diff --git a/EROI universal mining machine.ipynb b/EROI universal mining machine.ipynb
new file mode 100644
index 00000000..c5669240
--- /dev/null
+++ b/EROI universal mining machine.ipynb	
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymrio\n",
+    "from pymrio.core.mriosystem import IOSystem as IOS\n",
+    "from pymrio.tools.iomath import div0\n",
+    "from pymrio.tools.iofunctions import *\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "# import scipy.io\n",
+    "import scipy.sparse as sp \n",
+    "from scipy.sparse import linalg as spla\n",
+    "import operator\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pylab import *\n",
+    "import pickle\n",
+    "from openpyxl import load_workbook\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import statsmodels.api as sm\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "# Unfortunately, the database THEMIS is not open (you can ask to NTNU or thomas.gibon@list.lu) if they can provide it to you\n",
+    "# path_themis = '/mnt/dd/adrien/DD/Économie/Données/Themis/'\n",
+    "path_io = '/mnt/dd/adrien/DD/Économie/Données/'\n",
+    "# path_io = '/media/sf_U_DRIVE/Données/'\n",
+    "# years = [2010, 2030, 2050]\n",
+    "# scenarios = ['BL', 'BM']\n",
+    "# scenarios_dlr = ['REF', 'ER', 'ADV'] \n",
+    "# themis, EROIs_mixes, dlr, EROIs_Mixes = pickle.load(open(path_io+'Themis/EROIs.pkl', 'rb'))\n",
+    "# EROIs_mixes, dlr, EROIs_Mixes = pickle.load(open(path_io+'Themis/EROIs.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "themis = pickle.load(open(path_io+'Themis/themis.pkl', 'rb'))\n",
+    "erois_prices = pickle.load(open(path_io+'Themis/erois_prices.pkl', 'rb'))\n",
+    "# erois_prices_wo_GW = pickle.load(open(path_io+'Themis/erois_prices_wo_GW.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['zinc monosulphate, ZnSO4.H2O, at plant/ RER/ kg',\n",
+       " 'zinc oxide, at plant/ RER/ kg',\n",
+       " 'zinc sulphide, ZnS, at plant/ RER/ kg',\n",
+       " 'cadmium sludge, from zinc electrolysis, at plant/ GLO/ kg',\n",
+       " 'leaching residues, indium rich, from zinc circuit, at smelter/ GLO/ kg',\n",
+       " 'resource correction, PbZn, zinc, negative/ GLO/ kg',\n",
+       " 'resource correction, PbZn, zinc, positive/ GLO/ kg',\n",
+       " 'titanium zinc plate, without pre-weathering, at plant/ DE/ kg',\n",
+       " 'zinc concentrate, at beneficiation/ GLO/ kg',\n",
+       " 'zinc, from combined metal production, at beneficiation/ SE/ kg',\n",
+       " 'zinc, from combined metal production, at refinery/ SE/ kg',\n",
+       " 'zinc, primary, at regional storage/ RER/ kg',\n",
+       " 'zinc coating, coils/ RER/ m2',\n",
+       " 'zinc coating, pieces/ RER/ m2',\n",
+       " 'zinc coating, pieces, adjustment per um/ RER/ m2',\n",
+       " 'disposal, zinc in car shredder residue, 0% water, to municipal incineration/ CH/ kg',\n",
+       " 'mine, gold-silver-zinc-lead-copper/ SE/ unit',\n",
+       " 'Lead, zinc and tin ores and concentrates',\n",
+       " 'Lead, zinc and tin and products thereof']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "themis['ER'][2010].find('zinc', 'sectors')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Evolution of EROI/EROIs for different energy intensity 2.png b/Evolution of EROI/EROIs for different energy intensity 2.png
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diff --git a/Evolution of EROI/Evolution of EROIs Until 2050 - Accepted version.pdf b/Evolution of EROI/Evolution of EROIs Until 2050 - Accepted version.pdf
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diff --git a/Evolution of EROI/Evolution of EROIs Until 2050 - code.ipynb b/Evolution of EROI/Evolution of EROIs Until 2050 - code.ipynb
new file mode 100644
index 00000000..9f3a3fc7
--- /dev/null
+++ b/Evolution of EROI/Evolution of EROIs Until 2050 - code.ipynb	
@@ -0,0 +1,7916 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "\n",
+       "<i>The raw code for this IPython notebook is by default hidden for easier reading.\n",
+       "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.</i>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "\n",
+    "<i>The raw code for this IPython notebook is by default hidden for easier reading.\n",
+    "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.</i>''')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evolution of EROIs Until 2050 — code\n",
+    "\n",
+    "## _by Adrien Fabre_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction\n",
+    "\n",
+    "This file contains the output of my working paper [Evolution of EROIs Until 2050](http://bit.ly/futureEROIs), it provides only the main results. \n",
+    "\n",
+    "For non technical readers, there is a [pedagogical version](http://bit.ly/future_eroi) of the notebook, which explains the paper in a simplified way.\n",
+    "\n",
+    "The code uses the library [pymrio](https://pymrio.readthedocs.io/en/latest/intro.html), which I highly recommend to those who intend to work with Multi-Regional Input-Output data in Python. Pymrio was originally created by [Konstantin Stadler](http://github.com/konstantinstadler/pymrio). \n",
+    "All the code called in this notebook is open, and comes from [my fork](https://github.com/bixiou/pymrio) (i.e. version) of pymrio."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. The EROI of a Technology Is Not Intrinsic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. A Simple Model With A Unique Energy Technology\n",
+    "\n",
+    "The coefficient $a_{i,j}$ of technology matrix A represents the quantity of input $i$ required to produce one unit of output $j$.\n",
+    "\n",
+    "$$\\overset{A=\\left(a_{\\text{in, out}}\\right)=\\begin{pmatrix}a_{1,1} & \\cdots & a_{1,n}\\\\\n",
+    "\\vdots & \\ddots & \\vdots\\\\\n",
+    "a_{n,1} & \\ldots & a_{n,n}\n",
+    "\\end{pmatrix}\\leftarrow\\text{ inputs}}{\\underset{\\text{outputs}}{\\downarrow}}$$\n",
+    "\n",
+    "Below is an illustrative technology matrix with three inputs (and the same three outputs): an energy technology, materials, and energy. Let $m_e$ be the quantity of materials required to produce one unit of energy.\n",
+    "\n",
+    "$$A=\\begin{pmatrix}0 & 0 & 1\\\\\n",
+    "m_{e} & m_{m} & 0\\\\\n",
+    "E_{e} & E_{m} & 0\n",
+    "\\end{pmatrix}\\begin{array}{c}\n",
+    "\\text{energy technology}\\\\\n",
+    "\\text{materials}\\\\\n",
+    "\\text{energy}\n",
+    "\\end{array}$$\n",
+    "\n",
+    "One can check that $m_e$ corresponds to the definition above, and one can find the meaning of other coefficients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & 1\\\\m_{e} & 0.2 & 0\\\\0.1 & 0.5 & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "⎡ 0    0   1⎤\n",
+       "⎢           ⎥\n",
+       "⎢mₑ   0.2  0⎥\n",
+       "⎢           ⎥\n",
+       "⎣0.1  0.5  0⎦"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "import sympy as sym\n",
+    "from pylab import *\n",
+    "sym.init_printing()\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "m_e, e_e, m_m, e_m = sym.symbols('m_e e_e m_m e_m') # i_j: units of i required for 1 unit of j\n",
+    "unit_energy_output = sym.Matrix([0, 0, 1])\n",
+    "unit_tech_output = sym.Matrix([1, 0, 0])\n",
+    "# A = sym.Matrix([[0, 0, 1], [m_e, m_m, 0], [e_e, e_m, 0]]) # energy technology, material, energy\n",
+    "A = sym.Matrix([[0, 0, 1], [m_e, 0.2, 0], [0.1, 0.5, 0]]) # energy technology, material, energy\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can compute the system-wide EROI of the economy represented by the technology matrix above.\n",
+    "\n",
+    "The system-wide **EROI, or Energy Returned On Invested, is the ratio between the energy delivered by the system, and the energy required to build, operate, maintain and dismantle it.** In other words, it is the inverse of the amount of energy required to produce one unit of energy, when the series of all embodied inputs are taken into account."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "After some algebra (<a href=\"javascript:code_toggle()\">toggle code</a> below to see it), we find the formula for the EROI:"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML('''After some algebra (<a href=\"javascript:code_toggle()\">toggle code</a> below to see it), we find the formula for the EROI:''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIUAAAAuBAMAAAD0L8ciAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM0yiXZmVN0i77urRJnCTjQbAAAACXBIWXMAAA7EAAAOxAGVKw4bAAADAElEQVRIDe2Wz2sTQRTHv9MkTdLNpqv0UETIGkS9NTc92cW2Xiy1l3hS8NB6KEL35MGD6UEQQWhu/jgYf4IKYnvwVA8L0iqIbfAfaKooerCkRdGKdZ03m9mdkixklZ50DvNm3nv5zszb6XwKyBY/MSCG+4zDtvS1skNjBXIfWhwctNjqnJjIvP3oLdP4iftFulrZDptdJv+k67rObsQ3laTOW4ibNN87L6SUkBgmRz3PBHCERp+ADFaAR55b9JqD5BqNHDFt6qTGM6Bk8KgFDOABsKSs2O1A/0a/dKhrblLjJ7Bsi7A+ij4DS4UgN1eD/pWmC+PvoA9fL87Hhl4HYTQ02C+uMSr8muinjSCpYiEmNGZRqca1p8idwvkgLDXSvOD9nsZRiurqB6jUEGvMtamDKRNcdLKFBt9Hv0V+Vqc+5dAwL1rBPwuQ3TByVarcK8qiFs/nD9zJ5x2e7p8lYVJklTrZeE2TVNOMSd+8BHwE7ssgt0pNSzb5UzO8S9RoKJs2g076tl0msmt4C1yEvsHQU/QO72vM8etl0I/4osAFpMs08Zq8Y1m+gol7YJvImlfZHuZ4cbkPecfQXwNiFhKKBi5hV4GtI13DRDm9iWQdHZaVWTjjSfj7SNnsLnImv2kWcG1w5GUjLkxP8TlwBbg5vAJ9ik6a/FDuqsoUuQ82dLIgvsYNG+hz3e8yIdR2l2F4QakRmhoa0GxZD9bQCk0NDbD3xdDY/8B2VoA/rX/Z6tu5vX9bOyJrQcxlxZGqWrWIrBXM5Q/eC0VDvoPtsla8h+eAnYpGVNYK5h4DehUN/kRHYq1g7vIsBOkaOj6f2mOtx6mMO2Yr+4jI2gZzj6+rz2MlGmvTgrnJz9OPlX34Z2mPtd5ZziLxQ9kIr2kk1vKaluzb/B8ApSAhrPV3KtkiWUt2R53z/bSfAnnHtrK2mdeStcI+BLRyoNGatc28lqwV9o2BcUWCr9iCtc28lqwVNra49W9O1fPHzbz2Q20PAl7/OWsDXre9bFNi27z+DTbUSHxqAiVJAAAAAElFTkSuQmCC\n",
+      "text/latex": [
+       "$$\\frac{- 0.5 m_{e} + 0.72}{0.5 m_{e} + 0.08}$$"
+      ],
+      "text/plain": [
+       "-0.5⋅mₑ + 0.72\n",
+       "──────────────\n",
+       "0.5⋅mₑ + 0.08 "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L = sym.eye(3)-A\n",
+    "Linv = L.inv()\n",
+    "EROI = sym.simplify(1/unit_energy_output.transpose().dot((Linv-sym.eye(3)).dot(unit_energy_output)))\n",
+    "EROI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unsurprisingly, one can see in the Figure below that the EROI decreases with the material intensity of the energy technology, because extracting and processing material requires energy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f9286d0da58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array(range(31))/20\n",
+    "fig, ax = subplots()\n",
+    "\n",
+    "ax.plot(x, [EROI.subs(m_e, M) for M in x], label=\"EROI\")\n",
+    "ax.set_xlabel('Material intensity of the energy technology')\n",
+    "ax.set_ylabel('EROI')\n",
+    "# ax.set_title('EROIs for different energy mixes')\n",
+    "ax.legend(loc=1)\n",
+    "ax.grid(color='g', alpha=0.5, linestyle='dotted', linewidth=0.5)\n",
+    "ax.axhline(y=1, color='black', alpha=0.5, linestyle='dotted', linewidth=0.5)\n",
+    "fig.savefig('EROIs for different material intensity 2.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For an intensity above 0.6, it is below 1. **An EROI below 1 means that the energy technology is not worth developing, because it consumes energy rather than providing it. Such a system is not sustainable** (and not realistic): for it to happen the society should have accumulated energy in the past from an energy source no more accessible, and would waste this energy in that absurd technology."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. A Simple Model With A Mix of Two Technologies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let us consider two energy technologies, with the same energy intensity, but different materials intensities.\n",
+    "\n",
+    "Even if this example is purely illustrative, let us call them PV (for solar photovoltaic) and gas (for gas power-plant electricity). The numbers are completely made up, but they respect the fact that PV is more material intensive than gas. Here is our new technology matrix, where inputs (and outputs) are (in that order): PV, gas, materials, energy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$$\\left[\\begin{matrix}0 & 0 & 0 & p\\\\0 & 0 & 0 & - p + 1\\\\0.7 & 0.1 & 0.2 & 0\\\\0.1 & 0.1 & 0.5 & 0\\end{matrix}\\right]$$"
+      ],
+      "text/plain": [
+       "⎡ 0    0    0     p   ⎤\n",
+       "⎢                     ⎥\n",
+       "⎢ 0    0    0   -p + 1⎥\n",
+       "⎢                     ⎥\n",
+       "⎢0.7  0.1  0.2    0   ⎥\n",
+       "⎢                     ⎥\n",
+       "⎣0.1  0.1  0.5    0   ⎦"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = sym.symbols('p') # proportion of PV in energy mix\n",
+    "# m_pv (resp. m_oil) units of materials required for 1 kWh from PV (resp. oil)\n",
+    "m_pv, m_oil = 0.7, 0.1 # \n",
+    "m_pv, m_oil, m_e, e_pv, e_oil, e_e = sym.symbols('m_pv m_oil m_e e_pv e_oil e_e') \n",
+    "unit_energy_output = sym.Matrix([0, 0, 0, 1])\n",
+    "energy_mix = [p, 1-p] # proportion of [PV, oil]\n",
+    "A = sym.Matrix([[0, 0, 0, p], [0, 0, 0, 1-p], [0.7, 0.1, 0.2, 0], [0.1, 0.1, 0.5, 0]]) # order of ligns/columns: PV, oil, materials, energy\n",
+    "# A = sym.Matrix([[0, 0, 0, pv], [0, 0, 0, 1-pv], [m_pv, m_oil, m_e, 0], [e_pv, e_oil, e_e, 0]]) # order of ligns/columns: PV, oil, materials, energy\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you might have guessed, _p_ represents the share of PV in the energy (or electricity) mix.\n",
+    "\n",
+    "Using simple algebra, one obtains the formula for the EROI:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHQAAAAuBAMAAAALoSCFAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM0yiXZmVN0i77urRJnCTjQbAAAACXBIWXMAAA7EAAAOxAGVKw4bAAACaklEQVRIDd2Vu4sTURTGv9kZZ5LMJjOIhWwzYcRHtaS2EszCwhaKGIttzH+wqW1MIWzhggsKlpu1sRLTCYIy4vamsdUI2wgqRtQgusZzH2fuJEImBmxyCJl7z3d+cx9zuR/A4Vy7KJv36k84lX2yjLWrNXvnsF7PiGdxcpe6xT5uNjNpbmoZx3uVqjsajX6xALgdOFXqHhsiaJs0vMuywzKew+9WaIzE1PgJvAF1y48QVE2a0VT+QZpDv5qpCRKUh6o7NmE9Kst+R9W8NiSiPsrfZN+6hfLG3cYLJWqU5eDU2kchdJUq//cuwJZocf0dnOVtbChRoyxHZ1Bo0XybWbQP+6vqb/buLyXYGke1HH1GZR+IhGjFMmo8I0qV9sOghUshvTyOzz2I44Rq9Xpo990j4IpAOWgfPLFNxV34Q7wN8VQpZpukXKjC/Q7cZkw8/S5c8XGCgUDXYf1WqkZZXu7IUTMHwhyJUheFAVbh0opEaJSPhD1ApQNbv1eVYBsrNesL3KY4iD9xXn9zjbKMA6y04OkN1eiJxktgB3jz6RnKR433Os0oy84hlRUfavXvh9NOc4ymiZzGUj8tsOgb/UtEdGDmC+/O6fnAxaXohpozFndP/sPK2FDy/EYMPVEzo9/gRjjpSXx75PiNdfAqnPSk1FCm+w2wFU56El2ks/iNRGm1WU/i+5ku9Gl+o1GqMcGGgul+o1BRY2JvNr/hCW/2DGomPNVvGC3p2128gLZpFr+RqPKkdFg2lBy/kaiqSVE+Ejl+I1FVk6JsKHl+g+uh9iSDsqHk+M2Hx6tN6UmGHGs57bS7YH7zBzhRIXfFx2MaAAAAAElFTkSuQmCC\n",
+      "text/latex": [
+       "$$\\frac{- 0.3 p + 0.67}{0.3 p + 0.13}$$"
+      ],
+      "text/plain": [
+       "-0.3⋅p + 0.67\n",
+       "─────────────\n",
+       " 0.3⋅p + 0.13"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L = sym.eye(4)-A # sym.Matrix(3,3,[1, -m, -1, 0, 0.8, -1, -e, 0, 1])\n",
+    "Linv = L.inv()\n",
+    "EROI2 = sym.simplify(1/unit_energy_output.transpose().dot((Linv-sym.eye(4)).dot(unit_energy_output)))\n",
+    "EROI2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This corresponds to the system-wide EROI. But now that we have two technologies, we can compute the EROI of each of them.\n",
+    "\n",
+    "The EROI of a technology is the ratio between the energy delivered by one unit of this technology (over its lifetime), and the energy required to build, operate, maintain and dismantle it.\n",
+    "\n",
+    "In our example, the EROIs of PV and gas are, in that order:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$$- 0.697674418604651 p + 1.55813953488372$$"
+      ],
+      "text/plain": [
+       "-0.697674418604651⋅p + 1.55813953488372"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$$- 2.30769230769231 p + 5.15384615384615$$"
+      ],
+      "text/plain": [
+       "-2.30769230769231⋅p + 5.15384615384615"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EROIpv = sym.simplify(1/unit_energy_output.transpose().dot((Linv-sym.eye(4)).dot(sym.Matrix([1, 0, 0, 0]))))\n",
+    "EROIoil = sym.simplify(1/unit_energy_output.transpose().dot((Linv-sym.eye(4)).dot(sym.Matrix([0, 1, 0, 0]))))\n",
+    "\n",
+    "EROIpv\n",
+    "EROIoil"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that they depend on the energy mix: **the EROI of a technology is not an intrinsic property**.\n",
+    "\n",
+    "Indeed, **it depends on the whole economic system**, or more precisely, of all technologies used in their value chain. (The value chain what I called above the recursive or embodied inputs; its analysis is known as _structural path analysis_ in the literature).\n",
+    "\n",
+    "Here, **the higher the share of PV in the mix, the lower the EROI of both technologies**: this comes from the higher material intensity of PV.\n",
+    "\n",
+    "Put graphically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f9288028f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array(range(21))/20\n",
+    "fig, ax = subplots()\n",
+    "\n",
+    "ax.plot(x, [EROI2.subs(p, PV) for PV in x], label=\"System-wide EROI\")\n",
+    "ax.plot(x, [EROIpv.subs(p, PV) for PV in x], label=\"EROI of PV\", linestyle='dashed') # , linestyle='dashed'\n",
+    "ax.plot(x, [EROIoil.subs(p, PV) for PV in x], label=\"EROI of gas\", linestyle='dotted') # , linestyle='dotted'\n",
+    "ax.set_xlabel('Share of PV in the energy mix')\n",
+    "ax.set_ylabel('EROI')\n",
+    "# ax.set_title('EROIs for different energy mixes')\n",
+    "ax.legend(loc=1)\n",
+    "ax.grid(color='g', alpha=0.5, linestyle='dotted', linewidth=0.5)\n",
+    "fig.savefig('EROIs for different energy mixes - gas 2.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One can see that for highest penetration of PV, the EROI falls below unity. In other words, a renewable energy mix with 100% PV is not sustainable in this example. \n",
+    "\n",
+    "Even more worrying, if one computes the EROI of PV in an energy mix relying mostly on gas, one would find a high-enough EROI for PV (meaning, above 1).\n",
+    "\n",
+    "Hence, one cannot conclude that a technology is sufficiently efficient (or sustainable) just by computing its EROI in the current energy mix.\n",
+    "\n",
+    "Yet, EROIs computations are _always_ done from actual data of our economy, and could falsely represent the efficiencies of energy technologies in another energy mix, say, a 100% renewable one.\n",
+    "\n",
+    "In the next sections, I'll go into the data and compute for the first time EROIs of different electricity technologies under an energy transition towards renewables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Estimation of Current and Future EROIs\n",
+    "\n",
+    "### 3.1 Setting and Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "# One needs to install my fork of pymrio, available at https://github.com/bixiou/pymrio\n",
+    "#   To do this, clone my repository from github, go on the console on the pymrio folder, and\n",
+    "#   type 'python3 setup.py sdist', then 'python3 setup.py install'\n",
+    "import pymrio\n",
+    "from pymrio.core.mriosystem import IOSystem as IOS\n",
+    "from pymrio.tools.iomath import div0\n",
+    "from pymrio.tools.iofunctions import *\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "# import scipy.io\n",
+    "import scipy.sparse as sp \n",
+    "from scipy.sparse import linalg as spla\n",
+    "import operator\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pylab import *\n",
+    "import pickle\n",
+    "from openpyxl import load_workbook\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import statsmodels.api as sm\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "# Unfortunately, the database THEMIS is not open (you can ask to NTNU or thomas.gibon@list.lu) if they can provide it to you\n",
+    "path_themis = '/mnt/dd/adrien/DD/Économie/Données/Themis/'\n",
+    "path_io = '/mnt/dd/adrien/DD/Économie/Données/'\n",
+    "# path_io = '/media/sf_U_DRIVE/Données/'\n",
+    "years = [2010, 2030, 2050]\n",
+    "scenarios = ['BL', 'BM']\n",
+    "scenarios_dlr = ['REF', 'ER', 'ADV'] \n",
+    "# themis, EROIs_mixes, dlr, EROIs_Mixes = pickle.load(open(path_io+'Themis/EROIs.pkl', 'rb'))\n",
+    "# EROIs_mixes, dlr, EROIs_Mixes = pickle.load(open(path_io+'Themis/EROIs.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# themis = pymrio.themis_parser(path_themis)\n",
+    "# # Following lines computes all results, lasts ~3h\n",
+    "themis = pymrio.themis_parser(path_themis, dlr_files = True, compute_all = True)\n",
+    "for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']: \n",
+    "    for y in [2010, 2030, 2050]: _ = themis[s][y].erois_and_prices(recompute = True)\n",
+    "erois_prices = pd.concat([themis[s][y].eroi_price for y in [2010, 2030, 2050] for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']], \\\n",
+    "                         keys=[(s,y) for y in [2010, 2030, 2050] for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']], names=['scenario', 'year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "themis = pd.read_pickle(open(path_io+'Themis/themis.pkl', 'rb'))\n",
+    "erois_prices = pd.read_pickle(open(path_io+'Themis/erois_prices.pkl', 'rb'))\n",
+    "# erois_prices_wo_GW = pickle.load(open(path_io+'Themis/erois_prices_wo_GW.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['   Electricity  from Poly-Si                                             ',\n",
+       "       '   MG-silicon,at plant                                                   ',\n",
+       "       '   RER:silicon, solar grade-pc, at plant                                 ',\n",
+       "       '   silicon,solar grade,modified Siemens process, at plant                ',\n",
+       "       '   muti-silicon wafer 125/125.0.2                                        ',\n",
+       "       '   silicon cell,multi-crystal, pieces, producted in Factory              ',\n",
+       "       '   Polycrystal silicon module230w                                        ',\n",
+       "       '   1.63 m2 module                                                        ',\n",
+       "       '   Inverters                                                             ',\n",
+       "       '   Construction                                                          ',\n",
+       "       '   Operation and Maintenance                                             ',\n",
+       "       '   Poly Ground System                                                    ',\n",
+       "       '   External cabling, grid connection                                     ',\n",
+       "       '   Decommissioning  ',\n",
+       "       '   Poly Roof System                                                      ',\n",
+       "       '   External cabling, connection to grid                                  ',\n",
+       "       '   Electricity production from                                           ',\n",
+       "       '   Receiver, clean glass                                                 ',\n",
+       "       '   Mo back contact                                                       ',\n",
+       "       '   Laser scribe 1                                                        ',\n",
+       "       '   Coevaporate CIGS                                                      ',\n",
+       "       '   Reclaim metals                                                        ',\n",
+       "       '   Deposit buffer                                                        ',\n",
+       "       '   Mechanical scribe                                                     ',\n",
+       "       '   Deposit TCO                                                           ',\n",
+       "       '   Bus bar attach                                                        ',\n",
+       "       '   Laser scribeedge isolation                                            ',\n",
+       "       '   IV test                                                               ',\n",
+       "       '   Layup, laminate                                                       ',\n",
+       "       '   Final module assembly                                                 ',\n",
+       "       '   Final tests                                                           ',\n",
+       "       '   1.08 m2 module                                                        ',\n",
+       "       '   O&M                                                                   ',\n",
+       "       '   CIGS ground system                                                    ',\n",
+       "       '   Decommissioning                                                       ',\n",
+       "       '   Electricity from CdTe 11.6% CdTe                                      ',\n",
+       "       '   0.72 m2 module                                                        ',\n",
+       "       '   BOS Construction                                                      ',\n",
+       "       '   CdTe ground system                                                    ',\n",
+       "       '   CdTe Roof System                                                      ',\n",
+       "       '   External cabling => Final process        ',\n",
+       "       '   CSP parabolic trough plant, 103 MW, with TES                          ',\n",
+       "       '   Solar field                                                           ',\n",
+       "       '   HTF system                                                            ',\n",
+       "       '   TES                                                                   ',\n",
+       "       '   Power plant systems                                                   ',\n",
+       "       '   Construction & assembly                                               ',\n",
+       "       '   Dismantling                                                           ',\n",
+       "       '   Sodium nitrate                                                        ',\n",
+       "       '   HTF                                                                   ',\n",
+       "       '   External cabling => Final process                                     ',\n",
+       "       '   Electricity                                                           ',\n",
+       "       '   Site preparation and related                                          ',\n",
+       "       '   Receiver (tower elements)                                             ',\n",
+       "       '   Collector                                                             ',\n",
+       "       '   Cooling tower                                                         ',\n",
+       "       '   Power equipment                                                       ',\n",
+       "       '   Operation                                                             ',\n",
+       "       '   External cabling => Final process      ',\n",
+       "       '   Electricity from EX PC power plant                                    ',\n",
+       "       '   EXPC  power plant 550 Mwe                                             ',\n",
+       "       '   Coal transport and infrastructure                                     ',\n",
+       "       '   Coal extraction                                                       ',\n",
+       "       '   External cabling, overhead line     ',\n",
+       "       '   Electricity from IGCC plant                                           ',\n",
+       "       '   IGCC plant 629 Mwe                                                    ',\n",
+       "       '   External cabling => Final process          ',\n",
+       "       '   Electricity from SC pulverized coal plant wo CCS                      ',\n",
+       "       '   SCPC plant 550 Mwe                                                    ',\n",
+       "       '   CCS infrastructure                                                    ',\n",
+       "       '   Carbon capture                                                        ',\n",
+       "       '   CCS Pipeline                                                          ',\n",
+       "       '   CCS Well                                                              ',\n",
+       "       '   External cabling, overhead line      ',\n",
+       "       '   Carbon dioxide capture                                                ',\n",
+       "       '   CCS on-site infrastructure                                            ',\n",
+       "       '   IGCC plant 497 Mwe                                                    ',\n",
+       "       '   External cabling => Final process     ',\n",
+       "       '   Electricity from SC pulverized coal plant w CCS                       ',\n",
+       "       '   Electricity from NGCC plant                                           ',\n",
+       "       '   Natural gas transport                                                 ',\n",
+       "       '   Natural gas extraction                                                ',\n",
+       "       '   NGCC plant 474 Mwe                                                    ',\n",
+       "       '   Decommissioining                                                      ',\n",
+       "       '   Hydropower plant, 660 MW                                              ',\n",
+       "       '   Dam                                                                   ',\n",
+       "       '   Roadworks                                                             ',\n",
+       "       '   Transportation                                                        ',\n",
+       "       '   Buildings equipments                                                  ',\n",
+       "       '   Complementary works                                                   ',\n",
+       "       '   Decommissioning      ',\n",
+       "       '   Hydropower plant, 360 MW                                              ',\n",
+       "       '   Decommissioning          ',\n",
+       "       '   Wind turbine, misc. assembly activities                               ',\n",
+       "       '   Rotor blades                                                          ',\n",
+       "       '   Rotor hub, incl. nose cone                                            ',\n",
+       "       '   Bed frame/plate                                                       ',\n",
+       "       '   Generator (conventional)                                              ',\n",
+       "       '   Rare earth permanent magnet                                           ',\n",
+       "       '   Generator (rare earth PM, onshore)                                    ',\n",
+       "       '   Generator (rare earth PM, offshore)                                   ',\n",
+       "       '   Gearbox                                                               ',\n",
+       "       '   Transformer, low-voltage                                              ',\n",
+       "       '   Main shaft                                                            ',\n",
+       "       '   Cover                                                                 ',\n",
+       "       '   Tower, tubular steel, onshore                                         ',\n",
+       "       '   Tower, tubular steel, offshore                                        ',\n",
+       "       '   Foundation, gravity-based concrete, onshore                           ',\n",
+       "       '   Foundation, steel solution, offshore                                  ',\n",
+       "       '   Foundation, gravity-based concrete, offshore                          ',\n",
+       "       '   Transformer station (high-voltage), onshore                           ',\n",
+       "       '   Transformer station (high-voltage), offshore                          ',\n",
+       "       '   Foundation, steel solution, offshore, for transformer station         ',\n",
+       "       '   Foundation, gravity-based concrete, offshore, for transformer station ',\n",
+       "       '   Internal cabling, underground onshore                                 ',\n",
+       "       '   Internal cabling, submarine offshore                                  ',\n",
+       "       '   External cabling, underground onshore                                 ',\n",
+       "       '   External cabling, overhead line                                       ',\n",
+       "       '   External cabling, submarine offshore                                  ',\n",
+       "       '   External cabling, underground, for offshore wind farm                 ',\n",
+       "       '   External cabling (all, IO system)                                     ',\n",
+       "       '   Installation, onshore WF                                              ',\n",
+       "       '   Ships, marine gas oil fuel                                            ',\n",
+       "       '   Ships, heavy fuel oil fuel                                            ',\n",
+       "       '   Installation, offshore WF                                             ',\n",
+       "       '   Other capital costs (IO system)                                       ',\n",
+       "       '   Replacement of parts, category heavy components (HC), gearbox solution (Gr), onshore',\n",
+       "       '   Replacement of parts, category large parts (LP), gearbox solution (Gr), onshore',\n",
+       "       '   Replacement of parts, category small parts (SP), onshore              ',\n",
+       "       '   Replacement of parts, category heavy components (HC), gearbox solution (Gr), offshore',\n",
+       "       '   Replacement of parts, category large parts (LP), gearbox solution (Gr), offshore',\n",
+       "       '   Replacement of parts, category small parts (SP), offshore             ',\n",
+       "       '   Maintenance (excl. spare parts), onshore WF                           ',\n",
+       "       '   Maintenance, (excl. spare parts), offshore WF                         ',\n",
+       "       '   Other variable costs (IO system)                                      ',\n",
+       "       '   Decommissioning, onshore                                              ',\n",
+       "       '   Decommissioning, offshore  ',\n",
+       "       '   Electricity, nuclear, at power plant BWR                              ',\n",
+       "       '   Fuel elements                                                         ',\n",
+       "       '   Chemicals, use phase                                                  ',\n",
+       "       '   Construction elements                                                 ',\n",
+       "       '   Infrastructure elements and overhead costs                            ',\n",
+       "       '   Electricity, nuclear, at power plant PWR                              ',\n",
+       "       '   Operating expenses                                                    ',\n",
+       "       '   Decommissioning costs                                                 ',\n",
+       "       '   External cabling, overhead line   ', '   [not used]',\n",
+       "       'dried roughage store, air dried, solar, operation/ CH/ kg',\n",
+       "       'dried roughage store, cold-air dried, conventional, operation/ CH/ kg',\n",
+       "       'dried roughage store, non ventilated, operation/ CH/ kg',\n",
+       "       'housing system with fully-slatted floor, pig, operation/ CH/ pig place',\n",
+       "       'label housing system, pig, operation/ CH/ pig place',\n",
+       "       'loose housing system, cattle, operation/ CH/ LU',\n",
+       "       'slurry store and processing, operation/ CH/ m3',\n",
+       "       'tied housing system, cattle, operation/ CH/ LU',\n",
+       "       'barley IP, at feed mill/ CH/ kg',\n",
+       "       'barley organic, at feed mill/ CH/ kg',\n",
+       "       'fava beans IP, at feed mill/ CH/ kg',\n",
+       "       'grain maize IP, at feed mill/ CH/ kg',\n",
+       "       'grain maize organic, at feed mill/ CH/ kg',\n",
+       "       'protein peas IP, at feed mill/ CH/ kg',\n",
+       "       'rye IP, at feed mill/ CH/ kg',\n",
+       "       'rye organic, at feed mill/ CH/ kg',\n",
+       "       'wheat IP, at feed mill/ CH/ kg',\n",
+       "       'wheat organic, at feed mill/ CH/ kg',\n",
+       "       'ammonium nitrate phosphate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'ammonium nitrate phosphate, as P2O5, at regional storehouse/ RER/ kg',\n",
+       "       'ammonium nitrate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'ammonium sulphate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'calcium ammonium nitrate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'calcium nitrate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'diammonium phosphate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'diammonium phosphate, as P2O5, at regional storehouse/ RER/ kg',\n",
+       "       'lime, algae, at regional storehouse/ CH/ kg',\n",
+       "       'lime, from carbonation, at regional storehouse/ CH/ kg',\n",
+       "       'monoammonium phosphate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'monoammonium phosphate, as P2O5, at regional storehouse/ RER/ kg',\n",
+       "       'potassium chloride, as K2O, at regional storehouse/ RER/ kg',\n",
+       "       'potassium nitrate, as K2O, at regional storehouse/ RER/ kg',\n",
+       "       'potassium nitrate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'potassium sulphate, as K2O, at regional storehouse/ RER/ kg',\n",
+       "       'single superphosphate, as P2O5, at regional storehouse/ RER/ kg',\n",
+       "       'stone meal, at regional storehouse/ CH/ kg',\n",
+       "       'thomas meal, as P2O5, at regional storehouse/ RER/ kg',\n",
+       "       'triple superphosphate, as P2O5, at regional storehouse/ RER/ kg',\n",
+       "       'application, digested matter from biowaste in agricultural co-digestion, covered/ CH/ kg',\n",
+       "       'compost, at plant/ CH/ kg',\n",
+       "       'horn meal, at regional storehouse/ CH/ kg',\n",
+       "       'poultry manure, dried, at regional storehouse/ CH/ kg',\n",
+       "       '2,4-D, at regional storehouse/ RER/ kg',\n",
+       "       '2,4-D, at regional storehouse/ CH/ kg',\n",
+       "       'MCPA, at regional storehouse/ RER/ kg',\n",
+       "       'MCPA, at regional storehouse/ CH/ kg',\n",
+       "       '[sulfonyl]urea-compounds, at regional storehouse/ RER/ kg',\n",
+       "       '[sulfonyl]urea-compounds, at regional storehouse/ CH/ kg',\n",
+       "       '[thio]carbamate-compounds, at regional storehouse/ RER/ kg',\n",
+       "       '[thio]carbamate-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'acetamide-anillide-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'acetamide-anillide-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'aclonifen, at regional storage/ RER/ kg',\n",
+       "       'alachlor, at regional storehouse/ RER/ kg',\n",
+       "       'alachlor, at regional storehouse/ CH/ kg',\n",
+       "       'atrazine, at regional storehouse/ RER/ kg',\n",
+       "       'atrazine, at regional storehouse/ CH/ kg',\n",
+       "       'benzimidazole-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'benzimidazole-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'benzo[thia]diazole-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'benzo[thia]diazole-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'benzoic-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'benzoic-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'bipyridylium-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'bipyridylium-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'captan, at regional storage/ RER/ kg',\n",
+       "       'carbofuran, at regional storehouse/ RER/ kg',\n",
+       "       'carbofuran, at regional storehouse/ CH/ kg',\n",
+       "       'chlorothalonil, at regional storage/ RER/ kg',\n",
+       "       'chlorotoluron, at regional storage/ RER/ kg',\n",
+       "       'cyanazine, at regional storehouse/ RER/ kg',\n",
+       "       'cyanazine, at regional storehouse/ CH/ kg',\n",
+       "       'cyclic N-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'cyclic N-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'diazine-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'diazole-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'dicamba, at regional storehouse/ RER/ kg',\n",
+       "       'dicamba, at regional storehouse/ CH/ kg',\n",
+       "       'dimethenamide, at regional storage/ RER/ kg',\n",
+       "       'dinitroaniline-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'dinitroaniline-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'diphenylether-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'diphenylether-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'dithiocarbamate-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'dithiocarbamate-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'diuron, at regional storehouse/ RER/ kg',\n",
+       "       'diuron, at regional storehouse/ CH/ kg',\n",
+       "       'folpet, at regional storage/ RER/ kg',\n",
+       "       'fosetyl-Al, at regional storage/ RER/ kg',\n",
+       "       'fungicides, at regional storehouse/ RER/ kg',\n",
+       "       'glyphosate, at regional storehouse/ RER/ kg',\n",
+       "       'glyphosate, at regional storehouse/ CH/ kg',\n",
+       "       'growth regluators, at regional storehouse/ RER/ kg',\n",
+       "       'herbicides, at regional storehouse/ RER/ kg',\n",
+       "       'insecticides, at regional storehouse/ RER/ kg',\n",
+       "       'isoproturon, at regional storage/ RER/ kg',\n",
+       "       'linuron, at regional storehouse/ RER/ kg',\n",
+       "       'linuron, at regional storehouse/ CH/ kg',\n",
+       "       'mancozeb, at regional storage/ RER/ kg',\n",
+       "       'maneb, at regional storehouse/ RER/ kg',\n",
+       "       'maneb, at regional storehouse/ CH/ kg',\n",
+       "       'mecoprop, at regional storage/ RER/ kg',\n",
+       "       'metaldehyde, at regional storage/ RER/ kg',\n",
+       "       'metamitron, at regional storage/ RER/ kg',\n",
+       "       'metolachlor, at regional storehouse/ RER/ kg',\n",
+       "       'metolachlor, at regional storehouse/ CH/ kg',\n",
+       "       'napropamide, at regional storage/ RER/ kg',\n",
+       "       'nitrile-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'nitrile-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'nitro-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'nitro-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'orbencarb, at regional storage/ RER/ kg',\n",
+       "       'organophosphorus-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'organophosphorus-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'parathion, at regional storehouse/ RER/ kg',\n",
+       "       'parathion, at regional storehouse/ CH/ kg',\n",
+       "       'pendimethalin, at regional storage/ RER/ kg',\n",
+       "       'pesticide unspecified, at regional storehouse/ RER/ kg',\n",
+       "       'pesticide unspecified, at regional storehouse/ CH/ kg',\n",
+       "       'phenoxy-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'phenoxy-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'phtalamide-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'phtalamide-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'propachlor, at regional storehouse/ RER/ kg',\n",
+       "       'propachlor, at regional storehouse/ CH/ kg',\n",
+       "       'prosulfocarb, at regional storage/ RER/ kg',\n",
+       "       'pyretroid-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'pyretroid-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'pyridazine-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'pyridazine-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'pyridine-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'triazine-compounds, at regional storehouse/ RER/ kg',\n",
+       "       'triazine-compounds, at regional storehouse/ CH/ kg',\n",
+       "       'barley seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'barley seed organic, at regional storehouse/ CH/ kg',\n",
+       "       'clover seed IP, at farm/ CH/ kg',\n",
+       "       'clover seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'cotton seed, at regional storehouse/ US/ kg',\n",
+       "       'grass seed IP, at farm/ CH/ kg',\n",
+       "       'grass seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'grass seed organic, at regional storehouse/ CH/ kg',\n",
+       "       'maize seed IP, at farm/ CH/ kg',\n",
+       "       'maize seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'maize seed organic, at farm/ CH/ kg',\n",
+       "       'maize seed organic, at regional storehouse/ CH/ kg',\n",
+       "       'pea seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'pea seed organic, at regional storehouse/ CH/ kg',\n",
+       "       'potato seed IP, at farm/ CH/ kg',\n",
+       "       'potato seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'potato seed organic, at farm/ CH/ kg',\n",
+       "       'potato seed organic, at regional storehouse/ CH/ kg',\n",
+       "       'rape seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'rape seed organic, at regional storehouse/ CH/ kg',\n",
+       "       'rice seed, at regional storehouse/ US/ kg',\n",
+       "       'rye seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'rye seed organic, at regional storehouse/ CH/ kg',\n",
+       "       'sugar beet seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'wheat seed IP, at regional storehouse/ CH/ kg',\n",
+       "       'wheat seed organic, at regional storehouse/ CH/ kg',\n",
+       "       'application of plant protection products, by field sprayer/ CH/ ha',\n",
+       "       'baling/ CH/ unit', 'chopping, maize/ CH/ ha',\n",
+       "       'combine harvesting/ CH/ ha',\n",
+       "       'fertilising, by broadcaster/ CH/ ha',\n",
+       "       'fodder loading, by self-loading trailer/ CH/ m3',\n",
+       "       'grain drying, high temperature/ CH/ kg',\n",
+       "       'grain drying, low temperature/ CH/ kg', 'grass drying/ CH/ kg',\n",
+       "       'harvesting, by complete harvester, beets/ CH/ ha',\n",
+       "       'harvesting, by complete harvester, potatoes/ CH/ ha',\n",
+       "       'haying, by rotary tedder/ CH/ ha', 'hoeing/ CH/ ha',\n",
+       "       'irrigating/ CH/ ha', 'irrigating/ US/ m3', 'irrigating/ CH/ m3',\n",
+       "       'loading bales/ CH/ unit', 'maize drying/ CH/ kg',\n",
+       "       'milking/ CH/ kg', 'mowing, by motor mower/ CH/ ha',\n",
+       "       'mowing, by rotary mower/ CH/ ha', 'mulching/ CH/ ha',\n",
+       "       'planting/ CH/ ha', 'potato grading/ CH/ kg',\n",
+       "       'potato haulm cutting/ CH/ ha', 'potato planting/ CH/ ha',\n",
+       "       'slurry spreading, by vacuum tanker/ CH/ m3',\n",
+       "       'solid manure loading and spreading, by hydraulic loader and spreader/ CH/ kg',\n",
+       "       'sowing/ CH/ ha', 'swath, by rotary windrower/ CH/ ha',\n",
+       "       'tillage, cultivating, chiselling/ CH/ ha',\n",
+       "       'tillage, currying, by weeder/ CH/ ha',\n",
+       "       'tillage, harrowing, by rotary harrow/ CH/ ha',\n",
+       "       'tillage, harrowing, by spring tine harrow/ CH/ ha',\n",
+       "       'tillage, hoeing and earthing-up, potatoes/ CH/ ha',\n",
+       "       'tillage, ploughing/ CH/ ha', 'tillage, rolling/ CH/ ha',\n",
+       "       'tillage, rotary cultivator/ CH/ ha',\n",
+       "       'transport, tractor and trailer/ CH/ tkm',\n",
+       "       'sheep for slaughtering, live weight, at farm/ US/ kg',\n",
+       "       'tallow, at plant/ CH/ kg', 'wool, sheep, at farm/ US/ kg',\n",
+       "       'barley grains IP, at farm/ CH/ kg',\n",
+       "       'barley grains conventional, Barrois, at farm/ FR/ kg',\n",
+       "       'barley grains conventional, Castilla-y-Leon, at farm/ ES/ kg',\n",
+       "       'barley grains conventional, Saxony-Anhalt, at farm/ DE/ kg',\n",
+       "       'barley grains extensive, at farm/ CH/ kg',\n",
+       "       'barley grains organic, at farm/ CH/ kg',\n",
+       "       'barley straw IP, at farm/ CH/ kg',\n",
+       "       'barley straw extensive, at farm/ CH/ kg',\n",
+       "       'barley straw organic, at farm/ CH/ kg', 'corn, at farm/ US/ kg',\n",
+       "       'cotton fibres, at farm/ US/ kg',\n",
+       "       'cotton fibres, ginned, at farm/ CN/ kg',\n",
+       "       'cotton seed, at farm/ US/ kg', 'cotton seed, at farm/ CN/ kg',\n",
+       "       'fava beans IP, at farm/ CH/ kg',\n",
+       "       'fava beans organic, at farm/ CH/ kg',\n",
+       "       'fodder beets IP, at farm/ CH/ kg',\n",
+       "       'grain maize IP, at farm/ CH/ kg',\n",
+       "       'grain maize organic, at farm/ CH/ kg',\n",
+       "       'grass from meadow intensive IP, at field/ CH/ kg',\n",
+       "       'grass from meadow intensive, organic, at field/ CH/ kg',\n",
+       "       'grass from natural meadow extensive IP, at field/ CH/ kg',\n",
+       "       'grass from natural meadow extensive organic, at field/ CH/ kg',\n",
+       "       'grass from natural meadow intensive IP, at field/ CH/ kg',\n",
+       "       'grass from natural meadow intensive organic, at field/ CH/ kg',\n",
+       "       'grass silage IP, at farm/ CH/ kg',\n",
+       "       'grass silage organic, at farm/ CH/ kg',\n",
+       "       'green manure IP, until April/ CH/ ha',\n",
+       "       'green manure IP, until February/ CH/ ha',\n",
+       "       'green manure IP, until January/ CH/ ha',\n",
+       "       'green manure IP, until march/ CH/ ha',\n",
+       "       'green manure organic, until April/ CH/ ha',\n",
+       "       'green manure organic, until February/ CH/ ha',\n",
+       "       'green manure organic, until January/ CH/ ha',\n",
+       "       'green manure organic, until march/ CH/ ha',\n",
+       "       'hay extensive, at farm/ CH/ kg',\n",
+       "       'hay intensive IP, at farm/ CH/ kg',\n",
+       "       'hay intensive organic, at farm/ CH/ kg',\n",
+       "       'husked nuts harvesting, at farm/ PH/ kg',\n",
+       "       'jute fibres, irrigated system, at farm/ IN/ kg',\n",
+       "       'jute fibres, rainfed system, at farm/ IN/ kg',\n",
+       "       'jute stalks, from fibre production, irrigated system, at farm/ IN/ kg',\n",
+       "       'jute stalks, from fibre production, rainfed system, at farm/ IN/ kg',\n",
+       "       'kenaf fibres, at farm/ IN/ kg',\n",
+       "       'kenaf stalks, from fibre production, at farm/ IN/ kg',\n",
+       "       'maize starch, at plant/ DE/ kg',\n",
+       "       'palm fruit bunches, at farm/ MY/ kg',\n",
+       "       'potato starch, at plant/ DE/ kg', 'potatoes IP, at farm/ CH/ kg',\n",
+       "       'potatoes organic, at farm/ CH/ kg', 'potatoes, at farm/ US/ kg',\n",
+       "       'protein peas conventional, Barrois, at farm/ FR/ kg',\n",
+       "       'protein peas conventional, Castilla-y-Leon, at farm/ ES/ kg',\n",
+       "       'protein peas conventional, Saxony-Anhalt, at farm/ DE/ kg',\n",
+       "       'protein peas, IP, at farm/ CH/ kg',\n",
+       "       'protein peas, organic, at farm/ CH/ kg',\n",
+       "       'rape seed IP, at farm/ CH/ kg',\n",
+       "       'rape seed conventional, Barrois, at farm/ FR/ kg',\n",
+       "       'rape seed conventional, Saxony-Anhalt, at farm/ DE/ kg',\n",
+       "       'rape seed conventional, at farm/ DE/ kg',\n",
+       "       'rape seed extensive, at farm/ CH/ kg',\n",
+       "       'rape seed, at farm/ US/ kg',\n",
+       "       'rape seed, organic, at farm/ CH/ kg', 'rice, at farm/ US/ kg',\n",
+       "       'rye grains IP, at farm/ CH/ kg',\n",
+       "       'rye grains conventional, at farm/ RER/ kg',\n",
+       "       'rye grains extensive, at farm/ CH/ kg',\n",
+       "       'rye grains organic, at farm/ CH/ kg',\n",
+       "       'rye straw IP, at farm/ CH/ kg',\n",
+       "       'rye straw conventional, at farm/ RER/ kg',\n",
+       "       'rye straw extensive, at farm/ CH/ kg',\n",
+       "       'rye straw organic, at farm/ CH/ kg',\n",
+       "       'silage maize IP, at farm/ CH/ kg',\n",
+       "       'silage maize organic, at farm/ CH/ kg',\n",
+       "       'soy beans IP, at farm/ CH/ kg',\n",
+       "       'soy beans organic, at farm/ CH/ kg', 'soybeans, at farm/ BR/ kg',\n",
+       "       'soybeans, at farm/ US/ kg', 'straw IP, at farm/ CH/ kg',\n",
+       "       'straw organic, at farm/ CH/ kg',\n",
+       "       'straw, from straw areas, at field/ CH/ kg',\n",
+       "       'sugar beets IP, at farm/ CH/ kg', 'sugarcane, at farm/ BR/ kg',\n",
+       "       'sunflower IP, at farm/ CH/ kg',\n",
+       "       'sunflower conventional, Castilla-y-Leon, at farm/ ES/ kg',\n",
+       "       'sweet sorghum grains, at farm/ CN/ kg',\n",
+       "       'sweet sorghum stem, at farm/ CN/ kg',\n",
+       "       'wheat grains IP, at farm/ CH/ kg',\n",
+       "       'wheat grains conventional, Barrois, at farm/ FR/ kg',\n",
+       "       'wheat grains conventional, Castilla-y-Leon, at farm/ ES/ kg',\n",
+       "       'wheat grains conventional, Saxony-Anhalt, at farm/ DE/ kg',\n",
+       "       'wheat grains extensive, at farm/ CH/ kg',\n",
+       "       'wheat grains organic, at farm/ CH/ kg',\n",
+       "       'wheat grains, at farm/ US/ kg', 'wheat straw IP, at farm/ CH/ kg',\n",
+       "       'wheat straw extensive, at farm/ CH/ kg',\n",
+       "       'wheat straw organic, at farm/ CH/ kg',\n",
+       "       'electricity, at cogen with biogas engine, agricultural covered, alloc. exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen with biogas engine, agricultural, alloc. exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen with biogas engine, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen with ignition biogas engine, agric. covered, alloc. exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen with ignition biogas engine, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen, biogas agricultural mix, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, bagasse, sugarcane, at fermentation plant/ BR/ kWh',\n",
+       "       'electricity, bagasse, sugarcane, at sugar refinery/ BR/ kWh',\n",
+       "       'electricity, bagasse, sweet sorghum, at distillery/ CN/ kWh',\n",
+       "       'electricity, biogas, allocation exergy, at PEM fuel cell 2kWe, future/ CH/ kWh',\n",
+       "       'electricity, biogas, allocation exergy, at SOFC fuel cell 125kWe, future/ CH/ kWh',\n",
+       "       'electricity, biogas, allocation exergy, at SOFC-GT fuel cell 180kWe, future/ CH/ kWh',\n",
+       "       'electricity, biogas, allocation exergy, at micro gas turbine 100kWe/ CH/ kWh',\n",
+       "       'electricity, pellets, allocation exergy, at stirling cogen unit 3kWe, future/ CH/ kWh',\n",
+       "       'heat, at cogen with biogas engine, agricultural covered, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen with biogas engine, agricultural, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen with biogas engine, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen with ignition biogas engine, agricultural covered, alloc. exergy/ CH/ MJ',\n",
+       "       'heat, at cogen with ignition biogas engine, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen, biogas agricultural mix, allocation exergy/ CH/ MJ',\n",
+       "       'heat, biogas, allocation exergy, at PEM fuel cell 2kWe, future/ CH/ MJ',\n",
+       "       'heat, biogas, allocation exergy, at SOFC fuel cell 125kWe, future/ CH/ MJ',\n",
+       "       'heat, biogas, allocation exergy, at SOFC-GT fuel cell 180kWe, future/ CH/ MJ',\n",
+       "       'heat, biogas, allocation exergy, at micro gas turbine 100kWe/ CH/ MJ',\n",
+       "       'heat, pellets, allocation exergy, at stirling cogen unit 3kWe, future/ CH/ MJ',\n",
+       "       'maintenance stirling cogen unit 3kWe, wood pellets, future/ CH/ unit',\n",
+       "       'beet chips, at fermentation plant/ CH/ kg',\n",
+       "       'biogas, from agricultural co-digestion, not covered, at storage/ CH/ Nm3',\n",
+       "       'biogas, from agricultural digestion, not covered, at storage/ CH/ Nm3',\n",
+       "       'biogas, from biowaste, at agricultural co-fermentation, covered/ CH/ Nm3',\n",
+       "       'biogas, from biowaste, at storage/ CH/ Nm3',\n",
+       "       'biogas, from fat and oil, at agricultural co-fermentation, covered/ CH/ Nm3',\n",
+       "       'biogas, from grass, digestion, at storage/ CH/ Nm3',\n",
+       "       'biogas, from sewage sludge, at storage/ CH/ Nm3',\n",
+       "       'biogas, from slurry, at agricultural co-fermentation, covered/ CH/ Nm3',\n",
+       "       'biogas, from whey, digestion, at storage/ CH/ Nm3',\n",
+       "       'biogas, mix, at agricultural co-fermentation, covered/ CH/ Nm3',\n",
+       "       'biogas, production mix, at storage/ CH/ Nm3',\n",
+       "       'digested matter, application in agriculture/ CH/ kg',\n",
+       "       'disposal, biowaste, to agricultural co-fermentation, covered/ CH/ kg',\n",
+       "       'disposal, biowaste, to anaerobic digestion/ CH/ kg',\n",
+       "       'disposal, fat and oil, to agricultural co-fermentation, covered/ CH/ kg',\n",
+       "       'electricity, wood, at distillery/ SE/ kg',\n",
+       "       'ethanol, 95% in H2O, from corn, at distillery/ US/ kg',\n",
+       "       'ethanol, 95% in H2O, from grass, at fermentation plant/ CH/ kg',\n",
+       "       'ethanol, 95% in H2O, from potatoes, at distillery/ CH/ kg',\n",
+       "       'ethanol, 95% in H2O, from rye, at distillery/ RER/ kg',\n",
+       "       'ethanol, 95% in H2O, from sugar beet molasses, at distillery/ CH/ kg',\n",
+       "       'ethanol, 95% in H2O, from sugar beets, at fermentation plant/ CH/ kg',\n",
+       "       'ethanol, 95% in H2O, from sugar cane, at fermentation plant/ BR/ kg',\n",
+       "       'ethanol, 95% in H2O, from sugarcane molasses, at sugar refinery/ BR/ kg',\n",
+       "       'ethanol, 95% in H2O, from sweet sorghum, at distillery/ CN/ kg',\n",
+       "       'ethanol, 95% in H2O, from whey, at fermentation plant/ CH/ kg',\n",
+       "       'ethanol, 95% in H2O, from wood, at distillery/ CH/ kg',\n",
+       "       'ethanol, 95% in H2O, from wood, at distillery/ SE/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from Swedish wood, at service station/ CH/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, at distillation/ BR/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, at distillation/ US/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, at distillation/ RER/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, at distillation/ CN/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, at distillation/ CH/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, at service station/ CH/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, production BR, at service station/ CH/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, production CN, at service station/ CH/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, production RER, at service station/ CH/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from biomass, production US, at service station/ CH/ kg',\n",
+       "       'ethanol, 99.7% in H2O, from wood, at distillation/ SE/ kg',\n",
+       "       'ethyl tert-butyl ether, from bioethanol, at plant/ RER/ kg',\n",
+       "       'methane, 96 vol-%, from biogas, at purification/ CH/ Nm3',\n",
+       "       'methane, 96 vol-%, from biogas, from high pressure network, at service station/ CH/ kg',\n",
+       "       'methane, 96 vol-%, from biogas, from low pressure network, at service station/ CH/ kg',\n",
+       "       'methane, 96 vol-%, from biogas, from medium pressure network, at service station/ CH/ kg',\n",
+       "       'methane, 96 vol-%, from biogas, high pressure, at consumer/ CH/ MJ',\n",
+       "       'methane, 96 vol-%, from biogas, low pressure, at consumer/ CH/ MJ',\n",
+       "       'methane, 96 vol-%, from biogas, production mix, at service station/ CH/ kg',\n",
+       "       'methane, 96 vol.-%, from synthetic gas, wood, at plant/ CH/ Nm3',\n",
+       "       'methanol, from biomass, at regional storage/ CH/ kg',\n",
+       "       'methanol, from synthetic gas, at plant/ CH/ kg',\n",
+       "       'palm methyl ester, at esterification plant/ MY/ kg',\n",
+       "       'palm methyl ester, production MY, at service station/ CH/ kg',\n",
+       "       'petrol, 85% vol. ethanol, from Swedish wood, at service station/ CH/ kg',\n",
+       "       'petrol, 85% vol. ethanol, from biomass, at service station/ CH/ kg',\n",
+       "       'rape methyl ester, at esterification plant/ CH/ kg',\n",
+       "       'rape methyl ester, at esterification plant/ RER/ kg',\n",
+       "       'rape methyl ester, at regional storage/ CH/ kg',\n",
+       "       'rape methyl ester, production RER, at service station/ CH/ kg',\n",
+       "       'rape oil, at oil mill/ CH/ kg', 'rape oil, at oil mill/ RER/ kg',\n",
+       "       'rape oil, at regional storage/ CH/ kg',\n",
+       "       'soybean methyl ester, at esterification plant/ US/ kg',\n",
+       "       'soybean methyl ester, at esterification plant/ BR/ kg',\n",
+       "       'soybean methyl ester, production BR, at service station/ CH/ kg',\n",
+       "       'soybean methyl ester, production US, at service station/ CH/ kg',\n",
+       "       'soybean oil, at oil mill/ US/ kg',\n",
+       "       'soybean oil, at oil mill/ BR/ kg',\n",
+       "       'synthetic gas, from wood, at fixed bed gasifier/ CH/ Nm3',\n",
+       "       'synthetic gas, from wood, at fluidized bed gasifier/ CH/ Nm3',\n",
+       "       'synthetic gas, production mix, at plant/ CH/ Nm3',\n",
+       "       'vegetable oil methyl ester, at esterification plant/ FR/ kg',\n",
+       "       'vegetable oil methyl ester, production FR, at service station/ CH/ kg',\n",
