MC Analysis & Sensitivity Analysis - Scenario 3.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "id": "a62871be",
   "metadata": {},
   "source": [
    "### Scenario 3\n",
    "#### Construction year 'Wind park 1' = Construction year 'Wind park 2' = Construction year 'Artificial Energy Island'\n",
    "This notebook shows the basics of Scenario 3 as suggested by Wouter van den Haak:\n",
    "![image-4.png](attachment:image-4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb113ce",
   "metadata": {},
   "source": [
    "#### 0. Import basic methods (a.o. from source2.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9fd7be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from source2 import *\n",
    "import numpy as np\n",
    "BOLD = \"\\033[1m\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec38e936",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load basic info from the Input sheet\n",
    "mapping = load_input_from_xls(filename=r'Case 1 - Input Sheet.xlsm')\n",
    "Inputs = extract_scenario(mapping, 'Scenario1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89024b92",
   "metadata": {},
   "source": [
    "##### 1. prepare input data and generate objects\n",
    "#### 1.1 General input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40c2ccce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#General\n",
    "WACC = 0.0984\n",
    "\n",
    "#Time input\n",
    "startyear = 2030\n",
    "endyear = startyear + 29\n",
    "\n",
    "#Escalation\n",
    "escalation_base_year = 2030 \n",
    "lifecycle=30               \n",
    "escalation_rate=0.02    \n",
    "\n",
    "#Distances\n",
    "distance_windpark = 180_000 #meter (distance windpark from shore)\n",
    "distance_island_shore = 150_000 #meter\n",
    "distance_DC_collection = distance_windpark - distance_island_shore #meter\n",
    "\n",
    "#Windpark\n",
    "capacity_windpark1 = 12000 #MW\n",
    "capacity_windpark2 = 0 #MW\n",
    "\n",
    "#Dedicated energy for electrolysis / electricity\n",
    "dedicated_electrolyser_energy = 0.3\n",
    "dedicated_DCAC_energy = (1 - dedicated_electrolyser_energy)\n",
    "\n",
    "#Efficiency factors\n",
    "inter_array_cable_efficiency = 0.99 \n",
    "DC_collection_cable_efficiency = 0.95\n",
    "HVDC_cable_efficiency = 1 - (0.01 * (0.00005 *(distance_island_shore) + 4))\n",
    "H2_pipeline_efficiency = 0.95\n",
    "DCAC_efficiency = 0.9\n",
    "\n",
    "#Electricity to hydrogen conversion\n",
    "kWh_to_kg = 50 #kWh/kg hydrogen\n",
    "\n",
    "#Energy prices\n",
    "electricity_price = 0.18  # 0.18 EU/kWh\n",
    "hydrogen_price = 6 # 6 EU / kg "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e6a336",
   "metadata": {},
   "source": [
    "#### 1.2 Element data\n",
    "We are including the following elements:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f8ce36",
   "metadata": {},
   "source": [
    "##### 1.2.1 Foundations and cables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b04ea050",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Wind energy source & Transport',\n",
       " 'element': 'Offshore wind park',\n",
       " 'component': 'Foundation & cable',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 971621.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 12000,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 30,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.03333333333333333,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.015,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.35,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Wind energy source & Transport',\n",
       " 'element': 'Offshore wind park',\n",
       " 'component': 'Foundation & cable',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 971621.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 30,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.03333333333333333,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.015,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.35,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Wind park 1\n",
    "foundation_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Wind energy source & Transport', \n",
    "                               element='Offshore wind park',\n",
    "                               component='Foundation & cable')\n",
    "foundation_data['depreciation_rate']=1/foundation_data['economic_lifetime']\n",
    "foundation_data['unit'] = capacity_windpark1\n",
    "display(foundation_data)\n",
    "\n",
    "#Wind park 2\n",
    "foundation2_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Wind energy source & Transport', \n",
    "                               element='Offshore wind park',\n",
    "                               component='Foundation & cable')\n",
    "foundation2_data['depreciation_rate']=1/foundation2_data['economic_lifetime']\n",
    "foundation2_data['unit'] = capacity_windpark2\n",
    "display(foundation2_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf95a8fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wind park 1\n",
    "Foundation = Object(**foundation_data)\n",
    "\n",
    "#Wind park 2\n",
    "Foundation2 = Object(**foundation2_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fbd1661",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wind park 1\n",
    "Foundation.generate_cashflows(startyear=2030, lifecycle=30, debug=False)\n",
    "\n",
    "#Wind park 2\n",
    "Foundation2.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c786b05",
   "metadata": {},
   "source": [
    "##### 1.2.2 Turbines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9252a7a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Wind energy source & Transport',\n",
       " 'element': 'Offshore wind park',\n",
       " 'component': 'Turbine',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 1139979.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 12000,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 30,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.03333333333333333,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.03,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.08,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Wind energy source & Transport',\n",
       " 'element': 'Offshore wind park',\n",
       " 'component': 'Turbine',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 1139979.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 30,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.03333333333333333,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.03,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.08,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Wind park 1\n",
    "turbine_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Wind energy source & Transport', \n",
    "                               element='Offshore wind park',\n",
    "                               component='Turbine')\n",
    "turbine_data['depreciation_rate']=1/turbine_data['economic_lifetime']\n",
    "turbine_data['unit']=capacity_windpark1\n",
    "display(turbine_data)\n",
    "\n",
    "#Wind park 2\n",
    "turbine2_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Wind energy source & Transport', \n",
    "                               element='Offshore wind park',\n",
    "                               component='Turbine')\n",
    "turbine2_data['depreciation_rate']=1/turbine2_data['economic_lifetime']\n",
    "turbine2_data['unit']=capacity_windpark2\n",
    "display(turbine2_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fc2fa36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wind park 1\n",
    "Turbine = Object(**turbine_data)\n",
    "\n",
    "#Wind park 2\n",
    "Turbine2 = Object(**turbine2_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad95ac68",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wind park 1\n",
    "Turbine.generate_cashflows(startyear=2030, lifecycle=30, debug=False)\n",
    "\n",
    "#Wind park 2\n",
    "Turbine2.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3df5eb",
   "metadata": {},
   "source": [
    "##### 1.3.1 AC Substation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d51ba558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'AC Substation & Transport',\n",
       " 'element': 'AC Substation & Transport',\n",
       " 'component': 'AC Substation',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 185000.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 12000,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 40,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.025,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.03,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.2,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AC Substation 1\n",
    "AC_substation_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='AC Substation & Transport', \n",
    "                               element='AC Substation & Transport',\n",
    "                               component='AC Substation')\n",
    "AC_substation_data['depreciation_rate']=1/AC_substation_data['economic_lifetime']\n",
    "AC_substation_data['unit'] = turbine_data['unit'] + turbine2_data['unit']\n",
    "display(AC_substation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6597af52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#AC Substation 1\n",
    "ACStation = Object(**AC_substation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae46a177",
   "metadata": {},
   "outputs": [],
   "source": [
    "#AC Substation 1\n",
    "ACStation.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de951422",
   "metadata": {},
   "source": [
    "##### 1.3.2 DC Collection Cable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0458f25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'AC Substation & Transport',\n",
       " 'element': 'AC Substation & Transport',\n",
       " 'component': 'DC collection cable',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 2000.0,\n",
       " 'capex_per_unit_units': 'EUR / m',\n",
       " 'unit': 30000,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 40,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.025,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.03,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.2,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DC Collection Cable 1\n",
    "DC_collection_cable_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='AC Substation & Transport', \n",
    "                               element='AC Substation & Transport',\n",
    "                               component='DC collection cable')\n",
    "DC_collection_cable_data['depreciation_rate']=1/DC_collection_cable_data['economic_lifetime']\n",
    "DC_collection_cable_data['unit'] = distance_DC_collection\n",
    "display(DC_collection_cable_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2184eb5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DC Collection Cable 1\n",
    "DCCollectionCable = Object(**DC_collection_cable_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "61a4bf72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DC Collection Cable 1\n",
    "DCCollectionCable.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5713fa",
   "metadata": {},
   "source": [
    "##### 1.4.1 HVDC Converter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "84a2bd8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Converter & Transport',\n",
       " 'element': 'Converter',\n",
       " 'component': 'HVDC Converter',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 472500.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 12000,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 30,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.03333333333333333,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#HVDC Converter 1\n",
    "HVDC_converter_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Converter & Transport', \n",
    "                               element='Converter',\n",
    "                               component='HVDC Converter')\n",
    "HVDC_converter_data['depreciation_rate']=1/HVDC_converter_data['economic_lifetime']\n",
    "HVDC_converter_data['unit'] = turbine_data['unit'] + turbine2_data['unit']\n",
    "display(HVDC_converter_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a5257c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#HVDC Converter 1\n",
    "HVDCConverter = Object(**HVDC_converter_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "897f068b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#HVDC Converter 1\n",
    "HVDCConverter.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca76b473",
   "metadata": {},
   "source": [
    "##### 1.4.2 HVDC Cable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84ac8e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Converter & Transport',\n",
       " 'element': 'Converter',\n",
       " 'component': 'HVDC Cable',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 3800,\n",
       " 'capex_per_unit_units': 'EUR / m',\n",
       " 'unit': 105000.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 30,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.03333333333333333,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Converter & Transport',\n",
       " 'element': 'Converter',\n",
       " 'component': 'HVDC Cable',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 3800,\n",
       " 'capex_per_unit_units': 'EUR / m',\n",
       " 'unit': 105000.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 30,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.03333333333333333,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#HVDC Cable 1 (for electrolysis)\n",
    "HVDC_cable_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Converter & Transport', \n",
    "                               element='Converter',\n",
    "                               component='HVDC Cable')\n",
    "HVDC_cable_data['depreciation_rate']=1/HVDC_cable_data['economic_lifetime']\n",
    "HVDC_cable_data['unit'] = distance_island_shore * dedicated_DCAC_energy\n",
    "HVDC_cable_data['capex_per_unit'] = 3800 \n",
    "display(HVDC_cable_data)\n",
    "\n",
    "#HVDC Cable 2 (for DCAC)\n",
    "HVDC_cable2_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Converter & Transport', \n",
    "                               element='Converter',\n",
    "                               component='HVDC Cable')\n",
    "HVDC_cable2_data['depreciation_rate']=1/HVDC_cable2_data['economic_lifetime']\n",
    "HVDC_cable2_data['unit'] = distance_island_shore * dedicated_DCAC_energy\n",
    "HVDC_cable2_data['capex_per_unit'] = 3800 \n",
    "display(HVDC_cable2_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1908a260",
   "metadata": {},
   "outputs": [],
   "source": [
    "#HVDC Cable 1\n",
    "HVDCCable = Object(**HVDC_cable_data)\n",
    "\n",
    "#HVDC Cable 2\n",
    "HVDCCable2 = Object(**HVDC_cable2_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "19a9da39",
   "metadata": {},
   "outputs": [],
   "source": [
    "#HVDC Cable 1\n",
    "HVDCCable.generate_cashflows(startyear=2030, lifecycle=30, debug=False)\n",
    "\n",
    "#HVDC Cable 2\n",
    "HVDCCable2.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c33a053",
   "metadata": {},
   "source": [
    "##### 1.5.1 Electrolyser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "370e6e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Electrolysis',\n",
       " 'component': 'Electrolyser',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 535000.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 3600.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 25,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.04,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Electrolyser 1\n",
    "Electrolyser_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Electrolysis',\n",
    "                               component='Electrolyser')\n",
    "Electrolyser_data['depreciation_rate']=1/Electrolyser_data['economic_lifetime']\n",
    "Electrolyser_data['unit'] = (turbine_data['unit'] + turbine2_data['unit']) * dedicated_electrolyser_energy\n",
    "display(Electrolyser_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c00b8647",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Electrolyser 1\n",
    "Electrolyser = Object(**Electrolyser_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77567d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Electrolyser 1\n",
    "Electrolyser.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0e143d",
   "metadata": {},
   "source": [
    "##### 1.5.2 Desalination Unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6995fb35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Electrolysis',\n",
       " 'component': 'Desalination unit',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 3900.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 3600.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 15,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.06666666666666667,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Desalination Unit 1\n",
    "Desalination_unit_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Electrolysis',\n",
    "                               component='Desalination unit')\n",
    "Desalination_unit_data['depreciation_rate']=1/Desalination_unit_data['economic_lifetime']\n",
    "Desalination_unit_data['unit'] = (turbine_data['unit'] + turbine2_data['unit']) * dedicated_electrolyser_energy\n",
    "display(Desalination_unit_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e8897c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Desalination Unit 1\n",
    "DesalinationUnit = Object(**Desalination_unit_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c7b594e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Desalination Unit 1\n",
    "DesalinationUnit.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ebe142",
   "metadata": {},
   "source": [
    "##### 1.5.3 Compressor Unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "85ff5846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Electrolysis',\n",
       " 'component': 'Compressor unit',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 153000.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 3600.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 15,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.06666666666666667,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compressor Unit 1\n",
    "Compressor_unit_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Electrolysis',\n",
    "                               component='Compressor unit')\n",
    "Compressor_unit_data['depreciation_rate']=1/Compressor_unit_data['economic_lifetime']\n",
    "Compressor_unit_data['unit'] = (turbine_data['unit'] + turbine2_data['unit']) * dedicated_electrolyser_energy\n",
    "display(Compressor_unit_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c4bdd788",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compressor Unit 1\n",
    "CompressorUnit = Object(**Compressor_unit_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c0c30609",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compressor Unit 1\n",
    "CompressorUnit.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79da6a3f",
   "metadata": {},
   "source": [
    "##### 1.5.4 Storage Unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3e4f943c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Electrolysis',\n",
       " 'component': 'Storage unit',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 290000.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 3600.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 15,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.06666666666666667,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Storage Unit 1\n",
    "Storage_unit_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Electrolysis',\n",
    "                               component='Storage unit')\n",
    "Storage_unit_data['depreciation_rate']=1/Storage_unit_data['economic_lifetime']\n",
    "Storage_unit_data['unit'] = (turbine_data['unit'] + turbine2_data['unit']) * dedicated_electrolyser_energy\n",
