{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.32.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\__init__.py\n"
     ]
    }
   ],
   "source": [
    "from datetime import timedelta, datetime\n",
    "import json\n",
    "import pprint\n",
    "from copy import deepcopy\n",
    "import pytz\n",
    "\n",
    "import cufflinks\n",
    "cufflinks.go_offline()\n",
    "import numpy as np\n",
    "from numpy.polynomial import Polynomial\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import sys\n",
    "\n",
    "import os\n",
    " \n",
    "# Path to the folder containing the alternate version of PyRecoy\n",
    "\n",
    "alternate_pyrecoy_path = 'C:\\\\Users\\\\z004yn8c\\\\OneDrive - Siemens AG\\\\Documents\\\\GitHub\\\\Mooi-Kickstart\\\\pyrecoy\\\\pyrecoy'\n",
    " \n",
    "# Add the path to sys.path\n",
    "\n",
    "if alternate_pyrecoy_path not in sys.path:\n",
    "\n",
    "    sys.path.insert(0, alternate_pyrecoy_path)\n",
    " \n",
    "# Now import PyRecoy\n",
    "\n",
    "import pyrecoy2\n",
    "\n",
    "# Check the version or path to confirm\n",
    "\n",
    "print(pyrecoy2.__file__)\n",
    "\n",
    "\n",
    "from pyrecoy2.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\n",
    "from pyrecoy2.colors import *\n",
    "from pyrecoy2.converters import *\n",
    "from pyrecoy2.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n",
    "from pyrecoy2.framework import TimeFramework\n",
    "from pyrecoy2.casestudy import CaseStudy\n",
    "from pyrecoy2.plotting import ebitda_bar_chart, npv_bar_chart\n",
    "from pyrecoy2.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n",
    "from pyrecoy2.sensitivity import SensitivityAnalysis\n",
    "from pyrecoy2.prices import get_tennet_data, get_afrr_capacity_fees_nl\n",
    "from pyrecoy2.forecasts import Mipf, Forecast\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Development backlog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "* aFRR (can improve the optimisation case)\n",
    "* Report\n",
    "* COP seems to have fix values, rather than COP curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Meeting Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "##### Meeting 25-11-2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "* aFRR can help optimisation case\n",
    "* SDE++ should be included\n",
    "* Tsource sensitivity really gives interesting insights\n",
    "* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n",
    "* AP TNO: Update/verify COP curve\n",
    "* AP TNO: Update CAPEX\n",
    "* AP Mark: \n",
    "    * Create graphs on COP curve with different Tsource, Tsink\n",
    "    * Generate table and output in .csv (send it to Andrew)\n",
    "* Investigate opportunity to lower the COP and negative electricity prices\n",
    "    * Technically feasible, but not really needed/possible to do it in this project\n",
    "* Could be interesting to run this model on a usecase with higher delta T\n",
    "* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "# Kickstart: Heatpump Optimisation Framework\n",
    "\n",
    "***\n",
    "© Shahla Huseynova  \n",
    "July 2024\n",
    "##### Cases\n",
    "In this model, 4 cases are compared:\n",
    "* **Baseline** : All heat is supplied by steam turbine\n",
    "* **Heatpump case** : All heat is supplied by heatpump\n",
    "* **Hybrid case** : Steam boiler and heatpump run in hybrid mode, and are optimised on costs in real-time\n",
    "* **Heat pump & storage cases**: Heat pump only case is compared with different setup of storage +heat pump cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Loading config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "class Config():\n",
    "    start = '2023-01-01'\n",
    "    end = '2023-12-31'\n",
    "    # year = '2019'\n",
    "    # startdate = year + '-01-01'\n",
    "    # enddate = str(year) + '-12-31'\n",
    "    # start = datetime.strptime(startdate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
    "    # end = datetime.strptime(enddate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
    "    # start = start.astimezone(pytz.UTC)\n",
    "    # end = end.astimezone(pytz.UTC)\n",
    "    \n",
    "    hp_vdg_e_power = 1.5 # MW\n",
    "    # hp_ndg_e_power = 7.7 # MW\n",
    "    hp_min_load = 0\n",
    "    hp_lifetime = 25\n",
    "    hp_capex = 200_000 # EUR/MWth\n",
    "    hp_opex = 0.01 # in % of CAPEX\n",
    "    hp_devex = 0.005 # in % of CAPEX\n",
    "    \n",
    "    gb_power = 5 # MW\n",
    "    gb_efficiency = 0.9\n",
    "    \n",
    "    storage_power = 1\n",
    "    # storage_volume = 1000 *3/50\n",
    "    # storage_cap_per_volume = 50 * 1e-3\n",
    "    rated_capacity = 3\n",
    "    storage_lifetime = 25\n",
    "    storage_temperature = 144\n",
    "    # storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n",
    "    storage_min_level = rated_capacity * 0.00\n",
    "    # storage_min_level = rated_capacity\n",
    "    storage_capex_per_MW = 60_000\n",
    "    storage_capex_per_MWh = 0\n",
    "    storage_opex_perc_of_capex = 0.02\n",
    "    # storage_initial_level = 5\n",
    "    threshold = 20\n",
    "    \n",
    "    tax_bracket_g = 4 \n",
    "    tax_bracket_e = 4\n",
    "    \n",
    "    include_transport_costs = False\n",
    "    grid_operator = 'Liander'\n",
    "    connection_type = 'TS/MS'\n",
    "    \n",
    "    discount_rate = 0.1\n",
    "    project_duration = 12\n",
    "\n",
    "    forecast = 'ForeNeg'\n",
    "    gas_price_multiplier = 1\n",
    "    e_price_multiplier = 1\n",
    "    e_price_volatility_multiplier = 1\n",
    "    co2_price_multiplier = 1\n",
    "    tsource_delta = 0\n",
    "    tsink_delta = 0\n",
    "    energy_tax_multiplier = 1\n",
    "    \n",
    "    # Review the SDE implementation\n",
    "    sde_base_amount = 81\n",
    "    longterm_gas_price = 24.00\n",
    "    longterm_co2_price = 37.90\n",
    "    sde_switch_price_correction = 40\n",
    "    \n",
    "    day_ahead_buying_perc = 0.3\n",
    "    \n",
    "    afrr_capacity_fee = 25_000\n",
    "\n",
    "c = Config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "class Store():\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Model set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the file paths\n",
    "excel_file_path = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\Demand_Data_Smurfit_Preprocessed3.xlsx'\n",
    "output_directory = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\data'\n",
    "output_file_name = 'smurfit_demand_preprocessed3.csv'\n",
    "csv_file_path = os.path.join(output_directory, output_file_name)\n",
    "\n",
    "df = pd.read_excel(excel_file_path)\n",
    "# df = df.fillna(0) \n",
    "\n",
    "# # Save the DataFrame to CSV\n",
    "df.to_csv(csv_file_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def load_demand_data(c, s):\n",
    "    demand = pd.read_csv('data/smurfit_demand_preprocessed3.csv', delimiter=',', decimal='.')\n",
    "    dt_index = pd.date_range(\n",
    "        start=s.time_fw.start,\n",
    "        end=s.time_fw.start + timedelta(days=365), \n",
    "        freq='1T',\n",
    "        tz='Europe/Amsterdam')\n",
    "    dt_index = dt_index[:len(demand)]\n",
    "    demand.index = dt_index\n",
    "    demand['Total demand'] = demand['MW (VDG)']\n",
    "    \n",
    "    demand = demand[c.start:c.end]\n",
    "    return demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testetss\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n",
      "\n",
      "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def setup_model(c):\n",
    "    s = Store()\n",
    "    \n",
    "    s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
    "    # s.minute_ix = s.time_fw.dt_index(freq='1T')\n",
    "    mipf = Mipf(None, start=s.time_fw.start, end=s.time_fw.end, from_database=True, add_days_to_start_end=False).data\n",
    "    s.mipf = mipf\n",
    "    s.baseline = CaseStudy(time_fw=s.time_fw, freq='1T', name='Baseline')\n",
    "    s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', data=mipf)\n",
    "    #s.hpcase_sde = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + SDE', data=mipf)\n",
    "    #s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', data=mipf)\n",
    "    s.storage_case_PCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_PCM', data=mipf)\n",
    "    # s.storage_case_TCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_TCM', data=mipf)\n",
    "    # s.storage_case_battery = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_battery', data=mipf)\n",
    "    s.cases = list(CaseStudy.instances.values())\n",
    "    s.optcases = [s.hpcase, s.storage_case_PCM]\n",
    "    s.sde_cases = []\n",
    "    \n",
    "    s.demand = load_demand_data(c, s)\n",
    "    return s\n",
    "\n",
    "s = setup_model(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>MW (VDG)</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:00:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:01:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:02:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:03:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:04:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:55:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:56:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:57:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:58:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:59:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525600 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Tsource (VDG)  Tsink (VDG)  MW (VDG)  Total demand\n",
       "2023-01-01 00:00:00+01:00             70          144         1             1\n",
       "2023-01-01 00:01:00+01:00             70          144         1             1\n",
       "2023-01-01 00:02:00+01:00             70          144         1             1\n",
       "2023-01-01 00:03:00+01:00             70          144         1             1\n",
       "2023-01-01 00:04:00+01:00             70          144         1             1\n",
       "...                                  ...          ...       ...           ...\n",
       "2023-12-31 23:55:00+01:00             70          144         1             1\n",
       "2023-12-31 23:56:00+01:00             70          144         1             1\n",
       "2023-12-31 23:57:00+01:00             70          144         1             1\n",
       "2023-12-31 23:58:00+01:00             70          144         1             1\n",
       "2023-12-31 23:59:00+01:00             70          144         1             1\n",
       "\n",
       "[525600 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.demand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def increase_volatility_by_factor(col, factor):\n",
    "    mean = col.mean()\n",
    "    diff_to_mean = col - mean\n",
    "    new_diff = diff_to_mean * factor\n",
    "    return mean + new_diff\n",
    "\n",
    "def multiply_by_factor(col, factor):\n",
    "    mean = col.mean()\n",
    "    diff_to_mean = col - mean\n",
    "    \n",
    "    cond = diff_to_mean > 0\n",
    "    diff_to_mean[cond] *= factor\n",
    "    diff_to_mean[~cond] /= factor\n",
    "    return mean + diff_to_mean"
   ]
  },
  {
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     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def load_data(c, s):\n",
    "    \n",
    "    for case in s.cases:\n",
    "        case.add_gasprices()\n",
    "        case.add_co2prices(perMWh=True)\n",
    "        \n",
    "        case.data['Gas prices (€/MWh)'] *= c.gas_price_multiplier\n",
    "        case.data['CO2 prices (€/MWh)'] *= c.co2_price_multiplier\n",
    "        case.data['CO2 prices (€/ton)'] *= c.co2_price_multiplier\n",
    "        \n",
    "    for case in s.optcases:\n",
    "        case.data['NEG'] = multiply_by_factor(case.data['NEG'], c.e_price_multiplier)\n",
    "        case.data['ForeNeg'] = multiply_by_factor(case.data['ForeNeg'], c.e_price_multiplier)\n",
    "        case.data['DAM'] = multiply_by_factor(case.data['DAM'], c.e_price_multiplier)\n",
    "        \n",
    "        case.data['NEG'] = increase_volatility_by_factor(case.data['NEG'], c.e_price_volatility_multiplier)\n",
    "        case.data['ForeNeg'] = increase_volatility_by_factor(case.data['ForeNeg'], c.e_price_volatility_multiplier)\n",
    "\n",
    "\n",
    "    s.demand['Tsource (VDG)'] += c.tsource_delta\n",
    "    s.demand['Tsink (VDG)'] += c.tsink_delta\n",
    "    for case in s.cases:\n",
    "        case.data = pd.concat([case.data, s.demand], axis=1)        \n",
    "\n",
    "    s.eb_ode_g = get_tax_rate('gas', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
    "    s.eb_ode_e = get_tax_rate('electricity', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
    "    s.grid_fees = get_grid_tariffs_electricity(c.grid_operator, 2020, c.connection_type)\n",
    "    s.grid_fee_per_MWh = s.grid_fees['kWh tarief'] * 1000\n",
    "    return s\n",
    "\n",
    "s = load_data(c, s)"
   ]
  },
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       "                            DAM     POS     NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
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       "2023-01-01 00:04:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "...                         ...     ...     ...      ...      ...   \n",
       "2023-12-31 23:55:00+01:00  3.17   -6.81   -6.81    -7.10   -11.16   \n",
       "2023-12-31 23:56:00+01:00  3.17   -6.81   -6.81    -5.21    -8.12   \n",
       "2023-12-31 23:57:00+01:00  3.17   -6.81   -6.81    -5.61    -7.59   \n",
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       "2023-12-31 23:59:00+01:00  3.17   -6.81   -6.81    -5.70    -7.27   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-01-01 00:00:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:01:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:02:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:03:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:04:00+01:00              63.998                5.23   \n",
       "...                                       ...                 ...   \n",
       "2023-12-31 23:55:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:56:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:57:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:58:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:59:00+01:00                 NaN                 NaN   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "...                                       ...            ...          ...   \n",
       "2023-12-31 23:55:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:56:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:57:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:58:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:59:00+01:00                 NaN             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  \n",
       "2023-01-01 00:00:00+01:00         1             1  \n",
       "2023-01-01 00:01:00+01:00         1             1  \n",
       "2023-01-01 00:02:00+01:00         1             1  \n",
       "2023-01-01 00:03:00+01:00         1             1  \n",
       "2023-01-01 00:04:00+01:00         1             1  \n",
       "...                             ...           ...  \n",
       "2023-12-31 23:55:00+01:00         1             1  \n",
       "2023-12-31 23:56:00+01:00         1             1  \n",
       "2023-12-31 23:57:00+01:00         1             1  \n",
       "2023-12-31 23:58:00+01:00         1             1  \n",
       "2023-12-31 23:59:00+01:00         1             1  \n",
       "\n",
       "[525600 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data"
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  },
  {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:56:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.21</td>\n",
       "      <td>-8.12</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:57:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.61</td>\n",
       "      <td>-7.59</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:58:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-7.87</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:59:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>-7.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525600 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DAM     POS     NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:02:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:03:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:04:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "...                         ...     ...     ...      ...      ...   \n",
       "2023-12-31 23:55:00+01:00  3.17   -6.81   -6.81    -7.10   -11.16   \n",
       "2023-12-31 23:56:00+01:00  3.17   -6.81   -6.81    -5.21    -8.12   \n",
       "2023-12-31 23:57:00+01:00  3.17   -6.81   -6.81    -5.61    -7.59   \n",
       "2023-12-31 23:58:00+01:00  3.17   -6.81   -6.81    -5.74    -7.87   \n",
       "2023-12-31 23:59:00+01:00  3.17   -6.81   -6.81    -5.70    -7.27   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-01-01 00:00:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:01:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:02:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:03:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:04:00+01:00              63.998                5.23   \n",
       "...                                       ...                 ...   \n",
       "2023-12-31 23:55:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:56:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:57:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:58:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:59:00+01:00                 NaN                 NaN   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "...                                       ...            ...          ...   \n",
       "2023-12-31 23:55:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:56:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:57:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:58:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:59:00+01:00                 NaN             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  \n",
       "2023-01-01 00:00:00+01:00         1             1  \n",
       "2023-01-01 00:01:00+01:00         1             1  \n",
       "2023-01-01 00:02:00+01:00         1             1  \n",
       "2023-01-01 00:03:00+01:00         1             1  \n",
       "2023-01-01 00:04:00+01:00         1             1  \n",
       "...                             ...           ...  \n",
       "2023-12-31 23:55:00+01:00         1             1  \n",
       "2023-12-31 23:56:00+01:00         1             1  \n",
       "2023-12-31 23:57:00+01:00         1             1  \n",
       "2023-12-31 23:58:00+01:00         1             1  \n",
       "2023-12-31 23:59:00+01:00         1             1  \n",
       "\n",
       "[525600 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### COP curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def cop_curve_new(Tsink, Tsource):\n",
    "    Tsink += 273\n",
    "    Tsource += 273\n",
    "    Tlift = Tsink - Tsource\n",
    "\n",
    "    c0 = 0.0005426*Tlift**2 - 0.1178*Tlift + 6.962\n",
    "    c1 = 6.7058 \n",
    "    c2 = -1.79\n",
    "    \n",
    "    return Polynomial([c0, c1, c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$x \\mapsto \\text{19.2496754} + \\text{6.7058}\\,x - \\text{1.79}\\,x^{2}$"
      ],
      "text/plain": [
       "Polynomial([19.2496754,  6.7058   , -1.79     ], domain=[-1,  1], window=[-1,  1], symbol='x')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sourceT = 63 + 273\n",
    "sinkT = 140 + 273\n",
    "cop_curve_new(sourceT, sinkT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Create and assign assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def create_and_assign_assets(c, s):\n",
    "    heatpump_vdg = Heatpump(\n",
    "        name='Heatpump VDG',\n",
    "        max_th_power=c.hp_vdg_e_power,\n",
    "        min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n",
    "        cop_curve=2.5\n",
    "    )\n",
    "\n",
    "\n",
    "    capex_vdg = c.hp_capex*(heatpump_vdg.max_th_power) \n",
    "    heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, lifetime=25)\n",
    "\n",
    "    gasboiler = GasBoiler(\n",
    "        name='Gasboiler',\n",
    "        max_th_output=c.gb_power,\n",
    "        efficiency=c.gb_efficiency\n",
    "    )\n",
    "    gasboiler.set_financials(capex=0, opex=0, devex=0, lifetime=25)\n",
    "    \n",
    "    waterstorage = HotWaterStorage(\n",
    "        name='HotWaterStorage',\n",
    "        rated_power=c.storage_power, \n",
    "        # capacity_per_volume=None,\n",
    "        # volume=None, \n",
    "        temperature=c.storage_temperature,\n",
    "        min_storagelevel=c.storage_min_level,\n",
    "        # initial_storagelevel=c.storage_initial_level\n",
    "    )\n",
    "    capex_ws = c.storage_capex_per_MW * waterstorage.max_power + c.storage_capex_per_MWh * waterstorage.capacity\n",
    "    opex_ws = c.storage_opex_perc_of_capex * capex_ws\n",
    "    waterstorage.set_financials(capex=capex_ws, opex=opex_ws, devex=0, lifetime=c.storage_lifetime)\n",
    "    \n",
    "    s.baseline.add_asset(gasboiler)\n",
    "    s.hpcase.add_asset(heatpump_vdg)\n",
    "    s.storage_case_PCM.add_asset(heatpump_vdg)\n",
    "    s.storage_case_PCM.add_asset(waterstorage)\n",
    "    return s\n",
    "\n",
    "s = create_and_assign_assets(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.assets['HotWaterStorage']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def baseline_sim(case):\n",
    "    gasboiler = list(case.assets.values())[0]\n",
    "    data = case.data\n",
    "    demand = (data['MW (VDG)']).to_list()\n",
    "\n",
    "    minutes = iter(range(len(case.data)))\n",
    "    th_output = [0] * len(case.data)\n",
    "    gas_input = [0] * len(case.data)\n",
    "\n",
    "    for m in minutes:\n",
    "        th_output[m], gas_input[m] = gasboiler.set_heat_output(demand[m])\n",
    "\n",
    "    data['output_MW_th'] = np.array(th_output)\n",
    "    data['output_MWh_th'] = np.array(data['output_MW_th']/60)\n",
    "    data['gb_input_MW'] = np.array(gas_input)\n",
    "    data['gb_input_MWh'] = np.array(data['gb_input_MW']/60)\n",
    "    case.data = data.round(5)\n",
    "    return case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hponly(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    demand_vdg = case.data['MW (VDG)'].to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n",
    "\n",
    "    hp_vdg_input = [0] * len(case.data)\n",
    "    hp_vdg_output = [0] * len(case.data)\n",
    "\n",
    "    minutes = iter(range(len(case.data)))\n",
    "    for m in minutes:\n",
    "        demand = demand_vdg[m]\n",
    "        if demand != 0:\n",
    "            hp_vdg_input[m], hp_vdg_output[m] = hp_vdg.set_heat_output(\n",
    "                heat_output=demand - 0.66,\n",
    "                Tsink=Tsink_vdg[m],\n",
    "                Tsource=Tsource_vdg[m]\n",
    "            )\n",
    "\n",
    "    case.data['hp_output_MW'] = np.array(hp_vdg_output)\n",
    "    case.data['hp_input_MW'] = np.array(hp_vdg_input)\n",
    "    case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n",
    "    \n",
    "    for col in case.data.columns:\n",
    "        if col.endswith('MW'):\n",
    "            case.data[col + 'h'] = case.data[col] / 60\n",
    "\n",
    "    case.data = case.data.round(3)\n",
    "    return case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg):\n",
    "    \n",
    "    from_storage_vdg_MW = 0\n",
    "    to_storage_vdg_MW = 0  \n",
    "    \n",
    "    if neg < dam - c.threshold:\n",
    "        # overproduce\n",
    "        if demand_vdg != 0:\n",
    "            desired_hp_load_vdg = min(demand_vdg + ws.charging_power_limit - 0.66, hp_vdg.max_th_power)\n",
    "            e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg)\n",
    "\n",
    "            to_storage_vdg_MW = ws.charging_power_limit\n",
    "            to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 3))\n",
    "\n",
    "            extra_charging_power_constraint = ws.max_power - to_storage_vdg_MW\n",
    "        else:\n",
    "            e_load_vdg, th_load_vdg = (0,0)\n",
    "            \n",
    "            \n",
    "\n",
    "    elif pos > dam + c.threshold:\n",
    "        # take from storage\n",
    "        if demand_vdg != 0:\n",
    "            from_storage_vdg_MW = ws.discharging_power_limit\n",
    "            e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n",
    "            from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 3))\n",
    "        else:\n",
    "            e_load_vdg, th_load_vdg = (0,0)\n",
    "            \n",
    "        #print({f'from_storage_vdg_MW='})\n",
    "    else:\n",
    "        e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(\n",
    "            heat_output=demand_vdg - 0.66,\n",
    "            Tsink=tsink_vdg,\n",
    "            Tsource=tsource_vdg\n",
    "        )\n",
    "\n",
    "    return e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg\n",
    "# Neg-take price, Pos-feed price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hponly_with_storage(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    ws = case.assets['HotWaterStorage']\n",
    "    demands_vdg = case.data['MW (VDG)'].to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n",
    "    dam_prices = case.data['DAM'].to_list()\n",
    "    pos_prices = case.data['POS'].to_list()\n",
    "    neg_prices = case.data['NEG'].to_list()\n",
    "\n",
    "    hp_vdg_input = [0] * len(case.data)\n",
    "    hp_vdg_output = [0] * len(case.data)\n",
    "\n",
    "    minutes = len(case.data)\n",
    "    storage_levels = [None] * minutes\n",
    "    to_storage_list = [0] * minutes\n",
    "    from_storage_vdg_list = [0] * minutes\n",
    "    to_storage_vdg_list = [0] * minutes\n",
    "\n",
    "    new_tsources_vdg = case.data['Tsource (VDG)'].to_list()\n",
    "\n",
    "    \n",
    "    ws.set_chargelevel(ws.min_chargelevel)\n",
    "    \n",
    "    for m in range(minutes):\n",
    "        tsource_vdg = Tsource_vdg[m]\n",
    "        tsink_vdg = Tsink_vdg[m]\n",
    "        demand_vdg = demands_vdg[m]\n",
    "        \n",
    "        e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg = hp_storage_opt_enginge(\n",
    "            c, ws, hp_vdg, pos_prices[m], neg_prices[m], dam_prices[m], demand_vdg, tsource_vdg, tsink_vdg\n",
    "        )\n",
    "            \n",
    "        hp_vdg_input[m] = e_load_vdg\n",
    "        hp_vdg_output[m] = th_load_vdg\n",
    "            \n",
    "        storage_levels[m] = ws.chargelevel\n",
    "        new_tsources_vdg[m] = tsource_vdg\n",
    "        to_storage_vdg_list[m] = to_storage_vdg_MW\n",
    "        from_storage_vdg_list[m] = from_storage_vdg_MW\n",
    "        \n",
    "    case.data['hp_output_MW'] = np.array(hp_vdg_output)\n",
    "    case.data['hp_input_MW'] = np.array(hp_vdg_input)\n",
    "    case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n",
    "    case.data['tsource_vdg'] = new_tsources_vdg\n",
    "    case.data['to_storage_vdg_MW'] = to_storage_vdg_list\n",
    "    case.data['from_storage_vdg_MW'] = from_storage_vdg_list\n",
    "    case.data['to_storage_MW'] = case.data['to_storage_vdg_MW']\n",
    "    case.data['from_storage_MW'] = case.data['from_storage_vdg_MW']\n",
    "    case.data['storage_level_MWh'] = storage_levels\n",
    "    \n",
    "    for col in case.data.columns:\n",
    "        if col.endswith('MW'):\n",
    "            case.data[col + 'h'] = case.data[col] / 60\n",
    "\n",
    "    case.data = case.data.round(3)\n",
    "    return case\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# charge_times = print(sum(s.storage_case_PCM.data['to_storage_MW']!= 0))\n",
    "# discharge_times = print(sum(s.storage_case_PCM.data['from_storage_MW']!= 0))\n",
    "# # 154476 times out of 525600, the storage is used to charge/dischage\n",
    "# # from 39 000 to 110 000 discharge time increases\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>ForeNeg</th>\n",
       "      <th>ForePos</th>\n",
       "      <th>Gas prices (€/MWh)</th>\n",
       "      <th>CO2 prices (€/ton)</th>\n",
       "      <th>CO2 prices (€/MWh)</th>\n",
       "      <th>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>...</th>\n",
       "      <th>from_storage_vdg_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "      <th>storage_level_MWh</th>\n",
       "      <th>hp_output_MWh</th>\n",
       "      <th>hp_input_MWh</th>\n",
       "      <th>to_storage_vdg_MWh</th>\n",
       "      <th>from_storage_vdg_MWh</th>\n",
       "      <th>to_storage_MWh</th>\n",
       "      <th>from_storage_MWh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-11-21 17:55:00+01:00</th>\n",
       "      <td>145.80</td>\n",
       "      <td>92.74</td>\n",
       "      <td>92.74</td>\n",
       "      <td>144.20</td>\n",
       "      <td>144.20</td>\n",
       "      <td>45.735</td>\n",
       "      <td>76.35</td>\n",
       "      <td>14.72</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.014</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-09 22:35:00+02:00</th>\n",
       "      <td>97.48</td>\n",
       "      <td>75.66</td>\n",
       "      <td>75.66</td>\n",
       "      <td>81.45</td>\n",
       "      <td>78.85</td>\n",
       "      <td>26.914</td>\n",
       "      <td>82.19</td>\n",
       "      <td>15.85</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.014</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM    POS    NEG  ForeNeg  ForePos  \\\n",
       "2023-11-21 17:55:00+01:00  145.80  92.74  92.74   144.20   144.20   \n",
       "2023-06-09 22:35:00+02:00   97.48  75.66  75.66    81.45    78.85   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-11-21 17:55:00+01:00              45.735               76.35   \n",
       "2023-06-09 22:35:00+02:00              26.914               82.19   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-11-21 17:55:00+01:00               14.72             70          144   \n",
       "2023-06-09 22:35:00+02:00               15.85             70          144   \n",
       "\n",
       "                           ...  from_storage_vdg_MW  to_storage_MW  \\\n",
       "2023-11-21 17:55:00+01:00  ...                  0.0           -0.0   \n",
       "2023-06-09 22:35:00+02:00  ...                  0.0           -0.0   \n",
       "\n",
       "                           from_storage_MW  storage_level_MWh  hp_output_MWh  \\\n",
       "2023-11-21 17:55:00+01:00              0.0                3.0          0.014   \n",
       "2023-06-09 22:35:00+02:00              0.0                3.0          0.014   \n",
       "\n",
       "                           hp_input_MWh  to_storage_vdg_MWh  \\\n",
       "2023-11-21 17:55:00+01:00        -0.006                -0.0   \n",
       "2023-06-09 22:35:00+02:00        -0.006                -0.0   \n",
       "\n",
       "                           from_storage_vdg_MWh  to_storage_MWh  \\\n",
       "2023-11-21 17:55:00+01:00                   0.0            -0.0   \n",
       "2023-06-09 22:35:00+02:00                   0.0            -0.0   \n",
       "\n",
       "                           from_storage_MWh  \n",
       "2023-11-21 17:55:00+01:00               0.0  \n",
       "2023-06-09 22:35:00+02:00               0.0  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>95.817313</td>\n",
       "      <td>93.568163</td>\n",
       "      <td>103.438997</td>\n",
       "      <td>0.695674</td>\n",
       "      <td>0.144249</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.046697</td>\n",
       "      <td>194.977533</td>\n",
       "      <td>195.696551</td>\n",
       "      <td>0.271801</td>\n",
       "      <td>0.226532</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-500.000000</td>\n",
       "      <td>-1549.470000</td>\n",