+       "       'vegetable oil, from waste cooking oil, at plant/ CH/ kg',\n",
+       "       'vegetable oil, from waste cooking oil, at plant/ FR/ kg',\n",
+       "       'DDGS, from corn, at distillery/ US/ kg',\n",
+       "       'DDGS, from potatoes, at distillery/ CH/ kg',\n",
+       "       'DDGS, from rye, at distillery/ RER/ kg',\n",
+       "       'bagasse, from sugarcane, at sugar refinery/ BR/ kg',\n",
+       "       'bagasse, from sweet sorghum, at distillery/ CN/ kg',\n",
+       "       'grass fibres, at digestion/ CH/ kg',\n",
+       "       'molasses, from sugar beet, at sugar refinery/ CH/ kg',\n",
+       "       'palm kernel meal, at oil mill/ MY/ kg',\n",
+       "       'proteins, from grass, at digestion/ CH/ kg',\n",
+       "       'pulps, from sugar beet, at sugar refinery/ CH/ kg',\n",
+       "       'rape meal, at oil mill/ CH/ kg',\n",
+       "       'rape meal, at oil mill/ RER/ kg',\n",
+       "       'soybean meal, at oil mill/ US/ kg',\n",
+       "       'soybean meal, at oil mill/ BR/ kg',\n",
+       "       'syrup, from sugar beet molasses, at distillery/ CH/ kg',\n",
+       "       'vinasse, from sugarcane molasses, at sugar refinery/ BR/ kg',\n",
+       "       'vinasse, from sweet sorghum, at distillery/ CN/ kg',\n",
+       "       'electricity, wood, at distillery/ CH/ kWh',\n",
+       "       'ash, bagasse, at fermentation plant/ BR/ kg',\n",
+       "       'biowaste, at collection point/ CH/ kg',\n",
+       "       'grass fibres, at fermentation/ CH/ kg',\n",
+       "       'protein concentrate, from whey, at fermentation/ CH/ kg',\n",
+       "       'proteins, from grass, at fermentation/ CH/ kg',\n",
+       "       'vinasse, at fermentation plant/ CH/ kg',\n",
+       "       'vinasse, from sugarcane, at fermentation/ BR/ kg',\n",
+       "       'whey, at dairy/ CH/ kg',\n",
+       "       'yeast paste, from whey, at fermentation/ CH/ kg',\n",
+       "       'cladding, crossbar-pole, aluminium, at plant/ RER/ m2',\n",
+       "       'door, inner, glass-wood, at plant/ RER/ m2',\n",
+       "       'door, inner, wood, at plant/ RER/ m2',\n",
+       "       'door, outer, wood-aluminium, at plant/ RER/ m2',\n",
+       "       'door, outer, wood-glass, at plant/ RER/ m2',\n",
+       "       'glazing, double (2-IV), U<1.1 W/m2K, at plant/ RER/ m2',\n",
+       "       'glazing, double (2-IV), U<1.1 W/m2K, laminated safety glass, at plant/ RER/ m2',\n",
+       "       'glazing, triple (3-IV), U<0.5 W/m2K, at plant/ RER/ m2',\n",
+       "       'window frame, aluminium, U=1.6 W/m2K, at plant/ RER/ m2',\n",
+       "       'window frame, plastic (PVC), U=1.6 W/m2K, at plant/ RER/ m2',\n",
+       "       'window frame, wood, U=1.5 W/m2K, at plant/ RER/ m2',\n",
+       "       'window frame, wood-metal, U=1.6 W/m2K, at plant/ RER/ m2',\n",
+       "       'Borax, anhydrous, powder, at plant/ RER/ kg',\n",
+       "       'aluminium hydroxide, at plant/ RER/ kg',\n",
+       "       'aluminium oxide, at plant/ RER/ kg',\n",
+       "       'aluminium sulphate, powder, at plant/ RER/ kg',\n",
+       "       'ammonia, liquid, at regional storehouse/ RER/ kg',\n",
+       "       'ammonia, liquid, at regional storehouse/ CH/ kg',\n",
+       "       'ammonia, partial oxidation, liquid, at plant/ RER/ kg',\n",
+       "       'ammonia, steam reforming, liquid, at plant/ RER/ kg',\n",
+       "       'ammonium bicarbonate, at plant/ RER/ kg',\n",
+       "       'ammonium chloride from chlorosilane, at plant/ GLO/ kg',\n",
+       "       'ammonium chloride, at plant/ GLO/ kg',\n",
+       "       'argon, crude, liquid, at plant/ RER/ kg',\n",
+       "       'argon, liquid, at plant/ RER/ kg', 'arsine, at plant/ GLO/ kg',\n",
+       "       'barite, at plant/ RER/ kg',\n",
+       "       'biocides, for paper production, unspecified, at plant/ RER/ kg',\n",
+       "       'boric acid, anhydrous, powder, at plant/ RER/ kg',\n",
+       "       'boric oxide, at plant/ GLO/ kg',\n",
+       "       'boron carbide, at plant/ GLO/ kg',\n",
+       "       'boron trifluoride, at plant/ GLO/ kg',\n",
+       "       'calcium borates, at plant/ TR/ kg',\n",
+       "       'calcium carbide, technical grade, at plant/ RER/ kg',\n",
+       "       'calcium chloride, CaCl2, at plant/ RER/ kg',\n",
+       "       'calcium chloride, CaCl2, at regional storage/ CH/ kg',\n",
+       "       'calcium chloride, from hypochlorination of allyl chloride, at plant/ RER/ kg',\n",
+       "       'carbon black, at plant/ GLO/ kg',\n",
+       "       'carbon dioxide liquid, at plant/ RER/ kg',\n",
+       "       'carbon monoxide, CO, at plant/ RER/ kg',\n",
+       "       'cerium concentrate, 60% cerium oxide, at plant/ CN/ kg',\n",
+       "       'chemicals inorganic, at plant/ GLO/ kg',\n",
+       "       'chlorine dioxide, at plant/ RER/ kg',\n",
+       "       'chlorine, gaseous, diaphragm cell, at plant/ RER/ kg',\n",
+       "       'chlorine, gaseous, lithium chloride electrolysis, at plant/ GLO/ kg',\n",
+       "       'chlorine, gaseous, membrane cell, at plant/ RER/ kg',\n",
+       "       'chlorine, gaseous, mercury cell, at plant/ RER/ kg',\n",
+       "       'chlorine, liquid, production mix, at plant/ RER/ kg',\n",
+       "       'chromium oxide, flakes, at plant/ RER/ kg',\n",
+       "       'concentrated lithium brine (6.7 %  Li), at plant/ GLO/ kg',\n",
+       "       'copper carbonate, at plant/ RER/ kg',\n",
+       "       'copper oxide, at plant/ RER/ kg', 'cryolite, at plant/ RER/ kg',\n",
+       "       'deinking emulsion, in paper production, at plant/ RER/ kg',\n",
+       "       'diborane, at plant/ GLO/ kg',\n",
+       "       'explosives, tovex, at plant/ CH/ kg',\n",
+       "       'fluorine, liquid, at plant/ RER/ kg',\n",
+       "       'fluorspar, 97%, at plant/ GLO/ kg',\n",
+       "       'fluosilicic acid, 22% in H2O, at plant/ RER/ kg',\n",
+       "       'fluosilicic acid, 22% in H2O, at plant/ MA/ kg',\n",
+       "       'fluosilicic acid, 22% in H2O, at plant/ US/ kg',\n",
+       "       'flux, wave soldering, at plant/ GLO/ kg',\n",
+       "       'graphite, at plant/ RER/ kg',\n",
+       "       'graphite, battery grade, at plant/ CN/ kg',\n",
+       "       'helium, at plant/ GLO/ kg',\n",
+       "       'hydrochloric acid from benzene chlorination, at plant/ RER/ kg',\n",
+       "       'hydrochloric acid, 30% in H2O, at plant/ RER/ kg',\n",
+       "       'hydrochloric acid, 36% in H2O, from reacting propylene and chlorine, at plant/ RER/ kg',\n",
+       "       'hydrochloric acid, from Mannheim process, at plant/ RER/ kg',\n",
+       "       'hydrochloric acid, from the reaction of hydrogen with chlorine, at plant/ RER/ kg',\n",
+       "       'hydrogen cyanide from Sohio process, at plant/ RER/ kg',\n",
+       "       'hydrogen fluoride, at plant/ GLO/ kg',\n",
+       "       'hydrogen peroxide, 50% in H2O, at plant/ RER/ kg',\n",
+       "       'hydrogen sulphide, H2S, at plant/ RER/ kg',\n",
+       "       'hydrogen, cracking, APME, at plant/ RER/ kg',\n",
+       "       'hydrogen, from butanediol dehdrogenation/ GLO/ kg',\n",
+       "       'hydrogen, liquid, at plant/ RER/ kg',\n",
+       "       'hydrogen, liquid, diaphragm cell, at plant/ RER/ kg',\n",
+       "       'hydrogen, liquid, from chlorine electrolysis, production mix, at plant/ RER/ kg',\n",
+       "       'hydrogen, liquid, membrane cell, at plant/ RER/ kg',\n",
+       "       'hydrogen, liquid, mercury cell, at plant/ RER/ kg',\n",
+       "       'ilmenite, 54% titanium dioxide, at plant/ AU/ kg',\n",
+       "       'intral, at plant/ RER/ kg',\n",
+       "       'iron (III) chloride, 40% in H2O, at plant/ CH/ kg',\n",
+       "       'kaolin, at plant/ RER/ kg', 'krypton, gaseous, at plant/ RER/ kg',\n",
+       "       'krypton, gaseous, at regional storage/ CH/ kg',\n",
+       "       'lanthanum oxide, at plant/ CN/ kg',\n",
+       "       'lime from lithium carbonate hydration/ GLO/ kg',\n",
+       "       'lithium carbonate, at plant/ GLO/ kg',\n",
+       "       'lithium chloride, at plant/ GLO/ kg',\n",
+       "       'lithium fluoride, at plant/ CN/ kg',\n",
+       "       'lithium hexafluorophosphate, at plant/ CN/ kg',\n",
+       "       'lithium hydroxide, at plant/ GLO/ kg',\n",
+       "       'lithium manganese oxide, at plant/ GLO/ kg',\n",
+       "       'magnesium oxide, at plant/ RER/ kg',\n",
+       "       'magnesium sulphate, at plant/ RER/ kg',\n",
+       "       'malusil, at plant/ RER/ kg',\n",
+       "       'manganese oxide (Mn2O3), at plant/ CN/ kg',\n",
+       "       'natural gas liquids, from natural gas, helium extraction/ GLO/ kg',\n",
+       "       'neodymium oxide, at plant/ CN/ kg',\n",
+       "       'nitric acid, 50% in H2O, at plant/ RER/ kg',\n",
+       "       'nitrogen, liquid, at plant/ RER/ kg',\n",
+       "       'oxygen, liquid, at plant/ RER/ kg',\n",
+       "       'ozone, liquid, at plant/ RER/ kg', 'phosphane, at plant/ GLO/ kg',\n",
+       "       'phosphate rock, as P2O5, beneficiated, dry, at plant/ MA/ kg',\n",
+       "       'phosphate rock, as P2O5, beneficiated, wet, at plant/ US/ kg',\n",
+       "       'phosphoric acid, fertiliser grade, 70% in H2O, at plant/ GLO/ kg',\n",
+       "       'phosphoric acid, fertiliser grade, 70% in H2O, at plant/ US/ kg',\n",
+       "       'phosphoric acid, fertiliser grade, 70% in H2O, at plant/ MA/ kg',\n",
+       "       'phosphoric acid, industrial grade, 85% in H2O, at plant/ RER/ kg',\n",
+       "       'phosphorous chloride, at plant/ RER/ kg',\n",
+       "       'phosphorous pentachloride, at plant/ CN/ kg',\n",
+       "       'phosphorus, white, liquid, at plant/ RER/ kg',\n",
+       "       'phosphoryl chloride, at plant/ RER/ kg',\n",
+       "       'pigments, paper production, unspecified, at plant/ RER/ kg',\n",
+       "       'pitch despergents, in paper production, at plant/ RER/ kg',\n",
+       "       'portachrom, at plant/ RER/ kg', 'portafer, at plant/ RER/ kg',\n",
+       "       'potassium carbonate from manganese dioxide oxidation, at plant/ RER/ kg',\n",
+       "       'potassium carbonate, at plant/ GLO/ kg',\n",
+       "       'potassium hydroxide, at regional storage/ RER/ kg',\n",
+       "       'potassium perchlorate, at plant/ GLO/ kg',\n",
+       "       'potassium permanganate, at plant/ RER/ kg',\n",
+       "       'potassium sulphate, as K2O, from rape oil, at esterification plant/ RER/ kg',\n",
+       "       'praseodymium oxide, at plant/ CN/ kg',\n",
+       "       'rare earth concentrate, 70% REO, from bastnasite, at beneficiation/ CN/ kg',\n",
+       "       'rutile, 95% titanium dioxide, at plant/ AU/ kg',\n",
+       "       'sales gas, from natural gas, helium extraction/ GLO/ kg',\n",
+       "       'samarium europium gadolinium concentrate, 94% rare earth oxide, at plant/ CN/ kg',\n",
+       "       'secondary sulphur, at refinery/ RER/ kg',\n",
+       "       'secondary sulphur, at refinery/ CH/ kg',\n",
+       "       'selenium, at plant/ RER/ kg',\n",
+       "       'silicon carbide, at plant/ RER/ kg',\n",
+       "       'silicon carbide, recycling, at plant/ RER/ kg',\n",
+       "       'silicon tetrachloride, at plant/ DE/ kg',\n",
+       "       'silicon tetrahydride, at plant/ RER/ kg',\n",
+       "       'silicone product, at plant/ RER/ kg',\n",
+       "       'soda, powder, at plant/ RER/ kg',\n",
+       "       'sodium arsenide, at plant/ GLO/ kg',\n",
+       "       'sodium borates, at plant/ US/ kg',\n",
+       "       'sodium carbonate from ammonium chloride production, at plant/ GLO/ kg',\n",
+       "       'sodium chlorate, powder, at plant/ RER/ kg',\n",
+       "       'sodium chloride, brine solution, at plant/ RER/ kg',\n",
+       "       'sodium chloride, powder, at plant/ RER/ kg',\n",
+       "       'sodium cyanide, at plant/ RER/ kg',\n",
+       "       'sodium dichromate, at plant/ RER/ kg',\n",
+       "       'sodium dithionite, anhydrous, at plant/ RER/ kg',\n",
+       "       'sodium hydroxide, 50% in H2O, diaphragm cell, at plant/ RER/ kg',\n",
+       "       'sodium hydroxide, 50% in H2O, membrane cell, at plant/ RER/ kg',\n",
+       "       'sodium hydroxide, 50% in H2O, mercury cell, at plant/ RER/ kg',\n",
+       "       'sodium hydroxide, 50% in H2O, production mix, at plant/ RER/ kg',\n",
+       "       'sodium hypochlorite, 15% in H2O, at plant/ RER/ kg',\n",
+       "       'sodium perchlorate, at plant/ GLO/ kg',\n",
+       "       'sodium persulfate, at plant/ GLO/ kg',\n",
+       "       'sodium phosphate, at plant/ RER/ kg',\n",
+       "       'sodium silicate, furnace liquor, 37% in H2O, at plant/ RER/ kg',\n",
+       "       'sodium silicate, furnace process, pieces, at plant/ RER/ kg',\n",
+       "       'sodium silicate, hydrothermal liquor, 48% in H2O, at plant/ RER/ kg',\n",
+       "       'sodium silicate, spray powder 80%, at plant/ RER/ kg',\n",
+       "       'sodium sulphat from viscose production, at plant/ GLO/ kg',\n",
+       "       'sodium sulphate from sulfuric acid digestion of spodumene/ GLO/ kg',\n",
+       "       'sodium sulphate, from Mannheim process, at plant/ RER/ kg',\n",
+       "       'sodium sulphate, from natural sources, at plant/ RER/ kg',\n",
+       "       'sodium sulphate, powder, production mix, at plant/ RER/ kg',\n",
+       "       'sodium tetrafluorborate, at plant/ GLO/ kg',\n",
+       "       'sodium tetrahydroborate, at plant/ GLO/ kg',\n",
+       "       'spodumene, at plant/ RER/ kg',\n",
+       "       'stibnite ore, 70% stibnite, at mine/ CN/ kg',\n",
+       "       'sulphite, at plant/ RER/ kg',\n",
+       "       'sulphur dioxide, liquid, at plant/ RER/ kg',\n",
+       "       'sulphur hexafluoride, liquid, at plant/ RER/ kg',\n",
+       "       'sulphur trioxide, at plant/ RER/ kg',\n",
+       "       'sulphuric acid from viscose production, at plant/ GLO/ kg',\n",
+       "       'sulphuric acid, liquid, at plant/ RER/ kg',\n",
+       "       'tetrachlorosilane, at plant/ GLO/ kg',\n",
+       "       'tetrafluoroethylene film, on glass/ RER/ kg',\n",
+       "       'tetrafluoroethylene, at plant/ RER/ kg',\n",
+       "       'titanium dioxide at plant, sulphate process, at plant/ RER/ kg',\n",
+       "       'titanium dioxide, chloride process, at plant/ RER/ kg',\n",
+       "       'titanium dioxide, production mix, at plant/ RER/ kg',\n",
+       "       'trichloroborane, at plant/ GLO/ kg',\n",
+       "       'urea ammonium nitrate, as N, at regional storehouse/ RER/ kg',\n",
+       "       'xenon, gaseous, at plant/ RER/ kg',\n",
+       "       'xenon, gaseous, at regional storage/ CH/ kg',\n",
+       "       'zinc monosulphate, ZnSO4.H2O, at plant/ RER/ kg',\n",
+       "       'zinc oxide, at plant/ RER/ kg',\n",
+       "       'zinc sulphide, ZnS, at plant/ RER/ kg',\n",
+       "       'zircon, 50% zirconium, at plant/ AU/ kg',\n",
+       "       'zirconium oxide, at plant/ AU/ kg',\n",
+       "       '1,1-difluoroethane, HFC-152a, at plant/ US/ kg',\n",
+       "       '1,1-dimethylcyclopentane, from naphtha, at plant/ RER/ kg',\n",
+       "       '1-butanol, propylene hydroformylation, at plant/ RER/ kg',\n",
+       "       '1-pentanol, at plant/ RER/ kg', '1-propanol, at plant/ RER/ kg',\n",
+       "       '2,3-dimethylbutan, from naphtha, at plant/ RER/ kg',\n",
+       "       '2-butanol, at plant/ RER/ kg',\n",
+       "       '2-methyl-1-butanol, at plant/ RER/ kg',\n",
+       "       '2-methyl-2-butanol, at plant/ RER/ kg',\n",
+       "       '2-methylpentane, from naphtha, at plant/ RER/ kg',\n",
+       "       '3-methyl-1-butanol, at plant/ RER/ kg',\n",
+       "       '3-methyl-1-butyl acetate, at plant/ RER/ kg',\n",
+       "       '4-methyl-2-pentanone, at plant/ RER/ kg',\n",
+       "       'AKD sizer, in paper production, at plant/ RER/ kg',\n",
+       "       'DTPA, diethylenetriaminepentaacetic acid, at plant/ RER/ kg',\n",
+       "       'EDTA, ethylenediaminetetraacetic acid, at plant/ RER/ kg',\n",
+       "       'N,N-dimethylformamide, at plant/ RER/ kg',\n",
+       "       'N-methyl-2-pyrrolidone, at plant/ RER/ kg',\n",
+       "       'acetaldehyde, at plant/ RER/ kg',\n",
+       "       'acetic acid from acetaldehyde, at plant/ RER/ kg',\n",
+       "       'acetic acid from butane, at plant/ RER/ kg',\n",
+       "       'acetic acid, 98% in H2O, at plant/ RER/ kg',\n",
+       "       'acetic anhydride from acetaldehyde, at plant/ RER/ kg',\n",
+       "       'acetic anhydride from ketene, at plant/ RER/ kg',\n",
+       "       'acetic anhydride, at plant/ RER/ kg',\n",
+       "       'acetone cyanohydrin, at plant/ RER/ kg',\n",
+       "       'acetone from butane, at plant/ RER/ kg',\n",
+       "       'acetone, liquid, at plant/ RER/ kg',\n",
+       "       'acetonitrile, at plant/ RER/ kg',\n",
+       "       'acetylene, at regional storehouse/ CH/ kg',\n",
+       "       'acrylic acid, at plant/ RER/ kg',\n",
+       "       'acrylonitrile from Sohio process, at plant/ RER/ kg',\n",
+       "       'adipic acid, at plant/ RER/ kg',\n",
+       "       'alkylbenzene, linear, at plant/ RER/ kg',\n",
+       "       'allyl chloride, from reacting propylene and chlorine, at plant/ RER/ kg',\n",
+       "       'ammonium carbonate, at plant/ RER/ kg',\n",
+       "       'ammonium thiocyanate, at plant/ GLO/ kg',\n",
+       "       'aniline, at plant/ RER/ kg', 'anionic resin, at plant/ CH/ kg',\n",
+       "       'anthraquinone, at plant/ RER/ kg',\n",
+       "       'benzal chloride, at plant/ RER/ kg',\n",
+       "       'benzaldehyde, at plant/ RER/ kg', 'benzene, at plant/ RER/ kg',\n",
+       "       'benzyl alcohol, at plant/ RER/ kg',\n",
+       "       'benzyl chloride, at plant/ RER/ kg',\n",
+       "       'bisphenol A, powder, at plant/ RER/ kg',\n",
+       "       'butane-1,4-diol, at plant/ RER/ kg',\n",
+       "       'butanes from butenes, at plant/ RER/ kg',\n",
+       "       'butyl acetate, at plant/ RER/ kg',\n",
+       "       'butyl acrylate, at plant/ RER/ kg', 'butyrolactone/ GLO/ kg',\n",
+       "       'carbon disulfide, at plant/ GLO/ kg',\n",
+       "       'carbon tetrachloride, at plant/ RER/ kg',\n",
+       "       'cationic resin, at plant/ CH/ kg',\n",
+       "       'chemicals organic, at plant/ GLO/ kg',\n",
+       "       'chloroacetic acid, at plant/ RER/ kg',\n",
+       "       'chlorodifluoromethane, at plant/ NL/ kg',\n",
+       "       'chloromethyl methyl ether, at plant/ RER/ kg',\n",
+       "       'crude coconut oil, at plant/ PH/ kg', 'cumene, at plant/ RER/ kg',\n",
+       "       'cyclohexane, at plant/ RER/ kg',\n",
+       "       'cyclohexanol, at plant/ RER/ kg',\n",
+       "       'cyclohexanone, at plant/ RER/ kg',\n",
+       "       'dichloromethane, at plant/ RER/ kg',\n",
+       "       'dichloropropene, from reacting propylene and chlorine, at plant/ RER/ kg',\n",
+       "       'diethanolamine, at plant/ RER/ kg',\n",
+       "       'diethyl ether, at plant/ RER/ kg',\n",
+       "       'diethylene glycol, at plant/ RER/ kg',\n",
+       "       'dimethyl ether, at plant/ RER/ kg',\n",
+       "       'dimethyl sulfoxide, at plant/ RER/ kg',\n",
+       "       'dimethyl sulphate, at plant/ RER/ kg',\n",
+       "       'dimethylacetamide, at plant/ GLO/ kg',\n",
+       "       'dimethylamine borane, at plant/ GLO/ kg',\n",
+       "       'dimethylamine, at plant/ RER/ kg', 'dioxane, at plant/ RER/ kg',\n",
+       "       'dipropylene glycol monomethyl ether, at plant/ RER/ kg',\n",
+       "       'epichlorohydrin, from hypochlorination of allyl chloride, at plant/ RER/ kg',\n",
+       "       'esters of versatic acid, at plant/ RER/ kg',\n",
+       "       'ethanol from ethylene, at plant/ RER/ kg',\n",
+       "       'ethyl acetate from butane, at plant/ RER/ kg',\n",
+       "       'ethyl acetate, at plant/ RER/ kg',\n",
+       "       'ethyl benzene, at plant/ RER/ kg',\n",
+       "       'ethylene carbonate, at plant/ CN/ kg',\n",
+       "       'ethylene dichloride, at plant/ RER/ kg',\n",
+       "       'ethylene glycol diethyl ether, at plant/ RER/ kg',\n",
+       "       'ethylene glycol dimethyl ether, at plant/ RER/ kg',\n",
+       "       'ethylene glycol monoethyl ether, at plant/ RER/ kg',\n",
+       "       'ethylene glycol, at plant/ RER/ kg',\n",
+       "       'ethylene oxide, at plant/ RER/ kg',\n",
+       "       'ethylenediamine, at plant/ RER/ kg',\n",
+       "       'fatty acids, from vegetarian oil, at plant/ RER/ kg',\n",
+       "       'fatty alcohol, from coconut oil, at plant/ RER/ kg',\n",
+       "       'fatty alcohol, from palm kernel oil, at plant/ RER/ kg',\n",
+       "       'fatty alcohol, from palm oil, at plant/ RER/ kg',\n",
+       "       'fatty alcohol, petrochemical, at plant/ RER/ kg',\n",
+       "       'formaldehyde, production mix, at plant/ RER/ kg',\n",
+       "       'formic acid from butane, at plant/ RER/ kg',\n",
+       "       'formic acid from methyl formate, at plant/ RER/ kg',\n",
+       "       'formic acid, at plant/ RER/ kg',\n",
+       "       'fraction 1, from naphtha, at plant/ RER/ kg',\n",
+       "       'fraction 7, from naphtha, at plant/ RER/ kg',\n",
+       "       'fraction 8, from naphtha, at plant/ RER/ kg',\n",
+       "       'glycerine, from epichlorohydrin, at plant/ RER/ kg',\n",
+       "       'glycerine, from palm oil, at esterification plant/ MY/ kg',\n",
+       "       'glycerine, from rape oil, at esterification plant/ CH/ kg',\n",
+       "       'glycerine, from rape oil, at esterification plant/ RER/ kg',\n",
+       "       'glycerine, from soybean oil, at esterification plant/ US/ kg',\n",
+       "       'glycerine, from soybean oil, at esterification plant/ BR/ kg',\n",
+       "       'glycerine, from vegetable oil, at esterification plant/ FR/ kg',\n",
+       "       'heat, unspecific, in chemical plant/ RER/ MJ',\n",
+       "       'heptane, at plant/ RER/ kg', 'hexafluorethane, at plant/ GLO/ kg',\n",
+       "       'hexamethyldisilazane, at plant/ GLO/ kg',\n",
+       "       'hexane, at plant/ RER/ kg', 'hydrogen cyanide, at plant/ RER/ kg',\n",
+       "       'isobutanol, at plant/ RER/ kg',\n",
+       "       'isobutyl acetate, at plant/ RER/ kg',\n",
+       "       'isohexane, at plant/ RER/ kg', 'isopropanol, at plant/ RER/ kg',\n",
+       "       'isopropyl acetate, at plant/ RER/ kg', 'latex, at plant/ RER/ kg',\n",
+       "       'lubricating oil, at plant/ RER/ kg',\n",
+       "       'maleic anhydride from catalytic oxidation of benzene, at plant/ RER/ kg',\n",
+       "       'maleic anhydride from the direct oxidation of n-butane, at plant/ RER/ kg',\n",
+       "       'maleic anhydride, at plant/ RER/ kg',\n",
+       "       'melamine, at plant/ RER/ kg', 'methanol, at plant/ GLO/ kg',\n",
+       "       'methanol, at regional storage/ CH/ kg',\n",
+       "       'methyl acetate, at plant/ RER/ kg',\n",
+       "       'methyl acrylate, at plant/ GLO/ kg',\n",
+       "       'methyl ethyl ketone from butane, at plant/ RER/ kg',\n",
+       "       'methyl ethyl ketone, at plant/ RER/ kg',\n",
+       "       'methyl formate, at plant/ RER/ kg',\n",
+       "       'methyl tert-butyl ether, at plant/ RER/ kg',\n",
+       "       'methyl-3-methoxypropionate, at plant/ GLO/ kg',\n",
+       "       'methylamine, at plant/ RER/ kg',\n",
+       "       'methylchloride, at plant/ WEU/ kg',\n",
+       "       'methylchloride, at regional storage/ CH/ kg',\n",
+       "       'methylcyclohexane, at plant/ RER/ kg',\n",
+       "       'methylcyclohexane, from naphtha, at plant/ RER/ kg',\n",
+       "       'methylcyclopentane, from naphtha, at plant/ RER/ kg',\n",
+       "       'monochlorobenzene, at plant/ RER/ kg',\n",
+       "       'monochloropentafluoroethane, at plant/ GLO/ kg',\n",
+       "       'monoethanolamine, at plant/ RER/ kg',\n",
+       "       'n-olefins, at plant/ RER/ kg', 'nitrobenzene, at plant/ RER/ kg',\n",
+       "       'o-dichlorobenzene, at plant/ RER/ kg',\n",
+       "       'optical brighteners, in paper production, at plant/ RER/ kg',\n",
+       "       'p-dichlorobenzene, at plant/ RER/ kg',\n",
+       "       'palm kernel oil, at oil mill/ MY/ kg',\n",
+       "       'palm oil, at oil mill/ MY/ kg', 'paraffin, at plant/ RER/ kg',\n",
+       "       'penta-erythritol, at plant/ RER/ kg',\n",
+       "       'pentane, at plant/ RER/ kg', 'phenol, at plant/ RER/ kg',\n",
+       "       'phosgene, liquid, at plant/ RER/ kg',\n",
+       "       'phthalic anhydride, at plant/ RER/ kg',\n",
+       "       'polyvinylfluoride film, at plant/ US/ kg',\n",
+       "       'polyvinylfluoride, at plant/ US/ kg',\n",
+       "       'polyvinylfluoride, dispersion, at plant/ US/ kg',\n",
+       "       'propanal, at plant/ RER/ kg',\n",
+       "       'propylene glycol, liquid, at plant/ RER/ kg',\n",
+       "       'propylene oxide, liquid, at plant/ RER/ kg',\n",
+       "       'refrigerant R134a, at plant/ RER/ kg',\n",
+       "       'retention aids, in paper production, at plant/ RER/ kg',\n",
+       "       'rosin size, in paper production, at plant/ RER/ kg',\n",
+       "       'sodium formate, reaction of formaldehyde with acetaldehyde, at plant/ RER/ kg',\n",
+       "       'sodium methoxide, at plant/ GLO/ kg',\n",
+       "       'solvents, organic, unspecified, at plant/ GLO/ kg',\n",
+       "       'soya oil, at plant/ RER/ kg', 'soya scrap, at plant/ RER/ kg',\n",
+       "       'steam from catalytic oxidation of benzene, at plant/ RER/ kg',\n",
+       "       'steam from direct oxidation of n-butane, at plant/ RER/ kg',\n",
+       "       'steam from the production of formaldehyde/ RER/ kg',\n",
+       "       'tetrachloroethylene, at plant/ WEU/ kg',\n",
+       "       'tetrachloroethylene, at regional storage/ CH/ kg',\n",
+       "       'tetrahydrofuran, at plant/ RER/ kg',\n",
+       "       'toluene, liquid, at plant/ RER/ kg',\n",
+       "       'trichloroethylene, at plant/ WEU/ kg',\n",
+       "       'trichloromethane, at plant/ RER/ kg',\n",
+       "       'trichloropropane, from hypochlorination of allyl chloride, at plant/ RER/ kg',\n",
+       "       'triethanolamine, at plant/ RER/ kg',\n",
+       "       'triethylene glycol, at plant/ RER/ kg',\n",
+       "       'triethylene glycol, recycling, at plant/ RER/ kg',\n",
+       "       'trifluoromethane, at plant/ GLO/ kg',\n",
+       "       'trimethyl borate, at plant/ GLO/ kg',\n",
+       "       'trimethylamine, at plant/ RER/ kg',\n",
+       "       'urea, as N, at regional storehouse/ RER/ kg',\n",
+       "       'vinyl fluoride, at plant/ US/ kg', 'xylene, at plant/ RER/ kg',\n",
+       "       'basalt, at mine/ RER/ kg', 'bentonite, at mine/ DE/ kg',\n",
+       "       'bentonite, at processing/ DE/ kg',\n",
+       "       'calcareous marl, at plant/ CH/ kg', 'clay, at mine/ CH/ kg',\n",
+       "       'expanded clay, at plant/ DE/ kg',\n",
+       "       'expanded vermiculite, at plant/ CH/ kg',\n",
+       "       'gravel, crushed, at mine/ CH/ kg',\n",
+       "       'gravel, round, at mine/ CH/ kg',\n",
+       "       'gravel, unspecified, at mine/ CH/ kg',\n",
+       "       'lightweight concrete block, expanded vermiculite, at plant/ CH/ kg',\n",
+       "       'limestone, at mine/ CH/ kg', 'limestone, crushed, washed/ CH/ kg',\n",
+       "       'limestone, milled, loose, at plant/ CH/ kg',\n",
+       "       'perlite, at mine/ DE/ kg', 'pumice, at mine/ DE/ kg',\n",
+       "       'quicklime, in pieces, loose, at plant/ CH/ kg',\n",
+       "       'quicklime, milled, loose, at plant/ CH/ kg',\n",
+       "       'quicklime, milled, packed, at plant/ CH/ kg',\n",
+       "       'recultivation, bentonite mine/ DE/ m2',\n",
+       "       'recultivation, limestone mine/ CH/ m2', 'sand, at mine/ CH/ kg',\n",
+       "       'silica sand, at plant/ DE/ kg', 'vermiculite, at mine/ ZA/ kg',\n",
+       "       'anhydrite, at plant/ CH/ kg',\n",
+       "       'anhydrite, burned, at plant/ CH/ kg',\n",
+       "       'blast furnace slag cement, at plant/ CH/ kg',\n",
+       "       'cement, unspecified, at plant/ CH/ kg',\n",
+       "       'clinker, at plant/ CH/ kg',\n",
+       "       'lime, hydrated, loose, at plant/ CH/ kg',\n",
+       "       'lime, hydrated, packed, at plant/ CH/ kg',\n",
+       "       'lime, hydraulic, at plant/ CH/ kg',\n",
+       "       'portland calcareous cement, at plant/ CH/ kg',\n",
+       "       'portland cement, strength class Z 42.5, at plant/ CH/ kg',\n",
+       "       'portland cement, strength class Z 52.5, at plant/ CH/ kg',\n",
+       "       'portland slag sand cement, at plant/ CH/ kg',\n",
+       "       'stucco, at plant/ CH/ kg',\n",
+       "       'autoclaved aerated concrete block, at plant/ CH/ kg',\n",
+       "       'brick, at plant/ RER/ kg', 'light clay brick, at plant/ DE/ kg',\n",
+       "       'refractory, basic, packed, at plant/ DE/ kg',\n",
+       "       'refractory, fireclay, packed, at plant/ DE/ kg',\n",
+       "       'refractory, high aluminium oxide, packed, at plant/ DE/ kg',\n",
+       "       'sand-lime brick, at plant/ DE/ kg',\n",
+       "       'cement cast plaster floor, at plant/ CH/ kg',\n",
+       "       'concrete block, at plant/ DE/ kg',\n",
+       "       'concrete, exacting, at plant/ CH/ m3',\n",
+       "       'concrete, exacting, with de-icing salt contact, at plant/ CH/ m3',\n",
+       "       'concrete, normal, at plant/ CH/ m3',\n",
+       "       'concrete, sole plate and foundation, at plant/ CH/ m3',\n",
+       "       'lightweight concrete block, expanded clay, at plant/ CH/ kg',\n",
+       "       'lightweight concrete block, expanded perlite, at plant/ CH/ kg',\n",
+       "       'lightweight concrete block, polystyrene, at plant/ CH/ kg',\n",
+       "       'lightweight concrete block, pumice, at plant/ DE/ kg',\n",
+       "       'poor concrete, at plant/ CH/ m3',\n",
+       "       'ceramic tiles, at regional storage/ CH/ kg',\n",
+       "       'concrete roof tile, at plant/ CH/ kg',\n",
+       "       'fibre cement corrugated slab, at plant/ CH/ kg',\n",
+       "       'fibre cement facing tile, at plant/ CH/ kg',\n",
+       "       'fibre cement facing tile, large format, at plant/ CH/ kg',\n",
+       "       'fibre cement facing tile, small format, at plant/ CH/ kg',\n",
+       "       'fibre cement roof slate, at plant/ CH/ kg',\n",
+       "       'gypsum fibre board, at plant/ CH/ kg',\n",
+       "       'gypsum plaster board, at plant/ CH/ kg',\n",
+       "       'roof tile, at plant/ RER/ kg', 'anhydrite rock, at mine/ CH/ kg',\n",
+       "       'asbestos, crysotile type, at plant/ GLO/ kg',\n",
+       "       'dolomite, at plant/ RER/ kg', 'feldspar, at plant/ RER/ kg',\n",
+       "       'gypsum, mineral, at mine/ CH/ kg',\n",
+       "       'limestone, crushed, for mill/ CH/ kg',\n",
+       "       'limestone, milled, packed, at plant/ CH/ kg',\n",
+       "       'mastic asphalt, at plant/ CH/ kg',\n",
+       "       'natural stone plate, cut, at regional storage/ CH/ kg',\n",
+       "       'natural stone plate, grounded, at regional storage/ CH/ kg',\n",
+       "       'natural stone plate, polished, at regional storage/ CH/ kg',\n",
+       "       'packing, cement/ CH/ kg', 'packing, clay products/ CH/ kg',\n",
+       "       'packing, fibre cement products/ CH/ kg',\n",
+       "       'packing, lime products/ CH/ kg', 'quarry tile, at plant/ CH/ kg',\n",
+       "       'sanitary ceramics, at regional storage/ CH/ kg',\n",
+       "       'electronics for control units/ RER/ kg', 'blasting/ RER/ kg',\n",
+       "       'excavation, hydraulic digger/ RER/ m3',\n",
+       "       'excavation, skid-steer loader/ RER/ m3',\n",
+       "       'crushing, rock/ RER/ kg',\n",
+       "       'diesel, burned in building machine/ GLO/ MJ',\n",
+       "       'power sawing, with catalytic converter/ RER/ h',\n",
+       "       'power sawing, without catalytic converter/ RER/ h',\n",
+       "       'cooling energy, natural gas, at cogen unit with absorption chiller 100 kW/ CH/ MJ',\n",
+       "       'electricity mix/ BR/ kWh', 'electricity mix/ CN/ kWh',\n",
+       "       'electricity mix/ US/ kWh', 'electricity mix/ JP/ kWh',\n",
+       "       'electricity, high voltage, production AT, at grid/ AT/ kWh',\n",
+       "       'electricity, high voltage, production BA, at grid/ BA/ kWh',\n",
+       "       'electricity, high voltage, production BE, at grid/ BE/ kWh',\n",
+       "       'electricity, high voltage, production BG, at grid/ BG/ kWh',\n",
+       "       'electricity, high voltage, production BR, at grid/ BR/ kWh',\n",
+       "       'electricity, high voltage, production CENTREL, at grid/ CENTREL/ kWh',\n",
+       "       'electricity, high voltage, production CH, at grid/ CH/ kWh',\n",
+       "       'electricity, high voltage, production CS, at grid/ CS/ kWh',\n",
+       "       'electricity, high voltage, production CZ, at grid/ CZ/ kWh',\n",
+       "       'electricity, high voltage, production DE, at grid/ DE/ kWh',\n",
+       "       'electricity, high voltage, production DK, at grid/ DK/ kWh',\n",
+       "       'electricity, high voltage, production ES, at grid/ ES/ kWh',\n",
+       "       'electricity, high voltage, production FI, at grid/ FI/ kWh',\n",
+       "       'electricity, high voltage, production FR, at grid/ FR/ kWh',\n",
+       "       'electricity, high voltage, production GB, at grid/ GB/ kWh',\n",
+       "       'electricity, high voltage, production GR, at grid/ GR/ kWh',\n",
+       "       'electricity, high voltage, production HR, at grid/ HR/ kWh',\n",
+       "       'electricity, high voltage, production HU, at grid/ HU/ kWh',\n",
+       "       'electricity, high voltage, production IE, at grid/ IE/ kWh',\n",
+       "       'electricity, high voltage, production IT, at grid/ IT/ kWh',\n",
+       "       'electricity, high voltage, production LU, at grid/ LU/ kWh',\n",
+       "       'electricity, high voltage, production MK, at grid/ MK/ kWh',\n",
+       "       'electricity, high voltage, production NL, at grid/ NL/ kWh',\n",
+       "       'electricity, high voltage, production NO, at grid/ NO/ kWh',\n",
+       "       'electricity, high voltage, production NORDEL, at grid/ NORDEL/ kWh',\n",
+       "       'electricity, high voltage, production PL, at grid/ PL/ kWh',\n",
+       "       'electricity, high voltage, production PT, at grid/ PT/ kWh',\n",
+       "       'electricity, high voltage, production RER, at grid/ RER/ kWh',\n",
+       "       'electricity, high voltage, production RO, at grid/ RO/ kWh',\n",
+       "       'electricity, high voltage, production SE, at grid/ SE/ kWh',\n",
+       "       'electricity, high voltage, production SI, at grid/ SI/ kWh',\n",
+       "       'electricity, high voltage, production SK, at grid/ SK/ kWh',\n",
+       "       'electricity, high voltage, production UCTE, at grid/ UCTE/ kWh',\n",
+       "       'electricity, low voltage, production AT, at grid/ AT/ kWh',\n",
+       "       'electricity, low voltage, production BA, at grid/ BA/ kWh',\n",
+       "       'electricity, low voltage, production BE, at grid/ BE/ kWh',\n",
+       "       'electricity, low voltage, production BG, at grid/ BG/ kWh',\n",
+       "       'electricity, low voltage, production BR, at grid/ BR/ kWh',\n",
+       "       'electricity, low voltage, production CENTREL, at grid/ CENTREL/ kWh',\n",
+       "       'electricity, low voltage, production CH, at grid/ CH/ kWh',\n",
+       "       'electricity, low voltage, production CS, at grid/ CS/ kWh',\n",
+       "       'electricity, low voltage, production CZ, at grid/ CZ/ kWh',\n",
+       "       'electricity, low voltage, production DE, at grid/ DE/ kWh',\n",
+       "       'electricity, low voltage, production DK, at grid/ DK/ kWh',\n",
+       "       'electricity, low voltage, production ES, at grid/ ES/ kWh',\n",
+       "       'electricity, low voltage, production FI, at grid/ FI/ kWh',\n",
+       "       'electricity, low voltage, production FR, at grid/ FR/ kWh',\n",
+       "       'electricity, low voltage, production GB, at grid/ GB/ kWh',\n",
+       "       'electricity, low voltage, production GR, at grid/ GR/ kWh',\n",
+       "       'electricity, low voltage, production HR, at grid/ HR/ kWh',\n",
+       "       'electricity, low voltage, production HU, at grid/ HU/ kWh',\n",
+       "       'electricity, low voltage, production IE, at grid/ IE/ kWh',\n",
+       "       'electricity, low voltage, production IT, at grid/ IT/ kWh',\n",
+       "       'electricity, low voltage, production LU, at grid/ LU/ kWh',\n",
+       "       'electricity, low voltage, production MK, at grid/ MK/ kWh',\n",
+       "       'electricity, low voltage, production NL, at grid/ NL/ kWh',\n",
+       "       'electricity, low voltage, production NO, at grid/ NO/ kWh',\n",
+       "       'electricity, low voltage, production NORDEL, at grid/ NORDEL/ kWh',\n",
+       "       'electricity, low voltage, production PL, at grid/ PL/ kWh',\n",
+       "       'electricity, low voltage, production PT, at grid/ PT/ kWh',\n",
+       "       'electricity, low voltage, production RER, at grid/ RER/ kWh',\n",
+       "       'electricity, low voltage, production RO, at grid/ RO/ kWh',\n",
+       "       'electricity, low voltage, production SE, at grid/ SE/ kWh',\n",
+       "       'electricity, low voltage, production SI, at grid/ SI/ kWh',\n",
+       "       'electricity, low voltage, production SK, at grid/ SK/ kWh',\n",
+       "       'electricity, low voltage, production UCTE, at grid/ UCTE/ kWh',\n",
+       "       'electricity, medium voltage, production AT, at grid/ AT/ kWh',\n",
+       "       'electricity, medium voltage, production BA, at grid/ BA/ kWh',\n",
+       "       'electricity, medium voltage, production BE, at grid/ BE/ kWh',\n",
+       "       'electricity, medium voltage, production BG, at grid/ BG/ kWh',\n",
+       "       'electricity, medium voltage, production BR, at grid/ BR/ kWh',\n",
+       "       'electricity, medium voltage, production CENTREL, at grid/ CENTREL/ kWh',\n",
+       "       'electricity, medium voltage, production CH, at grid/ CH/ kWh',\n",
+       "       'electricity, medium voltage, production CS, at grid/ CS/ kWh',\n",
+       "       'electricity, medium voltage, production CZ, at grid/ CZ/ kWh',\n",
+       "       'electricity, medium voltage, production DE, at grid/ DE/ kWh',\n",
+       "       'electricity, medium voltage, production DK, at grid/ DK/ kWh',\n",
+       "       'electricity, medium voltage, production ES, at grid/ ES/ kWh',\n",
+       "       'electricity, medium voltage, production FI, at grid/ FI/ kWh',\n",
+       "       'electricity, medium voltage, production FR, at grid/ FR/ kWh',\n",
+       "       'electricity, medium voltage, production GB, at grid/ GB/ kWh',\n",
+       "       'electricity, medium voltage, production GR, at grid/ GR/ kWh',\n",
+       "       'electricity, medium voltage, production HR, at grid/ HR/ kWh',\n",
+       "       'electricity, medium voltage, production HU, at grid/ HU/ kWh',\n",
+       "       'electricity, medium voltage, production IE, at grid/ IE/ kWh',\n",
+       "       'electricity, medium voltage, production IT, at grid/ IT/ kWh',\n",
+       "       'electricity, medium voltage, production LU, at grid/ LU/ kWh',\n",
+       "       'electricity, medium voltage, production MK, at grid/ MK/ kWh',\n",
+       "       'electricity, medium voltage, production NL, at grid/ NL/ kWh',\n",
+       "       'electricity, medium voltage, production NO, at grid/ NO/ kWh',\n",
+       "       'electricity, medium voltage, production NORDEL, at grid/ NORDEL/ kWh',\n",
+       "       'electricity, medium voltage, production PL, at grid/ PL/ kWh',\n",
+       "       'electricity, medium voltage, production PT, at grid/ PT/ kWh',\n",
+       "       'electricity, medium voltage, production RER, at grid/ RER/ kWh',\n",
+       "       'electricity, medium voltage, production RO, at grid/ RO/ kWh',\n",
+       "       'electricity, medium voltage, production SE, at grid/ SE/ kWh',\n",
+       "       'electricity, medium voltage, production SI, at grid/ SI/ kWh',\n",
+       "       'electricity, medium voltage, production SK, at grid/ SK/ kWh',\n",
+       "       'electricity, medium voltage, production UCTE, at grid/ UCTE/ kWh',\n",
+       "       'electricity, production mix AT/ AT/ kWh',\n",
+       "       'electricity, production mix BA/ BA/ kWh',\n",
+       "       'electricity, production mix BE/ BE/ kWh',\n",
+       "       'electricity, production mix BG/ BG/ kWh',\n",
+       "       'electricity, production mix BR/ BR/ kWh',\n",
+       "       'electricity, production mix CENTREL/ CENTREL/ kWh',\n",
+       "       'electricity, production mix CH/ CH/ kWh',\n",
+       "       'electricity, production mix CN/ CN/ kWh',\n",
+       "       'electricity, production mix CS/ CS/ kWh',\n",
+       "       'electricity, production mix CZ/ CZ/ kWh',\n",
+       "       'electricity, production mix DE/ DE/ kWh',\n",
+       "       'electricity, production mix DK/ DK/ kWh',\n",
+       "       'electricity, production mix ES/ ES/ kWh',\n",
+       "       'electricity, production mix FI/ FI/ kWh',\n",
+       "       'electricity, production mix FR/ FR/ kWh',\n",
+       "       'electricity, production mix GB/ GB/ kWh',\n",
+       "       'electricity, production mix GR/ GR/ kWh',\n",
+       "       'electricity, production mix HR/ HR/ kWh',\n",
+       "       'electricity, production mix HU/ HU/ kWh',\n",
+       "       'electricity, production mix IE/ IE/ kWh',\n",
+       "       'electricity, production mix IT/ IT/ kWh',\n",
+       "       'electricity, production mix JP/ JP/ kWh',\n",
+       "       'electricity, production mix LU/ LU/ kWh',\n",
+       "       'electricity, production mix MK/ MK/ kWh',\n",
+       "       'electricity, production mix NL/ NL/ kWh',\n",
+       "       'electricity, production mix NO/ NO/ kWh',\n",
+       "       'electricity, production mix NORDEL/ NORDEL/ kWh',\n",
+       "       'electricity, production mix PL/ PL/ kWh',\n",
+       "       'electricity, production mix PT/ PT/ kWh',\n",
+       "       'electricity, production mix RER/ RER/ kWh',\n",
+       "       'electricity, production mix RO/ RO/ kWh',\n",
+       "       'electricity, production mix SE/ SE/ kWh',\n",
+       "       'electricity, production mix SI/ SI/ kWh',\n",
+       "       'electricity, production mix SK/ SK/ kWh',\n",
+       "       'electricity, production mix UCTE/ UCTE/ kWh',\n",
+       "       'electricity, production mix US/ US/ kWh',\n",
+       "       'electricity mix/ AT/ kWh', 'electricity mix/ BE/ kWh',\n",
+       "       'electricity mix/ CH/ kWh', 'electricity mix/ ES/ kWh',\n",
+       "       'electricity mix/ CS/ kWh', 'electricity mix/ FR/ kWh',\n",
+       "       'electricity mix/ GR/ kWh', 'electricity mix/ IT/ kWh',\n",
+       "       'electricity mix/ LU/ kWh', 'electricity mix/ NL/ kWh',\n",
+       "       'electricity mix/ PT/ kWh', 'electricity mix/ DE/ kWh',\n",
+       "       'electricity mix/ DK/ kWh', 'electricity mix/ FI/ kWh',\n",
+       "       'electricity mix/ GB/ kWh', 'electricity mix/ IE/ kWh',\n",
+       "       'electricity mix/ SE/ kWh', 'electricity mix/ NO/ kWh',\n",
+       "       'electricity mix/ CZ/ kWh', 'electricity mix/ HU/ kWh',\n",
+       "       'electricity mix/ PL/ kWh', 'electricity mix/ SK/ kWh',\n",
+       "       'electricity mix/ SI/ kWh', 'electricity mix/ HR/ kWh',\n",
+       "       'electricity mix/ BA/ kWh', 'electricity mix/ MK/ kWh',\n",
+       "       'electricity mix/ BG/ kWh', 'electricity mix/ RO/ kWh',\n",
+       "       'electricity mix, SBB/ CH/ kWh',\n",
+       "       'electricity mix, aluminium industry/ GLO/ kWh',\n",
+       "       'electricity, certified eletricity/ CH/ kWh',\n",
+       "       'electricity, consumer mix/ CH/ kWh',\n",
+       "       'electricity, high voltage, SBB, at grid/ CH/ kWh',\n",
+       "       'electricity, high voltage, aluminium industry, at grid/ GLO/ kWh',\n",
+       "       'electricity, high voltage, at grid/ CH/ kWh',\n",
+       "       'electricity, high voltage, at grid/ CS/ kWh',\n",
+       "       'electricity, high voltage, at grid/ DE/ kWh',\n",
+       "       'electricity, high voltage, at grid/ AT/ kWh',\n",
+       "       'electricity, high voltage, at grid/ BE/ kWh',\n",
+       "       'electricity, high voltage, at grid/ ES/ kWh',\n",
+       "       'electricity, high voltage, at grid/ FR/ kWh',\n",
+       "       'electricity, high voltage, at grid/ GR/ kWh',\n",
+       "       'electricity, high voltage, at grid/ IT/ kWh',\n",
+       "       'electricity, high voltage, at grid/ LU/ kWh',\n",
+       "       'electricity, high voltage, at grid/ NL/ kWh',\n",
+       "       'electricity, high voltage, at grid/ PT/ kWh',\n",
+       "       'electricity, high voltage, at grid/ DK/ kWh',\n",
+       "       'electricity, high voltage, at grid/ FI/ kWh',\n",
+       "       'electricity, high voltage, at grid/ GB/ kWh',\n",
+       "       'electricity, high voltage, at grid/ IE/ kWh',\n",
+       "       'electricity, high voltage, at grid/ SE/ kWh',\n",
+       "       'electricity, high voltage, at grid/ NO/ kWh',\n",
+       "       'electricity, high voltage, at grid/ CZ/ kWh',\n",
+       "       'electricity, high voltage, at grid/ HU/ kWh',\n",
+       "       'electricity, high voltage, at grid/ PL/ kWh',\n",
+       "       'electricity, high voltage, at grid/ SK/ kWh',\n",
+       "       'electricity, high voltage, at grid/ SI/ kWh',\n",
+       "       'electricity, high voltage, at grid/ HR/ kWh',\n",
+       "       'electricity, high voltage, at grid/ BA/ kWh',\n",
+       "       'electricity, high voltage, at grid/ MK/ kWh',\n",
+       "       'electricity, high voltage, at grid/ BR/ kWh',\n",
+       "       'electricity, high voltage, at grid/ CN/ kWh',\n",
+       "       'electricity, high voltage, at grid/ US/ kWh',\n",
+       "       'electricity, high voltage, at grid/ JP/ kWh',\n",
+       "       'electricity, high voltage, at grid/ BG/ kWh',\n",
+       "       'electricity, high voltage, at grid/ RO/ kWh',\n",
+       "       'electricity, high voltage, certified electricity. at grid/ CH/ kWh',\n",
+       "       'electricity, high voltage, consumer mix, at grid/ CH/ kWh',\n",
+       "       'electricity, low voltage, at grid/ AT/ kWh',\n",
+       "       'electricity, low voltage, at grid/ BE/ kWh',\n",
+       "       'electricity, low voltage, at grid/ CH/ kWh',\n",
+       "       'electricity, low voltage, at grid/ ES/ kWh',\n",
+       "       'electricity, low voltage, at grid/ CS/ kWh',\n",
+       "       'electricity, low voltage, at grid/ FR/ kWh',\n",
+       "       'electricity, low voltage, at grid/ GR/ kWh',\n",
+       "       'electricity, low voltage, at grid/ IT/ kWh',\n",
+       "       'electricity, low voltage, at grid/ LU/ kWh',\n",
+       "       'electricity, low voltage, at grid/ NL/ kWh',\n",
+       "       'electricity, low voltage, at grid/ PT/ kWh',\n",
+       "       'electricity, low voltage, at grid/ DE/ kWh',\n",
+       "       'electricity, low voltage, at grid/ DK/ kWh',\n",
+       "       'electricity, low voltage, at grid/ FI/ kWh',\n",
+       "       'electricity, low voltage, at grid/ GB/ kWh',\n",
+       "       'electricity, low voltage, at grid/ IE/ kWh',\n",
+       "       'electricity, low voltage, at grid/ SE/ kWh',\n",
+       "       'electricity, low voltage, at grid/ NO/ kWh',\n",
+       "       'electricity, low voltage, at grid/ CZ/ kWh',\n",
+       "       'electricity, low voltage, at grid/ HU/ kWh',\n",
+       "       'electricity, low voltage, at grid/ PL/ kWh',\n",
+       "       'electricity, low voltage, at grid/ SK/ kWh',\n",
+       "       'electricity, low voltage, at grid/ SI/ kWh',\n",
+       "       'electricity, low voltage, at grid/ HR/ kWh',\n",
+       "       'electricity, low voltage, at grid/ BA/ kWh',\n",
+       "       'electricity, low voltage, at grid/ MK/ kWh',\n",
+       "       'electricity, low voltage, at grid/ BR/ kWh',\n",
+       "       'electricity, low voltage, at grid/ CN/ kWh',\n",
+       "       'electricity, low voltage, at grid/ US/ kWh',\n",
+       "       'electricity, low voltage, at grid/ JP/ kWh',\n",
+       "       'electricity, low voltage, at grid/ BG/ kWh',\n",
+       "       'electricity, low voltage, at grid/ RO/ kWh',\n",
+       "       'electricity, low voltage, certified electricity, at grid/ CH/ kWh',\n",
+       "       'electricity, low voltage, consumer mix, at grid/ CH/ kWh',\n",
+       "       'electricity, medium voltage, aluminium industry, at grid/ GLO/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ AT/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ BE/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ CH/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ ES/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ CS/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ FR/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ GR/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ IT/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ LU/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ NL/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ PT/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ DE/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ DK/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ FI/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ GB/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ IE/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ SE/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ NO/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ CZ/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ HU/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ PL/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ SK/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ SI/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ HR/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ BA/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ MK/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ BR/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ CN/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ US/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ JP/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ BG/ kWh',\n",
+       "       'electricity, medium voltage, at grid/ RO/ kWh',\n",
+       "       'electricity, medium voltage, certified electricity, at grid/ CH/ kWh',\n",
+       "       'electricity, medium voltage, consumer mix, at grid/ CH/ kWh',\n",
+       "       'Anode, lithium-ion battery, graphite, at plant/ CN/ kg',\n",
+       "       'CD-ROM/DVD-ROM drive, desktop computer, at plant/ GLO/ unit',\n",
+       "       'CD-ROM/DVD-ROM drive, laptop computer, at plant/ GLO/ unit',\n",
+       "       'Cathode, lithium-ion battery, lithium manganese oxide, at plant/ CN/ kg',\n",
+       "       'LCD glass, at plant/ GLO/ kg',\n",
+       "       'backlight, LCD screen, at plant/ GLO/ kg',\n",
+       "       'cable, connector for computer, without plugs, at plant/ GLO/ m',\n",
+       "       'cable, data cable in infrastructure, at plant/ GLO/ m',\n",
+       "       'cable, network cable, category 5, without plugs, at plant/ GLO/ m',\n",
+       "       'cable, printer cable, without plugs, at plant/ GLO/ m',\n",
+       "       'cable, ribbon cable, 20-pin, with plugs, at plant/ GLO/ kg',\n",
+       "       'cable, three-conductor cable, at plant/ GLO/ m',\n",
+       "       'capacitor, SMD type, surface-mounting, at plant/ GLO/ kg',\n",
+       "       'capacitor, Tantalum-, through-hole mounting, at plant/ GLO/ kg',\n",
+       "       'capacitor, electrolyte type, < 2cm height, at plant/ GLO/ kg',\n",
+       "       'capacitor, electrolyte type, > 2cm height, at plant/ GLO/ kg',\n",
+       "       'capacitor, film, through-hole mounting, at plant/ GLO/ kg',\n",
+       "       'capacitor, unspecified, at plant/ GLO/ kg',\n",
+       "       'chassis, network main devices/ RER/ kg',\n",
+       "       'connector, PCI bus, at plant/ GLO/ kg',\n",
+       "       'connector, clamp connection, at plant/ GLO/ kg',\n",
+       "       'connector, computer, peripherical type, at plant/ GLO/ kg',\n",
+       "       'diode, glass-, SMD type, surface mounting, at plant/ GLO/ kg',\n",
+       "       'diode, glass-, through-hole mounting, at plant/ GLO/ kg',\n",
+       "       'diode, unspecified, at plant/ GLO/ kg',\n",
+       "       'electrode, positive, LaNi5, at plant/ GLO/ kg',\n",
+       "       'electrolyte, KOH, LiOH additive, at plant/ GLO/ kg',\n",
+       "       'electron gun, for CRT tube production, at plant/ GLO/ kg',\n",
+       "       'electronic component, active, unspecified, at plant/ GLO/ kg',\n",
+       "       'electronic component, passive, unspecified, at plant/ GLO/ kg',\n",
+       "       'electronic component, unspecified, at plant/ GLO/ kg',\n",
+       "       'ferrite, at plant/ GLO/ kg',\n",
+       "       'frit, for CRT tube production, at plant/ GLO/ kg',\n",
+       "       'funnel glass, CRT screen, at plant/ GLO/ kg',\n",
+       "       'inductor, low value multilayer chip type, LMCI, at plant/ GLO/ kg',\n",
+       "       'inductor, miniature RF chip type, MRFI, at plant/ GLO/ kg',\n",
+       "       'inductor, ring core choke type, at plant/ GLO/ kg',\n",
+       "       'inductor, unspecified, at plant/ GLO/ kg',\n",
+       "       'integrated circuit, IC, logic type, at plant/ GLO/ kg',\n",
+       "       'integrated circuit, IC, memory type, at plant/ GLO/ kg',\n",
+       "       'light emitting diode, LED, at plant/ GLO/ kg',\n",
+       "       'panel components, at plant/ GLO/ kg',\n",
+       "       'panel glass, CRT screen, at plant/ GLO/ kg',\n",
+       "       'plugs, inlet and outlet, for computer cable, at plant/ GLO/ unit',\n",
+       "       'plugs, inlet and outlet, for network cable, at plant/ GLO/ unit',\n",
+       "       'plugs, inlet and outlet, for printer cable, at plant/ GLO/ unit',\n",
+       "       'potentiometer, unspecified, at plant/ GLO/ kg',\n",
+       "       'power adapter, for laptop, at plant/ GLO/ unit',\n",
+       "       'production efforts, capacitors/ GLO/ kg',\n",
+       "       'production efforts, diodes/ GLO/ kg',\n",
+       "       'production efforts, inductor/ GLO/ kg',\n",
+       "       'production efforts, resistors/ GLO/ kg',\n",
+       "       'production efforts, transistors/ GLO/ kg',\n",
+       "       'resistor, SMD type, surface mounting, at plant/ GLO/ kg',\n",
+       "       'resistor, metal film type, through-hole mounting, at plant/ GLO/ kg',\n",
+       "       'resistor, unspecified, at plant/ GLO/ kg',\n",
+       "       'resistor, wirewound, through-hole mounting, at plant/ GLO/ kg',\n",
+       "       'separator, lithium-ion battery, at plant/ CN/ kg',\n",
+       "       'single cell, lithium-ion battery, lithium manganese oxide/graphite, at plant/ CN/ kg',\n",
+       "       'switch, toggle type, at plant/ GLO/ kg',\n",
+       "       'transformer, high voltage use, at plant/ GLO/ kg',\n",
+       "       'transformer, low voltage use, at plant/ GLO/ kg',\n",
+       "       'transistor, SMD type, surface mounting, at plant/ GLO/ kg',\n",
+       "       'transistor, unspecified, at plant/ GLO/ kg',\n",
+       "       'transistor, wired, big size, through-hole mounting, at plant/ GLO/ kg',\n",
+       "       'transistor, wired, small size, through-hole mounting, at plant/ GLO/ kg',\n",
+       "       'wafer, fabricated, for integrated circuit, at plant/ GLO/ m2',\n",
+       "       'CRT screen, 17 inches, at plant/ GLO/ unit',\n",
+       "       'LCD flat screen, 17 inches, at plant/ GLO/ unit',\n",
+       "       'desktop computer, without screen, at plant/ GLO/ unit',\n",
+       "       'keyboard, standard version, at plant/ GLO/ unit',\n",
+       "       'laptop computer, at plant/ GLO/ unit',\n",
+       "       'mouse device, optical, with cable, at plant/ GLO/ unit',\n",
+       "       'printer, laser jet, b/w, at plant/ GLO/ unit',\n",
+       "       'printer, laser jet, colour, at plant/ GLO/ unit',\n",
+       "       'HDD, desktop computer, at plant/ GLO/ unit',\n",
+       "       'HDD, laptop computer, at plant/ GLO/ unit',\n",
+       "       'ITO powder, for target production, at plant/ RER/ kg',\n",
+       "       'ITO, sintered target, at plant/ RER/ kg',\n",
+       "       'LCD module, at plant/ GLO/ kg', 'assembly, LCD module/ GLO/ kg',\n",
+       "       'assembly, LCD screen/ GLO/ kg',\n",
+       "       'battery, LiIo, rechargeable, prismatic, at plant/ GLO/ kg',\n",
+       "       'battery, NiMH, rechargeable, prismatic, at plant/ GLO/ kg',\n",
+       "       'cathode-ray tube, CRT screen, at plant/ GLO/ kg',\n",
+       "       'electrode, negative, LiC6, at plant/ GLO/ kg',\n",
+       "       'electrode, negative, Ni, at plant/ GLO/ kg',\n",
+       "       'electrode, positive, LiMn2O4, at plant/ GLO/ kg',\n",
+       "       'fan, at plant/ GLO/ kg', 'magnetite, at plant/ GLO/ kg',\n",
+       "       'mischmetal, primary, at plant/ GLO/ kg',\n",
+       "       'mounting, surface mount technology, Pb-containing solder/ GLO/ m2',\n",
+       "       'mounting, surface mount technology, Pb-free solder/ GLO/ m2',\n",
+       "       'mounting, through-hole technology, Pb-containing solder/ GLO/ m2',\n",
+       "       'mounting, through-hole technology, Pb-free solder/ GLO/ m2',\n",
+       "       'power supply unit, at plant/ CN/ unit',\n",
+       "       'printed wiring board, mixed mounted, unspec., solder mix, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, mounted, Desktop PC mainboard, Pb containing, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, mounted, Desktop PC mainboard, Pb free, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, mounted, Desktop PC mainboard, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, mounted, Laptop PC mainboard, Pb containing, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, mounted, Laptop PC mainboard, Pb free, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, mounted, Laptop PC mainboard, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, power supply unit desktop PC, Pb containing, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, power supply unit desktop PC, Pb free, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, power supply unit desktop PC, solder mix, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, surface mount, at plant/ GLO/ m2',\n",
+       "       'printed wiring board, surface mount, lead-containing surface, at plant/ GLO/ m2',\n",
+       "       'printed wiring board, surface mount, lead-free surface, at plant/ GLO/ m2',\n",
+       "       'printed wiring board, surface mounted, unspec., Pb containing, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, surface mounted, unspec., Pb free, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, surface mounted, unspec., solder mix, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, through-hole mounted, unspec., Pb containing, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, through-hole mounted, unspec., Pb free, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, through-hole mounted, unspec., solder mix, at plant/ GLO/ kg',\n",
+       "       'printed wiring board, through-hole, at plant/ GLO/ m2',\n",
+       "       'printed wiring board, through-hole, lead-containing surface, at plant/ GLO/ m2',\n",
+       "       'printed wiring board, through-hole, lead-free surface, at plant/ GLO/ m2',\n",
+       "       'sputtering, ITO, for LCD/ RER/ m3',\n",
+       "       'toner module, laser jet, b/w, at plant/ GLO/ unit',\n",
+       "       'toner module, laser jet, colour, at plant/ GLO/ unit',\n",
+       "       'toner, black, powder, at plant/ GLO/ kg',\n",
+       "       'toner, black, used for printing/ RER/ kg',\n",
+       "       'toner, colour, powder, at plant/ GLO/ kg',\n",
+       "       'toner, colour, used for printing/ RER/ kg',\n",
+       "       'use, IP network, videoconference/ CH/ h',\n",
+       "       'use, IP network, work at home/ CH/ h',\n",
+       "       'use, computer, desktop with CRT monitor, active mode/ CH/ h',\n",
+       "       'use, computer, desktop with CRT monitor, active mode/ RER/ h',\n",
+       "       'use, computer, desktop with CRT monitor, home use/ CH/ h',\n",
+       "       'use, computer, desktop with CRT monitor, home use/ RER/ h',\n",
+       "       'use, computer, desktop with CRT monitor, off mode/ CH/ h',\n",
+       "       'use, computer, desktop with CRT monitor, off mode/ RER/ h',\n",
+       "       'use, computer, desktop with CRT monitor, office use/ CH/ h',\n",
+       "       'use, computer, desktop with CRT monitor, standby/sleep mode/ CH/ h',\n",
+       "       'use, computer, desktop with CRT monitor, standby/sleep mode/ RER/ h',\n",
+       "       'use, computer, desktop with LCD monitor, active mode/ CH/ h',\n",
+       "       'use, computer, desktop with LCD monitor, active mode/ RER/ h',\n",
+       "       'use, computer, desktop with LCD monitor, home use/ CH/ h',\n",
+       "       'use, computer, desktop with LCD monitor, home use/ RER/ h',\n",
+       "       'use, computer, desktop with LCD monitor, off mode/ CH/ h',\n",
+       "       'use, computer, desktop with LCD monitor, off mode/ RER/ h',\n",
+       "       'use, computer, desktop with LCD monitor, sleep/standby mode/ CH/ h',\n",
+       "       'use, computer, desktop with LCD monitor, sleep/standby mode/ RER/ h',\n",
+       "       'use, computer, desktop, mix, home use/ CH/ h',\n",