    "display(Storage_unit_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c3808cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Storage Unit 1\n",
    "StorageUnit = Object(**Storage_unit_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "42c49b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Storage Unit 1\n",
    "StorageUnit.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe92020",
   "metadata": {},
   "source": [
    "##### 1.5.5 Compressor after storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "88db3592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Electrolysis',\n",
       " 'component': 'Compressor after storage',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 10200.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 3600.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 15,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.06666666666666667,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compressor after storage 1\n",
    "Compressor_after_storage_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Electrolysis',\n",
    "                               component='Compressor after storage')\n",
    "Compressor_after_storage_data['depreciation_rate']=1/Compressor_after_storage_data['economic_lifetime']\n",
    "Compressor_after_storage_data['unit'] = (turbine_data['unit'] + turbine2_data['unit']) * dedicated_electrolyser_energy\n",
    "display(Compressor_after_storage_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "da0f4fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compressor after storage 1\n",
    "CompressorAfterStorage = Object(**Compressor_after_storage_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5a14cb21",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compressor after storage 1\n",
    "CompressorAfterStorage.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7a599b",
   "metadata": {},
   "source": [
    "##### 1.6 DCAC Converter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86409a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Electricity',\n",
       " 'component': 'DCAC Converter',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 285000.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 8400.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 25,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.04,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.03,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.25,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DCAC Converter 1\n",
    "DCAC_converter_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Electricity',\n",
    "                               component='DCAC Converter')\n",
    "DCAC_converter_data['depreciation_rate']=1/DCAC_converter_data['economic_lifetime']\n",
    "DCAC_converter_data['unit'] = (turbine_data['unit'] + turbine2_data['unit']) * dedicated_DCAC_energy\n",
    "display(DCAC_converter_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2aec7899",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DCAC Converter 1\n",
    "DCACConverter = Object(**DCAC_converter_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "161216e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DCAC Converter 1\n",
    "DCACConverter.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b42dd1b",
   "metadata": {},
   "source": [
    "##### 1.7.1 Artificial Island"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d7467e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Facility location',\n",
       " 'component': 'Artificial island',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 1410000000,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 1,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 80,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.0125,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': 0.01}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Artificial_island_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Facility location',\n",
    "                               component='Artificial island')\n",
    "Artificial_island_data['depreciation_rate']=1/Artificial_island_data['economic_lifetime']\n",
    "Artificial_island_data['unit']=1\n",
    "Artificial_island_data['capex_per_unit']=1_410_000_000\n",
    "display(Artificial_island_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "91230058",
   "metadata": {},
   "outputs": [],
   "source": [
    "ArtificialIsland = Object(**Artificial_island_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f58b1dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "ArtificialIsland.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d8e621",
   "metadata": {},
   "source": [
    "##### 1.7.2 Mainland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2ff492de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "issue detected\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Facility location',\n",
       " 'component': 'Mainland',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 6000.0,\n",
       " 'capex_per_unit_units': 'EUR / MW',\n",
       " 'unit': 8400.0,\n",
       " 'unit_units': 'MW',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 80,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.0125,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.1,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.01,\n",
       " 'residual_value': []}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Mainland_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Facility location',\n",
    "                               component='Mainland')\n",
    "Mainland_data['depreciation_rate']=1/Mainland_data['economic_lifetime']\n",
    "Mainland_data['unit'] = (turbine_data['unit'] + turbine2_data['unit']) * dedicated_DCAC_energy\n",
    "display(Mainland_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b2e0ecd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mainland = Object(**Mainland_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "940749d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mainland.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b72b80",
   "metadata": {},
   "source": [
    "##### 1.8.1 H2 Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "da129c7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "issue detected\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'sub_system': 'Electrolysis, Electricity & Transport',\n",
       " 'element': 'Transport',\n",
       " 'component': 'H2 Pipeline',\n",
       " 'escalation_base_year': 2030,\n",
       " 'escalation_rate': 0.02,\n",
       " 'capex_per_unit': 1500,\n",
       " 'capex_per_unit_units': 'EUR / m',\n",
       " 'unit': 45000.0,\n",
       " 'unit_units': 'm',\n",
       " 'construction_duration': 3,\n",
       " 'share_of_investments': [0.2, 0.4, 0.4],\n",
       " 'economic_lifetime': 30,\n",
       " 'depreciation_flag': 1,\n",
       " 'depreciation_rate': 0.03333333333333333,\n",
       " 'yearly_variable_costs_flag': 1,\n",
       " 'yearly_variable_costs_rate': 0.02,\n",
       " 'insurance_flag': 1,\n",
       " 'insurance_rate': 0.005,\n",
       " 'decommissioning_rate': 0.02,\n",
       " 'residual_value': []}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "H2_pipeline_data = get_object_data(Inputs=Inputs, \n",
    "                               subsystem='Electrolysis, Electricity & Transport', \n",
    "                               element='Transport',\n",
    "                               component='H2 Pipeline')\n",
    "H2_pipeline_data['depreciation_rate']=1/H2_pipeline_data['economic_lifetime']\n",
    "H2_pipeline_data['unit'] = distance_island_shore * dedicated_electrolyser_energy\n",
    "H2_pipeline_data['capex_per_unit'] = 1500\n",
    "display(H2_pipeline_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "596f88bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "H2Pipeline = Object(**H2_pipeline_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5f485979",
   "metadata": {},
   "outputs": [],
   "source": [
    "H2Pipeline.generate_cashflows(startyear=2030, lifecycle=30, debug=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b1968c",
   "metadata": {},
   "source": [
    "##### 2. Generate combined cashflows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a032498d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# place escalated revenue values in the cashflow dataframe\n",
    "df_combined=combine_cashflow_dataframes([Foundation.df, Foundation2.df, Turbine.df, Turbine2.df, ACStation.df, DCCollectionCable.df, HVDCConverter.df, HVDCCable.df, HVDCCable2.df, Electrolyser.df, DesalinationUnit.df, CompressorUnit.df, StorageUnit.df, CompressorAfterStorage.df, DCACConverter.df, ArtificialIsland.df, Mainland.df, H2Pipeline.df])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f79aae1",
   "metadata": {},
   "source": [
    "##### 3. Calculate NPVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "71097a8c",
   "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>years</th>\n",
       "      <th>capex</th>\n",
       "      <th>opex</th>\n",
       "      <th>revenue</th>\n",
       "      <th>cashflow</th>\n",
       "      <th>cashflow_sum</th>\n",
       "      <th>npv</th>\n",
       "      <th>npv_sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2030</td>\n",
       "      <td>-8.482455e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.482455e+09</td>\n",
       "      <td>-8.482455e+09</td>\n",
       "      <td>-8.482455e+09</td>\n",
       "      <td>-8.482455e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2031</td>\n",
       "      <td>-1.730421e+10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.730421e+10</td>\n",
       "      <td>-2.578666e+10</td>\n",
       "      <td>-1.575401e+10</td>\n",
       "      <td>-2.423647e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2032</td>\n",
       "      <td>-1.765029e+10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.765029e+10</td>\n",
       "      <td>-4.343695e+10</td>\n",
       "      <td>-1.462955e+10</td>\n",
       "      <td>-3.886601e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2033</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.321614e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.321614e+09</td>\n",
       "      <td>-4.475857e+10</td>\n",
       "      <td>-9.972938e+08</td>\n",
       "      <td>-3.986331e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>2034</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.348046e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.348046e+09</td>\n",
       "      <td>-4.610661e+10</td>\n",
       "      <td>-9.261104e+08</td>\n",
       "      <td>-4.078942e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>2035</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.375007e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.375007e+09</td>\n",
       "      <td>-4.748162e+10</td>\n",
       "      <td>-8.600078e+08</td>\n",
       "      <td>-4.164942e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>2036</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.402508e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.402508e+09</td>\n",
       "      <td>-4.888413e+10</td>\n",
       "      <td>-7.986234e+08</td>\n",
       "      <td>-4.244805e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2037</th>\n",
       "      <td>2037</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.430558e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.430558e+09</td>\n",
       "      <td>-5.031469e+10</td>\n",
       "      <td>-7.416205e+08</td>\n",
       "      <td>-4.318967e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>2038</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.459169e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.459169e+09</td>\n",
       "      <td>-5.177386e+10</td>\n",
       "      <td>-6.886861e+08</td>\n",
       "      <td>-4.387835e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039</th>\n",
       "      <td>2039</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.488352e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.488352e+09</td>\n",
       "      <td>-5.326221e+10</td>\n",
       "      <td>-6.395301e+08</td>\n",
       "      <td>-4.451788e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>2040</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.518119e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.518119e+09</td>\n",
       "      <td>-5.478033e+10</td>\n",
       "      <td>-5.938827e+08</td>\n",
       "      <td>-4.511177e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>2041</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.548482e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.548482e+09</td>\n",
       "      <td>-5.632881e+10</td>\n",
       "      <td>-5.514934e+08</td>\n",
       "      <td>-4.566326e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2042</th>\n",
       "      <td>2042</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.579451e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.579451e+09</td>\n",
       "      <td>-5.790826e+10</td>\n",
       "      <td>-5.121297e+08</td>\n",
       "      <td>-4.617539e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2043</th>\n",
       "      <td>2043</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.611040e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.611040e+09</td>\n",
       "      <td>-5.951930e+10</td>\n",
       "      <td>-4.755756e+08</td>\n",
       "      <td>-4.665097e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2044</th>\n",
       "      <td>2044</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.643261e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.643261e+09</td>\n",
       "      <td>-6.116256e+10</td>\n",
       "      <td>-4.416307e+08</td>\n",
       "      <td>-4.709260e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2045</th>\n",
       "      <td>2045</td>\n",
       "      <td>-4.518002e+08</td>\n",
       "      <td>-1.676126e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.127927e+09</td>\n",
       "      <td>-6.329049e+10</td>\n",
       "      <td>-5.206535e+08</td>\n",
       "      <td>-4.761325e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>2046</td>\n",
       "      <td>-9.216725e+08</td>\n",
       "      <td>-1.709649e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.631321e+09</td>\n",
       "      <td>-6.592181e+10</td>\n",
       "      <td>-5.861456e+08</td>\n",
       "      <td>-4.819940e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>2047</td>\n",
       "      <td>-9.401060e+08</td>\n",
       "      <td>-1.792946e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.733052e+09</td>\n",
       "      <td>-6.865486e+10</td>\n",
       "      <td>-5.542669e+08</td>\n",
       "      <td>-4.875366e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>2048</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.800373e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.800373e+09</td>\n",
       "      <td>-7.045523e+10</td>\n",
       "      <td>-3.324091e+08</td>\n",
       "      <td>-4.908607e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2049</th>\n",
       "      <td>2049</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.836380e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.836380e+09</td>\n",
       "      <td>-7.229161e+10</td>\n",
       "      <td>-3.086829e+08</td>\n",
       "      <td>-4.939475e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>2050</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.873108e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.873108e+09</td>\n",
       "      <td>-7.416472e+10</td>\n",
       "      <td>-2.866502e+08</td>\n",
       "      <td>-4.968141e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2051</th>\n",
       "      <td>2051</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.910570e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.910570e+09</td>\n",
       "      <td>-7.607529e+10</td>\n",
       "      <td>-2.661901e+08</td>\n",
       "      <td>-4.994760e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>2052</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.948781e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.948781e+09</td>\n",
       "      <td>-7.802407e+10</td>\n",
       "      <td>-2.471904e+08</td>\n",
       "      <td>-5.019479e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>2053</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.987757e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.987757e+09</td>\n",
       "      <td>-8.001183e+10</td>\n",
       "      <td>-2.295468e+08</td>\n",
       "      <td>-5.042433e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>2054</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.027512e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.027512e+09</td>\n",
       "      <td>-8.203934e+10</td>\n",
       "      <td>-2.131625e+08</td>\n",
       "      <td>-5.063749e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2055</th>\n",
       "      <td>2055</td>\n",
       "      <td>-1.445833e+09</td>\n",
       "      <td>-2.068062e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.513895e+09</td>\n",
       "      <td>-8.555324e+10</td>\n",
       "      <td>-3.363378e+08</td>\n",
       "      <td>-5.097383e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056</th>\n",
       "      <td>2056</td>\n",
       "      <td>-2.949500e+09</td>\n",
       "      <td>-2.109423e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.058923e+09</td>\n",
       "      <td>-9.061216e+10</td>\n",
       "      <td>-4.408435e+08</td>\n",
       "      <td>-5.141468e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057</th>\n",
       "      <td>2057</td>\n",
       "      <td>-3.008490e+09</td>\n",
       "      <td>-3.310191e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-6.318681e+09</td>\n",
       "      <td>-9.693084e+10</td>\n",
       "      <td>-5.012938e+08</td>\n",
       "      <td>-5.191597e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>2058</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.351442e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.351442e+09</td>\n",
       "      <td>-9.928228e+10</td>\n",
       "      <td>-1.698399e+08</td>\n",
       "      <td>-5.208581e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>2059</td>\n",
       "      <td>1.238879e+10</td>\n",
       "      <td>-1.541698e+10</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.028190e+09</td>\n",
       "      <td>-1.023105e+11</td>\n",
       "      <td>-1.991260e+08</td>\n",
       "      <td>-5.228494e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       years         capex          opex  revenue      cashflow  cashflow_sum  \\\n",
       "years                                                                           \n",
       "2030    2030 -8.482455e+09  0.000000e+00        0 -8.482455e+09 -8.482455e+09   \n",
       "2031    2031 -1.730421e+10  0.000000e+00        0 -1.730421e+10 -2.578666e+10   \n",
       "2032    2032 -1.765029e+10  0.000000e+00        0 -1.765029e+10 -4.343695e+10   \n",
       "2033    2033  0.000000e+00 -1.321614e+09        0 -1.321614e+09 -4.475857e+10   \n",
       "2034    2034  0.000000e+00 -1.348046e+09        0 -1.348046e+09 -4.610661e+10   \n",
       "2035    2035  0.000000e+00 -1.375007e+09        0 -1.375007e+09 -4.748162e+10   \n",
       "2036    2036  0.000000e+00 -1.402508e+09        0 -1.402508e+09 -4.888413e+10   \n",
       "2037    2037  0.000000e+00 -1.430558e+09        0 -1.430558e+09 -5.031469e+10   \n",
       "2038    2038  0.000000e+00 -1.459169e+09        0 -1.459169e+09 -5.177386e+10   \n",
       "2039    2039  0.000000e+00 -1.488352e+09        0 -1.488352e+09 -5.326221e+10   \n",
       "2040    2040  0.000000e+00 -1.518119e+09        0 -1.518119e+09 -5.478033e+10   \n",
       "2041    2041  0.000000e+00 -1.548482e+09        0 -1.548482e+09 -5.632881e+10   \n",
       "2042    2042  0.000000e+00 -1.579451e+09        0 -1.579451e+09 -5.790826e+10   \n",
       "2043    2043  0.000000e+00 -1.611040e+09        0 -1.611040e+09 -5.951930e+10   \n",
