LCA_parameterized_model_Eolien_public.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parametrized model for onshore and offshore wind turbines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authors: Romain Besseau [romain.besseau@mines-paristech.fr](mailto:romain.besseau@mines-paristech.fr) and Romain Sacchi [r_s@me.com](mailto:r_s@me.com)\n",
    "\n",
    "The script below was used to generate life cycle inventories of wind turbines based on the [Danish wind turbines registry](https://ens.dk/sites/ens.dk/files/Statistik/anlaegprodtilnettet.xls).\n",
    "Results were used in the following publications:\n",
    "\n",
    "Romain Sacchi, Romain Besseau, Paula Pérez-López, Isabelle Blanc, Exploring technologically, temporally and geographically-sensitive life cycle inventories for wind turbines: A parameterized model for Denmark, Renewable Energy, Volume 132, 2019, Pages 1238-1250, ISSN 0960-1481, [doi.org/10.1016/j.renene.2018.09.020](https://doi.org/10.1016/j.renene.2018.09.020).\n",
    "\n",
    "and\n",
    "\n",
    "Romain Besseau, Romain Sacchi, Isabelle Blanc, Paula Pérez-López, Past, present and future environmental footprint of the Danish wind turbine fleet with LCA_WIND_DK, an online interactive platform, Renewable & Sustainable Energy Reviews, online, 2019, [doi.org/10.1016/j.rser.2019.03.030](https://doi.org/10.1016/j.rser.2019.03.030)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need first to import a few librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import brightway2 as bw\n",
    "from scipy.interpolate import InterpolatedUnivariateSpline\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the following assumes you have an BW2 project within which ecoinvent 3.4 cutoff is installed. If not, you should create a project and install ecoinvent before going any further. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bw.projects.set_current('my_project')\n",
    "eidb = bw.Database('ecoinvent 3.4 cut off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of datasets for power transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by creating datasets for high voltage and medium voltage power transformer. These datasets are based on the EPD of ABB's power transformers.\n",
    "\n",
    "This has to be executed once."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### creation of 500 MVA transformer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "if [act for act in eidb if 'Power transformer TrafoStar 500 MVA' in act['name']] == []:\n",
    "\n",
    "    act_transfo=[act for act in eidb if act[\"name\"]==\"transformer production, high voltage use\"][0]\n",
    "\n",
    "    new_act=act_transfo.copy()\n",
    "    new_act[\"name\"]=\"Power transformer TrafoStar 500 MVA\"\n",
    "    new_act[\"unit\"]=\"unit\"\n",
    "    new_act.save()\n",
    "\n",
    "    for exc in new_act.exchanges():\n",
    "        exc.delete()\n",
    "    new_act.save()\n",
    "\n",
    "    #electric steel\n",
    "    steel=[act for act in eidb if \"steel production, electric, low-alloyed\" in act[\"name\"] and \"RER\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=99640,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #transformer oil\n",
    "    steel=[act for act in eidb if \"market for lubricating oil\" in act[\"name\"] and \"GLO\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=63000,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #copper\n",
    "    steel=[act for act in eidb if act[\"name\"]==\"market for copper\" and \"GLO\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=39960,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #insulation\n",
    "    steel=[act for act in eidb if act[\"name\"]==\"market for glass wool mat\"  and \"GLO\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=6500,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #wood\n",
    "    steel=[act for act in eidb if act[\"name\"]== \"planing, board, softwood, u=20%\" and \"CH\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=15000,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #porcelain\n",
    "    steel=[act for act in eidb if act[\"name\"]==\"market for ceramic tile\" and \"GLO\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=2650,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #construction steel\n",
    "    steel=[act for act in eidb if act[\"name\"]==\"market for steel, unalloyed\" and \"GLO\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=53618,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #paint\n",
    "    steel=[act for act in eidb if act[\"name\"]== \"market for electrostatic paint\" and \"GLO\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=2200,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #electricity, medium\n",
    "    steel=[act for act in eidb if act[\"name\"]==\"market for electricity, medium voltage\" and \"SE\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=750000,unit=\"kilowatt hour\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #heat\n",
    "    steel=[act for act in eidb if act[\"name\"]==\"heat, from municipal waste incineration to generic market for heat district or industrial, other than natural gas\" and \"SE\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=1080000,unit=\"megajoule\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #output\n",
    "    steel=[act for act in eidb if act[\"name\"]==\"Power transformer TrafoStar 500 MVA\"][0]\n",
    "    new_exc = new_act.new_exchange(input=new_act.key,amount=1,unit=\"unit\",categories=\"\",type='production')\n",
    "    new_exc.save()\n",
    "    new_act.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### creation of 10 MVA transformer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "if [act for act in eidb if 'Power transformer TrafoStar 10 MVA' in act['name']] == []:\n",
    "\n",
    "    act=[a for a in eidb if \"Power transformer TrafoStar 500 MVA\" in a[\"name\"]][0]\n",
    "    new_act=act.copy()\n",
    "    new_act[\"name\"]=\"Power transformer TrafoStar 10 MVA\"\n",
    "    new_act.save()\n",
    "\n",
    "    for exc in new_act.exchanges():\n",
    "        print(exc.input['name'])\n",
    "        if exc.input['name']==\"steel production, electric, low-alloyed\":\n",
    "            exc[\"amount\"]=6820\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"market for lubricating oil\":\n",
    "            exc[\"amount\"]=6780\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"market for copper\":\n",
    "            exc[\"amount\"]=3526\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"market for ceramic tile\":\n",
    "            exc[\"amount\"]=53\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"market for steel, unalloyed\":\n",
    "            exc[\"amount\"]=9066\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"market for electrostatic paint\":\n",
    "            exc[\"amount\"]=95\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"market for electricity, medium voltage\":\n",
    "            exc[\"amount\"]=105200\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"heat, from municipal waste incineration to generic market for heat district or industrial, other than natural gas\":\n",
    "            exc[\"amount\"]=68760\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"market for aluminium, cast alloy\":\n",
    "            exc[\"amount\"]=65\n",
    "            exc.save()\n",
    "\n",
    "        if exc.input['name']==\"market for sheet rolling, steel\":\n",
    "            exc.delete()\n",
    "        if exc.input['name']==\"market for epoxy resin, liquid\":\n",
    "            exc.delete()\n",
    "        if exc.input['name']==\"market for glass fibre\":\n",
    "            exc.delete()\n",
    "        if exc.input['name']==\"market for kraft paper, bleached\":\n",
    "            exc.delete()\n",
    "        if exc.input['name']==\"market for paper, melamine impregnated\":\n",
    "            exc.delete()\n",
    "        if exc.input['name']==\"market for electrostatic paint\":\n",
    "            exc.delete()\n",
    "        if exc.input['name']==\"market for glass fibre\":\n",
    "            exc.delete()\n",
    "\n",
    "        if exc.input['name']==\"Power transformer TrafoStar 250 MVA\":\n",
    "            exc.input['name']=\"Power transformer TrafoStar 10 MVA\"\n",
    "            exc.input=new_act\n",
    "            exc.save()\n",
    "\n",
    "    #insulation\n",
    "    steel=[act for act in eidb if \"market for glass wool mat\" in act[\"name\"] and \"GLO\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=337,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()\n",
    "\n",
    "    #wood\n",
    "    steel=[act for act in eidb if \"planing, board, softwood, u=20%\" in act[\"name\"] and \"CH\" in act[\"location\"]][0]\n",
    "    new_exc = new_act.new_exchange(input=steel.key,amount=366,unit=\"kilogram\",type='technosphere')\n",
    "    new_exc.save()\n",
    "    new_act.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Foundation functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These functions are used to size foundation, electrical connexions, etc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pile_weight(p, pile_height):\n",
    "    #diameters, in meters\n",
    "    diameter=[5, 5.5, 5.75, 6.75 ,7.75 ]\n",
    "    #kW\n",
    "    power=[3000,3600,4000,8000,10000]\n",
    "    fit_diameter= np.polyfit(power, diameter, 1)\n",
    "    f_fit_diameter=np.poly1d(fit_diameter)\n",
    "    \n",
    "    #diameter for given power, in m\n",
    "    outer_diameter=f_fit_diameter(p)\n",
    "    #Cross section area of pile\n",
    "    outer_area=(np.pi/4)*(outer_diameter**2)\n",
    "    #Pile volume, in m3\n",
    "    outer_volume=outer_area*pile_height\n",
    "    \n",
    "    inner_diameter= outer_diameter\n",
    "    pile_thickness = np.interp(p, [2000, 3000, 3600, 4000, 8000, 10000], [0.07,0.10, 0.13, 0.16, 0.19, 0.22])\n",
    "    inner_diameter-= 2 * pile_thickness\n",
    "    inner_area=(np.pi/4)*(inner_diameter**2)\n",
    "    inner_volume=inner_area*pile_height\n",
    "    volume_steel=outer_volume-inner_volume\n",
    "    weight_steel=8000*volume_steel\n",
    "    return  weight_steel\n",
    "\n",
    "def transition_height():\n",
    "    pile_length=[35,55,35,60,40,65,50,70,50,80]\n",
    "    transition_length=[15,20,15,20,15,24,20,30,20,31]\n",
    "    fit_transition_length= np.polyfit(pile_length, transition_length, 1)\n",
    "    return np.poly1d(fit_transition_length)\n",
    "\n",
    "fit_transition_height = transition_height()\n",
    "\n",
    "def transition_weight():\n",
    "    transition_length=[15,20,15,20,15,24,20,30,20,31]\n",
    "    transition_weight=[150,250,150,250,160,260,200,370,250,420]\n",
    "    fit_transition_weight= np.polyfit(transition_length, transition_weight, 1)\n",
    "    return np.poly1d(fit_transition_weight)\n",
    "\n",
    "fit_transition_weight = transition_weight()\n",
    "\n",
    "def grout_volume():\n",
    "    transition_length=[15,20,15,20,15,24,20,30,20,31]\n",
    "    grout=[15,35,15,35,20,40,25,60,30,65]\n",
    "    fit_grout= np.polyfit(transition_length, grout, 1)\n",
    "    return np.poly1d(fit_grout)\n",
    "\n",
    "fit_grout_volume = grout_volume()\n",
    "\n",
    "def scour_volume():\n",
    "    scour=[2200, 2200, 2600, 3100, 3600]\n",
    "    turbine_power=[3000, 3600, 4000, 8000, 10000]\n",
    "    fit_scour=np.polyfit(turbine_power, scour, 1)\n",
    "    return np.poly1d(fit_scour)\n",
    "\n",
    "fit_scour_volume = scour_volume()\n",
    "\n",
    "def pile_height(P, fit_penetration_depth, sea_depth):\n",
    "    return 9 + fit_penetration_depth(P)+ sea_depth\n",
    "\n",
    "def penetration_depth():\n",
    "    #meters\n",
    "    depth=[22.5,22.5,23.5,26,29.5]\n",
    "    #kW\n",
    "    P = [3000,3600,4000,8000,10000]\n",
    "    fit_penetration= np.polyfit(P, depth, 1)\n",
    "    f_fit_penetration=np.poly1d(fit_penetration)\n",
    "    return f_fit_penetration\n",
    "\n",
    "fit_penetration_depth = penetration_depth()\n",
    "\n",
    "def transport_requirements(M_nacelle,  M_tower, M_rotor,  M_foundation,  M_all, LT):\n",
    "    #taken from Vestas 2012 LCA report\n",
    "    #https://www.vestas.com/~/media/vestas/about/sustainability/pdfs/lca_v903mw_version_1_1.pdf\n",
    "    trsp_truck_nacelle=1025*(M_nacelle/1000)\n",
    "    trsp_truck_rotor=600*(M_rotor/1000)\n",
    "    trsp_truck_tower=1100*(M_tower/1000)\n",
    "    trsp_ship_tower=8050*(M_tower/1000)\n",
    "    trsp_truck_foundation= 50*(M_foundation/1000)\n",
    "    \n",
    "    #transport to local waste facilities\n",
    "    trsp_end_of_life=200*(M_all/1000)\n",
    "    \n",
    "    #30 turbines per plant\n",
    "    trsp_maintenance_per_year = 2160/30/1000*LT\n",
    "    \n",
    "    #600 km by ship assumed\n",
    "    trsp_ship_offshore = 600*(M_all/1000)\n",
    "    return trsp_truck_nacelle, trsp_truck_rotor, trsp_truck_tower, trsp_ship_tower,trsp_truck_foundation, trsp_end_of_life, trsp_maintenance_per_year, trsp_ship_offshore\n",
    "\n",
    "def grout_and_monopile_requirements(P, sea_depth):\n",
    "    pile_length = pile_height(P, fit_penetration_depth, sea_depth)\n",
    "    transition_lenght = fit_transition_height(pile_length)\n",
    "    \n",
    "    density = 2400 #kg/m**3\n",
    "    m_grout = fit_grout_volume(transition_lenght) * density  \n",
    "    \n",
    "    m_monopile = 1e3*fit_transition_weight(transition_lenght)\n",
    "    return m_grout, m_monopile\n",
    "\n",
    "\n",
    "#Nexans cable at 150 kV, section, ampacity and power capacity in kW\n",
    "df150 = pd.DataFrame(index = [400, 500, 630, 800,1000, 1200, 1600, 2000])\n",
    "df150['I'] = [710, 815, 925, 1045, 1160, 1335, 1425, 1560]\n",
    "df150['P'] = 150 * df150['I']\n",
    "\n",
    "#Nexans cable at 150 kV, section, ampacity and power capacity in kW\n",
    "df33 = pd.DataFrame(index = [95, 120, 150, 185, 240, 300, 400, 500, 630, 800])\n",
    "df33['I'] = [352, 399, 446, 502, 581, 652, 726, 811, 904, 993]\n",
    "df33['P'] = 33 * df33['I']\n",
    "\n",
    "# defines cable requirements for offshore foundations\n",
    "def cable_requirements(P, park_size, dist_transfo, dist_coast, affiche = False):\n",
    "    copper_density = 8960\n",
    "    \n",
    "    #Cable of 300 mm² cross section allocated according to the wind turbine power\n",
    "    cross_section1 = 300 * P / df33.loc[300].P \n",
    "    if affiche:\n",
    "        print('cross_section1 : %s mm²' %cross_section1)\n",
    "    #\n",
    "    m_copper = (cross_section1*1e-6*(dist_transfo*1e3))*copper_density \n",
    "    energy_cable_laying_ship = 450 * 39 / 15 * dist_transfo # 450 l diesel/hour for the ship that lays the cable at sea bottom\n",
    "    #39 MJ/liter, 15 km/h as speed of laying the cable\n",
    "    \n",
    "    #Cross section calculated based on the farm cumulated power, \n",
    "    #and the transport capacity of the Nexans cables @ 150kV if the cumulated power of the park cannot be\n",
    "    #transported @ 33kV\n",
    "    \n",
    "    #Test if the cumulated power of teh wind farm is inferior to 30 MW,\n",
    "    # If so, we use 33 kV cables.\n",
    "    \n",
    "    if P*park_size <= 30e3:\n",
    "        cross_section2 = np.interp(P*park_size, df33.P.values, df33.index.values)\n",
    "    #Otherwise, we use 150 kV cables\n",
    "    else:\n",
    "        cross_section2 = np.interp(P*park_size, df150.P.values, df150.index.values)\n",
    "    if affiche:\n",
    "        print('cross_section2 : %s mm²' %cross_section2)\n",
    "        \n",
    "    m_copper += (cross_section2*1e-6*(dist_coast*1e3/park_size))*copper_density\n",
    "    energy_cable_laying_ship += 450 * 39 / 15 * dist_coast/park_size #450 l/h de conso, 39 MJ/l, 15 km/h de vitesse d'installation\n",
    "    \n",
    "    m_cable = m_copper*617/220\n",
    "    return m_cable*0.5, energy_cable_laying_ship*0.5\n",
    "\n",
    "# defines cable requirements for onshore foundations\n",
    "def cable_requirements_Onshore(P, affiche = False):\n",
    "    copper_density = 8960\n",
    "    \n",
    "    #Cable of 300 mm² cross section allocated according to the wind turbine power\n",
    "    cross_section1 = 300 * P / df33.loc[300].P \n",
    "    #Calculation of the cable length starting fromt eh road surface, with assumed with of 8 meters\n",
    "    cable_length = np.interp(P, [0, 2000],[0, 8000]) / 8    \n",
    "    \n",
    "    if affiche:\n",
    "        print('cross_section1 : %s mm²' %cross_section1)\n",
    "        print('cable length : %s m' %cable_length)\n",
    "    #\n",
    "    m_copper = (cross_section1*1e-6*(cable_length))*copper_density \n",
    "        \n",
    "    m_cable = m_copper*617/220\n",
    "    return m_cable*0.5\n",
    "\n",
    "def add_to_dict(dictionary, key, value):\n",
    "    if key not in dictionary:\n",
    "         dictionary[key] = value\n",
    "    else:\n",
    "         dictionary[key] = dictionary[key] + value\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's load inventories of a 30 kW, 150 kW, 600 kW, 800 kW, 2000 kW wind turbines. These files contains lifecycle activities that has to be linked with activities of the ecoinvent database. That will be done executing the function find_UUID(). As it takes quite long, the dataframe containing all the necessary information can be saved in a pikcle files and be loaded directly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### To create a pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "path = 'to define'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
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     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power</th>\n",
       "      <th>Phase</th>\n",
       "      <th>Component</th>\n",
       "      <th>Sub-component</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Database name</th>\n",
       "      <th>Market name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>MJ</td>\n",
       "      <td>5310.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for diesel, burned in building machine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Explosives</td>\n",
       "      <td>kg</td>\n",
       "      <td>10.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for explosive, tovex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Copper wire drawing</td>\n",
       "      <td>kg</td>\n",
       "      <td>17.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for wire drawing, copper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Aluminium sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>14.70</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, aluminium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Chromium steel sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>736.50</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, chromium steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Steel sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>317.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Electricity [kWh]</td>\n",
       "      <td>kWh</td>\n",
       "      <td>575.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for electricity, high voltage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Tower</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Steel sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>5299.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Tower</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Steel arc welding [m]</td>\n",
       "      <td>m</td>\n",
       "      <td>84.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for welding, arc, steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Tower</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Galvanizing [m]</td>\n",
       "      <td>m</td>\n",
       "      <td>74.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for zinc coat, pieces</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>Copper wire drawing</td>\n",
       "      <td>kg</td>\n",
       "      <td>3.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for wire drawing, copper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>Steel sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>63.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>Aluminium sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>12.04</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, aluminium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Rotor</td>\n",
       "      <td>Blades</td>\n",
       "      <td>Fiberglass -waste</td>\n",
       "      <td>kg</td>\n",
       "      <td>146.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>treatment of inert waste, inert material landfill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Rotor</td>\n",
       "      <td>Coupling</td>\n",
       "      <td>Rubber</td>\n",
       "      <td>kg</td>\n",
       "      <td>3.15</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste rubber, unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Paneling</td>\n",
       "      <td>Fiberglass -waste</td>\n",
       "      <td>kg</td>\n",
       "      <td>60.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>treatment of inert waste, inert material landfill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Hydraulic</td>\n",
       "      <td>Lubricating oil</td>\n",
       "      <td>kg</td>\n",
       "      <td>10.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste mineral oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Concrete</td>\n",
       "      <td>Concrete, inert waste</td>\n",
       "      <td>kg</td>\n",
       "      <td>51040.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste reinforced concrete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Steel, inert waste</td>\n",
       "      <td>kg</td>\n",
       "      <td>2367.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for scrap steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Power supply</td>\n",
       "      <td>Cable</td>\n",
       "      <td>HDPE granules</td>\n",
       "      <td>kg</td>\n",
       "      <td>219.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste polyethylene/polypropylene pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Power supply</td>\n",
       "      <td>Cable</td>\n",
       "      <td>PVC impact resistant</td>\n",
       "      <td>kg</td>\n",
       "      <td>158.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste polyvinylchloride product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Power supply</td>\n",
       "      <td>Cable</td>\n",
       "      <td>PP granules</td>\n",
       "      <td>kg</td>\n",
       "      <td>20.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste polypropylene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>HDPE granules</td>\n",
       "      <td>kg</td>\n",
       "      <td>27.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste polyethylene/polypropylene pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>PVC impact resistant</td>\n",
       "      <td>kg</td>\n",
       "      <td>6.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste polyvinylchloride product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Concrete</td>\n",
       "      <td>Concrete [m3]</td>\n",
       "      <td>m3</td>\n",
       "      <td>23.20</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for concrete, normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Material</td>\n",
       "      <td>Low-alloy steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>2367.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, low-alloyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Land use</td>\n",
       "      <td>Conversion of meadows and pastures [m2]</td>\n",
       "      <td>m2</td>\n",
       "      <td>1004.00</td>\n",
       "      <td>biosphere3</td>\n",
       "      <td>Transformation, from pasture and meadow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Land use</td>\n",
       "      <td>Conversion to urban area [m2]</td>\n",
       "      <td>m2</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>biosphere3</td>\n",
       "      <td>Transformation, to urban, continuously built</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Land use</td>\n",
       "      <td>Conversion to industrial area [m2]</td>\n",