+       "       'use, computer, desktop, mix, home use/ RER/ h',\n",
+       "       'use, computer, desktop, mix, office use/ CH/ h',\n",
+       "       'use, computer, desktop, mix, office use/ RER/ h',\n",
+       "       'use, computer, desktop, with CRT monitor, office use/ RER/ h',\n",
+       "       'use, computer, desktop, with LCD monitor, office use/ CH/ h',\n",
+       "       'use, computer, desktop, with LCD monitor, office use/ RER/ h',\n",
+       "       'use, computer, laptop, active mode/ CH/ h',\n",
+       "       'use, computer, laptop, active mode/ RER/ h',\n",
+       "       'use, computer, laptop, off mode/ CH/ h',\n",
+       "       'use, computer, laptop, off mode/ RER/ h',\n",
+       "       'use, computer, laptop, office use/ CH/ h',\n",
+       "       'use, computer, laptop, office use/ RER/ h',\n",
+       "       'use, computer, laptop, standby/sleep mode/ CH/ h',\n",
+       "       'use, computer, laptop, standby/sleep mode/ RER/ h',\n",
+       "       'use, computer, laptop, videoconference/ CH/ h',\n",
+       "       'use, computer, laptop, videoconference, certified electricity mix/ CH/ h',\n",
+       "       'use, computer, laptop, work at home/ CH/ h',\n",
+       "       'use, computer, laptop, work at home, certified electricity mix/ CH/ h',\n",
+       "       'use, network access devices/ CH/ h',\n",
+       "       'use, network access devices, certified electricity/ CH/ h',\n",
+       "       'use, printer, laser jet, b/w, per kg printed paper/ CH/ kg',\n",
+       "       'use, printer, laser jet, b/w, per kg printed paper/ RER/ kg',\n",
+       "       'use, printer, laser jet, b/w, printing per h/ CH/ h',\n",
+       "       'use, printer, laser jet, b/w, printing per h/ RER/ h',\n",
+       "       'use, printer, laser jet, colour, per kg printed paper/ CH/ kg',\n",
+       "       'use, printer, laser jet, colour, per kg printed paper/ RER/ kg',\n",
+       "       'use, printer, laser jet, colour, printing per h/ CH/ h',\n",
+       "       'use, printer, laser jet, colour, printing per h/ RER/ h',\n",
+       "       'videoconference, laptop, participant/ CH/ h',\n",
+       "       'videoconference, laptop, participant, certified electricity mix/ CH/ h',\n",
+       "       'work at home, corporate access/ CH/ h',\n",
+       "       'work at home, corporate access, certified electricity mix/ CH/ h',\n",
+       "       'sugar, from sugar beet, at sugar refinery/ CH/ kg',\n",
+       "       'sugar, from sugarcane, at sugar refinery/ BR/ kg',\n",
+       "       'anti-reflex-coating, etching, solar glass/ DK/ m2',\n",
+       "       'flat glass, coated, at plant/ RER/ kg',\n",
+       "       'flat glass, uncoated, at plant/ RER/ kg',\n",
+       "       'glass fibre, at plant/ RER/ kg',\n",
+       "       'glass tube, borosilicate, at plant/ DE/ kg',\n",
+       "       'solar collector glass tube, with silver mirror, at plant/ DE/ kg',\n",
+       "       'solar glass, low-iron, at regional storage/ RER/ kg',\n",
+       "       'tempering, flat glass/ RER/ kg',\n",
+       "       'glass cullets, sorted, at sorting plant/ RER/ kg',\n",
+       "       'glass, from public collection, unsorted/ RER/ kg',\n",
+       "       'packaging glass, brown, at plant/ CH/ kg',\n",
+       "       'packaging glass, brown, at plant/ DE/ kg',\n",
+       "       'packaging glass, brown, at plant/ RER/ kg',\n",
+       "       'packaging glass, brown, at regional storage/ CH/ kg',\n",
+       "       'packaging glass, green, at plant/ CH/ kg',\n",
+       "       'packaging glass, green, at plant/ DE/ kg',\n",
+       "       'packaging glass, green, at plant/ RER/ kg',\n",
+       "       'packaging glass, green, at regional storage/ CH/ kg',\n",
+       "       'packaging glass, white, at plant/ CH/ kg',\n",
+       "       'packaging glass, white, at plant/ DE/ kg',\n",
+       "       'packaging glass, white, at plant/ RER/ kg',\n",
+       "       'packaging glass, white, at regional storage/ CH/ kg',\n",
+       "       'benzene, at coke plant/ DE/ kg',\n",
+       "       'benzene, at coke plant/ GLO/ kg',\n",
+       "       'coke oven gas, at plant/ DE/ MJ',\n",
+       "       'coke oven gas, at plant/ GLO/ MJ',\n",
+       "       'hard coal briquettes, at plant/ RER/ MJ',\n",
+       "       'hard coal coke, at plant/ RER/ MJ',\n",
+       "       'hard coal coke, at plant/ GLO/ MJ',\n",
+       "       'hard coal mix, at regional storage/ UCTE/ kg',\n",
+       "       'hard coal, at regional storage/ WEU/ kg',\n",
+       "       'tar, at coke plant/ DE/ kg', 'tar, at coke plant/ GLO/ kg',\n",
+       "       'anthracite, burned in stove 5-15kW/ RER/ MJ',\n",
+       "       'hard coal briquette, burned in stove 5-15kW/ RER/ MJ',\n",
+       "       'hard coal coke, burned in stove 5-15kW/ RER/ MJ',\n",
+       "       'hard coal, burned in industrial furnace 1-10MW/ RER/ MJ',\n",
+       "       'heat, anthracite, at stove 5-15kW/ RER/ MJ',\n",
+       "       'heat, at hard coal industrial furnace 1-10MW/ RER/ MJ',\n",
+       "       'heat, hard coal briquette, at stove 5-15kW/ RER/ MJ',\n",
+       "       'heat, hard coal coke, at stove 5-15kW/ RER/ MJ',\n",
+       "       'NOx retained, in SCR/ GLO/ kg',\n",
+       "       'SOx retained, in hard coal flue gas desulphurisation/ RER/ kg',\n",
+       "       'electricity, hard coal, at coal mine power plant/ CN/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ AT/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ BE/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ ES/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ FR/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ IT/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ NL/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ PT/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ UCTE/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ DE/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ NORDEL/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ CZ/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ HR/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ PL/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ SK/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ CENTREL/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ US/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ ERCOT/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ FRCC/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ MRO/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ NPCC/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ RFC/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ SERC/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ SPP/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ WECC/ kWh',\n",
+       "       'electricity, hard coal, at power plant/ CN/ kWh',\n",
+       "       'hard coal, burned in coal mine power plant/ CN/ MJ',\n",
+       "       'hard coal, burned in power plant/ AT/ MJ',\n",
+       "       'hard coal, burned in power plant/ BE/ MJ',\n",
+       "       'hard coal, burned in power plant/ ES/ MJ',\n",
+       "       'hard coal, burned in power plant/ FR/ MJ',\n",
+       "       'hard coal, burned in power plant/ IT/ MJ',\n",
+       "       'hard coal, burned in power plant/ NL/ MJ',\n",
+       "       'hard coal, burned in power plant/ PT/ MJ',\n",
+       "       'hard coal, burned in power plant/ DE/ MJ',\n",
+       "       'hard coal, burned in power plant/ CZ/ MJ',\n",
+       "       'hard coal, burned in power plant/ HR/ MJ',\n",
+       "       'hard coal, burned in power plant/ PL/ MJ',\n",
+       "       'hard coal, burned in power plant/ SK/ MJ',\n",
+       "       'hard coal, burned in power plant/ NORDEL/ MJ',\n",
+       "       'hard coal, burned in power plant/ ERCOT/ MJ',\n",
+       "       'hard coal, burned in power plant/ FRCC/ MJ',\n",
+       "       'hard coal, burned in power plant/ MRO/ MJ',\n",
+       "       'hard coal, burned in power plant/ NPCC/ MJ',\n",
+       "       'hard coal, burned in power plant/ RFC/ MJ',\n",
+       "       'hard coal, burned in power plant/ SERC/ MJ',\n",
+       "       'hard coal, burned in power plant/ SPP/ MJ',\n",
+       "       'hard coal, burned in power plant/ WECC/ MJ',\n",
+       "       'hard coal, burned in power plant/ CN/ MJ',\n",
+       "       'hard coal supply mix/ AT/ kg', 'hard coal supply mix/ BE/ kg',\n",
+       "       'hard coal supply mix/ DE/ kg', 'hard coal supply mix/ ES/ kg',\n",
+       "       'hard coal supply mix/ FR/ kg', 'hard coal supply mix/ IT/ kg',\n",
+       "       'hard coal supply mix/ NL/ kg', 'hard coal supply mix/ PT/ kg',\n",
+       "       'hard coal supply mix/ CZ/ kg', 'hard coal supply mix/ HR/ kg',\n",
+       "       'hard coal supply mix/ PL/ kg', 'hard coal supply mix/ SK/ kg',\n",
+       "       'hard coal supply mix/ CN/ kg',\n",
+       "       'hard coal supply mix, at regional storage/ US/ kg',\n",
+       "       'hard coal, at mine/ AU/ kg', 'hard coal, at mine/ RNA/ kg',\n",
+       "       'hard coal, at mine/ EEU/ kg', 'hard coal, at mine/ ZA/ kg',\n",
+       "       'hard coal, at mine/ RLA/ kg', 'hard coal, at mine/ WEU/ kg',\n",
+       "       'hard coal, at mine/ RU/ kg', 'hard coal, at mine/ CPA/ kg',\n",
+       "       'hard coal, at mine/ CN/ kg',\n",
+       "       'hard coal, at regional storage/ AU/ kg',\n",
+       "       'hard coal, at regional storage/ EEU/ kg',\n",
+       "       'hard coal, at regional storage/ RLA/ kg',\n",
+       "       'hard coal, at regional storage/ RNA/ kg',\n",
+       "       'hard coal, at regional storage/ ZA/ kg',\n",
+       "       'hard coal, at regional storage/ RU/ kg',\n",
+       "       'hard coal, at regional storage/ CPA/ kg',\n",
+       "       'heat, air-water heat pump 10kW, at heat radiator/ RER/ MJ',\n",
+       "       'heat, air-water heat pump 10kW, at heat radiator/ CH/ MJ',\n",
+       "       'heat, at air-water heat pump 10kW/ RER/ MJ',\n",
+       "       'heat, at air-water heat pump 10kW/ CH/ MJ',\n",
+       "       'heat, at heat pump 30kW, allocation electricity/ CH/ MJ',\n",
+       "       'heat, at heat pump 30kW, allocation energy/ CH/ MJ',\n",
+       "       'heat, at heat pump 30kW, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at heat pump 30kW, allocation heat/ CH/ MJ',\n",
+       "       'heat, at heat pump 30kW, allocation price/ CH/ MJ',\n",
+       "       'heat, biogas, at diffusion absorption heat pump 4kW, future/ CH/ MJ',\n",
+       "       'heat, borehole heat exchanger, at brine-water heat pump 10kW/ RER/ MJ',\n",
+       "       'heat, borehole heat exchanger, at brine-water heat pump 10kW/ CH/ MJ',\n",
+       "       'heat, borehole heat exchanger, brine-water heat pump 10kW, at heat radiator/ RER/ MJ',\n",
+       "       'heat, borehole heat exchanger, brine-water heat pump 10kW, at heat radiator/ CH/ MJ',\n",
+       "       'heat, natural gas, at diffusion absorption heat pump 4kW, future/ CH/ MJ',\n",
+       "       'electricity, hydropower, at power plant/ CH/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ AT/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ BE/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ ES/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ CS/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ FR/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ GR/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ IT/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ LU/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ NL/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ PT/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ DE/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ BA/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ HR/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ MK/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ SI/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ CZ/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ HU/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ PL/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ SK/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ DK/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ FI/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ NO/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ SE/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ GB/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ IE/ kWh',\n",
+       "       'electricity, hydropower, at power plant/ JP/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ AT/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ BE/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ ES/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ CS/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ FR/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ GR/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ IT/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ LU/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ PT/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ DE/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ CH/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ BA/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ HR/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ MK/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ SI/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ CZ/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ HU/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ PL/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ SK/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ DK/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ FI/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ NO/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ SE/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ GB/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ IE/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ NL/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ JP/ kWh',\n",
+       "       'electricity, hydropower, at pumped storage power plant/ US/ kWh',\n",
+       "       'electricity, hydropower, at reservoir power plant/ CH/ kWh',\n",
+       "       'electricity, hydropower, at reservoir power plant/ FI/ kWh',\n",
+       "       'electricity, hydropower, at reservoir power plant/ BR/ kWh',\n",
+       "       'electricity, hydropower, at reservoir power plant, alpine region/ RER/ kWh',\n",
+       "       'electricity, hydropower, at reservoir power plant, non alpine regions/ RER/ kWh',\n",
+       "       'electricity, hydropower, at run-of-river power plant/ CH/ kWh',\n",
+       "       'electricity, hydropower, at run-of-river power plant/ RER/ kWh',\n",
+       "       'cellulose fibre, inclusive blowing in, at plant/ CH/ kg',\n",
+       "       'cork slab, at plant/ RER/ kg',\n",
+       "       'expanded perlite, at plant/ CH/ kg',\n",
+       "       'foam glass, at plant/ RER/ kg',\n",
+       "       'foam glass, at regional storage/ CH/ kg',\n",
+       "       'foam glass, at regional storage/ AT/ kg',\n",
+       "       'glass wool mat, at plant/ CH/ kg',\n",
+       "       'polystyrene foam slab, 100% recycled, at plant/ CH/ kg',\n",
+       "       'polystyrene foam slab, 45% recycled, at plant/ CH/ kg',\n",
+       "       'polystyrene foam slab, at plant/ RER/ kg',\n",
+       "       'polystyrene, extruded (XPS) CO2 blown, at plant/ RER/ kg',\n",
+       "       'polystyrene, extruded (XPS), HFC-134a blown, at plant/ RER/ kg',\n",
+       "       'polystyrene, extruded (XPS), HFC-152a blown, at plant/ RER/ kg',\n",
+       "       'polystyrene, extruded (XPS), at plant/ RER/ kg',\n",
+       "       'rock wool, at plant/ CH/ kg',\n",
+       "       'rock wool, packed, at plant/ CH/ kg',\n",
+       "       'tube insulation, elastomere, at plant/ DE/ kg',\n",
+       "       'urea formaldehyde foam slab, hard, at plant/ CH/ kg',\n",
+       "       'urea formaldehyde foam, in situ foaming, at plant/ CH/ kg',\n",
+       "       'lignite briquettes, at plant/ DE/ MJ',\n",
+       "       'pulverised lignite, at plant/ DE/ MJ',\n",
+       "       'heat, lignite briquette, at stove 5-15kW/ RER/ MJ',\n",
+       "       'lignite briquette, burned in stove 5-15kW/ RER/ MJ',\n",
+       "       'SOx retained, in lignite flue gas desulphurisation/ GLO/ kg',\n",
+       "       'electricity, lignite, at power plant/ AT/ kWh',\n",
+       "       'electricity, lignite, at power plant/ ES/ kWh',\n",
+       "       'electricity, lignite, at power plant/ CS/ kWh',\n",
+       "       'electricity, lignite, at power plant/ FR/ kWh',\n",
+       "       'electricity, lignite, at power plant/ GR/ kWh',\n",
+       "       'electricity, lignite, at power plant/ UCTE/ kWh',\n",
+       "       'electricity, lignite, at power plant/ DE/ kWh',\n",
+       "       'electricity, lignite, at power plant/ BA/ kWh',\n",
+       "       'electricity, lignite, at power plant/ CZ/ kWh',\n",
+       "       'electricity, lignite, at power plant/ HU/ kWh',\n",
+       "       'electricity, lignite, at power plant/ MK/ kWh',\n",
+       "       'electricity, lignite, at power plant/ PL/ kWh',\n",
+       "       'electricity, lignite, at power plant/ SI/ kWh',\n",
+       "       'electricity, lignite, at power plant/ SK/ kWh',\n",
+       "       'electricity, lignite, at power plant/ CENTREL/ kWh',\n",
+       "       'electricity, peat, at power plant/ NORDEL/ kWh',\n",
+       "       'lignite, burned in power plant/ AT/ MJ',\n",
+       "       'lignite, burned in power plant/ DE/ MJ',\n",
+       "       'lignite, burned in power plant/ ES/ MJ',\n",
+       "       'lignite, burned in power plant/ CS/ MJ',\n",
+       "       'lignite, burned in power plant/ FR/ MJ',\n",
+       "       'lignite, burned in power plant/ GR/ MJ',\n",
+       "       'lignite, burned in power plant/ BA/ MJ',\n",
+       "       'lignite, burned in power plant/ CZ/ MJ',\n",
+       "       'lignite, burned in power plant/ HU/ MJ',\n",
+       "       'lignite, burned in power plant/ MK/ MJ',\n",
+       "       'lignite, burned in power plant/ PL/ MJ',\n",
+       "       'lignite, burned in power plant/ SI/ MJ',\n",
+       "       'lignite, burned in power plant/ SK/ MJ',\n",
+       "       'peat, burned in power plant/ NORDEL/ MJ',\n",
+       "       'lignite, at mine/ RER/ kg', 'peat, at mine/ NORDEL/ kg',\n",
+       "       'compressed air, average generation, <30kW, 12 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, average generation, <30kW, 8 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, average generation, >30kW, 6 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, average generation, >30kW, 7 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, average generation, >30kW, 8 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, best generation, >30kW, 6 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, best generation, >30kW, 7 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, best generation, >30kW, 8 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, optimised generation, <30kW, 12 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, optimised generation, <30kW, 8 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, optimised generation, >30kW, 6 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, optimised generation, >30kW, 7 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, optimised generation, >30kW, 8 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, average generation, <30kW, 10 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, average installation, <30kW, 10 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, average installation, <30kW, 12 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, average installation, <30kW, 8 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, average installation, >30kW, 6 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, average installation, >30kW, 7 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, average installation, >30kW, 8 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, best installation, >30kW, 6 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, best installation, >30kW, 7 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, best installation, >30kW, 8 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, optimised generation, <30kW, 10 bar gauge, at compressor/ RER/ m3',\n",
+       "       'compressed air, optimised installation, <30kW, 10 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, optimised installation, <30kW, 12 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, optimised installation, <30kW, 8 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, optimised installation, >30kW, 6 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, optimised installation, >30kW, 7 bar gauge, at supply network/ RER/ m3',\n",
+       "       'compressed air, optimised installation, >30kW, 8 bar gauge, at supply network/ RER/ m3',\n",
+       "       'cold impact extrusion, aluminium, 1 stroke/ RER/ kg',\n",
+       "       'cold impact extrusion, aluminium, 2 strokes/ RER/ kg',\n",
+       "       'cold impact extrusion, aluminium, 3 strokes/ RER/ kg',\n",
+       "       'cold impact extrusion, aluminium, 4 strokes/ RER/ kg',\n",
+       "       'cold impact extrusion, aluminium, 5 strokes/ RER/ kg',\n",
+       "       'cold impact extrusion, steel, 1 stroke/ RER/ kg',\n",
+       "       'cold impact extrusion, steel, 2 strokes/ RER/ kg',\n",
+       "       'cold impact extrusion, steel, 3 strokes/ RER/ kg',\n",
+       "       'cold impact extrusion, steel, 4 strokes/ RER/ kg',\n",
+       "       'cold impact extrusion, steel, 5 strokes/ RER/ kg',\n",
+       "       'deep drawing, steel, 10000 kN press, automode operation/ RER/ kg',\n",
+       "       'deep drawing, steel, 10000 kN press, single stroke operation/ RER/ kg',\n",
+       "       'deep drawing, steel, 3500 kN press, automode operation/ RER/ kg',\n",
+       "       'deep drawing, steel, 3500 kN press, single stroke operation/ RER/ kg',\n",
+       "       'deep drawing, steel, 38000 kN press, automode operation/ RER/ kg',\n",
+       "       'deep drawing, steel, 38000 kN press, single stroke operation/ RER/ kg',\n",
+       "       'deep drawing, steel, 650 kN press, automode operation/ RER/ kg',\n",
+       "       'deep drawing, steel, 650 kN press, single stroke operation/ RER/ kg',\n",
+       "       'deformation stroke, cold impact extrusion, aluminium/ RER/ kg',\n",
+       "       'deformation stroke, cold impact extrusion, steel/ RER/ kg',\n",
+       "       'deformation stroke, hot impact extrusion, steel/ RER/ kg',\n",
+       "       'deformation stroke, warm impact extrusion, steel/ RER/ kg',\n",
+       "       'heat treatment, cold impact extrusion, aluminium/ RER/ kg',\n",
+       "       'heat treatment, cold impact extrusion, steel/ RER/ kg',\n",
+       "       'heat treatment, hot impact extrusion, steel/ RER/ kg',\n",
+       "       'hot impact extrusion, steel, 1 stroke/ RER/ kg',\n",
+       "       'hot impact extrusion, steel, 2 strokes/ RER/ kg',\n",
+       "       'hot impact extrusion, steel, 3 strokes/ RER/ kg',\n",
+       "       'hot impact extrusion, steel, 4 strokes/ RER/ kg',\n",
+       "       'hot impact extrusion, steel, 5 strokes/ RER/ kg',\n",
+       "       'laser machining, metal, with CO2-laser, 2000W power/ RER/ h',\n",
+       "       'laser machining, metal, with CO2-laser, 2700W power/ RER/ h',\n",
+       "       'laser machining, metal, with CO2-laser, 3200W power/ RER/ h',\n",
+       "       'laser machining, metal, with CO2-laser, 4000W power/ RER/ h',\n",
+       "       'laser machining, metal, with CO2-laser, 5000W power/ RER/ h',\n",
+       "       'laser machining, metal, with CO2-laser, 6000W power/ RER/ h',\n",
+       "       'laser machining, metal, with YAG-laser, 120W power/ RER/ h',\n",
+       "       'laser machining, metal, with YAG-laser, 200W power/ RER/ h',\n",
+       "       'laser machining, metal, with YAG-laser, 30W power/ RER/ h',\n",
+       "       'laser machining, metal, with YAG-laser, 330W power/ RER/ h',\n",
+       "       'laser machining, metal, with YAG-laser, 40W power/ RER/ h',\n",
+       "       'laser machining, metal, with YAG-laser, 500W power/ RER/ h',\n",
+       "       'laser machining, metal, with YAG-laser, 50W power/ RER/ h',\n",
+       "       'laser machining, metal, with YAG-laser, 60W power/ RER/ h',\n",
+       "       'surface treatment, cold impact extrusion, aluminium/ RER/ kg',\n",
+       "       'surface treatment, cold impact extrusion, steel/ RER/ kg',\n",
+       "       'warm impact extrusion, steel, 1 stroke/ RER/ kg',\n",
+       "       'warm impact extrusion, steel, 2 strokes/ RER/ kg',\n",
+       "       'warm impact extrusion, steel, 3 strokes/ RER/ kg',\n",
+       "       'warm impact extrusion, steel, 4 strokes/ RER/ kg',\n",
+       "       'warm impact extrusion, steel, 5 strokes/ RER/ kg',\n",
+       "       'warming, hot impact extrusion, steel/ RER/ kg',\n",
+       "       'warming, warm impact extrusion, steel/ RER/ kg',\n",
+       "       'drilling, CNC, aluminium/ RER/ kg',\n",
+       "       'drilling, CNC, brass/ RER/ kg',\n",
+       "       'drilling, CNC, cast iron/ RER/ kg',\n",
+       "       'drilling, CNC, chromium steel/ RER/ kg',\n",
+       "       'drilling, CNC, steel/ RER/ kg',\n",
+       "       'drilling, conventional, aluminium/ RER/ kg',\n",
+       "       'drilling, conventional, brass/ RER/ kg',\n",
+       "       'drilling, conventional, cast iron/ RER/ kg',\n",
+       "       'drilling, conventional, chromium steel/ RER/ kg',\n",
+       "       'drilling, conventional, steel/ RER/ kg',\n",
+       "       'milling, aluminium, average/ RER/ kg',\n",
+       "       'milling, aluminium, dressing/ RER/ kg',\n",
+       "       'milling, aluminium, large parts/ RER/ kg',\n",
+       "       'milling, aluminium, small parts/ RER/ kg',\n",
+       "       'milling, cast iron, average/ RER/ kg',\n",
+       "       'milling, cast iron, dressing/ RER/ kg',\n",
+       "       'milling, cast iron, large parts/ RER/ kg',\n",
+       "       'milling, cast iron, small parts/ RER/ kg',\n",
+       "       'milling, chromium steel, average/ RER/ kg',\n",
+       "       'milling, chromium steel, dressing/ RER/ kg',\n",
+       "       'milling, chromium steel, large parts/ RER/ kg',\n",
+       "       'milling, chromium steel, small parts/ RER/ kg',\n",
+       "       'milling, steel, average/ RER/ kg',\n",
+       "       'milling, steel, dressing/ RER/ kg',\n",
+       "       'milling, steel, large parts/ RER/ kg',\n",
+       "       'milling, steel, small parts/ RER/ kg',\n",
+       "       'turning, aluminium, CNC, average/ RER/ kg',\n",
+       "       'turning, aluminium, CNC, primarily dressing/ RER/ kg',\n",
+       "       'turning, aluminium, CNC, primarily roughing/ RER/ kg',\n",
+       "       'turning, aluminium, conventional, average/ RER/ kg',\n",
+       "       'turning, aluminium, conventional, primarily dressing/ RER/ kg',\n",
+       "       'turning, aluminium, conventional, primarily roughing/ RER/ kg',\n",
+       "       'turning, brass, CNC, average/ RER/ kg',\n",
+       "       'turning, brass, CNC, primarily dressing/ RER/ kg',\n",
+       "       'turning, brass, CNC, primarily roughing/ RER/ kg',\n",
+       "       'turning, brass, conventional, average/ RER/ kg',\n",
+       "       'turning, brass, conventional, primarily dressing/ RER/ kg',\n",
+       "       'turning, brass, conventional, primarily roughing/ RER/ kg',\n",
+       "       'turning, cast iron, CNC, average/ RER/ kg',\n",
+       "       'turning, cast iron, CNC, primarily dressing/ RER/ kg',\n",
+       "       'turning, cast iron, CNC, primarily roughing/ RER/ kg',\n",
+       "       'turning, cast iron, conventional, average/ RER/ kg',\n",
+       "       'turning, cast iron, conventional, primarily dressing/ RER/ kg',\n",
+       "       'turning, cast iron, conventional, primarily roughing/ RER/ kg',\n",
+       "       'turning, chromium steel, CNC, average/ RER/ kg',\n",
+       "       'turning, chromium steel, CNC, primarily dressing/ RER/ kg',\n",
+       "       'turning, chromium steel, CNC, primarily roughing/ RER/ kg',\n",
+       "       'turning, chromium steel, conventional, average/ RER/ kg',\n",
+       "       'turning, chromium steel, conventional, primarily dressing/ RER/ kg',\n",
+       "       'turning, chromium steel, conventional, primarily roughing/ RER/ kg',\n",
+       "       'turning, steel, CNC, average/ RER/ kg',\n",
+       "       'turning, steel, CNC, primarily dressing/ RER/ kg',\n",
+       "       'turning, steel, CNC, primarily roughing/ RER/ kg',\n",
+       "       'turning, steel, conventional, average/ RER/ kg',\n",
+       "       'turning, steel, conventional, primarily dressing/ RER/ kg',\n",
+       "       'turning, steel, conventional, primarily roughing/ RER/ kg',\n",
+       "       'MG-silicon, at plant/ NO/ kg',\n",
+       "       'Molybdenum concentrate, main product/ GLO/ kg',\n",
+       "       'aluminium alloy, AlMg3, at plant/ RER/ kg',\n",
+       "       'aluminium fluoride, at plant/ RER/ kg',\n",
+       "       'aluminium scrap, new, at plant/ RER/ kg',\n",
+       "       'aluminium scrap, old, at plant/ RER/ kg',\n",
+       "       'aluminium, primary, at plant/ RER/ kg',\n",
+       "       'aluminium, primary, liquid, at plant/ RER/ kg',\n",
+       "       'aluminium, production mix, at plant/ RER/ kg',\n",
+       "       'aluminium, production mix, cast alloy, at plant/ RER/ kg',\n",
+       "       'aluminium, production mix, wrought alloy, at plant/ RER/ kg',\n",
+       "       'aluminium, secondary, from new scrap, at plant/ RER/ kg',\n",
+       "       'aluminium, secondary, from old scrap, at plant/ RER/ kg',\n",
+       "       'anode slime, silver and tellurium containing, primary copper production/ GLO/ kg',\n",
+       "       'anode, aluminium electrolysis/ RER/ kg',\n",
+       "       'antimony, at refinery/ CN/ kg', 'bauxite, at mine/ GLO/ kg',\n",
+       "       'brass, at plant/ CH/ kg',\n",
+       "       'brazing solder, cadmium free, at plant/ RER/ kg',\n",
+       "       'bronze, at plant/ CH/ kg',\n",
+       "       'cadmium chloride, semiconductor-grade, at plant/ US/ kg',\n",
+       "       'cadmium sludge, from zinc electrolysis, at plant/ GLO/ kg',\n",
+       "       'cadmium sulphide, semiconductor-grade, at plant/ US/ kg',\n",
+       "       'cadmium telluride, semiconductor-grade, at plant/ US/ kg',\n",
+       "       'cadmium, primary, at plant/ GLO/ kg',\n",
+       "       'cadmium, semiconductor-grade, at plant/ US/ kg',\n",
+       "       'cast iron, at plant/ RER/ kg',\n",
+       "       'cathode, aluminium electrolysis/ RER/ kg',\n",
+       "       'cathode, copper, primary copper production/ GLO/ kg',\n",
+       "       'chromite, ore concentrate, at beneficiation/ GLO/ kg',\n",
+       "       'chromium steel 18/8, at plant/ RER/ kg',\n",
+       "       'chromium, at regional storage/ RER/ kg',\n",
+       "       'cobalt, at plant/ GLO/ kg',\n",
+       "       'copper concentrate, at beneficiation/ GLO/ kg',\n",
+       "       'copper concentrate, at beneficiation/ RNA/ kg',\n",
+       "       'copper concentrate, at beneficiation/ RLA/ kg',\n",
+       "       'copper concentrate, at beneficiation/ RER/ kg',\n",
+       "       'copper concentrate, at beneficiation/ RAS/ kg',\n",
+       "       'copper concentrate, at beneficiation/ ID/ kg',\n",
+       "       'copper concentrate, couple production Mo/ GLO/ kg',\n",
+       "       'copper telluride cement, from copper production/ GLO/ kg',\n",
+       "       'copper, SX-EW, at refinery/ GLO/ kg',\n",
+       "       'copper, at regional storage/ RER/ kg',\n",
+       "       'copper, blister-copper, at primary smelter/ RER/ kg',\n",
+       "       'copper, from combined metal production, at beneficiation/ SE/ kg',\n",
+       "       'copper, from combined metal production, at refinery/ SE/ kg',\n",
+       "       'copper, from imported concentrates, at refinery/ DE/ kg',\n",
+       "       'copper, primary, at refinery/ GLO/ kg',\n",
+       "       'copper, primary, at refinery/ RNA/ kg',\n",
+       "       'copper, primary, at refinery/ RLA/ kg',\n",
+       "       'copper, primary, at refinery/ RER/ kg',\n",
+       "       'copper, primary, at refinery/ RAS/ kg',\n",
+       "       'copper, primary, at refinery/ ID/ kg',\n",
+       "       'copper, primary, couple production nickel/ GLO/ kg',\n",
+       "       'copper, primary, from platinum group metal production/ ZA/ kg',\n",
+       "       'copper, primary, from platinum group metal production/ RU/ kg',\n",
+       "       'copper, secondary, at refinery/ RER/ kg',\n",
+       "       'copper, secondary, from electronic and electric scrap recycling, at refinery/ SE/ kg',\n",
+       "       'ferrochromium, high-carbon, 68% Cr, at plant/ GLO/ kg',\n",
+       "       'ferrochromium, high-carbon, 68% Cr, at regional storage/ RER/ kg',\n",
+       "       'ferromanganese, high-coal, 74.5% Mn, at regional storage/ RER/ kg',\n",
+       "       'ferronickel, 25% Ni, at plant/ GLO/ kg',\n",
+       "       'gallium, in Bayer liquor from aluminium production, at plant/ GLO/ kg',\n",
+       "       'gallium, semiconductor-grade, at plant/ GLO/ kg',\n",
+       "       'gallium, semiconductor-grade, at regional storage/ RER/ kg',\n",
+       "       'gold, at refinery/ CA/ kg', 'gold, at refinery/ US/ kg',\n",
+       "       'gold, at refinery/ ZA/ kg', 'gold, at refinery/ TZ/ kg',\n",
+       "       'gold, at refinery/ AU/ kg', 'gold, at regional storage/ RER/ kg',\n",
+       "       'gold, from combined gold-silver production, at refinery/ PG/ kg',\n",
+       "       'gold, from combined gold-silver production, at refinery/ CL/ kg',\n",
+       "       'gold, from combined gold-silver production, at refinery/ PE/ kg',\n",
+       "       'gold, from combined metal production, at beneficiation/ SE/ kg',\n",
+       "       'gold, from combined metal production, at refinery/ SE/ kg',\n",
+       "       'gold, primary, at refinery/ GLO/ kg',\n",
+       "       'gold, secondary, at precious metal refinery/ SE/ kg',\n",
+       "       'indium, at regional storage/ RER/ kg',\n",
+       "       'iron ore, 46% Fe, at mine/ GLO/ kg',\n",
+       "       'iron ore, 65% Fe, at beneficiation/ GLO/ kg',\n",
+       "       'iron scrap, at plant/ RER/ kg',\n",
+       "       'iron sulphate, at plant/ RER/ kg',\n",
+       "       'iron-nickel-chromium alloy, at plant/ RER/ kg',\n",
+       "       'leaching residues, indium rich, from zinc circuit, at smelter/ GLO/ kg',\n",
+       "       'lead concentrate, at beneficiation/ GLO/ kg',\n",
+       "       'lead, at regional storage/ RER/ kg',\n",
+       "       'lead, from combined metal production, at beneficiation/ SE/ kg',\n",
+       "       'lead, from combined metal production, at refinery/ SE/ kg',\n",
+       "       'lead, primary, at plant/ GLO/ kg',\n",
+       "       'lead, secondary, at plant/ RER/ kg',\n",
+       "       'lead, secondary, from electronic and electric scrap recycling, at plant/ SE/ kg',\n",
+       "       'lithium, at plant/ GLO/ kg', 'magnesium, at plant/ RER/ kg',\n",
+       "       'magnesium-alloy, AZ91, at plant/ RER/ kg',\n",
+       "       'magnesium-alloy, AZ91, diecasting, at plant/ RER/ kg',\n",
+       "       'manganese concentrate, at beneficiation/ GLO/ kg',\n",
+       "       'manganese, at regional storage/ RER/ kg',\n",
+       "       'mercury, liquid, at plant/ GLO/ kg',\n",
+       "       'metal values from electric waste, in blister-copper, at converter/ SE/ kg',\n",
+       "       'molybdenite, at plant/ GLO/ kg',\n",
+       "       'molybdenum concentrate, couple production Cu/ GLO/ kg',\n",
+       "       'molybdenum concentrate, couple production Cu/ RNA/ kg',\n",
+       "       'molybdenum concentrate, couple production Cu/ RLA/ kg',\n",
+       "       'molybdenum concentrate, couple production Cu/ RER/ kg',\n",
+       "       'molybdenum concentrate, couple production Cu/ RAS/ kg',\n",
+       "       'molybdenum concentrate, couple production Cu/ ID/ kg',\n",
+       "       'molybdenum, at regional storage/ RER/ kg',\n",
+       "       'nickel, 99.5%, at plant/ GLO/ kg',\n",
+       "       'nickel, primary, from platinum group metal production/ ZA/ kg',\n",
+       "       'nickel, primary, from platinum group metal production/ RU/ kg',\n",
+       "       'nickel, secondary, from electronic and electric scrap recycling, at refinery/ SE/ kg',\n",
+       "       'palladium, at regional storage/ RER/ kg',\n",
+       "       'palladium, primary, at refinery/ ZA/ kg',\n",
+       "       'palladium, primary, at refinery/ RU/ kg',\n",
+       "       'palladium, secondary, at precious metal refinery/ SE/ kg',\n",
+       "       'palladium, secondary, at refinery/ RER/ kg',\n",
+       "       'parkes process crust, from desilverising of lead/ GLO/ kg',\n",
+       "       'pellets, iron, at plant/ GLO/ kg', 'pig iron, at plant/ GLO/ kg',\n",
+       "       'platinum, at regional storage/ RER/ kg',\n",
+       "       'platinum, primary, at refinery/ ZA/ kg',\n",
+       "       'platinum, primary, at refinery/ RU/ kg',\n",
+       "       'platinum, secondary, at refinery/ RER/ kg',\n",
+       "       'precious metals from electric waste, in anode slime, at refinery/ SE/ kg',\n",
+       "       'recultivation, bauxite mine/ GLO/ m2',\n",
+       "       'recultivation, iron mine/ GLO/ m2',\n",
+       "       'reinforcing steel, at plant/ RER/ kg',\n",
+       "       'resource correction, CuMo, copper, negative/ GLO/ kg',\n",
+       "       'resource correction, CuMo, copper, positive/ GLO/ kg',\n",
+       "       'resource correction, PbZn, cadmium, negative/ GLO/ kg',\n",
+       "       'resource correction, PbZn, cadmium, positive/ GLO/ kg',\n",
+       "       'resource correction, PbZn, indium, negative/ GLO/ kg',\n",
+       "       'resource correction, PbZn, indium, positive/ GLO/ kg',\n",
+       "       'resource correction, PbZn, lead, negative/ GLO/ kg',\n",
+       "       'resource correction, PbZn, lead, positive/ GLO/ kg',\n",
+       "       'resource correction, PbZn, silver, negative/ GLO/ kg',\n",
+       "       'resource correction, PbZn, silver, positive/ GLO/ kg',\n",
+       "       'resource correction, PbZn, zinc, negative/ GLO/ kg',\n",
+       "       'resource correction, PbZn, zinc, positive/ GLO/ kg',\n",
+       "       'rhodium, at regional storage/ RER/ kg',\n",
+       "       'rhodium, primary, at refinery/ ZA/ kg',\n",
+       "       'rhodium, primary, at refinery/ RU/ kg',\n",
+       "       'rhodium, secondary, at refinery/ RER/ kg',\n",
+       "       'silver, at regional storage/ RER/ kg',\n",
+       "       'silver, from combined gold-silver production, at refinery/ PE/ kg',\n",
+       "       'silver, from combined gold-silver production, at refinery/ CL/ kg',\n",
+       "       'silver, from combined gold-silver production, at refinery/ PG/ kg',\n",
+       "       'silver, from combined gold-silver production, at refinery/ GLO/ kg',\n",
+       "       'silver, from combined metal production, at beneficiation/ SE/ kg',\n",
+       "       'silver, from combined metal production, at refinery/ SE/ kg',\n",
+       "       'silver, from copper production, at refinery/ GLO/ kg',\n",
+       "       'silver, from lead production, at refinery/ GLO/ kg',\n",
+       "       'silver, secondary, at precious metal refinery/ SE/ kg',\n",
+       "       'sinter, iron, at plant/ GLO/ kg',\n",
+       "       'soft solder, Sn97Cu3, at plant/ RER/ kg',\n",
+       "       'solder, bar, Sn63Pb37, for electronics industry, at plant/ GLO/ kg',\n",
+       "       'solder, bar, Sn95.5Ag3.9Cu0.6, for electronics industry, at plant/ GLO/ kg',\n",
+       "       'solder, paste, Sn63Pb37, for electronics industry, at plant/ GLO/ kg',\n",
+       "       'solder, paste, Sn95.5Ag3.9Cu0.6, for electronics industry, at plant/ GLO/ kg',\n",
+       "       'steel, converter, chromium steel 18/8, at plant/ RER/ kg',\n",
+       "       'steel, converter, low-alloyed, at plant/ RER/ kg',\n",
+       "       'steel, converter, unalloyed, at plant/ RER/ kg',\n",
+       "       'steel, electric, chromium steel 18/8, at plant/ RER/ kg',\n",
+       "       'steel, electric, un- and low-alloyed, at plant/ RER/ kg',\n",
+       "       'steel, low-alloyed, at plant/ RER/ kg',\n",
+       "       'tantalum, powder, capacitor-grade, at regional storage/ GLO/ kg',\n",
+       "       'tellurium, semiconductor-grade, at plant/ GLO/ kg',\n",
+       "       'tin plated chromium steel sheet, 2 mm, at plant/ RER/ m2',\n",
+       "       'tin, at regional storage/ RER/ kg',\n",
+       "       'titanium zinc plate, without pre-weathering, at plant/ DE/ kg',\n",
+       "       'zinc concentrate, at beneficiation/ GLO/ kg',\n",
+       "       'zinc, from combined metal production, at beneficiation/ SE/ kg',\n",
+       "       'zinc, from combined metal production, at refinery/ SE/ kg',\n",
+       "       'zinc, primary, at regional storage/ RER/ kg',\n",
+       "       'aluminium product manufacturing, average metal working/ RER/ kg',\n",
+       "       'chromium steel product manufacturing, average metal working/ RER/ kg',\n",
+       "       'copper product manufacturing, average metal working/ RER/ kg',\n",
+       "       'degreasing, metal part in alkaline bath/ RER/ m2',\n",
+       "       'metal product manufacturing, average metal working/ RER/ kg',\n",
+       "       'metal working factory operation, average heat energy/ RER/ kg',\n",
+       "       'metal working factory operation, heat energy from hard coal/ RER/ kg',\n",
+       "       'metal working factory operation, heat energy from heavy fuel oil/ RER/ kg',\n",
+       "       'metal working factory operation, heat energy from light fuel oil/ RER/ kg',\n",
+       "       'metal working factory operation, heat energy from natural gas/ RER/ kg',\n",
+       "       'metal working machine operation, average process heat/ RER/ kg',\n",
+       "       'metal working machine operation, process heat from hard coal/ RER/ kg',\n",
+       "       'metal working machine operation, process heat from heavy fuel oil/ RER/ kg',\n",
+       "       'metal working machine operation, process heat from light fuel oil/ RER/ kg',\n",
+       "       'metal working machine operation, process heat from natural gas/ RER/ kg',\n",
+       "       'steel product manufacturing, average metal working/ RER/ kg',\n",
+       "       'anodising, aluminium sheet/ RER/ m2', 'casting, brass/ CH/ kg',\n",
+       "       'casting, bronze/ CH/ kg', 'coating powder, at plant/ RER/ kg',\n",
+       "       'contour, brass/ RER/ kg', 'contour, bronze/ RER/ kg',\n",
+       "       'drawing of pipes, steel/ RER/ kg', 'enamelling/ RER/ m2',\n",
+       "       'hot rolling, steel/ RER/ kg',\n",
+       "       'powder coating, aluminium sheet/ RER/ m2',\n",
+       "       'powder coating, steel/ RER/ m2',\n",
+       "       'section bar extrusion, aluminium/ RER/ kg',\n",
+       "       'section bar rolling, steel/ RER/ kg',\n",
+       "       'sheet rolling, aluminium/ RER/ kg',\n",
+       "       'sheet rolling, chromium steel/ RER/ kg',\n",
+       "       'sheet rolling, copper/ RER/ kg', 'sheet rolling, steel/ RER/ kg',\n",
+       "       'tin plating, pieces/ RER/ m2', 'welding, arc, aluminium/ RER/ m',\n",
+       "       'welding, arc, steel/ RER/ m', 'welding, gas, steel/ RER/ m',\n",
+       "       'wire drawing, copper/ RER/ kg', 'wire drawing, steel/ RER/ kg',\n",
+       "       'zinc coating, coils/ RER/ m2', 'zinc coating, pieces/ RER/ m2',\n",
+       "       'zinc coating, pieces, adjustment per um/ RER/ m2',\n",
+       "       'selective coating, aluminium sheet, nickel pigmented aluminium oxide/ SK/ m2',\n",
+       "       'selective coating, copper sheet, black chrome/ RER/ m2',\n",
+       "       'selective coating, copper sheet, black majic/ US/ m2',\n",
+       "       'selective coating, copper sheet, physical vapour deposition/ DE/ m2',\n",
+       "       'selective coating, copper sheet, sputtering/ DE/ m2',\n",
+       "       'selective coating, stainless steel sheet, black chrome/ CH/ m2',\n",
+       "       'silicon, electronic grade, at plant/ DE/ kg',\n",
+       "       'silicon, electronic grade, off-grade, at plant/ DE/ kg',\n",
+       "       'silicon, multi-Si, casted, at plant/ RER/ kg',\n",
+       "       'silicon, production mix, photovoltaics, at plant/ GLO/ kg',\n",
+       "       'silicon, solar grade, modified Siemens process, at plant/ RER/ kg',\n",
+       "       'acrylic filler, at plant/ RER/ kg',\n",
+       "       'adhesive mortar, at plant/ CH/ kg',\n",
+       "       'anhydrite floor, at plant/ CH/ kg',\n",
+       "       'base plaster, at plant/ CH/ kg',\n",
+       "       'cement mortar, at plant/ CH/ kg',\n",
+       "       'clay plaster, at plant/ CH/ kg', 'cobwork, at plant/ CH/ kg',\n",
+       "       'cover coat, mineral, at plant/ CH/ kg',\n",
+       "       'cover coat, organic, at plant/ CH/ kg',\n",
+       "       'light mortar, at plant/ CH/ kg', 'lime mortar, at plant/ CH/ kg',\n",
+       "       'plaster mixing/ CH/ kg', 'thermal plaster, at plant/ CH/ kg',\n",
+       "       'electricity, at Mini CHP plant, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at Mini CHP plant, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at Mini CHP plant, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe Jakobsberg, allocation electricity/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe Jakobsberg, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe Jakobsberg, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe Jakobsberg, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe Jakobsberg, allocation price/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe lambda=1, allocation electricity/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe lambda=1, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe lambda=1, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe lambda=1, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen 160kWe lambda=1, allocation price/ CH/ kWh',\n",
+       "       'electricity, at cogen 1MWe lean burn, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 1MWe lean burn, allocation energy/ RER/ kWh',\n",
+       "       'electricity, at cogen 1MWe lean burn, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 1MWe lean burn, allocation exergy/ RER/ kWh',\n",
+       "       'electricity, at cogen 1MWe lean burn, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen 1MWe lean burn, allocation heat/ RER/ kWh',\n",
+       "       'electricity, at cogen 200kWe lean burn, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 200kWe lean burn, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 200kWe lean burn, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen 500kWe lean burn, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 500kWe lean burn, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 500kWe lean burn, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen 50kWe lean burn, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 50kWe lean burn, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 50kWe lean burn, allocation heat/ CH/ kWh',\n",
+       "       'electricity, natural gas, allocation exergy, at PEM fuel cell 2kWe, future/ CH/ kWh',\n",
+       "       'electricity, natural gas, allocation exergy, at SOFC fuel cell 125kWe, future/ CH/ kWh',\n",
+       "       'electricity, natural gas, allocation exergy, at SOFC-GT fuel cell 180kWe, future/ CH/ kWh',\n",
+       "       'electricity, natural gas, allocation exergy, at micro gas turbine 100kWe/ CH/ kWh',\n",
+       "       'heat, at Mini CHP plant, allocation energy/ CH/ MJ',\n",
+       "       'heat, at Mini CHP plant, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at Mini CHP plant, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe Jakobsberg, allocation electricity/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe Jakobsberg, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe Jakobsberg, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe Jakobsberg, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe Jakobsberg, allocation price/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe lambda=1, allocation electricity/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe lambda=1, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe lambda=1, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe lambda=1, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen 160kWe lambda=1, allocation price/ CH/ MJ',\n",
+       "       'heat, at cogen 1MWe lean burn, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 1MWe lean burn, allocation energy/ RER/ MJ',\n",
+       "       'heat, at cogen 1MWe lean burn, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 1MWe lean burn, allocation exergy/ RER/ MJ',\n",
+       "       'heat, at cogen 1MWe lean burn, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen 1MWe lean burn, allocation heat/ RER/ MJ',\n",
+       "       'heat, at cogen 200kWe lean burn, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 200kWe lean burn, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 200kWe lean burn, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen 500kWe lean burn, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 500kWe lean burn, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 500kWe lean burn, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen 50kWe lean burn, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 50kWe lean burn, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 50kWe lean burn, allocation heat/ CH/ MJ',\n",
+       "       'heat, at local distribution cogen 160kWe Jakobsberg, allocation electricity/ CH/ MJ',\n",
+       "       'heat, at local distribution cogen 160kWe Jakobsberg, allocation energy/ CH/ MJ',\n",
+       "       'heat, at local distribution cogen 160kWe Jakobsberg, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at local distribution cogen 160kWe Jakobsberg, allocation heat/ CH/ MJ',\n",
+       "       'heat, at local distribution cogen 160kWe Jakobsberg, allocation price/ CH/ MJ',\n",
+       "       'heat, at module cogen 160kWe Jakobsberg, allocation electricity/ CH/ MJ',\n",
+       "       'heat, at module cogen 160kWe Jakobsberg, allocation energy/ CH/ MJ',\n",
+       "       'heat, at module cogen 160kWe Jakobsberg, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at module cogen 160kWe Jakobsberg, allocation heat/ CH/ MJ',\n",
+       "       'heat, at module cogen 160kWe Jakobsberg, allocation price/ CH/ MJ',\n",
+       "       'heat, at system cogen 160kWe Jakobsberg, allocation electricity/ CH/ MJ',\n",
+       "       'heat, at system cogen 160kWe Jakobsberg, allocation energy/ CH/ MJ',\n",
+       "       'heat, at system cogen 160kWe Jakobsberg, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at system cogen 160kWe Jakobsberg, allocation heat/ CH/ MJ',\n",
+       "       'heat, at system cogen 160kWe Jakobsberg, allocation price/ CH/ MJ',\n",
+       "       'heat, natural gas, allocation exergy, at PEM fuel cell 2kWe, future/ CH/ MJ',\n",
+       "       'heat, natural gas, allocation exergy, at SOFC fuel cell 125kWe, future/ CH/ MJ',\n",
+       "       'heat, natural gas, allocation exergy, at SOFC-GT fuel cell 180kWe, future/ CH/ MJ',\n",
+       "       'heat, natural gas, allocation exergy, at micro gas turbine 100kWe/ CH/ MJ',\n",
+       "       'maintenance PEM fuel cell 2kWe/ CH/ unit',\n",
+       "       'maintenance SOFC fuel cell 125kWe, future/ CH/ unit',\n",
+       "       'maintenance SOFC-GT fuel cell 180kWe, future/ CH/ unit',\n",
+       "       'maintenance micro gas turbine 100kWe/ CH/ unit',\n",
+       "       'maintenance, Mini CHP plant/ CH/ unit',\n",
+       "       'maintenance, cogen unit 160kWe/ RER/ unit',\n",
+       "       'natural gas, burned in Mini CHP plant/ CH/ MJ',\n",
+       "       'natural gas, burned in cogen 160kWe Jakobsberg/ CH/ MJ',\n",
+       "       'natural gas, burned in cogen 160kWe lambda=1/ CH/ MJ',\n",
+       "       'natural gas, burned in cogen 1MWe lean burn/ CH/ MJ',\n",
+       "       'natural gas, burned in cogen 1MWe lean burn/ RER/ MJ',\n",
+       "       'natural gas, burned in cogen 200kWe lean burn/ CH/ MJ',\n",
+       "       'natural gas, burned in cogen 500kWe lean burn/ CH/ MJ',\n",
+       "       'natural gas, burned in cogen 50kWe lean burn/ CH/ MJ',\n",
+       "       'liquefied petroleum gas, at service station/ CH/ kg',\n",
+       "       'natural gas, at consumer/ RNA/ MJ',\n",
+       "       'natural gas, from high pressure network (1-5 bar), at service station/ CH/ kg',\n",
+       "       'natural gas, from low pressure network (<0.1 bar), at service station/ CH/ kg',\n",
+       "       'natural gas, from medium pressure network (0.1-1 bar), at service station/ CH/ kg',\n",
+       "       'natural gas, high pressure, at consumer/ CH/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ RER/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ BE/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ DE/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ FR/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ IT/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ NL/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ AT/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ ES/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ CZ/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ DK/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ FI/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ GR/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ HU/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ IE/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ SE/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ SK/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ GB/ MJ',\n",
+       "       'natural gas, high pressure, at consumer/ JP/ MJ',\n",
+       "       'natural gas, low pressure, at consumer/ CH/ MJ',\n",
+       "       'natural gas, production mix, at service station/ CH/ kg',\n",
+       "       'heat, natural gas, at boiler atm. low-NOx condensing non-modulating <100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at boiler atmospheric low-NOx non-modulating <100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at boiler atmospheric non-modulating <100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at boiler condensing modulating <100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at boiler condensing modulating >100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at boiler fan burner low-NOx non-modulating <100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at boiler fan burner non-modulating <100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at boiler modulating <100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at boiler modulating >100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at industrial furnace >100kW/ RER/ MJ',\n",
+       "       'heat, natural gas, at industrial furnace low-NOx >100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler atm. low-NOx condensing non-modulating <100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler atmospheric burner non-modulating <100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler atmospheric low-NOx non-modulating <100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler condensing modulating <100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler condensing modulating >100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler fan burner low-NOx non-modulating <100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler fan burner non-modulating <100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler modulating <100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in boiler modulating >100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in industrial furnace >100kW/ RER/ MJ',\n",
+       "       'natural gas, burned in industrial furnace low-NOx >100kW/ RER/ MJ',\n",
+       "       'blast furnace gas, burned in power plant/ RER/ MJ',\n",
+       "       'coke oven gas, burned in power plant/ RER/ MJ',\n",
+       "       'electricity, industrial gas, at power plant/ AT/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ BE/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ CENTREL/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ DE/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ ES/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ FR/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ IT/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ NL/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ NORDEL/ kWh',\n",
+       "       'electricity, industrial gas, at power plant/ UCTE/ kWh',\n",
+       "       'electricity, natural gas, at combined cycle plant, best technology/ RER/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ UCTE/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ AT/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ BE/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ ES/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ FR/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ IT/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ LU/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ NL/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ DE/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ CENTREL/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ NORDEL/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ GB/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ JP/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ US/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ ASCC/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ ERCOT/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ FRCC/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ MRO/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ NPCC/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ RFC/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ SERC/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ SPP/ kWh',\n",
+       "       'electricity, natural gas, at power plant/ WECC/ kWh',\n",
+       "       'electricity, natural gas, at turbine, 10MW/ GLO/ kWh',\n",
+       "       'natural gas, burned in combined cycle plant, best technology/ RER/ MJ',\n",
+       "       'natural gas, burned in gas motor, for storage/ GLO/ MJ',\n",
+       "       'natural gas, burned in gas motor, for storage/ DZ/ MJ',\n",
+       "       'natural gas, burned in gas motor, for storage/ DE/ MJ',\n",
+       "       'natural gas, burned in gas motor, for storage/ RU/ MJ',\n",
+       "       'natural gas, burned in gas motor, for storage/ NO/ MJ',\n",
+       "       'natural gas, burned in gas motor, for storage/ NL/ MJ',\n",
+       "       'natural gas, burned in gas turbine/ GLO/ MJ',\n",
+       "       'natural gas, burned in gas turbine/ CH/ MJ',\n",
+       "       'natural gas, burned in gas turbine/ DE/ MJ',\n",
+       "       'natural gas, burned in gas turbine/ NL/ MJ',\n",
+       "       'natural gas, burned in gas turbine, for compressor station/ DZ/ MJ',\n",
+       "       'natural gas, burned in gas turbine, for compressor station/ DE/ MJ',\n",
+       "       'natural gas, burned in gas turbine, for compressor station/ RU/ MJ',\n",
+       "       'natural gas, burned in gas turbine, for compressor station/ NO/ MJ',\n",
+       "       'natural gas, burned in gas turbine, for compressor station/ NL/ MJ',\n",
+       "       'natural gas, burned in gas turbine, for compressor station/ UCTE/ MJ',\n",
+       "       'natural gas, burned in power plant/ AT/ MJ',\n",
+       "       'natural gas, burned in power plant/ BE/ MJ',\n",
+       "       'natural gas, burned in power plant/ DE/ MJ',\n",
+       "       'natural gas, burned in power plant/ ES/ MJ',\n",
+       "       'natural gas, burned in power plant/ FR/ MJ',\n",
+       "       'natural gas, burned in power plant/ IT/ MJ',\n",
+       "       'natural gas, burned in power plant/ LU/ MJ',\n",
+       "       'natural gas, burned in power plant/ NL/ MJ',\n",
+       "       'natural gas, burned in power plant/ UCTE/ MJ',\n",
+       "       'natural gas, burned in power plant/ GB/ MJ',\n",
+       "       'natural gas, burned in power plant/ CENTREL/ MJ',\n",
+       "       'natural gas, burned in power plant/ NORDEL/ MJ',\n",
+       "       'natural gas, burned in power plant/ JP/ MJ',\n",
+       "       'natural gas, burned in power plant/ ASCC/ MJ',\n",
+       "       'natural gas, burned in power plant/ ERCOT/ MJ',\n",
+       "       'natural gas, burned in power plant/ FRCC/ MJ',\n",
+       "       'natural gas, burned in power plant/ MRO/ MJ',\n",
+       "       'natural gas, burned in power plant/ NPCC/ MJ',\n",
+       "       'natural gas, burned in power plant/ RFC/ MJ',\n",
+       "       'natural gas, burned in power plant/ SERC/ MJ',\n",
+       "       'natural gas, burned in power plant/ SPP/ MJ',\n",
+       "       'natural gas, burned in power plant/ WECC/ MJ',\n",
+       "       'natural gas, burned in power plant/ US/ MJ',\n",
+       "       'sour gas, burned in gas turbine, production/ NO/ Nm3',\n",
+       "       'sour gas, burned in gas turbine, production/ NO/ MJ',\n",
+       "       'sweet gas, burned in gas turbine, production/ NO/ Nm3',\n",