       "2044    2044  0.000000e+00 -1.643261e+09        0 -1.643261e+09 -6.116256e+10   \n",
       "2045    2045 -4.518002e+08 -1.676126e+09        0 -2.127927e+09 -6.329049e+10   \n",
       "2046    2046 -9.216725e+08 -1.709649e+09        0 -2.631321e+09 -6.592181e+10   \n",
       "2047    2047 -9.401060e+08 -1.792946e+09        0 -2.733052e+09 -6.865486e+10   \n",
       "2048    2048  0.000000e+00 -1.800373e+09        0 -1.800373e+09 -7.045523e+10   \n",
       "2049    2049  0.000000e+00 -1.836380e+09        0 -1.836380e+09 -7.229161e+10   \n",
       "2050    2050  0.000000e+00 -1.873108e+09        0 -1.873108e+09 -7.416472e+10   \n",
       "2051    2051  0.000000e+00 -1.910570e+09        0 -1.910570e+09 -7.607529e+10   \n",
       "2052    2052  0.000000e+00 -1.948781e+09        0 -1.948781e+09 -7.802407e+10   \n",
       "2053    2053  0.000000e+00 -1.987757e+09        0 -1.987757e+09 -8.001183e+10   \n",
       "2054    2054  0.000000e+00 -2.027512e+09        0 -2.027512e+09 -8.203934e+10   \n",
       "2055    2055 -1.445833e+09 -2.068062e+09        0 -3.513895e+09 -8.555324e+10   \n",
       "2056    2056 -2.949500e+09 -2.109423e+09        0 -5.058923e+09 -9.061216e+10   \n",
       "2057    2057 -3.008490e+09 -3.310191e+09        0 -6.318681e+09 -9.693084e+10   \n",
       "2058    2058  0.000000e+00 -2.351442e+09        0 -2.351442e+09 -9.928228e+10   \n",
       "2059    2059  1.238879e+10 -1.541698e+10        0 -3.028190e+09 -1.023105e+11   \n",
       "\n",
       "                npv       npv_sum  \n",
       "years                              \n",
       "2030  -8.482455e+09 -8.482455e+09  \n",
       "2031  -1.575401e+10 -2.423647e+10  \n",
       "2032  -1.462955e+10 -3.886601e+10  \n",
       "2033  -9.972938e+08 -3.986331e+10  \n",
       "2034  -9.261104e+08 -4.078942e+10  \n",
       "2035  -8.600078e+08 -4.164942e+10  \n",
       "2036  -7.986234e+08 -4.244805e+10  \n",
       "2037  -7.416205e+08 -4.318967e+10  \n",
       "2038  -6.886861e+08 -4.387835e+10  \n",
       "2039  -6.395301e+08 -4.451788e+10  \n",
       "2040  -5.938827e+08 -4.511177e+10  \n",
       "2041  -5.514934e+08 -4.566326e+10  \n",
       "2042  -5.121297e+08 -4.617539e+10  \n",
       "2043  -4.755756e+08 -4.665097e+10  \n",
       "2044  -4.416307e+08 -4.709260e+10  \n",
       "2045  -5.206535e+08 -4.761325e+10  \n",
       "2046  -5.861456e+08 -4.819940e+10  \n",
       "2047  -5.542669e+08 -4.875366e+10  \n",
       "2048  -3.324091e+08 -4.908607e+10  \n",
       "2049  -3.086829e+08 -4.939475e+10  \n",
       "2050  -2.866502e+08 -4.968141e+10  \n",
       "2051  -2.661901e+08 -4.994760e+10  \n",
       "2052  -2.471904e+08 -5.019479e+10  \n",
       "2053  -2.295468e+08 -5.042433e+10  \n",
       "2054  -2.131625e+08 -5.063749e+10  \n",
       "2055  -3.363378e+08 -5.097383e+10  \n",
       "2056  -4.408435e+08 -5.141468e+10  \n",
       "2057  -5.012938e+08 -5.191597e+10  \n",
       "2058  -1.698399e+08 -5.208581e+10  \n",
       "2059  -1.991260e+08 -5.228494e+10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-52284935736.79744\n"
     ]
    }
   ],
   "source": [
    "df_combined_npv=calculate_npv(df_combined, 2030, WACC=WACC)\n",
    "display(df_combined_npv)\n",
    "final_npv_sum = df_combined_npv['npv_sum'].iloc[-1]\n",
    "print(final_npv_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ac3761",
   "metadata": {},
   "source": [
    "##### 4. Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6194cfb0",
   "metadata": {},
   "source": [
    "##### 4.1. NPV Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "13f4890a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AC9nPswM/AdYDtgSGSfpDRFxaou57QKljOfcD7wJrSvpFRLzYTNncRe0FLTe7U1oT+B1wbRllhwNrA3sBd5cqJKkvsCzwTES80AZtbEQnS7o3IqaUU1jS74HLAAEPAbcAU4AlgNWBzYCbgbeAM4A5C06xK7B4do4xBcdGVt78jk3SnMD1wIbA18BdpOdlbuDXwKnAgZI2i4iXi9RfEXgS6A7cA7wILET6nhsPjCijGZNJn6XpykqaA/htVsZ/i03ruOy2C+l9/jNgJ2AP4BlJO0bEGyXq5v9uKjTdfkl/IS0RPxPp9b4MGAcsAPQDLgT2JX0GO7uHmfpdMg8wgPT+HiRprYh4s0S9sl6TLNF2B7AR8D/gTuAD0md2KWAHYHng361/CGZmMxb/AWBmrXFrYbJFUldgd+BM4BJJP0TENUXqjomIoc2dPCJC0oXAMFKy5aBi5SQtDfQFxtJM8qATex9YEDhB0s0RMbGF8teRLvQ3l7RARPy3RLm9stvhbdPMhvMW6QJyd8pIEmY9NP4JBDAwIh4sUqYP8DlARJxR5Hg/UjLm0ogY2eqWNwBJMwE3AL8C7gV2jIgv8o53BY4HjgTuk7Rqkff6bqREzIkRMaTg/AuV2ZQ7gG0kzZMfP7Mj0IOUdNu6zPN1CsV+P0haADgb+A3wgKTVI+LTItWn+91UiqQhpMTPWOA3ETFdT0xJmwGHlt/6hjYy/7XJPme3A5sAQ0ifmWLKfU22JyVi/gP0jYiv8w9m35NrVd5sM7MZl4cpmVmbiIjJETEc2C/bdZqk7lWc8mLSf41/30w38T1JPQkuiogfKw0g6dqsq/T6JY4Pyo6fXbB/bkknSnpV0vdZ1/YHJQ0sco5ZJB2UdbX/StL4bBjDbZJ+VWmbKzQWOI/Uu+LAlgpHxPfAlaRE/a7FymQ9DrYl/fe4WLKtWdmQgJD0UDNl/i/rnr5gwf5fS7pL0ufZEIa3JZ2StanwHL+QdE32XOcPdzhD0syVtrtCfyX1njhe2dCiFvwcmAN4qVgiBiAiHo+I/7VdE0uT9KTSEMTeJY4flr2GhxbsX1TSOZLeyZ7zLyT9W9IaRc4xu6RjlIaGfCPp2+z1vE7SalU+hB1IiZh3gG0KEyHZd9UQUvJxYeBvRc4xLrt9vvBARHxcZjsuALqRenUU2ov0+bynzHM1S9JJ2Wuyc4njq2XHby/Y30PSkZJekPSdpHGSnpC0fZFzSNIuSsMZP1MaqjJW0r2SftcWj6OULFm2Halnxk9IF/+tlr23hwKTgE2KJWKyuLmeGq2NM1f2nf+2VHK46B3Za7Nawf61JN0o6ROloVNjJf1L0sJFzrGk0pCht7LfSV9m36Pnq8TQ4Wplv3Mvze5O9xlvhT7Z7aWFiZgs3viIKKdHWkmSNsqe64tLHO+W/X75XFK3gmPbSxqR/R6fkP3+P7qwXFZ2PaXhpR9k34WfZN+rx1bTfjNrPE7GmFlbu4w0FGlBUjfmVomIT0j/WZ6LdPE/DaX/bu8C/EhK3OTPSzOyzDDnZrd7lzg+OLtt6gEiaXHgWeAI4DPgfNJF3QrAPZL2KjjHpaTeQjMDlwNnAY8AK1LFH/kVOJ7U5fsoSXOXUT73WPcocXwnYFbgmogYByBpZPa892vp5BHxGmnYRn9JyxYeV+oB8nPgtuw9kNv/F9KF61qk7utnkXqgHAaMUhr2kSv7C+Ap0rC5J4HTSENWPiMlC6f747mNfUQaBrMg8OcyyueSBQuXmbypiKRd1cycGUWcS/r7oPC9nLMn8APps56LsSppyMF+wOukXgy3A+sDjylvDp3sovQe0nvzG9JQkPOA0Vn5tctsZym5dv8jIsY3U+747HYnTZ/wvZA0vOlISbO1sh33k4ZGTTM/UHbRvQrpe6toElkl5oNqxvnZuUp9l+X2/ysvxpzAY8AJpCFxF5Ne0/mAqyUVJqmGkb7PFiR9nk4DHgAWIfVYaVfZxX+uTduXSm6UaTfSd/JNEfFSC3F/yL+vEnNGlaj7FWmI6JKkBOE0JC1K+j3wbEQ8m7d/N2AUsDHp+/IM4BnSe+kZSYvllV0IeDp7TC+TvhuvIA313Yk0vK695F6DSW1wrtz34HS/F5ptwNQ5mnqXUfxe4G3gd5J6FTm+LWkI1qX5r7uki4CrgaVJw0X/CXxJSrzfk/09kiu7ESlpuC7wIOl3wa2k78zcP6vMzAAPUzKzNhYRP0p6lDRkYk3ShXO+3io9WeVrEZE/t8lwYCvSxdVVBWU3J10U3BkRY1vZ1kckvQxsqzTPzee5Y5KWIP3x/HhE/F9etctIj237/LZmFzYjgbMk/Tsi/pv9sbcdKXmzVuH8Ie31H8t8EfGlpGHAKcDRwCEtlH9R0mjSfD39igx5ySVpqhmidC7Qn5TsOqzgWC4Bln/R2J80nOAJ0n+x/5d3bFfgkuz4wdnuXUgJo60i4rb8k0uai9Rrpb2dTHosh0o6v4XeFO+QLqbWICWWLgAeB14uY2hZe7iOdKG9u6ShEdF0oZUl3JYDrs59XrILkeuB2YD+EfFwXvmFSY/tIkm9swucn5P+C35rREwzREdp6EOxi6SyZG35ZXb3gebKRsQrkj4i9Y5ZnZSYyJkX+J6UNLlP0jTvuzIFcBHwV0lrR8QT2f69mJpEnq43XWtExBhJdwObSlox/zsrSyZtz/TDOc8gPb7DI+LkvPKzki4eh0i6MW9eqL2BD4GfFya5JM3bFo+jDI+RekzOD/QmJRzybdXMRfm1WTIY0oUypIvl9nYuKVGyNylBl29P0tw4+d93y2b3x5CG63yYd2xAdo4zmTq8bRBpXpU/RsSZ+SfPkrsV9xoth9IcL7tndx9rpmi5r8nNwOHAPpJmJw3hezYi3muL9kLTEOjzSb8PdwLOKShS7B8wu5Ie5y2kIY/f5x0bSppzaH/SawLp8z0T0C8i/pN/8hp+Tsyso4gIb968eStrI/1XNIBdWyh3Ulbu3Lx9/bJ9zW23FpxnJtIfpAEsU3Dsrmz/Fnn7epAm+Fusgse0f3aeQwv2n5jt3zlv30rZvhtKnGvL7Ph+2f05svujALXD69E7O/+YEvsfy+53I120/AAsmVcugA+KnHeP7NiVBfvXzPY/V7B/sex571Fmu7uSLuo+B7rl7Z+TlCh5K//5Iv0RHMDPSpzveeDTvPunMnX+lZp9DvL2/yq7v3d2/8K8MkOzfXsWeQ5HFHweJpJ6+BwOzNFCm0ZmdfoVOdYre30WquAxnpKdb9uC/ddk+9cv8r4/pcS5/pAd3yS7v2J2/+p2eG3mz3v+Zi2j/JNZ2d/m7VuB1GPnVVKPj+/IJvAtqLtwVvffBfvHZPu7ZmUmAxdnx3pm574ru79nVnZowTlmzl6zpSp47Jtm5zq7YH/uffiXvH3zZO16usS5ct91J+ft+4L0PdKt3DZV+NoF6Xq5pXKfZGXXzNuX++w1t22VV/6VbN9GrWjnQtlr06uCOk+Teo8smLevCylB9g0wW97+07O2bVriXLdkr93s2f0Ds/KD2+l1GZqdf2T281BS75tXs/0vA/MXqVfRa5LV+S3wcUGZL7LHvHmJ9i2VvR4zl/l45iElWv+vYP9yWbyHCvY/n712cxY5VxfS77HReftuys6zbHu8Ht68eWuszT1jzKw95LouR5FjD0dEv3JOEqmXzcWkXg97ki5KkfQT0oooH5HX8ybSf2tfK3Kq5lxOSh4NJl3EozSnyK7AV6T/+Ofkhk/0KtG7Z77sdoWsPd8ozdGwOfCCpJuAR4GnovnhE20qIn5QmqzyatJj/W0LVa4lXRBsK+nASF3tYepwi2l6xUTE+xW2Z7LSBM1/IXULvzo7tBNp0tThEZH/3lmb9MfwbyQVGw4xCzCfpk6Ueh0pAXCrpBtJPSRGRcTblbSzDVxImnx6N0lnxrQ9rKaRPYf9Ja1AWgFodVLyK7ftl/VUerfSRkSaf2G6ORhacB5p4tK9SRcXuf/qbg28GhGP5JXNfS4WL/G5WCa7XYGURH2FNKRp+2zY322k/6w/E9X3BKp06Eqx76oLSIndbSP1nvmcNGRylKSBEfFWVi73uJ4udfKI+EjSXcBvJf2RlNyZnRYmdo7UG6nS77K7yYamSDo87ztmMGkY0oV5ZdcgXUiWWlY7N6/SCnn7riJd+L8s6QbS6jpPRJH5PdpZc79fdovyJott7hzNitTLrdx5g3LOJfWE2p00LAzSxLeLAudFNuQzk/s89VWR+ZZICccupOE8z5JWFzoB+KekX5OG4owCXin4Hq1W32zL9wIpAdzce6Dc14SIuF7SLaSek+uSem6tS+ohu5Wky0lJ8MirU9H3ekR8Iel6YGdJfSLi8exQrlfM+bmySpMGr0RKuPyxxMi4H5j+c7IN8JSk60hJ9lER8UEl7TSzTqLe2SBv3rx1nI3ye8ZcQcF/e5naM2ZkhTEXIf0X8BOy/3yRugUH8Nc2elznZefrn90flN0/vaDcUbT8n74ALsmr0530n8Q38o5/nz1HC1TZ7t6U0TMm2yfSnBwB/DLbFxTpGZMdOz87fmB2P/cf/XG00Eujgtd1Eik5l9v3f6Q/bOcrKDupzOd98bw6a5MuoMfnHX+NNLysXT4HFPSMyfbleivcnd0fSpGeMc3EWp40ZCko6DlWUG4kJXrGVPE47yYNcVgqu39YFuMPBeUuKPP1OTavzlykhN/YvOPfkOaama2KNnfN3kNBQW+6EuU/zMqum91fnCLfU6T5r8YD/wVWyfadnj0/yxeUHZOdo2t2f/Ps/t6knjgf5x0r2jOmisd/eHa+3bL7q2X3bykot2OZr9mIvDpdSEnO/+Qdn0RKpi3dBm0PWugZQxp+mPs+6F3ks7drmbEezMrv0VaflxbidSfNMfIuMFO2746sDSsVlH2zzNemb16dFUhJ6K/zjr8PHNQGbR+a/x4l9Vj9CWlYTpCSPzMVqVfRa9JM/C6kfyCMo0hvmlaec63sXJdl97uREi7/BWbJK7dIma9FFJx/U+AhUu/GXJlngA1r8X7z5s1bx9k8ga+Ztalszofc6kRFV6ioRKTx8ncBC5CWXJ6JNP7+R6b9T281zstuc5NcTjduPJP7798fIkLNbE1LfEbE9xExNCKWJQ1F+T2pF8DvgRvbqP0tiohg6vws/yijSu6x5yZD3Y70H/3rIuKbNmjPh2QTvEpaIW/i3lsi4rOC4l8DX7XwnCvy5haIiCciYjPSRf86pIkWFyBNTNreq1g1iYg7SX+UbyRpw1bUf42pq/G0ekLsVjqPlMTLvQf2BCaQepPly30utmzh9TkuVyEivoqIgyPiJ6QeJnuSkmUHMPXzWLGImMzU751mX+esF9LCpORNbvLU3Eo1n+eXjYiHSEmV2YGRkrYjDef7d0yd86KUu0hJn6NJF4GXZO1sDxeTHk/uu2y6iXszudfs9BZes/65ChExJSLOjIiVSJ+lbUnDR7YgTWLa3hNjQ+ol0RX4b0SMqeI8uTlONqi6RWWINM/IpaRE+cC8iXufioJ5RZj62vRq4bV5OO/8r0bE70hDcFYnTTA/E3CmpFKTsbf2sfwYEWMj4g+k32EDSZ/bdpG9764nJT+hDb4HI62e9Rypx1pukYB5SJ/N/N55udfi+ZZ+/xSc/86IGED6/bNB1vafAXdI+mm17TezxuFkjJm1tV1JSYePSd1z20IuMbAn6Q+/xYH7oo0m9ouIF0ndureWtBbpIu6RiHi1oOiT2e16rYwzNiKuIg2xehNYtxaT+ObFf4T0X+x1JE23QlVB2edIF6grSlqTqRfk1UzcWyi3mtVgikzcm+dJYC5JP6s0QET8EGlZ6L+QhgxBmuOklnI9Sk6hdb93v81uq1k9pjXuIP13fTelZduXA66PqcPWcqr9XLwVEReRhkCMo/rXJ5ekPURS92bKHZ3dXhFTJ+XMfaesXZhciLTs+BakITzXkF7Lg2lBpIm7LyYNSQnSpL7tIktk3gisJWkd0sS9Y4D7CoqOJiW0W/uafRoRN0fEb0nJxqVIydR2kyXij8ruXt1c2TJcQuphs21LF8dtmGTK9cDcmyIT9+Zp9ecp0rLtz0bE30mvPaQhPu3lUFLy7y/KW9GunbT19+B5pJ5WO5N+/wQFwwcjDR97GfiZyluNcBoR8V1EPBQRh5CGks1CWiHLzAxwMsbM2oikrkrLOv+T9EfNwRExoY1OfzfwASmJcUy2b7o5FyT1kLR8/rKfFTiP9IfSTaQ/9s4vLBARz5DmfNlG0u6Fx7M2rChp/uzn+bLkTqGepP+wTyZ1Y87VHaq0ROfQVrS/XH/O4p5URtncc/wP0n/0X8z+ozgNSYtlz3uPCtvyIGn41i6kbuhvRESxBF7uP6IXZKvzFMbvKemXeffXU/FlSxfIbgtXgglJUWHbyxYRzwNXkuYe2L7wuKQlJB1UrM1KkxTkLkAfKTxeDkm9stenoiVuIy0lPJw0R8XF2e7pPhekBN/bwP7KW8K6oA1r594f2eMtllibizRc4PuCumUvJZy5ipQIXhq4MfvPd/75ukg6HtiBlDTOfacQER+Rvm8WBi7U9Mtav0gaTgfpb6hyn9OzSPPt/DrKmONC0szZa7ZUmefPl+tZdB1phavh2WvZJCI+JT1Pq0s6RnlL8+a1YSmlVeWQ1E3SBiqYNCObXyt3kTo+b3+ly6k3K/tOvZY03PV9ps670ipZr5qhpO/8OyWtXiLuRky7AhWSFspem2LfMc3FfJP0nbcZsA/wP9JrVOgcUqLodKWVlQrbNIuk9fLurylpgcJylP6+q2Qp6GZFmuvqAlKvkkOrOZek7SVtmCXdCo8tyNR/CDxScGyp7PWYubBeC64m9Xz5MykRfH+Jz+ZppPfJxUqrJha2bS5Jq+bd36BEErjo62FmnZsn8DWz1shfqrInqSfMeqQLk6+BvSOi2B+Z0PzS1gBnRMESshExRWki37+QlsT9hDRpYaE1SRdhD5P+aK/EDaSL/kVIQxRuLlFuB9J/gi+SdBBpSMT/SP/1/gXpv8NrA59m53pS0qukLtFjSSssbUZalvusiPg279y5P0LbawgDEfGGpOHAfmUUv5qUiMn94V9q0tHLSX/M9ifNXVJuW3LLjJ6W7Sr2X2Ii4kFJR5BWuHpTaULUd0kXmotnsR8jdfuHdFEwUNJI0rLR40hdxDcmTcqcv2xp7uJymmXH28FRpMlbly5yrBdp/oVTJI0CXiL9F3h+Upf8JUnvp9Ze7GxN6glwGannWiVyEy0vQlp95InCAhExSdI2pLkj7pT0OGliz/GkuSXWID2GhbJ9KwG3SHqW9Fg/Ik1+vSWp18nfC0JU9LnIvi+2JfUQ2QR4R9KdpF4vc5OSukuQeoxsHhGfFJxiD9Jn/PfAAEn3Z21civTZFemCeV/SsIP1IuLlFtr0OWm56HItQlqt5j3S0JayRcQoSf8hPc+TmJpIK3QAaYjY8aRJfx8jzZmxMGkOkjVIycN3SXOePACMkfRU1q5ZSZNNr0AarpXfk7DV32V5vx9mIq2w9jPS8KRZSD16dsyez2KaW0Z5TORNJBsRJ2RJqGOBp7P37TOk74sFSMNtl8n25TuRlEDejTT0qBLnknpeLkBa9Wq6C/OIeC1L9l9Mmiz5HlLSemam/q79jDSfFKTfSftLepi0Et1XpPfq5qReK2cUhGjr3zMnkD4zB0s6u8hrU+5rshZpTqJPsvfiu9n+JUhzsHQnJX4Lh/c+SPo9kPtMlyUixku6jKk9Jkv9/rlY0mqk35lvS7qXlBCcO4u5Pun7dZ+syqmkv3NGZu2ZSJq7aQDpc3NtuW00s04gZoCJa7x589YxNqZfqnIKadLNt0kXGgcAc5eo26+gbqmtd4n6P8niBXBCCzFGtvLxnZ7VL7pEb1652YEhpGE840j/yX+XtLLTYKBnVm5O0oXsQ6Q5I34g/Sd+JOkiRwXnvSV7jGUtiUkFE/gWHJ+PqRM9Fp3AN69sbnLW8RRZ2jMrMzIr068Vz/lc2WOeAMzTQtl1SatbfUT6A/cz0kX/acDqeeUGkv44fiV7nN8Br5N6JyxecM6VsrZfWUGbc5+DXUvs/1WJernl0oO8CXxJvUG2Il2oPUe6IJ6Utf1ZYBgFkxpX8hqQEjABXNrKz8UtWf39Wyg3P6nH1UvZ+2UcaTjejaTERm7S2kVJF3CjSInVH0g93+4GNi5y3udJ3zNzVdhukXpc3U763E0kTaI6ipTYKrkUOynJPCSLPZ70GX+N9B2xeFbmgNxnCFgsr+6YbH/XMtpYamnr3hT5bFfw2P+Q1b+hhXKzZI/j8ez99gPpQvNB4I9kn0lSIuDP2Wv0Punz+hlpSM0+5E16mpXPfZcW/SyUaEvh74IfSInxZ0nfQxtRZKLYgs9ec9vIEnVXIE0c/VL2PpuYvV/uJiUZupWItWu5jy2vbpfseQvgZy2UXTGL9V72XHyZtfFfwIC8cmuRekP9JyvzPSkpcwnw84Jz5r5vi/5uKNGOocXeowVlTs3KnNra14T0O35/0vfN6wWvxV2k75BiEwWPoZm/HVp4bLnv/49o4fNKSsTeQUqMTyR9d40G/kbeJN6k75xrSN9947LH8RJlfI978+at822KCMzMDLL/ZK0PLBepS3ktY4v0R/pDkeZh6BQk9SP1ZroyInZqvnS7xD+I9J/jFaOF3g2dUTZk4C3Sf/IXijaYvLnC+HMCX5Au8v5cy9gdWTY8aBdSMuTBOsR/DpgcEWvWOvaMTNKSpM/TqIho1Xw9VcbfgtS7ZNOIuKvW8Wc02dDHS4C/RcQxLRQ3M2tzHqZkZkYad08a7nJPrRMxmZ+Txt2fWIfY9ZS7wD6nTvH7koZYOBFT3CBSV/zza52IyaxH6iV0WksFLZH0E9LqZ6+SeuXVOn4vUo+DZicJ76QOY+owt3roC/zHiZg0zx1wCGm4VtEhSmZm7c09Y8ysU5O0L2l+ht1I87isHRGj69uqxiZpRVKX79VIF2x3RMTm9W2V5cvm6JmbNOyuK2lIRZusXmbtQ9IOwLKkRMxywKCIuKm+rTKlCeV3IM0/sxtpEuhVo2BSZasNSeuSklL9SPP3nBMRB9a1UWbWaTkZY2admqQxpDks3iGNia92yVRrQV7X8G9Ik77uF6Un5LQ6yFaXmkSad+dPEXF/nZtkLcgbZjkWOD0izqhrgwyYZijmeNJE4/tGxDv1bFNnlk0QfSxpfp2bgD/E1KXtzcxqyskYMzMzMzMzM7MamqnlImZmZmZmZmZm1lY8gW8nMtNMM0X37t1rFu/HH39kpplql++rdbx6xGz0ePWI2ejx6hHT8Tp+zEaPV4+YjR6vHjEbPV49YjZ6vHrEbPR49YjZ6PEAxo8fHxHhjhONrt5ra3ur3datW7eopREjRjR0vHrEbPR49YjZ6PHqEdPxOn7MRo9Xj5iNHq8eMRs9Xj1iNnq8esRs9Hj1iNno8SIigO9iBrh+9Na+m7NtZmZmZmZmZmY15GSMmZmZmZmZmVkNORljZmZmZmZmZlZDTsaYmZmZmZmZmdWQkzFmZmZmZmZmZjXkZIyZmZmZmZmZWQ05GWNmZmZmZmZmVkNOxpiZmZmZmZmZ1ZCTMWZmZmZmZmZmNeRkjJmZmZmZmZlZDTkZY2ZmZmZmZmZWQ07GmJmZmZmZmZnVkJMxZmZmZmZmZmY15GSMmZmZmZmZmVkNORljZmZmZmZmZlZDTsaYmZmZmZmZmdWQkzFmZmZmZmZmZjXkZIyZmZmZmZmZWQ05GWNmZmZmZmZmVkNOxpiZmZmZmZmZ1ZCTMWZmZmZmZmZmNeRkjJmZmZmZmZlZDTkZY2ZmZmZmZmZWQ07GmJmZmZmZmZnVkJMxZmZmZmZmZmY15GSMmZmZmZmZmVkNORljZmZmZmZmZlZDTsaYmZmZmZmZmdWQkzFmZmZmZmZmZjXkZIyZmZmZmZmZWQ05GWNmZmZmZmZmVkNOxpiZmZmZmZmZ1ZCTMWZmZmZmZmZmNeRkjJmZmZmZmZlZDTkZY2ZmZmZmZmZWQ07GmJmZmZmZmZnVkJMxZmZmZmZmZmY15GSMmZmZmZmZmVkNORljZmZmZmZmZlZDTsaYmZmZmZmZmdWQkzFmZmZmZmZmZjXkZIyZmZmZmZmZWQ05GWNmZmZmZmZmVkNOxpiZmZmZmZmZ1ZCTMWZmZmZmZmZmNeRkjJmZmZmZmZlZDTkZY2ZmZmZmZmZWQ07GmJmZmZmZmZnVkJMxZmZmZmZmZmY15GSMmZmZmZmZmVkNORljZmZmZmZmZlZDTsaYmZmZmZmZmdWQkzFmZmZmZmZmZjXUqZIxknaSFNm2Z4kyfSTdJelLSeMlvSjpj5K6NHPeXSSNljRO0teSRkrarJny3SUdJ+l1SRMkfSrpekkrNFNnUUkXS/pI0g+Sxkg6Q9JclT0LZmZmZmZm1mh8vduxdJpkjKSfAGcD45opsyXwCLA+cAvwT2AW4HTg2hJ1/gFcCiwEXABcCawI3C7pgCLluwH3A38BvgHOBB4AtgaekbRWkTpLAc8CuwGjs/a8A/wBeELSPC09fjMzMzMzM2tMvt7teDpFMkaSgEuAL4DzS5SZg/TmmgL0i4g9IuJPwMrAE8AgSdsV1OkDHAq8DfwiIg6OiP2B1YAvgX9I6l0Q6hBgHeBGYK2IODwidgAGAT2AiyUVvi7nAvMDB0XEVhFxREQMIL1JlwOGVfqcmJmZmZmZWcfn692OqVMkY4CDgAGkTNt3JcoMAuYDro2IZ3I7I2ICcHR2d9+COvtkt8Mi4qu8OmNIWcZuWUyg6UOSq/PniPgxr85twKPAT4G+eXWWBAYCuXPmOzZ7PDtJ6lnicZmZmZmZmVnj8vVuB9TwyZhsXNpJwJkR8UgzRQdkt/cUOfYIMB7ok3W7KqfO3QVlAJYCFgPeiIh3y6yT+/m+/DczQER8C4wiZRh/WeR8ZmZmZmZm1qB8vdtxNXQyRlJX4ArgfWBIC8WXy27fKDwQEZOBd4GuwJLZuXsCiwDjIuLjIud7M7tdtpwYbVzHzMzMzMzMGpivdzu2rvVuQDv7C7AKsG5EfN9C2V7Z7dcljuf2z9nK8rWsY2ZmZmZmZo3N17sdWMMmYyStScoOnhoRT7TFKbPbqLBeJeVbE6PZOpIGA4MBunbtysiRIys4dXXGjRvX0PHqEbPR49UjZqPHq0dMx+v4MRs9Xj1iNnq8esRs9Hj1iNno8eoRs9Hj1SNmo8fLdJX0TN794RExPL+Ar3cbQEQ03EZKMr0OvAJ0Kzg2lPRC7lmw/+ls/2olzvlSdnyF7H7P7P63JcrPmx3/b96+TbN9t5eoMyg7fl3evlOyfYeWqHNOdnzflp6Xbt26RS2NGDGioePVI2ajx6tHzEaPV4+YjtfxYzZ6vHrEbPR49YjZ6PHqEbPR49UjZqPHq0fMRo8XEQF8F77erfh6t6NtjTpnzGykMWUrABMkRW4jzcgMcEG274zs/uvZ7XRj0bKxeEsAk0nrnRMR3wEfArNJWqhIG5bJbvPHvpWM0cZ1zMzMzMzMrDH5ercBNOowpR+Ai0ocW5U0ru4x0guf69L1ELAjsBFwTUGd9UkzOD8SET/k7X8I2Cmrc0lBnY3zyuS8TZpcaVlJS8T0M0wXqzMiux0oaabIm2Fa0uykNdy/B54s+mjNzMzMzMyskfh6twE0ZM+YiPg+IvYstgH/zopdlu27Lrt/I/A5sJ2k1XPnkjQr8Lfs7nkFoc7Pbo+SNFdend7A/qQPSdObNutylqtzsqSZ8upsCaxH6mr2cF6dt4H7gNw58x1H6j52eZa5NDMzMzMzswbm693G0Kg9YyoWEd9I2ov0Jh0p6VrgS2AL0nJbNwLXFdR5XNJpwCHAi5JuBGYBfgfMDRwYEWMKQp0GbEYaL/eUpAdJa7H/hrS2++5RsL46sB/wOHCWpA2AV4G1gP6k7lpHVf8MmJmZmZmZWSPy9e6MpyF7xrRWRNwK9AUeAbYFDgQmkd5822WZvsI6hwK7Ap+QVi3aGXgZ2DwizilS/gfgV8DxpOW5DgY2BG4F1oiIp4rUeRtYHbiU9KY8FFgKOAtYOyK+aO1jNjMzMzMzs8bn690ZS6frGRMRQ0kzTJc6PgrYpMJzXgZcVkH570kTKx3bUtm8OmOB3Sppl5mZmZmZmXUevt7tONwzxszMzMzMzMyshpyMMTMzMzMzMzOrISdjzMzMzMzMzMxqyMkYMzMzMzMzM7MacjLGzMzMzMzMzKyGnIwxMzMzMzMzM6shJ2PMzMzMzMzMzGrIyRgzMzMzMzMzsxpyMsbMzMzMzMzMrIacjDEzMzMzMzMzqyEnY8zMzMzMzMzMasjJGDMzMzMzMzOzGnIyxszMzMzMzMyshpyMMTMzMzMzMzOrISdjzMzMzMzMzMxqyMkYMzMzMzMzM7MacjLGzMzMzMzMzKyGnIwxMzMzMzMzM6shJ2PMzMzMzMzMzGrIyRgzMzMzMzMzsxpyMsbMzMzMzMzMrIa6tqaSJAG/AjYE1gcWA+YFvgc+BV4AHgL+HREftklLzczMzMzMzMwaQEXJGEk9gIOAvUkJGGWHJpCSMN2BJYGlgG2BMyXdDpwaEY+3VaPNzMzMzMzMzDqqsocpSdoNeBM4gdQD5jhSz5g5I6JHRCwaEfOQEjw/BXYHbgI2Bh6VdJ2kxdr6AZiZmZmZmZmZdSSV9Iy5CLgVODEini5VKCICeC3bLpU0B7ALcASwK3B8axtrZmZmZmZmZtbRVZKMWT0inqs0QER8A5wt6QKgd6X1zczMzMzMzMwaSdnJmNYkYgrqTyD1ljEzMzMzMzMz67RatZpSMdlwpF7A11lvGDMzMzMzMzMzK1D2BL7FSOoi6QhJbwFfAWOAryS9le1vs2SPmZmZmZmZmVkjaHWyRNIswD1AXyCAscDHwEKkuWGGARtJGhgRE6tvqpmZmZmZmZlZx1dNz5hDgH7AncAKEdE7ItaOiN7AcsDtwHpZOTMzMzMzMzMzo7pkzA7AS8BWEfFm/oGIeBvYBngZ2LGKGGZmZmZmZmZmDaWaZMzSwN0R8WOxg9n+u4GlqohhZmZmZmZmZtZQqknGTARma6FMT2BSFTHMzMzMzMzMzBpKNcmYF4FBkuYrdlDSvMAg4D9VxDAzMzMzMzMzayjVJGPOAeYDRkvaQ9KSkrpLWkLSbsBT2fFz2qKhZmZmZmZmZmaNoNVLW0fE9ZJWBo4AhhcpIuDkiLi+tTHMzMzMzMzMzBpNq5MxABExRNK/gT2AVYBewNfA88DFEfFE9U00MzMzMzMzM2scrU7GSFof+CYingSebLsmmZmZmZmZmZk1rmrmjBkBDG6rhpiZmZmZmZmZdQbVJGM+B75vq4aYmZmZmZmZmXUG1SRjRgJ92qgdZmZmZmZmZmadQjXJmKOB5ST9VdLMbdUgMzMzMzMzM7NGVs1qSkcCLwFDgD0k/Qf4BIiCchERe1QRx8zMzMzMzMysYVSTjNk17+cFs62YIC19bWZmZmZmZmbW6VWTjFmizVphZmZmZmZmZtZJtDoZExHvtWVDzMzMzMzMzMw6g2om8DUzMzMzMzMzswo5GWNmZmZmZmZmVkOtHqYk6Z0yi0ZELNXaOGZmZmZmZmZmjaSaCXxnYvplrAF6AXNmP38ETKoihpmZmZmZmZlZQ6lmAt/epY5JWho4C+gJ/Lq1MczMzMzMzMzMGk27zBkTEW8B2wCLAMe2RwwzMzMzMzMzs46o3SbwjYgJwP3A9u0VoxRJ80jaU9Itkt6S9L2kryU9JmkPSUUft6Q+ku6S9KWk8ZJelPRHSV2aibWLpNGSxmUxRkrarJny3SUdJ+l1SRMkfSrpekkrNFNnUUkXS/pI0g+Sxkg6Q9JclT0zZmZmZmZm1pH5ercxtPdqSpOBBds5RjG/AS4A1gKeAs4AbgJ+DlwIXC9J+RUkbQk8AqwP3AL8E5gFOB24tlgQSf8ALgUWyuJdCawI3C7pgCLlu5ESVH8BvgHOBB4AtgaekbRWkTpLAc8CuwGjs/a8A/wBeELSPGU9I2ZmZmZmZtYIfL3bzpRsKOlkSU9miaKJWULqTUk3SNpX0iKtjVHNBL7NkjQv6Ukf214xmvEGsAVwZ0T8mNemIaQXeFvSMKqbsv1zkN5cU4B+EfFMtv8Y4CFgkKTtIuLavHP1AQ4F3gbWiIivsv2nkN5M/5B0R0SMyWvXIcA6wI3A73Jtk3QdcCtwsaQV89sMnAvMDxwUEWfnxT8NOBgYBuzT+qfKzMzMzMzMOhBf77YTST2Ag4C9gcWAXFJrAvAp0B1YEliK9DyfKel24NSIeLySWK3uGSPpLyW24yVdQnqD5DJoNRURD0XE7QUvMhHxCXB+drdf3qFBwHzAtbk3ZlZ+AnB0dnffgjC5N8Sw3BszqzOGlGXsRsruASmzllfnz/lti4jbgEeBnwJ98+osCQwEcufMdyzwHbCTpJ6Fz4GZmZmZmZk1Hl/vtg9JuwFvAicA3wPHARsCc0ZEj4hYNCLmIXVq+SmwOynhtTHwqKTrJC1WbrxqhikNLbEdDeySnftvEXFyFTHaQ26p7cl5+wZkt/cUKf8IMB7ok3W7KqfO3QVlIGXOFgPeiIh3y6yT+/m+Ih+0b4FRQA/gl0XOZ2ZmZmZmZp2Lr3db7yLSsK+1IuKnEXF8RDwYEd8UtC0i4rWIuDQitidNzfJHYF1g13KDVTNMqX+J/T8CXwGvRcTkEmXqQlJXYOfsbv6barns9o3COhExWdK7wM9I3ZFezTJziwDjIuLjIqHezG6XLSdGlXUGZnUeLFHGzMzMzMzMGpyvd6u2ekQ8V2mlLFlztqQLgN7l1lNEVBqrw8omIDoUuCsiNs3b/wawDLBMtix3Yb1RQB+gT0Q8IWlh4EPgw4hYtEj5mYGJwMSI6Jbt2wG4CrgqIn5fpM6GwH2krOCvs33Dgb2AvSLiwiJ1hgFDgCERcWKJxzwYGAzQtWvX1e6///6Sz09bGzduHLPNNlvDxqtHzEaPV4+YjR6vHjEdr+PHbPR49YjZ6PHqEbPR49UjZqPHq0fMRo9Xj5iNHg+gf//+E4H/y9s1PCKGt1SvM17vdmgR0Sk20iQ8AbwKzF1w7I3s2NIl6j6eHf9ldn/h7P4HJcrPnB2fkLdvh2zflSXqDMyO35O3b3i2b88SdU7Ijh9RznPQrVu3qKURI0Y0dLx6xGz0ePWI2ejx6hHT8Tp+zEaPV4+YjR6vHjEbPV49YjZ6vHrEbPR49YjZ6PEiIoDvwte7FV/vdrStojljJK1fyYQ0kn4haeeWS7YvSfuTltV6BegfEV8WFPk6u+1V4hRzFJRrqXyvgnKtidHaOmZmZmZmZtZJ+Hq3NiTNIekn2epUVat0At8RFExII+lwSV+UKL81cEkr2tVmJP0ROAd4ifTG/KRIsdez22ULD2Tj7pYgTYD0DkBEfEfqtjWbpIWKnG+Z7DZ/7FvJGG1cx8zMzMzMzDoBX++2L0ldJB0h6S3S3LhjgK8kvZXtb/U8vJUmY1Rk36zAnK1tQHuSdDhwOvAC6Y35aYmiD2W3GxU5tj5pBufHI+KHMutsXFAG0vrs7wPLSlqizDojstuBkqZ5rSTNTlrD/XvgySLnMzMzMzMzswbl6932JWkW4H5gGGli3rHA6Oy2d7b/gaxcxapZ2nqGJukY4CTgWWCDiPi8meI3Ap8D20laPe8cswJ/y+6eV1Ant377UZLmyqvTG9gf+IG8XkHZ2L9cnZPz32yStgTWI3UrezivztukSY5y58x3HNATuDzLXJqZmZmZmVkn4OvdmjgE6AfcCawQEb0jYu2I6E1aCep20uM6pDUnr2Zp6xmWpF2A44EpwKPAQdJ0nXrGRMSlkJaikrQX6U06UtK1wJfAFqQn+UbguvzKEfG4pNNIT/yLkm4EZgF+B8wNHBgRYwpingZsBgwCnpL0IGkt9t+Q1nbfPQrWVwf2I02odJakDUgTMq1FWlr8DeCoip4cMzMzMzMz67B8vVszO5CGf21V2O6IeFvSNqReSTuSEmMVachkDGnMG0AX4I8lyjwMXJq7ExG3SupLerG3JQ2/eov05jsry/RNIyIOlfQicABp+egfgeeAUyLijiLlf5D0K+AI0gt7MPANcCtwbES8UqTO21n28nhSF7FNgI+Bs4DjikzOZGZmZmZmZo3L17u1sTRwdpEEEgAR8aOku4EDW3PyhkzGRMRQYGgr6o0ivfiV1LkMuKyC8t8Dx2ZbuXXGArtV0i4zMzMzMzNrPL7erZmJwGwtlOkJTGrNyVszZ8x0GTMzMzMzMzMzswbyIjBI0nzFDkqalzQk6z+tOXlresYMlTS0SEOmtKYBZmZmZmZmZmYzmHOAa4HRkv5GWv3pY2BB0sS+RwPzAQe15uStScYUW966Oe5JY2ZmZmZmZmYdRkRcL2ll0hw4w4sUEXByRFzfmvNXlIyJiIZdCtvMzMzMzMzMLCcihkj6N7AHsArQC/gaeB64OCKeaO25G3ICXzMzMzMzMzOz1pK0PvBNRDwJPNnW53dPFzMzMzMzMzOzaY0gLendLpyMMTMzMzMzMzOb1ufA9+11cidjzMzMzMzMzMymNRLo014ndzLGzMzMzMzMzGxaRwPLSfqrpJnb+uSewNfMzMzMzMzMbFpHAi8BQ4A9JP0H+ASIgnIREXtUenInY8zMzMzMzMzMprVr3s8LZlsxQVr6uiJOxpiZmZmZmZmZTWuJ9jx5i8kYSesBn0bE69n9/YD9gcWA94BzIuL89