       "      <td>m2</td>\n",
       "      <td>4.00</td>\n",
       "      <td>biosphere3</td>\n",
       "      <td>Transformation, to industrial area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Foundation</td>\n",
       "      <td>Land use</td>\n",
       "      <td>Use of traffic area</td>\n",
       "      <td>m2a</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>biosphere3</td>\n",
       "      <td>Occupation, traffic area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Generator</td>\n",
       "      <td>Cast iron</td>\n",
       "      <td>kg</td>\n",
       "      <td>3382.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for cast iron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Generator</td>\n",
       "      <td>Chromium steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>8877.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, chromium steel 18/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Generator</td>\n",
       "      <td>Aluminium 0% recycled</td>\n",
       "      <td>kg</td>\n",
       "      <td>845.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for aluminium, wrought alloy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Generator</td>\n",
       "      <td>Copper</td>\n",
       "      <td>kg</td>\n",
       "      <td>986.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for copper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Brake</td>\n",
       "      <td>Chromium steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>613.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, chromium steel 18/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Frame</td>\n",
       "      <td>Chromium steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>16940.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, chromium steel 18/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Paneling</td>\n",
       "      <td>Fiberglass</td>\n",
       "      <td>kg</td>\n",
       "      <td>11294.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for glass fibre reinforced plastic, pol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Yaw - ball bearigs</td>\n",
       "      <td>Low-alloy steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>2389.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, low-alloyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Yaw - Drive</td>\n",
       "      <td>Chromium steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>1225.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, chromium steel 18/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Yaw - Brake</td>\n",
       "      <td>Chromium steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>816.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, chromium steel 18/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Hydraulic</td>\n",
       "      <td>Chromium steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>2042.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, chromium steel 18/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Hydraulic</td>\n",
       "      <td>Lubricating oil</td>\n",
       "      <td>kg</td>\n",
       "      <td>150.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for lubricating oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Copper wire drawing</td>\n",
       "      <td>kg</td>\n",
       "      <td>1586.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for wire drawing, copper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Aluminium sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>845.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, aluminium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Chromium steel sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>51643.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, chromium steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Steel sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>42216.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Electricity [kWh]</td>\n",
       "      <td>kWh</td>\n",
       "      <td>67500.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for electricity, high voltage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Tower</td>\n",
       "      <td>Material</td>\n",
       "      <td>Low-alloy steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>113210.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, low-alloyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Tower</td>\n",
       "      <td>Material</td>\n",
       "      <td>Epoxy resin</td>\n",
       "      <td>kg</td>\n",
       "      <td>547.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for epoxy resin, liquid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Tower</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Steel sheet rolling</td>\n",
       "      <td>kg</td>\n",
       "      <td>113210.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for sheet rolling, steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Tower</td>\n",
       "      <td>Assembly</td>\n",
       "      <td>Steel arc welding [m]</td>\n",
       "      <td>m</td>\n",
       "      <td>228.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for welding, arc, steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Rotor</td>\n",
       "      <td>Blades</td>\n",
       "      <td>Fiberglass -waste</td>\n",
       "      <td>kg</td>\n",
       "      <td>29741.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>treatment of inert waste, inert material landfill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Paneling</td>\n",
       "      <td>Fiberglass -waste</td>\n",
       "      <td>kg</td>\n",
       "      <td>11349.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>treatment of inert waste, inert material landfill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Hydraulic</td>\n",
       "      <td>Lubricating oil</td>\n",
       "      <td>kg</td>\n",
       "      <td>150.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste mineral oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>Cast iron</td>\n",
       "      <td>kg</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for cast iron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>Copper</td>\n",
       "      <td>kg</td>\n",
       "      <td>600.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for copper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>Low-alloy steel</td>\n",
       "      <td>kg</td>\n",
       "      <td>800.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for steel, low-alloyed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Input</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>Lubricating oil</td>\n",
       "      <td>kg</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for lubricating oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Disposal</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Transformer</td>\n",
       "      <td>Lubricating oil</td>\n",
       "      <td>kg</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for waste mineral oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2000kW</td>\n",
       "      <td>Maintenance</td>\n",
       "      <td>Nacelle</td>\n",
       "      <td>Transmission</td>\n",
       "      <td>Lubricating oil</td>\n",
       "      <td>kg</td>\n",
       "      <td>3150.00</td>\n",
       "      <td>ecoinvent 3.3 cutoff</td>\n",
       "      <td>market for lubricating oil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Power        Phase     Component       Sub-component  \\\n",
       "0      30kW     Assembly    Foundation            Assembly   \n",
       "1      30kW     Assembly    Foundation            Assembly   \n",
       "2      30kW     Assembly       Nacelle            Assembly   \n",
       "3      30kW     Assembly       Nacelle            Assembly   \n",
       "4      30kW     Assembly       Nacelle            Assembly   \n",
       "5      30kW     Assembly       Nacelle            Assembly   \n",
       "6      30kW     Assembly       Nacelle            Assembly   \n",
       "7      30kW     Assembly         Tower            Assembly   \n",
       "8      30kW     Assembly         Tower            Assembly   \n",
       "9      30kW     Assembly         Tower            Assembly   \n",
       "10     30kW     Assembly   Electronics         Transformer   \n",
       "11     30kW     Assembly   Electronics         Transformer   \n",
       "12     30kW     Assembly   Electronics         Transformer   \n",
       "13     30kW     Disposal         Rotor              Blades   \n",
       "14     30kW     Disposal         Rotor            Coupling   \n",
       "15     30kW     Disposal       Nacelle            Paneling   \n",
       "16     30kW     Disposal       Nacelle           Hydraulic   \n",
       "17     30kW     Disposal    Foundation            Concrete   \n",
       "18     30kW     Disposal    Foundation               Steel   \n",
       "19     30kW     Disposal  Power supply               Cable   \n",
       "20     30kW     Disposal  Power supply               Cable   \n",
       "21     30kW     Disposal  Power supply               Cable   \n",
       "22     30kW     Disposal   Electronics         Transformer   \n",
       "23     30kW     Disposal   Electronics         Transformer   \n",
       "24     30kW        Input    Foundation            Concrete   \n",
       "25     30kW        Input    Foundation            Material   \n",
       "26     30kW        Input    Foundation            Land use   \n",
       "27     30kW        Input    Foundation            Land use   \n",
       "28     30kW        Input    Foundation            Land use   \n",
       "29     30kW        Input    Foundation            Land use   \n",
       "..      ...          ...           ...                 ...   \n",
       "271  2000kW        Input       Nacelle           Generator   \n",
       "272  2000kW        Input       Nacelle           Generator   \n",
       "273  2000kW        Input       Nacelle           Generator   \n",
       "274  2000kW        Input       Nacelle           Generator   \n",
       "275  2000kW        Input       Nacelle               Brake   \n",
       "276  2000kW        Input       Nacelle               Frame   \n",
       "277  2000kW        Input       Nacelle            Paneling   \n",
       "278  2000kW        Input       Nacelle  Yaw - ball bearigs   \n",
       "279  2000kW        Input       Nacelle         Yaw - Drive   \n",
       "280  2000kW        Input       Nacelle         Yaw - Brake   \n",
       "281  2000kW        Input       Nacelle           Hydraulic   \n",
       "282  2000kW        Input       Nacelle           Hydraulic   \n",
       "283  2000kW     Assembly       Nacelle            Assembly   \n",
       "284  2000kW     Assembly       Nacelle            Assembly   \n",
       "285  2000kW     Assembly       Nacelle            Assembly   \n",
       "286  2000kW     Assembly       Nacelle            Assembly   \n",
       "287  2000kW     Assembly       Nacelle            Assembly   \n",
       "288  2000kW        Input         Tower            Material   \n",
       "289  2000kW        Input         Tower            Material   \n",
       "290  2000kW     Assembly         Tower            Assembly   \n",
       "291  2000kW     Assembly         Tower            Assembly   \n",
       "292  2000kW     Disposal         Rotor              Blades   \n",
       "293  2000kW     Disposal       Nacelle            Paneling   \n",
       "294  2000kW     Disposal       Nacelle           Hydraulic   \n",
       "295  2000kW        Input   Electronics         Transformer   \n",
       "296  2000kW        Input   Electronics         Transformer   \n",
       "297  2000kW        Input   Electronics         Transformer   \n",
       "298  2000kW        Input   Electronics         Transformer   \n",
       "299  2000kW     Disposal   Electronics         Transformer   \n",
       "300  2000kW  Maintenance       Nacelle        Transmission   \n",
       "\n",
       "                                     Dataset Unit   Quantity  \\\n",
       "0                                     Diesel   MJ    5310.00   \n",
       "1                                 Explosives   kg      10.00   \n",
       "2                        Copper wire drawing   kg      17.00   \n",
       "3                    Aluminium sheet rolling   kg      14.70   \n",
       "4               Chromium steel sheet rolling   kg     736.50   \n",
       "5                        Steel sheet rolling   kg     317.00   \n",
       "6                          Electricity [kWh]  kWh     575.00   \n",
       "7                        Steel sheet rolling   kg    5299.00   \n",
       "8                      Steel arc welding [m]    m      84.00   \n",
       "9                            Galvanizing [m]    m      74.00   \n",
       "10                       Copper wire drawing   kg       3.00   \n",
       "11                       Steel sheet rolling   kg      63.00   \n",
       "12                   Aluminium sheet rolling   kg      12.04   \n",
       "13                         Fiberglass -waste   kg     146.00   \n",
       "14                                    Rubber   kg       3.15   \n",
       "15                         Fiberglass -waste   kg      60.00   \n",
       "16                           Lubricating oil   kg      10.00   \n",
       "17                     Concrete, inert waste   kg   51040.00   \n",
       "18                        Steel, inert waste   kg    2367.00   \n",
       "19                             HDPE granules   kg     219.00   \n",
       "20                      PVC impact resistant   kg     158.00   \n",
       "21                               PP granules   kg      20.00   \n",
       "22                             HDPE granules   kg      27.00   \n",
       "23                      PVC impact resistant   kg       6.00   \n",
       "24                             Concrete [m3]   m3      23.20   \n",
       "25                           Low-alloy steel   kg    2367.00   \n",
       "26   Conversion of meadows and pastures [m2]   m2    1004.00   \n",
       "27             Conversion to urban area [m2]   m2    1000.00   \n",
       "28        Conversion to industrial area [m2]   m2       4.00   \n",
       "29                       Use of traffic area  m2a   40000.00   \n",
       "..                                       ...  ...        ...   \n",
       "271                                Cast iron   kg    3382.00   \n",
       "272                           Chromium steel   kg    8877.00   \n",
       "273                    Aluminium 0% recycled   kg     845.00   \n",
       "274                                   Copper   kg     986.00   \n",
       "275                           Chromium steel   kg     613.00   \n",
       "276                           Chromium steel   kg   16940.00   \n",
       "277                               Fiberglass   kg   11294.00   \n",
       "278                          Low-alloy steel   kg    2389.00   \n",
       "279                           Chromium steel   kg    1225.00   \n",
       "280                           Chromium steel   kg     816.00   \n",
       "281                           Chromium steel   kg    2042.00   \n",
       "282                          Lubricating oil   kg     150.00   \n",
       "283                      Copper wire drawing   kg    1586.00   \n",
       "284                  Aluminium sheet rolling   kg     845.00   \n",
       "285             Chromium steel sheet rolling   kg   51643.00   \n",
       "286                      Steel sheet rolling   kg   42216.00   \n",
       "287                        Electricity [kWh]  kWh   67500.00   \n",
       "288                          Low-alloy steel   kg  113210.00   \n",
       "289                              Epoxy resin   kg     547.00   \n",
       "290                      Steel sheet rolling   kg  113210.00   \n",
       "291                    Steel arc welding [m]    m     228.00   \n",
       "292                        Fiberglass -waste   kg   29741.00   \n",
       "293                        Fiberglass -waste   kg   11349.00   \n",
       "294                          Lubricating oil   kg     150.00   \n",
       "295                                Cast iron   kg    1500.00   \n",
       "296                                   Copper   kg     600.00   \n",
       "297                          Low-alloy steel   kg     800.00   \n",
       "298                          Lubricating oil   kg    1000.00   \n",
       "299                          Lubricating oil   kg    1000.00   \n",
       "300                          Lubricating oil   kg    3150.00   \n",
       "\n",
       "            Database name                                        Market name  \n",
       "0    ecoinvent 3.3 cutoff      market for diesel, burned in building machine  \n",
       "1    ecoinvent 3.3 cutoff                        market for explosive, tovex  \n",
       "2    ecoinvent 3.3 cutoff                    market for wire drawing, copper  \n",
       "3    ecoinvent 3.3 cutoff                market for sheet rolling, aluminium  \n",
       "4    ecoinvent 3.3 cutoff           market for sheet rolling, chromium steel  \n",
       "5    ecoinvent 3.3 cutoff                    market for sheet rolling, steel  \n",
       "6    ecoinvent 3.3 cutoff               market for electricity, high voltage  \n",
       "7    ecoinvent 3.3 cutoff                    market for sheet rolling, steel  \n",
       "8    ecoinvent 3.3 cutoff                     market for welding, arc, steel  \n",
       "9    ecoinvent 3.3 cutoff                       market for zinc coat, pieces  \n",
       "10   ecoinvent 3.3 cutoff                    market for wire drawing, copper  \n",
       "11   ecoinvent 3.3 cutoff                    market for sheet rolling, steel  \n",
       "12   ecoinvent 3.3 cutoff                market for sheet rolling, aluminium  \n",
       "13   ecoinvent 3.3 cutoff  treatment of inert waste, inert material landfill  \n",
       "14   ecoinvent 3.3 cutoff               market for waste rubber, unspecified  \n",
       "15   ecoinvent 3.3 cutoff  treatment of inert waste, inert material landfill  \n",
       "16   ecoinvent 3.3 cutoff                       market for waste mineral oil  \n",
       "17   ecoinvent 3.3 cutoff               market for waste reinforced concrete  \n",
       "18   ecoinvent 3.3 cutoff                             market for scrap steel  \n",
       "19   ecoinvent 3.3 cutoff  market for waste polyethylene/polypropylene pr...  \n",
       "20   ecoinvent 3.3 cutoff         market for waste polyvinylchloride product  \n",
       "21   ecoinvent 3.3 cutoff                     market for waste polypropylene  \n",
       "22   ecoinvent 3.3 cutoff  market for waste polyethylene/polypropylene pr...  \n",
       "23   ecoinvent 3.3 cutoff         market for waste polyvinylchloride product  \n",
       "24   ecoinvent 3.3 cutoff                        market for concrete, normal  \n",
       "25   ecoinvent 3.3 cutoff                      market for steel, low-alloyed  \n",
       "26             biosphere3            Transformation, from pasture and meadow  \n",
       "27             biosphere3       Transformation, to urban, continuously built  \n",
       "28             biosphere3                 Transformation, to industrial area  \n",
       "29             biosphere3                           Occupation, traffic area  \n",
       "..                    ...                                                ...  \n",
       "271  ecoinvent 3.3 cutoff                               market for cast iron  \n",
       "272  ecoinvent 3.3 cutoff              market for steel, chromium steel 18/8  \n",
       "273  ecoinvent 3.3 cutoff                market for aluminium, wrought alloy  \n",
       "274  ecoinvent 3.3 cutoff                                  market for copper  \n",
       "275  ecoinvent 3.3 cutoff              market for steel, chromium steel 18/8  \n",
       "276  ecoinvent 3.3 cutoff              market for steel, chromium steel 18/8  \n",
       "277  ecoinvent 3.3 cutoff  market for glass fibre reinforced plastic, pol...  \n",
       "278  ecoinvent 3.3 cutoff                      market for steel, low-alloyed  \n",
       "279  ecoinvent 3.3 cutoff              market for steel, chromium steel 18/8  \n",
       "280  ecoinvent 3.3 cutoff              market for steel, chromium steel 18/8  \n",
       "281  ecoinvent 3.3 cutoff              market for steel, chromium steel 18/8  \n",
       "282  ecoinvent 3.3 cutoff                         market for lubricating oil  \n",
       "283  ecoinvent 3.3 cutoff                    market for wire drawing, copper  \n",
       "284  ecoinvent 3.3 cutoff                market for sheet rolling, aluminium  \n",
       "285  ecoinvent 3.3 cutoff           market for sheet rolling, chromium steel  \n",
       "286  ecoinvent 3.3 cutoff                    market for sheet rolling, steel  \n",
       "287  ecoinvent 3.3 cutoff               market for electricity, high voltage  \n",
       "288  ecoinvent 3.3 cutoff                      market for steel, low-alloyed  \n",
       "289  ecoinvent 3.3 cutoff                     market for epoxy resin, liquid  \n",
       "290  ecoinvent 3.3 cutoff                    market for sheet rolling, steel  \n",
       "291  ecoinvent 3.3 cutoff                     market for welding, arc, steel  \n",
       "292  ecoinvent 3.3 cutoff  treatment of inert waste, inert material landfill  \n",
       "293  ecoinvent 3.3 cutoff  treatment of inert waste, inert material landfill  \n",
       "294  ecoinvent 3.3 cutoff                       market for waste mineral oil  \n",
       "295  ecoinvent 3.3 cutoff                               market for cast iron  \n",
       "296  ecoinvent 3.3 cutoff                                  market for copper  \n",
       "297  ecoinvent 3.3 cutoff                      market for steel, low-alloyed  \n",
       "298  ecoinvent 3.3 cutoff                         market for lubricating oil  \n",
       "299  ecoinvent 3.3 cutoff                       market for waste mineral oil  \n",
       "300  ecoinvent 3.3 cutoff                         market for lubricating oil  \n",
       "\n",
       "[301 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets=pd.read_excel(path + 'Wind turbines inventories.xlsx', sheetname=\"All\", dtype=None, decimal=\";\", header=0)\n",
    "del datasets['Unnamed: 9']\n",
    "datasets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Use of cement for foundation and reinforcing steel for foundation\n",
    "datasets = datasets.replace('market for concrete, normal','market for concrete, sole plate and foundation')\n",
    "datasets.loc[datasets[(datasets['Component']=='Foundation')&(datasets['Market name']=='market for steel, low-alloyed')].index, 'Market name'] = 'market for reinforcing steel'\n",
    "datasets.loc[datasets[(datasets['Component']=='Foundation')&(datasets['Market name']=='market for reinforcing steel')].index, 'Dataset'] = 'Reinforcing steel'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Diesel', 'Explosives', 'Copper wire drawing',\n",
       "       'Aluminium sheet rolling', 'Chromium steel sheet rolling',\n",
       "       'Steel sheet rolling', 'Electricity [kWh]', 'Steel arc welding [m]',\n",
       "       'Galvanizing [m]', 'Fiberglass -waste', 'Rubber', 'Lubricating oil',\n",
       "       'Concrete, inert waste', 'Steel, inert waste', 'HDPE granules',\n",
       "       'PVC impact resistant', 'PP granules', 'Concrete [m3]',\n",
       "       'Reinforcing steel', 'Conversion of meadows and pastures [m2]',\n",
       "       'Conversion to urban area [m2]',\n",
       "       'Conversion to industrial area [m2]', 'Use of traffic area',\n",
       "       'Use of industrial area', 'Chromium steel', 'Cast iron',\n",
       "       'Aluminium 0% recycled', 'Copper', 'Fiberglass', 'Low-alloy steel',\n",
       "       'Epoxy resin', 'Tin', 'Lead', 'Car [km]', 'Use of urban area'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.Dataset.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "market for diesel, burned in building machine\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\romain\\Miniconda3\\envs\\python36\\lib\\site-packages\\ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "market for explosive, tovex\n",