+       "       'sweet gas, burned in gas turbine, production/ NO/ MJ',\n",
+       "       'drying, natural gas/ NO/ Nm3',\n",
+       "       'natural gas, at evaporation plant/ JP/ Nm3',\n",
+       "       'natural gas, at long-distance pipeline/ CH/ Nm3',\n",
+       "       'natural gas, at long-distance pipeline/ RER/ Nm3',\n",
+       "       'natural gas, at production/ NG/ Nm3',\n",
+       "       'natural gas, at production/ RNA/ Nm3',\n",
+       "       'natural gas, at production offshore/ NO/ Nm3',\n",
+       "       'natural gas, at production offshore/ NL/ Nm3',\n",
+       "       'natural gas, at production offshore/ GB/ Nm3',\n",
+       "       'natural gas, at production onshore/ DZ/ Nm3',\n",
+       "       'natural gas, at production onshore/ DE/ Nm3',\n",
+       "       'natural gas, at production onshore/ RU/ Nm3',\n",
+       "       'natural gas, at production onshore/ NL/ Nm3',\n",
+       "       'natural gas, liquefied, at freight ship/ DZ/ Nm3',\n",
+       "       'natural gas, liquefied, at freight ship/ JP/ Nm3',\n",
+       "       'natural gas, liquefied, at liquefaction plant/ DZ/ Nm3',\n",
+       "       'natural gas, production DE, at long-distance pipeline/ RER/ Nm3',\n",
+       "       'natural gas, production DZ, at evaporation plant/ RER/ Nm3',\n",
+       "       'natural gas, production DZ, at long-distance pipeline/ RER/ Nm3',\n",
+       "       'natural gas, production GB, at long-distance pipeline/ RER/ Nm3',\n",
+       "       'natural gas, production NL, at long-distance pipeline/ RER/ Nm3',\n",
+       "       'natural gas, production NO, at long-distance pipeline/ RER/ Nm3',\n",
+       "       'natural gas, production RU, at long-distance pipeline/ RER/ Nm3',\n",
+       "       'natural gas, sour, burned in production flare/ GLO/ Nm3',\n",
+       "       'natural gas, sour, burned in production flare/ GLO/ MJ',\n",
+       "       'natural gas, sweet, burned in production flare/ GLO/ Nm3',\n",
+       "       'natural gas, sweet, burned in production flare/ GLO/ MJ',\n",
+       "       'natural gas, unprocessed, at extraction/ RNA/ m3',\n",
+       "       'sweetening, natural gas/ DE/ Nm3',\n",
+       "       'transport, natural gas, offshore pipeline, long distance/ DZ/ tkm',\n",
+       "       'transport, natural gas, offshore pipeline, long distance/ NO/ tkm',\n",
+       "       'transport, natural gas, onshore pipeline, long distance/ NO/ tkm',\n",
+       "       'transport, natural gas, onshore pipeline, long distance/ DZ/ tkm',\n",
+       "       'transport, natural gas, pipeline, long distance/ RER/ tkm',\n",
+       "       'transport, natural gas, pipeline, long distance/ DE/ tkm',\n",
+       "       'transport, natural gas, pipeline, long distance/ RU/ tkm',\n",
+       "       'transport, natural gas, pipeline, long distance/ NL/ tkm',\n",
+       "       'electricity, nuclear, at power plant/ CH/ kWh',\n",
+       "       'electricity, nuclear, at power plant/ DE/ kWh',\n",
+       "       'electricity, nuclear, at power plant/ UCTE/ kWh',\n",
+       "       'electricity, nuclear, at power plant/ US/ kWh',\n",
+       "       'electricity, nuclear, at power plant boiling water reactor/ CH/ kWh',\n",
+       "       'electricity, nuclear, at power plant boiling water reactor/ DE/ kWh',\n",
+       "       'electricity, nuclear, at power plant boiling water reactor/ UCTE/ kWh',\n",
+       "       'electricity, nuclear, at power plant boiling water reactor/ US/ kWh',\n",
+       "       'electricity, nuclear, at power plant pressure water reactor/ FR/ kWh',\n",
+       "       'electricity, nuclear, at power plant pressure water reactor/ CH/ kWh',\n",
+       "       'electricity, nuclear, at power plant pressure water reactor/ DE/ kWh',\n",
+       "       'electricity, nuclear, at power plant pressure water reactor/ UCTE/ kWh',\n",
+       "       'electricity, nuclear, at power plant pressure water reactor/ US/ kWh',\n",
+       "       'electricity, nuclear, at power plant pressure water reactor/ CN/ kWh',\n",
+       "       'electricity, nuclear, at pressure water reactor, centrifugal enrichment/ CH/ kWh',\n",
+       "       'MOX fuel element for LWR, at nuclear fuel fabrication plant/ UCTE/ kg',\n",
+       "       'U enriched 3.0%, in fuel element for LWR, at nuclear fuel fabrication plant/ US/ kg',\n",
+       "       'U enriched 3.8%, in fuel element for LWR, at nuclear fuel fabrication plant/ FR/ kg',\n",
+       "       'U enriched 3.8%, in fuel element for LWR, at nuclear fuel fabrication plant/ CH/ kg',\n",
+       "       'U enriched 3.8%, in fuel element for LWR, at nuclear fuel fabrication plant/ US/ kg',\n",
+       "       'U enriched 3.8%, in fuel element for LWR, at nuclear fuel fabrication plant/ CN/ kg',\n",
+       "       'U enriched 3.9%, in fuel element for LWR, at nuclear fuel fabrication plant/ UCTE/ kg',\n",
+       "       'U enriched 4.0%, in fuel element for LWR, at nuclear fuel fabrication plant/ DE/ kg',\n",
+       "       'U enriched 4.0%, in fuel element for LWR, at nuclear fuel fabrication plant/ UCTE/ kg',\n",
+       "       'U enriched 4.2%, centrifugal enrichment, at nuclear fuel fabrication plant/ CH/ kg',\n",
+       "       'U enriched 4.2%, in fuel element for LWR, at nuclear fuel fabrication plant/ CH/ kg',\n",
+       "       'fuel elements BWR, UO2 4.0% & MOX, at nuclear fuel fabrication plant/ DE/ kg',\n",
+       "       'fuel elements BWR, UO2 4.0% & MOX, at nuclear fuel fabrication plant/ UCTE/ kg',\n",
+       "       'fuel elements PWR, UO2 3.8% & MOX, at nuclear fuel fabrication plant/ FR/ kg',\n",
+       "       'fuel elements PWR, UO2 3.9% & MOX, at nuclear fuel fabrication plant/ UCTE/ kg',\n",
+       "       'fuel elements PWR, UO2 4.0% & MOX, at nuclear fuel fabrication plant/ DE/ kg',\n",
+       "       'fuel elements PWR, UO2 4.2% & MOX, at nuclear fuel fabrication plant/ CH/ kg',\n",
+       "       'fuel elements PWR, UO2 4.2% centrifuge & MOX, at nuclear fuel fabrication plant/ CH/ kg',\n",
+       "       'uranium enriched 3.8%, for boiling water reactor/ CH/ kg SWU',\n",
+       "       'uranium natural, at mine/ GLO/ kg',\n",
+       "       'uranium natural, at open pit mine/ RNA/ kg',\n",
+       "       'uranium natural, at underground mine/ RNA/ kg',\n",
+       "       'uranium natural, in uranium hexafluoride, at conversion plant/ US/ kg',\n",
+       "       'uranium natural, in uranium hexafluoride, at conversion plant/ CN/ kg',\n",
+       "       'uranium natural, in yellowcake, at mill plant/ RNA/ kg',\n",
+       "       'uranium, enriched 3.0% at TENEX enrichment plant/ RU/ kg SWU',\n",
+       "       'uranium, enriched 3.0% at URENCO enrichment plant/ RER/ kg SWU',\n",
+       "       'uranium, enriched 3.0% for boiling water reactor/ US/ kg SWU',\n",
+       "       'uranium, enriched 3.0%, at CNNC centrifuge enrichment plant/ CN/ kg SWU',\n",
+       "       'uranium, enriched 3.0%, at EURODIF enrichment plant/ FR/ kg SWU',\n",
+       "       'uranium, enriched 3.0%, at USEC enrichment plant/ US/ kg SWU',\n",
+       "       'uranium, enriched 3.8% for pressure water reactor/ FR/ kg SWU',\n",
+       "       'uranium, enriched 3.8% for pressure water reactor/ US/ kg SWU',\n",
+       "       'uranium, enriched 3.8%, at CNNC centrifuge enrichment plant/ CN/ kg SWU',\n",
+       "       'uranium, enriched 3.8%, at EURODIF enrichment plant/ FR/ kg SWU',\n",
+       "       'uranium, enriched 3.8%, at TENEX enrichment plant/ RU/ kg SWU',\n",
+       "       'uranium, enriched 3.8%, at URENCO enrichment plant/ RER/ kg SWU',\n",
+       "       'uranium, enriched 3.8%, at USEC enrichment plant/ US/ kg SWU',\n",
+       "       'uranium, enriched 3.9% at URENCO enrichment plant/ RER/ kg SWU',\n",
+       "       'uranium, enriched 3.9% for pressure water reactor/ UCTE/ kg SWU',\n",
+       "       'uranium, enriched 3.9%, at EURODIF enrichment plant/ FR/ kg SWU',\n",
+       "       'uranium, enriched 3.9%, at TENEX enrichment plant/ RU/ kg SWU',\n",
+       "       'uranium, enriched 3.9%, at USEC enrichment plant/ US/ kg SWU',\n",
+       "       'uranium, enriched 4.0% for boiling water reactor/ DE/ kg SWU',\n",
+       "       'uranium, enriched 4.0% for boiling water reactor/ UCTE/ kg SWU',\n",
+       "       'uranium, enriched 4.0% for pressure water reactor/ DE/ kg SWU',\n",
+       "       'uranium, enriched 4.0%, at EURODIF enrichment plant/ FR/ kg SWU',\n",
+       "       'uranium, enriched 4.0%, at TENEX enrichment plant/ RU/ kg SWU',\n",
+       "       'uranium, enriched 4.0%, at URENCO enrichment plant/ RER/ kg SWU',\n",
+       "       'uranium, enriched 4.0%, at USEC enrichment plant/ US/ kg SWU',\n",
+       "       'uranium, enriched 4.2% for pressure water reactor/ CH/ kg SWU',\n",
+       "       'uranium, enriched 4.2%, at EURODIF enrichment plant/ FR/ kg SWU',\n",
+       "       'uranium, enriched 4.2%, at TENEX enrichment plant/ RU/ kg SWU',\n",
+       "       'uranium, enriched 4.2%, at URENCO enrichment plant/ RER/ kg SWU',\n",
+       "       'uranium, enriched 4.2%, at USEC enrichment plant/ US/ kg SWU',\n",
+       "       'uranium, enriched 4.2%, centrifugal enrichment, for pressure water reactor/ CH/ kg SWU',\n",
+       "       'disposal, uranium tailings, non-radioactive emissions/ GLO/ m3',\n",
+       "       'low active radioactive waste/ CH/ m3',\n",
+       "       'nuclear spent fuel, in conditioning, at plant/ CH/ kg',\n",
+       "       'nuclear spent fuel, in conditioning, at plant/ CN/ kg',\n",
+       "       'nuclear spent fuel, in reprocessing, at plant/ RER/ kg',\n",
+       "       'radioactive waste, in final repository for nuclear waste LLW/ CH/ m3',\n",
+       "       'radioactive waste, in final repository for nuclear waste SF, HLW, and ILW/ CH/ m3',\n",
+       "       'radioactive waste, in interim storage conditioning/ CH/ m3',\n",
+       "       'radioactive waste, in interim storage, for final repository LLW/ CH/ m3',\n",
+       "       'radioactive waste, in interim storage, for final repository SF, HLW, and ILW/ CH/ m3',\n",
+       "       'tailings, uranium milling/ GLO/ m3',\n",
+       "       'diesel, burned in cogen 200kWe diesel SCR/ CH/ MJ',\n",
+       "       'electricity, at cogen 200kWe diesel SCR, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 200kWe diesel SCR, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 200kWe diesel, allocation heat/ CH/ kWh',\n",
+       "       'heat, at cogen 200kWe diesel SCR, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 200kWe diesel SCR, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 200kWe diesel, allocation heat/ CH/ MJ',\n",
+       "       'bitumen, at refinery/ CH/ kg', 'bitumen, at refinery/ RER/ kg',\n",
+       "       'diesel, at refinery/ CH/ kg', 'diesel, at refinery/ RER/ kg',\n",
+       "       'diesel, at regional storage/ CH/ kg',\n",
+       "       'diesel, at regional storage/ RER/ kg',\n",
+       "       'diesel, burned in diesel-electric generating set/ GLO/ MJ',\n",
+       "       'diesel, low-sulphur, at refinery/ CH/ kg',\n",
+       "       'diesel, low-sulphur, at refinery/ RER/ kg',\n",
+       "       'diesel, low-sulphur, at regional storage/ CH/ kg',\n",
+       "       'diesel, low-sulphur, at regional storage/ RER/ kg',\n",
+       "       'heavy fuel oil, at refinery/ CH/ kg',\n",
+       "       'heavy fuel oil, at refinery/ RER/ kg',\n",
+       "       'heavy fuel oil, at regional storage/ CH/ kg',\n",
+       "       'heavy fuel oil, at regional storage/ RER/ kg',\n",
+       "       'kerosene, at refinery/ CH/ kg', 'kerosene, at refinery/ RER/ kg',\n",
+       "       'kerosene, at regional storage/ CH/ kg',\n",
+       "       'kerosene, at regional storage/ RER/ kg',\n",
+       "       'light fuel oil, at refinery/ RER/ kg',\n",
+       "       'light fuel oil, at refinery/ CH/ kg',\n",
+       "       'light fuel oil, at regional storage/ CH/ kg',\n",
+       "       'light fuel oil, at regional storage/ RER/ kg',\n",
+       "       'naphtha, APME mix, at refinery/ RER/ kg',\n",
+       "       'naphtha, at refinery/ RER/ kg', 'naphtha, at refinery/ CH/ kg',\n",
+       "       'naphtha, at regional storage/ CH/ kg',\n",
+       "       'naphtha, at regional storage/ RER/ kg',\n",
+       "       'petrol, 15% vol. ETBE additive, EtOH f. biomass, prod. RER, at service station/ CH/ kg',\n",
+       "       'petrol, 15% vol. ETBE additive, with ethanol from biomass, at refinery/ RER/ kg',\n",
+       "       'petrol, 4% vol. ETBE additive, EtOH f. biomass, prod. RER, at service station/ CH/ kg',\n",
+       "       'petrol, 4% vol. ETBE additive, with ethanol from biomass, at refinery/ RER/ kg',\n",
+       "       'petrol, 5% vol. ethanol, from biomass, at service station/ CH/ kg',\n",
+       "       'petrol, low-sulphur, at refinery/ CH/ kg',\n",
+       "       'petrol, low-sulphur, at refinery/ RER/ kg',\n",
+       "       'petrol, low-sulphur, at regional storage/ CH/ kg',\n",
+       "       'petrol, low-sulphur, at regional storage/ RER/ kg',\n",
+       "       'petrol, two-stroke blend, at regional storage/ CH/ kg',\n",
+       "       'petrol, two-stroke blend, at regional storage/ RER/ kg',\n",
+       "       'petrol, unleaded, at refinery/ CH/ kg',\n",
+       "       'petrol, unleaded, at refinery/ RER/ kg',\n",
+       "       'petrol, unleaded, at regional storage/ CH/ kg',\n",
+       "       'petrol, unleaded, at regional storage/ RER/ kg',\n",
+       "       'petroleum coke, at refinery/ RER/ kg',\n",
+       "       'propane/ butane, at refinery/ CH/ kg',\n",
+       "       'propane/ butane, at refinery/ RER/ kg',\n",
+       "       'refinery gas, at refinery/ RER/ kg',\n",
+       "       'refinery gas, at refinery/ CH/ kg',\n",
+       "       'heat, heavy fuel oil, at industrial furnace 1MW/ CH/ MJ',\n",
+       "       'heat, heavy fuel oil, at industrial furnace 1MW/ RER/ MJ',\n",
+       "       'heat, light fuel oil, at boiler 100kW condensing, non-modulating/ CH/ MJ',\n",
+       "       'heat, light fuel oil, at boiler 100kW, non-modulating/ CH/ MJ',\n",
+       "       'heat, light fuel oil, at boiler 10kW condensing, non-modulating/ CH/ MJ',\n",
+       "       'heat, light fuel oil, at boiler 10kW, non-modulating/ CH/ MJ',\n",
+       "       'heat, light fuel oil, at industrial furnace 1MW/ CH/ MJ',\n",
+       "       'heat, light fuel oil, at industrial furnace 1MW/ RER/ MJ',\n",
+       "       'heavy fuel oil, burned in industrial furnace 1MW, non-modulating/ RER/ MJ',\n",
+       "       'heavy fuel oil, burned in industrial furnace 1MW, non-modulating/ CH/ MJ',\n",
+       "       'heavy fuel oil, burned in refinery furnace/ CH/ kg',\n",
+       "       'heavy fuel oil, burned in refinery furnace/ RER/ kg',\n",
+       "       'heavy fuel oil, burned in refinery furnace/ CH/ MJ',\n",
+       "       'heavy fuel oil, burned in refinery furnace/ RER/ MJ',\n",
+       "       'light fuel oil, burned in boiler 100kW condensing, non-modulating/ CH/ MJ',\n",
+       "       'light fuel oil, burned in boiler 100kW, non-modulating/ CH/ MJ',\n",
+       "       'light fuel oil, burned in boiler 10kW condensing, non-modulating/ CH/ MJ',\n",
+       "       'light fuel oil, burned in boiler 10kW, non-modulating/ CH/ MJ',\n",
+       "       'light fuel oil, burned in industrial furnace 1MW, non-modulating/ CH/ MJ',\n",
+       "       'light fuel oil, burned in industrial furnace 1MW, non-modulating/ RER/ MJ',\n",
+       "       'refinery gas, burned in furnace/ CH/ kg',\n",
+       "       'refinery gas, burned in furnace/ RER/ kg',\n",
+       "       'refinery gas, burned in furnace/ CH/ MJ',\n",
+       "       'refinery gas, burned in furnace/ RER/ MJ',\n",
+       "       'electricity, at refinery/ CH/ kWh',\n",
+       "       'electricity, at refinery/ RER/ kWh',\n",
+       "       'electricity, oil, at power plant/ AT/ kWh',\n",
+       "       'electricity, oil, at power plant/ BE/ kWh',\n",
+       "       'electricity, oil, at power plant/ ES/ kWh',\n",
+       "       'electricity, oil, at power plant/ CS/ kWh',\n",
+       "       'electricity, oil, at power plant/ FR/ kWh',\n",
+       "       'electricity, oil, at power plant/ GR/ kWh',\n",
+       "       'electricity, oil, at power plant/ IT/ kWh',\n",
+       "       'electricity, oil, at power plant/ NL/ kWh',\n",
+       "       'electricity, oil, at power plant/ PT/ kWh',\n",
+       "       'electricity, oil, at power plant/ DE/ kWh',\n",
+       "       'electricity, oil, at power plant/ DK/ kWh',\n",
+       "       'electricity, oil, at power plant/ FI/ kWh',\n",
+       "       'electricity, oil, at power plant/ GB/ kWh',\n",
+       "       'electricity, oil, at power plant/ IE/ kWh',\n",
+       "       'electricity, oil, at power plant/ SE/ kWh',\n",
+       "       'electricity, oil, at power plant/ CZ/ kWh',\n",
+       "       'electricity, oil, at power plant/ HU/ kWh',\n",
+       "       'electricity, oil, at power plant/ SK/ kWh',\n",
+       "       'electricity, oil, at power plant/ HR/ kWh',\n",
+       "       'electricity, oil, at power plant/ SI/ kWh',\n",
+       "       'electricity, oil, at power plant/ UCTE/ kWh',\n",
+       "       'heavy fuel oil, burned in power plant/ RER/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ AT/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ BE/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ ES/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ CS/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ FR/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ GR/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ IT/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ NL/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ PT/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ DE/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ DK/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ FI/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ GB/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ IE/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ SE/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ CZ/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ HU/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ SK/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ HR/ MJ',\n",
+       "       'heavy fuel oil, burned in power plant/ SI/ MJ',\n",
+       "       'crude oil, at production/ NG/ kg',\n",
+       "       'crude oil, at production offshore/ NO/ kg',\n",
+       "       'crude oil, at production offshore/ NL/ kg',\n",
+       "       'crude oil, at production offshore/ GB/ kg',\n",
+       "       'crude oil, at production onshore/ RME/ kg',\n",
+       "       'crude oil, at production onshore/ NL/ kg',\n",
+       "       'crude oil, at production onshore/ RU/ kg',\n",
+       "       'crude oil, at production onshore/ RAF/ kg',\n",
+       "       'crude oil, production GB, at long distance transport/ RER/ kg',\n",
+       "       'crude oil, production NG, at long distance transport/ RER/ kg',\n",
+       "       'crude oil, production NG, at long distance transport/ CH/ kg',\n",
+       "       'crude oil, production NL, at long distance transport/ RER/ kg',\n",
+       "       'crude oil, production NO, at long distance transport/ RER/ kg',\n",
+       "       'crude oil, production RAF, at long distance transport/ RER/ kg',\n",
+       "       'crude oil, production RAF, at long distance transport/ CH/ kg',\n",
+       "       'crude oil, production RLA, at long distance transport/ RER/ kg',\n",
+       "       'crude oil, production RME, at long distance transport/ RER/ kg',\n",
+       "       'crude oil, production RME, at long distance transport/ CH/ kg',\n",
+       "       'crude oil, production RU, at long distance transport/ RER/ kg',\n",
+       "       'crude oil, used in drilling tests/ GLO/ kg',\n",
+       "       'discharge, produced water, offshore/ OCE/ kg',\n",
+       "       'discharge, produced water, onshore/ GLO/ kg',\n",
+       "       'natural gas, vented/ GLO/ Nm3',\n",
+       "       'refinery gas, burned in flare/ GLO/ MJ',\n",
+       "       'transport, crude oil pipeline, offshore/ OCE/ tkm',\n",
+       "       'transport, crude oil pipeline, onshore/ RER/ tkm',\n",
+       "       'laminating, foil, with acrylic binder/ RER/ m2',\n",
+       "       'acrylic binder, 34% in H2O, at plant/ RER/ kg',\n",
+       "       'acrylic dispersion, 65% in H2O, at plant/ RER/ kg',\n",
+       "       'acrylic varnish, 87.5% in H2O, at plant/ RER/ kg',\n",
+       "       'adhesive for metals, at plant/ DE/ kg',\n",
+       "       'alkyd paint, white, 60% in H2O, at plant/ RER/ kg',\n",
+       "       'alkyd paint, white, 60% in solvent, at plant/ RER/ kg',\n",
+       "       'alkyd resin, long oil, 70% in white spirit, at plant/ RER/ kg',\n",
+       "       'melamine formaldehyde resin, at plant/ RER/ kg',\n",
+       "       'phenolic resin, at plant/ RER/ kg',\n",
+       "       'polyester resin, unsaturated, at plant/ RER/ kg',\n",
+       "       'printing colour, offset, 47.5% solvent, at plant/ RER/ kg',\n",
+       "       'printing colour, rotogravure, 55% toluene, at plant/ RER/ kg',\n",
+       "       'resin size, at plant/ RER/ kg',\n",
+       "       'urea formaldehyde resin, at plant/ RER/ kg',\n",
+       "       'white spirit, at plant/ RER/ kg',\n",
+       "       'wood preservative, creosote, at plant/ RER/ kg',\n",
+       "       'wood preservative, inorganic salt, containing Cr, at plant/ RER/ kg',\n",
+       "       'wood preservative, organic salt, Cr-free, at plant/ RER/ kg',\n",
+       "       'corrugated board base paper, kraftliner, at plant/ RER/ kg',\n",
+       "       'corrugated board base paper, semichemical fluting, at plant/ RER/ kg',\n",
+       "       'corrugated board base paper, testliner, at plant/ RER/ kg',\n",
+       "       'corrugated board base paper, wellenstoff, at plant/ RER/ kg',\n",
+       "       'corrugated board, fresh fibre, single wall, at plant/ RER/ kg',\n",
+       "       'corrugated board, fresh fibre, single wall, at plant/ CH/ kg',\n",
+       "       'corrugated board, mixed fibre, single wall, at plant/ RER/ kg',\n",
+       "       'corrugated board, mixed fibre, single wall, at plant/ CH/ kg',\n",
+       "       'corrugated board, recycling fibre, double wall, at plant/ RER/ kg',\n",
+       "       'corrugated board, recycling fibre, double wall, at plant/ CH/ kg',\n",
+       "       'corrugated board, recycling fibre, single wall, at plant/ RER/ kg',\n",
+       "       'corrugated board, recycling fibre, single wall, at plant/ CH/ kg',\n",
+       "       'folding boxboard, FBB, at plant/ RER/ kg',\n",
+       "       'liquid packaging board, at plant/ RER/ kg',\n",
+       "       'packaging, corrugated board, mixed fibre, single wall, at plant/ RER/ kg',\n",
+       "       'packaging, corrugated board, mixed fibre, single wall, at plant/ CH/ kg',\n",
+       "       'production of carton board boxes, gravure printing, at plant/ CH/ kg',\n",
+       "       'production of carton board boxes, offset printing, at plant/ CH/ kg',\n",
+       "       'production of liquid packaging board containers, at plant/ RER/ kg',\n",
+       "       'solid bleached board, SBB, at plant/ RER/ kg',\n",
+       "       'solid unbleached board, SUB, at plant/ RER/ kg',\n",
+       "       'whitelined chipboard, WLC, at plant/ RER/ kg',\n",
+       "       'paper, newsprint, 0% DIP, at plant/ RER/ kg',\n",
+       "       'paper, newsprint, DIP containing, at plant/ RER/ kg',\n",
+       "       'paper, newsprint, at plant/ CH/ kg',\n",
+       "       'paper, newsprint, at regional storage/ CH/ kg',\n",
+       "       'paper, newsprint, at regional storage/ RER/ kg',\n",
+       "       'paper, recycling, no deinking, at plant/ RER/ kg',\n",
+       "       'paper, recycling, with deinking, at plant/ RER/ kg',\n",
+       "       'paper, wood-containing, LWC, at regional storage/ RER/ kg',\n",
+       "       'paper, wood-containing, LWC, at regional storage/ CH/ kg',\n",
+       "       'paper, wood-containing, supercalendred (SC), at regional storage/ RER/ kg',\n",
+       "       'paper, wood-containing, supercalendred (SC), at regional storage/ CH/ kg',\n",
+       "       'paper, woodcontaining, LWC, at plant/ RER/ kg',\n",
+       "       'paper, woodcontaining, supercalendred (SC), at plant/ RER/ kg',\n",
+       "       'paper, woodfree, coated, at integrated mill/ RER/ kg',\n",
+       "       'paper, woodfree, coated, at non-integrated mill/ RER/ kg',\n",
+       "       'paper, woodfree, coated, at regional storage/ RER/ kg',\n",
+       "       'paper, woodfree, coated, at regional storage/ CH/ kg',\n",
+       "       'paper, woodfree, uncoated, at integrated mill/ RER/ kg',\n",
+       "       'paper, woodfree, uncoated, at non-integrated mill/ RER/ kg',\n",
+       "       'paper, woodfree, uncoated, at regional storage/ RER/ kg',\n",
+       "       'paper, woodfree, uncoated, at regional storage/ CH/ kg',\n",
+       "       'core board, at plant/ RER/ kg',\n",
+       "       'kraft paper, bleached, at plant/ RER/ kg',\n",
+       "       'kraft paper, unbleached, at plant/ RER/ kg',\n",
+       "       'chemi-thermomechanical pulp, at plant/ RER/ kg',\n",
+       "       'stone groundwood pulp, SGW, at plant/ RER/ kg',\n",
+       "       'sulphate pulp, ECF bleached, at plant/ RER/ kg',\n",
+       "       'sulphate pulp, TCF bleached, at plant/ RER/ kg',\n",
+       "       'sulphate pulp, average, at regional storage/ RER/ kg',\n",
+       "       'sulphate pulp, average, at regional storage/ CH/ kg',\n",
+       "       'sulphate pulp, from eucalyptus ssp. (SFM), unbleached, TH, at maritime harbour/ RER/ kg',\n",
+       "       'sulphate pulp, from eucalyptus ssp. (SFM), unbleached, at pulpmill/ TH/ kg',\n",
+       "       'sulphate pulp, unbleached, at plant/ RER/ kg',\n",
+       "       'sulphite pulp, bleached, at plant/ RER/ kg',\n",
+       "       'thermo-mechanical pulp, at plant/ RER/ kg',\n",
+       "       'waste paper, mixed, from public collection, for further treatment/ RER/ kg',\n",
+       "       'waste paper, mixed, from public collection, for further treatment/ CH/ kg',\n",
+       "       'waste paper, sorted, for further treatment/ RER/ kg',\n",
+       "       'waste paper, sorted, for further treatment/ CH/ kg',\n",
+       "       'electricity, PV, at 3kWp facade installation, multi-Si, panel, mounted/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp facade installation, single-Si, panel, mounted/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp facade, multi-Si, laminated, integrated/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp facade, single-Si, laminated, integrated/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp flat roof installation, multi-Si/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp flat roof installation, single-Si/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, CIS, panel, mounted/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, CdTe, laminated, integrated/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, a-Si, lam., integrated/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, a-Si, panel, mounted/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, multi-Si, laminated, integrated/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, multi-Si, panel, mounted/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, ribbon-Si, lam., integrated/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, ribbon-Si, panel, mounted/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, single-Si, laminated, integrated/ CH/ kWh',\n",
+       "       'electricity, PV, at 3kWp slanted-roof, single-Si, panel, mounted/ CH/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ CH/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ AT/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ AU/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ BE/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ CA/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ CZ/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ DE/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ DK/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ ES/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ FI/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ FR/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ GB/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ GR/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ HU/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ IE/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ IT/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ JP/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ KR/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ LU/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ NL/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ NO/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ NZ/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ PT/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ SE/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ TR/ kWh',\n",
+       "       'electricity, production mix photovoltaic, at plant/ US/ kWh',\n",
+       "       'CZ single crystalline silicon, electronics, at plant/ RER/ kg',\n",
+       "       'CZ single crystalline silicon, photovoltaics, at plant/ RER/ kg',\n",
+       "       'metallization paste, back side, aluminium, at plant/ RER/ kg',\n",
+       "       'metallization paste, back side, at plant/ RER/ kg',\n",
+       "       'metallization paste, front side, at plant/ RER/ kg',\n",
+       "       'multi-Si wafer, at plant/ RER/ m2',\n",
+       "       'multi-Si wafer, ribbon, at plant/ RER/ m2',\n",
+       "       'photovoltaic cell, multi-Si, at plant/ RER/ m2',\n",
+       "       'photovoltaic cell, ribbon-Si, at plant/ RER/ m2',\n",
+       "       'photovoltaic cell, single-Si, at plant/ RER/ m2',\n",
+       "       'single-Si wafer, electronics, at plant/ RER/ m2',\n",
+       "       'single-Si wafer, photovoltaics, at plant/ RER/ m2',\n",
+       "       'butadiene, at plant/ RER/ kg', 'butene, mixed, at plant/ RER/ kg',\n",
+       "       'epoxy resin insulator (Al2O3), at plant/ RER/ kg',\n",
+       "       'epoxy resin insulator (SiO2), at plant/ RER/ kg',\n",
+       "       'epoxy resin, liquid, at plant/ RER/ kg',\n",
+       "       'epoxy resin, liquid, disaggregated data, at plant/ RER/ kg',\n",
+       "       'ethylene, average, at plant/ RER/ kg',\n",
+       "       'ethylene, pipeline system, at plant/ RER/ kg',\n",
+       "       'methyl methacrylate, at plant/ RER/ kg',\n",
+       "       'methylene diphenyl diisocyanate, at plant/ RER/ kg',\n",
+       "       'polyols, at plant/ RER/ kg', 'propylene, at plant/ RER/ kg',\n",
+       "       'propylene, pipeline system, at plant/ RER/ kg',\n",
+       "       'styrene, at plant/ RER/ kg',\n",
+       "       'toluene diisocyanate, at plant/ RER/ kg',\n",
+       "       'vinyl acetate, at plant/ RER/ kg',\n",
+       "       'vinyl chloride, at plant/ RER/ kg',\n",
+       "       'bitumen adhesive compound, cold, at plant/ RER/ kg',\n",
+       "       'bitumen adhesive compound, hot, at plant/ RER/ kg',\n",
+       "       'bitumen sealing Alu80, at plant/ RER/ kg',\n",
+       "       'bitumen sealing V60, at plant/ RER/ kg',\n",
+       "       'bitumen sealing VA4, at plant/ RER/ kg',\n",
+       "       'bitumen sealing, at plant/ RER/ kg',\n",
+       "       'bitumen sealing, polymer EP4 flame retardant, at plant/ RER/ kg',\n",
+       "       'glass fibre reinforced plastic, polyamide, injection moulding, at plant/ RER/ kg',\n",
+       "       'glass fibre reinforced plastic, polyester resin, hand lay-up, at plant/ RER/ kg',\n",
+       "       'natural rubber based sealing, at plant/ DE/ kg',\n",
+       "       'polysulphide, sealing compound, at plant/ RER/ kg',\n",
+       "       'acrylonitrile-butadiene-styrene copolymer, ABS, at plant/ RER/ kg',\n",
+       "       'ethylene vinyl acetate copolymer, at plant/ RER/ kg',\n",
+       "       'ethylvinylacetate, foil, at plant/ RER/ kg',\n",
+       "       'fleece, polyethylene, at plant/ RER/ kg',\n",
+       "       'modified starch, at plant/ RER/ kg', 'nylon 6, at plant/ RER/ kg',\n",
+       "       'nylon 6, glass-filled, at plant/ RER/ kg',\n",
+       "       'nylon 66, at plant/ RER/ kg',\n",
+       "       'nylon 66, glass-filled, at plant/ RER/ kg',\n",
+       "       'polybutadiene, at plant/ RER/ kg',\n",
+       "       'polycarbonate, at plant/ RER/ kg',\n",
+       "       'polyethylene terephthalate, granulate, amorphous, at plant/ RER/ kg',\n",
+       "       'polyethylene terephthalate, granulate, bottle grade, at plant/ RER/ kg',\n",
+       "       'polyethylene, HDPE, granulate, at plant/ RER/ kg',\n",
+       "       'polyethylene, LDPE, granulate, at plant/ RER/ kg',\n",
+       "       'polyethylene, LLDPE, granulate, at plant/ RER/ kg',\n",
+       "       'polylactide, granulate, at plant/ GLO/ kg',\n",
+       "       'polymethyl methacrylate, beads, at plant/ RER/ kg',\n",
+       "       'polymethyl methacrylate, sheet, at plant/ RER/ kg',\n",
+       "       'polyphenylene sulfide, at plant/ GLO/ kg',\n",
+       "       'polypropylene, granulate, at plant/ RER/ kg',\n",
+       "       'polystyrene scrap, old, at plant/ CH/ kg',\n",
+       "       'polystyrene, expandable, at plant/ RER/ kg',\n",
+       "       'polystyrene, general purpose, GPPS, at plant/ RER/ kg',\n",
+       "       'polystyrene, high impact, HIPS, at plant/ RER/ kg',\n",
+       "       'polyurethane, flexible foam, at plant/ RER/ kg',\n",
+       "       'polyurethane, rigid foam, at plant/ RER/ kg',\n",
+       "       'polyvinylchloride, at regional storage/ RER/ kg',\n",
+       "       'polyvinylchloride, bulk polymerised, at plant/ RER/ kg',\n",
+       "       'polyvinylchloride, emulsion polymerised, at plant/ RER/ kg',\n",
+       "       'polyvinylchloride, suspension polymerised, at plant/ RER/ kg',\n",
+       "       'polyvinylidenchloride, granulate, at plant/ RER/ kg',\n",
+       "       'purified terephthalic acid, at plant/ RER/ kg',\n",
+       "       'styrene-acrylonitrile copolymer, SAN, at plant/ RER/ kg',\n",
+       "       'synthetic rubber, at plant/ RER/ kg', 'blow moulding/ RER/ kg',\n",
+       "       'calendering, rigid sheets/ RER/ kg',\n",
+       "       'extrusion, plastic film/ RER/ kg',\n",
+       "       'extrusion, plastic pipes/ RER/ kg',\n",
+       "       'fleece production, polyethylene terephthalate/ RER/ kg',\n",
+       "       'foaming, expanding/ RER/ kg', 'injection moulding/ RER/ kg',\n",
+       "       'packaging film, LDPE, at plant/ RER/ kg',\n",
+       "       'stretch blow moulding/ RER/ kg',\n",
+       "       'thermoforming, with calendering/ RER/ kg',\n",
+       "       'heat, at flat plate collector, multiple dwelling, for hot water/ CH/ MJ',\n",
+       "       'heat, at flat plate collector, one-family house, for combined system/ CH/ MJ',\n",
+       "       'heat, at flat plate collector, one-family house, for hot water/ CH/ MJ',\n",
+       "       'heat, at hot water tank, solar+electric, flat plate, multiple dwelling/ CH/ MJ',\n",
+       "       'heat, at hot water tank, solar+gas, flat plate, multiple dwelling/ CH/ MJ',\n",
+       "       'heat, at hot water tank, solar+gas, flat plate, one-family house/ CH/ MJ',\n",
+       "       'heat, at solar+gas heating, flat plate, one-family house, combined system/ CH/ MJ',\n",
+       "       'heat, at solar+gas heating, tube collector, one-family house, combined system/ CH/ MJ',\n",
+       "       'heat, at solar+wood heating, flat plate, one-family house, combined system/ CH/ MJ',\n",
+       "       'heat, at tube collector, one-family house, for combined system/ CH/ MJ',\n",
+       "       'textile refinement, cotton/ GLO/ kg',\n",
+       "       'weeving, bast fibres/ IN/ kg', 'weeving, cotton/ GLO/ kg',\n",
+       "       'textile, jute, at plant/ IN/ kg',\n",
+       "       'textile, kenaf, at plant/ IN/ kg',\n",
+       "       'textile, woven cotton, at plant/ GLO/ kg',\n",
+       "       'viscose fibres, at plant/ GLO/ kg',\n",
+       "       'yarn production, bast fibres/ IN/ kg',\n",
+       "       'yarn production, cotton fibres/ GLO/ kg',\n",
+       "       'yarn, cotton, at plant/ GLO/ kg', 'yarn, jute, at plant/ IN/ kg',\n",
+       "       'yarn, kenaf, at plant/ IN/ kg',\n",
+       "       'operation, aircraft, freight/ RER/ tkm',\n",
+       "       'operation, aircraft, freight, Europe/ RER/ tkm',\n",
+       "       'operation, aircraft, freight, intercontinental/ RER/ tkm',\n",
+       "       'operation, aircraft, passenger/ RER/ pkm',\n",
+       "       'operation, aircraft, passenger, Europe/ RER/ pkm',\n",
+       "       'operation, aircraft, passenger, intercontinental/ RER/ pkm',\n",
+       "       'operation, maintenance, airport/ RER/ unit',\n",
+       "       'transport, aircraft, freight/ RER/ tkm',\n",
+       "       'transport, aircraft, freight, Europe/ RER/ tkm',\n",
+       "       'transport, aircraft, freight, intercontinental/ RER/ tkm',\n",
+       "       'transport, aircraft, passenger/ RER/ pkm',\n",
+       "       'transport, aircraft, passenger, Europe/ RER/ pkm',\n",
+       "       'transport, aircraft, passenger, intercontinental/ RER/ pkm',\n",
+       "       'transport, helicopter/ GLO/ h',\n",
+       "       'transport, helicopter, LTO cycle/ GLO/ unit',\n",
+       "       'electric motor, electric vehicle, at plant/ RER/ kg',\n",
+       "       'operation, coach/ CH/ vkm', 'operation, electric bicycle/ CH/ km',\n",
+       "       'operation, electric bicycle, certified electricity/ CH/ km',\n",
+       "       'operation, electric scooter/ CH/ km',\n",
+       "       'operation, electric scooter, certified electricity/ CH/ km',\n",
+       "       'operation, lorry 16-32t, EURO3/ RER/ vkm',\n",
+       "       'operation, lorry 16-32t, EURO4/ RER/ vkm',\n",
+       "       'operation, lorry 16-32t, EURO5/ RER/ vkm',\n",
+       "       'operation, lorry 20-28t, empty, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry 20-28t, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry 20-28t, full, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry 28t, rape methyl ester 100%/ CH/ km',\n",
+       "       'operation, lorry 3.5-16t, fleet average/ RER/ vkm',\n",
+       "       'operation, lorry 3.5-20t, empty, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry 3.5-20t, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry 3.5-20t, full, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry 3.5-7.5t, EURO3/ RER/ vkm',\n",
+       "       'operation, lorry 3.5-7.5t, EURO4/ RER/ vkm',\n",
+       "       'operation, lorry 3.5-7.5t, EURO5/ RER/ vkm',\n",
+       "       'operation, lorry 7.5-16t, EURO3/ RER/ vkm',\n",
+       "       'operation, lorry 7.5-16t, EURO4/ RER/ vkm',\n",
+       "       'operation, lorry 7.5-16t, EURO5/ RER/ vkm',\n",
+       "       'operation, lorry >16t, fleet average/ RER/ vkm',\n",
+       "       'operation, lorry >28t, empty, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry >28t, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry >28t, full, fleet average/ CH/ vkm',\n",
+       "       'operation, lorry >32t, EURO3/ RER/ vkm',\n",
+       "       'operation, lorry >32t, EURO4/ RER/ vkm',\n",
+       "       'operation, lorry >32t, EURO5/ RER/ vkm',\n",
+       "       'operation, passenger car/ CH/ km',\n",
+       "       'operation, passenger car/ RER/ km',\n",
+       "       'operation, passenger car, diesel, EURO3/ CH/ km',\n",
+       "       'operation, passenger car, diesel, EURO4/ CH/ km',\n",
+       "       'operation, passenger car, diesel, EURO5/ CH/ km',\n",
+       "       'operation, passenger car, diesel, EURO5, city car/ CH/ km',\n",
+       "       'operation, passenger car, diesel, fleet average/ RER/ vkm',\n",
+       "       'operation, passenger car, diesel, fleet average/ CH/ vkm',\n",
+       "       'operation, passenger car, diesel, fleet average 2010/ CH/ vkm',\n",
+       "       'operation, passenger car, diesel, fleet average 2010/ RER/ vkm',\n",
+       "       'operation, passenger car, electric, LiMn2O4/ CH/ km',\n",
+       "       'operation, passenger car, electric, LiMn2O4, certified electricity/ CH/ km',\n",
+       "       'operation, passenger car, electric, LiMn2O4, city car/ CH/ km',\n",
+       "       'operation, passenger car, electric, LiMn2O4, city car, certified electricity/ CH/ km',\n",
+       "       'operation, passenger car, ethanol 5%/ CH/ km',\n",
+       "       'operation, passenger car, methane, 96 vol-%, from biogas/ CH/ km',\n",
+       "       'operation, passenger car, methanol/ CH/ km',\n",
+       "       'operation, passenger car, natural gas/ CH/ km',\n",
+       "       'operation, passenger car, petrol, 15% vol. ETBE with ethanol from biomass, EURO4/ CH/ km',\n",
+       "       'operation, passenger car, petrol, 4% vol. ETBE with ethanol from biomass, EURO4/ CH/ km',\n",
+       "       'operation, passenger car, petrol, EURO3/ CH/ km',\n",
+       "       'operation, passenger car, petrol, EURO4/ CH/ km',\n",
+       "       'operation, passenger car, petrol, EURO5/ CH/ km',\n",
+       "       'operation, passenger car, petrol, fleet average/ CH/ vkm',\n",
+       "       'operation, passenger car, petrol, fleet average/ RER/ vkm',\n",
+       "       'operation, passenger car, petrol, fleet average 2010/ CH/ vkm',\n",
+       "       'operation, passenger car, petrol, fleet average 2010/ RER/ vkm',\n",
+       "       'operation, passenger car, rape seed methyl ester 5%/ CH/ km',\n",
+       "       'operation, regular bus/ CH/ vkm', 'operation, scooter/ CH/ km',\n",
+       "       'operation, tram/ CH/ vkm', 'operation, trolleybus/ CH/ vkm',\n",
+       "       'operation, van < 3,5t/ CH/ km', 'operation, van < 3,5t/ RER/ vkm',\n",
+       "       'transport, bicycle/ CH/ pkm', 'transport, coach/ CH/ pkm',\n",
+       "       'transport, electric bicycle/ CH/ pkm',\n",
+       "       'transport, electric bicycle, certified electricity/ CH/ pkm',\n",
+       "       'transport, electric scooter/ CH/ pkm',\n",
+       "       'transport, electric scooter, certified electricity/ CH/ pkm',\n",
+       "       'transport, lorry 16-32t, EURO3/ RER/ tkm',\n",
+       "       'transport, lorry 16-32t, EURO4/ RER/ tkm',\n",
+       "       'transport, lorry 16-32t, EURO5/ RER/ tkm',\n",
+       "       'transport, lorry 20-28t, fleet average/ CH/ tkm',\n",
+       "       'transport, lorry 28t, rape methyl ester 100%/ CH/ tkm',\n",
+       "       'transport, lorry 3.5-16t, fleet average/ RER/ tkm',\n",
+       "       'transport, lorry 3.5-20t, fleet average/ CH/ tkm',\n",
+       "       'transport, lorry 3.5-7.5t, EURO3/ RER/ tkm',\n",
+       "       'transport, lorry 3.5-7.5t, EURO4/ RER/ tkm',\n",
+       "       'transport, lorry 3.5-7.5t, EURO5/ RER/ tkm',\n",
+       "       'transport, lorry 7.5-16t, EURO3/ RER/ tkm',\n",
+       "       'transport, lorry 7.5-16t, EURO4/ RER/ tkm',\n",
+       "       'transport, lorry 7.5-16t, EURO5/ RER/ tkm',\n",
+       "       'transport, lorry >16t, fleet average/ RER/ tkm',\n",
+       "       'transport, lorry >28t, fleet average/ CH/ tkm',\n",
+       "       'transport, lorry >32t, EURO3/ RER/ tkm',\n",
+       "       'transport, lorry >32t, EURO4/ RER/ tkm',\n",
+       "       'transport, lorry >32t, EURO5/ RER/ tkm',\n",
+       "       'transport, passenger car/ RER/ pkm',\n",
+       "       'transport, passenger car/ CH/ pkm',\n",
+       "       'transport, passenger car, diesel, EURO3/ CH/ pkm',\n",
+       "       'transport, passenger car, diesel, EURO4/ CH/ pkm',\n",
+       "       'transport, passenger car, diesel, EURO5/ CH/ pkm',\n",
+       "       'transport, passenger car, diesel, EURO5, city car/ CH/ pkm',\n",
+       "       'transport, passenger car, diesel, fleet average/ CH/ pkm',\n",
+       "       'transport, passenger car, diesel, fleet average/ RER/ pkm',\n",
+       "       'transport, passenger car, diesel, fleet average 2010/ CH/ pkm',\n",
+       "       'transport, passenger car, diesel, fleet average 2010/ RER/ pkm',\n",
+       "       'transport, passenger car, electric, LiMn2O4/ CH/ pkm',\n",
+       "       'transport, passenger car, electric, LiMn2O4, certified electricity/ CH/ pkm',\n",
+       "       'transport, passenger car, electric, LiMn2O4, city car/ CH/ pkm',\n",
+       "       'transport, passenger car, electric, LiMn2O4, city car, certified electricity/ CH/ pkm',\n",
+       "       'transport, passenger car, ethanol 5%/ CH/ pkm',\n",
+       "       'transport, passenger car, methane, 96 vol-%, from biogas/ CH/ pkm',\n",
+       "       'transport, passenger car, methanol/ CH/ pkm',\n",
+       "       'transport, passenger car, natural gas/ CH/ pkm',\n",
+       "       'transport, passenger car, petrol, 15% vol. ETBE with ethanol from biomass, EURO4/ CH/ pkm',\n",
+       "       'transport, passenger car, petrol, 4% vol. ETBE with ethanol from biomass, EURO4/ CH/ pkm',\n",
+       "       'transport, passenger car, petrol, EURO3/ CH/ pkm',\n",
+       "       'transport, passenger car, petrol, EURO4/ CH/ pkm',\n",
+       "       'transport, passenger car, petrol, EURO5/ CH/ pkm',\n",
+       "       'transport, passenger car, petrol, fleet average/ CH/ pkm',\n",
+       "       'transport, passenger car, petrol, fleet average/ RER/ pkm',\n",
+       "       'transport, passenger car, petrol, fleet average 2010/ CH/ pkm',\n",
+       "       'transport, passenger car, petrol, fleet average 2010/ RER/ pkm',\n",
+       "       'transport, passenger car, rape seed methyl ester 5%/ CH/ pkm',\n",
+       "       'transport, regular bus/ CH/ pkm', 'transport, scooter/ CH/ pkm',\n",
+       "       'transport, tram/ CH/ pkm', 'transport, trolleybus/ CH/ pkm',\n",
+       "       'transport, van <3.5t/ RER/ tkm', 'transport, van <3.5t/ CH/ tkm',\n",
+       "       'operation, barge/ RER/ tkm', 'operation, barge tanker/ RER/ tkm',\n",
+       "       'operation, transoceanic freight ship/ OCE/ tkm',\n",
+       "       'operation, transoceanic tanker/ OCE/ tkm',\n",
+       "       'transport, barge/ RER/ tkm', 'transport, barge tanker/ RER/ tkm',\n",
+       "       'transport, liquefied natural gas, freight ship/ OCE/ tkm',\n",
+       "       'transport, transoceanic freight ship/ OCE/ tkm',\n",
+       "       'transport, transoceanic tanker/ OCE/ tkm',\n",
+       "       'operation, average train/ AT/ pkm',\n",
+       "       'operation, average train/ IT/ pkm',\n",
+       "       'operation, average train/ FR/ pkm',\n",
+       "       'operation, average train/ DE/ pkm',\n",
+       "       'operation, average train/ BE/ pkm',\n",
+       "       'operation, average train, SBB mix/ CH/ pkm',\n",
+       "       'operation, coal freight train, diesel/ CN/ tkm',\n",
+       "       'operation, coal freight train, electricity/ CN/ tkm',\n",
+       "       'operation, coal freight train, steam/ CN/ tkm',\n",
+       "       'operation, freight train/ RER/ tkm',\n",
+       "       'operation, freight train/ CH/ tkm',\n",
+       "       'operation, freight train/ AT/ tkm',\n",
+       "       'operation, freight train/ IT/ tkm',\n",
+       "       'operation, freight train/ FR/ tkm',\n",
+       "       'operation, freight train/ DE/ tkm',\n",
+       "       'operation, freight train/ BE/ tkm',\n",
+       "       'operation, freight train, diesel/ RER/ tkm',\n",
+       "       'operation, freight train, diesel, with particle filter/ CH/ tkm',\n",
+       "       'operation, freight train, electricity/ RER/ tkm',\n",
+       "       'operation, high speed train/ DE/ pkm',\n",
+       "       'operation, high speed train/ FR/ pkm',\n",
+       "       'operation, high speed train/ IT/ pkm',\n",
+       "       'operation, long-distance train, SBB mix/ CH/ pkm',\n",
+       "       'operation, metropolitan train, SBB mix/ CH/ pkm',\n",
+       "       'operation, regional train, SBB mix/ CH/ pkm',\n",
+       "       'transport, average train/ AT/ pkm',\n",
+       "       'transport, average train/ IT/ pkm',\n",
+       "       'transport, average train/ FR/ pkm',\n",
+       "       'transport, average train/ DE/ pkm',\n",
+       "       'transport, average train/ BE/ pkm',\n",
+       "       'transport, average train, SBB mix/ CH/ pkm',\n",
+       "       'transport, coal freight, rail/ CN/ tkm',\n",
+       "       'transport, freight, rail/ RER/ tkm',\n",
+       "       'transport, freight, rail/ CH/ tkm',\n",
+       "       'transport, freight, rail/ AT/ tkm',\n",
+       "       'transport, freight, rail/ IT/ tkm',\n",
+       "       'transport, freight, rail/ FR/ tkm',\n",
+       "       'transport, freight, rail/ DE/ tkm',\n",
+       "       'transport, freight, rail/ BE/ tkm',\n",
+       "       'transport, freight, rail, diesel/ US/ tkm',\n",
+       "       'transport, freight, rail, diesel, with particle filter/ CH/ tkm',\n",
+       "       'transport, high speed train/ DE/ pkm',\n",
+       "       'transport, high speed train/ FR/ pkm',\n",
+       "       'transport, high speed train/ IT/ pkm',\n",
+       "       'transport, long-distance train, SBB mix/ CH/ pkm',\n",
+       "       'transport, metropolitan train, SBB mix/ CH/ pkm',\n",
+       "       'transport, regional train, SBB mix/ CH/ pkm',\n",
+       "       'air distribution housing, steel, 120 m3/h, at plant/ CH/ unit',\n",
+       "       'air filter, central unit, 600 m3/h, at plant/ RER/ unit',\n",
+       "       'air filter, decentralized unit, 180-250 m3/h, at plant/ RER/ unit',\n",
+       "       'air filter, decentralized unit, 250 m3/h, at plant/ RER/ unit',\n",
+       "       'air filter, in exhaust air valve, at plant/ RER/ unit',\n",
+       "       'connection piece, steel, 100x50 mm, at plant/ RER/ unit',\n",
+       "       'control and wiring, central unit, at plant/ RER/ unit',\n",
+       "       'control and wiring, decentralized unit, at plant/ RER/ unit',\n",
+       "       'elbow 90░, steel, 100x50 mm, at plant/ RER/ unit',\n",
+       "       'exhaust air outlet, steel/aluminum, 85x365 mm, at plant/ CH/ unit',\n",
+       "       'exhaust air roof hood, steel, DN 400, at plant/ CH/ unit',\n",
+       "       'exhaust air valve, in-wall housing, plastic/steel, DN 125, at plant/ CH/ unit',\n",
+       "       'flexible duct, aluminum/PET, DN of 125, at plant/ RER/ m',\n",
+       "       'ground heat exchanger, PE, DN 200, at plant/ RER/ m',\n",
+       "       'insulation spiral-seam duct, rockwool, DN 400, 30 mm, at plant/ RER/ m',\n",
+       "       'outside air intake, stainless steel, DN 370, at plant/ RER/ unit',\n",
+       "       'overflow element, steel, approx. 40 m3/h, at plant/ RER/ unit',\n",
+       "       'sealing tape, aluminum/PE, 50 mm wide, at plant/ RER/ m',\n",
+       "       'silencer, steel, DN 125, at plant/ CH/ unit',\n",
+       "       'silencer, steel, DN 315, 50 mm, at plant/ CH/ unit',\n",
+       "       'spiral-seam duct, steel, DN 125, at plant/ RER/ m',\n",
+       "       'spiral-seam duct, steel, DN 400, at plant/ RER/ m',\n",
+       "       'supply air inlet, steel/SS, DN 75, at plant/ RER/ unit',\n",
+       "       'ventilation duct, PE corrugated tube, DN 75, at plant/ RER/ m',\n",
+       "       'ventilation duct, steel, 100x50 mm, at plant/ RER/ m',\n",
+       "       'ventilation equipment, Avent E 97, at plant/ RER/ unit',\n",
+       "       'ventilation equipment, GE 250 RH, at plant/ CH/ unit',\n",
+       "       'ventilation equipment, KWL 250, at plant/ RER/ unit',\n",
+       "       'ventilation equipment, KWLC 1200, at plant/ RER/ unit',\n",
+       "       'ventilation equipment, Storkair G 90, at plant/ RER/ unit',\n",
+       "       'ventilation equipment, Twl-700, at plant/ RER/ unit',\n",
+       "       'ventilation equipment, central, 600-1200 m3/h, at plant/ RER/ unit',\n",
+       "       'ventilation equipment, decentralized, 180-250 m3/h, at plant/ RER/ unit',\n",
+       "       'energy reduction, ventilation system, 1 x 720 m3/h, PE ducts, with GHE/ CH/ MJ',\n",
+       "       'energy reduction, ventilation system, 1 x 720 m3/h, steel ducts, with GHE/ CH/ MJ',\n",
+       "       'energy reduction, ventilation system, 6 x 120 m3/h, PE ducts, with GHE/ CH/ MJ',\n",
+       "       'energy reduction, ventilation system, 6 x 120 m3/h, PE ducts, without GHE/ CH/ MJ',\n",
+       "       'energy reduction, ventilation system, 6 x 120 m3/h, steel ducts, with GHE/ CH/ MJ',\n",
+       "       'energy reduction, ventilation system, 6 x 120 m3/h, steel ducts, without GHE/ CH/ MJ',\n",
+       "       'ventilation of dwellings, central, 1 x 720 m3/h, PE ducts, with GHE/ CH/ m2a',\n",
+       "       'ventilation of dwellings, central, 1 x 720 m3/h, steel ducts, with GHE/ CH/ m2a',\n",
+       "       'ventilation of dwellings, decentralized, 6 x 120 m3/h, PE ducts, with GHE/ CH/ m2a',\n",
+       "       'ventilation of dwellings, decentralized, 6 x 120 m3/h, PE ducts, without GHE/ CH/ m2a',\n",
+       "       'ventilation of dwellings, decentralized, 6 x 120 m3/h, steel ducts, with GHE/ CH/ m2a',\n",
+       "       'ventilation of dwellings, decentralized, 6 x 120 m3/h, steel ducts, without GHE/ CH/ m2a',\n",
+       "       'DAS-1, fluorescent whitening agent triazinylaminostilben type, at plant/ RER/ kg',\n",
+       "       'carboxymethyl cellulose, powder, at plant/ RER/ kg',\n",
+       "       'fluorescent whitening agent distyrylbiphenyl type, at plant/ RER/ kg',\n",
+       "       'steam, for chemical processes, at plant/ RER/ kg',\n",
+       "       'sodium perborate, monohydrate, powder, at plant/ RER/ kg',\n",
+       "       'sodium perborate, tetrahydrate, powder, at plant/ RER/ kg',\n",
+       "       'sodium percarbonate, powder, at plant/ RER/ kg',\n",
+       "       'layered sodium silicate, SKS-6, powder, at plant/ RER/ kg',\n",
+       "       'polycarboxylates, 40% active substance, at plant/ RER/ kg',\n",
+       "       'sodium metasilicate pentahydrate, 58%, powder, at plant/ RER/ kg',\n",
+       "       'sodium tripolyphosphate, at plant/ RER/ kg',\n",
+       "       'zeolite, powder, at plant/ RER/ kg',\n",
+       "       'zeolite, slurry, 50% in H2O, at plant/ RER/ kg',\n",
+       "       'alkylbenzene sulfonate, linear, petrochemical, at plant/ RER/ kg',\n",
+       "       'esterquat, coconut oil and palm kernel oil, at plant/ RER/ kg',\n",
+       "       'esterquat, tallow, at plant/ RER/ kg',\n",
+       "       'ethoxylated alcohols (AE11), palm oil, at plant/ RER/ kg',\n",
+       "       'ethoxylated alcohols (AE3), coconut oil, at plant/ RER/ kg',\n",
+       "       'ethoxylated alcohols (AE3), palm kernel oil, at plant/ RER/ kg',\n",
+       "       'ethoxylated alcohols (AE3), petrochemical, at plant/ RER/ kg',\n",
+       "       'ethoxylated alcohols (AE7), coconut oil, at plant/ RER/ kg',\n",
+       "       'ethoxylated alcohols (AE7), palm kernel oil, at plant/ RER/ kg',\n",
+       "       'ethoxylated alcohols (AE7), petrochemical, at plant/ RER/ kg',\n",
+       "       'ethoxylated alcohols, unspecified, at plant/ RER/ kg',\n",
+       "       'fatty alcohol sulfate, coconut oil, at plant/ RER/ kg',\n",
+       "       'fatty alcohol sulfate, mix, at plant/ RER/ kg',\n",
+       "       'fatty alcohol sulfate, palm kernel oil, at plant/ RER/ kg',\n",
+       "       'fatty alcohol sulfate, palm oil, at plant/ RER/ kg',\n",
+       "       'fatty alcohol sulfate, petrochemical, at plant/ RER/ kg',\n",
+       "       'soap, at plant/ RER/ kg',\n",
+       "       'disposal, air distribution housing, steel, 120 m3/h/ CH/ unit',\n",
+       "       'disposal, air filter, central unit, 600 m3/h/ CH/ unit',\n",
+       "       'disposal, air filter, decentralized unit, 180-250 m3/h/ CH/ unit',\n",
+       "       'disposal, air filter, decentralized unit, 250 m3/h/ CH/ unit',\n",
+       "       'disposal, air filter, in exhaust air valve/ CH/ unit',\n",
+       "       'disposal, building, PE sealing sheet, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, PVC sealing sheet, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, bitumen sheet, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, brick, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, bulk iron (excluding reinforcement), to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, cement (in concrete) and mortar, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, cement-fibre slab, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, concrete gravel, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, concrete, not reinforced, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, door, inner, glass-wood, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, door, inner, wood, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, door, outer, wood-aluminium, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, door, outer, wood-glass, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, electric wiring, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, emulsion paint on walls, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, emulsion paint on walls, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, emulsion paint on wood, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, emulsion paint remains, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, fibre board, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, glass pane (in burnable frame), to final disposal/ CH/ kg',\n",
+       "       'disposal, building, glass pane (in burnable frame), to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, glass sheet, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, glass sheet, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, glazing 2-IV, U<1.1W/m2K, LSG, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, glazing 2-IV, U<1.1W/m2K, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, glazing 3-IV, U<0.5W/m2K, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, mineral plaster, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, mineral plaster, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, mineral wool, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, mineral wool, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, paint on metal, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, paint on metal, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, paint on walls, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, paint on walls, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, paint on wood, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, paint remains, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, plaster board, gypsum plaster, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, plaster board, gypsum plaster, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, plaster-cardboard sandwich, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, plaster-cardboard sandwich, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, plastic plaster, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, plastic plaster, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, polyethylene/polypropylene products, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, polystyrene isolation, flame-retardant, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, polyurethane foam, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, polyurethane sealing, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, polyurethane sealing, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, polyvinylchloride products, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, reinforced concrete, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, reinforced plaster board, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, reinforced plaster board, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, reinforcement steel, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, reinforcement steel, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, vapour barrier, flame-retarded, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, waste wood, chrome preserved, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, waste wood, untreated, to final disposal/ CH/ kg',\n",
+       "       'disposal, building, window frame, plastic, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, window frame, wood, to final disposal/ CH/ m2',\n",
+       "       'disposal, building, window frame, wood-metal, to final disposal/ CH/ m2',\n",
+       "       'disposal, control and wiring, central unit/ CH/ unit',\n",
+       "       'disposal, control and wiring, decentralized unit/ CH/ unit',\n",
+       "       'disposal, electronics for control units/ RER/ kg',\n",
+       "       'disposal, exhaust air roof hood, steel, DN 400/ CH/ unit',\n",
+       "       'disposal, exhaust air valve, in-wall housing, plastic/steel, DN 125/ CH/ unit',\n",
+       "       'disposal, facilities, chemical production/ RER/ kg',\n",
+       "       'disposal, flexible duct, aluminum/PET, DN of 125/ CH/ m',\n",
+       "       'disposal, insulation spiral-seam duct, rockwool, DN 400, 30 mm/ CH/ m',\n",
+       "       'disposal, outside air intake, stainless steel, DN 370/ CH/ unit',\n",
+       "       'disposal, overflow element, steel, approx. 40 m3/h/ CH/ unit',\n",
+       "       'disposal, sealing tape, aluminum/PE, 50 mm wide/ CH/ m',\n",
+       "       'disposal, silencer, steel, DN 125/ CH/ unit',\n",
+       "       'disposal, silencer, steel, DN 315, 50 mm/ CH/ unit',\n",
+       "       'disposal, ventilation equipment, Avent E 97/ CH/ unit',\n",