       "      <td>-1549.470000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>76.935000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>57.967500</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99.215000</td>\n",
       "      <td>84.130000</td>\n",
       "      <td>88.570000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>122.400000</td>\n",
       "      <td>114.020000</td>\n",
       "      <td>119.592500</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>463.770000</td>\n",
       "      <td>2037.740000</td>\n",
       "      <td>2037.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DAM            POS            NEG   hp_output_MW  \\\n",
       "count  525600.000000  525600.000000  525600.000000  525600.000000   \n",
       "mean       95.817313      93.568163     103.438997       0.695674   \n",
       "std        49.046697     194.977533     195.696551       0.271801   \n",
       "min      -500.000000   -1549.470000   -1549.470000       0.340000   \n",
       "25%        76.935000      47.000000      57.967500       0.340000   \n",
       "50%        99.215000      84.130000      88.570000       0.840000   \n",
       "75%       122.400000     114.020000     119.592500       0.840000   \n",
       "max       463.770000    2037.740000    2037.740000       1.000000   \n",
       "\n",
       "       to_storage_MW  Total demand  \n",
       "count  525600.000000      525600.0  \n",
       "mean        0.144249           1.0  \n",
       "std         0.226532           0.0  \n",
       "min         0.000000           1.0  \n",
       "25%         0.000000           1.0  \n",
       "50%         0.000000           1.0  \n",
       "75%         0.500000           1.0  \n",
       "max         0.500000           1.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-05-25 19:10:00+02:00</th>\n",
       "      <td>107.11</td>\n",
       "      <td>55.51</td>\n",
       "      <td>55.51</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29 06:32:00+02:00</th>\n",
       "      <td>121.06</td>\n",
       "      <td>191.89</td>\n",
       "      <td>191.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-27 09:18:00+02:00</th>\n",
       "      <td>94.92</td>\n",
       "      <td>66.79</td>\n",
       "      <td>66.79</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-02 17:41:00+01:00</th>\n",
       "      <td>141.39</td>\n",
       "      <td>1852.49</td>\n",
       "      <td>1852.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-07 11:29:00+02:00</th>\n",
       "      <td>-4.75</td>\n",
       "      <td>-107.08</td>\n",
       "      <td>-107.08</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29 14:01:00+02:00</th>\n",
       "      <td>98.60</td>\n",
       "      <td>86.81</td>\n",
       "      <td>86.81</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-21 01:48:00+01:00</th>\n",
       "      <td>108.00</td>\n",
       "      <td>-9.69</td>\n",
       "      <td>-9.69</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-24 20:10:00+01:00</th>\n",
       "      <td>96.70</td>\n",
       "      <td>78.90</td>\n",
       "      <td>78.90</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-23 14:29:00+02:00</th>\n",
       "      <td>-20.00</td>\n",
       "      <td>-28.95</td>\n",
       "      <td>-28.95</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-21 02:43:00+01:00</th>\n",
       "      <td>96.12</td>\n",
       "      <td>104.09</td>\n",
       "      <td>104.09</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-05 23:32:00+02:00</th>\n",
       "      <td>139.13</td>\n",
       "      <td>112.28</td>\n",
       "      <td>112.28</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31 00:13:00+02:00</th>\n",
       "      <td>65.13</td>\n",
       "      <td>423.84</td>\n",
       "      <td>423.84</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-20 06:31:00+02:00</th>\n",
       "      <td>73.02</td>\n",
       "      <td>88.69</td>\n",
       "      <td>88.69</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-25 21:13:00+02:00</th>\n",
       "      <td>118.13</td>\n",
       "      <td>75.20</td>\n",
       "      <td>75.20</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-18 17:53:00+01:00</th>\n",
       "      <td>160.00</td>\n",
       "      <td>120.84</td>\n",
       "      <td>120.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-09 23:59:00+01:00</th>\n",
       "      <td>46.18</td>\n",
       "      <td>-40.53</td>\n",
       "      <td>-40.53</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-08 20:41:00+01:00</th>\n",
       "      <td>153.77</td>\n",
       "      <td>193.90</td>\n",
       "      <td>193.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-11 12:27:00+02:00</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-17.39</td>\n",
       "      <td>-17.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-17 08:41:00+02:00</th>\n",
       "      <td>199.29</td>\n",
       "      <td>649.29</td>\n",
       "      <td>649.29</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-09 22:11:00+01:00</th>\n",
       "      <td>132.00</td>\n",
       "      <td>180.50</td>\n",
       "      <td>180.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM      POS      NEG  hp_output_MW  \\\n",
       "2023-05-25 19:10:00+02:00  107.11    55.51    55.51          0.84   \n",
       "2023-06-29 06:32:00+02:00  121.06   191.89   191.89          1.00   \n",
       "2023-08-27 09:18:00+02:00   94.92    66.79    66.79          0.84   \n",
       "2023-11-02 17:41:00+01:00  141.39  1852.49  1852.49          1.00   \n",
       "2023-08-07 11:29:00+02:00   -4.75  -107.08  -107.08          0.84   \n",
       "2023-06-29 14:01:00+02:00   98.60    86.81    86.81          0.34   \n",
       "2023-02-21 01:48:00+01:00  108.00    -9.69    -9.69          0.84   \n",
       "2023-03-24 20:10:00+01:00   96.70    78.90    78.90          0.34   \n",
       "2023-05-23 14:29:00+02:00  -20.00   -28.95   -28.95          0.34   \n",
       "2023-11-21 02:43:00+01:00   96.12   104.09   104.09          0.34   \n",
       "2023-04-05 23:32:00+02:00  139.13   112.28   112.28          0.84   \n",
       "2023-03-31 00:13:00+02:00   65.13   423.84   423.84          1.00   \n",
       "2023-10-20 06:31:00+02:00   73.02    88.69    88.69          0.34   \n",
       "2023-07-25 21:13:00+02:00  118.13    75.20    75.20          0.84   \n",
       "2023-01-18 17:53:00+01:00  160.00   120.84   120.84          0.84   \n",
       "2023-12-09 23:59:00+01:00   46.18   -40.53   -40.53          0.84   \n",
       "2023-03-08 20:41:00+01:00  153.77   193.90   193.90          1.00   \n",
       "2023-10-11 12:27:00+02:00    0.00   -17.39   -17.39          0.34   \n",
       "2023-04-17 08:41:00+02:00  199.29   649.29   649.29          1.00   \n",
       "2023-01-09 22:11:00+01:00  132.00   180.50   180.50          1.00   \n",
       "\n",
       "                           to_storage_MW  from_storage_MW  Total demand  \n",
       "2023-05-25 19:10:00+02:00            0.5              0.0             1  \n",
       "2023-06-29 06:32:00+02:00            0.0              1.0             1  \n",
       "2023-08-27 09:18:00+02:00            0.5              0.0             1  \n",
       "2023-11-02 17:41:00+01:00            0.0              1.0             1  \n",
       "2023-08-07 11:29:00+02:00           -0.0              0.0             1  \n",
       "2023-06-29 14:01:00+02:00            0.0              0.0             1  \n",
       "2023-02-21 01:48:00+01:00           -0.0              0.0             1  \n",
       "2023-03-24 20:10:00+01:00            0.0              0.0             1  \n",
       "2023-05-23 14:29:00+02:00            0.0              0.0             1  \n",
       "2023-11-21 02:43:00+01:00            0.0              0.0             1  \n",
       "2023-04-05 23:32:00+02:00            0.5              0.0             1  \n",
       "2023-03-31 00:13:00+02:00            0.0              1.0             1  \n",
       "2023-10-20 06:31:00+02:00            0.0              0.0             1  \n",
       "2023-07-25 21:13:00+02:00           -0.0              0.0             1  \n",
       "2023-01-18 17:53:00+01:00            0.5              0.0             1  \n",
       "2023-12-09 23:59:00+01:00           -0.0              0.0             1  \n",
       "2023-03-08 20:41:00+01:00            0.0              1.0             1  \n",
       "2023-10-11 12:27:00+02:00            0.0              0.0             1  \n",
       "2023-04-17 08:41:00+02:00            0.0              1.0             1  \n",
       "2023-01-09 22:11:00+01:00            0.0              1.0             1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'from_storage_MW','Total demand']].sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'hp_input_MW', 'hp_output_MW', 'cop', 'Total demand']]\n",
    "hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'Total demand']]\n",
    "storage_selection = s.storage_case_PCM.data[['hp_input_MW', 'hp_output_MW', 'cop', 'to_storage_MW', 'from_storage_MW']]\n",
    "\n",
    "selection = pd.concat([hp_selection, storage_selection], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>Total demand</th>\n",
       "      <th>hp_input_MW</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>cop</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-20 17:15:00+02:00</th>\n",
       "      <td>118.63</td>\n",
       "      <td>475.44</td>\n",
       "      <td>475.44</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-19 16:57:00+02:00</th>\n",
       "      <td>98.16</td>\n",
       "      <td>93.95</td>\n",
       "      <td>93.95</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-14 01:50:00+02:00</th>\n",
       "      <td>96.36</td>\n",
       "      <td>67.33</td>\n",
       "      <td>67.33</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-15 22:49:00+01:00</th>\n",
       "      <td>116.70</td>\n",
       "      <td>74.13</td>\n",
       "      <td>74.13</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01 13:06:00+02:00</th>\n",
       "      <td>54.90</td>\n",
       "      <td>90.10</td>\n",
       "      <td>90.10</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-22 11:45:00+01:00</th>\n",
       "      <td>123.96</td>\n",
       "      <td>87.50</td>\n",
       "      <td>87.50</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-03 11:04:00+02:00</th>\n",
       "      <td>68.14</td>\n",
       "      <td>-50.70</td>\n",
       "      <td>-50.70</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-07 07:05:00+02:00</th>\n",
       "      <td>109.20</td>\n",
       "      <td>86.49</td>\n",
       "      <td>173.56</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01 01:07:00+02:00</th>\n",
       "      <td>63.90</td>\n",
       "      <td>125.74</td>\n",
       "      <td>125.74</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-05 01:30:00+01:00</th>\n",
       "      <td>108.40</td>\n",
       "      <td>141.20</td>\n",
       "      <td>141.20</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-28 01:00:00+02:00</th>\n",
       "      <td>84.00</td>\n",
       "      <td>55.34</td>\n",
       "      <td>55.34</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-26 23:27:00+01:00</th>\n",
       "      <td>132.98</td>\n",
       "      <td>141.91</td>\n",
       "      <td>141.91</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-25 08:15:00+01:00</th>\n",
       "      <td>0.08</td>\n",
       "      <td>-40.00</td>\n",
       "      <td>-40.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-25 13:29:00+02:00</th>\n",
       "      <td>13.00</td>\n",
       "      <td>88.52</td>\n",
       "      <td>88.52</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-31 17:47:00+02:00</th>\n",
       "      <td>51.87</td>\n",
       "      <td>333.47</td>\n",
       "      <td>333.47</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-13 02:52:00+02:00</th>\n",
       "      <td>78.80</td>\n",
       "      <td>56.13</td>\n",
       "      <td>56.13</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-12 05:26:00+01:00</th>\n",
       "      <td>91.98</td>\n",
       "      <td>44.45</td>\n",
       "      <td>44.45</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-31 02:46:00+02:00</th>\n",
       "      <td>44.09</td>\n",
       "      <td>65.56</td>\n",
       "      <td>65.56</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-28 03:05:00+01:00</th>\n",
       "      <td>124.59</td>\n",
       "      <td>91.64</td>\n",
       "      <td>91.64</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-01 13:54:00+02:00</th>\n",
       "      <td>61.96</td>\n",
       "      <td>-259.73</td>\n",
       "      <td>-259.73</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM     POS     NEG  Total demand  hp_input_MW  \\\n",
       "2023-06-20 17:15:00+02:00  118.63  475.44  475.44             1       -0.400   \n",
       "2023-06-19 16:57:00+02:00   98.16   93.95   93.95             1       -0.136   \n",
       "2023-07-14 01:50:00+02:00   96.36   67.33   67.33             1       -0.336   \n",
       "2023-02-15 22:49:00+01:00  116.70   74.13   74.13             1       -0.336   \n",
       "2023-04-01 13:06:00+02:00   54.90   90.10   90.10             1       -0.400   \n",
       "2023-02-22 11:45:00+01:00  123.96   87.50   87.50             1       -0.336   \n",
       "2023-04-03 11:04:00+02:00   68.14  -50.70  -50.70             1       -0.336   \n",
       "2023-07-07 07:05:00+02:00  109.20   86.49  173.56             1       -0.136   \n",
       "2023-04-01 01:07:00+02:00   63.90  125.74  125.74             1       -0.400   \n",
       "2023-03-05 01:30:00+01:00  108.40  141.20  141.20             1       -0.400   \n",
       "2023-05-28 01:00:00+02:00   84.00   55.34   55.34             1       -0.336   \n",
       "2023-02-26 23:27:00+01:00  132.98  141.91  141.91             1       -0.136   \n",
       "2023-12-25 08:15:00+01:00    0.08  -40.00  -40.00             1       -0.336   \n",
       "2023-05-25 13:29:00+02:00   13.00   88.52   88.52             1       -0.400   \n",
       "2023-05-31 17:47:00+02:00   51.87  333.47  333.47             1       -0.400   \n",
       "2023-08-13 02:52:00+02:00   78.80   56.13   56.13             1       -0.336   \n",
       "2023-03-12 05:26:00+01:00   91.98   44.45   44.45             1       -0.336   \n",
       "2023-07-31 02:46:00+02:00   44.09   65.56   65.56             1       -0.400   \n",
       "2023-02-28 03:05:00+01:00  124.59   91.64   91.64             1       -0.336   \n",
       "2023-08-01 13:54:00+02:00   61.96 -259.73 -259.73             1       -0.336   \n",
       "\n",
       "                           hp_output_MW  cop  to_storage_MW  from_storage_MW  \n",
       "2023-06-20 17:15:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-06-19 16:57:00+02:00          0.34  2.5            0.0              0.0  \n",
       "2023-07-14 01:50:00+02:00          0.84  2.5           -0.0              0.0  \n",
       "2023-02-15 22:49:00+01:00          0.84  2.5            0.5              0.0  \n",
       "2023-04-01 13:06:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-02-22 11:45:00+01:00          0.84  2.5            0.5              0.0  \n",
       "2023-04-03 11:04:00+02:00          0.84  2.5            0.5              0.0  \n",
       "2023-07-07 07:05:00+02:00          0.34  2.5            0.0              0.0  \n",
       "2023-04-01 01:07:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-03-05 01:30:00+01:00          1.00  2.5            0.0              0.0  \n",
       "2023-05-28 01:00:00+02:00          0.84  2.5            0.5              0.0  \n",
       "2023-02-26 23:27:00+01:00          0.34  2.5            0.0              0.0  \n",
       "2023-12-25 08:15:00+01:00          0.84  2.5           -0.0              0.0  \n",
       "2023-05-25 13:29:00+02:00          1.00  2.5            0.0              0.0  \n",
       "2023-05-31 17:47:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-08-13 02:52:00+02:00          0.84  2.5            0.5              0.0  \n",
       "2023-03-12 05:26:00+01:00          0.84  2.5            0.5              0.0  \n",
       "2023-07-31 02:46:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-02-28 03:05:00+01:00          0.84  2.5            0.5              0.0  \n",
       "2023-08-01 13:54:00+02:00          0.84  2.5           -0.0              0.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DAM                 95.817313\n",
       "POS                 93.568163\n",
       "NEG                103.438997\n",
       "Total demand         1.000000\n",
       "hp_input_MW         -0.278269\n",
       "hp_output_MW         0.695674\n",
       "cop                  2.500000\n",
       "to_storage_MW        0.144249\n",
       "from_storage_MW      0.144035\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################\n",
    "\n",
    "# This is where we left\n",
    "\n",
    "########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def cost_function(th_load, cop, electricity_cost, alt_heat_price, demand):\n",
    "        return (\n",
    "            th_load / cop * electricity_cost\n",
    "            + (demand - th_load) * alt_heat_price\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hybrid_imb_optimisation(case, decimals, s):\n",
    "    gb = case.assets['Gasboiler']\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].round(decimals).to_list()\n",
    "    fore_neg = case.data[c.forecast].fillna(999).round(decimals).to_list()\n",
    "    gas_prices = case.data['Gas prices (€/MWh)'].round(decimals).to_list()\n",
    "    co2_prices = case.data['CO2 prices (€/MWh)'].round(decimals).to_list()\n",
    "    eb_ode_g = s.eb_ode_g\n",
    "    eb_ode_e = s.eb_ode_e\n",
    "    \n",
    "    gb_input = [0] * len(case.data)\n",
    "    gb_output = [0] * len(case.data)\n",
    "\n",
    "    minutes = range(len(case.data))\n",
    "    hp_output = [0] * len(case.data)\n",
    "    hp_input = [0] * len(case.data)\n",
    "\n",
    "    for m in tqdm(minutes):\n",
    "        dem_vgd = demand_vdg[m]\n",
    "        if dem_vgd != 0:\n",
    "            max_load = min(hp_vdg.max_th_power, dem_vgd)\n",
    "            min_load = hp_vdg.min_th_power\n",
    "            Tsink = Tsink_vdg[m]\n",
    "            Tsource = Tsource_vdg[m]\n",
    "            cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n",
    "            cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n",
    "            \n",
    "            cost_full_load = cost_function(\n",
    "                th_load=max_load,\n",
    "                cop=cop_max_load,\n",
    "                electricity_cost=fore_neg[m] + eb_ode_e - c.sde_switch_price_correction,\n",
    "                alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
    "                demand=dem_vgd\n",
    "            )\n",
    "            \n",
    "            cost_min_load = cost_function(\n",
    "                th_load=min_load,\n",
    "                cop=cop_min_load,\n",
    "                electricity_cost=fore_neg[m] + eb_ode_e,\n",
    "                alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
    "                demand=dem_vgd\n",
    "            )\n",
    "            \n",
    "            if cost_full_load < cost_min_load:\n",
    "                hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n",
    "            else:\n",
    "                hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n",
    "        else:\n",
    "            hp_vdg_input, hp_vdg_output = (0, 0)\n",
    "\n",
    "\n",
    "        hp_out = hp_vdg_output\n",
    "        hp_output[m] = hp_out\n",
    "        hp_input[m] = hp_vdg_input\n",
    "        remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n",
    "        gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n",
    "\n",
    "    case.data['hp_output_MW'] = np.array(hp_output)\n",
    "    case.data['hp_input_MW'] = np.array(hp_input)\n",
    "    case.data['gb_output_MW'] = np.array(gb_output)\n",
    "    case.data['gb_input_MW'] = np.array(gb_input)\n",
    "\n",
    "    for col in case.data.columns:\n",
    "        if col.endswith('MW'):\n",
    "            case.data[col + 'h'] = case.data[col] / 60\n",
    "\n",
    "    case.data = case.data.round(5)\n",
    "    return case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "aFRR\n",
    "* Bid in a volume (X MW) --> Strategy is to only bid in on aFRR down\n",
    "* Remaining demand is filed in by gasboiler\n",
    "* Bid price at switch price?\n",
    "* Assume direct response to 0% for now?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def calc_afrr_capacity(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    \n",
    "    capacity = 0\n",
    "    for hp in hp_vdg:\n",
    "        max_th_output = hp.max_th_power\n",
    "        cop = hp.get_cop(max_th_output, Tsink=135, Tsource=60)\n",
    "        e_power = max_th_output / cop\n",
    "        capacity += e_power\n",
    "    return capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Run optimisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def run_optimisation(c, s):\n",
    "    s.baseline = baseline_sim(s.baseline)\n",
    "    s.hpcase = hponly(s.hpcase)\n",
    "    s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)\n",
    "#     s.hpcase_sde.assign_algorithm(hponly)\n",
    "#     s.hpcase_sde.run()\n",
    "    \n",
    "#     s.optcase1.assign_algorithm(hybrid_imb_optimisation)\n",
    "#     s.optcase1.run(decimals=2, s=s)\n",
    "    \n",
    "#     s.afrr_case.assign_algorithm(aFRR_optimisation)\n",
    "#     s.afrr_case.run(s=s)\n",
    "    \n",
    "    for case in [s.hpcase, s.storage_case_PCM]: # [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
    "        case.mean_cop = case.data['hp_output_MW'].sum() / case.data['hp_input_MW'].abs().sum()\n",
    "    \n",
    "    return s\n",
    "\n",
    "s = run_optimisation(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4999999999999987"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data['hp_output_MW'].sum() /s.hpcase.data['hp_input_MW'].abs().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.499999999999999"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['hp_output_MW'].sum() /s.storage_case_PCM.data['hp_input_MW'].abs().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>ForeNeg</th>\n",
       "      <th>ForePos</th>\n",
       "      <th>Gas prices (€/MWh)</th>\n",
       "      <th>CO2 prices (€/ton)</th>\n",
       "      <th>CO2 prices (€/MWh)</th>\n",
       "      <th>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>MW (VDG)</th>\n",
       "      <th>Total demand</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>hp_input_MW</th>\n",
       "      <th>cop</th>\n",
       "      <th>hp_output_MWh</th>\n",
       "      <th>hp_input_MWh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:00:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:01:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:02:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:03:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
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       "    <tr>\n",
       "      <th>2023-01-01 00:04:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
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       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                            DAM    POS    NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:02:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:03:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:04:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-01-01 00:00:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:01:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:02:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:03:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:04:00+01:00              63.998                5.23   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  hp_output_MW  hp_input_MW  \\\n",
       "2023-01-01 00:00:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:01:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:02:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:03:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:04:00+01:00         1             1          0.34       -0.136   \n",
       "\n",
       "                           cop  hp_output_MWh  hp_input_MWh  \n",
       "2023-01-01 00:00:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:01:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:02:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:03:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:04:00+01:00  2.5          0.006        -0.002  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_data = s.storage_case_PCM.data[[\n",
    "    'storage_level_MWh', \n",
    "    #'to_storage_ndg_MW', \n",
    "    #'to_storage_vdg_MW', \n",
    "    'to_storage_MW', \n",
    "    #'from_storage_ndg_MW', \n",
    "    #'from_storage_vdg_MW',\n",
    "    'from_storage_MW',\n",
    "    'NEG'\n",
    "]]\n",
    "\n",
    "cops = pd.concat([s.hpcase.data['cop'], s.storage_case_PCM.data['cop']], axis=1)\n",
    "vis_data = pd.concat([vis_data, cops], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
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    "vis_data.loc['2023-10-02', :].iplot(subplots=True, shape=(6,1), dimensions=(1000, 800))"
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   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "storage_level_MWh       0.00\n",
       "to_storage_MW           0.00\n",
       "from_storage_MW         0.00\n",
       "NEG                 -1549.47\n",
       "cop                     2.50\n",
       "cop                     2.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_data.min()"
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   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1213.08, 1286.8299999999997)"
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     "execution_count": 64,
     "metadata": {},
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    "total_to_storage = s.storage_case_PCM.data['to_storage_MWh'].sum()\n",
    "total_from_storage = s.storage_case_PCM.data['from_storage_MWh'].sum()\n",
    "total_to_storage, total_from_storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
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     "execution_count": 65,
     "metadata": {},
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   "execution_count": 66,
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       "False"
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     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "hp_output = s.storage_case_PCM.data['hp_output_MW'].round(2)\n",
    "demand_and_to_storage = (s.storage_case_PCM.data['Total demand'] + s.storage_case_PCM.data['to_storage_MW']).round(2)\n",
    "hp_output.equals(demand_and_to_storage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
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      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\4223154146.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
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HuXfDamXkhtemxt/lif7t5dgHR8q2nXvcsbeu7i4rN26Ty1+anPA+2lHev2WtclKrUoa8M2FF/J1euKhTnACc9thoufiQxnJ02+rusvtGzHV9RRA7rWttAVca2NK/epUzpGvjSrJk3VYZt3CjHNG6mnw6fY18cH1POePJMVKlbCk5u1d9ueu92ZF4Vy6bJm9c2tUVuL/i5clubLbv3CNsfMOv7S7XDZoqC9Zkx/u8fssOaVqjrHw/d72c2KmmXNenqXunf57WSh78aK5s2bFHtu7Y7cZq5orN7rq6lTOEjYDWvGZZmbNqS8K7PHZue2ldp5wgXPR9fEz8t/IZaXJdnyby93dz3z2jVAnp1bSy/PWEFjJ/dbZc9PxE6dKoout7jyaVZNT82LxC6B29YIPcc3pr6ZZDZv2xGnx5VzcHnj6vg9w4eKpkbdst1cqVlDWbdzrsT+pc243jf87rIJe+MMndU+ckuDz40Xz5cuYaKV2qhOuvPs/vGGPz0sWd44d07KIGgn5ty5nH+jsCCcLJ57f0lsc+WyBvjlsh5x1QX/7Qu74oMfzjm7Nk6tIsJ6BA+rTd/Po0gVA/cFZbaVW7nBzzwEj3052n7CcTF2+QEZMz3RzRd+Ke3Puf786Rz2asdn299/TWcvxDo+Lfl94b7GqULxV/1rTlm+XqVybLNUc0kae/Wui+h/A7OqZdDencqKI88NF8SSsu0rdbXdc3/WYVv4ySxWXAAQ1k7IL1bkz5lpeu3+beQfHv06aamyv3nd7Gvf/tJ7aQv70TmyN/PbGF/P2d2TLihp6SnlZcXvh2iXwyPdN9a/qMQ1tVc/OHdvMb06RB1dIye+VmaV+volx8cEN5+stFMmXZRlmdtUMyN+2QU7rUkrfGxdYg/xn8m36+d10Ptw5UKlPSfZO0g/er6sje2i07pd/jY9w60rZOebn5mGauwP3fPpjvvqNNW3fKJ9PWyKldarvxRZlQongxObd3AzmxY834Mxet3SrnPztB7jujjTC2hW1ghkIHxYDfju9QQz6ZvkaKFyvm5i+NsRg2drns3rs3vjbpNcwvXUM6NqggExdvit+O+dnviTGydvPO+DFwueywxvLCN4ukQumS0rlhRWlfr4L83/BZUrVcSUdMH/10vptjvKPOQ503b1zRVU5/IrZGs2bOWrlZrjuyqVNQVSidJtcekbgunN2zrnw0NVMO2a+aDBu7Ir4mROGk64S/FoXztVXtsjJjxZaEufXoue1l9Px18t3sNVI6PU3KlEpz+9jhraq7b0Yb86xquVJSKq2427v8dWDqss1yzaDJMvD8jm7topUuWUIg7LT/O6mlHNSyavxeKA4GfrUoTzeeGtBeWtQsJ38cMt19C3Mzt7hv+7WRS6VFrbJuXZ6/Jlsuei72jHLpadKmbrn4+njRwQ1lQWa2bNy60+2z/hz3126ON65WVp7/ZpHbx2lPfrFQhoxZHn8n/3w9yLp6yQuT4vvn+9f1kOETV7pv6/qjmsqDH81zp+o+puvNpS9OStgfIJm8I3PrlmOby2fTM91azffKNVOWbZIR1/eUiUs2ub2Z59wyZJqUSy8po+avT8Dt9uObyt/eiz2X/Yp95KRHRsnlhzVxaz44g7ffUIzMW71FNm7d5b4THWu+H76B7k0qu+9W5wvXHt+hprw3aZV7X93H9Z7sSYwZ7YAWVeRvJ+/n/s36vmB1bDwOa11dvp29Nr5/soZ3rF/BnfftnHVy+1szE94R5SEyxPTlm+WqVyY7OaRi6TSZuixLrhk0JWFv4p1mrciKy1SsN9k797g9nz2oTsV0Nzdp9Pvi5yeJ7pWKmT6c/YW1SNcu9sXy6WkJmHKuPzbsl/q33wmVb8Dtxe8WSzERtxfTqpQtKUOv7JbnG/g9HBj44muuGxcNOOv30J3ffR+MGO7jELPRVy9fSnbt2SvDruwmfx42Q36YF1uoERLGL9qY9AldGlZ0gq1PDFvUKifl00tI9Qql5MMpsY3YF6ivOryxW2ARuFZnbZdNW3e5De+HuRscYaOdt399ObJdDTn7qXHub90QlQAiTKIR16YbF0Jv/aoZcvPrsUUdYeXJP7SXo/79g+zcvdcdG3pFV8ncvEOuyCGGVx/RxAk/2hByW9UtLzUrpMs7E1bGhcjnL+wkDavGLEOnPDZGLj6ogRzbPiYU3jNirnw0JVMuPaSRnNG9TlyAqla+lKzJ2uGEDgRuiBeCNIIRhAVh9aynxjpieFbPeoIlI6ohzA65rIsjhpe9NNltItk7dsmb41bKm1d1k+temyqL1251l4L/xuwd0qh6WRk1b70c36GW3HBUE/dO/+rbSh74cK5kbd/tNhn6OjuHACqG3IONHkLnt0fOaSdt65aX9dk75bTHcolh2fQ0uf7IJvIPjxhyHX2EDCAMIXxAKtkUfXKmZPGufq2lR45l5OgHRsqOnLEddGkXOec/4xKIYeWyJWX9lp3Sp011R8Ive3GyIIDxf5oKxG9f010e+jiHGOYIdfo8v18Q4pcLSQzDeefm6gH1HbGBwD379SIZNm6FnNu7nlxwQINcYjhslhMA/nhsMzmybY344296fbqMX7RB/nlqKzc/lBjecVJLmbR4k4yYusrNkT73/eCu6b9/PTl//wYO689nrJEujSvJff1ay3EPjYoTB735oEs7S+2KGfFnqQCi3x8/hET3qLbVpXOjyvLQR/MkrUQx6dutjiOGfFePfDo/viaklywu/XvXlwkLN7jvn/kCqUNw1jUB5QfjBEli7kHyVHnwx+Oau7n+/vU9nMCNIPrlrDXuW9PrEbwRwGkINvUghis2S7v6FeSyQxrJM18tkslLNzpSCDnEcjZ8fC4x9EkSz+BZxz40Ssqll3DfJE2fkZm1wykgWF9a164gtx7bNEYM358vTWqUlSyI4fQ18XWuUpk0KVasmPTvXU9O7lQ7jjHKmQufmyjM59tyBMvIDzo4iKKH718VWvozRJ1xLlZM4ootxmLouOWye/cewULptwRiWL+CTF2eJbty1r3PbuntFDqMiTa+3UsPaSjP5RDDTg0qCv9BDBH0Tu1axwnid/dr7b5bJYaMP+vHq5d2kbP/E1ujdS1RJUKUwujM7nXlo2mZcnDLqs4ijiCNABrVVDHZuFqZuNIrP2L4pxOay7/eja2dEEMUFqyv5TJKSEZaCTdHGeuvZ62NP4r9DcVJybQS8u7ElU4xpuuAfitKnLjI//bzEMORywQrbdge799OWtUu7xQb4M6ayrUQG/C64aimccUZ1yKQt69XPk4MLz6kocxdtcUpJtg3UPK9M2GVe4wScf7N/luvSml5deRSt4/TQmLIvgBR85uum7r2v3tddzcuz36dqwzifFXGMSZ4MFz8wiSZl5mrOGSMIEuQ35uOaSZfTF/t9gn6CzGcvHSTfHhDT5mweKPcOHiasDZjES9bKs0RXr/99bim8s8P5smevSJ/O2U/OaB5FTn50dFy6cEN5Zj2Nd1aMXh0IjHs2bSye5/N23a7NUjHWolhl0aVHMnyMTi2Q035YNIqufGopnJch1zlDu8yct56+dOwGe61eD7vQWMNXOiI4S45vE11+Xrmmvj++e8z27hvhxZFDOtVyZCXLuos05ZnydWvTHFySJVypWTK0iy59tUpcYUa16OYYqxU2e4TQ769upUy4sRwTuYWpxR97bKuTpb4+6n7JXgQjFm4QW7FsyBHeQ/2FTIghqPl8kMbydHtYvsR44AHwrvX9nCKfR0rf2yuOKyx9O1aW96ZuMp5B4kUk7WbY2sQ+7LOvcgP+jd88NdIDBctXioPPzFQrr3iImnYoN4+ozviky9k0tTpct0VF0lGesHW4W3bt8tDTwyUzMw1cvsfr5dKFWNKkQ0bN8nf7n7Q/ZvjixYvkRdfHZpwTogn1zzw6NNy4R/O/En6YsRwH6cDGz0ftOzd6zSNtw2dEdfgJdu09bEqhPsbFhpeFh4IJ+6eNBUk+DeLyxOfL5BG1crI8g3bHAlg0WIxXrUppslHAD6qTU059+mY0HFy51puw/LJi9/1mhVKOSEJkle/crrcMiS2qLMRPPWH9tLn/h9kNzuNiLxxRRdZnbVTrnw5RiTQgj3++YL47SCsbeuVlxoV0uXdfIghG9XFBzWMbyh3fzBXPp6aKRcd1FDQiqsAhUaQTQRi2LFBRVmxYZsTUg5sUVW+mb1Whl/TXc59erwbg7N71BXuE9UqlU6TIZfHLIaXvTjJWR83bd0t701aKUOv7CrXvjo1bu1rhiC7baeznI6evyEPMfz3h3PjVgP6qht8jQqlnJBNQ7u5eF2MaGp78Ky20qF+hTzEEGHmuiObyF3vJZLag1pUlf87uaXbOHlnJZu+JVqJGoS1Z5PK7lG+xQ4N63kDJziL4E2Dp0nWtl3O4rMhe6cc3rqa9O1ax1kMH+/fPj6ejB3WuTev7i6PfBIQwxxFht8vyMCgS7rEDyWzGEaNDVa8l75f4ojHc18vdsTw7F515aIDG8aJ4a3DZsm0CGJ44+vTZMKijQIR7NGksrNo0bCoQQwRohOIYe96cv4BDZy17YuZawSB577TW8sxD450grrfXr6kk9StlOviivsyFin9/jg3JLqQOYjho5/Mdxaxvt1qO9KrZLJ9/fLCfbCyQIp4d5RHzJfMrO2OvOiagNZ+ffYOuT+HGPrCO8Lj/SPmxokhiqLv565zxFDXHV8guwliWCVDZq/c4pQT9AEBEWIIKeS/EzrWlHcnxoRmGlr3ez+IzUksnBB38C1dsrisz97ljh/YsqrceVJLWbFxu1NAsL60qlNebjs2ZjG884P5gtC7edsuZ+FXKwPWMBpKAZ8Yqvb+H6e1kr/kCJaRH3RwkG/ijB513Rz3G5aML2auTSCGjMXQscudMk8Jrl6jhJu/OzSo4OacEsOPb+olZzw5NpIYQmpYszs2qCRdG+USw+Pa15SXf1jqiC6Km/DbgEj1f2a8ezzrDkogVSJA4iA+vsLo9G515JNpmQ53CA7WsWnLEsmK9kUtz/WrlHaeFrSQGKoV7Nbjmss9OUq1R85pL+MWrnfkpHyZNClZvLgjn/s3rxL3huFezOVaFTLcXMYCUqdShrxyScxzgPXjmlenyFMDOri1K2yFJYYPn9NW2tWt4MgQaxaKA77t10cvd4rRm49OJIbMUyy2WKxoEMNZKzZL1tZdTlCH6Lw3KeaF45Mc5mXdyqUdYXrrqmhiqOPj90XXTd0/2Y/eGr/CffM6jpwPMeGbUYshyg/1UOF3FKaszRBDxhxSDmHAgq5rPBZrJRsoMyFeZUvFSLvf/nRsU7l7RIwYsn+wj0BiUGCgiH3qi4XOU8ZvKC349vCEwaMDC+J3c9fJce1ryMSlm6RT/YpujH3rM0oXvHxYi47NIUd6T67965sxi5+/DuHptGDNFsnaulsOa11Nvp69VuasjBFknxh+M2ed3BFYDFXxoMQQOaRauXQhrOH616YmKOJ5pzkeMUQRtTXHYsh8RZ5Qi+HsVZudUvTVy7o4RfqdJ7d08oW2UQs2OCWVro/IeYznCQ+PkisObeTINm30/PXyx6EznEyCUicZMYSwvvz9EkcM122JyQysiW9fHbNW/96aEcO8I6rEcMvmbOnds6sc0+dQdxIE8/uRY2VLdrYjgzSf9ClxrFGjWpyETpoyLQ953Jc5ZMRwX9DLEcLLZ5SQtOLFHDG8dcj0uPuoCtjJHoHwgQDrC2VseBAiiOFHOcTQv8elhzaS/3yxUPwNn+txL8zMIYbn9KonR7etERc6VNvlkxf/njwL4RChiUWTBY6mxPDw+76H+7r22mVdHDHCnYN22aGN3GajDcJKv6qXx2K4Ik6WfIvhiWxUB+cSw7venyOfTFvthHYEZhWg0Mhv2b5L6lbJkA71KsiqjdvdRqgbF5j/4ZnxUrVsSTmrZ125Jx9iCJ5DPWIIIViXvdNZKYdc0VWuHjQlbtmEgPHMelXLyLgFEMOabrNWiyFur2o18LXTCKM6AAAgAElEQVTxuI6pm5nvVqq46MYXupJijbn2yCZ5rJ26oerGpdZe32qnVmkE6d5N8xLDgRd0dG5WCGgIzcShsAFhZT50v2rSr3sdZ/nFmok7Dq1VnXIyY/lmGXZVN6fh/WrmWkcMIC1odNnw/AYZwPKhrajEsH+vek54fufa7vLCt4udFffMHnXlkoNzieHNQ2fJjOV5LYY3DJ4mExdvFAT6A1tUkeMeHOVe4y8ntHAuUAjRjhjeH7MYqiXyzuGz5KtZa3OJ4QMjEyzonIt7LN+CNnUNCue7jwXum8ytxz9d4MgI+L747ZK48sQX5HGhnfhfbwHwZL7w/aHkUfLIvTZs2ekEJjD1rVnqbkjfEPYf+GiejF6w3n1r7eqXlylLspwLGW5RNARriOGslVukTZ1yTgH0ny8XOksEz4Ug+UIz1/iucBBg3FYh0KVKFHdCLE2J4dJ12+QPA8cL60ubuhXixPD/3p/nXPSwROCGeGz7GvLB5ExhzdwrxeT8A+rLKZ1zLYaqvUc4u+PtWcmWzoTfELghhqH7Ke64X81a51y21EMCRdbQscucBwTfot+wNPB901jDIDiqEKP/Zz81Nk6KOQfsWY+f+RJX0jTp0KCiswwyv1BWoXwZOmaF3NW3lVNchN+GKm64l64l6oHBunDD0U2c27M2LPyfTV/thG0ITpR7o56rlmNdNzjuu3Pyt1rNiOtD4UWDGI5ZsE4+n7FaKpYuKcX/q+AAB9+FnfOYy3UqZkjJkiWc5ahWxQx59dIYMVQlCt4mGnLg44wiB6untkE/LJVnc2Ld/PNUmXb94KnuW4Ao/fmE5jJk9HJpXKOs3HpMs7grINch9KMY8YnhzOUQw52O3CqZ4Vyf6DEveX8UBiqcP/HFAjd22qK9QNo7l1ndP1GmvTVuubz8/dK4MojrcXFl7VViiLs0fdGGYihr+y63r/Btfz1rjazK2i7L1m2LEx6fGGIt+9ObM6VMqeIJ7s7c749HN5F7Pprv9uu/nNhCDtuvmiOGrKdY9lDi4obsN/aU+ZlbZOuuvY4Y6lgrMWTv5bv1MeDbwj2cucN5fvMtfj4xJDYUQsx6wLfBGoxFl3b/Ga2lc8NYPDCE8f+C718VD2qNRg7BK0nXZd/1m3easyorbh1m/2f/0jWAfigxnLlys9sD1bsGLx28dbSNnL9e/jR0huvj+5Mznbsn3gDHPzxKrvSIoVpJmT+sBcmIIUr6V35YInuLFZP1ORbDchlp8s41v19iuHXrVtmydatMnDRNypcvJ3+5+Rpn3VLL3YG9e8jrb77rYD/i0AML7XaqRGn5iphS87Ybr5QO7dq4f4eEFAI14tMv5bIL+8tTz77s3kUb1y1fmemsfmVLl5bvRsZc/PV++pwBZ/dNuH/dOrWkdcvm8o/7HpGsrJiSrk7tmgkWvoSPI+cPJYa9unaWH8aOl8su6O9+eeq5l4Vjwz/42N0jIyPdWRaPOeIQ91zw+uSLbwQ8Tzz2SIchZHLZ8pWFxizqffxjRgwLQqiA39nosfikpxWTN6/q7gQT1eCpgJ3sFkoeVZPOuRCrymVKus3m42m5MR16H+ImcE/yyQdC3cj5G2VNVsxiCEnCxWHAMxPc30o81TUzfCc9jiUBYVjdQHDXgVT4Ag0uduu27HLxVjR9H70nm1ynhhWleoV0GT5+hRM8ac9d2FEaVY0F7LOo4s5Gv2lKDAfsX1/4L3S5ghi2q1NecFnDwqIb1xtXdJPzBo53sS5YGvMjhizUw3KIIfEdEKo1WTudsEqcD3GHaj1Ae4s7Tf0qZWTcwg1uM8VdRmMM7x8xJy4c+pZB1QrTnyjLLC6BCGshMYR0EWMYWjt7N6ss/zi1lejGxWbEtb6Lsm6GvvuLP1bq6sT/b3htmothw81ly/aYuxAEjE2RJD4an6UubUOu7CaPfRYjhmqxVkWGP3/QluOGo62oxJBxe3XkMuce9dJ3MWJ4evc6bn5ojOHNQ2bKjBWb5dZjm8lRnispmmI0xjcc2cTFrBArSPvT8c1l8n+tHCgbfGKIwuTC/yZZQOhA+EAQQSDxXaW1H74ig2PjF2905PqSQ2LxeVGNmLsuDSvJE18sdGSkb5fajvQqmfStFFhKpyzd5AQIf74oeeReG7N3ygNntnVzz7fgqZUeMo3Qec8Hc9x3wbem10NQcGGkQQzrVk53FkMsergrOmK4JHYNcS6+0Mw1oYs4ginEsLgUi8eKoVmHxGlsIOtIm7rl5c/HNXcWQ4hhw6ooWnY7l05cbVF28c579u51CS9O65JLDImxg0j48ZSFWaIRuHHb1bgmvSaMi+M47l9DsBju3iMbciyfej4KIEg2DYKONU6JIRZtrKL+NUoMUYxBovg+sNxDDBFG+cawwmrcUvht+DF4xIDizq7WZb65G49uGnfx5J0ICfhs+hqnGMNaE2XF0r7oOqGkheOh67POR1U0cA5KojEL1sun0zKlctlSTsEBDmFoBKQUi3rJksXkw8mZTkhHWKepFcf3RPDHMRS+8yOGqkzD+rgpe5fzwkBBMmzMMufRcdtxzROIIc/wPSrYm1Ao4aYJuT2yTfX4nuqTHCzLbr+asDJfYsi+4JM5nqXrprqKQhreHLfcrWfMM9xRfdwhYMQMDxg4IW7F5Xf23C07djtiyLeJy+6KjducslKVcT4xxFr252EzJaNUcacE8tstRzWWez+KefCwDh7RurqgiL3koAZyfMda8Thu/xqegcUQxRQESsdaiSFxw3y3viIUxdUXM9bIDUc3k+MDYugTOyzNf89xJX3go7m5xLBNdWcZnZ8ZC7m474zWbu2kRRFDVTwoMVRXf40BVIUa1/Mdz83cHCeG7P94hEQRQ/YVvJ9euaSL87BCqXhYq1xi+P289c57gTmCBxfyQrVypVz4wZWHNYqHw6iVFGsuXjlRxPDywxpLP/I9jFshg0YuFT4uJYZl0kvIe9f2KMxyV+A5xdfNlGI7E0NZCrzoJzhhT9WWsjetbJ47QdBGjZ0QJ4O+2+WqVasdqerRtZMjNlEELL9XU3LVoW1rZ3GDNHGvqy4Z4EhUfsQQd0+eG7qS8l4vDhoSJ4O+JY53wMUzihjy7B/rSgrhg5DS6tSq4f7N/333UZ/48W8aRLBb5/bxfkJQ1eq4r0NpxHAfEWSjR5uOcE8w9A2Dp8Y1eFExCeHjNG7M19YjIFQtW0qqlS/pNHJh02QdviYYoY4EAWtytE8I1riN4EZIixPDnMQj4T3V2sVmVrdK6bgbFyThcVxJc2K0uO6lizvJhuzdTlNKu+CgBs4FUBuEFUGNjfbt8SvihMsnhiyqlx/S0G1UtH+9N0c+nb46btEJBSiIYeta5VySCwRgdZOCkFzw3ARnMTy7Zz25N0fbH/avfOk0eTOHGBKvx8a3YtN2+WbWWhd0fvmLExPI3rZdu51rEa5+xFLc5BHD+0bMiQuHvBdaXZpa4vh3FAHXOCNN1KHviAAIMQxJLXEf/zqtlSNEbFyq7fetdkrUSMhwYPNYZkUfO+KFIPBo7rGukVhAE8mgySUui3sjgEEeaCowvf7f5CtozX1iqNYoH18ET87VVlRieFaPuvLaqGXO4j7ohxgxJA4DJYUSw5uGzHRJCkJiSGwoBPDKwxs7YqPEEAsi1rDPAmKoLqokNkCrjQLj32e0SXCV1n48d0EnaVQt15VUY0hCRYiPBYkWujaqLE99GRMIIYav/LA0TiZ9YfTcXvVk6rJNbr1QwZJrVKGENS4LYnhWjBiq+yjnKDnVRAjEHkIyUW7o9SghUEbQWJfQuGORa1mzvIuZhczELIbbnaXbF5q5JrSMOmL4wEjBcUBjWJUYaoIk+tGqbgX56/ExYngHxLBKGSf0IkSSOIhYQwgVhAssfWKoc913mw2/5ai/Ebhxi8aV328qvPrH6Ncbo5c5V1Is537T+D6OhcSQ2CEER1zbtfEtERf91BcLpHxGSecG3LtZFUcMcSfr2bSSW8PVoh9+G76rJeSABERKDLF+YfnwE2rh+YElr1fTKo7gRJEVfTddJzSmmON+SAJ/6x7lKwGUGH4yNdPFcJGhlAQ14bePWyFxebwnnheEDgzOIYZqxSHhlnqW+DjjDorHgrb8iKEm7eEejBX4oCB5e9xy92yEeI3B1nuFxHDa0k2yadtOR241aRnn+iQH6xd9fX9SZtxqE1oMo8IDdN3U/RPSQJI3XFLVs8fvtxLD/gPHx/cNfmfPzMZimL3LKWRIyrJk/Vb3PatruE8MX72ss/z1zVmSUbJYHlfim45sJPd/HFt/WAf5rnF7ZM04oUMtefjT+QmxxJzHPjJ3Vbbs3hMjhjrWWFIh+a1rl3PfrT/fNJwDEqYKXu3rl7PWyt+Gxyz+PjG8/8O5shCL4fbdckRADHWsuSYZMSQRz7WDpjo5pF7l0vHYPt9DC2+HeR4xxENh+6698XXLtxhqoiR16w4TnCnhUwupJiVDSXbVYY3j4TD6zmpRTEYMsdi+OnJJjBjmeC2ou35h1ruCzsl4/3wpvq7wHhcF3a+wv2879jnZUzUWT+q3ohK0wrqeRsUJ+tcW9bkhufNJasMG9X82Ysi9cRctU7a0sxyGcYVq6YTQDhv+gRzQs5ts2LhRxoyfLH1PPu4njS9k3IwYFnbG53OeHwsHMcTlBddQWjJtrt5Os3ypmwLHcRFlo0ErRVxO2DQmS+Ma+B1t1piFG+OBzP261Zaj29V0iRxoapH03R39+6q1C6EJAVIzgkEMIRdYVLTh/oT2Fi0ubcAB9Z27nDYIa/fGlR05wmKoWbd8YsiiihuGEkNNBoKlk9jDKGLYsmY5F2cCMVRLGe4fuEpWKVdSzikkMSTwv1PDCm5jJlEQbpCXvjjRZYGjQfZ27tojdSqVdm6KxBAQy6IWQ+KuVDj0ybm6vXIP33qouKjVICSGCFYQw5DUdm9SSe7u2zqehU3v48euqpbUj9nxscMVC6samnusXVhCVTjEInlOr/qOGPpZIFUAgnQ/+cUCp73mHSEDUe7RkAEst9qKSgxJNkTMEBbK10YuccQQsgDZU2J44xszXcbGkBgSGwohIsvmiR1rOQGIxnmTlm6Sz6evcRn81JVU5xfxL2z4SgyPuO97F5OTsJmd3zEhJb1m2lPFTNSSgCUHV9KnSaSxd6+c1qWO0wwrmfSFSyyl05dnOWLozxcV1iGZJM3AKgGmvjVL30ETIZANeeaKLEfw9Hq+EayNNIhhrQrpMmd1tnMfRNEBMUToW7M5RgyVtGm/iM/CRVKbEkM/aZVmHFR3Z6zauJL+9YQYMbz9PYhhaRe7hHUATTyWQwjVzt175JKDGzklgDbN/OrHvEXhHB5D4D6lS508cYlYXVFs+A0BGWJIgkxcxv3mJxYKiRAk/A9Pj48khk9+vkBwBSOG2JHl4bOE9QAFFLHQatEPvw2fONWpnCHL129LiNn2Yx55TxR8X85c6zwm2BuiXNbDdQKhWNe2MCZWvQP8uNlHzm4noxeuk4+mrI4lVtu918U5hx4wCNf1q5aW9LQSLj68Wvl0eePymMVQiSFuqapA9HEmkRKkXVt+xPDuvq1cRkw8OnBfJvkYCpLhE1a4BCK3n9iyQGI4ZclGl70b93hfUeCTHOYlXjofTs10yUNooculrwTU99Z1U8k3ayYWQ7KZRimQVFlzztPjEjLiso/gwgkxxBuAeGHK+UAaVPnH90eiLRJuEf98x1uz3FpOv/x2Q5/G8sAnMYvhLcc0c55DeOjgSsoa+dAn8+KWNL2OdX3eqmzZI3udZU3HWokhawqKHT98hf2DrN2QsBOC5DN84xob6xNDSrosWpPt1oPDW1eXr7AY5iRpK4gYquJBiaFmOdd12XfTxwI+f3WuxZBvAMKrCfQSiGFOPCyJbXCHZ+05qk0sWzpN4x3V+wB5AQ8PlGSsF5p4h5h1Ytc1KY6OlT82ajFkfgwelWMxzCGG6q5fmPWuoHN+jRZD3lmzkvpEJz/LXWFcIyGGz740WG64+pJ48hbfuravxFAtklj1fk5iqNZNtfqFMYOaXOa0k46JucJe0F82btwk73zwsRx5+MEy6PW3EjAoaH4U9LsRw4IQKuB33eg1cBiyhMsKzddI5ncbFeI09obz2OyrlyslVculJ6QH13tAgBA2fSsD2qyxizbF3RIQrCE0mipcLZK+9th/J03ygpaTejvq39+8VllBUNBsj1yDJYVNmkxgNI0R0/tBWHs2rSJVc4ihxt09e0FHhwmNzJlXHt7IaTBpSgyxdBJn6D9PMWlek2yhO52LhhIitHwXvzjJ+fyfm4QYkl3vrSu6ueQzYNKxYUVZvGar0zbiQnLxCxPjWSkR0HAzq1khw5GOo9vXkFuObuaEczT/EEO1NPgugH78DtYCjcNSXFQ4DIkhv0M87/sw5sKmrWvjynJvv1aiGk097rvMKFFT1yyEdh87FVwghiRqQfgoWaKY2yB7NK3sSkJADP0skCqgsgFi+fIzEaqF23/PMM12UYlhv251nCCF1ZHN8q3xK+XkzrXlmiNyieENb8x0WeZU0NHnKzH8w/71hcQcajHEqjBpyUYnzOBKemROjKHGLmr2YKwqaP39GFq99zPndZSmNXJrlWmsSagI8bHAyos1e+A3i2Xvnr0u6ytuZZSPIVOhYss1ZJiEzCFE+/NFFUoIVFg67u3Xxo2p7+6n3xwxTSRWIrYZywmCpF4PsXk4p84n2ToRrOau3uLS/4MjxBDFB14GuCgradP+hAQYwTR0udX4IbX0sb60qVPeuYIqMaxfubRk79ztCJoSNVwad+zcLZcc0siNmzZ1E/Nj3vJbO/3jEEPmjCa80N/UquGfi8D++qilsmu3xF1i9Xff/S8khij+Bgwcn2BlVIsh5SX4Dsj4enCLqq6sCH3skJMhU7NDht8Ga6sq2HQt8d8hJMgoEImZ7Nakkpvb+SUToz9KKNRDgGOq4NH+ajIR3z1aiSGlXpgzEAWEdz8DM9fj2YI7Z0ZaceeyjSITixlNrSV+//wxCN31sKo/FxFjiMcE3xQJbIjBI3MvCpJ3J610io47T94vDzH0XV4Za+J4ie/D48DPrOoraYgp4xsktEDd+UJi6CsBtS+6bur+iaKSUii4CoaeNFyjxJBMtFryheNa8gpiyPhTf5WkZhB6nYc+MXzxos5yx9sznbcSCjO/XXt4I3n4s5jFkJhZ9aRA0XNSx1rOVZpEMn6DVM3FpXNvLBZX5wXXomBjTWEf8BURmh2bdckvOcN98f7BC4jmE0MyKeOO60pi5FgMKV9BY69jz6MRe4hyxW+qeFBiqB4dlOvAU8BXXGD5n796SzyZFmO7befuODFs/F+lxrM55So0HlbjfRUzfTaKHeKdNRM68kKdSuluLcS6q9bSz2askX++OzvuapqMGJL8xxFDKeaU3TSyWH98Y6+EPv9e/igqQUs1i6HGROp4h8RQCWrLpk0ke9s2Oef0U4Rjzzw/SOrVqS1r12/4yeILeQcjhvv45YXE8OpBk+OuHVGuJ+HjVIg7ul31eGkKNqCa5dOdawuat7Cp651P8tBmjV+8Ie6WgJB0TPsa8dp0apGMsmRxf9Uqs3mQUEAXZYjhQ2e1jSf14Nxnzu/osgxiiaKRDdSvi4SlEMsJ74/FUBM8+MTwSLeoNo4Tw7+9M8tpwrEeXHBgw3h2Se07GxJp7zfnZJdT7SCLOUIDWPTvVT9fV9KQGCI0Iexg3YVcXvTCxHhWSjZp4p+oI4mgCjGknhjCOcTwnvdnxzXwfvyOr433hTHtg2Y7w4J6+hO55Sr4nRjGf+fENun5mjFTM/zpcd9qp/9WDTxaUc3MyfkkvWDTxDJx0+tTndaUbJm48XVrUtmVNYEYYs2ELNHUEs0GSL0+nxhGxc0yp7T2F9cXlRhCnpxrzWVdZAhZAcevdJptMrWqxfCG12c4q0VIDEmYwxhB+HDNVGLI5o41TEuaKDE8o0cdufTgRnLbsBmuFAmZbh/ISe4SfmdPD+ggzWrmxkuoS1GoCPGvw40NYvj8d0tkz569zpWUb0NJlp/8CUIE2YUY+koFdTflG9q4LVbHkLnnuzmqlVUz5OGpsHhNtrM46PV8I4+e0969HsSQuTpvdbbL5khsFsQQAR5iCKEMrWvgicCuLYoYqtCnhI7sf23qVJA7TooRw7++O1dixHCPm0dK1NRqjRCMYkCbCmk+CS7MEo3AfWKnWnkS1oSZNLkXJH3wKLKSxuqa+c0nSKF1nGRMxDOr9Y3rGDeUaRBD+t62XgWX7ANiyHrQsnZZl4hFXb3DbwM3YS2xoXPDJ4Z+XCnPY5/4ZvZ6V5oFN/j8PEA4VxVIUSVitM+6/6jiguMPn91OxixcJx9MXCU1K2XI1h17nPtfqOhkTUbAppwFRICSQWR4pmk8rh+77ONcWGKoyjS8PLDuQqaYG+9PXik1yqU7S6wmLNL7+x4VEENKwqCkY/1AkYELOc23fuG2TYZPCAkeBrSQGEaRcM2eq2EE7CWUQqHsS5RnAaSVLMOURfBLpbDmQsiIX8VrZ+S8dS5RFATKJ4ZqiWXfw6sHUq7lkrT/1xzeUB75LGbpVzdP1sWLDm7gMgDjzkkiGb+p55IqDnSslRiypnw7e128XiPXKgGPIoa4OWvpKJ8Y4hbtiOHOPc6V9IsZq2XhmlgSngKJYY7iAWUtCkHkkKbVyzivH/YuPxFTSAyJad6+K5oY+pgSehO6xipJZY7wzTHGzH28UHiOEkPt8+uXd3GJ95IRQ1yN8VogARfKblrxYiKf3ty7MMvdb+6cohDDopSRUMJUrUoVR4zCGEPfNRTQSOJCwyVz27bteVxDQ1fSqOvDeMZTTzzGxfYVNTOob40siBjyzhr/OOCcfvFYQnD99ItvxD/2U0wOI4b7iKJu9BCr4df0kCtfnuTiMWjJ3Hz0sSrEaWAzx9mAEBJIxRxFDBEo0TiplpJrcJGZsGhDPPaN8hfHtKsZzwinxNN3K/K7rlnT2Egp/qqFtNEcPnR2SAw7OOFIBRp9H70fmnNczLB45kcMwc3fUO58Z5azKJBcgTIFxCD6DTeexlXLOM0vrne6kZF1E2LDM/v3bhBPrx8OK7XX3roy12KIZp901tQFfPHiTvEkPVyHAE3CBf5PXArYXdunqUcM58StgZoQJnxelDCmmfiiLIZRFhJ1BdQ6TfoM32VG/02mPpQDuIpqZk7Ox1KBAIE7MMTQF4Sxspx/QEMXA4TgqunB1RLNBvj0V4ucK5y2qCyICMQkXtJWVGKoNTYhosPGLXNF1jUTrBLD61+f4QpCh8WUVRFD8WFKH5z48Gj3GiREmLJkg7OuUP9L3wlCRUwYNcDI4ovwrK6a4RhS4qN5jVxiqC5FmiwnaumAoHRtUlle/HaxI+GqxFEroz9fKGUBMYQ4+PNFk5CQxXLLjl1xYui7OZ5KAoOxK+IZ8hhDStcgWKqLHGP1RP8YMSSxEOsJAj7JrRDMSYzhLIZZ2x2hVMFH+6Xvrn9DDMOxVVdSFa4QkCmHgWuzEkMSlCAIMo80m7DeEyHYtxjqffxU/4VZopnL1BsNrQzq7ubfA4F98KhleepWco5PkEKLIe7SFz43IcETwLcYYpVoV6+89GlTI/4emvVTlUIhfrjPUfuMpnHJfmynnyWVc/AM+XbOemd5guD4cc0hToXJiq3E0LeCKzF8b8IqRwS2bN/jkr6ErpTsUygZmLvsU77ngCYE8Ymv/34hMcyvwL0SakIiqDurJZU+mrLKJRxDoRUSQ7/f7GfUZGS/wgLnzwe/PxBGXGK/m7vWlWWhUQIGy582XwmoxyCmWKnV4s9eMnTMcmep0pAPv9/qoXD6k2MTSqWw3+Oqz/eLcnbM/PXORRSyqGs8WXGxzLPvsu/d+fYsZwH26yHyrKsObSCPfRGL+dc9lv0UV1JiVHHn1PrI+m7quaTEUONdIYYTl2x0SgHcRn1yrN9HgcSwWRVH4Gl4BlE6BcLbp20Nl2FX6wdrcjbOQ4lELVC/oQQZckW3eCkUEjchP6nCzi+lgSVv0dotcZfZsEyL70qq1m1CXS54dmKCXMLzUS4iD6lSgTFG2eXc+73EOx9NW+1KvmgMYlSBe61jyHwfOmaZC1/w45xZY3+PrSBi6Gf0pP9+ZtGC8EiWlVTJl2ZCPbbPoTJr3vx4mQclW/pMEr+QfEZbmF1UiSfZRzt2aOOylzZr2tgRNf9ZRclKqtlG/X5GkUyO3fXvxxOw4dhjT78YT+pTEFaF/d2IYWGRyue80GJIRj117Ujm5qO3Q0hDWNPAZo6zSXAtxAp3obCphUXJHL+j7YcwqfYJwRpXUq01qMQzP2KoCyeCEdm/cImgYTFBw3nyIzGBm0ZdQ5JJaLISBFw/rTdEAZcdNMgkn9Esfr7FENc9tG1YhmiaJZKNC629T274HRe8RlVLu6ya9FM3AYR3it06YtirXh53TH1nMnG+nUMMETLY1EhjjrbVTxnP+RAjim8jeOB+dFS7mMUQSxwaYuoNamxSfhbYqOmiwlCUxfD6o5rIgx/NT7hMrVma4U9/9ImhWvA0YB7irOSI8yGjCMvE+twMMcwpfM9vCCkXHpRDDL3yAKpwYAMc+PVipyXVFrqTcVyVInpOUYmh1tiEiFLDkDmjCX+UGF77+gyZF2ExvPKVKS7rIHEe1OmKE8OjmrqkNF/NwpW0ZzxGVrOdUqcTgbF9/VgcHjGGYXviD+1lv1rl4ofVpQgX0LBAtJ4Emccd6pXvlzhiqJY9tTL6yhzILJkANSZZ76FCGcRw83Yshm3d3PPjwLT8DHGZJF4ioRJ1EBEy9Hq+kf8M6OBui0WR7xH3qibVyrpacBBD4nXXbt7uvlHfmsI14XeN0IKlX+v6cQ5ED6FPiQACMllJ/3ZyjBj+5Z25TkG2bedeRwxxCyStuzasbWd4FkO1NPlWs6hvKTwGUWLOEFUX++QAACAASURBVOPjNzDE1cxvKBAghoxP2HyCFBIr4scufn6iU05p8y2GrKHt6lWQI9tWj7+HknSNAQ6/DbXocz9dS/ykJQieD+SUkeAckqd8N3e9S0iCpcSPaw77UhhiqIpJDU/gHihKxi5c54RqMtlu3LbbxT6G+xlEiTkGoSK+yi/QrYma7j+zTZ7akjyD5EJHtMqN46K8w/Pf5iYw077cflJLOaRlVTnv2QmOTGhJJWIBWfOJwQ6Job8+QgxHz1/n6uZRJsEvueG7hlLapWRacTdXRigx/HyBU75oi7LOalkVHQcyGSPwU9YgtLhzHyWG/Z4YEy9txHH2HJIh8R06Mjt/vfMkoGl/IKwzVmS5fffp8zoIylRqTCI/+O3yg+vLk1/FYv5RsLC+sn4Qh01pGCx5YbbzMB+CjjVKUfafBlXKOGx8jwcNK2AfZz3y24jJq+J7sa4R/E6SLGIMt+/eI31aYzFcE8/0qvGknOcnr9H7Vi5XSoZd0dWFd2AxRA6B0KrCzu8DSrRFa7PjrqR8mzt274mvXb4rqa5dyCfIBoqZPlfjJVWpoLGNyDDXYzHMkWE+mLxK7v9wnivZggw1btEGufn1mNJHmyrCCAVCgUBYeyoQwzwLrXegKBbCZPf5KX4rambRn+KZv8Z7GDHcx1HJtRimucKmlELAskGL0jCGj1N3Fj9bGpsERKhK2XSnrQqbCtK++xna/kmLN8YXGUghxZU1I5yfJt4XbPTe6vqINp204+oG0rRmWbmvX2s59bFc10fi1bBMaTFptfjovRAOD20VI4ZvjVsRT9aAlrNJTowhuFG7j5gHGvES38xe57T+lx3aMO4SqPcED7R0ZAtFkFaBBuGdcgtsZGzELMxRjXpPw6/q7mIML4AY1isvWOLYVJ85v4Nc/HxuEWYEDtwtSdWORefIthDDxs6KiRAMMYSg0vIj2lHvoOQN173Tn4jVyNHm14zTYxojFmoefXdOtZxqwDxJFnwSr7Xv/DqFen/caS86qJHLWgpRUKFahRw2QGqL+cQwKqESCgrKJmgrKjHEuo0QyvOo7wQxJGECbqNKDK8ZPN2lNQ9dSVF8oEXv06a6XHVEYzkpx2KIFnvK0o1O85xADLvVca5aN78x3ZUiQZDHlVST0/hj8nj/dtKqdvn4IXUpCi3k/jWMWbfGleXVH5Y64qHnquDta65P6lxLFqze4iyGftM4RNxSsZLce3obpyjxLUkak6yp03GBWp+9w1lG9HpfK47FkPm8cHW2iwuDqJB1cfzCja7AMgIpwjFkQxvvh0ucNiwWuKRp+QaOq9CnCSCYCwiypKdXYkgyq2279jhXND9jJNejBIJoa1MhLVlJkKhvC7c2sjD7xeA5L3wex7DkQOw1s6p/P99NOCRWxI9d+sKkBGJIHCH1WCncjeUMRQNzV78lFbDVWyD8NtTixDuo9c/ve+hSi2cIY6Sxi8ncRH2CFIUZx3Qd9ZUdSgz5FutXyXDWZFw4w0zLEEHiVcmmyLdRsUzJeHH4MQs3yK1vTE9IauW/Q5j5MT9iqMo0atUSI4ZSDaXBJ9NjNRbvO721U+4RTqDN92pA0QmhocC9Xyyec32iC2EsXqyYU3DgYUB7LCCGUfH5uPBDdnT/ZI8bOnqZK2sQ5VmgxPC0x0Yn1MNkz+G7IqkZezBuiKowUgJGuRQUlRBDyg9hxUorVsxZc/126UEN5D85WcIhSCigSPZ20UGxDMC4c5Jh1G+hm7B6jaBQJsYQqz9k389roFbGKGL4/qRV8dAInxhCZont37EnRgyxGC7JyeqttT55L03k4r+jKh7IpHzdq7GEaq1ql3MKJ2IA/YzPITHUZFeq1CLbNDGKNJ2rWjpGMdNnE3dKLVFVKqiCm28Zha7mSSDuFeXuK5d0dgn8khFDShgRi0ozYrg0T9kIxV5dJaPWr8JY5fJb9/I7/lMSw1/63Yva12TnGzHcRzR1o9fipCQ20Sxb+bkZ+o9UdxY/8QPXcRxiFWbU41otPeELBWj70expwhOsXBBDLVquqejz0zBrfStHDMuXitfUgxje07eV9H08l8g8em47J/RqMWm1Xmi/WISxgCKIvjl2eTwmR4khWrLD7/1erunT2MU80Miq+N2cdc7KefmhuZYfvSfCLlno0PqxYWr8ppZjIEbj3F7188Tp+e/0zlUxV1KKC0MMJyzZ5DKThrW2sHiWLFFcKpUtKXNWbpE+bau7rKEI5whyBNWjvaYl09iHU0vJWxQxjIqpQjAleUOY9lrJIPdXVzV1sUSIQ+jQpjFKUUkguP8lBzd2WQP98gA6F3Ctef6bJQmupFFFniE7JHjRVlRiqPOZ50EQIYZgftuxzQskhmQr1KQSxGlSyJmGsIJmGaHBJ4Zk67380MYusx/CF8QQi7ifdVf7wTwnkYo2tNsIgaElzR9nhHGIoVqkNLGOxuH65QLoN5kHwzpkKrDiHsk8I8bQuYJ59RPD1Okks6BWIN+/Xo+1CguGw+O1KVKpTCmnrefbwcWYGCrKsazdvMMJpKF1LSx4j8WCrK9RxFBrfTEXcCX9x6kxYvjnd+ZK7YoZzlLN961F1xUzrCNgo00JZlTijmRLNcTwqHY1E0o7cH74PI4lixH146VDYohF+7KXJidkMvWJIffmPbBeKzFUK6CWZwi/DQi6usypB4ifDTZ0qcUzhNhYrLLjFm5MuntFJYoKL9Dv2Z/TZDIeu3C9s5YhQOP6nrlph4uh9Gs4QmSxqIMBChhNwMYzdBzvOb21I4hhKywxvO345o5AnAsx3LHbxatjUYVQoICk1ExIDH2vBojhD3PXuT0IF0a/1I+f1Zu44L3FRCYv3igf5SQACV1JfWu/9kfXTV0zseQRJw3xivIs0PCAUx4dnZDdlnmCWyHfIRZt1nwlhrrev3tddxd3iEKWPesf786S4sWLJZS94L0uOrC+DPwmZjHUGF5ilLU0jCZ688dEvQz0mPYVhTJErFaFDPdOvkyjcye0sHEPJUn82yeGdwyHGGY76ygxhozjUiWGp7VyCdFon89cI/8IrP+qeFBiqOuzKuzU+4rr8a5Ysi7XYogSHdlB1y6fGI5asEFuGzLdWWHxvAjjnhlLyLSWyFIX1lg4TK7X09sTVsgjnyzIU0bDx1kthi99vySHGBZLcE3/vbqSJl2o7MdfHQJGDPdxSEJiiDVKXTuiNpLwcerO4qfRZgOmRhPE0E/8odeGAhvHcXOYvDTLJYWhYUE5rn2teNFyTUUflRSF81VgxZWKxDdaOqFJjTJyT9/W0s+zcBFziLZdY2O0FIa+Hxpknl+5TCl5a/yK+MKnxHD33r2uLmICMcwpHwChveKwRnHLj94TnEiKg6YRQVoJtRIeXFxIPhMmcNHr2RjeuTqXGMYEqw0uZoWYGs2wyvkIHODBRoT7IiQFV1KsJY4Yvjsnns3Qt9oWNJVIs35suxrOHeqMJxMthlExVQh2JI1RK4reX8kgf6vWVu+N4NT38Vzrrloiwz5yLSTm0kOaOGIYVR6A8aIMiR9jGJVpFwzQZmsrKjHU+YzlljpiEEOKMlOcOW4xfG26U7iEFkN13UagIKGKWkuJMYEYYnFH0FPip/UR0boj6EBiiG0Js+DSl4fPbuuIo7ZPZ6x2Y48G3o898scday4CBDHAxHOq23eUlZF+L1q3NQ8xVE8DSA3ZPO/PIYZ++nvNkId7IwIurmnbd+511iy9HmHvpYs7u9eDGGJhYW3C8k5sFsQQi+HaLTuc1jq0rqmXgfaPpBwUyvaJIevOP05tFXfnYn2BTP/rtBxiOHyO1KpUWnbs3ONigbTMjN4zJIaaYTBZ5teo7wyB/8h2NVyMj9/C5/Gb7zYZ3ssfp9DihkUb7wT1FuBa1jpiVrEY0nDxPKFTrTgx1PUBy1Lv5lXyzDMsYmrlVGuyP86ULmCctBHTSb1aLCW41CdrUYmiwvNVuFcvFH5XYkhCqMbVSktm1g7n9hgqwXjf1nXKS+lSxZ23B+smXjM0Fbap3frHIT+eGGp5GhQf23fGyjmADxalcqVKuHfVUinaN195BTH8fs7auDusn9HZd4tk/pBwbNryrHhmyJAYRnmHaDkR5gHKUurFkmEZ98Oob16JIQosP2s1soK6FfbvXc9lVFZPAiWGlEvBGwliSGjAv96b5WLhydTqt/P3ryfPfxdLGqUWedY3wgZY/7A0hp5IoZuwzkUUUCSWIjstRNUPndBY6ChiqCSJd9A1gn/f/vZMZzHchQwAMZy22tWmpN1VADFUxYOGVuj6rAo7P9kf5G7p+q1xV1KIOyWoooihZpsmLAWPgNCLQZPKqFKB85pWL+vCD3xiSBjE458tEDLGYmlXd2p/bNR1/oVvl8hb43ElLRaX2TjPiGHSJc1+/IUQMGK4j0CrEEwMG/WPiIXQYOqokgXh43RB9jMCsgHh7oXFzXfj02sJCB8xJTGrGLE7CMJYDWhYIPF91wQx6qqaH5HRoHM2JVxJ1SUTYsiCfcaT4+KvTnp/NH6qCQ7fh3shVFbCtWjcyrjrlWrauJYskT4x1PIBziXw8MZxy48+1GVqrZAukEqIoeKmyQ0Qhvv3ru9ScUe1jJIl5N0cYsgYQbpIPkI2Rr+GH9ciACHsVMhIc2SEd+JdT3hotCOG/3xvjtNe08KC0cmmk2Y7iyKGfmIRvQcCweP9O+TZYHyNuMZaahB8aI3UwtVR2QG5/+WHQQynJBRP1+ejQcXFyyeGUQW1w8K8RSWG6hZJse8PJ2cKQgXzF6FZieHVr00X0ppT1gOrsjYsOLj7dm5YSW4/qUWcGIInac2xuH94Y085+t8j3SUQNYRtMuoiYKAgwGIYRQwROqlLp001x6GLpT/mEPVujSrJ0LHLnZVMk8So5dA/l37jBqblbfQ3jWVCENm2c5fcf2Ys+ZOf5RCCgGum1tRC0ETogbTo9Xwzgy6J1ZWjJALEEIshSpW7TmvtXOUcMdy83VlU/DT/XOO7t/M3VuETHx6VUO9RhT7V2iOAtaldzmXDxWL4p+FzpWbFdCeUkbwiTOiC4A5R06aWx2TkLeobAyuSWVBKxm9assE/FmZR9n/T8eJYSAyxaF/58pSEpDUhMcTKeEqn2s6y7DfmMtkZw3mmrt6cq+TCJ4a++zDnYH1B4NRsp8nWG9+zIL/z1MqiVnvOY00dt3C9DBm93GWCXrkxltQoXOt4X5QAjDnWYN0DuYcS/H/1bSV/GhrLduw3Lbyux/JzJdXv/cynxjpFi7paUky8dFpxwUU+JIb+GoXlme8ka9tOR6D8MfVdY5knO3btdWvJJzfFSgaExDBMYMI5KOTuHzE3XgYES94bY5a5dSfKswDFAfie8PDoBMszez5x7ShoCImYvDSXGKoikJqllKSBGLKe/+v9Wc791S97wTud16uuvPDDMtcHVbxQHgpi2O+/NUOxUIcKZ99FlOvUGwmF8tTlWS7ennXKT3akLuvsMad0TowxRCH86KcxhYZPDEnUQ+yfUw63ruHKg1CbkqalSfi3XwdR54gqHpQYPnBWG+lYv6K7B66evtWT72bpuq3xshzMVUo0KTFsWK20PJ/jSqrJa3DPvezFyXHM9Lm4BbOuqFKB8yCGLiupFw4zZOwKoZ4pXhrMwWTE8PlvFsvbE1Y6ZYTKbK7fv9PkM8nWKfvt14eAEcN9HJOQGBILoRqw/Kxz/iNVa+lnBMSlqFH10s7i5gvlep26hfr3oaYQmj3VZh+yX1VHDDUOUIuzqvCRX7dxs4JkPfBxTAMOMcQt7Oynxscvue+M1kIOE9wvaKFlgWMIGpVKl3TJRPSdlBji0oGg7hPDPw2b4ZJSQAjcRvNorjsk90PI5b1wt2GDUtyU1LHJEzuUHzFko3vvmliMIfFYaNMRVNHahsIL5LlsRgkhkymptCGGlNYgsYkjhu/OjkxcUdBU0uxtUcQwKtkGJIMAe42B0Pv7cX76by0yjAWUVOjaVLDUQvf+O3J/CBTE0C+erudA1F75YYkTrLT5Kd71WFiYt6jEUDPmEi/68ZTVjhgyfylercTwqlenubHIQwxfnORcySB4WK503uBKBDFEsZJADLvUlisPb+ySF6BIweLBfA6THdE3FCAQjlBA8IXocMwZDyxvzHus6mpdVMuhfz7xO0s3bMtDDFUrjxBJbB7EFWs1LmZommnq9kncG3VHiSFCSMT1VF3+sCRiUaRBDFF0gGGdyqVdaniI4bgFG1yMIcQwtK6F5SuII8UiyzeojbhErI8qnLG+gCmJJCCGt709V2pUTJddu/a42LiQbEF2WXO0qZCWLMFP1HfGOB3Wurr820vUwnlRCViSxYj6lrPwXVm/rh6UnBiicDqlS5148i59V4jQwS2r5iGGftZRVc75mVF9t1LuhQKQhEHNa5aJl0XKb93xPQvyO0eJoV9HF+JCYibqbxKvt2z91oQSHf69wB0lGmtpmfQS8RqAaoXxy+D414VFxPMjhqpMo7wPihbmKdZkMrKWKlFcHj83LzH0CQLE8OuZax0xZG30rah+MhnmCRa/+Zlb4iUDHv1svlNsaotSqobrJpa8IWOWuX07yrNAiSHrDXH62tjzixePxZuhuJiyfFPck0C9QqhZSgZSQjhYm+56f44UE3EeKH47t2ddeWVkjBjq94XHBMSQuY/Vzl/TOS8/7ybkhmnLNzkXUjJ4+8pudcUNY/K4n1rP+LdPDFEAowyDEEEMKXNCNmXaP09tJb2a5biSzliTJ15Yw3WUGN5/RmunEFSFnV8jFsvcMo8YotQgvjCKGDKXyNyNtRcPlHBNQgl/34i58bUE8o9CFuW2TwxfH71c/vPlQhe7iKtqMmJITdt3Jq50BDk7R5kPBkYM81up7PgviYARw31EW4VgjbMiFoIMbrTCWJNUU6cWAK6DUDaBGJYtlWcB53clef6r4742bVlWfLOBaJLxU+MANYaxoHdCU48g92CO5Q23nL+f2krO+U+uxRAyRgF46uPR1E3Vfx8SilTIiBFDjcdTV1Itwu67oHAvtMwIo5SGODWCGKK13Iu7z7LNcauIuiqB4x8OaJiQwc9/n/S0YvLeNT0cMRwwcILsV7usfDNnvbP8aWY5PR/hFlJYJj3NWX+xnFxN5rWHR7vYrH++NztP/bPCTCP6S3p3Ctlqtli9zs9EqMdw1YFMrt+yIyHbIvfQFOXqNqWkc8XG7QljpdaHqLTxkBjeCdLg18jT57NRkj3NFyLClPV6rm5oOraFwUPPUUUHG+7HUzNl+ISVrnTCnSflEsMrB01zySPCchUknYAYslHfc3qb+LyBEE9dtsm9u19mAWHNkeFXpzhChvBPAfmwPArvBmYIctpUQIhy5dZzcLWFGOJKSlOi4Vui9FzctBCKmM9+U5c1rGs7du6V+89s7azVWPMRoGkaO6eJDliHVOmj16MseePyGDGkrEe59JKyeF22c8nmG8YigvCC1RwFie9mxzVhDUDcBDWGU99XhT51t0JJgPB9b7/Wjhj+EWJYoZTs3LXXfd+he2MohGkiiWRxnFFzC1J7WKvqeRRDUQlYosZC7+mT/vBaFCVYmnU94xrWU+piPvJpTJEGkerXtU4eiyGWwYNa5LUYRilkfMuwTxK5vxYVL4ybaFQG4RA7ta75ysaQGJIcRLMwh9fzfUAIIYbsW1oDUGvL+cl1/GvD75hkHFhRwqYKr9MeH+MEe+YpigSIJwnCKMdCFnAEem0+QQDLr2aucV4rxEn6ZNl3i4TQQ8pQ6upaFhLDqHkXxobjsk99OhQcvpJBr9XyOGQJ9bPiIj+UKC6O+BJWgmVfcxWoV8jQK7u5zKrs6Xy/d78fs0rj5uu3s7vXkVdHx9YfSPSA3vVdaQWNndN4fv+a/JTYyA1kQmUvB2eNpeRaJdaRxHDsirgLtB9jyD7PGkQjXABiqK6wKBFQNNHUPdR/R5WxtHyTlrfQtcfPMos7KHLYe5NWuVuEco8ff61rjuYaUMz02WSYReGk6wHhHcyXsOSWzmE8CxpVLePCDVhj/ablKgZ+vci5ueI95a8nRgyjvjI79ksjYMRwHxEPiSGxEKFrR7JHqDAXCmEIU6QBJ2A+bFHufP6iyPksYrUqlHaafJofw5jsfdCcHrZf9bigw+Z58H7VXLZIbRDT7k0rxxM9+EHfeg5uaWR0hFhoQ+hqXrOcc01CG42Qs19O1kfit1jIiRM4vE31PEICGtX6VTNc7Sc0pOrSohppNqyTO9bKt4xAqRLFXHza4nXbnG8/BWh149Wi2wkbZckSklYitlEj7HdvUknQCEIMiQmKymhY0FTSeKHQEsN1flKE8D6h65ufBEL/DcHDEgu2/lipQsAvh6L3h1Re16epI0lRMY5YEdiE/TmYn3u0WgCwaBFYX5SmLotYDhes3uoEEJIpndS5toxfEMuS+f6UTCe4IVBmpBWTtBLFnVvZc98scsQGjfdRbWvECRnzD8GReCyfGCK4U0rluzlrHSGDPCEwhWUOeCb9v+noZvGuqIAQ5cqtJ/Ftdm9S2cUZ0TQxU1T9R76jlZu25iGGmoQExQAxw0e2qeHmte9eqdp6hM8TOtZyada1qeWJb/fOk1s58jdiyiqXtQ9t/d694u45ZsE6V6NufXaMGIZEKExG89bV3fNY8lFIYSGcm7nZkQMabnlkMnbE8K05Ur1CuhPqIYYhWUEIw8KzZO0253qHayuublF4JZtTuJY3rBpLqe+30EXOH5Oo+/mWsxAPFCUQw7DMhZI17kf/zuheN5IYHtC8ch7LdJRCxnd/DYukawKMZIXttV9RWTTDPute4scOo6T6bvZa54HCOCxcuzXuOh9ezzzkW8QdkL3s3N71XTzpkvVbnXCvsbDhdUoMWYMheVhyooghmSBJ5vTM14ukRLFizvuE7+CHeeskrXgxgayHBM6PlwO/z6evdqQQC50ff+h7z+B2u2xDrNwLZI+14/1JK11iq2QtXDch1a+PWubmYZTFVokhrp3hHpJfhlkdR0rTECMMMUQhes8HsSRo4Xw8vWsteWNsbL9GudSpfiW3JmviEw3bKMwazd5OYfiypdKcG6vfdO9hX4N4sTfTeF8UhE99sdD9rSSMd4DEbcyO5UFoUqO0zM/cGlc64BGB1w+WyddHx6yufoO83npMM5m6fJMrj0XW17TixSVz0zaXBdZPJsR+yvhqlmVdE/V+7Bckq8OTQ2smQvjI4g5m3RtXdl4ZxPcvW5ct703KjGdkZZ/q0qhSPFNpy1rlnTzy+YzV7t01aykeRmEGePb+O05sKU9/uUjeHB/zKvGbEcPCzEo75+dGwIjhf90t/KKVAD7gnH6uYKU2vwgmRS2vu+IiyUhPdz+HxBA3PlxWitrC+l5FvT48PwwmjyIj+T0jjGuJOs+vqRb1O8QWVxGfGHJeYbTYYVp0vb+61PC3Cs9awJ1jYfFs/71KlijmFnrcRval+c8r6n0UVz9IvjD3KIz1BK3ty98vSch0x72jksXoM9mwbziqmUu8U9B4FvSeyQrFF3RtlNKCsSQ2LUzygusdgmSp4sVl+catrgh0fk3dCMPC7AgYuProtVEWb+4Zusi+M3GVPPTxvEjXaX0HSA7EUN87zNjrvyv9RqgJLYaqlQ+TXYQF5/Vet5/YwiWUCBvXn9Gjngz8apH7CVJBQgZfcYVrMAmLsAiFWSzJiupnvhx2VTc5zStbwz2j4tiYCyTMiRPD8umya88eF9MbljtBEJy1arPLtPnyJZ1k9sotkSS9oDlUlN+TWXx9t/gQDwTHm17PSwz9Z0M8zuxR12Uu9htKKdbg0GU5TC4T9iPMoJpMgVQUDPTcqLUIxYnGoLFeQwwLowiDHKg7c0Hvoi7huAE++PE8YW5HEUPuowK/fhe4GY9eGFMADDyvo3Oz9hMC+WWiIIYkONGSDlFKTO5DPyGGfAd8E8RLFkQKuS5cN6nbOHjUcueKG9UoZ/LQWW1dfJqfxKkgvPh98OVdXGkHiCEK0ftGzEnIEqv36NulpgwdF7OU+U2J4W1Dp7tvsbANWQJrnSpS9TqNuWQOfzIt03lu0NhXUNI9nbPuQLKJ2ywoxAAZiDhDkh75CZf0eYw/ioLpy7KccljlAZV1wvJgYQbt4sUkwQ2ehDqDL+sSL42BGzCJ2Gis/df0aeLILa762jd9F61pqH/7iYxIokYMIl4ovjWQc6uVKyWUGHrqy4XyRo5V1x8HI4aFnZV23s+JQMoTww0bN8nf7n5QBpzdVzq0ayPh35OmTJMXXx0qt//xeqlUsYJQm4R20YCz3P91sVM3mtOfHBvXnBVl4FQTXJRrkp0basr9zb6gZ/jJD/I7N8r10T+XRZrYPCyDfouqgxc+I0rLzzk+yVEhQQu487tfvDi856+BGGq5gfyIb35Ya52/ZOPG5vzS94vzxALl5/rJvRCAbjyqmctcW9B4FjRntLRGQZt/1H2irAoICcwh3G38psQQ68EqiOGKRC22f65anUJiiADLXFLBL0yykt9GjYsr2SejYnz1GtzYcCXV+n/JiCFEYU3W9jzEUC0HYbKL/GLj/BqU/ruTROmsnnXjxBD3U4ihr7iCyJLpEYE4tNKFiVuwWPTzMt7yrChBm7nwwBkxYnjrW3OcMIQQTAmDUFGBeyxCF8SQbH5zVm3OE1tU0Nwr6u8a0xp1na8kCN01SXRyyxvTksYXIxSf06teNDFsWTWPy3JB313oLRCVTKeo/ffPj4oZ9uPdfbf1gp6DVYYsmSSJKajddHRTV3cSYoibHpjlRwx1P1ALH9/B2EUbZM+evS6e69iHRiVYNH1rKsTw42mZ8ZIOmkkzfD/6uWzDNncfiCilXfBcKKiFStR7T2/tiCGlcKIaWY7JpklxdCz3RWmvXtZZlq3f7oghCsr7P5ybUANP73Vqp5ry5oT8ieGtQ6Y7T4rCNogXY6tJ9fQ6dc+EHH/qEUMw7D1cGwAAIABJREFUxiMBd0kaxPDjm3q5ElXJmhLDoWNXyBNeJl7/eSjdZizb5OpZ6lqipCyUG0LPqvyIoSa6CUs63Xh0M5dMhjUyJIZhBmd/zpG0DTknyipMOMyQK7rKk18skCFjVuSBw4hhYWelnfdzIpDyxBBr4cNPDJRrr7hIGjaoJ9u2b5eHnhgoHdq2dlZDiGDdOrXiFsSQKIbEkFIBaOCL2qLSqhf1Hv75YTC5v9kXdF8/yUV+5xZEHlmk2RxeHZVIDKPq4IXPyK/+o09y1DXET/ceWjjC+x7QrIp8O/d/ZzFUzIpS+5A+JHNd1D7iDkn8Wei64rvXhHjUrZwhNx/d3BHDgsazoDmjpTV+DDGMmpu45zEPcBHymyOGOUXYV2dtT6rVV6E2JIbcz3fz8muIhv30N+q3xq+URz+d72p0fjItsUC0XocQhdCghDYZqceFed2WvMRQ7xUmiorKbMq5fz6huXNrChvKk3N61hUSHdCwNkEM/WQVfFPrN+90FpfQmh+6UqLpPt0rW8M9Qxd2jnHdg2e2dcTwljfnuIyGEENcWkPBnAyMczKxGG5w2fwQwKgZ9nO2/CzEPNO3XofEkEyQtw6dlpT4QLQgu6HFECLEvUOLYUHf3Rnd6zgXdm1RyXT2BatIYtiiatyNL5nHQdRzQ5e9/N6NmqPUe9TEIaHLrH+duiyqwgT3P0rN4J78woWdXEIfXBe1+a723PfDKavicWxR/eU6+gkxxDLK3kkG7JAMRPUlTA50V7/WzpV04uLoGpNKDH/MOjnoUojhNlcmirh4iCGhDmE7qWMNGT4xMWs552ipBIglLpKFbaxpuO1qUr3wOvYeYgUppUHDCk2NwudyYkYhhpQMorxDslYgMUwr7rwxpi3b5GQslQeUEIZyQ1ibEaWFT8YJJ3n98i6iZYjCkk7E/z7xxUKpXzkj3jd9/1Be8+Ut3JtZS6OswpXLlZJhV3R1xBcCHLbfMzFM5nlX2Llo5/0yCKQ8MQRmyN+osRPkLzdf41B/9qXBcsPVl0hGRnoCSeS3kEjqAq8p+099bHSke0dBwxmmcS/o/IJ+D60NUXF0+d2jMKnik23k3BcNGsTwtYAYFkbQ8JMC+O8YRXK0jhTnkQhh4pL8a3v9FMTwzlP2kzvemlkQ/JG/F4RZfjdNZtHSa9Bcv/TdkgSXKn7Lj2TzG0L9Lcc0d3FTP/bd9PlYm0gE8WMEHj/xkt4Pkk95BbS5fiM9PNYlrAWU5kim1Vdr1ogbeubJBumToGTxt/5GrckEko0HVk4s18Sk0JIRQ+KKSSxEXEphWn71E7XIdngPhPRze9eT53KIId8HWVA1FojzUQ4gZOHyFFqHwhipwZd3lTOD+ptRcWw+MbwZYli2pMu+h1tqaMFmrZm7OkYMyfpJHNPdQS3CwmBTlHOSjbevpAiJIZkgKb3gk5DwuViMB+xfX+4KXEkhQoe1zksMC1LChcqAqGQ6Rel7eG6UR4H/PRbV7R0SUBgXSc02qsQwGQ6hS/UpXWq5UjNYJl++uHOeNcc/n3VtxORVcffp/Dwo/H5iCeNvJTrJ8A3XTVw8B49a5jIeRzWIIRmiCyJJUdfiak2iFoghXgIPfjQ/jyKQ605oX13enZyoUOO4EkOt4VrYecO+W7x4sTz1Ev29h0L1ihfkG2LoW4BxJYUoJWvUoqU0FvHZT+bEJ4bnE3tOUjHKp6g8oGtQKDeESqv8iKHOwTBzN4mPqEuI5TGcC6GCRkNbeF9ikVk7o/ZC3nXYld3cfUnMF7bfKzEsyPOusHPRzvtlEDBiKCJu0g4aIllbsiUra3M8xlCth8cccYhzM6UlI4bDr+4m/Z4cF+neUdBwFjXZQkH3CzW3oU98suv7dq0lQ3OC1/M77+yedeTVkbma7PA8FsA+rarJG4FWrEHVDFm8Npa1Nb/ma3z9c6KE0OuPbCIP5pTWoKYWBYrza/s3rSzf5VibCsIvv9/zi+cqzP3O6lFHXhuVP2b53ePA5lVcAfFk7cIDG8igkctkm5cCnfPJrrrZS4ft36NG+VJy8zFN5eY3ZrhMf2RV+7GteY0y8tg57eSoB0cV+Ra9mlSWH4KkIZ3+m8CEhAehhZfxHjN/g2zfvUc2Zu9MqtWH9KBhp37lCY+OSXgv33J9SMuq8uWsxEQHevK713RzafFpw8avkKe/WiyH7ldVvpgZfT5EnDqGH02LCWbHta8hJK2Jaj2bVJINW3cVKpaJ60/sWFOIcwzbLUc3k3uDMg16znn715cXvoslA2pXt7yzivgeDcTPbMjeKVt37pFGVUu7eDJtoXX/pYs6yh8G5ia54bzSJYu7a/3GWvbA6a1k67YdctOw2VKlbCyb8PjFm+L1R/X8M7rXdgmHcDN94ty2Mm/1Vvl3PrVIizyx8rkgmYKIrIiasGK/WmVl5spcV2WE1juGz0oab4elacD+9eTeEYn1VK89orEc0rKanBK44p7do468mmRNIKHW2xNzE3+F77SvmOg34t9n/2aV5bucREJRvyd7Zuiul9+51x7eyLmSfjZjrZu7yfaTMqWKS/aO3Dl2Qoeabp3fsmO3vHRBxzxrjn8+GTnfn7zKKZFodStluG8gbGE/C+PZwj3CdfP/Tmohg0cvz/ebblu3vNzbt5Uc+3BiOabCjONz53dwBPdPb86UPx7TTB75bH4CLnqPY9pWkxFT83o0XEQdwy615OYhM2RyPsQ16j3YJ2iZQVkMPffigxq6xCvzVseyjdarnC6H7lc9YT9579rucnwBfSbhy99PaeEIE+tsVKOcBdnXSRKk8oBa6/wai1zLe+f3zvyOi/ugSzo5qzV1m3EDhnRru+rwRvLYZwulWY0yMjcz1jdtobzmr4O4ClNO5tgH844xys43Lu8sT3y+UIZHrOW/V2JYkOddYea/nfPLIZDyxBCipxZCYgg1xrDPYQfJoQf1LrTFECI2+KL20v+5KW7DKmprWr1MfGEt6rWFOb9744oyekHh3EeOa19d3o/QOPrPObVzDXlzfLTAy3mVSpeQQ1pWkbcnJmou61VKl6Ubtid95fwyX5YtVSIPtpceVF/+83VM8GUxnrMqcQH3H9SjcUUZVUgM8nvBW49uLPd8mJiCujD4c87JHWvI2xEuPgVd37VhBRm7KH9LKNef27OOI+EUafYbZTq2B8f0d0jMdUc0lNuHz5X8EhYU9G76O8lcHujXUk59MpE4FOb6Lg0ryLigf23rlHOZ7sLjVxzawLlBYS3AjUqFkajnqGAw+JIOcubTkxJOQXhZuj42D3s3rSjfz4v+Nl65sL0gaNLenrRaXvp+mRzYvJJ8Myc6RgchpXODCvLFrBiRP7JNVfl4WjSJ5Lys7buSzln/pY9uU1U+jLjX1Yc3kEc/ixakzupeW14bHdNMkyiGciZo27URn5O1dZcjdz4m/B7+/eQ5reXyQbmCU35j27xGabn71Jaye/ceufnN2VKpdJowKycv3SzVy5WU1V56/VM61ZBFa7c50nhf35ayYO1WeeKL6L4UZi4V5pxujSrImIXR35P/rdGPOZm5RPmvxzWVuz+c7wpl59ewjp7bq448/GksvkrbpQfXkwObVZZzn52ScLygNSEU8n/qfQLFAHPCb/5ekcwVvTBY53fOZQfXkyNbV5MvZ62TRz5fLMn2k1JpxRLWNb4pkhRt3rFbnj63TZ41x3fBZv5/OG21rN8Sm/N+khD/3UIS0bBqhpuXBbV+XWrKEC/Ryy1HNZI3J2TmIRJ6H4j9nSc2kzOC9aig5/D7I2fu576dv783T645rIE8883SPEoZt+a0qiIfz8irSOzfq46c0rGG/PntOUljs8N3wVWT2qX5hcgM6FVXvp6zThasiX0rtSuVkgOaVk7AZfDF7eXMZ3JLikT1l/XwL8c2keGTV8uL3yeGoOj5fLvElaPsVHlAlZ+hErRKmTRZ56114TP5Vp/p30Y+m7lOHv9ysdx5QlO5491chc4F+9eV575bJo2rlnbrkt+aVCst83P6y3FyGOi6cPcpLaRRtYzI/pYvnSYvDmjrxm5ERDjCT0UMZ6/aHKk0KMw825dzWtYqK9T79FsYnsVvoYFlX55p1/70CKQ8McRaOOLTLxMyjfoJZgrSdKi7AELsiOt7RmaiKsywFSXIvzD3C88pisWwMMlOwtiX8Hlh+QD9PfT7j+pLfsQwqgYj2Ti1VpCftTTqvqSL/3ZOdLa4wmK6L66kUUXOC/PcwiQmopDzK98vLVT2QH0m7r5/PqGl3PDaVFdUOYwHLcy76Tm4CJOm+8e4kkYlDcK1GkudnxWTZ+FKOnLeOtmybbdLmJIsDkiT/ES5kvq1zpLF3759TXdXGJ722uhl8syXi5KWfsGNrWeTyk4LTfPr4oV4EotITGiyzKr+Nfndi9T/936QN8aQa8n6qeVDcEOkJAzJZrQRP4TVkqQb4bfpY8T5uO31f2Z8gdMCN6pHz4nFGN44dPb/s/fdAVJUWfen03RPYhhgyDkz5GxOCIoJREQRc1rX7Lr7bfh2101+7q6uOa05K4qKgqhgRgxEyWnIGYbJM93T8fc7r+Y1NdXV3VUdhlH7/aNMV71w36v33rn33HvRMsch3qFfmDbwEiNMbi2txeKtFXj88iEoOVAbkYswboMmH4gVpEodVEJrFYiWrF3dPJUt15/SPYIOe/uEnhhX3Ebko1SXaH6j8hntnBsJ3mVGHNo55rvq1Ena6NZm6o71rKSSyhx00QIr6dVBK/z6fTUiAjMDeWj3HDVbhlTP2cv3iZQ2LNHGo2WjGHF5YH3afZPB0BiJm+tYr9BiyPx79Is0Wxho51BNPX775jrQ15r+ztpUFaxzQnFrzF8XqYxi8DN+b8xpqo2EHKsvIg9pIBSWofZZUlQ/WXsonFuX1ldax9URyfX2YG09Y3u1xD0XFIuUQzLVhfYZWvRX7a4We79UAEiffa37TDSXFFmnzPMq0xAxouyv31gbblIGhtK7m8VaH4xu2qttdoQ/MSumVXP2LWPw4PytIsG9tqQKGMr8vmbXWLLPP3n5EBEtVl2MMO+SbTfzfmol8LMHhjJVxc3XX9EoKikthgw+E48bHQaGdiu4+WkT1xqdrmhhtI2+H+85NUUq3rOx6G/y3XgpFLgBnqnKKyffM0JNMhOcRR0VLh79R02RiieDaL/T6f+u2RsTen3SiPbhaJVmKtAmHtd7NxEfQR6cf5rUXwBDMxczvfYZVISBIBIBhnrAlyCG/kqrdjWmBjNS3PdbylDloV9cMKYfkKQk611K1JYDPR9HOUamaChsADa86DCQSywfNa5dAg/pG3nO0HbhJMtauTFKaJ3Xb1h7H+27JFi+78MS3SWl9hemz9y+Ck8ji6EAhnU+ccHUggSttejF64bjiqdXxF269N989NLBYWBIJREswA87qyJ8XrnuqI1fsrVcvEPKFlOCpLNQ7tGCbzD/5fKGwBxaYBgtWbu6r1xzN5zaIwIY3jq+h0jozdxm6kJ/uXeXRV4Q5TPaOY+3x5mVm17wILUSUZsCwGz90Z6XietJuf7XB5tFPjmmKTBSGFWWFkPm36S/Vqw9h76L7y7bF07jE81iqPYRYx+MnsfaffNP5/YVgEib2kGOayCppNOKdUFDvLHTB/dwrVcAw1+d2RuPfbJV19/1tP6t8NmGSIshA+VMH9MJN72yyrAyin2izLg/kL6pVwg4P11XGgaG3EdO7tu6UXyBeXcod6NYxQgwZMop0mDVaSAYEZ7/pqWKuSpl0fqmatuWwHDuygNCGUXAzsA8ssjAUHprIZaCm9FNuxdlR3zrrJdK7/f+v7Lx/o+3Yu7K9AHDjMUw3teU+T2WBH72wJDCIfi75z+PheWUSB5Daik/aghyESs4QbTJMGJJS2Ypm8mTGCvqouxDvAsNN0BG06T2T130LiLacWk1f7HGrY7qFy0UuXz/aAPDWPnTYo1RGwRD71kmCn/xa3OJ5QneqeFmMAIzFzO99rl+X9IJBGFkzepd1BnllIQ9rTVNAsPyOq+gl8UKECEVDHrAUG0hYECDaPktZ/5ylEibwUIfTAZUiJa0m88wEigthl9sUCyGsYAho4TyImMkXxrripZmgZfE+6P4GNJC8MZihZZFoLO3wtPogkfwR4shqblaa4o2iTqjhl71bHxg2KddLh5rAIa/mrUJLbJtsMIiAkNpoyWrgeFDMwZh68E6kRIknSVWygf1b9rgO0aUQtz3bhqnDwzHFRdhkgYYxmNnnDmoqFFkXm0I/mTlFA8YatdAsu3J95lEnj6zEhhOHtEes5dHB8jqdpkuhkGKDtf6RB66WNY3AkMCzmqPAmqipQrSslHinSWyP1oFKQNBvfrtbuxQ0QzVfScw/OfUYpz7kHlf7KevVIDh795aB8ovmgLl5H6F+HJjJDNGAsMbX15leM9h37n/UQmnjXgtx8V6mStSgmGuqZP6tm4UTXfu7WNFvslYRQLDmUv24r9Rgs9Q6cbAQ2pLqZw77RzGu0eQIvvmjaOF3zZl+a9pxQJ0yyIDIukFYIplSWf04m5tXDhf49fOepkfc85tY4Uf9QcrI/3FU2UxTNV3mqp64jHvUtVOpp7USCADDJOUo9RWSmCYiMWEXTACmJLpaiz6lLbeWBYR+Wy8Cw01sBOHtBURxtTFCDVJq/mLNW61pSxWzj7WkQpg+JdJ/fCX9xKzGGoveUbn0wh9jCHyma7CTOEc/fV8BRjGA/rx6qWW+JUEgaFeqhaCGIaj5wVQXSQwPFxTDyqHtb+rn5VUIz1gqKYrq6lz2nG++ouRIsExy4vf7BLgO1ZOUIbUH9OrEAsbgtnEUgYQhBCQGQWG0fInkpZHjbdeUV9cSfHZV+FuFOKe1gBS8tgPrTVFG8yBVOFrnovvQ0rq1ROXKRbDO97aiBY5dhoMhfVXa5khlZIBb2gxZFRAXi5JkUtniRXZUx1tUAsM1TlTo/WPl1EmxtZGVr3l9B4iUqMWGMZjZ2iVdNHSLSQqL71zR80uiUfHS7RdLTCMRbnWtsGowFwnTLvCpO/aFCDq53m5ZyJxpmNhiZYjV9uG0Wis2n2TUbJf+XYXdpfp+ycWd8zHPVMHYNLD5oPPPHXFUJT9f+s+geGtp/fEw1G+k5P6FOIrHZcJCQzN0gwJDGs8gUbWOLW8qJz9bF1jYHhCn9aNzn6CoXhgeEzPlgI0xwKGjFa9Znd1I0uppA5rA+7JXMfR1qgEhjI/7T8vLBaylYWpdF75dndEJGX+HsuSfv/0QejayoWpjy2NaDrHacPc28bi3o+2iGi52vJTBYbxmHeJ7iOZ99IjgQwwTFKuEgjS+Zi5ehIFhtEoLkl2L/w688Ix8p+RYiS1RSxLCNugtWbikCLM0iRxNRLMQJu/LVaf1YAoHug042cZrc1kopKOG1CET9dHhhGPNydGtNfyEItXl/p3UnBIj6NfRTygH69eyv61X4xMaP3rUWVJR/T4gxFJlWkdI5X0UFU9GANkiwY46vVTj8akvjTEWhcvXTsCnVu5RLW0FtJqGC8nqNoCGeviTxDi84cMJdJm+9Hy78WyHqgtMbTkMaqhtJ7ICw4pYmQ5aC/NkqIlZfr0VUNx3fONg/joyZt0R/qaEBjeTmCYbReJz1fvqoa2TlqqGeRj6bZyMB0ELYaP6SS3jrf+zPweKwK02jqv9VlW50yN1h73Ls6HFhjSikhgrwUE8ZRF2hybRqj4ZmSht2eq2SXx6Hhm2lI/K4Eh85TSP/asIW0xL0r0Xm0bVFxuP1SH/VX1wscwliWKTArmgaQPLYteZGu9MRhl8Gj3Tfr7vvLNLmGZ1ysEhndf0F/XmhRPlk9ePhQVbgUY3nRaj6jfSbTo2wRw9Im87oWVhvZN2R+CoCqPojzSK1pgyDVFZZuaGvz+bWN0qZXq+iQwJMPhqS8aB2+Sz6mp3vHkFe93WsPfunE0ZH5aAvbfz1offk36j+p9I7EUJtzHurRyYdrjyyK6QMURraf//rAEH62ODN73UwWGFEQmj2G8Fdl8fs8AwyTnIlXAMFa+uSS7KF5XB1WIV58RABWN1ibr5gWQh73Wb8SIz4pekJlofVY7/8er28i44smG+aP+/n5iCbhP7t8aX0ZJcxCrXSPWZDVlMN4Y5O+8xP7jggECGMYD+vHqpGLjjRsiA0HEe4+/6yXtptWJ/nfM26UuEhjSV44lmi+P+p14/i2x/G9JnyR9j4X+hfQfiqc4Ua+zWMCQlit/AIaBoRYkyDHeNr5nVPql2hJDwHagyisCN8hCah2BIRN7ay/NMqG4fJaJm2ltiFcYmOG/VzQAwzc3Ij/bBovFIjT92m97yqgOIn0NgeG9FxULYBgth1m8do3+HssCrwaDWmD4+3P64p658b99dW5V2ScCw/EDizBZYymKpyzS7hlG9gKjcuBzepde+nF935A+xoy/t5l2uWYnDW8vaLIEhmcMKsLHa4wpzSiTnaVu7Kv0YOaNoyKssOp+EBgyr6AENdFSIWn7Ho99Ip/X7ptkNBAYHqjSj7w9oGM+/jGlPy54tHH6HCOyY3AmWvd//9Y6qH3rte8e27Mlvt0aqQSWwPDa538wtG+q9wh17lNte3rAkGkl1L6zDOKlXfvaeowAw1g0cCMyVD8jgaHMT6sNLiX9R/WU9lrmg7pe+ip2KnTikicjA3XJfNf/nFeC+Wt+XsDQ7Pxknj96EsgAwyRlL4Gh3WbB/CQshlrfmyS7FfG6kciW8iUjIDIarU3WQTroxKFt8Y4moEA0Hw91h7UX0liyUDv/x9MGpwQYntsXf58T/3Ko1+dYlMVYYzQis3jRDfXqJ/WGhyEd7ukP+qGOBtPoOpS0nEQs5nrUPoIYaqm1FxIJDHeXuUVwmlQAw1j+t7SS9SrKFWJ4+ssdIqBCrGA1fE59sY4FDGmdCgSBTfuNJbiP5tsYCxiqLTEEbLywSlod+6q2Esbbg3gxvfGl2CHnWSeB9NNXDhUWw9ve3Ig8l03MFYGhtkwZ2QE7yxRg+O8LB2BLqTuqf5HRtRjvuViBRdQRCLXA8Ldn9xGBUuIVPWB442k9MGFQJDCM5a/KdrRrzQjjIl7/1L/HA4ZmaP1m2tUCQ9JDP2mI5BuvHipm+P0zHyF9gM9/JDotU+t7HW+Ny7aNWma1+yaD6ry8aFc4b6J2LASGf5/cD1Mfj6QZxhv3Y5cNRpUnIIAhaaGMkKxXoqWmksCQfsI7NOkXkjl/rjmpKz5fXxrei7lGj+ndqlGgNUbinBxjnti+BIYy+rNenxitmsyDVBQJDGct3YfHP9sm2DN/emdDuGruTQSNevTjWDRVBhfid3W5TqAuGb3+ng82Y0FDrlv1WH7KFsNUzFmmjqaRQAYYJiln9UWYH3UiF2N2IV2aWTk8MxQMPZ8vrZiiWS/kc9SMMYExN1Z1SbVlVO3jofWJ0vb5uF6F+CbJBPf/e24f3D0n/uVQb1mpAYOZZRdvXKwrUR9BGYnNjMZer+8E5fEiBEYbM/PrMTeVuhBcVLp9jSJo8ncBDEvKsONwHQhsjQDDD+4YG9MPKRbN+onLh4C+Ziz//WK7oKXFA/jqYDqxADfrZYAdo8AwGoVVnbJFK2P1vFKmB6u8jXyF1MFFoqWJkXU+dtkQ3PRyfGBI/6xnrlKA4a0zNyDPZYfNCt0Q+aS67ir3YNm2CuF7tbW0LuqF18w3E+