      "market for wire drawing, copper\n",
      "market for sheet rolling, aluminium\n",
      "market for sheet rolling, chromium steel\n",
      "market for sheet rolling, steel\n",
      "market for electricity, high voltage\n",
      "market for welding, arc, steel\n",
      "market for zinc coat, pieces\n",
      "treatment of inert waste, inert material landfill\n",
      "market for waste rubber, unspecified\n",
      "market for waste mineral oil\n",
      "market for waste reinforced concrete\n",
      "market for scrap steel\n",
      "market for waste polyethylene/polypropylene product\n",
      "market for waste polyvinylchloride product\n",
      "market for waste polypropylene\n",
      "market for concrete, sole plate and foundation\n",
      "market for reinforcing steel\n",
      "Transformation, from pasture and meadow\n",
      "Transformation, to urban, continuously built\n",
      "Transformation, to industrial area\n",
      "Occupation, traffic area\n",
      "Occupation, industrial area\n",
      "market for steel, chromium steel 18/8\n",
      "market for synthetic rubber\n",
      "market for cast iron\n",
      "market for aluminium, wrought alloy\n",
      "market for copper\n",
      "market for glass fibre reinforced plastic, polyamide, injection moulded\n",
      "market for steel, low-alloyed\n",
      "market for lubricating oil\n",
      "market for epoxy resin, liquid\n",
      "market for polyethylene, high density, granulate\n",
      "market for polypropylene, granulate\n",
      "market for polyvinylchloride, bulk polymerised\n",
      "market for tin\n",
      "market for lead\n",
      "market for transport, passenger car\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\romain\\Miniconda3\\envs\\python36\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\romain\\Miniconda3\\envs\\python36\\lib\\site-packages\\ipykernel_launcher.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "def find_UUID():\n",
    "    list_unique_act=datasets[\"Market name\"].unique()\n",
    "    order_preference=[\"DK\", \"DE\", \"CH\", \"RER\", \"RoW\", \"GLO\"]\n",
    "    \n",
    "    for act in list_unique_act:\n",
    "        print(act)\n",
    "        for pref in order_preference:\n",
    "            list_act=[a for a in eidb if act in a[\"name\"] and pref in a[\"location\"]]\n",
    "            if len(list_act)==1:\n",
    "                datasets.loc[:,\"UUID\"][datasets.loc[:,\"Market name\"]==act]=list_act[0][\"code\"]\n",
    "                break\n",
    "            if len(list_act)>1:\n",
    "                list_act=[a for a in eidb if act == a[\"name\"] and pref in a[\"location\"]]\n",
    "                datasets.loc[:,\"UUID\"][datasets.loc[:,\"Market name\"]==act]=list_act[0][\"code\"]\n",
    "                break\n",
    "            else:\n",
    "                list_act=[a for a in bw.Database(\"biosphere3\") if act in a[\"name\"]]\n",
    "                if len(list_act)>0:\n",
    "                    datasets.loc[:,\"UUID\"][datasets.loc[:,\"Market name\"]==act]=list_act[0][\"code\"]\n",
    "                    break\n",
    "                else:\n",
    "                    datasets.loc[:,\"UUID\"][datasets.loc[:,\"Market name\"]==act]=[a for a in bw.Database(\"biosphere3\") if 'Transformation, from pasture, man made, intensive' in a[\"name\"]][0]['code']\n",
    "                \n",
    "        \n",
    "def get_act_country():\n",
    "    list_unique_uuid=datasets.loc[:,\"UUID\"].unique()\n",
    "    for uuid in list_unique_uuid:\n",
    "        try:\n",
    "            datasets.loc[:,\"Location\"][datasets.loc[:,\"UUID\"]==uuid]=bw.get_activity((eidb.name, uuid))[\"location\"]\n",
    "        except:\n",
    "            datasets.loc[:,\"Location\"][datasets.loc[:,\"UUID\"]==uuid]=None\n",
    "\n",
    "datasets[\"UUID\"]=0\n",
    "find_UUID()\n",
    "datasets[\"Location\"]=0\n",
    "get_act_country()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'market for waste aluminium' (kilogram, GLO, None)\n"
     ]
    }
   ],
   "source": [
    "#We add recycling of aluminium and chromium steel\n",
    "df_act = datasets\n",
    "df_act2 = pd.DataFrame(index = df_act.columns)\n",
    "\n",
    "new_act = [act for act in eidb if 'market for waste' in act['name'] and 'aluminium' in act['name'] ][0]\n",
    "print(new_act)\n",
    "df_act2[new_act['name']] = [None, 'Disposal', None, None,'Aluminium waste', 'kg', None, eidb.name, new_act['name'], new_act['code'], new_act['location']]\n",
    "\n",
    "df_act = df_act.append(df_act2.T)\n",
    "df_act = df_act.append(df_act[df_act['Dataset'] =='Steel, inert waste'].iloc[0].replace('Steel, inert waste','Chromium Steel waste'))\n",
    "df_act\n",
    "\n",
    "df_act = df_act.replace('ecoinvent 3.3 cutoff', eidb.name)\n",
    "\n",
    "#Saving data into a pickle\n",
    "df_act.to_pickle('activities and uuids.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To load the pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Loading data from the pickle\n",
    "df_act = pd.read_pickle('activities and uuids.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inventories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use values of the inventories. Values expressed in kg are normalized to get the share of different material.\n",
    "Values not expressed in kg are used directly. At that stage, 2 new dataframe are created and saved in a pickle. Once created, they can be directly loaded to save time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### To create the pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaN filled\n",
      "Maintenance is being normalised\n",
      "Assembly is being normalised\n",
      "Disposal is being normalised\n",
      "Input is being normalised\n"
     ]
    }
   ],
   "source": [
    "#Loading wind turbine inventories dataframe and creating 'df_inv.pkl'\n",
    "# All that has \"kg\" as unit\n",
    "df_inv = pd.read_excel(path + 'Wind turbines inventories.xlsx')\n",
    "\n",
    "df_inv = df_inv.replace('30kW', 30)\n",
    "df_inv = df_inv.replace('150kW',150)\n",
    "df_inv = df_inv.replace('600kW', 600)\n",
    "df_inv = df_inv.replace('800kW', 800)\n",
    "df_inv = df_inv.replace('2000kW', 2000)\n",
    "\n",
    "df_inv = df_inv[ df_inv.Unit == 'kg']\n",
    "\n",
    "#df_inv = df_inv.pivot_table(columns= 'Power', values= 'Quantity', index=['Phase', 'Component','Sub-component', 'Dataset'])\n",
    "df_inv = df_inv.pivot_table(columns= 'Power', values=['Quantity','Unit'] , index=['Phase', 'Component','Sub-component', 'Dataset'])\n",
    "df_inv = df_inv.T\n",
    "\n",
    "#Filling nan values\n",
    "df_inv = df_inv.loc['Quantity']\n",
    "df_inv.loc[0] = 0\n",
    "df_inv = df_inv.sort_index()\n",
    "df_inv.interpolate(method = 'index', limit_direction = 'both', inplace = True)\n",
    "df_inv = df_inv.drop(df_inv.index[0])\n",
    "\n",
    "print('NaN filled')\n",
    "\n",
    "df_inv_tot = df_inv.T.groupby(level=['Phase','Component']).sum(axis = 1).T\n",
    "\n",
    "for phase in set(df_inv.columns.get_level_values(0)):\n",
    "    print(phase + ' is being normalised')\n",
    "    for component in set(df_inv[phase].columns.get_level_values(0)):\n",
    "        for sub_comp in set(df_inv[phase][component].columns.get_level_values(0)):\n",
    "            for dataset in set(df_inv[phase][component][sub_comp].columns.get_level_values(0)):\n",
    "                #print(phase, component, sub_comp, dataset)\n",
    "                df_inv[phase][component][sub_comp][dataset] = df_inv[phase][component][sub_comp][dataset] / df_inv_tot[phase][component]\n",
    "\n",
    "df_inv.to_pickle('df_inv.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Loading wind turbine inventories dataframe and creating 'df_inv_not_kg.pkl'\n",
    "#All that has not \"kg\" as unit\n",
    "df_inv_not_kg = pd.read_excel(path + 'Wind turbines inventories.xlsx')\n",
    "\n",
    "df_inv_not_kg = df_inv_not_kg.replace('30kW', 30)\n",
    "df_inv_not_kg = df_inv_not_kg.replace('150kW',150)\n",
    "df_inv_not_kg = df_inv_not_kg.replace('600kW', 600)\n",
    "df_inv_not_kg = df_inv_not_kg.replace('800kW', 800)\n",
    "df_inv_not_kg = df_inv_not_kg.replace('2000kW', 2000)\n",
    "\n",
    "df_inv_not_kg = df_inv_not_kg[ df_inv_not_kg.Unit != 'kg']\n",
    "\n",
    "df_inv_not_kg = df_inv_not_kg.pivot_table(columns= 'Power', values=['Quantity','Unit'] , index=['Phase', 'Component','Sub-component', 'Dataset'])\n",
    "df_inv_not_kg = df_inv_not_kg.T\n",
    "\n",
    "for phase in set(df_inv_not_kg.columns.get_level_values(0)):\n",
    "    for component in set(df_inv_not_kg[phase].columns.get_level_values(0)):\n",
    "        df_inv_not_kg[phase][component].loc['Quantity'].plot()\n",
    "        plt.title('%s, %s'%(phase, component))\n",
    "        plt.ylim([0, df_inv_not_kg[phase][component].loc['Quantity'].max().max()])\n",
    "        plt.xlim([0, 8000])\n",
    "        #plt.show()\n",
    "        \n",
    "df_inv_not_kg['Assembly']['Tower']['Assembly']['Galvanizing [m]'] = df_inv_not_kg['Assembly']['Tower']['Assembly']['Steel arc welding [m]']\n",
    "df_inv_not_kg = df_inv_not_kg.loc['Quantity']\n",
    "\n",
    "df_inv_not_kg = df_inv_not_kg.drop('Electricity [kWh]', axis = 1, level = 3)\n",
    "df_inv_not_kg.to_pickle('df_inv_not_kg.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Once the dataframe saved, they can be loaded and some new activities also considered :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To load the pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'market for wire drawing, copper' (kilogram, GLO, None)\n",
      "'market for explosive, tovex' (kilogram, GLO, None)\n",
      "'market for sheet rolling, steel' (kilogram, GLO, None)\n",
      "'market for sheet rolling, aluminium' (kilogram, GLO, None)\n",
      "'market for sheet rolling, chromium steel' (kilogram, GLO, None)\n",
      "'market for road' (meter-year, CH, None)\n",
      "'market for transport, freight, lorry >32 metric ton, EURO6' (ton kilometer, GLO, None)\n",
      "'market for transport, freight, inland waterways, barge tanker' (ton kilometer, GLO, None)\n",
      "'market for excavation, hydraulic digger' (cubic meter, GLO, None)\n",
      "'market for cement, Portland' (kilogram, US, None)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['PVC impact resistant',\n",
       " 'PP granules',\n",
       " 'HDPE granules',\n",
       " 'Fiberglass -waste',\n",
       " 'Concrete, inert waste',\n",
       " 'Lubricating oil',\n",
       " 'Steel, inert waste',\n",
       " 'Rubber',\n",
       " 'Aluminium waste',\n",
       " 'Chromium Steel waste']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inv = pd.read_pickle('df_inv.pkl')\n",
    "df_inv_not_kg = pd.read_pickle('df_inv_not_kg.pkl')\n",
    "\n",
    "diesel_burned_activity = [act for act in eidb if 'market for diesel, burned in building machine' in act['name']][0]\n",
    "MV_transfo = [act for act in eidb if 'Power transformer TrafoStar 10 MVA' in act['name']][0]\n",
    "HV_transfo = [act for act in eidb if 'Power transformer TrafoStar 500 MVA' in act['name']][0]\n",
    "\n",
    "assembly_activities = list(set(df_inv['Assembly'].columns.get_level_values(2)))\n",
    "#Assembly activites to add\n",
    "Copper_wire_drawing = [act for act in eidb if 'market for wire drawing, copper' in act['name']][0]\n",
    "print(Copper_wire_drawing)\n",
    "Explosive = [act for act in eidb if 'market for explosive, tovex' in act['name']][0]\n",
    "print(Explosive)\n",
    "Steel_sheet_rolling = [act for act in eidb if 'market for sheet rolling, steel' in act['name']][0]\n",
    "print(Steel_sheet_rolling)\n",
    "Aluminium_sheet_rolling = [act for act in eidb if 'market for sheet rolling, aluminium' in act['name']][0]\n",
    "print(Aluminium_sheet_rolling)\n",
    "Chromium_sheet_rolling =  [act for act in eidb if 'market for sheet rolling, chromium steel' in act['name']][0]\n",
    "print(Chromium_sheet_rolling)\n",
    "Road = [act for act in eidb if 'market for road' == act['name'] and 'CH' in act['location']][0]\n",
    "print(Road)\n",
    "Truck_transport = [act for act in eidb if 'market for transport, freight, lorry >32 metric ton, EURO6' in act['name']][0]\n",
    "print(Truck_transport)\n",
    "Ship_transport = [act for act in eidb if 'market for transport, freight, inland waterways, barge' in act['name']][0]\n",
    "print(Ship_transport)\n",
    "Digger = [act for act in eidb if 'market for excavation, hydraulic digger' in act['name']][0]\n",
    "print(Digger)\n",
    "Cement = [act for act in eidb if 'market for cement, Portland' in act['name']][0]\n",
    "print(Cement)\n",
    "\n",
    "disposal_activities = list(set(df_inv['Disposal'].columns.get_level_values(2)))\n",
    "disposal_activities.append('Aluminium waste')\n",
    "disposal_activities.append('Chromium Steel waste')\n",
    "disposal_activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'market for electricity, high voltage' (kilowatt hour, DK, None)\n",
      "'market for steel, low-alloyed' (kilogram, GLO, None)\n"
     ]
    }
   ],
   "source": [
    "#Dataset for electricity directly used and steel low-alloyed\n",
    "Electricity_dataset = [act for act in eidb if 'market for electricity, high voltage' in act['name'] and 'DK' in act['location']][0]\n",
    "print(Electricity_dataset)\n",
    "Steel_dataset  = [act for act in eidb if 'market for steel, low-alloyed' == act['name']][0]\n",
    "print(Steel_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling models used in the publication are defined below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling models are based on regression from TheWindPower database (see Supporting information of the article: \"Exploring technologically, temporally and geographically-sensitive life cycle inventories for renewable energy systems: a parameterized model for wind turbines\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Scaling model: Rotor diameter (m) - Rated power (kW)\n",
    "def func_rotor_power(x, a, b, c, d):\n",
    "    y = a - b*np.exp(-(x-d)/c)\n",
    "    return y\n",
    "p_rotor_power_ON = [152.66222073,   136.56772435,  2478.03511414,    16.44042379]\n",
    "p_rotor_power_OFF = [191.83651588,   147.37205671,  5101.28555377,   376.62814798]\n",
    "\n",
    "#Scaling model: Hub height (m) - Rated power (kW)\n",
    "def func_height_power(x, a, b, c):\n",
    "    y = a - b*np.exp(-(x)/c)\n",
    "    return y\n",
    "p_height_power_ON = [116.43035193, 91.64953366, 2391.88662558] \n",
    "p_height_power_OFF = [120.75491612, 82.75390577, 4177.56520433]\n",
    "\n",
    "#Scaling model: Nacelle weight (kg) - Rated power (kW)\n",
    "def func_nacelle_weight_power(x, a, b):\n",
    "    y = a * x**2 + b*x\n",
    "    return 1e3 * y \n",
    "p_nacelle_weight_power_ON = [  1.66691134e-06,   3.20700974e-02] \n",
    "p_nacelle_weight_power_OFF = [  2.15668283e-06,   3.24712680e-02]\n",
    "\n",
    "#Scaling model: Rotor weight (kg) - Rotor diameter (m)\n",
    "def func_rotor_weight_rotor_diameter(x, a, b):\n",
    "    y = a * x**2 + b*x\n",
    "    return 1e3 * y\n",
    "\n",
    "p_rotor_weight_rotor_diameter_ON  = [ 0.00460956,  0.11199577] \n",
    "p_rotor_weight_rotor_diameter_OFF = [ 0.0088365,  -0.16435292]\n",
    "\n",
    "#Scaling model: Tower (kg) - D²*h (m**3)\n",
    "def func_tower_weight_d2h(d, h, a, b):\n",
    "    y = a * d**2*h + b\n",
    "    return 1e3 * y\n",
    "p_tower_weight_d2h = [3.03584782e-04, 9.68652909e+00]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to generate the wind turbine LCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dictionary(P, d = None, h = None,  M_tower = None, M_foundation = None, M_reinfSteel_foundation = None, \n",
    "                      V_conc_foundation = None, M_nacelle = None, M_power_supply = None, M_rotor = None, \n",
    "                      M_electronics = None, offshore = False, park_size = 50, dist_transfo = 1, dist_coast = 5, \n",
    "                      sea_depth = 5, print_details = False, lifetime = 20):\n",
    "    \"\"\"\n",
    "    This function generates the lifecycle inventory of a wind turbine\n",
    "    List of parameters : P (rated power) expressed in kW, d (rotor diameter) expressed in m, h (hub height) expressed in m,\n",
    "    offshore = True/False (False by default), park_size (number of wind turbines in a park) as integer,\n",
    "    dist_transfo (ditance to transformer) in meters, disct_coast (distance to coast for offshore) in meters,\n",
    "    sea_depth (for offshore) in meters, print_details (to print details) as boolean and lifetime (in years) as integer.\n",
    "    \"\"\"\n",
    "    #Setting parameters for scaling model with onshore-offshore distinctions.\n",
    "    if not(offshore):\n",
    "        p_rotor_power = p_rotor_power_ON\n",
    "        p_height_power = p_height_power_ON\n",
    "        p_nacelle_weight_power = p_nacelle_weight_power_ON\n",
    "        p_rotor_weight_rotor_diameter = p_rotor_weight_rotor_diameter_ON\n",
    "    \n",
    "    if offshore : \n",
    "        p_rotor_power = p_rotor_power_OFF\n",
    "        p_height_power = p_height_power_OFF\n",
    "        p_nacelle_weight_power = p_nacelle_weight_power_OFF\n",
    "        p_rotor_weight_rotor_diameter = p_rotor_weight_rotor_diameter_OFF\n",
    "        \n",
    "    #Using scaling model for missing values\n",
    "    if d == None:\n",
    "        d = func_rotor_power(P, *p_rotor_power)\n",
    "    if h == None:\n",
    "        h = func_height_power(P, *p_height_power)   \n",
    "    if M_tower == None:\n",
    "        M_tower = func_tower_weight_d2h(d, h, *p_tower_weight_d2h)\n",
    "    if M_foundation == None:\n",
    "        M_foundation = 1696e3 * h/80 * d**2/(100**2)\n",
    "    if M_reinfSteel_foundation == None:\n",
    "        M_reinfSteel_foundation = np.interp(P, [750, 2000, 4500], [10210, 27000, 51900])\n",
    "    if V_conc_foundation == None:\n",
    "        V_conc_foundation = (M_foundation - M_reinfSteel_foundation) / 2200\n",
    "    if M_nacelle == None:\n",
    "        M_nacelle = func_nacelle_weight_power(P, *p_nacelle_weight_power)\n",
    "    if M_power_supply == None:\n",
    "        M_power_supply = 620\n",
    "    if M_rotor == None:\n",
    "        M_rotor = func_rotor_weight_rotor_diameter(d, *p_rotor_weight_rotor_diameter)\n",
    "    if M_electronics == None:\n",
    "        M_electronics = np.interp(P, [30, 150, 600, 800, 2000], [150, 300, 862, 1112, 3946])\n",
    "    \n",
    "    M_all = M_nacelle + M_tower + M_rotor + M_foundation + M_electronics\n",
    "    \n",
    "    #A reprendre avec tous les paramètres possibles\n",
    "    if print_details:\n",
    "        print('Nominal power : %s kW' %P)\n",
    "        print('Rotor diameter : %s m'%d)\n",
    "        print('Hub height : %s m'%h)\n",
    "        print('Tower weight : %s kg'%M_tower)\n",
    "        print('Foundation weight : %s kg'%M_foundation)\n",
    "        print('Foundation reinforced steel: %s kg'%M_reinfSteel_foundation)\n",
    "        print('Foundation concrete volume: %s m3'%V_conc_foundation)\n",
    "        print('Nacelle weight: %s kg'%M_nacelle)\n",
    "        print('Rotor weight: %s'%M_rotor)\n",
    "        print('Electronics: %s'%M_electronics)\n",
    "        print('Total weight: %s kg'%M_all)\n",
    "    \n",
    "    dict_activities={}\n",
    "    \n",
    "    #Adding input element expressed in percentage of mass * M\n",
    "    phase = 'Input'\n",
    "    if print_details:\n",
    "        print(phase)\n",
    "    for component in set(df_inv[phase].columns.get_level_values(0)):\n",
    "        if print_details:\n",
    "            print('\\t' + component)\n",
    "\n",
    "        if component == 'Electronics':\n",
    "            M = M_electronics\n",
    "\n",
    "        if component == 'Foundation':\n",
    "            continue           \n",
    "\n",
    "        if component == 'Nacelle':\n",
    "            M = M_nacelle\n",
    "\n",
    "        if component == 'Power supply':\n",
    "            M = M_power_supply\n",
    "\n",
    "        if component == 'Rotor':\n",
    "            M = M_rotor\n",
    "\n",
    "        if component == 'Tower':\n",
    "            M = M_tower \n",
    "\n",
    "        if print_details:\n",
    "            print(M)\n",
    "\n",
    "        for sub_comp in set(df_inv[phase][component].columns.get_level_values(0)):\n",
    "            if print_details:\n",
    "                print('\\t \\t' + sub_comp)\n",
    "            for dataset in set(df_inv[phase][component][sub_comp].columns.get_level_values(0)):\n",
    "                df_inv_i = df_inv[phase][component][sub_comp][dataset]\n",
    "                if print_details:\n",
    "                    print('\\t \\t \\t' + dataset + '\\t M = %s'%M + '\\t pctg = %s' %(np.interp(P, df_inv_i.index.values, df_inv_i.values)) + '\\t %s'%(np.interp(P, df_inv_i.index.values, df_inv_i.values)*M))\n",
    "                    print('\\t \\t \\t'+ df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])\n",
    "                add_to_dict(dict_activities, key = bw.get_activity((df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])), value = np.interp(P, df_inv_i.index.values, df_inv_i.values) * M  )\n",
    "\n",
    "    #Adding onshore foundation:\n",
    "    if not(offshore):\n",
    "        if print_details:\n",
    "            print('\\t Onshore foundation')\n",
    "            print('\\t \\t M_fond = %s'%M_foundation)\n",
    "            print('\\t \\t concrete = %s' %V_conc_foundation + 'm3')\n",
    "            print('\\t \\t reinforced steel = %s' %M_reinfSteel_foundation + 'kg')\n",
    "        #V_conc_foundation\n",
    "        add_to_dict(dict_activities, key = bw.get_activity((eidb.name, df_act[(df_act['Component'] == 'Foundation')&(df_act['Market name'] == 'market for concrete, sole plate and foundation')]['UUID'].iloc[0])), value = V_conc_foundation)\n",
    "        #M_reinfSteel_foundation\n",
    "        add_to_dict(dict_activities, key = bw.get_activity((eidb.name, df_act[(df_act['Component'] == 'Foundation')&(df_act['Market name'] == 'market for waste reinforced concrete')]['UUID'].iloc[0])), value = M_reinfSteel_foundation)\n",
    "    \n",
    "    #Adding road\n",
    "    if not(offshore):\n",
    "        value = np.interp(P, [0, 2000],[0, 8000])\n",
    "        add_to_dict(dict_activities, key = bw.get_activity((eidb.name, Road['code'])), value = value)\n",
    "\n",
    "    #Adding land use\n",
    "    if False:        \n",
    "        phase = 'Input'\n",
    "        if print_details:\n",
    "            print('Land use')\n",
    "        if not(offshore):\n",
    "            for dataset in df_inv_not_kg['Input']['Foundation']['Land use']:\n",
    "                df_inv_not_kg_i = df_inv_not_kg['Input']['Foundation']['Land use'][dataset].dropna()\n",
    "                if print_details:\n",
    "                    print('\\t' + dataset + '\\t %s'%(np.interp(P, df_inv_not_kg_i.index.values, df_inv_not_kg_i.values)))\n",
    "                    print('\\t' + df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])\n",
    "                add_to_dict(dict_activities, key = bw.get_activity((df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])), value = np.interp(P, df_inv_not_kg_i.index.values, df_inv_not_kg_i.values))\n",
    "\n",
    "    \n",
    "    #Maintenance\n",
    "    if False:\n",
    "        if print_details:\n",
    "            print('Maintenance')\n",
    "        dataset = 'Car [km]'\n",
    "        df_inv_not_kg_i = df_inv_not_kg['Maintenance']['Nacelle']['Transport by car'][dataset].dropna()\n",
    "        if print_details:\n",
    "            print('\\t' + dataset + '\\t %s'%(np.interp(P, df_inv_not_kg_i.index.values, df_inv_not_kg_i.values)))\n",
    "            print('\\t' + df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])\n",
    "        add_to_dict(dict_activities, key = bw.get_activity((df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])), value = np.interp(P, df_inv_not_kg_i.index.values, df_inv_not_kg_i.values))\n",
    "\n",
    "\n",
    "    #Assembly activities not in kg\n",
    "    phase = 'Assembly'\n",
    "    if print_details:\n",
    "        print(phase + 'not in kg')\n",
    "    for component in set(df_inv_not_kg[phase].columns.get_level_values(0)):\n",
    "        if print_details:\n",
    "            print('\\t' + component)\n",
    "        for sub_comp in set(df_inv_not_kg[phase][component].columns.get_level_values(0)):\n",
    "            if print_details:\n",
    "                print('\\t \\t' + sub_comp)\n",
    "            for dataset in set(df_inv_not_kg[phase][component][sub_comp].columns.get_level_values(0)):\n",
    "                df_inv_not_kg_i = df_inv_not_kg[phase][component][sub_comp][dataset].dropna()\n",
    "                if print_details:\n",
    "                    print(dataset)\n",
    "                if component == 'Foundation':\n",
    "                    df_inv_not_kg_i = df_inv_not_kg[phase][component][sub_comp][dataset].dropna()\n",
    "                    if print_details:\n",
    "                        print('\\t' + dataset + '\\t %s'%(np.interp(P, df_inv_not_kg_i.index.values, df_inv_not_kg_i.values)))\n",
    "                        print('\\t' + df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])\n",
    "                    add_to_dict(dict_activities, key = bw.get_activity((df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])), value = np.interp(P, df_inv_not_kg_i.index.values, df_inv_not_kg_i.values))\n",
    "\n",
    "                else:\n",
    "                    s = InterpolatedUnivariateSpline(df_inv_not_kg_i.index.values, df_inv_not_kg_i.values, k=1)\n",
    "                    if print_details:\n",
    "                        print('\\t' + dataset + '\\t %s'%(s(P)))\n",
    "                        print('\\t' + df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])\n",
    "                    add_to_dict(dict_activities, key = bw.get_activity((df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[((df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)) & (df_act['Phase'] == phase)].iloc[0]['UUID'])), value = s(P))\n",
    "    \n",
    "    #Connexion_requirements\n",
    "    if offshore:\n",
    "        if print_details:\n",
    "            print('Raccordement offshore')\n",
    "        M, E_CLS = cable_requirements(P = P, park_size = park_size, dist_transfo = dist_transfo, dist_coast = dist_coast)\n",
    "        \n",
    "        phase = 'Input'\n",
    "        component = 'Power supply'\n",
    "        for sub_comp in set(df_inv[phase][component].columns.get_level_values(0)):\n",
    "            if print_details:\n",
    "                print('\\t \\t' + sub_comp)\n",
    "            for dataset in set(df_inv[phase][component][sub_comp].columns.get_level_values(0)):\n",
    "                df_inv_i = df_inv[phase][component][sub_comp][dataset].dropna()\n",
    "                if print_details:\n",
    "                    print('\\t \\t \\t' + dataset + '\\t %s'%(np.interp(P, df_inv_i.index.values, df_inv_i.values)*M))\n",
    "                    print('\\t \\t \\t'+ df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])\n",
    "                add_to_dict(dict_activities, key = bw.get_activity((df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[((df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)) & (df_act['Phase'] == phase)].iloc[0]['UUID'])), value = np.interp(P, df_inv_i.index.values, df_inv_i.values) * M  )\n",
    "        \n",
    "        #Energy for cable laying ship \n",
    "        add_to_dict(dict_activities, key = diesel_burned_activity, value = E_CLS)\n",
    "        \n",
    "        #Transfo\n",
    "        add_to_dict(dict_activities, key = MV_transfo, value = P / 10e3 / 0.85 * lifetime/35) #calcul au proratat de la puissance, facteur 0.85 en puissance active et apparente et durée de vie\n",
    "        add_to_dict(dict_activities, key = HV_transfo, value = P / 500e3 / 0.85 * lifetime/35)\n",
    "    \n",
    "    # Pour les onshore, Transfo moyenne tension au proratat de la puissance, et cable section et longueur suivant la puissance\n",
    "    if not(offshore):    \n",
    "        add_to_dict(dict_activities, key = MV_transfo, value = P / 10e3 / 0.85 * 19/35)\n",
    "        \n",
    "        M  = cable_requirements_Onshore(P)\n",
    "        phase = 'Input'\n",
    "        component = 'Power supply'\n",
    "        \n",
    "        for sub_comp in set(df_inv[phase][component].columns.get_level_values(0)):\n",
    "            if print_details:\n",
    "                print('\\t \\t' + sub_comp)\n",
    "            for dataset in set(df_inv[phase][component][sub_comp].columns.get_level_values(0)):\n",
    "                df_inv_i = df_inv[phase][component][sub_comp][dataset].dropna()\n",
    "                if print_details:\n",
    "                    print('\\t \\t \\t' + dataset + '\\t %s'%(np.interp(P, df_inv_i.index.values, df_inv_i.values)*M))\n",
    "                    print('\\t \\t \\t'+ df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])\n",
    "                add_to_dict(dict_activities, key = bw.get_activity((df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[((df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)) & (df_act['Phase'] == phase)].iloc[0]['UUID'])), value = np.interp(P, df_inv_i.index.values, df_inv_i.values) * M  )\n",
    "        \n",
    "    #Transport activities\n",
    "    trsp_truck_nacelle, trsp_truck_rotor, trsp_truck_tower, trsp_ship_tower,trsp_truck_foundation, trsp_end_of_life, trsp_maintenance_per_year, trsp_ship_offshore = transport_requirements(M_nacelle,  M_tower, M_rotor,  M_foundation,  M_all, lifetime)\n",
    "    \n",
    "    #Truck transportation\n",
    "    trsp_truck = trsp_truck_nacelle + trsp_truck_rotor + trsp_truck_tower + trsp_truck_foundation + trsp_end_of_life + trsp_maintenance_per_year\n",
    "    add_to_dict(dict_activities, key = bw.get_activity((eidb.name, Truck_transport['code'])), value = trsp_truck)\n",
    "\n",
    "    \n",
    "    #Ship transportation\n",
    "    if offshore:\n",
    "        trsp_ship = trsp_ship_tower + trsp_ship_offshore\n",
    "    else:\n",
    "        trsp_ship = trsp_ship_tower\n",
    "    \n",
    "    add_to_dict(dict_activities, key = bw.get_activity((eidb.name, Ship_transport['code'])), value = trsp_ship)\n",
    "\n",
    "    #Scour stuff\n",
    "    if offshore:\n",
    "        scour_volume = fit_scour_volume(P)\n",
    "        add_to_dict(dict_activities, key = bw.get_activity((eidb.name, Digger['code'])), value = scour_volume)\n",
    "\n",
    "    #Grout stuff and monopile\n",
    "    if offshore:\n",
    "        m_grout, m_monopile = grout_and_monopile_requirements(P, sea_depth)\n",
    "        add_to_dict(dict_activities, key = bw.get_activity((eidb.name, Cement['code'])), value = m_grout)\n",
    "        \n",
    "        phase = 'Input'\n",
    "        component = 'Tower'\n",
    "        sub_comp = 'Material'\n",
    "        M = m_monopile\n",
    "        for dataset in set(df_inv[phase][component][sub_comp].columns.get_level_values(0)):\n",
    "                df_inv_i = df_inv[phase][component][sub_comp][dataset].dropna()\n",
    "                if print_details:\n",
    "                    print('\\t \\t \\t' + dataset + '\\t %s'%(np.interp(P, df_inv_i.index.values, df_inv_i.values)*M))\n",
    "                    print('\\t \\t \\t'+ df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['UUID'])\n",
    "                add_to_dict(dict_activities, key = bw.get_activity((df_act[(df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)].iloc[0]['Database name'], df_act[((df_act['Dataset'] == dataset) & (df_act['Phase'] == phase)) & (df_act['Phase'] == 'Input')].iloc[0]['UUID'])), value = np.interp(P, df_inv_i.index.values, df_inv_i.values) * M )\n",
    "    \n",
    "    #Assembly depending on kg\n",
    "    for AA in  assembly_activities:\n",
    "        if print_details:\n",
    "            print(AA)\n",
    "        if AA == 'Explosives':\n",
    "            value = 10\n",
    "            add_to_dict(dict_activities, key = Explosive, value = value )     \n",
    "        if AA == 'Copper wire drawing':\n",
    "            value = dict_activities[ bw.get_activity((eidb.name, df_act[ df_act['Dataset'] == 'Copper']['UUID'].iloc[0]))]\n",
    "            add_to_dict(dict_activities, key = Copper_wire_drawing, value = value )\n",
    "        if AA == 'Steel sheet rolling':\n",
    "            value = dict_activities[ bw.get_activity((eidb.name, df_act[ df_act['Dataset'] == 'Low-alloy steel']['UUID'].iloc[0]))] + dict_activities[bw.get_activity((eidb.name, df_act[ df_act['Dataset'] == 'Cast iron']['UUID'].iloc[0]))]\n",
    "            add_to_dict(dict_activities, key = Steel_sheet_rolling, value = value )\n",
    "        if AA == 'Aluminium sheet rolling':\n",
    "            value = dict_activities[ bw.get_activity((eidb.name, df_act[ df_act['Dataset'] == 'Aluminium 0% recycled']['UUID'].iloc[0]))] \n",
    "            add_to_dict(dict_activities, key = Aluminium_sheet_rolling, value = value )\n",
    "        if AA == 'Chromium sheet rolling':\n",
    "            value = dict_activities[ bw.get_activity((eidb.name, df_act[ df_act['Dataset'] == 'Chromium steel']['UUID'].iloc[0]))] \n",
    "            add_to_dict(dict_activities, key = Chromium_sheet_rolling, value = value )\n",
    "            \n",
    "    #Disposal activities\n",
    "    for DA in  disposal_activities:\n",
    "        if print_details:\n",
    "            print(DA)\n",
    "        ei_key_disposal = df_act[ (df_act['Dataset'] == DA) & (df_act['Phase'] == 'Disposal')]['UUID'].iloc[0]\n",
    "        DA = DA.replace(' -waste','').replace('Steel, inert waste', 'Low-alloy steel' ).replace('Concrete, inert waste', 'Concrete [m3]').replace('Aluminium waste', 'Aluminium 0% recycled').replace('Chromium Steel waste','Chromium steel')\n",
    "        ei_key_input = df_act[ (df_act['Dataset'] == DA) & (df_act['Phase'] == 'Input')]['UUID'].iloc[0]\n",
    "        try:\n",
    "            value =  - dict_activities[ bw.get_activity((eidb.name, ei_key_input))] \n",
    "            add_to_dict(dict_activities, key = bw.get_activity((eidb.name, ei_key_disposal)), value = value)\n",
    "        except:\n",
    "            pass\n",
    "    \n",
    "    \n",
    "    #Electicity dataset\n",
    "    value = 0.5 * (M_nacelle + M_rotor + M_tower)\n",
    "    add_to_dict(dict_activities, key = bw.get_activity((eidb.name, Electricity_dataset['code'])), value = value)\n",
    "    \n",
    "    #Steel dataset\n",
    "    value = dict_activities[bw.get_activity((eidb.name, Steel_dataset['code']))]\n",
    "    del dict_activities[bw.get_activity((eidb.name, Steel_dataset['code']))]\n",
    "    add_to_dict(dict_activities, key = bw.get_activity((eidb.name, Steel_dataset['code'])), value = value)\n",
    "\n",
    "    return dict_activities\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, here we create the inventory of a 2 MW onshore wind turbine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nominal power : 2000 kW\n",
      "Rotor diameter : 91.3263992951 m\n",
      "Hub height : 76.7120593079 m\n",
      "Tower weight : 203925.473527 kg\n",
      "Foundation weight : 1356413.71578 kg\n",
      "Foundation reinforced steel: 27000.0 kg\n",
      "Foundation concrete volume: 604.278961717 m3\n",
      "Nacelle weight: 70807.84015999998 kg\n",
      "Rotor weight: 48674.2572552\n",
      "Electronics: 3946.0\n",
      "Total weight: 1683767.28672 kg\n",
      "Input\n",
      "\tRotor\n",
      "48674.2572552\n",
      "\t \tHub\n",
      "\t \t \tCast iron\t M = 48674.2572552\t pctg = 0.21769230769230768\t 10596.0113871\n",
      "\t \t \tecoinvent 3.4 cut off 0e718dd998cd3c6ca2aac0076cfe10d8\n",
      "\t \tBlades\n",
      "\t \t \tFiberglass\t M = 48674.2572552\t pctg = 0.5714230769230769\t 27813.5938477\n",
      "\t \t \tecoinvent 3.4 cut off 4d3553e0ea356529fcabfe3b721e6d14\n",
      "\t \tExtender\n",
      "\t \t \tChromium steel\t M = 48674.2572552\t pctg = 0.2108846153846154\t 10264.6520204\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\tNacelle\n",
      "70807.84015999998\n",
      "\t \tGenerator\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.10721791434161895\t 7591.868940989924\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \t \tCopper\t M = 70807.84015999998\t pctg = 0.011909075536874652\t 843.2559170683863\n",
      "\t \t \tecoinvent 3.4 cut off d6f0a3a0933866c2f8d7eb83db02c9aa\n",
      "\t \t \tCast iron\t M = 70807.84015999998\t pctg = 0.040848370654878376\t 2892.3849001270614\n",
      "\t \t \tecoinvent 3.4 cut off 0e718dd998cd3c6ca2aac0076cfe10d8\n",
      "\t \t \tAluminium 0% recycled\t M = 70807.84015999998\t pctg = 0.010206053578761747\t 722.6686104693575\n",
      "\t \t \tecoinvent 3.4 cut off d7b2398f8a10f8a4f809783a70e9598e\n",
      "\t \tBrake\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.0074039181583206515\t 524.2554535120901\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \tBearing\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.012380124163586734\t 876.6098529362029\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \t \tCast iron\t M = 70807.84015999998\t pctg = 0.012380124163586734\t 876.6098529362029\n",
      "\t \t \tecoinvent 3.4 cut off 0e718dd998cd3c6ca2aac0076cfe10d8\n",
      "\t \tAxis\n",
      "\t \t \tLow-alloy steel\t M = 70807.84015999998\t pctg = 0.15292170930260646\t 10828.05594929294\n",
      "\t \t \tecoinvent 3.4 cut off 1ee93e063d5ac749c5f62ffdfa960828\n",
      "\t \tPaneling\n",
      "\t \t \tFiberglass\t M = 70807.84015999998\t pctg = 0.13641085102785225\t 9658.95773566973\n",
      "\t \t \tecoinvent 3.4 cut off 4d3553e0ea356529fcabfe3b721e6d14\n",
      "\t \tCrane carrier\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.0010387226127497162\t 73.54970473415946\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \tCoupling\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.0005435176462062468\t 38.485310616711345\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \t \tRubber\t M = 70807.84015999998\t pctg = 0.00018117254873541562\t 12.828436872237114\n",
      "\t \t \tecoinvent 3.4 cut off b86006cfcfc3459fbc17ba535e753387\n",
      "\t \tHydraulic\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.024663622967847913\t 1746.3778728738794\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \t \tLubricating oil\t M = 70807.84015999998\t pctg = 0.001811725487354156\t 128.28436872237114\n",
      "\t \t \tecoinvent 3.4 cut off 73755d76796b5973edb5460525464d25\n",
      "\t \tFrame\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.2046041983718627\t 14487.58137437978\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \tYaw - Brake\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.00985578665120661\t 697.8669658496991\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \tYaw - Drive\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.014795758146725608\t 1047.6556778993643\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \tYaw - ball bearigs\n",
      "\t \t \tLow-alloy steel\t M = 70807.84015999998\t pctg = 0.028854747928593858\t 2043.1423791849643\n",
      "\t \t \tecoinvent 3.4 cut off 1ee93e063d5ac749c5f62ffdfa960828\n",
      "\t \tTransmission\n",
      "\t \t \tChromium steel\t M = 70807.84015999998\t pctg = 0.11038239485953089\t 7815.938971691666\n",
      "\t \t \tecoinvent 3.4 cut off 0c03435daea210f81f1d92699e4e33e1\n",
      "\t \t \tCast iron\t M = 70807.84015999998\t pctg = 0.11038239485953089\t 7815.938971691666\n",
      "\t \t \tecoinvent 3.4 cut off 0e718dd998cd3c6ca2aac0076cfe10d8\n",
      "\t \t \tRubber\t M = 70807.84015999998\t pctg = 0.0012078169915694375\t 85.52291248158078\n",
      "\t \t \tecoinvent 3.4 cut off b86006cfcfc3459fbc17ba535e753387\n",
      "\tFoundation\n",
      "\tElectronics\n",
      "3946.0\n",
      "\t \tTransformer\n",
      "\t \t \tLead\t M = 3946.0\t pctg = 0.00012670930857264497\t 0.49999493162765707\n",
      "\t \t \tecoinvent 3.4 cut off fb5aaef027f2870f68948dbbb363efef\n",
      "\t \t \tHDPE granules\t M = 3946.0\t pctg = 0.006842302662922829\t 26.999726307893486\n",
      "\t \t \tecoinvent 3.4 cut off 4f3e7413e2e5b016e5e3e633de952a1e\n",
      "\t \t \tLubricating oil\t M = 3946.0\t pctg = 0.25341861714528996\t 999.9898632553142\n",
      "\t \t \tecoinvent 3.4 cut off 73755d76796b5973edb5460525464d25\n",
      "\t \t \tCast iron\t M = 3946.0\t pctg = 0.38012792571793497\t 1499.9847948829713\n",
      "\t \t \tecoinvent 3.4 cut off 0e718dd998cd3c6ca2aac0076cfe10d8\n",
      "\t \t \tCopper\t M = 3946.0\t pctg = 0.15205117028717396\t 599.9939179531884\n",
      "\t \t \tecoinvent 3.4 cut off d6f0a3a0933866c2f8d7eb83db02c9aa\n",
      "\t \t \tTin\t M = 3946.0\t pctg = 0.00012670930857264497\t 0.49999493162765707\n",
      "\t \t \tecoinvent 3.4 cut off 32f61b5d85c9ec54dff497f07e317600\n",
      "\t \t \tLow-alloy steel\t M = 3946.0\t pctg = 0.20273489371623196\t 799.9918906042514\n",
      "\t \t \tecoinvent 3.4 cut off 1ee93e063d5ac749c5f62ffdfa960828\n",
      "\t \t \tPVC impact resistant\t M = 3946.0\t pctg = 0.0015205117028717398\t 5.999939179531885\n",
      "\t \t \tecoinvent 3.4 cut off 80c733a42592703c5cb2d5ebab325761\n",
      "\t \t \tAluminium 0% recycled\t M = 3946.0\t pctg = 0.003051160150429291\t 12.039877953593981\n",
      "\t \t \tecoinvent 3.4 cut off d7b2398f8a10f8a4f809783a70e9598e\n",
      "\tPower supply\n",
      "620\n",
      "\t \tCable\n",
      "\t \t \tHDPE granules\t M = 620\t pctg = 0.3549432739059968\t 220.064829821718\n",
      "\t \t \tecoinvent 3.4 cut off 4f3e7413e2e5b016e5e3e633de952a1e\n",
      "\t \t \tCopper\t M = 620\t pctg = 0.3565640194489465\t 221.06969205834682\n",
      "\t \t \tecoinvent 3.4 cut off d6f0a3a0933866c2f8d7eb83db02c9aa\n",
      "\t \t \tPP granules\t M = 620\t pctg = 0.03241491085899514\t 20.097244732576986\n",
      "\t \t \tecoinvent 3.4 cut off 53911325a4eda651c0a21f6fe3a4a3de\n",
      "\t \t \tPVC impact resistant\t M = 620\t pctg = 0.2560777957860616\t 158.76823338735818\n",
      "\t \t \tecoinvent 3.4 cut off 80c733a42592703c5cb2d5ebab325761\n",
      "\tTower\n",
      "203925.473527\n",
      "\t \tMaterial\n",
      "\t \t \tEpoxy resin\t M = 203925.473527\t pctg = 0.004808495301388047\t 980.574681288\n",
      "\t \t \tecoinvent 3.4 cut off b88fe2ebe8d73a8783b1b111f12ee7d3\n",
      "\t \t \tLow-alloy steel\t M = 203925.473527\t pctg = 0.995191504698612\t 202944.898846\n",
      "\t \t \tecoinvent 3.4 cut off 1ee93e063d5ac749c5f62ffdfa960828\n",
      "\t Onshore foundation\n",
      "\t \t M_fond = 1356413.71578\n",
      "\t \t concrete = 604.278961717m3\n",
      "\t \t reinforced steel = 27000.0kg\n",
      "Assemblynot in kg\n",
      "\tFoundation\n",
      "\t \tAssembly\n",
      "Diesel\n",
      "\tDiesel\t 29250.0\n",
      "\tecoinvent 3.4 cut off e3e6881ad361c2b885665388a6d8bc0f\n",
      "\tTower\n",
      "\t \tAssembly\n",
      "Steel arc welding [m]\n",
      "\tSteel arc welding [m]\t 228.0\n",
      "\tecoinvent 3.4 cut off fed294ef8248567ee4792261ddde81bb\n",
      "Galvanizing [m]\n",
      "\tGalvanizing [m]\t 228.0\n",
      "\tecoinvent 3.4 cut off c5199723e7104a11950d8881aa917908\n",
      "\t \tCable\n",
      "\t \t \tHDPE granules\t 124.36241951\n",
      "\t \t \tecoinvent 3.4 cut off 4f3e7413e2e5b016e5e3e633de952a1e\n",
      "\t \t \tCopper\t 124.930284439\n",
      "\t \t \tecoinvent 3.4 cut off d6f0a3a0933866c2f8d7eb83db02c9aa\n",
      "\t \t \tPP granules\t 11.3572985854\n",
      "\t \t \tecoinvent 3.4 cut off 53911325a4eda651c0a21f6fe3a4a3de\n",
      "\t \t \tPVC impact resistant\t 89.7226588247\n",
      "\t \t \tecoinvent 3.4 cut off 80c733a42592703c5cb2d5ebab325761\n",
      "Aluminium sheet rolling\n",
      "Steel sheet rolling\n",
      "Explosives\n",
      "Copper wire drawing\n",
      "Chromium steel sheet rolling\n",
      "Fiberglass -waste\n",
      "HDPE granules\n",
      "Lubricating oil\n",
      "PVC impact resistant\n",
      "Rubber\n",
      "Concrete, inert waste\n",
      "PP granules\n",
      "Steel, inert waste\n",
      "Aluminium waste\n",
      "Chromium Steel waste\n"
     ]
    }
   ],
   "source": [
    "dict_activities = create_dictionary(P = 2000, print_details = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We print the dicitonnary that contains the inventory, in which quantities and corresponding datasets are listed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'market for steel, chromium steel 18/8' (kilogram, GLO, None): 45164.842145887807,\n",
       " 'market for cast iron' (kilogram, GLO, None): 23680.929906741403,\n",
       " 'market for waste rubber, unspecified' (kilogram, CH, None): -98.3513493538179,\n",
       " 'market for scrap steel' (kilogram, CH, None): -261780.9312106054,\n",
       " 'market for steel, low-alloyed' (kilogram, GLO, None): 216616.08906471758,\n",
       " 'market for waste polypropylene' (kilogram, CH, None): -31.454543317984886,\n",
       " 'market for tin' (kilogram, GLO, None): 0.49999493162765707,\n",
       " 'Power transformer TrafoStar 10 MVA' (unit, GLO, None): 0.12773109243697478,\n",
       " 'market for sheet rolling, aluminium' (kilogram, GLO, None): 734.7084884229515,\n",
       " 'market for road' (meter-year, CH, None): 8000.0,\n",
       " 'market for waste mineral oil' (kilogram, CH, None): -1128.2742319776853,\n",
       " 'market for glass fibre reinforced plastic, polyamide, injection moulded' (kilogram, GLO, None): 37472.55158340768,\n",
       " 'market for polyethylene, high density, granulate, recycled' (kilogram, CH, None): 371.42697563982802,\n",
       " 'market for polypropylene, granulate' (kilogram, GLO, None): 31.454543317984886,\n",
       " 'market for electricity, high voltage' (kilowatt hour, DK, None): 161703.78547108438,\n",
       " 'market for waste reinforced concrete' (kilogram, CH, None): 26395.721038282725,\n",
       " 'market for lubricating oil' (kilogram, GLO, None): 1128.2742319776853,\n",
       " 'market for polyvinylchloride, bulk polymerised' (kilogram, GLO, None): 254.49083139161246,\n",
       " 'market for sheet rolling, steel' (kilogram, GLO, None): 240297.01897145898,\n",
       " 'treatment of inert waste, inert material landfill' (kilogram, CH, None): -37472.55158340768,\n",
       " 'market for transport, freight, inland waterways, barge with reefer, cooling' (ton kilometer, GLO, None): 1641600.0618917299,\n",
       " 'market for explosive, tovex' (kilogram, GLO, None): 10,\n",
       " 'market for synthetic rubber' (kilogram, GLO, None): 98.3513493538179,\n",
       " 'market for epoxy resin, liquid' (kilogram, GLO, None): 980.57468128754169,\n",
       " 'market for waste polyvinylchloride product' (kilogram, CH, None): -254.49083139161246,\n",
       " 'market for wire drawing, copper' (kilogram, GLO, None): 1789.2498115194085,\n",
       " 'market for zinc coat, pieces' (square meter, GLO, None): array(228.0),\n",
       " 'market for transport, freight, lorry >32 metric ton, EURO6' (ton kilometer, GLO, None): 730676.19452969846,\n",
       " 'market for copper' (kilogram, GLO, None): 1789.2498115194085,\n",
       " 'market for aluminium, wrought alloy' (kilogram, GLO, None): 734.7084884229515,\n",
       " 'market for diesel, burned in building machine' (megajoule, GLO, None): 29250.0,\n",
       " 'market for waste polyethylene/polypropylene product' (kilogram, CH, None): -371.42697563982802,\n",
       " 'market for concrete, sole plate and foundation' (cubic meter, CH, None): 604.27896171727662,\n",
       " 'market for waste aluminium' (kilogram, GLO, None): -734.7084884229515,\n",
       " 'market for lead' (kilogram, GLO, None): 0.49999493162765707,\n",
       " 'market for welding, arc, steel' (meter, GLO, None): array(228.0)}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_activities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculation with Brightway2 !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methode d'évaluation d'impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set_of_methods: \n",