mykmZmZmZmZmVmtRMR77Xn+cibwPQ9YGEDS/sDfgVuBfYFbgJOy/WZmZmZmZmZm1oJyhiktBbyT/bwncEBEXJY7KOlF4K/AP9u+eWZmZmZmZmZmjaWcZMy3wLykIUkLAy8UHH+ONGTJzMzMzMzMzKzDk/ROy6WAtJrSUpWev5xhSncBB2Q/jwB+W3D8d8AblQY2MzMzMzMzM5tBzQSoyDYn0DvbZqG8vMp0yukZcwQwStKjwFPAwZLWB14FlgN+CWzVmuBmZmZmZmZmNSeVPhZRu3bYDCsiepc6Jmlp4CygJ/Dr1py/xQxORHwCrAo8CmxMygStCQwEPgDWiYi7WxPczMzMzMzMzKwjiYi3gG2ARYBjW3OOcnrGEBFfA0OyzczMzMzMzMys04qICZLuB7YHjqy0fqvGNpmZmZmZmZmZdXKTgQVbU7GsnjHFSOpOmi9mWdIENgF8TZrM98mI+L615zYzMzMzMzMzm1FJmhfYGhjbmvoVJ2MkzQUMA3YCepQo9r2ky4CjI+Kr1jTMzMzMzMzMzKweJP2lxKGuwE+ALYFetGKIUu4klTRmTmAUsDzwHXA/8CapR4yAOYBlgHWAfYH+kvpExP9a0zgzMzMzMzMzszoY2sLxb4C/RcTJrTl5pT1jjiUlYk4Hjo2IccUKSZoNOB74I/AX4JDWNM7MzMzMzMzMrA76l9j/I/AV8FpETG7tyStNxmwFPBQRhzZXKEvSHCJpZdIYKidjzMzMzMzMzKxDiIiH2/P8la6mtBAwuoLyT2Z1zMzMzMzMzMxmWJLWl7RYBeVXkrRza2JVmoz5AliugvIrZHXMzMzMzMzMzGZkI4Bd83dIOlxSqbzGVsAlrQlUaTLmXmArSfu1VFDSAcAWwD2taZiZmZmZmZmZWQ2pyL5ZgTnbOlClc8YcA2wKnC3pUOA+4A3SakqQlnVaFhgI9AY+JU3ga2ZmZmZmZmZmVJiMiYgPJa0NnAdsCOwNREGxXCbpPmC/iPiw6laamZmZmZmZmTWISnvGEBHvAL+WtAQwgDSHTK/s8NfA68CIrJyZmZmZmZmZmeWpOBmTExHvAhe1YVvMzMzMzMzMzBpepRP4mpmZmZmZmZk1qsKpWNpFq3vGmJmZmZmZmZk1mKGShhbulDSlLYO0a88YSadIers9Y5iZmZmZmZmZtRFVuLVKe/eMmZe0xLWZmZmZmZmZ2QwrImo2lYvnjDEzMzMzMzMzq6GKesZIurzC8/epsLyZmZmZmZmZWUOrdJjS70kzC1cyLqomMxGbmZmZmZmZmXUElSZjvgU+APYrs/wRwMAKY5iZmZmZmZmZNaxKkzH/AVaKiIfLKSxp14pbZGZmZmZmZmbWwCqdwPcFYDZJS7VDW8zMzMzMzMzMGl6lPWMeBtYDFgXeLqP8rcCYCmOYmZmZmZmZmTWsipIxEXETcFMF5W8Dbqu0UWZmZmZmZmZmjarSYUpmZmZmZmZmZlYFJ2PMzMzMzMzMrFOStJ6k5fLu7yfpZUnfSnpJ0j7tEbfsZIyk7tUGa4tzmJmZmZmZmZm1kfOAhQEk7Q/8nTT/7b7ALcBJ2f42VUnPmHcl/UFSt0qDSFpJ0m3AYZXWNTMzMzMzMzNrJ0sB72Q/7wkcEBFHRcSVEXEMsBdwYFsHrSQZcx9wGvCxpPMk9W+up4ukJSXtK+kJ4DlgJWBEdc01MzMzMzMzM2sz3wLzZj8vDLxQcPw5YLG2Dlr2akoRsbOks4ATgMHZNkXSq8DHwFfArMA8wHKkByPgv8BRwOkR8UPbNt/MzMzMzMzMrNXuAg4AdiN1IPkt8J+8478D3mjroJUubf0MMFDSMsAewAbAysCKBUU/A24mLYN9U0RMqr6pZmZmZmZmZmZt6ghglKRHgaeAgyWtD7xK6mjyS2Crtg7aqtWUIuLNiDgiItYAepEa2AdYBVgkIhaIiN9ExLVOxLQNSYtKuljSR5J+kDRG0hmS5qp328zMzMzMzKy2fI3YNiLiE2BV4FFgY9IInzWBgcAHwDoRcXdbx62oZ0wxETEeeDPbrB1IWgp4HJgfuA14jfTm+AOwkaR1IuKLOjbRzMzMzMzMasTXiG0rIr4GhmRbTbSqZ4zV3LmkD9lBEbFV1itpAHA6qVfSsLq2zszMzMzMzGrJ14gdnJMxMzhJS5K6R40B/llw+FjgO2AnST1r3DQzMzMzMzOrMV8j1oak7tkq0ntLOlzSn7Ofm11ZulxVD1Oydjcgu70vIn7MPxAR30oaRfog/hJ4sNaNMzMzMzMzs5ryNWI7yubcGQbsBPQoUex7SZcBR0fEV62J42TMjG+57LbUUlpvkj5oy+IPmpmZmZmZWaPzNWI7kTQnMApYntTD6H7S8/k1aWLfOYBlgHWAfYH+kvpExP8qjhURbdNqaxeShgN7AXtFxIVFjg8jm2goIk4scnwwMBiga9euq91///3VNejZZ0sfW221ae6OGzeO2Wabrbp4FcSsdbw2jVlmUxZddBwffDBbsaa0S8z8eO0Vs9aP0c9px49XGLPWz2kjvoaFMTvsc9rM9/ezTHvSNnlOm/t9URCz1o/R79OOH68wZq3j1SJmh31OW/ldU4uYHfb7tIKmNOJ3TaH+/ftPBP4vb9fwiBieu1PtNaKVJul00iTIpwPHRsS4EuVmA44H/gicERGHVBwsIrzNwBswHAhgzxLHT8iOH9HSubp16xZVg9JbgREjRlQfr4KYtY7XpjHLbMo//jGiVFPaJWZ+vPaKWevH6Oe048crjFnr57QRX8PCmB32OW3m+7tdntPmfl8UxKz1Y/T7tOPHq/f3t5/TCk5a5ndNh/rs1/r7tIKmNOJ3zfTx+S6iNteI3qZ77t4FHqig/EPAu62J5Ql8Z3xfZ7e9Shyfo6CcmZlZ5zT93+xTNzMzs8bha8T2sxAwuoLyT2Z1Ktbmc8ZImhdYDxhPyihNaesYnczr2e2yJY4vk92WGi9oZmZmZmZmjcPXiO3nC6bOyVOOFbI6FWt1zxhJ+0p6StLceftWA14FbgTuAh73clpVG5HdDpQ0zeslaXbSxEHfkzJyZmZmVivN9cRpr9447v1jZma+RmxP9wJbSdqvpYKSDgC2AO5pTaBqhin9jjRo7su8facAcwGXkJIxawD7VBGj04uIt4H7gN7A/gWHjwN6ApdHxHc1bprVQOHf2aut1v5/c5eKV6vrivZ+jH5OO348MzOzunJS1OrM14jt6hjgc+BsSW9LOk/SwZJ2z7aDs31vA2cCnwJ/aU2gaoYpLQPcmbuTDU/qC1wYEXtn+54CdgBOrSKOwX7A48BZkjYg9T5aC+hP6np2VB3bZmZWc/l/744c2f5//9YzXj1iNnq8esRs9Hi1imlmZk18jdgOIuJDSWsD5wEbAnuTJkPOp+z2PmC/iPiwNbGqScbMQ8oC5ayT3d6St+9RYNcqYhgp8ylpddLSWRsBmwAfA2cBxxX0TjIzMzNrSE5wdex4hTH9nJq1nq8R209EvAP8WtISwADSHDK5yZK/Js3ZMyIr12rVJGO+BObNu98X+JGUncsJYNYqYlgmIsYCu9W7HWZmZmZmNuOodYKr1pzgKs3XiO0rIt4FLmqv81czZ8yrwOaS5pE0J2kOmacj4pu8Mr2BT6qIYWZmZmZmZmbWUKpJxpxJWk/7A2AssCBwbu6gpC7AusB/qmmgmZmZmZlZp+ZJg80aTquTMRHxb9JKSS+TxkwdFhFX5hX5FWmI0r1VtdDMzMzMzMxqx8kfs7JIOiVbWali1cwZQ0QMB4aXOHYvaZlrMzMzMzMzawCew8VsGvOSpmepWKt7xkjaVdJPWlvfzMzMzMzMzKwzqqZnzMVASHoHeBB4CHgoIj5vk5aZmZmZmZmZmdWIpMsrrNKntbGqScYcQFpzuy8wGNiLlJx5manJmYcj4tsqYpiZmZmZmZmZ1cLvgQBUQZ1WDdRrdTImIs4FzpUkYGVgg2xbF1gR+AMwRdIzEdHqbJGZmZmZmZmZWQ18S1oxer8yyx8BDGxNoKom8AWIiACez7Z/SJoF2Bc4EpgfWKvaGGZmZmZmZmZm7ew/wEoR8XA5hSXt2tpAVSdjsgYszdSeMf2BuUndet4mDVkyMzMzMzMzM5uRvQCsI2mpiGjVktXlanUyRtKOTE3ALEpKvnwE3ENKwDwYEWPbopFmZmZmZmZmZu3sYWA9Uo6jnGTMrcCY1gSqpmfMFaSJakYAJwEjIuK1Ks5nZmZmZmZmZlYXEXETcFMF5W8DbmtNrGqSMROBWYD1gZ7AwpIeBB6PiIlVnNfMzMzMzMzMrGHNVEXdOYENgX9k5zmCNDzpf5Lul3SEpDWy1ZbMzMzMzMzMzGZIkrrX8hytTsZExISIeDAihkTEWsA8wDbABcCCwDDgSeCL1sYwMzMzMzMzM6uBdyX9QVK3SitKWknSbcBh5dappmfMNCLiG2BUtj0BfE6a1LdXW8UwMzMzMzMzM2sH9wGnAR9LOk9S/+Z6ukhaUtK+kp4AngNWIs2pW5aqlraW1JM0Z0xuVaUVSQkYAf8jTWTjpa3NzMzMzMzMbIYVETtLOgs4ARicbVMkvQp8DHwFzEoaFbQcMC8p9/Ff4Cjg9Ij4odx41Sxt/SiwZnYOAd+TEi8PZbfPRkS09vzWgV11FRx1FH3ffx8WWwyGDYMdd6x3q8zMzMzMzMxKiohngIGSlgH2IHU6WZnU8STfZ8DNpJWXboqISZXGqqZnzFrAU0xNvjzRmgZYg7nqKhg8GMaPRwDvvZfugxMyZmZmZmZmNsOLiDdJixQhqQewCKlHzPfApxHxcbUxqknGzBUR31XbAGswRx0F48dPu2/8+LTfyRgzMzMzMzPrQCJiPPBmtrWZVidjChMxkuYgTdb7dTaZrzWilkaevf9+ZfvNzMzMzMzMOpmqVlOS1EXSEZLeIk1mMwb4StJb2f6qJgi2DmixxSrbb2ZmZmZmZtbJtDoZI2kW4H5gGNAbGAuMzm57Z/sfyMpZZzFsGPToMe2+7t3TfjMzMzMzMzOrqmfMIUA/4E5ghYjoHRFrR0Rv0jJPtwPrZeWss9hxRxg+HBZfnKYBTZtv7vlizMzMzMzMzDLVJGN2AF4CtspmGm4SEW8D2wAvA74K72x23BHGjOHhESNgo43goYfgO8/1bGZmZmZmZgbVJWOWBu6OiB+LHcz23w0sVUUM6+iOOQY+/zz1ljEzMzMzMzOzqpIxE4HZWijTE5hURQzr6Pr0gf794ZRTYMKEerfGzMzMzMzMrChJW0iqaqGjclUT5EVgkKT5ih2UNC8wCPhPFTGsERx9NHz8MVxySb1bYmZmZmZmZlbKrcB7ko6XtHh7BqomGXMOMB8wWtIekpaU1F3SEpJ2A57Kjp/TFg21Dqx//9RD5qSTYOLEerfGzMzMzMzMrJgHgYWBo4G3Jd0taWtJXdo6UKuTMRFxPXASsDgwHHgTGAe8BVwILAGckpWzzkxKvWPefx+uvLLerTEzMzMzMzObTkRsCCwJnAB8DPwauBEYK2mYpCXbKlZVY6EiYgjQB7gYeB54J7u9GFgnIo6ouoXWGDbaCFZbDU48ESZPrndrzMzMzMzMzKYTEe9FxNGkjidbAncA8wJHAm9Iuk/SIEldq4lTVeWsoU8CT1Z7Hmtwud4xW28N112Xlr82MzMzMzMzmwFlK0TfDtwuaUFgd2AP4FfABsDnki4FLoyINys9f01mCTYDYIst4Oc/h2HD4MeiK6KbmZmZmZmZzVAi4pOIOCEilgI2BG4A5gAOA15tzTmdjLHamWmm1Dvm1Vfh5pvr3RozMzMzMzOzSj0M3ERaOVrZVrGyhylJurg1AYCIiD1aWdcazaBBsOyy8Le/wbbbpuFLZmZmZmZmZjMwScsBewI7k+aQEfAucFFrzlfJnDG7tiYAEKRxVWbQpQsMGQK77gp33gmbbVbvFpmZmZmZmZlNR9KswG9JSZh1SAmYScDNwAURcV9rz11JMmaJ1gYxm8YOO8Bxx8Ff/wqbbureMWZmZmZmZjbDkLQysBewA2luGAFvAxcCl0TEp9XGqCQZsyXwZESMrjaodXIzzwxHHAF77w0PPAAbbljvFpmZmZmZmVknJ2lvUhJmFVICZiJpst7hEfFQW8aqZALfM4CNcnckTZF0TFs2xjqRXXaBRRdNc8eYmZmZmZmZ1d95wKrAm8CfgEUjYru2TsRAZcmYCUC3vPutnjXYjG7d4M9/hkceSZuZmZmZmZlZfV0D9I+I5SPi1Ij4vL0CVZKMeRf4taQF8vZFG7fHOpM994QFFnDvGDMzMzMzM6u7iNgxIh6uRaxKkjH/InXX+UjSlGzf0Gy4UnPb5LZvtjWE7t3hsMPg/vvhqafq3RozMzMzMzMzJC0maVtJ20j6SXvEKHsC34g4S9KnwKbAwkB/4H1gTHs0zDqJffaBE09MvWNuv73erTEzMzMzM7NOTNI/gD8ydVqWkHR6RPypLeNUspoSEXEtcG3WwB9JSzod35YNsk5mttng4IPhmGPg+edhlVXq3SIzMzMzM+vEIm8yjpEjp71vjU3SDsAhpClZXiMlZJYDDpH0XERc01axKhmmVOg4YGQbtcM6swMPhF69YNiwerfEzMzMzMzMOq89gMnAryLiZxHxU+DXwI/ZsTbT6mRMRBwXEV4Gx6rXq1dKyNx0E7z8cr1bY2ZmZmZmZp3TL4BbI2JEbkdEPADcBqzcloGq6Rlj1nb++Efo2RNOOKHeLTEzMzMzM7POaS7g9SL7XwPmbMtATsbYjGGeeWC//eDaa+HNN+vdGjMzMzMzM+t8ZgImFdk/iakT+rZZILMZwyGHwCyzwEkn1bslZmZmZmZm1jnVZMpmJ2NsxrHggjB4MFx+OYwZU+/WmJmZmZmZWeczVNKU/A34C0Dh/myb3JogTsbYjOVPfwIJTj556r6I0puZmZmZmZlZ21GFW6vyKk7G2Ixl0UVht93goovgo4/q0wYnf8zMzMzMzDqdiJipNVtrYjVcMkbSMpIOl/SQpLGSJkr6r6TbJPVvoe4ukkZLGifpa0kjJW3WTPnuko6T9LqkCZI+lXS9pBWaqbOopIslfSTpB0ljJJ0haa5m6vSRdJekLyWNl/SipD9K6lLes9LBHHEETJkCp5xS75aYmZmZmZk1FF8zzxiqSsZI6ivpjuwJndSW46eq8FfgJGAB4C7gVGAUsCnwkKSDilWS9A/gUmAh4ALgSmBF4HZJBxQp3w24nzR27BvgTOABYGvgGUlrFamzFPAssBswGjgdeAf4A/CEpHmK1NkSeARYH7gF+CcwS1b32jKej45niSXg97+Hf/0LPv203q0xMzMzMzNrJL5mLkHSTK3ZKomR07U1lbJGbgrcCnQB3ietxV3rxEsx9wB/j4jn83dK6kt6I5wi6YaI+DjvWB/gUOBtYI2I+CrbfwrpjfAPSXdExJi8Ux4CrAPcCPwuIn7M6lxHel4ulrRibn/mXGB+4KCIODsv/mnAwcAwYJ+8/XOQ3uRTgH4R8Uy2/xjgIWCQpO0iovGSMkOGpIl8TzvNqyuZmZmZmZm1HV8zl1ZsWeuWBK3IrVTTM2YoqaEbRUTviFgvIvoX26qIUbGIuLTwTZXtfxgYScqQ9Sk4nHsxh+XeVFmdMaSsWjdSZg4AScqr8+f8N09E3AY8CvwU6JtXZ0lgIJA7Z75jge+AnST1zNs/CJgPuDb3pspiTACOzu7uW/hYG8Kyy8Lvfgf//Cd8+WW9W2NmZmZmZtYQfM3crLGkziblbF8ydRLfilWTjPk5cF1E3FfFOWotl+Uq7MEzILu9p0iduwvKACwFLAa8ERHvllkn9/N9BZk/IuJbUrewHsAvy2zXI8B4oE/W/avxDBkC48bBWWfVuyVmZmZmZmadQae+Zs46mizR3AYsC5zN1HzKmHLOXaiaZMw4UiaoQ5C0OLAB6cV4JG9/T2ARYFx+N6w8b2a3y+btWy67faNEuHavExGTgXdJ3aGWLHHOjm3FFWHrreHMM+Gbb+rdGjMzMzMzs4bla+aWSfoN8CpwCqlHzJ+BkpMRN6eaZMyDwNpV1K+ZLAt2Fanr1ND8blVAr+z26xLVc/vn7CB1GstRR8H//peGK5mZmZmZmVmb8zVz87LVmp4gTQj8E+AsYKmI+EdETGzVOSOitY1ZnDS78dmkcWOtO1Hxc48BFq+gylUR8fsS5+oCXAP8BrgO2D6/rZIWBj4EPoyIRYvUnxmYCPwQEbNm+3YgvVGLxpU0ELgXuDciNsr2DQf2AvaKiAuL1DkBOBI4MiJOyva9ASwDLBMRbxWp8zgpIbZ2RDxZ4vEPBgYDdO3adbX777+/WLF2MW7cOGabbbaqz7PiEUcw+2uv8eQ11/Bj9+7tHq8StY7Z6PHqEbPR49UjpuN1/JiNHq8eMRs9Xj1iNnq8esRs9Hj1iNno8eoRs9HjAfTv338i8H95u4ZHxPD8Mr5mbrtr5uZIWpq08tTWpJ4wNwJHRMQ7lZ6rUNkz/kq6uMjul4HjgN0lvQD8r0iZiIg9KmzX28CECsp/VGxn9qa6kvSmuh74fZGkUS5T1oviimXaWqozRx3rTCP70A4HmHXWWaNfv36lira5kSNH0ibxTj8d+vRh/VdfhUMOaf94Fah1zEaPV4+YjR6vHjEdr+PHbPR49YjZ6PHqEbPR49UjZqPHq0fMRo9Xj5iNHi8zOSJWb6GMr5lbX6dFkuYmTRq8N2lC4yeAQ1uT0CmlkuWXdm3mWO9sKyaAipIxEbFBJeWLkdQVuJr0proa2DkiphSJ9Z2kD4FFJC1UZAzcMtlt/hi017PbZSmuLeusntV5Nr9w9viWIE2sVHVWboa29towYACccgrstx/MOmu9W2RmZmZmZlY3vmaepk6bXTNLmgX4I6kHTi9S0uuIiLipnPqVqGTOmCVaudV8ctnsCbyR9Ka6HNip2Jsqz0PZ7UZFjm1cUAbSC/I+sKykJcqsMyK7HShpmudd0uyk9de/B/Izbc21a33STNKPR8QPRY43lmOOgU8+gYsuqndLzMzMzMzMOjRfM5f0OnAiKYHzR2D59kjEQAXJmIh4r7VbezS8lGzioVuALYGLgN0Kl8Uq4vzs9ihJc+WdqzewP/ADcEluf9ZtK1fn5Pw3iqQtgfWAV4CH8+q8DdxH6kG0f0H844CewOUR8V3e/huBz4HtJDV1U5M0K/C37O55LTy2xtC3L6yzDvz97zCxVfMjmZmZmZmZdXq+Zm5Wbh4eAYcB70p6v4WtVTmPSoYplUXSvKQndjzwQAvZtfZwPrAJ6QX5EPiLpMIyIyNiZO5ORDwu6TTgEOBFSTeSxoX9DpgbODAixhSc4zRgM2AQ8JSkB0nrqP+G9Nh3L/KG3g94HDhL0gakJbHWAvqTulodlV84Ir6RtBfpDTZS0rWk5cS3IC3hdSNpgqXGJ8HRR8PGG8MVV8AelU5DZGZmZmZmZviauSXKHtPcFdarSKuTMZL2Jc0js3FEfJntWw24h6mNfkbSgILMVXvLdYGaF/hLM+VG5t+JiEMlvQgcQFp96EfgOeCUiLijsHJE/CDpV8ARwA7AwcA3wK3AsRHxSpE6b2fZuuNJ3ag2AT4mLYt1XO55LKhzq6S+pDfdtsCswFukD8FZbbmK1Qzv17+G1VeHE06AXXaBrm2eSzQzMzMzM2t0vmYuISIqmcqlKtVczf6O1Pso/8k4BZiL1D1pAWBTYB/g1CriVCQi+lVR9zLgsgrKf0+aYfnYCuqMBXarsF2jSG/Czi3XO2arreDaa+H3RVdmMzMzMzMzsxJ8zTxjqCbrswzwYu5ONjypL3BRROwZEZsDT5MyYGZtY/PNYcUVYdgwmFLrEXBmZmZmZmZm1asmGTMP8Gne/XWy21vy9j3K1AlwzKo300ypd8xrr8HNN9e7NWZmZmZmZmYVqyYZ8yVpjFlOX9KYscfz9gVpvJZZ29l2W1huOfjb36ATTZljZmZmZmZmjaGaZMyrwOaS5pE0J2kOmacj4pu8Mr2BT6qIYTa9Ll3gqKPgxRfh9tvr3RozMzMzMzOzilSTjDkTWAj4ABgLLAicmzsoqQuwLvCfahpoVtT228MSS7h3jJmZmZmZmXU4rU7GRMS/SSslvQy8DhwWEVfmFfkVaYjSvVW10KyYrl3hyCPh6afh/vvr3RozMzMzMzOzslW1hnZEDI+I1bPt9IJj90bEXBExvLommpWwyy6w6KLw17+6d4yZmZmZmZl1GFUlY8zqapZZ4PDD4bHH4JFH6t0aMzMzMzMzs7I4GWMd2x57wBxzwEYb0XfAAOjdG666qt6tMjMzMzMzMyupa7UnkLQQsAGwCNCtSJGIiL9WG8esqJtvhu+/h0mTEMB778HgwenYjjvWs2VmZmZmZmZmRVWVjJF0HHBEwXkERMHPTsZY+zjqKJg0adp948en/U7GmJmZmZmZ2Qyo1cOUJO0IHAM8CgwiJV4uA3YALgB+BK4FBlTfTLMS3n+/sv1mZmZmZmZmdVbNnDH7Ah8AG0XELdm+MRFxbUTsA2wG/BaYo8o2mpW22GKV7TczMzMzMzOrs2qSMSsCd0XE5Lx9XXI/RMS9wL3An6qIYda8YcOgR49p9806a9pvZmZmZmZmNgOqJhkzM/BF3v3vgV4FZV4CVqoihlnzdtwRhg+HxRcnpLRvrbU8X4yZmZmZmZnNsKpJxnwMLJR3/33gFwVlFgEmY9aedtwRxozh4YceggMPhFGjPGeMmZmZmZmZzbCqScY8TxqqlPMQsJ6knST1lLQpsG1Wzqw2Djss3Z56an3bYWZmZmZmZlZCNcmYO4CfSVoiu38S8DVwKfAN8G/SCktHV9NAs4osthjstBNccAF8+mm9W2NmZmZmZmY2nVYnYyLi0ojoERHvZvfHAmsA5wH3AcOBNSLiyTZpqVm5Dj8cJkyAM8+sd0vMzMzMzMzMplNNz5jpRMS7EXFARGwcEftGxP+15fnNyrLccjBoEJxzDnz9db1bY2ZmZmZmZjaNipIxkrpJGi3pQUkzN1NulqzMk82VM2s3Rx4J33wD555b75aYmZmZmZmZTaPSnjE7AqsBp0bEpFKFImIicAqwZlbHrLZWWQU22ghOPx3Gj693a8zMzMzMzMyaVJqM2QZ4JyLuaqlgRNwDvAn8pjUNM6vakCHw2Wdw8cX1bomZmZmZmZlZk0qTMasAIyso/wiwcoUxzNrGeuvBuuvCySfDxIn1bo2ZmZmZmZkZUHkyZl7gvxWU/y8wT4UxzNrOkCEwdixcfXW9W2JmZmZmZmYGVJ6M+R6YrYLyswETKoxh1nY22ghWXhlOOgmmTKl3a8zMzMzMzMwqTsaMBdaooPzqwPsVxjBrO1LqHfP663DLLfVujZmZmZmZmVnFyZiRwC8lrd5SQUmrAX2AEa1ol1nb2WYbWHZZOOEEiKh3a8zMzMzMzKyTqzQZcw4QwA2SVihVSNLywA3AFODc1jfPrA106QJHHAHPPw/33lvv1piZmZmZmVknV1EyJiJeB44HFgeel3SlpN0lDZS0oaTdJF0JPA/0Bo7L6pjV1447wk9+knrHmJmZmZmZmdVR10orRMTxkiYDxwI7ANsXFBEwCTgqIk6svolmbWCWWeBPf4KDDoJHH03LXpuZmZmZmZnVQaXDlACIiBOAZYG/kuaEeQ14Pfv5eGBZJ2JshrPHHjDffHCi35pmZmZmZmZWPxX3jMmJiPdIvWPMOoYePeDgg9PqSs8/D6usUu8WmZmZmZmZWSfUqp4xZh3WfvvBHHO4d4yZmZmZmZnVjZMx1rn06gUHHAA33give25pMzMzMzMzqz0nY6zz+cMfYNZZ4e9/r3dLzMzMzMzMrBNyMsY6n/nnhz33hCuugPffr3drzMzMzMzMrJNxMsY6p8MOS7ennlrfdpiZmZmZmVmn42SMdU6LLQY77QQXXACfflrv1piZmZmZmVkn4mSMdV6HHw4TJsCZZ9a7JWZmZmZmZtaJOBljnddyy8GgQXDOOfD11/VujZmZmZmZmXUSTsZY53bkkfDNN3DuufVuiZmZmZmZmXUSTsZY57bKKrDxxnD66TB+fL1bY2ZmZmZmZp2AkzFmQ4bAZ5/BRRfVuyVmZmZmZmbWCTgZY7buurDeenDKKTBxYr1bY2ZmZmZmZg3OyRgzSL1jxo6Fq66qd0vMzMzMzMyswTkZYwbw61+n+WNOOgmmTKl3a8zMzMzMzKyBORljBiCl3jFvvAE331zv1piZmZmZmVkDczLGLGfrrWG55eCEEyCi3q0xMzMzMzOzBuVkjFlOly5wxBHwwgtwzz31bo2ZmZmZmZk1KCdjzPLtuCMstljqHWNmZmZmZmbWDpyMMcs388zwpz/BY4/Bo4/WuzVmZmZmZmbWgJyMMSu0++4w33xw4on1bomZmZmZmZk1oE6RjJF0kaTItqWbKbeLpNGSxkn6WtJISZs1U767pOMkvS5pgqRPJV0vaYVm6iwq6WJJH0n6QdIYSWdImquZOn0k3SXpS0njJb0o6Y+SupT/LFjZevSAgw+Gu++G55+vd2vMzMzMzMzala+Za6/hkzGSNgd2B8a1UO4fwKXAQsAFwJXAisDtkg4oUr4bcD/wF+Ab4EzgAWBr4BlJaxWpsxTwLLAbMBo4HXgH+APwhKR5itTZEngEWB+4BfgnMEtW99qWHr+10n77wRxzuHeMmZmZmZk1NF8z10dDJ2MkzUd6k1xHekFLlesDHAq8DfwiIg6OiP2B1YAvgX9I6l1Q7RBgHeBGYK2IODwidgAGAT2AiyUVPr/nAvMDB0XEVhFxREQMIL1JlgOGFbRrjqz9U4B+EbFHRPwJWBl4AhgkabtKnhMrU69ecMABcOON8Prr9W6NmZmZmZlZm/M1c/00dDIGGJ7d7t9CuX2y22ER8VVuZ0SMIWXVupEycwBIUl6dP0fEj3l1bgMeBX4K9M2rsyQwEMidM9+xwHfATpJ65u0fBMwHXBsRz+TFmAAcnd3dt4XHZq31hz/ArLPC3/9e75aYmZmZmZm1B18z10nDJmMk7QpsBewTEV+0UHxAdntPkWN3F5QBWApYDHgjIt4ts07u5/vy34gAEfEtMIqUHfxlme16BBgP9Mm6f1lbm39+2GsvuOIKeP/9erfGzMzMzMyszfiaub4aMhkjaXHSeLQrI+LWFsr2BBYBxkXEx0WKvJndLpu3b7ns9o0Sp233OhExGXgX6AosWeKcVq3DDku3//hHfdthZmZmZmbWRnzNXH+KiHq3oU1lY84eApYBfp7rQiVpJKkL1DIR8VZe+YWBD4EPI2LRIuebGZgITIyIbtm+HYCrgKsi4vdF6mwI3EfK6P062zcc2AvYKyIuLFJnGDAEGBIRJ2b73sgexzRtzqszCugD9ImIJ0o8H4OBwQBdu3Zd7f777y9WrF2MGzeO2WabrcPHW+7kk5n/wQd58tprmTTXtBN4N8pjnFHi1SNmo8erR0zH6/gxGz1ePWI2erx6xGz0ePWI2ejx6hGz0ePVI2ajxwPo37//ROD/8nYNj4jhpcpXytfMM4iImOE20hixqGC7Mq/uodm+TQrOOTLbv3TB/oWz/R+UaMvM2fEJeft2KIxbUGdgdvyevH3Ds317lqhzQnb8iLx9bxRrc97xx7Pjvyznee3WrVvU0ogRIxoj3uuvR0gRRx5Zu5glNHq8esRs9Hj1iOl4HT9mo8erR8xGj1ePmI0erx4xGz1ePWI2erx6xGz0eBERwHfha+a6XDPXcus6XXZmxvA2MKGC8h8BSFqGNLvyJRFxV5l1v85ue5U43qugXDl15qhjHWtryy4Lv/kN/POf8Oc/w5xz1rtFZmZmZmbWufmaufV1ZggzZDImIjZoZdWfkc3iLGm3EmXeTBM7s3VE3BoR30n6EFhE0kIx/Ri4ZbLb/DFoubWOl6W4tqyzelZnmmXGJHUFlgAmk9Zdt/Z05JFw/fVw7rkwZEi9W2NmZmZmZp2Yr5mnqdMhr5kbbQLfMcBFJbZPsjI3ZPfH5NV7KLvdqMg5Ny4oAykL+T6wrKQlyqwzIrsdWLiWuqTZSeuvfw88WWa71ifNJP14RPxQ5Li1pZVXhk02gdNPh/Hj690aMzMzMzOz1hiDr5lnCA2VjImIFyJiz2IbU7NsQ7J9L+RVPT+7PUpS0wytknqT1lv/AbgkL07k1Tk5/40iaUtgPeAV4OG8Om+TJijKnTPfcUBP4PKI+C5v/43A58B2klbPizEr8Lfs7nnNPyvWZoYMgc8/hwunm0vKzMzMzMxshudr5hnHDDlMqdYi4nFJpwGHAC9KuhGYBfgdMDdwYESMKah2GrAZMAh4StKDpHXUf0Nay3z3KFgbHdiPNIHQWZI2AF4F1gL6k7paHVXQrm8k7UV6g42UdC3wJbAFaQmvG4Hrqn8GrCzrrAPrrw+nnAL77AOzzFLvFpmZmZmZmbU7XzO3vYbqGVONiDgU2JXUNWswsDPwMrB5RJxTpPwPwK+A44E5gYOBDYFbgTUi4qkidd4mjWe7lPSGOhRYCjgLWDsivihS51bS8mKPANsCBwKTSB+C7bKMo9XKkCHwwQdw5ZX1bomZmZmZmVnN+Jq5bXWanjER0a+MMpcBl1Vwzu+BY7Ot3DpjgVITJZWqMwrYpJI61k4GDoRVVoGTToJddql3a8zMzMzMzNqEr5lryz1jzCohpd4xb74JCy5I3wEDoHdvuOqqerfMzMzMzMzMOohO0zPGrM18/31Kynz+OQJ47z0YPDgd23HHerbMzMzMzMzMOgD3jDGr1DHHQOGww/Hj4cADYcQI+Pjj6Y+bmZnVwM0338yAAQPYbLPN6NatG8suuyxHH300n3/+eb2bNo2hQ4cy77zzVlRn4sSJDB06lBdeeGGa/WPGjEESd9xxRxu2sL523XVXVl999RbLffzxx2yyySb06tULSYwcObLsuvVy6qmn0r9//+n2jxw5ks0224x5552XWWaZhd69e3PQQQfx/vvv16GVbW/y5MmcccYZrLTSSnTv3p255pqLTTbZhMcee6zeTStLv379kIQkunbtSu/evdl777357LPP6t20GcYpp5zCBhtsUO9mWAfiZIxZpUr9UfDVVzBgACy8MMw5J6y1Fuy6a5pf5tZb4bXXYNKkGjbUzMw6k0MPPZTf/OY3LLnkkgwZMoT77ruPgw8+mNtvv5299tqr3s2r2sSJEznuuOOmS8YstNBCPPHEE6y77rr1aVgdDRs2jP/85z9cc801PPHEE6y66qr1blKzxo0bx9///neOOOKIafafddZZDBgwgO7du/Ovf/2LBx54gGOPPZbnn3+eLbfcsk6tbTtTpkxhq622YsiQIWyxxRbcddddXHrppXTp0oV+/fpx9dVX17uJZenfvz9PPPEEI0eO5JBDDuGaa65h++23r3ezZhj77LMPzz33HCNHjqx3U6yD8DAls0ottlgamlRokUXgsstS0uW11+DVV+GBB9K+nK5dYemlYfnlp24rrADLLQe9epWOedVVcNRR9H3//RR/2DAPiTIzsya33347p512GhdddBG77747I0eOpG/fvvTt25fBgwdz33331buJ7aZbt2788pe/rHcz6uK1115jrbXWYpNNOsaclddccw3dunVj4MCBTfuef/55DjnkEI4++miOP/74pv3rr78+u+22W0P0eDr77LO58847ufvuu9loo42a9m+55ZZst912DB48mL59+7LIIovUsZUtm3vuuZs+a+uuuy7jx4/nyCOPZJ999qlzy2YMs88+O9tuuy1nn302/fr1q3dzrANwzxizSg0bBj16TLuvRw/4+99hgw1g//3h7LNTIuaDD+Cbb+Dpp+Hyy+HPf4af/hTeeANOPTX1nFlrrdSTZuGFU8+aXP3774exY1MiZvBgeO89FDF1jhpPGmxmZpnTTz+dVVddld133326Y126dGHjjTcG0lAQSbz00kvTlOnXrx+DBg1qup8b6nLnnXfy05/+lB49erDpppvy5Zdf8tZbb9G/f3969uzJ6quvzosvvthUr9SQoZaGznz33XcccMABLLfccvTo0YMllliC/fffn2+++aapzOyzzw7AbrvthiT69+/PmDFjpou5yy67sOaaa04X45xzzqF79+6MGzcOgB9//JGTTjqJpZdeumlI12WXtbxAyJQpUzjxxBNZdtll6datG4suuii77rpr0/E777yTDTfckPnnn5855piDX/7yl9Mlwz744AN++9vfMv/889O9e3eWWmopjjnmmOliPfPMM/ziF7+gZ8+erLvuurz88stNxyTx4IMPcssttyCJ3r17l2zzCy+8wAYbbECPHj2Ya6652HHHHfnvf//bdHz99ddn7733brp/7733IolDDz20ad9NN93ELLPMwvjx4wH497//zWqrrUbPnj2Za665WGuttXj44Yebfe4uu+wyttlmGyQ17Tv77LOZd955iz5+gM0226zp51NPPZU11liDXr16scACC7D55pvz1ltvTVM+914ePnw4vXv3pnv37my66aZ8+OGH05SbMGECf/7zn/nJT35Ct27dWGmllbjrrruajp9yyinMOuusvPLKK037Ro8eTdeuXbnwwgubfZyFzjzzTPr37z9NIiZn2LBhTJgwgYsuuqhpX+/evTnssMP461//yoILLshss83GjjvuyNdffz1N3S+//JK9996bBRZYgFlnnZU+ffpM097999+f+eabj08//bRp30033YQk7r///ooeQzErrbQSwDTnb+l5bcvPZ+61vvrqq1l66aWZY4452Hjjjfnggw+aypT7nQfw2GOP0bdvX3r06ME888zDXnvtxbffftt0/H//+x977rknCy+8MLPOOiuLLbbYdL0Ot912W+644w6+/PLLsp5D69ycjDGr1I47wvDhsPjihASLL57ul+qpMvvssPrqsNNOKZFz003w8svw3Xfw+utw220pkfPrX6fJga+6Cg46KC2jvdhiqV72h0+T8ePTqk5mZjZjueqqtMreTDPVbLW9SZMm8fjjjxe90KvG+++/z1/+8hf+9re/MXz4cB5//HEGDx7Mdtttx3bbbceNN97I5MmT2W677Ygq50obP348U6ZMYdiwYdx999389a9/5aGHHuI3v/lNU5mHHnoIgKOPPponnniCf/7znyy00ELTnWu77bbj6aef5p133plm//XXX8+mm27KbLPNBsCBBx7I3/72NwYPHsydd97J1ltvze67795iT4y9996bY489lt/+9rfccccdnHrqqXz33XdNx999910233xzrrjiCm666Sb69OnDxhtvzKhRo5rK7LzzzowdO5bhw4dz9913c9RRR/HDDz9ME+f999/n/PPP56ijjuKaa67h008/5be//W3Tc/3EE0+wyiqrNA0dueWWW4q297PPPqNfv36MHz+eq6++mrPPPpuHH36YDTfckIkTJwIpGfPII4801XnkkUeYddZZp9u36qqr0qNHD95++20GDRrEgAEDuP3227nqqqvYbLPNmr0A/e6773jqqafo06fPNPsffvhhNthgA2aeeeZmn3dISawDDjiA2267jQsuuIApU6awzjrrTJekeOKJJzj77LObeou9+OKLbLXVVtOUGTRoEJdeeilDhgzh9ttvZ4011mCLLbZoGgZ36KGHsvrqq7PLLrswefJkJkyYwC677MLAgQPZc889W2xrzqeffsqYMWOmi5+z1FJLseKKK07zXEPqRfTAAw9wwQUXcNppp3HnnXdOE/eHH37gV7/6Fffffz+nnHIKt956K/PNNx+HHXYYn3zyCQAnn3wyvXr1akq0ffrpp+y7777ss88+bLjhhmU/hlLef/99ZpppJhZccMGmfS09r239+Xzqqac455xzOPXUUxk+fDjPPfccg3MLa1Rg1KhRbLDBBiy44ILceOONnHHGGdx1113sttvU1ZUPOeQQHnvsMU4//XTuvfdeTjjhhGkSiwB9+vRh0qRJPProoxW3wTqhiPDWSbZu3bpFLY0YMaKh47VbzB9/jPjkk4gRIyLOOy8iTQdcfFt++Yitt44YMiTiiisinnkmYty4NmtKwzynnThePWI6XseP2ejx2i3mlVdG9Ogx7fd0jx4RV17Zro/x448/DiDOP//8pn2l4o0YMSKA+L//+79p9vft2ze23Xbbpvu77LJLdOnSJd56662mfX/6058CiMsuu6xp35133hlAvPLKKzFixIh49913A4jbb799mvPvsssusdpqqzXdP/bYY2OeeeYp+ZgmTZoUjz32WADx3nvvRUTEt99+G0Bccskl0zzGwpiTJk2KeeaZJ0488cSm833wwQchKW644YaIiHjzzTdDUlx66aXTxN1pp51i9dVXL/ncvfrqqwHEmWeeWbLt+aZMmRKTJk2KgQMHxm677da0v2fPnvHvf/+7ZL3c83/FFVc07bvlllsCiFdffbVpX+Hrlqub/1wffvjh0atXr/j666+b9j311FMBxNVXXx0REffcc08Accstt0RExHrrrRf7779/dOnSJb799tuIiFhllVXisMMOi4iIG264Ieaee+6ynoOcUaNGBRAvvfRS074RI0ZEt27d4ogjjqjoXBERkydPjvHjx8dss802zXuyb9++0bVr1xgzZkzTvtx76e67744RI0bEAw88EECMHDlymnOut956MWjQoKb7b775ZvTs2TOOP/74OPjgg2OuueaKDz/8sKJ2/vOf/wwgbr311pJlttxyy1h++eWb7i+++OIx11xzNT33ERFXXnllSIpXXnklIiIuvPDCmHnmmeONN95oKjNp0qRYeOGFm16n3GOfaaaZ4vLLL49tttkmllxyyWnOW66+ffvGNttsE5MmTYoJEybEqFGjYvHFF4999tmn6bNYzvPaFp/PXLy+ffvGHHPMEV9++WVTmdNPPz2AGD9+fESU/5237rrrRr9+/aYp8+CDDwYQF198cURE/OxnP4uzzjqrxedq8cUXjyFDhrRYrjnAdzEDXD96a9/Nc8aYzWgkWGCBtPXrlyYALjZHzRxzpPlmXn0Vbr8dJk+eemyxxdKxwq3ClSvMzDqtP/4RCiaKbdGTT0JB7wbGj4c99mDl5ZdPQ1LLsfLKcMYZlcWG6f5DW63evXuz1FJLNd1feumlARgwYMB0+z788EO6dq3uz8orrriC0047jTfffHOaniZvvPEGiy22WNnn6dq1K9tssw3XXXdd00SxN9xwAz179mTTTTcF4MEHH2SmmWZi6623ZnLe788NNtiAa665hilTptClS5fpzj1ixAiAaYYlFfrggw846qijeOCBB/j4449J11WwzjrrNJVZeeWVOfLII/niiy8YMGBA0cfXu3dvFl100ab7P/3pT5vOv/zyy5f7dDB69GgGDhzIHHPM0bRvzTXXpHfv3jz22GNsv/329OnThy5duvB///d/bLzxxowePZqzzjqLG2+8kSeeeIK11lqLF198kaFDhwKw4oor8vXXX7PLLruw4447ss4669CzZ89m25HrrVFsFa1y37tPPvkkxxxzDM8999w0vXDeeOONacqtuuqqLL744k3311lnHeaff35Gjx7N+uuvzwMPPMCCCy7IOuusM93rf+mllzbdX3rppfn73//OwQcfzJQpU7j88stZeOGFy2prtTbccMOmXiIA22yzDb///e95+umnWWGFFXjggQdYbbXVWGKJJaZ5DL/4xS945plnmu6vs846HHLIIey1115MmjSJkSNHTnPeStx8883T9GBac801Oeuss5p6fZXzvLbV5zNnjTXWYK655mq6n/ucfPjhh03fTy0ZP358U2+q/HjrrrsuM888M6+//jqQPrennHIKXbp04Ve/+hXLLrts0fPNO++8Te93s+Z4mJLZjK7UHDXnngs335ySMd99B6+8koZA/e1vsO668Omn8K9/wd57w/rrw3zzpW399dO+M8+E++5L89JEQffyrJt93wEDatbN3syswytMxLS0v43MM888dOvWrc2XAJ6zIHk0yyyzTLc/t2/ChAlVxbrlllvYeeedWXvttbnhhht48sknm4bdtObc2223HS+88ELTRfp1113HFltsQffu3QH4/PPPmTJlCr169WLmmWdu2nbddVcmT57Mxx9/XPS8X3zxBT179pwmsZHvxx9/ZIsttuDxxx/n+OOPZ8SIETz99NNsvPHG0zyO6667jtVXX52DDz6YxRdfnJVXXpkHH3xwmnOVev4rfT4+/vhjFlhggen2L7DAAk0Jjdlnn52VV16ZF198kdGjR9O9e3d+8YtfsO666/Loo48yatQofvzxx6aE0nLLLcdtt93GO++8wyabbMK8887LDjvs0Owyx7l2d+vWbZr9iyyySFnv3ffff5+BAwcSEfzrX/9i1KhRPP3008w///zTPSfzzz//dPXnn3/+ptf1888/55