vZWH56atBI6q06uBFTBNwbJfqruj09yyKBIS2GWhATT8lwfO9WWFRyJMKkESWRGTnpWUPU34MZJZ2Zdukjx7mXFsN4AFldN7+/PeUebC+tA6MGx7K+aYFhvDUu2zFqmdXumwS8L327G+U1Xl1xDOiQB6Y8mpYAMHyEAZ3qFWAYKwq1Xl5YdkYCwyufWYGdZY3z8sWau3h+mUyV9Pm6UmwrVXIIc03xrJujSuD+zs2jMSWOlXR0z0L8a+oAkTbpmS/1QW8s/2Az64/P8i4y66bR4fQYWmAoacJGrcyyffoqtmvh1A3UJWNR/N/czfhkXaSFPAMMzc5i5vl0SCADDJOUaqqAYTxH6SS7CUZAZB4xI0WP2qd9L95BTs3Y2TrAMNXBDNRUuXhRyFIBDI3SyfTkHCtMfqx50fpl6T2bqI+gBIZmNPZ67VMT/+4tYxJSjGiDfLB+Xt4P13gbWbf4dwZa+X5Luch957LbwpeRWPKLBwxjWcgfuXQIBnZUgCEpjgymFA8Yqn0mYwFDAg8GZYmVi1E9rmggIhYwVFMVCdgO1njD/lbyciQTV8dTTjE/162vxgeGnLtnVcAwx2kHcx7r5U5jCpfdDcDw7ikEhrWCspvOEuvSrwaNWmAYK8iPur96lsVfntod4we1xRSN1SQei0GrTEq1Yi0eMEzXPNxyek+crwKG8ejZ6n6Q+r2vsl4AQ0aVnPZ4dFqmOo8n64hFAVS3Ec9fXT6r3Tc5LuYNraxT8iZqC4HhXZP74eInIv3P4smaUXvrCAxnrcdlx3YW/s56ZXjXFlixsyriJwkML3tmOfZECY6jV1+8M5sg9Yv1R4AhlReMzMw0ELIQgE19LDZ91ggw1FMixpNbtN8lMJQ+jdocxTJwmFk/Wyq4ivKdIGVXW2Qsin/M2RROaaR+JgMME53NzHuplEAGGCYpTTUwjOfLFKspbUStJLsV8boRK6B8ycjmG0/TzfGcPbRtRI4uo1Qeo+NXU+Xi+SYe26slvt1iLABPtPaTAYap9I/Q9i/RVBikvfzPm+ti5uYzMhcyP1MiFnPtBZztkcrFqIP0fVMXXs6Z4J5BZ7Kz7OJyGK/EA4aMdLdsu77S5KFLBmNw53zRxKOfbRPUaK0VR9s+U22s26vQQ2P54pIuS4plrJQb6rqjtUvQEc0vT+2jxoAah2q8jYJIqOnX8ZRTlMVtr62OJ24Rxe/5q4YJi+Etb25AjsMGXoikTNQVUKGxp7wey7ZXCCoXLcAM8pMmwTZtAAAgAElEQVTOEivQlzqXo9YXMVaQH3V/9fJK0idsgg4wjBcUTKu0MGu9iCdHPb9sM24H8eqP9rsEhqSv0wob75tS18MI2/SV5bf/+g2jMP3J6LRMbbRmI0o2tmWUsquN4M2gMASG6gBP6r4z8NGfJ/XV9T+LJ8sHLxkk8vURGMbyKR/aOR8rdWjbpHwyr+mMp5ZF+G7HajseY4XAkFRSuRfTwjimZ2GjYEKzbhqlG6VT3a4RYKild8eTWazfJctFWii16WhkYKh4CmdtG3TPKMrLwvU6/tjc7xf8+lgRp+DzhpRG6vczwDCZGc28myoJZIBhkpJUX4TjXUCTbCqp14d1aSHyiBkpRtIjxNN0sx09eqNZ7Vu8/saL6qd+/2gDw1TSYLRyMSMH9bsymE6sFAzx5oC/88LFS+xXDWkajLwjn9FL2h3NqkMq6Xclh0XAFmr/jfjKfHD7WJwdI4dWLGDICHO0trM88ulWoeiIt/YZUVVaAWMBQ/r80TfZKDCMFiTnF6d2j+qXp46QSrBdqgGGaqART7Fi1KpMcPXC1UeAYbadwBARdGHKVAGGHgHMqbHfdqgWL5jMx2lmrfHZWFY3taVIuw8SzBhJpaFnWeQcnaEDDOMxObQsA6NUSKMy0QOG8cCq0bpjPae1GJqh2fN7pe8xv31GDb78mcggH7JtbbRmoz6TRqOEa/dNKgBeXLSrUQJ2tRyodP3jef0x47/mLYbMj+f2K1RSbf5EdRuDOuZhTYNiSv13aTG85L/LRHRioyUeMCIwZLoKuReTns6z4KPVR6iSRiyGVEhQURmLSmrkbmJ0XLSEvn3zGBFQjCwFLTCUUaCNrhnZLgO6tcnLihqoi+Dvr+9v1A1ElwGGRmcv81w6JZABhklKNxELSZJN/mheZ9Q55ghSF6NUnh/NIE10VI8yaeL1mI9GS2UQr36jATXi1ZPM7wRI0j8lXj08zBnA4ZuSyATO0d6NBwzjtSmpUgx88P6KAxGRNWO9Hy96b7y2f4y/E9S/eM1wHKqow4xnI6mnsZKmp9oipie/WMBQ7cMXK0hNrHnRCwZExdxdk5mmwHz+unSugXiRnNPVNi1rTFUifQybwkrJsZhJhZTI2OmTSX54tNQOidSpfudv5/fHn9/dIPwzZy9vfLbK54o75GKdxmc7mXbjMQmSqftoviuBISm5eiyFRKOIxxtTLKCZAYbxpJf5vSkkkAGGSUo5AwyjC1APGMbTPiY5Hc36dT3LWKo6rKU0Ga1Xj/Zm9N1UPWckT6Nsi4oFUju/NRFEKFlgSPrVlxtLMbxbgQioYCYgR7JBfVIl46ash1a3l64djn1lNbji+TURTceyxqSaUaA37ljgU02tVdNKzchPz+eTbIF/TCludsAwXmARM+M286wEhvNWH8R9H5bADKPFTDvaZ818u8m0k65375rUD399b2PM9EJGXEHM9C/dbi5m+pLKZyUwJPVXj6Vgxu81Vf3KAMNUSTJTTzISyADDZKQHJBRsI8kmfzSv6wVEMUvL+NEM1kBHzQAgA9U1eiRe0vVo9RkNqGG2P2aejxUlUlsPNf6k3zUlMLxoTEd8ufEwRnUvwNyVkbmnYo315wgM6Z/18nUjsOtQDa55KRIYxgrsES8Ajpl1Fe3ZWOBT7U9llE6obUfP55NsgXsuLI4IPpOK8SRTRyzrbTL1xnuXUVqnjuoACQyNBDyLV6eR33/swPAP5/bB/83ZjFiuA3o+20Zk83N7hgqid24eg+e/3omXv4kM5BPPZSAd8soAw3RINVOnWQlkgKFZiWmez1gMowtQHTFUPvVTpaUYWUaJWiCM1B0vUma0OmLlwDPSbiqeMZpMWrYVKyG9Xn+S9f1lPquvNpZidI9C07kef47AkIDqletGYOuBKtzwyrqIKYn1HTSF4igWnV0NTONFY4y29vVyzJEtcO9Fg3DBo82LSprqKKdG9wMtMGROz/U6fnFG6zP63I/d+iVTpsTy9+3VJlvkA82U2BKQwPC5hTvxik6E16bwtdX2MAMMM6u2OUggAwyTnIUMMDQHDH/sGttklovRSHeJtBErSXus+owG1EikT0bfMZozTNZndqzJAkMqOBhUh4CUPlFmyoSBRZivk8jYTB0/tmfpp/fa9SOweW8lbnp9fUT3GR2VKSr0SlPsD7Ho7GrFVaL+0Iz+qE25wVQhD1wyuNkBQzV1tinXGa2qF47uiA9WHcR/PipBKqNNNuU4mrotGRk3lv9bt9Yu7Dis/301dX+bc3syxdIzX+3Aa9/tiehqrKBk6RpXBhimS7KZes1IIAMMzUhL59kMMIwuwHOGtmuUyyhJUf/oX0+ndj7RQ4ya+8c/23ZUZWs0Z5jspNmxJgsMmYuQVNLj+xRiwdpSU7IaP7CN6XdMNdAMHyY98fVfjMT63RW4beaGiB52a51tKJpsuoYWi7WgtiglGqjkyhO6RPgsUfnx6KVDYiZjT9d4Y9V7tIAho3dOUwHDXm1zRQqaTIktAem/GitdVJdCJ3aVG486+nOVuQSGT3+5A69/HwkMm8rvVS3/DDD8ua7G5jXuDDBMcj4ywDC6AM8e0lZohDNFkUC8fFDJyMlMnkp1O7FSHSTTHzPvmo2MqE4gb6SdZIEhI75+takMJ/ZthU/WmQSGxW2wwOQ7RsbUnJ+hAuSNG0Zi1c5y/PqtjRFdTWcQJiNyiZeWw0gdsZ7R5s7js1zjT14xtPkBwxw7yuv8yQ7Z9PsSGM5ddRD3f1SCRCPAmm74R/6C3K9jsSY6Fjix10Q6ih+5SBLuvgSGT36+HW8u2RtRT1P5vWaAYcJTmHkxTRLIAMMkBZsBhtEF+HMM1R9rORlNrpzIkmQqhdW7qk2/ytxWz3y5w/R7qXzBbGREdQJ5I/2Ye/tYnBMjj2G8OiRtK5HIr4mmEYnXp+b8O33zZv5yFFZsK8Nv39kU0dWfenCMS8Z2ErnY1IUyefqq4Zj62JJmNXVNkR5Eb8AEOBeN7ggJDGPRi5uVwI5yZ64+qSue+2onYrEm0umycJSHn9LmqaidfcsYPPH5drylAwxTHd3VSOczFkMjUso8k24JZIBhkhLOAMPoAkx34I0fWyCbdAY+YDj8tXtqTK9m5ujTy+FkuqIkXjBLsVUnkDfS7Jzbx+DcBxMP+iGTxCcS+fXnCAypiX/rxlFYvOUw/jh7c8QUpTOfp5H1kO5nSJHUWiDor/jCtc0PGHKuKt1NbzHUAkOzfsbpnsPmWr+0RseiORblOXCoxtdch9Bs+iWB4WOfbcPbS/dF9MvsOZOKgWWAYSqkmKkjWQlkgGGSEswAw+gCTMelWB2cIp3UzCSXRZO/3r9DHjbsMw8MrzihC178eleT91fdoFmrhVnaWbLAcGzPlvh+awXMBr3hGMcNKMKn680FrDmqk5GCxvldzrpxFL7ZdAh/mbMlokazFt8UdKlJq5g6ugNmLWl80aS/4svXj8SFzcxieLSA4fWndMPFYzoJH/T7P96CjJXL2BKlzN5YvAexFIGtcu0oq216sG9sBM3nKaatee/WsXj00214Z1kkMDwalPcMMGw+6+Pn3JMMMExy9jPAMLoA03EpVoezZ/TD0mpvkjP403i9d9tclCQQvOHSYzvrhupuSqmYTWreqdCFPVGiWur1+/3bxuC8hxK3GI7oVoDlOypF/sQVOytNiSYd34CpDhyFh/Nddrx90yh8tf4g/jFva0QPjobvTlOKYfKI9pi9fH9Ek2/eOBrTHm9eVFKz316q5MiUHtNVwPBo5VNM1Xiaqp4pozrgnaX7EEsReLTAflPJIFXtSGD48Cdbdb9XRhLeWda0aT8ywDBVs5upJxkJZIBhMtL7CSe4TwVNMxGfrHjToQ4hn6EfHZGWWSuafFPPHyreHKT6d7NJzc0mHn/vtjGYlAQwlP6bidB1TxvQBp+tNxewJtXyber6OJ/v3jwKn649gH99FBnxdmjXFli5s6qpu9Vk7Z03vB3eX3HgqAJDo2k/jhYwpG8z9545Kw/ggY+3wCxroMkms5k1JNdWrCiueU4bauoDzaznza87VGC9d+sYPLhgi+73ms68w9GkkQGGzW+d/Bx7lAGGSc76T9ViaPayrifGU/q3wRcbUnspVmtDj3bY+ySXTkpf79IqG7sS0G7q+UOltGMGKjMblMdsiP1kgaEMQpBI0JSfIzBknsDZt4zGx6v24z8LtkesAGmBNbA0fpSPnDO0LeaujIzG/OovRmLGf5c1yZiMptrg5bja0/S0Qy0wPFr9aJLJSGEjTJ3z4eqDiHX2ZWdZ4fYGU9jqT7MqCQwfmL8Fc36IVOTopVEyqnBJVGIZYJio5DLvpVICGWCYpDR/qsAwFXSURIJ1xJsOdaCSRC7q8er/sf6eqHbzglEddB3vm1IOZq3TZi+RyQJDSdNNhFr0cwSGpHu/d+tofPDDPjz0aWTE21HdW2Lp9oqmXGJN2pa8vGsbffG64bji6RVN0hc15T5Wg2oGRpN0rKERLTA0qxxqyr42p7ak334sOn2W3QKvP9Scut0s+8K1TzeD+z8u0VXk6EXLTvc6zQDDZrlUfnadygDDJKf8pwoMzUaK1BNjrCS8iYpdnfPupx7EwoyMEg3ecP7I9nh3WaQ/lJm2k33WbLRWo9YQ2a/Zt47B5IcT9zGU2nk9DXK8saeDTh2vzaP9O+fn/VvH4L1le/HYFzsjuiOD+Rztfqar/fED22DB2kimxDNXDcO1z/+QrmYb1UurbZ03Pp0wFcyQRAZ0zUldMeOYznj/hwN4cP4WpNsSk0gfm+M7MnVOIntRcxzP0eyTBIb3fVSCeTr5lvXozekOeJcBhkdzRWTalhLIAMMk18JPFRia9eOiGLWWn+P7tMKizWVJSrjx6+oDMVbI7pQ22gwr04Ips7kA5ZCi+UM1wyEn3KVkgaHUzpulsLLDP0dgyATyc24dg7eX7MF/v4qMeHtc70J8U1Ke8Hw21xelwiKalfiJy4fgly+tapLuGwV8RgFkqjvNfHyXqoBhqus/2vWZVXYZ7a88UzPBeoxKLPpz/Ebm3DYG/563GR+tiYwcred/mwqFeayeZ4Bh8vOaqSF5CWSAYZIy/KkCw0QCu2gpfsf2KsS3W1J7AVRTaMb0aInF2366lLRYS1MLwplAu7xO31coFlUzHVbdJD+plL/+7i1jcP4jiVsMpTXW6GVbPYB0+NmmXEAprpCX4rm3jcHM73bh2UWNE72zqZ/qmpP7XzRlwMMzBuPWV1enWNr61RmlWx8tKinzp1527BGLYZMIJU4jVGjU+1Ljm2eWHm90/PLMS7flymh/fszPUSny3DXDcd+HJbrUdj06dqLMHKNyygBDo5LKPJdOCWSAYZLSbUpgGC2CXDoSsSYSzEQLTtJxeKmd7tNhkUxyOTTZ62Y0/Ufr8tdkwojT0C9P7Y4nPo8MgmK0f1I7n4gVIBW+ukb72ZyeYwCWZdsrsK8yMp3MiX1bY+Gmw82pu0n3hdZCfpNltT5E861+YPog3PH6mnBb6bQ+MK3BN5vLsL+yPubY0u0zFa1xMj+evmooPl13WFBJm0MxCqaN9FXuuamm6v/c93Ijstc+w+9x1c5KVLobK075zV55Qlc8GeVs0HNZSNSX32i/M8DQqKQyz6VTAhlgmKR0mxIYRsvbl45gDolE/GyKvII92uRgW2mdmLWfIk3PqKbZjPUqljUxkeXPtu1WRBy0idTVFO+cObgIH61OPMl8qi6MiQDLppBPOtro3z4XG/bX6lYt/aTS0W5T1ymVdfzGbFYLSmt8OL53KywqiaTQ//PCYvzurXXhLprNx2lmbDeP64F5qw5g6yFlr4xWjAapMdO20WeZ1/GbkrJmAwxTqcSRdd16ek8wT15TlVRaPZuqz+lu5+IxncQ608tJyCBIz3wZGSArWp8SCUBmZnwZYGhGWpln0yWBDDBMUrJNCQyjOZyP7VmI77emlrIZK09SNJElQj81K351IvcJA4swf23iF36zbTfF80Y1wmbyj6UasLOPdhtQEYW62hRyMtNGstFxjYL1eH1KVT3x2mkOv/drn4uNUYDhTylSq1S60KpMYHiwyoto+/Hfp/THn97ZEJ6eRJRvRueWwJBpDbYc1Afnsp6juSaZvmPx1nI8tKDpgFMs+SXqp61Xp7QG3za+Z5OOz4zC0Oha4nM/ZqXW9LGdxP1IT0nCAEivfrfbsCh6FuXEVbYYrkznwQwwTEZ6mXdTJYEMMExSkmaBod4Ga5TOE43GkI5gDn3b52HT/hpT0unUyoU9ZR5T75h9uH+HPGzYp/QrWr4ws3U2p+eNBjgxo91OtV8EQandZkV5ra85iS5qX9JhUU9k4Om6tCXSl3S/E4veLkPup7sPTVG/VLowWJfVYhHfxJAuLbBEx/f5rkn98Nf3Noa7pWY/pLqvt5zeEx+vPoBNB5ovMGT6juXbK5sUOMWScyojfcrgbXec0QsPfNx0VFm9SJqJri21NfloWpYT7b9875KxnbB0R6XufWbyiPaYvdx4VO5E7kVm+p8BhmaklXk2XRLIAMMkJWsWGOppaGNd8tXUkGh+f+kI5pBIKoh0asDlNA3slIe1exRgmGr/jSSXQkpeNxoN1iiAZKdSeeFhffQdddgsOFzz4wCG6jWTkklKsJJ0+Nwm2JW0vxYrx+hPydIvvy3+12KBCAA1oH0eVuysjJDx78/pi3vmbgr/PZ3WBwLD+WsOYmMc5d7RpB4yfcfq3VWGgGFTWKxSSe2V7Jlfndkb939UkvbvTTaQSnaI+l5ilMnSZAM10dAlx3TCDzsrsW5vpKL7jEFF+FgnImm06tN9lmSAoYmJzTyaNglkgGGSojULDPWChsTazNVAsnubHGxv8K9TdzsdPjuDOudjze5qU9JR0zxNvWji4SFd8rFql9KvaaM74s0le0283fwfNercbjRcOS9+hTmOuEEozEiGWmlaDEurIwOL6NVj1CJupg9mno323ZipIxXPpjPYSCr6l8o6erfNQclBff+2aAngU9l+U9UlwQSBgNVqQUWtH73aZof3KHU/fjOxN+79UAEJzNtHRV9JHKpnouOgb9uCdQexXucyrK7zaAJDpu8g+8MIlbQpLFZdW2dj52F3oiJv9F7nQhd2l3ugnvOUVBynklS6c6jvJVRqef1BeFIUtbUpZCHbIF109Z5K3W/SbCCsoV1bYOXOqrR1PwMM0ybaTMUmJJABhiaEpfeoWWCoF8gi1mauPhCjaZjT4bOTSI5ANc0zSbFGfX1414KwNp7hzl/+1rh/QLr6lMp6jV5OjGqGqYgoyLFjX0Xs6IRmxkC/KofdKvypjJSjbSlLNZXWyJj1njE6Z4nW35ze61GUjW2H9C/Z8SjgTQECUiUr+b0SCJBKWuH2gX/TU6rdPqFXONAKFX5dC11xqZ6J9pO+bZ+sOxhmV0Sr52gmln94xhBsOWgMGKYqAFQseaZSsSnZM/9zVh+RJ6+pSiLRxKP1TX0vIUOFqTzqvIGmGkrK2uE9Ye3eaizfEWnFH9mtAMt0/h6tcbPPmx1EBhialVjm+XRIIAMMk5TqWQ9+D4+JzZLWFrc3iHr/kXxJscCA2sIYzW9n/MA2WLC2NMmRNH49kQ0w3TQL9lDtL8Ykyc99tTOl4z7alRkN+mMU7NAfMM+VYmCY6xAWj3ih8KUsUxnUIZH5aYpLpZF+pZrSa6TNo/VMtzbZ2FGqDwzPHdYOc344ELVrzWW+jMhOKusYrZAWw8o6r6Bur98X6dt302k98Nhn20S13Nd5iY9H9TTSB71nCAw/XX8oLuujKSia0cZw//RB2FFaZ8hiaIY6n6jMUqnYlP6jWvqwtm+pBuappCer7yXcw72BEKo0KR8SlXVTvnfZcZ2Ff+H3WyNzHg/omBfXqq7uazoC/anrzwDDplwZmbaiSSADDJNcG+c+tBi19fqJxfWqJp2MWjc1JSOWplLN7R/QIVf3wnHmoCJ8ZIInb2TIY3u21N1IY72biJXRSF8ab8xH+nXDqd2j5iAyW29zeT7W5URN+zIKMnihIpVzb3nqggKRxsoLpVErZCrpTc1lnhLph1GacCJ1N7d3YoV1nzS8Pd5bET3gQ1OAgFTJSyrrCAToY1jl9qFVnlM30MX1p3TDU18oofGpsOncKtvUpdRMn2md/HzDobTS3sz0R+/Zf00rFsHKjKRzKMrPwiGD1PVE+5VKxaYEaP97bh/cPSe6xTDVNPtUBkdR30uoiCQw/LEEHFOvgcuP6yKi8+qlkDEbFyFa7uRUKVgywDDRrzfzXiolkAGGSUrz/EcWm8rnxgOupj4At8rKGEtrpU5LEO3gOmtIW8xbdTDJkTR+PZFIpyO7F2DZ9ki6Rio7ptbYNXUo8FSOI1pdsXw71f6mRkEG6YvUSu9JITCk9jjLYRxspjv3E2WZas17Oua6KeSQjn4nUmesQB6TRrTHezEiARoNwJRIv1L9jlTW0dJvAVDl8SHf5dBNE3HViV3x/EKF4UDmCGUkA2mlul8Ehl9sOIQf0ugPlWyfmb7jUJXXEDA0olxKNvVGqhSb3Is4t0yP8Kfz+uLv7x8JOKSVmZm0Q0bknUpwqw5AR/n7AiHDfuVG+pqOZ/QA2hUndMG2Q3X4auPhiCbN0vtP6tdatx692BGJjC8DDBORWuadVEsgAwyTlOjUx5agzETYflp6qj0BYWWUFqBYYEAdGUwdeEXd7XjUrESGaNYpm22kk2YhL/7H9CrEd1uUnI1NHfEtETmafWdY1xZRL3Pqw8doahCuN7vVIgIhpKqYBZuppDdFG0OqDuZUyUivnnSmJ0hnv2PVHU1T3rGlC3sr9NdcvGjCRkDA0Rqvtl25d9NSw1Lt8cFlt2GbTpCwS4/tjFcafKLJHKFyx2yAL6PjZpqErzaUmvKfMlp3qp77y6R+OFzrwyMGEsDLYC6x2k7WlzlVik0C1E4tXUI5oE1Rou2/zIOZKpmmMjiKui6eN4FAyLD7QKrGY7YevZRAV57QRQQV+mx9pLuN2XNj3IAiQdHWllSlCckAQ7Mznnk+HRLIAMMkpXrxk0sNB+FgU7wMVLt9AhxKmmiszVy94UQDDfE08IkM8ZT+bfDFBnN+iyf0aYWvN5cl0lzcd7iBB4IhjOhWgG8bgGE8/424lTbDB0b3aNkoB5qaPqr2vTJqfaLmmkExdpWlJtoeRUaLTpbNahhsRqNAp1L8ZvI6prJdM3WlMriFmXbT9SyDxNBMpmY/yLbatXDiQJV+wKMpIzvgnWX7onbL6NpO17jM1CutTKSAWxBClScQ9XtTR1GmcoUAeHVDhGUzbRp5lsBw4cbDWLo90q/KyPtN8QxpllXugCFgaCSycLK+zKlSbBKcUCFHYPjXyf1w1+wjuSu1cjVrsYo3L6nM2ao+i+hvyPM3lcyTeGNJ5Hc9Gjot9XvL3abSUkRrO1o8h1RRnTPAMJFZz7yTaglkgGGSEp3x1DLDvlZsiprPao9f0E+ltpCRNtftrW4UkEZ2Sx3iPppGc8qoDnhnafSLViJDPL24DT5ZZw4YRqNZ6LVvNkw6L/71gRBGdG2Bb0oUi2E8bWwi4z7a72gpvGowqAY/Ri5KHAsPdISAnSkEhvQ3cdpthusc3CU/4gKcLO1LO09HMxWE0bXcr31e2oKNHI11y/UYDIWEkktbYl2U4u1XTWFhTpW8ZJAuUvhYqt0B+EMhXZ9eNSAmcGhX4EybD+CvzuiFRZsPm/YTT5VcjNTDiJ1UKhixGBpRqiRraU6VYpOWy7b5TpGK5G/n98ef390QVRxGg4gZkSefObZXYVhxavSdaM+pzyJ+k/5AyPCen2zbib6v53tPYHiw0oMPUuBuEy2eg1HXjnjjygDDeBLK/N4UEsgAwwYpr1y9Fvf85zHxr44d2uHPv7sDLQtaiH9/uOBzvPjqW+L/hw0diNtvvBYup1P8+/Knlxu2nMiLOoEhnbiltpCAb92eGrh9kRcstRY0WkCYC0d3xFspzudnNvErx2YmbYZZCgflUOcNgiBaOpHTR+VP70Q/dJviA0p1Gyf2ay00/bKoNaDq/zd6eRYHejCUsvxc7BcPX1J7dxjM+aUX4VaP8pOMLKNdsJoi9YHRKJpqn51kxtpc3iX4oxVBj0ofK8/m1NEd8P6KAyIvml5JZQCNdMtqTI+WWLytAqSUstR6AmIf14vYq6b8E8TQqpouH0DS7L/ZfDhMu0+3HBKpn1ZNnz+IRz9VIrXGKkbWhNFUP9HaSVU+YCp82+Q7sflALeKdUakCFHJMibiARJOHWtFLYE4lEP0mm3PRS9dxzUldhW9krIBXRscULQer2SA20drLAEOjM5F5Lp0SyABDAASFL742qxEYlELX/vbMi6+Ln669Yrr479XP/aCbdD7apPGiXun24XCNT1CJGNmRgG/Nnhrd6KZqzXu0gDAXj+mENxbvSek6OXtI20YaNiOXXzNg0qw/CMFIbX0AQ7q0wKIGuuq/pxXjf95cl9Jxp7uyeJYyLbhWa0DVSgIjGnSOhVETmRrFKIgzMn6u22h+VHrv61G0Uh10IdoFK9VULb3xGbVW6llOjci7uT5DmrIvENSl0utRuqSfMBVZ7/+wX+RF0ys/JgAt92RSSoMIobY+KPZ3XkS1RW1toM9W+3xn2nwA7zyzN74rKdONxNhc1tPN43qIrhgBhkbo6H3a5QowlmiJ5j9mtj7u061ys0SOynumDsDvZ62PWkUq8w6ykVMHtMHnOr50ZsfA58cXt8GCBtYQ2Q4EhsnIN5E+GH1Hsjb0FKbXntwNZbXeCFaV2ejHbIMymbsyMtCfXiqxRCLOZoCh0RnPPJdOCfzsgWFFZRXuf+QpXHP5xejWtXOErAkEO3Vsj4njTxW/aYHidS+s1I1AF23SGL2OIc0Zelv60pD+wQSsejmC1JaQE/q2wtebIn34ZhzTGa9+l9pE79qANkYiBWrBZOf9AbQAACAASURBVKyFa/QyLeuQFNzBnVuE/RgfnD4Yt7++Op3fR8rrjgeItOBaDQzVSoJoOS21HeaFyu0LmVJexBs0gSEtcUa1x3rr1uyhHK9P0awFyVoR4rXL342mDokVWMhIO83tGQbTqQ8EdWmTej6fLodVpOmhrx2BoTplj3ps0YJsNbfxsz/SQkMmQwghwWoora7XtaKqgQfXJemG6fIB/PXE3vhuS5nuedFc5Mh0QwyMZQQYGom2maxCIVVpn3hmF+RkiZQl/7ywGL97K7ryMtUBqSYMLML8tZHBURKZc3W0c8o2FArppstKpG4j75hJASEV13oR3gkMGddh5uK9jZo1EtBI/QLPvNMHttHNwaq39hLxe88AQyMrI/NMuiXwsweGO3buxj/ufRjV1TVhWZ9+6onCIuipr8eDjz+DoYOKw8CQzz/0+DO47cZrBZD85UurTPkNkRJTUecVWnap3WJuHPoY6uUIUl86o/nwMU/PS9/sSula0UYONBIQ4rzh7QRFzEgx66zNA7TC7cPATvnhy84jlw7BLa+sMtJcs3kmFsWOnTxnaNtGGkmCMFL1aF1RrwWjyZh5oaqrD+pGSUxUKLQU8ZCkD42RoqfFTjZQhLbdaNRaI5YGI2OI9YyRb4PvpyrqYbL9TdX7tCKQNsmIf9qipwCRGvSLxnQU+4QedZ71MMDU8h3pTXuTKhnItc25pS9vndePvRX1uimM1DRx7mfcA0lDTUf5zcTe+H5LOb7aFBmiPx3tJVLn1Sd2BV0KjADDWJG7ZdvJppvQ7r2JjInvcM9myhICw3svGojfzFwbtSqjzA+jfTGjnI1XpzqoHeVPYJiu9Cp6fYnHrlG/I88TvUB+153SDXWeQITy3OgZKtuh+8O44ta6dxy9tSf7ZCaVUgYYxluVmd+bQgI/e2BIC+CHn3wR9hukBfFv/3wA4087CaeedJwAhhNPPwVDBw8U86EFhv9+dyk2HvJCnu9ZNqBPkROWgBchR454x+Krwd4aCxj/o18bOyo8ARyoCeGkLlaUeQIY2KkAO6utqKp1wxLwoMJjwe5qZfqHFAURcOSLPDyjOwLL9vhR47ehV9sc7DrsRoHLiosGWvHoYl94vRS4LKj0hNDCaUHHwmxs2K/4BfRqbcehGj/oytirlQUhew72V3pwuPaIb+OgohDWHLLg4sHZWLO/DvtrLCh1AxOK22BPhRvl1bXY29A32eDAjrlijMf1aYdF22vh84ew6WBjX4RebbJwuNqLinrAZbeA0dO+LilHUZ4Dh2qO9F296HOzLOhRaANCIThcudhxuA4DWgawaE9IPPafiQW488NKtMxxoFOBHRZ/HWDNQsiWpcg9UC/erQ44sOOwB9kOq/CJYqJelo55QGtXEHspA18O+rbLAX/aouo7Ay/quH5G/TYp86r6ENrmZ+FgA52sm6sSjuwWKCm3oF9rwGF3AKEAdlWF4PWH0Ne5H6GsAgTtLhzfxY7ZG4M4UBtChzxlLdV56uHxh9A6145DtcC6QwEc26uloIfyEprntIncmCy59gB6Ow/B36IH9lT40CU/gGq/HdvLfGiZbUeF2y+eoyx6tVTeKfMxtUC9WB/ZVmUugiELZOyh1jmAzWrHwRo/erd2oEt+EDurQthSFkSeA+hWlIu1e2tRmGtHQbYD20vd6NsKyLJz7vwY1cGKJQezUOP2YkdlCFl2C8Z2dqK8zgs3GNa9Dl0KrCjIaliH1iyU1VtFn2QpLrKhpCwYnjvtBJze04H9NSERJZNzLm7pAApatMCikgq0cAJdW4QQsmUDVhv2VdKqo8iCJd9pQXW98g5L37Y5cNgt2F/lFbRvvcK4Ph3yQih0WbAwhl6mSwuLmOsTe+agvNqNPTUWlLuD6NM2B86QG9b6ClR6gR2eAtGMw2ZF3yIH6jzcV470iReN9tl+ZX1b7eLZnWX1IphVu1yALLouBRYUOK1gtvWQzSnWvVwbBdk2VLoVGbd1epFv82BLneJHrS55Tgtq6kPIslmiypvfPP1q6n0hfLohOvjgGiZuZNrWjvlAuRu4ZnQLPLesRljXWLgecp02lDfMx2ndgM92QMggy6LMZchTjrVVSl8HFQGl9VlibtQlPyuEai8XADCgfTZsAe4HDmwr96M2ktkpnutXGBTzHHLk4XCtP+wbSAuE1QIB8HoUWuAJZok1w5JtBxo+I5zStxBfbCrHqE4OBAkMg47wfiv7luMIwWKxwWKhXAPimynKs6B1rgtLdx8B1b1b2+CyWcQa3lERCK/HAifQpTALIatDfGdcd+0Lc8W+RLpi95ZW5OZki+a4D3P/vmOMFUvKCsQeq6wpZR/r1MKKPVWK3NV7W9+iLFTUeiG3Ps6veu3IsfQuysHuCo+w9sr3W+XaxbfEeeR+Jtdi6xwrDtcpbXHv6VToxK6yelR5lO+OeyRlLOcxN8uKni38CNaVYm1dW9BtsyA/VzzbO7cW87YCdkbB9QO5DsAfsqDef+T7OL/YhU0VNlTV1mFX5ZG/K2smhE3lVtG/aGXi4Db4cHWpmF+73YpqTxCD2trg9QdAok6nfH7rVsDmgDdkx6YDkf52RTnc3+0Y3KUldpe7cenYTvj9u0fyGLbJ8gAWG0rrHaIbx3R2oKbhO+BeKYrVAQT9CDk4B27U1EdSrju3sGB3lTIWyrsoh997Fka08eHlNUF0zA+hlQsIWrOx7oAHhVl+lHuVPaNrgQUtXA6UlPkbWe2lRV/K5+ohFiw+YMPuKqBzKxd65dRhS5UNCHpBneDBOkt4j5BrQC1bftN0/+Ba4rqVpSjXBsarUget6l7owJ6qgKCmK/MFuLKy4A5YYQm4xbkeDAbF2ccizzzu6wx+PKZ7AWq9fvRpm4uSPQcQsrlgCfrFPnjm0C7YU+HB64uVAH292zjhzLKD3/h3WyMVUFzHdpsVdfUB5W7RMguHq+pQ4wVmDAT+u6LxGurb1oVBhR5sKrNge0UI8rjo29oKl78CPn8A62sLxb4ux8d+KPu/IpeNZRbYrBbMu+PYqOsz80NGAk0lgQww1ABDCp7BZlauWYcbrrkMTz77ckyLoWP+rSjduxXvtPktHHu/Q4HDi3OP6Q/XmudROWm2mMfCmSfha9spWN7pF+hcugAerx+lHSfigr3/i07+ragb83vU95iIrD0Lkfv1/6LE3xGfeYbCEgpg7KjR6DLsDHzx8Sy0PrQQtX4rdgXaYfz0X2Ph5jL08SxF/32v4w3/ubBXbEIgtyN6tiIQcKNbXgDDzroBsz79Hlk7P8XEEd2xfvVi1AWdOKf9PlRNfAVrl36KdauWAXYnctv2wIzK+/B09QSc0CMPA0rfweL6Pljf6mycd84UMZYDr16CT4tuQSC/Kxx7FyEvWI1zp1yClrPOQM2pD8LXdoS4/Ly7fD+y1z4PS69xqHN1xQWt1qJk8Txsqm+DVoWFOO2Cm/Dyt3swtEsLrN9cAtuWD0T93m6nI5DXBY79i9E5uA0TugVhrT2A7/rcJfwbuq76B0qcI2FzZOHs1tswy3kl+rTLwfGFh9Bi/rXwDL4G7uIrRF05S++Dte4Qtgz7Gz5YuR/tC7JhObQGNbtWoL7H2Th919/RY8QELF+6CGuCA3DOjNuEZW7OSsXqaavegTZ7PkB5Tn/x/7L4C/vB1/G4iG80b91z6NS+HfYfPIheZ9yOtZ+/gpA1C2cGP4RjxFWYvbsNjtv/BAYUD4G9ogSfBcaiuqYaF/hmhusiWF+OoVjU8VaM2/Jb9O7dH84d88Xv/sK+CGa3xbMHhuGYU88VQWW+LSkXKVBKV84TfexoL8PZ2UtRccHH+GaHB1j0L1R1nYiDzt6KMmHJbHEJbtnvFExfe4God/lxr+Lr3VZMsbyPzjsUH1oWrgOWwTkHYOlzFlauXY+zR3RFh12z8K7nZByuqUeX0A4cc+UDmLl0H3q2yUa31jn44v3nMd36LrI7DoRj/xJRR+0xf8TBJW/gw5xLkdd5EC7E+3Ds/x67TnhCyHti2WPoU/O1eNY94DKs7TBDRJ8NBILI3vY+rum6Fa+1/C0q6xSQRgWKa8ORvk7rVQNXbktxqbLW7oXFWwfY7Fha/E8s3bgD/bc/gdNdK1Fz8n/gaz8aS3dUYtsnT8DScBnr0L499h4oRTCvI2zVOzHx4luQm1eAFTursGaPognheg4U9ICtUgmWMdi5A8dkbcShYAFmZV0Oe/lG+DocA8e+7+Dpf4kAZvay9RjveRvf1XZDv+IRGLXvGXzR6kqssQ7F5OHt0Xn+RbC6D2Kbvz0+cQ+BZ8AM5OfmYFr+dyj9YTZmd/xHeD74rZy47DJ4Bl2N+l7nib9/tW43Dn7zAkY5t+C7gqmYEJgnKOrB3A6oG3UnvtxUhl0HSuHa+CY6jT4fJXUtxXtjq95GF88azD3cG/4OY+Fv2Vv83bX+VXTsNwa7nf2Q47ChokHe/M25eZa4oGX7DuPcq/4Y7tfby/bDu3cFbOUlqO+j7BO0LoRqD2HCgfuwxtcNlYEc9Mvag4pgLiYM64EX/FPgbfAxJK2Y9LsN+2rh3PwOTrZ9i4XuYkwZUoA81MDirRFKn2cPjYalZQ/8ouy3+ObkeVi6rRJWTxmcW94TbXa1H8I21zDYavbg4ilTkb/genh6T8EnGyuwtcd1og7XxplCdgRQWTs/w7SijSjw7EDF1PnYWBoIR/Ek6OVFbeeGJZjsm4ndxz2M7zfsgmvzLLS1VeJwdi/UdDsXvdvmoORgHXqVzkPIEkJdzyk4tGcrsnbMR6CgJyzeKnTybYZ34BXY7W8NW9UO9Nz3Fjz2Qji6HYO929bD1qY/agsH4WLPMyjIdSFkc+CL3TZsajtV9HPQtocx9oTxqO81GW9/tACDDr2J/pc8gpqAA3PmvINzWqxG3vi/iXVfMPdiPFszHie41mHz2McF3Tvr4BIUli1BKGTB4O5F+Hq3BaGsfBS6S3Cg/41Cdhe13YA133yAtb3vhN1mwcWjO2D+6oM4WOuHvXwD7AdXwdNvGs4b1g7flJShas0cFLq3oMLVA72OmYRtnz2F7P4TUR7Mw0k778Z3luMwqKAGaypyEcjrjO6DTxARM7/eXK7LXuC31WbgOEzoWA3bt//Gcy1+jwnlT6PNtGcFQOIZ+pxvKgqzfNjd4Tz02vI4KvtdhUNoLfqfu/czXNZhK2qPuwtlb/8SHzvPh7+1oszND5bhqt034ZVez6Om5GvYahQtjr91MXztx4bXcf+OediwtwZdtjwDJ+cmZyiuqLkXdfvW4rWs6zCu7i30buFH/YDpKO9+Pua+fB8sQa+ox992BHI3vIQhWduxueMlOHPc6aJeKhbe/+GAWA+wZWGY5wv4ikZgw+7DCDnycVw7N/plK4oV7ocs3i6niLsE1+SXK9bj0LK34ek7VTzP9Yc2A3Bq3Vv4oqKbeH6kswTDWtbBPfxmuBf+G6/l34kJFU+jv2M3ak6+F6/PXYC+jr3Y5OuIQG4HTMhehQ79x+Bd33iU1vjgLHkXwZa9UNB9pPi3/E6m5H6L/O6j8Um72wRIumDNdHh7nCm+t2Xe3lhe3xPOQVNQESoAZUfmgG/rF+JsY2k9Zgb2urMwY2wnvL54r4hqyv4P7tsTh3L648DimQi6WsPqOYzTxw7FV8GxqC7bj/xt7+LyFovg7XoqfF1PR96Xd8LTezLsFVvweM4fRN0EqmU/vIcenhXY6muHiRfegNyCNuI3rhWug6wtcxFo0R3uEbdhzd4arNhRKfbws087Gdk9T8Sq3dVYuasqPP/yf1rmOpDjUBSTvdvl4Niehdg592/4wdcLV3j/ixc6P4L6rV+Lb9Mz4FJM61GB9l/+EsGc9vj4cCds6XE9nJvfxuAOWTimZi52+ovwsXs4HKOua+QuNKWfBZ0XXCyanVl3IiwdRuKc86ZF9Cfzh4wEmloCP3tgSAvgsy+9gV/dcn2jKKR79u4XdNJ4PobOBbfCtn8p6k/5J7IW3y+sEb6BM5C18hnUXThPWIZyXjkRoYJucJ/3OrIW/Q0Wby3qT/0XsmedA4u7DN5j/wB/73Ng2/UlnF/8vtEa8B77e/h7nwvH8idg3/i2YhUDUHfZN8phsuEt2Ne8jEDHsbBv+QCBdiMRbNkdttJ1COUUiX7Zdn8N5+f/A9/gq+BY/bx4L9iiCzyTZsK+9UM4ljyEkDMfgW7j4VjzgnJodp8A+3YFkPiKZ8A38ibx/zkvHyfqDHQ5CVnf/ROWukOoP+GvyJk5AZ7TH0Kww+hw/9XPsp2s7/4FBLwIdBiD+tMfDD9n3b8UrgW3in/Xn3ofAp2Pg2PpQ7AeXo8QL1e1+1A/7oFw+4EOYxHKLoTFVyf6wmI9vBGueVfBN+Qa+IZeI/7G9ix1B1B/2v0or65Di2wXnNs/gGPFk6g/40m43rsIvmHXwfHD00JW7guUC6Ys9p1fIOvLPyDQ6VjY9nwb/ru/z2R4j/mfiG+V4/X3mAD7zi9Rd8nncM27BiF7NmwHVsA79jcI5bWH89Nfwdf3fFgrtiJY2AvW8i2wHVwZrovAMJTdBp7JbwhZU1a2fYuVOSvsg1BuO4RcLcWaURfnF7+DbddX4T/VXfwJ4MhBzhunwzv6V/D3Okv85vzkdoTyOsI79k6xLlnc57+FUF4nOBbfD8fGWRHjIpD0j/gl7CufhX/gJbBteheh/C6wlpfA4i4VY4VNidLL4vrgaljLNoi1aDuwTPzNe8JdyFr6ELxDr4e/72Q4fngStj3fwXO2st5ccy8X9bH4Bl8O37AbxP/Xub0oXHA1LDmtxfqSxVJfgew3lTGxiDWR01qAMWv1Hlh8teIi5pnwGCwV25A9Z4Z4rv60+xDopID6nNdOBWhV5nrnvG2bj0Cn42Hbswh10z4CnI2taWJ+u5wM+64vI2TENWHfPBu+Mb+GY/F9cE95V8yV+D7XvirWvW/4L+BY/SJ8Ay6Gf4ByAch++3yxRmWR7do3vAnHmlfgnvp+o7Zy3job3mHXwt/nfPF3S30lst+cqMhtyNWwbfsYyO2AYH5HeI/5nfJM7X5kvzNFyDrYqq/4W9bCP8F6eAOs9ZXwjrwN/t5nKzLhehl1R/jf6sZZR8hZAGvlNtRd8kWjfnFc9pI5cJ+vrB9/IAjv7h/Q5qubI2TlK54O38hbIv7OP/CbtFYpF3d+Jxa/R8wlv/VQdiv4u54C58I/o26Gstat5ZvhmqsoglgC7UbAdmA5PGc+BddH18PXbyr4HVOOlrqDyH57MjxnPSf+n99MKLe9kI/7oo8Qyoq0ntrXvwHH2lfhnjoHlsodyH5fCToWbDMInolPhdtlnxAKov6kf8C291s4P70T/u7jYK3aDWvZRngmPo1gm4HhPT6U3Rr+IVeLtcH93Tf4CrFHh7jmaO2o3i32SEvNHmS/eyG8Y38Nf98psK9/E1lLH0TdxQsAR66yB9ceEPujun/sWPiMWPoIHOsVJQr3devhtQjldhDnlnzGtvVjOL+7JzyvBPZllXVo3TJXnDuONS/DfYGi6BTztOAWWPcvU+Qw7n5l/x//CEL5ncRaC7YeIPY+AuNAxzHwjYhcB+oFIM6KU/8t5jrr67/CN+ZXYj9yX/ShYFPw91BOW4Rchag/7d/InjUJnjMeR7DtMFGN86s/AhYr6k/8G7JnnQvfwEvhH3CRskYOb4Br3tVwT3kHWd/fG97L+Q16R90esQ7F+0OuEvJ2LrgNtv1L4Ot/ofiW2QfWyzWc88Z4wFcLf/8L4R10BXJmnSPq8o65A/5+Fzaql/cEgnH7zs/h73467Ns/EftwsEU3sW5oFZf7PM9W/n/d9M9gKd+C7LmXwT35TYTyO8M1+0IEuA7WvCS+fVnYL7bL+0PlyN+iYNm/xE+UKdeV+vsgEPP3PAu+QZeJP2e/f4nY/3yDrxT/5n7EfYkl0O00saZZuF+Kvjcocfk3z7kvC1Api/Prv8C2Tbk3UN78vtSFa9nX/wIEOx0L13vTEWzVT3wfnG9+qzzzeW7C7oK/66kI9DxDnFncX/mt8zsKt/XpHbDtVcC0+4L3EcpRgCHXCvc++/YFCBb0gHfMr8Lv8MyrP+lvCHRVYkYYLc6PfynGYt/2MTwTn4Hzm7/DUrNfnH3Ww+vgmnet2O/5LXomv6XMk+r8YzvyW5Nt8hvPnn0ECHI+fMN/abRLmecyEkibBH72wFD6EbZp1UoAQUklveKSqYI+Gi8qqXP+jbAd+KEBGP5H0ER8Ay9B1g9Poe6i+YISkvPqSWJT5+buXHgX4PfoA8Odn8P55f82mmxucP4+58Gx7DE4Nr0t3lVvMgowfKkBGM5DoN0wsRkKYJjbrgEYLoTz89/CN+gKONa8KN4P5neGZ/KbCjBc/CDgzIe/2zg41r4sfvd3Hy82VpaYwLD2oDiMFWD4IIIdxhzZhFUgUgDDRX8Xv2mBoW3vdwIwsYSB4ZIHxIFBYGit2RMGBRIsIbsQqK9B/Wn3ivfk4c8D3Tf0OvE3AQzFpUkFDLfNheOH/6L+jMfFwUQQ6Vj5LHhR4+VPXew7PkPWV39EoP0ocYmShRc5AnZtEcChAVDzEFCAoVOsD17sQnkdlAtj73NhqdopgKGNl/NSVXACuwvBnCIB2lkfD11rxRZlzlr2EodTKJvAsPE64eFPBYAsYWD4+jh4x9ypAYbtBVANA8OGS4dj8X/g2Ph2xLgEMBz+iwZgOB22ze8jlNdZgD9eTniB4UEui+uDq8TcBdoODYNe7wl/RtaSB+EddoMCDFc8Adueb+A5R1lvPCB5UIr1pjogBTCcfxUsOa3ExVMWi6cc2W8pYEauKc5hyO4Sl3GhQAkDw63InnNpo/XFf+S8dooAbCxy3qiUsO3+JiowDHQ8Vlz8tcXf6xzYt8yFb8ydoBw9k15DsEV3ONa/Afu6NwQA9Q+7HnYCw+Lp4UsrgQpBSnjeqExytYR9/Uw41hIYNl6TOW+dBe/Qa8WllaURMBx8FWxU5uS0Q7BFZxUw3Ifsdy6A56znEWzdT7zHdc05snoq4B1FYKhcanO4XkbfLtaotrCvIWdLWMs3RVxyHKtfgH3ju3BPVZQrAhjuXII2X98RKasBF8M7SlEEaQsvVAT2oo4+5wEBHyzealj8bgEK/N1OFXvoEWC4Ca65yoWWRa65+gmPg3uzr98UoagRwE4PGOa0FX/n71w/2kJgmLXqedRd9DEsFUfWEUFeo0sqgWEwgPqT7w4r4qhE4FisFSXiMhlsUyxAKpVNBKH+YdfBvvY1+HtPhG/ItXB+9hsFGNqzYa3eJfY8eXk8AgzfQNbSh8PrM+vbu2GpK1WAYQOICK8lqTxc9ggc6yQwHC8usqGcdgJAS6WOAIbf3h2Wa2NgOAuO1S+H55b1u+bfDOuB5QLsesY9cAQY5nVE9rsXINiqv7B8Wv11Aqx7R0euA7WsFWD4rwZg+Df4Rt0mlIPuCz8Q8y6AoSNPgCkBDAnwVYpINTDMefNMeAdfpQKGysXdPeXtBmD4nbK+COh0+iWUL0Ouhr/fBZCKX1+/C8TeKAD9gGnwDbxMKFHgq4O/31T4Bk4X3xgL62Td6uKcfzOQlSeUd/5up4FnC8+DMDAMBsJKNLHHHFyhAYYzhTIu+92p8PebDPvqVwToloX94hoRwHDEb1CwXDkXvSffjSzVnYL3A6unHP4eE4UCjoXKDu5/PDtZqIiVYyGA4ppm4X7p73JKWGHMv3nOfQnBBrYB/00FiW37J+J5Kok4X+oigGG/KQh2JjC85AgwHH2HAN88C10fXqvMT48zEehFYEhF1blCceg569lwdc4YwJCKJ56HAhiO/XX4nZxXThD3FQJeM8X50S/EWBRg+DSci/4OS/Ue1F26ENbStXB9eJ1Q6FJRyrObCi71+ce2IoBh1S5kv6coL1gywNDMjGSeTacEfvbAkMKVYHDvPkVzf8WMC8PBZvjvWHkMnR/fANvBVag/5R5kff8fxWJYPF0BhrTaSGCY10lYZsQBFvAKTR4PIHjKBcjgxmfb8Znyu6p4x/4W/r6T4Fj2KByb3okEhutnissmrVoEX4GiwQgRTPDwz22vAMNdXwntOA8zCfyC+Z2EZqsRMOx6suoCMQ727Z8qG1bxJfCNVDS+ERZDAi9aDN88UwMMQ8h5+fiwdTEmMNy9CM7PfyPqDwPDxffBWrYZoZY9Ya3eHQYFAhi2HwXktAa81eJ5Fqm1o9bTN+x68TdFm35QAwznwLGCwPBRuN6/NGxFDblawX3h3Eay5/hpWQm0HSLmWBaCAO9xjS12UjYSUIeBoc0h3vWN/Q2Cue3g/OzXAqTxwhcs7A3bwTXioh0utizxnASGvIjTOsYSLOgpLhMhVwG8xzVeJ6yXQEuWI8DwNHhHE4wqIMr1yW2ifu8xvz0CDCfNRKhFFzi+vxeOTe9G7DcCGPISu+o5+Iunw1YyR1gYeYiTTuOevgAhu+ILJNqYc7m4DAeLBsF6aI34m/f4PyFryQPwDr8xDAztuxfBfe4r4vfsWeeJQ5UlAhh+fIUCDCcoeUZZLO7DwjIgS6D9aGFR4qXaUrVLUE1JK6VVQX1h5uUz0FmxlHItyRIGhp2OEZbMaBZDrZIg/H7PieJb8o2+A44lD4gLDK0mjnWvCisPv3n/0GthX/2SuET6BygUoqjAcO1rwsoTAQzfnAjvsOtUwPCI5dQ36HLYdnwKZLcRjABpVbbU7BUXSn1gWA7vyFsVECaA4WnCiiL/rV4MlHfIWSjmVnvJcax6Ho6NbyksCQkMd3yHNouOXMrCshpwkQCjeoX9ZH9FHb3OEYwLS32VmE9aynmpdn71Z3EhYyG4pSJCFrnmaG2jpYFWR2ExvPCDsCWkkcUwp0iwHtznvy2+LW0RwJB7+fTPxH7k+kCxThL4eM5+Lvy4c+GfgIBfnAOS+RHofAIs1XthrdwathiGgaE9B/4RNyjAsMd4YSng0O5hGgAAIABJREFUNyyslo4cWKt2wjP+YViqdyF79kVHLIbrXkfWskcUObtaKgwUd5mwLlrLNsH1wRGQfMRi+LBQUAiZdj9dKA25B5DJIJU6tq0fwfnN3WG5NgaGb8GxmkqKI4yKMDCkHMY/hJyZZygWw9z2yJ59IYKFfYUFj1TLQNEQXYaFWtbyXBEWw0V/FxZlx7JHhKWXF275rZI1QWUgv5v60/4jzj0WoVC10mL4d0W5Mez6I8Cw4eLO85dnNBWRQhZRgeFZ8A65RgGG828RgI1gxrHxHWExF8Bw8FVHgGHfKQLUSGuyULQ0WCvlGHlPgCNfsBEk64CyEsCQ/tBBn5gPFrHHHFotQLulvATZcy+He9IbCLXoqgDDPufBvuYVsffKwnPCe+xvFWA4/E4UrPiP+Ml7wl+Q9fVfws/xLBPAsOfZjYFht9PDTBu1JSvQ5WSxplmE4rPrKeJ7kiUCGH71R3GPYZFWTvU8h4Fhp7Hi/OV80hLoG3U7fAOmifOCln4xPz3OUCyGn/5KgEQyFTxnK4wnMedqYDj1PYSyi8L9FOwJWrRb9ooEhif8BYHuCtXXaCFYDeZ1FqBYAMOv/yrOcH5jYWDoagWLpwyeSa8r1lDV+cd2IoChioHA32nl9o1QqN2ZkpHA0ZRABhgmKX1uGLwge0/+P0F9UYDhJcha/riijZXAMLe9oFYIqmgo0AAMzwKosTcEDB9RrDkNFo7woU+LhACGx4mLKbXSBBzsk9CuCmCoUFQJWKXmmJcgXobsW+bBsaTBYtj5BEGXEZtyg1ZTbFixgGHNftSf8BfQkkHNcbBjg89GKAihnWugndpL5iLr2/8TdZNuqqYFqim0YWBIyk/5ZsViVrULngmPhjd9aqCRWwR4KgXoY5GbMylZvmG/EH9T06zCVNJt78Ox4inUT3gErjmXwTfoSkGf5cHqnqZcamWhxTRr4V3igk+Kiyz+nhMF0GlUAvUNVBvF0iqA4QdXCz83HnakygRz2wtaDw85S+1eMTbb/hWwVm1vVJW05qqBi5Bbi+4I5XdUqKQRwPDORnRXSTUj/UdYDBssQgIY5hQJaxIt2SykOJPqnPXdvwUdUlsEMBx6Deyrnoe/+CLYtswTwNB2aJVQVLgvni+0+bJQrrRyBlsXC8DOIoDh4v/AO/ImQYN0LH8c9t0LRdsstDjDq1x0Ii2Gl8PiKoTnjCfCbUQCw1EguIejARjSsk6LoRYYNqxH0WYjYKjQuyR1V49aKJQSGiVBeE00WNh5wXEsfRD1Ex4VlhIqYuwbZgH+ekWGa15WLIbFCiWRlDiL+0h4+bqpc4HsVoK6RzppBDCceQa8w3+hDwyLZ8C26wvAWYhgQdfwGpF0RF1gSCo7L7JqYEig2HdyxDogdZeWCc6tLjBc/7rCkmgAhr5tX6P1twqdVV2iUfiEPN6ZIqidoo6eE0UgIVpGSCkNA0NaDKMBw4ZvlZRhoYTpMwk2AsNpH6qA4bMCDAoqaXZroWTgZU65pDcudgKx5Y+h7tKvw3R1PkHLK+UpiwIMfWK/CyuUOh4rQK61aoegtvKiSCqhtOBQicD6A91OhXfkLQ3AMA+w58JaTWD4iGAWZL93sfIN02q19lU4lj8Wps6RukfrOfdTqRyTfaq7bBFVKMLy5liv+DFzX1eAYVuhsDoCDD+Ec9E/oLyj+IiGqaSC1kw6rRoY3gTrgRWKHMY9pCgGadHPbQfX7Gniwk+KJP3kA22KI/arxlI+okQMA8MRvxTuE4KSnd06vFeFCnvBc9p9guqoVvI4v/yDOHsFMHz1JHhH3BhWvlhL18D14fUCqHAPkvTDqMCQFkda5ftNBS19tKxSwUClmbD09rtAuCAQgMLvFt8iFbt0ZWDhXMrvW46TYIc+gmQbCKrorq8Eo0exGBIY1oeVj8LSdHiDQlNsUEbIPZrWWO7l9jWvweI/EtCF/fId+zthja4c9isU/KCcizwnsr454qcsgKG7DP5eBIaKkkNYDLudFmbaSGUEf2NfpbuG2P86HddIARkBDL/8g/jeWNyTFSunuhDY+vpOQbDTGKFApLWRiibfqFsFxZ5rUoDoBhaHsBgKWvYEse+Quhr+5j65PUy/Jc2ZdFoW9tM34CLYStcj2LJnI6WEUFafSGA4PuJbj/UHWpx5LgtgeOZTcFIhQ2A44ytFIfPhtUJpQAYH+8gzkHcxNRtI63KhdnFg2xlgaGpKMg+nUQIZYJikcCVtzkcfrPVvicPJP2C6uBxSa+Ubeq1Cg3G2hL/fFNh2LVQsefQxfOtscajTguE95jfC941ATV3o8+QbcqXQwklQJzZdAeo+EBHS7GtfR6Dz8QowbNVfAYaH1wurQYC+i1s/FJcV0n4IoMTBT6vR2N/Avuk9ccALKmmnY4VWVGzKOsDQvuldQcVh3wkssxbdLWhe1CI61r2G+vEPifHQT47a8kY+hlvmhQ8oLZVUUjbZLoFeoOMxggZKvz7SiEKObJAaJjd90mFC2W2FJU36KkrfBB7gIOVswIVwLHtcUEnpy+Pevx624gvh3PoeHCufQf3pDwgKGq0sjjUviQPfN/bXglZGIMwiL1Ty8JLzwn/7hl0rDs1wCQNDxdKqAMOrxHrg4SCAYU5bcRnlIWdxHxSHlm3vYmERVZdgftewj6H277QWWnxu+IdcKeYofEh+emcjiiPXHdefsACN+TUCXY4X69O262tBYSVQk8BIXoopc/vmxn6Wsn7OGem6vNRzPQlg2OD7qAVRrvdnCO2uvHSzDnFBWXyfQrPqfS5IzbF4KsU4xbyqaJ2kPNMfj0VQST++AnC1QP2Z/w2PNwIYthupWFatdhENmBFpGaWP64YXCh7UYn2dcg+oBZdrSVZIfzDOWxgYXvyxuMipi6D2apQE4TUhfWUaKHDSl5EggNp/KnSCrfsLJYF/2LXiEkSNv2vW5LDfsJAT/XlCIdg2zRaWJlq6ZCEN3LHqBUHD9HOda6mkxZcq82d3ItBhdFh5YanZJ+h9nnNeUCw5Kh9Di7scge7jhGWelymxXnixbahfPf7smWeKyxcvclSE8bsP92318wLMCqDRAAz9275Cq28jLevch7iH2De+Iy6oaktd9jvni29W1NHjDMXqRF8qP30MWyPY9VRBg+XFPMD2/R7FJ6mhSJ8lzjOVYQIY7vhc+BAeoZKqgKE9R8hfXnAJAixVu8NAmYCK+4CwDEj/J+Hv21fIUxbFZ3MzAv0vEBb9rIV/EWuJfkgC5J35XwSLBgsKIfvPQrokLZKkDtLaTd8sKlNCWbmwVu4UrAQCR/r9cq/mnHCfJXtEWtKyFv5ZfEfce9XWFvHtSCppBDBcK3yq+bwEhqTI0bdPvqMGhmyflm6eUQToZEVIFwrKof6MR5D9xhlC2Wbd8aXoL5U09KkjMKVfq/fEI1arRh9Vwz/CFkNvjTgnfMOvFwo8yoCW47DFsKCH2LtpMZRrkOubVqVgq97wEhjSstXrHIQKugiWjFQauifTYnhveN+ilY/gnIUWWsqfylTH0oeVb1QAw5uEJS9sMaSSrN8U+IbfgOw3JohvmP5v/l4T4fpI2bPIruEZwu9VnPt9Jin0yPoqsdfzbCTNUVBjXQUI5bQX1GFJKZeWJs95r4i9gHuX3KOF71rPiYJmLt1KRKO2LPi7nCj2sKqht6PFSsWHn0rnrG8Vix8LGUVcL4E+ZwvZsHC/5rfEb4qF9wxaa8XznU8U3yqL1uedf1PvKfy32tedrioEUyxkg9g3zRb7E0FzoMMocf7yfOedgRZiKszodsG1xcJzMtBzgqLg6XYarBXb4Dnv1fBYyAgIn0EaYMh7kPAft2WBdzN+i3IM3hP/Ktaw0cL7AvtNn8GwxXDhXULpI5gEPF9IJc1qIZRY3Ee5b8i9SLYjDQCcN85fsO1gQZOVxVd8KXwjMxZDo/OSeS59EsgAwyRly8AHpEKQHig0kRYb/MUXi8OFhRsHL1vCB4tab3sOgu1HCIshg0ZIB3LPWc8IzZjaoTy8YTBQQ90BOEhJayi8CNAxnpdoBgcQwHDbx0IDRy2usBi26IJgTpsw2FMPlcDQ3+k4RQtKa09WnrhMMoCEOBAatJr8f9+A6fCNukX4adHPRmgQfbWCZ68uBHV2OuiT1nLcHwRV8YjFcE74gIqwGNK/ZdFfRVXSYkjrIq2MLMGiIfCc+WR4Y6fWEwwa4S4TdCsW6ZsgaToisM7aV8QlM9huGCxbFsB9zstwbZkN+8qnFWD4wdVhei1BQJDWoNI1qJv2oaiTWl5enki7lX5Pcrx01idNJ1z8bqFBlgAjbDEUdLcN4hIYZPAZXla7nSYOSloMSadU+5iJ8TbQfCMshvmdRTYGairVQQH4jppWI/pkz0bd9E+R8+rJwtoQbDdU0Ft4IQy0HQ7v8X88YjE852VQE88LhJx/vc9CWA77X6isMwLDhoijtMZQWyoL26GVRF0EMPz+3/COvg3+7mcIGhYDVfCyxiKslwwvrkslvQwWZ4tGPl20+GS/PSnchAw6ItYu59vPS4GzwWKo0LHE+jrpH2H/ksYWwwZg2G64uAhGsxiqfT71ZCQpcPLyQUAm/OR4SfEq0U29I24WQXwkUFHXIwIT1ZXCtnuhAAtuWhAbithH/B4RTIHWDBa1r6W8kPDvtErTr1M80xDkoJHF8Mv/FRRmfkMiAIu/TlhnhIWZVt2G+tV9y359PEK5bQXoJyXRe+Lfwj87Vj4Hx+rnhGVNtB8Iwr/lc7T6XmNZb3iD+x218NKyKitSU2vF5Y3A0FMhLCqksQa7j1OAldUumAoElY2AYYNPLuVPa79C0f9cWLXDQXjOIjBk8JkjfsIyOAz9j+27FoYpzsJiSOomL4AMdNNw+ef3wv1EFgI0Wpz57VIRRTok1yQDeXDvYLAr7luNgeGtwpc0UDQ07LclxuzIhaVqh/D5kkG55JwTfNM/V/pv0VLGqK3cB+nzR4qnLGIuLFahpJRnB8G8jUG9aPktXSf2CO4VVEgSkMl3GgHDcICzMYLWy7PLNV+xGAoL3vhHxVkm6buyfRF4xOYQ+1z9yQpbRL9Ii+E9YixZ39wd9v2mPJS9SlHmkNlAX3ZhMSQlsMcEEQSHwXDknqgocIqFYkXM26FVcH10A9yTZsJJF4V9SqRktcWQ3xv98AUoq9im0LX7TwsDYBlciuc4LV4Ef2GLYZ/zxJ7u+kQJZENaMFkP3NOo/GPQJ6lA5u9UfEo6q/h3w54jZSMBBQN20UeO9GD3ua8i1LKHUPDw26aCQAbO0sq0asjNaLFKYdjIID7hutsUizmMsBh2OSkc+ISyosxE3zrT91Nx19BjTHjOeVGxDjcU+smSLssirZz8fwnOhfyGXiNAFgMCyeIbcZMI2qcOREeFlb/nmcL3lvcRBlciQA63pQaGqkA3cl+Xbhjy/iLHUH/8nxHoeWbU1aj9QdYn3Q2o5KGfM/cTMiSoVBAWQ0c+LL5qJeiS390oPoCQZdH/Y++q4+2orvU6eiXuRowIJCQEK+4SXBo0SCmv7WupvEft1b3lVWhflRoVChSXoCG4B01CBElCAsESPHLt2Pt9a8/aZ8+c0SP33oTZ/0DumdmzZ83M3vtb61vfmsnOTRFOA2sHrCZtA0PkL/Tg4gNjCzTAAjEwrNGo4MljAcJkzd4rjhieoT1u7WfcTS1IUpeWUt78CmAIpau7v8gTi7OxIuiWDZrmid87D/slA0lEvADGCvAWwuM8YKKKTAAYDhhPxZYh7nljAAign66cZwHDPryRQdSRJzE3YHjzmZT4YG0AMLyGKN2q6KVXKBoK+gLgEM+lEximV91GEFHg+zrkFwxysUmR6KmpAMiL07CZRH1HKQqYJUgiC4+Mu+PEq1QeRtsGKg6fRYmXFlD7cVdQ8+obKY2I4aEXsZcPXrrMisvZBvDgpeBFP/1OHosAQ4ApABGzYZEwveCg9WDjLJFWDQxLRd6AAzBwlBUqp4g0dr6vgOEr9/Pm3mzewHAMJYoFXpDg4TU3W6CIyoaH+0o3K6cEaFUf+TIVR+6igGHzYCqM3F1FDIVKety/qDRoMmUfvZAFVLwa8ggZGK5VnnUs4mimIhzb7abTKqKgXft+i6PArNg3/gimjqIPUbH0BYbzz1bA8JhL9NCcoMrcXGEDnsh3ceQM9FMzN6zzgB9oGpENGFoRcqGKegLDfuM4AuTVNDC06OFlYNhRpspamyA4LRAhs71XU05ipwFHFJzA0FJRNVUPbcDQUk5Ef6bjogwM/85zAxoimXgvk+3vMLUYdVcVMDxY0fB2rJRNBzCFJDuozybFDP1BwCnz7N90xAnAsLDqLhr0hHL4OFvnkX8kKP1VAEMj15Qj4sm0AoaIADcDGB6ugCEUKAEM+41mgCkN1Dw4JZi6/MiPOHLEwHDuXXZguGU9Mf3QashfLY7YldVy02vvZdohf0YCDE+/k5WEm+9UqoGlAROo/YR/6/MRMWRWBoDhjI/xXMe0vba3CBEt3C8ogjZguPsXFDAcMk0r3cKxhFy0xMa1rBIpYEMDw6WXsniW5G8hQgP6JebB5BtP6OMxMKbbJlKc8yopAnhuiPwjbw/UU6xPiLBrYAi1V0TdDSopQCWcbFi34LjkufPOz1Jyw2IGKx2zL3YHhhDzgZhW//F8jvfEYqUdHHShKiPy6IWa4s8OiH2/rRgFsHv/cQoY3jCHBbiQO20DhjgW7yki+6A8n3k/JTcs4eeGPL2mxy/S85YNGFp5+ApQrlF0bQDDO89nAS3O61t5s4pATT2Jc+LEUYPfsGYBWKJBURnCLgxQB02mjuP+pfOu8bupNM3/NkS68G/kkmNPgQgTIrKgqCIXG1F93Dci6SIi52bTjTPPp/5LFe1e5iP9fQAYdnzAdhMVUqaSbneAzm+D8yMJlgPGNmZvVvVGc2NMVALDckoDvg98J2gaGKaaKLcLgOFutu9WwHTy9cep+R4VRQPQZ2B43//wXgXOEugiSLNFDOdcz0q7Mk5+V3QETzm25bfO/b7DUddQLd+hnrMhyMfA8MHv8N4Cub4Qx+OIYbqPovdaQQCZi0zbQ4RK5mywABDBlmam7IQaW3xQbIEGWSAGhjUaVqIjAAocQZGI4dPKY1cBDNkLt38lMARv/e4LbLQyPWHsdj7RlrdspQQAuFhQBsDmxesov92BvGEHDbE4FDlxAIYTvYFhyxAFDCEkAuGQpr42z7XI9vPiYsnLYwGB1w7XhkcM1COzIQrHCoyJNFP2bMBw5c3aO1YBDF+8iaNJaBoYPvJDSr80n/+GxVpAAS9OQ3fiaBOEZZBDhiY0KqHpwLPIwHDLBhUxfOlOaj/u39S8+gZKL76EgbUChmey95UjuQCGby/XeVL4HQua5A6Y98qe7APK948NDahFkNhGHpEGhsUcb1DgYYYHnVVOt9ufqGujAoZr7rKJCPD9ekQM2QMPYNj+VsXGXOTj9RhtwPBLFjA8kxdL0HgQ7RRgKGUMTDDu9lnARsivSb98D4NcbMj4HTcS//FvU1lS+kEEOfvYT7lMhgaGVt4tjrEBQyMJn6mk88+iRLafTezDCaoQJQQNCa0SGJZFOSTKwNc0cwwtYCh5IV7AUCTJvaYNOHGgICxiCtjIQVhCRQytHMrdPsv5JJ7AsO0t5XVvHkRtBpWUoybFnFINtYAbxA5arlWKopIHhf83HReSMwTRFTdgCMCFfDAGhogwc35WWS1P7pW/PQt4oTwIvndpmWcvIUQNJY+GgeGLd9Kgp+ysAjleGA+gHyJ6IA0CN3D48D2AKprKEuiuKKqNCEBx4hGaigmQD+cCvlNpEt0XbzyYHABjLP1vqS1CGAi2N4EhVD1R8gfRuPTqO3SZEA0MT7mFkh+s5cgUmpQfkuvagCHKFS38Oc9TAPmsjDz7Yo5k24Hh5xkYcq6xCKIADGf7sQMuP/PjzARAE2cAi/ws+auOxoBmh3cLwBDU+6Z7v6xtwcqtyXQlMITTEBHDd1/QwlHinOMyJKmsAxgq9kVx1B5Mh4ToiwaGiOAd+UcuHVMRMYSQTrYfz2dmeaKKb0fy0Q/6iVIlBTC0hNIAlLv2/U4ZGEJN+4jfKmC41/9wLqxEmaCg2bXv15nWCiohmBpMAd6wmMeLCBZHDK15C1FxKWUguZ8AYnD8dO16PjvBRFwOkWdhU4BWirI0GhhOPo7BHpcswXc461MKGKJMlRVZFgcyz09WSRWxgzNvGSkgyKuHwnFh6AwFDC1WBwPDCUdoETm3eWjTTp+mfssV7R6UV6So6O9jyDQG98hT1MBw3hmUH7u/Liki6x6PdfRetlJRTsZEBZXUFISxopzoB0CTo+0A1hwx3JXzPqUJMDQVyvE885MADL/G5YjwXJBCI80GDA0FVB0xzPTltdUsj4Pf4GiQ8k1u9jP/hjI54tiXMiOI/mMdx7cNpyhYATwHWYCwDAzVc9S2twSrJA1CFKy1DSxmVtCY4t9jCzTaAjEwrNHCsgnGApZ68xkLGJ7G+W1ooDABMJhNA8Orj9KS05hsmu65wJ43YJ3E3P/2d22lBJCLxLk2089i5UOhOWCjhMWEa1UNnOQNDBE5gpLp6tvKwHDoTrxx4QXBSDLXwHDeGSyGoIBhG2UfLlPJcA5TSSFyUCqxchw2mZLTBW+r0CaKI/fQFFCcl37hOlUD0gCGZj0kUwFQAcPpXP4D+TtCMZWFR8ZdBoagku5KCWz2jr+Kmlddz2UXulDf6c7PcK4XK/bBs812W6HUZAFwBBha1BDzGfKG5YDypheRXmxIRLWN6xjO/ww/T0QwkDtQah2lVE5H7805U1hkM8gptWrp6QXEExiO4MgOlGzN3A8eqyUfr8cowPDyA6hrry9xvS/kkiAyysAQJUasiKFQDBFhkYix62fBNCrUhVNS6xoYOupVmQIi0o/kuqD+Y37coSxWgXw1qY3mBwwH33EmUXM/m9hHBTA0vO6cRwM7pZtVxNCqccnvl+EttgFDS21PaFxSP820A+cKW8pzXtMGnjO+feRIQdhIA0OI4aC2okE1E2qj7b2aciJHp1lhtgIYKrqtFzDUdDdLfVKonlBphSy6KKXieohcp94FlfQdVSKBEvwsOMKMTbEljuO8fziesEHDxq7TUIkFWAFoYfGZbF+mkhZfuI0GPl3ObzL7EnGYCmB47XGs7Mdz0HYHKFoW/l3sImoayNFefENocG4gh0nk7fE3vJeI0MmmCwI2DAyh8KiB4SXsMLIBw0N+zg4bAMPMizdp1oAAQwiHwY6IzPN1rPJDck8mMCxgTnnilxy1wrPEBhK2gs1EmIbfg90/y3m/xX5jWeAEjcEwRCxA153xMc6v4nu1auMxZffZS3QEqeneL1GikFPA0KpXK2PSwNCoT4p75PqwzYOZGivCURoYWqVn7BHDq7jeYnHk7uzQQgQJEThE4pCu0HHknzhfvgIYplv4maF+