      " [('ILCD 1.0.8 2016 midpoint', 'ecosystem quality', 'freshwater and terrestrial acidification'), ('IPCC 2013', 'climate change', 'GWP 100a'), ('ILCD 1.0.8 2016 midpoint', 'ecosystem quality', 'freshwater eutrophication'), ('ILCD 1.0.8 2016 midpoint', 'ecosystem quality', 'freshwater ecotoxicity'), ('ILCD 1.0.8 2016 midpoint', 'resources', 'land use'), ('ReCiPe Endpoint (H,A)', 'resources', 'fossil depletion'), ('ReCiPe Endpoint (H,A)', 'resources', 'metal depletion'), ('ILCD 1.0.8 2016 midpoint', 'resources', 'mineral, fossils and renewables'), ('ILCD 1.0.8 2016 midpoint', 'human health', 'ozone layer depletion'), ('ILCD 1.0.8 2016 midpoint', 'human health', 'carcinogenic effects'), ('ILCD 1.0.8 2016 midpoint', 'human health', 'non-carcinogenic effects'), ('ecological scarcity 2013', 'water resources', 'total'), ('ILCD 1.0.8 2016 midpoint', 'human health', 'respiratory effects, inorganics'), ('cumulative energy demand', 'fossil', 'non-renewable energy resources, fossil'), ('cumulative energy demand', 'nuclear', 'non-renewable energy resources, nuclear'), ('cumulative energy demand', 'primary forest', 'non-renewable energy resources, primary forest'), ('cumulative energy demand', 'biomass', 'renewable energy resources, biomass'), ('cumulative energy demand', 'geothermal', 'renewable energy resources, geothermal, converted'), ('cumulative energy demand', 'solar', 'renewable energy resources, solar, converted'), ('cumulative energy demand', 'water', 'renewable energy resources, potential (in barrage water), converted'), ('cumulative energy demand', 'wind', 'renewable energy resources, kinetic (in wind), converted')] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Huge set of methods\n",
    "\n",
    "#Acidification\n",
    "Acid_method = [m for m in bw.methods if 'acidification' in str(m) and 'ILCD' in str(m) and not 'no LT' in str(m)][0]\n",
    "#Climate change\n",
    "CC_method = [m for m in bw.methods if 'IPCC 2013' in str(m) and  'climate change' in str(m) and 'GWP 100a' in str(m) and not 'no LT' in str(m) ][0]\n",
    "#Eutrophication\n",
    "Eutro_method = [m for m in bw.methods if 'freshwater eutrophication' in str(m) and 'ILCD' in str(m) and not 'no LT' in str(m)][0]\n",
    "#Ecotoxicity\n",
    "Ecotox_method = [m for m in bw.methods if 'freshwater ecotoxicity' in str(m) and 'ILCD' in str(m) and not 'no LT' in str(m)][0]\n",
    "#CED\n",
    "#non renewable\n",
    "CEDfossil_method = [m for m in bw.methods if 'cumulative energy demand' in str(m) and 'non-renewable energy resources, fossil' in str(m)][0]\n",
    "CEDnuclear_method = [ m for m in bw.methods if 'cumulative energy demand' in str(m) and 'nuclear' in str(m) ][0]\n",
    "CEDbioNR_method = [ m for m in bw.methods if 'cumulative energy demand' in str(m) and 'non-renewable energy resources, primary forest' in str(m) ][0]\n",
    "#renewable\n",
    "CEDbiomR_method = [ m for m in bw.methods if 'cumulative energy demand' in str(m) and 'renewable energy resources, biomass' in str(m) ][0]\n",
    "CEDgeoth_method = [ m for m in bw.methods if 'renewable energy resources, geothermal, converted' in str(m) and '' in str(m) ][0]\n",
    "CEDsolar_method = [ m for m in bw.methods if 'renewable energy resources, solar, converted' in str(m) and '' in str(m) ][0]\n",
    "CEDwater_method = [ m for m in bw.methods if 'renewable energy resources, potential (in barrage water), converted' in str(m) and '' in str(m) ][0]\n",
    "CEDwind_method = [ m for m in bw.methods if 'renewable energy resources, kinetic (in wind), converted' in str(m) and '' in str(m) ][0]\n",
    "#Land use\n",
    "Land_method = [m for m in bw.methods if 'land' in str(m) and 'ILCD' in str(m) and not 'no LT' in str(m)][0]\n",
    "#Depletion \n",
    "Fossildeplet_method = [m for m in bw.methods if 'fossil depletion' in str(m) and 'resource' in str(m) and 'H' in str(m) and not 'w/o LT' in str(m)][0]\n",
    "Metaldeplet_method = [m for m in bw.methods if 'metal depletion' in str(m) and 'resource' in str(m) and 'H' in str(m) and not 'w/o LT' in str(m)][0]\n",
    "Deplet_method = [ m for m in bw.methods if 'ILCD' in str(m) and 'resource' in str(m) and 'mineral, fossils and renewables' in str(m) and not 'no LT' in str(m)][0]\n",
    "#Ozone\n",
    "Ozone_method = [m for m in bw.methods if 'ozone layer depletion' in str(m) and 'ILCD' in str(m) and not 'no LT' in str(m)][0]\n",
    "#Human health\n",
    "HH_carcinogenic_method = [m for m in bw.methods if 'carcinogenic effects' in str(m) and  '' in str(m) and 'ILCD' in str(m) and not 'LT' in str(m) and not '-' in str(m)][0]\n",
    "HH_noncarcinogenic_method = [m for m in bw.methods if '-carcinogenic effects' in str(m) and  '' in str(m) and 'ILCD' in str(m) and not 'LT' in str(m)][0]\n",
    "#PM emissions : ILCD\n",
    "PM_method = [ m for m in bw.methods if 'ILCD' in str(m) and 'human health' in str(m) and 'respiratory effects, inorganics' in str(m)][0]\n",
    "#Water\n",
    "Water_method = [m for m in bw.methods if 'water' in str(m) and 'scarcity' in str(m) and 'water resources' in str(m) and not 'no LT' in str(m)][0]\n",
    "\n",
    "\n",
    "list_methods = [Acid_method, CC_method, Eutro_method, Ecotox_method, Land_method, Fossildeplet_method, Metaldeplet_method, Deplet_method, Ozone_method, HH_carcinogenic_method, HH_noncarcinogenic_method, Water_method, PM_method, CEDfossil_method, CEDnuclear_method, CEDbioNR_method, CEDbiomR_method, CEDgeoth_method, CEDsolar_method, CEDwater_method, CEDwind_method]\n",
    "print('set_of_methods: \\n %s \\n' %list_methods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start a LCA calculation that will calculate the impacts for all the methods contained in list_methods for as many wind turbines as contained in the dicitonnary dict_activities (in this case, one).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater and terrestrial acidification)</th>\n",
       "      <th>(IPCC 2013, climate change, GWP 100a)</th>\n",
       "      <th>(ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater eutrophication)</th>\n",
       "      <th>(ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater ecotoxicity)</th>\n",
       "      <th>(ILCD 1.0.8 2016 midpoint, resources, land use)</th>\n",
       "      <th>(ReCiPe Endpoint (H,A), resources, fossil depletion)</th>\n",
       "      <th>(ReCiPe Endpoint (H,A), resources, metal depletion)</th>\n",
       "      <th>(ILCD 1.0.8 2016 midpoint, resources, mineral, fossils and renewables)</th>\n",
       "      <th>(ILCD 1.0.8 2016 midpoint, human health, ozone layer depletion)</th>\n",
       "      <th>(ILCD 1.0.8 2016 midpoint, human health, carcinogenic effects)</th>\n",
       "      <th>...</th>\n",
       "      <th>(ecological scarcity 2013, water resources, total)</th>\n",
       "      <th>(ILCD 1.0.8 2016 midpoint, human health, respiratory effects, inorganics)</th>\n",
       "      <th>(cumulative energy demand, fossil, non-renewable energy resources, fossil)</th>\n",
       "      <th>(cumulative energy demand, nuclear, non-renewable energy resources, nuclear)</th>\n",
       "      <th>(cumulative energy demand, primary forest, non-renewable energy resources, primary forest)</th>\n",
       "      <th>(cumulative energy demand, biomass, renewable energy resources, biomass)</th>\n",
       "      <th>(cumulative energy demand, geothermal, renewable energy resources, geothermal, converted)</th>\n",
       "      <th>(cumulative energy demand, solar, renewable energy resources, solar, converted)</th>\n",
       "      <th>(cumulative energy demand, water, renewable energy resources, potential (in barrage water), converted)</th>\n",
       "      <th>(cumulative energy demand, wind, renewable energy resources, kinetic (in wind), converted)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10260.17503</td>\n",
       "      <td>1.722377e+06</td>\n",
       "      <td>839.727862</td>\n",
       "      <td>8.372464e+07</td>\n",
       "      <td>1.263579e+07</td>\n",
       "      <td>58523.064312</td>\n",
       "      <td>51924.329436</td>\n",
       "      <td>166.022108</td>\n",
       "      <td>0.125687</td>\n",
       "      <td>0.794192</td>\n",
       "      <td>...</td>\n",
       "      <td>1.034870e+07</td>\n",
       "      <td>1772.044509</td>\n",
       "      <td>2.046748e+07</td>\n",
       "      <td>2.444796e+06</td>\n",
       "      <td>11742.11277</td>\n",
       "      <td>3.052372e+07</td>\n",
       "      <td>10265.660062</td>\n",
       "      <td>531.38249</td>\n",
       "      <td>1.421547e+06</td>\n",
       "      <td>305023.411014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>1 rows × 21 columns</p>\n",
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       "   (ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater and terrestrial acidification)  \\\n",
       "0                                        10260.17503                                         \n",
       "\n",
       "   (IPCC 2013, climate change, GWP 100a)  \\\n",
       "0                           1.722377e+06   \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater eutrophication)  \\\n",
       "0                                         839.727862                          \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater ecotoxicity)  \\\n",
       "0                                       8.372464e+07                       \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, resources, land use)  \\\n",
       "0                                     1.263579e+07   \n",
       "\n",
       "   (ReCiPe Endpoint (H,A), resources, fossil depletion)  \\\n",
       "0                                       58523.064312      \n",
       "\n",
       "   (ReCiPe Endpoint (H,A), resources, metal depletion)  \\\n",
       "0                                       51924.329436     \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, resources, mineral, fossils and renewables)  \\\n",
       "0                                         166.022108                        \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, ozone layer depletion)  \\\n",
       "0                                           0.125687                 \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, carcinogenic effects)  \\\n",
       "0                                           0.794192                \n",
       "\n",
       "                                              ...                                              \\\n",
       "0                                             ...                                               \n",
       "\n",
       "   (ecological scarcity 2013, water resources, total)  \\\n",
       "0                                       1.034870e+07    \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, respiratory effects, inorganics)  \\\n",
       "0                                        1772.044509                           \n",
       "\n",
       "   (cumulative energy demand, fossil, non-renewable energy resources, fossil)  \\\n",
       "0                                       2.046748e+07                            \n",
       "\n",
       "   (cumulative energy demand, nuclear, non-renewable energy resources, nuclear)  \\\n",
       "0                                       2.444796e+06                              \n",
       "\n",
       "   (cumulative energy demand, primary forest, non-renewable energy resources, primary forest)  \\\n",
       "0                                        11742.11277                                            \n",
       "\n",
       "   (cumulative energy demand, biomass, renewable energy resources, biomass)  \\\n",
       "0                                       3.052372e+07                          \n",
       "\n",
       "   (cumulative energy demand, geothermal, renewable energy resources, geothermal, converted)  \\\n",
       "0                                       10265.660062                                           \n",
       "\n",
       "   (cumulative energy demand, solar, renewable energy resources, solar, converted)  \\\n",
       "0                                          531.38249                                 \n",
       "\n",
       "   (cumulative energy demand, water, renewable energy resources, potential (in barrage water), converted)  \\\n",
       "0                                       1.421547e+06                                                        \n",
       "\n",
       "   (cumulative energy demand, wind, renewable energy resources, kinetic (in wind), converted)  \n",
       "0                                      305023.411014                                           \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bw.calculation_setups['multiLCA'] = {'inv': [dict_activities], 'ia': list_methods}\n",
    "bw.calculation_setups['multiLCA']\n",
    "myMultiLCA = bw.MultiLCA('multiLCA')\n",
    "myMultiLCA.results\n",
    "df_impact = pd.DataFrame(columns = list_methods, data = myMultiLCA.results)\n",
    "df_impact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can formulate a calculation function that takes directly the attributes of the wind turbine as arguments and normalize the life cycle impacts by the electricity production, a given life time and load factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_impact(dict_activities = None, P = None, lifetime = 20, load_factor = 0.3, list_methods = list_methods):\n",
    "    bw.calculation_setups['multiLCA'] = {'inv': [dict_activities], 'ia': list_methods}\n",
    "    bw.calculation_setups['multiLCA']\n",
    "    myMultiLCA = bw.MultiLCA('multiLCA')\n",
    "    myMultiLCA.results\n",
    "    df_impact = pd.DataFrame(columns = list_methods, data = myMultiLCA.results)\n",
    "    df_impact\n",
    "    \n",
    "    df_impact_kWh = df_impact / (P * lifetime * 8760 * load_factor)\n",
    "    return df_impact, df_impact_kWh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_impact, df_impact_kWh = calculate_impact(dict_activities, P = 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <td>4.091636e+06</td>\n",
       "      <td>54623.717844</td>\n",
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       "      <td>142.381654</td>\n",
       "      <td>0.106451</td>\n",
       "      <td>0.785612</td>\n",
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       "      <td>9.349119e+06</td>\n",
       "      <td>1403.516777</td>\n",
       "      <td>1.910194e+07</td>\n",
       "      <td>1.857511e+06</td>\n",
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       "      <td>1.275264e+06</td>\n",
       "      <td>9765.282442</td>\n",
       "      <td>477.250599</td>\n",
       "      <td>1.194313e+06</td>\n",
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       "   (ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater and terrestrial acidification)  \\\n",
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       "0                                         822.244175                          \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater ecotoxicity)  \\\n",
       "0                                       8.295610e+07                       \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, resources, land use)  \\\n",
       "0                                     4.091636e+06   \n",
       "\n",
       "   (ReCiPe Endpoint (H,A), resources, fossil depletion)  \\\n",
       "0                                       54623.717844      \n",
       "\n",
       "   (ReCiPe Endpoint (H,A), resources, metal depletion)  \\\n",
       "0                                       51499.121812     \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, resources, mineral, fossils and renewables)  \\\n",
       "0                                         142.381654                        \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, ozone layer depletion)  \\\n",
       "0                                           0.106451                 \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, carcinogenic effects)  \\\n",
       "0                                           0.785612                \n",
       "\n",
       "                                              ...                                              \\\n",
       "0                                             ...                                               \n",
       "\n",
       "   (ecological scarcity 2013, water resources, total)  \\\n",
       "0                                       9.349119e+06    \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, respiratory effects, inorganics)  \\\n",
       "0                                        1403.516777                           \n",
       "\n",
       "   (cumulative energy demand, fossil, non-renewable energy resources, fossil)  \\\n",
       "0                                       1.910194e+07                            \n",
       "\n",
       "   (cumulative energy demand, nuclear, non-renewable energy resources, nuclear)  \\\n",
       "0                                       1.857511e+06                              \n",
       "\n",
       "   (cumulative energy demand, primary forest, non-renewable energy resources, primary forest)  \\\n",
       "0                                        1025.927857                                            \n",
       "\n",
       "   (cumulative energy demand, biomass, renewable energy resources, biomass)  \\\n",
       "0                                       1.275264e+06                          \n",
       "\n",
       "   (cumulative energy demand, geothermal, renewable energy resources, geothermal, converted)  \\\n",
       "0                                        9765.282442                                           \n",
       "\n",
       "   (cumulative energy demand, solar, renewable energy resources, solar, converted)  \\\n",
       "0                                         477.250599                                 \n",
       "\n",
       "   (cumulative energy demand, water, renewable energy resources, potential (in barrage water), converted)  \\\n",
       "0                                       1.194313e+06                                                        \n",
       "\n",
       "   (cumulative energy demand, wind, renewable energy resources, kinetic (in wind), converted)  \n",
       "0                                      287204.055616                                           \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Impacts per life cycle\n",
    "df_impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0.038923</td>\n",
       "      <td>0.00052</td>\n",
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       "      <td>1.012657e-09</td>\n",
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       "      <td>0.000013</td>\n",
       "      <td>0.181716</td>\n",
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       "      <td>0.00001</td>\n",
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       "      <td>0.000093</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.011361</td>\n",
       "      <td>0.002732</td>\n",
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       "   (ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater and terrestrial acidification)  \\\n",
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       "\n",
       "   (IPCC 2013, climate change, GWP 100a)  \\\n",
       "0                               0.015354   \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater eutrophication)  \\\n",
       "0                                           0.000008                          \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, ecosystem quality, freshwater ecotoxicity)  \\\n",
       "0                                           0.789156                       \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, resources, land use)  \\\n",
       "0                                         0.038923   \n",
       "\n",
       "   (ReCiPe Endpoint (H,A), resources, fossil depletion)  \\\n",
       "0                                            0.00052      \n",
       "\n",
       "   (ReCiPe Endpoint (H,A), resources, metal depletion)  \\\n",
       "0                                            0.00049     \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, resources, mineral, fossils and renewables)  \\\n",
       "0                                           0.000001                        \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, ozone layer depletion)  \\\n",
       "0                                       1.012657e-09                 \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, carcinogenic effects)  \\\n",
       "0                                       7.473481e-09                \n",
       "\n",
       "                                              ...                                              \\\n",
       "0                                             ...                                               \n",
       "\n",
       "   (ecological scarcity 2013, water resources, total)  \\\n",
       "0                                           0.088938    \n",
       "\n",
       "   (ILCD 1.0.8 2016 midpoint, human health, respiratory effects, inorganics)  \\\n",
       "0                                           0.000013                           \n",
       "\n",
       "   (cumulative energy demand, fossil, non-renewable energy resources, fossil)  \\\n",
       "0                                           0.181716                            \n",
       "\n",
       "   (cumulative energy demand, nuclear, non-renewable energy resources, nuclear)  \\\n",
       "0                                            0.01767                              \n",
       "\n",
       "   (cumulative energy demand, primary forest, non-renewable energy resources, primary forest)  \\\n",
       "0                                            0.00001                                            \n",
       "\n",
       "   (cumulative energy demand, biomass, renewable energy resources, biomass)  \\\n",
       "0                                           0.012132                          \n",
       "\n",
       "   (cumulative energy demand, geothermal, renewable energy resources, geothermal, converted)  \\\n",
       "0                                           0.000093                                           \n",
       "\n",
       "   (cumulative energy demand, solar, renewable energy resources, solar, converted)  \\\n",
       "0                                           0.000005                                 \n",
       "\n",
       "   (cumulative energy demand, water, renewable energy resources, potential (in barrage water), converted)  \\\n",
       "0                                           0.011361                                                        \n",
       "\n",
       "   (cumulative energy demand, wind, renewable energy resources, kinetic (in wind), converted)  \n",
       "0                                           0.002732                                           \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Impacts per kWh\n",
    "df_impact_kWh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "There are many ways to visualize such results.\n",
    "Here, we take the per kWh LCA results calculated for all currently operating wind turbines in Denmark.\n",
    "Then we use Matplotlib's map library Basemap to plot them. Another very good library for static maps is Geopandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ros\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2728: DtypeWarning: Columns (10,14,20,40,74) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     52.849981\n",
       "1    985.505588\n",
       "2     34.847178\n",
       "3     58.760033\n",
       "4     27.457860\n",
       "Name: Carbon footprint per kWh, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all=pd.read_csv(\"Complete_dataframe.csv\")\n",
    "df_all[\"Carbon footprint per kWh\"]=df_all[\"('IPCC 2013', 'climate change', 'GWP 100a')\"]/df_all.loc[:,\"1977\":\"2050\"].sum(axis=1)*1000\n",
    "df_all[\"Carbon footprint per kWh\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ros\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\mpl_toolkits\\basemap\\__init__.py:1708: MatplotlibDeprecationWarning: The axesPatch function was deprecated in version 2.1. Use Axes.patch instead.\n",
      "  limb = ax.axesPatch\n",
      "C:\\Users\\ros\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\mpl_toolkits\\basemap\\__init__.py:1711: MatplotlibDeprecationWarning: The axesPatch function was deprecated in version 2.1. Use Axes.patch instead.\n",
      "  if limb is not ax.axesPatch:\n"
     ]
    },
    {
     "data": {