NPPpnmtZ955pkZOnQoY8eOnabetttuC6TJa/OHzZUrl3x6r9g/1jLvvffedJP3Fj6G7t27M9tss03zGJ588snpHsM999wz3WPYfvvt+eGHH/j5z3/OeuutV/FjyBkwYABPP/00jz32GIcffjijR4/m6KOPbjpe7vNa7eczPxH3/+3de5xN9f7H8dd3LmbMuEWJQ4YScukixe8Qg0Qp4nQhhW5KSkW6ITqhTiXVIaVO0uXkdlS6USd0QbpRuXfBxCkqIZfBzHx/f6y9t71n9pjbnr1m1n4/H4/vg1l77fVea+09373ms9f6rkj8nvzxxx9kZ2dz0003heQlJSVx+PDhwPt68uTJXHzxxfz973+ncePGnHzyycycOTPP8pKSkkrcJ0ps0JkxImWdfyyakSOxGRmYcHdTqlDhyNkvwXJynFtxr1sX2ubOheDruitVOnJnp8xMmD8fDh7EwJEBg4PXRUTE64pxZgr164c/kzEtjVWPP15qd9dITEykbdu2LFy4kHHjxh113uTkZIDAWCF+O3fuDHvGQlEdbflHM2fOHFq3bs1TTz0VmFbQYLBHk56eTq1atZg1axb9+/dnxYoV3HPPPYHHq1evTkJCAkuXLiUuLu93k+H+mAen8LVv3z727NkTtiDz/fffs3Llyjx3zTlw4EDIfHXq1OGFF14gJyeHzz77jLFjx9KjRw8yMjKoUaNGcTc7rNq1a4cMsOq3fft2zjzzzMDP55xzDu+++y4fffQRbdu2JS4ujnPOOYfXX3+dw4cP07Rp05B16969O927d2f37t28/fbb3Hbbbdxyyy1h/zgFZ5+DMwhq8B/Q6enpvPPOO2RlZR317KoFCxawf/9+3njjjcBZOLn/MPcLt707duwIjDFUvXp16tSpw+uvv55vnt8NN9xAvXr12LFjB2PHjmXChAkFPidYzZo1qV+/PvPnz2fo0KF5Ht+0aROrV6/OM4Bx7m04cOAAe/fuDdmGVq1aMXXq1JD5vvzyy5BxebKyshg0aBAtWrRgzZo1TJs2rVhjqgAcc8wxgYG427Zty6+//srjjz8eeB8Vdr+W9Pfzjz/+KPQ6F6bPq1atGsYYxo4dG/bOZJs3bw7M9+STT/Lkk0/yzTff8PDDD9OvXz9OPfXUwBk54LzH/e93kaPRmTEi5UG/frB5Mx8uWgSbNxe+KOIfQPL882HYMHj2WfjkE/jtN9i+HZYsgalT4Zpr4JhjYPFimDMn/Gn2Q4bA7NmwenWpf8srIlIu5Xcm4/jxpR5922238cUXX4S9G1BOTg4LFiwACFz2sm7dusDjP/30U+A0/JKqWbMmiYmJIcvfu3cvy5cvP+rzDhw4kOeMiVdynZVZlG+84+LiuOSSS5g1axazZ8+mSpUqIcWRTp06kZ2dze7du2nVqlWe5s/KzX+J1osvvpjvdkDo2R9btmwJGbw393q2adOGMWPGsH///qOePVFcrVu3ZuHChSF3hfn888/ZvHkz7dq1C0w755xz+O6773jnnXdo37494Azsu2LFCj744IN8z6ioWrUqV1xxBb169Qq5k09ujRs3BpziQ7BbbrmFX3/9lfH5/J7478Rz4MAB4uLiQgo2s2fPDrmsxO+rr74KOdtm6dKl7NixI3AXn86dO/PLL79QqVKlsK+/34svvsjbb7/Nyy+/zMSJE3n44Yf57LPP8t3G/Nx666188MEHYW8xP2rUKJKSkrj22mtDpr///vuBOwuBc4mQMSawfp07d+b777+nXr16IeveuHFjWrRoEXjehAkT2LBhA2+88QZ33XUXd9xxR6C4UFL3338/4BRT/etUmP1a0t/Pwgz27FeYPi81NZU2bdqwYcOGsHnhCtWnnnoqjzzyCDk5Oaxfvz4wPScnh4yMjHwvYRIJpjNjRGKRMVCzptM6dAh9LC4u72VLALt3w+WXO/+Pj4eGDZ3bdJ9yivNv06bQuHHeP0RERGJF0JmMZGQ443f5z2RcsqRUoy+66CKGDRvGtddey9KlS2nQoAHGGNavX8/TTz9N/fr16datG3Xr1uWss85i9OjRpKSkkJOTw4QJEyL2LW5cXBw9e/Zk0qRJpKWlUa1aNSZOnBi4/CA/Xbp0YciQIYwfP57WrVvzzjvv5Llsp0KFCjRo0IDZs2fTvHlzNmzYkOfOPMEuv/xyJk+ezKRJk+jVq1dIgaVx48bceOON9OnThzvvvJNWrVqRmZnJmjVr2LhxY763Lm7cuDGDBg1i+PDh7Nixg/bt27Nr1y7mzp3LzJkzadKkCXXr1mX48OE88MAD/Pnnn4wZMybkEpTdu3fTtWtX+vfvT6NGjTh48CATJ06kVq1anJL7DNcIGDZsGFOnTqVr167cdddd7N27l7vvvpsWLVoELsEBpxiTk5PDsmXLmDhxIuDcujgxMZHPP/+c2267LTDvM888w/Lly+nWrRt/+ctf+O6775gzZw79+/fPdz0aNGhA7dq1+fLLL+nYsWNg+umnn85jjz3Gbbfdxtq1a+nTpw/HHnssmzZt4vnnn2f37t1ccMEFgT/Qr776aq699lrWrFnDo48+mucyFXCKghdeeCFjx44lMzOTu+66i5YtW9KtWzeWLFlCly5d6Nq1K126dOGuu+6iWbNm7Nmzh1WrVpGZmcmDDz7I1q1bufXWWxkxYgRt2rShTZs2zJs3jwEDBrBy5crAGRf169cnPT09ZKyZ3G655Rb++9//0qtXL+644w7S09P5888/+de//sVbb73FSy+9lOcyJf8tuUeMGMHPP//MiBEj6NWrV+AMjP79+/P000+Tnp7OHXfcwYknnsjvv//O3LlzWblyJbfffjsrV65k3Lhx/POf/6RBgwaMGTOGt956i2uuuYYPPvggMFaPMYYxY8YExgQqrLp16zJgwABefvlldu3aVaj96leS388rr7yySOtYmD7v4YcfpnPnzoFCUeXKlcnIyODtt9+mR48egDOGTK9evWjevDnGGJ599llSU1NDbtW9YcMG9u7dGzJGlEi+3B5BWC16TXdTKv+ZUclLS7Nh79x0wgnWrlpl7b//be2oUdb27m3tKadYm5BwZB5jrD3xRGsvvNDaO++09oUXrF2xwto9ewrOfflla9PSbI4xzjq8/HJpb6m11qOvocuZyiv/mV7PcyMzWnlz58616enpNjU11SYmJtqTTz7ZDh8+3P7888+Beb777jvboUMHm5KSYhs1amRff/31sHdTCr4jj7XWTp8+3QIhd2IJvpORfxt/+eUX26NHD1u5cmVbr149+8wzzxR4N6WsrCw7fPhwe9xxx9nKlSvb3r17208//TTPnZkWLlxoW7RoYZOSkixgN23alO8dnHJycuwJJ5xgAbtgwYI8+yonJ8dOmjTJNm3a1FaoUMEee+yxtn379iF35gnm376srCw7fvx426BBA5uYmGjr1KljBw4cGJjvs88+s2eddZZNTk62DRs2tNOnTw/Z/szMTHvdddfZRo0a2YoVK9oaNWrY7t2722+++SbP/g9+34TbzsLcTclaa7/66ivbsWNHW7FiRVu1alXbt29f+8svv+TZxnr16tmUlBR76NChwLRu3bqF3NXKWmuXLVtmL7jgAlu7dm2blJRk69evb++8806bmZkZdt/5DRkyxHbq1CnPPrXW2kWLFtkLLrjAVq9e3SYkJNi0tDQ7aNAg+9133wXmmTFjhj3xxBNtcnKybd26tf30009tWlqaHT58eJ59MnXqVHvCCSfY5ORk261bN5uRkRGSmZmZae+77z570kkn2cTERHv88cfbrl272rfeestaa23Xrl1t8+bN7cGDBwPL3rp1q61WrZodNmxYYNpxxx1nR4wYke82+/MOHz5sH3vsMduiRQubnJxsq1WrZrt162Y//vjjPM9JS0uzw4YNs2PGjLE1a9a0KSkptk+fPvaPP/4ImW/Xrl126NChtm7duoH34jnnnGM/+eQTe/DgQduiRQt73nnnhTxn5cqVNjExMXBXoH379lnATpkyJd9tCN6vuf3www82Pj7eTpgwwVpb8H71K8nvZ/DdlHKvU7i7JxWmz7PW2k8//dR27drVVq5c2aakpNhTTjnF3n777YHfuTvuuMM2b97cVqpUyVatWtWmp6fbjz76KGQZjz32mG3QoIHNyck56v4sCLqbUkw011dALXpNxZjynxmVvKPcmjWsgwetXbPG2jlzrL3/fmsvv9zaU0+1tkIFm6eY07Wrtbffbu2zz1q7dKm1/lsRFjUzgjz5Grqcqbzyn+n1PDcyvZ7nRqbX89zILO28r776ysbHxweKg6WRl1/RwC+SmT/++KONi4uzP/zwQ0TzcheYiqKoeYsWLbJVqlQJufV5aWeWVFnOa9OmjX3ggQdKnKliTGw0XaYkIqEKM2BwsAoVjlymFCwrCzZtcu7yFNyeecYZg8avVi3444/w49SMHKlBg0VERDzijDPOoGvXrkyePLnAwabLg2XLlnHxxRdz4oknur0qxbZs2TKuu+66fO8QJoW3YsUK1q9fz7vvvuv2qkg5oWKMiOTVrx/068eHS5YU/+4fCQlw8slO69nzyHT/HZ6CCzTTp4dfxpYtcPPN0KyZU+xp1gwicLcPERERccfEiRNZuHCh26sREf369aNfOf/SaOTIkW6vgmfs3LmTGTNmhB3HSCQcFWNEJLr8d3iqXx/8tw9ctCj87WArVICXXoI9e45Mq1nzSGFGRRoREZFypUmTJjRp0qTUlr+klAfLjoZI3e1Iouv88893exWknFExRkTcN348DBoUevlSSgpMmwZXXAHbtsGaNc5ZNGvWOO3FFyHoNp3UrBlanPH/P78izSuvwMiRdMh9xxMREREREZFSpmKMiLivoHFq6tZ1WteuR55j7ZEijb+tXVu4Is3atTB8OOzfjwHnrJxBg0LXRUREREREpJSoGCMiZUNRx6kxJv8izdatoWfRhDuTJrf9++Huu1WMEZF8Hdp3iMSURIwxEVumtZbD+w9TIbVCxJYpIiIiZZ+KMSLiLcbACSc4LVyRZs0ayO+a3q1boU4daN7cOYPG/2/TplC5cnTWX0TKpEP7DvFChxeo164eXSd1jUhBxlrLwtsXkvFJBgM/HKiCjIhIlKi4LmWBijEiEhuCizRpaeEHDK5WDc49F1avhqefhgMHjjyWluYUZ4ILNU2aQMWKUdsEEXFPYkoi9drVY8UTKwBKXJDxF2JWPLGC1re2JjElMVKrKiIiR6HiupQVKsaISOzJb8DgyZOPXKaUnQ2bNjln0qxefeTf996Dw4edeeLi4KST8p5J06iRcyeoYBowWKRcM8bQdZJztl1JCzK5CzGR+mNAREQKpuK6lBUqxohI7ClowGCA+Hho2NBpPXsemX74MHz/fWiBZs0amD/fKeAAJCQ4BRl/cWbnTnjmGcjM1IDBIuVYJAoyKsSIiLhLxXUpK+LcXgGR0jBv3jw6depEtWrVSEpKolGjRowaNYrffvstZL7p06djjOGqq64Ku5z09HSMMRhjSEhI4KSTTuL2229nz549YefJ3bZu3QrA/fffT2pqKps3bw5ZfkZGBpUqVWLkyJGR3QEx4IUXXqB169akpqZSpUoVOnTowPz580PmGThwYOC1iIuLIy0tjauvvprt27c7RZDNmzn/vPMwW7Zgrrwyz+v3ySefADBjxgzi4uJYsWIFJCbCKafApZfy5/Dh1Fm+nH4tW8K+ffD1184ZMHfe6RRxvvgCxo6FJ56AzMzQDdi/H26/HTIynPFsYsjYsWND9nOtWrW48MIL+eabb9xeNZEC+Q/iW9/amhVPrGDh7QuxhfwdjsZBu//z78ILLzzq55/bxo4dy7HHHluk5xw6dIixY8eyatWqkOmbN2/GGMNbb70VwTXMX/369THG8Oqrr4ZM37t3L8YYXnjhhcC0o34OAc2bN+eiiy7KN+uee+7hlFNOyffx9PR0LrnkkpJtkM+6desYOnQoqampGGPYvHlzRJef28CBA7nhhhtKZdkiBSlJX+6nQoyUlM6MkaMrQYeSXpLcEvxxOnz4cB5//HGuvvpqbr/9dqpUqcLatWt5+umnWbNmDa+99lpgXv+B1Ouvv86BAweoGGb8j44dOzJhwgSysrL4/PPPGT16ND/99BNz587NM09uNWvWBODuu+/m5ZdfZujQoSEFg6FDh3LccccxatSoYm9vJOT/MqeXam5xX+bBgwfz7LPPctNNNzFu3DiysrKYOXMmPXv25KGHHuKuu+4KzNukSROmT59OTk4Oa9asYeTIkaxdu5bly5cTFxcXMk9uzZo1A2DAgAE8//zzDB48mM8//5z4+HgARo8ezd69e5k4cSIkJcGppzot2L59zuC/4Tb211+dsWiqVHHOomnRIvTfGjUKtT/M/YX4Pf2wUIsqEjum+L+nVatWZcGCBYDzh9R9991Hly5dWLduHdWrV4/UKoqUiuJ8qxqNg/bgz79zzz2Xtm3b5vv5Vx4dOnSI+++/n/r163P66acHpteuXZvly5fTpEmTqK7PhAkT6NOnT4Gv49E+h/r27cvf//53/vjjD4455piQ5/3xxx988cUX3Hffffku+6mnniIxMTKXRIwYMYK9e/cyf/58UlNTqV27dkSXL1LWlOQMGRViJBJUjBFPefPNN3nsscf417/+xTXXXBOY3qFDBwYNGsR7770XmLZjxw4WLVpE586d+eCDD3jrrbe49NJL8yyzevXqtGnTBoB27dqxb98+Ro8eza+//hp2nnCSkpKYPHky3bp1Y/78+fTo0YM333yTN954g/nz54ctAkl4r7/+Ok8//TRTp07lxhtvDEw///zzqVWrFvfeey9dunShZcuWAKSmpgZem7/+9a9UrFiRq666ii+//JKzzjorzzz5eeqppzj99NOZMmUKQ4cOZdWqVUyePJlJkyZRq1at/J+YmuqMERNuwODjj3fOnPn2W+dyp9mzncuZ/GrVylugadrUWWY5l5CQENjnbdq0oX79+vzf//0fCxYs4IorrnB57UQKVpSDeGstP0z5gW3/2VZqB+25P/+WLFlChw4dwn7+eU1SUlKBfXikpaens2TJEt544w0uvvjio857tM+hvn37MmrUKObNm8e1114b8rz//Oc/ZGVl0adPn3yX3bRp0xJvi9/69es588wz6dy5c6ksvzzK74s68Y6yWlyX2KDLlMRTJk2aRMuWLUMKMX7x8fGcH3RL49mzZ5Odnc3kyZOpU6dOntON83PmmWcC5LnkqCBdu3blkksuYejQofz2228MHTqUHj16HPX0ZMnriSeeoGHDhlx//fV5Hrv33nupXLkykydPzvf5xX39mjVrxm233cbo0aPZtm0bN954I6eeeio33XRTwU8eP94ZIDhYSgpMnAg33ghTpsCHH8Lvv8O2bbBgATz6qHNr7p07YepUuOYaOPts5yybhg3h4oth9GiYNYtTdkBCdt7Yvt/ApkmQPdb5t28ZvgrotNNOA+Cnn34KTHvuuedo1qwZSUlJpKWl8fDDDwcemz59OklJSezatStkOWvWrMEYwwcffBCY9sYbb9CqVSuSk5OpVasWd955J4f9gzBz5HKJlStX0qZNG1JSUjjjjDP4+OOPQ5ZtjMnz3gp3qUVGRgZ9+vShevXqpKSk0LVrVzZs2FC8HSNlWmFOc/cftJdmIQYK//m3ZMkSjDGsXr06ZJ7cl6MMHDiQVq1a8fbbb9O0aVNSUlLo3r07O3fu5Pvvv6djx46kpqbSqlWrkEsM87tkyL+8/Ozbt4+bb76Zxo0bk5KSQoMGDRgyZEjIZcGVK1cG4Oqrr8YYQ8eOHdm8eXOezAEDBnD22WfnyZg8eTIVK1Zk7969AOTk5PDQQw/RsGHDwCVdM2bMyHcdg5155pl069aN8ePHF2r+3M8FZ1+deOKJnH322cycOTPPfDNnzqRx48acfPLJ+S4r9+tW2P4smH///fDDD8ydOxdjDOnp6SVa/osvvki7du2oXr06xxxzDB07duSLL74o1P7xe/vtt4mLi2PTpk0h0zdt2kRcXFzImcYF9fPr16+nT58+nHDCCXTr1o1mzZrx+OOPk5OTE5jH/7uxcOFCevToQaVKlbj55puLtM5SPhXlkiUVYiSSVIwRz8jKymLZsmV069atUPO/+uqrnHHGGTRp0oTLL7+cd955h927dxf4PP8f8cFnQ1hrycrKCmnZ2Xn/On788cf5/fffOeuss9ixYwdPPvlk4TZOAOc1Xr58ORdddFHgUqFgVatWpWPHjnz00Uf5LiPc6+dfdu6W29ixY6lSpQrt2rXjs88+Y+rUqWHXI49+/WDaNEhLwxrjXJo0bVrewXuNgb/8xSnCDB8OL7zgjDuzdy9s3Ajz5jln0rRsCRs2wIMPQp8+rH0K9o2Hr5+Cl/8Dd38MDy+E5+ZD/d1OR19/Nzz7ZtktyGRkZADQoEEDAB555BEGDx7MxRdfzFtvvcXgwYMZPXp0oBjSu3dvgDyXXcyaNYuaNWsG/pCYPXs2vXv35uyzz2b+/PmMGTOGadOmcc8994Q8b//+/QwYMIAbbriB//znPyQlJdGrVy/2B99xqxB27txJu3bt2LBhA08//TSzZ89m3759nHvuuRwIvlW6eMbRDuKDD9rr/K1OqR20Hz58uEiff4WVkZHBfffdx7hx45g2bRrLli1j0KBB9OnThz59+jB37tzAmRtFHWsht/3795Odnc348eN59913eeCBB1i0aFHIGauLFi0CYNSoUSxfvpwpU6ZQu3btPMvq06cPn3/+OT/++GPI9NmzZ9O9e3cqVaoEwC233MK4ceMYNGgQb7/9Nr169eKaa64p9Ngzo0aN4osvvghccllYuT+H+vbty+LFi9mxY0dgnu3bt7NkyRI6depUpGVD0fsz/2VetWrVonPnzixfvpynnnqqRMvfvHkz/fv3Z86cOfz73/+mbt26tG/fPs9rcjTdunXjL3/5S54C2QsvvMBxxx3HBRdcABSun9+2bRuNGzfmqaee4qGHHuL6669nzJgx/OMf/8iTe+2113Laaacxf/78PGcriXcVpbiuQoxEii5TEs/Ys2cPBw8epF69egXOu2XLFpYvX85DDz0EOAdujz32GK+99hoDBw4MmddfaMnOzuazzz5j/PjxtGrVirp16/LDDz8AzoCJua+pTktLy3P2RZ06dbjhhhuYOHEi9913H2lpacXf4Bj022+/cfDgwaPut7S0tDwHxllZWeTk5LB27VruvPNOTjjhhMA3kwBffvll2Gvic38Ip6amctddd3HLLbfQv39/WrduXfiV79cP+vXjwyVLAoWCQouPh5NPdlqvXkemZ2bC+vVc+fczaLEDmu+Ac7ZAv2/DLyb1MDy2EN49GXaVgbOu/QWvLVu2cPPNN3P66afTs2dP9uzZw/3338+oUaMYM2YMAF26dGH//v2MGzeOwYMHU7VqVbp168asWbO4++67A8ucNWsWl156KfHx8VhrGTFiBP379w/5wyIpKYkhQ4Zwzz33UMM3Ls+BAwd4/PHHA3/41K5dmzPOOIOPPvqoSH/gTpo0iX379rFq1arA2Ddt27alfv36PP/88wwZMqRkO03KpPxOcw8+aE/qmVRqB+2///57oT//imLnzp0sX76ck046CYBvvvmGRx55hBkzZtC/f3/A6Se7d+/O+vXrS5R13HHHMXXq1MDPWVlZNGjQgHbt2pGRkUG9evUCl5aedNJJtGnThszMTJKSkvIsq0uXLtSoUYPZs2cH+odt27bxySefMHv2bAC+//57pk6dyvTp0xkwYAAA5557Lj///DP3338/F154YYHr3LZtWzp06MD48eML7CeO9jl0+eWXM3z4cObMmRPoI+bMmUNOTg4dO3YscD1yK2p/5r/MKykpiRo1ahR4yVdhlh88zk1OTg5dunTh888/5+WXXz7qGDjB4uPjGThwIDNmzGDMmDEYY7DWMmPGDK666ioSEhIK3c937tw5cPnV4sWLOeecc9i/fz/PPvtsnuL8pZdeygMPPFCodRRvya8vBxVipHSoGCOeU5iO0X868OWXXw7AWWedRcOGDXn11VfzFGNyF1ratm3L888/H5LTqVOnPN+uhDtA/PPPP5k5cybGGD78sBRGVJU8chdamjdvzty5c0kJumzolFNO4cUXXyxwWdnZ2YHXfvny5Rw6dIgKFSqUynoXSnIynH46r5wWOrlKJvzxUPhTH2vtgz/+AVsrwzfHw7fHw7c1nX/XHwuHovSp8Pvvv4e8LjVq1ODzzz8nKSmJJUuWsG/fPi699NKQM5Q6derEAw88wNatW0lLS+Pyyy9nwIABDB48GIBVq1axceNGnnvuOQA2btxIRkYGl112WZ7lZGZmsnr1ajp06ABAYmJiSJHMP06C/45ohfXf//6XLl26UKVKlUBm5cqVOfPMM4t8ir6UL7kP4v0H8v6D9mj0+ZH+w6B+/fqBQgxAw4YNAULO1vBP27ZtGwkJJetAXnrpJR577DG+++479u3bF5i+cePGIhWaEhIS6N27d0ixds6cOaSmptK9e3cAPvjgA+Li4ujVq1dI/9C5c2deffVVsrOzC3Xm46hRo+jSpQsffvhhSJE/WEGfQ7Vr16ZDhw7MmjUrUIyZNWsW7du357jjjiv0dvtFqj8ryfLXrVvHvffey7Jly0LO+Nm4cWORsq655homTJjAkiVL6NixI4sXL2bLli1cffXVgeUVpp/PzMzkwQcf5JVXXmHLli0h82ZlZYW8d/3vEYlN4QoyST2TVIiRUqFijHhGlSpVSEpKClzucDSvvvoqLVu2pGrVqoExJ3r06METTzzBjh07AndBgiOFloSEBNLS0vLc7QDgmGOOOeq18H5jxoxh3759zJo1i8suu4xXXnmFfrkvVZF8HXvssSQlJbEl3GC4Plu2bKFOnTqBn/2Flvj4eOrUqRPy2vqlpKQU6vWbMmUKq1evZt68efTp04dHHnmkTN6WfE8yZFR1Lk3KbXsKTPwrtNgBp26Hc3+ECr5L5g/HwYYaoUWab46Hn6oCET7mqFq1Kv/973/Jzs7m66+/5o477uCKK65g6dKlgVvw+u9mldtPP/1EWloaPXr0IDExkY8++oiePXsya9Ys6tSpQ7t27QACy/Gfyh5uOX5VqlQJ3F0LCBTZMnPfkrwAv/32G59++imzZs3K81jwoJjiTf6DeP8BPBT+zhwlUaNGjUJ//hVFtWrVQn72/14ETw/+XfFf/lMcr732Gv3792fw4MFMmDCB6tWr8/PPP9OrV68i/x6Cc8brs88+y8aNG2nUqBGzZs2iR48egcFYf/vtN7Kzs6latWrY5//888/UrVu3wJxzzz2X1q1bM378eObNmxd2nsJ8DvXt25cbbrghUNBYunRpyJlCRRGp/qy4y//zzz8577zzOP7443nsscdIS0sjOTmZ6667rsjrcOKJJ5Kens706dPp2LEj06dP5+yzzw58PhS2n7/rrrt47rnnGDNmDPHx8XTo0IE33niDcePG5XnvHn/88UVaR/GePAWZJ5zpKsRIpKkYI56RkJBA27ZtWbhwIePGjct3vvXr1/P1118DhC2sBJ8m7J+nMH+oF+Tbb7/ln//8J0888QSXXnop/fr1Y8SIEVx00UVUqVKlxMuPBQkJCfzf//0fb7/9No8++mjIwSA4l6otWbKEXkGX8hS20FKQX375hfvuu4/bbruNiy++mDvuuIMJEyZw5ZVXlsnLze7t7IwRk3pk/EL2JcLt3eDVoLtvJ2RDo9+dwkyL7U6Rpu1PcEXQ2J67kmB1zbxn0uTxyiswciQdMjKcO0iNH593XBx/bkJC4HVp3bo1FStWDIwv4L+856233gp7UNy4cWMAKlWqRPfu3Vm8eDHgjBtw2WWXBQ6S/MuZNm0aZ5xxRp7l+MenKaykpCQOHToUMm3nzp0hP1evXp0ePXowevToPM/3Dz4q3uU/jT3YwtsXBg7qS0tiYmKhPv8AkpOTAcK+l3MPRl0cR1v+0cyZM4fWrVuHXGpSkrOJ0tPTqVWrFrNmzaJ///6sWLEi5HKU6tWrk5CQwNKlS/N8lgBhCyb5GTlyJD169OCzzz4L+3hhPof+9re/MWTIEGbPno21lvj4eC655BK+/Taf607LsOXLl7N161bef//9kNuNF2ZcvnCuu+46rr/+eh588EHmzZvHxIkTA48Vtp+fM2cOt9xyC3feeSdLliwJDE4djv7QFnCvuC6xRcUY8ZTbbruNHj16MGPGjMA14H45OTm89957LFu2jPj4eObPnx9yqQrArbfeyquvvhrxcR2stQwePJjTTjstcDvmRx99lMaNGzNmzBgmTZoU0Twvu/XWW+nVqxfPPfccgwYNCnnsoYceYs+ePaVy94Nhw4ZRuXLlwLXuI0eO5OWXX+a2227LM4hsWeAvuEz4AOrtds6UubdzaCEGICse1tZ02swWR6ZXyXTGoGmx3Veo2QFXfAvVgq+0+Xeac8vtFi1gzx54/nnIzHROotmyBfyvTyHO/rryyiv5xz/+wT/+8Q8WLVpExYoV+d///lfg6eJ9+vThsssu48033+THH38MuQVs48aNqVOnDps3bw57962iqlu3LuvWrQvcNj0nJycwoKhf586dmT17Ns2aNdPtUGNMuPEE/D+Dc5p7aSrM51+3bt0CZ3sEv5d/+uknNmzYQKNGjUq8HjVr1iQxMZF169YFpu3du5fly5cftXB94MCBPJf3vvLKKyE/F+UMj7i4OC655BJmzZpFcnIyVapUCRkvpVOnTmRnZ7N79266dOlSqG3Lz0UXXcRpp51WYCHsaKpXr07Xrl2ZOXMm1lrOO++8wJhW5Y1/sPLg13PZsmVs3rw530u5jqZ3794MGTKEPn36kJOTU6x+Pvf7Kzs7O+wdrET8jlZcV0FGIkXFGPGUiy66iGHDhnHttdeydOlSevbsSaVKlVi/fj1PP/009evXZ82aNXTp0iXsKa0DBgzgjjvuYMuWLUU622Hnzp18+umneaY3a9aMypUrM336dJYvX87y5csD38DVqlWL+++/nxEjRnDNNdfQokWLPM+XvC6++GJuvPFGhgwZwtq1a7nwwgvJyspi1qxZvPDCCzz44IOBPzAKa9++fWFfv4YNG3LssceyaNEiXn31VebMmRM4lblixYo8/vjj9OrViwULFkT8LiaR8OqpeYsvhbUnGZbVc1qAhRN2O4WZFtvhoZpt4dtvYeFCCHP3Kfbvh2HD4NxzoYDTvo0x3HvvvfTr148vv/ySsWPHcuutt7Jlyxbat29PTk4OGzduZPHixSHFr+7du5OcnMwNN9xAgwYNQm5nGxcXx8SJE7nqqqvYs2cP559/PhUqVODHH3/k9ddfzzN2UEF69erFlClTSE1NJTMzk+eeey7ktrvgFO1efvllOnXqxC233EKdOnXYvn07H374Ie3ataNv376FzpPyI7+BHYNPc6+ztQ7p6emldhCf+/OvQYMGGGNCPv/8xZizzjqL0aNHk5KSQk5OTuCyoEiIi4ujZ8+eTJo0ibS0NKpVq8bEiRMLLE526dKFIUOGMH78eFq3bs0777wTcot6cIoxDRo0YPbs2TRv3pwNGzbw17/+Nd9lXn755UyePJlJkybRq1evkDG+GjduzI033kifPn248847adWqFZmZmaxZsyZk7KnCuvfeewPj0BVX3759A5cuv/TSSyValpvatGlDpUqVuP7667nzzjvZunUrY8eODbmEuCiSk5Pp168fU6ZMoW/fviGXyRW2n+/SpQtTpkyhYcOGbN26lYcffpiDBw9GaIvFa3L36Uk9kzj4xsGQQX1VkJGIsNaqxUhLSkqy0bR48WLX8ubOnWvT09NtlSpVbGJioj355JPt8OHD7ccff2wB+8orr4Rdxv/+9z8bHx9vH3roIWuttR06dLB/+9vfjprZoUMHC4RtH3/8sf3999/tscceawcNGpTn+VlZWfbUU0+155xzTpG3MRqinVfYzJycHDt9+nR79tln25SUFFupUiXbvn17+8Ybb4TMN2DAAHvmmWcedVldu3bN9/V76aWX7MGDB22TJk3seeedF/b5F1xwgW3YsKHNzMyM2PZFWqlnHjxorTHWQv6tZk1rzz3X2mHD7LyLL7Ydqla1Ntc+y8rKsieffHJgX7/00ku2ZcuWNjk52VarVs2effbZduLEiXnizz33XAvYu+++O+zqvfPOO7Zdu3Y2JSXFVq5c2Z522ml25MiR9vDhw9Zaa8eMGWNr1KiR53mA/ec//xn4+c8//7T9+/e3lStXtscff7x94IEHwj5327ZtduDAgbZmzZq2QoUKNi0tzfbr18+uXr26SLs1mNd/98vz70VOTo5999Z37VjG2ndvfdfm5OQU6fFI83/+paamhnz+/fzzz4F5vvvuO9uhQwebkpJiGzVqZF9//fU8n3fh+s/p06dbwP7555+BaZs2bbKAffPNNwP79JdffrE9evSwlStXtvXq1bPPPPNMnuXl/t3Jysqyw4cPt8cdd5ytXLmy7d27t/30008Dy/ZbuHChbdGihU1KSrKA3bRpU8g6BMvJybEnnHCCBeyCBQvy7KucnBw7adIk27RpU1uhQgV77LHH2vbt29sZM2aE3bf+7UtLS7PDhw8PeSw7O9s2adLEAnb69OlH3Y/52bt3r01JSbHJycl2z549IZn5yf26FbY/CyctLc1edtllEVn+u+++a5s1a2aTk5NtixYt7Ntvvx32PdaoUaOjrpPf+++/bwH7/vvvh328oH7+l19+sRdffLGtXLmyPeaYY+yIESPstGnTQt7PixcvtoD99ttvC7VOhaX+tHzlheuzFy9eHPW+HNhny8Dfj2ql21xfAbXotVgqxng10+t5bmR6PS9qmWlpNt8izOOPW3vNNda2amVtcvKRx+LjrW3WzNorrrD2oYesfecda7dutbaIBzh6DZXnVmZhD85zcnLss397NmoH8daW332qPHczy2reiBEjbL169Wx2dnbUMiPF63luZJZWXn59uj8vmgUZFWNio+kyJRERKbnx450xYvbvPzItJQUeeyx0zJjsbPjuO/jmmyPtk0/g3/8+Mk+NGnDqqUfaaadB06aQ+zKHIgwYLBJp1oa/NCkcYwwnDTmJunXr6jR3kSLYsGEDa9euZerUqYwZMybsYMsikVCYPj3cba/Vl0tJqBgjIiIl5y+CjByJzcjA5FcciY+HJk2cdtllR6bv2uWMP+Mv0Hz9NTz77JHiTlwcNGp0pDizcyc89RQcOFCsAYNFSqIohRg/HcSLFN0NN9zAihUr6NGjB0OHDnV7dcSjilpcV18ukaJijIiIREa/ftCvHx8uWUJ6enrRnlutGpxzjtP8cnLgxx+PFGe++Qa++AJmzw6/jP374Y47oHfvvGfRiERIcQoxfjqIFymaJUuWuL0K4nEqroubVIwREZGyKS4OGjZ0Wu/eR6b/+SdUreqMPJPbL79ApUrQuLFzBo2/nX461KoFOlCSEihJIcZPB/EiImWDiuviNhVjRESkfKlc2RkjZsuWvI8deywMHuycSbN8OcyceeSx444LLdCcdhqccgokJkZv3aXcikQhxk8H8SIi7lJxXcoCFWNERKT8yW/A4McfDx0zZtcu5/KmVaucAs3XX8PkyXDwoPN4YqIzOPDpp4cWaWrUyJupAYNj2uH9h8n4JKPEhRi/4IP4jE8yOLz/MBVSK0RiVUVE5ChUXJeyQsUYEREpfwo7YHC1atC+vdP8srJg40anMOMv0ixcCDNmHJmnTp3Q4sxPP8GYMbB/vwYMjlEVUisw8MOBJKYkRuwg238Qr0KMiEj0qLguZYWKMSIiUj4Vd8DghATnbJimTaFv3yPTd+w4cvaMv0jz3ntO8Sac/fvhnntUjIkhpXFwbYzRQbuISBSpuC5lhYoxIiIiADVrQpcuTvM7eBDWroWWLcM/56efnMGCTz89tGmwYBERkTJLxXUpC1SMERERyU9SEpxxBqSlhR8wuGpVaNYMPv889JbbNWvmLdA0agTx8dFZbxEREREp01SMERERKUh+AwZPmXLkMqXgwYJXrnT+nTQJDh92Hq9YEU49NbRA06IFpKbmn6tBg0VEREQ8ScUYERGRghRmwOBwgwUfOgTr1jmFGX+bNQueecZ53BjnjJlwlzm98kqgAKRBg0VERES8RcUYERGRwijOgMEVKhy5I9OAAc40ayEj40hxZuVK+PRTp0jjd/zxsHs3ZGaGLm//fhg5UsUYERERkXJOxRgREZFoMsYZgyYtDXr2PDL9jz+O3Mlp1arQW20H27IF/vUvZyybZs2ccW1EREREpFxRMUZERKQsOOYYSE93GsCSJeEHDTYGrrvO+X9ionOL7pYtneLMGWc4Z+FUrhyllRYRERGR4jDWWrfXQaLEGJMDHIhiZAKQ5eE8NzK9nudGptfz3MhUXgRUh4Q0qBAXNC0H2AKHdpZ+vif3qcuZXs9zI9PreW5kej3PjUyv57mR6fU8gIrW2riCZ5PyTGfGxJavrLWtohVmjPnCy3luZHo9z41Mr+e5kam88p/p9Tw3Mr2e50am1/PcyPR6nhuZXs9zI9PreRI7VG0TEREREREREYkiFWNERERERERERKJIxZjYMk155T7T63luZHo9z41M5ZX/TK/nuZHp9Tw3Mr2e50am1/PcyPR6nhuZXs+TGKEBfEVEREREREREokhnxoiIiIiIiIiIRJGKMSIiIiIiIiIiUaRijIiIiIiIiIhIFKkYIyIiIiIiIiISRSrGiIiIiIiIiIhEUYLbKyClwxhTDWgPNAGOAbKBHcDn1trlpZDXAegENAKqARbYDWwEFllrP4x0ZiHWqTpQyVqbEe3s0mKM+QvQBsgCPrTW7o5C5lfAPGvtuChkGaApkApsttbuKMWs6sD1wNlARWAzMNNa+1EJl9sW+Mxae7jEK1n07LrAccBaa+1B37Q44ErgTOAQ8IG1dkEEM6uhvkZ9TWQy1dcUbbnqa2Ksr/Gtl6f6Gzf6Gl9uVPobL/Q1vmW70t/EQl8jMc5aq+ahBlQHngcycTqPcO07oHeE8s4CvvUtNyeflg18A7SK8r6YDmRFaJ8+AXwNfAmMA6rmM++YkmYCXYHVvtfwM+Bs3/T+wL6g13EXcEkU9mMOMC2Cy2sEVA8z/Tpge9D2ZQHvAnVLmDcM+AiID5rWKigr93v1sQjsr+3AI0Cj0n59fJkGeCZo32UALYEk4ONc25kNzAFMCTPV1xxZN/U1kdmP6muKvr/U18RQX+NbvxL3N7He1/iyI9bfeL2vCdpfUetvYqGvUVOz1jpvWvEGY0xV4FOgMfArTkW8Pk5F+WucD4C2vmaA0dbaCSXIawJ8AaTgdIzv4nRSu33LrwKcDFwAtAP24nwAry9uZhHXbzrQ31obX4JlVAI+x/mgNUEPZQB/s9Z+mWv+McB9xc00xjTCOcCrgHNQUg34DegGLAM2AB8AdYBeOB9Cp1lrNxQz78VCzHYl8D3OewvAWmsHFCfPl5kNjLXWPhA07Sbgnzj7eBvO+7cJkOzLPsNau6+YeZ8Cu6y13Xw/J+K8T+sB84DXgD+AFsDtOL8vV1prXy1mXk7Qjxb4EOeAYp4tpW+UjDGXATNxvrlZgfOt2B6cg/YJwOvAYpxtuxGoAQy21k4rZp76mtD1U19TcJ76GvU1xclTX5N3HUvU33i9r/FlRrW/8Xpf48uIan/j9b5GJMDtapBa5BrwKM6H2HAIFNoMTgU9G7jMN60J8JVv2l9LkDcL5/TAiwoxb0/fvDOjuD+mA9klXMb9vn36JvB/ON88TOXINzhtcs0/piSZwDTffurs+7kdToV+PbAASAya90Lfuk0pQZ7/G4X8vv0L93hJ92kOzoGd/+fKOAe6u4DuQdOr4BxQZAN3lyDvN+CJoJ+7+9bhwTDz1vXN/1EJt+9ZnIOFn4P24Q7gYeDkUnivL8I5eKjp+7mmL28X8GiueU/AOaD5pAR56mtCM9XXFJynviZ0XvU1hctTX5M3t0T9jdf7Gt9yotrfeL2vCdrGqPU3Xu9r1NT8zfUVUIvgi+lU2t/P57H3cK719P9cE6dqPqcEeduBl4sw/7+B7SXIyyhi+7MkH66+zG9wrg9PyDW9B843Yn8AZwVNL+lBy1rgrVzT5vk6/TZh5l8GrClB3j7fAcPNQIcwLZ0jB22B6SXcp7kPWnr6pt0eZt5U4H/ApyXIOwCMD/r5Dt/+DHuaMPAcsCcS2wfEA38DFuKcnuw/ePkAuJygg9AS7tOt5DrdGuegKRuoH2b+GTjfqhU3T32N+pqi5qmvyTu/+pqC8zzd1/iWEdX+xut9jW8ZUe1vvN7X5N7GaPQ3Xu9r1NT8TXdT8pa6ONXacFYBzfw/WGcAsfk431AUVxXgpyLMv8X3nOKqW8SWWoIsv5OAhdbarOCJ1tr5wHk4dyRbYIxpGYEscE4xXZNr2kbfv6vDzL/K95ziOgPn26lHgL8CH1trPwxqS3zz/Rw8vQR54TTEOeX1tdwPWOcU3vdxThstrv/hnI7t538t9+Yz/z4idKc5a222tfY/1tquOO+lB4FfgI44B/HbjDEPG2NOLmFUdZxvvoL96vv3lzDz/4xzGn5xqa9RX1NU6mvyUl9TMK/3NRD9/sbrfQ243994tq+BqPU3Xu9rRADd2tpr9uKcqhdOXZzTRINtxRklvLi24NxpoEC+0eQ743yrU1zbga+ttXGFaUBhrhkuSBb5fLhZa5fhXPOcCCw0xpwWgbxs8v5eHvDlhVuPXb78YrHWbsQ5UHkQ59TlZcaYkhwgFIf/OvT/5fP4Nkr2AfsecKEx5iTfz8twTj3tmXtGY0wSzkCDG3M/VlLW2i3W2pE4B5l/861XdZxvtNaVcPHbcA6Igp3o+zfc69kE51ud4lJfo76mSNTXhFJfU2he72sg+v2Np/sa33Ld7m9ioq+BUu1vvN7XiAAqxnjNp0BvY8yZwRN9P/cmb8X3OJwPveKaBZxljJlpjEnLbybfYzNxbkH37xLkrQROMcYU9pbstgRZfhk4tyQMH+Dc4q47zoBs7xNUOS+mHUCtXNNWA7Pzmb8O8HtJAn3fcPwd58ClCrDSGDPcd6BZWqoaY+oZY+rhXOcLzmmf4dSkZO/Th3AOBv9rjOlmrf0MZ38+aYwZbIypbYypaIw5C+eU5ZNxrskvFb79/Zq19nycA4vxhP+Wpyg+A3oYY9pB4BaUPXFOD3/IGJPsn9EY0wXnuvwvSpCnviaU+ppCUF+jvqYYvN7XQPT7G8/3NeBKfxOzfQ2USn/j9b5GxFHS65zUyk7DueY1GziIc0DxIM7Bgv8WbRflmv9b4L8lyEsGPuHItaLrgDdwvrV50ff/dRwZJO0TIKkEeQ/6lnVaIed/gZKP4/AvnA/VigXM14mg2zOWIO81inCtNM4Hz4cRfA8l49zuMtv3ep1M5G8363+/5G5hb2cJLAW+KGFmV9/rmI1zKuv7wOEw65CDcy17XAm3774iPqfYeb7nN8f51iYb55sh/7+n4Jzmm4Fz28clQdtd4ACVR8lTXxM6v/qa4r2m6mvU1xSU5+m+xpcZ1f4m1vqaoNe11Pobr/c1QdsYtf7G632Nmpq/ub4CahF+QeF6YD+hI8QfIteo7TgjvT8CXFDCvArAPTin9uY3Yv1m4G6gQgmzzgImAacWcv7GlHwASP9tFm8sxLzn4Zx6W5KDlqtxBkArcF/5PqhygAdK4X3U2fea7vV9CEXyD6Tp+bSBYeY9ybeNUyOQ2wDnYHpfPu/Tr4Br8Y2iX4KcIh+wRGi/XoRzXf5+nNuWtvNN7wLszNUfjI5AnvqaI/Orryn+eqivKX6O+prQ+cpdX+PLi2p/E6t9jW/5pdLfeL2v8WVFvb/xel+jpmatDdy6SzzEGHM8cAHOaaG/4QzUVtJrmguTexLOQUJV36TdwAZr7Q+lnV1afKcNnwT8aa3N79rf4PkbAbVt5AeeDJdVHef61i3W2l2lsPyqOAeIZwD/sdaOi3RGIdbheJzrgH+w1m6N0DITcA74auJc1/0Hzvv0jwgtPw1nRP/dkVheJBhjjsE5VTsJWG6t/TlCy1VfEyHqa9TXFGP56mtKmfoab/U1vgxX+5vy2Nf4MspUf+O1vkZil4oxIiIiIiIiIiJRpAF8RUSk3DPGVPHdNcKTeW5kej3PjUyv57mR6cY2ioiIRILOjPEo3+l72dbaPUeZpx5Q31r7UQmzjscZOX2DtXZn0PTeQDrObRTfttZ+UJIct/LcyPR6nhuZXs9zI9ONbTzKumQDz1lrbyjtLDfy3Mj0ep4bmV7PcyMzWnnGmBZAeyAV+BFYYMPfmrncZno9z41Mr+e5ken1PIkxbg9aoxbZBrQGvubIKOqfAK3zmXcMJb8DyP0cGcV8PzDIN/0pjozi7h9lflIEti+qebGwjdqn5T8vVraxgPWJ2J0xymJeLGyj9mn5zyvv2wgMAlrmmpYEvEzeu9T8AnQub5lez4uFbdQ+Lf95amr+5voKqEXwxfQNyOY7MNmHM9BcDs5t2m4IM3+JijE4tz3M8XVK84D/4Yw63s83fSrQA7gJ2ObrwM4tL3mxsI3ap+U/Lxa2EecWlgW1HJz+z//zlvKSFwvbqH1a/vNiYRsJc8cYnP4sB/gdeAl4DOfWxP7c+iXcp1HN9HpeLGyj9mn5z1NT8zfXV0Atgi8mPOvrIO7GGQ/IAJcDO3D+GBqSa/6SFmPewCn41Pb9XNPXYe0i1zfhOIWiQ8DM8pIXC9uofVr+82JhGzlyhk1OUVp5yYuFbdQ+Lf95sbCNvuffF/RzHZwzADcCdXPNe49v/icisE+jlun1vFjYRu3T8p+npuZvrq+AWgRfTOc6xo/DTG8ArMc5oLkhaHpJizE/Ai/lmjbdl9M4zPzzgc3lJS8WtlH7tPznxcI2Aqtxij+DjjJPDpG7VCGqebGwjdqn5T8vFraRvH+QXemb9rd85v8GWFueMr2eFwvbqH1a/vPU1PwtDvGSvwDLc0+01m4CzgHWAVOMMddGKK82zinBwbb6/t0UZv4fgOPLUZ4bmV7PcyPT63luZEY7ryXwHDDVGLPAGFOnBMsqi3luZHo9z41Mr+e5kenGNgarA1ic8ffCWQrUK+eZXs9zI9PreW5kej1PYpSKMd6yB0gI94C19legI87pds8YY/pHIG8fzsjiwbJ8eYfCzJ+F8815eclzI9PreW5kej3Pjcyo5llrD1lrhwOdgSbAamPMwOIur6zluZHp9Tw3Mr2e50amG9uYS6bv3935PL6byB9LRzvT63luZHo9z41Mr+dJjAr7h7uUW5uBM/N70Fr7qzGmM/Ah8C9gZQnzfgbq5pr2Fs6gnuGcAPxajvLcyPR6nhuZXs9zI9ONbcRau8R3i8l/As8bY/4GXG+tzS+3XOW5ken1PDcyvZ7nRmaU804P+sKqvu/fusD3YeatA+wsh5lez3Mj0+t5bmR6PU9EY8Z4qQEP4ww2dXwB89XFuWwgh5KNGfMS8FMh5zU4ly+8WV7yYmEbtU/Lf16sbGOY5fYCtuMMHOy/trvUbqcb7bxY2Ebt0/Kf57Vt5MggwP5b2Pp/vimf+dcDi8tTptfzYmEbtU/Lf56amr/pzBhveR24CugPPJLfTNbarcaYjjhnyJTkesdpwPfGmAo2/KUJwc4BqgHvlaM8NzK9nudGptfz3Mh0YxtDWGtfM8YsxRlP4sVILrss5LmR6fU8NzK9nudGZinnXZ3P9DxjYRlj2gGNcO4uV54yvZ7nRqbX89zI9HqeCADGWuv2OoiIiBSb77TiM4Dl1trZXstzI9PreW5kej3PjUw3tlFERCRSVIwREREREREREYkiXabkYcaYROBEnEsELM7I3z9aaw8rr3xkej3PjUyv57mRqbzyn+n1PDcyvZ7nRqbX89zI9HqeG5lez3Mj0+t5EsPcHrRGLfINuAxYDBzkyEBU/nYQWARcqryym+n1vFjYRu1T5ZWHTK/nxcI2ap+W/7xY2Ebt0/KfFwvb6MY+VYvtpsuUPMQYEwe8ClyCcweT/TgDT+32/VwFaACk4FR55wB9bTHfBF7PcyPT63luZHo9z41M5ek1LOt5bmR6Pc+NTK/nuZHp9Tw3Mr2e50am1/NEAtyuBqlFrgG34tyGbSnQEYgPM0880AlYhlPlHaq8spPp9bxY2EbtU+WVh0yv58XCNmqflv+8WNhG7dPynxcL2+jGPlVTs1ZnxniKMeZrIBE43RZwu1ljTBKwCjhkrT1NeWUj0+t5bmR6Pc+NTOVFNs+NTK/nuZHp9Tw3Mr2e50am1/PcyPR6nhuZXs8T8YtzewUkohoCbxbUiQBYaw8C833PUV7ZyfR6nhuZXs9zI1N5kc1zI9PreW5kej3PjUyv57mR6fU8NzK9nudGptfzRAAVY7wmE6hehPmr+56jvLKT6fU8NzK9nudGpvIim+dGptfz3Mj0ep4bmV7PcyPT63luZHo9z41Mr+eJOKyL10ipRbYB7wB7gDMKMe+ZwJ/AW8orO5lez4uFbdQ+VV55yPR6Xixso/Zp+c+LhW3UPi3/ebGwjW7sUzU1azVmjKcYY/4KfAhk4YwIvhDYiDMSOEBVoBHQDeiDMxBVB2vtcuWVjUyv57mR6fU8NzKVp9ewrOe5ken1PDcyvZ7nRqbX89zI9HqeG5lezxMJcLsapBbZBvQEduCMCJ6dT8vxzdNTeWUv0+t5sbCN2qfKKw+ZXs+LhW3UPi3/ebGwjdqn5T8vFrbRjX2qpqYzYzzIGFMZuBTn1myNcaq54FR3NwCLgLnW2j+VVzYzvZ7nRqbX89zIVJ5ew7Ke50am1/PcyPR6nhuZXs9zI9PreW5kej1PRMUYEREREREREZEo0t2URERERERERESiSMUYDzLG9DLGPGGMmWiM6XKU+QYYYxYpr+xlej3PjUyv57mRqbzI5rmR6fU8NzK9nudGptfz3Mj0ep4bmV7PcyPT63kirg9aoxa5BhhgNkcGmPIPQDUfqBZm/jFAtvLKTqbX82JhG7VPlVceMr2eFwvbqH1a/vNiYRu1T8t/Xixsoxv7VE3NWksC4iVXA5cAPwFPA4eBAcCFwCfGmE7W2h3KK9OZXs9zI9PreW5kKk+vYVnPcyPT63luZHo9z41Mr+e5ken1PDcyvZ4n4nC7GqQWuQZ8DOwEagZNiwcexanwfgMcG/RYSavIns6LhW3UPi3/ebGwjV7Pi4Vt1D4t/3mxsI3ap+U/Lxa2Ufu0/Oepqfmb6yugFsEXE3YBz+Xz2FBfZ7IKOMY3raQdl6fzYmEbtU/Lf14sbKPX82JhG7VPy39eLGyj9mn5z4uFbdQ+Lf95amr+pgF8vaUCsD3cA9baJ4FbgFOB940x1ZRXJjO9nudGptfz3MhUnl7Dsp7nRqbX89zI9HqeG5lez3Mj0+t5bmR6PU8E0N2UvGYbUC+/B621U4BhQEtgIVBVeWUu0+t5bmR6Pc+NTOXpNSzreW5kej3PjUyv57mR6fU8NzK9nudGptfzRBxun5qjFrkGzAO2FmK+u3BOtztEyU7p83ReLGyj9mn5z4uFbfR6Xixso/Zp+c+LhW3UPi3/ebGwjdqn5T9PTc3fXF8BtQi+mHCdr4PoXoh5R/vmLUnH5em8WNhG7dPynxcL2+j1vFjYRu3T8p8XC9uofVr+82JhG7VPy3+empq/6dbW3jIPZ+TvfQXNaK19wBiTAdRXXpnK9HqeG5lez3MjU3mRzXMj0+t5bmR6Pc+NTK/nuZHp9Tw3Mr2e50am1/NEADDWWrfXQUREREREREQkZmgAXxERERERERGRKFIxRkREREREREQkilSMERERERERERGJIhVjRERERERERESiSMUYEREREREREZEoUjFGRERERERERCSKVIwREREREREREYkiFWNERERERERERKJIxRgRERERERERkShSMUZEREREREREJIpUjBERERERERERiSIVY0REREREREREokjFGBERERERERGRKFIxRkREREREREQkilSMERERERERERGJIhVjRERERERERESiSMUYEREREREREZEoUjFGRERERERERCSKVIwREREREREREYkiFWNEREQk4owx84wx1hhz+1HmOcsYc9gYs8kYUyWa6yciIiLiJmOtdXsdRERExGOMMTWAb4HqwFnW2m9zPZ4CrAQaAh2stZ9Efy1FRERE3KEzY0RERCTirLW/A1cDFYBXjDFJuWaZCDQCHlIhRkRERGKNijEiIiJSKqy1C4HJQAvgQf90Y8wFwI3Al8BYY0yCMeYmY8ynxpg9xpj9xpiVxpibjTF5jlWMMQONMf8xxvxojDnge85SY8yV4dbDGLPEd8lUBWPMfcaYDcaYg8aYF3yPVzDGDDXGfGWM+cOXv9kY84Yx5txS2DUiIiIS43SZkoiIiJQaY0wyTtHlFKAL8DWwGqgMtAR+BN4EugIbgCVAJtAROBV42Vp7Va5lHgDW4lwG9TNQA7gAqAOMs9aOzjX/EqAD8BZwFvAusAPYYa2daIz5N9DXt16LgAPAX4B2wDxr7R2R2h8iIiIiAAlur4CIiIh4l7U20xjTD1gBzAC+AY4HbrLWbjDGjMUpxEwGbrPWZgMYY+KBacA1xpi51to3ghbb3Fr7Q3COMaYCTpHlbmPM09babWFWJ8333N+CnlcV6INTMGrtzw96vEYJNl9EREQkLF2mJCIiIqXKWrsKGI1z5sr5wNvW2qm+S5BuBn4Bbg8uhPj+PxywQL9cywspxPimHQKm4HzR1DmfVRkdXIjxPxUwwEEgJ8xyfy/EJoqIiIgUic6MERERkWh4FLgdqAWM8E1rhHOJ0XfAKGNMuOcdwLnEKcAYUw+4C6foUg+omOs5dfJZh89yT7DW7jHGvAlcBKwyxvwH+BhYYa3dX/BmiYiIiBSdijEiIiJS6qy1OcaYg74fD/j+9V8CdDIw5ihPr+T/jzHmRJyiyjE4RZP3gN1ANlAfGADkvnOT3y/5TL8cp7hzBXC/b1qmMWYucIe1dvtR1k1ERESkyFSMEREREbfs9v37mrW2dyGfMwyniHO1tfaF4AeMMX1xijFh2XzuWmCtPQCMxbmz0wlAe2AgcCVOgeecQq6biIiISKFozBgRERFxy3pgF9DGGJNYyOc09P37nzCPdSjpCllrf7LWvoIzqPB3QDsN4isiIiKRpmKMiIiIuMJamwX8E6gNPGmMyT32C8aY2saYpkGTNvv+Tc81X1fguqKugzHmOGNM6zAPpeLcfjsLOFTU5YqIiIgcjS5TEhERETc9AJwG3AhcZIxZBGwDauKMJdMWGAms9c3/FHA1MMc32O42oDnQDZiNM/5LUdQBPjXGrAO+An4CqgAX4gw2/KS19s9ib52IiIhIGCrGiIiIiGustYeNMRfjjM8yEKcIUgn4FdiEc0vsV4Lm/8YY0xEYB1yAcyzzNdAb55KnohZjNuMMHpwOdASOBXYCG4C7gZnF2CwRERGRozL5jGUnIiIiIiIiIiKlQGPGiIiIiIiIiIhEkYoxIiIiIiIiIiJRpGKMiIiIiIiIiEgUqRgjIiIiIiIiIhJFKsaIiIiIiIiIiESRijEiIiIiIiIiIlGkYoyIiIiIiIiISBSpGCMiIiIiIiIiEkUqxoiIiIiIiIiIRJGKMSIiIiIiIiIiUfT/KeniZPT2RQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DEV: Project development costs\n",
    "# INV: Investment / procurement costs\n",
    "# INST: Installation / construction costs \n",
    "# O&M: Yearly variable / O&M costs \n",
    "# DEC: Decommissioning costs \n",
    "# RES: Residual value   \n",
    "\n",
    "create_npv_plot(df_combined_npv, title='Supply chain: Scenario 3 \\n DEV: yes, INV: yes, INST: yes, O&M: yes, DEC: yes, RES: yes' )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "436b2218",
   "metadata": {},
   "source": [
    "##### 4.2. Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "348f490a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# collect the npv calculations per item (without revenues)\n",
    "Foundation.df_npv = calculate_npv(Foundation.df, 2030, WACC=WACC)\n",
    "Foundation2.df_npv = calculate_npv(Foundation2.df, 2030, WACC=WACC)\n",
    "\n",
    "Turbine.df_npv = calculate_npv(Turbine.df, 2030, WACC=WACC)\n",
    "Turbine2.df_npv = calculate_npv(Turbine2.df, 2030, WACC=WACC)\n",
    "\n",
    "ACStation.df_npv = calculate_npv(ACStation.df, 2030, WACC=WACC)\n",
    "DCCollectionCable.df_npv = calculate_npv(DCCollectionCable.df, 2030, WACC=WACC)\n",
    "\n",
    "HVDCConverter.df_npv = calculate_npv(HVDCConverter.df, 2030, WACC=WACC)\n",
    "HVDCCable.df_npv = calculate_npv(HVDCCable.df, 2030, WACC=WACC)\n",
    "HVDCCable2.df_npv = calculate_npv(HVDCCable2.df, 2030, WACC=WACC)\n",
    "\n",
    "Electrolyser.df_npv = calculate_npv(Electrolyser.df, 2030, WACC=WACC)\n",
    "DesalinationUnit.df_npv = calculate_npv(DesalinationUnit.df, 2030, WACC=WACC)\n",
    "CompressorUnit.df_npv = calculate_npv(CompressorUnit.df, 2030, WACC=WACC)\n",
    "StorageUnit.df_npv = calculate_npv(StorageUnit.df, 2030, WACC=WACC)\n",
    "CompressorAfterStorage.df_npv = calculate_npv(CompressorAfterStorage.df, 2030, WACC=WACC)\n",
    "\n",
    "DCACConverter.df_npv = calculate_npv(DCACConverter.df, 2030, WACC=WACC)\n",
    "\n",
    "ArtificialIsland.df_npv = calculate_npv(ArtificialIsland.df, 2030, WACC=WACC)\n",
    "Mainland.df_npv = calculate_npv(Mainland.df, 2030, WACC=WACC)\n",
    "\n",
    "H2Pipeline.df_npv = calculate_npv(H2Pipeline.df, 2030, WACC=WACC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bdef2a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine the values of certain items\n",
    "foundation_cables_value_npv_sum = -Foundation.df_npv.npv_sum.iloc[-1] - Foundation2.df_npv.npv_sum.iloc[-1]\n",
    "turbine_value_npv_sum = -Turbine.df_npv.npv_sum.iloc[-1] - Turbine2.df_npv.npv_sum.iloc[-1]\n",
    "AC_substation_value_npv_sum = -ACStation.df_npv.npv_sum.iloc[-1] - DCCollectionCable.df_npv.npv_sum.iloc[-1]\n",
    "hvdc_converter_value_npv_sum = -HVDCConverter.df_npv.npv_sum.iloc[-1] \n",
    "hvdc_cable_value_npv_sum = - HVDCCable.df_npv.npv_sum.iloc[-1] -HVDCCable2.df_npv.npv_sum.iloc[-1]\n",
    "electrolysis_value_npv_sum = -Electrolyser.df_npv.npv_sum.iloc[-1] -DesalinationUnit.df_npv.npv_sum.iloc[-1] -CompressorUnit.df_npv.npv_sum.iloc[-1] -StorageUnit.df_npv.npv_sum.iloc[-1] -CompressorAfterStorage.df_npv.npv_sum.iloc[-1]\n",
    "dcac_converter_value_npv_sum = -DCACConverter.df_npv.npv_sum.iloc[-1]\n",
    "artificial_island_value_npv_sum = -ArtificialIsland.df_npv.npv_sum.iloc[-1]\n",
    "mainland_value_npv_sum = -Mainland.df_npv.npv_sum.iloc[-1]\n",
    "H2_pipeline_value_npv_sum = -H2Pipeline.df_npv.npv_sum.iloc[-1]\n",
    "\n",
    "# create a new data point for each combined value\n",
    "data_hydrogen_npv = [foundation_cables_value_npv_sum, turbine_value_npv_sum, AC_substation_value_npv_sum, hvdc_converter_value_npv_sum, electrolysis_value_npv_sum, artificial_island_value_npv_sum, H2_pipeline_value_npv_sum\n",
    "]\n",
    "\n",
    "data_electricity_npv = [foundation_cables_value_npv_sum, turbine_value_npv_sum, AC_substation_value_npv_sum, hvdc_converter_value_npv_sum, hvdc_cable_value_npv_sum, dcac_converter_value_npv_sum, artificial_island_value_npv_sum, mainland_value_npv_sum\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2e8c93cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17821526077.126797\n",
      "27275595922.91795\n"
     ]
    }
   ],
   "source": [
    "#Cost allocation to hydrogen and electricity\n",
    "dedicated_data_hydrogen_npv = []\n",
    "for item in data_hydrogen_npv:\n",
    "    if item not in [electrolysis_value_npv_sum, H2_pipeline_value_npv_sum]:\n",
    "        dedicated_data_hydrogen_npv.append(dedicated_electrolyser_energy * item)\n",
    "\n",
    "data_hydrogen = sum(dedicated_data_hydrogen_npv) + electrolysis_value_npv_sum + H2_pipeline_value_npv_sum\n",
    "\n",
    "print(data_hydrogen)\n",
    "print('27275595922.91795')\n",
    "\n",
    "dedicated_data_electricity_npv = []\n",
    "for item in data_electricity_npv:\n",
    "    if item not in [hvdc_cable_value_npv_sum, dcac_converter_value_npv_sum, mainland_value_npv_sum]:\n",
    "        dedicated_data_electricity_npv.append(dedicated_DCAC_energy * item)\n",
    "\n",
    "data_electricity = sum(dedicated_data_electricity_npv) + dcac_converter_value_npv_sum + mainland_value_npv_sum + hvdc_cable_value_npv_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1a1b1b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare input for pie chart\n",
    "\n",
    "# combine the values of certain items\n",
    "foundation_cables_value = -Foundation.df_npv.cashflow_sum.iloc[-1] - Foundation2.df_npv.cashflow_sum.iloc[-1]\n",
    "turbine_value = -Turbine.df_npv.cashflow_sum.iloc[-1] - Turbine2.df_npv.cashflow_sum.iloc[-1]\n",
    "AC_substation_value = -ACStation.df_npv.cashflow_sum.iloc[-1] - DCCollectionCable.df_npv.cashflow_sum.iloc[-1]\n",
    "hvdc_converter_value = -HVDCConverter.df_npv.cashflow_sum.iloc[-1] -HVDCCable.df_npv.cashflow_sum.iloc[-1] -HVDCCable.df_npv.cashflow_sum.iloc[-1]\n",
    "electrolysis_value = -Electrolyser.df_npv.cashflow_sum.iloc[-1] -DesalinationUnit.df_npv.cashflow_sum.iloc[-1] -CompressorUnit.df_npv.cashflow_sum.iloc[-1] -StorageUnit.df_npv.cashflow_sum.iloc[-1] -CompressorAfterStorage.df_npv.cashflow_sum.iloc[-1]\n",
    "dcac_converter_value = -DCACConverter.df_npv.cashflow_sum.iloc[-1] \n",
    "artificial_island_value = -ArtificialIsland.df_npv.cashflow_sum.iloc[-1]\n",
    "mainland_value = -Mainland.df_npv.cashflow_sum.iloc[-1]\n",
    "H2_pipeline_value = -H2Pipeline.df_npv.cashflow_sum.iloc[-1]\n",
    "\n",
    "# create a new data point for each combined value\n",
    "labels = ['Foundation & cables', 'Turbine', 'AC Substation', 'HVDC Converter & cables', 'Electrolysis', 'DCAC Converter', 'Mainland', 'Artificial Island', 'H2 Pipeline']\n",
    "data = [foundation_cables_value, turbine_value, AC_substation_value, hvdc_converter_value, electrolysis_value, dcac_converter_value, mainland_value, artificial_island_value, H2_pipeline_value \n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d2747530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create pie plot\n",
    "fig, ax = plt.subplots(figsize=(16, 10), subplot_kw=dict(aspect=\"equal\"))\n",
    "\n",
    "def func(pct, allvals):\n",
    "    absolute = int(np.round(pct/100.*np.sum(allvals)))\n",
    "    return f\"{pct:.1f}%\"\n",
    "\n",
    "\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"))\n",
    "\n",
    "ax.legend(wedges, labels,\n",
    "          title=\"Components\",\n",
    "          loc=\"center left\",\n",
    "          bbox_to_anchor=(1, 0, 0.5, 1),\n",
    "         fontsize=15)\n",
    "\n",
    "plt.setp(autotexts, size=15, weight=\"bold\")\n",
    "\n",
    "ax.set_title(\"Cost diagram - Scenario 3 \\n Wind park 150 km from the coast \\n W2H / C(D, P, IC, R)-O(Y, I, D)-E-DCF / PD30 - WC12\", fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bc12d2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d6113afe",
   "metadata": {},
   "source": [
    "##### 5. Energy production + LCOE & LCOH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "52e39630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wind_energy_production_per_year_park1: 52560000000 kWh\n",
      "wind_energy_production_per_year_park2: 0 kWh\n",
      "    Year  Wind Energy Production (kWh)\n",
      "0   2030                             0\n",
      "1   2031                             0\n",
      "2   2032                             0\n",
      "3   2033                   52560000000\n",
      "4   2034                   52560000000\n",
      "5   2035                   52560000000\n",
      "6   2036                   52560000000\n",
      "7   2037                   52560000000\n",
      "8   2038                   52560000000\n",
      "9   2039                   52560000000\n",
      "10  2040                   52560000000\n",
      "11  2041                   52560000000\n",
      "12  2042                   52560000000\n",
      "13  2043                   52560000000\n",
      "14  2044                   52560000000\n",
      "15  2045                   52560000000\n",
      "16  2046                   52560000000\n",
      "17  2047                   52560000000\n",
      "18  2048                   52560000000\n",
      "19  2049                   52560000000\n",
      "20  2050                   52560000000\n",
      "21  2051                   52560000000\n",
      "22  2052                   52560000000\n",
      "23  2053                   52560000000\n",
      "24  2054                   52560000000\n",
      "25  2055                   52560000000\n",
      "26  2056                   52560000000\n",
      "27  2057                   52560000000\n",
      "28  2058                   52560000000\n",
      "29  2059                   52560000000\n"
     ]
    }
   ],
   "source": [
    "wind_energy_production_per_year_park1 = (turbine_data['unit']) * 1000 * 4380\n",
    "print('wind_energy_production_per_year_park1: {:.0f} kWh'.format(wind_energy_production_per_year_park1))\n",
    "wind_energy_production_per_year_park2 = (turbine2_data['unit']) * 1000 * 4380\n",
    "print('wind_energy_production_per_year_park2: {:.0f} kWh'.format(wind_energy_production_per_year_park2))\n",
    "\n",
    "total_wind_energy_production_per_year = []\n",
    "for year in range(startyear, endyear+1):\n",
    "    if year < (startyear + turbine_data['construction_duration']):\n",
    "        # Wind park 1 not yet operational\n",
    "        total_wind_energy_production_per_year.append(0)\n",
    "    else:\n",
    "        # Both wind parks operational\n",
    "        total_wind_energy_production_per_year.append(wind_energy_production_per_year_park1 + wind_energy_production_per_year_park2)\n",
    "\n",
    "# Create a DataFrame to hold the data\n",
    "production_data = {\n",
    "    'Year': range(startyear, endyear+1),\n",
    "    'Wind Energy Production (kWh)': total_wind_energy_production_per_year\n",
    "}\n",
    "df_wind = pd.DataFrame(production_data)\n",
    "\n",
    "# Print the table\n",
    "print(df_wind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d55e135e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total hydrogen onshore: 6732805165.02 kg hydrogen\n",
      "Total electricity onshore: 744152149818.00 kWh\n",
      "Total hydrogen converted to electricity onshore: 3366402582.51 kWh\n"
     ]
    }
   ],
   "source": [
    "# Multiply every annual value by the cable efficiency factors\n",
    "wind_energy_production_per_year_after_cable_losses = []\n",
    "for energy in total_wind_energy_production_per_year:\n",
    "    energy_after_cable_losses = energy * inter_array_cable_efficiency * DC_collection_cable_efficiency * HVDC_cable_efficiency\n",
    "    wind_energy_production_per_year_after_cable_losses.append(energy_after_cable_losses)\n",
    "\n",
    "# Calculate energy after electrolysis and transportation onshore and convert to kg hydrogen\n",
    "hydrogen_onshore_per_year = []\n",
    "for energy in wind_energy_production_per_year_after_cable_losses:\n",
    "    energy_after_electrolysis = energy * dedicated_electrolyser_energy / kWh_to_kg\n",
    "    energy_after_pipeline = energy_after_electrolysis * H2_pipeline_efficiency\n",
    "    hydrogen_onshore_per_year.append(energy_after_pipeline)\n",
    "\n",
    "# Calculate energy after DCAC converter and transportation onshore\n",
    "electricity_onshore_per_year = []\n",
    "for energy in wind_energy_production_per_year_after_cable_losses:\n",
    "    energy_after_DCAC = energy * dedicated_DCAC_energy * DCAC_efficiency\n",
    "    electricity_onshore_per_year.append(energy_after_DCAC)\n",
    "    \n",
    "#Calculate electricity after hydrogen conversion\n",
    "electricity_after_hydrogen_per_year = []\n",
    "for energy in hydrogen_onshore_per_year:\n",
    "    electricity_after_hydrogen = energy * 0.5\n",
    "    electricity_after_hydrogen_per_year.append(electricity_after_hydrogen)\n",
    "\n",
    "# Print summed values\n",
    "total_hydrogen_onshore = sum(hydrogen_onshore_per_year)\n",
    "total_electricity_onshore = sum(electricity_onshore_per_year)\n",
    "total_hydrogen_converted_to_electricity_onshore = sum(electricity_after_hydrogen_per_year)\n",
    "print(\"Total hydrogen onshore: {:.2f} kg hydrogen\".format(total_hydrogen_onshore))\n",
    "print(\"Total electricity onshore: {:.2f} kWh\".format(total_electricity_onshore))\n",
    "print(\"Total hydrogen converted to electricity onshore: {:.2f} kWh\".format(total_hydrogen_converted_to_electricity_onshore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8d7dffc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net Present Value hydrogen: 1933827275.34 kg\n",
      "Net Present Value electricity: 213738804116.79 kWh\n",
      "Net Present Value hydrogen converted to electricity onshore: 966913637.67 kWh\n"
     ]
    }
   ],
   "source": [
    "WACC = WACC\n",
    "\n",
    "present_value_hydrogen = []\n",
    "for i, production in enumerate(hydrogen_onshore_per_year):\n",
    "    pv = production * (1 / ((1 + WACC) ** (startyear+i - escalation_base_year)))\n",
    "    present_value_hydrogen.append(pv)\n",
    "    \n",
    "npv_hydrogen = sum(present_value_hydrogen)\n",
    "print(\"Net Present Value hydrogen: {:.2f} kg\".format(npv_hydrogen))\n",
    "\n",
    "present_value_electricity = []\n",
    "for i, production in enumerate(electricity_onshore_per_year):\n",
    "    pv = production * (1 / ((1 + WACC) ** (startyear+i - escalation_base_year)))\n",
    "    present_value_electricity.append(pv)\n",
    "    \n",
    "npv_electricity = sum(present_value_electricity)\n",
    "print(\"Net Present Value electricity: {:.2f} kWh\".format(npv_electricity))\n",
    "\n",
    "present_value_electricity_after_hydrogen = []\n",
    "for i, production in enumerate(electricity_after_hydrogen_per_year):\n",
    "    pv = production * (1 / ((1 + WACC) ** (startyear+i - escalation_base_year)))\n",
    "    present_value_electricity_after_hydrogen.append(pv)\n",
    "    \n",
    "npv_electricity_after_hydrogen = sum(present_value_electricity_after_hydrogen)\n",