q6koXPHtmMAQVNLH/lc77BA1ZnYDouWW3TUwtGwCpgSeC1gUiMJgLPIdMzC0KLaIYDU98Ut3YGjlfjLw2bROqSdPO41zoZEjbAJDRAhZvMuidoOWiflVWC8oPZKbcRaPGRFV0EBNer3pxMI9OdUrpbYmon0qsgZgqFgdYBhwWSlQST3app0+Rf2Wq3p/yDVHjpw02IWppACGVnkKiBsh4oncYjQbMDRqDvPa2287GxvECQxN5oqAWfRpispwzt/wWWp9tBoE4yCGg1JBTfepdx7PkyOG93+dnUfJza+y80qa7VpWLV78poGhtW5XAEOrbrSnAY0fxOHL36ZVf1IBw28yDZ/nhba3lZJqKqvEAa3/ylykbT9oCiHCqoGhJVSmbRADwzCPJD6mGywQA8MajQyqGDYiiBSx3DRTSU9jRTW08MBQUavcGhdsBzA0SgmAxgiBA4l4aWDYOpwKw3fhXDkscpxj6FaCAJssq8SFRAyxaEiRdLNem0ysUnzaGxj+ktIrrmagA+qUTXzGoJJikseGUBpoTqA7oQFQIlqJ/CCpo4hxiUy15I/wArXldVYIMxceGbcGhpvfpOKo3Six8nZqP+HqMjBExBV1zqxEcYCIAqtoPkftyLvhBVIpzro13rBYhX/xO5LOIc4hOY6IUDDtLLeFF1J4bplK+tD3CKUVqNDBNCPXZ2NRLJ05hqC0Qt2Si0ZbUWcZm1Ce9FgFGMJDilIZw3dmEQOOjI7ag8G9Fp859u/sYQfQT69RUVrXBmA45aNMe4PCqmxmpZi7nGMKiMjfJIKDsSB3hIVMjNqR5r2aSfgcMbxjrooYGmIfIqgi/QMMssiLtdFCXh9qGuI9NEsaYJPJapfOiKFVuF484p4RQyuPxMtEGhjudA6LHjBVFLmTuQ6tIoi/IyLivAf0iU0nNhoAhk6lXLERBDMgnMHvnVG2w7Z5Nd5PeK3x7boCw82v83VAu+WIIRT9LCEI5z2qiKHygsM5A1qU/oYX/5UyS//B6p8s6Q9g+PzNNPAZxQRwNqmvVgEMrzlGl2eBkweODC5jUswpYLj9kTpiiDmw2Ge0lrfHNYT2DZVDFhGZeBS/0wAIWoTHJccQ3zK+aYB6MDBERAdOI/QDChvUXRGl4HfMct7IfcFJp2rTjeZoLvL6EH3iiGHHu5oya4sYWoJlnLdpzTOYPwgCU++voTyK3lvAUAqAg64L2q4UFufxFPPcv6nujHFp8RmjPinmVkTSuB7l+6vLwFByDH2AIedMgkqKUiAWxRLzcOdRAIbHsWNQ6tGKXRBhB23VrPno9j5wJAfzKSKGAIbWvIx1FbmyGhj2Hc0gE98V8rZZfXL901xWAqA695ELWJjGBIaYp6AuitqimYUX6XnLFJ/RCtRWRFy+MUknAPiTwu6INjG9VYDh9kdTadgMLveDhvUaOgNILYCSNK5r0usrIoQO9UqphwcBLjgRkZ8o4kgMDMcdosuPuNly0/RPUL8Vqt6EqVOZAAAgAElEQVSfWaKK31sGhu/zPIOSG2jNNwEY7svKoPzv2z+p1aRROqPTyuNnx5hVxF2ui3UZfUoTFVf8W95R/L8pKoO1EEBa1Ed5nLt+mvUSwJRATiEavoXCpKMVMAQte9NrNpXmoIghcsyxF4HzV+5NRQy/xTmWYZqs6zweS6CMgSEo3J3vc448qKFSYsPsE45PUz8AawtsgjkB5ZmcNF9znGHGFh8TW6BRFoiBYY2WlU0w526g1h2AIXJ9LPpG+xl3ciTJ2Vg2/I2ndMTQyUe3TTAWJcL8m+TQOPvFZps9khBNGTSV85TclCaxeQOIMoGAU4ra7JupkVbxaYCKzOJLWJ3QbJKHAU85NqFewFA8aVBdQz6FqYKmgaG10XJ7PFiI2F6bX+XNKSiQ8DRionbmb+B8eGALlKT2E66h5pXXcT2+roMv5M0CNgc6/wbiM+++qDeFvsAQG5aDfsIbRRQTR52/lquP5igHhAyQ09R05+eV1D4iGLt+mnMgUFOKy20Uc57AEGPGRo+LIhuNC3ADGOY2c0TXzNsRypM+3ACGqKGIhZWVOVNZthHX0bSolKDpptZC9dbbC839cn7NHEqve1DXQcOfRQiDFS33/TaXP5GahM7nx8Bw/KGckwQKo6huugFDU01UABveD6jwQvEQAFWaDRgOnc6UW1dgaGwKnMAbfSFHFzlGXsBQNhte0wZAH5xCsun0B4aqvqDZmMpm1bX0AoY4XuhQNmBobl7HH0qFSccoRb8Js7W4iWzAsvd/g8vBQFmPa+elskyfVMDwc5TcsJRtXACVa8EX+H3mja4lxATBCXi/pWUEGFp15xgYrriRBi5W9dQq3gNxbO0C2l25DqEpyOU8B86QAqikD32Pf4I4R/K1hSyGJE0iuqBuQ71T7t0EhmBgYLxmE1l/qaumQZWl0Mg0bippxVQp94M+TIcKA0OUlXj6d+yYY2AIMGWVLxEAgvOwQVZiXao2JBrmD8wXlGnmCIpskjUwtOwsG3JdZ2/a6VQaOkNHU9EXnGbYsErOGvc/Zl8VMQQwxHt+xp3UdNsniJJZpYJr5RzaIoYrrqT0in/rHEn+Bpddzrl3ZpO8d+dzE8ceM0wKnTxvOpsGhogYLvwpK4JC3RRgrzR0mna0wu6c0whguPtnOZUCKRwQYnP7jvAcNTA84XLKoI6hVS8Q3ygcMIWpc6jUOoQdrfJ+C+iU6JkJDJ1jh/OhNGSqFpzj9zm3mdcVUZgWBzLOdUYIhfaq32Gr7AH6KYw7UAFDS1GYgeF2B9jSSpzj2bzjudT3efVtQngF9VCl4f3GmuQEhmABiLMTYk5SnglrFd5dNAaGVhF33Z/hIJEyRfo+Mv0oP/kYBvDyDOQ3k93A47TmAREB4nd17IGs9gqRKKZlo75pMsORdNB5A4GhYRi8J2D1YC+k1UEr3sLKP+DbxZyEJvoBDAzv+x+2I4vhdG5yB4YOdgnn2h57Cc+jfM9W2oFcNQaGIR5IfEi3WCAGhjWaGZ5SeITZE/v2MkUlnXYKS22jYYOJ6EjFYgJg+PqTWmxGKFquw7E8X+ZvThlqc1GBuAgDw8E7eAND5JqN3ltvGHkiHrWnln92jgMLrBSfZmC45G9MRTIb5/Osvp03RPAQAyhoKqkRMRSvo3gUM5agAvoCYMEGEFQ3s5CueR0GvAPGM7ULRaZBgcQCj0ncFRi2DqdiKUFtJ11PzSuvpfQSAMOfUNNd/6U3IOifqaTvvkBtZynAYXpOnfaQwr9cF7DvGAZ+WEA0MDxjAW+oAVZBe8TCh00j8jJBo0FOl1fEkO1wxG95fGZjae98O9NT8eykhiOP1RAM4HNSWRZe4A0XSmWM2IUFEPg5A1QiYihU0qP/Qqm199k2EF6fBTZTaQijtI7g3B1+x616VQwM9/kWR0skT8zZD1NJtzuQWq87jgt0S0F05OOgvica1OmgUmcDhpYgA5c5QB5rtp+ObvOzM4oJIyeVSnlWW1RU0ue0ciUil7pmn5FjKOMUqpQXldTLLvJ3vAeZRX/m9wqbeQGGyJ0SeXmhTSU2vcbFwG3vtqWqib/5AkOLDmUDhlZ0DOdiQ12YfCwDC+QkSfRdypjYgGG6lSjbh9pPnqeA4W6gCS5lKX2oPmLTjXqKXOC+72jeoGGTYyoEQhAF3zGKiKOUCYBhafm1NGDJb1xNxjmnyCVzAkODXl/xzY3ZT0UMrVwuOMcYGK69Sx8q+cAC0OXebcDQQa/DySIEIY4ByZUUpxXnYVJC01wxhwmtDc4eCH/xMwMwnHoiAxnYCNEEbCDxPSP6AuVSGT/oxunnr2cqvwgaYe4DRR4RXChbIlrC40OZFzixLDuLiqrk+qGWXnHEzgxupHF6wp2f0Xmh/O2P3scChgOY4o71qQnlPhIZFhUSaqkdGP5b1VsctjPnT8MhlV72L113T1/PEkRzPjcBhkgPoK4tzAqxtVJBKVgf8CMlPrPwZzpfFuAAOfP4pti+fUZwXnnLDSerXL6dzwsEhkI1BbUx+ySAoZq38I2mX3tUlUzpp+ZmtTZtoK49vsAicuIcZEqyJczmvD+IwZQGjtdrPiJf+N6RIiHKyyaLwqzvynOXVepG+hURE6SJFCYcynOX0P3RD6/bHmWF0MemHc6mfi9cru8RAFvPbwMnqxJTiBhqKunpvJbqNe3285h+z2MbsaumAbtR6c3IeSUw7MtUUMyDEtWVcUAUyhQ6k3nJjHpjLUXEEHsBgGcGhqkMfxeIytuopEbNRDeHHyLL/ExW3Upde32Nv9EwTRREeU618tBZOOu+r3LOON4ppNWIWrHZp1ObgKPrx/5dpxY58z8xRoDouMUW6GkLxMCwxicgHm5ecFEjkCOGp2iPdHhgWFkSwW9oSH7PPFGp9AbJ5MIoCxgOmeYNDHHcGADD8qbKmRRvXl8BQ0Wb9YwYTjxSlTKAQMBx/7IDQyPHUBeVtrygomiI6wkwNOshVWw0Bk6m0sAJnO8I4RQs6BBeYIrliN15EbJN0C1DqUhJajvpBmpeeY0Chgf9mD2OOXj3X1AJ7axk995KLlqL5g8MleIq5OSLfUcp2uA1x5SBITZcd/23ihi0v8P0ImwS4DVn8ZxE0h8Ygq5l0db0hgGeZACMYt4mCsBjtfIh9X3bgOEXVY7hraqgMYNKRAwFGB71J0q9fL8vPUn6hb1QYgMRFA0MT7iSgTqXOkBZiid/SQQlSZfWtddXmCLUet3xDO5EAdYODM9kIQQbMOw/gTeuXEB8xZUKGL6qSiPwszNqdOI5Msh0BYZfpfwUpQTqtoGQaLazBIfX8c5blPprIlDhDgz/k4UfXIGhBYDRLzYWGIc0c7xdrsBwtgYo7G2ffBwr+kluDPpBFAJgDN8X3nWOTqWaODqoI4a7ftoChgMVMLz7AqZMAcSKffD8kV8jTQND611gYLj0Shqw9A8e74FS5JMNoRwEdoWbMjO/t2P2tYChFTHc99uUfO1RnRfNNhPBCQugy70rYKgitKjvmXn277ZxQSyI6Z9WDqVEjDUwREkfAENLNRqUVQBpNIAxCH/x9fuOYrsjIolNMxQgAXYgtgMKuS1iuMunGBhisyu1G1l9EXUcAQx3OlvnQWpgiBzIZZdxbjXK+JjiKHD+wEkmTQNDo9wOi3iASpodoMQ8Tp/PEakSgKH1b0SQ7cDwClUrF3UY1z3AwC697LLqgCHEaw5T+eS6aWD4QwsY/lzn9DEwHLKDXk9Bz+s46o8KGM78OOV2+U92ZjrnSjPyK0qXHcdeSpknf6UjnWoue5Ty289mtVPYTtM4rYLrsgb4AsMJUFgeyfVw+RsDTRwg5MUb2EkCZ4kpquQEgjJWbQ8rRw1OOJRVUMBQCUcxMMR75KMevXnKGdR3paoP64zMcRSbgeGJZWB402mqBJJVzB7lMVC0HQ2K3nCuoTEwtKKZMlYbMLQU2uU3AFxE+OEMdEZ1heItx2J9zO38CXY8QDGZv3cwBBgYfotLYmCuSqTS1DXj45yuYwOGJ17N+a4yTvsLBifAHK7BCcc16nHKGuA8zvlvm+PNBIageEMN+Jh/cP6zKzB0ML3gNIIjWwIFTuZCbtppLFoWt9gCPW2BGBjW+ASkyK0UAIeMOgNDK+EbGzuhIpiX4pzENxAxVEqFzkTloGHJRsZ5HCbj4qjdGaRyseSWQUpm29FYhGTMXiyGIM0pm22eAg86ktRZYY+ppH/ljbvtnizKGm+aTrxaAcODLuQkclN8RuoLCR0KEvGZpWpRRUQInkKzHpJz7FjckNQP5T4AUGzMoGamgOFuOodEL1AojI2I4UdvouYXr6b0s/+grgN/oMp9oGi0lYNp5qXgXJNS4xyDiL9ApbDYZwRLfSN6LGqueO6sMtu2QRXwnXkelfpvx5sPjmglk1xzMr3yJtdHLeIc5o8iv82LppH7ocZazgvhc5IZjnxyxPAjF1Bx2CwWMeBzoTTHwFDVBsPCn3oFwPDqoNeO7ZV+7TH2rEN8Ak1kyRUw/BYXsod6oVsDMCxsdyC1XH8Ci5SwWAk2HbaIoQDDVTrKKRt20NBSGGemn66XhfNFTZT/n3NeikSpFt5AQ1BIShowMJ06R13TJWIoeSFVA0OLpgg7gcLLwBB1DPNt5YihFelAzgzo2WZDThIAMJodGKpab9KEDmXbuBiRQbyf+SkAhl/Tnm6ci00rnBg2YIhrWfmeHDHc5VOs8osoGTZ2KKOD6D7nqKLm4ZY3bfmh6Fe+YRGcADCkpVdo2XznuwDPOArHA6QBHEqT+dTt3YHzDdRWTSXd91uUXPewjVIsNcRk02WWj/EFhhYFWKiEUkBe09yxWU8k9TM082OlhiE/MwBDFCB/9u9sK+r8gKP8yMvDGmEDhrM+SekXbmD2ACIT6tvcR+UlQdZ/pzOpCaU5EDHc4wIrd/1iLo6NqBk7e6w8aFyTgaFRzBxKqMijlmfG/Y/ak1kRpWxflbN1+h2q0Hgyrf5t5YjageHllF5+FUflkYPuCQwPvUjnRNre6UFTmQoJ1U6UHpHas/oYDQx/oOoYPv4LjuIhpw/vMQNDaz3lfEXkNALgW8wCV2BoRd6RKwrKK9Rd4URUwFDV6BMnV2H8IVxvEPUTde07DQwVrdIJZMz7Q1QaKRsyfyra4hZKr7yR0wfa51zPgjhSlsgJBL1qowK85accz3MX6P5YN5A/yWWlfHLBN08+nfquUnO5sHhkvIiIw8YMDGepOoItAgytYvbNt53LJX54LnXUEDbXIP7dpJJWAMMWKoyHquw3K8C7yWLgZ2E5rExxJnYETTqGc4p53FvWUyKZoq6dz1PR3Lv+q1wy6cSrGNyjuc3ryL+Gem9qzQIy1wC3ecb8m+QDsi0txXH+ru79Es8FiNyjhJSZLynny1yk/41o97H/1PtBp4PKzHkNGlf8e2yBRlogBoY1WrflysN508eqWaBWAhjuMEd7pEMDQ0dSd9CwJIem4jiIqIz8CMtDYyFXOYYuwBDevNF7sodOLxqOJHizb+SeNKOOYdsGb2BoedR4QzvnBi4AXAaG85gihCaLi4hhZJ75PWWWq/y2MjD8ilJmdGlYJEqDtudCxKjvhI0iRwyRe+coFszXQ/0zAjC8uQwMD/ieotlNOVHTcgDUkOsj+UW+wHDMfrxBghposc9wpj7CKyx5msidgxADezm7NrJCZan/GN58gApL6WwAMPyFzi/SC4vhgTRzP/B7823/wZEA3RgYgkq6H28q4fkVYMiqsIf9sgwMZ/+BhSukCLbfuwd7pV5/3A4MLSl1roFnFbKX0gzOvhgYjj2AWq47kSN6XGDbCQyt8ihcUNyKcgrFEwIVDAwRMTTejwpgyBHDVgUM316uSxqYNQBdgaGVF1I1MLRoivDUw1OugSEivZb6rFDgIEoEUQqzmbnGNmBoKTfKsa7A0Pr+cAy87RwxvP/rekODv4uQlAaGiE6Vihwp0RHDWZ+g5FvLee4oTDic32NsgLhWlzVPgZmA/DRprOaJSJYV2WBguORf1H+5otQ7m87FtDaE8jvyb7gWmEtDpBsRFFA30WCD1LqHbJFjOU02XTZgaFF3sQmFUI7ZELEAc0HT3E9CftgYzrtFrqKzmfmxIjyDYxgYopzAsn8pIZyuTbyBRGkPROtAOZXxY4xpOKXyXfqecY+gMnLEcPpcXQKhDAzVXAkVUqiEyhzF4GTELloABWOBEmrTgs9pQR5+L7A2vPsCU4cxN+E9b771Y+xIAnDGvIV7qwSGVzIwgThIZGA4cDKLpzQ9hNpv7+gSQ9qmxbwqbXSAAQwtWjTeYwUMlZgKADnqyAEYImoEu/gBQ6xbcGChjAeiO5mn/s8AhnM4YghFzuLgqQyqdcTZEmCSeRV0UTOX1XwfEJWmTF/t5ENeH/YEXCPYiqwjSoR1AM0JBM0cUFu/2x/Newl8d0IdZmA4fGemJHu1zZNOob6rFX3UzC/ma/efwOsk5qcyMDyV2QtwavKadus5lHxPOX2dNYQhXsZOLqvZgeFZnLeqW7qZ8mMPZvEgswwFj8sCWfp7nfFxVnI2vw8bMEQkv209M7K6doa4z1w7MLSYCujPFRhufzQlil2UWnsPde35Zc4DDtPMerk2YIgyMigTc+QfwSNwBYZapdS6EDs1jv8XM4vQsCfILPunHkYMDMM8kfiY7rBADAxrtDKrpRU6GZBgMsXkU4C6pxVNcSZ/6wnVUj+TfzuTuoOG5Uf7lHzH4rCZnsBQLRKqGLQek1X7ye3a2AQ2zztLSa97RQwtZUcofLafcivX/5EcQ0QukFhuNgjHYEOPyIF4W7mg8QE/5ChF6o3HXc2AcRcHT6bkey9RxwlXsOBC6tWFvLlyrTOXaqICQMicW6n5xaso/ew/qWu/b6tNsxQuNuxRBoaOKJwxGllE2SPfMpyKI3dj0QQdMTzlFvaeY8HlWkozzlV5LI/9rxKl6NqoJOOxSXNpTsUyPkTqJFm5Nl37fZffOzTQwZx9wfZI3Gd6Ssf7ehHCZgWeaBHggLhA8pUHtAiP37snqnbF1uFaBRRjLTUP5GgoajtmFv6iojaj9MniM9vtTy3XAxg2a6Efu/jMWUoA5b0XOZqBJrlt6J8FOxAxfP0xPVShJ/N7PWgKl0wpZVqo86g/2yKGpqKnKzC0qFLYiMHZgGgWvmV8227UbaetNCCxJO05t6jzA1sZGsmrE7VQsw8z1xgOFFD9pNmopJaIjhkxNL9nAGlsdqGCafv70OlMJU6tuZsSpbzK27GaODUAnLh+J4SsJgIYflmLIZnzlNBAcX/Z+7/GG26JlAIYJhb9nfo9Z6ds6mtZkX2JFMjfW648TOXRujRsQhHxkgg/A8NXHnB1IMmmS+4dEXiIgwGwuQJDq26e3J/Of/YChgbN18yHxvyTH384lxAyS5uAPsm5YSYwnHkeA0OJFuKWcX9c4gICIShkbglCSV3MzFO/41xgoabi+8B3gk1raeSulLHK/qAvEbfg3OSO99ii2oGJKGVuswKGt5ytRa0ARosQwHnnOVo/+gQaMrAP18xDXi8o2nDGgG3AVFJLBVgelTMKJH+Hww0bYszTyS3rtZK0+YiZ2WABQ6h7CliCCi7sqIVuoIw8+XieqxDxQ1kSJ+jg+aJpINu148SruTwRqL5gqMA+Mm7QC0GLh82xDrHj0srnR4QJarGpdQ/zvOoHDOGoRE6z1KwDrR10f47MWpRjMxJusgJ4vjJYAra5YMD2VJhynFLFtdZKqFkWUFbKEN5yfipbJp5EfdYoJkrFtfqN43USgFOrks47nccugmZmrqCpPszzj7EGcf/9xnCNXkTNUB9Y2A58cQidDduZFbFLA8bZ8l8lF1/GjvUR+dkA31IfGX3DiQrntQB29V5/ntXYRXgJf0O+M751NFdgCPCOMb5yHztc4JQ0mQq4rhLk29tmTrOkkJSiMgXrWJgnkXYHho4Hg+8QDCc4kPlbHHugLU8+Boau0378xx6wQAwMazS6TEIsp/zeS54b4qDLBNVHCzrf/F3yHXlCbh6kC/MG9VGR62CcwLkoNwMYvsNgT6mS2qmkAopwGjy1JjB0m6yhmoeoJhZrM0EeCojZ+77JeQluDZsFbHqT767iBQEe+9RrAIZlT6bzvGJ2IG05GcDwSkov+Sd17Q9g+A1bfSqhkYUDhkoVFLLtHBnId3DkQkcMT7mZI37Ig8RGFxQ+zjFc+FPPjYA5ZpPi6vXcIE6AzR+abBDNY2Uzw6pwL86zL9rGAh8FGKJ/bH5NYMgqq6UCb94YGD6GOpfum3sGhmP2VZE0Kw/SuZjLfZllJoQOh9p16eevUcDQy3EwcDKbQQNDI2IoaoNeGwjZgMDLjXxQvMeo4Qaqrc0b7vFQBHRgYwng4la6wx8YluuERQWGQd+383cB2/J3KfWCTRorVwIYTjiU32NEEvBfpwOL68RtXEvN885U3VjPlIHhM3+lfpY6ouc7bG0I5XcpAeB1vBltgZBQ6uV7XQWznDL9LJq0ZgGDdFdgaKkTa0BhUfc8I4YGaIeQj2zUGRha5QRM2h0UO5UKtBExnPFxjjJVAMMtG9imJvtB3luABJTQkAikbOIx9xbH7GUDhhIttVHQR+zG+WOSwtB+2u1qXreAI+zO0dNll9G6Y25TwHDZv7g+LZczen2hJzD0e2YA2pink5vfYMVqZ2NguP/3mYIpZR+C3mf5xtyAoZzLQiFWGShcN/vk/zFNmu9TgOGwWVQaMct2XUmrkHnHGXkLGpv8LpRjYRbh71LyRY5x/ts2h1vOVh0xvO4E9Rxee8RzCFsmHE991t7i+juulejaQvkdkaNplasQYDh6H8J7agOGQ6YzjRXOb3aCS60+q3cBb1DsTb6/1r7GGM5WZ+qLWQ6LnwWURqHSu2Z+RX1k542IGm0kYDjuYEqUigzE2DH2+kJWWuZSXURaXRzaDWYT+jn+JqWoTEcLKOKUag4HDJsGUMfxl2tgaDoz2QY7nkpwXMYttkBPWyAGhjU+AQE8LKf8wcvVA0PLw1njcPh0THxY/JBzEgkYengu0SfnoiBiiJpcAIaL/sry5maTCRd/Q+5Y69WzFZV0zD66DpV5vOTJwFNrqqxBkASgTerkOW2CaEiJgeELLIqB5PQ0okcoIu7RitkBtOXk26j5xX8zMMxBFfHBb+tcFpwm0UYNDF2icNI9hHtQ6Bm5BaXmIVxQHYu1XnTm3Mj5XexBLXQRwA7nGC78WcXGwG3IZiTT654kOoPfoThaUT7EyvlEDafUCzdp5UPuz1jgoZjIEUNLhCfoHeTaZK3D9AYL5VHgjYUnHsAQdFku9OvSmEo6Zh8Wj4CCb9vZKorcevn+TGlEA00IxZZtwBDgu+0tLlGAouClTB9vxwEisgnimo2gnYFWLeIA6FeKt7s5K8QjzrmSj/6E3/v00ss5euh8393uTyJVyJFCH6DMJtrfsh0KCheKYCNaj8LXZjPBmg0YFroIVF1poq5q5sAEPTfn72buGX4TRV+AqtQ7z1GxdRgh/woCNl0H/IgjPs7NDIu6vLeaWm49R3WfSFLb2Q+zKmniqYup34v+JVByMz9GuV3Kha6FgeH5HVvlRPA7nAwos+IUm8Jv+N4QtZPGIOClu1g8xknh4nduykkM0gREIY8I73Vm+RWUecZFQMfIjzWFshgYjj2QI1pmlANOJDhEELkS5VAAcAUMP9DjREQPzxTvhlmCRSKG2ad+TennrqHOQ37BFEgAcgaRiHqN3d8GDCUqY46DmS3vvKipq6COcoqAMQZs0gEGXz3mVho8ZLAChogYDp7KIBx0/8zSy5QKd4gmRd6bHvgmi5wgamJrQiXd73uUyG2k7BOqpm1Qk3qCYA4gqu3WzLI/ADjom0Xi8I4gXxrOPIiBjdydMk+US6voWoOWA80UcAoal+17bh5M7afeqtY/i0ouDkg5zvkdmufLMxQnKiKGcI54OcVwbtuEY6l17W2uw2RKfm4L5Xb4KM9BaDrH0FK6bsH78IHKc9Y1hAUYOnrFvZT6jVMCSh+8bGMg8fmW8JGTAYMc+dSbT5W/Tyv3Gaqh2ccu9DUx5gvMG8jvT76phObaj7+CdQfQ3OZ1OE6wKLDzdvgsdmKKAi/OUerio6lr76/Zrm3S/TUwNPZqXDIq08ezBrXtXcj0JZToAh3YrUkEPMr7FR8bW6ARFoiBYY1W1cBwyHQVHbLEZKJ260zqjnq+bTGBwAADw10VMPRRMDPPkzwut2sz5WjeXPZuewLDkR/Rm/UyMPwJ57UAJDpbx+zfU3HEbpR97CeUXlVeyFDCoOnBb+l6U87zsHmGQhsktaH4xh771xfqhddt/MV0X9py6nxqfuEKSj97KeX2+QZvdE21Odn0hgKGEHA57P+oaf6niSBlX8yrmlij9uJFGzmWTfdB+VGpu4FSzMDw8V+EEhoyi5V7vQv5ibPZy84L2y3nuIgBHc65KFiUUy/eSFjk3Bq8nsl1D1IGQhghmhStlg0WNrCJUoGLdDMwhDIiirq7NOR3AFS33KhEV8TWSlilxH8rA8PnOXcSTZ4NShQwMMz2tW0sbO/xgO0pkUwYwPBZap6vwAdEgqD2iOYKDK2OsEEAiEeZBpbmf+OpcBHDGedSZtml/F5hrK7AcOfzWGrfFRgaucZ2YGh57PX4FA26rsDQ2qxxbbh3AQyHU2H8wezgEKDsZDZwxPCdF3T+qjxTAMPkE7+nvquUOqJXg/R8btfz9c9+zwQHmbRYiPsk196lyw+Y10BNMETWpAH4pdbcxYAIgAzPyGwi0iGlAoT6CaGXzDMXVw7fyI8FcBaFXAaGY/ZVdFczKi/A0KDKAYCnV93sAIa7MSsD74aZNysRQ0S8QKUGnR9UNBQnh5ooovbFcQdR5skyqNKOOmMcALsKGCp2BUALwKXkv/E3YtW+e+PwK2jAqIlsq/SKq1lFGZv5yMBwwAQWqOG6ywkAACAASURBVOK81o2v8P/bmgaG3+VxZJ/8te87Iz8WJhzBeYm+wPCI3+gah4i6ccTw7RVqLrCAIZgyxdF72Wxnqhzj2KqBYdNAQlTWfK+dQNAvjQS0e7xbUJgFWGVg2H+8qzNE7NI27mhqfaWsZmwak4EaA8M5LsBQrWktN89lkMff2+ApBDVXOF05mu9ovBb3Gc5iYAwMN71iO0LEvCSnWD87hxaAsERMkTrPOWMXpepsB4aXU2ng9nyKKzActSc7IvGuSK1YpMeAeYLGwLDPcC63ZDa8r83zzuA/aWCY7a+/F3yH1DQgHDCESjaAIZyiLg2iaHCcxi22QE9bIAaGNT4BDQwH70CJja96CicEXcb06gYdG/Q7gFjq7aWcTxIFGPopozLliIHhBxYw/EvFRtmkPiHq13r1kdR50E+oMGyWqlvnaFCngxBK0yM/oNRLZSGLNuTnARha6nEVi1GfkXrTxADs/q/buPpu9ilm+tCWU+60gOE/KYeN/8Pft6nNCTiPAgxZkIOBYYEXHYm6oOh70/3fNIDhGUT9xzJdyTUP0jFop5qc2z2JMiovbFbkwDwuP+EwVp1FToYChq+5vjrYBCfXPaRzt4LeLycwxAaWikUWvwFQzT7yQx39c/bFwHD0XuylRvMFhkb9QQEkACjp569TEcP1z7gOlRXsEgk+hiOGG5ZQs0UnAwgBGEHzAyFSDgbvF+rzJdcvrvCGu11cKIzhgKFBwbQ6M98NGzB0bMx0xLDtLZWvWUWriBhamzXk76TeeZ43SlAUxveFXC5EVIRqKZdTwLBcJ5Kf6VkPUr6UpOTjv9YiGF7DA0gHWJcWCAwR8bAcHBgTA0OXecLMA0Lf7GhBaZ58hzswnKCcKALmJDfaGxiW82ORSyxCSHh+rD66yk7lkzI8Zg4VADgDQ0uUBOPEHAqBFmywzcLnGhg+8Suuj9d54I+pMP5QVrXFdw3nTHHC4XZg6IjKcP9IeXh3paZ6MzC8aa6tRIjYbv2Bf6R+42dxbnJ6xTWs3IlvjnPMlyFiqCJvQQ2lbNpPuFIBw/dfovaTrrGfYgOGmxi8hWl4N8FIge3xDNwa5jaIJ6EhTy/75K/swHDdQwy0imMP4HIl0kxQzu+PIewUZmxyjAhI2YBh63ClPCvfvA9bSFITbMCw35hyzqXLYNrGzqbWdUrt2dl4jUdqgxswHLUnq+ey+vhGBfCKgyZRx3GX+QJD3CPPzxvXVQJD696cpRnMurO4DpwRuT2+wEqu2YWOOpeOmxDxLhswtATQNOXVcQ6nOyTTHPF2q1XLZadahxLy9s1mOu8kJ9Dcq0EMDfcOrYHAlmqidgBDyynqPD4GhoEWjA/oJgvEwLBGQ8uEb/LOq+rSkdRdVR/WSVzLD8Bw1J6cUA3Z7zCNc+Xa7LQ3OY8pRwCGrLD5MaZ+mvQjHKfpN5wzoLzy8Nyjmd576VOKPpty7/it47hLKfP4RRXiBnIeb76G7kSpt5dzkWk3YRu3++2ccR4lMxkujcERoYd/YAeGUgNtt/NZjIGFB6yIn7M/2BaLKOTisVlmYPjG41okpeOkayj74He0IAw2WwwMn/iVjWLm9Vz85NG1HfqO5oR9iD7g2ZhCQjhGVNSQhwhZfK5Z59KwKKfWL7Krmvq8MAoYDtEbLJX0X2QBAwaGlmqk67WQYzhqT12moQwM99WHi3KmWWZCRCFYWAkU5lKJyya4NQgucGmBLKikf7FRSWEvvJteGwjpT3JiWLxi6T8p9eojtg28l3mEhof8TtBe4eFHVM9sIrhiy82zDjCjB77AcB+VH2uq5oX5xs1jnA4KoVjB/qn1S6iUblKOpZfvLQuZOOqYgc6JjX/Tgs/rrple+tICSq24irLvGUq5bu/e9LMJOUPSgoFhud4rF85+ab5+D202hjK0EQE3HS3O/EOeuxxCEF0HfJ8VHUFHzTztQiU18mMrgCHKCTiKoSPiBDl9UPRgTzRESQAg7cBwFytiuI5tL7l/oINzFPSJi/i+INCFPltumMPfAYRhUHMSQl7SEB2UYu76b8NmUgrq2RbtHhFxntcNpgtAA67x1t4/pT5TDmQFV0TpiwMn8jzBwHD55ZpKHvTeYX5ALjhT69cvpq5Dfsa1XwU8o/4c5mJ8L4jqeqlRV8zBw2exMivKwUAgx61Jbix+w1yAaCTmFTQumg7xtab+Kgr3xhO6CxOU44/VAkNQ9uH8MOtmOunYfk5hycVDriS+TwiXcKkgK+rpds/tYw+nlnXuqqU4l3LtXFJLxFe0c2HE7lTY4aOUWfhz/U5ibcvPOJPyU+awZoCzIWJYgmBPMUeJzo0Vc6RWeXXUDnUCb5nzIcYEVo1fE9ErW47h8ZcxxdZrXuf7TiR5DRQnY/vJN7FiOxyVmUV/4TUN3700zK3ppZdqh6kGhoYyK9Y79C15rEHfgtSDdTvOTA0J6if+PbZAIy0QA8MarRu0kQndfR2BoUzGmHwTm9/kvMBam0mtAf3CrQi10LDMa3HR+Heec708vOP4HcDVBJms2olxe8jWA8Aihw3nSP1Ic3Pld68SJRFKmg3QW4p0+nyfZwIbI+cmUejgSDEDFav4NW86QC3+YI32zGPDn4dX2qqZGPQ8nJEZr+PxjEupLKXefq6CSivFiJ0iIxV9VfHuOWXLpU/nhsp5LXhrKdtfb86w2eHcj/WL9aF4vqwKOGACwWkQueF+mJiaouKQKSy2wKVkLPoYrpd8b5VnRJqfn5Ubg/eTS45YdeaCxqLLOTQNpOLACa40RxbvYc99p+e3IddB9A6lBJIbFtnGC9Cf6HyPKJHxzLUMGqvzd/l+dZkF5HxauaJeeVBQnCw19bPRvqN8kwBfifZy9MQJZCrfnzIw5ALy7e+5ik5VbL4NkCX0NrPvyvps46jUOtj1+cl5rGBY6FCiY0bUL6yT0G0cUoDcjCjJ+8h5vRvXMXAENS+16nbtDELNNJQWMAW7zBxFGbNzjnZSJvm7aRmirtF3HCX7DKbk5jd5bikOAIVxsVVTbkN4dgxUoYdO03bCu1TK9tPfpKwnTpXswPfXmrdMh2TF+zJge50bDAohchz9RMrKdmqxCWg5C7sHjs3nAOf74TWXms8CdFekKWRWXM4U+SChtWSXqotZ8Y2D3ZLvImrqy3NQcehMyjx/NX/nWNNQDsLp8MW3gRJDrg5m5KqjeeSUa3s6ynE5he54zt/xFEqtvMUz5cHsC2OH+Jw4NLDulDLNqr6tQ/3c61FgjYaTQIQDce9wfohzLtHVZnMM63fAWC/xzuZ3+RTrFYRpck3XZ2PVkg3TT3xMbIFGWiAGhjVat27AsMZxuJ3uVTi3AZfy7NJP6RQTLSusGZ7aMGMLC5rC9FXrMX4TvbNvLzn3WsewrZ7vt+Gr9p6xGcfCL1GDavvprvMAsgAMPVUIqwD19Rw7A5mOd22qllH6N5WMw5wXhoYdpp+t/Ri3qKfznvycclHvn+fq/uM8WRxR+6vn8U5aYlDfCkTv5JmjHHR+rb9XgEyH0qdX/07BllrHgfNNwTiv/pyF2utxXbfajciZh2pvLc3Lae3Xp3NO8WNOcT8OBzJo/VAbr0cTBk09+or7iC1QrQViYFit5azzejUwNBT8arzNqk9HvpeXzD88kQwMPcpSeC5UDjpb1YOrw4kYv1ctQmf3VdOR6jDOrbELt4hHrfeBaAgDQ4/6kbX2X+/zsRlkYGjUbKz3NWrpDxs8BoYhI6rOazll64PGErhpC+pgG/k9N+10XfvV65b8nHJRzcB5ZMjvDJlXGLX/Wo53i3oG9cfpFuuVomV3t2rAC8bYCEdZKLDpZNLUwWBuJTqqFfgxh1MNiHXOKVFLh4lQWR3MonPu69FX3EdsgWotEAPDai1nndergaGjgH2Nt1rV6X70IAUMp0b23Fa7sFZ1AwEngUrnLBPhdYqomjViHNtin848lHrcI2hcKC8S9pnV45q19IENrBJNeLyWbhp2Lgv9MDAsl1yIcjERawp7jpMiGva8be04SNsHlZfxKpxejS0UMBzjm9tWTb/1OKeaeSIUIKrH4Fz6cEu5CHOpKOyUMP3hGIiyeIl4he2jmuPchO56ynHqBIJRFeJFmKsaOzjPiSOG9bBi3EetFoiBYY0W7N3AcCznpPRk8yuBwcBw0JTInttqF9ZG2MGNEuN1HZEeb8Q4tsU+G7EREgW9MMXqe4NNWU0PMusRo+rdNXaADwaGRo5dlGtH3aBH9eZHGcvWdGyYOqd+c2/Ue8VczcDQI188an/1PL4aymxUh0Q9x2uWMYnSbxQnZNh+TSXxsOfU4zi3Eh095Th1pqYE5XA671+EyuphlxgY1sOKcR+1WiAGhjVasFcDQ0tAo8ZbrOl0vxIYiPyhTlJkj2UP51WZBokimBCVNleT4beBk4OEbKq5RbxzpZbBoUpPVNN/vc9hYEgQ5+kZ2lvQ/TAwbH+76vqtUSMW9RQBCbq33vx7mHI2foXTo94bA0MW/Hgh6qkNPz4KnV8GEya3ruEDj3iBKGtN2K5FiTjs8fU6zk0nAOWXwgrH1Gsc6KciQhgy51PGgDrBmad/V5chxcCwLmaMO6nRAjEwrNGAvRkYBqpR1njvYU73E4tQyp4AhovCdNUrj4nile9RL3WvtJ7/oKrZ8AXdJt65UvMAz5qOQed39+8oOaCAYe/8Ror9xqmIoVHuIIqN3Eoq+J0fleYVZSxb07FhhMX8CqdHvVcGhiiTYKn7Rj2/kcdXM09sjcCwnkBfA2SUL3lraSMfj2vfbt9xVCGqeg26VgYSFIIzz/yxLsOJgWFdzBh3UqMFYmBYowF7NTB0SETXeKtVne63OVHAcLKvJHxVF+3Gk6Is1lGjI914G73yUvUUz9A3mG5WRdo9aiD2NkMwMCyVfAta9+SYGRgiYphvq2oYUQWGelN+cVU33I0n1TO6ysAQ5TLeX92NdxDuUtXME1sje6MR+bXVCPeEeyoBR7mwfnrqmVQjWGPeHWrjZhb9uS5miYFhXcwYd1KjBWJgWKMBezUw9ClYX+Nthz7dLycInjoGhhuWhO6vtx3oVovMa4w9RdvpbTYLO54wUZGwfenjUlniDXPbW5FP7YkTuM4jKjJueLYnLh94TVYXhC2tgumBJzgOiCocUqt3P+r4tubj/QqnR70vBoZQ8/3gpainNvz4auaJwph9KfXaow0fWz0v0IhoedTvr5734+yrxxg1Naqu5mZ9gjJL/lYX08TAsC5mjDup0QIxMKzRgL0aGFqFimu8xZpO9/NaI8lbAcPeuekNc+NRvLhR622Fuf62fEwUYZ8odojyzKL024hjIVGfKBUp+dayRnRfc59MpW5/u3pgaBWZDjuQqMIQYfvdJo+rYy62ys0dQsmNa3udqaqZJ7ZKYJhuoUS+va72b0Qed7UD7MkSItWOGeflZp5HmaX/qKULfW4MDOtixriTGi0QA8MaDdirgWHzYM7/6cnm5+XkQsPIMdyagSFoiSFruDVCZbMnn22jr11PuX1zrPWk2DXaBqBaJkqFXlkmAPdehMAVIoaFrqpMETk/rI5gp6oBf0hP6s2iTX61cr0eV0/ls/W21yfy99fAG9haUy1yO51DmeWX1cUyMTCsixnjTmq0QAwMazRgrwaGboXgIypu1Wge8uXvp5upOGhyr42GhLn3KDlPxUFTKfnei2G6jY9h0DGOkpteqbst6kmxq/vgHB0iykyFPCXffb7Rl6qq/1oFrhohwV/VjcQn+VpAiTYNpOSmV3udpapx9MTAUD3Gami4jXoBwI7YGp3EuelzKbPiyrqYJQaGdTFj3EmNFoiBYY0G7NXAMNu/or5YozbbnmaMPfzaNPEmONrHxvlrjajDab2TW0NdSQUMc72yTACeZu3AcFKvFDSJ9qZu+0dvbWq+QU+kO0ojRMrf62aHbZB9euL3qEJUPTHGRl8zBoaNtnDcfxgLxMAwjJV8junVwDDTt0JGvhG1kHxNGC94ZWDYf0KvzNGp8RNo2OlMU9z0WsP6L4w9kFLrHmxY//XoGJtLBoa9sEyAAoajKLH5japvtVF04aoHFJ/oagEAQ2rqV9Oz7k2m7Y5vPxLQiR2oFAlI96aXqY5jiYFhHY0Zd1W1BWJgWLXp1Im9Ghim+1Aiv8V2h9XkY9Roovh0ywINi4BtoxauFXQEmaU7NodBYwj6nYFhvrPXRtX86pQG3Rt+73YGQ5hBxcdUWICBYbYPJbas3yas0x3ffmHErqHrj25N9PZGvQDFIdMo+c5zjep+q+g3BoZbxWPa5gcZA8MaH3FvBobk4oWMgWGND7yG0xsdAathaL3y1FpBR9BN5ccdTOlX7g86rEd/h2ARSkH0xjIBMEytzyj+Jnr09Qp9cQaGmZatpsxL0I11CzAcuQel3nwqaCj8ezV5kqE63ooO6k1COD1lthgY9pTl4+uaFoiBYY3vQ68Ghi40zmqkvWs0UXy6ZYFaN9EfNkNGqRFZjW3y4w+l9Mv3VnNqt50DLzrl2ii58eVuu2aUC6G2HQrcV9tqzVGs9rrxedEswMAw3USJ9neindhLj+4WYDhqL0q98XgoC2xNJXRC3VAVB8U5+EQxMKzixYlPqbsFYmBYo0l7NTB0ubfepEJWo+m3utNjr3C0R1Yr6Ai6Wn7CYZRee0/QYfHvPhaIosrr1s027SypsXB2r3rx0s1USmYrxMx61RgjDCY/9iBKr3sgwhnRD42ifBrn2taerxz9CfW+M2Jg2PueyYdxRDEwrPGpx8CwRgPGp8cW8LBAo73o+QmHU3rt3bH9e9ACjQb/PXhrRNsSMOwBQzaSZtwdwDDKNeK5qAdesF54yRgY9sKH8iEcUgwMa3zoMTCs0YDx6bEFvIBh8yBKdLzXMPvkJ8ym9NoFDes/7jjYAo0G/8EjaOARsdJkTcZtZM5Zd+QXR6Gq5yfOpvSaeC6q6YXZBk6OgeE28BC3gVuIgaHxEDs6O+nXF1/Cf7ngs5+k5qYm/v877rqPLr3iWv7/XWbtZPttqwOGg6b0Wun7beB7im+hjhZoNPU2BoZ1fFhVdlVqGkiJzverPLt3n1ZKt1Ai3967B9mLR1ccvCMl332+ISPsFmAYwfGUn3QMpVff3pB7jTvdeiwQA8Ot51ltyyONgaH1dAUULl6y3Ab+lixdTpf++zr67te/SAMH9KdLLr2Sz/jkuXP5vzEw3JY/j/jeetICDQeGsZe+Jx8vX7uU7b/N5K05jVlyKRfUMINvg9HJRta16xZgOPEoSq+ZH+qR56ecSOmV80Id+6E/aBuujRwDww/9290rDBADQ+sxAPCNGT2S/7Vk2QodFZS/H33EIeo3B1Dc+oDhVEq+92KvePniQcQW8LNAo0FDfuKRlF5zZ/wQetAC23L9tu68t1pFgHrwFfC8dHHoTpR8e3n9h5bKUn7MfpR+5b769230mJ90LKVX3xbqGrkdTqbMC9eHOvZDf9A2nLsbA8MP/dvdKwwQA0MiWxQQtFEBhnhCoJbOmjGdBBi+/Mqr9JuLL6H//uwnafy47ba6iGFh8FRKvRsDw17x9cWD8LVAozfWue2PpsxLd8RPoQct0Ohn3IO31q3R0G0xV7MwbGdKvfVs/R9hqonyo/duuCppbspHKbPyxlDj79rxdMo+f3WoYz/sB5XSrZTIt22TZoiB4Tb5WLe6m/rQA0MAwddef1NTQ92A4dGHH0yzZu7ED7engGGu/0TKbFxT8wvWNWAyZT9YVXM/Xh20j9yPWt58pGH9xx1/eCzQ6BytLWOPoD7r7vrwGLQX3mkp1USJQmcvHFntQypm+lEyt6n2jkL0kG8ZTun2DZ5Hbo127hw8k5reXRri7qMdAlu0D9+TWt94KNqJEY/ePPF46rvmllBnbZx8OvVfFQNDp7FKyQwlijnbnxu9LoR6YA06KAaGDTJs3G0kC3zogSGoonffV7lAQGTmM584h/70t8t6RcSwWCfRmEYqveHNi2vDRfr+4oP9LNDgvKn89kdTOo4Yxu9gg3KWGp0jaz64Yr/tKLnpVe9n2eBvqREvUWHELpRav7j+XaebKT9mX0q/fG/9+zZ6zE07jTLPXRPqGrmZ51Fm6T9CHfthOsiNUdCtubvdbOwYGHazwePLuVrgQw8MnVYxI4ZQJe0tOYb1AnSNVHpjYBgLesRTTb0s0OBckig5QPW6pbifXmiBBoGm7lRcLQ7YnpIfvORp3K2RflcYsRul1j9T/xem24DhXMo8p8Tqglpul09RZvFfgw770P3u9g1ty/TzGBh+6F7xXnnDMTB0PBYnMOwtqqT1AnTFIdMo+c5zDXsZY9nthpk27rjOFshPOo7Sq2+tc69xd1ubBRoFmkoNrsNp2jnIcbg1RlkKI3an1Pqn6/46gYpYGLNP4yOGO51DmeWXhRp/btdPU2bRn0Md+2E6qNQ6nBJtFkXaiuzHwPDD9AbE99oTFoiBYQAwxM+9oY5hvaS7Gw4Mp5xA6ZU398S7HF8ztkAkC+QnH69USQtdkc6LD962LNCojWZ3CsIEKXg26h4b+SYURu1JqTeeqPsl4AgojNm78cBwxscps+yfocaf2/UzlFn0p1DHfpgOKvYbQ8lNr/EtS25ho9Wqe9K+ccSwJ60fX1ssEAPDGt8FlKvgDeYqe5I5FurElvWUaH/bdoXCsJmUequcUF/qM4KoVCp7xayjC8N3ptSGsiJbcdgMSr61rMbREuXHHxpqQax2I7GtyW53Z55QzQ+3l3UAZwZ1bqbkpld62cjUcPJwYrwEYLhtip/0SqPzBq+Vh9ZblAUbVeqh1DK0Yv5v1DPJB9TMa9Q9Nup+0G9h9N6Uen1h3S+B6GlhzF6h1sFaLp6b9UnKLLkkuIt0M+V2Po8yz/yRnOt+8Mlb7xFh9hjFARMp+YES3ZN3OAaGW+8zj0e+dVggBoY1PicAw45j/07Nt/2Hrafc9LmUWr+Eku+sKP8dC8DUkymz4gr9N+RRUDFnA4v4MTfjXMosu7R8nANQVjvsXEh6i+mpi3KtKAn3UfrtqWOL/SdQcuPanrr8Vn3d3A6nULJtA6XWPdgr7yMGhj3zWEqtw5QzzOE065nRUMPKSuA+E21vdctt5Xb+D8o8+3fPa22Nm+nC6H0o9fpjdbcfA8Pt9qb02nvq3rfZYVh6KMaT3/ljDAxzMz5GmWX/aui4ekvncIrDee7XTIq05Btuy87aOGLYW97OD/c4YmBY4/NnYHjcpdR867l2YDhtLqXeWkLJt8vAEJ7y/NQ5lFlxuQEMdycq5WzRQQaGDpWywvBZlNqwpMbRAnCGW3iK/cZVFenJTT/LBnxrHnAPdxAk6tDDw+vVlwcwxOY//cr9vXKc+SknKippvqNXjm9bHRTyhojAkuge0BRkx0aBJlt+VNAgavx9mwSGY/al1GuP1miZytMRqeKIYaOB4e6fpczTFweOH+9ffuY5lHn6DxXrfuDJW/EBpT4jKbHlTX9gaDClJAIfA8Ot+KHHQ98qLBADwxofEwPDEy6n5pvPtgPD6WdRasNiSr69XP8dC1J+6kdtCenIowCVzQn6nDSUwohdKbV+UY2jjQAMA1TuvAYSFnjWfCPd1IETGG7LNZTqbdLcjqdSog3A8L56d12X/vJTTqL0mvkxMKyLNcN34kWfD99DfY9sGDAMERGp150wMFx+uWe+LG+m8+1bVT5tYbsDKPVq/WsNKmCIiOHd9TK/az+5Pf6LMk/9NvAaiITlZ5xNmad/T6Hpp4G99v4DwrCSTGVaiTB2p9pvd1sxjhh2t8Xj67lZIAaGNb4XDAxPvFJFDA0RC1A2kxsW2/MJXYHhXkTFrgrQl9v1Pymz6C96dPWS7s6FTIgvDpxEyfdXR7ZOkOc6cofdeIIb6HPaoVGbyG68zW67FAPD9ncanstT7Q3lpn6U0i/NVxvmuHWbBRAp4IhhAI2suwbUqAhEqe8oSmx+o1tuIzfrE5RZBmDoni/Lm+lCh90J0qD6jfW64cLYAxtCQ0eumooYNhgYfuSLlHny/wLNAZGi/PQzKfP078i57geevBUfEFh705FnKkCyO9V+u9u8MTDsbovH14uBYQPeAQaGJ11LzTedauudgeFbS2wUUSxI+akn2SOGo/dREUNHvSanSlm9pLuduYteJikOnEzJ91dFttjWvLC5JcNXAEN43js/iGyXD+MJTCXtfL/hG7BqbZvbYQ6lVwMYtlXbRXxeFRYAYKJSsX7AsEaAw84egKY6q9N2KzBExHDFvz2j37yZhgPEpE03qH5jFa+E6yn5cQc3hIaugOGejaeS7vkVyjxxUaA5EAnL73iaBQzrqE7a4DqwgTcWcEAoYGhEjSW9JQaGtVo+Pj+2gL8F4ohhjW8IgGH7nBuo5YY5dmA441wVMTTyAjmXAFRSU1RmzH68WDvrNbWfcgs13fsVSr77AvdbGLUXpd54vHK0xuTvG82yNk+hgeGgqVQYf1CkoruYuDuP/AO1XHd8oFWDCvoW+4+n5MaXK/ppJNXGzX5OgOymNJjb478p89RvAu856ID8hMM9QVR3FGMHDZhah1LmiV/ZhhqFxtw5+w+UWrOA0ivnEUcMOz6g9NoFQbfeI78rYHgnJfJbGnL9eouPhBFraMiNROgUKoIdJ1xBmBe9Gjz/aCJDH6F710PDqBv6XYMjhoi0WaDJr8xEGHVPyQfvDlq95DGCqZFecZWnkwP3RLkO27teq91qfW5B54dV0EY/bWfeR83zP6PXS9/nne3PiqcyL9Urf5+vaazHXft8g7KP/W/QbVKp72jK73gK005zu3+Ocw3r0hzAP7/90ZR+6Y5QXVcTrQWrqThoEmWevzbUNbzWePPkjhP/TamX7+N9iDhpu7MMTKgb8Tmo/eR51Hzrx0I7k+OIYa0Wj8+vhwViYFijFRkYnjyPWhAxNKmkM8+j5IZFlFq/WF8BG5D8FADDcm0j5FHAW518oLdzFAAAIABJREFU40nbSNpOuYWa7/0qJd99nv/uKd1tTP6+njTrOKeojefmbfAOChgadNYgU2Gi7zzyYmq59tigQymobpOX6EsQoAy8sM8BrsBw0BRKvrey/Axd8oZyISlDQWPLT5jtCaK4GDvy4Yr5oG6q/j034xyi1uGUeeKXtj6i0Jg7Z19MybV3UebFGxUw7PyA0mt6KzA8hdKr72gcMAwhrhDlYWEDmdj8epRTuv1YbN46jr8sBDBMUHLTq3UZX61UUJyPuVsoxX6iMWGo5F0H/YSyD3yLwtL2azGCOAtAJU0vBzB0d3IwMMx3USK3ybYe9Wb2Q4WjzCfCiQ01lMFlvfSzKZ43cvvTa+9Sa2vU/H2fSJyZjtC177cp++iPAx9vsf9YKkCt/KlfU273L3DksB4NYncmG4LXkNW3huo6P+6QyLnh0YFhsOI30nRSr9zH+xBRKO3OMjChjOVzUNsZd1Mr9oYd74XqKgaGocwUH9RgC8TAsEYDMzA89VZqueFkW34HAFhywxIbRZSTzKecZAeGYw9UVNLX7dHAtlNvo+Z7vlwGhh4Kbebk7+vpthaJsDmAxcE7UmH8weWiuyFoKQBzHDG85uhAq+YCFNuKDkAmHYaVAA8cgMsBYYChG/0lF5IyFDSm/MTZniCKF3VsZBpYcy8381yiFkQMncBw94qItte9VALDTQrQ9sIG4MrAMLe5IaMLI64Q5cJhqFdR+mvEsaGAYf+xRCUAw/rUt6w1gqCAYV6DKr/IbBgQ2nnw/1LT/d+gsHNtLc+hDAw/qSKGHu8yNtMMfrs2loFhyxDOAe6tzekoQ1kHL+BbGzDcrSKVwxdYpls885LNKGzX/t+j7MM/CDQvSiIVdvgo5yPm9riAAWI9mjMizCrMK+eF6jo/4bDIVFukuxQHbR8+YhhCx6DjxKso9cq9ChgOnc4q71sVMJx7L7XcdFro0jwxMAz1esYHNdgCMTCs0cAMDE+7g1quP8kODGd9gpJvImL4THkhbh5MmJwzS/+h/wbPHDzVzkK+bafeTs33fpmS7zzHxxa2259Srz5cMVpz8vejrslxYamYxSHTVcTwmT/yNf0WZRkUNoWds38fEhj6e0YBTN28v7ndztdjqvHRVdrSJX/QrKOEE9zoL117f52yC39a83D8ilTnJx+vgGEDSyvkZn7cAob2vJjCyI9Q6k17RNvrZjuP/CMl1yxQEcNpp1Gic1No+lLNBozYQeOBYXUlX7xuA5GF5MZ1Ee+yew8PBQz7jSNKJFyp4pFHm24mBms1CNnAYYdIvIAqv8hsGEXEzkN+Rk33fa1bFCZF8h/zevq5a8rAz+HI4xIhhU4bpa3bqMlV5jI6HWV+0VoGhrd/Qq+XvsCOI4YQn1FMhsLIPSj15lOhXz0/Cq5JNe464AeUfeh7gf0iXaEw5XgFDPf8coVjLrADjwOc9gJ1PvPCDaG682OveHXQGGB4NaVeuYeBYWH4zqzZUGoZRon23lHqJsiYbWfeTy03QoQt3HhjYBhk0fj37rBADAxrtDIDwzMWUMu1x9uB4S6fUsDQWHBYfWzyCXZgOP5QBQxfsxfybTvtDmq+50tlYOih0GYDhj7UNfF0hxWHgXeOI4ZWHaYwFCpE+Tpn/45arj5KWdVHFCLIMyreQefjqWsOhqNz3vSxpHtZ2a8CGLp4Obv2/SZlH72wxjeJyC8HhIHhy/cQ5RonlJLb+Tyi5iEVggmgXaXeeCLU/TEwBJX0hRsoN+0M3qimV98e6tzuPig37XRKr7rdRq+r5xiQb5f8YE3dugyTkxP6YjUKtniC10GTqOM4g0rqAgpwH5RI1sU2cFiVWgbWlK/IwBBiOFY0zS/SW2oeTImOd33N3HnoRZwfHnauDf3MXA4UgRsnMHQqLHOJkDyA4fu6l+6iJjspjWHv1zkf+kVrowHDgRaV1AKGEeY3jN1vLTTH2HXQhZR94JuBt4s1pjD5WM7tzu31Vco8/ovAc8Ic4LQXHGFh8//yE49UNV4jtMjAUFhBPnMRhP1SL99NmUV/JklpKLWOoETb+ggj67lD2856UEUMA+o1yghjYNhzzyq+ctkCMTCs8W0AMGybew+1Iq/OiOYgFy755mJbpAUUiPyUEyjz7N/1VZFHgTyA1Kv2Qr5tp82n5nu+qIFhfuxBlF73QMVozUXKd0MD0NP5fmBun1ygOHQnCxiqRPgwnnIscAwMr5qtuvHxFAd5RsU76LzheuZgOPt2U+5zRi6dQBF9dO33Hco+8qOq3yTZOOUnHeMJogAMUy/fXzuI8QPrO/8HUcuQio2JZ36ryx13HvknCxheT7npc1XEMGReS9UGrPJEAFeAVpNeV2VXrqdVW/LFawygnCU3rnX9OXJ9zRDU8GpsURw0mTqO+5fOMXQDBbgPSqaqKodT8c1m+xO+W5tQVUTQi/MZGFpqw36R2TA0ts7DfkVN93yJGslu0PN0vzEMihkYPn+tvgdnVItLhBRzNuqoqDxW85yjnBNGsMetP+d86EcZjgQMmwcRWBA6Yjh67wrGjt/9+a2FZp6/RI6DbMVO2O2PYYdcvdgnvGZDidbIbcN8l3nuKptAjtfY/NYir3MQeS0OnBgafOq11Gef0P7R65gpw8DQYq5IlDzIrr3hd7yXSDNKbAlXtiYGhr3hqcVjiIFhje+AAob3Uuu1x9iB4a6fVhFDI9ICOk8elJElf9NXBWUjkdtCqdcesY2k7fQF1Hz3BZR8ZwX/3UuhzfQK+m9oVD5J2M1KYdhMKo5DxFAlwofZEBWHTKPOI36jgaHfZjXIM+olCFAvBVC3x45oAMC9mbBfAQytPAfz/LC5JF6vmmyc/MQBGBi+8kDNIMaPEozNJTUPqgSGHvmtbvejgOHdlHnhOspNm8v0vPSqW2r8yhpzOoBretVtNdvUE8gN3iGUSmLYu/MSZOLvEyUXjPyxwD6rpPcF9VscPIU6jr20DAwzfSvy3hBJpWTaJuoU1K/nt9M0gErNQyj5wUv6kKgRqkpg6K6IzHZuHUaJNn9aWOfhv6amuy8IPddWe+84T5yBChhepyOCzveBS4Qgx9DIKfR7n2oZk/PcMHmZbtdzzod+okAKGH5Sr5d+48c8DxCjgWGE+U2thd65meZvEjkOsiWvtdsfxfNuvdgnbuPMTT+bMisuD+XkZYZKxHk7OjBU6SJ+1FwoviNyycBw9D6Uev0x2uqAISKGIYW2YmAY9LXEv3eHBWJgWKOVGRie9SC1Xj3bDgx3O5+SbzxjKzHB9YomAxheoq8KykYih4jhQ7aRVABDj2Rw03upqWYu0QAtax5SDhsRu+LYg8rA0EWN02k6eD47D/8ttVx1OP/kB0KCPKNeeR/1UgB1e+xKua/TtpEF2JU8T5zjFsnsOuCHlH3ou1W/SfIM/RZjRJoZGNZYQ9EPQCDKTU0DXYDhfhWOC6+bZWD48j3sNc5NP5OdHmEFD6o2YJUnArimVzcQGA6ZHmqjGnb4fhHI3gMMp1LHsf8sA0MXwIr7YGBoleIJe/9e3ywDw/deLAPDiCCZgSGRjq74AvAQ8yBYE00LvkBBAlu13LecK4JE+HbTz1+vaa7OqBYAZALA0AC1XgJf9RiX2UcY+q0rMJxyAqVX3lx+rn1HUWKze+QlEjBsGUKgPZaBYfj5jde11uGUaNvgaibTcdB52C+p6Z4vB5qzMGIXKkw4grIAhjWyT2x2bxlqEz2R8ilhRLGiCNXINRHRKw6cED5iKGIyPt9r+5wbLWD4J62z0J31QQMfXsABHDGcdzolQuaGx8CwVovH59fDAjEwrNGKDAzPeZRarzy0EhgiYvj6wvLC1mekihgu/qv+G/IoOGK47kHbSNrOuIua7/5vVuFCy084Qstr2yf/svdS1/lxUU3T6nW7f54yT/8+8K6xWBXHHsi1ldDCLCagn3Yc8VtqvfIwPsfPExjkGfXKa8vt+aWKOnuBNxPyADdJd4jwSNQW3biVbhB5+pCXqThMNhOBwHDdw4H5TUFj8NukYXOJDSU2KGbzEj5yu5YdGJ5FlG9jIZre2ABcGRjWCLa97g3fQ/Lt5XW7dc+NPARYUk3R7iPT2pB81eJgBzB0E3RiYJgJVVogyHjYpIPNYH6jUYEIMwUSCR1N8wXgIUqQQICracHn61uTzsMQwhJhYPjCDfoenFEtBoYQ2DFEepxOryBbV/t7GLaJW99Q8E6vvEn/5LcGRQaGI3fXCtAoGeV0zPrdq1/Eyvyt8/D/o6a7vxhoNoDUwsTDKLvw51Qr+8S2N3AAWClVVRw01eZIcRtgDvWWI87bVQNDK83FbRztJ99E6Zfmszq6lNDortzYwAcXxLpIZYnFZ26eS4kPKmsyu/UfA8NAq8cHdIMFYmBYo5FbL9uP2s55pBIY7v45RSV9rZw7CE9XfvJxdmA46VhKdG2mlCN/EHmLzXd9oQwMPUoZmIuubBzdInUymYbN0QOVkyOGlnR2GOELUGI6D/+NskUQMAzwjBY86D1BFNRaHqebpLtTBMcNsHYe/FNquv/rVV9aPKB+Xlr8BlXaCnn5qPlUPt5uiGWUsgMqgaGH8JHbDXcc9WdKgUrKEcOziQptoZXwqjZglSfmpp/F+Y+NAoZeebJVDpe8NnSgIiNnzxQWCbxGpg9Rzr3mXeC5PgcUh+xAHcf8oxwxdOQ54VSoMEKYygRz1V4TG/Fi6zBKvbVUdxGG7mnbQDMwTOroit/GOcymFAJMTXeeT7mQTrhq751t2V/RXhUwvFHfgzOqhciiAoZvloFWnR0XXvdRrfqpE5x4rkGpJlXg/o5P6vXSF9i1DKXCyN3KwDDC/MY2t/I63a5hvh9CKQ56vlhTCuMPoezCn1FYJdOgPnn9dTgxRJG8MHwWpTYs8e0it8MpnA4QpTEwHDSRMs9dE+q04rAZlHxrmS81t/2UmzkPnIHhhMMpvfbuUE5qzwFEXC993yMXmrzt+HQzpxm13HIWJd4PJ0IWA8NQr058UIMtEAPDGg3cesWBikrqjBju/gVKvvm0DRhiQSlMOsYODCcfr4DhK/fZRoIJpfmuzxvA8CjXenDmJkgXgHXL67Gk7sPm6HGx2nEHsYS22sxNChSLwIKDTRGiqLwwZfp5iqUEeUa9vLhde/8Pe1Yb0WBLzsMxIkgVwNAFsHYe+gtquverVQ9JvP5OD7nZIQPD1x6tyG/yi8oGbVycv+d2/QyVmvpX2Dc/7mBKv3J/qPtjYAgq6XPXUG6nc4jy7ZE3GKEuVIeDAFwVMCwrNdahWy3u4BZdrqV/rxIurKqJiFfIIso8hmxfoq7612/EHNRxrAEMXfKxALwolQ61iQ+yF+ZUgKDU+kX60KhUM2YKJFKaHugmMCWdh2FO4Btonv/puhYr97KDDRi+OE/fg9MGmGMSxQIlNr+uuwoDEILsH+b3anPCnODEk+JrbcCjAUNEDJXqZpT5jddCn7Ix5vvRecRv1boQUHsWTtDCuIMo+9j/UteBP6bsg98OY9bAY5xODKkB7OV0NTuEYnPmuasDr2EeAIDLVNKQwFCXn/BxVrafcgulX7qdS1SJUmqYb9Bz4EFRvgh3HJQ7i1zn9rl3U8ut51DivdWheo6BYSgzxQc12AIxMKzRwACELD7jBIZ7/Bcl33zGVnvQFRhOOZEIAh1r77GNhD2gCz5HiU2vMVDxKmVgemN1AVi3vB7xLIcsoMvS06hj+MSveFxh6Cegn6LAuQaGPrkDQZ5RLxXWoNzEWh4nS7oDGBobbPFqSr9ugFVUCKu9tpQ1CASGry+sqNfmVJ4LGoOfEiGEiQA0nVTSKBunjqP+QqmX72X1O+S0MDB8/tqgYfXI7wCuDAyjAKowI7U2H1HqP4bp1hMYwqGBTX9AGQXzGpFzEsMMEPPEkGnUcczfynOAi1gLgFcplbVF+UJ2X3EY3udSn+G2skBR1TYZGCbT+tty0sfNi4apJdlx9F+p+Y5PUW6P/9JU/GrvL+g8UarliOHKm8v34IhqwSZERUoaIhj1dlx4jTVMlNXtXGd5BS8qNRgy7XPvihgx3EM7Wr2E3bzux489Y757TCm+9yuBtWexphTG7l93YOgEUCI855WWYgeGcynz3JVBr5/tdwaGA8aHnu/FMeHnOGg/9Vam+zMwtFS7w3yDnu+iOM3roMrsJ0KE62MtRSmz5ls/HkjdlfHGwDDSKxcf3CALxMCwRsOiNAM+fndguMiWu4AJrTDpWKZFSAMYUMDwbttIOGdi/n9yhAh5IWb+GTx+iHIg/9D0DEtOk5uctt5AhCigi1wEbDRAJdHA0ENIw4xYAUxCeCEIGIKyCYqIHOf2CPIeYjtd+36Lso/+pMan5n66AoY52wbbCQzdNhFQYm266785Pw+UNEQwoLbmuThBFW/UnnpjIqC7/fT5lFy/iJru/wafapYnwHuCfFVnPSRnBBUbk84j/0At1x3venk/YQ03YIj3Lr/jyby42ZqH59UODM8lKrSH9iAHPdR6P3sAVyjvOYFhbsbHKbPsn+7DCeFxlkg5imin3ng86LZC/+5V2xMbby5ebihOolM4alLrF7v2X29gyDTsZIoVQjuOuaQ8B7gIhjAwTDdxsWoe57CZkUAiHCmlfmPY6cb/32ckqxVKy007zfOdc6M1MjBMZbWwibazC+1MQIEfeAQwRrF1Zmcs+iM7m7xareBMnEqVwHAcJTe9wvmXmGubbzqDKFGipCGC4UaL5zkQQjxGLmLoF9TjwGojPBXAcLBSsXQ2vQH3oZLymrnxVWWTPiOoMHxXGzBMvb2cCsNmaUEa8xrIm8489lNdKsaPPWOCxs7Zf6DsvV+lUssgKg7ZscL5q/cA4w6m4nb78brWecjPqem+/6nV5NS+z3coMXQqNd9yju4L0UisX2igZ2Yf/bHndUCzz6y4ItI4IgNDa37yez/aT7ud52gGhpYYURRgyDWmpXyWod4ctbyP07mBaCHKcfl9J5hjsaY333ZeaKGtGBhGeuXigxtkgRgY1mjY1muOItQcbLnyMFUc3WrYFGCTb4rKYNEoTDraBgxzO8yhZOcmSq29yzYS0FOb7zyf6V7IIakAhl0bFT+/72hND9LUDNe8HkUFze35lYoC5k4T5GZ9gnI7f4KTz6XYrhMgyTkmXRRKoqDPMOCDRy6VcaWrlVqGUfsp8wKA4WzXRbqeqm0VGwyu9ZW3Kbk5N60oLyJqdnK+iE2IiAU2HiIa5PZ6YbNWGPURSr90B/8sIhDtZ9zJUWYNDAdsr2X4kW+D0ieoWWY2p4gP3jGMxxMY+ggPQEWxlOlD2YVl8RksxvkdAAzPtV3XS3EW0ZLUWlBJETH8OFGhQ9XOqkPr2vfbKprpR82KkEOSm3GuBQztBctzO/+HrdaoOXQ/pV39TViRclfKVg2eai8xGzxzCFgl2t+2WRmOmtT6pystn2qiEjY1dRTdYRo2JanUMtgWMXTb9OF9Z2BogdaouZhwbpT6jVbAcOBkdo6Z4iEQFcqs+LfrG+cFDGEP+bZ07pOLiJc4a3yB4bH/4M1gDuyMxX/yjRh5PqOQ34uAFAaGKL1iUUVlnHgu7SfPo+Z5c7lHs96j2/vJZS1Q07E3AMPpcymzohy18nSMZPoR5k4/KmlhzH5cMgD3XwEMJxzBjonCsJ1d1xxQg7OP/UzPxXjnku+vcn1CJmgEoMze+xUqNQ8k5N46WUHSAZygxdF7K2Dol5YQwiklfbbv812iIZOp5daP6XF2HvhjKggwfOkO39q7rGCKedvHqeE0gKKSIscwHAVVnCJ+Ef720+6g9KqbGRhir5R54QbyZQQ45v/2M+7WKukYrzjNo9bWdM5j6KeU7VOxHtvWiqaBBGALJ5GpbO73acfAMOTEFx/WUAvEwLBG86Kwfdupt7kAwwsosX6RrSg9A8PJx/AkJw15FMmujZRas8A2EgaGCz7LFDFMKqYwCRZ0RBnhcTcnLKn950ZxkKhU115fraAKOk2ATQaihukXb9THVuSjWIuUGXnAwoCEewaG+D0JYLipwsKIqEFtzDdiONE9p7Jr/+9S9uEf1vjU3E/nTRGAoSHpXgEMXcYlYhMceaAEbzz81Ch5YwJVvNW380BkIwrvpgKGSsjG3GQoYPikjQqGY5z2YGB45MXUcu2x7hsXnxIKEMsoZVocwPAkyu84h5pvKW8weIH1SLxnYAgq6Yor+R1CXciolCSvh8vA8ImL/KlZEYAXgGt69S0VkTaOlhvKwbbFPkQpBKH3uubJRtjcVWy8PCJreE9Qw9BZX88zYlmNimnAF4dvmt/9FnvEUMopmKcDVAGIpdY/w3+ODAwHTrLA4MOE6GOx72hKGznaoAhnll/mOmK3/EOMGREEoVlqB5tLjrRE3L1ACi7acdyl7Ejh0jqIGOY7PK3npb4cdoIzgWFq9e1lcGvlhMtcq4BhQke92O4uoiscfWZgWBapCTsW1+NSWSqCQRGyjpvZhzNq5fWe6MiMX8Rwu/0psel1BneVwHA2pd5eSoWhM+3A0AIZmNMYGFpg0DcH1QCNAJRN9wAYImI4xQcYzqbi6D05gudX4iIKmGnf97tEg6dwfpu0zoMupMK4g/mfcEpmH/mR56NlBVNQSX3eXdVRsz4mMjAcuQdTwCXq7TaY9tPvYIo0A0OLCeBH5XXWMNVpPlbnolgclB/oHEsFMMSckelrc7Q4z8G8ghxJ0MrDqlPHwLCm2SY+uU4WiIFhjYZsue4Epuq0XHm4rTA6NgWJ9YttGxZMgIVJR9mB4bTTKNG5kSWZzdZ29sPUfOdnqJTMsLCCqdAG76cChkts3jPxPrvJg0t+UtfeX2P1M7+GJHWOpqy8SQuROD3bEjkxJ9jC6L0J+XYAfDxBJ1MewHAEtZ98oz8wnHQs5xY4m020poZNttv9q01RweYtd25GJM/BPB/0SdB+VdSEqAhqm6GSWLFg9B2l6mitvpV/EkBfCQzLnmkGhm8+U7EQOXM1GRge9UdqueYY10fst6Ht2u3zRFkHMJw6h/I7nOQCDN2FhTqOvsSKGFrAsNAVmZLk9W5ytBgRQ7/NSoR3gp0fq26uBIa7nW/7Rs3xhNlQiGPGNTczwvicdvDaGGOjCmq5M8qDeSL12iOV5gQwRDSsjrmVDAxBowYwPPqv+tt228ThHSylWnQ0M6oICsAQvlVECQEysWlLG4wLPyqwW74b5kvMV6AZmt+j27MWIOYLDI+/jCl8WAPSi/5sWxcqnmmNdGPJu4OicGoVgOGrag4aNIWS761kEIRacAwMEykd9cIxbrR4LmsBYOhRLzDycplupmILgKGybZTmBPgyT1bMp00DCJEl34jh2AM5Xx+sGVCPC8N3KVNJJx6pIoZDZ9iBofWtYk6D4JnUyvTNQR28g6YN4jtAjiGiS8XBkxUwdPn+oR9QHLUHAzU/JdMo+eRt+32fEoMm2YHhwT9lZwBaIDCc9UlKL7+SEnl/5WKTQQFHlBKfCRkxHLUns2D8qLmIBKdfvMkChirv0RcYOhx3XErMEsPDfYtYX9SSNk4HF/qBQwLvk1fT0fr5n6akz37APD8GhlFmiPjYRlkgBoY1WrblhjnUPucGd2C4YQmlX75XXwHe5sLkoyjz9MX6b7lpZ7C3X6JH8gNKYDTN/wxRth+rUeZ2OJkyL1yvNi4AhvktTMUyJ0nxPrsV4JWNTNc+3+Akd7+GXDMW5lh5M2UX/lRd05rE5TzxXpqLFSKZnYdepIAhIkqJhDsw7DOSbdb674M9qSpeNf1MYBiG1hfl8apaXwCGZW95JTA8TgM66VvEJlTUhKjYd6TOn3K7Pq5THLEb0xjZtiM/Qqk3n2QBheQbT5cjhtbmDseARpN6A8Bwra1Lp4qdAoZ/opZrjna9dY6AojZmqVDxe9fuXyBKt1D28bLqK2ik+aknVgJDj8gZA0OOGP6bQMlEzqZX9CbKs8GxAIaZxy+yUbadfTg9xr7vOQNDRAztFMzc7p+jzNN/cD01zIZC6Io6T9agN0XNbTEH4ZUzCIop7qECGHrI8GMMeM5arCZClNXLnio3zYoYmsBwwERKfmCXasd48ZzwzvP775ML6fr9mMBw2Awq9t3OptjsRwV2o7YyMMz00U4Xobi5My8U4PKi1mO8HSdcQc03n0WouZp+BsDQe3MdRiHS7x0WJogChvM1ABNHoA0YJtO2jaybCIkChiWbemnU79Q8Hu8a51xvDFfHzTxXCrLL35CqgAhTxTffPIjaT73NNzIDQJTY/CaDO0SNGRhaVH4As+Rbz1JxyE42YChzCUcMH/+lzm/0q09q1oZUwPCrFjCcxMDQbX7KTzqOiqN2YyYMcvSbFnzBfe5xEXLyejZt+/+QEgMn2oHhIT8jsBjQ0mvm+zJvmJq8/CpPVXG5ri2VJDIwVDnYfsJ2cJbCQY09kzgKzNz7infBoYJcAQytEh6RStrAudE6zJafi+8KgD/57guen4f+9u48n5IB5UGkkxgY1jLbxOfWywIxMKzRkizLP+00arnyCCq1DtULILzF2FgkX3mQFxsAJaawIGJobDpBj0MuIqJjphABJgiOKBULHOHDZpVy7ZR6+T4WXqBcG1OxsEEpDJrC+YBCHYNHFL9TqUTU1Je9v0KJFAEPJ08flFZq6s+5VbgW13hbdQuDSEQPQS8yN/jY0PCGDyqelugFCqEjeR7AENFDTJxYgCombwGGDiVXHCeAEHlC7F3lPyLqdDn/b9cB36fsQ99Xf7aiir7y9BEiNPAKEuq7Fbr0ZtYZzUDOHewqoI43gsdcQs23f5KgpFoaMYuS6x4s50+N2pMQeTAjGogWlvqPYeANb2l+xtmU2LiOUGcKG6jMwl/ws4U9UTAcDcAeiyOoUGhyz52H/Iya7vsa24pt1jyI8hOPoJarj+JNAACDmd+A+wFtSaiSEl3G+4Eal8n3X2JnAIM6prnuxBs7U8SAr+9SuFzHkyCdAAAgAElEQVRskVp7H9un6+ALKfn6E5RZ9i8uXYLvo5Tt6xpNDVJ4w3gK4w6kpvmfs220ZRzYXOE+sfkL9HIDwOfaKD/tNKXkWAEMv0CZp3/HNmYxova3mbJY2G4/Sr3ygKJsQqzEcZ685xKJkY03A7Hmgfwd+jkzTNDiVnzcS6gEea94RvKNYPNJ6SaORJuOKb2Zw++ZlvJ367Hh9poaMXcUJh7O9yOba8xtAGiJYichgi5eemc0ACCwsP3RHFVGtAAtN30uA9Xkm0/5OlRkPIgIwlOPeTP3EeRyL7axC4QK7JaL5BkxZFqYcrqIo8ZNLVEEUYTBYeYsCq2+48SrqHneGZzPnV70J0rkvMuCCJ1THHcCkoO+B7GF0Bpxz6mXFlCi0MEOAukP3w3mleZ5Z1Kp70h+dyXKkXehxWMOLOx4amCkM+yyifcdm3CnQ8vvfE65mHAYUcsQneMOZygXgXdJI9CUvfn/yXn3buIkmJuTW97kjTzeAcyD8u5CwCuB7/yt5RoY4huCUmh6zQKe37OP/0rX3XQ6CzHPFgdP0beENRSlFbCO8txc6OJIWvKd56kw5QQbGwHfeW7n8zhHOPvwDzg/vGnB59Uc7wA5Mufblch3qqAp5vuMpY6DLqR0JsXPnefxfmOo69BfUrE/1GmDgSG0ApJvL6PMor/4PmqTjqxSByA2VplTzuJUyIPOt2sgiDkbexxzrsOzh9MSkW/sSeBcgQgO9kxMb136D1/qqdhGUh2wj8osuYSfNeYrnmPb3iaUZkq+9pieg/TcaNhcjbmN8jM/xvNTetlles7HN4Xf4QSVeYz3JJjbNixWc33f0dT+0evY6Y9zsW9gZxyYVMk0C3XxO/bijZq9EQPDsDNLfFwjLRADwzpZF8pXhVF76HpvmNAwsUFJsumeL/EkwYsNxGee/p2+KhTsUJgYEyS8vjIRywSBCablxpOp64AfESIQ2Ye+Q4l8lwUMnyYANET4sHEHkIAgA18rkWYVulLrSPbMy2LGeVqP/rh8bLqVSq1DqOPYSxlcYEOHXDOADIwp++iFhARuRC2zD31Xj5sn1tefZCAjG2QskAAq3Mcun6JS//GuNZlkwnTSbwVYAGQJnRV/w0YfwButa//vU/ZhBQxFDdRP2S8K/QaLEucbtQzS+SCVwPBEKmx/FBewlqZVCK1IKxZ2yZ+CCBE2KixbbjUsHgAW6ZXzCJt6gF2zYfEDcMPmM7dXuT4i8vwkz0UiuIjQMl3Jymdge+U2UctVRxKcAIl3nrMVmHeWFNEbyOlnE8RnxBlgLlDYSFYAQ5f6dPL8UmvvZ5pfx4lXUmbRnyn9/LWWMt90KrWO0ADGvGc/ehaOk/HgOzM32uI9hnc+/fz1qtZj10b/zcywmZRs20D5ycdRGrXfnMAQapJP/YbpvolCOwsJIccXNOymuy+gxJY3OJLvJTAkZQqkxAy+e2x+Uu8s5+/SCyiYUXkRWjBvxOs9hxOnsP0RWjmWI2BNA6g4aJIu4m32g826AoYqUiobrrBToXzneMfxrjPwbepPhdH7UOL91dR51J8NYGgX6hDF46Z7vqyVREGHBojOPPU7yqy8sYIq7FTS7Tj275RZ8g/+Fto/ej3T/BBVkIa5CcrPbjmenhHDbH8qNfWj1DvPcTSJKW4uhcy7DvoJZR/4Fgnzwox2CiDpOOkaar7pNP52088AGFbmWeu5YNwhnG6gAaclfGRulv3oyxoYImK4egGVWgayU0ocgaCRYrOMCCYiXQApIp4ljjWMRQH7Uews6Djxahavcqrchn0/bO8aHKIAho6osV9foGB3HXQh06ClPizsDaehU/2bxy7KqxYw5HUQkUWDcp4fdwgl29ZbSt52YNhx3D8ZrMDhKLZBn117fZmFwDC/Z5/4pf7enRFurKlIMUCDcwpiI1ib4GDIPvBNSm58lYoDx1Oic1M5B98yAJhAuT2/zDUVGRge+SdquvMz/Kszl1HeL/P6cKqYAj08B4/6//a+A0yq6nz/7M7MNqoIIkVBxQbYEOyoKHYNGBuWxPSiKfqLSfzlb5qJifkZNZrENJNobNjFhgXFEkURFURUbFSpKgLL1tnd//O+535nzr1zZ2ZXl2EWvvM8eQw7M/ee+55zzznvV97vYNN4+OWmsm6JI4Z4N5G/KE3ul2scsOe3lSdMza3j8g4782iDWscQzkHESxwxxD5DhfW61U5d1BG9vsMz9Zq3P9yUtTRxHcd7BEOFI4ZB7nc+ZW15Z4Ugyr4hezLreTaspVoojPqpWX8IPZ+POfu8YSXzBNH8UGVoJNAY99ad7vnxHRD75Gs32NzJHoNMw0RbqgnzwNYRXW6jdZB7u9XOBuJx/r6uxPDTrDL6m85GQIlhJyFKDw2IYRA6isUeiz4OGDhM0huVrDHpnY41FbOudXclMXz7Ph7Ipc4QPnTEcMNyU33PKaZx7K9oRQU5K0s30jJcvvwlSwz3Pc9U3X+OOwhhQTJlKWyZNhdn2QyXxyaqnnJowqGjrdsAg8MWGkgdQgrTw8/MkIRJj5vkBy+QlLrD16hvm/Jls2gJFtELWGWbDv+tqbnlUB42YZ2s9H4jv3XEMHLI56YWeN8knBV/K2uuc8pi9Bg+/xsecMS6mq9eXHst79yIIX1ftRVJlnj4oocAkLXWHY5xmzf7DBVCkNm9v2GaR37BVD7+PVpxYR0lMew1hMYBaTiQmXIQw3tNy9DxpnFsWEwnNfcGk5r9d5Pe5fOmaf8MofTrITliKKUyAvVB3qNpvam5/RjTdOBPaCFPBiHIfMZt9zUNR2VKikhYFMJ0gDmIUsWLv3PzD7+JJYY5ihJj/ODVTi79r6n/3G0kuMk378gQw279rec5oh6XL2fLfx+yiGGg3NohYrjNnqYcJWAgf+4VBXdzO6j1iXkFVVXkH6FcCDwLGEdu7hXdcwoK0Mo986qMRzvVnXL1FCBAIfUcHiQc3qTsgh86Lv3KNc+bh58TEEOrHCv5L/A2i1fEX+ZIDCtq3HubtzxHzPoILwEOnsh9rnzsfObamFQNiSHIGkQ3nMfQy7nCpRwxnH6RSSx9nldvHPtL0wJi+PKfrPXcU3fG59F3EAf51Ox/mvI175r6z99tUjOvdGH2vMe+59G7HieuEudNwgGvraIH39PEBzNM6zZ70aAXl8vUePhvSRYk8sIn62KkqJ94p6m+7zQDoS8YRvIZKtI7HE3yLgXFnbfTOyznW8OEQMKQBo8hFDAxLmLQChHDfiOp6Iv7ofmCZuh721Y7Mky2vjOJIQh3dd9QbmOhLdcRw6XPm8rpdv3DWgZDTiwxDMTMkOcNjyG8i/Ai+5ED8OLjncc7yFB+eAwD8S8YReHxg8FRsAGxaBpzIcP6G47/Fw//kjcezbfHnkpjH4nhW6bq4a+YhvHXmNYBATFc/4Fp67U9Fbobj7w6lPOW3vVU04SQ44WPkZhKvjrX6si7I953//7Nu2fXG6wfcKhpOvwyU1m31KnRSpqHYJ94f6qpzCM+U3cm8iGr8+oA8F0LEcPfm/Lls2KJIQwV5Sy9tcLNO3jd4M3zPbAg8JYYPsd52NZzOxoSGUoa5H5n5SR6e4lT4w1CRrOJ4VB67UgMI6Quijn7XLucGhJovogMVNWhrg6V1BQE0YJGYvj6TSax7EX2Hc+ABmNSGYTt4DGEwaI8SaVa1GWueuhcU/7xO/yeEsNCq4N+XgwElBh2EsokhgPHOE8TwoiYF7biJda4Y4HhVDU3kArPSoVNB54j/M/PbcoQwxUGeYwiNU1iCAnplkYuPrCO0mN4/9kMZUwuedreqzzJ8E/Kun/wvAtTlRw9OTTRu1DdN0wMR19gw+zefchUzLjM1E2aZpLLXgh5/3D4ssRwkcttwqIOi3rNLYeZ5j2+ZNp6xXsMxZIGzKKHJud9C7yW9gTTYA8EEko643L+TdRA89WLi5OnzzXklL4HUSYxtIenqJcGB/aWHY42VY98010GB9Wqh7/OkC0Sw2kX8ICAZyMx7L0D/yYNITT0GL59Tw5ieKNJzf5bNjH06iE5YhiEHkk+gwWp1tTcfrRpOvD/BcTwLndvHFZwaJGDu3gWOkwMY+rT4SaWGD5lkkuftcQQYTxv3G7aavowhwekhcQwEuKbL28HcxkqvWg5iSEMLG/dxYM9hFjyNRxEHDH0ioLLb+SwA4xJDFe9liGGT15EEYt8xFCUf6XuFsJoLTF8nUq9uYiCn28WreHGuRjJ83X9HfEFSwwD5VhHDPsOD4U8y/cRZgUiV1a3in/69MRwtql87DzDMi2JioAYvmcajv1rhhhGVHAltBGeIBHGgWEEBhJLDKdkhQJHc8saTrqJBgccpkgMZ10TErxASDSiMuLEf2LDS0EM4WHttb1JLJ1hWpHz+MHzsaIY9MQ8dTHzXSEWEiKGQT4lc87v+TwNCSl4DMWDHVNKRcLpxPMDggcy6ecw5isALp52EsMF0/gcDEPvvw8JIkpVYD7QYxghhr5XmkaxrXayxPBzt3WexxDEEB7DPCIdLG3klaCRfQSGA0cMA69czeTxNk3Ca8AHmIeI4ao5IQMMom3KNqwiuYsSw/oT/2PathoWJobdB5imMRcwFLTh+H/TswSxt7j3MEwM55uqh7+cIYbPXEKl2Laeg41p2mDF2W4+hHsztzZHDB83Fc/+3CBHG54pNMkTlUcVohiKLIgpzVI/8DDTdNivM8QwUUkVbESXSEu8/4ipfC63ujfUPCWCKN9aCgM4DDNojeOuMOUr4j2GMFTAA80Q0V1OZuqLREj5hh8QeBJDGBYn3m7aeoAYBqGkgcEnq1yIt5eI6JJ4DrOIIRSc61ZbYhghdcR8691d6gX6hbHDO4TmE8M6eBFxTnjn3pCKN4yuyK/HPoTzD94ltIpnLmG4soF6dFOtEsO8O7R+uKkRUGLYSSMAsQ8WLQ/U8RBGRCXJlS8zmRyWLCy09Bi+lAlfwKZjieF9zPGSMFNHDOtWmuq7TzaNh11mWrYfZz2GJIZNtGqD4OFQQGI45Ah6LHkvxLG3tbl6X7KZSI6eHJqQt4BcQBAyNHoMx1xg0rud7orgwnpIYvj0/3NoIdwU+WPl6xY7sRbcH2IoCD9p3uNc09Zzey6I0bwqRwzvOC6rllqGGH7X5h5xJ250IS0IJU298Dt6FcS6mk/AIW/+YWTsWXgboU/5iOFup1liOPXr7tcNJ/3HEkPk85AYXkjPCTZC1DJr3WoHGgekISQKh6Hk/LvoKYHHxG+puf9h/bMsj6FXD0nIsIQeiReW12neYGomH2WYvwqP4Vs2nIVQDtw/ZLUWzwKEHhCCJ4JDeUNJqTTYN1aCHuOXWPyUSS55xhLD1/5lkm9MJnlgvmJ1XxLDqAgLD8IfvhkrihMihhFjgliP4bWFZxLGEieqkuPdxjMj3wiiOiQifs22RAVDqeHxQ54sieHK2RliOP1HATHsRu8EW+TAL2GGLg+NYZ27dIwYxhRpzzXPQepbdjzahfuKBwwEGAewaCMxrOjmnrujoaQSGQDCjLA3Cs+UlZuWAQfQM9RwzF9M9a2w/DcyfNGXanfEcPqPXe1BiYYgMQRRj3hUMQ5Y66Q1TLiVhIsew5PvYv6RX4ybheVnXROvutlzSJYQCvFCSYGe21liuO0+tkai77EJyIvUmXMGNi8/U+Yiwsyq74aH+ccMaZV6kXHCQ+6QHBzwxSvie1HyqTC6UHASwyesx3DFLCNkGt4OvHOIKMH7x1DSBY/aZWK30+g1QaPSK4jhx/M7lxjS0AZiGF/3jzePGIlkH8HhuvLJH/ArjQf/jCH8MHjB8OU3RLzAQOCI4eBDTPmq10IGGIQqwxBilbwHm9Zt9nQew/oTb+KzI5RTsMF3sHaDmJIYvnwtibZdQzOeffw7RAw/foceoIajrjWt247m/le+fhlzyrEuNx5xFSNqUBYJLUMMp3FvR+RO1UNBfrdHUjhGgRfZfx8QLSC5xYJJ/aBxpunQSzPEMFnF+YD8f2kFieFZT3Fdy1dSinMIXvEXQTjbTOO43zNPOC6UFAYUEsN1i5hik5p/l/MA+sYVEviWZtZ/rp94h2nzQ0mxLr/8J6e4K8/iny+E2ElkQBYx3GpnzgPWR/TKccm1/JQG9AtEFu8zWiiU9PSpVjvAE+jDdywxnEyjV1vvHU39SVYbwRFD5Fmifmx5isZCGI+qHvqSKf/4bX5PPYbR3Ur/vSkQUGLYSahbYri/8zTBa5AmMbR5OIiLd8Rw5lXurth0EijgCgtacKDBglyHhRlhlHWreMiQGkSOGLY20yoVIoZDjyIx5b3oMWyhUA0JZP9R/K+UN3AkEt7Fim5hYrjf/3DDQqgN8hFhPbTE8Ceu3xQqWfYiF04pqpweOt40jb2U3j0I1iB8BmQymiODTRf5A9V3nph1iHfEcPQFFPWRJhuUJYZX0KsgYac5ZfmDpPtoUfhcQ876aqkaFumWkKJo2BBCvkDmxKrLzQDy9FNt7UcSwyd+wAMyCAeJYZ+dQkpzBYnh6zex/lkWMfTqdAlJEEVUwZTP1lxnYFmPJYaDDrCHk0DCWzwLGC8YGLjRvfh/BuVSpEVDSRE6iIN0nNJghhjCY3grxQIgew6y3dpvD2JLMZpkt5BniJ7Lj950B6bQGJWnTN3ZT/NPUS+zI4Yn3mCSb97JOV4oN4qW4FqfGHo12yDiMwrE8PcM04aYgpSLwSEI3iJ4DDFPXEmSyMFW8njlAGTl6nexRDKRyllYXnKEOYS7n5El+55rnoPUt+wAj6GtWUbFvFQP07LtPu7g7+OJzxhKGhDiDhNDyQELiKGUOAgTQ1j+G1yum9zfEcOnLubhDy1MDB/IysmLEmLkwKVe+XOGGL76N5N6/cbM2hR4MbAeJRdOC02lOJJlFQYDYohQ0m1HsW+hvNdgjCWn1xFDz4sr3grkJCFHD2FilhhaD3Zc7U+XWxjUXpSokVDuYlCTMG7dChHDhU/yvUT6ghgC0Re8e5gbJIZphJIGxNALQ8R7hHDKxEdv8b1FGaZc4kod2TJtaH5feiJztahSpyOGQX4+58ghv6BBDiHy0dq4bd0HmfqT7zSVj3zTlp0AMVw9N/SeIZcbz0PBtp7bcS2SUNJYYthziGke/V3mbyPUFIYGV3czUgYGtWQh5MP9es17VAJ1xPDZn5ry9ctNW48BXJcbj7gypMadIYZPMFWD0ScPfonXiuZdi7EgFFkQU7OzfrsjmYtfWfcBRZCY4wyP4bhMmarE+4+ayufCBkl/fBihUZ4sSAwZHQGPYVtrfmK47Rju9dhLpB6hC/n23iESeBi9lzydTQxHf8+kZl2bpWIaqqW8zZ62vnNgAMoihn12Y444ieE7U7JKd/kpDXiHytcu9Ihhph4h6iu2VfRyUVWCXcPRf2LuItePrYaZhhP/w49IDGEMSDeYsnoI0CCUdHfTdMAP6WEu/8iqmyox7Mjqot/dWAgoMewkZFE3rmXgfo5QZIihzcNB+INJVjBJvYIWNttIDN97kDkyLl4/WUUyxo2m7kNTfffnDHJbWrY7jOEmVAIlMXyeBWsRkgH1MclXwSYvoaSs8SXE8IPnKWKDDUik9GEpbSuvCBPD/S8iKRGrIvoCWWnk1kgDiS3/4AVTVrvUFVUWIZWa244MiOF2lhhGhEqwMecSOBBiKF5LuZ8jhmN/aYlhc63N7Xvoy7G5RPK7uNCxXENuZfSrrccwODxlEcPhZzLXEzmFEgJFYviIrf1IYohww3XAZXFADIc5pTncm8qm8Bi+dWe8x3DezSb1ynXZxDDIocE1HDEMcjLhna2fEKjBBaG3jQf/1CTgMXzzDvfILYMP4gaeIYajeOBBOCHmkSWGV5i6c551v8kihkFOVlx4GCze1mP4rKk/6RYe2JOv30pCCCs9PIf4W/SQzEMPNkcvpCzTAY8YRrzMjhie9B+TfOMOztNooffoeIMMI3ckvctE5vf65UlwiEqjXMWLV/CAKeq/IgSE+Vy+bolpS1U7Yhh9FiENQu7oLd1qWGFi6JWXiBOViMuZw7Nh3jGU9P5z+KiOGA4YxUNKtDEnEB7DoFZdx4mhDRnH4RvzHhEKZa1N9LBiTtBjGNR1jYo3Ne/1VdO851dN5dM/4TxBkzB5eP5YPgRzwJsH0edGDl/Fy3805WveIyFIvfZPk5pjIx7QXCjv0KPDdemCPOKoEArxqtqaniSuqYGImIRZE1OU90jXm8bxVzMiQESw/DB28TDWn/YgjV7wHDPHEHlFuYhh4PHBGOK9kKiRUIhqxHPkj6eEnMLbT8Xqyt58B8Srhb5g/mWIITyGVinarxMIUuuI4Uk3dx4xpJhX3/yy/iht5HmJsTdhn4L3v/KJC+0cCcKNa1CGBx4Xr8H4iTkRJoavh0LKsTeW1X3ECB7sCa399+ChHg1eHXh3Qh7DXjtYYvjE/xiEmlbM+mOmvEqkDEyIGH6ywFQ/cDY9RyBjMOSW1y6zCseYP1h7PTXu9G6nMZcRkT4gD9xLAgNP1NsucyJkQIohhnXbHW2aD/mpqaxbZqqmnEEjXOuAfZkXLK0gMcT6X5YoTAxRFxnnmbYWSwxXvpwlhoN74j2BgQQeaRc2HURI+R5QEvjWJq4NeLdB+hGaSTG2fb7NkN5o7mWoljIM4CD/COuu/cAZ2BG1xbGHoAzCQ0EMA9X1uPeJfcZ6tnYB64CiMZR0zbtcm1BfEQa2aK5mw9F/pjEO/UcOIc53aBQNRCgp8gyhal2WYF+gIaDEMGuL0j9sYgSUGHbSAFTfeYINJQ0IBUIEIFiB0BUoWGIxA1lLDzs+HJN+wg0m8d5DXExEtAKexWxiaIvTkhi2NtP6hHCnliFHmOa9rRy5qCBik/eJYeKDF60VfOmzDPXEBiRS+uxXWXmEGP6Ih2bZPCwxnOnq6/FQMeZCyj2X1X7g6vtAohuHYoQxwouBvAoSw279Q+F62JgbJk6mJ1TynGQYJJSmab8fGNTQkxYihi9eyTAhhnA+8MXsXCI/56Dn0HZLpYOcQBQGB5nk4idJwKOCH1BrtcTwqy4EinXLSAy/aJpHnMO8FHhRaR2lx3AX5mJJg+gDxjj55u05iOEt9Ijg+YGDNITtwRrKTSuwWiNHFWIHfj6D5GQi/MoSw0zBYb+kCK8ThMKJKAiJ4czfu5w+fCdKDEVISQo++69QFjGcd5NJzr3ZEsP++/Dgmnr9BgqW+Ll2JIYIp4krXu950OGZl9A83FfyTRo+dzNzGTFPo/X8oq84Q4Rql5n0ridbYugV8wbJgyUbSpcsBt1Uy4NGeueTad1laJgQw2Asos8iXnkRhqC3FF4feAyTlTkLy4ek32NEJeJy5vgujvxSQAzP5qMi5wrP0TJg39hi05YYoozNMn4/X92/uOVRcsDwPAjfQ5gsDvaWGCKU9DpTPfkoSvBHi5I7YvjMJZR6RwsRw/cetMTQV5QMQuSlL8gnQzi+I4av30gCJg2eOpRcwSFTcoXlMx4YIwqZJIYYIxDDpTNsrviiJ0OiN0L+pQA5IiNw6PfDCiUnrP70hw0MhSSGs//uPNixHsNAgl/IuUSN+HmVPkHNmsvwtK+eyzBwEsOqPlZsLFCoRn0/eO0sMRwZeAwDYhjcm+/RVruY1q0Dj+GJN3UiMUQO+9afihiWL59pqoLcbJkjNXceb0xDOIdYRD4yxHAsS0v4kQPYm8oa1gRqs1FieItp671DmBj23sk07fsdU/XEhQYeRRgipCxB9D3Enofro5WtXWSq7z/TI4a/MOUblvGdtB5DEMMjSRLRHDFcPJ17pdTA5JggvF7C1b0c49A6MfKLjMDwW932x5jmgy8xlfXLqY5rowdGURhOWmLBY6YyUPeOe8dRRxl1SQuFkjJs/sUrSHjyEsNBB5myhk84LlBRRui3m+ueB5YEHmebRU8yPBhhwiCGyTn/NGmoDb/yZ0v6vPqBfm1Z8ZRzvQUxDAzsjhj2G8nSUCSGgeq6//x+CDdFjKB87BND5MqmG1hzGIQ7iiOJ4dv3sBwP1gMR9XORXihZgTrJ8BiCGO73Axq35XnUYxg3G/VvxUZAiWEnIY7QIRLDoGgu8ktAACQPB4tEWyJlWnaExzAT0tEQIoYXMYQNXov6M4MQqPqPTc1dJzIMBEqiGWLYQqIXIoZSF4gkNGFMa6tp7TnIgBgi+RzCNJABR0ioHJoQUoNFPZRjeMDF9GrJold31nRrvX3qYocWSGz5UhDDZS6kEMQU+RbIAyEx7AFi+BMeuBBy6g5nPYewlAEEGnxvDT53xDDwWspvQsRw5lUkB8g1IiGOHBz9ortZCmZ5xhuHWFOWZL09SMhjA8gihsjnGnoEF3MJh2Q/HvkWSSGJIcINoYAH6yiI4da7hVRM4X0qg8fwzcmxxDA571ZT8cqfsonhY+dl6iPCar3sBdNwzN9ocfTzGUBoa2493DQe/HOTWPO2Sb5hE+DRRFGSMuQtjS7kTEL8LDG80oVu4jdZxBDe70QqVB9Rrg8vbsZjeDPzX0gMq/owtNGAGM79d1Z4MUPnoMzWHFMMPEQMjw95AhwxnHArQ1YTy1/KmlNZh+n++/KwRmI4H8TQEiQ0zB1LDH9HMSeQm8SKl4Mcwx/y/UNebVuy0pH0aKi0lDQQjwxLAcBAsXouDQK5ciBFPAr9gAECByK/Ree5fAZSQY9hULOMecMkhqNtqZKIF5ahwJU9nKcfZM33uBVaEiXUD7mDsKIj5M00rTWtA/Yn6cLhSESCosIxjhg++zOTCMI8w8TwIRsRkc4IjEQJHsQgKmZexXmJPCQRp5B+i2IoDutiqHNrT6Bi6z+jJYZ9A48hiCFyxac5o4nMC5SdkBI5YmDzw/ok9A+HThgw0A+GkgZ1XkkMW5tD4yEqpELOfeEjISLRqAW/73KQJTFc/LQNJYXXM/BqgaTCGOOIIXIMgz1K7o3rwUAIDwfGtAHE8FGnjsoAACAASURBVO4JBT3vheYJcWP+Zh9XHD7uN1HDiqQkQHW7aprNzZaImeq7TrSheF4To1iYGL4ZCoXF3oTfsaB6zyGmdZuRGY/h5241bb2GskaieFNBlJtGf8dUPf49Swxfuc4pBkffwxAxXLfEVE85I0QMy+pWGIPaqRBMQ51fL08yQwyf4l6JfbFqis2tjxoEhOzDMMP9KYgW8MOo8be6Iceb5oP+11TWrzBV951GIxz2f6xL0goTQ6sY3CFieARyDHN4DFFTt3ENia6si26uex5YEnhEQ5EY3kNCbYnhv0161LeowcByFR+/5Z5FavESs4Bk0li1fomph7qqMUzlgMeQwmNrF1li+P5UCkj5OeIhYrjdocz9Qz/QsNaVfbKQKRCi2op+Vj5ziesLjGKpt+/j2QkeX6R6oGHfgMGrraycOac4n+FzKNIqMWzPSqLfKSYCSgw7CW0Sw4H7u7wFWK1Brly4Vd/hpq08aYnhC5mQDuQvJN5/mJZ9CYEC4YBFiq1hjam58wRuKNgYLDFMm7K2FsaxQ80Phy1sJqKCiNh1RwyZYxgQw8XTbX2jpy6mhROHptb+o7hhIV9PNgKRBk8sfNxUPvtzhmMwlBTFeoMG1dXypc+FieFOxzOvDXkglhgOssQwYqXHIRlkqvreU0OHcj5ukHzftP+PKQ4iLUMML6V0OKy/CEdFqIx4P+W7vgUxS8Esz3jjAAbvaVu3frbsCIhhRAmS+VxDxlliGIRAcTN/9Nu0hDaPONvWrkJheWyCYy40LVvvTs+Kw26Xk0kQUH8KeTPIn/EbiBws1FkeQ68+Ig9+y2daYvjQuaF8BniUoQyL64Kc+nWuEHqMfFUJZxKPhxzYGV4DYhjkuKJfWcSw73B2N66OnyWGT1vxmZNADG8xybk30WuAwwnq/1li2DtE8OhZQh5SRFiCN0pUGhgn0OCZx9hLk7wujAFwSyyflTWnokMOsgJrchqqwW/f5wgSvodDFPK8Kmb8lgYH1B7DgYLlKva/iMqF5WtBDCsyxDASKi3vmHiBcLhp7TXUlK+aa0yqKmcOZOjAFyMqEZczhz6DVDDHMDhQso5pqptpGTAmtvwDlXdRbgPqqvh9IBvf3qUwQwzfYK4tDlMgP5YYLjTIs5FcUPf+BAI9VO7d8yv0VuDwhBYihiAtIIZ+aGGkEDvy5hC+5ojhm7czB0yaKIZKBIX/XHGGIo5PTT8ashJLn6PnE55GP0xUyIuUyBHy7+d9SugfFA/x/FgLoS4soc3M7WxLh8pxSE6hI4j7WeOgH14XFd/xn8eJR4EYLnnGegyX/tcZy3AAxngjssKpkgoxDOo94nrYM5gH+9EbzKnrNGIYhOniurla1LCC9RxedwiZgJhxjhxuI2bi6iuC1CEvUiIq8D2qQgequ/g9iWHjWqs2S2KIUNIHeW2IZFFB2yeGfXYzTfueb6oe/y5D4ite+TN/y2sFufxuvvkew/VLTfV9p/MdwN6K9YIRDDX96K1lnV8vT1KIIXLqWDNx4p0kc2g+SeF9tz+cdZIlDYTvruf1lf7UDT3RNB/44wwxrOxlieGhv3ZDkJ8YlhnrMTShfMi48aMR5oX/YxQTvKG5iCGNbE1radgUFWScITDX/XUPuZplbWkajTLE8DbWBmwe9S1TMesaG32BfPSg+RFJTpkduYTwDAbnKEcMES0CgxKI4YJHOOa++FFUIRV576iVioY9nMSwudZ6IpNVNIIiLF4awugpJvj+I8xjRekeNEcMExU2wgqhpP1GMIwYYkNCdNVjmHOZ0A+KiIASw04CG8n6lhjavAUpyOus6shfAzHc6QRTMeM37q7IX4DlCod3CYEC4aifZA9NCJtB+AzCNJAfhlpHPFy0tXJRArFo3vPLATGcSHVThCjgXgjJYHjUMogR7MtSGtiYQPDk0IQ+4/AbIoaBNDjCIVCH0BLDmaZy+o9cv0FicYhCGJ6EZkFUBc+NPJCmEV8wpsdASwyhyLjGqm6hgSgyZOa+07OULR0xDAoay29CxHDWNTyIQpEQ5DKLGHqhq9F8hHzDzfIEUHLttg0tlsgpyiaGXzItQ8eZqgfPdV6vhgmTA2J4liWGSDRH2NLKVy0x7DsiJFYDmXjUiErNuyWeGL45mbUus4mhtXqiYQMECUJpAIgd+PkMMBxA+Q5qpySG8zKeJ3iYcRCvvu1I+3yDDuKBRw7slhhe5YgY7gUhHclfw79B4iAQYIlhWwhS5swufcYSwxNv4rxOQmymamvOQZPqbolhVZ+Q54z5MwjTieQP8eIhYhgWLMoQw9tN8o1beTDxvdNx4w0vcHntUuKbePveMDGs7GWJ4fO/4QGMxJAewwmWGD73K5KftgSIoZWvhxy/n9coAiXiiQNRQ03PgsTQ83z7+V/yDJJDHH0m3AcGIiGGrGOarLERDO9MyVL5JDGs7OlCwDtMDIMcMFnbYFCBVyREDIOQX0dqAlVPRwyf+yVD1dFEcRm1ypILQAybw4qSQSSEPDcOdRUzLrdhXhPvZH62SOZz7T3k5wwLRD63CIy4taf3sCyFTBLDbtswn4neNhDDBY867wPHuLIXiUXj0X9mzrhEXvj5XhL6h7UbHlMQVOYYBgSFxNC0hcZDvCYi3Y8UBIYxB+8l37fBY52Ca3Ts5SALNVN6DKv7kiCKEUFEMmKJYaDyyDV56+E2d+ujeQZRLJ1GDOG9hlAV14r4FjUSSa463mUQM86Rccj5PTg2xFWiJULE8OP5mZDyRAXxKGtcZ9Vmew4NPIZ5iCHC/Eadb6oeO5+ks+KVv7gxiHqiofSNPqMhtaL63tM8YvhLO/7VW9vau4dfzv1R1rkMMXyGBlvZ0zjuqKcZrDFc8wMxJd+DHkcMNww9yaQP/JGpqF/BeprAF2sv8jalidE3dkTKEi7HPK5upP8bGmFe+J19tiOuDOoYZiJU3NoFI1sDiOHLLnQ9kwt8pCvzRQIPo/eCx0z9Kfeatpr+dg+ZeyNVTCteutqmD3iGBl95XDBiLuHaxe4chdrCVq13DM8ilhg+ynXCF0LzQ99xrcSH88LEcO1irk1C4BC15RvMSQzfe5DeaNTKxL/RcG6DGFdbsooeyxAx9NTGlRjmXCb0gyIioMSwk8DGRsoDxbsP8IpRYsj8NUi673QilT7dIYfE8BGGS4jMPQ4QSG62O+JabiRZxBDnZeTB7HgMawbiUChFsSmiAmLY1mJauw9m0jwOb7CCi9w6iqyDxMLijaRwCXkAAYNoScuOx/H6CJOAIiSJ4ZM/dP1GqGxiybM2ZDKoUQWPJcgtiCyIIWoowgoaVVdDKAhywqrum0SBFr+JoAys7TjYSQsTw2sZJoTY/+p7Ts7KJZIaRvitX5eo0FDDe0Yl1xoQw+kMGckihgjbAzF84Is88NBzOfEO5pGmd59kiSESzevXmMSq2aZp3+/xEEKxmqBhnCwxvDkHMbydllFRrJPfoeQFxpKHhO3HMWwSFkmIHeA5JRwYz1Bz81gKNlhieIu7NwgE/u7EQYJcJHgscMggMXzpapebgR9mEcOBB/BwC6u8yK7LDYQY4nCKkDRIl2NTx4EVHizUz0MJi6ggEYnh2vez8od4XS/nNuoxEKMDLO3JebdQ/IAW2TyNanPrlxDfxNv3WGIYeLQwps2jzjMVz19GgwO9DMgxHPY5Swyf/7UlhuWpDDGM5NCKQIkc9jEf23qAGL4WKiwf7aIfogbPdDR3CNZ0CXXzfwuyZYnhJP7ZEUOsRyCGUkcv+BEOivCAybvnhxQWekc49wL1YRzOMK+x7pWtX2pageu6RQyjE8+uIzhB3q8QQxBsCWkME8NHbA61ZyDwC7Hj/vDIkRii5h7GPaIu6JSXI4SS2Gy1c5ZCJolh920tMUTe9qCD2Dc/l0ze9cajr2O+sHiF/Xwv8fDAS1F921GWGCLHMFB/JTEsKwuNhxyOEQqKsFMxDvqeyFyiQ3gepyoMYrjkWdNatTVTBoQ8iEgGDFnYF0xrkyPLTgU7KIXAHEmEkp7w704jhpyLlb3yE8NgHZW554jhyldM1WPf4Z9hbMG4xOWlt261E9eaEDFc804mdxhlooaMJ+5Ui+y1A701zmM4YbItreR7DPuNtMTw0W/To1jx6l+cim7UEw0hIhht0KB4WX3PKQynRk41rmmJYZ8MMfTyJFEvuGn0Bbw2iWGwp/lj63AJ3n+fmMblB2/YYYJJH3CRqWhYScMpozX670PFcGl5iaFfNzYwIOZaFyC8UzEDxLAxPzGUtXT5TCPrTVwuMPZ85hiSGN7HvdgaF2+yxHDmVQbj7Rsa/P1e1kjMdXoMz7D5tI4YYo1CeOjpD1MoEB5dP/c3VDpjh6NNYvXrJOto9BiuW8K1KUMMM7U28R0YamGMQkoGPMbwHKM5Ypjqxjxs5hjCYzj6AuoViAdUiWF7diD9zsZGQIlhJyGMDatlEA5i9/OKTQf9xKB4MV54vPiw/sFKlB52gqlEXHvQ4FWxxPAW5qTgQIpDmyxopnGdqbnjWLcxOo9hWRkTnNM7HmvSe5xriWFQl8qSUJSrSNvDzvKXbI7hgkd5HahmSmFlhBbC2xEihoE0eIYYPhMQw4vcARokNrH4mYAY2hpVDLc74EcMfW0acY4jhtGQGISCUH1tyllZwjBCDIWcCk4ZYvgr5hmwFtEpU3iAieYgMZ9r3UL+NG/h9MjYU4WypYliOSSGgaCGX0ONh/DtD2POjpAbkBIQw+bdJ5n0iLNs2Ag8hqtfM037nGda++/palPhlihjYFLVJvX6TTmI4R2mYtYfsonhtAsZ0kushxwReAz/RrEDP58B0uEooowyAJYY2lpKaBK66nLAgvyODDF8MCCGNjcDLYsYDh5rTOMnViwmkr9GYvjBs/ReWGJ4h0nCQ0hiuB+JEfLZol42GC4wZrGlJtpBDLF5gxgmVrwSW0bDH2pLDH2P4VKnMIsxJTF87lecV5YYvpwhhjN+y40d+cIIi+Ic6zEo5HVsPPqPzGmRmnRQ4UWhZhJDr7B8dOnxQ0Xjis7HecA4n/b+umkZAmJ4huuPSXazEQxQ+QzKJcj9WEIAxHDdIv6p48QwCPWTtW3wIaZs7cIwMbzrJI6l83YJMQwMECDY4s2T/DF6DBc+ZkNJvT6LwcutmWc+biqeu8yWq5h4R5aIhBPYGnaSM9TJb6PRC/g7PA6t3QYYeHaZtz34EBrN/JA9edcbj/kL33WJvAgRQ+TJrpzN/CMIjODQnHoVxNCWQ6HoD4ihR3plzRfpfuwb8Fb712V4HN77GGEmOchiriG0H5555J8JeYH3EgdfEMMWCG+0NGaIoVecHOsHjWirX2c4f4iARcqxROdtvn9TqKqqd0hEJfr9qJEIJAtkq3zlq/TYoaEwPAwQcXnpUFNF+KtPDMs+ec+9k8y3HTKOZS4QignDpCWG1ohbP+F2AwEb31gBb0/TqPMoKsZ6rLP/7vL6EBkjv8XvQ8QwqDvsiOFzv7LRBFW96QnHXPeNW44YLv0vI3JQdxLRR1yrA4VNwUv2bJ+Yxnn7N+x4sknv/z+momGVqb73lIAYwmOYSVlAqGblsz+LHzo/p/u2o0JlhaI/oHceHkPkTx55lSlf9pJJvRnjMYSoDIS8EJ2yzzfpSZe57hNdPCPOLTgT1Z86hTUwSQzn3WJDSV+8MoYYZjQMZGz8XEL02RHDQQeRVJIYLrS1I0PlLvrvm4nK2fE4U776NYb38hqPfNOW52pYkyGGXkkVfAeG2uR7KIVxH+tYomwJGkOK0w2MPChHLj1yDPvtYZpGf4/GNfGAKjHsyOqi391YCCgx7CRk4bmix/CdKbwiNnx45RA7jhhyLPJoWLj8+kEkhgseY9FvyY3B4oFQB3uhWiary8aYIYYJA6sfDovpkV+wxHD3SfTQWBIaEEOEkiKEov8oWsEbj7ySdfbcAooFe/0HHjE82DSO/QVFUSR+HjWNrMfwIhePj0XdEsNVLkxUJP2x8cFzhoMWrKDRDc4Rwwe+4LyNMgyOGAbkVP4OMRUcGBEOk3rlTySkUi8sGmLnewXyKfpFh54qlFi88xFDHMJBDO8/x5EbkJKqR88zzbudbokhNoGGT6gW2LT3N01r/72YkygNBJ6es7k3xhPDt+6g6mKWx/CJH2QEEIYeaRLLX7ahpFMmmdZ+e/L/O7zg+T3015YYvn6T+zsMCU0H/8yJg0hOBrxbICOwooOU1k3K1H+LEkPm3qEmGDa4QF3PjZ8jhvAY/odqu8nXAmKIgvGpKpOacz0twX4OEPP+1oIYfpj9RoaIYbi+misR8Pl7aFXGwTyqOpk1zsiFg8dwN3gM77UexuDwCwJLyzTEZ7YfZ4khQkkRJg2P4Qu/M2W0+CY8Yrh9yPMtXqVmKcjccwjzba3HMFNYPtovPyQ6LkQseiB18wlzcujRLjdJVGNbBh1I0hQl2wjjtcTQGk86TgyP5iFT1jbMB3jvWhGmtW4xD0NVd32OebZCcKTcgxggkMMph2vJHyMxXPS4DSUNSjywf14hdvwb+T2Vz12aIYaSKxQAgsM3IhWinkZ8HBdabonhQBqy4HVr2W4s++avKyJw4YjhEVcwgsL3Kso6h/4hBA+HZnoMA9VbEkNEcnjP5kqbBHNFamD6QkQSHeDnXcrYizCNI4ZQVF70pAujzRDDL5mWfiMYAu7SHYKwVVwL62Tb1rtZYnj8P0PEME5Ntb3bJo0iKKEB4aUczRcP4R4ZqFuXr5rNdRWtYfwfaHiANw5eOb9JGH2IGK5d4CIHKL4y5HDupcCGNRv7Ds8Qw4m303ATIobwsIEYTv06iSNyRZl3TgPoBLfP49/wUOPdRSurX22q75pAZd7WbfZmhAFKTsFryjy8w34TCodN736GaRr9fRZEx3ySUiccE4+k8D3Y5WTWOxaCmOvdrd3pVNOy3/dNReNqEmmstS3b7N1+Yuivt5OPzgpF97HHOgDvfUFiiBBRCHktecYZzNxcD6KXiO2wkxi1A6NR/Sn3UwiOxPCN26i8CwVUlv75cJ7rhhSz9zFirdpPFrhzlCOGg8dS94HEMCgR4ocy+2JZNOyvejVMDGuXcT0VAucr53KeHvd3k3z/UZOcfzcNc41HXs1+ImQVqRut1VuzViiix1C+qWnf7yoxbO9iot8rGgJbPDFsaGw0f7juejN7Tmah+d8fnG/22mOEG4Spj083N95irUZ77zXCXHDe10xVZWVokBjSOOggLtzcLBwxnG9r7W072m4qw04MyUSTGC58nGFjDMv476W0KmUTw6u50JAYmhZTVpZguAU8kOkRIIaTnJKhqGvSYyjEcNvR3AglzI2bG8Klho5nzHvGYwhieClLMkhCPGrakRg+8QOPGF5C4ghrqEgtwxMGtVJYPC0xHGCJ4YD9nacLGCBHQEpNIE/Ib44YBnWr5DPJdSAxfPU6HrZEFp4WR9REC7xXfuiqn0xe6K0iEWjeYNq6b2sSi55i6FG0uDasnSSGU85yZTiQnA7LNp7fEsNL6fFIfPi6adrr66a1/9602EuzxLCbSc29IZ4Yzr/TVMy8OpsYPnkRSQqeEwcRhDiyNMB9Z4TyGXAfhgSDGK5526TmZuTMYUjAhiziIBK+iOdCLTXkRlhiGIgfxXgMKSm+YYVJgCBFxGKosrvkWc4dEsP5d5skQkeRYzj4IHqcSQwj4ZfM/Vi32JTVrcweJv+gElFLzBDDe01y3k0msQLE8P28Q82yCusWUyo+MR85hhCTsXXqcIgiMaT4zJGW4COUdKcTSAxR35Ch03j/Vr7K+/geah4Ojv2bLV8y+vsURUHoGt4FSwy751RNDeUO7fkVhtz6LY7o8DC0zzdMeshRzCey/dnOmER1EBL5cFZJGBJDWK6Dsg2fnhjatQ39Lv/4TUsM1y8xDeOvMVV3TzTldaucsIQUMXfE8IXLeYBiCFogLGKJ4TQrruUZCKTsh2CBQ1kl6kkylPQOrp8U5Qqay6Pe2eZc+y2eGA40rd0R7jsgIIaH8uDve2bEkNF4zF/pmXKRF55CpBzk0T+8fyB9yTnXZzxXIIaJitCziXezOSjeLXuAH1bM6IAVL4c8jfJMoppMYvjBDFtqZ+E0Z/irnzTNtKVqWDQ9ixgG3klcCx6WNvEYHvePMDEM8isLrZ9xn0Popa2qlxNqivtO1EiEcEDgUL5qDkM5+U4ddS09MMjfQx5feExtWYAwMVzkPOKY64iMgeIxsCExhMcwiO7BHKKCthfejBqETaO+zUN7/UQQw+s5z/i+BQRN+hAihkHd4QwxvCwghj1tSYfDLgthmyGGz9PwKoq2HJOglJC/b8DQJiJz7MveX2eNP7/VDjvdtIz5rqlo/JCpFlhrSQwPybwjoh8QO6bJaqu6CbGv24/NCkX3VTwZtk1iWE8SVL5sZrzHEJ5A4L94uhGDmZvrngeWNX5b09nE8K076Glk/u1WO4cMDX6t4ubdT2eUCksSoQbhaVbzwRFDCBOtes0Sw8VWCdb3WPupI1hvkeeKVBFeAx7DDSt45nHE0MuD5Tw97h809LNG8cADaNBHAzGk0bmmn0kg37aszLT038s0j/qOrY8YEF31GH6aVUZ/09kIbPHE8JO168xd9z1kzpn0eZK9OXPnmT/9/UZzyQ+/Z4ZsP5j/vvHWu8zPLr7Q9O7V01x/ow2T+Nq5VlJaGkI20gMP8IihzZHD4YVhPIGwSXrnk0IhHFBuTC6YxtpuYj1GqJcsaKh9VDN5vGk48mrT6ohhKy3PCLcA0UyPOMcSw6DYLb2T5SlaKBEiA3U3bHTYCEVunZsbwqUQLrHmPVq60GpuOtg0HvZr04IctiDvwRLDl1jsVw548G6SPEF9M1AIA+GBmAIsps0jz+GGRGLo1SnCPeDRazjxxpBMs+BoieFXTOOhv2IpDodvkOuAg1Ty1b/wsCWbKInhkqddqFVIcjoSjpPvBRKBAoaUQXwmjhiO+ja9IPDQ4nsodYC8EEsMTzHp4WfygEFP00dvmKY9vswaklK0mJv57mcEuXb/zkEM72IuRZbHcPqPbY5huoGHcRLDY//Cw5Kfz+CPI/Ip4JmUhvkC1VlHDIfaxH8cLDF/SAxfvsbUnRGIH5EYLjBV99saefCskTCtX2ISaxcahDr7zRLD/5rE0qcZ3gVDCQ7HDCUddLAxyRQPMr5gAH7P8E4QwyDszr8mSJvIjkdzjBwxPGVKxmMYMTZExxxkv2zdItOy2+kmMf8eHiBlXiPfjAeQGaiDNz4QEXrFpHc83hLDmVfaIsckhq/w0lGlSxwOsNlLXVJ8juvioGsqe4TqJvp98z1UcSUkogdS+S0s6egr8onYnx7bM1RZcuWimOIg1FYBYmgJtIR25Xs3Qv0cKh7DgBhinVv1umkdGHgMSQxPNuV1K104pnidMsTw/2y+JELQhBi+8heTXPREUAR6lbtlVIjHEUOIz0y4nYdN1ICTJlERcXhJrcHQ/II4UI/BnJMwaqS3P8x6ZrwDqxgyHDGUyIvg/cFhuaXfXnw/M8TwFyY55x9hYpiqDtXZdAq2oy9g8W56YJ6FmIknyAEj3crZsWHWYWL4AkPvEI6LsH6QYpHVp3ESghwtzfQio8nBnO/fNnvRi4bySpi/IBQuNzIiFJU1T4L83Lj5A6OJJYZWqCmuRY1Ejhiufo1lgNBE5RPGD0S4+A2GQIinIcQX94G3FcZOlKRhakD11ia93aGmrAnE8LF2EUMYYZv3/hZLAbEkComhXRNhAEzNt3lnxHHspXz/bEetijhERxCOirBgeuwrutsi8BD+8so0IS8d4YQIsSQxnPSoqZ5sayJm5bcHtU1lbNmXfb7BcGW/1Q6bZFrGnO8Rw21tjuHBP3Vfy0sMU92cYTBaN5YXCISk5Nmpso4ajSCGy1/KKrOD75HwNdeTXDsjiMx1z4DDGr/wGL77oKk79QGK9jBP/U0hhr8zLX12yRgaEpWmFWsrvMgtTRnjOIxUa99nVBGa5OfTyLLyVZ6v5HwTKnfhqZBDJA7CfSFiWLeK74Ujhp5Xm8MPYrhwmq1RPPggakOgUf0UhkesMTAQkhjubZqRx6rEMHZd0D9uOgS2eGIYhR5E8dLLrzbnnnUqvYYggoMGbmuOO2ocvxolivJ7qmMOOpAeEi6YB//Ukq5P3iMpgPUIuV9YIP26NySGC5+gUqOIJsCij5AStnQ981VghW8dMCbwGLbS8oxwC1wvPfxs5hdJXhIOCyxw39pM74FV4xrNw47Lf9r1FJN690EefiAiEiKGhyPH5TDm20Bxq+6c//IaldMucApeILGJhU9y05P4eBAeeEmq7z7ZNI84k4dhlsYIZLYFK+T5NJx4Q0imWT5zxPCwy6yVN2gQc4AYjCWGf2Wsv2yiOEjAQwWPDzdUjwxG6xCGxjuSOyM5Za09BjLMBOQuy2O47/nMmwIRl/qMJIaPf5eWZBLD5y+zxXxRx3DkF0zrgH1Dqp4I+aXH8LV/5iCGd7NkRBYxfOpibrx4TpAIHILoMbznlFA+A56ROYYMJQUxvME9tggEyYYv4YthYnitqTsjED+KEEOQNISjIs8usW4xD0J+Y/mVpf+1HsMTbmC5BJtTCI/hIZyXIIbRvDx4lZm/EfEG4NohYigH1mDsXFHxUx+g+ilDSVH2Ik+LJ4bdOL9A9pv3/ob1GMIzitpnyDGExxA5WS9dbYmhKcsQw4jqLkLxkFfcvP8P6WGEIQReEUsMe+YspxESlYjxBERz7eQRMXYQ14BxCg1eGhB4qDgmFjwayn8kniCGlb1dGLcjhu3MJXM5YIHRi2V5VswmMYQ4A4rAV91zCg9r4gWNEkPgAsMW5rJPDBOLn6T4hJAS9Deab0li+Mwl1mM44Xa3TgkeUoQ+eoAnNvCKeVL3/BvFgbbjeoU1D+sV1nGfWIohQ7zBcg+XFwq8QQyXv2iJ4a2HM2okORvE0NZwpop+twAAG29JREFUZSgpQomD0FL8TbybYkQAyUDek+89pjri6rkhTORZhTxYj+GLtgbrgscMDrWoYSmy+iSGDCVtzgikHfILhr3bNXNv5iknVs7hXhAihpGw76xXK8+8QT5fW1VPF3Yd91oCd994IfVwpdQTfiNEC6UgIHTkN8mvDhHDdUssMQy8NGnkwTbX2RJNvYeZ1n7DMx7Dk+9kLr6f9wq9ABLDh75kieGcfznhJ3ql3p7iIlQQxQIizxaoiDtiOOO3lhimumGmMe/bL9PkiGGQqyaKthyTaKmkwPDrrwPu3fXIWu0uZ5oW1GIVj2H3gST+IWIYqb8XArSiuzMMVt8RrhvL72G80dIN3I9JDJtqabyGATlaf5XvMGr3BviLEq+cd2Su8pI7TyROMGLXnfogRXtADBPz7zbpvb7Oe4WIYbLKtNb0pxEK/UHUC+o6cj9BWYpTreaDI4aItFkxyxLD4HzjGyb80jAwdLNW8ITb7TUe/ZYpr1vN99cRw9VzGR0iDcQQRmWWohp8CMuMobFeYnOdjeBahUiT8oAY2nBl9RjGrQz6t02FgBLDCPKLFi8111x3vfn+eV8z/fv3Y5jpXiOHO2Lofw6PojRLDA9ylkRYY0FYHDEcdBAt4SiqjfwXt5BMuNUk4DF87V92kX3mEh7cxNKFxQ5hlKifBcJjQ0lhtUtZgYSdJ5j08LMsMQzCz+idJDFMm9aeg5mLZotd383rVD72HZu38+4Dpnnnk03iw7mm4VjxGMLK9TsrGhHkPWARZB3DaRdmE8OGj5xCWPPwM03zvt+1SqEjzjatNdtYYuhZv+GZgReTBHDq17LU6lwoadAHh2+Q60BiOPtvzB9BmFT15PEk4FRIDWqf+RtqdHP1hxsqgShaLc0WQf6IB8UkxGcaP+HiDqIjDc+HHCSU2nDE8JT7TOVj3zMtu05knidIBYnhmrc5NiiYLaUEuEmCGFb2IEESBT6/XyBTyKUQD6x8hrCXxDIQww3Mw4G4DQ4gFD4asB8P5NIYSnr4b5no7ockikCQbPhyAHXEcMEjJjXzDxnxI2xh6xZmiqenupv0sONJOMvXLc3KCYSoECyx8CbDK4wNPjXnH9ZjuN1YWkrjieF+tiZm5NCH58GcqT/T5jzKgVUkxuWgD0MK1E9hjZXQ5lyLKjyX8IIijIuhpOsWunmNsU9HiGH5CoSSHmtYd+vlP5ryj+az7IALJYXM/8f4m22u5MoBF/MgA68mQojKV7xq2qp7ZxE1N/881dE4L140187NyVzEcLuxJvH+Y1nKvxgLSwxtGLfcy5dtz4Ud/u48OgEx5AHqgxdNy8DRnBMkhveeZmtF7nA0iUoWMZx5pUm+B2K4wdVoRSgpPM0khh55iiovYj3COln+8bumYeJk5t0yzD1ojUddy9CxOLyiCsn4CYkhatt1688yK/AqIGTP/z0NGXUfUnEV6oQZYngUvSDADjlD6IslhuMorpGc/U+HP4hhW2WPsFARchWf+SkFj1Izr6L3icQwwI14I1T3o3mx8yZMDGcGxPBR13fW/0xU2nQGEMM0cgwDj+HYX1FBGQ17C7BJrJ7NvSBEDLsPyOnl5o/zEUN4yyt7OSNK3LySyAv3Hux4HEkMREKwR/CdOu4fJK5YR0U0Sb6P/MjGY/8W9hjSyLTMhod362/Sgw82ZfBYvT81hhjeTU+OH0oK4xHWRBh1kSqA8HdR0W0eflaoPij2JMwZNOwn8PgBQ5QvsXvBGq5hWDNAhlCn0NUQDfZMEBBE5IhwEcdk4IEup5zvabC/+/My8+7WmLJ0HfuwftdzTOuob5qKpo+4NwBfEH+kEEiT/LrY8fDE76J1Y/F9+yxlzlCbmnE5915EI5V/MJNCetEGMouxgDHbGUGC807oeUAg4TF8535GBGHuWGJ4T7Au/8a09Nk1owiN80RguMDzixgPsCMxPMWGksOojXQYGlSXv2SJYXC+8Q0TPuYwACSWvsA1htdAKGn9RzbHMKirK+KC8rwwCmLvg2YEPNdNh/+WHzli2GuojfqBQn3/UcybxHqC3F40DSWNm5H6t2IjoMTQQ1zyDYUIyr+PG3+4yzmMEsPausZij1nW/RL1q02PmZeZhqHHmaqFU826A39hWit6b/J+fbYOhOvjfbZrGdPYlDYVySTWY5OoXWp6vPx7s37MxaalZtu8l674cI6pmffvzQPTzoX0sw6JqVg509S8Pdl8MtbmYfitaul0U/X+FNPUe1eTXLfIrDvk8s98v7gL9Jh1uUlvPdLU73CiqV74sEmtnm3WjckULMZvej33v6a5/xhTN+zzG6UPhS6a+miu6Tbvn2bdfj9lOYJP01Jr3jLd5v7VrDvgUtMKz9VGbq1txjSn06Yylcx5p+S6hab77D+YDSO/Zpr7jDTVCx40qQ/nmnVjrOEM4jU9X/q12TDy66a5TybnG591e/NGk6hd5r6b73G4Pr50manb9SweyGreutnU7v19k+65g6leNNWkVr1i1o3JhKJ2BJrqBQ+YyiVPmLUHX27aEoEnpSMX6OTvQnW013M/zsKsx6tXmpYeO7R7DmOpwJpZVZE9fj1evsK0bDXMNAw+0vR84WemfudTTeOAQ7KepOb9e01izbtm/b6ZMke5Hrfb6/+gMXPD8IxIVydD077LlRX+ml0n5rRr7hW+WvY3yps+MT1n/MLU7nW+SffeucOXaGpqMclkuSkvb8fDdPjq7ftBzbv3mMT6xWb9PhcEP+i8vnSf/UdGkaT77GqqFjxs1h5syZffus2/zSQ+edus2z+TV9m+nm/ab3WvCWtXbNre6N23VASUGAYjLySwb58+Ln8wShTx1SgxrG9o2lLnzkZ+7s7bSNDRusYmU5VKmvLy8o3c7xK+fOdCWpQHbWpKwzhtKvIQjKJ0RG/SIQRaW1tNY2PaVFdXdOh3+uUSQaCtzWyobzLdtrSDaokZzz7NbKhvbOJ6mSipvW4zAPbTDEYHf1NdpetlByHTr28EBJQYIkwlUCb1SaFg3d4cw40wNnrJTkRgzfo607O6yiSSWzAx7EQ8i3WpuvomEsMa3TCLBXmn3Cfd0mpqNzSY3j0RdqatqyHQ1tZmPl5bZ7bujdw4bV0JgbXr601NdYVJJRNdqdvaV0VAESgRBLZ4YhjnFfTHpr2qpCUyntqNHAgoMeyaU0OJYdccNyWGXXPcpNdKDLvu+Ckx7Lpjpz1XBEoBgS2eGCI09NdXXGvWr68Njcf4cWNdSGl76hiWwmBqH3IjoMSwa84OJYZdc9yUGHbNcVNi2LXHDb1XYtj1x1CfQBHYlAhs8cRwU4Kv9y4eAkoMi4d1Z95JiWFnolm8aykxLB7WG+NO6jHcGKgW55pKDIuDs95FEdhcEVBiuLmOrD5XCAElhl1zQigx7JrjpsSwa46begy79ripx7Drj58+gSKwqRFQYripR0DvXxQElBgWBeZOv4kSw06HtCgXVGJYFJg32k3UY7jRoN3oF1aP4UaHWG+gCGzWCCgx3KyHVx9OEFBi2DXnghLDrjluSgy75ripx7Brj5t6DLv++OkTKAKbGgElhpt6BPT+ioAioAgoAoqAIqAIKAKKgCKgCGxiBJQYbuIB0NsrAoqAIqAIKAKKgCKgCCgCioAisKkRUGK4qUdA768IKAKKgCKgCCgCioAioAgoAorAJkZAieEmHgC9vSKgCCgCXQGBOXPnmRtvvcv87OILTe9ePbtCl7WPioAioAgoAoqAItABBDZLYugXrd97rxHmgvO+ZqoqKzsAS+GvXn/jbWba9GfdF889+zRz3FHj3L9xiPrtlX/mvwcO6B86TPmf4fNoH6PX/t8fnG/22mNE4U51oW80NDaaP1x3vVm16sMQNp+sXWcuvfxqPklnHUDlXrPnzHMIRTHNN16FxnpLGC8BTnCKzvepj083N95yp4n+vTOmZCH8Zc4sW76St/PHttC7Jv2Wfm6M/ncGBp19Df+d6NGju7nkh98zQ7YfnPc2hYhhIayj76GPtb9mx62Z0c831rre2Th39vWiOOD648eNNV8798xOvVV71kz/3Sm0h/ljHX1fo/tjpz5ICVxMnnd97YbQewb8Pli2otPHTt/DEhh07YIi0IUR2CyJoX+InTrtqU4nhtg0b558jzl14gm0nMtm/Z1vnEsCh39fc9315vvnfY2HLWwAc15/w/UD/x647Tb8rmzAffv04QaBTeSu+x4y50z6PMls9FpdeK6Fui7PvaG2zhx0wGhHqoHN8y/MMhvq6jqNGEYxxcb5p7/f6DbpfOOFTucb6y1lvPx3CgSwe/fu5n+++w3Of2Bw1R//bmpra81RRx4WMpB81vla6F2TebTXyOG8b9xY5nrXoteWA9y5Z5262Rlico2DjN1XvzjpMxPDfOsa7g+CjybrHAxAgjXeyWUrVrm5g+9++PHHsWtm9FqfdY51ld/Hzc/oHO6sZym0ZkaNBP7YFnpno2Md3R876xlK5TryjvXr28dUV1c7IrixiKG+h6Uy8toPRaBrIrBFEcOod8D3LGBjq6+vNxvq6w08S+21omPYo4fT6IJfiNzl2xg318OqYHbg6FFmxqxXzLe+8gW+QX/9100Gf5vy8GOOGEat5L6FXLDrVl1tnnthVrus51FMOzJe0bGOvvab63hFjS277rSjqaqu4kEe+DXUN5j5771vhKBFvQK+R8E/RMa9P/mW0jgi+M//THYktdD45HvXCv22ay7x+XsdJYbRscEhXoxr899+t0OhpD7WDQ2NNB74BDR6L7+nhbyTmzuZiBu1QpjgN7m85/L3sQfuZx5+fLpZv742K1Il30yJrmsYu0EDt3VEPl/fCr1X7XmurvzuyTt2yoTjzN1Tprp3IG7f+fUV13Js/PNH3FzP9+5EsdL3sCvPHu27IlB8BLYYYghopz76pDnumCPoicNi+fiTzzjygYX2xVmvOi9SRw4ecZumWMb9zTqXJ6LQAcn3bhV/imycO8ph4bjxh9NTgAbPDv4//uvnMj397AwzdMh29GhEvbNC9jsSbhsl6lH885G7QsQv6o3cOOhtuqsKUfjy2aebf99yh8F/H5g6zZx03Hj+W4ghvocGr7hgdtQRhzqvnk/mOnIwjOIf99t871O+z6Jza9OhXLw7b0xi6GMdZxzLt8a2h8BLlEXx0Nq0d5L5uf/ofWLDD/O9Z+g5PLTbbNOXXlg0hPLL+1royfzx69+/X9Zv8xk/C62ZUe9wob50tc/9d+yN+e+48FGfGMata7Lv9+rVM2RU6YiXH1jpe9jVZoz2VxHYtAhsMcQwmmNYiBz4lvJC+YnRw2bUmppvY8x1KPa9ZB0hPZt2OrX/7j4xHLL9dtz4arpV03O4aPGSnJ6JOO+sH6ZbqAdx1uuOjFcuYrG5j5fg6r8X0595nmG/CAUed+hBeQ+a+d6RKP75xjCfR0ve01xjlOtd870sW0qOoWC8sYhhFGu8H74xgIa6SIi99CkfyZB8U80xrHWviewPUcyja6wfupsP/+j7F10z/etK7nuhMcM1o3mQYtTbEnIMxVvukzyfJOYaOyHu/hrZkbOJvoeFTgT6uSKgCEQR2KKIYTQk0Q/XaM+BM276xFk72+uBao93qZC1tatO6ejhIrrx+R7DOCEEOcB3xLMbzecU7No7Xu2xbG+u4xVHDFeuXO0O+1VVlVnEUA7x8ls/BNj3PP757zeGQgxzzek4/NvrMWzPu1Yo5K2rvmv5+r0xiGEc1u31GLbXa9uR935zHDd5JuBwz/1TGenyydq1TvDMf2YQRxjfosSwPQQjbs2Me09yEcP2rJkdiRjoimMZfcfEU4hQXBGfiRuLXGQQOe9+GG8uTPQ97IqzRfusCGx6BLYYYohDLOL3cwnEfBpimGvTa0/OWnsOqj5xac9GsOmnU/t7EGd19smHEEMhHBI29mk9hrlIoVjOfXW4uENOew44m/N4xRFD35MeHZcoXtH3yxcfQohwIVXFXPhHPVFxh9aOvGsbSxCi/W9Gcb/Z2cQwF9Zx4W/ROdFeUgiE4jyQxUWuNO7mG6K4luUQW4szWBUi1/nWzKiXP5eBxhcQyoVYR0MjSwP59vci+nzyb1+MppDH0M9TxBgjsiZfyRh9D9s/PvpNRUARCCOwWRPDfLH10YWzo8QwV7iaHFryqZLms5DiwPPfF14yZ59+MkeqI4elrjS5O0oMo6Imkq9W6HADTAp5ggqpyBYa6y1hvD4NMcRvQPhyHTDbmx+aD/84MRr/3cv3ruGw5SsAR3O0utL79Gn7GsXHf59wTeShoSEvrZD4TCHPjz+OUaJSSKDrljvuNYccMMYpp3bEUPNpsSm13wHfl155LWRE8TFHf+EVlLUR/8bnaFGPYaHIhkJrZnSs40LF5f2P4uirZpLMRvL9Sw33z9qfOOIra59EUUTXnjhih98gdL+QIU3fw886Yvp7RWDLRmCzI4ZYFHPVD/RD2yBwgiYlJTpCDP2cJH/6+Hkv7e2H/F7CWiWxP1/Nvc1hyraXGMIq6mMJnHp0q3FlEdpDDKMhxIJfNLQxbt4UGms5PG/u49VRYuhjLmM2fPddsw61hcrJFMIfXsvod6Jqw369UTxHNIQ8Xz3SzeFdiz5DPrz8sG3gdPxR46g22x5iGA0djmIdDQn38znloBztq58/J+8nvrMl5hjGvQvR/Lzod+RzIY1S6xP/zpdP25410x8zfzwKvbMSvQP1TbQtKcdQaoUKRsN328WtidE1M1pbtL1GYn0PN8dVW59JESgeApsdMSwedHonRUAR+CwI4AAzZtSeW0zNwM+Clf5WEfgsCBTyEH6Wa+tvFQFFQBFQBDYfBJQYbj5jqU+iCHQZBDRHrMsMlXZ0M0BAieFmMIj6CIqAIqAIFAEBJYZFAFlvoQgoAoqAIqAIKAKKgCKgCCgCikApI6DEsJRHR/umCCgCioAioAgoAoqAIqAIKAKKQBEQUGJYBJD1FoqAIqAIKAKKgCKgCCgCioAioAiUMgJKDEt5dLRvioAioAgoAoqAIqAIKAKKgCKgCBQBASWGRQBZb6EIKAKKgCKgCCgCioAioAgoAopAKSOgxLCUR0f7pggoAoqAIqAIKAKKgCKgCCgCikAREFBiWASQ9RaKgCKgCCgCioAioAgoAoqAIqAIlDICSgxLeXS0b4qAIqAIKAKKgCKgCCgCioAioAgUAQElhkUAWW+hCCgCioAioAgoAoqAIqAIKAKKQCkjoMSwlEdH+6YIKAKKgCKgCCgCioAioAgoAopAERBQYlgEkPUWioAioAgoAoqAIqAIKAKKgCKgCJQyAkoMS3l0tG+KgCKgCCgCioAioAgoAoqAIqAIFAEBJYZFAFlvoQgoAoqAIqAIKAKKgCKgCCgCikApI6DEsJRHR/umCCgCioAioAgoAoqAIqAIKAKKQBEQUGJYBJD1FoqAIqAIKAKKgCKgCCgCioAioAiUMgJKDEt5dLRvioAioAgoAoqAIqAIKAKKgCKgCBQBASWGRQBZb6EIKAKKgCKgCCgCioAioAgoAopAKSOgxLCUR0f7pggoAoqAIqAIKAKKgCKgCCgCikAREFBiWASQ9RaKgCKgCCgCioAioAgoAoqAIqAIlDICSgxLeXS0b4qAIqAIKAKKgCKgCCgCioAioAgUAQElhkUAWW+hCCgCioAioAgoAoqAIqAIKAKKQCkjoMSwlEdH+6YIKAKKgCKgCCgCioAioAgoAopAERBQYlgEkPUWioAioAgoAoqAIqAIKAKKgCKgCJQyAkoMS3l0tG+KgCKgCCgCioAioAgoAoqAIqAIFAEBJYZFAFlvoQgoAoqAIqAIKAKKgCKgCCgCikApI6DEsJRHR/umCCgCioAioAgoAoqAIqAIKAKKQBEQUGJYBJD1FoqAIqAIKAKKgCKgCCgCioAioAiUMgJKDEt5dLRvioAioAgoAoqAIqAIKAKKgCKgCBQBASWGRQBZb6EIKAKKgCKgCCgCioAioAgoAopAKSOgxLCUR0f7pggoAoqAIqAIKAKKgCKgCCgCikAREFBiWASQ9RaKgCKgCCgCioAioAgoAoqAIqAIlDICSgxLeXS0b4qAIqAIKAKKgCKgCCgCioAioAgUAQElhkUAWW+hCCgCioAioAgoAoqAIqAIKAKKQCkjoMSwlEdH+6YIKAKKgCKgCCgCioAioAgoAopAERBQYlgEkPUWioAioAgoAoqAIqAIKAKKgCKgCJQyAkoMS3l0tG+KgCKgCCgCioAioAgoAoqAIqAIFAEBJYZFAFlvoQgoAoqAIqAIKAKKgCKgCCgCikApI6DEsJRHR/umCCgCioAioAgoAoqAIqAIKAKKQBEQUGJYBJD1FoqAIqAIKAKKgCKgCCgCioAioAiUMgJKDEt5dLRvioAioAgoAoqAIqAIKAKKgCKgCBQBASWGRQBZb6EIKAKKgCKgCCgCioAioAgoAopAKSOgxLCUR0f7pggoAoqAIqAIKAKKgCKgCCgCikAREFBiWASQ9RaKgCKgCCgCioAioAgoAoqAIqAIlDICSgxLeXS0b4qAIqAIKAKKgCKgCCgCioAioAgUAQElhkUAWW+hCCgCioAioAgoAoqAIqAIKAKKQCkjoMSwlEdH+6YIKAKKgCKgCCgCioAioAgoAopAERBQYlgEkPUWioAioAgoAoqAIqAIKAKKgCKgCJQyAkoMS3l0tG+KgCKgCCgCioAioAgoAoqAIqAIFAEBJYZFAFlvoQgoAoqAIqAIKAKKgCKgCCgCikApI6DEsJRHR/umCCgCioAioAgoAoqAIqAIKAKKQBEQUGJYBJD1FoqAIqAIKAKKgCKgCCgCioAioAiUMgJKDEt5dLRvioAioAgoAoqAIqAIKAKKgCKgCBQBASWGRQBZb6EIKAKKgCKgCCgCioAioAgoAopAKSOgxLCUR0f7pggoAoqAIqAIKAKKgCKgCCgCikAREFBiWASQ9RaKgCKgCCgCioAioAgoAoqAIqAIlDICSgxLeXS0b4qAIqAIKAKKgCKgCCgCioAioAgUAQElhkUAWW+hCCgCioAioAgoAoqAIqAIKAKKQCkjoMSwlEdH+6YIKAKKgCKgCCgCioAioAgoAopAERBQYlgEkPUWioAioAgoAoqAIqAIKAKKgCKgCJQyAkoMS3l0tG+KgCKgCCgCioAioAgoAoqAIqAIFAEBJYZFAFlvoQgoAoqAIqAIKAKKgCKgCCgCikApI6DEsJRHR/umCCgCioAioAgoAoqAIqAIKAKKQBEQUGJYBJD1FoqAIqAIKAKKgCKgCCgCioAioAiUMgJKDEt5dLRvioAioAgoAoqAIqAIKAKKgCKgCBQBASWGRQBZb6EIKAKKgCKgCCgCioAioAgoAopAKSPw/wEhBJUL5DAjpgAAAABJRU5ErkJggg==",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([hp_output, demand_and_to_storage], axis=1).resample('H').sum().iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Financials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def collect_cashflows(c, s):\n",