      "image/png": 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HDw8PvvjiiwzbFyGEECKvmzJlCtOnTwfgxRdfZNOmTdja2qarrtu3b/P777/z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8c14RqSE2d2o2wkLiDOvnIqKFZ3NxSLdyZuGjfENFER77nJe6wMY/ePBSA5PZlt57Zx7vo5B4EX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69+8fUbQDtI9qzyf4hbuCELZRCFEci4gPdyHCTdIRq7OBT7YL2IsIrViNWHW3ECK2AJyykx36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+vWrWPMmDFs2bKF6tVdoafPPfcc8+bNo3379jRv3pw333yTBg0KV+PkdvuqWLGi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\n/X51TVb1wTZ5G83l5rSUW2pGvhMBjqhHWGpfygC990q8ValK5bmV2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IygTiXsZOF7CeQm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\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AViu3z+zl/JGfUVD4yPARv2t/p1hcMUx3TVhkC3flu+zK2MW7t9/Nd269Nb3Zpt3GZHEy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BsLjfle/X3ODpIF1pnWMdM6EyNGgglmlW4VJcQSLssesRzhLekt9+cpQwW5AmG6sCyP28nU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WJ4ggPpQ/TJVnhdTQMQUwoRbT0UBboS0LXRZiNBopWbJkSuhCdvntt9/o2bMnx44dw9PT\nPpZ+9OjRBAYGMnr06Az2zuVFoygKnp6e+P3ux7NCz1BkBeWegvJIQawqUlesy8+uPzvd947tDt2l\n7txSbjkNQ/SX/DnqcdTueYiUI2llbsVTHIsmzdTNpLu2e8rnJ7YnVJGrpNyTikVB0Kl5GTpZxx7D\nHkqKJR3auSHdoLulO3e5ixwlkxiciNd7Xuwatis3t+EfQE4M99xQmVxeKo4dO8aCBQuyte3Zs2d5\n7bXXst22oih8G/ItQlPHZy+SJM+yC2qH65r06weUgmLfAWYz0XI04YRTkYp23najYmSmbSab2YwP\nPmjRcl45r07HyuCuuKshMgCvAYtRP48j1WiHjI12AAVs2DjEoVSjPV0PISGxQL+AM9IZGuka8T7v\nk0BCyv52iE6WpcGNzMOVnpeYmBgePXpEnjx5sFgsuLm5ERAQQOXKlalcuTKff/45reu3Zp77PGIb\nx5LQKkENXwoFf/z/tPP6K3ARXajgYq+YlJUs4bT90xj7w1ioAOwC9/3uqiqNGyBDLLE0l5pzVjyb\n5fElRaJvTF/CrKkFvsJt4SQqiXzj8022riFCjqBVQiueKc8AdTA5xjCGIfohTrc/dPuQo9EOIMDJ\nyJOM/2U83/b41mG1RbZQy1ILm8aWssyY9BOqhBJKqGMWWHL9BCMQjvqMeYF7EXWgFxkZSd68ebNt\ntH9p/ZKxprFYB1txmexCM6EZq22rKa0tnXp9hw7Rp0/21JtyeTEIgkDbtm3ZsnQL2vFaBFFAKCxA\n0qTdCU4QZAyioliRj7UfMzpmNKel0yTqEkl0S0y9b5JnPpMnHSUIEAMcBrEBQgCFKORguBeiEC00\nLeyWdZI72Z+rPrVTt2JlsHkwu113O1zTTNtM7qI6SsQ8Im5r3IhpHcOYpmPYW2Vvjr6fXP5e/n3z\nwbnk8gcwGo0OHtmMOHfuHNWqVcvWthGWCOrcqsO10GtomzuOh11wUSUfIdXATkOX1VBp3jEq2irS\nwdaBUrZS9LX2VduWIyhvK8985nOPe1zkIueUc2qSa5IhkSAmIEoigiyoBlgnoDHqwCA9zpJRISXE\nJs4Wp/YMznoHESrqKzLebTyB2kDHOM60FV7TfnZyzCGCc0PsjyIIAv7+/mi12pRchiFDhhAeHk6v\nXr146623qGGqQROhCZplGjgMHAf/mv6MEke9VEl627dvZ2yjsepA7wNw/dIV0VuEOFJnS2zwhCeE\nK1kntu027eaK9YrdMgWFY9ZjGOWsi8HIskyjhEYpRjuoM0tTLVMpZi7GCesJh3183Xxx1WUQUqbA\nlftXnK4aZx2HTbQ5XZfpAFdANdxl1Jmzp5D3qarsVLRoUe7fv4/F4pgLkp4W1haMVkZj/dqKppgG\nbTstd3V3aZnYMuW7UhSFS5cuUbVq1Szby+XF8s0332Baa0IKcwz1k5C4yEXWyeuomViTXfIunohP\nVKM9fb2N5F+baoiP1I90aE8QBMbqxlKU1KJ0+clPP20//AT7TjxSicz0/nyYQeb2U8V+UCAUE8AV\nYsXYjBvL5R9JruGey0uF1WrFZsvgZZ0GWZa5dOkSpUplXSURoM+9Phw/dRwlQUEsb/9YadDQQGiA\nr9k3wyfudkl46ma06+h3Kbv4xvoNXaWuKclxKTh5OcjINJOb0VHpyNCGQxnZbqRzj7eS7je9ka3B\n3vhOd9xkD3sRoUjqiyY7EXQSIIOf7MdUYSojdCOysdOLw9XVlZ49e1K5cmW+/PJLNmo2Mlc3l+Bi\nwbwZ+CbbNdvprumedUP/EQ7IB3hz85sAiJVF3EPc0dZOGnTagLTiMjaIVZy/4BVFISwsjISEBCLk\nCMd7FUhUElMS7zJjnXUdFsWJ0auATbDRWXasfFszoCZl8jhRKUq633Ra5yE62+XtWRvozpCBtBFe\nCpyMPgnAnTt3cHFxSUlszIin8lP2KHvUAdIscPnCBUEUQACz3sx083RAnT1ycXHJVuXmXF4s/v7+\ndJrSCdNIE4qUcQcniZLar2c0UZfUp/bW9WaT+yYa6xo73ayhtiHbXbYzVjOW4ZrhbDVsZYjO0bnR\nSmiVaX+bl7xOl5cQSth9tv5gBR8oXbK00+1z+eeSa7jn8lLRqFEjtm/fnuV2oigSHBzM9OnTs9w2\nyhbFNcs1WAqG7gaER4LqkbNBfik/fYQ+fKr5VNWHdpanKZNh1dSZykw1oTUrwzhp39Ka0nxn+I73\nde+zn/2O2yW1877wPh6KR4YKM2hxWsipMIX5Ov5rOsR2YI95D7M1s3mFV7JluDcTmnFZf5nzhvP0\n1fXNeoc/ialTpzJnzhwiIyIZrBnMFu0WVmtXU1V8ebyaT5Wn9Hrci+h10YilRFw+dUEsnO51kO7e\ncFaA6Mcff6R27dp06NCBVq1aUTO6ptNY+IJiwWzFyJ+QHT3qQMog0ybYOGA7YH+agsDSzkupUaiG\nOuMkoYY+xYFeq6dpuabOWqSkUjLz+zajdXGkzp4lYbPamDBhAkFBQYwdO5YSJUo43TWZ//E/tZbB\nSRDLiYhFk777pBm00/JpAJ48eZJS5Od5MCpGnsnPMhx05ZI5699eTzGvYljftCIvkJFOOenARVRn\nR7LDwxkyTHedTmFRjbXZY9vD26a3GWIewknpZMpm3oI3Q/RDGK0fTYAY4LSpufokMfh0tQaQVFnY\n+QbnsrbDtcMpiRr7Lt+UMX9uJvD7QL50+TLD68/ln0mu4Z7LS8Xw4cP57rvvMpTDS8uMGTNYuXIl\nJ05kYEwkIQgC0h0JDoOuk059qT8EImGMeQxTtFPQCBrK6cpBLPYv/WSDPYMOX1Ik5G3ZKFakqJ6W\nwbrBAEySJnGc4/btJuvAy7DAtoB4nBeoQQB0oJf1qQmmCrgqrkRaIgkllOMcp4+xD6PiR7FFs4X8\nQv4sjaBl+mV4CtkrYpMVMXIMH1o/pIelB2elrGOv01K0aFHee+89XnvtNT766KNshTX81xhrG0vE\n2gg0TTS4/+KOpoR9/Jb8UEa+J6f8TYtT3Gly6cOHD4mKimL//v106tSJ3q/3psmdJnZSpYXFwoz2\nGJ2tEKTeut7OV6R5Rh4pjxxWF/QqyJa3tvBliy8p51qOvOQlME8gPev2ZESQ85md7/XfpyaaOuMp\nagJqmtkpjVmD7pb99yAgUOBKAXbu3Mn58+f56KOPsrzW0vGl1bZPgqaKY+zcG9o3ADWx2KHAWjaI\nUqJoZW1Fflt+8kp5yW/LT3VrdX6TfkNRFGRZVv9VZGTljxdD+68iCAKnNp9i6cClVNhWAWN7I/Kj\ndN+XjOroyCzpX0kNn5xgnsA71ncIUULYLG+mj6UP8605qyFxXjyvDlKT+vNkh1AJsQRlhbIO27c0\ntqSapRrXuQ6A4ZKBwNqBHKt6LGUwkcu/h9zk1Fz+c8iyzLNnz/DzcwzwDg4O5qOPPkrRtM2M/Pnz\nM3bsWCZMmMC2bdsy3M5P44d2hRZdZx2CR2rvHagNpJ1nu5TPk/JPotudbkTHR4MPqR6azJI444AL\nQEsyfloVMGBgjm4O+QTVO3dBueC4XZp4cxu2VGnHDF44N91uEi6Fs0HeQB7yMMkyKXWgkTQIOCmd\nZLttu6rVnvwSST9YABrLjdEKL6a72WzdzBBpSErb+6X9VBQqssNlR7bbGDlyJK1ataJHjx507tyZ\nunXrvpBzSyYqKoozZ85QtGhRSpQokeMqmn82P/MzbALdQJ2qPvQYkECJUTAvNmMNsYIA+kF6XIa6\nsMx9mdN2PvjgAzw9PenQoQMXL16kWLFijHpjFG3faou1q5UiAUUY6D6QfJrseY2raKvgo/Xhme2Z\n/Ypkw0iG1prWGe7fo1YPutXoxpP4J3i7emPQpS+YkIqn1pOxlrFMNU+FB6TWVpCAR8Au+LT9p4SV\nC+Ox7TEJ0QnEWeJ4bHiMxWwhTorDV+NLLa9a1LlcB30tfbZ17petXwYFgOOgaZnu3lCgkaYRoN5H\nz+Nx72jryAFSZyYkJE5zmsZyY7uZNAEBHTqKU5yR4kje1ryd42P9G1AUhQMHDhAbG4ter6dx48bo\n9fqsdwR8fHwICgqiX79+vPfTe/x05CfiiEMbrMVD8MBoNSILsiqX66wvlZMKpKEOOkPkkJQ6HQDP\neMaPth/pp+2Hi+DipAFHNBoNhZRCKcmmyS7YO9zhqHKUukJqfzbINIgwwuz2N+U3oXmoIa+Q80Fh\nLn8/uR73XP6ThIWFcenSJYflGo2GDz/8kDlz5mSrHZvN5iDPlh5FUYjdEkvtXrXxFr1xFVwpqy/L\nlPxTSCSRXtZeVLVWZYx2DNWLVFf1gNN7aDJI4HT/3R164DShNXkbAJ84Hzs93kc4eiXTIyI6DYcB\n8FTUay6lKcX/6f6PfbZ99uE8yQMOEb43f48ffuo5Jnt/kn+TYtpXGVZleT7ZZYhtiMN3FaaEcdhy\nONttCIJAhQoV/lAYQloURWHv3r2MGTOGGjVqEBgYmBI64enpSbVq1Zg/f3628iv+bO4r95GQwBM1\npEQLiruC5WcLiR0S0Rq0CCcEOAS2ozY8gz0RwjP2IPfv35/ExETCwsLo06cP+/btw83oRsjrIZwb\nfA7rEytzrHPobO3MQutCJMW5rn8yJ1xPUENfIzX8IElyERm60hVXTebx3qIokt8rf6ZGezJD3IZw\nyf0SFY9VVGVUF6Am6m6GUm6luHb6GldnXOXwzMMcP3mcy9bLRLlGgQ9Uy1eN/PnyE+kRyZIrS/Av\n6p/l8UC9V36K+gk+RjXckz3uMupA/RzUt9ZnoGkgZ86coWxZRw9qZtxQbnCEI9k7FxQsWLjKVUbI\nI9gibcnRsf4tHDhwgEaNGhEcHMx7771H9erVOXr0aLb3z5cvH40bN6bQ5UKUXlca6zAr3n29mfFo\nBnvd9xJgCXCsnQEpM5bJcecnpBM84IFD+/e5z03lZrbPJ5roVDWvNBgxcltRFZYOHDiAl5cXoSdD\nURQlJU5fURQsKy1Elct5VfBc/hnk6rjn8p/l0qVLPHr0iAYNGthViEtMTKR48eJs376d0qXtE3Ns\nNhvLly/H09OTU6dOsXnzZtauXUvDhg0zPM7du3epXr064eHhXDdfJ1FO5BWXV7imXON16XWnyXoZ\nkkZ5pg1t2PvbXowNjZkPsRV1v16WXnzh8wUAVa1Vecxjx+2S4+nTLktrjCctCxFDqKpNjfluaGyY\nMs1qhwzBBONucGezshkjxpRr0KOnvdieGboZ6MXsebeyIsIaQT1bPafrfPElzDXM6TpnKIpCvXr1\nWLp06R/yuO/fv59Ro0aRkJBAcHAwTZo0oWbNmikevfj4eM6ePcv06dN5+PAhM2bMoG3btn+bek20\nEk1lW2Uiv4hE3CqCDuQzMgTB2MljmVJxCvfke9xV7lJNqcbChQuZMGECBQsWxNvbm4IFCzJz5ky7\nGa0hQ4ZQtmxZxo0bl7IsISGBj8Z+xNKjS9Fv0qfI1vniy1HNUdzFzLXyJVlig7SB+bb56NEzSTuJ\nV3WZz5I9LyariTG/jOFkxEkskoWiPkWJvRfLhYg0M1cuQEOgBg4hbonvJVKsczGuDb+WZfGus9JZ\nqt6oCp8BG8FjlQdCwaS8mORHtp56vDzN87Bq3qocydJuk7fRRsq6ToUzalCDE7rMQwP/jURHR7Nk\nyRJ2797NoUOHiI9PDRPs0qULVapUoXLlygQFBWWYCHz58mXq16/P06eqOkvv3r0JDQ3lk08+oX//\n/pyRzxAsBasbp5mVFBAI0YVQRVuFK9IVOlo6EkOMXduFKMROl534Cr7Zuh5JkWhha8FlLtstL0AB\nQrWh5BfyYzKZyJ8/qUiUN+ACLp+6IN+VsWyxIO4SkfI5DqLXrVvHzp07WbJkSbbOJZcXQ24Bplxy\nSeLJkyccO3aMhg0b4uHhkbJ8/Pjx3Lt3j6+//tpu+7i4OAIC1KSg4cOHM27cuCxjTHfv3s3kyZMJ\nCQmxW17TWpP73M/+yRqBr6GirSKhk0IRRZGSN0piKmLKel8ZtHFabudVvS31rPWIIMJxOycFmNxl\nd4yKEUVQ8FP82KTbREmNfQGPFsYWXMBJ+A0QoA3gKU9xxRUDBopTnPpifYaKQzMNj5EVmX3SPu4q\nd2mpbanGyWfBJeslmtuaO13nggvXXZ0MLjJg2rRp7Nq1iwMHDjyXaselS5cYM2YMZ8+eZfz48XTt\n2jXTEuKKorBz504mTJhA/vz5mTlzJjVq1MjxcV8EbWxt2HZ5GywEmgONoLx7ec5oz6AXHAdZT58+\nJTIykmfPnrFq1SoiIiJYt24dANOnT2fFihV06dKF/v37U7Vq1ZRBSV1zXY52PoquoA6XL1LDAOpT\nn3W6dS/8umRZ5j73yUMeXMTshR2k319SJLYe38qIZSOw2tINuv2B/tglIkrnJRJHJmLYbWB98fUE\newRneoxG8Y3Yr98P40HcJuI+zskApjTIkoy5tRnTA1OOQq22SlsJljM/h4zwxJNY3X87kdVsNnPo\n0CE2bdrkMPPq4+ND27Zt6dGjB0FBQeh09vkMJ06c4NSpU+zcuZMpU6YgSRIDBgzA19c35XlYaV3J\np7ZPkZAIIIBlhmWUE8ultPGm6U1+U35L+axBQ0exI98YslfnIJnN0mYmy5NTPPg++NBL7MXHmo9T\ntvnkk0+Y98s85AMy3ACGo4aBHYCAwgFEutlXrrZYLBgMBtq0aZNbqfcvJtdwzyWXNFgsFvbt20f5\n8uUpUqQIoCbVlS9fnp9++snO23r9+nX69u1LdHQ0t287KeqSjg3mDfT5tg+2czZ8p/syxTCF7obu\n7I/dTw+XHpnLzaVFAS6C27dufNL9E/q16AdAZ1NnjohHsm4naZr9bl415rGrtataSCn9McChrXzk\nY7d2d6bxjlMtU1kkLVJj49MgaARVTz4JAwbGiePor+mf6enel+/T19yXq1zFgoUCFKCnpicf6j/M\n/DJlmSLmIk7X9RJ68YXLF5nun8zKlSuZPXs2hw4dwt8/eyEOkiKxXlrPpgebiPg0gmu/XGPEByN4\n++23cXHJvpFos9lYtWoVn3/+OU2bNmXOnDlO8zH+TJ4pz+gp9eSccg4rVopTnAWaBQ7KOo8ePSJf\nvnx2swNWq5X69etTo0YNSpQowaxZswgNDaVatWro9Xrq1q3Lrl270Ov1+Fh9iImJgfrg8r4Lup6q\nIeSNNxd1Fx3OS1GU556JGGEdwToldTBQSCjEUc3RTAdTGTH2h7Gs2LvCcYU38A4p8czybRnjx0bk\nHjJ0g6l+U/nE75MM2zUrZnwSfTDpTDAYdE91uLzp5N6pDJYNFjyue/BkxZMcnfuv0q+0k9tlvaET\ndOiw6F6uZO3o6GhCQkIYP348hQoVombNmhw7doxr167RsWNH3nzzTUqUKEFCQgKvvPKKw/6SJJE3\nb15OnDiR6uHOBKNi5FPLp5yVzyIiUl9TnzG6MWiEjAdnkiIxT5rHD9YfkAWZIcIQymvLE0AAi1hE\nopJIH7EPVcQqdvstXryYX7b/wqFfDqnhcTYgFjR+Gq4ZrhGoCXQ41h95BnN5fnJiuOfGuOfyn0ev\n1xMUFMSDBw84eVKV3vL39+fHH3+kZ8+eTJ06FS8vL+bNm8eAAQPo0qUL165dy7LdLeYtvGF6A+MV\nI2IpESNGRppHMjJ2JEMiclBcSAL2AmvBpakLQY2CuP7sOqcenWK2ZrZjh+6sgJLFvhLpp5pPKUGq\nJJ077tQSajkdAGiSfjLjY93H9BZ7U4IS5CMf5SmPu8bdzmgHMGMmVA512sYj+RHLbMvYK+1llGUU\nYYRhQTUSHvCA5dJyrkuZe8xFUeQ1wTFsQIOGz/WfZ7pvMtu3b2fKlCmEhoZm22i3KlaaxTajZ2hP\n1r2+jhNXTuD3ox/dhnbLkdEOoNVq6devHydPnkQURQYNGpQtlaMXiY/gQ4g2hLPas5zSnuKI7oid\n0W40Ghk9ejQFCxbk3r17dvvqdDqaNm3KggULGD16NA8ePKBq1ap88cUXBAYGYrFYGDNmjHqtaFVj\ndwOYJptIaJ+Ada3V6f125MgRvL29efbsmcO6rPjF+otqtKepg3BPuUctW/ZKuT9+bB9W1rBCQ1z0\nqX9XxaZge2xDcVVABPmRjHGKkYQ+CcgtZOgMecQ8tHdvn+WxrHKSF/8mDmo+gJr/4gfWUCsjOzoW\n68kK8Q+81l9Gx5yvry+9evXi4sWLNG/enNWrVxMUFMTOnTspWrQoI0aM4NVXX6V58+Y0b96cY8eO\n2e2v0Who0KABP/zwQ7aO5yq4Mt0wnVDXUEJcQ+gl9GJg1ECCHgTR4VEH1savddinpbkl063TiSSS\ne8o9xsnj6GzpTGNLYwrIBZipnelgtIM6u+Dv7U+CIYF2tCOfNh/BfsE8c33m1GgHuHr1Kvv27cNq\nzUGIZy5/Kbke91z+kyiKwrNnz/Dy8rKbZr527RpxcXFUr14dUKujjhs3jgsXLtCkSRPKly/PBx98\nkK2p6TwxedQS1e3A9R1XtC2chIVkZ/x8C1Umsiwgghgv4r7HHbPZTFGvonSt1ZUlBZaoMevpiyZB\nSrGcNe5raOTaKGVxnBLHcnk5EUoEXcWuVKYyLaQW3OCG3eEb0IC1OseXhTOsipUEErgl31LjaJ1c\nXy1q8YvuF7tlA60D2ansVL0+ybH2TnBBNZZKUILV+tXk1zj3YM0wzWCRsggJiTKUYa1hLb5i1vGh\nJ0+epGvXrvz66685imufZZ7FyFUjYTcIuwTch7ojuAlUrleZ0KrOByrZwWQy0bBhQyZNmkS3bt2e\nu50Xyc2bN3n99dcpUKAA586dIyoqysFrffbsWTWu98wZ1qxZk3Luo0aNYsuWLdy6dYv169dzoPUB\nZjJT3ckI7AbehqE7hzK19FS7NlesWMGwYcNo0qQJS9ctZYltCdel61TSVmKg60DcBPsKN2bZzCLj\nIm5IN1hvWI8iOnk3KXBXf9dxeRqOHj1KUFAQpUuXpnfv3nTp0gVPT09avt2S64+vI4gC5ktm1ajV\ngfY1Lbb9NuiM6n33VY2xNzzeYIX/iiy/X88YT+Kt8VAW3L92R9QnfbdaoJLanvxUPBstKAAAIABJ\nREFUJqF2Aj9G/kh3z5wVBduqbCXY9nyhMlpJi9Xl5TbYbt68yYgRIzh//jzDhw+nU6dOeHt7Y7Va\nWbVqFTNmzKB27dpMmTKFChUqAHD79m3q1avH7Nmzad3aUfXIZDOx7fo2YiwxtCvZjiXiEnbZdhGn\nxBFljcJoMqb0iV6iFx97fUwfjz6AmtDa3pLxgFCDhsOGw0513xMSEqhatSq7du2iUiXHOgxpWb9+\nPZMnTyYsTM0T+vDDDxk3bhyzZ88mMDCQbt26pVSizuXFkxsqk8tLiaIoDBkyhD179nD3rvqyrlu3\nLmvWrLHzrJ45cwZXV9ccqzWkR5OgQdbKUBzct7sjFnHi6XISU26HDXiGKg+ZlgTgV/W/RdyLsL3d\ndppbm3Nff9++PStghLf0bzHNa1qW53xYPswEaQJ3uIMOHWUow0LtwhzLgi20LWSydbJj0REFBomD\nmKSdlLLogPUA3ZXudsmvTgtRpUNE5KT+ZIbGe065fv06rVq1YtGiRQQH58ywqXqwKmdHnYVL4NbV\nDU1RdWDnms+V8J7hf+i8Tpw4Qbdu3ViwYAEdO3b826ept2zZQvv27dmwYQMDBw5k6NChBAUFUadO\nHQcJvcjISAoXTtWBVhSFadOmsXLlSmJjY3nw4AFtrG3Ywx4sWNCjp/DIwhS/WJyGDRuSJ08e8uTJ\nw927d/nyyy/ZunUrQ0cO5X7wfZ52SS3RXklTidGuo5ljmsND6SEaNEQoEdgUmyrjaMD5c6bADd0N\nDIJzhZmoqCi6du1KeHg4iqJQtGhRIiIiMJvNlC5dmgQlgYcPH6LYFCzxFhRPBamGBOOAQuAr+tLQ\npSF9vfrS3r19tv52nY2d+fmzn9GEa3Cb6wb3UDXjK5Ai+WpdbcW0z4S4VsSoNTrNO8iIEEJoK7V1\nHBw7U65Kiww6WYfF9eUKlcmIvXv3MnfuXPbs2UPr1q3p3bs39evXx2QysXjxYr755htef/11Pv30\nUwIDAzl27Bht27Zl48aNVK2aOnt17uE53t/zPjef3URGxsPNA2MFI1LZNJ2ghFqpOOnvU1VXlRB/\nNWdqlGUUP0o/ZnquQWIQKwwrnK779ttvuXDhAmvWrMlw/+RE1kaNGnH79m2uXbtGs2bNOH78OC1b\ntuThw4c8evSIU6dO/e3904skkcSUOgbJs8ceeCBkO8b1xZEbKpPLS8m3337L8ePHmT9/PuvXr+eb\nb75h7969FChQgBIlSvDmm29y4MABqlatSkSEk8TNHLBX3ousk1X5tjgQCmXwvCVpT0PSv4nq9sio\nXvZowMvJfq5Jv8CdhDvMvzY/1WhP+6sDPKCCa4VsnXcpoRR9hb6MF8cToglho26jU6NdURSOy8eZ\nLc1mv7zfYQo9TAlLvabkVUmzATXlmuyz7WOmZSZHpCNMlCc6Sl86I51RISPT09IzW9eVlsePH/PF\nF1/w1Vdf8d1337FkyRJ++OEHOnfuzKRJk3JstAM8+PEBYqSIxxCPFKMdQDJJfzi8oGbNmnz//feM\nHTuW4OBgbt269Yfa+6MYDKlG7ooVK4iPj+eDDz4gb968dO3a1U7SMq3RDurLZ+zYsVy5coXLl1XF\nixBdCNHaaC5rLhOtjebIhCO0bNmS2NhYNmzYwDfffMO2bdvYtWsXNWvWJLpCNPdv2Cd1n5fO8078\nOxyzHeO2cpsbyg0130IgY6M9CW0GBRAOHjzIq6++yo0bNxg1ahRnz55FkiTKly9PjRo1qF27NsXz\nF6dH+x6sW7WORw8fsXblWnyv+dKxVEfG+Y7jVvFb/FLoFzp4dMi2QfOl8UuEhQIuw1zUgXc+VKM9\nzSSfNdQK7dVn4Ffp12y1m4wrrvZv9rQD5vTL0hbwkchS6edlomnTpmzcuJFr165Rq1YtPvroI6pX\nr87cuXPp0qULp0+fJiAggJo1a/Lee+9RpEgRhgwZwvz59sWUPjnwCdefXUdOehHEJ8YjXZDAnGYj\nDWpfDmqYl3CP/db9yIpMBbLu2+OUuAzXBQQEcPv2bZ49e4aiKNy4kTrjmpCQwPbt22nbti1BQUGs\nXLmS/fv34+HhwYULF/j555+ZO3cu69evx2w289tvv2V4nH8T5zlPPaUeflY/vGQvPPHES/Eij5KH\nMpRhAQsA9T3o7OfvJtfjnst/gnPnzvHaa68RFxeHh4cHJUuWpFSpUvj5+bF582Y6dOjA1q1biYmJ\nUWUbr1+nZs2adkZKTuhq68oGZQMcA+E9AY+9HhlvrIDBaIAjYN5jVvWz8wBFgEakdtjp9iEh6d/L\n0M63HVvKbsnU6K0t1WaT66YMT2O2NJvV8mrucx8DBspQhpXalfgL9nHeVsXKAGkAR5WjxBOPK65U\noxrfa7/HVVBHE/0s/dgp7XR6HC+8sGLFiBE33DBitO/skqX00nvdrY7fRXaUYg4dOsSzZ8+oX78+\noijSoUMHSpUqRbFixTAajRiNRkwmE7Vr1+a9995z2kZiYiLt27fHZDIxYsQI2rdvbxcuNWThEBbO\nWIh7X3vDpkj+IswsNpOvv/6aunXrMnJkzmOSk7FYLHz77bcsXLiQ48ePpyRS/9WEhYXRrl07zp61\nr0j79OlTGjZsyI4dOyhXrlwGe2eP06dPM2zYMA4dOsTevXtp1Cg1xKv6yeqcbnMaTR0Nhr4GNNUc\nw9YUSVGfDwMIeYUMve0l5ZLsd9mfukhR2L9/P4sXL+bIkSP4+fnRsmVLPvvsM0ANpatXrx5Dhw5l\n4sSJ9s0lvfsqVKjAzp07KV++fI6vW1EUBn45kF3HdiF/I6fkeJgMphRjW0lQiK8QD9cBH5jHPN7V\nvZvtY0QRRXGKE6/EpxrmyaQ14pND1pIfTQ0ECUHs1Dl/rl92FEXh+PHjLFmyhA0bNlC7dm169+5N\nrVq1mDNnDmvWrMFgMPD111/TqlUrAB4lPCJoXRBPjE4SjGsDZdJ8tqD2iUl1C/ToKSOWYbHLYupZ\nnUvgJhNiCHFILE/m4sWLTJgwgUOHDjF//nx69+5NuXLl+O6772jevDn169enZcuWvP/++2i1WkJC\nQujVqxf/+9//7GYOFi1axOHDh9mwYUOOvrd/GiZMVFIqEW4LT6lF4tB/JM8Kp+16BDUsyQsvgghi\nCUvw5MVUAofcUJlcXkJ69OhB5cqVmTRpEo8fP8bLK9WNvWPHDlasWEH58uWpXLky7dq1Izw8HDc3\nNwePYXZJMdy/B812DW4LM4n9U+Cq5io3799k7M9jOaE9AZFAX1TjPSOSw1Ak0CXqsHpYMw+7UeCi\n7iLegrfDquvydTpIHdSY/DQ0FZqySmtfHGmuNJfp8vQUD1Eyg8XBTNBMAOCwdJguli6ZnEwWaHCM\ndXcSVuSHH+ddz2fYTGxsLNWrV6d8+fKcOHECm81Gr169WLx4cY6mdH/44QeWLFlCv379mDdvHo8f\nP2bPnj0EBqoJXPfu36NIiSIIJQQ0+TSI+UW4AaaT9lKdDe805DvP7yguFs/2sdMzc+ZMdu/ezb59\n+9Bq//ri1rIsU758eQYNGsTgwYNTlicb7j/88AMNGjRw2O/IkSN8uuhTfj/yO62+bcX3Tb93Ksn4\nyy+/0LFjR0CVkkxOZE2m9bPWhD4JhTXAEhBcBdWYSVCNWuIAE+AO6MDtFzc0FR0TuLWSlnCXcHSC\njtjYWNauXcuSJUvQaDS0a9eO/fv3U6RIERYsWJASw79v3z5GjRrF48ePKVq0qMPgZfXq1ezbt4+S\nJUs6nHd2eDvubZZOXYrWpMXwgQENGjoYOnDdcJ0znAHA+qsV0yoTJKVOmEQTBk3OHAxv8zZLpaVq\nRc+0z5kzr3sSBSjATs1OKomZx0I/L5cvX+a7774jOjqa1q1b07NnzmfT/ikkJiayceNGlixZwuXL\nl5kxYwZ16tRh06ZNvPvuuymD/lhzLM3WNuNewj3HRhoAxZL+L6PmgLjgUGyvvqY+/XX9GSANyPB8\n6gn12OCSuUHds2dP+vTpw5MnT3j33XeZMWMG69ato1KlSowcOZKiRYsC0KFDB65cucKECRNwc3Oj\ncuXKBAYGEhsbS6VKlTh//nyKZPK/kUXKIgYrg1XDPH0hxLRkEeYaRBA7eXGD3NxQmVxeKuLi4ggN\nDaVHjx40atSI/PnzU7ZsWUaNGgVAy5Yt+fHHH5kwYQIdOnRAFEX8/f15+PDhcx9zkDgIL7wgAjQF\ns05kdde4U7FwRQb1GAStgBFAAM5H+2BX3AUNWF2zlzAWYglxunylstLBaAcIV8IdQj0OygcdjHaA\n0/LplP/X09RTq6U6Q0F9CcWRcSx7ek9gcpXMdMzVzM2gAZXt27dTq1Yt9u3bR1RUFPHx8SxZsiRb\nRvvjx49Zvnw5EydOZP78+VSsWJFOnTrx8eyPSciTQLW21dh8bDMAhQoW4vSJ07Tr3I4A3wB8b/va\nG+1JMwWHvzxM4/jGnLWedXLE7DFixAgMBgOTJk167jb+CKIosmPHDubMmcOyZcs4dOgQEydOpHHj\nxnTt2tVpMaCoqCgaBDfgf8X/h+19G+t7rsfnog/Hbccdtg0ODmbEiBFMmTKF0aNHO6wf7TYafx9/\neBc4BsrXCp6zPBFXi2py6wXgPqou9ReQ+GYicqTj/XpCe4LrV64zcuRIKlWqxOHDh5k3bx4NGzZk\n+fLlBAcHM3fuXLvEW09PT2RZRq/X06FDB0D1sFeoUIGAgAASExOpX78+mzdvzvH3+q31W5ZolyBf\nk1WlHTeQdBIb2Ug4qXkStlAbJOUi+uCTY6MdYCEL+UrzFZWUSnjKnrjgggEDBtGACy64SC4YFAOu\niiv5yU9rWv+pRvtvv/1Go0aN8PT05ODBg8TFZRza8W/Azc2N3r1789tvvxEaGsqYMWM4f/4877//\nvt1MnZfBiwp5HUNddN46SPIZueNOVbEqpQ2lnfaBN+WbNNI14pjumOPKJA4rWVeNjoqKIl++fLzz\nzjvIssxHH33Erl27UBSFJk2aUKdOHUaMGMGFCxeYPHkyU6ZMYdWqVbRs2ZLz58/j5eVF+wHtKdGw\nBL7rfekkdVJndf5l7FR2Zk80IottfuM3bpO1ZPSfwV/vzskllxfMpk2bePXVV8mTJw8bN27EbDYT\nHh5OvXr12L9/v4N8F4CXlxexsc9faKSF2IIhyhCWVFnC02+fYiDzl2ukFElhTWG2abZBAdQnz4ba\nOYjYG7LJnva0VU4F1KlUPZl2KHlF50mmXk4D6VXd5vQYBIPTJDY99glyxw3HaWFuwU3UpCtvvIkn\nHilRIp3cuyNp2097vUleDi1axohjaKjPuGItqJro3t7qDEN2w55iY2P56quvmDt3Lo0bN6Z06dL0\n6tWLpk2bMnDeQEKPhyLnk0nYnUCHsR1o07QNWz/eSuUKlfl58s/s3r2bkSNHYvYzQ10wvmVELCBi\n3WhFKCxgxcog86BMX7SZIYoiixYtokGDBjRu3JjmzZ0XnPozKV68OLt37yYoKAhfX19at27NmjVr\nqFu3rtNB0SrjKmwxNgwtDIglRJR4BXMXM632teJJAfswAY1Gw6xZszI8dmN9Y5Z7LmeGcQbRmmjy\n18vPJLdJTE2cyj7rPhJJBNR7RG4vIz+SSeyciGG0QVV2coG6oXXpt7wfV69eZdCgQSlewiVLlvDb\nb79x6tQpu4JsydSoUYOTJ0+ybt06li9fjpeXFzqdjoiICF5//XU2bdrEvn37clw4K06JY5oyDWWm\nghAuoJ+tV/uApKn6eFQDSLEq2HbaYDoUoxjhmudLfBYQGMEIRmhGODUG/0qOHj1Kp06daN++PXv3\n7sXd3Z233nrr7z2pF0j16tXZvHkzwcHBrFy50mFgO6f5HIbtGUbYkzBsko0iXkWY0HACR1yPcEu5\nRWdtZ+pq67LUspSJ5okO7WsFLSIiecQ8OT43WZbZdmcbkiIRWCKQ0NBQGjVqlPIM+/j4sHz5cmRZ\n5uTJk4SEhDBr1ix69uzJ+PHjmTx5MuvXr6djx4588MsH/DL+FzT1NcSMj2HT7E14jvBkgdsCimmK\nZXoeAQEBWara/FUY5TSVyDPyqmclKgFYFAthQhjFyPza/wxyDfdc/vWs+n/2zjssiqtt47/ZvksR\nLCgqNrDF3sUSNYo9FrDgm6ixG6PRWKIm0Rg1GnvslWjssRsltrxKNHZjxV6jiB0RhO0z3x9DW3ZR\nsKS8H/d1cenOzpw5M7s75z7PuZ/7WbHCYdlVq9VSpkwZfvrpJ7p27cr48eMZOXKkQyTkTWTGT1RO\npE+DPhTtWhTJJCHoMk5QTU6Qi3aPdoikpzwgkrV2aZH+9TMgJ85OLsjtKOwKgnRBLrvQTdGN9eJ6\nonCslFdFqOJ0L7orunPMfoxYUv203XEnVOFoS6dT6Niv389t8TZRUhRFhCI0MDbguT1NFCYjZZ2L\n61VYFVwxXEGlUKEWXAn/nVGhQgVGjRrF2rVrCQ19sW2eyWRi7ty5TJ48mYYNGxIREUGRIkVS3o+J\nj2HPH3vADgqtAnUhNdZrVnZ57OL+0/vk884HwOzZswkNDaVv375UMlbCKsmrIdrPUycOT8WnL+37\nLfEWAywDuCfdo72iPcPUw1Kivz4+PixatIiOHTvSq1cvRowYgZdXeuuht4vixYtz8+bNTP1W5uee\nDzMhsUMihq0GNB9pEC+LxHSOwbbDlmXJTzNtM5ppmzls26beRrglnM3mzZRVlaWvvi/37PeIGhrF\nzaI3mbx8MjeG3iCHPgfGACP9+/cnODgYjUZDYmIic+bM4ZtvvmHHjh0uSXsyBEGgbNmynD59mjNn\nzhAVFUXt2rVTqmjGx8djt2fCFikNwsVwHmx4AAvBsNOA4J50T9PdWvshO/gDBaC5qvkLKw//W3D7\n9m1iYmK4fPkyI0aMoFWrVlmqBPtvQI0aNVizZg2dOnViw4YNVKpUKeU9D60HS5sv5bn5ORbRQk69\nvFJZlaoObXTUdOQHyw/8KTlGccsqysrBlCSNtd3FMqa/4O+0bU/UHj4+9LFMVCVQV1Bj+dZCjx49\nKF68uMO+CoWCatWqUa1aat2D5Gdj+/btefToEYO/GIxmswbVuyrc9rphW2vDstBCP/pRiUp4Cxnb\n8V65cgUvLy+6detG1apVU3KPkvOPPD09adasWZZrYrwykoNiybcy7Qp3FtTgrj6LvwLZGvds/Ktx\n79493nnnHS5duuTSY/b69eu0a9eOsLAwhwQ4gH379lG/fn0nYiJJEtevX8fPzy9TUdyiVYry4OsH\nqOq4HmQFBKLUMmFua23LMdJEYjOoZgqkRqCTCX5ysowL0q60K1mrXkstVa0M+/mr+CuT7ZO5xz10\n6KgsVGamciY6wflhucy+jBXiCp7ylBzkIFgRzADlgAzbTsa7Ce9yPT5NMqkS2R0n/Tid/DrZoi7J\nFnOM5xh6ufd66XnSIjIykg4dOtCmTRuqVKlCwYIF8fPzw8/PD4PBgM1mY9myZXzzzTcpRN9VBcSV\n+1cyfEmqblk0iiTsTYB6MGvwLAY0k6+/X79+HDt2jGXLltExV0fuiHec2sop5OScR8ba/E3WTQyw\nOd5PTzw5rz3vIN24e/cuEydOZMeOHVSvXh1BEFCr1TRu3Jjg4OBMVWr8K1DdVJ3j4nGYBcISAf0i\nPajA1M2EeEOkcOHCNGvWjH79+r2xyJvJZHIa6GNiYnj06FGK1euTJ0+YM2cO8+bNo0aNGgwbNswh\n4c4Vrl+/TqtWrRg+fHiGycxZxc/mn2mdtzWGrQaU5dL8GNJF9kwjTFgLWWE4lKc8Z9SvLrn6J+H2\n7dspGur/ZWzevJk+ffowZcoUgoODs3z8fut+xpvHc1e8i07QUUZZhnn6ebgL8kTzgfUBlW2VHY5R\noWKEfQQWu4X2bu3Jr8qPKIqUWF8Co2R02Ne220b9hPr88ssvWeqX1WpFW1qLpocG9YdqBDfHQcgX\nX06oTwAQZ4lj6JmhnI89j4RECY8SfFf+Oy4fv8zq1au5desWOp3O4S86Oppz584RGhpK9+7dqVy5\n8luznWxhbcEvwi/OUfVk2Wr6sfYFGvgtbKG18PKia5lBdnJqNv7fYMaMGZw4cYIFCxa4fP/27du0\na9eOsWPH0r59e4f3jh8/TqlSpfDwcMwMr1atGidOnEixi3sZig4sSrQyGu2XSSQ/7czdAvUe1GNh\n4YV4qDyYYJ/AXDGdbjupDDU5cCS4mViuAzkKc0N1I1PROUmSiCIKTzxdJrGmhSiJPOMZnni+sBx3\nWuy27KZbbDdnJ4tkr+3kh6OVFFcQbMiaeKCApgBH8xzN8kM7OjqasLAwoqKiuHv3bsqfwWBArVZT\nokQJvv76a6pXr55hG8euHqPtt20d+m48bcRmtbE7fDdBFeTVDEmSmDFjBpMmTaLPtD7MajDLISdA\nQGC0djS9tb0zPFcRYxGsOOct9FH0YbR2tNP2q1evplTzff78Obt27WL37t28++67TJky5bVrErwu\nIsVIKpoqyhGoycBKQAAtWgxPDDx9Kq9AaLVaJk6cyKFDhzhw4AA//vgjTZo0yfL5bt26RdGiRYmN\njU2RSqXHtWvXqFGjBi1atGDgwIGUKFHC5X7pERoaSr169Vzq718VR08cpX7n+qgPpltJSiOHkySJ\n5xWey0mppaAznVmuXv7G+pCNvwYnTpygU6dOBAYGMnnyZNzcsmaxKUkSf4p/4iF4uJTH2EQb42zj\n2Cfuo6xUltOxp/nTLkfpfRQ+9HLvhf8zf7of7O7ctlXCbYobYXPCaNGiRab6I0oi65+vJ3RdKPSU\nV4yU1RzHAxUqrqmvISDQ+WBn9j/e7/C+2lNNkQpFKHa2GMWeFOPDOh9SJG+RlPfv2O4w8cJEzmw8\nw/2N98mXIx89evTggw8+IHfurNUYeRlaWFvwiyJp4uKq1kEyeZdwJO5p89GSaPBWaSutFK3eSL+y\niXs2/t+gcuXKjBkzhgYNGjhsF0WRsLAwJkyYwJAhQxg2bJjTcv3t27exWq34+zsuM968eZMLFy7Q\nvHnzTBHIIhuLcGfqHdw2uMlyFj3yj/weKYS0hnsNNpbciBUr3e3dOSIewSgkVcu7CawBqgIVkAVs\nOsis05QGDTfVNzO38wtgES1oFJkv9OIKRslIueflMCYaHZcctYBeroi6Wr2aNg/acF+873S8l86L\nPd57yK/Mn7ItRorBhg0fIWvRZUmSePLkCbGxsfj7+2f4WRpF+UPSK/SUG1iOmKepSbySTcJy3MJ7\nld6jdu3aDB06NCXKe/jwYUJDQynVqhQXh16UC+WgobulO11sXYiPjyc2NpZ169Zx/fp1DAYDbm5u\neHh4sGrAKtmZJh088OCS/lKmri8xMZGwsDBmzJhB165d+e677/4WF5pkfGf9jnHWcZgwoUBBgCKA\nU5pT6BQ64uLiGDFiBPPnz0ev1zN9+nQSExP56aefOHLkSJYnal27dmX58uU0a9aMsLAwfH19Afkz\nnzp1KlFRUZw5c4YKFSowfvz4TLcbGxtLmTJliIqKcnCmel1MmzaN38/9ztPpT7nFLRKlRGyiDZto\nS3G2sJ+yk9gvEZJKJDxQPHhjxcey8dciPj6e/v37c/jwYWYsnsGGkhu4Ld2mlaoVnbWdMy0FfBEk\nSaLlw5actp522O6r8GWQeRDDjwx3uTr7oflD9szaQ2RkZKZWlDvc68C2hG2YLCZoCB7nXzAw2UCI\nFJCi0/FECfgj6V8FKGwK6uSvw5r+a4gwRjD06VDuiXLtBo2oodzpcuTdlpedO3eyfPlyWrV6M+QY\noKXYknB7uDNJzwzSSjztsFXx9xD3bFeZbPxrcfHiRe7du8e778oJjJIkcfXqVRYsWECTJk1Yt24d\nBw4cYOTIkS4JTb58+VIqJqZF0aJFadGiRabJxOf1Pkc8K8oPq2XIZFyD7BqQNO5GJkZyMuEkGkFD\nSVtJjHajHGkWkZNV8wEngZ+BLcikP5MowOtZcw0xDiEgLgD/5/74x/nT39j/ldvSC3pC9aFyUSlN\n0l8OQAcFEguwVbMVP6Ufrd1aOw8qSjAoDLgJcoTqgO0ApY2lKWcsRyVjJco8L5MltxZBEMidOzeF\n/Quz3bqdJeYlPBBTnYQui5cpZyxHCXMJSphLUNFYkVXjVlG4YGE0Wg0arYZChQpxbN8xmjRpwvHj\nx6lZsyaXL18GIDAwkD/++APFNQUJlROwvGPhfqH7TC87nbp169KxY0eGDh2Kp6cngwcPpnPnzjRu\n3JhDhw5h/8O1NtJA5kuKGwwGBgwYwJEjRzhw4AA//PBDpo99GxihHkG8Lp5Luks80T3hou5iih2k\np6cn8+bN4+DBgxQtWpStW7eSL18+rly5wt69e7N0ngsXLrBjxw5u3rxJ+fLlqVixIhs3bgQgPDyc\nxUsWc8t6i/iC8ahqqnhmfIZdsrPXspfN5s0kSAkZth0eHk6DBg3eKGn/73//y6RJkxj40UC2qbex\nR7WH4+rj3NLeYrJmMp6SJ0hg3WmFpNpgjWiUTdr/xfDw8ODHH3+kw1cdaBnSktXzV3PMdoxRllHU\ni6/Hn9F/cvTo0dcySHgsPuau/a7T9nviPc55nsvQraxzx86ULFmSadOmvfQcR4xH2J24GxMmyCjX\nPu3wqQKpjATFkANY+ZDd06JJzeUCRJXI/nv72XV6F1PjpqaQdgCLwsLlKpfpObsnGzdupFevXty8\n+fqBqdTuSqn9TjaFsCHLY9LUNHAqYpYsnclk5e+3ieyIezb+lTCbzYSGhlKoUCHGjx9PVFQU7dq1\nIy4ujqCgIJo1a0ZwcPBLk6Bu3rzJgwcPqF69uoO2OCsQJRFVDxXKSCX65foUsg7IP/AHwCOYXmQ6\nzXM2p1RcKZxMaGKQH4wPkB8kXsi2kWly6DzxpDjFOcvZlIRIgHyP81Hg5wLE2eIwi2aKeRXj+zbf\nk8czz0v7vtC0kLGWsU7bh2iGMFg3OJN3QM6wj5Pi2CpuJUKK4A53iLZHY5ZrYBlxAAAgAElEQVTM\nCKJAQVNBcthzYBbNuCncaKdvxxLrEq6ZklwzVIASmqqbEuYZxh37Hepa6jrJSdQ2NecM5/BQZm45\n4rr9Or2NvbkqXsWOHXfJHSFRwMfThzuKOykFcJKRgxxc0F9w2ZYkSSxdupTx48fTo0cPGjRoQO3a\ntTEYDNy7dw83Nzfc3d0zjHqvWrWKiRMnEhMTg7RUIqGSM4FcrVpNPXU9F0e/GCdPnqRjx47Mnj2b\nkJCQV/4u/xWwWCxMnTqVw4cPk5iYSN++fZ1kbC9C//79iYyMZMKECdSuXTtluyRJ7N67mzb/aYOq\nsyrForNQrkLca3lProMAKFAwRD2EbrpufG76nKP2o5RWlGaubi5TvpyCn58fw4cP55D5EJtMmyij\nKsMHhg/QCK+2GlW4cGFmzJhBUJDrxHGAlbaVfDXqK6R8ErNHzOYD1b/X4zwbqVA+UyLeEqEnKN2V\n6KbrUPgoKPZZMc6sl4MQP/zwAyEhIVledXouPue9++9xV3Qk70qUDHIfxIzrM2Tr0TR5VAZ/A3sr\n7eXMzjN8/PHHxMTEvPBZMeLxCCbFTpLrjrQH/UQ9qlaqVElnUgTdCfHIK8nJj8JdgAvnSH+tP8Zu\nRqJFZ5/7nm49+cb7G+bMmcOmTZs4duzYG9G9t7AnadztpE4m0tcUSYtksp4WSde8la20UmZLZbKR\njUxhwoQJ/Prrr6xdu5anT5/SokUL+vTpw9ChQ7P847579y4XLlzAy8uLChUqoNFkfYCudb8Wh8sf\nxrA+XfIZgAny3shLeKlwBp8ezP539jv7OcUDO4E6Sf+CHKmohEzyn0Mfrz6MzjWa9Zb1DJIGOS71\nxQFL5XMhgUEw8EWDL+hWp9sL+10yvqRLL96XJVcmQ5RERllG8Zv4G3cVd7EIlpRIT05yMkgYRHWx\nOp88+YTr9tSkVQ/Bg97uvTnCEW7ab6JGTQVVBaZ7TEcv6Oln6sdWyYVXtgTtzO2Y6T3zpX0DCEkI\n4Yj9iPziKHAcWVtfBPgPLgedjZqN1FTWzLDNS5cusX79eg4fPsyff/7J9u3bKV++/Ev7EhwcjJeX\nF8WbFWfsqLGIzUW0I1JncK1pzTz9vExdlyvs3LmTcePGUadOHebOnfvWkrv+bjx69IjPPvuM9evX\nY7FYWLJkCQEBAdSrV49Ru0Yx/vPxCFECgruA0luJrrkOAoB0UnqFoECUUkdrKUai4siKfNDyA5aG\nLuUPxR/Yo+wwEdQH1PRs15N5U7P2+UiSRIkSJQgLC3NwGnGFFStWsHbtWvbv3//C/bLx78BN+02K\nPS8mv7ACwaBUK9FN1OGj9qHp4qbMnz+fMmXKoNPpKFasGHXq1KFbtxc/s9Oi15Ne/GJ0TDQtqSrJ\nDp8dvP/wfc5bzsND5DEiL9TQ1WCTzyaaNm1Kt27d6Nmz5wvbXxW3im4Pu2HtbUVTRoO2V5o8Li1y\ngMlVnMKKXGchOTK9B3mMSod33N7B1tXGFdsVh+1KlEz2mkyoeyiSJOHn58f58+dfuWBiWrSwJWnc\nky2IXencwdEYIpn2Jo8XScYRW5V/j1Tm3+81lY3/dzCZTMyePZvNmzeTkJBAq1at6NGjB8OGDXul\n9goUKECBAgWIiYnh4MGDqNVqKlWqlKWkokZejTjc4TDWbVZn4q6Gmpqa+Gp9OXTwEDgbmsgPhrvI\nUYk/kX+Z0cBuUjzRwwqFUW1ENYbahzq6ywjIcpQQYBXgAYlFEpl8dTKtK7Ump5tsP2YTbXxt/5pd\n7MILL2YoZ6T4R6dHslf2yzDROpGV4kps2Jwy8GOIIUwMIywmLCV5KhnxUjz/Nf2X8LzhmCQTSpQO\nus9H0qMMz3nVejVTfTNKRm6Lt+UXN4G9pOQcUIsMhYI77HLJyhqKGi7Jb6lSpRg1ahQAGzdupFGj\nRsycOROz2czx48cRBIFGjRrRoEEDh8TJcePGUbZsWXmCBej6yzISAwa2a7dTUvF6CaZNmzalVq1a\ntGzZkq+++opvv/32tdr7pyJPnjysXLkyRcteoUKFFKvGk/dOQhuQrkhIayVQyuRZuC+k1k5IQlrS\nDpDYPZHD1sPkLp2bY6pj8vejP6iLqlF8rGDd1nXMI2vEffTo0eTIkYNSpUq9dN/du3fTqVOnLLX/\nOoiT4nDHHYXwz12dyQiRkZEsXLiQDh06uKzi+0/A79bfU19sBeGGgKq7ioS6CdzyvMUqyypatmzJ\nxo0b+e2337h//z6ffPIJwcHBGSZcp8cs71koUXLGfAYbNvxUfkz2moxWoWVEzAgG/DCA2LhYxOci\nBqOBeHM8dePrcvHiRVauXPnS9jt4dGD6s+mcLHMSMTrNJNeSZH9swzWLFHGUk3jjRNwFm8DEdhOJ\n0EVw5/kdjKQ64JRSlSLYTXblEQSB9957j0mTJjF79uxM3ZcXwR13uW8KXNoSO11H2sdEOomM6m+i\n0NkR92z867BkyRLWr1/Phg0bGDlyJDabLUNXmVdBQkICp06dQq1WU6NGjUwdY5EsaH/XIvQUcD+c\nziPaCoVXF2b7B9up0KsC4reio5wG4BoQBtQH9iFHztNDCYW/Lsyf6j9dJ66agVNASeT3zaC1aonI\nFUFBClLBXsG5eqoFl3o9tULNLfdbL7tsmhibEElkqgYwPezIMiAXy5D+Sn/2+7qOLk6xTOF72/fO\nOk0RuiV0Y7zPyxMOrZKVes/ryb7Ia4CLSW9ogCGQQU0qB4Qrw6moebF94KFDhxg1ahRFixalYsWK\n2Gw29u7dS3R0dIoeHuSJhGc1T5QdlWi6Oq7qtFC0YJF20cs7lAk8fvyYZs2a0atXL4YMGfJG2vy7\nER0djYeHh5MDVHr4rfEjqnoUHAHlciWGBQa4BexHrsRqI8NEtIQuCYi9RTyaeRCvipdXZoqC+1l3\nLIssqBJVJMzKWB+fHosXL+a7775jz5495MnzYtnatWvXaNiwIX/++edLr/F1MdQ2lOmK6UiCfCP8\n7f5cUV751xD4wYMHs2bNGtq2b8uGnzbwNEZ2LKpRowabNm36x1ikXrZdplRC0oRtJwgjBfSz9SQO\nTOQ/p//DKvdVTse0aNGC1q1bZ3kCZ5bM2CQbbgo52HTnzh2CgoLkwoR+uShRsgQBvgF4eHjg6emJ\nu7s7Me4xzIqchcluomNARzoW7+gyUPHI/oieF3sSXiecaturoU3U8luT3/Da64W9sB1JLWE/Zke8\nK6J6V4XCTyEHnWJJfX7bkaWgDwELqEU1Hcp0YPIHk5Ekifnx8/nF+AsmyURRdVG+9frWIccjNjaW\nRo0aMXjwYPr06ZOle5Meq+