+       "       'disposal, ventilation equipment, GE 250 RH/ CH/ unit',\n",
+       "       'disposal, ventilation equipment, KWL 250/ CH/ unit',\n",
+       "       'disposal, ventilation equipment, KWLC 1200/ CH/ unit',\n",
+       "       'disposal, ventilation equipment, Storkair G 90/ CH/ unit',\n",
+       "       'disposal, ventilation equipment, Twl-700/ CH/ unit',\n",
+       "       'disposal, ventilation equipment, central, 600-1200 m3/h/ CH/ unit',\n",
+       "       'disposal, ventilation equipment, decentralized, 180-250 m3/h/ CH/ unit',\n",
+       "       'disposal, antifreezer liquid, 51.8% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, bilge oil, 90% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, capacitors, 0% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, catalyst for EDC production, 0% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, emulsion paint remains, 0% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, hazardous waste, 25% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, paint remains, 0% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, refinery sludge, 89.5% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, separator sludge, 90% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, solvents mixture, 16.5% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'disposal, used mineral oil, 10% water, to hazardous waste incineration/ CH/ kg',\n",
+       "       'process-specific burdens, hazardous waste incineration plant/ CH/ kg',\n",
+       "       'disposal, concrete, 5% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, emulsion paint, 0% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, glass, 0% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, gypsum, 19.4% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, inert waste, 5% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, limestone residue, 5% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, mineral wool, 0% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, natural gas pipeline, 0% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, packaging cardboard, 19.6% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, paint, 0% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, plastic plaster, 0% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, polyurethane, 0.2% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, slag from MG silicon production, 0% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, steel, 0% water, to inert material landfill/ CH/ kg',\n",
+       "       'disposal, zeolite, 5% water, to inert material landfill/ CH/ kg',\n",
+       "       'process-specific burdens, inert material landfill/ CH/ kg',\n",
+       "       'disposal, drilling waste, 71.5% water, to landfarming/ CH/ kg',\n",
+       "       'disposal, refinery sludge, 89.5% water, to landfarming/ CH/ kg',\n",
+       "       'disposal, wood ash mixture, pure, 0% water, to landfarming/ CH/ kg',\n",
+       "       'disposal, LCD module,  to municipal waste incineration/ CH/ kg',\n",
+       "       'disposal, PE sealing sheet, 4% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, PVC sealing sheet, 1.64% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, aluminium in car shredder residue, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, aluminium, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, anion exchange resin f. water, 50% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, biowaste, 60% H2O, to municipal incineration, allocation price/ CH/ kg',\n",
+       "       'disposal, biowaste, 60% H2O, to municipal incineration, future, alloc. price/ CH/ kg',\n",
+       "       'disposal, bitumen sheet, 1.5% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, building wood, chrome preserved, 20% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, cation exchange resin f. water, 50% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, cement-fibre slab, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, coatings in CRT screens,  to municipal waste incineration/ CH/ kg',\n",
+       "       'disposal, copper in car shredder residue, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, copper, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, digester sludge, to incineration, future, allocation price/ CH/ kg',\n",
+       "       'disposal, digester sludge, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, emulsion paint, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, expanded polystyrene, 5% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, glass, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, hard coal ash from stove, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, lead in car shredder residue, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, lignite ash from stove, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, municipal solid waste, 22.9% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, newspaper, 14.7% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, packaging cardboard, 19.6% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, packaging paper, 13.7% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, paint, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, paper, 11.2% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, plastic, consumer electronics, 15.3% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, plastic, industr. electronics, 15.3% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, plastics, mixture, 15.3% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, polyethylene terephtalate, 0.2% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, polyethylene, 0.4% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, polypropylene, 15.9% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, polystyrene, 0.2% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, polyurethane, 0.2% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, polyvinylchloride, 0.2% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, polyvinylfluoride, 0.2% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, raw sewage sludge, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, residues, mechanical treatment, CRT screen, in MSWI/ CH/ kg',\n",
+       "       'disposal, residues, mechanical treatment, IT accessoires, in MSWI/ CH/ kg',\n",
+       "       'disposal, residues, mechanical treatment, LCD screen, in MSWI/ CH/ kg',\n",
+       "       'disposal, residues, mechanical treatment, desktop computer, in MSWI/ CH/ kg',\n",
+       "       'disposal, residues, mechanical treatment, industrial device, in MSWI/ CH/ kg',\n",
+       "       'disposal, residues, mechanical treatment, laptop computer, in MSWI/ CH/ kg',\n",
+       "       'disposal, residues, mechanical treatment, laser printer, in MSWI/ CH/ kg',\n",
+       "       'disposal, residues, shredder fraction from manual dismantling, in MSWI/ CH/ kg',\n",
+       "       'disposal, rubber, unspecified, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, steel in car shredder residue, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, steel, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, textiles, soiled, 25% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, tin sheet, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, vapour barrier, flame-retarded, 4.5% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, wire plastic, 3.55% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, wood ash mixture, pure, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, wood pole, chrome preserved, 20% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, wood untreated, 20% water, to municipal incineration/ CH/ kg',\n",
+       "       'disposal, zinc in car shredder residue, 0% water, to municipal incineration/ CH/ kg',\n",
+       "       'electricity from waste, at municipal waste incineration plant/ CH/ kWh',\n",
+       "       'electricity, biowaste, at waste incineration plant, allocation price/ CH/ kWh',\n",
+       "       'electricity, biowaste, at waste incineration plant, future, alloc. price/ CH/ kWh',\n",
+       "       'electricity, digester sludge, at incineration plant, future, alloc. price/ CH/ kWh',\n",
+       "       'heat from waste, at municipal waste incineration plant/ CH/ MJ',\n",
+       "       'heat, biowaste, at waste incineration plant, allocation price/ CH/ MJ',\n",
+       "       'heat, biowaste, at waste incineration plant, future, allocation price/ CH/ MJ',\n",
+       "       'heat, digester sludge, at incineration plant, future, allocation price/ CH/ MJ',\n",
+       "       'process-specific burdens, municipal waste incineration/ CH/ kg',\n",
+       "       'process-specific burdens, slag compartment/ CH/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ AT/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ BA/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ CZ/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ DE/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ ES/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ FR/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ GR/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ HU/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ MK/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ PL/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ SI/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ SK/ kg',\n",
+       "       'disposal, lignite ash, 0% water, to opencast refill/ CS/ kg',\n",
+       "       'disposal, spoil from coal mining, in surface landfill/ GLO/ kg',\n",
+       "       'disposal, spoil from lignite mining, in surface landfill/ GLO/ kg',\n",
+       "       'disposal, tailings from hard coal milling, in impoundment/ GLO/ kg',\n",
+       "       'transport, municipal waste collection, lorry 21t/ CH/ tkm',\n",
+       "       'Al fraction, mechanical treatment, CRT screen, at plant/ GLO/ kg',\n",
+       "       'Al fraction, mechanical treatment, IT accessoires, at plant/ GLO/ kg',\n",
+       "       'Al fraction, mechanical treatment, LCD screen, at plant/ GLO/ kg',\n",
+       "       'Al fraction, mechanical treatment, desktop computer, at plant/ GLO/ kg',\n",
+       "       'Al fraction, mechanical treatment, industrial devices, at plant/ GLO/ kg',\n",
+       "       'Al fraction, mechanical treatment, laptop computer, at plant/ GLO/ kg',\n",
+       "       'Al fraction, mechanical treatment, printer, laser, at plant/ GLO/ kg',\n",
+       "       'Al fraction, mechanical treatment, shredder mat. of manual dismantling, at plant/ GLO/ kg',\n",
+       "       'Co powder, hydrometallurgical processing Li-ion batteries, at plant/ GLO/ kg',\n",
+       "       'Co powder, pyrometallurgical processing Li-ion batteries, at plant/ GLO/ kg',\n",
+       "       'Cu fraction, mechanical treatment, CRT screen, at plant/ GLO/ kg',\n",
+       "       'Cu fraction, mechanical treatment, IT accessoires, at plant/ GLO/ kg',\n",
+       "       'Cu fraction, mechanical treatment, LCD screen, at plant/ GLO/ kg',\n",
+       "       'Cu fraction, mechanical treatment, desktop computer, at plant/ GLO/ kg',\n",
+       "       'Cu fraction, mechanical treatment, industrial devices, at plant/ GLO/ kg',\n",
+       "       'Cu fraction, mechanical treatment, laptop computer, at plant/ GLO/ kg',\n",
+       "       'Cu fraction, mechanical treatment, printer, laser, at plant/ GLO/ kg',\n",
+       "       'Cu fraction, mechanical treatment, shredder mat. of manual dismantling, at plant/ GLO/ kg',\n",
+       "       'Disposal, power adapter, external, for laptop, to WEEE treatment/ CH/ unit',\n",
+       "       'Fe fraction, mechanical treatment, CRT screen, at plant/ GLO/ kg',\n",
+       "       'Fe fraction, mechanical treatment, IT accessoires, at plant/ GLO/ kg',\n",
+       "       'Fe fraction, mechanical treatment, LCD screen, at plant/ GLO/ kg',\n",
+       "       'Fe fraction, mechanical treatment, desktop computer, at plant/ GLO/ kg',\n",
+       "       'Fe fraction, mechanical treatment, industrial devices, at plant/ GLO/ kg',\n",
+       "       'Fe fraction, mechanical treatment, laptop computer, at plant/ GLO/ kg',\n",
+       "       'Fe fraction, mechanical treatment, printer, laser, at plant/ GLO/ kg',\n",
+       "       'Fe fraction, mechanical treatment, shredder mat. of manual dismantling, at plant/ GLO/ kg',\n",
+       "       'Li salt, hydrometallurgical processing Li-ion batteries, at plant/ GLO/ kg',\n",
+       "       'MnO2 powder, pyrometallurgical processing Li-ion batteries, at plant/ GLO/ kg',\n",
+       "       'Ni-Co-Fe residues, pyrometallurgical processing NiMH batteries, at plant/ GLO/ kg',\n",
+       "       'copper, secondary, from cable treatment, at plant/ GLO/ kg',\n",
+       "       'dismantling, CRT screen, manually, at plant/ CH/ kg',\n",
+       "       'dismantling, CRT screen, mechanically, at plant/ GLO/ kg',\n",
+       "       'dismantling, IT accessoires, mechanically, at plant/ GLO/ kg',\n",
+       "       'dismantling, LCD screen, manually, at plant/ CH/ kg',\n",
+       "       'dismantling, LCD screen, mechanically, at plant/ GLO/ kg',\n",
+       "       'dismantling, desktop computer, manually, at plant/ CH/ kg',\n",
+       "       'dismantling, desktop computer, mechanically, at plant/ GLO/ kg',\n",
+       "       'dismantling, industrial devices, manually, at plant/ CH/ kg',\n",
+       "       'dismantling, industrial devices, mechanically, at plant/ GLO/ kg',\n",
+       "       'dismantling, laptop, manually, at plant/ CH/ kg',\n",
+       "       'dismantling, laptop, mechanically, at plant/ GLO/ kg',\n",
+       "       'dismantling, printer, laser, manually, at plant/ CH/ kg',\n",
+       "       'dismantling, printer, laser, mechanically, at plant/ GLO/ kg',\n",
+       "       'dismantling, shredder fraction from manual dismantling, mechanically, at plant/ GLO/ kg',\n",
+       "       'disposal, CRT screen, 17 inches, to WEEE treatment/ CH/ unit',\n",
+       "       'disposal, LCD flat screen, 17 inches, to WEEE treatment/ CH/ unit',\n",
+       "       'disposal, Li-ions batteries, hydrometallurgical/ GLO/ kg',\n",
+       "       'disposal, Li-ions batteries, mixed technology/ GLO/ kg',\n",
+       "       'disposal, Li-ions batteries, pyrometallurgical/ GLO/ kg',\n",
+       "       'disposal, NiMH batteries/ GLO/ kg',\n",
+       "       'disposal, building, brick, to recycling/ CH/ kg',\n",
+       "       'disposal, building, brick, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, cement (in concrete) and mortar, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, cement-fibre slab, to recycling/ CH/ kg',\n",
+       "       'disposal, building, concrete gravel, to recycling/ CH/ kg',\n",
+       "       'disposal, building, concrete gravel, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, concrete, not reinforced, to recycling/ CH/ kg',\n",
+       "       'disposal, building, concrete, not reinforced, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, mineral wool, to recycling/ CH/ kg',\n",
+       "       'disposal, building, plaster board, gypsum plaster, to recycling/ CH/ kg',\n",
+       "       'disposal, building, plaster-cardboard sandwich, to recycling/ CH/ kg',\n",
+       "       'disposal, building, reinforced concrete, to recycling/ CH/ kg',\n",
+       "       'disposal, building, reinforced concrete, to sorting plant/ CH/ kg',\n",
+       "       'disposal, building, reinforced plaster board, to recycling/ CH/ kg',\n",
+       "       'disposal, building, reinforcement steel, to recycling/ CH/ kg',\n",
+       "       'disposal, desktop computer, to WEEE treatment/ CH/ unit',\n",
+       "       'disposal, fluorescent lamps/ GLO/ kg',\n",
+       "       'disposal, industrial devices, to WEEE treatment/ CH/ kg',\n",
+       "       'disposal, keyboard, standard version, to WEEE treatment/ CH/ unit',\n",
+       "       'disposal, laptop computer, to WEEE treatment/ CH/ unit',\n",
+       "       'disposal, mouse device, optical, with cable, to WEEE treatment/ CH/ unit',\n",
+       "       'disposal, printer, laser jet, b/w, to WEEE treatment/ CH/ unit',\n",
+       "       'disposal, printer, laser jet, colour, to WEEE treatment/ CH/ unit',\n",
+       "       'disposal, treatment of CRT glass/ GLO/ kg',\n",
+       "       'disposal, treatment of batteries/ GLO/ kg',\n",
+       "       'disposal, treatment of cables/ GLO/ kg',\n",
+       "       'disposal, treatment of printed wiring boards/ GLO/ kg',\n",
+       "       'electronics scrap, for precious metal recovery, at preparation plant/ GLO/ kg',\n",
+       "       'glass cullets, from fluorescent lamps treatment, at plant/ GLO/ kg',\n",
+       "       'glass cullets, funnel glass, for CRT glass production, at plant/ GLO/ kg',\n",
+       "       'glass cullets, mixed glass, for CRT glass production, at plant/ GLO/ kg',\n",
+       "       'glass cullets, panel glass, for CRT glass production, at plant/ GLO/ kg',\n",
+       "       'iron and steel, hydrometallurgical processing Li-ion batteries, at plant/ GLO/ kg',\n",
+       "       'mercury, from fluorescent lamps treatment, at plant/ GLO/ kg',\n",
+       "       'non-Fe-metals, hydrometallurgical processing Li-ion batteries, at plant/ GLO/ kg',\n",
+       "       'non-Fe-metals, pyrometallurgical processing Li-ion batteries, at plant/ GLO/ kg',\n",
+       "       'rare-earth activated phosphors, from fluorescent lamps treatment, at plant/ GLO/ kg',\n",
+       "       'secondary metals, from fluorescent lamps treatment, at plant/ GLO/ kg',\n",
+       "       'shredding, electrical and electronic scrap/ GLO/ kg',\n",
+       "       'steel, pyrometallurgical processing Li-ion batteries, at plant/ GLO/ kg',\n",
+       "       'disposal, H3PO4 purification residue, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, ash from deinking sludge, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, ash from paper prod. sludge, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, average incineration residue, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, basic oxygen furnace wastes, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, carbon SPL, Al elec.lysis, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, catalyst base CH2O production, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, catalyst base Eth.oxide prod., 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, cement, hydrated, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, decarbonising waste, 30% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, drilling waste, 71.5% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, dross from Al electrolysis, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, dust, alloyed EAF steel, 15.4% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, dust, unalloyed EAF steel, 15.4% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, filter dust Al electrolysis, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, frit for CRT tube production, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, green liquor dregs, 25% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ ES/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ PL/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ CZ/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ HR/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ SK/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ AT/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ BE/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ DE/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ FR/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ IT/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ NL/ kg',\n",
+       "       'disposal, hard coal ash, 0% water, to residual material landfill/ PT/ kg',\n",
+       "       'disposal, lead smelter slag, 0% water, to residual material landfill/ GLO/ kg',\n",
+       "       'disposal, nickel smelter slag, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, non-sulfidic overburden, off-site/ GLO/ kg',\n",
+       "       'disposal, non-sulfidic tailings, off-site/ GLO/ kg',\n",
+       "       'disposal, pollutants from rail ballast, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, redmud from bauxite digestion, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, refractory SPL, Al elec.lysis, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, residue from TiO2 prod. Cl, 56% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, residue from TiO2 prod. SO4, 30% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, residues Na-dichromate prod., 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, salt tailings potash mining, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, slag, unalloyed electr. steel, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, sludge from steel rolling, 20% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, sludge, NaCl electrolysis Hg, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, sludge, NaCl electrolysis, 0% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, sludge, pig iron production, 8.6% water, to residual material landfill/ CH/ kg',\n",
+       "       'disposal, sulfidic tailings, off-site/ GLO/ kg',\n",
+       "       'disposal, waste, Si waferprod., inorg, 9.4% water, to residual material landfill/ CH/ kg',\n",
+       "       'process-specific burdens, residual material landfill/ CH/ kg',\n",
+       "       'disposal, aluminium, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, asphalt, 0.1% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, bitumen, 1.4% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, emulsion paint, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, gypsum, 19.4% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, hard coal ash from stove, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, inert material, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, lignite ash from stove, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, municipal solid waste, 22.9% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, newspaper, 14.7% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, packaging cardboard, 19.6% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, packaging paper, 13.7% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, paint, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, paper, 11.2% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, plastic plaster, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, plastics, mixture, 15.3% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, polyethylene terephtalate, 0.2% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, polyethylene, 0.4% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, polypropylene, 15.9% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, polystyrene, 0.2% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, polyurethane, 0.2% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, polyvinylchloride, 0.2% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, refinery sludge, 89.5% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, residue from cooling tower, 30% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, sludge from pulp and paper production, 25% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, tin sheet, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, wood ash mixture, pure, 0% water, to sanitary landfill/ CH/ kg',\n",
+       "       'disposal, wood untreated, 20% water, to sanitary landfill/ CH/ kg',\n",
+       "       'process-specific burdens, sanitary landfill/ CH/ kg',\n",
+       "       'disposal, catalyst for EDC production, 0% water, to underground deposit/ DE/ kg',\n",
+       "       'disposal, catalytic converter NOx reduction, 0% water, to underground deposit/ DE/ kg',\n",
+       "       'disposal, catalytic converter for cars, 0% water, to underground deposit/ DE/ kg',\n",
+       "       'disposal, hazardous waste, 0% water, to underground deposit/ DE/ kg',\n",
+       "       'disposal, sludge from FeCl3 production, 30% water, to underground deposit/ DE/ kg',\n",
+       "       'disposal, spent activated carbon with mercury, 0% water, to underground deposit/ DE/ kg',\n",
+       "       'disposal, waste, silicon wafer production, 0% water, to underground deposit/ DE/ kg',\n",
+       "       'treatment, CRT tube production effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, LCD backlight production effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, LCD module production effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, PV cell production effluent, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, black chrome coating effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, ceramic production effluent, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, concrete production effluent, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, condensate from light oil boiler, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, fibre board production effluent, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, glass production effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, heat carrier liquid, 40% C3H8O2, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, liquid crystal production effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, lorry production effluent, to wastewater treatment, class 1/ CH/ m3',\n",
+       "       'treatment, maize starch production effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, particle board production effluent, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, pig iron production effluent, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, plywood production effluent, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, potato starch production effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, rainwater mineral oil storage, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, sewage grass refinery, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, sewage whey digestion, to wastewater treatment, class 4/ CH/ m3',\n",
+       "       'treatment, sewage, from residence, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, sewage, to wastewater treatment, class 1/ CH/ m3',\n",
+       "       'treatment, sewage, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, sewage, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, sewage, to wastewater treatment, class 4/ CH/ m3',\n",
+       "       'treatment, sewage, to wastewater treatment, class 5/ CH/ m3',\n",
+       "       'treatment, sewage, unpolluted, from residence, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, sewage, unpolluted, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, soft fibreboard production effluent, to wastewater treatment, class 3/ CH/ m3',\n",
+       "       'treatment, tube collector production effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'treatment, wafer fabrication effluent, to wastewater treatment, class 2/ CH/ m3',\n",
+       "       'tap water, at user/ RER/ kg', 'tap water, at user/ CH/ kg',\n",
+       "       'water, completely softened, at plant/ RER/ kg',\n",
+       "       'water, decarbonised, at plant/ RER/ kg',\n",
+       "       'water, deionised, at plant/ CH/ kg',\n",
+       "       'water, ultrapure, at plant/ GLO/ kg',\n",
+       "       'electricity, at wind power plant/ RER/ kWh',\n",
+       "       'electricity, at wind power plant/ CH/ kWh',\n",
+       "       'electricity, at wind power plant 2MW, offshore/ OCE/ kWh',\n",
+       "       'electricity, at wind power plant 600kW/ CH/ kWh',\n",
+       "       'electricity, at wind power plant 800kW/ CH/ kWh',\n",
+       "       'electricity, at wind power plant 800kW/ RER/ kWh',\n",
+       "       'electricity, at wind power plant Grenchenberg 150kW/ CH/ kWh',\n",
+       "       'electricity, at wind power plant Simplon 30kW/ CH/ kWh',\n",
+       "       'electricity, at cogen 6400kWth, wood, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 6400kWth, wood, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 6400kWth, wood, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen 6400kWth, wood, emission control, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen 6400kWth, wood, emission control, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen 6400kWth, wood, emission control, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen ORC 1400kWth, wood, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen ORC 1400kWth, wood, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen ORC 1400kWth, wood, allocation heat/ CH/ kWh',\n",
+       "       'electricity, at cogen ORC 1400kWth, wood, emission control, allocation energy/ CH/ kWh',\n",
+       "       'electricity, at cogen ORC 1400kWth, wood, emission control, allocation exergy/ CH/ kWh',\n",
+       "       'electricity, at cogen ORC 1400kWth, wood, emission control, allocation heat/ CH/ kWh',\n",
+       "       'heat, at cogen 6400kWth, wood, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 6400kWth, wood, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 6400kWth, wood, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen 6400kWth, wood, emission control, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen 6400kWth, wood, emission control, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen 6400kWth, wood, emission control, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen ORC 1400kWth, wood, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen ORC 1400kWth, wood, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen ORC 1400kWth, wood, allocation heat/ CH/ MJ',\n",
+       "       'heat, at cogen ORC 1400kWth, wood, emission control, allocation energy/ CH/ MJ',\n",
+       "       'heat, at cogen ORC 1400kWth, wood, emission control, allocation exergy/ CH/ MJ',\n",
+       "       'heat, at cogen ORC 1400kWth, wood, emission control, allocation heat/ CH/ MJ',\n",
+       "       'wood chips, burned in cogen 6400kWth/ CH/ MJ',\n",
+       "       'wood chips, burned in cogen 6400kWth, emission control/ CH/ MJ',\n",
+       "       'wood chips, burned in cogen ORC 1400kWth/ CH/ MJ',\n",
+       "       'wood chips, burned in cogen ORC 1400kWth, emission control/ CH/ MJ',\n",
+       "       'charcoal, at plant/ GLO/ kg',\n",
+       "       'logs, hardwood, at forest/ RER/ m3',\n",
+       "       'logs, mixed, at forest/ RER/ m3',\n",
+       "       'logs, softwood, at forest/ RER/ m3',\n",
+       "       'waste wood chips, mixed, from industry, u=40%, at plant/ CH/ m3',\n",
+       "       'wood chips, hardwood, from industry, u=40%, at plant/ RER/ m3',\n",
+       "       'wood chips, hardwood, u=80%, at forest/ RER/ m3',\n",
+       "       'wood chips, mixed, from industry, u=40%, at plant/ RER/ m3',\n",
+       "       'wood chips, mixed, u=120%, at forest/ RER/ m3',\n",
+       "       'wood chips, softwood, from industry, u=40%, at plant/ RER/ m3',\n",
+       "       'wood chips, softwood, u=140%, at forest/ RER/ m3',\n",
+       "       'wood pellets, u=10%, at storehouse/ RER/ m3',\n",
+       "       'heat, hardwood chips from forest, at furnace 1000kW/ CH/ MJ',\n",
+       "       'heat, hardwood chips from forest, at furnace 300kW/ CH/ MJ',\n",
+       "       'heat, hardwood chips from forest, at furnace 50kW/ CH/ MJ',\n",
+       "       'heat, hardwood chips from industry, at furnace 1000kW/ CH/ MJ',\n",
+       "       'heat, hardwood chips from industry, at furnace 300kW/ CH/ MJ',\n",
+       "       'heat, hardwood chips from industry, at furnace 50kW/ CH/ MJ',\n",
+       "       'heat, hardwood logs, at furnace 100kW/ CH/ MJ',\n",
+       "       'heat, hardwood logs, at furnace 30kW/ CH/ MJ',\n",
+       "       'heat, hardwood logs, at wood heater 6kW/ CH/ MJ',\n",
+       "       'heat, mixed chips from forest, at furnace 1000kW/ CH/ MJ',\n",
+       "       'heat, mixed chips from forest, at furnace 300kW/ CH/ MJ',\n",
+       "       'heat, mixed chips from forest, at furnace 50kW/ CH/ MJ',\n",
+       "       'heat, mixed chips from industry, at furnace 1000kW/ CH/ MJ',\n",
+       "       'heat, mixed chips from industry, at furnace 300kW/ CH/ MJ',\n",
+       "       'heat, mixed chips from industry, at furnace 50kW/ CH/ MJ',\n",
+       "       'heat, mixed logs, at furnace 100kW/ CH/ MJ',\n",
+       "       'heat, mixed logs, at furnace 30kW/ CH/ MJ',\n",
+       "       'heat, mixed logs, at wood heater 6kW/ CH/ MJ',\n",
+       "       'heat, softwood chips from forest, at furnace 1000kW/ CH/ MJ',\n",
+       "       'heat, softwood chips from forest, at furnace 300kW/ CH/ MJ',\n",
+       "       'heat, softwood chips from forest, at furnace 50kW/ CH/ MJ',\n",
+       "       'heat, softwood chips from industry, at furnace 1000kW/ CH/ MJ',\n",
+       "       'heat, softwood chips from industry, at furnace 300kW/ CH/ MJ',\n",
+       "       'heat, softwood chips from industry, at furnace 50kW/ CH/ MJ',\n",
+       "       'heat, softwood logs, at furnace 100kW/ CH/ MJ',\n",
+       "       'heat, softwood logs, at furnace 30kW/ CH/ MJ',\n",
+       "       'heat, softwood logs, at wood heater 6kW/ CH/ MJ',\n",
+       "       'heat, wood pellets, at furnace 15kW/ CH/ MJ',\n",
+       "       'heat, wood pellets, at furnace 50kW/ CH/ MJ',\n",
+       "       'logs, hardwood, burned in furnace 100kW/ CH/ MJ',\n",
+       "       'logs, hardwood, burned in furnace 30kW/ CH/ MJ',\n",
+       "       'logs, hardwood, burned in wood heater 6kW/ CH/ MJ',\n",
+       "       'logs, mixed, burned in furnace 100kW/ CH/ MJ',\n",
+       "       'logs, mixed, burned in furnace 30kW/ CH/ MJ',\n",
+       "       'logs, mixed, burned in wood heater 6kW/ CH/ MJ',\n",
+       "       'logs, softwood, burned in furnace 100kW/ CH/ MJ',\n",
+       "       'logs, softwood, burned in furnace 30kW/ CH/ MJ',\n",
+       "       'logs, softwood, burned in wood heater 6kW/ CH/ MJ',\n",
+       "       'pellets, mixed, burned in furnace 15kW/ CH/ MJ',\n",
+       "       'pellets, mixed, burned in furnace 50kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, hardwood, burned in furnace 1000kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, hardwood, burned in furnace 300kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, hardwood, burned in furnace 50kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, mixed, burned in furnace 1000kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, mixed, burned in furnace 300kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, mixed, burned in furnace 50kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, softwood, burned in furnace 1000kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, softwood, burned in furnace 300kW/ CH/ MJ',\n",
+       "       'wood chips, from forest, softwood, burned in furnace 50kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, hardwood, burned in furnace 1000kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, hardwood, burned in furnace 300kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, hardwood, burned in furnace 50kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, mixed, burned in furnace 1000kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, mixed, burned in furnace 300kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, mixed, burned in furnace 50kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, softwood, burned in furnace 1000kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, softwood, burned in furnace 300kW/ CH/ MJ',\n",
+       "       'wood chips, from industry, softwood, burned in furnace 50kW/ CH/ MJ',\n",
+       "       'azobe (SFM), standing, under bark, in rain forest/ CM/ m3',\n",
+       "       'bark chips, softwood, u=140%, at forest road/ RER/ m3',\n",
+       "       'bark chips, softwood, u=140%, at plant/ RER/ m3',\n",
+       "       'chips, Scandinavian softwood (plant-debarked), u=70%, at plant/ NORDEL/ m3',\n",
+       "       'diesel, burned in chopper/ RER/ MJ',\n",
+       "       'eucalyptus ssp., standing, under bark, u=50%, in plantation/ TH/ m3',\n",
+       "       'fibreboard hard, at plant/ RER/ m3',\n",
+       "       'fibreboard soft, at plant (u=7%)/ CH/ m3',\n",
+       "       'fibreboard soft, latex bonded, at plant (u=7%)/ CH/ m3',\n",
+       "       'fibreboard soft, without adhesives, at plant (u=7%)/ CH/ m3',\n",
+       "       'glued laminated timber, indoor use, at plant/ RER/ m3',\n",
+       "       'glued laminated timber, outdoor use, at plant/ RER/ m3',\n",
+       "       'hardwood, Scandinavian, standing, under bark, in forest/ NORDEL/ m3',\n",
+       "       'hardwood, allocation correction, 1/ RER/ m3',\n",
+       "       'hardwood, allocation correction, 2/ RER/ m3',\n",
+       "       'hardwood, allocation correction, 3/ RER/ m3',\n",
+       "       'hardwood, stand establishment / tending / site development, under bark/ RER/ m3',\n",
+       "       'hardwood, standing, under bark, in forest/ RER/ m3',\n",
+       "       'industrial residual wood chopping, stationary electric chopper, at plant/ RER/ kg',\n",
+       "       'industrial residue wood, 3-layered LB production, softwood, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, GLT production, indoor use, u=10%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, GLT production, outdoor use, u=10%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, LTE production, hardwood, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, LTE production, softwood, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, from planing, hard, air/kiln dried, u=10%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, from planing, hardwood, kiln dried, u=10%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, from planing, softwood, air dried, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, from planing, softwood, kiln dried, u=10%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, hardwood, including bark, air dried, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, hardwood, including bark, u=70%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, mix, hardwood, u=40%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, mix, softwood, u=40%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, plywood prod., indoor use, hardwood, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, plywood prod., outdoor use, hardwood, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, softwood, forest-debarked, air dried, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, softwood, forest-debarked, u=70%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, softwood, plant-debarked, u=70%, at plant/ RER/ m3',\n",
+       "       'industrial residue wood, wood wool production, softwood, u=20%, at plant/ RER/ m3',\n",
+       "       'industrial wood, Scandinavian hardwood, under bark, u=80%, at forest road/ NORDEL/ m3',\n",
+       "       'industrial wood, Scandinavian softwood, under bark, u=140%, at forest road/ NORDEL/ m3',\n",
+       "       'industrial wood, hardwood, under bark, u=80%, at forest road/ RER/ m3',\n",
+       "       'industrial wood, softwood, under bark, u=140%, at forest road/ RER/ m3',\n",
+       "       'laminated timber element, transversally prestressed, for outdoor use, at plant/ RER/ m3',\n",
+       "       'medium density fibreboard, at plant/ RER/ m3',\n",
+       "       'meranti, standing, under bark, u=70%, in rainforest/ MY/ m3',\n",
+       "       'oriented strand board, at plant/ RER/ m3',\n",
+       "       'paranß pine, standing, under bark, in rain forest/ BR/ m3',\n",
+       "       'particle board, cement bonded, at plant/ RER/ m3',\n",
+       "       'particle board, indoor use, at plant/ RER/ m3',\n",
+       "       'particle board, outdoor use, at plant/ RER/ m3',\n",
+       "       'plywood, indoor use, at plant/ RER/ m3',\n",
+       "       'plywood, outdoor use, at plant/ RER/ m3',\n",
+       "       'provision, stubbed land/ BR/ m2',\n",
+       "       'provision, stubbed land/ MY/ m2',\n",
+       "       'raw cork, at forest road/ RER/ kg',\n",
+       "       'residual wood, hardwood, under bark, air dried, u=20%, at forest road/ RER/ m3',\n",
+       "       'residual wood, hardwood, under bark, u=80%, at forest road/ RER/ m3',\n",
+       "       'residual wood, softwood, under bark, air dried, u=20%, at forest road/ RER/ m3',\n",
+       "       'residual wood, softwood, under bark, u=140%, at forest road/ RER/ m3',\n",
+       "       'round wood, Scandinavian softwood, under bark, u=70% at forest road/ NORDEL/ m3',\n",
+       "       'round wood, hardwood, under bark, u=70%, at forest road/ RER/ m3',\n",
+       "       'round wood, primary forest, clear-cutting, at forest road/ BR/ m3',\n",
+       "       'round wood, primary forest, clear-cutting, at forest road/ MY/ m3',\n",
+       "       'round wood, softwood, debarked, u=70% at forest road/ RER/ m3',\n",
+       "       'round wood, softwood, under bark, u=70% at forest road/ RER/ m3',\n",
+       "       'roundwood, azobe (SFM), debarked, u=30%, CM, at maritime harbour/ RER/ m3',\n",
+       "       'roundwood, azobe (SFM), under bark, u=30%, at forest road/ CM/ m3',\n",
+       "       'roundwood, eucalyptus ssp. (SFM), under bark, u=50%, at forest road/ TH/ m3',\n",
+       "       'roundwood, meranti (SFM), under bark, u=70%, at forest road/ MY/ m3',\n",
+       "       'roundwood, paranß pine (SFM), under bark, u=50%, at forest road/ BR/ m3',\n",
+       "       'sawdust, Scandinavian softwood (plant-debarked), u=70%, at plant/ NORDEL/ m3',\n",
+       "       'sawn timber, Scandinavian softwood, raw, plant-debarked, u=70%, at plant/ NORDEL/ m3',\n",
+       "       'sawn timber, hardwood, planed, air / kiln dried, u=10%, at plant/ RER/ m3',\n",
+       "       'sawn timber, hardwood, planed, kiln dried, u=10%, at plant/ RER/ m3',\n",
+       "       'sawn timber, hardwood, raw, air / kiln dried, u=10%, at plant/ RER/ m3',\n",
+       "       'sawn timber, hardwood, raw, air dried, u=20%, at plant/ RER/ m3',\n",
+       "       'sawn timber, hardwood, raw, kiln dried, u=10%, at plant/ RER/ m3',\n",
+       "       'sawn timber, hardwood, raw, plant-debarked, u=70%, at plant/ RER/ m3',\n",
+       "       'sawn timber, softwood, planed, air dried, at plant/ RER/ m3',\n",
+       "       'sawn timber, softwood, planed, kiln dried, at plant/ RER/ m3',\n",
+       "       'sawn timber, softwood, raw, air dried, u=20%, at plant/ RER/ m3',\n",
+       "       'sawn timber, softwood, raw, forest-debarked, u=70%, at plant/ RER/ m3',\n",
+       "       'sawn timber, softwood, raw, kiln dried, u=10%, at plant/ RER/ m3',\n",
+       "       'sawn timber, softwood, raw, kiln dried, u=20%, at plant/ RER/ m3',\n",
+       "       'sawn timber, softwood, raw, plant-debarked, u=70%, at plant/ RER/ m3',\n",
+       "       'softwood, Scandinavian, standing, under bark, in forest/ NORDEL/ m3',\n",
+       "       'softwood, allocation correction, 1/ RER/ m3',\n",
+       "       'softwood, allocation correction, 2/ RER/ m3',\n",
+       "       'softwood, allocation correction, 3/ RER/ m3',\n",
+       "       'softwood, stand establishment / tending / site development, under bark/ RER/ m3',\n",
+       "       'softwood, standing, under bark, in forest/ RER/ m3',\n",
+       "       'three layered laminated board, at plant/ RER/ m3',\n",
+       "       'wood chopping, mobile chopper, in forest/ RER/ kg',\n",
+       "       'wood wool boards, cement bonded, at plant/ RER/ m3',\n",
+       "       'wood wool, u=20%, at plant/ RER/ kg',\n",
+       "       'wood, cork oak, under bark, u=70%, at forest road/ RER/ m3',\n",
+       "       'EUR-flat pallet/ RER/ unit',\n",
+       "       'azobe, allocation correction 1/ GLO/ m3',\n",
+       "       'azobe, allocation correction 2/ GLO/ m3',\n",
+       "       'industrial residual wood, azobe (SFM), u=15%, CM, at sawmill/ RER/ m3',\n",
+       "       'industrial residual wood, paranß pine (SFM), u=15%, at sawmill/ BR/ m3',\n",
+       "       'paranß pine, allocation correction 1/ GLO/ m3',\n",
+       "       'paranß pine, allocation correction 2/ GLO/ m3',\n",
+       "       'roundwood, meranti (SFM), debarked, u=70%, MY, at maritime harbour/ RER/ m3',\n",
+       "       'sawn timber (SFM), azobe, planed, air dried, u=15%, CM, at sawmill/ RER/ m3',\n",
+       "       'sawn timber, paranß pine (SFM), kiln dried, u=15%, at sawmill/ BR/ m3',\n",
+       "       'sawn timber, paranß pine (SFM), u=15%, BR, at maritime harbour/ RER/ m3',\n",
+       "       'preservative treatment, logs, pressure vessel/ RER/ m3',\n",
+       "       'preservative treatment, sawn timber, pressure vessel/ RER/ m3',\n",
+       "       'compost plant, open/ CH/ unit',\n",
+       "       'dried roughage store, air dried, solar/ CH/ m3',\n",
+       "       'dried roughage store, cold-air dried, conventional/ CH/ m3',\n",
+       "       'dried roughage store, non ventilated/ CH/ m3',\n",
+       "       'dung slab/ CH/ m2',\n",
+       "       'housing system with fully-slatted floor, pig/ CH/ pig place',\n",
+       "       'label housing system, pig/ CH/ pig place',\n",
+       "       'loose housing system, cattle/ CH/ LU',\n",
+       "       'milking parlour/ CH/ unit', 'shed/ CH/ m2',\n",
+       "       'slurry store and processing/ CH/ m3',\n",
+       "       'tied housing system, cattle/ CH/ LU',\n",
+       "       'tower silo, plastic/ CH/ m3',\n",
+       "       'agricultural machinery, general, production/ CH/ kg',\n",
+       "       'agricultural machinery, tillage, production/ CH/ kg',\n",
+       "       'harvester, production/ CH/ kg',\n",
+       "       'slurry tanker, production/ CH/ kg', 'tractor, production/ CH/ kg',\n",
+       "       'trailer, production/ CH/ kg',\n",
+       "       'stirling cogen unit 3kWe, wood pellets, future/ CH/ unit',\n",
+       "       'anaerobic digestion plant covered, agriculture/ CH/ unit',\n",
+       "       'anaerobic digestion plant, agriculture/ CH/ unit',\n",
+       "       'anaerobic digestion plant, biowaste/ CH/ unit',\n",
+       "       'anaerobic digestion plant, sewage sludge/ CH/ unit',\n",
+       "       'ethanol fermentation plant/ CH/ unit', 'oil mill/ CH/ unit',\n",
+       "       'synthetic gas plant/ CH/ unit',\n",
+       "       'vegetable oil esterification plant/ CH/ unit',\n",
+       "       'air separation plant/ RER/ unit', 'phosphate rock mine/ US/ unit',\n",
+       "       'phosphate rock mine/ MA/ unit',\n",
+       "       'phosphoric acid plant, fertiliser grade/ US/ unit',\n",
+       "       'silicone plant/ RER/ unit',\n",
+       "       'storage building, chemicals, solid/ CH/ unit',\n",
+       "       'chemical plant, organics/ RER/ unit',\n",
+       "       'liquid storage tank, chemicals, organics/ CH/ unit',\n",
+       "       'methanol plant/ GLO/ unit', 'mine, bentonite/ DE/ unit',\n",
+       "       'mine, clay/ CH/ unit', 'mine, gravel/sand/ CH/ unit',\n",
+       "       'mine, limestone/ CH/ unit', 'mine, vermiculite/ ZA/ unit',\n",
+       "       'cement plant/ CH/ unit', 'ceramic plant/ CH/ unit',\n",
+       "       'concrete mixing plant/ CH/ unit',\n",
+       "       'explosive production plant/ CH/ unit', 'building, hall/ CH/ m2',\n",
+       "       'building, hall, steel construction/ CH/ m2',\n",
+       "       'building, hall, wood construction/ CH/ m2',\n",
+       "       'building, multi-storey/ RER/ m3',\n",
+       "       'facilities, chemical production/ RER/ kg',\n",
+       "       'hydraulic digger/ RER/ unit', 'building machine/ RER/ unit',\n",
+       "       'conveyor belt, at plant/ RER/ m',\n",
+       "       'industrial machine, heavy, unspecified, at plant/ RER/ kg',\n",
+       "       'power saw, with catalytic converter/ RER/ unit',\n",
+       "       'power saw, without catalytic converter/ RER/ unit',\n",
+       "       'absorption chiller 100kW/ CH/ unit',\n",
+       "       'distribution network, electricity, low voltage/ CH/ km',\n",
+       "       'transmission network, electricity, high voltage/ CH/ km',\n",
+       "       'transmission network, electricity, medium voltage/ CH/ km',\n",
+       "       'transmission network, long-distance/ UCTE/ km',\n",
+       "       'electronic component machinery, unspecified/ GLO/ unit',\n",
+       "       'electronic component production plant/ GLO/ unit',\n",
+       "       'network access devices, internet, at user/ CH/ unit',\n",
+       "       'router, IP network, at server/ CH/ unit',\n",
+       "       'printed wiring board mounting facilities, SMT type/ GLO/ unit',\n",
+       "       'printed wiring board mounting facilities, THT type/ GLO/ unit',\n",
+       "       'printed wiring board mounting plant/ GLO/ unit',\n",
+       "       'sugar refinery/ GLO/ unit', 'flat glass plant/ RER/ unit',\n",
+       "       'glass etching plant/ DK/ unit', 'glass tube plant/ DE/ unit',\n",
+       "       'glass production site/ RER/ unit',\n",
+       "       'glass sorting site/ RER/ unit', 'coal stove, 5-15 kW/ RER/ unit',\n",
+       "       'industrial furnace, coal, 1-10 MW/ RER/ unit',\n",
+       "       'hard coal power plant/ RER/ unit',\n",
+       "       'hard coal power plant/ CN/ unit',\n",
+       "       'hard coal power plant, 100MW/ GLO/ unit',\n",
+       "       'hard coal power plant, 500MW/ GLO/ unit',\n",
+       "       'hard coal briquettes production plant/ RER/ unit',\n",
+       "       'hard coal coke production plant/ RER/ unit',\n",
+       "       'open cast mine, hard coal/ GLO/ unit',\n",
+       "       'underground mine, hard coal/ GLO/ unit',\n",
+       "       'underground mine, hard coal/ CN/ unit',\n",
+       "       'borehole heat exchanger 150 m/ CH/ unit',\n",
+       "       'diffusion absorption heat pump 4kW, future/ CH/ unit',\n",
+       "       'heat distribution, hydronic radiant floor heating, 150m2/ CH/ unit',\n",
+       "       'heat pump 30kW/ RER/ unit',\n",
+       "       'heat pump, brine-water, 10kW/ CH/ unit',\n",
+       "       'reservoir hydropower plant/ CH/ unit',\n",
+       "       'reservoir hydropower plant, alpine region/ RER/ unit',\n",
+       "       'reservoir hydropower plant, non alpine regions/ RER/ unit',\n",
+       "       'run-of-river hydropower plant/ CH/ unit',\n",
+       "       'run-of-river hydropower plant/ RER/ unit',\n",
+       "       'foam glass plant/ BE/ unit', 'rock wool plant/ CH/ unit',\n",
+       "       'tube insulation plant/ DE/ unit',\n",
+       "       'lignite power plant/ RER/ unit',\n",
+       "       'lignite briquettes production plant/ RER/ unit',\n",
+       "       'lignite dust production plant/ RER/ unit',\n",
+       "       'open cast mine, lignite/ RER/ unit',\n",
+       "       'open cast mine, peat/ NORDEL/ unit',\n",
+       "       'air compressor, screw-type compressor, 300 kW, at plant/ RER/ unit',\n",
+       "       'air compressor, screw-type compressor, 4 kW, at plant/ RER/ unit',\n",
+       "       'aluminium casting, plant/ RER/ unit',\n",
+       "       'aluminium electrolysis, plant/ RER/ unit',\n",
+       "       'aluminium hydroxide, plant/ RER/ unit',\n",
+       "       'aluminium melting furnace/ RER/ unit',\n",
+       "       'aluminium oxide, plant/ RER/ unit', 'anode plant/ RER/ unit',\n",
+       "       'blast furnace/ RER/ unit',\n",
+       "       'blast oxygen furnace converter/ RER/ unit',\n",
+       "       'electric arc furnace converter/ RER/ unit',\n",
+       "       'facilities anode refinery, secondary copper/ SE/ unit',\n",
+       "       'facilities blister-copper conversion, secondary copper/ SE/ unit',\n",
+       "       'facilities precious metal refinery/ SE/ unit',\n",
+       "       'magnesium plant/ RER/ unit', 'mine, bauxite/ GLO/ unit',\n",
+       "       'mine, gold/ CA/ unit', 'mine, gold/ US/ unit',\n",
+       "       'mine, gold/ ZA/ unit', 'mine, gold/ TZ/ unit',\n",
+       "       'mine, gold/ AU/ unit', 'mine, gold and silver/ PG/ unit',\n",
+       "       'mine, gold and silver/ PE/ unit',\n",
+       "       'mine, gold and silver/ CL/ unit',\n",
+       "       'mine, gold-silver-zinc-lead-copper/ SE/ unit',\n",
+       "       'mine, iron/ GLO/ unit',\n",
+       "       'non-ferrous metal mine, surface/ GLO/ unit',\n",
+       "       'non-ferrous metal mine, underground/ GLO/ unit',\n",
+       "       'non-ferrous metal smelter/ GLO/ unit',\n",
+       "       'scrap preparation plant/ RER/ unit',\n",
+       "       'metal working factory/ RER/ unit',\n",
+       "       'metal working machine, unspecified, at plant/ RER/ kg',\n",
+       "       'rolling mill/ RER/ unit', 'metal coating plant/ RER/ unit',\n",
+       "       'solder production plant/ RER/ unit',\n",
+       "       'Mini CHP plant, common components for heat+electricity/ CH/ unit',\n",
+       "       'Mini CHP plant, components for electricity only/ CH/ unit',\n",
+       "       'Mini CHP plant, components for heat only/ CH/ unit',\n",
+       "       'PEM fuel cell 2kWe, future/ CH/ unit',\n",
+       "       'SOFC fuel cell 125kWe, future/ CH/ unit',\n",
+       "       'SOFC-GT fuel cell 180kWe, future/ CH/ unit',\n",
+       "       'assembly, generator and motor, Mini CHP plant/ CH/ unit',\n",
+       "       'assembly, generator and motor, cogen unit 160kWe/ RER/ unit',\n",
+       "       'assembly, module cogen unit 160kWe/ RER/ unit',\n",
+       "       'catalytic converter, three-way, 19.1 litre/ RER/ unit',\n",
+       "       'catalytic converter, three-way, Mini CHP plant/ CH/ unit',\n",
+       "       'cogen unit 160kWe, common components for heat+electricity/ RER/ unit',\n",
+       "       'cogen unit 160kWe, components for electricity only/ RER/ unit',\n",
+       "       'cogen unit 160kWe, components for heat only/ RER/ unit',\n",
+       "       'cogen unit 1MWe, common components for heat+electricity/ RER/ unit',\n",
+       "       'cogen unit 1MWe, components for electricity only/ RER/ unit',\n",
+       "       'cogen unit 1MWe, components for heat only/ RER/ unit',\n",
+       "       'cogen unit 200kWe, common components for heat+electricity/ RER/ unit',\n",
+       "       'cogen unit 200kWe, components for electricity only/ RER/ unit',\n",
+       "       'cogen unit 200kWe, components for heat only/ RER/ unit',\n",
+       "       'cogen unit 500kWe, common components for heat+electricity/ RER/ unit',\n",
+       "       'cogen unit 500kWe, components for electricity only/ RER/ unit',\n",
+       "       'cogen unit 500kWe, components for heat only/ RER/ unit',\n",
+       "       'cogen unit 50kWe, common components for heat+electricity/ RER/ unit',\n",
+       "       'cogen unit 50kWe, components for electricity only/ RER/ unit',\n",
+       "       'cogen unit 50kWe, components for heat only/ RER/ unit',\n",
+       "       'construction work, cogen unit 160kWe/ RER/ unit',\n",
+       "       'control cabinet cogen unit 160kWe/ RER/ unit',\n",
+       "       'electric parts of Mini CHP plant/ CH/ unit',\n",
+       "       'electric parts of cogen unit 160kWe/ RER/ unit',\n",
+       "       'gas motor 206kW/ RER/ unit', 'gas motor Mini CHP plant/ CH/ unit',\n",
+       "       'generator 200kWe/ RER/ unit',\n",
+       "       'generator Mini CHP plant/ CH/ unit',\n",
+       "       'heat exchanger of Mini CHP plant/ CH/ unit',\n",
+       "       'heat exchanger of cogen unit 160kWe/ RER/ unit',\n",
+       "       'heating, sanitary equipment Mini CHP plant/ CH/ unit',\n",
+       "       'heating, sanitary equipment cogen unit 160kWe/ RER/ unit',\n",
+       "       'micro gas turbine 100kWe/ CH/ unit',\n",
+       "       'operation start, cogen unit 160kWe/ RER/ unit',\n",
+       "       'planning, cogen unit 160kWe/ RER/ unit',\n",
+       "       'planning, cogen unit Mini CHP plant/ CH/ unit',\n",
+       "       'sound insulation cogen unit 160kWe/ RER/ unit',\n",
+       "       'stack PEM fuel cell 2kWe, future/ CH/ unit',\n",
+       "       'stack SOFC fuel cell 125kWe, future/ CH/ unit',\n",
+       "       \"storage 10'000 l/ RER/ unit\",\n",
+       "       'storage 650 l Mini CHP plant/ CH/ unit',\n",
+       "       'supply air input/spent air output cogen unit 160kWe/ RER/ unit',\n",
+       "       'natural gas service station/ CH/ unit',\n",
+       "       'pipeline, natural gas, high pressure distribution network/ CH/ km',\n",
+       "       'pipeline, natural gas, high pressure distribution network/ RER/ km',\n",
+       "       'pipeline, natural gas, low pressure distribution network/ CH/ km',\n",
+       "       'gas boiler/ RER/ unit',\n",
+       "       'industrial furnace, natural gas/ RER/ unit',\n",
+       "       'gas combined cycle power plant, 400MWe/ RER/ unit',\n",
+       "       'gas power plant, 100MWe/ RER/ unit',\n",
+       "       'gas power plant, 300MWe/ GLO/ unit',\n",
+       "       'gas turbine, 10MWe, at production plant/ RER/ unit',\n",
+       "       'pipeline, natural gas, long distance, high capacity, offshore/ GLO/ km',\n",
+       "       'pipeline, natural gas, long distance, high capacity, onshore/ GLO/ km',\n",
+       "       'pipeline, natural gas, long distance, low capacity, onshore/ GLO/ km',\n",
+       "       'plant offshore, natural gas, production/ OCE/ unit',\n",
+       "       'plant onshore, natural gas, production/ GLO/ unit',\n",
+       "       'production plant, natural gas/ GLO/ unit',\n",
+       "       'nuclear power plant, boiling water reactor 1000MW/ CH/ unit',\n",
+       "       'nuclear power plant, boiling water reactor 1000MW/ DE/ unit',\n",
+       "       'nuclear power plant, boiling water reactor 1000MW/ UCTE/ unit',\n",
+       "       'nuclear power plant, boiling water reactor 1000MW/ US/ unit',\n",
+       "       'nuclear power plant, pressure water reactor 1000MW/ CH/ unit',\n",
+       "       'nuclear power plant, pressure water reactor 1000MW/ DE/ unit',\n",
+       "       'nuclear power plant, pressure water reactor 1000MW/ FR/ unit',\n",
+       "       'nuclear power plant, pressure water reactor 1000MW/ UCTE/ unit',\n",
+       "       'nuclear power plant, pressure water reactor 1000MW/ US/ unit',\n",
+       "       'nuclear power plant, pressure water reactor 1000MW/ CN/ unit',\n",
+       "       'uranium conversion plant/ CN/ unit',\n",
+       "       'nuclear fuel fabrication plant/ GLO/ unit',\n",
+       "       'nuclear fuel fabrication plant/ US/ unit',\n",
+       "       'nuclear fuel fabrication plant/ CN/ unit',\n",
+       "       'uranium conversion plant/ US/ unit',\n",
+       "       'uranium enrichment centrifuge plant/ GLO/ unit',\n",
+       "       'uranium enrichment centrifuge plant/ RU/ unit',\n",
+       "       'uranium enrichment centrifuge plant/ CN/ unit',\n",
+       "       'uranium enrichment diffusion plant/ US/ unit',\n",
+       "       'uranium mill/ US/ unit', 'uranium open pit mine/ RNA/ unit',\n",
+       "       'uranium underground mine/ RNA/ unit',\n",
+       "       'final repository for nuclear waste LLW/ CH/ unit',\n",
+       "       'final repository for nuclear waste SF, HLW, and ILW/ CH/ unit',\n",
+       "       'interim storage, nuclear waste to dispose in final repository LLW/ CH/ unit',\n",
+       "       'interim storage, nuclear waste to dispose in final repository SF, HLW, and ILW/ CH/ unit',\n",
+       "       'nuclear spent fuel conditioning plant/ CH/ unit',\n",
+       "       'nuclear spent fuel conditioning plant/ CN/ unit',\n",
+       "       'nuclear spent fuel reprocessing plant/ RER/ unit',\n",
+       "       'catalytic converter, SCR, 200 litre/ RER/ unit',\n",
+       "       'catalytic converter, oxidation, 20 litre/ RER/ unit',\n",
+       "       'cogen unit 200kWe diesel SCR, common components for heat+electricity/ RER/ unit',\n",
+       "       'cogen unit 200kWe diesel SCR, components for electricity only/ RER/ unit',\n",
+       "       'cogen unit 200kWe diesel SCR, components for heat only/ RER/ unit',\n",
+       "       'refinery/ RER/ unit', 'chimney/ CH/ m',\n",
+       "       'industrial furnace 1MW, oil/ CH/ unit',\n",
+       "       'oil boiler 100kW/ CH/ unit', 'oil boiler 10kW/ CH/ unit',\n",
+       "       'oil storage 3000l/ CH/ unit', 'oil power plant 500MW/ RER/ unit',\n",
+       "       'diesel-electric generating set production 10MW/ RER/ unit',\n",
+       "       'pipeline, crude oil, offshore/ OCE/ km',\n",
+       "       'pipeline, crude oil, onshore/ RER/ km',\n",
+       "       'platform, crude oil, offshore/ OCE/ unit',\n",
+       "       'production plant crude oil, onshore/ GLO/ unit',\n",
+       "       'regional distribution, oil products/ RER/ unit',\n",
+       "       'well for exploration and production, offshore/ OCE/ m',\n",
+       "       'well for exploration and production, onshore/ GLO/ m',\n",
+       "       'packaging box production unit/ RER/ unit',\n",
+       "       'paper machine, at paper mill/ RER/ unit',\n",
+       "       'paper mill, integrated/ RER/ unit',\n",
+       "       'paper mill, non-integrated/ RER/ unit', 'pulp plant/ RER/ unit',\n",
+       "       'waste paper sorting plant/ RER/ unit',\n",
+       "       '3kWp facade installation, multi-Si, laminated, integrated, at building/ CH/ unit',\n",
+       "       '3kWp facade installation, multi-Si, panel, mounted, at building/ CH/ unit',\n",
+       "       '3kWp facade installation, single-Si, laminated, integrated, at building/ CH/ unit',\n",
+       "       '3kWp facade installation, single-Si, panel, mounted, at building/ CH/ unit',\n",
+       "       '3kWp flat roof installation, multi-Si, on roof/ CH/ unit',\n",