    "    s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n",
    "    s.storage_case_PCM.data['nomination_MWh'] = s.hpcase.data['hp_input_MWh']\n",
    "    s.storage_case_PCM.generate_electr_market_results(nom_col='nomination_MWh', real_col='hp_input_MWh')\n",
    "\n",
    "    \n",
    "    for case in [s.baseline]: #, s.optcase1, s.afrr_case]:\n",
    "        case.add_gas_costs(gasvolumes_col='gb_input_MWh')\n",
    "        case.add_co2_costs(volume_cols='gb_input_MWh', fuel='gas')\n",
    "        case.add_eb_ode(commodity='gas', tax_bracket=c.tax_bracket_g)\n",
    "        \n",
    "    for case in [s.hpcase, s.storage_case_PCM]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
    "        \n",
    "        case.add_eb_ode(commodity='electricity', tax_bracket=c.tax_bracket_e, cons_col='hp_input_MWh')\n",
    "        case.add_grid_costs(\n",
    "            power_MW_col='hp_input_MW',\n",
    "            grid_operator=c.grid_operator,\n",
    "            year=2023, \n",
    "            connection_type=c.connection_type\n",
    "        )\n",
    "        \n",
    "    for case in s.sde_cases:\n",
    "        case = calculate_sde_subsidy(c=c, s=s, case=case)\n",
    "        case.add_cashflow('SDE++ subsidy (€)', case.sde_results['corrected_amount'])\n",
    "    return s\n",
    "\n",
    "s = collect_cashflows(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def calculate_financials(c, s):\n",
    "    for case in s.cases:\n",
    "        case.calculate_ebitda(c.project_duration)\n",
    "\n",
    "    for case in [s.hpcase, s.storage_case_PCM]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
    "        case.calculate_business_case(\n",
    "            project_duration=c.project_duration, \n",
    "            discount_rate=c.discount_rate, \n",
    "            baseline=s.baseline\n",
    "        )\n",
    "        \n",
    "    return s\n",
    "\n",
    "s = calculate_financials(c, s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\1396106630.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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          "width": 1.3
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         "mode": "lines",
         "name": "Total demand",
         "text": "",
         "type": "scatter",
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AgAABAgQIEFi/gIK5fmPPQIAAAQIECBAgQIAAgRACCmaImE2SAAECBAgQIECAAAEC6xdQMNdv7BkIECBAgAABAgQIECAQQkDBDBGzSRIgQIAAAQIECBAgQGD9Agrm+o09AwECBAgQIECAAAECBEIIKJghYjZJAgQIECBAgAABAgQIrF9AwVy/sWcgQIAAAQIECBAgQIBACAEFM0TMJkmAAAECBAgQIECAAIH1CyiY6zf2DAQIECBAgAABAgQIEAghoGCGiNkkCRAgQIAAAQIECBAgsH4BBXP9xp6BAAECBAgQIECAAAECIQQUzBAxmyQBAgQIECBAgAABAgTWL6Bgrt/YMxAgQIAAAQIECBAgQCCEgIIZImaTJECAAAECBAgQIECAwPoFFMz1G3sGAgQIECBAgAABAgQIhBBQMEPEbJIECBAgQIAAAQIECBBYv4CCuX5jz0CAAAECBAgQIECAAIEQAgpmiJhNkgABAgQIECBAgAABAusXUDDXb+wZCBAgQIAAAQIECBAgEEJAwQwRs0kSIECAAAECBAgQIEBg/QIK5vqNPQMBAgQIECBAgAABAgRCCCiYIWI2SQIECBAgQIAAAQIECKxfQMFcv7FnIECAAAECBAgQIECAQAgBBTNEzCZJgAABAgQIECBAgACB9QsomOs39gwECBAgQIAAAQIECBAIIaBghojZJAkQIECAAAECBAgQILB+AQVz/caegQABAgQIECBAgAABAiEEFMwQMZskAQIECBAgQIAAAQIE1i+gYK7f2DMQIECAAAECBAgQIEAghICCGSJmkyRAgAABAgQIECBAgMD6BRTMBcYnPnZHet0b3777qGdddWV6xfVH0vmHDq0/Hc9AgAABAgQIECBAgACBCQkomAvCuvW24+myxz8uXfXMK9MDJ0+mN998ND32MY9JRw5fN6GYnSoBAgQIECBAgAABAgTWL6BgrmjcFM4TH/+EXcwV3TycAAECBAgQIECAAIHtF1AwV8z46LFbdo6wg7kinIcTIECAAAECBAgQILD1AgrmChE3n8c89qvvSzfdeEO65OKLVjjSQwkQIECAAAECBAgQILD9Agrmkhk35fIX3nEsvfpVL0tXXP6kJY/ysG0RuO/LD6SDjziYzj/v4LZMyTyWFLj3/pPpvAP706FDj1jyCA/bFoH7vnwyHTy4P50v+22JdOV53P+VU+lMSulRjzxv5WMdMG2B+x84nc6c+Wp61CPd1HHaSTr7TQgomEuoK5dLIG35QxTMLQ94zvQUzLjZK5hxs29nrmDGXQMKZtzszbxcQMFcYOiy2PJFtg0jKJjbkOKwOSiYw9y24SgFcxtSLJuDglnmN+WjFcwpp+fcNy2gYC5IoLmpzweO337Woy688NEuld30yt3j51cw9xh8RE+nYI4ojD0+FQVzj8FH+HQK5ghD2aNTUjD3CNrTbKWAgrmVsZpUbQEFs7bodMZTMKeTVe0zVTBri05vPAVzepnVOmMFs5akcSIKKJgRUzfnlQUUzJXJtuYABXNrolx5IgrmymRbd4CCuXWRLj0hBXNpKg8kcI6AgmlREFhCQMFcAmlLH6JgbmmwS0xLwVwCacsfomBuecBzpqdgxs3ezMsFFMxyQyMEEFAwA4TcM0UFM272Cmbc7NuZK5hx14CCGTd7My8XUDDLDY0QQEDBDBCyghk35J6ZK5iWhIIZdw0omHGzN/NyAQWz3NAIAQQUzAAhK5hxQ1YwZd8joGDGXRoKZtzszbxcQMEsNzRCAAEFM0DICmbckBVM2SuY1kAmoGBaEgSGCyiYw+0cGUhAwQwUdjZVn8GMm71LZONm387cDmbcNaBgxs3ezMsFFMxyQyMEEFAwA4RsBzNuyHYwZW8H0xqwg2kNEKgmoGBWozTQNgsomNuc7vy52cGMm70dzLjZ28GUvR1Ma4DAcAEFc7idIwMJKJiBws6mqmDGzV7BjJu9gil7BdMaIDBcQMEcbufIQAIKZqCwFcy4YWczVzAtBZ/BjLsGFMy42Zt5uYCCWW5ohAACCmaAkHumaAczbvYKZtzs7WDKXsG0BggMF1Awh9s5MpCAghkobDuYccO2gyn7TMAOZtwloWDGzd7MywUUzHJDIwQQUDADhGwHM27IPTO3g2lJKJhx14CCGTd7My8XUDDLDY0QQEDBDBCyghk3ZAVT9j0CCmbcpaFgxs3ezMsFFMxyQyMEEFAwA4SsYMYNWcGUvYJpDWQCCqYlQWC4gII53M6RgQQUzEBhZ1N1k5+42btENm727cztYMZdAwpm3OzNvFxAwSw3NEIAAQUzQMh2MOOGbAdT9nYwrQE7mNYAgWoCCmY1SgNts4CCuc3pzp+bHcy42dvBjJu9HUzZ28G0BggMF1Awh9s5MpCAghko7GyqCmbc7BXMuNkrmLJXMK0BAsMFFMzhdo4MJKBgBgpbwYwbdjZzBdNS8BnMuGtAwYybvZmXCyiY5YZGCCCgYAYIuWeKdjDjZq9gxs3eDqbsFUxrgMBwAQVzuJ0jAwkomIHCtoMZN2w7mLLPBOxgxl0SCmbc7M28XEDBLDc0QgABBTNAyHYw44bcM3M7mJaEghl3DSiYcbM383IBBbPc0AgBBBTMACErmHFDVjBl3yOgYMZdGgpm3OzNvFxAwSw3NEIAAQUzQMgKZtyQFUzZK5jWQCagYFoSBIYLKJjD7RwZSEDBDBR2NlU3+YmbvUtk42bfztwOZtw1oGDGzd7MywUUzHJDIwQQUDADhGwHM27IdjBlbwfTGrCDaQ0QqCagYFajNNA2CyiY25zu/LnZwYybvR3MuNnbwZS9HUxrgMBwAQVzuJ0jAwkomIHCzqaqYMbNXsGMm72CKXsF0xogMFxAwRxu58hAAgpmoLAVzLhhZzNXMC0Fn8GMuwYUzLjZm3m5gIJZbmiEAAIKZoCQe6ZoBzNu9gpm3OztYMpewbQGCAwXUDCH2zkykICCGShsO5hxw7aDKftMwA5m3CWhYMbN3szLBRTMckMjBBBQMAOEbAczbsg9M7eDaUkomHHXgIIZN3szLxdQMMsNjRBAQMEMELKCGTdkBVP2PQIKZtyloWDGzd7MywUUzHJDIwQQUDADhKxgxg1ZwZS9gmkNZAIKpiVBYLiAgjnczpGBBBTMQGFnU3WTn7jZu0Q2bvbtzO1gxl0DCmbc7M28XEDBLDc0QgABBTNAyHYw44ZsB1P2djCtATuY1gCBagIKZjVKA22zgIK5zenOn5sdzLjZ28GMm70dTNnbwbQGCAwXUDCH2zkykICCGSjsbKoKZtzsFcy42SuYslcwrQECwwUUzOF2jgwkoGAGClvBjBt2NnMF01LwGcy4a0DBjJu9mZcLKJjlhkYIIKBgBgi5Z4p2MONmr2DGzd4OpuwVTGuAwHABBXO4nSMDCSiYgcK2gxk3bDuYss8E7GDGXRIKZtzszbxcQMEsNzRCAAEFM0DIdjDjhtwzczuYloSCGXcNKJhxszfzcgEFs9zQCAEEFMwAISuYcUNWMGXfI6Bgxl0aCmbc7M28XCBswbz7ni+ln3/bO9KPv+gF6YrLn9Qr+clPfTq99g1vTffee9/uYy57wqXpphtvSJdcfFF5AkaYhICCOYmY1nKSPoO5FtZJDGoHcxIxrfUkFcy18o56cAVz1PE4uZELhCuYD5w8md5889H00RN3pAsvfHR69atetrBgvuXmo+nl1x+Z+7iR5+z0CgUUzELACR+uYE44vMJTVzALAbfgcAVzC0IcOAUFcyCcwwiklMIVzDb1VXYwFUxfKwpm3DWgYMbNXsGMm307cwUz7hpQMONmb+blAgrmipfIujy2fNFNcQQFc4qp1TlnBbOO4xRHUTCnmFrdc1Yw63pOaTQFc0ppOdexCSiYCwpmHtjRY7ekL951V3rF9UfS+YcOpfsfODW2TJ3PGgROnXow7T+wPx08sH8NoxtyzAKnTj+UDuzblw4clP2Yc1rHuZ069VDav39fOij7dfBOYszTpx/aOc9HPOLAJM7XSdYTOP3gV1M6c0b29UgnNdIF5583qfMd28kqmCsWzOamP+9813vSK1/6kp2b/DTvbvqz/QKnTj+Y9u9XMLc/6XNnePL0g+lAUzIOeIEZLf+Hv+5lHy337nxPP/hQOpNSOu+gr/9o6+Dh7M+k8w4ejDZ1800pXfBIBbNkISiYhQWzBN+x0xFwiex0sqp9pi6RrS06nfFcIjudrNZ1pi6RXZfs+Md1iez4M3KG4xVQMLOCeettx9NtH/zw7q8h+dDtH0lPueLJu3eQbS6Rbf4cOXzdeFN1ZtUFFMzqpJMZUMGcTFTVT1TBrE46uQEVzMlFVu2EFcxqlAYKKBCuYHZ/TUmb97OuunL3M5V5wTzxsTvS69749t2l0X1swPUSdsoKZtjok4IZN3sFM2727cwVzLhrQMGMm72ZlwuEK5jlZEaIKKBgRkz94TkrmHGzVzDjZq9gyl7BtAYIDBdQMIfbOTKQgIIZKOxsqgpm3OwVzLjZK5iyVzCtAQLDBRTM4XaODCSgYAYKW8GMG3Y2cwXTUnCJbNw1oGDGzd7MywUUzHJDIwQQUDADhNwzRTuYcbNXMONmbwdT9gqmNUBguICCOdzOkYEEFMxAYdvBjBu2HUzZZwJ2MOMuCQUzbvZmXi6gYJYbGiGAgIIZIGQ7mHFD7pm5HUxLQsGMuwYUzLjZm3m5gIJZbmiEAAIKZoCQFcy4ISuYsu8RUDDjLg0FM272Zl4uoGCWGxohgICCGSBkBTNuyAqm7BVMayATUDAtCQLDBRTM4XaODCSgYAYKO5uqm/zEzd4lsnGzb2duBzPuGlAw42Zv5uUCCma5oRECCCiYAUK2gxk3ZDuYsreDaQ3YwbQGCFQTUDCrURpomwUUzG1Od/7c7GDGzd4OZtzs7WDK3g6mNUBguICCOdzOkYEEFMxAYWdTVTDjZq9gxs1ewZS9gmkNEBguoGAOt3NkIAEFM1DYCmbcsLOZK5iWgs9gxl0DCmbc7M28XEDBLDc0QgABBTNAyD1TtIMZN3sFM272djBlr2BaAwSGCyiYw+0cGUhAwQwUth3MuGHbwZR9JmAHM+6SUDDjZm/m5QIKZrmhEQIIKJgBQraDGTfknpnbwbQkFMy4a0DBjJu9mZcLKJjlhkYIIKBgBghZwYwbsoIp+x4BBTPu0lAw42Zv5uUCCma5oRECCCiYAUJWMOOGrGDKXsG0BjIBBdOSIDBcQMEcbufIQAIKZqCws6m6yU/c7F0iGzf7duZ2MOOuAQUzbvZmXi6gYJYbGiGAgIIZIGQ7mHFDtoMpezuY1oAdTGuAQDUBBbMapYG2WUDB3OZ058/NDmbc7O1gxs3eDqbs7WBaAwSGCyiYw+0cGUhAwQwUdjZVBTNu9gpm3OwVTNkrmNYAgeECCuZwO0cGElAwA4WtYMYNO5u5gmkp+Axm3DWgYMbN3szLBRTMckMjBBBQMAOE3DNFO5hxs1cw42ZvB1P2CqY1QGC4gII53M6RgQQUzEBh28GMG7YdTNlnAnYw4y4JBTNu9mZeLqBglhsaIYCAghkgZDuYcUPumbkdTEtCwYy7BhTMuNmbebmAglluaIQAAgpmgJAVzLghK5iy7xFQMOMuDQUzbvZmXi6gYJYbGiGAgIIZIGQFM27ICqbsFUxrIBNQMC0JAsMFFMzhdo4MJKBgBgo7m6qb/MTN3iWycbNvZ24HM+4aUDDjZm/m5QIKZrmhEQIIKJgBQraDGTdkO5iyt4NpDdjBtAYIVBNQMKtRGmibBRTMbU53/tzsYMbN3g5m3OztYMreDqY1QGC4gII53M6RgQQUzEBhZ1NVMONmr2DGzV7BlL2CaQ0QGC6gYA63c2QgAQUzUNgKZtyws5krmJaCz2DGXQMKZtzszbxcQMEsNzRCAAEFM0DIPVO0gxk3ewUzbvZ2MGWvYFoDBIYLKJjD7RwZSEDBDBS2Hcy4YdvBlH0mYAcz7pJQMONmb+blAgpmuaERAggomAFCtoNSplbpAAAgAElEQVQZN+SemdvBtCQUzLhrQMGMm72ZlwsomOWGRgggoGAGCFnBjBuygin7HgEFM+7SUDDjZm/m5QIKZrmhEQIIKJgBQlYw44asYMpewbQGMgEF05IgMFxAwRxu58hAAgpmoLCzqbrJT9zsXSIbN/t25nYw464BBTNu9mZeLrCRgnn3PV9Kr3n9m3bO/qYbb0iXXHxR+UyMQGCNAgrmGnFHPrSCOfKA1nh6CuYacScytII5kaDWcJoK5hpQDRlGYCMFs9E98bE70uve+PazoC97wqUKZ5ilN62JKpjTyqvm2SqYNTWnNZaCOa281nG2CuY6VKcxpoI5jZyc5TgFNlYwc452V/POz35+958UznEumohnpWBGTP3hOSuYcbNXMONm385cwYy7BhTMuNmbebnAaApmdyq33nY8HXv3e5OCWR6wEeoIKJh1HKc4ioI5xdTqnLOCWcdxyqMomFNOr+zcFcwyP0fHFhhFwTx67Jb0geO37ybxrKuuTK+4/kg6/9Ch2OmY/WgEFMzRRLHnJ6Jg7jn5aJ5QwRxNFBs7EQVzY/Qbf2IFc+MROIEJC2ykYOaXwyqUE15BQU5dwQwS9IxpKphxs1cw42bfzlzBjLsGFMy42Zt5ucAoCuZzr352OnL4uvLZGIHAmgQUzDXBTmBYBXMCIa3pFBXMNcFOaFgFc0JhVT5VBbMyqOFCCWykYObC+R1lx7ij+clPfTq9813vSa986Uv8WpVQXyIPT1bBDBj630xZwYybvYIZN3s7mLJXMK0BAsMFRlEw+wrnGG7y072cdwznMzxqR5YIKJgletM+VsGcdn4lZ69gluhtx7F2MLcjxyGzUDCHqDmGwMMCoymYs34v5ph2Mu1gxv6SUTDj5q9gxs1ewYybvR1M2SuY1gCB4QIbK5j5nWObKYypUOakCubwRbYNRyqY25DisDkomMPctuEoBXMbUiybgx3MMr8pH61gTjk9575pgY0UzPay02byN914wyQ+09hXMP/1h35p0xl6/j0Q+OpXz6R9+1La1/wff0IJyD5U3GdNVvZxs29nfuarZ3b+5779vvdHWw1nzpxJ6Yzso+Xezvdnv/PFUadeZd4bKZhVznyPB+ktmL+vYO5xFBt5uq+e+ZuCmbzI2EgAG3xS2W8Qf8NPLfsNBzCCp29KRlMx93tzcQRp7O0pyH5vvcf2bD/7XQpmSSYK5pJ6LpFdEmpLH+YS2S0NdolpuUR2CaQtfYhLZLc02BWm5RLZFbC27KEukd2yQE1nTwU2UjC7d2adN9sx3bVVwdzTdTm6J1MwRxfJnp2Qgrln1KN7IgVzdJHs+QkpmHtOPponVDBHE4UTmaDARgvmvfd9Ob36VS9LV1z+pNHSzSrDz7362enI4etGe85OrL6AglnfdCojKphTSar+eSqY9U2nNqKCObXE6p2vglnP0kjxBDZSMFvmW287no69+72jvntsvCVhxrMEFMy460LBjJu9ghk3+3bmCmbcNaBgxs3ezMsFNlowm9N/4OTJ9Oabj6aPnrgjHX7hD6Vrr7m6fFZGIFBZQMGsDDqh4RTMCYVV+VQVzMqgExxOwZxgaJVOWcGsBGmYkAIbL5itevMZx9e+4a07/zn2y2ZDrpTgk1Yw4y4ABTNu9gpm3OztYMpewbQGCAwXGE3BPPGxO9Lr3vj2nZn8zE//ZLrqmVcOn5UjCVQWUDArg05oOAVzQmFVPlUFszLoBIezgznB0CqdsoJZCdIwIQU2XjC7N9Fx85yQa3ASk1YwJxHTWk5SwVwL6yQGVTAnEdNaT1LBXCvvqAdXMEcdj5MbucBGC+bRY7ekDxy/3U1+Rr5InF5KCmbcVaBgxs1ewYybfTtzBTPuGlAw42Zv5uUCGymY7a7lFH5NSTmxEbZBQMHchhSHzUHBHOa2DUcpmNuQYtkcFMwyvykfrWBOOT3nvmmBjRbMOz/7+bnzv+wJl6abbrwhXXLxRZt28vzBBRTMuAtAwYybvYIZN3s7mLJXMK0BAsMFNlIwh5+uIwlsRkDB3Iz7GJ5VwRxDCps5BwVzM+5jelY7mGNKY2/PRcHcW2/Ptl0CCuZ25Wk2axJQMNcEO4FhFcwJhLSmU1Qw1wQ7oWEVzAmFVflUFczKoIYLJaBghorbZIcKKJhD5aZ/nII5/QyHzkDBHCq3PccpmNuT5aozUTBXFfN4An8roGBaDQSWEFAwl0Da0ocomFsa7BLTUjCXQNryhyiYWx7wnOkpmHGzN/NyAQWz3NAIAQQUzAAh90xRwYybvYIZN/t25gpm3DWgYMbN3szLBRTMckMjBBBQMAOErGDGDbln5gqmJaFgxl0DCmbc7M28XEDBLDc0QgABBTNAyApm3JAVTNn3CCiYcZeGghk3ezMvF1Awyw2NEEBAwQwQsoIZN2QFU/YKpjWQCSiYlgSB4QIK5nA7RwYSUDADhZ1N1Wcw42bvEtm42bczt4MZdw0omHGzN/NyAQWz3NAIAQQUzAAh28GMG7IdTNnbwbQG7GBaAwSqCSiY1SgNtM0CCuY2pzt/bnYw42ZvBzNu9nYwZW8H0xogMFxAwRxu58hAAgpmoLCzqSqYcbNXMONmr2DKXsG0BggMF1Awh9s5MpCAghkobAUzbtjZzBVMS8FnMOOuAQUzbvZmXi6gYJYbGiGAgIIZIOSeKdrBjJu9ghk3ezuYslcwrQECwwUUzOF2jgwkoGAGCtsOZtyw7WDKPhOwgxl3SSiYcbM383IBBbPc0AgBBBTMACHbwYwbcs/M7WBaEgpm3DWgYMbN3szLBRTMckMjBBBQMAOErGDGDVnBlH2PgIIZd2komHGzN/NyAQWz3NAIAQQUzAAhK5hxQ1YwZa9gWgOZgIJpSRAYLqBgDrdzZCABBTNQ2NlU3eQnbvYukY2bfTtzO5hx14CCGTd7My8XUDDLDY0QQEDBDBCyHcy4IdvBlL0dTGvADqY1QKCagIJZjdJA2yygYG5zuvPnZgczbvZ2MONmbwdT9nYwrQECwwUUzOF2jgwkoGAGCjubqoIZN3sFM272CqbsFUxrgMBwAQVzuJ0jAwkomIHCVjDjhp3NXMG0FHwGM+4aUDDjZm/m5QIKZrmhEQIIKJgBQu6Zoh3MuNkrmHGzt4MpewXTGiAwXEDBHG7nyEACCmagsO1gxg3bDqbsMwE7mHGXhIIZN3szLxdQMMsNjRBAQMEMELIdzLgh98zcDqYloWDGXQMKZtzszbxcQMEsNzRCAAEFM0DICmbckBVM2fcIKJhxl4aCGTd7My8XUDDLDY0QQEDBDBCyghk3ZAVT9gqmNZAJKJiWBIHhAgrmcDtHBhJQMAOFnU3VTX7iZu8S2bjZtzO3gxl3DSiYcbM383IBBbPc0AgBBBTMACHbwYwbsh1M2dvBtAbsYFoDBKoJKJjVKA20zQIK5janO39udjDjZm8HM272djBlbwfTGiAwXEDBHG7nyEACCmagsLOpKphxs1cw42avYMpewbQGCAwXUDCH2zkykICCGShsBTNu2NnMFUxLwWcw464BBTNu9mZeLqBglhsaIYCAghkg5J4p2sGMm72CGTd7O5iyVzCtAQLDBRTM4XaODCSgYAYK2w5m3LDtYMo+E7CDGXdJKJhxszfzcgEFs9zQCAEEFMwAIdvBjBtyz8ztYFoSCmbcNaBgxs3ezMsFFMxyQyMEEFAwA4SsYMYNWcGUfY+Aghl3aSiYcbM383IBBbPc0AgBBBTMACErmHFDVjBlr2BaA5mAgmlJEBguELJgnvjYHel1b3z7jtplT7g03XTjDemSiy+aqfjJT306vfYNb0333nvf7r8vOmZ4HI4cq4CCOdZk1n9ebvKzfuOxPoNLZMeazN6dlx3MvbMe2zMpmGNLxPlMSSBcwWwK41tuPppefv2RdMXlT0q33nY8nfj4J9Irrj+Szj906Jzs8sdPKVznWk9AwaxnObWRFMypJVbvfBXMepZTHUnBnGpy5eetYJYbGiGuQLiC2RTKz9z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    "fig_demands_over_time = s.demand['Total demand'].resample('H').mean().iplot(\n",
    "    title='Smurfit Kappa: Heat demand by Hour in MW', \n",
    "    yTitle='MW', \n",
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      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\1493830805.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
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        },
        "title": {
         "font": {
          "color": "#4D5663"
         },
         "text": "Smurfit Kappa: Source and Sink temperatures"
        },
        "width": 800,
        "xaxis": {
         "autorange": true,
         "gridcolor": "#E1E5ED",
         "range": [
          -0.5,
          1.5
         ],
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": ""
         },
         "type": "category",
         "zerolinecolor": "#E1E5ED"
        },
        "yaxis": {
         "autorange": true,
         "gridcolor": "#E1E5ED",
         "range": [
          65.88888888888889,
          148.11111111111111
         ],
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Temperature in degrees C"
         },
         "type": "linear",
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "image/png": 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",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demands_fig = s.demand[['Tsource (VDG)', 'Tsink (VDG)']].resample('H').mean().iplot(\n",
    "    kind='box',\n",
    "    title='Smurfit Kappa: Source and Sink temperatures',\n",
    "    color=recoygreen,\n",
    "    yTitle='Temperature in degrees C',\n",
    "    legend=False,\n",
    "    asFigure=True,\n",
    "    dimensions=(800, 400)\n",
    ")\n",
    "demands_fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh', 'nomination_MWh', 'Prod. vol.', 'Cons. vol.',\n",
       "       'Imb. vol.', 'Day-Ahead Result', 'POS Result', 'NEG Result',\n",
       "       'Imbalance Result', 'Combined Result'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp_comparison = pd.concat([s.storage_case_PCM.data[['tsource_ndg', 'tsource_vdg']], s.storage_case_PCM.data[['Tsource (NDG)', 'Tsource (VDG)']]], axis=1)\n",
    "# temp_comparison.columns = ['Tsource (NDG) -after', 'Tsource (VDG) - after', 'Tsource (NDG) - before', 'Tsource (VDG) - before']\n",
    "# temp_comparison.resample('15T').mean().iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-01 00:00:00+0100', tz='Europe/Amsterdam')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['tsource_vdg'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " 0,\n",
       " 29.635,\n",
       " 0,\n",
       " 0,\n",
       " HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0),\n",
       " Heatpump(name='Heatpump VDG', max_thermal_power=1.5, cop_curve=2.5, min_th_power=0.0))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case = s.storage_case_PCM\n",
    "hp_vdg = case.assets['Heatpump VDG']\n",
    "ws = case.assets['HotWaterStorage']\n",
    "ws.storage_level = 47.083 + 25\n",
    "pos = 35.650\n",
    "neg = 43.850\n",
    "dam = 68.400\n",
    "demand_vdg = 0\n",
    "tsource_vdg = 29.635\n",
    "tsink_vdg = 134.840\n",
    "\n",
    "hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tsource_VDG_before = s.hpcase.data['Tsource (VDG)'].round(2)\n",
    "Tsource_VDG_after = s.storage_case_PCM.data['Tsource (VDG)'].round(2)\n",
    "Tsource_VDG_before.equals(Tsource_VDG_after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "test_hp = Heatpump(\n",
    "    name='Heatpump for Testing',\n",
    "    max_th_power=1,\n",
    "    min_th_power=0.3,\n",
    "    cop_curve=2.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "def create_load_trace(gas_price, Tsnk, Tsrc, name):\n",
    "    loads = []\n",
    "    eprices = list(range(1000))\n",
    "    for eprice in eprices:\n",
    "        _, load = test_hp.set_opt_load(\n",
    "            electricity_cost=eprice + s.eb_ode_e,\n",
    "            alt_heat_price=gas_price / 0.9,\n",
    "            demand=1, \n",
    "            Tsink=Tsnk,\n",
    "            Tsource=Tsrc\n",
    "        )\n",
    "        loads.append(load)\n",
    "    trace = go.Scatter(x=eprices, y=loads, name=name)\n",
    "    return trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "date = s.baseline.data.index[0].strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_steamboiler = s.baseline.data[['Total demand', 'output_MW_th']].abs().loc[date, :].iplot(\n",
    "    subplots=True, \n",
    "    title=f'Steamboiler only case on {date}',\n",
    "    subplot_titles=['Total demand', 'Steam boiler output (MW)'],\n",
    "    legend=False,\n",
    "    dimensions=(800, 400),\n",
    "    colors=[recoydarkblue, recoygreen], \n",
    "    asFigure=True,\n",
    "    shape = (2, 1)\n",
    ")\n",
    "fig_steamboiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "mode": "lines",
         "name": "Total demand",
         "text": "",
         "type": "scatter",
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   "source": [
    "fig_heatpump = s.hpcase.data[['Total demand', 'hp_output_MW']].abs().loc[date, :].iplot(\n",
    "    subplots=True, \n",
    "    title=f'Heatpump only case on {date}', \n",
    "    subplot_titles=['Total demand', 'Heatpump output (MW)'],\n",
    "    legend=False,\n",
    "    dimensions=(800, 400),\n",
    "    colors=[recoydarkblue, recoygreen], \n",
    "    asFigure=True,\n",
    "    shape = (2, 1)\n",
    ")\n",
    "fig_heatpump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Heatpump only</th>\n",
       "      <th>Heatpump + Storage_PCM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total net volume (MWh)</th>\n",
       "      <td>-1,051</td>\n",
       "      <td>-2,515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total exported to grid (MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total consumed from grid (MWh)</th>\n",
       "      <td>1,051</td>\n",
       "      <td>2,515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total nominated volume (MWh)</th>\n",
       "      <td>-1,051</td>\n",
       "      <td>-1,051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Absolute imbalance volume (MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>1,464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean selling price (€/MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean buying price (€/MWh)</th>\n",
       "      <td>96</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total day-ahead result (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-100,723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total POS result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total NEG result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-170,525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total imbalance result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-170,525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total combined result (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-271,248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Heatpump only Heatpump + Storage_PCM\n",
       "Total net volume (MWh)                 -1,051                 -2,515\n",
       "Total exported to grid (MWh)                -                      -\n",
       "Total consumed from grid (MWh)          1,051                  2,515\n",
       "Total nominated volume (MWh)           -1,051                 -1,051\n",
       "Absolute imbalance volume (MWh)             -                  1,464\n",
       "Mean selling price (€/MWh)                  -                      -\n",
       "Mean buying price (€/MWh)                  96                    108\n",
       "Total day-ahead result (€)           -100,723               -100,723\n",
       "Total POS result (€)                        -                      -\n",
       "Total NEG result (€)                        -               -170,525\n",
       "Total imbalance result (€)                  -               -170,525\n",
       "Total combined result (€)            -100,723               -271,248"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = ComparisonReport(\n",
    "    cases=s.optcases, \n",
    "    kind='electr_market_results',\n",
    ")\n",
    "report.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Heatpump only</th>\n",
       "      <th>Heatpump + Storage_PCM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gasboiler OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas consumption costs (€)</th>\n",
       "      <td>393,966</td>\n",
       "      <td>393,966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2 emission costs (€)</th>\n",
       "      <td>140,949</td>\n",
       "      <td>140,949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas taxes (€)</th>\n",
       "      <td>33,689</td>\n",
       "      <td>33,689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heatpump VDG OPEX (€)</th>\n",
       "      <td>-3,000</td>\n",
       "      <td>-3,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Result on electricity market (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-271,248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity taxes (€)</th>\n",
       "      <td>-999</td>\n",
       "      <td>-2,390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grid transport costs (€)</th>\n",
       "      <td>-11,391</td>\n",
       "      <td>-33,504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HotWaterStorage OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-1,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td>452,492</td>\n",
       "      <td>257,263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciation (€)</th>\n",
       "      <td>-12,000</td>\n",
       "      <td>-14,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA + depr (€)</th>\n",
       "      <td>440,492</td>\n",
       "      <td>242,863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Heatpump only Heatpump + Storage_PCM\n",
       "Gasboiler OPEX (€)                           -                      -\n",
       "Gas consumption costs (€)              393,966                393,966\n",
       "CO2 emission costs (€)                 140,949                140,949\n",
       "Gas taxes (€)                           33,689                 33,689\n",
       "Heatpump VDG OPEX (€)                   -3,000                 -3,000\n",
       "Result on electricity market (€)      -100,723               -271,248\n",
       "Electricity taxes (€)                     -999                 -2,390\n",
       "Grid transport costs (€)               -11,391                -33,504\n",
       "HotWaterStorage OPEX (€)                     -                 -1,200\n",
       "EBITDA (€)                             452,492                257,263\n",
       "Depreciation (€)                       -12,000                -14,400\n",
       "EBITDA + depr (€)                      440,492                242,863"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc', baseline=s.baseline, comparison='relative')\n",
    "casereport.show(presentation_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\styling.py:42: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year 0</th>\n",
       "      <th>Year 1</th>\n",
       "      <th>Year 2</th>\n",
       "      <th>Year 3</th>\n",
       "      <th>Year 4</th>\n",
       "      <th>Year 5</th>\n",
       "      <th>Year 6</th>\n",
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       "      <th>Year 9</th>\n",
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       "      <th>Year 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CAPEX (€)</th>\n",
       "      <td>-301,500</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
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       "      <th>Regular Earnings (€)</th>\n",
       "      <td></td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
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       "    <tr>\n",
       "      <th>Irregular Cashflows (€)</th>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td></td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
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       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciations (€) -/-</th>\n",
       "      <td></td>\n",
       "      <td>-12,000</td>\n",
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       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBIT (€)</th>\n",
       "      <td></td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income tax (Vpb.) (€)</th>\n",
       "      <td></td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOPLAT (€)</th>\n",
       "      <td></td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciations (€) +/+</th>\n",
       "      <td></td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free Cash Flow (€)</th>\n",
       "      <td>-301,500</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simple Payback Period</th>\n",
       "      <td>0.8478165594669402 years</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>IRR (%)</th>\n",
       "      <td>118%</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>WACC (%)</th>\n",
       "      <td>10%</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>NPV of explicit period (€)</th>\n",
       "      <td>1,928,710</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>Discounted residual value (€)</th>\n",
       "      <td>50,184</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>NPV (€)</th>\n",
       "      <td>1,928,710</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Year 0   Year 1   Year 2  \\\n",
       "CAPEX (€)                                      -301,500                     \n",
       "Regular Earnings (€)                                     452,493  452,493   \n",
       "Irregular Cashflows (€)                                        -        -   \n",
       "EBITDA (€)                                               452,493  452,493   \n",
       "Depreciations (€) -/-                                    -12,000  -12,000   \n",
       "EBIT (€)                                                 440,493  440,493   \n",
       "Income tax (Vpb.) (€)                                    -96,873  -96,873   \n",
       "NOPLAT (€)                                               343,619  343,619   \n",
       "Depreciations (€) +/+                                     12,000   12,000   \n",
       "Free Cash Flow (€)                             -301,500  355,619  355,619   \n",
       "Simple Payback Period          0.8478165594669402 years                     \n",
       "IRR (%)                                            118%                     \n",
       "WACC (%)                                            10%                     \n",
       "NPV of explicit period (€)                    1,928,710                     \n",
       "Discounted residual value (€)                    50,184                     \n",
       "NPV (€)                                       1,928,710                     \n",
       "\n",
       "                                Year 3   Year 4   Year 5   Year 6   Year 7  \\\n",
       "CAPEX (€)                                                                    \n",
       "Regular Earnings (€)           452,493  452,493  452,493  452,493  452,493   \n",
       "Irregular Cashflows (€)              -        -        -        -        -   \n",
       "EBITDA (€)                     452,493  452,493  452,493  452,493  452,493   \n",
       "Depreciations (€) -/-          -12,000  -12,000  -12,000  -12,000  -12,000   \n",
       "EBIT (€)                       440,493  440,493  440,493  440,493  440,493   \n",
       "Income tax (Vpb.) (€)          -96,873  -96,873  -96,873  -96,873  -96,873   \n",
       "NOPLAT (€)                     343,619  343,619  343,619  343,619  343,619   \n",
       "Depreciations (€) +/+           12,000   12,000   12,000   12,000   12,000   \n",
       "Free Cash Flow (€)             355,619  355,619  355,619  355,619  355,619   \n",
       "Simple Payback Period                                                        \n",
       "IRR (%)                                                                      \n",
       "WACC (%)                                                                     \n",
       "NPV of explicit period (€)                                                   \n",
       "Discounted residual value (€)                                                \n",
       "NPV (€)                                                                      \n",
       "\n",
       "                                Year 8   Year 9  Year 10  Year 11  Year 12  \n",
       "CAPEX (€)                                                                   \n",
       "Regular Earnings (€)           452,493  452,493  452,493  452,493  452,493  \n",
       "Irregular Cashflows (€)              -        -        -        -        -  \n",
       "EBITDA (€)                     452,493  452,493  452,493  452,493  452,493  \n",
       "Depreciations (€) -/-          -12,000  -12,000  -12,000  -12,000  -12,000  \n",
       "EBIT (€)                       440,493  440,493  440,493  440,493  440,493  \n",
       "Income tax (Vpb.) (€)          -96,873  -96,873  -96,873  -96,873  -96,873  \n",
       "NOPLAT (€)                     343,619  343,619  343,619  343,619  343,619  \n",
       "Depreciations (€) +/+           12,000   12,000   12,000   12,000   12,000  \n",
       "Free Cash Flow (€)             355,619  355,619  355,619  355,619  355,619  \n",
       "Simple Payback Period                                                       \n",
       "IRR (%)                                                                     \n",
       "WACC (%)                                                                    \n",
       "NPV of explicit period (€)                                                  \n",
       "Discounted residual value (€)                                               \n",
       "NPV (€)                                                                     "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BusinessCaseReport(s.hpcase).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
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          "dash": "solid",
          "shape": "linear",
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         "mode": "lines",
         "name": "Gas prices (€/MWh)",
         "text": "",
         "type": "scatter",
         "x": [
          "2023-01-01 00:00:00+01:00",
          "2023-01-02 00:00:00+01:00",
          "2023-01-03 00:00:00+01:00",
          "2023-01-04 00:00:00+01:00",
          "2023-01-05 00:00:00+01:00",
          "2023-01-06 00:00:00+01:00",
          "2023-01-07 00:00:00+01:00",
          "2023-01-08 00:00:00+01:00",
          "2023-01-09 00:00:00+01:00",
          "2023-01-10 00:00:00+01:00",
          "2023-01-11 00:00:00+01:00",
          "2023-01-12 00:00:00+01:00",
          "2023-01-13 00:00:00+01:00",
          "2023-01-14 00:00:00+01:00",
          "2023-01-15 00:00:00+01:00",
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    }
   ],
   "source": [
    "#price chart\n",
    "from copy import deepcopy\n",
    "\n",
    "data = deepcopy(s.baseline.data)\n",
    "data = data[data.columns[:2]]\n",