    "print(\"Net Present Value hydrogen converted to electricity onshore: {:.2f} kWh\".format(npv_electricity_after_hydrogen))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2820badd",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cost = final_npv_sum*-1            # EU\n",
    "\n",
    "total_cost_hydrogen = data_hydrogen\n",
    "\n",
    "total_cost_electricity = data_electricity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "355ab33f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LCOH: 9.22 EU/kg\n",
      "LCOH2: 18.43 EU/kWh\n",
      "LCOE: 0.1612 EU/kWh\n",
      "Levelised Cost of Total Energy (LCOTE): 0.05 EU/MJ\n"
     ]
    }
   ],
   "source": [
    "LCOH = total_cost_hydrogen / npv_hydrogen\n",
    "LCOH2 = total_cost_hydrogen / npv_electricity_after_hydrogen\n",
    "LCOE = total_cost_electricity / npv_electricity\n",
    "\n",
    "npv_hydrogen_energy_produced = npv_hydrogen * 141.800 #MJ / kg\n",
    "npv_electricity_energy_produced = npv_electricity * 3.600 #MJ / kWh\n",
    "\n",
    "LCOTE = total_cost / (npv_hydrogen_energy_produced + npv_electricity_energy_produced)\n",
    "\n",
    "print('LCOH: {:.2f} EU/kg'.format(LCOH))\n",
    "\n",
    "print('LCOH2: {:.2f} EU/kWh'.format(LCOH2))\n",
    "\n",
    "print('LCOE: {:.4f} EU/kWh'.format(LCOE))\n",
    "\n",
    "print('Levelised Cost of Total Energy (LCOTE): {:.2f} EU/MJ'.format(LCOTE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b698bce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SupplyChain efficiency: 71.13 %\n"
     ]
    }
   ],
   "source": [
    "total_wind_energy_production = sum(total_wind_energy_production_per_year)\n",
    "\n",
    "hydrogen_energy_produced = total_hydrogen_onshore * 141.8 #MJ / kg\n",
    "electricity_energy_produced = total_electricity_onshore * 3.6 #MJ / kWh\n",
    "\n",
    "electrical_energy_input = total_wind_energy_production * 3.6 #MJ / kWh\n",
    "\n",
    "SupplyChain_efficiency = (hydrogen_energy_produced + electricity_energy_produced) / electrical_energy_input \n",
    "\n",
    "print('SupplyChain efficiency: {:.2f} %'.format(SupplyChain_efficiency*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "83bce454",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mTotal cost (NPV): 52.28 billion EUR\n",
      "LCOH: 9.22 EUR / kg\n",
      "LCOH2: 18.43 EUR / kWh\n",
      "LCOE: 0.16 EUR / kWh\n",
      "Levelised Cost of Total Energy (LCOTE): 0.05 EUR / MJ\n",
      "Supply Chain efficiency: 71.13 %\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 10), subplot_kw=dict(aspect=\"equal\"))\n",
    "\n",
    "def func(pct, allvals):\n",
    "    absolute = int(np.round(pct/100.*np.sum(allvals)))\n",
    "    rounded_pct = round(pct, 2)\n",
    "    if rounded_pct == 0:\n",
    "        return \"\"\n",
    "    return f\"{rounded_pct:.1f}%\"\n",
    "\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"), radius=total_cost/50_000_000_000)\n",
    "\n",
    "ax.legend(wedges, labels,\n",
    "          title=\"Components\",\n",
    "          loc=\"center left\",\n",
    "          bbox_to_anchor=(1.2, 0.5),\n",
    "          fontsize=15)\n",
    "\n",
    "plt.setp(autotexts, size=15, weight=\"bold\")\n",
    "\n",
    "title = ax.set_title(\"Cost diagram - Scenario 3 \\n Wind park 100 km, island 70 km (offshore) \\n W2H (70%) - W2E (30%) - PD30 - WC6 / C(D, P, IC, R) - O(Y, I, D) - E - DCF\", fontsize=20)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "cost_in_billions = total_cost / 1_000_000_000\n",
    "print(f\"{BOLD}Total cost (NPV): {cost_in_billions:.2f} billion EUR\")\n",
    "print(f\"LCOH: {LCOH:.2f} EUR / kg\")\n",
    "print(f\"LCOH2: {LCOH2:.2f} EUR / kWh\")\n",
    "print(f\"LCOE: {LCOE:.2f} EUR / kWh\")\n",
    "print('Levelised Cost of Total Energy (LCOTE): {:.2f} EUR / MJ'.format(LCOTE))\n",
    "print('Supply Chain efficiency: {:.2f} %'.format(SupplyChain_efficiency*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7dbe79",
   "metadata": {},
   "source": [
    "##### 6 - Revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4dac309b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make df_combined again (only cashflows no npv yet)\n",
    "df_combined=combine_cashflow_dataframes([Foundation.df, Foundation2.df, Turbine.df, Turbine2.df, ACStation.df, DCCollectionCable.df, HVDCConverter.df, HVDCCable.df, HVDCCable2.df, Electrolyser.df, DesalinationUnit.df, CompressorUnit.df, StorageUnit.df, CompressorAfterStorage.df, DCACConverter.df, ArtificialIsland.df, H2Pipeline.df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e9d916d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a set of escalation years and values\n",
    "escalation_years = []\n",
    "escalation_values = []\n",
    "previous = 1\n",
    "for index, year in enumerate(list(range(escalation_base_year, escalation_base_year + lifecycle + 1))):\n",
    "    previous = previous * (1 + escalation_rate)\n",
    "    escalation_years.append(year)    \n",
    "    escalation_values.append(previous)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d9b13815",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create list of revenue years\n",
    "revenue_years = list(range(startyear, endyear+1))\n",
    "\n",
    "# calculate revenues for both electricity and hydrogen\n",
    "electricity_revenue_value_per_year = [energy * electricity_price for energy in electricity_onshore_per_year]\n",
    "hydrogen_revenue_value_per_year = [energy * hydrogen_price for energy in hydrogen_onshore_per_year]\n",
    "\n",
    "# create a list of the combined electricity and hydrogen revenue values (summed)\n",
    "revenue_values = []\n",
    "for e, h in zip(electricity_revenue_value_per_year, hydrogen_revenue_value_per_year):\n",
    "    revenue_values.append(e + h)\n",
    "\n",
    "# escalate the combined electricity and hydrogen revenue values\n",
    "for i, revenue_year in enumerate(revenue_years):\n",
    "            revenue_values[i] = revenue_values[i] * escalation_values[\n",
    "                [index for index, escalation_year in enumerate(escalation_years) if\n",
    "                 escalation_year == revenue_year][0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8479f622",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the escalated combined electricity and hydrogen revenue values to df_combined\n",
    "for i, revenue_year in enumerate(revenue_years):\n",
    "    df_combined.loc[revenue_year, 'revenue'] = revenue_values[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b29d7ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the npv values\n",
    "df_combined_npv = calculate_npv(df_combined, 2030, WACC=WACC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9230bb70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Internal Rate of Return (IRR): 2.74%\n"
     ]
    }
   ],
   "source": [
    "import numpy_financial as npf\n",
    "\n",
    "# Calculate the NPV of the cash flows\n",
    "df_combined_npv = calculate_npv(df_combined, 2030, WACC=WACC)\n",
    "\n",
    "# Extract the cash flows from the DataFrame and convert to a one-dimensional array\n",
    "cash_flows = df_combined_npv['npv'].values\n",
    "\n",
    "# Calculate the internal rate of return (IRR)\n",
    "irr = npf.irr(cash_flows)\n",
    "\n",
    "# Convert the IRR to a string with two decimal places\n",
    "irr_str = \"{:.2f}%\".format(irr * 100)\n",
    "\n",
    "# Print the IRR\n",
    "print(\"Internal Rate of Return (IRR): {:.2%}\".format(irr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fa090c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "create_npv_plot(df_combined_npv, title='Scenario 3 with revenues\\n Wind park 100 km, island 70 km (offshore) \\n W2H (70%) - W2E (30%) - PD30 - WC6 / C(D, P, IC, R) - O(Y, I, D) - E - DCF\\nIRR= ' + irr_str )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d718a3bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.701930676803709\n"
     ]
    }
   ],
   "source": [
    "final_npv_sum_revenue = df_combined_npv['npv_sum'].iloc[-1]\n",
    "print(final_npv_sum_revenue/1000000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0518fc",
   "metadata": {},
   "source": [
    "##### 7 - Probabilistic calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f798adf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a0e432b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optie 2\n",
    "\n",
    "# Create a list of capex_per_unit's\n",
    "Foundation_capex_per_unit = stats.norm(foundation_data['capex_per_unit'],foundation_data['capex_per_unit']*0.1)\n",
    "Turbine_capex_per_unit = stats.norm(turbine_data['capex_per_unit'],turbine_data['capex_per_unit']*0.1)\n",
    "ACStation_capex_per_unit = stats.norm(AC_substation_data['capex_per_unit'],AC_substation_data['capex_per_unit']*0.1)\n",
    "DCCollectionCable_capex_per_unit = stats.norm(DC_collection_cable_data['capex_per_unit'],DC_collection_cable_data['capex_per_unit']*0.1)\n",
    "HVDCConverter_capex_per_unit = stats.norm(HVDC_converter_data['capex_per_unit'],HVDC_converter_data['capex_per_unit']*0.1)\n",
    "HVDCCable_capex_per_unit = stats.norm(HVDC_cable_data['capex_per_unit'],HVDC_cable_data['capex_per_unit']*0.1)\n",
    "Electrolyser_capex_per_unit = stats.norm(Electrolyser_data['capex_per_unit'],Electrolyser_data['capex_per_unit']*0.3)\n",
    "DesalinationUnit_capex_per_unit = stats.norm(Desalination_unit_data['capex_per_unit'],Desalination_unit_data['capex_per_unit']*0.3)\n",
    "CompressorUnit_capex_per_unit = stats.norm(Compressor_unit_data['capex_per_unit'],Compressor_unit_data['capex_per_unit']*0.3)\n",
    "StorageUnit_capex_per_unit = stats.norm(Storage_unit_data['capex_per_unit'],Storage_unit_data['capex_per_unit']*0.3)\n",
    "ArtificialIsland_capex_per_unit = stats.norm(Artificial_island_data['capex_per_unit'],Artificial_island_data['capex_per_unit']*0.1)\n",
    "CompressorAfterStorage_capex_per_unit = stats.norm(Compressor_after_storage_data['capex_per_unit'],Compressor_after_storage_data['capex_per_unit']*0.3)\n",
    "DCACConverter_capex_per_unit = stats.norm(DCAC_converter_data['capex_per_unit'], DCAC_converter_data['capex_per_unit']*0.1)\n",
    "Mainland_capex_per_unit = stats.norm(Mainland_data['capex_per_unit'], Mainland_data['capex_per_unit']*0.1)\n",
    "H2Pipeline_capex_per_unit = stats.norm(H2_pipeline_data['capex_per_unit'], H2_pipeline_data['capex_per_unit']*0.1)\n",
    "WACC_stat = stats.norm(WACC, WACC*0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "850c6bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "nr_of_realisations = 1000\n",
    "\n",
    "Foundation_capex_per_unit_realisations = Foundation_capex_per_unit.rvs(nr_of_realisations)\n",
    "Turbine_capex_per_unit_realisations = Turbine_capex_per_unit.rvs(nr_of_realisations)\n",
    "ACStation_capex_per_unit_realisations = ACStation_capex_per_unit.rvs(nr_of_realisations)\n",
    "DCCollectionCable_capex_per_unit_realisations = DCCollectionCable_capex_per_unit.rvs(nr_of_realisations)\n",
    "HVDCConverter_capex_per_unit_realisations = HVDCConverter_capex_per_unit.rvs(nr_of_realisations)\n",
    "HVDCCable_capex_per_unit_realisations = HVDCCable_capex_per_unit.rvs(nr_of_realisations)\n",
    "Electrolyser_capex_per_unit_realisations = Electrolyser_capex_per_unit.rvs(nr_of_realisations)\n",
    "DesalinationUnit_capex_per_unit_realisations = DesalinationUnit_capex_per_unit.rvs(nr_of_realisations)\n",
    "CompressorUnit_capex_per_unit_realisations = CompressorUnit_capex_per_unit.rvs(nr_of_realisations)\n",
    "StorageUnit_capex_per_unit_realisations = StorageUnit_capex_per_unit.rvs(nr_of_realisations)\n",
    "CompressorAfterStorage_capex_per_unit_realisations = CompressorAfterStorage_capex_per_unit.rvs(nr_of_realisations)\n",
    "ArtificialIsland_capex_per_unit_realisations = ArtificialIsland_capex_per_unit.rvs(nr_of_realisations)\n",
    "DCACConverter_capex_per_unit_realisations = DCACConverter_capex_per_unit.rvs(nr_of_realisations)\n",
    "Mainland_capex_per_unit_realisations = Mainland_capex_per_unit.rvs(nr_of_realisations)\n",
    "H2Pipeline_capex_per_unit_realisations = H2Pipeline_capex_per_unit.rvs(nr_of_realisations)\n",
    "WACC_stat_realisations = WACC_stat.rvs(nr_of_realisations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a0df9069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "plt.hist(ArtificialIsland_capex_per_unit_realisations, density=True, bins='auto', histtype='stepfilled', alpha=0.2)\n",
    "ax.set_xlim([round(min(ArtificialIsland_capex_per_unit_realisations)), round(max(ArtificialIsland_capex_per_unit_realisations))])\n",
    "ax.set_title(Artificial_island_data['component'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7361aaf0",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must have equal len keys and value when setting with an iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [80]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     48\u001b[0m Mainland\u001b[38;5;241m.\u001b[39mgenerate_cashflows(startyear\u001b[38;5;241m=\u001b[39mstartyear, lifecycle\u001b[38;5;241m=\u001b[39mlifecycle, debug\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m     49\u001b[0m H2Pipeline\u001b[38;5;241m.\u001b[39mgenerate_cashflows(startyear\u001b[38;5;241m=\u001b[39mstartyear, lifecycle\u001b[38;5;241m=\u001b[39mlifecycle, debug\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m---> 51\u001b[0m Foundation\u001b[38;5;241m.\u001b[39mdf_npv \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_npv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mFoundation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstartyear\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mWACC\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mWACC\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m Turbine\u001b[38;5;241m.\u001b[39mdf_npv \u001b[38;5;241m=\u001b[39m calculate_npv(Turbine\u001b[38;5;241m.\u001b[39mdf, startyear, WACC\u001b[38;5;241m=\u001b[39mWACC_stat_realisations)\n\u001b[0;32m     53\u001b[0m ACStation\u001b[38;5;241m.\u001b[39mdf_npv \u001b[38;5;241m=\u001b[39m calculate_npv(ACStation\u001b[38;5;241m.\u001b[39mdf, startyear, WACC\u001b[38;5;241m=\u001b[39mWACC)\n",
      "File \u001b[1;32m~\\OpenSCSim\\source2.py:485\u001b[0m, in \u001b[0;36mcalculate_npv\u001b[1;34m(df, escalation_base_year, WACC)\u001b[0m\n\u001b[0;32m    483\u001b[0m \u001b[38;5;66;03m# calculate the npv through the years from the 2nd year up to the end and add the values to the 'npv' column\u001b[39;00m\n\u001b[0;32m    484\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m year \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39myears\u001b[38;5;241m.\u001b[39mtolist()[:]:\n\u001b[1;32m--> 485\u001b[0m     df\u001b[38;5;241m.\u001b[39mloc[year, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnpv\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcashflow\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mloc[year] \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m ((\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m WACC) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m (year \u001b[38;5;241m-\u001b[39m escalation_base_year)))\n\u001b[0;32m    487\u001b[0m \u001b[38;5;66;03m# add the cumsum of npvs to the 'npv_sum' column\u001b[39;00m\n\u001b[0;32m    488\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnpv_sum\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnpv\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mcumsum()\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:716\u001b[0m, in \u001b[0;36m_LocationIndexer.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    713\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_valid_setitem_indexer(key)\n\u001b[0;32m    715\u001b[0m iloc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miloc\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39miloc\n\u001b[1;32m--> 716\u001b[0m \u001b[43miloc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setitem_with_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1688\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1685\u001b[0m \u001b[38;5;66;03m# align and set the values\u001b[39;00m\n\u001b[0;32m   1686\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m take_split_path:\n\u001b[0;32m   1687\u001b[0m     \u001b[38;5;66;03m# We have to operate column-wise\u001b[39;00m\n\u001b[1;32m-> 1688\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setitem_with_indexer_split_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1689\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1690\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_single_block(indexer, value, name)\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1743\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer_split_path\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1738\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(value) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_integer(info_axis):\n\u001b[0;32m   1739\u001b[0m         \u001b[38;5;66;03m# This is a case like df.iloc[:3, [1]] = [0]\u001b[39;00m\n\u001b[0;32m   1740\u001b[0m         \u001b[38;5;66;03m#  where we treat as df.iloc[:3, 1] = 0\u001b[39;00m\n\u001b[0;32m   1741\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_with_indexer((pi, info_axis[\u001b[38;5;241m0\u001b[39m]), value[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m-> 1743\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1744\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust have equal len keys and value \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1745\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhen setting with an iterable\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1746\u001b[0m     )\n\u001b[0;32m   1748\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m lplane_indexer \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(value) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex):\n\u001b[0;32m   1749\u001b[0m     \u001b[38;5;66;03m# We get here in one case via .loc with a all-False mask\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: Must have equal len keys and value when setting with an iterable"
     ]
    }
   ],
   "source": [
    "LCOH_stat = []\n",
    "startyear=2030\n",
    "for index, value in enumerate(Foundation_capex_per_unit_realisations):\n",
    "    Foundation = Object(**foundation_data)\n",
    "    Foundation.capex_per_unit = Foundation_capex_per_unit_realisations[index]\n",
    "    Turbine = Object(**turbine_data)\n",
    "    Turbine.capex_per_unit = Turbine_capex_per_unit_realisations[index]\n",
    "    ACStation = Object(**AC_substation_data)\n",
    "    ACStation.capex_per_unit = ACStation_capex_per_unit_realisations[index]\n",
    "    DCCollectionCable = Object(**DC_collection_cable_data)\n",
    "    DCCollectionCable.capex_per_unit = DCCollectionCable_capex_per_unit_realisations[index]\n",
    "    HVDCConverter = Object(**HVDC_converter_data)\n",
    "    HVDCConverter.capex_per_unit = HVDCConverter_capex_per_unit_realisations[index]\n",
    "    HVDCCable = Object(**HVDC_cable_data)\n",
    "    HVDCCable.capex_per_unit = HVDCCable_capex_per_unit_realisations[index]\n",
    "    Electrolyser = Object(**Electrolyser_data)\n",
    "    Electrolyser.capex_per_unit = Electrolyser_capex_per_unit_realisations[index]\n",
    "    DesalinationUnit = Object(**Desalination_unit_data)\n",
    "    DesalinationUnit.capex_per_unit = DesalinationUnit_capex_per_unit_realisations[index]\n",
    "    CompressorUnit = Object(**Compressor_unit_data)\n",
    "    CompressorUnit.capex_per_unit = CompressorUnit_capex_per_unit_realisations[index]\n",
    "    StorageUnit = Object(**Storage_unit_data)\n",
    "    StorageUnit.capex_per_unit = StorageUnit_capex_per_unit_realisations[index]\n",
    "    CompressorAfterStorage = Object(**Compressor_after_storage_data)\n",
    "    CompressorAfterStorage.capex_per_unit = CompressorAfterStorage_capex_per_unit_realisations[index]\n",
    "    ArtificialIsland = Object(**Artificial_island_data)\n",
    "    ArtificialIsland.capex_per_unit = ArtificialIsland_capex_per_unit_realisations[index]\n",
    "    DCACConverter = Object(**DCAC_converter_data)\n",
    "    DCACConverter.capex_per_unit = DCACConverter_capex_per_unit_realisations[index]\n",