yr+JAPU8ekl/m3JxdITAsBNIKGa4pr+Al+r9WflCazpTLZ+F+FKIqU\nLl2aGTNm8M4771C5cmXOnj37RpbQ0uPatWvEx8e/dIk7GfUs9djvvx/DTwaUpdKw2IfAGvi0xqcs\nWLkAy+cWyItDdjp7gSNAT2AJpOfXKSgE9EKOYKSHFZm8p7Nlz09+2qvaMxMX8hIRmbyn+1nnVOXk\nS82XHLAf4B3hHbqpu2EQnBMn6xvrc5WkCLgrj3kb8BSXxL2Opg4/+fzk4kIgUUqknrEe0UQ7PFhz\nxuUkIncEuVSZqyLYL7EfW21bZXvC5NVYD+AzwECm3ATu6p0TwFxBkiSO3TjGlftXeLfEuwRWCOTO\nnTusWbOGXLlyUaJdCSpPqIz0nYRQREBZTYmqmgplDSXF/YqzX//mJBJRUVHUqlWLK1euvJQ0/tNw\n+PBhLly4QI8ePQC5Ou4777xDoUKF2LVr1wvzVoSbglyk7DdQTFLgts5N/n7/BjTFsWJiuuq+pm9N\nWH+xQiugBTADVDlUMtnqloi+nZ74bvEOxxglI4ssi4i0RxKiDmFz/80cPHiQgIAAjhw5wo4dOwgI\nCHjh9Z44cYL//Oc/jBkz5rVJycsQZgujp7Kn4+9UAq1di0nlKlrwz0J4eDj9+/enyoIqnLedx2Qz\noVPqKJ63OIo1Cs5vO8+5c+f+ukI+L4HHM4/UVc2uoM6vxrrMyu17t/HL4Uz4du/eTZcuXdi6davL\nWhOZwePHj2natCk6nQ5Jkjh//jxTpkxh8ODUnKWKDSty7o9ziBVFqAq6YjpCS4SytNHSlH1EUUzR\nv+/Zs4fGjRtTp04domOjuRF5A6GkgJBDQDzm+HD3+92P2Gex8jiUXB04KaGzWGROhvqPoUm1Fui0\nWfuMzp07R4cOHbhz585ryQCtkpVKtkqcV52XNyQHrtLKYFy5zSRH55O2N5eaE64Kf+V+pEe2q0w2\n/mfx888/4+HhQZ06dVi0aBFt27Z9K6QdICAggJiYjBi0M7aptiG0EbBtT8MIEpC11UB0fDT69npZ\nF2hGjqqbkImFP+iL6SldqDRuTV4g0UlOYk1+qNiT/kRk4u7iWfiYx6xghev2krWKamTirZL/VIKK\n4dbhbBG3MME+gfdN7/NQeuh0eFGhaOqL9CRYTLpOF0Us/BR+fJHjiwwv0yAY2K/fT0epI+5WdzxM\nHjR41oDwnOGZJu0A0/TTaKJqgqpMmoSqeORokBLHIhsZPDIPWw+/9DzxxniCZwfz4cIPGbF+BK1m\ntkKfW8+OHTv4/PPPGTBgABd3XkQ3WAd3QZonYStlw7TbREKzBG60vMHPP/+c5YqcGaFgwYK0aNHi\nb3eaySxiY2Px8vKiVq1aBAcHM3ToUIoVK4a/vz+NGjXi+vXrnDp1iq+++gqALl26IAgC8fGpRPrx\n48epqyqeID1P+kJqgFI4fcfTJ4hrv9BimC/7/gvDBJQecjIhyPuXoYzD/lH2KKo/r85g02CWPFpC\ny14tWbp+KWPGjuH9999n+/btLyXt4eHhdOjQgYULF7510g7QS9HL+XsugFlppk1im7d+/pfhofiQ\n5tbmFLYWpp21HXGio77i8uXLxOni2PV0F3dNd3lie8Jd810iHkSwr8c+YgrEsCDsza2+vi4uuF8g\nF0nPq8lgW29Dp9Rx6+wtl/s3btyYGTNm0LZtW65cueJynxchPj6edu3aERwcjIeHB6VKlaJMmTLk\n6ZmHkoklaW1sTaw1lvN/nEfTToNar0ZYIWCaZWL79e1cenqJ6qbq6BJ1qO+pMfxo4N0P3qVTp060\natWK33//najaUXAHpPySTNoVoGykRPudllzHcpEvTz70Kr08thlJHeuscLtWDIe3LM4yaQcoW7Ys\noihy48aNLB+bFmpBzU7lTpram+Jh90CJEgEBRBDsAkhyLQ5BKaROPABBFFDZVOS256aL1IX1yvWv\n1Y/XQTZxz8a/CpMnT+bTTz8lISGBJUuWvNEiKa6QFXcOT4Un69utx77eDreR5S/bgRugV+sJeSeE\nHNXSaBfTzOgFb4FL3S/xq/pXrtS/QrWAas4nUIBar5aj849Ita9Kjh4+zuAaRIX8YHKFZMKqQiY4\nSaT9IQ9l3XoSLnGJMZYxTodP0k6iLGVRowYRNHYNuaRcBBJIIVMh+YGdnLmfVMCirrYuW322UkFb\nwXWfkqAX9Ex3m85lz8tc8r7ESt+VFNJkrfqiDh2P1Y+x1bRBWUhxW9yNrLlMlvgkR1Jc4BIv91Uf\nvXk0x24cI9Ei5wY8fv4YYwEj4yaNo2TJktSpU4dPu35KsSXFYBtQBRgArAb1dTUDhwxk/PjxfPvt\nt+zZsydL15gRevTowaJFixDFjOwSXg8PHjxg0aJF7Nu3L0sTXFd48uQJz549o0aNGgQEBBAbG4uv\nry+rV68mMDCQ3Llz8+2337Jy5Up27tzJlClTWLt2bYqs5MGDB/LKwsikBguBdE/C9t+k73AOHCdn\nyd/JNIolQRBQlleiG6zDbZ0bmtYabLttWDdb0cZoCY0PdejzANMAIsVIxEQROoJgFnDb70ZCvQQ6\ndOjgVBTrtv0256znsElyn5I1zb/88ssb9al+EZLlMa6wVbOVq/aM80cei485ZD/EM/GZ6+O3biVP\nnjxs3ryZZcuWyROpJFwXr1PjeQ3yx+XnvYT3XNZwuCxeprC9MDvYwW1us5GN5Lfnd9i3U+dOmHOZ\nSfg8AfuVNJNcM0gmCdNAE19P/BqLxeLU/t8BP6Ufj3M85qr7Vfbm3Iuv1pccbjleSMo7derEd999\nR4sWLdi61UWCfgYwm8188MEHVK5cmYIFC6Yk0d8Iu0EXVReucIWfpZ/xjvFGqClgPWZF21SL20g3\nFGYFj39/TJNnTTj+43HMjcyIpUTMq80cq3CMufvmUq9VPdT11FimWmCU7MWuX6rH/bI7hmUGNB9q\nsPhYuCRcwmhI0qvbkYm7HcgBtpxwsOJlHv/55wuuxDUEQaBu3bpERERk+dj0KKgoyA7VDuJUcdhU\nNkSViKSSENUiklpCVIqIgogkSEhKSd6mFrGqrTxSP+JH1Y8uV6D/KmQT92z8a3Dw4EHu379Pq1at\nWLduHYGBgX97xcj0aFOnDd5x3hTcUBDCgfvgpnajsX9j6hauywda1zZvXm5eqJSpiS4zus6gsE/h\n1B0U4FnMk+CKwZAL2SM3LRTI5CT9mGoF03kTOleheFcQwMdJgC/jT9H5Yesj+BCuC2eOeg7DlMPY\nrd7NWc1ZNqg3MEozityK3CntooCquqqszL2SvKq8mevPK+KOeIfPTJ/R3txe1uArgA+BfkBbUDRQ\nUO1pNd55+A5lLWXpIfbIsK2OQseXnu/CXRcWksXhwdMHBAYGYrfbWbhwIQH7AsgxIQeacRp06MhH\nPlbqVjK241iCgoKYOnUqISEhDpHkV0WVKlXw9PRk9+7dr92WK2zdupU+ffowZNgQqgVWc0r2zAr8\n/f3p3bs3mzdvJjQ0lIcPH1KoUCF69+5Np06duHbtGh06dCAwMJBDhw6RN29eqlatyvLly7l37x6X\nL1+mYMGCeFX1khvMBawB42Ajtv02yEAtpFKqqCg45jBIZonE0ESM84x4bPSgxK4SVPStSEy5GMyS\nOWW/G+INeZXrI1AVUKGbrMPubWenbadDe7FiLE1jmvLu43dpFtOM6k+qs824jalTp9K1a1eqVXMx\nSX9byIi3J01ga9tqs82+zaG2xRPxCQabgTzkoTa18bJ7UdRa1MkPf9q0aTx+/Jjg4GCmT5/Ol19+\nCcAJ6wlKHSnFsQ7HuDfmHvts+ygWXywlcfzZs2d07dqV97a9J/uFi6n9TCCBtva2KefI4Z0D79He\nSKESiRMSkexpLsgG4hORRHviW5usvggWi4UffviB1atXO62cBSgDWDZkGR7uHqjVakJCQl7YVpcu\nXdi0aRPjxo3jo48+cpgE3RXvMsQ4hC6JXZhknESClIDFYqFXr17kzJmTadOm0b9/f548eYImh4aE\nMkmf0wmgExAA1v1W7Ofs2I7bEBQCkkVCfV/NncZ3UCxToP9Ej/t5d9xWuKH5SEOv3L0YtnoY1t5W\necV3NxhWGFAFqRD0SbPhZD90AB8QfATZztgdyE9KXpdospH4zPXk72WoU6cO+/bte6Vj/5eQ7SqT\njX8NZs6cSb9+/VAqlQQEBDBnzhysVmuKo8TbQFa1dIIgkNMrJ1PqTOGC5gI3n96kbem21CgoO5T0\n0/TjR/OPRNtSfWvVgppZhlkO7RT1KUr4iHAW7F7AtUfXqFmyJh/V/oj74n1+ivnJmbgDuAEHkAs/\neSNHOfRAObhL5nTaGjQUFgoTLTn76mYUYVAJKlqqWjptb65vTl5lXuYlzCNRSqScqhyD3AehEt7u\nY2ehZSET7RNTPeBtpEb88wK5QJwrcvzycXgOBfIUQF9Lj7qN2sEfH6CUohQG1csjK2knXckQFAK+\nJXwxm83ExsYSFBREUFAQDx8+pGbNmuz7cJ/sMoNMXrZs2cKoUaMoU6bMG0lQFASBHj16MHfuXJo2\nbfra7aVHDq8c+NbyJX5yPLdCb1HZVJmRmpF0VL58ouMK8+fPB1JXuVavXs2sWbMICgris88+Y/ny\n5RQsWJBevXoBchJ5165dAcibNy8PHz7kcO/D9FH34ez+s0hPJFgOxi5G9D/qUQU6f0Y2ycYV1ZUU\n2Zn9rB3zDDOin0iO5TlYbVhN07im2LFziEOMezyO8frxfOn+JXbRDp+CUlKim6OTl9YtoJYcn0ft\nYttx0XIx5fUD2wM+vfQp4gaRy5cu81eiuL04V4WrGcrCHike0V5sz4fShyxRLUGSJHwkH0Sl6FD6\n/Zb9FmVtZR0qNu/fv5+EhAR8fX3p2LEjP/74IwDtTe2x3bQh3BLgIkh1JIwNjYQkhnDc/Thnz55l\nxYoVCP8V4D1kO1wNUBsYCFerpa4CGFQG/N38iaobBetAvC6iLKEENdgf2zGNMOGz1ueNa9yTi/vZ\nbDIz9fHxIXfu3A773L59m759+1KyZEl+++03FixYkDJ+hIeHs2OH7Fa1bNkyvLy8Xng+CxbW1FqD\n4bSB30f9TtlaZekR2oNitYuxoOICovXy83nP4z0sX7kcy0oLtQJrsXr1arRaLWPHjqVIkSL0qNVD\njnjPBRaBpo0GzR8apGcS9tl2FLnl35q6ghr9NT3WcVYU9RVO454YJSIeFKEaMAT0M/UI3oI8cU1E\n/v1YQbJK2A/bsYZb4WnSCo8AeIDqHRU6PwWVdxcmf9fSr/Q55M+fn+ho57Hp/xuyk1Oz8a9AXFwc\nfn5+nDlzhly5ZM3g+++/T8+ePfnwww/f2nn37dtHgwYNMr3/jh07GD58OL///nuGpF8URcIsYayw\nrKCQshDT9dPJTW6iiCIPedArXLFyGR8aP2QfGUQcbMBMZE19LmAossPMC6BAgZgmc7QqVZkuTKej\ntSP30pRv9cabSepJtFC1eHGDfzOskpVSplJy5C49kvXsq5ClMmkeZe4GdxYPWsz6kuvZKm5FhYpR\nilF002TOU3ly+GTm7p2LzZ4qL8rtnpvSt0tTvlR5fvvtN3bt2pXy3uLFi9m0aRMHDhxAEASmT59O\neHg4W7ZseaXrTg9REpnweAL7Hu/jZJOT1N1Ul5W1V1JA83pVdtNiiGkI04tOx7DegGmUCTFQJP83\n+dmv3I+/wv+NnefgwYOMGTOGjz/+mLZt2zr8rjZu3Ei/fv3w8vKib9++fPbZZzx8+JC8efPSqFEj\ntu7aSoFlBYibFYfbf91cloBPTk6z7rBi+twEvUHXR0eXNV1Y9Pki8AVKIEftPwOKwyPPR+QZlgfF\nUQWGjQYEg5DSln+CP3tz7UUlqDCKRko9LOUgOwMwfmGkSpEqHJly5I3dp8zAJtpkWZurnI6ryDk0\nQ0A4J6DOoUb0ErF52+S8HC/kgIB30v89YHXu1bRt3NaBKA8ZMoS9e/eydOlSKlasiOczT+LvxUNN\nUAersf5mhcOQU5OTJ15PuHjxIm3btiVhSwIP5j5A3VaN4CFg22PDPMdM/hP5uZs/NfBw+fll2p1o\nR+TMSDRaDdoPtIgqkcReiUjjJbqHdCfMI+yN3rdBgwaxZs0avL29EQSBmJgYtmzZQmBgYOq9tdlo\n2rQpuXLl4tq1a/j4+JAvXz6mT59OuXLlcHd3JzAwkKVLl77gTDJCCGEzm1OrfJ6GEptL8OjXRzw9\n/RShkCBbnUZJ0AY+HfAp31f+ntOnT7N582Z27NjB7NmzqZ+rPuZQM8o8StSt1aiap4mQR4L3PW8K\nUYhyucoxutpo3rG/4/RdJRosSyyYF5tRvatC01aDsoNSzhERoXZcbS5euEj0pmisP1vl30k7KFCk\nAF5PdVwXbmAxSiiPgv6ICkuMksJFitChQwc++uijLCXPz5o1i4cPHzJzZubqePybkJ2cmo3/OWze\nvJk6deqkkHaAokWLkpCQeXu2vwLTp0+nX79+L4zUKxQKeul6sd9zPyvdVjLbOpvC5sIEmgMJMAdQ\n2Vg5wyTF29zO+ORPgDPIS5ZGILfr3QwYKEYx5gnzmK+YT13qUo1qdBO6sVq1Gn+VP7M1s6kp1MQf\nfypTmVHqUW+dtIuiyPvm9ylgLkABSwGKmIuw1ro2S20csB9wTdohlahfwUky8DzxOav+u4rZmtnc\n1t3mhu7GS0m7RbJwRbxCrBTL0GZDCa0eSqFchcjplpOS+UoyvMVwlHYlnp6emM1mh2O7d++O0WhM\niUp27NiR48eP4+npiaen52sv9Y96OIrFsYu5pLqEqoeKiC8iaHGnhdOKwutgn3IfjABjHyO6iTpY\nBtF7opkqTn1j5wCoXbs2e/bsITg42Ol3FRISwooVK+jcuTODBg0C5GhoQkICe/bswaAwMI1p1PSo\nScOrDVGjxjzHjOlLE7ZfbSkJrJJFwvyNGdViFaNHjuZRgUfUaVFHJqn3QF1AjcZfIzvOJEDJ7SUR\nfhEwrEki7ZAiB7sp3iTCHAHIyZb2dH6r4k0R239teH6cCT/S18Qq6yoEm4BglxPrptincFdKs/r2\nO3LdiHAgEIReAqpCKtzOuqH+WY1mjgb9cD26jjp0NXVo82rRJGpQX1Sj3KVk+MThVK5cmePHj6c0\nOW3aNE6dOkXFirIESSto5clPKFh/tUIg0BGe131OREQEbm5uPHv2jP55+6P7RoeyohKFvwJNXw2a\nDhry9cznIN0p6V6Sk++epG3ntlj3WRF9RIxfGZG6SORvm5+p7m/2+7d8+XK2b9/O8ePHOX78OMeO\nHWP+/Pm0bt2aX3/9NWU/lUpFkyZNyJkzJxs3bqROnTps27aNhg0b4ubmRkJCAjNmzHjp+aKI4hCH\nkH6ToC+yw1EhePrNU3x3+cJVkCZKSOUk2Cjfy/CwcIoUKUK7du24cuUKFy5cQK1W49fTD3UVNfof\n9ahD1KmkHaAs6IP0/NLqFybVnsRU01Q5XyMtTEAUqOupcV/mjn6AHmVBJSQtFNn22ohqG8WjLo/k\nwMhqYCfk7pWb4cHDiRx4jXOh55jm2Z+1geOJOnef+OfPWb58OXfv3qVKlSr06dOHkydPZuqziIyM\npEKFF+dG/X9AtlQmG/8KrFy5kg8+cNSHP3z4EE/Ptzv4ZUUqc/HiRQfG9zsAACAASURBVCIjI1mz\nZk2mj/nd+js/iI7OHw94QGVLZc7ozzjt31XZldH20c4NGYFfkCOCyfJxLTJBTXcJwQQzST0p5XVL\npbPMJVAZyEblxkxfx5tAbUttbitup/TXKlgZIg2hsK0wgarAFx+cBL2Q8WoFVlJdeFwhC6qosdax\nLJYWp6xWFJOKEdEhArPFTExCDPly5EOlVHFmyxkeP37slCynVCqZPn06HTt2pFWrVhQoUABJkvD0\n9GT27NkpchG73f5C+0OXlylZ2Z+4PyVypu6gxrLNQuSWSDb03ECnHG+myI8VK3wM0k0J0yAT+pl6\njF2MnJt1Tq5K+xehcePGNG7c2GGbwZAqb/L19SXqdhRHah/h27nfsvbaWs6sPIP1DytcBEVVBQov\nBVJxCUUjBd8YvgHAo5iHTFDOgLWTFVVTlewS9RxifotB016D4CU41jCQQMwhclQ8SkOpIXmVedEJ\nOoxSanEZ0xwT9IS+vn3f6n0RLILspJP0vbZLdr6QvmC1ZTWfjv2UJeWXkDgoUZZSqEC/Qo+qhkpm\nBQLytYm4tnlNSogPEUIov6U8LVu2pEePHnz99ddotY52PV9rv2agaSDiJBEWAptByCUwcNRAunbt\nioeHBzVq1KCfqh9PrE9YwQosWNCh4/MvPmdJ9SWcPn3awZZXrVCzKXgTrY61Yne73eh8dLz3xXvM\n0M/AW3DllftquHfvHr1792b79u14e6e2GxQUxIoVK+jUqRMHDx6kRIkSAJw8eZJ69eqRK1cuPv30\nU0JCQli4cCErV65k+fLlL5XIgEzcn/AE5oPyoRL7ZTvch0fej3BTusl2tuWAtkAxYAIoWiiYMmUK\nv/32G/PmzcPd3Z1KlSrRtEVT5h+Zn+E4pknKzrZIFn41/4poF+WkfT1yWDcS2W1FKzi4MEnPJSyz\nLVhmWxg3fxwtW7Ykwh7BkoQlaAQNg9wGUVlTGYAAnzIM+tixcFL16tWpXr06kydPZsmSJXTt2pW8\nefPSu3dv2rRp47J6+dOnT9m/fz8jR450eu//G7Ij7tn4x+P+/fucOHGCZs2aOWxv06YN8+a9eon4\nzCAr0rA//viDOnXqOA1cL8Ig2yCX2x/zGJPoHDnupupGQdLZX5qRpR8NgUrIiUD5ka0h05FUNWq+\nUn6V6f79VbCJNgfSngIBPrJ95LDppP0kS61LuWJ3dmaooaiBh6vqVBIy6YoHiuB0Hg+DB12Dumaq\nr/ut+1koLUQUxBTJwQ3hBu9Z3+PhvYdMHD2RDz78gMFDBuPj48OdO3ecIu4AlStXpnXr1gwfPhyA\njz/+mPLlyxMbG8vAgQPx9PTE29ubq1czVyk2GUbRSIKYuhIlqAT0I/TYJ9s5+/hsltp6EUoLpeXr\nnwRiMRFLmIUca3NwbOQxcg/OTaOYRpyzn3tj53tVNGvWDI1Gg1KpJGxaGB43PdAX0sNs4AqIfURs\neW0wOZXIALTSJDm9VAA2gc1f3gcP4FdQ1lKmkvZk6UmSU808YR7FzMWwi3ZCdKmJiPYrduyH7QT0\nCaCtLjXp8k3DYDU4kHZI7WPkL5FsWrsJywQLgk7A7YgbbnvdZNIOjoVpXElqpNR9+in70a5dOw4e\nPMjZs2epUqUKkZGRDrv31/ZngnYC3oI3yr5KPLp6MOvALEaFjuL48eN07NiRAQMGADDYMphBiwcx\ndOFQBoUNwn21XMzOanW9UrT52800qtCI7/t+zzb3bQQoX2y/+TJs2rSJEiVKMGXKFO7evZvyes6c\nOU771qpVi379+jF6tBxIOXr0KBERESkRYVESOZDvANs+2cZTw1O63epGT3NPhwRnVyhLWQpRCPqA\n/YRdJtIJ8kQsSoySo+AG4BLQHbwXeBNzLIZPPvkEd3d3Ro8eTatWrVhiX8KqD1Zh32vH9osNKd55\nLCshyBOOeDGeeCkpIV5C1q0/x6nWAcg6dtP3JixrLKz5fQ1t2rRBpVLRSNuItTnXstx7eQppfxly\n5crF8OHDuX79OiNHjmTVqlWULVuWCRMmcP/+faxWK1FRUZw4cYJevXoREhJC+fLlM9X2/zKyI+7Z\n+Mdj7dq1NGvWzCGKBlCtWjW+++67t3puNzc3nj9/jru7+0v3jY6OxtfX12l7rBjLE/EJhZWFnRIz\n49LXgU6DCDGCpgrHpEKFQsFh7WEWWRexXFzOIx6RqE+ENmQcSU7z8C0jlJGjNv8wHJIOZfjec0Eu\nYGKSTHQzd+OkdJLnPMcbb+oq6jJXMzel6qNCUBCmDqOntWfKvTVgQGvV8lT1VCZWLZHdd24CCVAo\ndyFCaodQ651amerrIHGQywnGNdM1Qv8Typ07d1I258mdB28v7wzt6UaNGkVQUBCNGzdm7dq1tGzZ\nkoEDBwJQunRpli1bRvHixTPVr2R4KDzIp87HA/ODlG3KCkrcarkRNSMKnDnIK2GBcgG3bbc5qziL\ncYkRoa2AMcyIZoeG2EGx/LfAf6nkXokKxSowb9a8TFchflXs3LmTs2fPurSIrVmzJl27dmXYsGFo\ntVrui/cpYiqCGbMsf2klezd/oEpd1VMoFLTQtCDcEi77wJcC9gB1QFVfhbK6MsXi1AFJ9qoWq4Xy\n1vJc87xGYWVhtpi2cG3TNUp0L8GBogdeq4jMi2AUjRiVRrkfItAe2UI2GbFyVFe/UY+2nBZFrnTx\nu2R+lzwRSY8kMt9B0YF8Qj5ATg5evXo1y5Yto02bNpw7dw69PnX1a7huOMN1w7kx7AbVq1cnZJQ8\nmdHr9SkSJ0mS6NevH3FxcZQtWxar1YrVaiUoKIhixYq5vFalUsm2bdve2L20Wq1cvXqV+Ph46tev\nT9myZfHx8XHQssfYYrhrvUsxbTH69u1LpUqV2LJlC8OHD2fw4MGUKVMGURLpbOtMhBQBOUH1iYoH\nex4Q1jWMOHMc63TrADh//jyXLl0iODiYkydPsnXrVvz8/KjXsB7Xa1yXAzJDAG+Q7JJsJZo8h9EB\nT0DlryL8UDhl8pdJGSMfSA8oZyzHU6+nMBWMM4zQC7QTtGg+1IACygvlmaKdAoC3whsfhQ+PxEdp\nbi5yRP8y8iQQkOIkEiclonBX8OTgE3J65nwj912pVNKmTRvatGnD+fPnmT17NlWrViUxMTElV6BS\npUpMmzbtjZzv347s5NRs/ONRtWpVvvrqKxo2bOiw/eLFi3Tt2pWLFy9mcOTr488//8Rut2c4cKTF\nwIEDyZcvH/379wdkycLA+IEctx3nOc/xFXzpq+9LB12HlGN6G3sTjuvqa8tUywhSBwEwK2oWEU8j\nGFt0LGXdy6bs80x6xlj7WA7YD3BXzJxzTGWhMtt02zK171+Fq+JV6tvqu4zuudvduay/zDeWb1hk\nX+Twtho141Tj6Kzu7LDdIlnYa9+LBQtByiBmGGcw1zjXse1Y8Ir14kjpI3gYXLu4/Pzzz2zZsgVf\nX1969epFkSJFCLAEYBSMzjs/QNbvpoEoikh2WQKTkXdzZGQkTZs2pXLlythsNvr06UPz5s1fyy0p\nIiGCYQ+GEW2XHRjcBDeaWJvwS8tf2L17d4r++HUhSRJ7pD1clC4S9jSMczXOoZ+pR1VdhSRKSI8k\nGhxswNHRRwkPD39r1oeSJKXIiy5cuEDp0i93rThjP0Nba1seig/RClp6qHowWT3Zqd3C8YW5Y70j\na8Ebgv4HPap6SRPw5BoA6WFHLqyGY+Xdli1bMnLkyLfi8pOMGdYZDI4eLNeROAWKDQoMaw0p/bRF\n2DB1NaGorMBtj4tJfHJdB3AptQPII+bhtOK0y/N369YNf39/Jk2a5PL9kJAQ8ufPz7fffutAuKdN\nm0Z4eDgHDhz4WyufxsXFUbt2bdq0aeMwCRQlkWEPhrE/cT9PxafkVealrUdbvLd6M3fuXIKCgpgw\nYQJKpZLt9u30EVMLalnXWjHNMMEuKORbiFO6U+QUcjJhwgS+/PJLChQogE6no0WLFty/f59N+zZh\nK2yTpUWNkSPgHYHpQBdk97AEwFuuAeKR6MGp306ljFPjLeMZZRslR+d/ACaBuoUa7VAtgpdAA6EB\nKzxWONz/zcbNfBP/TSp5T5JMcQ14BuJDkcSxiUiBEreW3aKwZxq74rcAs9mMSqXKslTw34qsJKdm\nR9yz8Y/GlStXuHv3LvXq1XN6z2w2Z0mW8irw8fHh9OnTmSLu0dHRDoRoTMIYfrb+nOIMECfFMSZx\nDEdNR2mtb01dTV3ma+ZT2FI41T0gCd54U1dVlyvGKzTY00COmovQ5FoTlO5KbgfJSao5hBxMU00j\nRhFDOXO5TF3TWeksCWICbop/TuS9uKI47pJ7SnQ9LYYrZSnJKfspp/esWNlt3+1E3DWChqaqVHL0\nue5zNps3Ey2mWolpvbX86PcjHhrXpD25QM7UqVO5du0aDRs2ZObMmdRpUoc90h5nQvPEuQ1BEEg0\nykWZLBaLS+2mr68vHTp04NatW2zYsMHlPllFfbf6bPPbxoLYBcTaY+no2ZFAQyB+ffxYunTpG3Nl\nEASBxkJjGtOYxW6LoQJId+XvsqAQEPIKeIV4Mdt7Ni1btmTHjh1Urpy5ZfRXwYQJE6hfvz7r16/n\n3XfffeG+FZQVuKF8cRXGJ9ITOVdABWwBZXUlqrpphs3kAmhpvwtpnGoAnkpP8Ra8kSSJyMjINzZp\nygg7tu+A4KQXOUG/V4+QS0jpm7q5GmGd4FIGAaTq9ZPh4vpGMQqAGDGGo9JRKggVyK/ID8CkSZMI\nDAwkNDTUQZeejHnz5tGyZUv69evHzJkz0Wg07Nq1i8WLF3P06NG/lbQDnD59mvv37+Pu7p4yaff1\n9WWddh0b4jek5I7cst1icexi5ofO52QPx+TKNaJjnpM6VI34WMTS0ELsgVhi8seQU8jJw4cPGTp0\nKO3ataN06dIpRPpAwgEetHuAuFxEUVqBYYmBhEYJSAESyupKdLt0CF4CgkLev8qGKrz33ntERERQ\npEgRbttuy6S9Hii8FOjW6FCWTv1g86nzOa1StNW3pYyqDPMS5pEgJXBSOsl96T4EgGSTMHaQE4C9\nhnq9ddIOvPWx/d+MbI17Nv7RWLlyJW3btkWlcp5j/hXEXa/XYzS6iK66QHqpzDHrMSdC/kx6xlrL\nWno87UGXp12QFBJb1FvIQWpFVV98maKegk7Q0WBvAzlyl6w7tYM9zk6nc44JhjkVOWksOCboZQQb\nNu5whzPiGY6LxzklnmKIbQjj7ON4ID14eQNvCQfUB/AWvVPJkAjdxe4gQYX4Cvxh/SO1WmwaqHl5\nZFqlUHHE6wjj3MZRTlWONto2nMhxgqqaqhkec/36dcqUKUPPnj357rvv+PnnnxkxYgSqr1RIFsmx\nHxIU3+EsaREEgZKlSiJJEqdPu45Q5sqVi+nTp7Np06Y3QtqTkU+djzF5xvB9vu8JNMhLAQEBAURF\nRb2xc6RFQaEg5APpQeqNUaPmPdV7NG/enO+//55mzZpx6pTzBOxVERcXx8aNG4mLi+OLL77g+++/\nRxAEgoKCWLduXYbHmc1mjh49+lL3Hk/BU86ZWA7CDwK6sTpZqpB8iWmrF4P8O02bAC2AG/IEOTo6\nGpVKRb58+V75el8Gm2jj18G/yjIKb9BN1qEopkjto1Xuo6qhClUTF3G7dNIfhaCgkdgIvahHISnQ\ni3p6i70JUYTQztKOSrZK9LT3pKa1Jg3MDRBFER8fH8aOHUv37t3ZvXs3UVFRDrlCefPmJSIigmfP\nntG+fXv++OMPPvnkE9avX0/BggWd+/QXo1KlSgwdOpQ7d+6wadMmvvzyS5o0acL0VtNlVyY1KZOb\n59Jz/o+9846L4uz+9jXb2AUpoohiAxUblqhYEAuW2DWaxN4ldqNJ1FiiJsbHGmOMscfeorF3Y8ES\nGypWsIMKiKgUlb5l5v1jZGHdxRZ9HvN7ufzsR5hyzz3Dzsy5z33O96x5usaqDVsF7+yG2CG4Cjjd\ncsJL8OLZs2dER0dTqlQpypcvb2FIeyo95b9VAIiXRSSDJOci/OWA7hcdCleF2Wh3FVz5qf9PfPnl\nlzRo0ICUlBQaC40hCBT2Cuz/sLcw2gGccOJW6i26hneld3hvotPkZ0JpdWlmu8zm97y/syjPIpTP\nT9S4wyjfK19DP22/f3yNc/ln5Hrcc/lgkSSJdevWsWTJEpvrc/JgvmsMBgOnT5/GxcXF/LHlFYqN\njbV4KVvp4WYjlVSO6I+wInUFXzh8wSXlJQ6bDpNBBo2VjbPUUWyFRktw7N4xWVkgG8u1yzlpPEl/\nQ38SSJBfwDYi29SoaWNsQ4rwgpSmBDvFnUxVTqWRopH1ju+ZAooChNqFki6mEyFG0NXQlWUsszwH\nEYuEOQcc6K16Pa11paCkj7YPfbR9XrpdXFwcW7ZswWQy4eXlBcjfxZIlSxJ0NohKnSuR3jwd3e86\nFF4KEOFzw+cMaDuAntt7cj86KzTC08uTTRs24ePjw/Lly/H19TWHdPwTUqQURhpH8jd/Y8JELWqx\nQLkAO8XLB7KFCxd+b4b7L+pfqFuoLs8eyrkFatTUFmrTTiknYbZq1QqTyUTz5s3566+/3oms2+ef\nf05YWBidO3dm5syZBAYGcv78eUJCQl6a1Jt5/6amphIdHU1KSopNT7hG0FD5QmVuTb6F/UZ7FEXk\nv7eb0Y3HwvOQgkwjXYlVnomryhWNID+jLl++/N4T674P/h7JWUIoLKCbqZMNNhH5kz2/M4cQmBe9\n7eUox0qlLFl6xHiEqYaprDetZ7WwmjR1WpZijWDipnSTPqY+rFCsoEuXLkRGRjJlyhSuX79OWloa\nQ4cO5YcffkCpVOLg4MC2bdsYOnQoDRo04Pfff6d27dfLMXnfODo6mhPGM5EkCbvidqgT1CirKLOu\n52Ms6mBkMlY5lgOmA1ZSoIJWoNaRWlAPAgMD2bx5s7mgWHZWalZSJq2MLEc6GFI/S8V+kz2OxRzR\nJ+kxiPIf013hThddF0qrS1O6X2lWrlzJ9evXaVu1LQ47HTA2NVp51j0ED2Ifx9JQbIjoIPf90MND\nDFYNZlSRrPP21fjys/3PfB/7Pfen38dhtQOBDoFMd7QdApXLf4/cGPdcPliCg4Pp1q0bISEhNpOP\nDh06xNy5cy20dN8HkiSRnp7OkydPePLkCYmJiVYqITqdjoCAAMLCwihQQK7tPPDZQHYYdrzQGBZT\n1I00jVjluirHYxfeWth20qkW7rfKOaa9uKG4PHCwMXZQCkpMypwyWaEiFdmr2vvekudexUn9Sdqb\n2luvEMmKu8xMvBMhVBv6TiXghgwZwqpVq1CpVNSrVw8fHx927tzJ/fv3qf1NbY72PwrfgfKhEvu5\ncjKYj8qH/QX3c/PWTWb+PJP4+HgKFizI2NFjKVy4MGPGjGHhwoW4u7uzevXqfxzr3dTQlFAs1TuK\nUpTT6pcX9ImKiqJJkybvzXhfv2s9AwYPoP6f9fms9Ge0UbbhiP4Iv6X9RrwYj4vChYr7K7Lxu42E\nhIRQuLDtglCbNm1CqVTSpk2bHGNcjUYjarUaPz8/Lly4wOLFi+nYseNrDeY3bNjAvn37uHbtGnfv\n3kUQBP7zn/8QGBhote26P9bxnw3/wXWBK3r0eCu8UagUbE/fjs3JnufdLSgV5ITmBFpBHiT8+OOP\nSJLEzJnvVmc8k6ioKMpWLos4RUTVXmV9/2Ya8JlVhK26rcRV5coz4RlKlPjgwwIWUIhCLDUsZapx\nKmk8n33MoQ1HyZHrmutWy6Ojoxk4cCBFihRhzZosD7UkSVy5cuWDVwpJkVLI/21+TAYT2glZThsh\nTWA+82njJCsQGSQD56Xz6AQda4xrWM96s/GultT0vNmTMyPP8PTpUzIyMggICMgx4bLX4F4crHaQ\n1L6pMB0cVjswoMMASpcpTUrJFCQviRaOcnVqgOTkZPz8/Fi3bh1+fn64FXbDfbU70aWikRQSSkGJ\nl8KLIaohDI8djkln+Q7QpGq4UfwGGmXW/fPw4UN69OhB1apVbSrr5PLuyI1xz+X/BGvWrKF9+/Y5\nGpB6vf6/EgcnCAI6nQ6dTmdTNUaSJG7cuIFGo+H69esIgoCbmxvT8kzjwbMHhJnCSCVVfmm+MO51\nEKzjzFNTU3n06BFxcXHyHfqijS1A6UKlX9rnLnRhFatkIyLbcf0V/pxQnHjpvrHEEk88+XOq4PSe\nsWm0g7Wx8Pzn8cbxzFW/3kslQUxgkWkRrrjSW9kbjcLayAsMDGTVqlUYjUbi4+NxcXHhjz/+IH/+\n/Ph97ieXDzeAemqW1ZYqyXHspb1Ls3jhYqs2p06dyqBBg6hXrx5fffUVJ068/G/wMq6L17nKVavl\nUURxzHSMesqcY7sLFizIo0ePMBqNNsPP/imdWnXi6f2nfP/J9wz/czhXK1xlRMoIHkmP5A1McLfx\nXfzO+zF9+nTmzJljs50OHTqYVUbmzp1r8xmgUqmIjIxk165dCIJAjx49qFOnjnmW5GXExMQQcimE\nUsNLUaFuBRzXOxISEmJluEdFRTF2zFi+/vpr+uTJmqmpnFrZttGeSSw0iWuCtqrW3M7y5cs5eTJn\n9aR/yurVq/m87eds7bDV9gbZpSttYMKEyWhin2ofLoILBZAdECbJxFrj2iyj/SW8GBqYSZEiRRg8\neDA//fSTZZcE4ZVG+/jx49m5cydOTk6kpKSQlpZGnz59GDFixCv7Y4tdu3YRGRlJu3btbD7PbbFR\n3Eh6uXQUyy1HK5KdRD21fL8dEY/wvel77nAHDRpKUpKFwkIOchA1avop+1GyQkmkPRJBQUFkZGS8\n9Nwb1GiA7rSONcPWwETYU3MPJ0+eZOeOnVy9epXIyEj2N9rPsGHDqFmzJt988w0NGjSgdu3aSJKE\nR34PPj/5OQ0+aoAJE4WUhcgr5GVa7DRMWmvHjV6jZ/+T/bTKJ9f1OHfuHN27dzfr8+fy4ZBruOfy\nwbJnz56XFjMqXLgwly5dIjU11Uoq8r9Jeno63bt3Z9iwYdStW5fjx48jiiLu7u5sdd5KiCGEDekb\n2Ja2zWzgAeRX5GeAg1yERZIkYmJiuH37Ng4ODhQsWBAfHx9WllhJz797yp5zCdlY1cF003QuXLhA\nRkaGVZVVV1dXRnuMJsE+gT3CHkSliBIlvehFfWV9TphebjQKCOa43A8eBRwTj+W4OkFMoGlGUx7w\nQDYqsjlvpxuns0hYZFbuefr0Kbt372b79u0ATJs2jUGDBlm0t3TvUgIjA0lXpCPkybKAiqqKcuPG\nDcLCwvD398fd3Z0Xyazo+SayiHq9niFDhnDt2jWUSiXDhw+HZran5wE2S5upR86Gu1qtJl++fERH\nR+Pp6fna/XgT+vfvj6OjIwMGDKDC7go8Eh5ZrE+QEkgJTGFN4zWMGTPGynhKl9KZsn4K3/f4nvnz\n5+Pl5ZWjkVa0aFEGDhzIwIEDuXr1qpXR3qpVK3bv3s2PP/7I+PHjzcvv57/Pbfvb3A24C4DBaKCo\noahV+3///TcVK1akTx/ZaI+Pj+f27dtWYWoWPAbOQLhbuHnR6NGj+fLLL82Fet41kiSxevVqZs+Z\nzVZeYribd8CmAZ9AAqFiKJ8qP7VYlkCC5YYvhKxltlmGMjn2sV69eowZM4b9+/dbFcx6Ge7u7ly6\ndMkcojh48GDz3+NNOXr0KK1btwZg+qzpzLoxi1qKWhQWbM/8ZPL06VOYAHa/veAoUsAw0zAWCYsY\nbxpPBHKycxpphBLKQmkhO9SWs64iIvvr7GenuJMMMsiTloeZ6pl8rPrYYrsaNWpYJJG3aNGCFi1a\nmH9PSUlh5cqV9O/fH61Wi0Kh4MyZMwAkJSXx2Wef8f2E76njX4fq1aszL3UeG9I3EKOJscgjytYx\nCqoLms+3Q4cOLFmyhE8++eRVlzWX/zK5hnsuHyRJSUnExsa+9EVXqVIlqlWrxpw5cxg9evR/sXeW\njBkzhuLFizNy5EgEQaBOnTqcOHECSZIoWLAgvhpffDW+eCm82Jy+mWfiM/Ip89Hfvj8VVRW5ceMG\nsbGxeHh4ULduXYsY6Ma6xtz/5D6fXvyU68+uM6zUMPp79Dcb7BqNxsJzKooiiYmJhIeH0y2lGzM/\nmomjo6PFei1a0rEu7gSABE9NT7kkXKKWqtZ7u2bvDAk88LC5Kl1Kp2ZGTXm2A6y8jRlkMEgaxC3k\nWOi+ffuSnp5OYGAglStXZs6cOTx58oSxY8ea9wlwCKCZezMOpB8wD8JKKksyOHUwLVu2pEaNGnz1\n1VdUq1aNzz//nNatW+Pk5MSDBw8YOnQovr6+bN++nZo1a9KxY8ecT0uSiIiIYOfOndy7d48lS5YQ\nGxvLmDFj0P6mRfpBQqhubXnZGe0YljwMb5U3vex7kUdhXX+gcePGrFq1ylw45n3QuXNn1qxZw+XF\nl6G/9XqTm4nOnTszY8YMizLwS9OW8lPqT9xpcAe73+3Q99AzatQoatasia+vr4U2+IuUL1/ealmN\nGjXYv3+/OTF448aNLFq0iKjpUaTdTMP+lj1KbyWSKHFLusW89HkM1g62aic5ORmtVkupUqUwmUw4\nnnWUi5y9+AaNBYLl5c6arITz06dPs3Dhwpddsn/EuXPn0Ov1+NX0y1kt5jVxwcXqd2ececxjyw0z\njXcFIIGb5MYG9YYc27W3t8fHx4f4eBvySy9h8ODBqFQqUlNTuXfvHr169cLV9e30w9PT5eeeax1X\nYvPH8vl3n1NwQkHaaNqwULkwx9ndK+OuoGqsypIBzcZBDhIkBnGHO/KCbJMOIYQQZgzDR+VjXjYi\nYwR/SlmJ0+mkE2gIZBWrCFAFmJeXKVOGR48eERERYVPVzMHBgUGDBtGvXz927NjBRx99ZHZgjRo1\nioULF1K/fn0eP37MygsrmeMyh2Sn5KwBl5TtAzjrnfF1kpP1lyxZQpMmTXKN9g+U3Bj3XD5IgoOD\nGTBgAMeO5exNBbh16xZNmzbl8uXLrz3t+a7J1PrN7tGTJImTIelR1AAAIABJREFUJ09SokQJi35J\nkkQGGQh6gatXr5KUlESpUqXw8LBtfP4TMjIyuHTpEpIk8dFHH5nDitYb1zNaGi2XrM+8nbOrYhig\nkaoRq+xzjr3/p0Q/i6b/nv5EJ0bj7ujO/BbziXOI44H4gCEMsb1T9tLymUgQogyhoNJaqWOuYS5T\njVOzFuRQun2fch8VlRXp0qULvXv35tNPZW/jw4cPqVKlCoMGDaJdu3YUK1ZM3kWSOJlxki2pW/BS\netHTsSeHdhxiw4YN7Nq1i9TUVHbt2sWaNWs4evQoRYoUISoqimHDhjF+/Hhu3rxJq1at6N69u3mw\nl51jx44xadIkoqKiKFWqFFOnTmXHjh2EhITwxx9/sGvXLvqP64+pqgntOC2KUvJATyNqIBH0zzOa\nvVXerHJeRTF1MYv2b9y4QfPmzdm8efMrJRP/CXfu3MHH1wfFVoWsbJIZlWSERlIjpqdMx9/fnxIl\nStCkSRP8P/HnC68v5HoEeuBHYDHYudmR8TiD7t27s2pV1ndSkiRMJtNLQ36SkpJo3rw5MTExXLhw\nARcXFypVrUSoRyjiRyLKq0rs59ijX64n414GDj87yMbNc2JiYqhZsyaJiYk4OjoSGxuLspoS7Tda\nFJUV4IIcMmMC0oALwDNwdHCkQ60OBDgF0FDZkKpVq7Jr1y7KlMnZI/1PGDJkCM7OzowePZq+hr7s\nkfbkGBIDZHncMw3v54acwqTgruouSsHyRpuln8V80/yscJnnkpcVpArU0NWgAQ1oqG74yn5+9NFH\nbN269Y1j2lesWMHw4cPx9fUlLi6OY8eO4eDwdrOCno08iTZEo+6oxrDdgAkTug06ljkto5Oyk819\nypUrR+TvkSjL2863WKpYSqAYaFM6UxAForVyTkm6lE7V9Ko85alVG2UoQ5AuyGLZ/PnzmTt3LuvX\nr6d27dqvnXd0+vRpdu/ezcmTJwkKet6mEhzOOKBwzRbuYwJlqpK8hrzsLLKTYrpipKSkULlyZQ4f\nPmxzMJzL++FNYtxz5SBz+SC5cuXKaz00vL29+fLLL6lUqRLTp083e1Re5MjTIxQJLoLj3464nXRj\n7sO3T7TJyMjg0aNH3Lx5k7lz57JixQpzQmomgiBQu3Zt7ty5w8WLF4mKiiIhIYGbN29y7sQ5zp07\nh5eXF/Xq1XsvRjvIOrg1atTAx8eHM2fOcOnSJUwmE51UnbimvMZXfEVhU2FZ7zfj+ee58sQzKeeK\nrv+UiMQI6i6py8U7F4l7EkdYVBj1l9enU3gnhjwdYql+kYkASkmJIApZMfsizBZm2zTaAc6Yzryy\nLwKCOXnQ2dmZJ0+emNe5u7uze/duwsPDCQgIYNKkSfI+goC/1p+fXX9miPMQuVJpwYLcvHmTLVu2\ncP36ddq3b8+OHTu4c+cOa9euJTIykkmTJqHRaKhQoQKnTp1i//79VKxYkVGjRnHs2DGCg4P55JNP\nGDR0EPcH3ictPI1S+0uxZu0a9u/fT6lSpahTpw7t2rUj4WYClWpWIrVFKswGL5MXpqcms9EOcMt4\niwlJWV71IGMQrQ2tGVViFAPmDqBt27ZERLxcx/yf4OXlxagRoxB/FJG0kuydVgF2cEhziE2um7h6\n9Srjx48nNTWVdh+34/79+xANNAdluBLlx0qEYvK7LDMhfMWKFXh5eaFQKFCr1fz5559s3LhRDmd4\nAUdHRxYuXMidO3fIkycP8+fPJ+JmBGJhEfqB6YgJ4wkjhk0GKIbF9QPw8PDgypUr3L9/n25rusEw\nMEWYSJuaBjHIxWkeIpelvwwqkwpNBQ0pdVNYql1Kd313fNJ8sHe2t/huvUtMJhMr16/kr6Z/USWu\nyquNdshan5k38tyAF1Uip6RTVpt/o/mGyerJVKc6BaQCeIle/KD6gZ2OO5mknvRaRjtAnTp1GDVq\nVI6VhHNi5syZrF27lo0bN1KmTBlatGjBnj173qiNTFx/dcUUZkLZWIlujQ5VARVpjdNY/nB5jvsk\nJCRkaeHboIGige3wIwEkQTIXQkshJcdcAatwJGDQoEFMnjyZbt26UaxYMbN3/cGDBxYSm8nJyeze\nvZuzZ8+SkZFBrVq1mDRpEtu2bUOr1eLVywv1QLWl0Q4o1UrGFx7PJe9LFNPJA/yZM2fi7++fa7R/\nwOR63HP5IBk2bBgFChRg6NChr7X9rVu3+P7777ly5QpTp06lY8eO5pCTy6mX8Q32xZCRZREqVApm\nVZjFsILDuHr1KhcuXODp06dm1ZjMn588eWL++dmzZzx58gSTyYSzszPOzs54e3vzww8/5PiQkySJ\npKQkEhMTSU5Oxs3Njfz5878TScA3JS4ujtDQUAoXLkypUqUQBIFxaeNYznILrxsiNKABa3TW+sTv\ngoarG3Ij+ob1Clcgc2bWAdnIe36Z8oh5uGR3Ca1CS6QpEiNGSihfXhRriWEJ3xuzJVXZ8Lg7S85c\n1ciJnp07d6Zly5YMHDjQqq0zZ87wxRdfcPz4cZter+TkZCZNmkRkZCRhYWGUK1eOZcuWvVSzW5Ik\nwsLC2Lp1K9u2bSM+Pp5GoxuxrMcy+dxFoD8obiiY8ccM+ubrS8+ePfH39zfHfEdHR/Ppp5/ywO0B\nT/7zxCLuHqCsqix7XfdS3lTeymBI+SyF5aOW06NZj5wv4j8kISOBAlULINQQ0I7TIuTNKgSkNWm5\nrb1tvp4NRjQgJD0EHoK6pFzlERPom+iZNXwWnTt3xtXVlYCAAI4ePWrzeD4+PuzevZvixXMuECOK\nIgVSChAvxsPvwAygDzAanJROPHW0HgAA1LhVg7MPzkIg6HrrUFV/7uk3yrJ8C6suJEIRwXDTcKt9\n0/3TObvp7DuRwHyRdY/X0bVAV3Srdagaq+QZgFcZ7iLy/WBjsqKYWIxTCmvj/V1gMBjo1q0bjo6O\n/Lb2Ny5KF4mX4skv5CdAFYBasM74TUxMxNXVlSNHjnDu3DnOnj1LUFAQjx8/5vz58zaLPL2Muoa6\nHP/mOGpRjXaSVp4FnZqB3XY7Lv11yWZYirOzM8qdSowVXohDeh5q4i15c0u4ZdsVKsL3wvf00/RD\nkiSqplflEY+sNqtGNXbodthoQH5W3Lp1i/3797N//37CwsJITU2lZMmSuLq6EhISQpUqVcxhkuXL\nl6d69erUqlVLrmdQBk59eUqeZc2OGsroyrBftR+VoGLu3LksX76cY8eOvdd6A7lY8yYe91zDPZcP\nko4dO9KsWTM+//xzq3UmkylHibjjx48zbtw4FAoFs2bNom7dulS8UJHQh6FW27qkudBmWxs2bdqE\nVqvFYDBgNBrN/+eEi4sLM2bMoFMn29OqHzqRkZHcuXMHFxcXxpUdx3nFeZvblaUsO1Q7bCrfnDee\np7vUnSeS7EV0Ep0IVgbjpHZ65fF95vnw5JkN76M9clnv53xi9wm/Ov2KUlC+1UAnQ8qgUnolkslW\njfX5AEWMENE91bG3+l5KSaVYvHgxK1as4MyZM+TJYx0XnpaWhq+vL87OzkycOJFatXKO/9fr9Uyb\nNo3Vq1dz/PhxSpUq9dp9tjPYyV5fA9ALFA8V2K+2R6FVEKgIpO3VtvTo0QM3NzcGDx5sHqBW61uN\nq6evYj/PHoVn1rWqqq6K4CQQQoj1OQ1OQ9VYRVrg6xUYexuuSlep8LAC0kQJNoPdGDvU3dQISrly\nZ3YpzwvRF/i4w8cY8xixX2GPYCe/w/KNykfnkp25cuUKffv2pUGDBowYMYLFixfTpUsX9u7dS8uW\nLQkPD+fUqVMsXbr0lcmLYcYwKqVUskjyFRAYajeU2drZ5mWbNm3i77//pnTp0lwodoGleZfCHFA+\nUqIbo5OL4KQDd6BDyQ6cq3GOCMlyFkOMFElpkYIp1vReBuwfx33MwboH0dTXoP5SjcJdYW1AZvcG\nm5BnCfICNm7XvGJeQhXWz8sYYwzhhnDKasripnR76/6mp6dTtl1ZEt0TkX6QzLkCZRVlWahbSF1F\nXX788UeOHz/Oli1bcHBwwMnJiaJFi+Lv70/16tWJiori0qVL/Pzzz28cfjTFNIUJsyaguK1AO1We\nbXPEkTbL27Bj1g527txpVd13zZo1fPvdtzzb8wxFQYXNkDuz1KaNKrqeez1RJikpmbckZ/3Pkmif\naLG7ChVr1Guoq6r72ufx5MkTIiIiePz4MTVr1sTFRc5NSElJ4fLly4SEhLB69WoGDRrE1KlTyTsz\nL5G1I7PCIpWAvVzAaZ9qH/tX7GfWrFkcO3bMHBaYy3+PXMM9l389rVq1okePHjRv3txieWZM+7p1\n66hYsaLNOEdRFNm0aRMTJ05EEARiVDEY7YyyYahD/l8EIVjAr6If165do0KFChQuXBg3NzfzJ3/+\n/Bb/29vbIwgCx48fZ8SIEe9V2u2/wfGnx+mq7YpRkfMg5RM+Yb56vsWyGDGGGqYaltJvzz1PSlHJ\nZuVmqmty1imv/nt1YuJirFc4k1WqHbnaaWVNZZY7LSefIt9rnpUlcWIc9dPr80SQBwqSUUK7WIvj\nNkdirsdQu3Ztrl27RpEiRRg/fjyhoaHs27ePpKQkkpOT0ev13L59G7VajcFgoGrVqtSpU4cZM2a8\n8ti9evXis88+o0uXLq/dX8Hw/Ln9NwiBAg7HHRB0cnhQUbEoB7QHsBftOXDgAEuXLuXcuXMsXLiQ\n2u1qU2lWJeKnx6ObqkMVoMJBcGBMnjGM04yzeaz0SekYXA0kfpeIi8LF5jb/lBQphUKGQiQJSXAR\nGAaKNAW6tTrs8tsRZheGvWBv9rqvTlvNorRF3JXu4iQ44aP0of7G+hz96yh//fUXIHttVSoVn3/+\nOQ4ODpQpU4Zly5YRFBT0RgbHIcMhumV046n4FI2goa+mLz/ZWcoVent706ZNG3777TcMBgOUQg6P\nUYK6jRptXy3EAwlQxKEImtYas7JIJhlzMjBFmjCu+odZozng/9ifk1+ehBOgKC5XykQAwT6bDWAA\n7iN72GOAO0AdwNO6vY9MH7Fbudv8u0kyMSxhGCcyTvBIfEQhRSEaaxszNe/Ut6r1MC51HMseLyPj\nlwwMmw3yLNuXQGmoIFTgsOEwbvnkgUFGRgYajQZJkt5ZXQlREqkwoALxXvEI/QTcceczxWd8rfya\nHTt28PXXX7NmzRor5ZuGYxty7Pwx7NfloGCWOQbMnvwJ8nXfnm27/EA7rAZXnnjSWNGYHzQ/vPRc\n9ZKeZJLJS95XXpOlS5cSEhJCnz59aNehHamVUjFJJgtFIHvsqSZW4/bN2xw5cuSNHA25vDtyDfdc\n/vU0aNCA4cOHU79+fYvlQ4cOJTw8nAcPHhAdHU3p0qU5fPgwarX1FKter+fBgwf0utiLq9FXIQ2k\nVAkpTYJUUN9X43jFkd9//52WLVu+dt9OnTrFV1999d4LP71vtpi28KX45Uu3cdO7sT1uO87Ozjg5\nOaFQKGhvaM9J6WSWZ+m5V+lFOik7MUM5w2p25FjkMbpu6