+       "       '3kWp flat roof installation, single-Si, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, CIS, panel, mounted, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, CdTe, laminated, integrated, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, a-Si, laminated, integrated, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, a-Si, panel, mounted, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, multi-Si, laminated, integrated, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, multi-Si, panel, mounted, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, ribbon-Si, laminated, integrated, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, ribbon-Si, panel, mounted, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, single-Si, laminated, integrated, on roof/ CH/ unit',\n",
+       "       '3kWp slanted-roof installation, single-Si, panel, mounted, on roof/ CH/ unit',\n",
+       "       'electric installation, photovoltaic plant, at plant/ CH/ unit',\n",
+       "       'facade construction, integrated, at building/ RER/ m2',\n",
+       "       'facade construction, mounted, at building/ RER/ m2',\n",
+       "       'flat roof construction, on roof/ RER/ m2',\n",
+       "       'inverter, 2500W, at plant/ RER/ unit',\n",
+       "       'inverter, 500W, at plant/ RER/ unit',\n",
+       "       'inverter, 500kW, at plant/ RER/ unit',\n",
+       "       'photovoltaic cell factory/ DE/ unit',\n",
+       "       'photovoltaic laminate, CIS, at plant/ DE/ m2',\n",
+       "       'photovoltaic laminate, CdTe, at plant/ US/ m2',\n",
+       "       'photovoltaic laminate, CdTe, at plant/ DE/ m2',\n",
+       "       'photovoltaic laminate, CdTe, mix, at regional storage/ RER/ m2',\n",
+       "       'photovoltaic laminate, a-Si, at plant/ US/ m2',\n",
+       "       'photovoltaic laminate, multi-Si, at plant/ RER/ m2',\n",
+       "       'photovoltaic laminate, ribbon-Si, at plant/ RER/ m2',\n",
+       "       'photovoltaic laminate, single-Si, at plant/ RER/ m2',\n",
+       "       'photovoltaic panel factory/ GLO/ unit',\n",
+       "       'photovoltaic panel, CIS, at plant/ DE/ m2',\n",
+       "       'photovoltaic panel, a-Si, at plant/ US/ m2',\n",
+       "       'photovoltaic panel, multi-Si, at plant/ RER/ m2',\n",
+       "       'photovoltaic panel, ribbon-Si, at plant/ RER/ m2',\n",
+       "       'photovoltaic panel, single-Si, at plant/ RER/ m2',\n",
+       "       'slanted-roof construction, integrated, on roof/ RER/ m2',\n",
+       "       'slanted-roof construction, mounted, on roof/ RER/ m2',\n",
+       "       'wafer factory/ DE/ unit',\n",
+       "       'auxiliary heating, electric, 5kW, at plant/ CH/ unit',\n",
+       "       'evacuated tube collector, at plant/ GB/ m2',\n",
+       "       'expansion vessel 25l, at plant/ CH/ unit',\n",
+       "       'expansion vessel 80l, at plant/ CH/ unit',\n",
+       "       'flat plate collector, at plant/ CH/ m2',\n",
+       "       'heat storage 2000l, at plant/ CH/ unit',\n",
+       "       'hot water tank 600l, at plant/ CH/ unit',\n",
+       "       'hot water tank factory/ CH/ unit', 'pump 40W, at plant/ CH/ unit',\n",
+       "       'solar collector factory/ RER/ unit',\n",
+       "       'solar system with evacuated tube collector, one-family house, combined system/ CH/ unit',\n",
+       "       'solar system, flat plate collector, multiple dwelling, hot water/ CH/ unit',\n",
+       "       'solar system, flat plate collector, one-family house, combined system/ CH/ unit',\n",
+       "       'solar system, flat plate collector, one-family house, hot water/ CH/ unit',\n",
+       "       'aircraft, freight/ RER/ unit', 'aircraft, long haul/ RER/ unit',\n",
+       "       'aircraft, medium haul/ RER/ unit',\n",
+       "       'aircraft, passenger/ RER/ unit', 'airport/ RER/ unit',\n",
+       "       'disposal, airport/ RER/ unit', 'helicopter/ GLO/ unit',\n",
+       "       'bicycle, at regional storage/ RER/ unit', 'bus/ RER/ unit',\n",
+       "       'disposal, bicycle/ CH/ unit', 'disposal, bus/ CH/ unit',\n",
+       "       'disposal, electric bicycle/ CH/ unit',\n",
+       "       'disposal, electric scooter/ CH/ unit',\n",
+       "       'disposal, electric vehicle, LiMn2O4/ RER/ unit',\n",
+       "       'disposal, lorry 16t/ CH/ unit', 'disposal, lorry 28t/ CH/ unit',\n",
+       "       'disposal, lorry 40t/ CH/ unit',\n",
+       "       'disposal, passenger car/ RER/ unit',\n",
+       "       'disposal, passenger car, diesel EURO5, city car/ RER/ unit',\n",
+       "       'disposal, passenger car, electric, LiMn2O4, city car/ RER/ unit',\n",
+       "       'disposal, road/ RER/ ma', 'disposal, scooter/ CH/ unit',\n",
+       "       'disposal, tram/ CH/ unit', 'disposal, tram track/ CH/ ma',\n",
+       "       'disposal, van < 3.5t/ CH/ unit',\n",
+       "       'electric bicycle, at regional storage/ RER/ unit',\n",
+       "       'electric scooter, at regional storage/ RER/ unit',\n",
+       "       'lorry 16t/ RER/ unit', 'lorry 28t/ RER/ unit',\n",
+       "       'lorry 40t/ RER/ unit', 'maintenance, bicycle/ CH/ unit',\n",
+       "       'maintenance, bus/ CH/ unit',\n",
+       "       'maintenance, electric bicycle/ CH/ unit',\n",
+       "       'maintenance, electric scooter/ CH/ unit',\n",
+       "       'maintenance, electric vehicle, LiMn2O4/ RER/ unit',\n",
+       "       'maintenance, lorry 16t/ CH/ unit',\n",
+       "       'maintenance, lorry 28t/ CH/ unit',\n",
+       "       'maintenance, lorry 40t/ CH/ unit',\n",
+       "       'maintenance, passenger car/ RER/ unit',\n",
+       "       'maintenance, passenger car, diesel, EURO5, city car/ RER/ unit',\n",
+       "       'maintenance, passenger car, electric, LiMn2O4, city car/ RER/ unit',\n",
+       "       'maintenance, scooter/ CH/ unit', 'maintenance, tram/ CH/ unit',\n",
+       "       'maintenance, van < 3.5t/ RER/ unit',\n",
+       "       'operation, maintenance, road/ CH/ ma',\n",
+       "       'operation, tram track/ CH/ ma', 'passenger car/ RER/ unit',\n",
+       "       'passenger car, diesel, EURO5, city car, at plant/ RER/ unit',\n",
+       "       'passenger car, electric, LiMn2O4, at plant/ RER/ unit',\n",
+       "       'passenger car, electric, LiMn2O4, city car, at plant/ RER/ unit',\n",
+       "       'road/ CH/ ma', 'road vehicle plant/ RER/ unit',\n",
+       "       'roads, company, internal/ CH/ m2a',\n",
+       "       'scooter, ICE, at regional storage/ RER/ unit', 'tram/ RER/ unit',\n",
+       "       'tram track/ CH/ ma', 'van <3.5t/ RER/ unit', 'barge/ RER/ unit',\n",
+       "       'barge tanker/ RER/ unit', 'canal/ RER/ ma',\n",
+       "       'maintenance, barge/ RER/ unit',\n",
+       "       'maintenance, operation, canal/ RER/ ma',\n",
+       "       'maintenance, transoceanic freight ship/ RER/ unit',\n",
+       "       'operation, maintenance, port/ RER/ unit',\n",
+       "       'port facilities/ RER/ unit',\n",
+       "       'transoceanic freight ship/ OCE/ unit',\n",
+       "       'transoceanic tanker/ OCE/ unit', 'ICE/ DE/ unit',\n",
+       "       'disposal, ICE/ DE/ unit', 'disposal, locomotive/ RER/ unit',\n",
+       "       'disposal, long-distance train/ CH/ unit',\n",
+       "       'disposal, railway track/ CH/ ma',\n",
+       "       'disposal, regional train/ CH/ unit', 'goods wagon/ RER/ unit',\n",
+       "       'locomotive/ RER/ unit', 'long-distance train/ CH/ unit',\n",
+       "       'maintenance, ICE/ DE/ unit',\n",
+       "       'maintenance, goods wagon/ RER/ unit',\n",
+       "       'maintenance, locomotive/ RER/ unit',\n",
+       "       'maintenance, long-distance train/ CH/ unit',\n",
+       "       'maintenance, regional train/ CH/ unit',\n",
+       "       'operation, maintenance, railway track/ CH/ ma',\n",
+       "       'operation, maintenance, railway track, ICE/ DE/ ma',\n",
+       "       'railway track/ CH/ ma', 'railway track, ICE/ DE/ ma',\n",
+       "       'regional train/ CH/ unit',\n",
+       "       'plastics processing factory/ RER/ unit',\n",
+       "       'ventilation components factory/ RER/ unit',\n",
+       "       'ventilation system, central, 1 x 720 m3/h, PE ducts, with GHE/ CH/ unit',\n",
+       "       'ventilation system, central, 1 x 720 m3/h, steel ducts, with GHE/ CH/ unit',\n",
+       "       'ventilation system, decentralized, 6 x 120 m3/h, PE ducts, with GHE/ CH/ unit',\n",
+       "       'ventilation system, decentralized, 6 x 120 m3/h, PE ducts, without GHE/ CH/ unit',\n",
+       "       'ventilation system, decentralized, 6 x 120 m3/h, steel ducts, with GHE/ CH/ unit',\n",
+       "       'ventilation system, decentralized, 6 x 120 m3/h, steel ducts, without GHE/ CH/ unit',\n",
+       "       'hazardous waste incineration plant/ CH/ unit',\n",
+       "       'inert material landfill facility/ CH/ unit',\n",
+       "       'municipal waste incineration plant/ CH/ unit',\n",
+       "       'slag compartment/ CH/ unit',\n",
+       "       'lorry 21t, municipal waste collection/ CH/ unit',\n",
+       "       'sorting plant for construction waste/ CH/ unit',\n",
+       "       'facilities for mechanical treatment of WEEE scrap/ GLO/ unit',\n",
+       "       'manual treatment plant, WEEE scrap/ GLO/ unit',\n",
+       "       'mechanical treatment plant, WEEE scrap/ GLO/ unit',\n",
+       "       'residual material landfill facility/ CH/ unit',\n",
+       "       'sanitary landfill facility/ CH/ unit',\n",
+       "       'residential sewer grid/ CH/ km', 'sewer grid, class 1/ CH/ km',\n",
+       "       'sewer grid, class 2/ CH/ km', 'sewer grid, class 3/ CH/ km',\n",
+       "       'sewer grid, class 4/ CH/ km', 'sewer grid, class 5/ CH/ km',\n",
+       "       'wastewater treatment plant, class 1/ CH/ unit',\n",
+       "       'wastewater treatment plant, class 2/ CH/ unit',\n",
+       "       'wastewater treatment plant, class 3/ CH/ unit',\n",
+       "       'wastewater treatment plant, class 4/ CH/ unit',\n",
+       "       'wastewater treatment plant, class 5/ CH/ unit',\n",
+       "       'pump station/ CH/ unit', 'water storage/ CH/ unit',\n",
+       "       'water supply network/ CH/ km',\n",
+       "       'water treatment plant, deionisation/ CH/ unit',\n",
+       "       'water works/ CH/ unit',\n",
+       "       'wind power plant 150kW, fixed parts/ CH/ unit',\n",
+       "       'wind power plant 150kW, moving parts/ CH/ unit',\n",
+       "       'wind power plant 2MW, offshore, fixed parts/ OCE/ unit',\n",
+       "       'wind power plant 2MW, offshore, moving parts/ OCE/ unit',\n",
+       "       'wind power plant 30kW, fixed parts/ CH/ unit',\n",
+       "       'wind power plant 30kW, moving parts/ CH/ unit',\n",
+       "       'wind power plant 600kW, fixed parts/ CH/ unit',\n",
+       "       'wind power plant 600kW, moving parts/ CH/ unit',\n",
+       "       'wind power plant 800kW, fixed parts/ CH/ unit',\n",
+       "       'wind power plant 800kW, fixed parts/ RER/ unit',\n",
+       "       'wind power plant 800kW, moving parts/ CH/ unit',\n",
+       "       'wind power plant 800kW, moving parts/ RER/ unit',\n",
+       "       'cogen unit 6400kWth, wood burning, building/ CH/ unit',\n",
+       "       'cogen unit 6400kWth, wood burning, common components for heat+electricity/ CH/ unit',\n",
+       "       'cogen unit 6400kWth, wood burning, components for electricity only/ CH/ unit',\n",
+       "       'cogen unit ORC 1400kWth, wood burning, building/ CH/ unit',\n",
+       "       'cogen unit ORC 1400kWth, wood burning, common components for heat+electricity/ CH/ unit',\n",
+       "       'cogen unit ORC 1400kWth, wood burning, components for electricity only/ CH/ unit',\n",
+       "       'wood pellet manufacturing, infrastructure/ RER/ unit',\n",
+       "       'furnace, logs, hardwood, 100kW/ CH/ unit',\n",
+       "       'furnace, logs, hardwood, 30kW/ CH/ unit',\n",
+       "       'furnace, logs, hardwood, 6kW/ CH/ unit',\n",
+       "       'furnace, logs, mixed, 100kW/ CH/ unit',\n",
+       "       'furnace, logs, mixed, 30kW/ CH/ unit',\n",
+       "       'furnace, logs, mixed, 6kW/ CH/ unit',\n",
+       "       'furnace, logs, softwood, 100kW/ CH/ unit',\n",
+       "       'furnace, logs, softwood, 30kW/ CH/ unit',\n",
+       "       'furnace, logs, softwood, 6kW/ CH/ unit',\n",
+       "       'furnace, pellets, 15kW/ CH/ unit',\n",
+       "       'furnace, pellets, 50kW/ CH/ unit',\n",
+       "       'furnace, wood chips, hardwood, 1000kW/ CH/ unit',\n",
+       "       'furnace, wood chips, hardwood, 300kW/ CH/ unit',\n",
+       "       'furnace, wood chips, hardwood, 50kW/ CH/ unit',\n",
+       "       'furnace, wood chips, mixed, 1000kW/ CH/ unit',\n",
+       "       'furnace, wood chips, mixed, 300kW/ CH/ unit',\n",
+       "       'furnace, wood chips, mixed, 50kW/ CH/ unit',\n",
+       "       'furnace, wood chips, softwood, 1000kW/ CH/ unit',\n",
+       "       'furnace, wood chips, softwood, 300kW/ CH/ unit',\n",
+       "       'furnace, wood chips, softwood, 50kW/ CH/ unit',\n",
+       "       'chopper, mobile, diesel/ RER/ unit',\n",
+       "       'chopper, stationary, electric/ RER/ unit',\n",
+       "       'planing mill/ RER/ unit',\n",
+       "       'preservative treatment, infrastructure/ RER/ unit',\n",
+       "       'sawmill/ RER/ unit',\n",
+       "       'technical wood drying, infrastructure/ RER/ unit',\n",
+       "       'wooden board manufacturing plant, cement bonded boards/ RER/ unit',\n",
+       "       'wooden board manufacturing plant, organic bonded boards/ RER/ unit',\n",
+       "       'Paddy rice', 'Wheat', 'Cereal grains nec',\n",
+       "       'Vegetables, fruit, nuts', 'Oil seeds', 'Sugar cane, sugar beet',\n",
+       "       'Plant-based fibers', 'Crops nec', 'Cattle', 'Pigs', 'Poultry',\n",
+       "       'Meat animals nec', 'Animal products nec', 'Raw milk',\n",
+       "       'Wool, silk-worm cocoons', 'Manure (conventional treatment)',\n",
+       "       'Manure (biogas treatment)',\n",
+       "       'Products of forestry, logging and related services',\n",
+       "       'Fish and other fishing products; services incidental of fishing',\n",
+       "       'Anthracite', 'Coking Coal', 'Other Bituminous Coal',\n",
+       "       'Sub-Bituminous Coal', 'Patent Fuel', 'Lignite/Brown Coal',\n",
+       "       'BKB/Peat Briquettes', 'Peat',\n",
+       "       'Crude petroleum and services related to crude oil extraction, excluding surveying',\n",
+       "       'Natural gas and services related to natural gas extraction, excluding surveying',\n",
+       "       'Natural Gas Liquids', 'Other Hydrocarbons',\n",
+       "       'Uranium and thorium ores', 'Iron ores',\n",
+       "       'Copper ores and concentrates', 'Nickel ores and concentrates',\n",
+       "       'Aluminium ores and concentrates',\n",
+       "       'Precious metal ores and concentrates',\n",
+       "       'Lead, zinc and tin ores and concentrates',\n",
+       "       'Other non-ferrous metal ores and concentrates', 'Stone',\n",
+       "       'Sand and clay',\n",
+       "       'Chemical and fertilizer minerals, salt and other mining and quarrying products n.e.c.',\n",
+       "       'Products of meat cattle', 'Products of meat pigs',\n",
+       "       'Products of meat poultry', 'Meat products nec',\n",
+       "       'products of Vegetable oils and fats', 'Dairy products',\n",
+       "       'Processed rice', 'Sugar', 'Food products nec', 'Beverages',\n",
+       "       'Fish products', 'Tobacco products', 'Textiles',\n",
+       "       'Wearing apparel; furs', 'Leather and leather products',\n",
+       "       'Wood and products of wood and cork (except furniture); articles of straw and plaiting materials',\n",
+       "       'Wood material for treatment, Re-processing of secondary wood material into new wood material',\n",
+       "       'Pulp',\n",
+       "       'Secondary paper for treatment, Re-processing of secondary paper into new pulp',\n",
+       "       'Paper and paper products', 'Printed matter and recorded media',\n",
+       "       'Coke Oven Coke', 'Gas Coke', 'Coal Tar', 'Motor Gasoline',\n",
+       "       'Aviation Gasoline', 'Gasoline Type Jet Fuel',\n",
+       "       'Kerosene Type Jet Fuel', 'Kerosene', 'Gas/Diesel Oil',\n",
+       "       'Heavy Fuel Oil', 'Refinery Gas',\n",
+       "       'Liquefied Petroleum Gases (LPG)', 'Refinery Feedstocks', 'Ethane',\n",
+       "       'Naphtha', 'White Spirit & SBP', 'Lubricants', 'Bitumen',\n",
+       "       'Paraffin Waxes', 'Petroleum Coke',\n",
+       "       'Non-specified Petroleum Products', 'Nuclear fuel',\n",
+       "       'Plastics, basic',\n",
+       "       'Secondary plastic for treatment, Re-processing of secondary plastic into new plastic',\n",
+       "       'N-fertiliser', 'P- and other fertiliser', 'Chemicals nec',\n",
+       "       'Charcoal', 'Additives/Blending Components', 'Biogasoline',\n",
+       "       'Biodiesels', 'Other Liquid Biofuels',\n",
+       "       'Rubber and plastic products', 'Glass and glass products',\n",
+       "       'Secondary glass for treatment, Re-processing of secondary glass into new glass',\n",
+       "       'Ceramic goods',\n",
+       "       'Bricks, tiles and construction products, in baked clay',\n",
+       "       'Cement, lime and plaster',\n",
+       "       'Ash for treatment, Re-processing of ash into clinker',\n",
+       "       'Other non-metallic mineral products',\n",
+       "       'Basic iron and steel and of ferro-alloys and first products thereof',\n",
+       "       'Secondary steel for treatment, Re-processing of secondary steel into new steel',\n",
+       "       'Precious metals',\n",
+       "       'Secondary preciuos metals for treatment, Re-processing of secondary preciuos metals into new preciuos metals',\n",
+       "       'Aluminium and aluminium products',\n",
+       "       'Secondary aluminium for treatment, Re-processing of secondary aluminium into new aluminium',\n",
+       "       'Lead, zinc and tin and products thereof',\n",
+       "       'Secondary lead for treatment, Re-processing of secondary lead into new lead',\n",
+       "       'Copper products',\n",
+       "       'Secondary copper for treatment, Re-processing of secondary copper into new copper',\n",
+       "       'Other non-ferrous metal products',\n",
+       "       'Secondary other non-ferrous metals for treatment, Re-processing of secondary other non-ferrous metals into new other non-ferrous metals',\n",
+       "       'Foundry work services',\n",
+       "       'Fabricated metal products, except machinery and equipment',\n",
+       "       'Machinery and equipment n.e.c.', 'Office machinery and computers',\n",
+       "       'Electrical machinery and apparatus n.e.c.',\n",
+       "       'Radio, television and communication equipment and apparatus',\n",
+       "       'Medical, precision and optical instruments, watches and clocks',\n",
+       "       'Motor vehicles, trailers and semi-trailers',\n",
+       "       'Other transport equipment',\n",
+       "       'Furniture; other manufactured goods n.e.c.',\n",
+       "       'Secondary raw materials',\n",
+       "       'Bottles for treatment, Recycling of bottles by direct reuse',\n",
+       "       'Electricity by coal', 'Electricity by coal w CCS',\n",
+       "       'Electricity by gas', 'Electricity by gas w CCS',\n",
+       "       'Electricity by biomass&Waste', 'Electricity by biomass w CCS',\n",
+       "       'Electricity by oil', 'Electricity by nuclear',\n",
+       "       'Electricity by hydro', 'Electricity by ocean',\n",
+       "       'Electricity by geothermal', 'Electricity by solar PV',\n",
+       "       'Electricity by solar CSP', 'Electricity by wind onshore',\n",
+       "       'Electricity by wind offshore',\n",
+       "       'Transmission services of electricity',\n",
+       "       'Distribution and trade services of electricity', 'Coke oven gas',\n",
+       "       'Blast Furnace Gas', 'Oxygen Steel Furnace Gas', 'Gas Works Gas',\n",
+       "       'Biogas', 'Distribution services of gaseous fuels through mains',\n",
+       "       'Steam and hot water supply services',\n",
+       "       'Collected and purified water, distribution services of water',\n",
+       "       'Construction work',\n",
+       "       'Secondary construction material for treatment, Re-processing of secondary construction material into aggregates',\n",
+       "       'Sale, maintenance, repair of motor vehicles, motor vehicles parts, motorcycles, motor cycles parts and accessoiries',\n",
+       "       'Retail trade services of motor fuel',\n",
+       "       'Wholesale trade and commission trade services, except of motor vehicles and motorcycles',\n",
+       "       'Retail  trade services, except of motor vehicles and motorcycles; repair services of personal and household goods',\n",
+       "       'Hotel and restaurant services', 'Railway transportation services',\n",
+       "       'Other land transportation services',\n",
+       "       'Transportation services via pipelines',\n",
+       "       'Sea and coastal water transportation services',\n",
+       "       'Inland water transportation services', 'Air transport services',\n",
+       "       'Supporting and auxiliary transport services; travel agency services',\n",
+       "       'Post and telecommunication services',\n",
+       "       'Financial intermediation services, except insurance and pension funding services',\n",
+       "       'Insurance and pension funding services, except compulsory social security services',\n",
+       "       'Services auxiliary to financial intermediation',\n",
+       "       'Real estate services',\n",
+       "       'Renting services of machinery and equipment without operator and of personal and household goods',\n",
+       "       'Computer and related services',\n",
+       "       'Research and development services', 'Other business services',\n",
+       "       'Public administration and defence services; compulsory social security services',\n",
+       "       'Education services', 'Health and social work services',\n",
+       "       'Food waste for treatment: incineration',\n",
+       "       'Paper waste for treatment: incineration',\n",
+       "       'Plastic waste for treatment: incineration',\n",
+       "       'Intert/metal waste for treatment: incineration',\n",
+       "       'Textiles waste for treatment: incineration',\n",
+       "       'Wood waste for treatment: incineration',\n",
+       "       'Oil/hazardous waste for treatment: incineration',\n",
+       "       'Food waste for treatment: biogasification and land application',\n",
+       "       'Paper waste for treatment: biogasification and land application',\n",
+       "       'Sewage sludge for treatment: biogasification and land application',\n",
+       "       'Food waste for treatment: composting and land application',\n",
+       "       'Paper and wood waste for treatment: composting and land application',\n",
+       "       'Food waste for treatment: waste water treatment',\n",
+       "       'Other waste for treatment: waste water treatment',\n",
+       "       'Food waste for treatment: landfill',\n",
+       "       'Paper for treatment: landfill',\n",
+       "       'Plastic waste for treatment: landfill',\n",
+       "       'Inert/metal/hazardous waste for treatment: landfill',\n",
+       "       'Textiles waste for treatment: landfill',\n",
+       "       'Wood waste for treatment: landfill',\n",
+       "       'Membership organisation services n.e.c.',\n",
+       "       'Recreational, cultural and sporting services', 'Other services',\n",
+       "       'Private households with employed persons',\n",
+       "       'Extra-territorial organizations and bodies'], dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.set_printoptions(threshold=np.nan)\n",
+    "themis['BL'][2010].sectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(themis, open(path_io + 'Themis/themis.pkl', 'wb'))\n",
+    "export_all_excel(path_io+'Themis/', themis)\n",
+    "# for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']:\n",
+    "#     pickle.dump(themis[s], open(path_io + 'Themis/themis_'+s+'.pkl', 'wb'))\n",
+    "pickle.dump(erois_prices, open(path_io + 'Themis/erois_prices.pkl', 'wb'))\n",
+    "# pickle.dump(erois_prices_wo_GW, open(path_io + 'Themis/erois_prices_wo_GW.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compute EROIs, I use input-output tables (i.e. technology matrices) representing the global economy. \n",
+    "\n",
+    "The original database is [Exiobase](https://www.exiobase.eu/), and the model I used is [THEMIS](https://sci-hub.tw/https%3A//pubs.acs.org/doi/abs/10.1021/acs.est.5b01558), which is precisely what we need: input-output tables (IOT) of 2010, and prospective IOT for 2030 and 2050, under two [scenarios](http://gen.lib.rus.ec/book/index.php?md5=4D5AF94A02A48B799CF77641509C1C6B) of the International Energy Agency: BaseLine (with a high share of fossils) and Blue Map (with a high share of renewables).\n",
+    "\n",
+    "Footnote 7 affirms that neglecting to subtract from _fuel input_  secondary fuels used by thermal plants in non final steps is a good approximation, in the sense that the variation of _fuel input_ it implies is below 0.1% for all technologies. Here is the proof of this statement for scenario BL and year 2010."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 Main Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">Baseline ('BL')</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Blue Map ('BM', +2°C)</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Advanced ER ('ADV', 100% renewable)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2010</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>biomass w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass&amp;Waste</th>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ocean</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geothermal</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar CSP</th>\n",
+       "      <td>21.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar PV</th>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind offshore</th>\n",
+       "      <td>9.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind onshore</th>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>13.2</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>0.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>13.9</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>12.2</td>\n",
+       "      <td>19.76</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>34.29</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>45.97</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>28.01</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>40.22</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>36.74</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>64.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario      Baseline ('BL')                                   \\\n",
+       "year                     2010         2030         2050          \n",
+       "stat                     EROI    mix  EROI    mix  EROI    mix   \n",
+       "biomass w CCS               –   0.00     –   0.00     –   0.00   \n",
+       "biomass&Waste            11.4   0.01   6.3   0.02   5.9   0.03   \n",
+       "ocean                     5.5   0.00   2.4   0.00   2.9   0.00   \n",
+       "geothermal                5.4   0.00   5.2   0.01   5.1   0.01   \n",
+       "solar CSP                21.6   0.00   8.9   0.00   9.1   0.01   \n",
+       "solar PV                  9.3   0.00   7.4   0.01   7.2   0.01   \n",
+       "wind offshore             9.4   0.00  11.0   0.01  10.5   0.01   \n",
+       "wind onshore              9.5   0.01   9.3   0.04   8.1   0.04   \n",
+       "hydro                    13.2   0.16  11.9   0.14  11.9   0.12   \n",
+       "nuclear                  10.5   0.14   7.3   0.11   7.0   0.10   \n",
+       "gas w CCS                   –   0.00     –   0.00   7.5   0.00   \n",
+       "coal w CCS                  –   0.00     –   0.00   6.2   0.00   \n",
+       "oil                       8.4   0.06   9.8   0.02   9.9   0.01   \n",
+       "gas                      13.9   0.21  15.0   0.21  14.9   0.23   \n",
+       "coal                     12.9   0.42  11.5   0.45  11.5   0.45   \n",
+       "Total (PWh/a)            12.2  19.76  10.9  34.29  10.7  45.97   \n",
+       "\n",
+       "scenario      Blue Map ('BM', +2°C)                      \\\n",
+       "year                           2030         2050          \n",
+       "stat                           EROI    mix  EROI    mix   \n",
+       "biomass w CCS                   4.6   0.00   4.0   0.01   \n",
+       "biomass&Waste                   5.5   0.06   5.2   0.05   \n",
+       "ocean                           3.7   0.00   5.8   0.00   \n",
+       "geothermal                      5.2   0.01   5.4   0.02   \n",
+       "solar CSP                       8.2   0.02   7.9   0.06   \n",
+       "solar PV                        6.4   0.02   6.0   0.06   \n",
+       "wind offshore                   7.7   0.03   6.3   0.04   \n",
+       "wind onshore                    7.1   0.08   7.3   0.08   \n",
+       "hydro                          12.8   0.18  13.1   0.14   \n",
+       "nuclear                         7.3   0.19   7.4   0.24   \n",
+       "gas w CCS                       7.9   0.01   9.1   0.05   \n",
+       "coal w CCS                      7.1   0.05   7.1   0.12   \n",
+       "oil                             9.5   0.03   7.3   0.01   \n",
+       "gas                            17.3   0.14  19.7   0.11   \n",
+       "coal                           11.6   0.18  12.4   0.01   \n",
+       "Total (PWh/a)                   9.1  28.01   8.0  40.22   \n",
+       "\n",
+       "scenario      Advanced ER ('ADV', 100% renewable)                      \n",
+       "year                                         2030         2050         \n",
+       "stat                                         EROI    mix  EROI    mix  \n",
+       "biomass w CCS                                   –   0.00     –   0.00  \n",
+       "biomass&Waste                                 5.2   0.05   4.6   0.05  \n",
+       "ocean                                         4.8   0.01   4.9   0.03  \n",
+       "geothermal                                    3.8   0.03   3.9   0.07  \n",
+       "solar CSP                                     9.3   0.07   7.8   0.22  \n",
+       "solar PV                                      5.4   0.14   4.7   0.21  \n",
+       "wind offshore                                 6.5   0.04   6.4   0.10  \n",
+       "wind onshore                                  7.2   0.17   5.8   0.24  \n",
+       "hydro                                        11.0   0.13  10.9   0.08  \n",
+       "nuclear                                       8.3   0.02     –   0.00  \n",
+       "gas w CCS                                       –   0.00     –   0.00  \n",
+       "coal w CCS                                      –   0.00     –   0.00  \n",
+       "oil                                          10.0   0.01     –   0.00  \n",
+       "gas                                          16.5   0.18     –   0.00  \n",
+       "coal                                         10.4   0.16  11.5   0.00  \n",
+       "Total (PWh/a)                                 8.1  36.74   5.8  64.04  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results(themis, longnames=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3dbc263ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93f5ba90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d95969dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d95a16400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3dbc3b29b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, to_plot=True)\n",
+    "for scenario_fig in scenarios+scenarios_dlr:\n",
+    "    if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "    else: tot_name = 'Total (PWh/a)'\n",
+    "    EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                                   'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "    InteractiveShell.ast_node_interactivity = 'last'\n",
+    "    colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "    fig = plt.figure(figsize=(7,5)) # 7,5\n",
+    "    _ = ax = fig.add_subplot(1, 1, 1)\n",
+    "    _ = ax.plot(years, EROIs_mixes[[(scenario_fig, year, 'EROI') for year in years]].loc[tot_name], \\\n",
+    "            label='EROI', color='black', zorder=20, linewidth=3)\n",
+    "    _ = ax.set_ylabel('EROI')\n",
+    "    _ = ax.set_ylim([5, 13]) # 5, 10; 13, 18; 11.5, 17.5\n",
+    "    # ax.set_zorder(1)\n",
+    "    _ = ax2 = ax.twinx()\n",
+    "    _ = ax2.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[(scenario_fig, year, 'mix') for year in years]].iloc[:15], \\\n",
+    "                  labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "    _ = ax2.set_title(scenario_fig)\n",
+    "    _ = plt.ylabel('Share in the mix')\n",
+    "    _ = plt.xlabel(scenario_fig+' scenario')\n",
+    "    _ = xticks(years)\n",
+    "    _ = handles, labels = ax2.get_legend_handles_labels()\n",
+    "    _ = ax2.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.1,0.5)) \n",
+    "    _ = ax.legend(loc='center left', bbox_to_anchor=(1.1,0.97))\n",
+    "    _ = ax.set_zorder(ax2.get_zorder()+1)\n",
+    "    _ = ax.patch.set_visible(False)\n",
+    "    _ = fig.savefig('Evolution of EROI in '+scenario_fig+'.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7fTFIPa9BNBs7sx1iMGSr49ztTiuMP4KY8q3PZOns/xwHUbkwQswZ/Qq7yk3GHCus6RAffGUAmgBsgWiGBcRsLHmO7/4XAPcwMdNJEMSHXQfEydgRDLCKyRjLB/BHh3z1EIPljjkVuQ7ASUf9OyA+nMoGqKcdYlDYFgA1EFf8nQOxB5RzGNc9CdEnuBOiBWPAbEbApRSUSRjaB50zegx1HwIAHP7Bn18hhsa5bD3E+/XWqxHEYfX6fxDvnToiMjpe64ZozwRxJfk8xMl+B0Sl3B/fZJS5BWLWl4FWMDmjwwYA/2CMVTLG6vpeEJMtrHMs1gDAfxFRJ0S3oG0Qsz5dP4CV5y2IridDBbADlz8XvgPxObIH4kp2D8SJcx/3QHTraQXwAoA7GWONAMAY2wcxUPcQRJeYCohxAc58D0Cuk5Lyb4ir5Y0QY1peG4bMfTwA0bUo3yHPRxieG9xwrj0CcbJ/dJDPwPDG8O0Qf6csiLEbb/Qv4LCy3Ajxf1sL0b3p93AsHvHxf3zT303o4Ycfbu0798ADD0QbDIYZf/3rX4OeeuqpBgAQjWB0WT2MMQx0nCNCw3imcoYJEX0F4HE2yhuPcVwLEf0RQAlj7BV3y8JxLSSmLHwLwJzhKBBjIM9JAN9njOW6WxbO6EPf7EC8zLFizhln8PF/cLKzs8tTUlKahi45eqjV6pnd3d2XzanmzJmT8OKLL1bNnTu357HHHgutqKhQ7N+/vyQ3N1dx0003xVdWVp6TSMSszzabDREREdM/++yzIplMxtasWRNXXFx82WJTdna2f0pKSuTof6vxB7cMuBDG2AKuCHgejLEn+INgYsLEfP/XjQdFAAAYY3O5IjB5YIyZGGNJXBEYv/Dx37NRKBTsT3/6U01WVpbm7NmzyuTkZNO0adO6n3766UuWqKeffjo4OTm5Ozk52XSluiYzo6YMENGbRNRARLlOx35DRDkkZqDZT0TDyazD4XA4HA6Hw5lk9I8Z2LRpU2j/Mlqtlm3cuLH+hRdeMADAu+++W15cXKyMiIhIDg8PTy4uLla+++675WMuvAcxam5CRPQdiD7v2xhjyY5jXsyRs57E3XCTGGOPjooAHA6Hw+FwOJxrYjy4CY0l3E1oFGCMHcW38zODfXvzqr5sNBwOh8PhcDgcDscNjPk+A0T0O4iZBtpxhVzhRPQIxLzDgLjV+HDT+3E44xEVY2xYyjfv+5wJBO/3nMnKsPs+h+NuRjWbEImbVu3qcxPqd+6/ASgZY/1Tow1UTxdjTON6CTmcseFa+zDv+xxPhvd7zmRlIvTh7Ozs0unTp7cKgjDhvTjsdjudO3fOJyUlJdrdsrgDd2qt7wG4w43tczgcDofD4XAGJrexsVFvt9sndIJ+u91OjY2NegCTNtPbmLoJEVEcY6zY8fEWiJvzcDgcDofD4XDGEVar9aG6urotdXV1yZjYqejtAHKtVutD7hbEXYxmNqF/AlgMcffNeog7Jd4MIAHiP74CwKOMsZph1OXx5jbO5Ia7S3AmI7zfcyYrvA9zPAmP2IGY31QcT4dPijiTEd7vOZMV3oc5nsSYZxPicDgcDofD4Yxvzpw5EyiVSrcAmOhuQpOBS65Qs2fPbuh/kisDHA6Hw+FwOJxvIZVKtwQFBSUGBARMioxCExlHkHRSXV3dFogxu9+Ca3ocDofD4XA4YwgRvUlEDUQ0YAYbEtlMRBeIKIeIZo21jACSAwICOrgi4PkIgsACAgLaIVp5Lj8/xvJwOBwOh8PhTHa2Alh5hfOrAMQ5Xo8AeHUMZOqPwBWBiYPjtxxw3u/xbkKbNm0iAPPdLQdnUlP1yiuvVI11o5s2bfIFMHWs2+VwnMh55ZVXjGPd6KZNm2IAGMa6XQ4HAIwXQnq27f955kjqYIwddWzMOhi3AtjGxCwvXxORNxEFM8YujqRdDmcgPF4ZgPgdNrtbCM6k5u8AXhuijJSIMpw+v84Ye32E7aYA+MMI6+BwRsKDuPJGPaPR7wHgXgBrXVAPhzM8GGzSFmmZLMurd3vxAzIA64a4YqR9PxSA8yJTteOY25SB3/zmN4murO8Xv/hFgSvrG4zNmzf7ZWRkaLZt21Y5Fu15IhNBGeBwPAErYyzN3UJwOK5C0iYpVRWobEMU4/2e49lY0KIqUtVrzmhCWJdc+Tc8pG9CkHwYV4607w+06y932eGMCjxmgMPhcDjDg8Eiq5UV+n7oW+//T/9oTZZG4m6ROByXw2CTNEtK9Pv1pYFvBHp7HfVKtHfJ6v+KjdomBAWMkRTVAMKdPocBqB2jtscVL7/8sl98fHxSQkJC0tq1a6OKiork8+fPj4+Pj0+aP39+fHFxsRwA3nvvPf2MGTOmJiYmJl1//fXxVVVVfMF7mHBlgMPhcDhXxgajskhZ4P+Wf4/vdt8EWbOM++tzJh4WtKjyVQX+b/t3+n/oH6MsUUYTI8FKdG4zHgtph5/3GEqzA8ADjqxC8wC0T8Z4gYyMDOWLL74YfOTIkaLCwsL81157rfLRRx+NuO+++5qLioryv/vd7zZv3LgxHABWrFhhzMrKOl9QUJB/5513tjz33HNB7pbfU+BaE4fD4XAGhMzUqM5RN2syNVFkJZf6C3M44wIGu6RVUqbN0JKiVBFJjHydTjK7YM98yf5UUjd0Slc2S0T/BLAYgD8RVQP4FQCZ2Cr7G4A9AG4GcAFAN4DvubJ9T+Gzzz7zSk9Pbw0ODrYCgMFgsGVmZmr27t1bAgAbN25s+fWvfx0GAGVlZfK1a9eGNTY2ysxmsxAeHm5yp+yeBFcGOBwOh/MtBKNQqT2ltSiLlFHEaKzcIjicscOKVmWxsk57Whss6ZLEXF6AmSGxZP3e9ouZJihlrm6eMXbvEOcZgB+4ul1PgzEGIhpWrMRjjz0W8aMf/ahu3bp17bt27dI999xzIaMt30SBuwlxOBwOB2CwSxulxT6f+lQHvB0QoSpUxRAj/ozgTBwY7JIWSYn+gL4scEugXn9Ynyjpklzm+kOwd0FqynnO9lzaaCgCnOGzcuXKjh07dvjW1dVJAKC+vl4yc+bMri1btvgAwGuvveablpZmBIDOzk5JRESEBQC2bt3q5z6pPQ9uGeBwOJzJjB29ikpFmfaYNlDaIY1ztzgcjsvpswJkaIMlxoGsAN8gwNpsldjLf2P93WwGYaCMPpOWsUoF6kxaWlrvE088cXHhwoVTBUFgycnJ3a+++mrlhg0bIv/yl78E+fn5Wbdt21YOAD/72c9q77333hiDwWBOS0vrqqysVIy1vJ4KVwY4HA/FbrdDEPjCLecasaJNVaC6qD2ljRTMAo8H4EwsHLEAmjMaQVminEKMfIa6RE49Na2CtukPtp/PHgsROcPj8ccfb3788cebnY99/fXXRf3LrV+/vm39+vVt/Y//8Ic/bAbQ3P845xs8WhkgIjWA95ctW+YdGxvbxidGnMkCEXmpVKr/XbRokS4qKqrT3fJwPAfqpYuaM5pOda46muw8KJgzwbgKK4AzGqH9QhVCejbbnkwZTfE4nPGIRysDANYDSP/8889x8uRJU1JSUkNycnKTQqGwu1swDme0ICJfAPt6enrSDhw4YF+1alVReHh4l7vl4oxjGJikXVKmPaGVKMuVUwAEu1skDsdl9FkBzmpIeUEZORwrgDO+0ou5Z22zpFvZI9NHS0QOZzzj6crApUh7o9GoOHXqVHhmZmZIbGxsU2pqaoO3t7fZncJxOKOEDo7JnM1mE/bt2xe3Zs2aouDg4G43y8UZb4ibhJXqjum8Zc2yaHeL4ypaW1vVPj5XNd/jTESsaFNeUF7Unr46K4AzYfLizH2WNbqP2D2xrhaPw/EUPF0ZWENEj0ul0v+0WCwSALBYLJKCggJDQUGBITw8vDUlJaU+LCysi4jHAXEmBoyxCiJaJgjC13a73cdqtUr27NkTl56eXhgYGNjrbvk44wAbjMoLyirtCW2opEeS4G5xXAWJA/l/CILw5LJlyypiY2Pb3S0TZ4xhsEvaJOWaMxqmvKCMIkbXuBEYsyYoz2Zt7d0Y8BlWTXGtkByOZzFqygARvQlgDYAGxliy49gfAKQDMAMoAfA9xthlwR7DhTFWtWnTpp+Zzeblubm5fvn5+YbOzs5L0eNVVVU+VVVVPr6+vl3JyckNU6dObZVIJMPKV8vhjGcYY0XXX3/9kxkZGa9bLBaJ2WyW7t69O/7WW28t9PX15RutTFLITI3qbHWzJlMTTbYJGQ/wYwAv2e12HDlyJDIwMDDfy8vL4m6hOGPAN1aAIIlRMkIrl717lvJo/vO9vws5get5LnrOpGc0I263AljZ79gBAMmMsRkAigD8tysaksvl9lmzZjXed999uStWrLhgMBi+FVDZ0tKiOXr0aNTbb789/eTJk0Hd3d0SV7TL4biT1NTUsptvvrlIKpXaAaC3t1e2c+fO+Pb2drm7ZeOMLUKnUOH1uVdJwBsB/toM7VSy0UTtA28DqAUAs9ksPXDgQLTdzkPEJixiLECJ1+depYFbAr30h/SJEqNkRP5hRNbWhco95/+7969TuCLA4YiMmmWAMXaUiCL7Hdvv9PFrAHe6sk1BEBAbG9seGxvbXl9fr8rOzg4sKyvzs9vtBAA9PT2ys2fPhmZnZwdHR0c3p6am1vv7+/NVVI7HEhIS0n3TTTcV79u3L85mswnd3d3yHTt2xK9du/a8Tqezuls+zijCYJM2SUt1X+nU8jr5pHBzYIw1EdF9AA4BoIaGBu2JEydCbrjhhlp3y8ZxId+2AlxTLMBASAVT7TLZzqZ7e3dElSPq2pUKwqS0Rr2S/IpLrY2bcjeN+b4FnIFxZ8zAfwD4YLQqNxgMPTfeeGOF0WisycnJCTh//nygyWSSAmLQZXFxcUBxcXFASEhI+4wZM+ojIyM7eVwBxxOJiIgwLlu2rOTgwYOxdrudjEajYufOnfFr164tVKvVNnfLx3ExdvQoKhTlumO6IEmnZNJtEsYYOxITE3OotLR0KQDk5OQEh4WFdU6ZMoWn2PVk+mIBzmqYsngksQADo5R0li6X7O5eY/oisg7BXtdcEcGCf+KiC0XjcNyOW5QBIvoZACuAd69Q5hEAjzg+XrOcWq3Wev3111+cM2dOXX5+vm9ubq6hvb1d1Xe+trZWX1tbq9fr9T3Jycn1iYmJLTKZjMcVcNzGtfT9mJiYDqvVWnro0KEYxhja29tVDgtBkVKp5ArBRMCKVnWeuk5zWhMpWCbeJmFX0++XL19++NNPP72uoaFBBwBffPFF1N13352v0Wi4NczTsKJNWaK8qD3liliAgfGS1+cvsh/GUvOJmHZ4q4a+YlBs2IpKfBcRLhOOMyRPPfVU8EcffeQbHBxs9vPzs86cObNbr9fb/vGPfwRYLBaKjIw0ffTRR2U6nc7+5ptv+jz//PMhgiAwnU5ny8jIKHS3/J7AmO/SRUQbIAYWr2OMDTrpZoy9zhhLY4ylQVQcRoRUKmUzZsxovvfee/NXrVpVFBoa+q0sFO3t7apjx45Fvv322zOOHTsWYjQaPT3TEsdDuda+n5CQ0LZw4cKyvs+tra3qnTt3xprNZr4bnwdDPVSj+1JXHLgl0Et3XJcoWISRTGbGLVfT7yUSCVuxYkWZXC63AmK8zIEDByJ5/ICHIFoByrw+9yoJ3BLopf9i5LEAgxGkKs5aaDtGN1jPxI5QEbBjM0rxAFzmtsQZmqNHj6p37tzpc+7cufzdu3eX5OTkaABg3bp1rbm5uQWFhYX5CQkJPZs3b/YHgBdeeCF4//79RYWFhfn79u274F7pPYcxnfAS0UoATwNYxBhzS050IkJkZGRnZGRkZ3NzsyIrK8tQUlLiZ7PZBAAwmUzSnJyc4Nzc3KApU6a0pKam1gcFBfW4Q1YO52qZNm1ai9VqFY4fPz4FAJqamrS7d++OWbNmzQVu8fIgGJikVVKqO6GTKioVkyIe4Grx8vKyLFq0qOzAgQNxAHDx4kV9RkaGYc6cOfXulo0zCH1WgNPaIEmnJGp0G2PWOG1GzpSuJtX17HScBfKRzXd+jWI8jgmTptdTOHz4sHbVqlVtWq2WAWArVqxoA4AzZ86ofvnLX4Z2dnZKurq6JIsWLWoHgLS0NOO6deu1D/QkAAAgAElEQVQi77jjjtZ169a1ulV4D2LUVgyJ6J8ATgBIIKJqIvo+gJchbph0gIiyiOhvo9X+cPDz8zMtW7as8v7778+ZPXt2tVqtvrRJmd1up7KyMr9PPvkk6eOPP44vLi7W81UnjieQkpLSdN1111X1fa6rq/Pau3dvtM1m40Ex4x0Gi6xadt7vA78m/w/8Y7gicGViY2M7kpKSLk3+z549G1pTU6Nxp0ycfvRZAb5wsgJ0jo4V4BvsPbO8Dub4Gs2q77BjCSNWBP4TBfglVwTcwWAOJI888kjUyy+/XFlUVJT/9NNP15pMJgEA3nvvvcrf/va3tVVVVfLU1NRpdXV1PHvkMBg1ZYAxdi9jLJgxJmOMhTHG3mCMxTLGwhljqY7Xo6PV/tWgUqlsc+bMqV+/fv25xYsXl/r5+XU5n29oaNAdPHgw9t13300+c+ZMQF+n43DGK2lpaQ2pqamXMqzU1NR4f/bZZ9yNYrxiQ6fyvLLA/y3/Xt+dvlOlrdIAd4vkKdxwww01vr6+XQDAGKPPP/88uqenh08A3I0VbcoiZYHfe35t/v/0j1IVqmKI0ag/O4lsbUt0Hxd2dkSoVmF/oh2SkbW5AQX4IyZcjI6nsHjxYuNnn32m7+7upvb2duHgwYPeANDd3S1ERERYTCYTvf/++7595fPy8hRLly7t+vOf/1zr4+NjLS0tnahpll0K94t3QiKRIDExsTUxMbG1urpak52dbaiqqvLp00yNRqPi1KlTEZmZmaFxcXFNqampDXq93jxEtRyOW5g3b95Fi8Ui5OXlBQFARUWF78GDB+3Lly+vEASuz44HyEQN6mx1qyZrwm4SNupIpVJ24403ln788cdJFotF0tXVJf/888+nrF69upRniBtjGOySdkmF5qzGrixyfUagoRAEU92t6nebDnWuVfwYfxn5/bQWBdjKFYE+3JEKdNGiRd0rV65sT0pKmhYaGmqaMWNGl16vtz3zzDO1c+bMSQwNDTUnJiZ2G41GCQD85Cc/CSsvL1cwxmjBggUd8+bN427ew4ArA4MQFhbWFRYWVtre3i7PysoKLC4u9rdYLBIAsFgskvz8fENBQYEhPDy8NSUlpT4sLKxrqDo5nLGEiLBw4cIai8UiKSoqCgCAkpISf6lUal+yZEkVnyi5D6FTqNCe1NqUxcooAgW6W55rQZCYLup9SsaFqcnHx8e8YMGCikOHDkUD4u7zWVlZATNnzmx0t2yTAivalaXKWs1pjUHaIR3lWICBkUuNZXcrtlreMP5E9jx+OnKXnqU4j0+4IjAe+NWvflX30ksv1XZ2dgrz589P+K//+q/6BQsWdD/99NOX3d/79+8vcYeMng5XBoZAr9ebFy1aVD1v3rzavLw8v7y8PIPRaFQAoi9bZWWlT2VlpY+fn19XcnJyfUJCQqtEwi3UnPEBEWHJkiWVVqtVKC0t9QOAwsLCQJlMZlu4cCHfqGksYbBJG6Qluq90WnmD524SRoKlyT8wp1XvVxaimpY0boLSp06d2lpdXd1YXFwcAACnTp0KDwkJMRoMBr4yOBqIVgBxX4AiZSQx91m2NIrGgntpm+pXXS8Jb+Ch2BFXeB2KsB/xLhCN4wLWr18/pbi4WGUymeiee+5pXrBggVsS0ExkuDIwTBQKhX3WrFmNqampjSUlJfpz584Z6uvrdX3nm5ubNUeOHIk+ffq0eerUqQ0zZsxoUqlUPL87x+0IgoAVK1aU7927V6isrPQBgNzc3GCZTGafN29enbvlm/DY0aMoU5TrjuuCJUaJx04wiGxtPv4Fdb7+BdHKqfFNPt/7H7vEy2tcrXwsWrSoqqGhQdve3q6y2+104MCB6LvvvrtALpePCwvGhEC0AlzUnNYESDuko7IvwNXgqynLuc/yL///Z3rL9DHuHLkikIQSHEcMJGOfep0zMDt37iwbuhRnJHBl4CoRBAFxcXHtcXFx7XV1dars7GxDeXm5r91uJwDo7u6Wnz17Niw7OzskJiamOTU1td7Pz8/kbrk5kxtBEHDTTTeV7d69W6itrdUDQGZmZqhMJrPPnj27wd3yTUgsaFHnqes1GZooz94kzN6l971Q6R+YEyv19VL7PfJUszwycqq7pRoImUzGbrzxxtJ///vfiTabTejs7FQeOnQo4qabbip3t2weTV8sQKbGpixURhGjcfD7M1uYPjvnjq7PQu6w7jR/gWUjd0+KQjnOIAJSjCsll8MZbbgyMAKCgoJ6goKCyo1GY3V2dnZgYWFhgMlkkgKAzWYTioqKAoqKigJCQkLaU1JS6qdMmdLJ/bQ57kIqlbKbb765ZOfOnXF9Vq1Tp06Fy2Qy24wZM5rdLd9EgbqpRpuh7Vblq2KIke/QV4xXmEmnrywNCDobKVUiyOv2uyq0ixaNfOV1lPH39++dN29e5bFjxyIBoLS01C83N7cjOTm5xc2ieR5WtCvLlLWaU5pAd8UCDIy9N8H3y4Kb274OW2L/0nYWs8NHXGUIqpCDYCghc4GAHI5HwZUBF6DVaq033HBD7Zw5cy4WFBT45ubmGtrb2y/tdFhbW6uvra3V6/X6nuTk5PqkpKQWqVQ6bnxtOZMHmUzGVq9efWHHjh3xTU1NGgA4duxYpFQqtSclJfENWq4VBrukVVKmO66TKaoUEe4WZ2Qwq1p78YIhJCNEKjPFqOfOLfG+554YkstHOTe865g+fXpzTU2NV3l5uS8AHD9+fEpwcHAXt9IOAwYm6ZCUa85q7MpC98YCDASRrX227+7Shc2Fodchk4qQEDTiSv1Qizz4QwuFC0TkcDwOrgy4EJlMxmbMmNE8ffr05vLycl1OTo6hzyUDANrb21XHjh2LzMjICEtISGhISUlp1Gq1VnfKzJl8KBQK+5o1a4q3b98e39raqgaAo0ePRslkMntcXFy7u+XzKBjM8mp5qe6Yzk/aKo1xtzgjg9mVquYLQaEn/WXyrqmy8PBi34cf9pf6+4+ryeBwICIsXbq04sMPP9QYjUaFzWYT9u/fH3PnnXcW8J24B8GGDmWpsmb8WQG+QRDM9Yt8Pqqf3twQPA0F8mqEjzx1qR71yIcXvKEaujCHMzHhysAoQESIiorqjIqK6mxublZkZWUZSkpK/Gw2mwAAJpNJmpOTE5KbmxscGRnZkpqaWs8zXnDGEpVKZUtPTy/+9NNPEzo6OpSMMfriiy9ipFJpcVRUVKe75Rv32NChKlTVaE9qw4VeYRz4T48MuaLtQlDoKZ1C2RYvaDQXfTZsqlAmJ8e5W66RoFAo7CtWrCjZvn17ot1up7a2NtXRo0fDli1bVjX01ZME0QpQoc5UW1XnVVHjzQrgjFTaVX6z7r3ukGaLbywu6FrgN/KdpjVowTkoEAitC0Sc8Oy+5RaX9o/VO3Zc874FM2fOnJqZmXm+sLBQvmbNmrji4uI8V8o22eDKwCjj5+dnWrZsWeX8+fNrzp07F1BQUBDY09MjAwC73U6lpaV+paWlfgaDoXP69On1MTEx7XxDKM5YoNForOnp6UXbt29PMBqNCkf2ldhVq1YVhYeH830zBoBMVK/J1LSrc9RRE2GTMKmsqzwo5KRUpWmKhURi1K1cfV63alU8TZBBKCgoqCctLa3q1KlTEQBQVFQUGBYW1pmQkNDmbtncig2dilJFtfaUNlDaIY10tzhDoVQ2F94pe0egVm91LAr8jdApR14p2pAFhnCM6cZoHNeQmZl53t0yTCQmxIDvCajVatvcuXPr7r///nOLFi0q9fX1/dZkq76+Xnfw4MHY9957L/ns2bMBZrOZ/zacUcfLy8uSnp5epFarzYAY+L5v3764ixcvqt0t23hC6BDK9fv1pQFvBgRqMjXxZCOPDjKUSHprgsO+Ko+K2x2p0jQFK5OTzwe/8ILgtXr11ImiCPQxa9asxrCwsEuT/y+//DKyra1N7k6Z3AYDk1fLCwO2Bki9D3onSjukfu4WaSj02opzDwhvqro7QykJ+aEuUQTk6MQpmBGLcf/9OcCzzz5riIuLmxYXFzftueeeCwQAtVo9091yTSS4ZWCMkUgkLCkpqTUxMbG1pqZGk52dbejL/Q4AnZ2dipMnT0acPXs2NC4urjE1NbVBr9db3CkzZ2Lj7e1tXrNmTdH27dunmkwmqdVqlezZsyc+PT39fGBgYK+75XMbDFZZg6xU95VOJ2uQRbpbHFcgCOZ6f0N2p96nLBYAJP7+Zb4PP6yWh4d7vKvTYBARli9fXv7hhx8mdXd3yy0Wi2T//v3Rd9xxR6FEIpk08QNkonr9fr1JUa0Y+e68YwKzG3xyc7/bvcuQZ0prW4QjcXZIRq6oStGNozBiOoJdICRnlPnyyy/V7733nt+ZM2cKGGOYPXt24rJly7grq4vhyoCbICKEhYV1hYWFlba1tcmzsrICi4uL/a1WqwQALBaLJD8/P6igoCAoIiKiJSUlpSE0NJS7bnBGBT8/P9Pq1auLdu3alWA2myVms1mye/fu+FtuuaVw0mVgsaNbUaqo0J3w7E3CnCGytvgG5DX6+BXGEcFACkWT/rvf7dDMm+f2TaPGApVKZVu6dGnp7t27pzLG0NzcrDl27FjId77znRp3yzbqMFiUhcoLXke84shOHvLMZ6bIgOOFd7YeivjcuuriWnw6lUEYeV5uASbsQzPmYuSpSDljwuHDh7U333xzm5eXlx0AVq9e3Xro0CHdUNdxrg4PGRgmNt7e3ubFixdXz58/vzY3N9c/Pz8/0Gg0KgCAMYaKigrfiooKXz8/v67p06fXx8fHt0okfE8UjmsxGAw9q1atKtq9e3eC1WoVent7Zbt27Yq/9dZbC729vc3ulm/UsaBZnatu1JzRRHr2JmFOkK3Dx7e41i8gN5YEuy+IejULFpTp77wzlmQyf3eLN5aEh4d3paam1mRmZoYCQF5eXlBYWFhndHR0h7tlGy0Eo1DpvdtbKWuReU5/JltHYuCB8rWNp+Letj9Y/hDecI3sBAs+xkUsQ6RL6uOMCYxNGuOdW5lQvqGejkKhsM+ePbth3bp1ucuWLbsQGBj4LVNYc3Oz5vDhw9HvvPPO9FOnThl6enq4RsBxKSEhId033XRTsUQisQPijto7d+6M7+zs9Ggf+SshdAnVusO6C4FvBProvtZNFSzCyH2S3Y69x8u7tCAmfrvc35AzlQS7RB4VVRT0u9+ZvO+9N5Fksgn7e16JOXPm1AUFBV2a/B8+fDiqs7Nz4i2K2dGtOaUp9H/bP1zWIgt0tzjDRRAsDbODtpff3nAy8SX7UxUuUwQAG7aiEmu5IuBpLF261Lhnzx7vzs5OoaOjQ9izZ4/PkiVLuJuQi5l4g+AEQBAExMfHt8fHx7dfvHhRlZ2dbSgvL/dljBEgTtDOnDkTlpWVFRIbG9uUmpra4OvrO7lcOTijRkREhHH58uUlBw4ciLXb7WQ0GhU7duyIv+22286r1Wqbu+VzFUKnUOF12EtQVCsmkMsAs2h0NRcMwRnhEqk5EQAEna7a9/vfhyI+fkK4PI0EQRCwYsWKsn/9619Jvb29MpPJJD1w4ED02rVriyZK3LS0UVrsvcfbIOmWeEhsgIhU2l15g99H7d+5WJr0JP5Y+if8p6viWOzYjFI8AI9OlTseGEkq0GtlwYIF3ffdd1/zrFmzEgHg/vvvb7zhhht4KnYXw5WBcU5wcHBPcHBweWdnZ3V2dnZgYWFhgNlslgJi5pfCwsLAwsLCwNDQ0LaUlJSGiIiITqKRu1ZyJjfR0dEdS5YsKf3iiy9iGGPo6OhQ7tixI37t2rVFSqXSsxUCK9p1X+sa1OfUE2hywGwqdeMFQ+gpg0zWLa6mSqUdXunpddrly+OIDwqX0Gq11sWLF5ft27cvHhAzuZ08eTJo/vz5de6WbURY0ep12KtFVazyuH6tULYUrdB+aJ1ZXz/1QbxV8TYecJ3i+msU43GMS8WIiFYC+AsACYAtjLEX+p2PAPAWAG9HmWcYY3vGXFA38+yzz9Y/++yz9c7Huru7MwEgISHBzPcYGDlcGfAQdDqddcGCBbVz5869mJ+f75ebm2vo6Oi45M5QU1PjXVNT4+3t7d2TnJxcn5iY2CKVSrmzHeeaiY+Pb7NYLGVHjx6NAoDW1lb1zp07Y2+99dZiuVxud7d8Vw2DTVGuKPb63CtSsAgeN2EaGMYUytYLhtBT3gpFR9+Ex6qaNavYe/36KEGpnPTWgIGIiorqTE5OvpibmxsMAFlZWaGhoaHGiIgIo7tlu2oY7PIKeZH+oD5SsAgetwu2VluVly79tyy6qSM6HTtr92C1677Df6IAv8S4jJcgIgmA/wOwAkA1gNNEtIMxlu9U7OcAPmSMvUpESQD2ANzVieN6uDLgYchkMpaSktI0Y8aMprKyMq9z584F1tbW6vvOt7W1qb766qvI06dPh02dOrUhJSWlUaPRWN0pM8dzmTZtWovVahWOHz8+BQCampq0u3btiklPT78gk8k8RtkUjEKl915vuaxJNmFSaMrknaWGkJNKlbrlkmIjDQoq8X3kEb0sKGhcToDGE9dff31tXV2drqmpSQsAX3zxRfTdd9+d50mucNRLF733etvldXIP7NfM7udbkH+beY8uoM3ktwhHmo/jhikuq34DCvDH8akIOJgD4AJjrBQAiOh9ALcCcFYGGAAvx3s9gNoxlZAzaRg1ZYCI3gSwBkADYyzZcewuAM8CSAQwhzGWMVrtT3SICNHR0R3R0dEdTU1NyqysrMDS0lI/m80mAIDJZJJmZ2eHnDt3LjgqKqo5NTW1flLnjHc/UiJy7u+vM8Zed5s0V0FKSkqTxWIRTp8+HQ4A9fX1Xnv37o1ZvXp1ybjP025Dh/ZrbZ0mRzNhVsgl0p4qQ/Bpu0ZXdyktKKlU9T7r13erZs4cbyvD47bfSyQS3HjjjaUfffTRNLPZLOnp6ZEdPHgwMj09vWTce1UxmFW5qlLdMV0cMfLARBLMFGo4eeH29sP+ql4oZ+NMdy6mh7qs+rUowFa3KwJD9f1QAFVOn6sBzO1Xx7MA9hPR4wA0AJaPhqAczmhaBrYCeBnANqdjuQBuB/DaKLY76fD39+9dvnx5ZXd3d01OTk7A+fPnA3t6emQAYLfbqaSkxL+kpMTfYDB0zJgxoyE6Orp9ogTLeRBWxliau4W4VtLS0hosFouQlZUVCgA1NTX6ffv2Ra1atap0XPalPteJz/VTBLMwIRQBQWK6GGDI7Pbyrvxmwi8I3dqlSyu9br01jsZnvuFx3e/1er1l4cKFZZ9//nksILpbnjlzJjAtLa3B3bINhqRdUua929tL2i71QGsAALJ1RIccrr6t4esQu0VpTUI2K0eU6zIeLcV5fOJ2RQAYuu8PpHH2X1y5F8BWxtgfiWg+gLeJKJkx5nlumpxxzagpA4yxo0QU2e9YASCuanNcj1qtts2bN6/uuuuuqz9//rxPbm6uoaWlRd13vr6+3uvAgQNeOp2ud9q0aQ3Tpk1r9kjfb45bmDdvXp3FYpHk5eUFAUBlZaXPwYMHpyxfvrxiPCkEQpdQ5b3PWyprmBguQUSWJv/A3Ba9b3Es0aV00HZFQkKRz3/8R5hEp5sQ39NdxMfHt9fU1DScP38+EAAyMjLCQkNDjcHBwd3ulu1b2GDUfq2t9WQrFwmWxsTQvY231GZPMdr0HcnI1dQh2GvoK4fJdSjCfnjK/6ca+NbmZ2G43A3o+wBWAgBj7AQRKQH4Axi3yirHMxm3MQNE9AiARxwfx62c4xGJRMKmTZvWkpSU1FJdXa3Jzs42VFVV+fSd7+zsVH799dcRZ86cCY2Pj29MSUlp9PLyMnMlbXwwXvs+EWHhwoU1FotFKCoqCgSAkpISf6lUal+yZEmV2/uPDR3aU9o6dZY6juBuYUYOka3dx+/8Rd+A/FgidmmDMMHbu9LvkUdk8sjICaUEuLPfL1y4sLq+vl7b2tqqZozRwYMHo++66658pVI5LhZLZHWyIu+93qFCr+dauSTSnqqU4O0dN1cXxl9kIReTkRvQDm+VyxpIQgmOIwYSj9k/6TSAOCKKAlAD4B4A9/UrUwlgGYCtRJQIQAmgcUyl5EwKxs1Eoz8O37rXAYCIutwsjkdCRAgPD+8KDw8vbWtrk2dlZQUWFxf7W61WCQD0rfLm5eUFERGTy+U2x8sql8utCoXC1vdXoVBYlUrlpb9KpdKqVCptKpXK6kmBpJ7AeO77RIQlS5ZUWa1WSWlpqR8AFBYWBspkMvvChQtr3CIUg11eJS/SH9RHCCbPnSx9g71L71NS6W/IiRYE26UJP8lkrV63396sXbQo1p3SjRbu7PdSqZTdeOONpR9//HGS1WoVjEaj4osvvpiyatWqMncquWShJq+DXkZluWdnhZIr2i7M9f/UtKSqPLEYceUpyA7rhUrusgaiUI4ziIAU49FVbkAYY1YiegzAZxDThr7JGMsjoucAZDDGdgB4AsDfiegnEF2IHmRu3JL39OnTLnW/uu6668Zk34LNmzf7ZWRkaLZt21Y5knpycnIUjz/+eHhZWZlSKpWyqVOn9rz22muV4eHh1kOHDqmfeuqp8KamJhkRsTlz5hi3bNlS1dbWJjzwwAORtbW1cqvVSmFhYaYjR45ccNV3cxXjVhnguBZvb2/z4sWLq+fPn1977tw5//z8/MCuri5F33nGGJlMJqnJZJICUFyhqssQBIE5FAjbQEqEQqGwKZXKS3+dFAnbuA9A5VyGY+Om8r179wqVlZU+AJCbmxskk8ls8+bNG9Nc7UKXUK3fpxfkDZ6YTaU/zKT1qioNDD4zRSKxfPPQJTKr584t8b7nnhiSy32uUAFnBPj6+pquv/76ir5UuhUVFb45OTmdKSkpTWMuDINNUaIo1n+hjyYb+Q99wfhFo60pWKTZya6rqZt2BrOK5+NElAVy1809QlCFHARDCY/bVduxZ8Cefsd+6fQ+H8ANYy0X53K6u7spPT097vnnn6+677772gFg586durq6OikArFu3Lmbbtm2ly5cv77Lb7Xjrrbd82trahKeffjp06dKlHb/4xS8aAODkyZOus4a5EK4MTDIUCoU9LS2tYdasWQ3FxcXeubm5gc3NzZq+LETXgt1up97eXllvb+9VD8ZSqdTurEjI5fJLCoTzX2drRN/f8eSnPtkQBAErV64s2717t1BTU6MHgMzMzFCpVGofk+BLG4za09padeZEcAliVrWm7oIh5HSIVNb7rZU3WXh4se/DD/tL/f3HQ0DkhGfatGktNTU1upKSEn8AOHnyZHhwcLBxLDOxCd1Ctfceb4ms0dNjXpjdx7fw/ArslybWt8R/jqXnb8T+eDskrhu4/VGLPPhDe3ULWBzP4eWXX/bbvHmzgYiQmJjY8+mnn5YVFRXJN2zYENnc3Cz18/Ozbtu2rTwuLs783nvv6V944YVgi8Ui+Pj4WD/44IPS8PDwQVOrx8fHJx0/frzQ19fX5uvrm/rb3/626rHHHmteu3Zt1IMPPti0du3azr6yr7/+uu+sWbOMfYoAAKSnp3cCwI9//OOQu+++u3n58uVdgPh8/N73vtcKAHV1dbIbb7zx0jVz584dl7snj2Zq0X8CWAzAn4iqAfwKQAuAvwIIALCbiLIYYzeNlgycwREEAQkJCW0JCQltAGC1Wqmnp0fS29sr7e3tlfT29kpNJlPfX6nJZJKYTCap2WyWmM1mqclkkpjNZqnFYpHY7fZrnoxZrVbBarXKu7uvPlZPJpNd5takUCguKRQDuTU5LBR2t/u3TwAkEglbtWpVyc6dO+Pq6+t1AHD69OlwmUxmH7XVVAa7vFperD+gD/N8lyBmV6paLhhCT/rJ5cZvTfwEjeaiz4YNZmVy8gTZHM1zWLx4cVVjY6O2o6NDabPZhAMHDsTcddddBaOebMGOXnW2ulx7UhtPjDx8pYOZg4JOl67q+lIV0dkZ9QHuLrgHH7hWodWjHnnwgjfG5UorZ+RkZGQoX3zxxeATJ06cDw4OttbX10sA4NFHH4247777mh9//PHmP//5z34bN24MP3jwYMmKFSuM99xzz3lBEPDSSy/5P/fcc0F///vfqwerPy0tzXjw4EFtTEyMKSwszPTVV19pH3vssebMzEzNW2+9VeFcNjc3VzVr1qwBJyr5+fmqBx54oHmgcz/4wQ8aHnzwwehXX321e/HixR0bN25sjoyMtIzk/zIajGY2oXsHOfXJaLXJuXakUinT6XRWnU53VRuUMcZgsViEKygSl5QHZ2Wi7+9IZLZYLBKLxSLp6uq6Kt9TIoJMJrukNDi7N/W3RigUCqtKpepTJGwymYwrEk7IZDK2evXqCzt27IhvamrSAMDx48enSKVS+7Rp01pc2RZ1U433Z96Q18kThi49vpHLO0oMoV9rlKq2bys0EolRt3JltW7Vqngax6YvxUVFecS7EdaAIwF2dLhbGtcil8vtK1asKPnkk0+S7HY7dXR0KI8cORK+YsWKiqGvvjYkrZISn10+vhKjxMOtAQBgN4aHHa5Nbz7tFdDTE/JXPFbwQ/zVtYqABs04BwUCoXVpvZxxxWeffeaVnp7eGhwcbAUAg8FgA4DMzEzN3r17SwBg48aNLb/+9a/DAKCsrEy+du3asMbGRpnZbBbCw8NNV6p/4cKFxiNHjmjLy8vlDz30UMM//vGPgLKyMpler7fq9XqXKP933HFHx4IFC8598skn+n379ulnz56ddO7cubyQkJBxtRksdxPijAgiglwut8vlcrter78qbddut8NsNkt6e3slfcqEyWSS9vb2SpytEX0KhbMiYbFYrjlQjDEGRz1So9E4ovgIuVxuM5lMD7766quJAH7OGDNeq1yeikKhsK9Zs6Z4+/bt8a2trWoA+PLLL6NkMpktPj6+fajrh8QGo+aMpkZzVhPn6SumUmlXhSHktKDWNvTfHMymTE4u9tmwIULQaPabocIAACAASURBVMblhJCs1OV70rcy6u9R/ppyTaTjsEf/HoMRGBjYO3fu3MoTJ05MAYALFy74h4aGdiQlJbW6tCEbOnRf6RrU+eoJERROgqUpJnx/+y21WX5ai8Xn5/hN4e/wc9cqAkq0IQtAOLxdWi9n3MEYAxENO67wsccei/jRj35Ut27duvZdu3bpnnvuuZArlV+xYkXn66+/HlhdXW36/e9/X7Njxw6fd955x2fevHmXPcenTZvWe/To0QGVz8TExJ6MjAz1+vXr2wY6bzAYbI8++mjLo48+2rJkyZLY/fv3ax988MEBy7oLrgxw3IYgCOhbcff2vrpx3W63o88K4aRIXPrbFwzd363JbDZLRiE+Yp7j9d/XWq+no1KpbOnp6cWffvppQkdHh5IxhkOHDsXIZLLiqKiozqFrGAAGJquRFXkf8A4VegWPtgZIJL01AcFnzTqv6qjLzvn7l/k+/LBaHh4+LpUAWYusOuxfYV2hH4dGScySSRO7kJKS0lRTU+PVFyR/7NixyKCgoG5fX98rrjYOC7FvF3rv944QTMKEUAQk0p7qhLDdPelVBcFym125Ea+WvIZHXXvfytGJUzAjFq7bpIwzblm5cmXHnXfeGfvTn/60PigoyFZfXy8xGAy2mTNndm3ZssXnBz/4Qctrr73mm5aWZgSAzs5OSUREhAUAtm7d6jdU/bGxsZbW1lapxWKhpKQk8/z5843/93//F/Tiiy9elnXo4Ycfbv7Tn/4U9P777+vvueeedgD46KOPvCIiIixPPvlkw9y5cxNvueWW9qVLl3YBwCuvvOK7Zs2ajqysLNWSJUu6dDqdvbW1VaioqFBERUWZXfufGjlcGeB4JIIgQK1W29Rqte1qr7VareSsSPRZI4bj1nSF+AgTY2xcBgaNFRqNxnrLLbcUffrppwlGo1Fht9vpwIEDsStXriyOiIi4KosJ9VCN92feTH7Rs12CBMHS6G/IbvfyLo0h+vaOo6RQNHnfc0+7eu7c/lYC92ODSZ+jL4/aEqXT5+nD3C2OOyAiLFu2rPzDDz9Ud3V1KaxWq3DgwIHoO+6447xUKr3mLGhkonr9fr1JUa0Yl8rftSBXtF1IDt5tX1VxYQoxwnfxQdVHuMu18S5SdOMojJiOYJfWyxk2Y5UKtI+0tLTeJ5544uLChQunCoLAkpOTuz/++OPyV199tXLDhg2Rf/nLX4L6AogB4Gc/+1ntvffeG2MwGMxpaWldlZWVQ1r+U1NTu2w2cRqxePHizueffz50+fLlly1gabVatn379gs//OEPw59++ulwqVTKEhMTe1599dXK8PBw67Zt20qfeuqpsObmZpkgCGzevHnG+++/v+306dPqn/zkJxESiYQxxuj+++9vWrRo0fja0BAAuTFl7bAhoi7GmGagc5s2bZIBODHGInEmIX3xEQ63pkvuTLW1tV8VFBR8zRj722DXXqkPX4kh+v4SAH+42jpHm7a2Nvn27dsTuru75QAglUptq1evLgoJCRl6ALSjS3NWU63J8GyXICJrq69/QYOPf0Gc067BfSd7NQsWlOnvvDOWZLJxlQ5R0impD90e2hr+z/Ap0i7pcAIzHwRD7mAnR6PfA8CmTZt+DmDt1dZ7LdTW1qp37NgxlTFGADB16tT6JUuWDBqUOCgMVmWhstjriFcc2WnCLMSptbXnZ/rux7LK8ngrpL0rsa/5CywLH/rKq0CACfvRgGVwbb3XTjsTNwMblGvt++OJ7Ozscrek1uWMGtnZ2f4pKSmR/Y9PmAGJwxltnOMjvLy8LsVH/H/2zjy+qSr9/59zb/a1adM9aZPuLbtoQSiLDIwg4DIuoAioKDPDqN+vzsBX/c2Myzh+3fA76qAgKsgogoIKRQRkkR0UkK1Luqb7vqdps93z+6OLla60SZvCfb9eeTU399xzn6Q3N+c553k+T0xMzMnU1NR1Q2mbN+Hj42OfN29exo4dO+JsNpvA6XSy3333Xcz8+fPTu5VopKDCEmGmz16fkOEdEsRZfHwzC/wCLkUxDHdlTQAqMhozfR97LJD18fGecBsOTkWmItfwsUHkd9ovHEDgUJvkTYSEhFjHjx9feObMGT0ApKenB+p0uobo6Og+58MwFibf51sfibBa6D3/9wFDqY9vRvoE8Q+YmF8S3wxx/RQcbTyDm9w7YCdwYDtK8BsY3NovDw9PO7wzwMPD43b8/Pxs8+bNMyUnJ8e1hlixu3btir3jjjvS/fz8fhVzTZpIsfp7tUtcJB7GUqFcs1KdlxsQ9LORYZ2dBnyMUlnou2wZxDExXvMemSamKmhvUHn4pvBQUbWIlzDtgRtvvLG8uLhYVVxcrAaAI0eOGAMCAlJ6FU3gYJWfkRfIz8pjhn89jI5QR0DQ2ZwpzlPMyJLK2EbIqm7EGZqOeHeH8LiwEfm4E94XSsfDcw0xbJfheXh4vJuAgIDmOXPmZAgEAg4AbDabYNeuXTG1tbUtUrAcrLKzsnT/T/yDxEVib1n+v0qoQ64oSjPGJDuDQn+KZ1in5Fe7BYJ61V13ZQS9+mqoOCZm6GPvKThpnjQ7/qX4nKTbkjTR/4qOF1WLVENtlrdDCMHMmTPNUqnUAQB2u539/vvvI9pijbtCUCnI1P5H61ScVcReW44AZwnVHy6cYz0iHFlZGVsDn7I4pLPpiHd3pWQO7yAHS3hHgIfH0/DOAA8Pj8cICQmx3nrrrZksy3IAYLVaRck7k2MaMxvztZu0TuWPyrjhmRtAOams3GSI+tYSEnY8XiCwXSk555TecENa8Ouvi5SzZsWQIS5OQRykPmB/QFriA4m1iUsTIwMOBkQMz8996JDL5c4ZM2bktG1XVFQoTp482Vm60Ika1QFVtt+XftGslb2mHC1CnFVG496q26t+FBrq6yPKEFAUjUx5IfTul/l8EZl4AvyKFQ/PIMCHCfHw8HiUsLAwy8yZM7O///77KI7jiKXRIt61f9ech/GwSYmrK3I39FAqEtdlB4WeVokldV3mNgiCgrJ9ly9XC4OChjw+XFwmzgvbHGYP2hVkZFyMG+2x1QMnSoCvuJai8tcHYWFhljFjxhRfuHAhBAAuXboUrNPpGgwGQwMoOFGeKEO9X21gHMw1N5vNss2FEeF76NzCVJnabvc3IzxvFC4FWqCU9H70VfI00vB3DPn3h4fneoF3Bnh4eDxOhCGibEb4jOIDuQemUVBUo1qyCZuiH8bDGTJcvTzsUCAUWnIDQ0+LpLKqLnXhiVRapnnwQat03LihHQi60OT7o2+ecb1Ro8hRhLuv44ZK4PtK4H05cDAU4GIBMNeTMwAAEydOLCktLVWWlZUpAeDQoUPGe+ffe0h/VN8sKhVdM3KhHRGJ6rOjwvYI5uWaNFKXS5WChOzxOBtmg8T9alhLkYbVvCNwtRBC/gPgcUppXet2OICPKaU9qh7x8AC8M8DDw+NhhGXCDPUedVCgNVAlhTQnGckRAFCBClmbQyCG2C2l3z0ByzYVBoSccSmUJZ0KhgEAGMaqmDEjX3XHHdGEZftdGXugCGuERbptusbQbaHhrI1106C0vBjYWQe87wucCwTg7rjwYQfDMJg1a1bul19+mdBao0R4+MvDEx7CQ1lDbZsnkMlL0mMDDwjn5OQECjlOcgI3Z0zB0SgOrPvDzO5EGjbyjkA/OQbgNCHkaQChAFYC+LNbzzDSzf+byxjUugX9oa6ujvnjH/+oP3r0qFIsFlMfHx/n66+/XjhjxozG/Px8wYoVK8IuXLggE4lEVKfT2d59992CESNG2JYtW6Y/fvy4ihBCRSIR3bZtW3ZcXJzXFRtrg3cGeHh4PAKxkVL1frVNnP+LStB4jK+xw563F3vDAaAUpfL/4D9RS7E0UwihVxU9YRh7mX/QeYvKx9zdTD8njo3N0DzyiI5VKodmRpiDXX1ZnWtcb1SoL6lDB94h5YC8AmBrE7A2EDCHAOgcF3+do1QqHUk3JJ06cPJAEgDkI1/9A34InIEZZUNtm/ugVK3JMiWojzMzc8zhDCD4Frelz0dyLAXj/hyYGUjH17wj0F8opesIISkADgGoBDCOUlo6xGYNexYtWmQIDw+3mc3myyzLIjU1VXTx4kUpx3G4/fbbox544IGqXbt25QDAiRMnpMXFxcKTJ0/KS0tLhenp6SksyyI7O1uoUqm8dsIL4J0BHh4ed8OhWXZJZlacVEQTSjrNlN+MmyvtsDOHcEgPAIUoVH6GzyIfxIPZAvS/squ7IMRZ5RdwucrHNyOKkK419xkfn3y/5cuFIoNhSJwA1sKWBycHV4d9FhYmtAgHWJfBZQfSCoBPXMCGEKDKjaFF1yAuWBSnFcVTLkyJqUNd6RmcCQKAYzgWaoDBEoGIxqE2ceBQh3/QOfMY9hwmm4tiAGADHkp7BBs8M1hPRAb2wWtkd4cjhJDFAP4GYAmA0QB2E0IeppReGFrLBsbKlSuDt23b5hscHGz38/Nzjhs3zvrSSy+VrV69WrthwwZ/h8NBDAaDbdu2bblKpZL7+OOPNf/7v/8bwjAMVSqVrjNnzpg69vfggw+GzZkzp27RokV1s2bNivTx8XF9+eWX5v/7v//T5ubmit55553itrYpKSnin3/+Wf7NN9/ktC36JiQk2BMSEuw7d+5UCgQCumrVqoq29pMmTWoCgBdeeCEwMDDQ0XZMZGRkzxLEXgCvJsHDw+M2BOWCTO2nWpvyhDKuK0egjWmYVj4Zk4vats0wq7diq5HDEE6eEFe9RpuaHhn7jVrjlxHTqXIwACIU1qgXLMgKfuWVMJHB4G5N9Z6hcCoyFJkjnxmZlzQvKSByXWSc0CKU9a8zRyNwPAN4OBtQUWBUJPBmDFB1pSoSTweEpcIM/03+kF+QxwDAbMwuDkRgIwBw4MjX+DrCCuuQhYq5B64xRH+kaJLrlGtyUVEcBehrWJXuMUcgAdk4jkiw/HhkgNwNIIlS+jml9FkAfwDwyRDbNCCOHDkiS05O1ly6dCn122+/zb548WJ7RedFixbVXL58Oc1kMqXGxsY2vfPOO1oAePXVV4P37duXYTKZUvfs2dMpdG/q1KkNR44cUQJAaWmpKCMjQwIAx48fV0ybNs3Sse358+clCQkJVoGg87z5xYsXpWPGjLF2ZffixYur9+/f7xMXF5fw2GOP6Y4fP96Xau5DCv/l4+HhGTDERsp8dvvk+W33i2YbWXVfjpmFWaU34ab2ZexMZGq2YZth8B0Crknlk50WGfuNWBtwOY4wXOc7PyF22cSJacFvvCFXTJvWZQKxp2CamZrg5OC0ifdMtI5fPj7a75RfP2furTXAt+nA/DxAIgWSYoCNkYBV7F6Lrz2Ig1Sp96hzfb/2jWGamXaHSQABvRf35oggcgFAAxpE27E9nGLIF7j6BSHOqnDjvroZDT87x1ZUxFHA9WesznwGr3lmBcwIM84iDAIMcwdq6KGU3kkpLe+w/SOAxCE0acD88MMPijlz5tQqFAqq0Wi4WbNm1bbtO3v2rHT8+PGxMTExCdu3b/dLSUmRAMCNN95oWbRokWH16tVap7OzWN2sWbMsp06dUpw9e1YSExPTpNVqHXl5ecKzZ8/KZ8yYYel0QD+IjIx0ZGVlXX7ppZcKGYbBbbfdFrtjxw6lO/r2FHyYEA8PT//h0CxNkeYqTyijCUeu+n5yG24rcsDBnMf5AABIRarfTux03YE7Cgg8Lc1PHQplYXZA8Fk9K7B3O+sp1OszfR97TCvQagcvnpmCkxZKzeGfhNOAAwFGQommfx1VlwHfVgPv+QCnggH0s5/rFAqXOEecqT6gjiAu4tdVEy209ttwm/kbfBMJANnI1hzHcf8kJFV01d5bYdnmonDDXnZmaYYruLExigNxPISN+f/BEs+E74SgAJcQDAncr0jkaerqiqHu05yHxyGErKKUvk4IeaebJk8OqkFuhNLunerly5cbt23blnXzzTc3vfPOO36HDx9WAsDmzZvzDx48KN+5c6d67NixI86fP58SFBTUrlhnNBoddXV1guTkZPWUKVMaqqurBZs2bdLI5XJOo9H8aiZq7NixzWlpaTKXy4UrtSFGjRrV9M0333R7P5VKpfS+++6rv+++++oDAwMdX331lc8dd9zR0N/PwtPwKwM8PDz9QlAhyNJ+pm1WHVPF98cRAAACgttxe0ECEirbXjuP8wG7sdsNybA9nJc4a3SGQ+XB+pNxrMAu76oNI5eX+K1YkRfw7LPRAq12UAbRxEEa/A/6pyUuSqxJXJwYEbg/MPLqioNRDigqAN5OB2IrAb9AYEl8qyPAcxUQKyny/cq33GefTxxxEVFPbcdibO1ojG4f/B/CIX0BCrw+NKANoag+JzJyFzuvMJUENzbqnWCb5yO5+D9Y4hmZXC2KkQIt5Bh+q1L5+Sa8807AUJvRgTZFnrPdPIYt06dPt+zdu1dttVpJXV0ds3///vbidlarlQkLC3PYbDayZcsW37bXU1JSxDNmzGj817/+VazRaJw5OTmdvrvjx4+3rFu3LmDmzJmW6dOnW9asWRM0YcKETqsCI0aMsI0ePbrx6aefDuG4Fj/h0qVL4k8//dRn/vz5DXa7naxevbpdYe3w4cOyb7/9VnHs2DGZ2WwWAoDL5cKlS5ek4eHhXqskBPArAzw8PFcJsZMK1QFVo8QscUu4DAMG9+CevM/xOZuJTA0A/ISfgkQQuWZhltvVMATCRrPeuN9PILB17XCwrEU5e3ahcs6cGMIwgzJhIi4X5+s/19uCk4ONjPNqi4NxDiCzEPjUDnwYDJTqPWPldQKHZtkFmVlxWhFzNY7YfMwvKEKRogpVUhdcZDu2R/wBf0iTQOLVKiJSWVm6MfSg8racbInc6fSxQ2j5DQ7UHcMUzySSq1GGFKjgg2HjLLVz7lwakpPjAdQNtSltUEqTW/96Pj9gkKVAp02bZp09e3ZdQkLCiNDQUNvo0aMb1Wq1CwCeeeaZ4sTExPjQ0FB7fHy81WKxsADw1FNP6cxms5hSSpKSkuonTpzYdGW/SUlJlqNHj6pGjhxps9ls9rq6Onbq1Kldztp/+umn5hUrVujDw8NHSqVSzsfHx/XGG28UMAyDnTt3Zq9YsUL/r3/9K0gsFrdLi5pMJvHvf//7cLvdzgDA2LFjG5955pnyrvr3FkhPyzAD6piQjwHMA1BOKR3Z+povgK0ADADMAO6jlNb0oa9GSmmXs3crVqwQAjjpJrN5ePrD+vfee29dTw16uob7e9yKFStuAfDG1fbZbyhs0hRpjvJ4/0KCesMJJ/kMn0XmIrd9/f0W3FIwDdPcdhOVKwvTg3UnukwOBuCSjByZqVm6NIyRy/uZmHsVuNCkOaPJi/ggQqPIVnSpWtQ9zibg50LgYwJ8qgMs7q8C2zceAqWXu9vpieseAFasWPFXAHdebb+9wdaw2ZpdGl/WwvZrJagUpeIP8WGCE04GAOIRX7UAC8xuNdJtUKrW5KQb/U5K5+TkBIo4TtoESe1EnLJdxJirvB77iBxVSAMLPXx6b+xFUOrEvn05OHWqLWSqjj7/fI/FvPp77fcXQsiNAP4fgHB0mOillI7ub58XLlwwjxkzprL3lp6jrq6OUavVXENDA3PzzTfHrl27Ni8pKanLxF2e3rlw4YJ2zJgxhitf9+TKwEYA/wawqcNrzwA4QCl9lRDyTOv2/3jQBh4eHjcgqBJk+Xzno2UbWI/FzQsgoA/ggexP8El0IQpbqrvikF4IITcJkwb4g8Q1BwSdK1D75nSZCMlqtbm+jz0mE+n1HpcKFdQJinXbdQ26L3QGtvlqioM11wHHSoB1EmCnDrBHe87K6wwX6pXHleWyFNmAVruCEGT7LX6bvxu7DQCQhjS/H/FjfSISq91ip9ugTm3g+ZwI2UXBb7PNOpZSQQMUlWNxnslBpGccAQlqcR4Ydo6Ay9WILVsqkZXl7dKnn6Gl0NglYChl2dzLgw8+GJ6ZmSm12Wxk4cKFVbwj4Bk85gxQSo8QQgxXvHwHgOmtzz8B8AN4Z4CHx2shdlKhOqSySHLcExLUG0II6WIsztqIjdElKFEAwD7sCxdBxN2IG/s1oCKMo0JvOMSJJbWdBs9ELK70WbiwTjZhgmdio9vgYFelqMzGD40ynws+ur4fWF8B7KkE3lcCh0MB6h1Zi9cKFFRYJMzw2eejZ2yMW67xRCRW5SBHmY50P6Dl+g1DWGMQgmzu6H+gEOKsCdEfs0RzWa6puYVxBCBV8C0ZicvyUgSrPHJSERrwI+yIgjfF2veO3V6Fjz5yobx8ONTeqKCU7hxqI9xNcnJy7lDbcD0w2DkDgZTSEgCglJYQQrq9MRBClgNY3rrJ5zbwXDd4xbVPYZemSrOVx5VRxEX8B/PUYoi5xVictQEbYipQIQOAb/GtUQQRNxqja3s7viMicV22znAghGWdneKTxbGx6X4rVkQSoVDb1bHugG1kK4J3BVeGfRoWJmwQ9mFmkVKgvBj4uh543w+4GABgUD//oWKwr3tiI+Xq79XN4gLxAIu2deZO3Jm/FmvltaiVOOFkvsSXkX/AH9KGusq2UNSQow/f7z+ivtRyQ1lZPAAUIaRgFC751cDXM6FxAlhxBBaMwvBKYrdYCrF2rQqNjV2qSHkhzxNCPgRwAEC740kp/WroTOIZLnjtIJtS+gGAD4CW2LshNoeHZ9AY6mufrWazfb7z0QjqBYMnpXkFMshcS7AkcwM2xFajWkJB8Q2+iRBCmBWP+Pree6CcyicnIyD4bCzprFFK5dOmpfssWOCZ90fhkmfLcw0bDQK/Y37hBL05U5wTMBcCm5uBD4KAglAAHlVT8kYG7bqncEpMkkzVEVUUcRGPyFpKIOHuxt05G7Ex3gUXqUKVdBd26e/CXfmeOF/vUJdak53pH3jWMLG0pDCytjYeADIQnTMOP4daIfeMqg8DG/agChMwvJLay8qy8OGHYXA6e1SR8jIeBhAHQIhfwoQoAN4Z4OmVwXYGygghwa2rAsEAvDq7mofneoI4SKXqB1W9JEvi2ZCZPqKE0rkESzI2YENsHerEHDiyHdsjF2BBZjSieygOwzUGhZ4uU6oLOsfjE+JQL1hgVkyd6nZHgLExtQH7A0oMGwwh4kpxLyEnLhtwuQDYwAGbdECNwd328HSGsTD5Prt9xMIqoccdXT30TdMxveAADoQBwAVc8DfCWD8WY69qdWugEOKqDdYfq1fKSkJm5OdXBlitUQBwDuMyJ+KU0QGRZ8YBBA5sRwl+A4NH+vcUJlMatm6NBaXDTXp9DKV01FAbwTM8GWxnYCeApQBebf27Y5DPz8PDcyUUDkm6JEt1VBVFXMRjITP9wQc+jjaHwAKLyAkn8wW+iF6ERSYDDJ0SyRjWVqI3HhCJRJaITp2xrMVvxYoaSXy8+xJvKaikSGIO/084F7gv0Ego6SE50tYAnC4B1guAL/WAbVArGV/XcGiSn5Xnyc/IYwmIp6vZtZOEpAozzMpsZGsAYDd2G3TQpWqhHRTNcaGwIVdnPKiVEqtkdnaOQ+lw6ADgOCZlTMWRKA6spwa8LmxEPu6EV0ws9AlKORw7loGDB4dsRXSAnCKEJFBKU4faEJ7hh8ecAULI52hJFtYSQgoBPI8WJ+ALQsgyAPkA7vXU+Xl4eHqHrWGzfXb7+AxlSFBv+MHPvhiLMzZiY1wTmgQOOJjP8XnMYiw26aBr15CWSKsyQsN/MDCMq9PSPhGLK/1XrYIwONgt4QrEQSx+J/wKjB8a/WUFMmP3LRurgP0VwPsy4HsdwHm7Isk1h6BSkOWz28efbbwa5Sb3QEBwN+7Oex/vyxvQILLDzn6JLyMew2MmAQQezB+gnMonxxQQfDZO7nTkz8nO0YpdLjkApCEu28OOAId3kIMlGD5qVxxnw44dhbh4cdCvETeSBGApISQXLTkDBAAdiLRoJ0aOdO/vxOXLg1a3IDExMfbNN98smDp1Kq9G1AWeVBO6v5tdPWrz8vDwDAIOVKmOqOqkGdJhMXMXiEDbg3jQtAmb4mywsTbY2M/wWcxDeCg9EAGNGm1aljbgcpc/5IxaXRDw//6fhlUoFAO1Q1QpKtBv0TeF7AgxMo7uioNVlQA7a4H3NcBPQQCGSwLitYUTtarDqipphnRIV2BkkLnuwl05n+LTWA4cKUOZfA/2hMzDvCJPnI8QV12w7kStXFkS72e1ZszMy4tgKRUAQDGCC27AOb0HHQHgRWTiCbg9KdtjOJ312LSpAQUFw+Je2AOzh9qA6wGHwwGh0COpRkPKcIuJ4+HhGQgUDolJkhawIUApzZB2DqXxYkIR2vwAHsgQQeQCgCY0CTbhk2gE7a7rzhEQ6vWZQS+9FDQgR8CFZs1PmvQblt9QevM9N+t123QxjIPp8GtAOSA/D3gzHYiqArTBwCPxrY4Az2BDwYnyROn+G/0l3uLsRiCicTImtw/+z+BMUCpS3S7jKRBazIbob4lcWRJuqK1Nu9VsjmlzBGqhLhuFS77NkHouKfZppOHvw8gRaG4uw5o1LhQUDPuEfUppXlePobZroJhMJlFERMSIhQsXhkdFRY2YPHlytMViIYmJibFHjhyRAUBJSYkgNDR0FAA4nU4sX75cFxMTkxATE5Pwz3/+s5Nq5VdffaUaO3ZsXEJCQvycOXMi6urqGAD4y1/+Ejxy5Mj46OjoEffff384x7XkYScmJsY+/vjjoTfddFPsyy+/7Jk6HEMM7wzw8FwnsLVsjt/nfnXqg+p44iLDSSWjnXCEW+/DfVkCCDgAaIRV/G7lz9Mq7PZOUzXSG25I93/mmSjSz2kcQZ2gJHxjuGnyvMkYvXJ0nDJD2WFwzzmA1GzgWRMQaAXCw4GVcUA2vwowhJBmUqLZoSnW7NbEMQ5mqKoyd8ktuKUsDGHtSlg7sdNYi1o3TTFSTqnOTTNE7Q4TsE2ysWVlpknFxe0rV02Q1I7Fuc7HQAAAIABJREFUeVE1/DxXEXcp0rAaXhtu2ImaGjP+9S8Vamv7VW2aZ/DIz8+XPPnkk+VZWVkparXatWnTpm7/Z6tXr/bPy8sTp6SkpGZkZKQ++uijVR33l5SUCF555ZXgI0eOZKSmpqbdcMMN1n/84x+BALBy5cryy5cvp2VmZqY0NTUxW7Zsaa/rUltby/7000+mF198scxz73To8FppUR4eHjfhQLXqqKpGavKOWdKBEoUoyz3iKYe+tB2a4QJIldMpfiUvL+Z5g8HkIxQ6AbiUc+dmqubOvfr4Xw4OZZoy1/iRUao5p9EDHbXRHY3A2SLgQwbYogcar4nP85qAwiFNkWYpjyljCCXsUJvTFQwY3IN7ctdibYIVVmEzmgXbsM34CB7JYAYyL0dc9cGhJ6sVquJ4QmlTUmFhmb6hoX123gFB4804acuDwXMzmnciDRuHkSOQn2/CJ59EguOGdAxECJkN4G0ALIAPKaWvdtHmPgAvoEUm9AKl9IFBNdILCA0NtU2aNKkJAMaNG2c1m83dSuEePHhQ9Yc//KGibQ4oMDDQ1XH/Dz/8IM/OzpYkJibGAYDD4SDjx4+3AMB3332nfOutt4Kam5uZ2tpaQUJCQhOAOgC4//77vayKuHvhnQEenmsVCqckS5Kp+kEVQZzEd6jNcQ/Urg28YJ7vVxbjVxea/X5RUSQHkHKHQ/LPvLzovxmNF8OXLy+QjR9/VY4Aa2Urg3YHVYZvCtcJ6zsWB2uqBQ6XAmslwC494OITgL0Mpp4xa77VKAS13psE34YKKucduCP3c3weAwCFKFTux/7g3+K3Jf3pTyBozNMbD/oIhE0GluNqfpuba9fYbIa2/RyIYx52VV3A2DA3vYXOzEA6vh5GjsC5c2lITh5yewkhLIA1AGYBKATwEyFkZ0c1IEJINIBnAUymlNb0VKi1tX04gGhK6X5CiBSAgFLa4Ll3MTiIRKL2ZHuWZWlTUxMjEAioy9Uyzrdare0KYZRSEEK6Tc6nlCIpKan+ysrGVquV/PnPfw4/ffp0alRUlOPpp58OaW5ubvfSlUol17m3awc+TIiH5xqErWNz/bb61aj3q+OJk3imoNAgQ4izSm88UKXxy4gBgElqdd3DwcG5bb8CxXa77DWLJZaLje1bwigFJ8+RZyX8LSF38m2T/aL+HRUnrBcqgJoyYHMaMKUIkPkAc+KAHQbA5ZUzztctLlgUJxQZ/p/5GwS1Aq+SxO2JWMQ2TMCE9sH/SZwMyUTmVea0UE6pzkszRH+rFwib1GKns2h+VhajsdnaZ/8pwD2MDfn7cKvnHIFEZGAfhoeDTKkTe/dmeIMj0EoigCxKaQ6l1A5gC4A7rmjzGIA1lNIaAKCUdlubiRDyGIBtANa1vqQD8I3brfYS9Hq97ccff5QDwGeffdYeNjRz5sz6tWvX+jscDgBAWVnZr+7b06dPbzxz5ozi8uXLYgBoaGhgLl68KLZarQwABAUFOevq6pjk5OTrKnyMXxng4bmWcKJGeUxZJUuTXVMa9kJhQ67eeCCAFdh/FZM/Q6OpsXGc+dOyMgMAmIuKNKtWrRK89dZbmRKJpNvZIcbG1Cb8PaHO77RfFEApUFIIbLcA72uBtEAA12SS2LWC9LK0VvGTAkwzMzwGolfwW/y2OB/5yhKUKNqqa/8Rf0xRQOHq9WDiaggKPV2pVBXGA4DKZsu+NSdHJ6T0V07/83gxcxOWei6ZNwHZOI5IsMNgUtHlasSWLZXIyvKm6yUUQEGH7UIAE65oEwMAhJDjaAkleoFSuqeb/v6EFgfjNABQSjN7W0m4agZRCrQ3nnnmmbIFCxZEbNmyxW/KlCntuThPPfVURUZGhjguLm6EQCCgS5curXjuuecq2vaHhIQ4161bZ164cGGE3W4nAPD8888XjR49um7RokUVCQkJI3Q6nX3MmDGeq4LuhRBKPSh17CYIIY2U0i4Tn1asWCEEcHKQTeLh6cj69957b11PDQghNgCXOrz0AaX0g9467uXavwXAGwAACqc4W5ylOqQyMk7mmlgJaIFSpSo/PTD0dCwhXQ86WH//nO/0+vGffPppuzrS2LFj615//fXsjsvLbUjzpNnjnhgXKKxvagJWVwHrQ4AStyu7XCc8BEovd7fTE9c9ALxIXvwrgDuvylIvoxrVwg/wQUIzmgUAYIChbimWZhF0XxNNILDm64wHVUKh1QcAgi2W9On5+THkilX+j/BI2qP4yHMz4EaYkYpQSOD9Got2exU++siF8nJ3Dozr6PPP9yiT3tu1Twi5F8CtlNJHW7cXA0iklD7Roc0uAA4A96Flpv8ogJGU0k5VrAkhpymlEwghP1NKxxFCBADODaTOwIULF8xjxoyp7O/xPN7HhQsXtGPGjDFc+Tq/MsDDMzg4KaU3eqJjpp4x+3znIxNWC4dzwZwu4JoCQ84Uq3zM3Q5qRDExJu0TT0Q9zLI1Do4r2rx5cygAnD9/Xv23v/3N+Morr+SwbOsqMQen7ktdVuT7kbHADxnAPAPQ6D8ob+X6xWPX/XDHF76OuZhr3o7tUQBghll9GIcDpmN6F6EglCpUBaag0FMxhIABpVxcdbXphrKyTt+NfZiV7lFHIAQFuITgYeEIWCyFWLtWhcbGoVD56u3aLwTQsQiiDkBxF21OUUodAHIJISYA0QB+6qK/w4SQ5wBICSGzAKwAkNxv63muK7x/ea8XhEVCDg5UgUPzUNvCwzOYCEuFDuURZab/Z/4GYbXQvcvBQwzD2MvCIvdZVD7m7hR7qHzq1DT///7vWNI62l++fHnpXXfdVdrW4PTp05oXX3zRwHEc2Ea2YuwTY8sj3w/TA49mA7fEAo3X0AoKz3BkFEbVjcO4dqnCIziiM8Ms+3UrzhIUeio3WHcqrtURsE8oKcnpyhG4gNFZc/Cd50JhtChGCrSQw/u/O2VlWXj77QA0Nnpg1Y/2Hs7VOz8BiCaEGAkhIgALAey8os03AG4BAEKIFi1hQznd9PcMgAq0rEb8HsBuAH91g5081wHDfmXAd6cvg9YKn5ShTk7CWTkp18TJuWaX3OXgZJyLk3OUk3KUk3IMJ+GEVESFVEgllKVSMJChm/ADHh5vxvdrXyFaZomuKcTimkyd4ZCeYZ1d68QT4lAvWGBWTJ3aaTD05JNPFjU1NTF79uwJAIAjR474/eOpf1TvMe1pFjUXlQKjHEDBNZVPwTO8mYu5RUUoUpajXMaBI1/hq4g/4o+pUkg5VtBUoDccUAhF1ggAYDiu/pb8/PpAq7XTNZwPfd4EnA73WHVhNcqQAhV8IPVI/+7EZErD1q2xoNTtn4WKaa7YFbK5ukXts/9QSp2EkMcB7EVLPsDHlNIUQshLAM5QSne27vstISQVgAvASkppVTf9cQDWtz54eK6KYe8MdIRwRMBaWRVrZVXo8uvSGUooR8XUwkm5JpfM1czJOTsn45wuuYtyshYHgkooy4k4IRVRCRW0OxDX1GfHwzP0UE6tycoICP65+3AnlrX4rVhRI4mP79IJIoRg1apVBc3NzcwPP/ygBYBDFw5FP4LJ9k04K2Lg/sEBD89AEEBA78E9OeuxPsEBB1OPevHX+Cr8UcXU3SH6UzGEUBYABC5XxezcXEZlt+uu7KMKviWjcTHABolnQnfkqMIliBGA/lfyHgwo5XDsWAYOHvRImFSSJM/0XeinBgXjcEvRRkrpbrTM4Hd87e8dnlMAT7c+eoQQMhktHko4WsZ2pLWLYVVpnmdouO4HtIQShjQTBdPMKAQ1ff84OCHXxEk5Kyfjmjk5Z3PJXE5OznGtDgThpBzLiTkhFVExFVAJWMhBMCyrvvLweB7OEqw/UalQFnfrCBCxuNJ/1SoqDA7Wd9cGABiGwQtPvnDqH6f+Mf5A84FgAPgMZ0aEAkWvAaU9HcvDMxQEIMA2G7PNyUiOAIAMZPpekNdPCSV+lQAgczjy5uTkaMUuV6ek6kbIqkbjoqwOPp6ZsZegFucB6OHjkf7dBcfZsGNHIS5edHvuFAuu6W3/7wr/5PNTmzqTN4YlfwTgKQBn0bKKwMPTZ657Z6C/MA5GyjgYKep7b9sGZam91YFocslc9tZVCJdL7qKclCNUShlOzAmoiIpbw5hkYIbBkiwPzwBg2eYivfGATChqNHTXhlGrCwKee07DKpW9zkxqTmvSR/51ZOQux58PzcGhOT+A0wDA60CoHOD+DnSr1c3DM1SMx/iaTKTnpSMzHAC+KC/Xx8lklpuAizPN5giW0k6/13YILTfhJ64YoWqPGCVCA36EHVHw7pwkp7MemzY1oKDA7VXBg9mGgiO6j2VRohpvD8mso5R+N9RG8AxPenQGCCGX0FICu9MutCw/9Vuy6nqEuIiItbAi1sL6CPsoxEAZ6uTELQ4EJ+dsnIyzu2QuFyfjOE7GEU7KEU7CCaiYivg8CJ7hhlRWbgoJOxLBMFy3XwihXp/p/5e/GEhbffluIE7SGPNGTGnQXl8dsCxfgo2x3wK5MwH2JKACgOcBvRzg/gzwcnk8XoVcUZT25xA29q+5wsAyh0PioJR5Ny9Pl8VxdhboVP3UBcY2E/tr05DQKWzILQhgxRFYMArBHunfXTQ3l2HdOhFqa0Pd2zHl7ldeNm0K/CpG0Bqq5Y0QQm5ofXqIEPIGgK8A2Nr2U0rPuetcI0e6t9L05cvotW6ByWQSzZs3LzozMzPlavvftWuXcvXq1YGHDh3K6p+F1w+9rQzMGxQreLqFcETANrEqtukq8yBEtJGTcVZOytlccpedk3NOTsa5XDIX4aQcqJQKOBEnoiIqoiwVd+ny8fQJ4iT2obZh+EGdvv4p2X7+qT0WRZKMG5fm++ijcYSQ7sXXAYjKRfnjnhinlJQVs8B4F1AcCQAygO4Bsm8Bos+hJd55JRAuB7g/ANXuez88PP2FswaEnClR+5jjAQZP6HTZL5rN8Q5KmXKOUz8G6LcAeR2PoIDrAWwuOoqpnokHZ2DDHlRhAnoMyRtyamrMWLcuEDabW1fQJcRR+0XQl3XzFRneUq24J1Zfsd1RzpQCmDGItgwLHA4Heplbuu7o0RmglOYBACHECGAEWi6sNEppd9JWPF4AoYQhNiJnbIwcNUNtzXWBCBuG2oThAyGu2tCwwxapvLInR4BTzp2boZo7t+cfYwoucF+gKfbV6AhC38kF/hwL0F85DiqA2wdkTQNiUgAZBfA4YJQD3GKgU/EeHp7BgmWbi3TGgxKRyNIe3hIhkdT8l1D485t2+3gA2ApoZwD1y9FyN6cAXYk3sr/AAs9IiBI4sB0l+A0MHunfXeTlmbBpUyQ4zq3hzvGiiuwjuo8DtWxTuDv79RSU0jbp0Ygrx2aEkGsiedjlcmHhwoXhZ86cUQQGBtrffvvtgiVLlhhTU1PTAODSpUvihQsXRqSkpKRt27ZNtXLlSr2vr69z1KhR1rY+nn766ZCSkhJhfn6+yNfX17l161bzkiVLwi9evChjWRavv/56wfz58xuG7l0OLT2GkhBCVISQLwAcAPAIgEcB7CeEfEkI4St28vDwXBUCQWOeIfpbRiqv7D60gZBmzbJlZtXcuT0mAhIbqR357MiCuP8N8iV0UjXwdNyVjkAbfoBrP5ARDTQBLdl1y4CIr1rDh3h4BhuZoiTdGL0rUCSytBfEYjmu5tbc3LrX7HbM67By9TRguIwWbf9/4/H01fiLp2oJuLAR+bjTyx2Bc+fSsHFjrDsdAQLqeFZzJD01fE2klm3ybtWkrtnWxWtfDroVHiA/P1/y5JNPlmdlZaWo1WrXjz/+KFMqla4TJ05IAWDdunXaBx54oMpqtZLHH3/csHPnzqyffvrJVF5e/qvp/4sXL8r27t2blZycnPvaa68FAEBGRkbq5s2bc5YvX26wWq09rkBfy/QWV/4OgFQAUZTS31FK7wIQiZaiFv/2tHE8PDzXDnJlUboh+lu9QNDc/QBcKKz1X7myRjZ+fI8zWtJ8ac7EBRNZv1Nnm4BgDfBjr3HNQa0OQVhrPK0DIAuAqNuAiK2A2nHV74iHpz9wTQHBZ7JCw47GEeaXwazY6Syen5XF+DY3BzEAPgHy9K3XaiPALAQivsAc05N411OhKxz+jRwsgduTcN0GpU7s3ZuB5GS3fgZqprnstH595Svag8OuijshJI4QcjcANSHkdx0eDwHoulbLMCM0NNQ2adKkJgAYN26c1Ww2ix966KHK9evXa51OJ3bs2KFZtmxZ1fnz5yU6nc42atQoG8MwWLRo0a+Cq2fPnl2rUCgoAJw4cUKxZMmSqtY+m0NCQuyXLl26Jj6v/tCbMzCZUvpCazELAC1Zw5TSlwDc3N+TEkL+ixBymRCSQgj57/72w8PDMxygNv+gs5kh+uMtFVS7gchkJYHPPw+RwdD9wJ6DU7dVl3bT0jH+otpHy4Fb44DmPkv2hgHO/UBGMGAHACdAvgM0C4GoEGD07wHdT9fIDyiP98GwtuLwyL2Nak3Or4qGqWy27NszM/1kTme7KpAvwH0G5AhaRTxSANkCBP3GY8b9A5n4kxcXMXS5GrF5cxFOnXLrqsgMaU56ifFNzU2SYu9OlO6eWLTkd/oAmN/hcQOAx4bQLrchEonasxpZlqVOp5MsXbq05tChQ+otW7b4jBo1yhoUFOQCWmrNdIdcLu84lvWozcON3pwBty+ZEEJGouUCTQQwBsA8Qoj33oB4eHj6DWEcFXrj9zU+vtk9fsdZf//coJdf1gh8fbvVMmetbOXYJ8eWRb5PJYQaKfCffs1gRgP2fUDGWKCx4+uVgPADIDARGDECiH8ZCCjh5Zd53IRMXppujE72F4kbtB1fD7JY0udmZxuFlIqvPGYKYP07UPjLKxsCgc3ulxF9Gmn4K3pM5h9S7PYqfPBBI7Ky3BbHL4CrcX3AzqwDuk1xUsY5bGsAUUp3UEofBjCPUvpwh8eTlNITQ22fp5DJZHTatGl1Tz/9dNhDDz1UCQBjx45tLiwsFKWkpIgBYMuWLb7dHZ+UlGT59NNPfQHg4sWL4pKSEtHo0aO9sX7EoNDbD91xQsjfAfyDdnCjCCF/A3Cqn+eMB3CKUmpt7eswgLsAvN7P/nh4eLwQkag+W2c8EMKyjh6VPkQxMSbtE09EEZbtVr5PmarMGP2XEcEC678rgJXd5gb0lZGA7Wcg/SdA8iGg/QrwrcQver+pgOxvgOxFQDcdqFsKVN0D1Em6llrm4ekBrtk/6OcCH9/sX4egUEpjq6tN48vKegxNeQzan19EvNyFo60DmxVGIDEFiHJPZNtDSMNq90pGuhWLpRBr16rQ2OjXe+O+EcrW5x/Vf6w0Cmujem89PKCUnvT0OfoiBTqYLFmypPq7777T/O53v6sHWhyEd999N2/evHlRvr6+zgkTJljS0tK6/P1ZtWpV+eLFi8NjYmISWJbFunXrzFKp9Lq9v/fmDDyBlqp2WYSQ82j5IRwH4Ge0JBP3h8sA/kkI8UNLMt9tAM5c2YgQshzA8j7aycNzzTD8r33KqXxyTQHBZ+II6XF1kcqnTDH53H9/94MhF2zG9ca8sC1KNZBkAc64ddByE9B8E1D4b6DwK0C9CfDbD/jYW1dFnQDZD/jsB3yeAJx3AtWPApU3A018IQ/3Mvyv+84wrK1EZzgkEIvrf70yRqk9saQkP6q2tkdHoAGKynG4qHSB5ANjlUCZEKhjgQURwCkT+livplvuRBo2eLEjUFaWhQ8/DIPTXTP3lFuqPG/6KHBnLEso/xUeBsTGxto71hh46aWXytqeHz58WHH//fdXCgS/3C7uueee+nvuuadTTYK33nqruOO2TCaj27dvN3vG6uFHb9Ki9QDuJYREAkhAyw/k/1BKs/t7QkppGiHkNQDfA7AAuADA2UW7DwB8AACEkMYr9/PwXKsM72ufawwKPV2mVBf0PMAgxKG+7z6zYtq0bgdDgjpBydj/GsvKzUc54E5fwOYxYWghgAVA3QKgrgJgNwKazwDtBUDe1qYWEGwEAjYCAdFA0wNA1TKgSt/F/Yvn6hne131nuiuox3Bcwy35+XWBVmuPs9I2iOrH4ywpRbAKgAv4JAeYG9uihXVOAfwlBHi7uKc+emQG0vG1FzsCJlMatm6NBXXPoF1KHDVfBW9pmC3P9t73zNNnZs2aFZmXlyc+fPhwxlDbci3Qp9mX1sF/uwNACIkF8BdKab+SUyilH6FlxQGEkFfwq5hIHh6e4QjD2kr0hgMikdjSs7Y1y1r8/vjHaklCQrd5BJrTmvQRf4sKZO2P1gCfD6rChz/gWglUrgQqLwDi9YB2G+BX1mEaNhOQvgjoXgZ0U4C6xUDVQqBWxocR8YDatIHn8zR+mZ1i8AUuV8Xs3FxGZbf3WDXYCbZ5Gg5bMhET8surt1qAPxcBr7dW2n03GJjRANxx9dro45CJffCUPOnAoJTDsWMZOHjQbYP20aLSrIO6T4L92KYwd/XpbRBCxADuBmBAh7Fdq+DLNcf333/f70lpns70VmdgNCFkX6vyz8uEkEBCyHa01B1I7e9JCSEBrX/DAPwOwOf97YuHh2foEUuqMozRu3xFYkuPcb1ELK4MeO65JklCQpc/ysRJGmNfjc0e/T8SEWuPZIHPh7RozhjA9m+gqBC4+DWQcTtQLQHaFSlcAH4A1MuAiGBgzINA2CFAxnXfJc81DMPYS8Mi9tVp/DI7DbRlDkfeHVlZMpXd3uN3hANx3oNtpacxMaTz3n+WAje3Dv4pgEeNQMHVhVQFoBAnYADbq4DI4MNxNnzzTS4OukfikwFnf8H3UPqF8LVRfmyTvPcjhjU7ANyBlpXKxg4PHp5e6e0msh7A+wBOApgN4ByAzQAWUUoHknW9vTVnwAHgT5RSvk4uD8+whLo0fqZMbeDFXn+8GbW6IOC55zSsUtllQR9RhSh/3BOjJZLSfzuAZztVEh5KBADuBBruBBqqAWYToPkU0J4F2t9LPcB+Bvh/BvgbgeaFQNVjQJWx5T7Hc40jkVWYQsOOGBjG1UkVyLepKWOW2RzBUtpzaC5An8C7uTtwZzerZgIAW3KAcSOAagFQKQTuNwKHM4Fu8+87GIlanIMSkoEmG3gAp7MemzY1oKDALXUONExT6UHdRjJW3HOC9jWEjlI6e6iN4Bme9DYzIKaUbqSUmiilb6NlRuyZAToCoJROoZQmUErHUEoPDKQvHh6eoYEQV31I2NGivjgCwtDQrKCXXgrq0hGg4AL2BaRNXBAjkJROpcAzA1YL8iS+APffQNUZwJQCXHoKKAlprVvQRi4g+V8gNAoYnQRErwV8G3q/3/IMS6hNG3AhQ284FNuVIxBeV5d+a25udG+OAAC8hv8xvYc/9SK1HeYE1uf+sn1cBfw9qFczGdhwAFaEwv3SpAOlubkMa9a4UFAQ6o7ubpVlmYqNb/qNFZcFuqO/YcIJQsiooTaCZ3jS281JQggZh1/qDVgAjCatVR0opec8aRwPD493wgqaCvTG/UqhsPcYXMm4cWm+jz4a13bf6Aixk7qE5xNqtCdPE2CcvyeThD1BAmB/Cyh+AyjeAyg2AtrdgMbaOvDnABwHVMcB1UrANReoeRiomgVYeM9g+MMw9jKd4QeIJbWd4+8pdY2pqMgaUVnZp5npz7Ew7Vm82sc4+d/VAytKgfdanYDXQoFbGoCZ3YWFULyPAkyC90lp1tSYsW5dIGy2HiWI+4IQLsv6wJ1lS1UXvLdmgudIAvAQISQXLZWrCVrqxI4eWrN4hgO9OQOlAN7qZpsCmOEJo3h4eLwXmbzUFBJ2NJKQXmc6OeWcORmq+fO7HOBIC6Q5Y/8rViaqXs4BXwzrpXwWwFzAMhew1AP5/wF8PgO0pwBlW0axBWC3AtqtgDYMsN3XGkYUc8WqAs/wQCKtyggN/yG8q9UAQmlTUmFhqb6hoU+D0qNIMj2Az68yYfatYuCEEjgvb8leWRIBXEgF/F2dmj6MdCz3QuWgvDwTNm2KBMcNWEo2TFCbd1T3sTpMWO+WMKNhyBxPn2Ak3HsNXcbA6xZMmzYtavv27blarbbzdd8FJpNJNG/evOiOcqXu5O677zbMmzev7uGHHx5W4e+9SYtOHyQ7eHh4vB7q0AZczNVoTb0PcAhp1jz8cLHsxhs7D/IpnKHbQrMi33MICY2TAaW9hzgMI1QA9yeg+k9AdSYg+hDw3QJo84H2QWM+IH4TCHkTCJkANCwCqpYANeoOyck83gq1+wVcMvtq07tU42E5rmaW2WzzbW429qW3dMTm3IJD/ZixF1Ngaw5wYwLQwAIlImCRAdiT/auItBuQiY+90BE4dy4NyclusIu6lqvOZrwX8O11WTuAEKJqlYG/elWpa4DDhw9nDbUN7sLpdKJjzYTBpDc1oVUdnt97xb5XPGUUDw+Pd0GIs0pnOFih0Zp6lyMUCGr9V66skd14YyclIMbKVI75rxFFUWu2EkITIoFSlUcM9hKiAftrQGkucHkfkH4/UKlomcZt5zSgfBIwBANj7gYMyYCyT1NcQwwH4shD2HUlpcowjgq9cX91d46AyOksnp+Vxfg2N/fJwS1BUOF4nA11QdCH7N+uiLEDa8y/bH/vA7zm374ZgCIcQ3j/+vYQlDqxd2+GOxwBGbFX7Q/dVLwucFf89egItLK59e9ZtBRwPdvh0amg63Dir3/9a+DLL78cAADLli3TT5w4MQYAduzYobzjjjuMABAaGjqqpKREYDKZRBERESMWLlwYHhUVNWLy5MnRFouFAMDRo0dlsbGxCWPHjo176623Aro6F8dx+P3vf6+Ljo4eERMTk7B+/XoNAOzatUuZmJgYO3v27Aij0Tji9ttvN3Jcy5zNihUrQiMjI0fExMQkLF++vF0u+PDhw4px48bF6XS6URs2bND01v+ECRNi5s+fb4yNjR0BAO+9957vqFGj4uPi4hIeeOCBcKfT86VsevvyLOzw/NncI84/AAAgAElEQVQr9vFZ6zw81wECoSXXGL1LLJVVdSF1+GuITFYa+MILEBkMwVfuU6QrMm++N6rR5+JMCfDX6yqmlwEwC2jcDOSVABfWATlTgPqON+AmgPkK8LsdiAkDRv03EHK5w2rCUOIE25QPfd5OzE/7C97IGI8zpSLYGQPyvDbR292IJdWZxuhkpURa0+VAX2mz5dyRleUrczr7lKBbC3XZSFzWWCEf4P94cS2wpPyX7ef1wAkpJKjFGcghhZuq97oBl6sRmzcX4dSpAdc4GCcuySwyrpb9Rpard4dpwxVK6bzWv0ZKaUTr37bHkEozD5RbbrnFcvz4cQUAnD9/XtbY2MjabDZy5MgRRVJSUqeVkPz8fMmTTz5ZnpWVlaJWq12bNm3SAMCyZcsMb731Vv758+fTuzvXpk2bfC5duiRNS0tLOXDgQMbf//53XV5enhAA0tLSpGvWrCnIyspKyc/PF3///feKsrIydvfu3ZrMzMyUjIyM1FdeeaWkra+ysjLhmTNn0nfs2JH5/PPPh/bW/8WLF+VvvPFGUXZ2dsq5c+ck27Zt8z1z5kx6enp6KsMwdO3atT3KEbuD3tYjSDfPu9rm4eG5pqBUoSowBYWeiiGkdyUcVqvNDXj22SBGKv11IiAHm/FDQ27Y5gsUmGwYbknC7kYB0OVAzXKgxgwI28KIsgFJW5tiQPQ2EPw2EHwDYFkEVC0FavyuWFXwBM0Q1+UhvOosxtsOYobwCKZqshDlS8F41wzzoEEdvv4p2X7+qd3mtQRZLOm35OfHkD4qRjVBUjsW50XV8HOT9v37hcCPSiBdCjgI8EAEtv+cC73Ge0Lw7PYqfPSRC+XlA7qOGHD2f/gdND/ne8w7i6bxuI2kpCTr0qVL5TU1NYxYLKajR4+2HD16VHby5Enlu+++m39l+9DQUNukSZOaAGDcuHFWs9ksrqqqYhsaGti5c+daAOCRRx6pOnjwYCeH/ejRo8r77ruvWiAQQK/XOydMmGA5duyYTK1Wc6NGjWqMjIx0AMCIESOs2dnZohkzZljEYjG3cOHC8Llz59YtWLCgrq2v22+/vZZlWYwfP765qqpK2Fv/o0ePboyLi7MDwJ49e5SXL1+WjRkzJh4AmpubmYCAAI8vDfTmDNBunne1zcPDc83ANQcEny1Ua3L7lNgrio42aZ98Moqw7K/CHQT1gpIxT0U1K7IfFwPb+hRDfT1hABwvA2UvAWXHANmHgHYn4FvXQTT+HKA4ByieA8J+C9Q8BFTdDtQPNLKUAtQCRVUWompOY4LzEG4RH8FUbSmC1YAXyk8OAYRxVOrCDzsk0uquvweU0tjqatP4sr5r2TsgsE7G8eY8GNw4UJdRYGs2MDEBaGKAPAnWPzIRt31tdt85BoDFUoi1a1VobBzQDKcvYy05rNsgGCmu4B2B6wCxWEx1Op1tzZo12sTERMuYMWOa9u/fr8zLyxOPGzeuk8S9SCRqH5eyLEubmpoYSim6ELLrBKXdD2nFYnHHfuF0OolQKMT58+fTdu7cqdqyZYvm/fffDzh16lQGAEgkkvb2bf321L9M9kudSkopuffee6vWrFlT1KvRbqS3WYwxhJB6QkgDWiRF6zts83q2PDzXIAxjLwuL2Neg1uT2JamRypKS0v2feir2SkdA86PGNPEeRYMie5Q/7wj0DANgKmDdBOSXABc2ANm3AHUdP1AbQJIB37uB6FBg9J+A0J87rCb0BAfirIJvyTFMNr2GVemz8V2+D2qbVWjQ3oCfo/+ItfFfYEFEKYKv6RyOq0EsqcmMiE5WSKTVnULeAACUOhJLSrKvxhHgQBzzkVz5M27wwIz9aBvwZl775jff+OHddz0eXtArZWVZePvtADQ2Dujamic3pRcZV2tHiiv8e2/Nc60wadIky5o1awKnT5/eMHPmzIZPPvnEPyEhwcowfUsR0Wq1LoVC4dq7d68CADZu3OjbVbtp06Y1bNu2zdfpdKK4uFjw448/KqZMmdJtBee6ujqmurqaXbBgQd3atWsL0tLSZD3Z0df+Z8+eXb9r1y5NUVGRAADKysrYjIwMj4f69aYm1M+kJh4enuGISFybpTcc1DGss/dBJiEO9b33mhXTp/9qMEScpDH6/wwFwd9uJMDz/AzeVSIF6ENA7UNAbQEg+Bjw/RzQmoD28KtyQPgeEPQeEDQKaFwEVD0CVPsDLhcYWxkCyy9hVONRTCGHcIvyLMb72yAJBtD1wJanA9Sh0aZlawMudzvIZziuYXpBQW1QY2OfVYAowC3DR/l7Mdtz0pdjVvyIyINKfLVdCwBYtSoMSUmN6GIWdVAwmdKwdWssaP+Te0VwNmwM+qbifmX3/w+eFgghSQCiKaUbCCH+ABSU0tzejusr7pACvVqmTZvW8M477wTNmDGjUaVScWKxmE6ePNlyNX189NFH5kcffdQglUq5GTNm1HfVZvHixbX/n73zDo+qSv/458ydlk56JSQkBBJKaKJiQcACdkVEV0Xsouuu+lNcRRdB3V0V61pAUSEWREHFwoqLItgABQ0tvZEK6XWSzNx7fn8kYRFDSEIm9X6e5z6TuXPuve9kzp0533Pe8uOPP7rHxsaOFELIxYsX54WHhzt2797d6jkrKiqUCy+8MLqhoUEAPP7447lt2dDe80+YMKH+4Ycfzp8+fXqMpmmYTCb54osvHoiJiXFqCmrR1tJFb0EIUSulbNW3crFYbAJ+6maTdHSO5PVFctHythq01Yc7e9xisXgq8HRHz9k6UvPyTk8NCP61fT+4BkOt7/z5pdaRI39XdMxcYs6N/2tgvWv+ZZ6weyBV/3Q6P4LLG+D3MfiUtzKRIzBorpx0qI6/VElmV0O3hGbMk5K9x3rRGf0eYLFY/DBwaUfPe/zrOkpDh2ypd3EtPWYlXKOqFs/IyjJ4NjZ2aNZ9EY8mL2GR8wa0fhSQjS9qlQtjx8aSldUk6GNibOzcmYS7e/f92Eup8f33qXzzzQm930hjefb3g9/wCTHW9LYVq0r+JKe31aCzfb+zCCEWAROB4VLKGCFECPChlPK0zp4zMTExOz4+vqTLjNTpcRITE/3i4+Mjjt4/UFNx6ejoHEarCQ774UB7hYCwWEoCFi6s+50QkGgBm/yTTrkyv9o1PzZSFwJdz1hcCu4l7tsnuXbNKdzwvZVTD4Hxf76paIZatgdJromB4Hi4NQx2nHBV14GC2VKRHhnzmWtbQsDFbj9wSXq6a0eFwFvMS3KqELBQyU5cccOCp6fG6tWZtPhPp6a6cPvt3ZdxR9Ma+OSTrBMRAgLpuNNrR3J6xAtDeqEQ6K1cBlwM1AJIKQsAjx61SKfP0DPVDXR0dHoFilKfPzjya1eTuTaiPe0Nnp55AQ895KV4eh7+kRGNojL28bBC/633usD6dp1H59hI0CrxKk4lpvInTlU3M9Xle073K8XPG/A+omkuFBXAWz7wrh/sO8JntdQIrwc2bbF18KdSuLEMQpyfsLrPIR3evinpfoG72xy8+thsaedkZ0coUnZoyWUT05Nv5C3nFf0S2PmKasI5nOeck0+28dhjuTzwQJNgf/ddf84+u4p58yqcZgeAw1HJqlW15OV12hXKTTSWbgh913amS47uFtQxGqWUUgghAYQQ3bYqodP30cWAjs4AxepanBIavmWowaC1a3BjCg1N91+wYIgwmQ63d8lzyYy/W7NZSiYNgVJ351nbP9EQ9mL8D+1jZM0PnCY3M9V9Oyf71+EWCLRjdSVIhQeLm7adVljhC+t8ofiIzzTJFR5xhUfD4KxKuL4UZleCtff7iDoZIRyloeFb613cStoceA6prEyanJ8/QnQwpXYiY9LPY6Nz42ZeIJszGfaH/ffdV8zmzR58+WWTgLzrrghOOWU/zSkMu5z6+oMsX26mouK49UiOxSRLXuqmsIRwD0PHVl50APhACLEcGCSEuAW4EXi9h23S6SPoYkBHZ8AhHT5++zN8A/a1u/CXdezYZJ9bbhkuWnK0SRwhH/snR//7PaOQT4x0mqn9CAeKrYCQQ78xtm4rZyqbmeqZSLy/ivGYbikdY0I9TMiHF/NhvSes8oWvvKGxeQCrCvh6UNP2FwdcWgY3lcLkuoHoMWq2VGaEDdkcpBjbGHhKqcYXF6ePLCnp8Mz+AQbnnMz2IRqK8/6515DEXbRum8EAb7+dQ3y8GwUFZmpqFObMGcqOHSkckSqxSygvz2b58kAaGjrllqag1T/p998D/+f9k55woJNIKZcKIc4BqoDhwN+llP/tYbN0+gi6GNDRGUAIoVaEhG+tcXUrbq8Q0Dxmzkz1vOiiwzOnhjpDyaiHBxV577oyEPbpaf5aoWcLd5mAK6qatpIDsNIb3vODX49wG6gwwsqApm2YDa4ubRIG4QPAjUg6BvmkpvkHJbY5wBdS2k7LyysKr67ucLXsMryLxrA7oAGr86K4x5BOAm270vj5qbzzTgbnnDMCVRXs3u3GX/8ayrJleV1mR05OCgkJUWhap8YT/kpt/pawtyyx5hJdCHQSIYQCbJRSng3oAkCnw+hiQEdngGA01uUMHrrJ22isDzt+a0CIBu958/JdTzrp8IDDPcUtbcx9SQ2m6ptHQOOA//5oKdyVQVTFNk6x977CXX4q3FfStO22NLkRfegLRUfkrU5zgSVh8EQYnF4Jc0vhqoqmQlb9CyEc5SHh39W6uhW3KQQUTSs/Jzu7wae+vsP1MWpxLYsn0VrJIOcFb/tSwI8MxtAOt6WpU+t46KF8Hnus6b5fvjyQadOquPLKVlMsdohdu5L47LNOxkNIeblbUvLq4LXDzKJzQkKnCSmlKoSoE0J4SSkrj3+Ejs7v0W9AHZ0BgJt7flLw4B9jhGhn7RCjscL/nnvqzJGRQwHQaIh4yzt1yNuPeMCnf/RPHgBIUMvwOZTMiOofmaxtZqrLD5wWUIWXH+DX0/YdnzEN8GIBPFcAn3vASl/40hvqm91YVGCLV9N2twoXNbsRnVXbH9yITOaqzMER3wQqxkbvttqZHY6CmZmZrm4OR4cLgzViqjmZ7Woeg1stbNQlWKhiV3PmoPayaNFBtmzxYOvWJoF6222RTJq0n4gIe6dskNLBV19lsm1bp4SAGUfVu0HrSq/wSHJeYPXAox7YI4T4L80ZhQCklH/pqguMeuWVLv289t5xxwnXLZgyZUr0unXrsvz8/NT2tE9JSTFfeOGFw9LS0va19xq33XZb2Ndff+01ffr0ykWLFhXNmDEj2m63G5577rkDM2bM+EPNg0mTJg1funRp7plnnlnXkffSk+hiQEenXyMb/IN2HRjkk9HuL3Hh6loU8OCDFqOvbwiAscpYNHqB7aBn8tRoKBsQGSpaKdzlvpMJAf2jcJcCXFLdtJUfgATvpmxEPx8RAF6twHv+TVtEPVxVCjeXQlTnBo89ilS9vNNT/YN+HSFE2zPpHg0NmTOyskJMmtauys5HomJoOJevKvYxqn0rb51BYOdLqn6XOag9KAqsXp3N2LFxFBebqKgwMmvWUL78Mh1//3YNog6jqrW8/34J6emdcusZZirN3BL2ll+wsWbAVyUXQswAXqDpplwhpfzXMdpdAXwInCSl/OUYp/uieRtQbNmyJd3Z13j33Xf9i4uLf3NxcZGvvfaad3R0dP1HH32U7Yxr2e12jsjR0W30yHSPEOIeIcQ+IcReIcRqIUSHv3h1dHTaRhjsJYMjN5UP8slo90y+4ueXFfTYY15GX19vgEE73ZJOmf1JhWfyOfH9VQg0YqrJJDJ7LbOS7uLFtNHsPmSm0RRKweAZbBzxBA8P/5HTQp3q/91jeGvw11LYkQJJe+DeAgg9KttMthX+FQrDxsBpw+BVH6juE0sFQqgVoeFbCwKCf409nhAIqqlJvjAjI6IzQkCCei3v5G/hLOcJAYDnyOKsDgqBFkJCHLz5ZhaG5o9u1y53xo+P5ccf2+/O1NhYymuv1ZKe3uHYF4F03DPox+TkIf+ODNZrB7T4+b8MzATigKuFEHGttPMA/gJsb+t8UspVrW3OsL27ePjhhwMff/zxAICbbrpp8CmnnBIDsH79eo9LLrkkEiA0NHR0YWGhMSUlxTx06NCRV1111ZDo6OiRp5122rCamhoB8N1337kOHz48buzYsSOeffbZgNaupWkat912W9iwYcNGxsTExL3++uveANOmTYu22WyGcePGxS5cuDBo0aJFYZs3b/YaMWJEXFVVlWHWrFkRLccsXrz48LlXr17tPXr06NiIiIhRX375pTtAXV2duOKKKyJiYmLiYmNj4z777DMPgBdffNF35syZQ6dNmxZ9xhlnxAA88sgjgaNGjYqNiYmJu+eeezqdoau9dPvKgBAilKaOHSeltAkhPgCuAlZ2ty06Ov0Vk7kqc3Dk10GKYnc9fusmzNHRKX5//Wu0UBRFOERd1EtKaugnc0Mhqd8FCTtQ6t/gpqx/8mBQDhHegJ4WlRGN8EwhPFUIX7nDW76wwQdqm0ePEvjRs2m7X4MLysA0Toh398leWMreZK7ODIv4JsBobGi74JaUMqa8PGViUVGn89ov4KmM97nauQGwV5PEX4+ROai9XHhhNQsX5h2OH8jLszBtWixLl+bw5z+XtnlsTU0ey5Z5Ulvb4bSfHqKh+MvQt+2TXfL02gH/YxKQLqXMBBBCvA9cAuw/qt1jwFPAfW2dTAgxDPgnTcLisKCVUg7tQpu7lalTp9YsXbo0EDj022+/uTY2NhoaGhrE1q1b3U8//fTqo9sfOHDA+s4772ROnjw55/zzzx+akJDgfccdd5TddNNNEc8999yBCy64oOa2225rVUwnJCQM2rNnj0tSUtK+wsJC46RJk2LPPffcmm+++Sbd1dV1XHJy8n6AwMBA+y+//OKWkJBw4LvvvnMtLCw0tbgclZSUHHbDdTgcYs+ePUlr1qzxWrJkSciMGTNSn3zyyQCA1NTU/b/++qv1/PPPH5aRkbEXYNeuXe67d+/eFxgYqH700Uee6enp1t27dydJKTn77LOj//Of/7jPnDnzDy5JXUVPze4YARchhBFwBQp6yA4dne7CKIT45YjtVudcRkoPr6ykIVFfRnZACEjX009P8r/33uFCURRzqSl34o1700I/mTqqvwkBCfIHJqcOIafhdpbHNgsBnd+hADNr4IMcKEyEV7Lg1Krfp9ivNcAHfvBuApAshDjW7HI39fsjkZrnoIykIVH/iTQaG9oWeVLaJxUWZpyIEHiJO5OXcr9zhcAo0nnnOJmD2suSJQd5881MXF01ABoaBHfdFcE114Rjs7W+enLwYDovvBBAbW2HZ/QnWw+k5g99xmOyS57TZzd7Gcfr+6FA7hHP85r3HUYIMQ4YLKX8vB3Xewt4FXAAU4EE4O1OW98LOP300+v27NnjVl5ebrBYLHLixIk13333netPP/3kMW3atD8MjENDQxsmT55sAxg3blxddna2pbS0VKmurlYuuOCCGoAbb7yxVdH73XffeVx55ZVlRqORwYMHO04++eSa77//vs3f0BEjRjTk5uZarr/++sFr16719Pb2PuxyN3v27HKAyZMn1+bl5ZkBfvzxR/e5c+eWNttXHxIS0rhnzx4rwBlnnFEVGBioAnz55ZeeW7du9YyLi4sbOXJkXEZGhjU5OdmpHjTdvjIgpcwXQiwFDgA24Csp5VdHt2u+cVpuHj22Qaev45BSTmxPw873fa0uMHRHkafXgfbPHgrh8Lriikz3qVNjkWh+W017Y5cs8TCoG+Pbf92+QQHBuXNYY/ieM/QUhu3GQ4P5ZU1buqkpG9H7fpBzZPBqjpTSdowTdEO/P/IcakVw2A+Vbh5Fx70HhJQ1Uw8cKA+qrY3uzLUAPuWipLt4yblBsD4U8lM7Mwe1lxtuKGfsWBtXXBFFZmbTIOO99/zZs8eVdesyGTbsf65iKSlJrFkzHCk7NHmooNme8/8y765BOwbq/Xa8vt/a53l4hU0IYQCeA+a183ouUsqvhRBCSpkDPCqE+A5Y1F6DexsWi0WGhYU1vPzyy36TJk2qiY+Pt23atMkjJyfHMm7cuPqj25vN5sP/P0VRpM1mM0gpaSmP0xadWdz09/dX9+7du//jjz/2fOWVVwLWrFnj8+GHH2YDWK1NRR2NRiOqqorjXcO1RZw3t7v77rsL77///pIOG9VJun1lQAjhTdNSWCQQArgJIa49up2U8jUp5cTmm2kA5L7W0WmiM33fYGgsGhK1sc7T60D7l4QNhlrf+fPz3adOjRGNoir2sdLEkYsuijKoG/tVYF8dLuV38WJaKAWDv+eMLirwNRCJtsO/iiBzL2xKhquLwVBNF7l4nuh3vtFUkxUx7AuDm0fRcf3ZjapafEFGRn1QbW3bLkRt8DMT0y7lE+e6vZipYidW3DuQOai9jBtXz65dSVxwQfnhfXv2uDFpUiyffOKBlBrffZfM++/HdlQIBCo1eclD/l1316AdAzLzWDvJA47sf2H83kvCAxgFfCuEyAZOAT4VQhxLYNQ3C4g0IcSfhRCXAa36x/clJk+eXPPyyy8HnnXWWdVnn3129apVq/zj4uLqDIb2dUk/Pz/V3d1d3bhxozvAypUrW830NWXKlOq1a9f6OBwOCgoKjDt27HA/44wzaltr20JhYaFRVVXmzZtX8fjjj+fv2bOnzZWE008/veadd97xAdi9e7elsLDQPGbMmD+ImpkzZ1a9/fbbfpWVlQaArKwsU35+vlMnxXtixv1sIEtKWQwghPgImAy80wO26Oj0eSzWsrSwiM3hBoPa7gGDMJtL/Rcs0EwhIUOs+UrG2Lvfa7QUvzrOmXZ2NyqGhlVcn3knL0fV46K7A3UZBmB6bdP2yK0Qt7dn7ZGa56CslIDgX4YLcfwJLhe7/cD5mZm+FlXtdEB8JpHZp/N9pMTQdbP1RyOw8x+qiOhkwHB78PLS+PTTTB5/PIAlS8JQVdGcaSiG887bw0knjaCdg64mpHaV+96Ut4M+ijG2N43xwOVnYJgQIhLIpyl28k8tLzbXCzicslgI8S1wXxvZhO6mye36LzTFGUwDru9Kg7siFWhHmTJlSvWLL74YNG3atFpPT0/NYrHI0047rUO+82+88Ub2zTffHOHi4qJNmzat1foa1113XcWPP/7oHhsbO1IIIRcvXpwXHt52Ecbs7GzTTTfdFKFpmgBYsmRJm8X8FixYcOi6664bEhMTE6coCsuXL892cXH5w3LB5ZdfXrVv3z7rSc01flxdXbV33303KzQ01GkT46K7476EECcDbwIn0eQmtBL4RUr57zaOqZVStvrFvVgsNgE/OcFUHZ328voiuWh5Ww3a6sOdPW6J4ZEpXj4Zt/gHJnZodtLg6ZkX8NBDXoqHp0vw5/V7hz17V6iQaf0mNkCC3MGktNl8GJBL+KCetqefM09KjikGnNHvARaLxQ8DlwqhVgWF/Vju7lHYruw23jZb2rnZ2RGKlJ3ODFWMX8FQMn1q8HBuFrxnSOVeus/FZuNGd669diglJf/730RGVjB7djaursdNP2oV9or3g9ZWXOKeEuFMM3sJlfxJTm+rQXv6vhDifOB5mgJ13pRSPiGEWELTmOjTo9p+S9tioMtJTEzMjo+P7zZXFR3nk5iY6BcfHx9x9P6eiBnYLoRYC+yiaSn4V+C1zp5vWNwH0t7oVq6qlnqHw9KoOqx21WFVHQ6rVFWLVFWL0FSzUVVNitSMZk1TrFIqVhAuIPpEejwdndaIGvExQsgOCQFjaGh6wP33hyuqpXrk/T9l+PyycDSo/WYG7yABeVezms1MG6h+ygMGo6k2Z3DE1z5GU327hMCQysqkyfn5I0Trvtrtohr3kjHsdne6ELiSJO49wcxBHeW882rYunUr55wzgfz8JhGdlTWIZctiufLKDMLCjhUXwghTccaWsLcCA4x1Ed1lbg/TsdoMx0BKuQHYcNS+vx+j7VltnUsIEQPcDwzhiLGdlHLaCRuq0+/pkcBcKeUiui6oRZjMtd4m2nTtasUGNE0116mq2aaqlnrVYbU7HFa7qlpU1WFFdVikqloMqmpWNM1k0jSjWWqKVUqDCxj0ugg6PY4QHfPjtcbHJ/nccstw9wyZGn/Po1ZTzeaxzrKtu7FhrVjIE4ee5+5hTnXd0OkVBAT/Uuk5KHNwe9yCkFIdU1ycPqqk5IQG1w2YqyawUxQR7Nwc+XFksLqLMgd1hPLybNavD+SGGzLZsCGUXbsCAaiqsrBy5QjOOy+Hk04qO/IQgbTf7/1DxpN+mwZKylCJK2lE0BtrjnwILANep4vEis7AYcBm6RECg2JsdFWMja7QsdStUgq1WUTYVIelQXVYG1WHRXWoVlV1WKXqsAhVNQtNMxs1tVlISMUqpcEVhNlJb0lH51hoHjNmpHpeePHQ8Hezf4t8454RUN7u+gO9GQ1hf48/pd/OsqG1uOurAQMEL+9ML9qRAENIWX9aXl5heHX18BO5ngOlfgpbatKIcW56TB8K2UZol2YOag85OSkkJEShaUaMRsnFF+cRFlbLhg0ROBwGHA4DX3wRSW6uOxdfnIvRKL0M9Qc3hr6tnWzNHxhCwEIWEVhxIwao7GlzWsEhpXy1p43Q6ZsMWDFwIgghFaOxwd1obOhwjgdNM9g11WxTVWu96rA0OByWRlW1qg6HVWtejRCaajGoqsmoaSaT1BSLJhUrTUJC/7x0OoYQDd7z5uV7jpg4aPT8lZmeKSvH97RJXcUuxqXN5kPfTKK6151Cp0+gaFrF2dnZNt/6+hPKjqUhHFewtmg7p0R0kWmtY6aKX7DiQfeuPO/alcRnn/3xHho/vpzgYBtr1kRRUdFk0+7d/hw86HrStad8tXnshkA3g73rsxz1NkzkEo7Ei16ZZU0I0ZId5zMhxB3Ax0BDy+tSyrJWD9TROQJ9cNnNGAyayWCoNxlN9R1eatY0pVFVLRYmg90AACAASURBVHWqw9IkJFSrQ3VYHc2rEVJVLUJVzYrWLCQ0qVikprjq8REDFKOx0u/uu2sDqrxto2fd4Kk0ZveLQXMxfgXX8bZjIzP0tIU6rWJ2OApnZma6uDkcwSdyHgnyLv6dtZ5LndvXBHY2UEkknU512mGkdLBxYybbtx/7eyE4uJ7bbkti3boI0tObMnIdPOiWvmz9RV/fRubFE/hDFdh+g8JBwqjBl6ieNuU47KSpPkHLatL9R7wmgT5bgVin+9DFQB/CYFDNBkOd2WSq69BxUiI1zdji1lSvqtZG1WF1OBxWVXVYNFW1oKkmoWkmg6aZFE1TjFIzGqVUzFIaTFIKKwgLtKNyh06vQLi4FAXe9zdt+JrE4tBP/jmqPwQJN2CuepRHC59iwTANRRe3Oq3i0dCQOSMrK8SkaSc8w76U+1Je4U7nu8E8RRbTuzFzkKrW8v77JaSnH/+aLi4a11yT6fb1FxbbD7+M1CSivBbjZc8R87eLyH/sSor6VZSOgTKCKSGAaASBPW3O8ZBS9soVC52+hS4GBgBCIBTF4aIoDhfMHQu0bkFKNE0zNmiauV5TTY2aampUVbNDVS0OTTVrqmbSVIcFTTNJTTUbNM0kNM2oaJpRkZpiahYWZimFRRcWzkXx9c0Ou/lu+7j7nja55m/r85WENYR9LVek38QbkTV4nJDvt07/JrC2NnlaTk6M6IKCmh8wO3kBTztfCMwiifu6MXNQQ0MxK1ZASUm7sjCB1K7z3J3y1u07h391KqnXvUpUaQ1GTcI/PiV0RyZu799Ftq97Hw9aFdTgTx4hRGGg1cJUvREhxElArpSyqPn5XGAWkAM82qVuQl+M6tp+esHeLq1bMGnSpOFLly7NPfPMMzs2Y9rMvffeG/LOO+/4+fj4OFRVFY8++miel5eX9vDDD4f+9ttvyS3t7HY7QUFB8bt27do/ZMgQe9e9g55DFwM67UIIDIrisCqKw3qieRSahUW9ppkbNNXUoKpmh6aa7KpqUTXVrKqqWVNVs9Q0s9BUE00rFkZF04xGqSkmTSompMEipaFZWOi0YB4alTIi/tz6kTdcP0xoNX0+SHg3ozOuYO2gNGL6hYuTjpOQUsaUlydPLCrqkn7yPaelzOED5wuBWDJYQ/cJ3PLyLF57LZD6+nZ9N1iFvWJd8JrK893SYwFmjqXml8fZP+t5hu7Kxh1g014GTVhI7Id/If2kKP5QTbUPUI8PWQwmEqUHsjidOMtpKuaKEOJM4F/AXcBYmtK2X9FzpvVuHA4HRuPvh8G33377wSVLlhzctWuXdfr06cNLSkoSb775ZnNKSop5+PDhjQDr16/3jImJsfUXIQC6GNDpAZqFRdNKRZcIC1ODppoaNM3coKomu6aa7WqTqFA11SxV1Sw11Sw0zYSqmRSpmgyaVIxSU4yaVMzNwsLa1zM9ucdP3H1qQZ3J/4m7+vxqQCk+hfNY2fg5F/V2f92BiZlq4ilkRi+YDZbSflJRUfaw8vIuEQLJDM88i2+ju+JcbeJNEdsJRTnxVYx2kZaWzOrVMcj2pSQeZT6Y/m3YymBfxfa7FYQIf+w/Pkrq/LcIe2sLAQA5JVjOfIzY564j5/bp9JWAVQeepDOEEEzdXNOha1GOmP2fA7wmpVwHrBNC/NaDdp0wVVVVhosvvnhoYWGhWdM0sWDBgoJbbrmlfP369R5/+9vfBquqSnx8fF1CQkLO0ZV8r7nmmvDExES3+vp6w0UXXVT+3HPPFQCEhoaOvvrqq0s2b97sedtttx269dZby1u79vjx4+sVRaGoqMh44YUXliUkJPg88cQTRQCrV6/2mT17dl/p5+1CFwP9ASkdAhxCSrsAtflv1dC8CdAMUmoGKaWiaapBSqm0bJomBaAJITQh0IRAA9H8XMiWx6Z9Btn0t0ETQpEgpBAGCUrzowEhjBIU2bRMryCcmwGpSVjYXRTF7gKdWhk8zBHCol7TzI2qarI3rVqYHapq0TTVrKmqCU01S1UzGTTVJKRmUoTBccxiPN2EI3jkxMTJG7+JsJTl+fawLSdEA+bqf/BQ/uM8HKPHBfQiBA7CyGcmdczFk1MJwUAMTZVTe84sKWumHjhQHlRb2yUBvoUE5U1gZ6iK0bnvy0w1P2PulsxBUjrYsiWdLVvaNettQGt82Gdr5mLfb4/Z3mJCvnkruadEU3P320TYGjHU2zHMf5PIbWm4LbuJPKsJeazjexgNV9KJxBdLn1wJOBpFCGGUUjqA6cCtR7zWp8d4H330kWdQUJD922+/TQcoLS1V6urqxG233Rb51VdfpYwZM6bhsssui3j66af9//73vx868thnn302PzAwUHU4HEyePHn49u3bXU4++WQbgNVq1Xbu3JnS1rW/+eYbN4PBIIODgx3XXXdd2e233x7xxBNPFNlsNrF582avZcuW5TrvnXc/fbqj9BhSakDLANxxePAN6hGDcM0gpaZIKZsfWwbjNA/CMUqJommieZ8wappofjQoUoqmSF5pUDRNMWqaYpBSUaRUFE1TFClNipRGg5RG0fQ5GqGbU9K1Ew1UTQiHJoSmCaE2b5ra9Lxlv6YKITUhNNVgkM1tZPO+w4/Nf6M1tUETAvX3jy2ipkXQIIUQWpOAES1/y2Zhc/jvZkGDaFCksUGRQrjTJGoUmkRNWwNTl276V/4RIWrGeASkjfzwnXECrc8OnjWEYz2XpN3AWxGVDOoPP9B9HzdKmUwx12DickLxoJ0+5t2DUVWLz8vKEl6NjV2SgacSz0Oj2TOoDjfnuh4KHHxGBVHdkDlIVWtZs6aEtLR23VODDLaDX4euYry1qF3tb51G+fgIbFe8QHROSVOi7VXfEZB4ALeP7iYjMoDe5UZhIYNI3HDtxmBt57Ma2CKEKAFswHcAQohoemc9hHYzfvx428KFCwfPnz8/9JJLLqmcMWNGzU8//eQSFhbWMGbMmAaAefPmlb788ssBwO/EwKpVq3xWrlzp53A4RHFxsSkxMdHaIgbmzp3b6moAwLJlywI/+OADXzc3NzUhISHTYDAwZcqUurq6OkNiYqJl9+7dLmPHjq319/fv+VXRLqTPi4GA2lqtymzOM8CRA3DNoGnyiBlwjE3PMTYNvDE2DcJ/N/hufjQomqY0D8QVpXkQbpSyyXFdSqOhaYBobt50joMBFIOUCrK3ThQdHwlHChm1WeBomhAqQjT2hE1BdXUNo6trSv337RvXE9fvKvYTm3EFaz2TiOvLS/V9H4V6YsjnUuxchx+x+AG9cqUppLq68tSCAheLqrp3xflsWCvG8pupFL8uOV+b/JMMzu2GOIH6+oOsWKFQWtouEXeOa0by+uDVQ10Mjg79rk0cSv2uJ9h/9UtEfrWHQQC/5eA28RHiVt1O5oXjekH6URO5DEHi2evThHYYKeUTQoivgWDgKykP/9AaaIod6LOMGTOmYdeuXfvXrVvntXDhwtBNmzZVXX755RXHOy45Odn80ksvBe7cuTPJ399fnTVrVkR9ff3hyTIPDw/tWMe2xAwcvf/SSy8tS0hI8ElJSXGZM2dOv3IRgn4gBs7OyTEAYT1th07/RkCTUJSytSiHHhGF07KzLdC7Zms7QjmDDt7EG3Ufc3m/+4HuI0h8KGI6FVyHK+cSiqVvDJbOys31Arpk4G7HWHcaP9RnExnUFedrk8tI5oFucE0pKclkxYpgGhqOu2ppRK19LeCzohu8fuu0XT7uaP95gIxFawn8x3rCNAllNRgvfZaYBy8mb/EVHOyR9KMKRYRRh2//zrUvpdzWyr7UnrClK8nOzjYFBAQ47rjjjjIPDw9t1apVvkuWLCnKz88379271zJq1KiGhIQE3zPOOON3grO8vFxxcXHRfHx81NzcXOO3337rNWXKlBMSpXPnzi27/PLLo6urq5X33nsv+4TeWC+kz4sBHR2dvkUjppql3Jf3CI/pcQHdjZlqxlLIHOBqgggmmKYZxQGJhrBfxGclvzI+3OkXG04mH3aDe0pychIffDC8PYHCg42VOd+HveEVbqo6YRFoEPDYbA5OiqJu3nKGltVgVDV4/BPCdmTgvvrPZPm4c8wZ2S7FQCnBlDbXCtC/Y7qKLk4Fejx27tzp8uCDD4YZDAaMRqN85ZVXclxdXeWyZcuyZ8+eHdUSQHzfffcVH3ncqaeeahs1alTdsGHDRoaHhzdMmDCh5kRtmTBhQr3VatVGjx5d5+np2T39uBsRsg+4bgghaqWUbsd40QT81L0W6ej8jteRcnlbDdrsw509ToipwNMdPWdPIUHdwPlpc0kYXIZvh/8XOp2gJfD3fOqYixenEIyBrpyjnSdh7zEv74x+39TgYeDSjp73SCRoN7Mi801ucn7moEEc5ABeTg0YltLB119n8MMP7XBBktpNnr+mLA/4bLgi2pddqCNkHcJ0+fNE/ZbD4c9wiB8Na/9K+sShTkw/KqgmgHyCicbg1MnOSkbI6W2a0sm+35tITEzMjo+PL+lpO3S6jsTERL/4+PiIo/frillHR8fppDIscyy/lVzIFyN0IeBk3CnhXJJZRQaVODjAEJYRy2RCulgI9Gke5dHUbhECJmr4GaNThYDDUc277xa0Rwi4isbSTaEJ+SsCP411hhAAiAzA/tNiUq4/439BnS3pR1/fjLcTLlmPD0mMwUQoI5wsBHR0+h36DaOjo+M0KvEsvp1l1e9zdb/22e1RWgJ/L28O/B2OH+DX02b1ZlZyfdISFjk/YF3g4BNKiXZibI/NVsTrr5soLz+uq9NES37af0MTwgYpDU4PDLeakCtvJ/eUYdTe8zZD6u0YbI0Ybl3B0J/SOPTqDeRZTjz9qAMv0gkntI/XCtDR6VF0MaCjo9Pl2DHWvcBfDzzEP6LtmP172p5+hsSXQqZTyVxcOYdQzH0j8Lc38DXTkm9gZfcMHB8ng/OdmDmouDiDFStCaWxsc9VBQat/0u+/B/7P+6duT6l5+3TKJkRQN/vF/6UffWsLAYk5uH50D5lD/DqVflTDjXQi8OsntQJ0dHoUXQzo6Oh0GRK0rzg39RreDSvFT/+R7irMVDGOIq6kJfA3BAjpabP6GnsYlX4uX3XPgPgiknjIibPV+/Yls27dcSsKByg1eVvD3rION5f2WG79k6Ko3/kESVf9m4hNe5vSj+7Kxn3CQuLenk/GzLG0P8DTSgYR/a5WgI5Oj6KLAR0dnS4hk8jsK1hr/ZXxugg4UQQOBpPPBc2Bv5MIxoBnT5vVlznA4JyT+HlIt2SwGkYmHztpRUBKO199lcW2bce5z6R2tcfelITAj2KMQvZopWgAX3fUjX8j45EPCPrXZ4RqEkprMF70DMMXXkLeolnHST9q4gBDEP2xVoCOTk/T7WJACDEcWHPErqHA36WUz3e3LTo6OidONe4ld/Jy5dvM1X+kTwR3SjmNYv6Eict6X8XfvkwZ3gfH8pt/A9bW6oR0LYM4yM8EozghQYfDUcV771WSldXmrLhV2CvWBa+pPN8tvVf50RsEPDGHopOjqZ23nKHltU3pR5d8TNiODNze+zPZ3m5HpR9VKGIwdfj071oBOjo9SbeLASllCjAWQAihAPnAx91th46OzonhQLG9zJ3ZC3hqWCMWPWC1o7Qe+NsrK/72ZWpxLYsn0VKOj6vTL2aihu0Y8eK4xb46TF1dIa+9ZqWycnBbzcaYi9K/CVsV7KvYeq2YvHgC1T8/xv7Lnydq94Gm9KNf7sZ7wkJc1t5NxvgI6ptrBZQRQJReK6CXkDmqa8Xl0K6tWzBp0qThS5cuzT3zzDPrOnuOl156yfeFF14IklIipeSaa64pWbJkycGvv/7a7Z577hnc2NhoaGxsFJdeemn5s88+W/Diiy/6Llq0KCwwMNBut9vFHXfccfD//u//+lw61p52E5oOZEgpc3rYDh0dnXYiQdvM1LRreDe4iOBeNfPYy5H4UcjZVHIdrpytB/46m0ZMNSezXc1jsI/TL9aSOSjGCSs6Bw+m88Ybg7HbLcdqYkBreNTn2+xHfLc6L2C5C4kKxL5tMSm3vsHgd77HHyCrGOvpixnxj5v48e4/44VBF8c6zsPhcGA0/m8Y/MEHH3i+8sorAf/9739TIyIi7HV1deLVV1/1BbjpppsiV69enXHqqafaHA4HiYmJh4P2L7roovKEhIQD+fn5xlGjRo288sorKwYPHuzogbfUaXpabV8FrG7tBSHErUKIX4QQv9DzokVHp9vozX3/AINzTmHbwel8M7yIYN2H/XiYqeJkUnmOVAqoppgQVhPL+QzB3Ls+256mq/u9iqHhXL6q2Meo7slm9SgZnO8EIbB7dxLLlkW1JQR8DXUFieGvVvUVIdCCixn59nwOvDSPLIuxyT3I1ohyz6ucccsiIhoa9boYA5mqqirDWWedFT18+PC4YcOGjXz99de9AdavX+8RGxsbFxMTEzd79uwIm832h35yzTXXhI8aNSo2Ojp65D333HM42UJoaOjo++67L3jChAnD33zzzd/VvHjqqaeC//Wvf+VFRETYAVxdXWXLLH9ZWZkxPDzcDmA0GpkwYcIfiueFhoY6wsPDG9LT081d+59wPj0mBoQQZuBi4MPWXpdSvialnCilnAj0KYWlo3Mi9Ma+X4tr6c28nj6EA0N2cHJwT9vTaxE4GEIOd5DENgqx4cE2YribGIL1AOC26Mp+L0G9lnfyt3BWWBeZ1zYXkszfuzhgWEo7Gzak8fHHsXCsQbGUl7glJecPfSZglKW4r6bwtd95BT988ya7BgfR0LJzxVoCT/sTMQcKddE8UPnoo488g4KC7CkpKfvT0tL2XX755VV1dXXitttui1yzZk1GamrqfofDwdNPP/2Hvv/ss8/m7927Nyk5OXnfDz/84LF9+/bDrntWq1XbuXNnyq233lp+5DFpaWkup512WqsuRrfeeuvB2NjYUeecc07U008/7VdXV/eHe3L//v3m3NxcS1xcXENr5+jN9OTKwExgl5TyYA/aoKOj0wYOlPpl3JbkS6nnG9zs/GqtfRF3SjiPZBLIpBIH2QzhZWI5meB+WfFXVes4eDCdLVuSeesttafNaY2/8a/0bit0N4xMPuniNJd2ewUrVx7i55+HHauJRTiqPgz6IPuTkDUjLELtiwNmDTdSGEkNUcROPgmx6yOSpp1CRUuDnftwnzCLuI3f496Thur0DOPHj7d99913nvPnzw/98ssv3X19fdXExERrWFhYw5gxYxoA5s2bV/r99997HH3sqlWrfOLi4mLj4uLi0tLSrEe69cydO7f86PbHY+nSpYU//fRT0tlnn131wQcf+J511lmH7/nPPvvMe8SIEXFXXXXV0Oeffz4nMDCwV34vtkVPfoFczTFchHR0dHoWCfIHTkudw5qgAkL1uIAjUahn+OHAX39i+nnFXykdVFXlk55uY88edw4cCEHKFmHY4ykrj+YV5ic9xQPd02c9OcSOLs4cVFOTz+uvu1FVFXqsJiNMxRnfhq0MCDTWRnbZdbuTploB7rj+fjXFzxv1qxVkPPwCQU+9QaimQUk5pgtuJ2bRHeQtvJ1Dhp52btbpNsaMGdOwa9eu/evWrfNauHBh6KZNm6ouv/zyiuMdl5ycbH7ppZcCd+7cmeTv76/OmjUror6+/nDP8fDw0Fo7Ljo62vbDDz+4XnzxxdWtvT5y5MiGkSNHFt97773Fvr6+Y4uKihT4X8xAZ99nb6BHbishhCtwDvBRT1xfR0fn2OQTknsmWwvO4PvhBYR69bQ9vQCJP/lcTRJfkEMdRvYRxWOMIKafBjjabEWkpCSxdm0W//ynyvPPD+Hzz0eQkxN2vCJXPclnXJh8J690jxAwUss2DAzqwsxBhYVpvPCCP1VVg1p7WSDtD3pvTU6KeDkq0Fj7h9nQXo+JHKLJJY4oXAlsrYmiwD/vpWjdC6QO8mxyF1NVxN//zeCL72BoZXWPxzrqdBPZ2dkmDw8P7Y477ii7++67D/7222+uY8eOrc/Pzzfv3bvXApCQkOB7xhln/G7wXl5erri4uGg+Pj5qbm6u8dtvv23X79iCBQuKHnroobADBw4YAWw2m3j88ccDAN5//30vTWvSEHv27LEqiiL9/Pz63ArAseiRlQEpZR16Cj0dnV5FHS7l9/N0ySvceUzXhAGDhSrGU8Qc4CqCCSQUOOZMbZ/Hbq+gqOgQ+/fD3r1B1NQEAUE9bVZH2Mn4tEtY3z0BtAIHH1FCbBcFDEsp+e23ZD799JhCZpDBVvR16CrGW4v6XlE/hULCseHd/loBl55N9chhJF1+F1F703AF+GIL3hNm4fLhC2SMi+UPAZw6TqaLU4Eej507d7o8+OCDYQaDAaPRKF955ZUcV1dXuWzZsuzZs2dHqapKfHx83X333Vd85HGnnnqqbdSoUXXDhg0bGR4e3jBhwoR2VbieM2dOZVFRkXH69OnDpZQIIbjmmmtKAN555x3fv/3tb4OtVqtmNBrlihUrso7MRNTXEVLKnrbhuAghaqWUbsd40QT81L0W6ej8jteRcnlbDdrsw509ToipwNMdPefRqBgaVnF95p28HFWPS5/LgtAlCByEk89F1HEtgziJoH7p79+CptVTVlZASkoje/Z4c/Bgq7O07WCeXLRo77FedEq/b2rwMHBpy9NMIrNjSQprxNI9v86PkswiumZQrmkNfPFFLrt2HTMm5zzXtORPgt+PshpU5xdN60oMlBJCKf4MQ3TufrLVI25+hMHvfc7hIFFXK9rLfydr3mUc12XESVQyQk5vq0Fn+35vIjExMTs+Pr7P5czXOTaJiYl+8fHxEUfv7z+yRkdHp0NIkDuYlDabDwNyCR94cQFmqplKPtdi4lLCcO/HFX+l1KipySczs4Y9e9zIygpB0/pFRddi/AriSQzqNiEwswuFgN1ewapVdeTntyoEzDiq3whcf+hazz19azVAUEUABYQQjTgxLwAXK/LdpzlwSjy19z/NkIZGRF09hhseIuqn3zj40sPkmfqWRNLR6XXoYmCAoiHsNlyqyvGuq8Sr0YAmFFSMOAwKqlBQDc37hAHN0PJoQBMGNMWAJgTS0PyoGNAMAqkIpKFp0/06ezMHCci7mtVsZlrXZkHpCxip40ZyWEokHl00qOuNNDQUk5tbyt69JpKTQ2hoaLNybV+kBrfSMex2r8HDevzWXUAUWXzWRZmDamryWL7ck5qakNZejjKVZW0Ne9M3xFjTlwrT2fAlmzCGonTtvXXXtZROGEndnHuJyivCAvDaBwT+loTr2hfJHBzUO9Iw6+j0RXQx0A9xoNiq8agqw8dWQEhDDkO0NIaJDKLMaQxzySbCvRh/d4nBFyfGbig4NCMO1YRdM+LQTNg1BVU1YZct+1r2Nz+n5W8zjbK5nTTi0Mw00vLchF2aaZRmGjHikEYctDw3YceEHQX18HMjDtG8X5qwt4ge0fJay6agCiMOFNQWQSQU1MPPjxJFLZuox2rvKwEwNqwVC3ni0PPcPUxi6L9uMK1hoIEryeQlwvGl/62EOBzVHDpUxP79Gvv2BVBR4Q/01dzzx6UBc9UEdspuK37nSTE7COySzEF5eamsXBmJ+ke3H4F0/HXQtvRn/DYON3TStaYHsONFBkMIw+i8e2vyOGy71pE05x4iN+/AC2DHHjwmzCLu3afJOGcytc66to5Of0YXA30ICbIRc00lXlUl+NXnEebIIlJLJ1pJY5g5gyi3bCI8avBwgS7McNFJVIwGFaOhge6ZtOtBTL098kbF0Pgef8qYz6tDa3EfWKsBAjszyWAFIQT3IxGgaY1UVOSTltbA7t1eFBQEAd0W/G1CrRljKWo1RZ+zqca98Tw21qQyvNVZ9S7HSC0/AT5NgaydRkrJL78ks2FDq/3Q01BfvDHkbfspLvl9ZcVKw400IgjA0j2rbP4+qP99k/QHnyV46VuESAnFZZhm3srwxX8m78Fb+076USHEDOAFmlL0rpBS/uuo1+8FbqapCF8xcKOUMqfbDdXp9+hioJegIRw2XCorGFRXRFB9LoPVDKJkOtGmVGIs2US45zLYw47ZA+h7KeV0eoydjE+7grV+2UT2n4Fw+1A5k3RWEkBkP3AHklKjrq6QrKwq9u51JT09BFXttjzzHqKhZIzlYOlMtzQudkvxGW055A8Y4LXuMuEwUWSIYgK6RwiAyocUE0fECZ1F0+r59NN8EhNbvQ+numSlfBbyXoSbwW45oet0F8eoFdAdKAo8dT+FJ8dTe9NChlbWoKgq4uEXGLwtEbd3nybH050eEartRQihAC/TlGY9D/hZCPGplHL/Ec1+BSZKKeuEEPOBp4A53W+tTn9HFwPdgAOlvgb3yjJ8bIUEN+YwRE0n2pBOtDGNYa7ZRLgdJNDD2W47OgOLQ/jnX8fb6lecN9BShUomkE4Cg4jr/oFKl9LYWEp+fgn79yvs2xeEzdZNKU6lGqDUFp1sza+62C3FdIFbakCwsabXFFcrJqD7QkYfJo1LT1BMNjaWsWpVAwUFf/D/N6LWvhrwReHNXrv6Rl81kUMECh70eCzDrHOpGh3D/sv/QtS+5vSjn3+Lz4RZuK57kfQxw2noaRvbYBKQLqXMBBBCvA9cAhwWA1LKzUe03wZc260W6gwYdDFwAjS77VRX4VlbjH9dPqGOTIZqGUQpqcSYs4h0zWSoZ3NwW7/3ldFpE6MQ4pcjnr8mpXTKlGo9lqrFLCp8igXDNJQ+smDeRcSSwSpcOan73GW6FFWtpaSkkKQkB3v3+lFa6kc3TBAoaPVDTBVFU1yybZe6pbhOc80McjfYu0J4dFu/dwrnkcxjJygEqqpyee01L2prfY5+KdxYkfNd2Jte4aaqY6YV7TU01Qqox5teVfU4JoLGHWtIvvkRwld/0SRW0w9gnXw1ca8sImvuJT2WfvR4fT8UyD3ieR5wchvnuwn4Txfa12FeeWVUl64u5JkbjwAAIABJREFU33FH99UtePvttwfFxcXVT5gwoR5g0qRJw5cuXZp75pln1nWXDUfy+eefezzzzDOBmzdvTu+J6x+NLgaOQbPbTlUlXrUHCWzIZbA9gyjR7J9vzWSoay6DvRqxeALdE8Cm05dxSCknOvMCGsL+IbPTb2ZFZA0efWOWsauIIIe3UDir52crO4SUDior88nIqGP3bg9yc0OQ0ukDQ6uwV8SZi0vOdc1wXOKW7HmStSBIETLCCZdyer93GpFk8/kJisoDB1JISIhCVY/6rZXqrZ47014J+CJGEb23ojMABkoIoRx/ojtbK8DZuLog31tKzsljqFmwlCGNdkStDcP1fyNq228UvfAQ+T2QfvR4fb+1/2Wr4WdCiGuBicCUrjBsIPLJJ58McjgclS1i4ERwOBz0p4JjMEDFgAOlvha36lJ8a4sIaswmQs1kqCGVGGMGUS6ZDHVvdtvxAf4wm6Oj09tIZEz6bD70TiNmYMUFBJHHMhxccoL+3N2JzVZETk45e/daSE0NxW53cn0DKQcZ6g+NtxRWXOCWKi52S/GNNpf7AoOce90+jAfF/EIARpROHS+lxvbtKWzc+If70V00lHwR+l79mS45vTuORVBFIAUEE43oHe5hx+OvcymdMBLbnHuJKjiEGeDV9wnatR+3dS+SGRrYq9KP5gFHpvsNAwqObiSEOBtYCEyRUvZmtyencf/99wevXbvWJzg4uNHX19cxbty4ujlz5lTcfvvt4WVlZUar1aqtWLEiZ9y4cfWpqanm66+/PqK0tNTo6+vrSEhIyM7OzjZt2rRp0LZt2zyefPLJ4HXr1mUArF692vvOO+8cUl1drSxbtix7xowZNQ6HgzvvvDPshx9+8GhsbBS33HLLofvvv7/k888/93jssceCAwIC7Pv373fdsGFD2owZM4ZNmjSpZteuXe6xsbF1N954Y8mSJUtCS0tLjStXrsycOnVq3ebNm13vvffe8Pr6eoPVatVWrlyZFR8f3+s+x34lBlqy7VTjUVOMf10BIfYsImUGUYZUYiwZRLlkE+FZySAXmtx2+m3aPZ2BQSk+hXNJaNzABb3fzaAr8aGQ57FxHb2/cJbdXkFh4SH274d9+4KoqQkCgpx1OYG0hyjVhZNdcusudks2z3RLD/RVbIFAZ6sMDyyM1LKNzmcO0jQbH39cyN69fxACp1pzUzeGvh3uYWjszYNrG35kE9r1tQK6g9MnULfrI/ZfeTeRW39pSj+6fTce42cR995SMqaf0mvSj/4MDBNCRAL5wFXAn45sIIQYBywHZkgpD3W/iT3P1q1bXT/77DPvPXv27Lfb7WLs2LFx48aNq7v55puHvPbaazmjR49u+Oabb9zmz58fvm3bttTbb789/E9/+lPpXXfdVfr888/7zp8/f/CmTZsyzj777IoLL7yw8oYbbihvObfD4RB79uxJWrNmjdeSJUtCZsyYkfr888/7eXl5qXv37k2y2WzipJNOGnHRRRdVAezevdvt119/3TdixIjGlJQUc25urnXNmjWZEyZMyBkzZkzsu+++6/vLL78kv/fee4OeeOKJ4KlTp2bEx8fX79ixI9lkMvHJJ594LFiwIGzjxo0ZPfcfbZ0+LwbOYrNmpT43nWjXPMI8G7Dq2XZ0BgST2O7YyYTAARUX4EEx/6SC+URj6J0uC2haPaWlBaSmNrJnjzcHDwbixFl4I2pttKns4HTXrMZL3JPdzrDmBFkNarizrtfPObHMQY2Npbz5poODB38nUhU023P+X+bdNWhHb07ra2cQ6YQT7sxaAd1BoC/qNytJX7CU4OdWNaUfPVSKacYtDF9yF7kP3ExxT6cflVI6hBB/BjbSlFr0TSnlPiHEEuAXKeWnwNOAO/ChEALggJTy4h4zugf49ttv3WfOnFnh7u4uAXnOOedU1NfXG3799Vf32bNnH3YLbWxsFAC//vqr23/+858MgPnz55ctXrw47Fjnnj17djnA5MmTa++//34zwKZNmzyTk5NdP/30U2+A6upqZf/+/Vaz2SzHjBlTO2LEiMaW40NDQxsmTZpkA4iJibFNmzatymAwMH78+LrHH388BKCsrEyZM2dOZHZ2tlUIIe12e6/83erzYmALZxn4/VKbjs6A4GcmGRkolZ5dKOcRDrGAYSi9bEVPSpWamgIyMmrYs8eN7OwQNM1pKxZuorFstOVgyQzXdHmJe7L3GPNBf4PoAyskfYEHTyBzUGVlDsuX+2Kz/S7gO1ipzt0a9qZrtLm8twa1a7iTRgSBmPu2CDgSRYFnHqDw1LHU3vQwQ6tqUBwOxEPPEb59N+4J/+r59KNSyg3AhqP2/f2Iv8/udqN6GVL+MYxC0zQ8PDwcycnJ+1s5pN1YrVYJYDQaUVVVNF9PPPPMMwdmzZpVdWTbzz//3MPV1fV3/cVsNh82zmAwHD6foiiHz/fAAw+ETpkypfq///1vRkpKinnatGm9Mp6vz4sBHR2dfoyZKu6hgMVEY8G7p805TENDMbm5pezdayY5OZiGBidNSEjNX6krmmTJr7rQPUW5wDXNf7CpSo9lcgbnkMw/OikEsrJSeOedKDTtiN9Uqc31SEx5M3D98F4bJOxCOhF44tLHU/C2wRXnNacfvYuo/RlNrl/rv8Zn4hW4rHuRjNExvTr96IDnrLPOqpk/f/6Qurq6QrvdLjZt2jRo7ty5xWFhYY1vvvmm94033liuaRrbt293OfXUU23jxo2rXbFihfedd95Ztnz5cp+JEyfWALi7u6tVVVXHvQ/POeecyldffdX/wgsvrLZYLHL37t2WiIgIe2ftr6qqUsLCwhoBli9f3mvdA3UxoKOj0/swUsstHOBphuLWC/yWHY4qDh06yP79Gnv3BlBZ6Y8TYo4MaA1DjJWFZ7pk2y52T3E5xzUzyMPQGAJ0V4GtgUkE2WzoROYgKTV+/DGVTZt+10ddRWPZR8Fras5zy+iNM+0aFrIJx4wHAyLWaHgkjT9/SPINDxH+wZdNwdBpObhMvprYVxeRfe3FPZZ+tE/RnalAW5gyZUrdjBkzKuPi4kaGhoY2jBkzptbLy0tdvXp15i233DLkySefDHY4HOKyyy4rO/XUU22vvvrqgeuvvz7ihRdeCGoJIAa45ppryubPnx+xbNmywLVr1x7TZ/+ee+4pyc7OtowePTpWSil8fHzsGzZs6LSP/wMPPFB08803R7744otBZ5xxRtXxj+gZRGtLML0NIUStlNKt9dcwAT91s0k6OkfyupQsb6tBW324s8cJwVSafEr7Dwr1XEUWL/H/7b13nBzFmfD/re6ePLM5Ka+EJECI3wICIWSRRbLB2IAQRujIYILDEYy5nw0i3R0vhjtjDAZkTMYEHRy+F8yRhMASkkFmQTmuNueduBM61PvHzK5WQmGTdlZSf/UZdXdVdXVN7TMzz1P11FPl5OHJWjssK0UwWM/GjXG+/jqP+voydh0KcEC4hBE+zNHaPNu7xTjfvy7nBHdNqSZk/yLYZIcruFSu2l3mvpD7dD6/An7Q13p3iZ9WqvBQSN/aaZqdLFrUxNq1O8TdP8ZVv/HDUc+NzlOT2ZPfXaHSRAEdlFKG8+CNJPXIsxTd+QhjU/r2z/NNl9L4n3dS14dokSEOk6fvqUB/ZX84UVlZWVVRUdGa7XaEQiElNzfXikQiygknnHDoH/7wh22zZs3Kyv4A+zuVlZVFFRUV5Tun2zMDNjY22Uegcy6beJrRlGbBb1lKi1isgaqqCN9842bz5lGYZvlgPyRXSbYc7Wro+K5vo/i+b33hoc62Qux9SrKHSpylWH02BJLJFv74R2hp6TYEVKzEA4Ufbruj4G/DyeUmRoAayggQYBR2RCluuYLW46bSecktHFLfkg4/+vuX0+FH3/gtW0aWDKvwozbAZZddNm7jxo2eZDIpLrnkkjbbEBh8bGPAxsYmm5icxkb+RBljh9gI0PUgNTWNrFmjsWbNCOLxge62uwMCaZSp0YaZnpro933rnN/1bSwtUuMlQMlgPmeISaHSgYMoLnQ8mNlu0ACweIVGjuzjjrodHVU8/XQx8Xi3AVGiRusWj37WdbizdTgYAmk3oBIsChmHMgzc7IYZJx5L55f/xZo5P2fCZ1+mjfFlXxE45gKmvPIwm049nh2UTdMi2pmkoyNGvCWE2dBB6ly7V4eMv/zlL1uz3YYDnawYA0KIPGAhMJX0jntXSSltVx8bm4MHi+PZzHPkc+gQKiumGaWqqo7PP3ezefMYpBw0dwkNs3OCo6PpNO/W5Pm+dd5TPFUj3Iq5/0U6E4TRCOEggRsDDyoe3LjJxUEOgp57FuxPLk078gs2MKePsrdp0zpefnkSssuVS1pz/Gs2vFi2aKJTWNkdXFNppIAOyhiFw44utTfKijA/fpaNtz/EyP98nhEATW04zriaQ6+8iL9f8H22NgbxtITIj6fwkw7x2UXo3Ow028Zmn5CtL6/fAn+VUl4khHBCPzd3sbGx2f+YyiZeIMBR/Viw2R8sK05tbTXLl7tYu3YMUg7K6K1XpNqnOptbz/Jtluf71uUd7WooUUQfR5mzg4GSGd13ksQDeHDgxoebPFRyONBdl05jHQ/2wRCQ0uTTTzfx8cfd97iFHvxz2RvB8/3rszdGLIgSoJYycvAzkn24md2BgJToCZ22cCex1jCpxiDqtJnU/CjJhEVvckQqhWqaKAtf5fjPKpn4wx9S7XJlN/yojc1QMOTGgBAiBzgJuAJASpkCUnu6x8bG5gDgELbyJ5ycOAQRTCwrSWNjNStWKKxaNRbTHKABIK0CJd50nLsudJ5vg3qeb33x2OEd4jOWGd2P4cbEjcCDGw8BnOQh2CfRkPYLxrKNd/sgg6YZ4/XXW1i/vluGjnA2b148+k+lRWq8fF80cW8tws02SrAooNx2A9oRKTFTBu2ROJG2CMnGIKKhA3dTkNxgjDx2YTAdejj6tUWsefVVJra2pgMXrFtH4VNP4Z07l80lJXb4UZsDm2zMDEwAWoA/CSEqgC+Bn0kph8sW4TY2NoPJSGp4Govv7uNRcyl1Wlqq+eILi6++Gouu93vmQcFKjtbCjSd6tnWe71vvOdO7uTRXTY6AtDvBMMBCIYhGGCdJ3Fh4cODBg5t8NHzQx0WxBwN+WllJEc5e/vYlEs0sXKjQ1lYOabm4q+CTrXcXfjL0CrhKA4UEKbXdgKRE6ibBWIJQe5RkUxCroQN3YxB/W4QCKftu7BYXk7ruOta++Sbj1q6lEKCtDc/ChRx+3nlsPfJIQvvkzdjYDAOyYQxowDHAT6SUy4UQvwV+Cfy6ZyEhxHXAdT3usbE5KDhgZL+Qeh4jySX70AiQ0qSjo4Yvv0zy5ZdjSSYP2ftN38aJET7U2dYy27tZ/4F/XWCGu7bUKaxxg93cPpLILNbtxEUKDwoenLgJ4CYPwXCemegz+1zuVeJ8htnryEHt7Vt56qkykukQoQVKZ8NHo59TKlxNQ2cICCLkUEsZefgYTsbokGCYRGJJOjqixJtDyIYOtMYOAi1hCkyLfBjcjQidTuTFF1O1dCnRDz9krGUhUinURYuYWFNDw9lnU68Mz+3jhoa/rxrcIA/HTR3yfQtsdk02FI1aoFZKuTxz/QZpY2AHpJRPAU9BOl7v0DXPxia77Peyn0Mz/4cw13IIyuDH5kdKi3C4hq++SvD3v48kFivvaxUuYYSPcjU0nu9bL8/3rSuY4motZuj95CWCEBoRnMRxY+JBxY0XDzk4yOEgUv72sdxbvEQDFb0cUd+wYR1//vNkpFRAyvN8G9a9VvbaRLdiOga5XbvCwM02SoECyhFZCLU7hJgWsXiSYDBGrCWMmVH4fU0hClIGASAwlO0RAr7zHVpHjaLzjTc4JBpNhx9dsYIRDQ34Lr6YrYEhbZGNzb5nyI0BKWWjEKJGCHGolHI9cDqwZqjbYWNjM8h4aeNu2riViaj7IHxmNFrL119HWL58BOFwn0btFazUeEew/jzf+sRlga/zp7kbShka5V/vXqzrIoUb8ODEgw83+SjkwcG7AdSQcQvrmdsLpVpKg8WLN7NkyWGQnjF6vuzN1rmB1fteIdeop5AwpYxGo18zXMMVS5JMpGgPpRfu6g0dqI0deJpCFHQmh6dLW3k5nddfz9rXXmN8TU36u6Kmhpwnn+TwCy6gMtvtO5hYsGBB6UsvvVQEMH/+/Ja77rqr+bHHHit89NFHS4UQHH744fG33npra319vXbllVeOq6urcwI88sgj1WeeeWbs448/9t5yyy1jE4mE4na7rWeffXZrRUVF8tFHHy38n//5n7x4PK5UV1e7zjnnnOAf/vCH2uy+2+yQLReEnwAvZSIJbQGuzFI7bGxsBoqLELfRwK+ZiCvtaztoxOONrF4dZNmyYtrbR/f+RikLlHjjqd6q4GWBrz1nezeOciuDvYlYBkEUlSBO4ri6Q3G68JCLg1wE+/veAvs3J7Oeh3thCJhmlD//uY1Nmw4FmOxo3bJ49LNFI7TovvPPF4TJoZ4R5OFlJDBynz1rHyMlRlKnLRIn1ppeuKs0tONuCpEX7iSX/XCWKxDAuPJKNr73HiOXL0+3PxrF+eKLHPv88+I64GkppcxyMw9oPv30U+/LL79c+OWXX66VUjJt2rTDZ8yYEfvNb34zYtmyZetGjBhhNDU1qQDXX3/9mFtuuaXprLPOim7cuNF51llnTdqyZcvqioqKxIoVK9Y5HA7eeuutwC9+8YvR77333maANWvWeCsrK9d4PB5r4sSJU2+77bamiRMn6tl910NPVowBKeVXwLHZeLaNjc0g4SDK9dTwfzgEzyBGNEkmW1i3rpVlywpoaiqjl+ESXcIIHeNqaLrYv0rMDawuHaFFB8vH2kShA40oLhKZ0X0tM7qfh/qtGOQ2w4UxbON/ezHKnkg08dRTGh0d4wRSvz3/b5sfLPpgX60N0PGwjVJU8hmH2H+iAUmJpZt0ROKE2yMkm4LQ0IG7IUggGCVfHoA7HCsKnHMO9aNHE/vLXxifSqFaFgJ4EpguhLjWNgj2HYsXL/Z/97vfDebk5FgA3/ve9zqWL1/uO++88zpGjBhhAJSWlpoAf/vb33I2btzo6bo3Go2qHR0dSnt7uzp37tzxVVVVbiGE1HW923111qxZ4cLCQhNg4sSJic2bN7tsY8DGxqbvqAdZHGqVBJexld9STu4g+TPregcbNzaybFketbUj6EUkEAUrNcHRUXeeb33yssDXece4G8uA3H62IJ4Z3Y9lRvfpEYozH0ERUNTPum2ygY82VlK418hBra1bePrpkaRS7jwl3vS/o16wjnPXD76CrlFLETFKGI02BOF1+4mUSMMilInUE28OIhs6cDYGCbRFKDAtCmGQZwD3A448klBpKWtffZVD2troUji32IbAvmVX3SuEQAjxrQwpJV988cVav9+/Q94111wz9uSTT468//77m9evX+887bTTusMEO53O7rKqqu5gKBxM2MaAzYGJQEclhYaBho4DAycGLsxM3HULDxY+LHxIfAj8SPwI/EAAhQACP2rmXMWPhh8NHxpenPjQ8OBE4eCIL6GQ4gds4UlGUzQIRoBhhNm6tZ5ly3xs3TqavUYGkbJQiTee6t0amhf4xp1x/elrpCITlSa8hDN/Xz9u8jKhOD17vdtm/yAdOcigaC9K69q1a3n99UORUjnDu3n9f494ZbxHMZyD1g5BkFwaKKMAL31wc9v3mCbRzlQ6Uk9m4a6joQN/S4h83bTXsuyKkhKS113HukWLGLFhAyuAf892mw50TjvttOhVV11Vft999zVKKXnnnXfyf//731ddd9114//lX/6lqayszGxqalJLS0vNWbNmhR988MGS++67rwlg6dKlnpkzZ8bD4bA6evToFMCTTz5pD+rsAtsYsBk6+qKgewE/Eh9knDAUApBRzgU5qARQ8XUr6I5uJd2DhooDGIrIHwc+AoPZbOaPlDJmgC4NphmjpqaWzz93s2HDGKTcY31uoQePcTU0XxxYzcX+1WV9dv1RaMNFBwF0cvDjpxRl//bNttkrFi/QwFF7iBwkpcEHH2xm6dLDHZjRhaX/3fRPOV8Pys7UpN2AqihDI49xiOwq1VIiUwatbRGCNa0YW5vxVLdQFE/Z7m39weXC+tGPWHPPPVwspTy4ZoWzEAp01qxZnZdeemnbMcccczikFxCfeeaZsVtvvbXhxBNPPExRFDl16tTORYsWVT311FM111xzzdjJkydPMU1THH/88ZGZM2dW33HHHY3XXHPN+EcffbTsxBNPDA/1e9gfEPvDDJcQIial3GW0ASFwAMuGuEkHFipxHCTRMHGg48TEhYELCw/mICroB+oI+tMy7T+6W/Ykw/29TwhOBR7qa519wOIENvE8hUwcgFuAZSWor69m+XIHa9aMwbJ2OwihYCUPybj+zAt8XXCMu7EvPsidOGnCR4IcXORQlAnPeVAgJZYEQ0pMKdGlxJIS09r+siwLy5KYpoVlWWBYmKYFpoVlWmCYSMME00ofDRNhpNMxTIRuIoz0SzEsRDzF3fOuk7tVEPaF3Kfz+RXwg11m/jNreWQPM1eGEebll0Ns3TpmvNZR9emYZ/JHaZH+updtx0ENRcQoZhxadmaZpMRK6LS0hglVt2JVNeGtbqUkZeDORnsOYEJ33y1P31OB/sr+cKKysrKqoqKiNdvtsBk8KisriyoqKsp3TrdnBg4eTHJoZwxhpqBzLArT8XIUheRlPKRtbNJIKtjMcwSoYHL/apA6TU3bWLECvv56HKa5m3qkLFTiDad6t4bnBb72nO3dNNKtmL2J3mKg0YSHKDkIcsjHTRFi32xwJiUSMKXEkJmjJTGkxMoo2UbmKM20si2ttIItDQtpmsiu84wyLU0LMso1xvajYlgI3UAYFoph9nhZqJlzzbBQzPS1ZlpopokqQQG6XFyG6vOsDtFzescs1u/REOjsbGDhQpfoaB9xc+6Kdf9Z/O6hihjAXhiCDvJopIwiPIzpdz39QEr0ziTNLWGi21pgaxP+unaKDZNSDsCFvMOMg2tGwOaAxzYGDjRU4hTSzgRiVGAyHSfTCHA4BTj7vkW7zUHGJLbwLG5m9mOBo5QGbW3VfPmlycqVY0ildlmHW+jBaa6GposDq5S5/tVlpVps7247Cm24aceP2cPdZ1TfmoeeSNHWESPSFMSobcMR7sRpWCi6kVa2Mwp2l5KtdCnblkQl/X1pf2cOV0ZTzQd7iBzU3LyZhQtHB4xo+N3RLzZ9x1PTX5e3JF6qKcNBLmMRg7sL7q6wJMlYguamILGqZsTWZnIaOyi2ZN8+AwcbAqELoSZVoaUURdM14TQ0xWk4VLfhUNyWU/VYTtUrXZoPp+qTbs0nXFpAuLWA4lK9ilvza24toLo0n8OpejWn6nO5VK/ToboPykWmNgcu9g/b/oqbICMIcigJjkFwHG6mkcsY8sD+gbDpI2PYxtMIzurlDq1dSGkRDFbzj38k+eKL0cTj37q/p+vP/JzKgqNcTaXseXFiDBdN+EiSg4sAJTh6H8HEkqQSKdo6okQbgxg1rTjr2slpC1Mo02FKexWq1GY/wksbX1KAaze/ad98s47/+q9DT3Rv2/jO2BfH+RXd1ednOKihmDjFjEVl0kCbvDssi3g4TnNjB/GqZtStzeS2hCiSDO3MwxBiKkJNKUJNKkLTNcVpaIpT1xRXT4Xdcqpe6dS8uDU/btUvXJpfcWt+xaX5Vbfq11yaT3OpfodT8zpdqs/pUN1ORaj7au1YfB/UaWOTNWxjYDgjMMiljbFEOAKdY1E5Dj9HUUDAjvZgMwiUUMdj6MyhvNf3SCmJRmuprIzx+ecjicXKdy5QpHQ2nuqtCl0W+Npz5p5df9LuPl4i5KAQoAAPRbB3o8SySHSmlf5YQwdWXRvOunZy2yPkyz0sMlaFI+x15AU9zsJOqTgNgUSAEKAgu86lECAsLCGlhSVNIaUlJBIpTSGRwpIWMp3fldd1VNLrCqWwsBSkzORJBdLnZK4lmTQpFSCTjwJd1zZ7RSXB3zAo2YWxKKXOX/+6VVuxbOzvSt7Z9OPcL/vm9qbQTh5NlFGMe/CVccMkEuqktb6dZFUz2rYW8tsiFAB92mF7CJACpVthVxVN1xRXepRdcZkO1WO5VK/pUD3SlVbWcWk+4VJ9wqX5VY8WUFyaX3WpPodL82uZkXanU/W4NMWpge2qamOTTWxjYDig0Ukx7RxCJ0dhchxOjiWHyRSg2f6fWUFKHcvSM0cTy9IxTQPTtDBNE8MwMQyJYVhEIjGmTMl2i/tGHk08RJRrerEhUxexWD2rVoX5/PNSgsEdFCO30IPHuuqb5wZWizlp159dR/1RaMFNBwEscvDjo2xv7j6WRTyWpK09QmdDB1ZtWunPD8Z2PwumCkfY48gNOR15MV31J2KaV4YVjxJUXa4tyWb/qtjmgubourF7e8sqqqUJ1XQomqUJ1XII1dKEZmlCNZ3CYWmKUzqEZnW9nIomtfS5dAhNOhTNcglH17nsSncqDnqUwSkcmTQVh9DQhIZTaFKA1BAoQkFIKVUhpILsshqEIkBBZKwHCek8FCRCCiGQUkkbOiClSBs+6R2TMgaQQFoSpBBSCjJ5dBlD0sSShrSkhSUNpLSkKQ0saSGlKX3OAnNvfbiPsXie+l1GDjKMMC++GB5T97VrSfkzyXJHqLej+Ul8bKMMFzmMRVAwGA3VTYLBKG117aS2NuGsaqEw3EkeEBiM+veGKhxhtxaIBFzFnXnukYbXkStcqk92jbCnXWN8qkvza+4uhV3xOJya1+1QPA4hhAvo+4yKjY3NsMc2BoYOiZcgIwlyGEmmITgOD8eQxwhyAG+2GzgskdJCyi6l3OhWyi3LwjSNjFJudSvmui7RdbqPqZQglUofdV0hlVIzaSq6rmaOGrru6D4aRtfUcm9ofx35AAAgAElEQVSnlz/h7rv3YScMIj7auJc2fsZE1F4YmYlEE2vWtLNsWRGtrd2+/RnXn/rv+9YnL8upzN+l648gipMW/CTIwU2AErTdr1sxLWKxBO2tEWINHVDbiru+g/xwJ7nw7RjtitCiXkduh6IFoknVl4ypHiusetQO4fRsTrX4V8e2FIVSmwY0mmtiKqY0laSZGkg1+y17M4YAZROPZq+BP2U9l+5iwXAsVs/TTzmvlR9Hnyj/n8mqkHubZZE4qKaYZMYNqH8L5zOkdNrao3TUtmFsbcK9rYXCWHKfzuZamuIKebTcaI67JJ7vHmUWecuVYt8EV7G33JfvGZWnKa4cOHiia9nY2PQe2xgYbAQ6ebQxjihHonMcGsfh50gK8JHPXjdWGsZIafRQylNYloFpmlhW10i5iWmmlfJUysIwtivlySToukDXRUYZV7pfOyrlGqmUhmE40HVHJgxlFkakpFSQuoLUVSENDcvUhGU4ME2HsAynMC2nMC23MKwRWiQ2tG3rB26C/JJG7mQizr343qdSbaxf38Lnn+dRX18GlIKUxWpn/WmedNSfM32bR7nEDht+6ThoxEuUABo5FOCmkF3EMTdMwtEEHa1hEg0dUNOGu76d/FiCHGCHUHxphT+nBs0XS6ieZERxm0HFrbUpmqcq1Z6zrrOqpDPVdqD6UmedXhhD2VtI+R3W89tdGAKNjZt8f3rC93bx84nTvFV7XiSs0EYezYygBFffXXOkxErqtLZGCNW0Ym5twlPTSnFCH+xdeoXhVN0hryM/kuMqSRZ4xlhF3vFqsW+8u8g7zpfnHpGnCG23vy+WtPSYGW8LGdHOFj2YbEq1GYY0UYWCJjRUoQgVBVWoIp2mCgUFRShCFYpQEKRzMtfdaYqSLqUIgRCqEEKQPleEUBREV54iEEIIoaRnpEQ6TWTSQREIBVCEEPbi3GHK1MenDs6O8xlW3bhqr/sWrF+/3nnuuedO2rhx4+qe6XPnzh33i1/8omnatGmJwWxTf7nllltGvvnmm/mapvHrX/+67p/+6Z+CAC+++GLes88+W/jBBx9sBrjzzjvLXnrppaLq6upVAC+//HLuwoULiz/66KNNfXneCy+8kDdlypTEYL1/2xjoLw6iFNPBJDo5GslxuJhGDhPJRx1mCxSltDDNGLoeI5FIEo+niERMIhFJKKQSjao9lHO1WyHvUs4NIz1aLuWQRlMRSENBxlUsXROWqSINhzBNh7BMpzANlzAslzAtlzAstzCkWxjSq+jSIwy8IiW9ii68wsCvpIRPSQmv0BW/klK8Qlcy15pX0VWf0FW3YmheoaseoTvcwnC4FVMjHaaxN7uR+uCFfdwb/cRBlJup5V85BPceNgzT9SBbtjSydGmA6upRQKFb6MFj3fXrLwmsUub4V48o0Tq7ZgYkCi14CGbcfQL4KEXs6FOtm4SicTpawiTq26G2DV99O/nxFDuMUCpCjbm0QLvT5anrVN2piOK2OhSX1iZU71a9I2dTvGZESm+3v6tstnMeIV7fycVNSkll5brp7z2m/u/oF/Ny1eTufNATGTcgDzmMQfROaZcSI56ipSVMuLoFubUJX20bJbpJCVAykLcjEEmn6gv5nPnRXHdZqtAzVhZ7J2hFvnJ3oWdcIOAqylGE+i0DQ7eMWMyKx7Ylmmua9fZUbbLZrE40iqpEvbIlUeeqSjR4apPNgaAR8WTuHUQDZd8gQCrpGSlLFYpUhGKpKFIViqUJzVKFItN5qlRRLIeiSgVVakKxVKFKh9CkKhSZPlczR81ShYIqVKmhSlUomXIqDkXrSus6x6FoUkFBE6rUhIqacd/L3IcQovNmjs12Vx3UvPrqq9uy3YYuNm3a5Fi0aFHBhg0bViuKIqurq7u9Ck477bToz372s+6BhuXLl/v9fr9ZV1enjRo1yvjb3/7mP+GEE6J9feZbb72VZxhGyDYGhgYLHx2MIszhGdee6Xg4mkJKsrx7o5Q6hhElmewkkUgSi+lEIhbhMIRCKqGQk3DYTTjspbPTj5QB+uybKi01PTpuqMKKa8IyHMIyHcI0HMKynJhml0LuVnTL06WQCx2Pokuf0Mko3cKn6Pgyirhf0bsUcsUrdM2TVsg1r6KrbmE4vEJ3uIShKaI7lKO9sKyvqMS5gir+g/EEdmMEGEaEbdvq+fxzN5s2jVWwPBMd7fXfz1u/bl7O1wVHuZpKgDwEYVw04aeGHDz4KUFLK0BSInWTYCRKdUuIVF07Sl0b3voOCpM6uUAupBV+h+oL6oqrJu5xp8KK02pTnM5WVM82PZi7NVE/yiJoL5i16R1vk0vP3y/LSjnf+7+b/33Lv2r/PObzXa0NkDjZRjEpihiHyh53G7Ykqc4ELU0hotXpGP6B+g5KTIu+7YCdQRFqp0v1h/zOgs4890i90DtOFnvHO4p84z2FnrG5Pme+H7o+U9JKylQkYnTG2oxQ4qtEbVNNaGVDVaJebI3Xa1WJeve2RKOnNtmck5QpHzvNpu3vSBAmpmpKU2X47okaupn/k+02HDQYhsEFF1xQvmrVKu+ECRMSr7/+etXpp58+6Te/+U3NSSed1Pnkk08WPPzww2VSSjF79uzgE088UQfg9XqPvvzyy5uXLFmSk5ubaz7wwAO1d9xxx5j6+nrngw8+WD1v3rzQ+vXrnZdeeun4eDyuAPz2t7+tPuOMM2Lbtm1zXHjhhROi0ahqmqb43e9+t2327NnRuXPnln/99dc+IYScN29e6913393scDiIRqNqOBxWiouLzUMOOUTvavvIkSONQCBgrlq1yjV16tRkU1OT47zzzuv46KOP/PPnzw+uWLHCf99999UBzJs3b2xlZaUvkUgo5513Xsd//Md/1APceOONo9577708VVXlKaecEp4zZ07HBx98kPf5558HHnzwwRGLFi3aDPDjH/94bHt7u+Z2u62FCxduO/roo3ttKNjGAIBCknzaKSfK/4fBcTg4Fj9HUphxdRia0RTT7MQwYiQSceLxFNGoQTQqCQYF4bAjo+B7iER8JJMe2JXbkZQqMuESRtwtjKRfSXXkOJIteUrCKFTjVpHaSYkaFcVqp1qqxtRiLeYoVDodXsVweEVKdQvD4VEMzSN0h5b2s91fFo2ZpDeCMQETkbkWWJn0rnOJwERkVluKTHp6ZWX6p2f7CkuZeXWdi+5rBTLpAo3h4yakkORCtvIEYyjchQuFZcWpqalhxQona9eMKlZigdM9W0KXjvimOuP6Myrj7tNODiFyKMRFgZT4dZOOUCehlgaqa9tQ6trwNQYpTBlpORSocU31tCUVZzSmuNo6PA5aheZoQfXWmuG82mTTSGmHvbUZbHQ9OO61x2s/0O8ZMTG/Y8fvaoVW8mmljFJcu46YZVkkIgmaGzvo3NaCsrWJ3KYgxX2R1e2Lc4s689yjjCLvOFHkHe8s9o33FnjG5Lo1v9eUlha3Es6QEets1YPJhmSL8ffY5nBV26fRLfE6R1WiwVOdbPQ1pzr8Fla3IW1jc7BTVVXlfvLJJ6vOPPPM2Jw5c8ofeuih4h55jgULFoz68ssv1xYXFxsnnnji5BdeeCFv/vz5wXg8rpx66qmRJ554ou6MM8445Fe/+tWoTz/9dMPKlSvdV1555fh58+aFRo4caXz66acbvF6v/Oabb1w/+tGPJqxatWrtM888U3D66aeHHnzwwUbDMIhEIsqyZcu8DQ0Nji6XpdbWVhXA7XZbhYWF+rnnnnvI4sWLN3o8nh3M2GnTpkUXL17sN02T8ePHJ2fOnBl79913cy+55JLg+vXrPSeddFIM4JFHHqkrLS01DcNg5syZhy5fvtxTXl6eeuedd/K3bNmySlEUWltb1aKiInP27NnBc889N3TllVd2AJxwwgmTn3rqqW1HHnlk8qOPPvLdcMMNYz///PMNve3jg8sYcBKmlA4mkeh27TmWXMaTj9L30Z69IqWJacZIpdLuOZ2dKaJRi1BIEg6rhEIa4bCbUMhLLObHNL2AVyANB2bcJcyEV9GTASWp5yhJo0CJdxapndEid2dLsS+mlGkxtUSNacVqzFGkdrqL1E53npLwqEL2JkybBSQRpBDopBVoA+ikK+QI3UpyV4iRLiV5d8oyPV5pxVlB7JQGAiWjTKf/KT3+3/HYda5mztRMmvqtc7pf31r0K2W3QSAlWMjMEaxMnpQgu9Izu81KKbFk5n6ZzpNSZl49zjuTiKxruAKDs9jEHxnJyJ1mAiwrSWNjNStWqO41XwWOVavlJYFV+pzy1fESV6eKG4Uc4uTQIj0Upkw8oU70piB6fS1ttW0km4IU6CaFAsUrVHd7QjiiEUVra1cdba2a5mgViq/GiOY36+2jyXZ8GZuDh2i09qrXbup40vXCFM0pu3ZDjuOnmjK85DAGKOoqblpEw520NnSQyMTwz2sNUwTsKbKUpSnOsEfLjeS4SuL5ntFmoXecKPaOdxf7xnt9zhJHClIdRlhvTrXL2lQzlYlGtkZWm1ua/zdZnWyM1iSaCJnR/cZVx8ZmOFFWVpY688wzYwDz589ve/TRR7vd8j777DPfjBkzIiNHjjQA5s6d2/7JJ5/458+fH3Q4HPKiiy4KAxxxxBFxl8tluVwuOX369HhdXZ0TIJVKiauvvnrcmjVrPIqisG3bNhfAjBkzYtdff325ruvKRRdd1DFz5sz4YYcdlqypqXFdfvnlY84777zQD3/4wzDAZZddVv7QQw/VfPbZZ/4f/OAHE955553Nd999d6nP57PuvPPOlpkzZ0aXLl3qM02T448/PnrSSSfF7r///pFLly71jh8/PuH1eiXAc889V/Dss88WGYYhWlpaHJWVle5jjjkm7nK5rEsuuWTc9773vdDcuXNDO/dPKBRS/vGPf/jnzJnT7TqZSqX6tPbmQDQGLAK0MZowU0gxDYXp+DiKfAoZeDQFy0pm3HPiGfccg0jEIhQShMMqwaCTcNitRkKaIx7BI3Tdr+ipgJK08pQEhWpcFiqdlGgxq0SNGcVqLF7ij5klubFEkdrpLlDjXr+iO0m79ASAVEZhTyIwEBgo6CiYKFgoJFFJoBDOqMMCFSEVsATCUsAUSEsgLIEw00fFBEwLSzewDAtppRXbtDZsgQXSskBKhCUzwQclmFamjERIiTTN7jLCtNJTvJYFVroeJXOfsNIvLGv7UUoUc3t+V3nRXbeVbpMlMS0LRabbJKTEMCWK3F5WldvvV6xMnpQo6Zjt7OuY7d67v7MPa98zFrPYyLMUc0gPI0BKnZaWau3LFfqk1R+Kc52r5SU532jHHNYYxo+0AqgJF8HGFKmmIFZdPVrdN4imEMKylIClOFJxoRlhoSXahBZvVpVQq6b6681YQYcRybrtY2MTaKpa98Y7Z7nPdG84ErBwUkUJOkWMQ+FQwyQUDLOlvp3k1macVc0UBGPk8233TsOheEJeR140112ayPeMtgo8Y4XbVYLLVaIILUe0GRGrNtlsVSXqxd8Sdc6q4Jeu6sb/661LtgSSMrUvNrWysbHJsPOa8p7XUu7el0zTNKko6Z9+RVFwuVwSQFVVTNMUAA888EBpSUmJvmjRoq2WZeHxeKYBnHPOOdElS5asX7RoUe4VV1wx/qc//WnTzTff3LZq1ao1b775Zs7jjz9e8uqrrxa8/vrrVUuXLs159913N59//vmRyy+/fMz8+fPHbt682f3SSy9tBTj55JOjTz75ZIllWeL6669vyc/Pt5LJpPjggw8C06dPjwKsW7fO+dhjj5VmZjjMCy+8sDyRSCgOh4Ovvvpq7dtvv53z5z//Of+JJ54o2XnE3zRNAoGAsW7dujX97eP93xi4G4sG1mZcewIcTiGu3Ycv/BZSSiwrhq53ZtxzdKJRQ0TCljvabnkjbdIbbcMbaVX8kVY1Vw9TqMZlkRoTpWqMEjWmFqudjmItRoEWV4q1mCjMj6cchVYSBSOjsFuWwDTBtATSFFimQJqAAdIQad8WQ6A0gagHdBAmqLqFSBmQMhApA6HrKEkdJWWgpAwcuomaMtBSBpqevnYYJg7TAoFiIIQOwpRgWAhDIkxLYFpp3V10+chY6bF7iRCkR70zWyMhLKDbpyZ9RKanB9Kfwq68zL3pYXORLpcpLyTI7gjmPdJk95QDUoLoaocUQnTXBViZz75ECGv7/aSnJDKNFGAJgUwHVReWzNQBkKnPyoRfTz9bKj3fFwhFIoW1/X0iQelqryVQMnYKZNItKRQLmchKYNH/H51LaGNqxv9ZSpOO9m3j1iwJf2fVIi7wrOTsgg2aeaRpBlVEawr5lzYcdTUUtITw6TjCMaEkgkIzWoVqtqDIFlXIeuL5nVZiFCSz8a5sbPbKTYv/pfXemv8YW+BJRClgbbIAX9DArG3D3LqJum0tFEYT6TUrApFyqt6gz1kQHJVT0Kg4clPCkWfhzMPS8tSE4tRqUi1aZbxOq0o05FS3L/M1p97xW1j2GhYbm2FAQ0OD84MPPvDNnj079vLLLxfMnDkz+u677+YBnHTSSbE77rhjTENDg1ZcXGy8/vrrBTfeeGNzb+sOhULq6NGjU6qq8thjjxWaZnpqe8OGDc7x48enbr311tZYLKasXLnS29DQEHK5XNYVV1wRnDx5cvKqq64aD3DooYfGn3jiicKf/OQnbb///e9rp0yZcsS4ceOSEydO1AGOOeaYREtLi2P58uX+5557rhpg6tSp8Weffbb4vvvuqwXo6OhQPR6PVVBQYNbU1GiLFy/OPfnkkyOhUEiJRqPK3LlzQ6ecckp08uTJRwL4/X4zHA4rAAUFBdbo0aNTzzzzTP5VV13VYVkWy5cv95xwwgm93il7/zcGFqDATr7RUhoYRlTVk53uVDTui4dSgXh7KrezxczpbDVzo00yEGkkJ9IkAtEmCohRoMYoVGKiSIuKQiUmAmpC0SXSAHQJuhtSLoQuUfV0mpqSKAagW1BnoG5NouoGqm4idEOQNHGlDIRugikUS4JhpgfFTQthmWCZYBkIaZA2EIy0z47UAQNhZbR5dFAMAQaKTIFlgNDBMoUgJclMGUhMFEsXlplSLUfcTDpMabq/1WfDd1HW/sq3+3gouB8HUhb6om1Vx1a91zyr8nnrLGuppilRR6MbX0M7nn+rUmRUaJ0dKHqTUGQzCi1CyGZSrpTURyAZYSv9NvsbN235N7FGYVNtkpzaKqWkU3emTMWZ1FW3kVQ8ibjmDiZyfVpS9Tk7MN3bEo3e2mRTWTheYwcjsLHpJ70JBbovmDBhQuKZZ54pvPHGG8eNHz8+edttt7V0GQPjxo3T77rrrrqTTz55spRSnH766aHLLrss2Nu6f/7znzdfeOGFh7z11lv5s2bNing8HgvgvffeCzz66KNlmqZJr9drvvTSS1urqqocV199dbllpYcn77333lqAF154Yeu111477ne/+12py+WSN998c9Nbb72Vt2DBgtIFCxY0KYpCRUVFLBKJqF2zEzNmzIi+8sorRaeeemoM4IQTTohPnTq1c9KkSUeMHTs2OW3atChAMBhUzz333InJZFIA3H///TUA8+bNa7/hhhvK//CHP5S+8cYbm1955ZUt11577bgHH3xwhGEY4oc//GF7X4wBsacpluGCECImpdxlxIRTFt+j5sZb/5gbaRB50XoC0SYlJ94icpROqQhTxEGkJCIhIWUiEgYiZQlhgkwr4lgGaSXcyCjhXQ70OoiUEMIAYYCSSivmig6qDooBWuZaMxGqAZoupSOF6bBHlQ4qnpZ3yyf3VGBPMtzf++b+9+UnTGhYfkNeZL1SE0dpk8JoEorSguJqgpw2aebbcmizj7lC3i1X7S5zX8g9wJR7lJ8khXZaTHH5OqxkTkrq+//Als3+REjeLU/fU4H+yv5worKysqqioqI12+2wGTwqKyuLKioqyndOz8oXqBCiCoiQ8Y6RUvY7YO8nnywQHhhvgMMATfbKN3xXBtBujKLhbyvZHKS89dXzjlR6VqzbiSrtfGav4LU5sFmLzEXqYzD1vRe2sRmmCCHOBn5LOvjFQinlv++U7wKeB6YBbcBcKWXVULfT5sAnm6Mpp0opB8PiFPEDLM6yjU1vSKV/QOzdOrNHOpTt9pC1O4ew3R66dnsYW7mb1/Y8pUeErq5zZafz7WnscN7zJdguIeljOu7Wjvkic911VLrLKpn8rjhe29OU7jhfTtvytLHpD0IIFfg9cAZQC/xdCPG2lLLnItCrgQ4p5UQhxCXAg8DcoW+tzYHOgTC1agHrst0Im4Oaliw9NwykfzjSCidsDwtL9zroHfdQ2FEJ3Z727VCx0KV49gwbu2Mo2fQ8XJdSuvMeDPRQbOnO23Uau7h3u1L67XJipzSxQ963y3WFtRU7lO8KTtulCG9XiEXmWvRQiLuC4KbTh5ch1t+2yJ2OVh/vT/XzuQOlAft73yZ7hAehjunAJinlFgAhxJ+B8+n6Tk9zPrAgc/4G8JgQQsj9wb/bZr8iW8aABP43E+DlSSnlUzsXEEJcB1yXufQIIfa0sZMGGIPfzIMGu/8Gxg/FAvEKe+5DjxDiix7XT+1K7sGW/SHG7r+BMUcgbLnfP7H7cACIBeJqBvadPwqo6XFdCxy/Ux3dZaSUhhAiRHqfCtuP32ZQyZYx8B0pZb0QogR4XwixTkq5pGeBzIdmlz8aOyOE+GIg6w4Oduz+GziD2Ye27A8ddv8NDFvu91/sPhwYg9B/u5rN23nEvzdlbGwGTFYijUgp6zPHZuBN0tNlNjY2NjY2NjYHA7XAmB7Xo4H63ZURQmhALtA+JK2zOagY8pkBIYQPUKSUkcz5mcC9Q90OGxsbGxsbG5ss8XdgkhBiPFAHXAJculOZt4HLgWXARcBH2VwvMJWph++9VO9ZxZ73LVi/fr3z3HPPnbRx48bVO+fNnTt33C9+8YumadOmJQazTUPNxx9/7L399tvHtLa2OoQQcvr06dGFCxfWBAIB67XXXsu59957R3V2dipSSs4444zQU089VVtZWem69tpry8PhsJpKpcTxxx8ffeWVV7YNpB3ZcBMqBd7MbCetAS9LKf86wDp7NbVss1vs/hs42epD+283MOz+Gxi23O+/2H04MAbUf5k1ADcD75GO9/WMlHK1EOJe4Asp5dvAH4EXhBCbSM8IXDLQRh8ovPrqqwNSfocDNTU12rx58w55/vnnt8yePTtmWRbPPfdcfjAYVNatW+e89dZbx7799tubjj766ISu6zz88MPFADfddNPYn/70p01dm6utWLFiwJspDrmbkJRyi5SyIvM6Qkr5wCDUaX+pDQC7/wZOtvrQ/tsNDLv/BoYt9/svdh8OjMHoPynlO1LKyVLKQ7p0ISnlXRlDACllQko5R0o5UUo5vSvy0MGEYRhccMEF5ZMnT55y9tlnT4hEIgrA9OnTD12yZIkX4MknnyyYPHnylEmTJh1xww03jOq61+v1Hn3DDTeMOuKIIw6fOXPm5I8//tg7ffr0Q0ePHn3kSy+9lAvp2Ydp06YdOmXKlMOnTJly+Pvvv+8D2LZtm+PYY4899LDDDpsyadKkI/7617/6DcPgwgsvLJ80adIRkydPnnLPPfeU7NzW0aNHH2lZFq2traqiKNPeffddP8C0adMOXbVqlatn+Ycffrjk4osvbps9e3YMQFEUrrzyyo4xY8YY//qv/1p26623Nhx99NEJAIfDwS9/+csWgObmZse4ceO6I7lNnz691zsN7w57d1IbGxsbGxsbG5thR1VVlfvHP/5xy4YNG9YEAgHroYceKt4p37FgwYJRixcv3rBmzZrV//jHP3wvvPBCHkA8HldOPfXUyOrVq9f6fD7zV7/61ahPP/10w+uvv77pvvvuGwUwcuRI49NPP92wZs2ata+++uqWf/7nfx4L8MwzzxScfvrpoXXr1q1Zu3bt6uOPP75z2bJl3oaGBsfGjRtXb9iwYc1NN93U1rMtmqYxfvz4xMqVK93vv/++f8qUKZ2LFy/2x+Nx0djY6Jw6dWqyZ/k1a9Z4jj322M5dve/169d7jj/++F3m3XTTTU3f/e53J5900kmT7rnnnpLW1la1/z2cxjYGbGxsbGxsbGxshh1lZWWpM888MwYwf/78tqVLl/p75n/22We+GTNmREaOHGk4HA7mzp3b/sknn/gBHA6HvOiii8IARxxxRHzWrFkRl8slp0+fHq+rq3MCpFIpcemll5ZPnjx5ypw5cw7ZvHmzG2DGjBmxV155peiWW24ZuWLFCk9+fr512GGHJWtqalyXX375mDfeeCMnPz//W5suzpw5M/Lhhx8GPvnkk8Dtt9/esGzZssCSJUt8FRUVewqV3Cd+9rOftX3zzTerL7jggvYlS5YEjjvuuMPi8fiA9r0ZlsaAEGKMEOJjIcRaIcRqIcTPMukFQoj3hRAbM8f8TPphQohlQoikEOK2neo6WwixXgixSQjxy2y8n6GmH/13vhDiayHEV0KIL4QQs3rUdXmm/EYhxOXZek9DTT/68BQhRCjTh18JIe7qUVevZdCW/YFhy/7AyJbc9/PZtuz3wJb9gZFN2bfZPUKIPV7vaT21pmlSUdJqrqIouFwuCaCqKqZpCoAHHnigtKSkRF+7du2ab775Zo2u6wrAOeecE12yZMn6UaNGpa644orxjz32WGFxcbG5atWqNaeeemrk8ccfL7nkkkvKd37mKaecEv3ss8/8K1eu9M2ZMycUDofVDz/8MDBr1qzIzmUPP/zw+BdffOHdVdsnT56cWL58+S7zAMrLy/Wf//znbR9++OFmTdP44osvBrRuYFgaA6Q38rhVSnk4MAO4SQgxBfgl8KGUchLwYeYa0gtrfgr8pmclYvt23+cAU4AfZeo50Olr/30IVEgpjwKuAhZC+ksQuJv0RijTgbu7vggPAvrahwCfSimPyrzuhX7JoC37A8OW/YGRLbnvz7Nt2RQGwGIAAAVISURBVN8RW/YHRjZl32Y3NDQ0OD/44AMfwMsvv1wwc+bMaM/8k046KbZ8+fJAQ0ODZhgGr7/+esEpp5wS3XVt3yYUCqkjRozQVVXl8ccfLzTN9GD/hg0bnKNGjdJvvfXW1ssuu6x15cqV3oaGBs00Ta644org/fffX/fNN998S1k/5ZRTYitXrvQriiK9Xq884ogjOp9//vniU0899Vttuu2225pfe+21wo8++sjXlfb4448XVFdXa3feeWfjI488MuLrr792AZimyYIFC0oB3njjjZxkMikAqqurtWAwqPZcQ9AfsrXp2B6RUjaQ3m6eTAjStaR34jsfOCVT7DlgMXBHZr+CZiHE93aqqjfbfR9w9KP/egqpj+2bmpwFvC+lbAcQQrwPnA28so/fQtbpax/uoao+yaAt+wPDlv2BkS2578+zbdnfEVv2B0Y2ZX9/YW+hQPcFEyZMSDzzzDOFN95447jx48cnb7vttpae+ePGjdPvuuuuupNPPnmylFKcfvrpoa4oO73h5z//efOFF154yFtvvZU/a9asiMfjsQDee++9wKOPPlqmaZr0er3mSy+9tLWqqspx9dVXl1uWJQDuvffe2p3r83g8sqysLHXsscfGAE488cTo22+/XbCrRb5jxowxnn/++S2333776La2NoeiKHLGjBnR+fPnB8eOHRt/8MEHa370ox9NiMfjihCC2bNnhwD++te/5tx2221jXS6XBXDPPffUjh07dkC7iYsshqztFUKIcmAJMBWollLm9cjrkFLm97heAESllL/JXF8EnC2lvCZzPR84Xkp585C9gSzT2/4TQvwQ+DegBPielHJZZurdLaW8P1Pm10C8q38PFnrTh0KIU4BFpDeJqQduy4SJ67cM2rI/MGzZHxjZkvvePrvH9QJs2d8BW/YHRjZlfzhRWVlZVVFR0ZrtdtgMHpWVlUUVFRXlO6cPVzchAIQQftIftp9LKcP9qWIXacPb+hlE+tJ/Uso3pZSHAT8A7uuqYldFB7eVw5s+9OFKYJyUsgL4HfBWVxW7KLvXPrRlf2DYsj8wsiX3fXz2bqvo77MPBGzZHxjZlH0bm2wxbI0BIYSD9AfyJSnlf2WSm4QQIzL5I4DmvVTTm+2+D0j6239SyiXAIUKIIg7i/oO+9aGUMtw17S6lfAdw9LcPbdkfGLbsD4xsyX1fn70H7L+dLfv9Ipuyb2OTTYalMSCEEKR33lsrpXykR1bX1txkjv+9l6q6t/sWQjhJ79739mC3d7jR1/4TQkzM3IMQ4hjACbSR3hnxTCFEfmYB2ZmZtAOefvRhWY8+nE76s9VGH2XQlv2BYcv+wMiW3Pfn2XvAln1b9vtMNmV/GGN1+cfb7P9k/pbWrvKG5ZoBkQ5x9inwDdsb/i/AcuA1YCxQDcyRUrYLIcqAL4CcTPkoMEVKGRZCfBf4T7Zv9z3gHY+HO/3ovzuAfwJ0IA7cLqX8LFPXVZl7AR6QUv5pyN5IFulHH94M3EA6IkUcuEVKuTRTV69l0Jb9gWHL/sDIltz389m27PfAlv2BkU3ZH65UVla+XVZWNqW4uDikKMrwUxZteo1lWaKlpSW3sbFxTUVFxfd3zh+WxoCNjY2NjY2NjU32+PLLL0s0TVtIeiH1sPQksek1FrDKMIxrpk2b9i1XQdsYsLGxsbGxsbGxsTlIsS09GxsbGxsbGxsbm4MU2xiwsbGxsbGxsbGxOUixjQEbGxsbGxsbGxubgxTbGLCxsbGxsbGxsbE5SLGNARsbGxsbGxsbG5uDFNsYsLGxsbGxsbGxsTlI+X86EY+dXOvPwwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d959396d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, to_plot=True)\n",
+    "# f, axarr = plt.subplots(2, sharex=True)\n",
+    "# axarr[0].plot(x, y)\n",
+    "# axarr[0].set_title('Sharing X axis')\n",
+    "# axarr[1].scatter(x, y)\n",
+    "if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "else: tot_name = 'Total (PWh/a)'\n",
+    "EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                               'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "InteractiveShell.ast_node_interactivity = 'last'\n",
+    "colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "fig = plt.figure(figsize=(10,4)) # 7,5\n",
+    "_ = ylims = [5, 13] # 5, 10; 13, 18\n",
+    "\n",
+    "ax1 = fig.add_subplot(1, 3, 1)\n",
+    "_ = ax1.plot(years, EROIs_mixes[[('BL', year, 'EROI') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI', color='black', zorder=20, linewidth=3)\n",
+    "_ = ax1.set_ylabel('EROI')\n",
+    "_ = ax1.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "_ = ax12 = ax1.twinx()\n",
+    "_ = ax12.stackplot(years, EROIs_mixes.fillna(0)[[('BL', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax1.set_title('BL (business as usual)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax12.get_legend_handles_labels()\n",
+    "_ = ax1.set_zorder(ax12.get_zorder()+1)\n",
+    "_ = ax1.patch.set_visible(False)\n",
+    "# ax12.get_yaxis().set_visible(False)\n",
+    "_ = ax12.tick_params(labelright=False)\n",
+    "\n",
+    "ax2 = fig.add_subplot(132)\n",
+    "_ = ax2.plot(years, EROIs_mixes[[('BM', year, 'EROI') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI', color='black', zorder=20, linewidth=3)\n",
+    "# ax2.set_ylabel('EROI')\n",
+    "_ = ax2.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax22 = ax2.twinx()\n",
+    "_ = ax22.stackplot(years, EROIs_mixes.fillna(0)[[('BM', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax2.set_title('BM (+2°C)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax22.get_legend_handles_labels()\n",
+    "_ = ax2.set_zorder(ax22.get_zorder()+1)\n",
+    "_ = ax2.patch.set_visible(False)\n",
+    "_ = ax2.tick_params(labelleft=False)\n",
+    "_ = ax22.tick_params(labelright=False)\n",
+    "# ax2.get_yaxis().set_visible(False)\n",
+    "# ax22.get_yaxis().set_visible(False)\n",
+    "\n",
+    "ax3 = fig.add_subplot(133)\n",
+    "_ = ax3.plot(years, EROIs_mixes[[('ADV', year, 'EROI') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI', color='black', zorder=20, linewidth=3)\n",
+    "# ax3.set_ylabel('EROI')\n",
+    "_ = ax3.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax32 = ax3.twinx()\n",
+    "_ = ax32.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[('ADV', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax3.set_title('ADV (100% renewable)') # ER (+2°C wo CCS nor nuclear)\n",
+    "_ = plt.ylabel('Share in the mix')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax32.get_legend_handles_labels()\n",
+    "_ = ax32.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.25,0.45)) # (1.1,0.5)\n",
+    "_ = ax3.legend(loc='center left', bbox_to_anchor=(1.25,1.03)) # (1.1,0.97)\n",
+    "_ = ax3.set_zorder(ax32.get_zorder()+1)\n",
+    "_ = ax3.patch.set_visible(False)\n",
+    "# ax3.get_yaxis().set_visible(False)\n",
+    "_ = ax3.tick_params(labelleft=False)\n",
+    "_ = plt.subplots_adjust(wspace=0.08, hspace=0.08)\n",
+    "\n",
+    "_ = fig.savefig('Evolution of EROIs IEA-ADV.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Implications of a Decreasing EROI\n",
+    "\n",
+    "### 4.1 Regressions price ~ EROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R^2 inv: 0.55 ; p = 18.16 + 85.11 / EROI\n",
+      "R^2 log: 0.61 ; log p = -0.57 · log EROI + 1.99\n",
+      "\"true\" R^2 inv: 0.55 ; standard-error:  0.29 ; N =  2079\n",
+      "\"true\" R^2 log: 0.58 ; standard-error:  0.28\n"
+     ]
+    }
+   ],
+   "source": [
+    "# N = 2079, Inv regression: p = 18 + 85/EROI (R^2=0.54); log regression: p = 100·EROI^-0.57 (R^2=0.57)\n",
+    "\n",
+    "# Preparation\n",
+    "EROI_for_prediction = np.array([i/100 for i in range(1,10000)], ndmin=2, dtype=float).transpose()\n",
+    "inv_EROI_for_prediction = 1/EROI_for_prediction\n",
+    "log_EROI_for_prediction = np.log10(EROI_for_prediction)\n",
+    "erois = np.array(erois_prices['eroi']) # erois_prices_wo_GW yield almost same results\n",
+    "prices = np.array(erois_prices['price']) \n",
+    "erois_cleaned = erois[np.logical_not(np.logical_or(pd.isnull(erois), pd.isnull(prices)))]\n",
+    "prices_cleaned = prices[np.logical_not(np.logical_or(pd.isnull(erois), pd.isnull(prices)))]\n",
+    "EROI_ = np.array(erois_cleaned[np.logical_not(np.isposinf(np.array(erois_cleaned, dtype=float)))], ndmin=2, dtype=float).transpose()\n",
+    "p_ = prices_cleaned[np.logical_not(np.isposinf(np.array(erois_cleaned, dtype=float)))]\n",
+    "EROI = np.array([EROI_[i] for i, x in enumerate(p_) if EROI_[i]>0 and x>0])\n",
+    "p = np.array([x for i, x in enumerate(p_) if EROI_[i]>0 and x>0])\n",
+    "\n",
+    "# Inv regression\n",
+    "reg_inv = LinearRegression().fit(1/EROI, p)  # R^2: 0.54 ; p = 18 + 85/EROI\n",
+    "print('R^2 inv:', round(reg_inv.score(1/EROI, p), 2), '; p =', round(reg_inv.intercept_, 2), '+', round(reg_inv.coef_[0], 2), '/ EROI')\n",
+    "p_predicted = reg_inv.predict(inv_EROI_for_prediction)\n",
+    "\n",
+    "# log regression\n",
+    "reg_log = LinearRegression().fit(np.log10(EROI), np.log10(p)) # R^2: 0.61; p = 94 + EROI^-0.57\n",
+    "print('R^2 log:', round(reg_log.score(np.log10(EROI), np.log10(p)), 2), '; log p =',round(reg_log.coef_[0], 2),'· log EROI +',round(reg_log.intercept_, 2)) \n",
+    "log_p_predicted = reg_log.predict(log_EROI_for_prediction)\n",
+    "\n",
+    "# Both regression explain same share of variance\n",
+    "TSS = sum((p - np.mean(p))**2)\n",
+    "iSS = sum((p - reg_inv.predict(1/EROI))**2)\n",
+    "lSS = sum((p - 10**reg_log.predict(np.log10(EROI)).flatten())**2)\n",
+    "print('\"true\" R^2 inv:', round(1-iSS/TSS, 2), '; standard-error: ', round(sum((p - reg_inv.predict(1/EROI))**2)**0.5/p.size, 2), '; N = ', p.size)\n",
+    "print('\"true\" R^2 log:', round(1-lSS/TSS, 2), '; standard-error: ', round(sum((p - 10**reg_log.predict(np.log10(EROI)).flatten())**2)**0.5/p.size,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To have complete regression results\n",