    "data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n",
    "data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n",
    "\n",
    "data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices', colors=recoycolors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
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   "source": [
    "# #price chart\n",
    "# from copy import deepcopy\n",
    "\n",
    "# s.optcase1.data['DAM'].resample('D').mean().iplot(dimensions=(800, 300), title='Electricity Prices', colors=recoycolors)\n",
    "\n",
    "# _source_output = s.optcase1.data[['hp_output_MWh', 'gb_output_MWh']].resample('M').sum()\n",
    "# _total_output = _source_output.sum(axis=1)\n",
    "# _data = _source_output.divide(_total_output, axis=0).rename(\n",
    "#     columns={'hp_output_MWh':'Heat pump', 'gb_output_MWh':'Gasboiler'}\n",
    "# ) * 100\n",
    "\n",
    "# production_fig = _data.iplot(\n",
    "#     kind='bar',\n",
    "#     barmode='stack',\n",
    "#     colors=[recoydarkblue, recoygreen],\n",
    "#     title='Hybrid case: Heat production per Month by Source in % share',\n",
    "#     yTitle='Share of production in %',\n",
    "#     dimensions=(600, 400),\n",
    "#     asFigure=True\n",
    "# )\n",
    "\n",
    "# production_fig = production_fig.update_layout(legend_traceorder=\"reversed\")\n",
    "# production_fig"
   ]
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   "execution_count": 92,
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   ],
   "source": [
    "report = ComparisonReport(s.cases, kind='capex').report\n",
    "report = report[report.index.str.contains('CAPEX')].T\n",
    "capex_fig = report.iplot(\n",
    "    kind='bar', \n",
    "    barmode='relative',\n",
    "    colors=recoycolors,\n",
    "    title='CAPEX by Casestudy',\n",
    "    yTitle='CAPEX in €',\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True,\n",
    ")\n",
    "\n",
    "capex_fig = capex_fig.update_layout(legend_traceorder=\"reversed\")\n",
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  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": [
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   },
   "outputs": [
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       "                            \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cashflow_report = ComparisonReport(cases = s.cases, kind='cashflows')\n",
    "\n",
    "fig = cashflow_report.report.T.iplot(\n",
    "    kind='bar',\n",
    "    barmode='relative',\n",
    "    colors=recoycolors,\n",
    "    title='OPEX breakdown',\n",
    "    asFigure=True,\n",
    "    yTitle='€',\n",
    "    dimensions=(800, 600)\n",
    ")\n",
    "\n",
    "ebitda_report = ComparisonReport(cases=s.cases, kind='ebitda_calc')\n",
    "scat = go.Scatter(\n",
    "    mode='markers',\n",
    "    y=ebitda_report.report.loc['EBITDA (€)', :].values, \n",
    "    x=ebitda_report.report.columns, \n",
    "    line=go.scatter.Line(color=recoydarkgrey),\n",
    "    marker=dict(\n",
    "        color=recoydarkgrey,\n",
    "        size=20,\n",
    "        line=dict(\n",
    "            color=recoydarkgrey,\n",
    "            width=3\n",
    "        ),\n",
    "        symbol='line-ew'\n",
    "    ),\n",
    "    name='EBITDA (€)'\n",
    ")\n",
    "fig.add_trace(scat)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ebitda_report.show(comparison='relative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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",
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    "_series = SingleFigureComparison(s.cases, 'ebitda', label='EBITDA').report\n",
    "ebitda_graph = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='Yearly EBITDA by Casestudy in €',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400), \n",
    "    yTitle='EBITDA in €',\n",
    "    asFigure=True,\n",
    "    yrange=[_series.min() * 1.2, max(_series.max() * 2, 0)]\n",
    ")\n",
    "\n",
    "ebitda_graph.update_traces(\n",
    "    text=_series.values/1000_000, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}M €\", \n",
    ")\n",
    "\n",
    "ebitda_graph"
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  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
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    "_series = SingleFigureComparison(s.optcases, 'npv', label='NPV').report\n",
    "\n",
    "npv_graph = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='NPV by Casestudy in €',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400), \n",
    "    yTitle='NPV in €',\n",
    "    asFigure=True,\n",
    "    yrange=[0, _series.max() * 1.1]\n",
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    "\n",
    "npv_graph.update_traces(\n",
    "    text=_series.values/1000_000, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}M €\", \n",
    ")\n",
    "\n",
    "npv_graph"
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  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
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   "source": [
    "irr_report = (SingleFigureComparison(s.optcases, 'irr', label='IRR').report * 100)\n",
    "irr_fig = irr_report.iplot(\n",
    "    kind='bar',\n",
    "    title='IRR by Casestudy in %',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400),\n",
    "    yTitle='IRR in %',\n",
    "    asFigure=True,\n",
    "    yrange=[0, irr_report.max() * 1.2]\n",
    ")\n",
    "\n",
    "irr_fig.update_traces(\n",
    "    text=irr_report.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.0f} %\", \n",
    ")\n",
    "\n",
    "irr_fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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    "_series = (SingleFigureComparison(s.optcases, 'spp', label='Simple Payback Time').report)\n",
    "spt_fig = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='Simple Payback Time by Casestudy in Years',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400),\n",
    "    yTitle='Years',\n",
    "    asFigure=True,\n",
    "    yrange=[0, _series.max() * 1.2]\n",
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    "    text=_series.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f} years\", \n",
    ")\n",
    "\n",
    "spt_fig "
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_cops = (SingleFigureComparison(s.optcases, 'mean_cop', label='COP').report)\n",
    "cop_fig = mean_cops.iplot(\n",
    "    kind='bar',\n",
    "    title='Mean COP by Casestudy',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400),\n",
    "    yTitle='COP',\n",
    "    asFigure=True,\n",
    "    yrange=[0, mean_cops.max() * 1.2]\n",
    ")\n",
    "\n",
    "cop_fig.update_traces(\n",
    "    text=mean_cops.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}\", \n",
    ")\n",
    "\n",
    "cop_fig "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Sensitivity analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "We have agreed that for the sensitivity analysis we will vary the following key assumptions in the flex-model:\n",
    "1. Market prices of gas and electricity. We will use the actual prices for 2019, the actual prices for 2020.\n",
    "2. aFRR prices +/- 30%\n",
    "3. CAPEX\n",
    "4. CO2 price\n",
    "5. Tsource +/- 10 degrees Celsius\n",
    "6. Tsink\n",
    "    * Roam off the peak / lower pressure\n",
    "    * Stabalize / running average per hour/ 2 hours\n",
    "7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n",
    "8. Energy Tax and ODE +/- 30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def setup():\n",
    "    _c = Config()\n",
    "    s = setup_model(c=_c)\n",
    "    return s\n",
    "\n",
    "def routine(c, s):\n",
    "    s = load_data(c=c, s=s)\n",
    "    s = create_and_assign_assets(c=c, s=s)\n",
    "    #s = preprocessing(c=c, s=s)\n",
    "    s = run_optimisation(c=c, s=s)\n",
    "    #s = postprocessing(c=c, s=s)\n",
    "    s = collect_cashflows(c=c, s=s)\n",
    "    s = calculate_financials(c=c, s=s)\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testetss\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n",
      "\n",
      "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.4 s\n",
      "Wall time: 41.1 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%time _s = setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 3.58 s\n",
      "Wall time: 11.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1928709.8797698324"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time result = routine(c, _s)\n",
    "npv = result.hpcase.npv\n",
    "npv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0459737342bf41bd970b008382e28530",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'to_list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18312\\4147334808.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m80\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m120\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'storage_temperature'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mkpis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'npv'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'irr'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'spp'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mean_cop'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msens\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSensitivityAnalysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\sensitivity.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, c, s, routine, param, values, output_kpis)\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_generate_configs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0moutput_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_output_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcases\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkpis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_sensitivities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\sensitivity.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, s, routine, output_kpis, output_dict)\u001b[0m\n\u001b[0;32m     44\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_sensitivities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0m_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m             \u001b[0m_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     48\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mkpi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mcase\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcases\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m                     \u001b[0moutput_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkpi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18312\\3648020848.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(c, s)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_and_assign_assets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;31m#s = preprocessing(c=c, s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun_optimisation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m     \u001b[1;31m#s = postprocessing(c=c, s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcollect_cashflows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculate_financials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18312\\3066241253.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(c, s)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrun_optimisation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbaseline_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhpcase\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhponly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhpcase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstorage_case_PCM\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhponly_with_storage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstorage_case_PCM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#     s.hpcase_sde.assign_algorithm(hponly)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18312\\3686723783.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(case)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbaseline_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mgasboiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mdemand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'MW (VDG)'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mminutes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mth_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   6295\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6296\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6297\u001b[0m         \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6298\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6299\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'to_list'"
     ]
    }
   ],
   "source": [
    "# sensitivity:Storage temperature\n",
    "values = range(80, 120, 10)\n",
    "param = 'storage_temperature'\n",
    "kpis = ['npv','irr','spp', 'mean_cop']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: Storage volume',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity: Water storage volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(_s.demand['MW (VDG)'] + _s.demand['MW (NDG)']).to_list()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time _s = setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = range(100, 500, 100)\n",
    "param = 'storage_volume'\n",
    "kpis = ['npv','irr','spp']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[case.name for case in s.cases][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: Storage volume',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('irr', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='irr in %',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: IRR',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='years',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: payback time',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='COP',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: COP',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity: CAPEX per MW storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = range(4_000, 10_000, 2_000)\n",
    "param = 'storage_capex_per_MW'\n",
    "kpis = ['npv', 'irr', 'spp', 'mean_cop']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX',\n",
    "    yTitle='NPV in Euro',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: NPV in euro',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX/MW',\n",
    "    yTitle='years',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: payback time',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity: CAPEX per MWh storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = range(1_000, 5_000, 1000)\n",
    "param = 'storage_capex_per_MWh'\n",
    "kpis = ['npv', 'spp', 'mean_cop']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX/MWh',\n",
    "    yTitle='NPV',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: NPV in Euro',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX/MWh',\n",
    "    yTitle='years',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: Payback time',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity: threshold price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = range(10, 60, 10)\n",
    "param = 'threshold'\n",
    "kpis = ['npv', 'spp', 'mean_cop']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='threshold',\n",
    "    yTitle='NPV',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: NPV in Euro',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='threshold',\n",
    "    yTitle='years',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: SPP in years',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='threshold',\n",
    "    yTitle='COP',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: SOP',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Heat Pump CAPEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3, 2]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.hp_capex *= value\n",
    "    configs[value*100] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "\n",
    "sens_capex = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX factor (%)',\n",
    "    yTitle='Payback Period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Heat Pump CAPEX (€)',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_capex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: CO2 prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "mean = 24.86\n",
    "co2_prices = [10, 25, 50, 100]\n",
    "\n",
    "for price in co2_prices:\n",
    "    _c = Config()\n",
    "    multiplier = price / mean\n",
    "    _c.co2_price_multiplier = multiplier\n",
    "    configs[price] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('ebitda', case_names=['Baseline', 'Heatpump + SDE', 'Heatpump only', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Mean CO2 price in €',\n",
    "    yTitle='EBITDA in €',\n",
    "    yrange=[output.min().min() * 1.1, 0],\n",
    "    title='Sensitivity: CO2 prices > EBITDA',\n",
    "    colors=[recoypurple, recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_co2_spp = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Mean CO2 price in €',\n",
    "    yTitle='Payback Period in Years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: CO2 prices',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_co2_spp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Mean CO2 price in €',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[output.min().min() * 10, output.max().max()*1.1],\n",
    "    title='Sensitivity: CO2 prices > NPV',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Gas prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.gas_price_multiplier = value\n",
    "    configs[value * 100] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Gas price factor (%)',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[output.min().min()*1.5, output.max().max()*1.1],\n",
    "    title='Sensitivity: Gas prices',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Day Ahead buying amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0, 0.5, 1]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.day_ahead_buying_perc = value\n",
    "    configs[value * 100] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Volume in %',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Volume bought on Day-Ahead market in %',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Electricity prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.e_price_multiplier = value\n",
    "    configs[value * 100] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_eprices = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Electricity price factor in %',\n",
    "    yTitle='Payback Period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Electricity prices',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_eprices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Electricity price volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.e_price_volatility_multiplier = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_evol = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Volatility factor',\n",
    "    yTitle='Payback period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: E-price volatility',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_evol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: aFRR capacity fee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [10_000, 25_000, 50_000]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.afrr_capacity_fee = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_affr_fee = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='aFRR capacity fee in €/MW',\n",
    "    yTitle='Payback Period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: aFRR capacity fee',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_affr_fee"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Energy tax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.energy_tax_multiplier = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Factor',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Energy taxes',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Tsource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [-25, -10, 0, 10, 25]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.tsource_delta = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.loc[-25, 'Heatpump only'] = np.nan\n",
    "sens_tsource = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Tsource delta',\n",
    "    yTitle='Payback Period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Tsource',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_tsource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Tsource delta',\n",
    "    yTitle='COP',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Tsource > Mean COP',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Tsink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [-25, -10, 0, 10, 25]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.tsink_delta = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_tsink = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Tsink delta',\n",
    "    yTitle='Payback Period in Years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Tsink',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_tsink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_tsink_cop = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Tsink delta',\n",
    "    yTitle='COP',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Tsink > Mean COP',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_tsink_cop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Time period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def routine2(c, s):\n",
    "    s = setup_model(c=c)\n",
    "    s = load_data(c=c, s=s)\n",
    "    s = create_and_assign_assets(c=c, s=s)\n",
    "    s = run_optimisation(c=c, s=s)\n",
    "    s = collect_cashflows(c=c, s=s)\n",
    "    s = calculate_financials(c=c, s=s)\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "configs = {}\n",
    "start_values = ['2018-01-01', '2019-01-01', '2019-11-01']\n",
    "\n",
    "for value in start_values:\n",
    "    _c = Config()\n",
    "    _c.start = value\n",
    "    _c.end = (pd.to_datetime(value) + timedelta(days=364)).strftime('%Y-%m-%d')\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "sens = SensitivityAnalysis(_s, routine2, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "result = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "result.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Start date',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[0, result.max().max()*1.1],\n",
    "    title='Sensitivity: Modelled time period',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "renderer = 'svg'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Presentation Flexible Heatpumps__\n",
    "\n",
    "ENCORE meeting: 17-12-2020\n",
    "Mark Kremer\n",
    "\n",
    " \n",
    "  \n",
    "   \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Central question:__\n",
    "#### *Can Smurfit Kappi shorten the Payback Period of an investment in a Heatpump by operating it in a flexible manner?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Flexible operations on imbalance market__\n",
    "\n",
    "Benefiting from fluctuations in electricity market prices by ramping the asset up- and down (increasing and decreasing the electricity consumption)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_example_1 = s.hpcase.data[['DAM']].iloc[:60*24].iplot(\n",
    "    title='Day-Ahead prices on arbitrary day in €/MWh',\n",
    "    yrange=[-100, 200],\n",
    "    colors=recoygreen, \n",
    "    yTitle='Price in €/MWh',\n",
    "    xTitle='Time of Day',\n",
    "    dimensions=(800, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "\n",
    "fig_example_1.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imbalance prices are very volatile, with prices below -100 and above 200 €/MWh on a daily basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_example_2 = s.hpcase.data[['DAM', 'POS']].rename(columns={'POS':'IMB'}).iloc[:60*24].iplot(\n",
    "    title='Imbalance Prices on arbitrary day in €/MWh',\n",
    "    colors=[recoygreen, recoydarkblue], \n",
    "    yTitle='Price in €/MWh',\n",
    "    xTitle='Time of Day',\n",
    "    dimensions=(800, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "\n",
    "fig_example_2.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to benefit from these fluctiations, if you have __flexibility__\n",
    "* Storage options\n",
    "* Hybrid installations (e.g. with gas-powered assets)\n",
    "\n",
    "In this case we are looking at a __hybrid set-up of a Steamboiler and a Heatpump__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Simulations & mathematical modelling__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To answer the central question, we have build a simulation model\n",
    "* The model simulates the operations of a hybrid set-up of a Heatpump and a Steamboiler over the timespan of 1 years (on a 1 minute basis)\n",
    "* The goal of the model is to minimize the operating costs, in order to reach the shortest Payback Period\n",
    "* We are taking into account all major investment and operating costs, including:\n",
    "    * Asset CAPEX\n",
    "    * Commodity costs for gas and electricity\n",
    "    * Energy taxes\n",
    "    * Grid transport costs\n",
    "    * SDE++ subsidies\n",
    "    * Maintenance costs\n",
    "    * CO2 allowances\n",
    "* The output of the model is a Payback Period for an investment in an heatpump, in different scenario's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Casestudies__\n",
    "5 main scenario's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. Steamboiler only (baseline, baseload)\n",
    "2. Heatpump only (stand-alone, baseload, without SDE++)\n",
    "3. Heatpump + SDE\n",
    "4. Heatpump + SDE + Steam boiler (hybrid set-up) on Imbalance market\n",
    "5. Heatpump + SDE + Steam boiler (hybrid set-up) on aFRR (secondary reserve market)\n",
    "\n",
    "Besides that, we modelled 11 sensitivities:\n",
    "* Heatpump CAPEX\n",
    "* Gas & CO2 prices\n",
    "* Electricity prices & volatility\n",
    "* Energy taxes\n",
    "* Bidding strategies\n",
    "* Source temperatures (affecting COP)\n",
    "* Sink temperatures (affecting COP)\n",
    "* Time period (2018, 2019, 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Smurfit Kappa case__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is based on the context of Smurfit Kappa (paper factory)\n",
    "* Currently a Steamboiler is providing the 20-30 MW of average heat demand for drying processes\n",
    "* We add a 31 MW heatpump (to make sure it can cover entire demand)\n",
    "* The steam demand must be fulfilled at all times, by either the heatpump or the gasboiler\n",
    "* The heatpump and steam boiler can both respond very quickly (within minutes) within a flexible range (30%-100% for heatpump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_demands_over_time.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Source temperatures of around 65 degrees C\n",
    "* Sink temperatures of 125-170 degrees C \n",
    "* Average Temperature lift of about 85 degrees C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "demands_fig.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Heat pump__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COP roughly between 4 and 1.5, depending on load, Tsource and Tsink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cop_curve(Tsink, Tsource):\n",
    "    Tsink += 273\n",
    "    Tsource += 273\n",
    "\n",
    "    c1 = 0.267 * Tsink / (Tsink - Tsource)\n",
    "    c2 = 0.333 * Tsink / (Tsink - Tsource)\n",
    "    \n",
    "    return Polynomial([c2, c1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sourceT = 63\n",
    "sinkT = 140\n",
    "cop_curve(sourceT, sinkT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_cop_curve.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Optimisation__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* At each moment in time, we calculate the cheapest option to produce the required heat. \n",
    "* Taking into account the COP fluctuations, due to changing Tsource and Tsink\n",
    "* Taking into account fluctuating market prices (electricity, gas, CO2)\n",
    "* We are predicting real-time electricity prices using our forecasting models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Some example days:__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steamboiler only is following demand pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_steamboiler.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar pattern for heatpump only case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_heatpump.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hybrid set-up is responding to price fluctuactions, steam boiler taking over at high prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_optcase.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_optcase2.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Business case__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CAPEX of around 6 M€ (200.000 €/MW), which need to be earned back by savings in operating costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "capex_fig.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Savings in EBITDA compared to the baseline are about 1.5 mln € without subsidy, and up to 4.5 mln € including subsidy\n",
    "* The optimisation on aFRR allows for a 30-40% improvement in EBITDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ebitda_graph.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resulting in a Payback Period of 5.4 years without subsidy, and 1.8 years with subsidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spt_fig.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The added value of the optimisation is limited (in absolute terms), which is explained by the high COP of the heatpump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "production_fig.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The heatpump is filling in 95-98 % of the demand. \n",
    "* Because of its high COP, it is almost always cheaper to run than the steam boiler\n",
    "* Switch price is on average around 90€/MWh (excluding subsidies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Sensitivities__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If CAPEX is 200%, subsidy is needed to keep a good Payback Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_capex.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subsidies are protecting the business case againsts low CO2 prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_co2_spp.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The businesscase is quite sensitive to Tsource and Tsink differences, because they directly impact the COP\n",
    "* The Smurtfit Kappa case, with a temperature lifte of about 85 degrees C on average, looks favorable. \n",
    "* When the temperature lift is higher, the COP will decrease and the business case will degrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_tsource.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_tsink.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_tsink_cop.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_eprices.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Conclusions__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The business case for a Heat Pump seems favourable\n",
    "* Flexible operation, using aFRR, can improve the operational results by 30-40%\n",
    "* However, this only results in a marginal improvement of the business case\n",
    "* SDE++ has a very favourable effect on the business case, but is not critical\n",
    "* The business case is notably sensitive to the temperature lift required, and is therefore strongly dependent on the specific use case."
   ]
  }
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