IpozKZFpkI2JLKr+T0vttRY3ZiVTivf\n/ASfkyFlsFa/lhPJJ4j8KRLjOSOdO3SmY8eOBAUF4ePjQ1xcHKVLl6ZDhw40bNiQli1bsm/fPubO\nncujR4/QarUMHz6c4OBgtm3b9lr1A0aOHEm5cuUYNmzYa/dVYVDIA6KnwEdgN8wOTaAGTKCTdGy3\n246PIkuh4syZM3Tt2pXr169z3f46gw4O4lKXS1Q7XI2OhTrSz6Ef7kHumE5a/4GMx4yIDUQe/+cx\n+RXvT7P/G+M3/CL+kmXMfAeK0woKrCiAs8oZnaCjnroeEx0mohSUpElpXDZcJr8iPyVVJUlMTKRp\n06Z069YNf39/ypQpI3tAnyeFb9++nbCwMJYsWUJQUNBLw2TehJikGIq7F6fetXqcaHyCjIcZkCSv\nE3wE1DXU2NW1My9z0bgwttVYvtV8a25DypBIrp5M8Z3FuVv97jvp14v0TezLkrQlcBgU8xSQBqqm\nKuy+ff4dlZCTfc8gq95kfhV0QHMwK78+H3yfFc7iIWTl3cx5OoeZSTMtqoDaYce0vNPo4NDhjfvr\nk+BjnqkT40T0y/UY1hmgCahHqDla5Sh+Kr83bvdNaNSoEb0H96Z049J44plVqRo4efIkvXv3pmrV\nqnh7exMXF0dcXByPij3iYsRFHDbkkBSbaRq9aHqJwBKycncUQCPARqSfBg3DlcMZorFO0BclkQn6\nCRwVj5JMMgUowFD1UFqqcn53RURE0Lx5c+7fv8+FCxfoFdOLK1wxr1ejppnQjEHKQVSsWDHHgmi5\nvH9yk1Nz+deTkpJiU5v95s2bTJo0iVu3bpGamoqzs7NZd/tFNBoNxYsXZ23ztThUdUBVTYW6rhpl\nOSWGYwbKUIaLFy++kdEOcjzz2yoafEjUVtTGnpfr36tVapRKJdHR0QQHB3PixAmuZ1y3fDnZlhRn\nvWk9xYzFKJ9RnghTlheyXrF6/Nr6V1ydXdHpdLg4u1CubjkcSmS7ptmmnG8ZbyFKORzkNbAT7Ch9\ntjT7P9pPnht52LNzD8OHD6dIkSLUrl2bhIQE6tevj7u7O48fP8bb25uHDx+ybNkyTp06hVarZfny\n5ezcuROfpT60NLSkfnJ9AlMD5TjpHKhatSq//fYbN2/efO2+euMtGwDOwCHImJ9Bxq8ZSBkS+clP\nMcHSo1yjRg3atGlDseLFaOPThuRFybRs05KGGxrSz6EfQUFB6L/Qo1fq0WssP2InEeVo5Xs12gFm\nqWYxVZiKnWiHIAmovleBAuJnxRMjxRAuhrMqYxWTUuTkX52go6amJiVVJQHImzcvW7duZfHixdy+\nfZvZs2fz7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SsWVOyK0aixxN4bqIigdNgy8/1YaVIEBCnO7bVXMuWcec+yLM+c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jNhcIrkvKy8ur/TjLyso4f/48Dg4OhIWFoVKpKC8vx8HB4bpz0gSNQ6lU4uzsTEJCAg4ODpSV\nlWFnZ0d0dPR1U0RZGck1m81UVFSQkZFRpQBVpVJdF9d/JycnIiIiZMe30oH39vZukw68JEkkJycj\nSRJhYWFyZNDR0ZFOnTphNBopLy9Hp9Oh1+txdHTEYDDg5OSEu7s7eXl5aDQaJEnCw8MDJycnnJyc\nqh2rxWJBo9FUi8ILBM1BTk4OBoOBgIAALl26hI+PD0FBQdja2tb6O/T09CQhIQGr1Up0dLQcmQc4\ne/YsSqUSlUqFi4uLnJposViQJInAwMBqqYilpaUABAQEyM3jVCoVpaWlJCcnU1JSgp+fH1lZWTg4\nOMi1IJ07dyYvL4/ExES6du161eM0mUwtEsxolOMuCvAENyMeHh6kpqbi6+srR7gsFgtqtRpPT0+S\nkpKoqKgAwN/fHz8/v9Y0V9AKhISEYDQaqaioQKVSXXXqtK1htVo5c+aMXARma2tLSEiIPK1rNBox\nmUyNctwlSWrR+4ejoyPh4eEYDAZyc3PJycnB19cXHx+fNuPAl5aWkp2dDUB4eHi18+Pg4FAl4ldR\nUUFRURFhYWEUFxeTkZGBg4MDgYGBtGvXrtb9VFRUkJSUhIuLi7g2CZqdyqL2yMhIcnJyaN++Pd7e\n3tfczsnJicDAQDIzM0lISMBoNOLh4UG3bt2oqKigpKQEvV6Pn58f8fHxREZGYmNjg0qlqvHa4uDg\ngJ+fX5VrsZOTE25ubtjb2+Pj44NarcbOzg6LxSI7/kqlEj8/P7RaLWVlZbVG3a1WKyUlJW3fcRcI\nbkYCAwMpKCggNzeXlJQUebmzszO+vr44OTnJP+Ly8vLWM1TQaiiVSjnaeb2hVCrp3LkzOp1OntKu\njMxmZGSg1Wrx9fVtcH5mRkYGGo2Gzp07t/gMRGWevtFoJCcnh9zc3FZ14C0WCwUFBeTl5SFJUrVm\nSFfDzs5OLqrz9va+pjMkSZLs4Pv5+eHr69skxyAQXI2CggLUajUODg6UlJQQFBRU5209PT1JSUnB\nZDLh6emJl5cXKpUKlUpFVlYW3t7e8szgtdJY7Ozs8Pf3r7LM1ta22rIrC1wrcXV1pbi4uMb9FBYW\nyoWuf2/41FwIx10gqCfl5eU4OTnVWmnu7OyMxWJBq9WKWSnBdYlKpcLV1ZXU1NQqhZ1arZYOHTo0\nOKqk1+spLi5GpVLRmj1B1Go1YWFhlJWVkZ2djVarlQvfWgqDwcDFixdp164d7du3b/JZmcpi6MqZ\nwPj4eOzs7AgLC2vwQ5dAUF9yc3Np3749APb29jWmmtZG5f3TxsYGo9HIuXPn6N69u5x+mpmZidVq\nrXOTo8bg7OxMQUFBtQfewsJC0tLSCA0NvepMV1MiHHeBoB7k5uaSlZWFnZ0dXbp0kZdbLBaKi4sx\nGAzyn6urK4GBga1orUDQcCpl1iqjularFYvF0qip4JKSEjw8PDCbzWRlZREaGtqqqSqVNSlarZYL\nFy4QGhraIkpRJpOJxMREgoKCmm1/lbnuRUVFZGZm4unpKasCCQQtQUlJCYD8UOrq6kphYWG9HlLV\najVGoxGDwYCHh4d8vYiMjESv12Nvb19vidSGUBlo0Gg0cpqgJEnk5OQQERHRooXewnEXCOqIxWIh\nOzubqKgoEhISSEtLk/OXL126hFqtlqfKHB0dheKS4LpGqVQSGBhIcnIynTp1kuUDG0PllLavry8X\nL16UI1WtjZeXF2q1mkuXLqFQKJq1aNNqtZKYmIinp2ezPiTY2NhQXl4ud3QWQQRBS5Obm1slhatd\nu3ZkZWXVa4zK6LqPj48cua+kJVMRTSYTxcXFWK3WKjNzoaGhLa7OJBx3gaCO5OTk4OLiglqtJiQk\nRL4pZmVlERQU1CK5bQJBS+Lu7k5hYSGFhYWo1epGR7YsFgtlZWUkJCRUiea3BZycnAgJCSE1NZUu\nXbo0mxpUamoqdnZ2zV4Y6uzsjMlkkpu9iECCoCWpjJIHBgZiMBhwdHTEarXW+xpS+b1t7QL/Sn32\niIiIVk+BFY67QFAHzGYzGo1G1kP+e0TOzc1NyKoJblicnZ3R6XSyznJjcHBwQKPR1CjX1hpYrVaS\nk5OpqKjAbDbj7e2No6Mj+fn5zfJQkZOTg9FolHWkmxNHR0ccHR0pLCysFqkUCJqb3NxcVCoVFy5c\nQKFQ0L59e6xWa50fiC0Wi9wRVa/Xt7r8rI+PT5sp6BaOu0BQB7Kzs3F3d68xv1d0TxXcyFRqh1cW\nlTYGPz+/NiVBWNncKCIiApVKRUJCAu7u7uj1+iaXrCwqKiI3N5cOHTq0aF6/QqFo1UJgwc2H2Wym\nsLCQsLAwUlNT8fb2JjMzE4VCUWshqV6vR6FQoFAo0Gg05Ofny3U23t7erX6fbe0o+98RjrtAcA2s\nVitarbZWFRmB4EbFbDZTVFSExWJBp9M1WL1Br9dz8eLFGvNUWxOFQkG7du2oqKiQ5Tvt7OwoLCwk\nPj6ekJAQ1Gp1o/djMBhISUkhPDy8xVNWFApFq0crBTcXeXl5uLm54ezsTEBAABkZGXTr1g2lUlnj\nQ2t5eTnx8fHY29tjsVhwc3Ojc+fOKBSKRhfE34gIx10guAZ6vR6LxUJJSQmenp6tbY5A0GJotVo5\n6tUYGUGNRiM3MbGxscHV1fWqDnF5eTkmkwmlUomNjU2Vf5uadu3aodPp5Bm1yk6NWq2W+Ph4vLy8\naN++fYMjbgUFBaSlpREUFNQquv4i4i5oSaxWKxqNhsjISABZqOFqs3XFxcW4u7sTHBxc7T3Rebw6\nwnEXCK6BTqcDIC0tTTjugpsKrVaLs7MzwcHBjU6TkSSJiIgICgoKSE5OxtPTs8a0meLiYtLS0nBw\ncMBisWC1WmUpysoc2Uptcjc3NyRJIisrC3t7e7y9vevtYDs7O6PVaoHLTVrKysqAy0ozbm5uXLx4\nETc3t3prNFutVjIyMiguLm5xubi/Ixx3QUui0WhwdHSUlVcKCwuv+cBqa2srmhXWA+G4CwTXQK/X\ny1E5SZIwmUxtfuquoqKCvLw8Oc+wpRpDCG4swsLCmiRK3L59exISElAoFHLkvLYbdaW+e00qEpIk\nYbVaKS0tpaCggIyMDJydnSkqKkKlUlFWVlZj1O5qqNVqLBYLRqNRfkCoRKVS0a5dO0pLS+v8G7Ja\nrXJnZXt7ezp06NCqUUPhuAtaCpPJJEsmA5SVlaHVauncufNVt3NzcyM9PR2j0dgkqWk3OsJxFwiu\nQUVFBZ6enuh0OjIzM9FoNHTs2LHNXmCsVitJSUk4OjoiSVKrRfoE1z9Nldpha2tLZGQk6enpODg4\nEBgYWKsjbLFYau1gWun4u7m54ebmhslkorCwsNbc2bri4+NDYmIiSqWS8PDwKu95e3uTkpKCTqcj\nODhYbmpUVFSEyWSS+zeo1Wr5gcLJyQl/f/82oTalVCqF4y5oETIyMvDw8JAL2vPy8ggICLhmoEuh\nUODs7Ixer2+z99W2hHDcBYJrYGtrK0fMioqKAEhJSWnxYlVJkjAYDNja2l71QpiRkYGTkxMODg6N\ndmgEgqbCzs6OiIiIa65ntVopLCzEzc3tmjd8W1tbfHx8kCSJs2fPypJz9f3O+/r61ir1plar6dix\nI3l5eZw7dw6lUomrqyu+vr7Y2dlhNBoxGo1otVrUajUdOnRoUzNyKpVKLr4VCJqL0tJSSktL8fDw\nIC8vDzs7OxwcHKo0YKqNiooKSktL67SuAMQdXSC4BiqVCqPRiEqlktNkysrKMJlMLbJ/q9VKamoq\ncXFxpKamkpKSIufhXkl2djZ6vZ7g4GD0en2LNa3YvXs3Tz31FDExMQQHB9OtWzcmTpzIgQMHqq27\nd+9eJkyYQPfu3QkODqZv377MnTtXfihqKC+//DL+/v74+/szZsyYem+/dOlSefuePXs2ypbmZvHi\nxfj7+6PX6zl//jxJSUno9foq62zduhV/f38OHz5cZblGo8Hf359u3bpVG/eLL77A39+fCxcuyPsw\nm83NeixXEhoaitFoJD4+nm8IXtMAACAASURBVAsXLsjf6atFjS0WC2q1mpycHC5cuIDZbGbMmDEN\n+h7UhEKhwNfXl65du9K1a1eCgoJwcXHBwcEBd3d3AgICCA8Px9/fv05Oe1Padi0cHR3lOh2BoDmQ\nJIm0tDTat2+PXq8nICCAyMhIQkNDa6w5qUx3y8vLIzU1lXPnzuHh4SGi7XVEOO4CwTUICAigpKRE\nviFXVFQAVHOU6orJZKqzM2SxWOQp/EqnQalUcv78eU6ePCk/PFitVi5dukRpaamcU2symZpdes5s\nNvPCCy8wYcIE7O3teffdd9m4cSOzZs0iPz+f2NhYueW6xWJh+vTpTJgwATc3NxYsWMDXX3/NuHHj\n+Oqrrxg5ciQlJSWNssfHx4cdO3awYMGCem8bGxvLjh07GDp0aKNsaAkqndjKfGwHBweysrKqPEz2\n798fgCNHjlTZ9siRI6jVarRaLQkJCdXec3d3lxuNtQYuLi6EhITQrVs3goKCZKfg/PnztTqgCoVC\nLma1sbFpNvnD63H2ql27dg2+VgkEdaGy2ZKbm9s1f3sGg4Fz586RkZGBwWDA3t6e6Oho/P39W8ja\n6x+RKiMQXAN7e3s6depEamqq7IQC8nR+XTGZTOTm5lJQUICNjQ1eXl7o9XpcXFzw8vKqsq4kSeTn\n58t6uEFBQfJ7fn5+lJaW4uXlJU+DJycn4+TkRHh4OEqlEovFQkVFRaOVQK7FzJkz2bZtG//85z8Z\nOXKkvPy2224jNjaWLVu2yDn2M2fOZPPmzaxfv76Kczxo0CC6devG448/zqeffspbb73VYHvs7Ozo\n1atXg7atjLa3JeWg8vJy7O3t5ddms5msrCxyc3MBSE1NxdbWFn9/f9m5ValUREVF4efnR0hISI2O\n+4ABA0hISODIkSNVungePXqUfv36tXqzkcrjrtRWDwgIoLi4mJSUFNzd3fH395edaIvFQlJSEvb2\n9kRFRVU5X4LLEXeDwdCgFCKB4FpoNBpyc3Pl64i3tzc5OTk13htzc3PJzs5uM52Tr1fEr1ggqAOV\nOtZ/b0BTVFSE0Wi85rYmk4mMjAwuXLggR84DAgKoqKjAw8NDboMOl52Q3Nxczp49S0lJCaGhoVWc\ndrhcMFipFFFYWMjFixfx8vIiNDRUvjEnJSXh5ubWrE7MgQMH2LBhA9OmTavitP+dynSAffv28fXX\nXzNjxowaI9pDhw4lMDCQ3bt3N4utW7duZcCAAYSGhjJkyBB27drV6HSFylSS8+fP89BDDxEWFkaP\nHj1YuHBhtajT2bNnmTBhAh07diQsLIxRo0ZVc6grx7tw4QKxsbFEREQwefJk+f3KtBilUomPjw8A\nISEhBAcHo1Ao+Ouvvxg9ejRLly6VI/H9+/fnzz//rDLDc+TIEfr160ffvn2r2JCcnExubi633npr\nFbvS0tJ4/PHHiYiIoHfv3nz00Ud1imjX9fxc7bj37dvH/fffT1hYGH369GHRokUkJCRw8eJFSkpK\nKC8vJykpiQMHDjB+/Hiio6O544472LlzZzV7Xn75Zfr06VNteU3fg7Nnz/L000/TuXNnwsLCGDBg\nAB9//HG1da71mQJs27aNAQMGEBISUqttzYlSqcTBwaFK0EEgaAqysrLIycmp8sDs7u6OxWIhOzu7\nyrqVqTHR0dHCab+C+haPC8ddIKgH3t7edOrUCR8fH1QqFenp6bU6MZU6zpUOe5cuXQgKCsLW1lZ2\ntD09PfH29iY5OZnExETOnTtHeXk50dHRREVF1ZijrlKpiI6Oxs7OjoKCAkJDQ6sU1hUUFFBSUtLs\nU4+ffPIJarWaadOmXXPdZcuW4eLiwjPPPFPrOoGBgeTk5DSliQDs37+fadOmERkZyerVq5kyZQpv\nv/02ycnJTTL+008/zcCBA/niiy948MEHWbp0KR999JH8/unTpxk5ciSFhYUsXryY1atX4+7uzmOP\nPcZff/1VbbynnnqK/v37s379ep577jl5eVFRES4uLrRr106ucbBarTg6OrJ+/XpeeOEFnn32WT7/\n/HNZleXWW29Fr9cTFxcHXNZIv3DhAv369aNfv35VHM3K/1/puE+cOJEBAwbwxRdfMHz4cBYtWsSm\nTZua7PzUdtz79u3jiSeewMnJiZUrV/LBBx9w8eJFpk2bhiRJ5OTkEB8fz5kzZ5gzZw7h4eGsWbOG\nqVOnMmfOHJKSkups4985efIk999/P6mpqcybN4+vvvqK5557roojUtfP9LfffmPq1KlNZltDqYy6\nCwRNgSRJpKamUlRURHR0dLUAUUREBFqttsr1vLS0FFdXVzEjVgOzZs2q1/oiVUYgqCeV0/dBQUFc\nunSJixcvEhoaWk3Crri4GIPBQJcuXa6aa+7v7y9L26nV6joVtzk7O9fo1CcnJ2M0GomOjm5W7eii\noiJ+//13Ro8efc1umnl5eRw7dozx48dfVZpSq9U2i9784sWLiY6O5osvvpBTQDp27Mg999xTTfqv\nIYwfP54XX3wRgMGDB1NaWsrKlSt59tlncXV15b333qN9+/Zs3rxZ/mwHDx7M4MGDWbp0KevWrasy\n3qRJk3j22Wer7cfOzg6DwUBBQYGcy56amso///lPPvnkE+bMmVMlQg//c8KPHDnCLbfcwtGjR7Gz\ns6N79+64u7uTlZVFeno6QUFBHDlyBGdnZ7p27VpljOeff57Y2FjgclrT77//ztatW+VljT0/tR33\nPffcQ0hICBs2bJBTvnr37s3tt9/Oxo0beeeddwCYPXs2kZGRrFu3Tp5xioqK4r777quTis2VvPPO\nO3h4eLBjxw75+zpgwIAq69T1M128eHGT2tZQJElq9fQnwfVPpeKTRqNBoVAQFRVVY/pVpfzruXPn\n8Pb2xsbGBr1e36bSENsKBQUFrFq1ql7biIi7QNBAFAoF4eHh+Pr6kpCQgFarrRJ9t1qtqNXqaxaI\nKhQKHB0dcXV1bZSMnCRJlJSUEBER0ezSb+fOncNqtdZJErMy2hsdHV3rOgaDgYyMDLp06UJZWRlP\nPfUUAwcOZNiwYcTGxpKamtogOy0WC3/99Rf33XdfFcelUtGmKRg1alSV16NHj0av13PhwgWMRiOH\nDx9m5MiRKJVKzGYzZrMZSZIYNGgQR48erTbevffeW+N+vL29iYqKIjw8XHZ4//3vf7NixQpmzZrF\nxIkTq20TEhJCQECAHE0/cuQIPXv2lKUZvby8ZNWZI0eO0KdPn2oPfMOGDavyumPHjmRmZtbx7Fz9\n/NR23AaDgbi4OEaNGlWlTiM4OJg+ffrINlssFk6dOsX9999fxYHo2bNntRSzumAwGDh27Bhjxoyp\n9SGzrp9pU9vWGCqLdgWChlBWVkZqaiqnT58mPz8fLy8vIiIirlozodfrsbe3l793er1e9BSpAXd3\n92s2qLoSEXEXCBqJl5cXTk5OpKWlkZmZKRfVmUymWhvJNDUmk4nMzEwcHByavSAVLk97AnXS3a1U\ntPDw8Kh1na1bt1JWVsaIESMAmDBhAkOGDAFg7dq1vPbaa2zevLnedlZGp68s/q2r7XXhynEqX+fk\n5BAcHIzFYmHp0qUsXbq0xu2vLBqsTU+8JrZv307Hjh154IEHan1A7NevH7/88guSJHHkyBEGDx4s\nv9e3b1+OHj3KgAEDSE9P54knnqi2/ZVFZnZ2dvVqT3618/N3/n7cRUVFSJJU47nw8fEhIyMD+N/n\nW9Nn2ZDPt7i4GKvVetU0s6Kiojp9pk1tW2MQjrugIVTmqmu1Wry8vOrcoyAtLQ2dTkdYWJi8rFI0\nobmVzq43FAoFc+fOZfTo0XXeRjjuAkETUNl4pbJJkk6nQ6VS4efn16z71el05OXlUVxcjJubGxER\nES0yJV7peGRlZV1z3cpzUFuU1mAw8NlnnxEaGsro0aOxs7OTnXa4HKH87LPPGmSnh4cHtra2aLXa\nau9pNBrat2/foHGvHOfvRcsajQa4fNyurq4olUqefvppHn744Rq3vzJqVZ/P79tvvyU2NpapU6ey\nYcOGGmdabr31VrZu3cqff/5JXFwcb7zxhvxev379WL9+vRzBrpSQbEqudn7+zt+P283NDYVCQV5e\nXrXx8vLy5OK2ys+3cswr9xsYGCi/tre3r7H3QmFhoTxe5ed1ZWHd36nrZ1of25obq9UqHHdBnalU\nNcvMzMTFxYWOHTvW2+G2WCxykTxcjiwXFhYK2cca+Pv9ri6IVBmBoAlRKBQ4OTnh6+tLYGBgk0S/\nJUkiOTmZ8+fPc/bsWU6fPs2pU6c4ceIEly5dQq1W06VLF4KDg1tM7q1bt274+vry7bff1hh9NRgM\nnDp1CoBbbrkFX19fNm3aVM1xKi8v55VXXiEjI4NPPvmkxmjO2rVrueeeexpkp42NDT169ODHH3+s\nUrn/119/kZaW1qAxr2T79u1VXm/btg0nJyc6duyIo6Mj/fr14+zZs3Tv3p2YmJhqf42hQ4cOfPfd\nd1y6dImxY8fWqHNe6Yx/+umnSJJE79695ff69u1LcnIyP/zwA2q1mh49ejTKnpq42vmpDUdHR7p3\n784PP/xQ5eafnp7O8ePH5WOysbEhJiaGHTt2VElTO3HiBOnp6VXGDAwMRKPRkJ+fLy9LSUmpUijq\n6OhI37592bJlS62KUXX9TOtjW3MjIu6CuqLT6Th//jwajYbw8HCCg4Pr7bQHBwcTGhpKamqq3JvD\n09MTjUbD+fPnSU1NRaPRyD1RbnbqG2wTEXeBoI2j1+sxGAwEBwdjY2ODUqlEqVRiY2PTagVntra2\nLFiwgGeffZYRI0bw7LPPEhwcTGlpKceOHeObb77h7bffJiYmBltbWz744AOeffZZRo0axTPPPIOv\nry+JiYmsW7eO7OxsvvjiiyoOZSUff/wxycnJ9VIxuZIZM2YQGxvL008/zRNPPEF+fj5LlizBx8en\nSR50NmzYgNVqJSYmhl9//ZV//etfzJgxQ85DnzdvHg8++CBjx45l7Nix+Pj4UFBQQFxcHFartd6K\nAlcSHR3Nd999x8MPP8y4ceP417/+VaXINyoqCi8vL37++We6d+9eJSrfrVs3nJyc+Pnnn7n99tub\nZRr7WuenNl5//XWeeOIJnnjiCZ566in0ej2LFy/G2dmZ559/Xl6vps930aJFsmRmJSNHjmThwoVM\nmzaNyZMnU1BQwCeffFIthWvu3Lk8+OCDjBw5ksmTJxMQEEBqaipnz57l/fffB+r+mdbVtuZGOO6C\na1FRUUFGRgY6nY6AgIBGSzY6OzsTEBBAWloaSqUSf39/YmJiKCsrQ6/Xo9friY+Px9fXt8V/D9c7\nIuIuELRxSkpKcHFxwcnJCQcHB+zs7FCpVK2uEnHvvffy/fffExQUxLvvvktsbCxvvfUWp0+f5oUX\nXqgSJR8+fDjfffcd7u7uvPXWWzz++OOsWrWKQYMGsX///hqnCj/77DN+/PFHNmzY0KiipjvuuIPl\ny5eTmJjIxIkTWb58OXPnzsXHx6dGZZ76sm7dOn777TeeeuopvvvuO1555RVeffVV+f3u3bvzn//8\nB3d3d2bPns3YsWN5++23uXDhQjXpxYYSGRnJli1byMjIIDY2Vq5BqOTWW29FkiT69etXZbmNjQ29\nevVCkqQms+VKrnV+auPOO+/kq6++oqSkhMmTJ/PGG28QFRXF999/XyXNZtCgQSxfvpykpCQmTZrE\nihUrePfdd6uptoSFhbFq1SpycnKYOHEiK1asYN68edWUhWJiYti+fTsBAQHMnj2bxx9/nBUrVlSZ\n4q/rZ1pX25ob0XxJcDVKSko4d+4cdnZ2dOrUqcl01t3d3enUqRN+fn7k5ORw9uxZtFotJpMJe3t7\nbG1tyc3NrTGFTVA7ivoIvysUCunv62dmZjZKBUMgEFyb+Ph4vL29rxmhvJFYuXIlW7ZsYdOmTXXu\nTvvyyy9z6NAhDh8+jEKhuGqEMSsri9tuu42XXnqJ6dOnA5dTkiwWC9OnT+fgwYOcOHHiqvtbvHgx\nS5YsIT09vUUKgq8XJEmiuLiYTz75hBUrVojz0wa4cOECYWFhQtVDUA2tVktmZiahoaHNIsf7d3Q6\nHQaDAbPZjNVqxdnZGRcXl1YPQrUGZWVlVeQxXVxckCSpTidCXE0FgjZOfVU8rneysrJ45513CAkJ\nkYv/bGxs2LVr1zW3zcjIICgoiP79+7NlyxbgsnzfvHnzGDhwIB4eHqSlpbF8+XLUajXjx4+Xt122\nbBkLFy4EEAVUDUCSJLRaLXl5eahUKoqKilrbJMF/qbyGCMddUIkkSWRmZlJYWFil82lz0q5du2Z/\nOLgZEI67QNDG8fT0JCsr66bJAwwICLiqqkdtzJgxg6effhqgys3BxsaGvLw8Zs2aRWFhoVxcuGrV\nqipyg+PGjZNTdsRMYv2oLKA2Go2yFKparW5tswT/5WZ7+Bdcm8p89ujoaDEjdp0hUmUEgjaOTqcj\nPT39qg2MBILWJDU1FbPZjNFoxNXVFScnp6vq9gtaFo1GQ3l5eRVZTsHNi8FgICEhgY4dOwqnvZVo\nTKqMqFYRCAQCQYPRarWUl5fj5eWFra0tQUFBVZx2i8VCfn4+ubm5rWjlzY2dnZ2Q3hMAl2fHUlNT\n8ff3F077dYr41ASCNk5FRcVNWbwjuD4wm820a9eOwsLCGqPs8fHxODg4YDKZ0Gq1+Pj44OXlJb7T\nLYi9vb1IlREAyH0M/h7tFVxfiIi7QNCGKSkpIS0tTRRLCtosKpUKrVaLTqerJiNXUlKCjY0N4eHh\nREREyFrRTdX8SlA3KiPu9UmNFdx4mM1mMjMzW7Rrr6DpERF3gaCNUlJSQnJyMmFhYaISX9BmcXNz\nw9bWtkZZt0qHsbS0lOLiYry9vYXT0ApUNmwzmUyiLu0mJjMzEzc3N6EudJ0jIu4CQRvl0qVLwmkX\ntHlUKhWurq41pr44ODgQFBRETk4OkiRhMplE1LeVEOkyNzd6vZ6ioiIxe3sDIBx3gaANYjQaUSgU\nwmkXXPe4uLhgNBqxWq2UlJRgNBpb26SbEltbW+G436RUFqQGBARctTGd4PpApMoIBG0QnU6Hs7Nz\na5shEDQapVJJhw4dKC0txd7eXkzTtyCVBcHFxcWYzWa8vb1b2yRBK6DValEqlUKi9QZBOO43EFar\nlaKiImxsbLC1tf3/7d17WJNXngfwby5ckiAhJFxCkKsSRKWjYp0yutXa0WnHVRw72nZWpK2rtXbH\n6dhaL7VVqB0vYzsqrdrdrsjUrVoVQe1YbbUzQ51t6/0CAorhkiCQhATI/fLuH2zeIRIgkACJPZ/n\n4Wl98yY5ycl73t973nN+B1wuF2azme7tenDiGOG72traSOBOPDSCgoIGZWVG4p/sdjvu3r0LHo+H\n+Ph48Hg8ksnnR8gxIXXEiBFDXRTCS0jg/hCx2+2orq4G0DHuVCwWo66uDsHBwWAwGCRw9yNtbW1k\nLCJBDCClUgmr1Yphw4aBx+MNdXG8rr6+HhwOBwkJCUNdFGIQUBQFs9nsdIFstVpRXV2N8PBwspLx\nQ4SMce/k22+/RWRkZJc/f7lSZbPZGDVqFN3bbjKZEBwcTPe6W63WoS5iFwqFAmvWrMFTTz2F+Ph4\nREZGdpsqzmg0YsOGDRgzZgzi4uLw1FNP4R//+Mcgl3jgGY1GMBiMIc/+cPLkSbz00kvIyMhAYmIi\nJk+ejE2bNqG9vb3LvhqNBitXrkRaWhqSkpIwf/58lJeXD0GpfxzOnz+PZ555Bunp6YiPj8f48eOx\nZMkSVFRUdNlXLpdj8eLFSElJwciRI/Hiiy+ivr5+CErtG/R6PSoqKtDS0gKr1Yq6ujrcuHEDbW1t\nXn+v5557DmKxGJs3b+7y2EAeM2q1GjqdjqyU2klfzu8ajQavvfYaUlNTkZCQgHnz5qGsrGwISt0R\nkPc2oVun06G8vBw3btzA5cuXoVQqodFocOvWLXA4HMTExAxSaftHoVBg3bp1mDVrFhITE+lOR1eM\nRiNyc3PxyCOPIDExEbNmzXIZB9jtduzcuRMTJ05EQkICpk+fjpMnTw70RxkUpMfdhffeew8/+clP\n6H/70+piPB4PUVFRaGpqgsViQVpaGgwGA8xmM5hM37tOu3fvHoqLi/HII49g0qRJ+Oabb7rd93e/\n+x2++uorvPPOO4iPj8d///d/Y8GCBTh16hTGjh07eIUeYFarFQEBAUNdDOzevRsSiQRr1qyBWCzG\nzZs3sX37dly4cAEnTpygf08URWHRokWoq6vDpk2bwOfzsWvXLsybNw9fffWVz580/JFGo0F6ejpy\ncnIQHh4OuVyO/Px8zJo1C+fOncPw4cMBdASpv/71rxEYGIgdO3aAwWBgy5YteOaZZ3Du3Dm/GG/e\n0tICrVYLi8UCsVhMT9hWq9VQKpXgcDgQiUTgcDhob2+HyWRCYGAgQkJCugwNMZlMuHPnDiQSCb0A\njc1mQ1lZmdcvlIuKiroN9gbqmLFarWhuboZSqYRUKiUTEV3o7fxOURQWLlyI2tpavPfeewgLC8OO\nHTvwq1/9CufOnRuU9swx7FWtVqO1tRV2ux1MJrPLH4vFAoPBgMFgQExMDBgMBmQyGRobG2EwGBAW\nFgYOhwOKonx6mJRMJkNJSQnS09MxadIk/PWvf+1239///vf4+uuvsX79esTHx2Pfvn14/vnnceLE\nCYwZM4beb8uWLdizZw9Wr16N9PR0HD9+HEuWLMGf//xnTJ8+fTA+1oDxn4i0E7lWjuPtx6GklBAx\nRMgKyYKEL/Ha648cORIZGRlee73BJhKJoFQqERgYCCaTCR6P5/GtYLlGjuPNx6G0KSFiiZAVkQVJ\nmOff+WOPPUaf3D799NNuA/ebN2/i2LFj2LFjB5577jkAQGZmJqZMmYKtW7fiz3/+s8dlGSg1NTUw\nGo1ISkpyKyB3ZH8wGo0IDg7udX+5So6S6hI0m5oRERSB2UmzIRF6Xjf79++HSCSi/52ZmYmwsDCs\nWLECFy5cwOTJkwEAX375Jb7//nscOXIEP/vZzwAAGRkZmDRpEj766CO8++67HpfFX8mb5Ci5UoJm\nXTMieBGYPW42JJGe183cuXMxd+5cp23jxo3DlClTcOrUKbz88ssAgAMHDqCmpgalpaVITEwEAKSl\npSEzMxOFhYX0fr6Coig0NDSAoihIJBLU1tZCr9fTqQw7DwOor69HfHw8DAYDqqqqwOfz0d7ejpCQ\nEPr44XK5EIlECAsLA9CxCjGLxYKmTYP9JfvRamgFh83B1PFTvToGX6vV4p133sHGjRvxyiuvdHnc\n28eM43tTqVQICwtDamqqW22HL5LL5Sg+fhxKpRIikQhzsrIgkQze+f306dP47rvvcOzYMbqNy8jI\nQEZGBvLz8/Hee+95rSydURSF1tZWqFQqaLVa8Hg8CAQCxMXFgcViwW63d/vH5XLBZrNht9sBdMwp\nMZvNYLPZaGlpQV1dHWJjYz2enNoml8NaUgJmczPsERFgz56NYV6om5/+9Ke4ceMGgI42q7vA/dat\nWygqKsIHH3yAZ599FkBHDDF16lRs27YN+/fvB9AxDG7Pnj149dVXsWzZMgDAz372M8hkMmzatIkE\n7oPJZrMhtzYXBWEFMET+M6XY1vatyLmXg7fj3h7SHobr169j3bp1uHr1KoRCIXJycmAymbB9+3Y0\nNTUNWjnYbLbTlacnbDYbcstzUcAqgIHb6Ttv2Iqc+hy8Pcqz79zduwBffvklAgICMGfOHHobm81G\nVlYWdu3a1eWk/iClUol169bh7NmzYLFYeOqpp/D0008jOzsbRUVF9MnT21paWtDa2tqnOx5BQUGI\njIxEZWUlpFJpt5/LZrMh7+95KFQWwhDwz7rZVrMN2aJsrJ+y3qO66Ry0Ozh6qhoaGuhtZ86cQXR0\ntNN3GBoaip///Oc4ffp0r0FITU0N1q5diwsXLoDH4+GZZ55BcnIyVq1ahe+//57uPfYnNpsNeUfz\nUHijEAZ0qpvSbcgem4318zyrG1ccc1g69yCeOXMGEyZMoIN2AIiLi8PEiRPx5Zdf9hq4X79+HevX\nr8e1a9cgFAqRnZ0Ns9mM999/3+k34C2OXNM2mw1hYWGw2WwwmUywWCxITEykL3wtFgsYDAb4fD74\nfD7UajXa2towatQo+nt1ZFS5d+8eUlJSwOPxwOVyUXCyAJ+Xfg6T7Z+pEfee2ovsGdlY/7J36iUv\nLw9SqRRz5851Gbh7+5h58sknERcXh23btuHixYt+GbTbbDbk5eaisKDAKWXoH7duRXZODta/PTjn\n9y+//BLR0dF00A501M2MGTNw+vTpXgN3mUyGNWvWOLVnI0aMwOuvv46LFy8iLi6O3peiKLS3t0Ot\nVqOlpQVBQUEQCASQSCRd7gQ4etl7wmQywWAwoNFoEBwcTM9vqKys9KjH3WazQZ2Xh7jCQvA71Y12\n2zbUZmcjfL1nx01f44DZs2fT29hsNubMmYP8/Hw6Djh//jzMZjPmzZvn9Px58+bhtddeQ21trVM9\nPEiv12Pjxo04ceIEzGYzpkyZgldeeQWzZ8/Gn/70JyxYsKB/H9RL/Cpwz63Nxe7Y3V22G0IM2B2y\nG6gFNiZu9Ph9li1bBrVaDT6fj2nTpuGtt97qdbU/lUqFefPmITo6Gvn5+QgICMDevXu7HaflL3LL\nc7F7mIvvnGvAbuwGyoGNYzz/zntTUVGBuLi4Lrf2U1NTYTabce/ePaSmpnb7/BdeeAHl5eVYt24d\nEhMTcerUKaxdu3agiw2dTgeTyQQOh9Onhi0mJgYGg4HubXQl7+952KvdCzzQiW8IMHRs/zuwYeoG\nD0rflWMs4ciRI+ltFRUVkEqlXfaVSqX4/PPPodPpur3jYzabsWDBAphMJmzevBkikQgHDhzw+7GI\neUfzsPfG3i7bDTDQ2zfM3+Dx+9hsNthsNtTX12PTpk2IjIxEVlYW/XhFRQVmzpzZ5XlSqRQnTpzo\n8bVVKhXmz5+PqKgo7TnpVgAAHs1JREFU7Ny5EwEBAfj4448HtE1jMpmw2+2IjIxEdXU1wsLCkJaW\n1uVOleNulNlsRl1dHRgMBqxWK8rLy+leSAaDATabjZiYGHpiXt6ePHz610+7vK/BYsDeU/9fL8s3\nePQZvvvuOxw5cgRfffVVt/t465h59913YTabcebMGXz77bcelXuo5eXmYu9uF+cag4HevmHjwJ/f\nKyoqXJ5LpFIpDh8+TN/VccVsNuPXv/41TCYTtmzZApFIhE8//bRLe6bX66FWq6FWq8FisSAQCCCV\nSj0eruUYC5+amkoH/mazGXq93univa/UeXkYu7dre8Y3GDB2717cABCxYUO/X99dlZWVLuMAqVQK\ns9kMmUwGqVSKyspKBAUFdfnMjmPO8TrdeeONN3Dy5EmsXLkSjzzyCEpLS7F8+XLvf6B+8pvAXa6V\noyCsoMd9CsIKsFS7FDH8/o1BCw0NxbJly5CZmYlhw4bhxo0b2LFjB55++ml8/fXXPebA3bNnD/R6\nPQ4dOkSPgZs2bRomTJjQr7L4ArlGjgJWQY/7FLAKsFSzFDFhAzvur6Wlhb7d3Zljm0aj6fa558+f\nx3fffYf//M//pHvsn3jiCSxcuHDAJ+mJRCKwWCx6PG1fcLlc6PV6l9mA5Co5CpWFXYL2zgqVhVii\nWoIYoXfqpqGhAdu2bcO//Mu/OI0R1Wg0LnvFO9dNd0HIoUOHUFNTgy+++ALjxo0D0FE3Tz75JORy\nuVfKPdjkTXIU3ijscZ/CG4VYMnUJYiI9q5unn34a169fBwAkJibi888/d7pTotFouj1utFptj6+9\nd+9e6PV6fPbZZ3SbNnXqVDz66KMelbknXC4XAoEAOp0OAoEANpsNAQEBsFgsaGtrA4/HQ1BQEIxG\nI3Q6HSoqKsDlcungnMFg0D2TD/Ywyu/LUXiml3o5U4glzyxBTFT/6sVisWDVqlV4+eWXe0xq4I1j\n5rPPPoNIJEJ0dDReeuklTJs2rV9l9gVyuRyFBQU97lNYUIAlS5f2e4x5T+f34uJihISEwG63o7m5\nGSKRCPX19fRFIEVRdHKHsrIypKenu7yrcfDgQdTU1OD06dMYP348AGD69Ol03TQ1NaGtrQ02mw0C\ngQBJSUlezfZisVjogJ3FYqGqqgptbW0IDw/v9xy3NrkccYU9HzdxhYVoWrIEwwZ4/H9LSwv4fH6X\n7Y7jpqWlhf5vaGholzbgwf1cuXPnDoqKirBu3To6WH/88cdhMBjwySefeOVzeMr3Zit243j7cRhC\nel5xzxBiwPH24/1+j7Fjx2Ljxo2YOXMmMjMzsXTpUhw8eBDNzc34r//6rx6fe+nSJWRkZDg1KhwO\nBz//+c/7XZ6hdrz5uNPwGFcMXAOOK/v/nburu1n17iyffunSJbBYLDz99NNO22fNmuWVsvUkODgY\nYrG4Xz0pXC6321UmS6pLnIbHuGIIMKC4urjP7+uKTqdDTk4O2Gw2PvjgA6fHupv45E7dXL58GRKJ\nhA7aAYDBYOCXv/yl54UeIiVXSpyGx7higAHFVzyvm127duHUqVP46KOPEBISgmeffdatHnF362bC\nhAld2rQnn3zSozK7YjQaoVQq0djYiNDQUAAdw9siIiJgMplQWVmJlpYWVFVVoby8HDweD0KhEHw+\nHzabDTU1NaipqUFAQAA9Ye9BJd+UwGDppV4sBhSf73+95Ofnw2g0YsWKFT3u58kx8/333yMqKgox\nMTEYMWIEoqKiwGAwBqU9GyjFx4/3uqKuwWBA8fGBOb/v2LEDOp0ORqORrgMmk4nAwEBwOByEhITQ\ngTqDwcDt27dRVVVFZ+8xm82gKAqXLl1CbGwsHbRbLBY0Nzdj0qRJ9L+HDx+O0aNHO90J8haLxQKL\nxYLbt2/j5s2b4HK5iIuLQ2RkZL9f01pS4jQ8xhW+wQBrsXfONb1x57jx9JxEURT+9V//1Wm7L52T\n/KbHXUkpvbqfu9LT05GcnIwrV670uF9jY6PL22v+vFKd0ubmd+7mfp4QCAQue2AdvYauehUdGhsb\nERYW1uV2u6/XTU+Be7Op2a3XUJo8rxuj0YhFixahtrYWx44d69LjFRYW5rIHw926cTWW3tU2f9Gs\nc7NudJ7XTUpKCgBg/PjxeOKJJ/Doo49i165d2Lp1KwCAz+e7vBul1Wpd9lx11l2b5s26MRqNqKqq\nAovFAo/HA5vNRkNDAwwGA4KDg+mJemKxmL5rVVVVRQdA1dXVdBaZqKioHt+rucW9eim/U46bN28i\nKSmpT1l36uvrsXPnTvzxj3+E2WyG2WymHzObzdBqtQgJCQGLxerXMWO1WqFQKFBTU4OIiAikpqY6\nBSe+3p71RKl081zj5n7uSkxMhEQiQV1dHX0HRCQSwWg0dvk9WSwWAB3DLTgcDjQaDVQqFR0sW61W\nVFVVgcfjobKyEhRFQa/Xg8/n02PNxWLxgK4ZcP/+ffr/U1JSvDLXgdns3nHD9HLduBIWFtZjHOC4\nOy0QCKDVarsE8A/u54pjPuKD7ZwvHV9+E7iLGO6dLNzdry/cSaUUFRXlslFpdvNH74tELDe/czf3\n84RUKsUXX3wBvV7vdDKtqKhAYGBgj+P3oqKioNFoYLFYnIJ3X6+bgIAAMBgMl6uoRgS514iYVWZc\nu3YNoaGhdHaCvrBYLFi8eDGuXLmCw4cPY9SoUV32kUqlLrMAVFZWQiKR9HiiioqKQmVlZZft3j5B\nD6YInnt1I+J597hxBAgymYzeJpVKXeZ2r6yspIP+7nTXpnmzbmprayEWi7ucJO12OwwGA52L3PH7\npyiKXp8C6PjMAoHArcXlIgTu1Uu0MJouW0/zZlx9FqPRiFdffbXLY7t378bu3btx9uxZjBkzps/H\njFqthlwuh1AoRHJyMkpLS7uck3y9PeuJuxeD3rxoNBgMqK6uBpvNdmoX3a2b8PDwLllakpKSUFpa\nioiICFAUhWHDhoHJZLpc+8Lb9Ho9jEYjAgMDERER4bUJynY3A1b7IHS2SKVS/OUvf+kSB1RWViIw\nMJC+QEpJSYHJZIJMJnOKDRznmp7aPsfdCaVS6TQO3peOL78ZKpMVkgVOe8+3lTjtHGSFZPW4T19d\nvXoVd+/epW99dWfChAm4ePEiFAoFvc1gMODs2bNeLc9gyorIAkffy3eu5yBL5N3v3JWZM2fCYrGg\npKSE3ma1WlFcXIypU3tO5TZhwgTYbDZ88cUXTtt7m5znCa1Wi4aGBty6dQsmk6n3J3QjISEBNTU1\nTr8rAJidNBscSy91Y+Hg1cdfhVQqhU6n63VM84PsdjuWL1+O0tJSFBQUdDtfY8aMGWhoaMCFCxfo\nbW1tbTh79qzLiZGdjR8/HnK53OmOFkVROHXqVJ/K6ktmj5sNDnqpG3AwZ9ycHvfpq+bmZty5c8dp\n0Z0ZM2bg8uXLqKmpobfV1dXhhx9+cKtuLl261KVN62nSZV9ZLJYuF6UA6DS2kZGRTo/rdDqw2Wz6\neBcKhW6vCD176mxwAnqul2BWMH455ZeIj4/v88J7o0ePxtGjR7v8AR3ZLI4ePUoHEX05ZgwGAxQK\nBaRSKYYPH46MjAzU19fj8uXL9D4URfn1hO45WVm9DhvhcDiYk+Wdc43ZbEZVVRVaWlogk8mczu+e\ntGcZGRlQKBS4e/cu+Hw+mEzmoLVnwcHBsNlsANDt5Nn+YM+eDW0vdaPlcMCe4932zJUZM2bAYrE4\n/dYdccDjjz9OtwtPPPEEAgMDcezYMafnHz16FKmpqT1OTB0/fjwYDEaX+MCXji+/6XGX8CXIuZfT\nkT2mGzmaHMQk9n9yxMsvv4z4+HiMHTsWfD4fN27cwM6dOyEWi7F48eJen1tQUIAFCxbg9ddfR2Bg\nIPbs2YOgoCCfXvigJ5IwCXLqczqyx3Qjx5bj8cRUxwFy7do1AMDXX38NkUgEoVCIzMxMAB3jE7Oy\nsrB+/XpYrVbExcWhoKAAtbW12O0iE0Fn06ZNw6RJk7By5UqoVCokJibi5MmTuHXrFgD3U1G5y2az\n4c6dO/R4Ok/SZPH5fKSlpaGyshIhISH0+F+JUILn+c/jE333k2VyInIQI4zB5cuX6fHAfbFmzRqc\nOHECK1asAJfLxaVLl+jHxGIxPWRm5syZyMjIwH/8x39g/fr19GIyFEW5TIXX2YIFC/Dhhx/ipZde\nwurVqyEUCnHgwAF6eIc/HjuSSAmyx2a7zCrjkD0226OJqS+88ALGjh2LtLQ0DBs2DHfv3sXHH38M\nNpvtlOLx3/7t37Bv3z7k5OTgzTffBIPBwNatWxETE4OFCxf2+B5Lly5FYWEhnnvuOaxcuRKBgYHY\nu3cvAgMDvVYvLBYLVqvV7Rzq3U22dYckWoLsGdl09hhXFv1iER4Z80i/Xp/P59Pt1YNiY2OdHnP3\nmKEoCjU1NYiNjaUD22effRa7du3CCy+8gDVr1tCZSxwX5r640F5vJBIJsnNyXGaVccjOyfFo8SPH\n+X306NHQaDRQKBTYt28foqOj8eKLL9L7+Wt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      "text/plain": [
       "<Figure size 1008x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib import cm\n",
    "\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "cdict = {'red':   ((0.0, 0.0, 0.0),\n",
    "                   (0.5, 0.0, 0.0),\n",
    "                   (1.0, 1.0, 1.0)),\n",
    "         'blue':  ((0.0, 0.0, 0.0),\n",
    "                   (1.0, 0.0, 0.0)),\n",
    "         'green': ((0.0, 0.0, 1.0),\n",
    "                   (0.5, 0.0, 0.0),\n",
    "                   (1.0, 0.0, 0.0))}\n",
    "\n",
    "color_map = mcolors.LinearSegmentedColormap('my_colormap', cdict, 100)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14,16))\n",
    "my_map = Basemap(projection='merc', lat_0=56, lon_0=11,\n",
    "              resolution='h', area_thresh=0.1,\n",
    "                llcrnrlon = 7, llcrnrlat = 54,\n",
    "                 urcrnrlon=13, urcrnrlat=58)\n",
    " \n",
    "my_map.drawcoastlines()\n",
    "my_map.drawcountries(color=\"grey\", zorder=1)\n",
    "my_map.fillcontinents(color='grey', zorder=1, alpha=.1)\n",
    "my_map.drawmapboundary(fill_color=\"white\")\n",
    "\n",
    "#We filter out teh really bad wind tubrines with a too low load factor\n",
    "df_data=df_all[(df_all[\"Estimated load factor\"]>0.1)]\n",
    "\n",
    "\n",
    "data=np.array(df_data[\"Carbon footprint per kWh\"])\n",
    "\n",
    "p_max=max(data)\n",
    "\n",
    "x,y = my_map(df_data[\"lon\"].tolist(), df_data[\"lat\"].tolist())\n",
    "my_map.scatter(x, y, marker='o', edgecolor=None, zorder=1, cmap=color_map, c=color_map(df_data['Carbon footprint per kWh']/1000))\n",
    "\n",
    "\n",
    "l1 = plt.scatter([],[], s=100,  edgecolor='black', linewidth='0.1', color=color_map(5), cmap=color_map)\n",
    "l2 = plt.scatter([],[], s=100, edgecolor='black', linewidth='0.1', color=color_map(10), cmap=color_map)\n",
    "l3 = plt.scatter([],[], s=100,  edgecolor='black', linewidth='0.1', color=color_map(20), cmap=color_map)\n",
    "l4 = plt.scatter([],[], s=100,  edgecolor='black', linewidth='0.1', color=color_map(30), cmap=color_map)\n",
    "l5 = plt.scatter([],[], s=100, edgecolor='black', linewidth='0.1', color=color_map(40), cmap=color_map)\n",
    "l6 = plt.scatter([],[], s=100,  edgecolor='black', linewidth='0.1', color=color_map(50), cmap=color_map)\n",
    "l7 = plt.scatter([],[], s=100,  edgecolor='black', linewidth='0.1', color=color_map(100), cmap=color_map)\n",
    "\n",
    "labels = [\"5 g\", \"10 g\", \"20 g\", \"30 g\", \"40 g\", \"50 g\", \"100 g\"]\n",
    "\n",
    "leg = plt.legend([l1, l2, l3, l4, l5, l6, l7], labels, ncol=7, frameon=True, fontsize=16,\n",
    "handlelength=2, loc = 8, borderpad = 1, handletextpad=.2, title='$CO_2$ [g] per kWh produced', scatterpoints = 1)\n",
    "plt.title('Carbon footprint per kWh', fontsize = 40)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can serve interactive maps with Leaflet, which is what was done with [LCA_WIND_DK](http://viewer.webservice-energy.org/lca-wind-dk/) :-)"
   ]
  }
 ],
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