    "    Mainland = Object(**Mainland_data)\n",
    "    Mainland.capex_per_unit = Mainland_capex_per_unit_realisations[index]\n",
    "    H2Pipeline = Object(**H2_pipeline_data)\n",
    "    H2Pipeline.capex_per_unit = H2Pipeline_capex_per_unit_realisations[index]\n",
    "    \n",
    "    Foundation.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    Turbine.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    ACStation.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    DCCollectionCable.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    HVDCConverter.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    HVDCCable.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    Electrolyser.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    DesalinationUnit.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    CompressorUnit.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    ArtificialIsland.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    StorageUnit.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    CompressorAfterStorage.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    DCACConverter.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    Mainland.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    H2Pipeline.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    \n",
    "    Foundation.df_npv = calculate_npv(Foundation.df, startyear, WACC=WACC)\n",
    "    Turbine.df_npv = calculate_npv(Turbine.df, startyear, WACC=WACC_stat_realisations)\n",
    "    ACStation.df_npv = calculate_npv(ACStation.df, startyear, WACC=WACC)\n",
    "    DCCollectionCable.df_npv = calculate_npv(DCCollectionCable.df, startyear, WACC=WACC)\n",
    "    HVDCConverter.df_npv = calculate_npv(HVDCConverter.df, startyear, WACC=WACC)\n",
    "    HVDCCable.df_npv = calculate_npv(HVDCCable.df, startyear, WACC=WACC)\n",
    "    Electrolyser.df_npv = calculate_npv(Electrolyser.df, startyear, WACC=WACC)\n",
    "    DesalinationUnit.df_npv = calculate_npv(DesalinationUnit.df, startyear, WACC=WACC)\n",
    "    CompressorUnit.df_npv = calculate_npv(CompressorUnit.df, startyear, WACC=WACC)\n",
    "    ArtificialIsland.df_npv = calculate_npv(ArtificialIsland.df, startyear, WACC=WACC)\n",
    "    StorageUnit.df_npv = calculate_npv(StorageUnit.df, startyear, WACC=WACC)\n",
    "    CompressorAfterStorage.df_npv = calculate_npv(CompressorAfterStorage.df, startyear, WACC=WACC)\n",
    "    DCACConverter.df_npv = calculate_npv(DCACConverter.df, startyear, WACC=WACC)\n",
    "    Mainland.df_npv = calculate_npv(Mainland.df, startyear, WACC=WACC)\n",
    "    H2Pipeline.df_npv = calculate_npv(H2Pipeline.df, startyear, WACC=WACC)\n",
    "    \n",
    "    # combine the values of certain items\n",
    "    foundation_cables_value_npv_sum = -Foundation.df_npv.npv_sum.iloc[-1]\n",
    "    turbine_value_npv_sum = -Turbine.df_npv.npv_sum.iloc[-1]\n",
    "    AC_substation_value_npv_sum = -ACStation.df_npv.npv_sum.iloc[-1] - DCCollectionCable.df_npv.npv_sum.iloc[-1]\n",
    "    hvdc_converter_value_npv_sum = -HVDCConverter.df_npv.npv_sum.iloc[-1] \n",
    "    hvdc_cable_value_npv_sum = - HVDCCable.df_npv.npv_sum.iloc[-1] \n",
    "    electrolysis_value_npv_sum = -Electrolyser.df_npv.npv_sum.iloc[-1] -DesalinationUnit.df_npv.npv_sum.iloc[-1] -CompressorUnit.df_npv.npv_sum.iloc[-1] -StorageUnit.df_npv.npv_sum.iloc[-1] -CompressorAfterStorage.df_npv.npv_sum.iloc[-1]\n",
    "    dcac_converter_value_npv_sum = -DCACConverter.df_npv.npv_sum.iloc[-1]\n",
    "    artificial_island_value_npv_sum = -ArtificialIsland.df_npv.npv_sum.iloc[-1]\n",
    "    mainland_value_npv_sum = -Mainland.df_npv.npv_sum.iloc[-1]\n",
    "    H2_pipeline_value_npv_sum = -H2Pipeline.df_npv.npv_sum.iloc[-1]\n",
    "\n",
    "    # create a new data point for each combined value\n",
    "    data_hydrogen_npv_stat = [foundation_cables_value_npv_sum, turbine_value_npv_sum, AC_substation_value_npv_sum, hvdc_converter_value_npv_sum, electrolysis_value_npv_sum, artificial_island_value_npv_sum, H2_pipeline_value_npv_sum\n",
    "    ]\n",
    "\n",
    "    #Cost allocation to hydrogen\n",
    "    dedicated_data_hydrogen_npv_stat = []\n",
    "    for item in data_hydrogen_npv_stat:\n",
    "        if item not in [electrolysis_value_npv_sum, H2_pipeline_value_npv_sum]:\n",
    "            dedicated_data_hydrogen_npv_stat.append(dedicated_electrolyser_energy * item)\n",
    "\n",
    "    data_hydrogen_stat = sum(dedicated_data_hydrogen_npv_stat) + electrolysis_value_npv_sum + H2_pipeline_value_npv_sum\n",
    "    \n",
    "    total_cost_stat = data_hydrogen_stat \n",
    "    LCOH_stat.append(total_cost_stat / npv_hydrogen)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "c2968946",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cost_stat = []\n",
    "startyear=2030\n",
    "for index, value in enumerate(Foundation_capex_per_unit_realisations):\n",
    "    Foundation = Object(**foundation_data)\n",
    "    Foundation.capex_per_unit = Foundation_capex_per_unit_realisations[index]\n",
    "    Turbine = Object(**turbine_data)\n",
    "    Turbine.capex_per_unit = Turbine_capex_per_unit_realisations[index]\n",
    "    ACStation = Object(**AC_substation_data)\n",
    "    ACStation.capex_per_unit = ACStation_capex_per_unit_realisations[index]\n",
    "    DCCollectionCable = Object(**DC_collection_cable_data)\n",
    "    DCCollectionCable.capex_per_unit = DCCollectionCable_capex_per_unit_realisations[index]\n",
    "    HVDCConverter = Object(**HVDC_converter_data)\n",
    "    HVDCConverter.capex_per_unit = HVDCConverter_capex_per_unit_realisations[index]\n",
    "    HVDCCable = Object(**HVDC_cable_data)\n",
    "    HVDCCable.capex_per_unit = HVDCCable_capex_per_unit_realisations[index]\n",
    "    Electrolyser = Object(**Electrolyser_data)\n",
    "    Electrolyser.capex_per_unit = Electrolyser_capex_per_unit_realisations[index]\n",
    "    DesalinationUnit = Object(**Desalination_unit_data)\n",
    "    DesalinationUnit.capex_per_unit = DesalinationUnit_capex_per_unit_realisations[index]\n",
    "    CompressorUnit = Object(**Compressor_unit_data)\n",
    "    CompressorUnit.capex_per_unit = CompressorUnit_capex_per_unit_realisations[index]\n",
    "    StorageUnit = Object(**Storage_unit_data)\n",
    "    StorageUnit.capex_per_unit = StorageUnit_capex_per_unit_realisations[index]\n",
    "    CompressorAfterStorage = Object(**Compressor_after_storage_data)\n",
    "    CompressorAfterStorage.capex_per_unit = CompressorAfterStorage_capex_per_unit_realisations[index]\n",
    "    ArtificialIsland = Object(**Artificial_island_data)\n",
    "    ArtificialIsland.capex_per_unit = ArtificialIsland_capex_per_unit_realisations[index]\n",
    "    DCACConverter = Object(**DCAC_converter_data)\n",
    "    DCACConverter.capex_per_unit = DCACConverter_capex_per_unit_realisations[index]\n",
    "    Mainland = Object(**Mainland_data)\n",
    "    Mainland.capex_per_unit = Mainland_capex_per_unit_realisations[index]\n",
    "    H2Pipeline = Object(**H2_pipeline_data)\n",
    "    H2Pipeline.capex_per_unit = H2Pipeline_capex_per_unit_realisations[index]\n",
    "    \n",
    "    Foundation.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    Turbine.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    ACStation.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    DCCollectionCable.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    HVDCConverter.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    HVDCCable.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    Electrolyser.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    DesalinationUnit.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    CompressorUnit.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    ArtificialIsland.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    StorageUnit.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    CompressorAfterStorage.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    DCACConverter.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    Mainland.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    H2Pipeline.generate_cashflows(startyear=startyear, lifecycle=lifecycle, debug=False)\n",
    "    \n",
    "    Foundation.df_npv = calculate_npv(Foundation.df, startyear, WACC=WACC)\n",
    "    Turbine.df_npv = calculate_npv(Turbine.df, startyear, WACC=WACC)\n",
    "    ACStation.df_npv = calculate_npv(ACStation.df, startyear, WACC=WACC)\n",
    "    DCCollectionCable.df_npv = calculate_npv(DCCollectionCable.df, startyear, WACC=WACC)\n",
    "    HVDCConverter.df_npv = calculate_npv(HVDCConverter.df, startyear, WACC=WACC)\n",
    "    HVDCCable.df_npv = calculate_npv(HVDCCable.df, startyear, WACC=WACC)\n",
    "    Electrolyser.df_npv = calculate_npv(Electrolyser.df, startyear, WACC=WACC)\n",
    "    DesalinationUnit.df_npv = calculate_npv(DesalinationUnit.df, startyear, WACC=WACC)\n",
    "    CompressorUnit.df_npv = calculate_npv(CompressorUnit.df, startyear, WACC=WACC)\n",
    "    ArtificialIsland.df_npv = calculate_npv(ArtificialIsland.df, startyear, WACC=WACC)\n",
    "    StorageUnit.df_npv = calculate_npv(StorageUnit.df, startyear, WACC=WACC)\n",
    "    CompressorAfterStorage.df_npv = calculate_npv(CompressorAfterStorage.df, startyear, WACC=WACC)\n",
    "    DCACConverter.df_npv = calculate_npv(DCACConverter.df, startyear, WACC=WACC)\n",
    "    Mainland.df_npv = calculate_npv(Mainland.df, startyear, WACC=WACC)\n",
    "    H2Pipeline.df_npv = calculate_npv(H2Pipeline.df, startyear, WACC=WACC)\n",
    "    \n",
    "    cost_data = [\n",
    "        -Foundation.df_npv.npv_sum.iloc[-1],\n",
    "        -Turbine.df_npv.npv_sum.iloc[-1],\n",
    "        -ACStation.df_npv.npv_sum.iloc[-1],\n",
    "        - DCCollectionCable.df_npv.npv_sum.iloc[-1],\n",
    "        -HVDCConverter.df_npv.npv_sum.iloc[-1], \n",
    "        - HVDCCable.df_npv.npv_sum.iloc[-1], \n",
    "        -Electrolyser.df_npv.npv_sum.iloc[-1],\n",
    "        -DesalinationUnit.df_npv.npv_sum.iloc[-1],\n",
    "        -CompressorUnit.df_npv.npv_sum.iloc[-1],\n",
    "        -StorageUnit.df_npv.npv_sum.iloc[-1],\n",
    "        -CompressorAfterStorage.df_npv.npv_sum.iloc[-1],\n",
    "        -DCACConverter.df_npv.npv_sum.iloc[-1],\n",
    "        -ArtificialIsland.df_npv.npv_sum.iloc[-1],\n",
    "        -Mainland.df_npv.npv_sum.iloc[-1],\n",
    "        -H2Pipeline.df_npv.npv_sum.iloc[-1],\n",
    "    ]\n",
    "    total_cost = sum(cost_data)\n",
    "    \n",
    "    total_cost_stat.append(total_cost/1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6de0218e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'total_cost_stat' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [81]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtotal_cost_stat\u001b[49m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'total_cost_stat' is not defined"
     ]
    }
   ],
   "source": [
    "total_cost_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "5c3ce53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "from scipy.stats import norm\n",
    "\n",
    "mean_total_cost = np.mean(total_cost_stat)\n",
    "median_total_cost = np.median(total_cost_stat)\n",
    "std_total_cost = np.std(total_cost_stat)\n",
    "lower_bound = mean_total_cost - std_total_cost\n",
    "upper_bound = mean_total_cost + std_total_cost\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "# Plot histogram\n",
    "n, bins, patches = plt.hist(total_cost_stat, density=True, bins='auto', histtype='bar', color= 'white', edgecolor='black', alpha=0.2)\n",
    "\n",
    "# Get probability density function (PDF)\n",
    "y = norm.pdf(bins, mean_total_cost, std_total_cost)\n",
    "\n",
    "# Scale PDF to match histogram\n",
    "y = y * np.max(n) / np.max(y)\n",
    "\n",
    "# Plot PDF with smooth line\n",
    "plt.plot(bins, y, 'r-', linewidth=2)\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=12)\n",
    "ax.tick_params(axis='y', labelsize=12)\n",
    "\n",
    "# Set y-axis tick formatter in scientific notation with 0 decimal places\n",
    "fmt = mtick.FormatStrFormatter('%.0e')\n",
    "ax.yaxis.set_major_formatter(fmt)\n",
    "\n",
    "# Set x-axis and y-axis limits\n",
    "ax.set_xlim([bins[0], bins[-1]])\n",
    "\n",
    "# Label axes and title\n",
    "ax.set_xlabel('Total cost (EUR)', fontsize = 14)\n",
    "ax.set_ylabel('Probability', fontsize = 14)\n",
    "ax.set_title('Total cost for 1000 samples \\n Std. Dev. Artificial Island = 30%', fontsize = 18)\n",
    "\n",
    "# Add vertical lines for mean and median\n",
    "ax.axvline(x=mean_total_cost, color='g', linestyle='dotted', label='Mean')\n",
    "\n",
    "percent_within_1std = (norm.cdf(upper_bound, mean_total_cost, std_total_cost) - norm.cdf(lower_bound, mean_total_cost, std_total_cost)) * 100\n",
    "plt.axvline(x=lower_bound, color='b', linestyle='--', label='Lower Bound')\n",
    "plt.axvline(x=upper_bound, color='b', linestyle='--', label='Upper Bound')\n",
    "\n",
    "ax.legend()\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1.02), borderaxespad=1, fontsize = 14)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "4a48c33b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original total cost: 52679022749.25 EUR\n",
      "Mean total cost: 51.84 billion EUR\n",
      "Standard deviation of total_cost: 2.55 billion EUR\n",
      "Chance the total cost will fall within the mean plus or minus the std. dev.: 68.27%\n",
      "total_cost value or value range with highest probability: 52107706751.19 - 52781819197.68 EUR/kg\n",
      "Probability of total_cost value or value range with highest probability: 68865158.34%\n"
     ]
    }
   ],
   "source": [
    "mean_total_cost_billion = mean_total_cost / 1000000000\n",
    "std_total_cost_billion = std_total_cost / 1000000000\n",
    "\n",
    "print(\"Original total cost: {:.2f} EUR\".format(total_cost))\n",
    "print(\"Mean total cost: {:.2f} billion EUR\".format(mean_total_cost_billion))\n",
    "print(\"Standard deviation of total_cost: {:.2f} billion EUR\".format(std_total_cost_billion))\n",
    "print(\"Chance the total cost will fall within the mean plus or minus the std. dev.: {:.2f}%\".format(percent_within_1std))\n",
    "\n",
    "# Find bin with highest probability and use bin center value as total cost value or value range with highest probability\n",
    "max_bin_idx = np.argmax(y)\n",
    "max_bin_center = (bins[max_bin_idx] + bins[max_bin_idx+1]) / 2\n",
    "if max_bin_idx == 0:\n",
    "    max_bin_width = bins[1] - bins[0]\n",
    "elif max_bin_idx == len(bins) - 2:\n",
    "    max_bin_width = bins[-1] - bins[-2]\n",
    "else:\n",
    "    max_bin_width = (bins[max_bin_idx+1] - bins[max_bin_idx-1]) / 2\n",
    "\n",
    "if max_bin_width > 0:\n",
    "    max_bin_area = norm.cdf(bins[max_bin_idx+1], mean_total_cost, std_total_cost) - norm.cdf(bins[max_bin_idx], mean_total_cost, std_total_cost)\n",
    "    max_bin_prob = max_bin_area * max_bin_width\n",
    "    print(\"total_cost value or value range with highest probability: {:.2f} - {:.2f} EUR/kg\".format(max_bin_center-max_bin_width/2, max_bin_center+max_bin_width/2))\n",
    "    print(\"Probability of total_cost value or value range with highest probability: {:.2f}%\".format(max_bin_prob))\n",
    "else:\n",
    "    max_bin_prob = y[max_bin_idx]\n",
    "    print(\"total_cost value with highest probability: {:.2f} EUR\".format(max_bin_center))\n",
    "    print(\"Probability of total_cost value with highest probability: {:.2f}%\".format(max_bin_prob))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a911a395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "from scipy.stats import norm\n",
    "\n",
    "mean_LCOH = np.mean(LCOH_stat)\n",
    "median_LCOH = np.median(LCOH_stat)\n",
    "std_LCOH = np.std(LCOH_stat)\n",
    "lower_bound = mean_LCOH - std_LCOH\n",
    "upper_bound = mean_LCOH + std_LCOH\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "# Plot histogram\n",
    "n, bins, patches = plt.hist(LCOH_stat, density=True, bins='auto', histtype='bar', alpha=0.2, color= 'white', edgecolor='black')\n",
    "\n",
    "# Get probability density function (PDF)\n",
    "y = norm.pdf(bins, mean_LCOH, std_LCOH)\n",
    "\n",
    "# Scale PDF to match histogram\n",
    "y = y * np.max(n) / np.max(y)\n",
    "\n",
    "# Plot PDF with smooth line\n",
    "plt.plot(bins, y, 'r-', linewidth=2)\n",
    "\n",
    "# Convert y-axis values to percentages\n",
    "y = n * 100 / len(LCOH_stat)\n",
    "\n",
    "# Format y-axis as percentages\n",
    "fmt = '%.2f%%' \n",
    "yticks = mtick.FormatStrFormatter(fmt)\n",
    "ax.yaxis.set_major_formatter(yticks)\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=12)\n",
    "ax.tick_params(axis='y', labelsize=12)\n",
    "\n",
    "# Set x-axis and y-axis limits\n",
    "ax.set_xlim([(lower_bound-1), (upper_bound+1)])\n",
    "ax.set_ylim([0, 1.4])\n",
    "\n",
    "# Label axes and title\n",
    "ax.set_xlabel('LCOH (EUR/kg)', fontsize = 14)\n",
    "ax.set_ylabel('Probability (%)', fontsize = 14)\n",
    "ax.set_title('LCOHs for 1000 samples \\n Std. Dev. All Components = 10%', fontsize = 18)\n",
    "\n",
    "# Add vertical lines for mean and median\n",
    "ax.axvline(x=mean_LCOH, color='g', linestyle='dotted', label='Mean')\n",
    "\n",
    "percent_within_1std = (norm.cdf(upper_bound, mean_LCOH, std_LCOH) - norm.cdf(lower_bound, mean_LCOH, std_LCOH)) * 100\n",
    "plt.axvline(x=lower_bound, color='b', linestyle='--', label='Lower Bound')\n",
    "plt.axvline(x=upper_bound, color='b', linestyle='--', label='Upper Bound')\n",
    "\n",
    "ax.legend()\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1.02), borderaxespad=1, fontsize = 14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Get probability density function (PDF)\n",
    "y = norm.pdf(bins, mean_LCOH, std_LCOH)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbe21292",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Original LCOH: {:.2f} EUR/kg\".format(LCOH))\n",
    "print(\"Mean LCOH: {:.2f} EUR/kg\".format(mean_LCOH))\n",
    "print(\"Standard deviation of LCOH: {:.2f} EUR/kg\".format(std_LCOH))\n",
    "print(\"Chance the LCOH will fall within the mean plus or minus the std. dev.: {:.2f}%\".format(percent_within_1std))\n",
    "\n",
    "# Find bin with highest probability and use bin center value as LCOH value or value range with highest probability\n",
    "max_bin_idx = np.argmax(y)\n",
    "max_bin_center = (bins[max_bin_idx] + bins[max_bin_idx+1]) / 2\n",
    "if max_bin_idx == 0:\n",
    "    max_bin_width = bins[1] - bins[0]\n",
    "elif max_bin_idx == len(bins) - 2:\n",
    "    max_bin_width = bins[-1] - bins[-2]\n",
    "else:\n",
    "    max_bin_width = (bins[max_bin_idx+1] - bins[max_bin_idx-1]) / 2\n",
    "\n",
    "if max_bin_width > 0:\n",
    "    max_bin_area = norm.cdf(bins[max_bin_idx+1], mean_LCOH, std_LCOH) - norm.cdf(bins[max_bin_idx], mean_LCOH, std_LCOH)\n",
    "    max_bin_prob = max_bin_area * max_bin_width\n",
    "    print(\"LCOH value or value range with highest probability: {:.2f} - {:.2f} EUR/kg\".format(max_bin_center-max_bin_width/2, max_bin_center+max_bin_width/2))\n",
    "    print(\"Probability of LCOH value or value range with highest probability: {:.2f}%\".format(max_bin_prob * 100))\n",
    "else:\n",
    "    max_bin_prob = y[max_bin_idx]\n",
    "    print(\"LCOH value with highest probability: {:.2f} EUR/kg\".format(max_bin_center))\n",
    "    print(\"Probability of LCOH value with highest probability: {:.2f}%\".format(max_bin_prob * 100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e976d8b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a scatter plot of LCOH vs. Foundation capex per unit\n",
    "plt.scatter(Foundation_capex_per_unit_realisations, LCOH_stat)\n",
    "plt.xlabel('Foundation capex per unit (EUR/m)')\n",
    "plt.ylabel('LCOH (EUR/kg)')\n",
    "plt.title('LCOH vs. Foundation capex per unit')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f60c61b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a43c6687",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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