHNUTvfTogiq8NcscbEcIbhXCpYeeptibPjViPrf9HA3k/GvtEa+44N7NfsH2Z6NLtqhWk+kyBhaBuaLaQTOmdnS3zsn4+3eCu3e8WIlaedpBhPBXbFpiHPrWMHRxgf3pgV15MjGabHcz3vNGXGfnmxwsxYjqFckrrtUqrae7J4mk6JSW7pZKss3C1j30qNfVuiaxo/9/YXrFrNmcskyGWSG9o763g+C8t1/vH+M3z2kfP82Jtu49qDI6u6XRK63nnlqesrKUaxaUjv9Rc8Z7rHlv3z+pdewze+sPc6w0zKwPN9qnWXmfFsqKvPFavsn9iYu4edhJzkFs3As2mDyuxgNfYdHW2SCWNVK9j4cIbxdQ6u7ob0cv/+192Ihsa/Pnwe6CDFxBVPVcau2CQuL4lpSdFZpldKKmn0VYiNrpicGc+Fv9OwPW68uvNgkN1ONp1o09i7j8erNdxwbsRGsmMz9m58x9ddF/q0bxRvf+2NlCo2Rw8M84A1y5OnF/F9f7Vtbxd3R8f1lE+MYtN45xtv5czlUujhMkiiAWxmPBdaBx+aKvDyW/cPfH+2A7ccnWhxlBUDrcWowXYloVZ3W8zCNopdF9gLPSbGrIfp0Ww4rOOGI6N8+alZZpfE7W9UitH1zsVT1e6o2EyuPzzK9YdHt+S1pROkoWtMDmcYyiY5dmiEN3/tsT07vL19/UE2LUYN1m13lZZeazgtve8bxa4L7JvNDVeJWYqPnxKuxH23OyopRrELkVOUpseyQbtxgp/6rju3eK+2F6Ene10E9rlilaFsinRg27DRhVPY4Rr7VnBwMs9QLsljz8wDtGnsnYun7fa+CsVuQWabM+sc6rEXCGfI1mzmilV+5iNf5O++cAoQ82IzG1w4BZWxrxlN07h63zDPXhLmRv1l7Kp4qtidyGxzo/rndyOyt71ad7nnsydp2F4o5dYa7oZb9oLK2NfFUC7JclAkavWK6VI89dTKU8XuZCSfIp00OHpgeKt3ZdsiA/cTzy7y4FOzaMBSScQPIcWojH1bkM8kw86b1V0xnUbjqYxdsTvJphP82jteuiftA/pFauzHzy4BcHimQLEsAnu1vjlSjHo31kE+4hzZrrF3WnnquB4J1RWj2KWkN6FdbzchA/uZQL49un+YYkmYttUabthZtJGowL4Ooi2Vq9odu8w83autXwrFXkdKMcVyg9FCiomRDLWGS73hUms4G+4TAyqwr4uowVhUYknoGj6sytptR2XsCsVeJZnQw8//5EiWkbxYSDa7VMX3N94nBlRgXxetUkyrxg606Oy241Gq2luyKlChUGw9mqaFWfvkSCYcBn5pUVhBbLRPDKjAvi5apJiIxCI9ZaKdMYsrwg1yYoNc9xQKxc5D6uwTIxmG8yKwX1wQgV1l7NuEFimmbTQetAZ2afM7MawydoVir9KasYtYcGlB9LKrrphtQjeNXX4dLaDOL4vq9/iIytgVir2KXKQ0OZKlkEuiaxoXAykmswkLlFQf+zrIpoVdsOevdncEkbGfmy1x/GyR5aBfdXxIBXaFYq8Szdh1TWM4n+TivJRiVMa+LdA0LSygRj1gDK1ZPP2HL53hD//B4vSlFUbyqQ33W1YoFDsHGdjlhKuRfJpS1WY4l+TARH7DX09l7Oskn0myUrG7ZuynLq4A8Ogz8xyeHtqSfVQoFNuDG68ew3a8MMEbKaTgEvyHr7mWtLIU2D6EGbu+unhaq7ucnysDYpD1hNLXFYo9zYtu3MeLbtwXfn/DkTFcz+elt2zOMBIV2NdJISigdiqenr60EurvruerjhiFQtHCN77wSF8zZdeLEn7XST7oZW/R2IPA/sz5ZaA5g3KjJscrFApFP6jAvk7ynTJ2Qwb2IoVskhfdJG69pkayg99BhUKxZ+lLijFN8/3Am4Jv77Us68fbHr8N+AgwDPwb8IOWZXUey71LKHTQ2OXK07OzZW66eoybj47zX771Zm65dm9Oa1coFFtDz4zdNM1XAa8GbgduA+40TfMNbZv9MfAjlmVdD2jA923wfm47JkeyJAytZTlw1F7g6IERdE3j+TdMK2dHhUIxUPrJ2C8A77YsqwFgmuYTQKj6m6Z5FZC1LOu+4EcfBX4W+K2N3dXtxQtunObYoZFwRRm0Zu/HDo1sxW4pFApF78BuWdbj8mvTNK9DSDJ3RTY5gAj+kgvAoY3awe2KoetMjbZq51HfmGvVqDCFQrFF9N3uaJrmTcC9wHstyzoeeUgHogbkGrB6PlwMExOFtWy+bSnW3PDrqw4rXV2hUKyfqan1L2zst3h6F/Bx4F2WZX2s7eGzQLTLfh9wfi07MT9f6jhSbqdRLArvh3wmwezsyhbvjUKh2KlMTQ3FxhBd12IT4n6Kp4eBe4C3dAjqWJZ1GqgFwR/grcAnez3vbkQuF375rQe2eE8UCsVepp+M/T1ABviQaZryZ3cD3wK8z7KsB4DvBD5smuYw8GXgf27Cvm579k/kef/bns/hmd0hLSkUip2J5vtbKoFcDZzcLVKMQqFQbARrkGKuAU6tenzT9kyhUCgUW4IK7AqFQrHLUIFdoVAodhkqsCsUCsUuQwV2hUKh2GWowK5QKBS7jK2eoGRA0+5WoVAoFIK4uBh5rOPA1K0O7PsBxsY2fkq3QqFQ7GT69NDaD5xo/+FWL1BKA89HOEK6PbZVKBQKhcBABPX7gXr7g1sd2BUKhUKxwajiqUKhUOwyVGBXKBSKXYYK7AqFQrHLUIFdoVAodhkqsCsUCsUuY6v72PcMpmm+BfgZIAn8GnAS+IXIJgeBL1qW9drB7932IhjY8u/Aay3LOhX5+Y8Ab7Qs6xVbtGvbAtM0348YKg9wr2VZPx78PAn8PfBzlmV9Zot2b9vQ6TiZpvlq4JcR7YJfBt5uWVZjq/Zxs1AZ+wAwTfMg8PPAS4HbgO8HTlmWdZtlWbcB3wgsAz+6Vfu4XTBN84XA54Dr235+I/CTW7JT2wjTNF8FvBq4HXEu3Wma5htMMd7sM8BLtm7vtg/djhPwu8B3WJZ1M5ADvnvLdnITUYF9MLwK+LRlWQuWZZWBvwTeGHn8l4G7Lcs6viV7t734PuCHiQxEN00zDfw28L6t2qltxAXg3ZZlNSzLsoEngCPAf0acR1/cyp3bRnQ7TgYwbJqmgRj5Wd3Cfdw0lBQzGA4gTjTJBeAFAKZpXge8Anj74Hdr+2FZ1tsBIvN1AX4R+D2EfLWnsSzrcfl1cO68CbhLJgWmab5ri3ZtW9HtOAGnEXc2y4jz6S+3Yv82G5WxDwYdiC7x1QAv+Pr7gf9tWdaqZcEKME3z64EjlmX9/lbvy3bCNM2bgE8B71V3et2JHidgBfgl4GbEcvz7gA9t3d5tHiqwD4azBIZnAftoSg3fCnxs0Du0g/iPwE2maT4EfAR4nmmaf7a1u7S1mKZ5F/DPwE9alvUHW70/25UOx+llwGOWZZ2wLMsDPoy4W951KClmMPwT8AHTNKeAMvBtwPebpjkJZC3L2vMSQzcsy/pe+bVpmq8APmBZ1pu3bo+2FtM0DwP3AG+2LOvTW7w725Yux+kx4FdM05yxLOsS8HqEidauQwX2AWBZ1jnTNH8a+BcgBXzEsqwvmab5AkQ2r1D0y3sQRb8PReoQd1uWdffW7dK2pONxAv4b8C+maTrA0wgpdNeh3B0VCoVil6E0doVCodhlqMCuUCgUuwwV2BUKhWKXoQK7QqFQ7DJUYFcoFIpdhgrsCoVCsctQgV2hUCh2GSqwKxQKxS5DBXaFQqHYZajArlAoFLsMFdgVCoVil6ECu0KhUOwyVGBXKBSKXYYK7AqFQrHLUIFdoVAodhkqsCsUCsUuY2ATlEzTfBvwCsuy3jao11QoFIrthmmaVwNPAV9te+h1lmWd6bD9BwAsy/pAv6+hRuMpFArF4DlvWdZtm/XkAw/spml+DfDzQA4YBX7Usqy/Nk3zo0ARuBM4CHzQsqzfH/T+KRQKxVZgmuYM8NvAYcAD/h/Lsv4pePgFpml+ESgAv2NZ1q/HPddWZOzvAN5uWdaTpmm+Evh14K+Dxw4DLwNuBj4DqMCuUCh2IwdM03wo8v2fIJLa37Ms629M09wPfM40zduCx/cDLwUM4EHTNP/Vsqzo77ewFYH9u4DXmqb57cCLEFcgyT9aluWbpvkYML4F+6ZQKBSDYJUUY5rmHHCDaZofDH6UBK4Nvv6YZVnlYLu/Bb4GeKjbk29qV4xpmi8zTfNA8K0GOMBngRcADyIkGS3yKzUAy7L8zdwvhUKh2IYYwCsty7otCPovBB4NHnMi2+mAHfdEm93u+L3AtwZfPxd4BrgeeB/wSeD1iD9GoVAo9jqfBn4IwDTNG4HHELVIgDeappk2TXMMeC3wL3FPtNlSzC8Cf2Sa5juAs8CbgEngccQV59NAzjTN/Cbvh0KhUGx33gH8jmmajyCUjO+yLGvFNE2A08DngSzwC5ZlPRH3RJrvK9VDoVAodhNq5alCoVDsMlRgVygUil2GCuwKhUKxy9iU4qlpmu9HFEoB7rUs68dN03wV8CGE+P9nlmX9TNvv/CHwacuyPhp8/zLg14AUcBL4T5ZlLW7G/ioUCsVuYsMz9iCAvxq4HbgNuNM0zf8I/B6ivfE5wPNN03xNsP2BoOH+jW1P9fvAWy3LugVhlvPejd5XhUKh2I1sRsZ+AXi3ZVkNANM0n0D0rh+3LOtk8LM/Br4d0cv+nQhLgfm253mOZVm2aZpJhHfMI5uwrwqFQrHr2PDAblnW4/Jr0zSvQ0gy/wsR8CUXgEPB9r8cbPvStuexTdO8BfgnRM/7T230vioUCsVuZNOKp6Zp3gR8CiGhPANEG+Y1hHtZLJZlPWpZ1gzwc8CfbcZ+KhQKxW5jUwK7aZp3Af8M/KRlWX+AWHW6P7LJPuB8zO9nTNP81siP/hhhSaBQKBSKHmxG8fQwcA/wFsuyPhb8+IviIfOYaZoG8BaEvt4NG/hN0zTvDL5/E/C5jd5XhUKh2I1sRvH0PUAG+FDgcQBwN/A24OPBY58A/rLbE1iW5Zqm+WaEb4IBnAPevgn7qlAoFLsO5RWjUCgUuwy18lShUCh2GSqwKxQKxS5DBXaFQqHYZajArlAoFLsMFdgVCoVil6ECu0KxTkzTfJtpmn+3jt/7cGSNhkKx4ajArlAMnq9H2GooFJvCZg+zVig2FdM0CwiL5+sQ/kMPAn+K8Bd6BrgZSAI/YFnW503TvB74TWAIYXPxEPBmy7Jqpmk6wC8BrwHywE9ZlvV/TdPcB/whYhA7iBkD/y34er9pmvcCRwAHseL6CdM0DwG/BVyNCOJ/YFnWL5um+fPAAeBPTNP8bsuyvrhZx0axd1EZu2Kn8wZgyLKs24DnBz87CrwQ+BXLsm5HBP5fCB77PkSQfRFwDLgG+ObgMQOoWJZ1J8LG4vdM05wKfucZy7LuAF4GXGea5kjktd4ZzA34N8TKa4A/Af4l+PldwHeZpvkdlmX9NMIn6TtVUFdsFiqwK3Y6nwNuMk3zM8BPIqZuPQ2ctizroWCbLwPjwdc/AcyapvnjiIz6AFCIPN9vAFiW9QjwKPBy4O+BbzNN8xPADyDM7YrB9l+yLOvp4OuHgGnTNPOIYP6bwXMVgY8i7gQUik1HBXbFjiYY3nIM+EVgGOHfPwRUI5v5NDXtPwW+HzgN/Coi6Ef1bifytQ64lmXdj8jsfwchrXwpUvy0O7yOzmoNXUdIQgrFpqMCu2JHY5rmf0FILf9oWdZPAP8A3BHzK98AfNCyLOnv/0KEBCP57uB57wBuAP7VNM1fAv6bZVn3AO8EHkdo9x2xLGsFuA/44eC5RoLn/VSwiYMK8opNRAV2xU7nDxGB+aumaT4IjBA/RvGngL8yTfNR4LeBf0Vk/JK7TNP8MmJG75uDAeq/BtxmmuZjwAOI4eofI57vBL4ueJ0vAf8XIccQfP3Hpmm+ut8/UqFYC8rdUaEIME3TB6Ysy5rb6n1RKK4ElbErFArFLkNl7AqFQrHLUBm7QqFQ7DJUYFcoFIpdhgrsCoVCsctQgV2hUCh2GSqwKxQKxS5DBXaFQqHYZfz/nwQOdJAcWToAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n.stores_t.e.loc[\"2013-01-01\":\"2013-02-01\"].sum(axis=1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carrier\n",
       "DC                    1638.704966\n",
       "H2 electrolysis          0.095464\n",
       "H2 fuel cell             0.142548\n",
       "battery charger          0.181392\n",
       "battery discharger       0.201546\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.links.iloc[:].groupby([\"carrier\"]).p_nom_opt.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "93061c673ce33a785d7c590269df2a13242973b51d41b0d96d8b2a166e219992"
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