+    "loglog = sm.OLS(np.log10(p), sm.add_constant(np.log10(EROI))).fit()\n",
+    "print(loglog.summary())\n",
+    "inv = sm.OLS(p, sm.add_constant(1/EROI)).fit()\n",
+    "print(inv.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3de0c335f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Both regressions\n",
+    "fig = plt.figure()\n",
+    "ax = plt.subplot(111)\n",
+    "_= plt.scatter(x=EROI, y=p, marker = '.', s = 3)\n",
+    "_= ax.plot(EROI_for_prediction, p_predicted, color='r', linestyle='--', label='inverse fit')\n",
+    "_= ax.plot(EROI_for_prediction, np.power(10, log_p_predicted), color='g', label='log-log fit')\n",
+    "_= ax.set_xlim(0, 30)\n",
+    "_= ax.set_ylim(0, 80)\n",
+    "_= plt.ylabel('price')\n",
+    "_= plt.xlabel('EROI')\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "_ = ax.legend(handles, labels, loc='upper right')\n",
+    "_ = fig.savefig('regression price ~ EROI.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R^2 inv: 0.54 ; p = 20.58 + 72.11 / EROI\n",
+      "R^2 log: 0.58 ; log p = -0.46 · log EROI + 1.9\n",
+      "\"true\" R^2 inv: 0.54 ; standard-error:  0.98 ; N =  104\n",
+      "\"true\" R^2 log: 0.62 ; standard-error:  0.9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93f13160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# REGRESSIONS price ~ EROI restricted to BL 2010\n",
+    "# N = 104\n",
+    "\n",
+    "# Preparation\n",
+    "EROI_for_prediction = np.array([i/100 for i in range(1,10000)], ndmin=2, dtype=float).transpose()\n",
+    "inv_EROI_for_prediction = 1/EROI_for_prediction\n",
+    "log_EROI_for_prediction = np.log10(EROI_for_prediction)\n",
+    "erois = np.array(erois_prices.loc[('BL', 2010)]['eroi']) # erois_prices_wo_GW yield almost same results\n",
+    "prices = np.array(erois_prices.loc[('BL', 2010)]['price']) \n",
+    "erois_cleaned = erois[np.logical_not(np.logical_or(pd.isnull(erois), pd.isnull(prices)))]\n",
+    "prices_cleaned = prices[np.logical_not(np.logical_or(pd.isnull(erois), pd.isnull(prices)))]\n",
+    "EROI_ = np.array(erois_cleaned[np.logical_not(np.isposinf(np.array(erois_cleaned, dtype=float)))], ndmin=2, dtype=float).transpose()\n",
+    "p_ = prices_cleaned[np.logical_not(np.isposinf(np.array(erois_cleaned, dtype=float)))]\n",
+    "EROI = np.array([EROI_[i] for i, x in enumerate(p_) if EROI_[i]>0 and x>0]) # EROI_[i]>2.5\n",
+    "p = np.array([x for i, x in enumerate(p_) if EROI_[i]>0 and x>0])\n",
+    "\n",
+    "# Inv regression\n",
+    "reg_inv = LinearRegression().fit(1/EROI, p)\n",
+    "print('R^2 inv:', round(reg_inv.score(1/EROI, p), 2), '; p =', round(reg_inv.intercept_, 2), '+', round(reg_inv.coef_[0], 2), '/ EROI')\n",
+    "p_predicted = reg_inv.predict(inv_EROI_for_prediction)\n",
+    "\n",
+    "# log regression\n",
+    "reg_log = LinearRegression().fit(np.log10(EROI), np.log10(p))\n",
+    "print('R^2 log:', round(reg_log.score(np.log10(EROI), np.log10(p)), 2), '; log p =',round(reg_log.coef_[0], 2),'· log EROI +',round(reg_log.intercept_, 2)) \n",
+    "log_p_predicted = reg_log.predict(log_EROI_for_prediction)\n",
+    "\n",
+    "# Both regression explain same share of variance\n",
+    "TSS = sum((p - np.mean(p))**2)\n",
+    "iSS = sum((p - reg_inv.predict(1/EROI))**2)\n",
+    "lSS = sum((p - 10**reg_log.predict(np.log10(EROI)).flatten())**2)\n",
+    "print('\"true\" R^2 inv:', round(1-iSS/TSS, 2), '; standard-error: ', round(sum((p - reg_inv.predict(1/EROI))**2)**0.5/p.size, 2), '; N = ', p.size)\n",
+    "print('\"true\" R^2 log:', round(1-lSS/TSS, 2), '; standard-error: ', round(sum((p - 10**reg_log.predict(np.log10(EROI)).flatten())**2)**0.5/p.size,2))\n",
+    "\n",
+    "# Both regressions\n",
+    "fig = plt.figure()\n",
+    "ax = plt.subplot(111)\n",
+    "_= plt.scatter(x=EROI, y=p, marker = '.', s = 3)\n",
+    "_= ax.plot(EROI_for_prediction, p_predicted, color='r', linestyle='--', label='inverse fit')\n",
+    "_= ax.plot(EROI_for_prediction, np.power(10, log_p_predicted), color='g', label='log fit')\n",
+    "_= ax.set_xlim(0, 30)\n",
+    "_= ax.set_ylim(0, 80)\n",
+    "_= plt.ylabel('price')\n",
+    "_= plt.xlabel('EROI')\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "_ = ax.legend(handles, labels, loc='upper right')\n",
+    "_ = fig.savefig('regression price ~ EROI BL2010.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To have complete regression results\n",
+    "loglog = sm.OLS(np.log10(p), sm.add_constant(np.log10(EROI))).fit()\n",
+    "print(loglog.summary())\n",
+    "inv = sm.OLS(p, sm.add_constant(1/EROI)).fit()\n",
+    "print(inv.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 Predicted average global price of electricity (in €/MWh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BL 2010 27.0\n",
+      "BL 2030 28.0\n",
+      "BM 2030 30.0\n",
+      "ADV 2030 30.0\n",
+      "BL 2050 28.0\n",
+      "BM 2050 30.0\n",
+      "ADV 2050 32.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('BL 2010', round(themis['BL'][2010].eroi_price['price'][('World', 'total')]))\n",
+    "for y in [2030, 2050]: \n",
+    "    for s in ['BL', 'BM', 'ADV']: print(s, y, round(themis[s][y].eroi_price['price'][('World', 'total')]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Contact\n",
+    "\n",
+    "**Adrien Fabre**, 2018  \n",
+    "[personal website](https://sites.google.com/view/adrien-fabre)  \n",
+    "adrien.fabre@psemail.eu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "### Theory\n",
+    "* Input-Output Analysis: [wikipedia](https://en.wikipedia.org/wiki/Input%E2%80%93output_model), [Miller & Blair (2009)](http://gen.lib.rus.ec/book/index.php?md5=8803A190575D7587AA8112F76BB82070), [Eurostat](http://ec.europa.eu/eurostat/documents/3859598/5902113/KS-RA-07-013-EN.PDF/b0b3d71e-3930-4442-94be-70b36cea9b39?version=1.0)\n",
+    "* EROI: [King et al. (2010)](http://careyking.com/wp-content/uploads/2013/10/ASME-ES90414_EROIMethodology-Wind_2010_King.pdf), [Murhpy et al. (2011)](http://science-and-energy.org/wp-content/uploads/2016/03/Murphy-et-al-2011-Order-from-Chaos-EROI-Protocol.pdf)\n",
+    "* EROI is not intrinsic: [King (2014)](https://sci-hub.tw/https%3A//doi.org/10.1016/j.energy.2014.05.032)\n",
+    "\n",
+    "### Estimations of EROI\n",
+    "* [Dale (2010)](https://ir.canterbury.ac.nz/bitstream/handle/10092/5156/Dale2011GlobalEnergyModelling-ABiophysicalApproach.pdf)\n",
+    "* [Weißbach et al. (2013)](https://sci-hub.tw/https%3A//www.sciencedirect.com/science/article/pii/S0360544213000492)\n",
+    "* [Hall et al. (2014)](https://sci-hub.tw/https%3A//www.sciencedirect.com/science/article/pii/S0301421513003856)\n",
+    "\n",
+    "### Link between EROI and affluence\n",
+    "> Think of a society dependent upon one resource: its domestic oil. If the EROI for this oil was 1.1:1 then one could pump the oil out of the ground and look at it. If it were 1.2:1 you could also refine it and look at it, 1.3:1 also distribute it to where you want to use it but all you could do is look at it. Hall et al. 2008 examined the EROI required to actually run a truck and found that if the energy included was enough to build and maintain the truck and the roads and bridges required to use it (i.e., depreciation), one would need at least a 3:1. Now if you wanted to put something in the truck, say some grain, and deliver it that would require an EROI of, say, 5:1 to grow the grain. If you wanted to include depreciation on the oil field worker, the refinery worker, the truck driver and the farmer you would need an EROI of say 7 or 8:1 to support the families. If the children were to be educated you would need perhaps 9 or 10:1, have health care 12:1, have arts in their life maybe 14:1 and so on. [Hall (2011)](http://pakacademicsearch.com/pdf-files/agr/740/1773-1777%20Volume%203,%20Issue%2010%20(October%202011%29.pdf)\n",
+    "\n",
+    "* [Hall (2011)](http://pakacademicsearch.com/pdf-files/agr/740/1773-1777%20Volume%203,%20Issue%2010%20(October%202011%29.pdf)\n",
+    "* [Hall et al. (2009)](http://dieoff.com/_Energy/WhatIsTheMinumEROI_energies-02-00025.pdf)\n",
+    "* [Lambert & Lambert (2011)](https://mahb.stanford.edu/wp-content/uploads/2014/03/sustainability_LambertLambert_2011.pdf)\n",
+    "* [Lambert et al. (2014)](https://www.sciencedirect.com/science/article/pii/S0301421513006447)\n",
+    "* [Fizaine & Court (2016)](https://sci-hub.tw/https%3A//www.sciencedirect.com/science/article/pii/S0301421516302087)\n",
+    "\n",
+    "### Data\n",
+    "* [Exiobase](https://www.exiobase.eu/)\n",
+    "* [THEMIS](https://sci-hub.tw/https%3A//pubs.acs.org/doi/abs/10.1021/acs.est.5b01558) Thank you a lot Thomas Gibon for providing me the code and helping me!\n",
+    "* [IEA scenarios](http://gen.lib.rus.ec/book/index.php?md5=4D5AF94A02A48B799CF77641509C1C6B)\n",
+    "* [Energy [R]evolution](https://www.greenpeace.org/archive-international/Global/international/publications/climate/2015/Energy-Revolution-2015-Full.pdf) (commanded and funded by Greenpeace, realised by a team of the DLR using their model [REMix](https://www.dlr.de/tt/en/desktopdefault.aspx/tabid-2885/4422_read-12423/))\n",
+    "* [Cecilia 2050](https://cecilia2050.eu/publications/168)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix\n",
+    "\n",
+    "### A. Updating a Matrix A To a New Given Mix\n",
+    "\n",
+    "See the function `change_mix` in [the code](https://github.com/bixiou/pymrio/blob/master/pymrio/tools/iofunctions.py).\n",
+    "\n",
+    "### B. Example of Non-Decreasing Relation Between EROI and Price\n",
+    "\n",
+    "See [this page](https://www.desmos.com/calculator/ne4oqunhsm) for the computations.\n",
+    "\n",
+    "### C. Complete Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EROIs and mix for Greenpeace scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">Reference DLR ('REF')</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Energy [R]evolution ('ER', +2°C, no CCS nor nuclear)</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Advanced ER ('ADV', 100% renewable)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2010</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>biomass w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass&amp;Waste</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ocean</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geothermal</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar CSP</th>\n",
+       "      <td>35.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar PV</th>\n",
+       "      <td>13.7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind offshore</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind onshore</th>\n",
+       "      <td>9.7</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>12.2</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>0.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>12.2</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>14.9</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>11.8</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>12.1</td>\n",
+       "      <td>22.60</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>36.26</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>50.11</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>33.60</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>49.20</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>36.74</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>64.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario      Reference DLR ('REF')                                   \\\n",
+       "year                           2010         2030         2050          \n",
+       "stat                           EROI    mix  EROI    mix  EROI    mix   \n",
+       "biomass w CCS                     –   0.00     –   0.00     –   0.00   \n",
+       "biomass&Waste                   8.5   0.02   6.4   0.03   5.0   0.03   \n",
+       "ocean                           4.7   0.00   2.0   0.00   2.5   0.00   \n",
+       "geothermal                      5.6   0.00   3.8   0.01   2.5   0.01   \n",
+       "solar CSP                      35.5   0.00   9.3   0.00   8.0   0.01   \n",
+       "solar PV                       13.7   0.00   7.0   0.02   5.3   0.02   \n",
+       "wind offshore                   9.1   0.00   8.6   0.01   7.8   0.01   \n",
+       "wind onshore                    9.7   0.02   9.1   0.05   7.2   0.05   \n",
+       "hydro                          12.2   0.16  11.4   0.14  11.2   0.13   \n",
+       "nuclear                        12.2   0.11   7.3   0.10   7.1   0.08   \n",
+       "gas w CCS                         –   0.00     –   0.00     –   0.00   \n",
+       "coal w CCS                        –   0.00     –   0.00     –   0.00   \n",
+       "oil                             8.4   0.05  11.1   0.02  11.4   0.01   \n",
+       "gas                            14.9   0.23  15.3   0.23  15.6   0.25   \n",
+       "coal                           11.8   0.40  11.3   0.40  11.3   0.39   \n",
+       "Total (PWh/a)                  12.1  22.60  10.7  36.26  10.1  50.11   \n",
+       "\n",
+       "scenario      Energy [R]evolution ('ER', +2°C, no CCS nor nuclear)         \\\n",
+       "year                                                          2030          \n",
+       "stat                                                          EROI    mix   \n",
+       "biomass w CCS                                                  –     0.00   \n",
+       "biomass&Waste                                                5.3     0.06   \n",
+       "ocean                                                        4.3     0.01   \n",
+       "geothermal                                                   3.6     0.03   \n",
+       "solar CSP                                                    8.5     0.05   \n",
+       "solar PV                                                     5.6     0.11   \n",
+       "wind offshore                                                5.6     0.03   \n",
+       "wind onshore                                                 7.2     0.15   \n",
+       "hydro                                                       11.0     0.14   \n",
+       "nuclear                                                      8.3     0.02   \n",
+       "gas w CCS                                                      –     0.00   \n",
+       "coal w CCS                                                     –     0.00   \n",
+       "oil                                                         10.0     0.01   \n",
+       "gas                                                         16.6     0.21   \n",
+       "coal                                                        10.7     0.19   \n",
+       "Total (PWh/a)                                                8.4    33.60   \n",
+       "\n",
+       "scenario                   Advanced ER ('ADV', 100% renewable)               \\\n",
+       "year           2050                                       2030         2050   \n",
+       "stat           EROI    mix                                EROI    mix  EROI   \n",
+       "biomass w CCS     –   0.00                                   –   0.00     –   \n",
+       "biomass&Waste   4.7   0.06                                 5.2   0.05   4.6   \n",
+       "ocean           4.5   0.03                                 4.8   0.01   4.9   \n",
+       "geothermal      3.7   0.07                                 3.8   0.03   3.9   \n",
+       "solar CSP       7.7   0.17                                 9.3   0.07   7.8   \n",
+       "solar PV        4.4   0.20                                 5.4   0.14   4.7   \n",
+       "wind offshore   5.9   0.08                                 6.5   0.04   6.4   \n",
+       "wind onshore    6.0   0.22                                 7.2   0.17   5.8   \n",
+       "hydro          11.1   0.10                                11.0   0.13  10.9   \n",
+       "nuclear           –   0.00                                 8.3   0.02     –   \n",
+       "gas w CCS         –   0.00                                   –   0.00     –   \n",
+       "coal w CCS        –   0.00                                   –   0.00     –   \n",
+       "oil             9.2   0.00                                10.0   0.01     –   \n",
+       "gas            17.2   0.06                                16.5   0.18     –   \n",
+       "coal           10.8   0.01                                10.4   0.16  11.5   \n",
+       "Total (PWh/a)   5.9  49.20                                 8.1  36.74   5.8   \n",
+       "\n",
+       "scenario              \n",
+       "year                  \n",
+       "stat             mix  \n",
+       "biomass w CCS   0.00  \n",
+       "biomass&Waste   0.05  \n",
+       "ocean           0.03  \n",
+       "geothermal      0.07  \n",
+       "solar CSP       0.22  \n",
+       "solar PV        0.21  \n",
+       "wind offshore   0.10  \n",
+       "wind onshore    0.24  \n",
+       "hydro           0.08  \n",
+       "nuclear         0.00  \n",
+       "gas w CCS       0.00  \n",
+       "coal w CCS      0.00  \n",
+       "oil             0.00  \n",
+       "gas             0.00  \n",
+       "coal            0.00  \n",
+       "Total (PWh/a)  64.04  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results(themis, scenarios=['REF', 'ER', 'ADV'], longnames=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quality-adjusted EROIs and mix for THEMIS/IEA scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">Baseline ('BL')</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Blue Map ('BM', +2°C)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2010</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>biomass w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass&amp;Waste</th>\n",
+       "      <td>20.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ocean</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geothermal</th>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar CSP</th>\n",
+       "      <td>44.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>16.9</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar PV</th>\n",
+       "      <td>17.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>14.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind offshore</th>\n",
+       "      <td>19.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind onshore</th>\n",
+       "      <td>18.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>0.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>25.6</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>26.3</td>\n",
+       "      <td>0.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>19.2</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>13.9</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>22.5</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>0.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>20.2</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>20.4</td>\n",
+       "      <td>19.76</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>34.29</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>45.97</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>28.01</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>40.22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario      Baseline ('BL')                                         \\\n",
+       "year                     2010            2030            2050          \n",
+       "stat                 EROI adj    mix EROI adj    mix EROI adj    mix   \n",
+       "biomass w CCS               –   0.00        –   0.00        –   0.00   \n",
+       "biomass&Waste            20.8   0.01     12.6   0.02     11.7   0.03   \n",
+       "ocean                     9.1   0.00      4.4   0.00      5.5   0.00   \n",
+       "geothermal               11.5   0.00     10.3   0.01     10.2   0.01   \n",
+       "solar CSP                44.6   0.00     17.7   0.00     18.2   0.01   \n",
+       "solar PV                 17.5   0.00     14.8   0.01     14.5   0.01   \n",
+       "wind offshore            19.6   0.00     21.2   0.01     20.5   0.01   \n",
+       "wind onshore             18.4   0.01     18.1   0.04     15.8   0.04   \n",
+       "hydro                    25.6   0.16     23.0   0.14     23.0   0.12   \n",
+       "nuclear                  19.2   0.14     13.4   0.11     13.0   0.10   \n",
+       "gas w CCS                   –   0.00        –   0.00     14.4   0.00   \n",
+       "coal w CCS                  –   0.00        –   0.00     11.7   0.00   \n",
+       "oil                      12.9   0.06     15.6   0.02     16.0   0.01   \n",
+       "gas                      22.5   0.21     24.6   0.21     24.4   0.23   \n",
+       "coal                     20.2   0.42     18.2   0.45     18.2   0.45   \n",
+       "Total (PWh/a)            20.4  19.76     18.7  34.29     18.4  45.97   \n",
+       "\n",
+       "scenario      Blue Map ('BM', +2°C)                         \n",
+       "year                           2030            2050         \n",
+       "stat                       EROI adj    mix EROI adj    mix  \n",
+       "biomass w CCS                   9.5   0.00      8.5   0.01  \n",
+       "biomass&Waste                  11.6   0.06     11.1   0.05  \n",
+       "ocean                           7.0   0.00     11.3   0.00  \n",
+       "geothermal                     10.6   0.01     11.4   0.02  \n",
+       "solar CSP                      17.3   0.02     16.9   0.06  \n",
+       "solar PV                       13.3   0.02     12.9   0.06  \n",
+       "wind offshore                  15.4   0.03     13.0   0.04  \n",
+       "wind onshore                   14.5   0.08     15.4   0.08  \n",
+       "hydro                          25.2   0.18     26.3   0.14  \n",
+       "nuclear                        13.6   0.19     13.9   0.24  \n",
+       "gas w CCS                      16.2   0.01     18.8   0.05  \n",
+       "coal w CCS                     13.7   0.05     14.3   0.12  \n",
+       "oil                            15.5   0.03     11.8   0.01  \n",
+       "gas                            29.0   0.14     33.5   0.11  \n",
+       "coal                           18.4   0.18     19.6   0.01  \n",
+       "Total (PWh/a)                  17.0  28.01     16.0  40.22  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results(themis, stats=['eroi_adj', 'mix'], scenarios=['BL', 'BM'], longnames=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quality-adjusted EROIs and mix for Greenpeace scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">Reference DLR ('REF')</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Energy [R]evolution ('ER', +2°C, no CCS nor nuclear)</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Advanced ER ('ADV', 100% renewable)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2010</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI adj</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>biomass w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass&amp;Waste</th>\n",
+       "      <td>15.7</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ocean</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geothermal</th>\n",
+       "      <td>12.1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar CSP</th>\n",
+       "      <td>73.2</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>16.1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>17.6</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>16.1</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>0.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar PV</th>\n",
+       "      <td>25.8</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind offshore</th>\n",
+       "      <td>17.3</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>15.1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind onshore</th>\n",
+       "      <td>18.6</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>17.6</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>14.8</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>23.5</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>22.1</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>21.9</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>21.4</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>21.4</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>21.1</td>\n",
+       "      <td>0.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>22.8</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>16.4</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>16.4</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil</th>\n",
+       "      <td>12.8</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>23.8</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>25.8</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>27.1</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>28.2</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>27.2</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>18.6</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>20.1</td>\n",
+       "      <td>22.60</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>36.26</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>50.11</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>33.60</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>49.20</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>36.74</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>64.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario      Reference DLR ('REF')                                         \\\n",
+       "year                           2010            2030            2050          \n",
+       "stat                       EROI adj    mix EROI adj    mix EROI adj    mix   \n",
+       "biomass w CCS                     –   0.00        –   0.00        –   0.00   \n",
+       "biomass&Waste                  15.7   0.02     12.6   0.03      9.8   0.03   \n",
+       "ocean                           7.8   0.00      3.6   0.00      4.6   0.00   \n",
+       "geothermal                     12.1   0.00      7.6   0.01      5.0   0.01   \n",
+       "solar CSP                      73.2   0.00     19.1   0.00     16.1   0.01   \n",
+       "solar PV                       25.8   0.00     13.7   0.02     10.5   0.02   \n",
+       "wind offshore                  17.3   0.00     16.0   0.01     15.1   0.01   \n",
+       "wind onshore                   18.6   0.02     17.6   0.05     14.1   0.05   \n",
+       "hydro                          23.5   0.16     22.1   0.14     21.9   0.13   \n",
+       "nuclear                        22.8   0.11     13.6   0.10     13.3   0.08   \n",
+       "gas w CCS                         –   0.00        –   0.00        –   0.00   \n",
+       "coal w CCS                        –   0.00        –   0.00        –   0.00   \n",
+       "oil                            12.8   0.05     17.8   0.02     18.2   0.01   \n",
+       "gas                            23.8   0.23     25.0   0.23     25.8   0.25   \n",
+       "coal                           18.6   0.40     17.8   0.40     18.0   0.39   \n",
+       "Total (PWh/a)                  20.1  22.60     18.5  36.26     17.7  50.11   \n",
+       "\n",
+       "scenario      Energy [R]evolution ('ER', +2°C, no CCS nor nuclear)         \\\n",
+       "year                                                          2030          \n",
+       "stat                                                      EROI adj    mix   \n",
+       "biomass w CCS                                                  –     0.00   \n",
+       "biomass&Waste                                               10.4     0.06   \n",
+       "ocean                                                        8.4     0.01   \n",
+       "geothermal                                                   7.2     0.03   \n",
+       "solar CSP                                                   17.6     0.05   \n",
+       "solar PV                                                    11.5     0.11   \n",
+       "wind offshore                                               11.6     0.03   \n",
+       "wind onshore                                                14.9     0.15   \n",
+       "hydro                                                       21.4     0.14   \n",
+       "nuclear                                                     16.4     0.02   \n",
+       "gas w CCS                                                      –     0.00   \n",
+       "coal w CCS                                                     –     0.00   \n",
+       "oil                                                         15.9     0.01   \n",
+       "gas                                                         27.1     0.21   \n",
+       "coal                                                        16.6     0.19   \n",
+       "Total (PWh/a)                                               15.9    33.60   \n",
+       "\n",
+       "scenario                      Advanced ER ('ADV', 100% renewable)         \\\n",
+       "year              2050                                       2030          \n",
+       "stat          EROI adj    mix                            EROI adj    mix   \n",
+       "biomass w CCS        –   0.00                                   –   0.00   \n",
+       "biomass&Waste      9.2   0.06                                10.3   0.05   \n",
+       "ocean              9.1   0.03                                 9.3   0.01   \n",
+       "geothermal         7.3   0.07                                 7.6   0.03   \n",
+       "solar CSP         16.1   0.17                                19.0   0.07   \n",
+       "solar PV           9.2   0.20                                11.3   0.14   \n",
+       "wind offshore     12.2   0.08                                13.3   0.04   \n",
+       "wind onshore      12.4   0.22                                14.8   0.17   \n",
+       "hydro             21.3   0.10                                21.4   0.13   \n",
+       "nuclear              –   0.00                                16.4   0.02   \n",
+       "gas w CCS            –   0.00                                   –   0.00   \n",
+       "coal w CCS           –   0.00                                   –   0.00   \n",
+       "oil               13.3   0.00                                15.8   0.01   \n",
+       "gas               28.2   0.06                                27.2   0.18   \n",
+       "coal              16.6   0.01                                16.2   0.16   \n",
+       "Total (PWh/a)     12.0  49.20                                15.5  36.74   \n",
+       "\n",
+       "scenario                       \n",
+       "year              2050         \n",
+       "stat          EROI adj    mix  \n",
+       "biomass w CCS        –   0.00  \n",
+       "biomass&Waste      9.1   0.05  \n",
+       "ocean              9.7   0.03  \n",
+       "geothermal         7.7   0.07  \n",
+       "solar CSP         16.2   0.22  \n",
+       "solar PV           9.9   0.21  \n",
+       "wind offshore     13.2   0.10  \n",
+       "wind onshore      12.1   0.24  \n",
+       "hydro             21.1   0.08  \n",
+       "nuclear              –   0.00  \n",
+       "gas w CCS            –   0.00  \n",
+       "coal w CCS           –   0.00  \n",
+       "oil                  –   0.00  \n",
+       "gas                  –   0.00  \n",
+       "coal              15.3   0.00  \n",
+       "Total (PWh/a)     11.9  64.04  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results(themis, stats=['eroi_adj', 'mix'], scenarios=['REF', 'ER', 'ADV'], longnames=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EROIs and mix by world region:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">BL</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">BM</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">ADV</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2010</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>China</th>\n",
+       "      <td>8.8</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>India</th>\n",
+       "      <td>10.9</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OECD Europe</th>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OECD North America</th>\n",
+       "      <td>28.4</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>26.1</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>25.6</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>20.2</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>0.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OECD Pacific</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Economies in transition</th>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Latin America</th>\n",
+       "      <td>67.3</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>65.7</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>61.3</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>106.4</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>134.2</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>71.2</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>67.8</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rest of developing Asia</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa and Middle East</th>\n",
+       "      <td>10.9</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>12.2</td>\n",
+       "      <td>19.76</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>34.29</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>45.97</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>28.01</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>40.22</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>36.74</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>64.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario                   BL                                      BM         \\\n",
+       "year                     2010         2030         2050          2030          \n",
+       "stat                     EROI    mix  EROI    mix  EROI    mix   EROI    mix   \n",
+       "China                     8.8   0.17   8.8   0.26   8.9   0.27    9.2   0.25   \n",
+       "India                    10.9   0.04  11.7   0.08  11.7   0.09    8.4   0.06   \n",
+       "OECD Europe              11.4   0.18  10.3   0.13  10.1   0.11    9.1   0.13   \n",
+       "OECD North America       28.4   0.26  26.1   0.19  25.6   0.17   20.2   0.20   \n",
+       "OECD Pacific              7.4   0.09   6.5   0.07   6.4   0.06    5.1   0.07   \n",
+       "Economies in transition   8.1   0.09   8.1   0.07   8.2   0.07   13.1   0.08   \n",
+       "Latin America            67.3   0.05  65.7   0.05  61.3   0.05  106.4   0.05   \n",
+       "Rest of developing Asia  13.0   0.05  11.8   0.07  12.6   0.09    7.6   0.07   \n",
+       "Africa and Middle East   10.9   0.07   8.4   0.08   7.9   0.10    3.9   0.08   \n",
+       "Total (PWh/a)            12.2  19.76  10.9  34.29  10.7  45.97    9.1  28.01   \n",
+       "\n",
+       "scenario                                ADV                      \n",
+       "year                      2050         2030         2050         \n",
+       "stat                      EROI    mix  EROI    mix  EROI    mix  \n",
+       "China                      8.9   0.26   8.7   0.25   7.7   0.22  \n",
+       "India                      8.2   0.09   7.1   0.09   6.3   0.12  \n",
+       "OECD Europe                9.0   0.11   8.6   0.11   7.3   0.09  \n",
+       "OECD North America        18.2   0.17  18.4   0.19  13.8   0.14  \n",
+       "OECD Pacific               4.6   0.06  10.2   0.06   9.2   0.05  \n",
+       "Economies in transition   13.6   0.07  13.7   0.07  12.8   0.07  \n",
+       "Latin America            134.2   0.06  71.2   0.06  67.8   0.06  \n",
+       "Rest of developing Asia    5.8   0.08   5.1   0.07   3.7   0.10  \n",
+       "Africa and Middle East     3.4   0.10   3.0   0.10   2.5   0.16  \n",
+       "Total (PWh/a)              8.0  40.22   8.1  36.74   5.8  64.04  "
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res, themis = results(themis, regional=True)\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are similar results for an older project of future IO tables, [Cecilia 2050](https://cecilia2050.eu/publications/168) :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cecilia = pymrio.cecilia_parser('/var/www/FutureIOT/') # <--- PATH to modify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>step</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">-1: original</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">0: balanced</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1: growth</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2a: new mix</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2b: new techno</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">3: 2° scenario</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>var</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>wind</th>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>18.3</td>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "step    -1: original       0: balanced       1: growth       2a: new mix  \\\n",
+       "var             EROI   mix        EROI   mix      EROI   mix        EROI   \n",
+       "wind             1.1  0.00         0.4  0.00       1.0  0.00        18.2   \n",
+       "hydro            5.1  0.14         3.5  0.13      12.9  0.19        12.7   \n",
+       "coal             8.1  0.46         5.7  0.46      13.8  0.48        11.0   \n",
+       "gas              9.1  0.20         6.6  0.19      19.7  0.17        24.9   \n",
+       "nuclear         11.6  0.20         9.1  0.22      24.4  0.16        15.7   \n",
+       "total            7.9  1.00         5.5  1.00      14.3  1.00        14.0   \n",
+       "\n",
+       "step          2b: new techno       3: 2° scenario        \n",
+       "var       mix           EROI   mix           EROI   mix  \n",
+       "wind     0.04           19.1  0.04           18.3  0.04  \n",
+       "hydro    0.27           13.4  0.27           12.0  0.25  \n",
+       "coal     0.20           11.8  0.20           11.0  0.21  \n",
+       "gas      0.19           26.6  0.19           24.9  0.19  \n",
+       "nuclear  0.30           17.7  0.30           17.5  0.30  \n",
+       "total    1.00           15.2  1.00           14.4  1.00  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EROIs_cecilia = pd.DataFrame()\n",
+    "EROIs_mixes_cecilia = pd.DataFrame()\n",
+    "steps = [-1, 0, 1, '2a', '2b', 3]\n",
+    "for step in steps:\n",
+    "    eroi_s = cecilia[step].erois()\n",
+    "    EROIs_cecilia[step] = eroi_s\n",
+    "    eroi_s = eroi_s.rename(index={'Power sector': 'total'})\n",
+    "    cecilia[step].elec_supply = dict()\n",
+    "    cecilia[step].elec_mix = dict()\n",
+    "    cecilia[step].elec_mix['total'] = 1\n",
+    "    cecilia[step].elec_supply['total'] = cecilia[step].impacts('Total Energy supply', secs = cecilia[step].energy_sectors('electricities')).sum()\n",
+    "    for sec in cecilia[step].energy_sectors('electricities'):\n",
+    "        cecilia[step].elec_supply[sec] = cecilia[step].impacts('Total Energy supply', secs = sec).sum()\n",
+    "        cecilia[step].elec_mix[sec[15:]] = round(cecilia[step].elec_supply[sec]/cecilia[step].elec_supply['total'],2)\n",
+    "    cecilia[step].elec_mix = pd.DataFrame(pd.Series(cecilia[step].elec_mix), columns=['mix'])\n",
+    "    EROIs_mixes_cecilia[[(step, 'EROI'), (step, 'mix')]] = pd.DataFrame(eroi_s).join(cecilia[step].elec_mix)\n",
+    "EROIs_cecilia = pd.DataFrame(EROIs_cecilia).rename(columns = {-1:'-1: original', 0:'0: balance', 1:'1: BAU', '2a':'2a: new mix', '2b':'2b: techno', 3:'3: 2°'})\n",
+    "# EROIs_cecilia\n",
+    "EROIs_mixes_cecilia = pd.DataFrame(EROIs_mixes_cecilia, columns = pd.MultiIndex.from_product([steps, ['EROI', 'mix']], names=['step', 'var']))\n",
+    "EROIs_mixes_cecilia.rename(columns = {-1:'-1: original', 0:'0: balanced', 1:'1: growth', '2a':'2a: new mix', '2b':'2b: new techno', 3:'3: 2° scenario'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### D. All Figures\n",
+    "\n",
+    "#### Quality-adjusted EROIs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+Pj4WtVpt1Gg0hpiYGGNwcDBvQM0YY+MMEUEIMaQhJPfcc0/M//zP/9QtX768/eOPP1Y9+eSTUZ6ObywZ1QPFx6uAgABrSkpKa0pKSisAtLW1KRzLPgTW1dWpuru7T9uAuru7W15WVhZaVlYWumfPHgQEBJidyVZsbKzR39+f+9EZY2yMW7hwoeH6669P+N3vflevVqtt9fX10oyMjM7XX3895Je//GXLunXrQjMzMzsAwGg0SmNiYiwAsH79+jDvRj76cHI1BgQHB/cEBwc3T506tZmI0NTU5FNRUeGciaiyWCynzUTs6OhQlpaWKktLS8MBICgoqCsyMtIYHR1t0Gq1Rh8fH56JyBhjY0xmZmb3Aw88UDtnzpxkiURCaWlpppdffrnilltuiXvxxRfVzgHtAPDII4/ULF26dHJERERPZmZmZ0VFhfIs1TMXwnWg9EglhOgkIv8LqWPVqlVzADzvppBGDbvdjvr6ej9nstXY2BjQdwNqV0IIhIaGOvdENGo0mg65XD7y/5IwxkaL4rVr165wR0Xu+G0YTvn5+WXp6elN3o6DuUd+fn54enp6XH/nuOVqjJNIJIiMjDRFRkaaANRbrVZRXV3tX1VVFVhTU6Nqbm72J6JTY+OICM3Nzf7Nzc3+hw4dinQs8+86E7FTKpUOckfGGGNsfOPkapyRyWQUGxvbERsb2wEAZrPZuQF1YG1traq1tfWMDajr6+tV9fX1qgMHDkAmk9kiIiJOJVsTJ07s4pmIjDHG2H9xcjXOKZVK++TJkw2TJ082AIDJZHJuQB1YW1urMhqNfTegllZXVwdVV1cHff/991Aqlc4NqA2xsbGG4ODgHk62GGOMjWecXLHT+Pn52ZKTk9uSk5PbAKC9vV3usuxDYFdX12kzEc1ms6yioiKkoqIi5Ntvv4Wfn19PZGSkQaPRGGNiYowqlYo3oGaMMTaucHLFBhUUFGQJCgpqSUtLayEitLS0KCsqKpwbUKt6enpO+ztkMpkUx48fDz9+/Hg4AAQGBnar1WpDdHS0MSYmxujr68sbUDPGGBvTOLliQyaEQFhYmDksLKwxIyOj0W63OzegDqypqVE1NDSorFbraTMRDQaDj8Fg8Dl69OhEAAgNDTVFRkYaoqOjjdHR0R0KhYKXfWCMMTamcHLFzptEIoFare5Sq9VdAOptNpuoqanxq6ysDKytrVU1NTWdsQF1S0uLX0tLi19RUZFaIpFQeHi4c9kHQ1RUlEkqlfKyD4wxxkY1Tq6Y20ilUtJqtZ1arbYTQG1PT49zJqJzA+ozZiI2NDQENDQ0BOTn50dJpVL7xIkTjVFRUUatVmuIiIjo4g2oGWNj1dq0tSnurG/VoVXF7qyPnT9OrpjHKBQK+6RJkwyTJk0yAKju6uqSOgbHq2prawMNBsNpMxFtNpuktrY2qLa2Nmj//v1QKBTOmYjGmJgYQ2hoqJlnIjLG2Pl78MEHIzdv3hwaGRnZExYWZs3IyDAFBQXZ3nrrrQkWi0XExcWZN2/efFKlUtnffPPNkD//+c9REomEVCqVLS8vr8Tb8Y8WnFyxYePr62vT6XRtOp2uDQCMRqOsoqIisLq6WlVXVxfY2dmpcC3f09Mjq6ysDKmsrAzZt28ffH19LWq12jkT0RAUFMQzERljbIh27tzpt23btpDCwsLDFotFTJs2TZ+RkWFavnx56wMPPNAEAPfee2/U6tWrwx955JGGp59+OvKLL744Gh8fb2lqauLVo88BJ1fMa1QqlTU1NbUlNTW1BQBaW1sVrsmW2Ww+7e9nV1eX/OTJk2EnT54Mc1xvdpmJaPDz8+OZiIwxNoBvvvkmYNGiRW0BAQEEgBYsWNAGAPv37/d99NFHNUajUdrZ2SmdO3duOwBkZmZ2LF++PO66665rXb58eatXgx9lOLliI0ZISEhPSEhIU3p6ehMRobGx0ce57EN9fb3KarWe9i8no9GoNBqNE44dOzYBAIKDg7scMxENWq22Q6lU8kxExhhzGGgv4ZUrV8Zv3ry5dObMmV2rV68O27FjhwoA3nnnnYqvvvrKf+vWrUHTpk1LPXjwYJFareZ/xA4BJ1dsRBJCYOLEid0TJ07sBtBgs9lQV1fn51j2IbCxsfGMmYhtbW2+bW1tvsXFxRFCCAoLC+uMiooyRkdHGzQaTadMJuOZiIyxcWvevHkdd999d6zJZKq1WCziyy+/DL755psbTSaTJCYmxmI2m8WmTZtCIyMjLQBQVFSkzMnJ6czJyencvn178IkTJxSO2eHsLDi5YqOCVCqFRqMxaTQaE4A6i8UiqqurA6qqqlQ1NTWBLS0t/q7/KiMi0dTUFNDU1BRQUFAQKZVK7Y4NqI1ardagVqtNPBORMTaezJ0717Rw4cJ2vV6fqtFozFOnTu0MCgqyPfzwwzVZWVkpGo2mJyUlxdTR0SEFgPvuuy+6rKxMSUQiOzvbcMkll3BiNURioGbCkUQI0UlE/hdSx6pVq+YAeN5NIbERpru7W1JVVaWqrKxU1dbWBra3t/sOVl4ul9siIiKMUVFRxpiYGEN4eHg3z0RkbFgUr127doU7KnLHb8Nwys/PL0tPT2/yZgzt7e2SoKAgu9FolMycOTPplVdeKc/OzjZ5M6bRKj8/Pzw9PT2uv3PccsXGBB8fH3tCQkJ7QkJCOwB0dnbKKioqnHsiqjo6OpSu5S0Wi7Sqqiq4qqoq+LvvvoOPj49FrVYbNRqNISYmxhgcHNzjnU/CGGOec9NNN8UeO3bM12w2iyVLljRzYuUZnFyxMcnf39+akpLSmpKS0goA7e3tivLyclV1dXVgXV2dqru7+7QNqLu7u+VlZWWhZWVloXv27IG/v785MjLyVLIVEBBg9c4nYYwx99m2bdtJb8cwHnByxcaFoKCgnqlTpzZPnTq1mYjQ3NzsU1FRoaqurg6sr69XWSyW02YidnZ2KktLS5WlpaXhjuu7IiMjjY6ZiEYfHx+eicgYY6xfnFyxcUcIgfDw8O7w8PDuiy66qNFut6O+vt6vsrJSVV1dHdjY2Bhgs9lOG+3e3t7u297e7nvkyJGJQgiEhoZ2RkZGGrVarUGj0XTI5fKRP3iRMcbYsODkio17EokEkZGRpsjISBOAeqvVKmpqavxdNqD2J6JTo90dLV/+zc3N/ocOHVJLJBKaMGFCh2MDamNkZGSnVMqLGTPG2HjFyRVjfchkMoqJiemIiYnpAICenh5JZWVlQFVVVWBtba2qtbX1jA2o6+vrVfX19aqDBw9CJpPZJ06caNRoNAatVmucMGECb0DNGGPjCCdXjJ2FQqGwT5482TB58mQDAJhMJqlzvFZtba3KaDSetgG11WqV1NTUBNXU1AR9//33UCqVzg2oDTExMcaQkBDegJoxNmJkZGQkHzhw4EhJSYniqquuSjx27FiRt2Ma7Ti5Yuwc+fn52ZKTk9uSk5PbAMBgMMidyVZdXZ3KZDKdtgG12WyWVVRUhFRUVIR8++238PPz61Gr1cbo6GhDTEyMUaVS8QbUjI1Dn1x9dYo767ty69bi87nuwIEDR9wZB+PkirELFhgYaElLS2tJS0trISK0trYqy8vLVTU1NYF1dXWqnp6e0/4/M5lMihMnToSdOHEizHF9t8sG1EZfX1/eu4sx5hGPP/54xMaNG8MBYMWKFY2PPvpog5+fX4bJZDrg7djGEk6uGHMjx0xCc2hoqDkjI6PJbrejoaHB17EnoqqhoUFltVpPG4BlMBh8DAaDz9GjRycCQGhoqEmtVhu0Wq0xOjq6Q6FQ8LIPjLELtmvXLr933nknbP/+/cVEhOnTp6f86Ec/Mno7rrGIkyvGPEgikUCtVnc5Njutt9lsoqamxq+qqiqwpqZG1dTUdMYG1C0tLX4tLS1+hw8fVgshKDw8vDMqKsoQHR1t1Gg0nVKplJd9YIyds2+++SbgiiuuaAsMDLQDwJVXXtn69ddfq7wd11jEyRVjw0gqlZJWq+3UarWdAGotFouoqqoKcC770NLScto+aUQkGhsbAxobGwPy8/MhhIBMJrPJZDK7TCazyeVyu/O1TCazy+Vy57NdLpfb+jzb5XK5TaFQnHp2vualI9hYJIRYCOBFAFIArxPR033OxwB4G0Cwo8zDRPTpsAc6TEbDXsJjBSdXjHmRXC6n+Ph4Y3x8vBEAurq6pC7LPgQaDIbTZiISESwWi9Sxory830rPg0QiIZekzTVxOy2B6y9Zc75WKBSnJW5yudzOS1AwbxFCSAGsAbAAQBWA74UQW4nosEux3wN4n4heFkLoAXwKIG7Ygx0mOTk5HbfddlvcH//4xzoiwqeffhqyfv36E3/605+8HdqYw8kVYyOIr6+vTafTtet0unYAMBqNsoqKisDq6mpVXV1dYGdnp+JsdZwPu90uenp6ZD097t2vWiqV2l0TNtfWNedr1ySuv4Stz7NdLpfbeSkLNgRZAEqJ6AQACCE2AbgGgGtyRQACHa+DANQMa4TDLDs727Rs2bLmiy66KAXoHdA+e/bsLm/HNRZxcsXYCKZSqaypqaktqampLQBgtVpFT0+PxGKxSB3Pkp6eHqnz2Wq1SiwWi8TRuiWxWCwSq9UqdT5brVaJo4zztbTvAHt3stlsEpvNJjGbzW6t1zVZG6hbdKCWNmfrmjNZc7a4SaVS4qRtTNEAqHR5XwVgRp8yjwP4QgjxKwD+AOYPT2i9znfphAvx+OOP1z/++OP1rsecMwWTkpJ6eI0r9+DkirFRRCaTkUwmswFw23INRORM2gZM2JwJXZ9k7bQkzXnMmbD13Z/RnZxJojvr7DuezbV1rW9LW9+xbX0TNtcxbTwBwaNkQog8l/evEtGrjtf9Zcp9/yyWAlhPRM8KIWYC+IcQIo2IeIYuuyCcXDE2zgkhIJfLSS6XW/39/c9+wRDZ7XY4W89cEzeXVrczWtecyZprS5vFYpHabLZTx/rOrnQXD49nO6Olzdm65pqkuSZwCoXCLpPJzhjH5mxp4/FsAAArEWUOcK4KgNblfTTO7Pa7HcBCACCib4UQPgDCATS4O1A2vnByxRjzCIlEAqVSaVcqlXYAVnfVa7PZ0Ld1zfF8WsJ2tpY21y5Sm80m8VTS5hjPJu3p6XHrlEypVGqXSqV21ySt78SD/maOymQy+0AJ2xgbz/Y9gEQhRDyAagBLACzrU6YCwI8ArBdCpADwAdA4rFGyMYmTK8bYqCKVSiGVSu0+Pj5u7bqxWq3CNUnrO7atb+Lmmqz1GdN2WuLmzhhdOcezeWoSgmt36EAtbc5jzsTMpdXttMRNJpMN+3g2IrIKIe4BsB29yyy8SURFQognAeQR0VYADwB4TQhxH3q7DG8lXq+AuQEnV4wxhv+OZ3NsP+SW/R5dx7P1k7i5Jmz9TUDot6VtGCchuPX3wZmgCSF0L7/88g8AHiKif7nzHn051qz6tM+xR11eHwYw25MxsPGJkyvGGPMQ1/Fs7qzXbrfDZaKB65i2M7pFXbpHz9rS5uFJCFKr1eocz5YBwNdT92LM2zi5YoyxUUYikcA5G9Gd9TonIfQ3c3SgxM01Wes7Y9T5PMB4tg53xs6Gz+rVq8Py8vL8c3NzKy6knoKCAuWvfvUr7cmTJ31kMhklJyd3rVu3rkKr1Vq//vprvwcffFDb1NQkF0JQVlZWx+uvv17Z1tYmufnmm+NqamoUVqtVREdHm3fs2FHqrs/mLpxcMcYYA3DGJAS3sdlsoqenR9LT0yPp6uoq+/DDD58EcMyd9xiNvv/++xR31nfxxRcP+7pZ58tkMonFixcn/vnPf65ctmxZOwBs27ZNVVdXJwOA5cuXT87NzT0xf/78Trvdjrfffjukra1N8tBDD2lycnIMf/jDHxoA4D//+c+IbAHlubyMMcY8SiqVkq+vry0oKMiiVqsNRLSfiAzejms8eumll8J0Op0+KSlJf+2118YDwNGjRxUzZ87U6XQ6/cyZM3XHjh1TAMA777wTNHXq1OSUlBT9rFmzdJWVlYM2yOh0On1TU5PUbrcjODh42ksvvRQGANdee238Rx99dNoG0a+++mroRRdd1OFMrABg8eLFxosvvrj72WefnXjjjTc2z58/vxPoTfp//vOft2q1WmtdXZ1cq9X2vbNWAAAgAElEQVSemsUxY8aMEbnCPCdXjDHG2DiQl5fn88wzz0Tu2LHjaElJyeF169ZVAMAvfvGLmGXLljUfPXr08M9+9rPmu+++WwsACxYs6Dh48OCR4uLiw9dff33Lk08+qR6s/szMzI4vv/wyYP/+/T7R0dHm3bt3BwDAgQMH/C+77LJO17KHDh3yveiii0z91XP48GHfzMzMfs/98pe/bPjVr34VN2PGDN1DDz2kLisrc9uadO7EyRVjjDE2Dmzfvj1w8eLFrZGRkVYAiIiIsAG9yc/KlStbAODuu+9u2b9/fwAAnDx5UjFnzpxEnU6nX716tfrIkSODdsHNmTOnY8eOHQH//ve/VXfccUdDcXGx78mTJ+VBQUHWoKAgt3Q1X3fddYbS0tLCn//8500lJSW+06dP19fU1Iy4IU6cXDHGGGPjABFBCDHkdbzuueeemFWrVjUcPXr08EsvvVRuNpsHzRkWLFhg3Ldvn2rPnj0Bl19+uTEsLMy6YcOGkEsuueSMyQupqandP/zwg19/9aSkpHTl5eX1ew7oTQp/8YtftHz00Ucnp06d2vnFF18EDPUzDRdOrhhjjLFxYOHChYatW7eG1tXVSQGgvr5eCgAZGRmdr7/+eggArFu3LjQzM7MDAIxGozQmJsYCAOvXrw87W/0JCQmW1tZW2cmTJ330en3PzJkzO9asWaO+9NJLz0iu7rzzzub9+/cHbNq0Kch5bPPmzYHfffed729+85uG999/P+yrr746tR/X2rVrQysqKmRbt25VGY1GCQC0trZKysvLlfHx8e5dSdcNPJZcCSHeFEI0CCEOuRybJoTYJ4Q4KITIE0Jkeer+jDHGGPuvzMzM7gceeKB2zpw5yUlJSfpVq1ZpAeDll1+u+Mc//hGu0+n07777btjatWsrAeCRRx6pWbp06eTp06cnhYWFDWmttmnTpnXGx8d3A8C8efOMDQ0N8vnz5xv7lgsICKAtW7aUrlmzZmJsbGza5MmTU9evXx8eGRlp0Wq11tzc3BMPPvhgdFxcXNqkSZNSd+/erQoJCbF///33ftOmTUvR6XT6rKyslBUrVjTNnTu33/FZ3iQ8tdK/EOJS9K5jkktEaY5jXwB4nog+E0JcAeC3RDRvCHV1EtEF7Si7atWqOQCev5A6GGOMXbDitWvXrnBHRe74bRhO+fn5Zenp6U3ejoO5R35+fnh6enpcf+c81nJFRDsBtPQ9DCDQ8ToIZ+5QzhhjjDE2qg33CPtfA9guhHgGvYndrGG+P2OMMcaYRw33gPa7AdxHRFoA9wF4Y6CCQoiVjnFZeeCV5BljjDE2SgyYtAghtqG3G69fRHT1edzvFgD/43j9TwCvD1L/qwBedcTSOVA5xhhjjLGRZLAWoWc8cL8aAHMBfAMgB7y3FGOMMcbGmAGTKyLaAQBCCB8ACehtxTpORN1DqVgI8S6AeQDChRBVAB4DcCeAF4UQMgDdAFZeUPSMMcYYYyPMgGOuhBAyIcRfAVQBeBvABgCVQoi/CiHOupcPES0lokgikhNRNBG9QUS7iWg6EaUT0Qwi2u++j8IYY4yxkay9vV2ybNmyWK1Wm5aQkJCamZmZ5FwstKKiQnbVVVdN0mq1aZMnT06dO3duQkFBgdJms+HWW2/VJiYmpup0On1aWlrKkSNHFN7+LIMZrFvwbwBUAOKJyAgAQohA9HYXPoP/jp1ijDHG2LlKQ4pb6zuEYrfW5wHLly+Pi42NNZeVlR2SSqU4fPiwoqCgwNdut+Pqq69OWLZsWfPHH398AgD27t3rW1NTI//222/96+rq5EeOHCmSSqU4fvy4PDAw0C17FXrKYLMFrwJwpzOxAgAiMqB3xt8Vng6MMcYYY+714IMPRsbHx6fOmjUrcfHixfGPPvpoBAA8++yz4WlpaSlJSUn6H//4x5OdW8y8+eabIYmJialJSUn6zMzMpL713XTTTTEbN24MAoAFCxZMvuGGG+IA4Pnnnw+/9957o1zLFhUVKQ8cOOD/4osvVkulUgCAXq/vWbJkSfvHH3+skslk9Nvf/rbRWX7WrFldCxcu7KitrZVHRERYnNdMnjzZMmHCBJtHviA3GSy5Iupn+XYismGQWYSMMcYYG3l27tzpt23btpDCwsLDn3zyyfGCgoJTq9svX7689dChQ8UlJSWHk5KSulavXh0OAE8//XTkF198cbSkpOTw559/Xtq3zksvvdS4c+dOFQDU1dUpjh496gMAe/bsCZg7d+5pewoePHjQR6/Xm2SyMzvNCgoKfNPT0/vdxmbFihUtX375ZXBycrL+zjvvjN6zZ4/vBX0Rw2Cw5OqwEOLmvgeFEDcBOOK5kBhjjDHmbt98803AokWL2gICAigkJMS+YMGCNue5/fv3+06fPj1Jp9Pp/+///i+sqKjIBwAyMzM7li9fHvfss8+GW61nbi+4YMGCjn379gXs37/fR6fTdYWHh1vKy8vl+/fv98/JyTljw+bzMXnyZEtpaemhJ598skoikeCKK65I2rJli8oddXvKYGOufgngAyHEbQD2o7e16mIAvgB+MgyxMcYYY8xNBttLeOXKlfGbN28unTlzZtfq1avDduzYoQKAd955p+Krr77y37p1a9C0adNSDx48WKRWq091ycXHx1va29tl27ZtC5ozZ46xpaVFlpubG+Lv728PCQk5bVzUtGnTuouLi/1sNhucXXxOU6ZM6froo49CBorP19eXbrzxRsONN95oiIiIsHzwwQfB11xzzRkbQo8UA7ZcEVE1Ec0A8CSAMgAVAJ4koiwiqh6m+BhjjDHmBvPmzevYvn17kMlkEu3t7ZIvv/wy2HnOZDJJYmJiLGazWWzatCnUebyoqEiZk5PT+cILL9SEhIRYT5w4ccYsvenTp3esW7du4vz58zvmzZvXsWbNGvWMGTPOaLVKTU01T506tfP++++Pstt7867CwkLlhg0bghcvXmzs6ekRzz77bLiz/I4dO/w++eSTgN27d/uVlZXJAcBms6GwsNA3Nja2x81fj1uddVsZIvoKwFfO90KIYAC/JKL/9WRgjDHGGHOfuXPnmhYuXNiu1+tTNRqNeerUqZ1BQUE2AHj44YdrsrKyUjQaTU9KSoqpo6NDCgD33XdfdFlZmZKIRHZ2tuGSSy7p6ltvdnZ2x65duwLT0tLMZrO5p729XXrppZf226q0YcOGslWrVmljY2PTfH197cHBwba//e1vlRKJBFu3bj2+atUq7QsvvKBWKpUUHR1t/vvf/15ZUlKivOuuu2J7enokADBt2rTOhx9+uMGT39WFEgM1EwohtAD+ACAKwEcA3gHwRwA3A3iHiIZtKQYhRCcR+Z+95MBWrVo1B8DzbgqJMcbY+Sleu3btCndU5I7fhuGUn59flp6e3uTNGNrb2yVBQUF2o9EomTlzZtIrr7xSnp2d3e9Acja4/Pz88PT09Lj+zg3WcpULYAeA/wOwEMA+AEUAphBRnbuDZIwxxphn3XTTTbHHjh3zNZvNYsmSJc2cWHnGYMlVKBE97ni9XQhRD+BiIjJ7PizGGGNs5BBC/APAPUTU7ngfC+BNIvqRdyM7N9u2bTvp7RjGg0HHXAkhQgAIx9s6AH5CCH8AIKIWD8fGGGOMjRS7AfxHCHE/AA2ABwE84N2Q2Eg1WHIVhN4lGITLsR8czwRgkqeCYowxxkYSIlonhCgC8DWAJgAZPESGDWTA5IqI4oYxDsYYY2zEEkKsQO8kr5sBTAXwqRDi50SU793I2Eg04DpXjpXYna9n9zl3jyeDYowxxkaY6wBkE9G7RPT/APwCwNtejomNUINtf3O/y+u/9zl3mwdiYYwxxkYkIrqWiBpc3n8HIMuLIXlNVlZW0s6dO/28HcdINtiYKzHA6/7eM8YYY2OOEOK3RPRXIcTqAYrce96Vp6WlnPe1/Tl0qNit9XmYxWKBXC73dhgeMVjLFQ3wur/3jDHG2FjkTFj2D/AYNUpKShSTJk1KXbJkSWxCQkLq7NmzEzs6OoRrS1Rtba1Mo9FMAQCr1YqVK1dG63Q6vU6n0//v//7vxL51fvDBB4HTpk1L1uv1KYsWLZrU3t4uAYDf/OY3kWlpaSmJiYmpS5cujXVud5OVlZV0zz33aC6++OKkp556KmIYP/6wGqzlKlkIUYDeVqrJjtdwvOeZgowxxsY8ItrmeB4T46sqKip8NmzYcGLWrFnlV1xxxaTc3NwBN0t+9tlnJ5SXlyuLiooOy+Vy1NfXn7bbcm1trexPf/pT5M6dO48GBgbaH3nkEfUf//jHiGeeeab2wQcfbHjmmWdqAeDaa6+N37RpU9CyZcvaAaCtrU36/fffl3j2k3rXYMmVe5srGWOMsVFKCJEJ4BEAsXD57SSiqV4L6jxoNBrzrFmzugAgIyPDVFZWphyo7FdffRX4i1/8otHZdRcREWFzPf/NN9/4Hz9+3CcrKysZACwWi5g+fXoHAHz22Weq5557Tt3d3S1pa2uT6fX6LgDtALB06dIxv07mYEsxlA9nIIwxxtgIthG9C4cWArB7OZbzplAoTg3rkUql1NXVJZHJZGSz9eZNJpPp1JhqIoIQYsBhQESE7OxsQ99V300mk3jggQdi//Of/xxOSEiw3H///VHd3d2nhiGpVKpR+/0N1WBjrhhjjDHWq5GIthLRSSIqdz68HZQ7aLVa83fffecPABs3bjzVTTh//nzDK6+8MsFisQDAGd2C8+bN68zLyws4dOiQEgCMRqOkoKBAaTKZJACgVqut7e3tkm3btg3Y9ThWcXLFGGOMnd1jQojXhRBLhRA/dT68HZQ7PPzww/VvvPHGhIyMjOSmpqZTPVr33XdfY3R0dE9ycnJqUlKS/o033gh1vS4qKsq6bt26siVLlkzS6XT66dOnJxcWFvqEh4fbli9f3qjX61MXLVqUkJ6e3jn8n8q7BNHIn/gnhOgkIv8LqWPVqlVzADzvppAYY4ydn+K1a9eucEdF7vhtOId7bQCQDKAI/+0WJCIa8rqP+fn5Zenp6U2eiI8Nv/z8/PD09PS4/s4NOOZKCFGIQZZcGG2D+BhjjLELkE5EU7wdBBsdBpsteJXj+ZeO5384npcDMHksIsYYY2zk2SeE0BPRYW8Hwka+s84WFELMJiLXvQUfFkLsAfCkp4NjjDHGRohsALcIIU4CMKN3zUfiXhzWn8Farpz8hRDZRLQbAIQQswAMSx83Y4wxdr6EEAsBvAhACuB1Inq6nzI3AngcvcNg8olo2QDVLfRUnGzsGUpydTuAN4UQQej9y9cO3riZMcbYCCaEkAJYA2ABgCoA3wshtrp26wkhEgH8PwCziahVCHHG9i5OY2XZBTY8zppcEdF+AOlCiED0zi5s93xYjDHG2AXJAlBKRCcAQAixCcA1AFzHTN0JYA0RtQIAETUMe5RsTDrrOldCiAghxBsA3iOidiGEXghx+zDExhhjjA1GJoTIc3msdDmnAVDp8r7KccyVDoBOCLFHCLHP0Y04ZpWUlCgSExNTz+fajz/+WHXZZZcluDumsWoo3YLrAbyF3j2VAOAogPcAvOGhmBhjjLGhsBJR5gDnRD/H+i4vJAOQCGAegGgAu4QQaUTU1m+FQsQCSCSiL4UQvgBkRGQ8v9CBtDT37uF76BCK3VnfUFgsFjj3HmT/NZQV2sOJ6H04Fk0jIisA2+CXMMYYY15VBUDr8j4aQE0/ZbYQkYWITgIoQW+ydQYhxJ0ANgNY51LfR26NeBjYbDYsWbIkNiEhIXX27NmJeXl5Pnq9/lSSV1hYqExNTU0BgM2bNwfGx8enTp8+PWnz5s3BzjL3339/1NKlS2Nnz56d+NOf/jTeZDKJ66+/Pk6n0+lTUlL027ZtU3njs40kQ0muOoUQYXBk/EKIS+DY2Zoxxhgbob4HkCiEiBdCKAAsAbC1T5mPAFwGAEKIcPR2E54YoL5fApgNwAAARHQMwIAD4EeqiooKn3vvvbehtLS0KCgoyPbdd9/5qVQq2969e30BYN26deHLli1rNplM4p577onbunVr6ffff1/S0NBwWvNUQUGB3/bt20u3bdt28i9/+ctEADh69Ojhd95558TKlSvjXDeAHo+Gklzdj96/kJMd61vlArjXo1F5gE+Jj0VeKy+RNcmOSdukJyVGSbkwiWphFvWwoAU2dIDQ4+04GWOMXThHL8s9ALYDKAbwPhEVCSGeFEJc7Si2HUCzEOIwgK8BPEhEzQNUaSaiU78RQggZBtnFZKTSaDTmWbNmdQFARkaGqaysTHnrrbc2vfbaa+FWqxVbtmwJuf3225sPHjzoEx0dbZ4yZYpZIpFg+fLlp30vCxcubAsICCAA2Lt3b8DNN9/c7KizOyoqqqewsNBn+D/dyDGUMVdFAOYCSEJvH3YJRuGGz0FfBcnR+xkGRYLsJKMeklEPKchK8t5nu8JuJQXZSE52UpLdrrATKYhIQSA5wS6zS0hOgmQkhQwSkpGMpCSDBHKSkBwSKCCghBh93x1jjI1GRPQpgE/7HHvU5TWhtwHh/iFUt0MI8TsAvkKIBQBWAdjmxnCHhUKhOJUQSqVS6urqktxyyy2tf/nLX6I2bdpknDJlikmtVttOnDgBIQZufPL393fur4jRsEfxcBtKcvUtEV2E3iQLACCE+AHARR6LyosECYmwCB9Y4IMu99dPUrKSjMwkJwvJyWJX2C2kIKsjgbOTguykOJW8geQEkpNwPCQkI6njPxmkkJGUFBBQQAI5BBTuj5gxxhiAh9G77mMhgLvQm7S97tWI3MTPz4/mzp3bfv/998e89NJLZQAwbdq07qqqKkVRUZEyNTXVvGnTptCBrs/Ozu7YsGFD6NVXX20sKChQ1tbWKqZOndo9bB9gBBps42Y1eqet+gohMvDfmReBAPyGIbYxSdiETNiEDGb3192n1c1CcrKc1urWm7TZHS1u/211k/+31Y1kJIUUUpKSHBLIuNWNMcYAIrIDeM3xGHNuvvnmls8++yzkpz/9qQHoTbj+/ve/l1911VUJoaGh1hkzZnQUFxf79nftb3/724YVK1bE6nQ6vVQqxbp168p8fX3HdXOWGKg5TwhxC4BbAWSid2CgM7kyAlhPRB8MR4COWDqJ6IK23HlCPDEHwPNuCmlcIilZSEY9JKceR4ubxdF1ajtLq5vEpdVN0qfVTQ4JlBDgubyMjQ/Fa9euXeGOitzx23AO95qN3m1yYtHbMOHcW3DSUOvIz88vS09Pb/JMhBfm0UcfjWhvb5e++OKLfWdUsgHk5+eHp6enx/V3brCNm98G8LYQ4joi+j9PBcdGD2ETcmETcpjhL4XUrXWfanWTOx69SdugrW52uV2c6jI9vdVNBinkJHF0mfY+uNWNMXYh3gBwH4D9GGPLES1YsGByeXm5cseOHUe9HctYMZQxV9GOrW+M6G0OvQjAw0T0hUcjY+PKaWPdPOCsrW5Kl1Y3eb+tbpLekW6OSQpSkjvGuim41Y2xcaGdiD7zdhCe8K9//eu4t2MYa4aSXN1GRC8KIX6M3jU9fo7eFds5uWKjxjC3ulnsCruV5GQjJdlIQTZH0tZfq1tv4tbb6iY71WXKrW6MjQhCCOfkra+FEH8D8AHw31GzRPSDVwJjI9pQkivnWKsrALxFRPlisPmZjI0zw9LqJnfMMJWR1RP3YGzYEAxY6+0gzsmzfd67brdDAHKGMRY2SgwludovhPgCQDyA/yeEUMGxFQ5jzPNOtbqN64nNbAwZkQO6B0JEzhXcJxHRaau3CyGGPJidjS9D6W64Hb3re1xMRCYACvR2DTLGGGPjxeZ+jv1z2KNgo8JQkqtsAAEApgohLgWQCiB48EsYY4yx0U8IkSyEuA5AkBDipy6PWwHPDAXwprlz5yY0NTUNeWBqSUmJIjExMdVT8Vx33XVxb731Voin6veUoXQLPujy2gdAFnqnog7azyyEeBPAVQAaiCjN5fiv0LvfkxXAJ0T023MNmjHGGBsmSej9LQsGsNjluBHAnRdScRqQciHX93Wodw/FC7Jjx45Sd8QyElitVshkQ0lz3O+sLVdEtNjlsQBAGoD6IdS9HsBC1wNCiMsAXANgKhGlAnjm3ENmjDHGhgcRbSGinwO4ioh+7vK4l4j2eju+c/H73/8+4qmnnpoIALfffrv2kksu0QHAli1bVNdcc008AGg0mim1tbWykpISxaRJk1KXLFkSm5CQkDp79uzEjo4OAQC7du3yS0pK0k+bNi35ueeem9jfvex2O+66667oxMTEVJ1Op3/ttddCAODjjz9WZWVlJS1cuHBSfHx86tVXXx1vt/cO4161apVm8uTJqTqdTr9y5cpoZ107duwIyMjISI6Ojp7ibMUarP4ZM2boFi9eHJ+UlJQKAGvXrg2dMmVKSnJysn7ZsmWxVqvn5wWdzxTvKvQmWIMiop0AWvocvhvA00RkdpRpOI/7M8YYY8OKiL71dgwX6rLLLuvYs2dPAAAcPHjQr7OzU2o2m8XOnTsDsrOzjX3LV1RU+Nx7770NpaWlRUFBQbbc3NwQALj99tvjnnvuuYqDBw8eGeheubm5wYWFhb7FxcVF//73v48++uij0eXl5XIAKC4u9l2zZk1laWlpUUVFhfJf//pXQH19vfTTTz8NOXbsWNHRo0cP/+lPf6p11lVfXy/Py8s7smXLlmOPPfaY5mz1FxQU+P/tb3+rPn78eNEPP/zgs3nz5tC8vLwjR44cOSyRSOiVV14Jc+83e6azJldCiL8LIVY7Hi8B2AUg/zzvpwMwRwjxHyHEDiHExedZD2OMMcbOQXZ2tqmwsNC/tbVVolQqKTMzs2PXrl1+3377rSonJ6ejb3mNRmOeNWtWFwBkZGSYysrKlM3NzVKj0Si98sorOwDgtttua+7vXrt27VLdeOONLTKZDFqt1jpjxoyO3bt3+wHAlClTOidPnmyRSqVITU01HT9+XBEaGmpTKpX2JUuWxL799tvBAQEBp1YluPrqq9ukUimmT5/e3dzcLD9b/VOnTu1MTk7uAYDPP/9cdejQIb/09PSU5ORk/e7duwNPnDihdPd329dQOiPzXF5bAbxLRHsu4H4hAC4BcDGA9x3TW8/Y4FAIsRLAynOIkzHGGGMDUCqVFB0dbV6zZk14VlZWR3p6eteXX36pKi8vV2ZkZJyx2ItCoTj12yyVSqmrq0tCRBjKUpcD7VvsjMOlXlitViGXy3Hw4MHirVu3Bm7atCnk5Zdfnrhv376jAODj43OqvLPewer38/Ozu5QXN9xwQ/OaNWuqzxq0Gw1lzNXbLo+NF5BYAb1dih9Qr+/Qu15W+AD3fZWIMokoE71JHWOMMeYVQgilEGKZEOJ3QohHnQ9vx3WuZs2a1bFmzZqIefPmGefPn298++23J+j1epNEMrRRQuHh4baAgADb9u3bAwBg/fr1of2Vmzt3rnHz5s2hVqsVNTU1su+++y5gzpw5nQPV297eLmlpaZH+7Gc/a3/llVcqi4uL/QaLY6j1L1y40PDxxx+HVFdXywCgvr5eevToUcWQPuwFGLBFSAjxPhHdKIQoRO8qtK4IveOpXiCiLedwv4/QO8vwGyGEDr1rZo2qBeUYY4yNS1sAtKN3trz5LGVHrLlz5xpXr16tzsnJ6QwMDLQrlUqaPXv2GV2Cg3njjTfK7rjjjjhfX197Tk6Oob8yK1asaNu7d29ASkpKqhCCnnjiiaqYmBhrQUFBv3W2tbVJr7rqqgSz2SwA4KmnnqocLIah1j99+vTu3//+99U/+tGPdHa7HXK5nFavXl2h0+l6zuUznysxUNOaECKSiGqFELEDXBsOYCMRJQ9w/bsA5jnK1QN4DMA/ALwJYBqAHgC/IaKvzhqkEJ1E5H+2coN5QjwxB8DzF1IHY4yxC1b8GD22wh0VueO34Rzudch1WaHzkZ+fX5aens4NCmNEfn5+eHp6elx/5wZsuSKiWsdz+QBFyoUQywe5fukAp24a6BrGGGNshNorhJhCRIXeDoSNfIN1CxpxZnfgKUQUSET7PRIVY4wxNrJkA7hVCHESvd2CAgAR0VTvhsVGosFarlQAIIR4EkAderv0BIDlAFTDEh1jjDE2MizydgBs9BjKEgc/JqIZLu9fFkL8B8BfPRQTY4wxNiIIIQKJyIDe7W4YG5KhJFc2x9iqTejtJlwKwObRqBhjjLGR4R307i24H72/ga6LPBGASd4Iio1sQ0mulgF40fEgAHscxxhjjLExjYiucjzHezsWNnoMZRHRMiK6hojCiWgCEV1LRGXDEBtjjDHGhtHcuXMTmpqapEMtX1JSokhMTEw9l3vcdddd0QkJCal33XVXdE1NjWzq1KnJKSkp+s8//zygv/JZWVlJO3fuHHRR0ZHmrC1XQggfALcDSAXg4zxORLd5MC7GGGNsTEtbuzbFnfUdWrWq+ELr2LFjR6k7YhnMxo0bJzQ2Nh709fWlV199NSQhIaH7gw8+KPPEvSwWC+RyuSeqHtRQ1rv/BwA1gB8D2AEgGjywjzHGGBtVfv/730c89dRTEwHg9ttv115yySU6ANiyZYvqmmuuiQcAjUYzpba2VlZSUqKYNGlS6pIlS2ITEhJSZ8+endjR0SEAYNeuXX5JSUn6adOmJT/33HMT+7uX3W7HXXfdFZ2YmJiq0+n0r732WggA5OTkJHR1dUkyMjJSHnnkEfVjjz0W/fXXXwclJyfrDQaD5LrrrotzXvPEE0+cqvvdd98NmTJlSkpcXFyas4XLZDKJ66+/Pk6n0+lTUlL027ZtUwHA6tWrwxYtWjQpJycnYc6cOToA+MMf/hCRlpaWotPp9Pfdd1+U577lXkMZc5VARDcIIa4horeFEO8A2O7pwBhjjLGRRAiRDSCRiN4SQkwAEEBEJ70d11BddtllHc8880wEgIaDBw/69fT0SMxms9i5c2dAdnb2GY0mFRUVPhs2bDgxa+kxGmQAACAASURBVNas8iuuuGJSbm5uyKpVq1puv/32uOeff77iyiuv7Ljrrrui+7tXbm5ucGFhoW9xcXFRbW2tLCsrK+Xyyy/v+Oqrr0r9/Pwyjhw5chgAIiIiLHl5ef65ubkVu3bt8qutrZUfO3asCABcuyetVqsoLCwsfu+994KefPLJqIULFx79y1/+MhEAjh49evjAgQM+V1xxReLx48cPAcAPP/wQUFBQUBQREWH74IMPAktLS30KCgqKiQjz589P+OyzzwIWLVp0Ttv+nIuhtFxZHM9tQog0AEEA4jwVEGOMMTbSCCEeA/AQgP/nOCQHsMF7EZ277OxsU2FhoX9ra6tEqVRSZmZmx65du/y+/fZbVU5OzhmJhkajMc+aNasLADIyMkxlZWXK5uZmqdFolF555ZUdAHDbbbc193evXbt2qW688cYWmUwGrVZrnTFjRsfu3bsHHTeVnJxsrqysVN5yyy3azZs3B4aEhJxameCGG25oBYBZs2Z1VlVVKQBg7969ATfffHOzI77uqKionsLCQh8AmDNnjiEiIsIGAJ9//nngzp07A/V6vT41NVV//PhxnyNHjvicGYH7DKXl6lUhRAiA3wPYCiAAwB88GRRjjDE2wvwEQAaAHwCAiGqEEKNqQW2lUknR0dHmNWvWhGdlZXWkp6d3ffnll6ry8nJlRkZGd9/yCoXi1C4tUqmUurq6JEQEIUTfomcYaN/iwUyYMMF26NChwx9++GHg2rVrJ7733nuh//znP8sAwMfHhwBAJpPBZrOJs93Dz8/P7hrLr3/969oHH3xw2PZ1HMpswdeJqJWIdhLRJCKaSETrhiM4xhhjbITood5fcwIAIcSwbBjtbrNmzepYs2ZNxLx584zz5883vv322xP0er1JIhlKRxYQHh5uCwgIsG3fvj0AANavXx/aX7m5c+caN2/eHGq1WlFTUyP77rvvAubMmdM5WN21tbUym82GW2+9te2pp56qLiwsHLSlKzs7u2PDhg2hAFBQUKCsra1VTJ069f+zd+fxVZT3/sA/35k5axKWLGwJISDZIxGCKBRkES22Yq1IUTEu1apob+uCWq/+FKneW4tLayku10sVq9StVet1KSiIC2JZGtlBbRAQZM9+lpnn+/tjZk4OIUCALCT5vl+v42zPPOc5xwPz4ZlnZg4Jieedd17l888/n1pRUaEBwL///W/P9u3bm9K5dNyOqXIiesu954cQQgjRibxMRE8B6EZEPwPwUwD/08ZtOmajR4+uevzxx3uNGzeupkuXLsrn8/H3vve9Yxp79L//+7/l1157bVYgEFDjxo2rbKxMaWnpgU8//TQxPz+/kIj4/vvv35aZmWkeqd7y8nLPNddck6WUIgCYOXPmtiOVv+OOO3aVlpb2y8nJKdB1HU899VR5IBA4pDvroosuqly7dq3/9NNPzwPsXq0XXnjh3+np6Udsz4mgY+m6I6JVzDy4pRpzhPetYeYT+lfC/XT/KACPNVOThBBCHJ/19/F9pc1RUXMcG47x/c4BcC7su7S/x8wLjmX/srKy8uLi4lY7NSVaVllZWWpxcXFWY9uOtVts1Yk3RwghhGg/iEiHHabGAzimQCU6p6OeZCWi84lIA+TGoUIIITofZrYA1BJR17Zui2gfmtJzdQmA3xPRawD+xMwnfAdYIYQQop0JAVhNRAsAxAZmM/Mv2q5J4mR11HDFzJcTURcAlwL4ExExgD8BmM/Mcqd2IYQQncH/OS8hjqpJY66YudLpuQoAuBn2/T5uJ6LHmfkPLdlAIYQQoq0x83Nt3QbRfjTlwc0XALgawCmwnzM4jJl3EVEQwHoAEq6EEEJ0aESUDeC/ARQAiN3dm5kHtFmjxEmrKXcNuxjAY8w8iJlnMfMuAGDmWtj3+RBCCCE6uj8BeAKACWAsgHmwOxw6rGHDhuUuWbLkiDfyPJJbb721T48ePQbl5eUVZGdnF77wwgtd33rrraTTTjstL75cNBpFSkpK8ZYtWzwn3uqTQ1NOC+5g5iXxK4joIWa+k5nfb6F2CSGEECeTADO/T0TEzFsAzCCijwDcd9w1/l9RfrO1DgB+uKZNLzgzTROGcXCsuOGGG76bOXPmdytXrvSfffbZuXv27Cm79tprvRs3bvTm5uZGAOCNN97okpOTU9evX79ooxW3Q03puTqnkXXnNXdDhBBCiJNYyLkt0WYi+jkR/RhAj7Zu1LGorKzUxowZMzA3N7cgOzu78H/+53+6A8Abb7yRlJ+fX5CTk1MwefLkrLq6ukMeHjh16tTMoqKi/IEDBxbecsstfdz16enpp06fPr13SUlJ7ty5c7sf7r2HDBkS0nUdO3fuNM4///x98+bNiz02Z/78+cmTJ0/e19yfty0dNlwR0TQiWg0gj4i+iHv9G8AXrddEIYQQ4tgR0QQi2khEXxLRr45Q7mIiYiIaeoTqbgYQBPALACUASgFc2bwtbll//etfu/Tq1Su6cePGdZs3b1570UUXVdbW1tL111/f/6WXXvpq06ZN60zTxKxZs9Ia7vvoo49uX7NmzfoNGzas/eSTT5KWLVsWcLf5/X61YsWKjdddd93+w733Bx98kKBpGvfu3dssLS3d99e//jUZAOrq6mjRokVdS0tLD7tve3SknqsXAUwE8IYzdV8lzHx5K7RNCCGEOC7OXdX/CPtMSwGAS4mooJFySbAD07Ij1cfM/2TmambexsxXM/NFzPxZS7S9pQwZMqTuo48+6jJt2rT0d999NzElJcUqKyvzZ2RkhAcNGhQGgKuuumrvxx9/nNRw3+eeey65oKAgv6CgoGDz5s3+srKy2KD+K6644rDB6Mknn+yZl5dXcPvtt2fMmzfva03TMHr06Nra2lqtrKzM9+qrr3Y97bTTatLS0qyW+dRt40hjrpiZy4nopoYbiCiZmTtUF54QQogOZRiAL5n5awAgor8A+BGAdQ3K/RrAbwFMP1JlRJQD4HYA/RB37GTmcc3Y5hY1aNCg8MqVK9e99tprXe++++70hQsXVl500UUHjrbfhg0bvLNnz+65YsWK9WlpadakSZOyQqFQrHMmKSlJHW5fd8xVw/UXXnjhvnnz5iVv3LgxMGXKlA6XJ47WcwUAKwAsd6Yr4paFEEKIk1U6gK1xy9ucdTFENBhAX2Z+qwn1vQJgJYB7YIcs99VulJeXe5KSktSNN9647+abb/7uX//6V/C0004Lbd++3btmzRofAMybNy9l1KhRB90gfP/+/XogEFDJycnW1q1bjcWLF5/wY4CuuOKKfa+++mrKp59+mnTppZceNeC1N4ftuWLm851p/9ZrjhBCCNFkBhHF/2P/aWZ+2pk/ZFA2AHZnnMHpjwG4qonvZTLzE8fVypPEihUrAnfddVeGpmkwDIPnzJmzJRgM8pNPPlk+efLkUyzLQnFxce306dN3x+83fPjwuqKiotrs7OzCzMzMcElJSfWJtqWkpCTk9/vVqaeeWtulS5fD9ny1V8TMjW8gGnKkHZl5ZYu0qPG21DBzwonUcT/dPwr2HyQhhBBtZ/19fF9pc1R0pGMDEQ0HMIOZv+8s3wUAzPzfznJXAF8BcINCLwD7AFzAzMvj6nGvavsFgF0A/gYg7G4/liEyZWVl5cXFxXuaWl6c3MrKylKLi4uzGtt2pDFXjxxhGwNoN+eZhRBCdDr/BJBNRP0BbAdwCYDL3I3MXAEg1V0mosUApscHK8cK2Mc8tycs/lQgA5A7tItDHOm04NjWbIgQQgjRXJjZJKKfA3gPgA5gLjOvJaKZAJYz85tNrEeGxohj1qQHNxNREQ59ntK8lmqUEEIIcaKY+W0AbzdYd+9hyo5pbD0RnQ5gKzPvdJavADAJwBbYpx073JVu4sQd9Q7tRHQf7Icz/wH285R+C+CCFm6XEEIIcTJ4CkAEAIjoLAC/gf1cwQoATx9hP9GJNaXn6mIAxQBWMfPVRNQTwDMt26zmF0z8NmpGE75ipenMus6suS8Pgzxg8gLkAagpjwQSQgjROehxvVNTYF+R+BqA14joX23YLnESa0q4qmNmRUQmEXWBfbVEuxvAl575sQfAKUcrp5RmsjIiivWoUkaUlRFVyrCU8ljKMiylPIqVoZQyWCkP21MDrAxSSifFhsZK15h1YqXrzJrhvgDyMpMHIK+EOCGEaBd0IjKY2QRwNoDr4rY1aWiN6Hya8sNYTkTdAPwP7KsmqgF83qKtakOapgxoEUNv4fdhpVmK9Ygd3vSoUh5TKcNkZZhKGUopj2VPDaUsjxvgoOwXMeuaUobGrOusNI1Zd0IcGYDmiQtxLf1RhBCiI5sP4EMi2gOgDsBHAEBEA2GfGuywhg0blvvwww9vPeuss2qPt47Zs2en/P73v+/FzGBmTJ06dc/MmTO/e//99xNuueWWvpFIRItEInThhRfuf/TRR799/PHHU+67776Mnj17RqPRKN14443f3Xbbbe3u9hVHDVfMfKMz+yQRvQugCzPLg5tPEGlK16EC0KOBo5c+fsxkKaXbPXBsRJzwZirLMJ0Ax0p5lBPomJ2eOOX0xLEyiJVOivVDTqeCDwpx8i84IUSHw8wPEtH7AHoD+AfX3xxSA/AfJ1T510X5J9i8gw1Ys75Z6ztGpmnCMOoPBS+//HKXOXPm9FiwYMGmrKysaG1tLT3xxBMpAHDNNdf0nz9//lfDhw+vM00T8c8qnDhx4v558+Z9s337dqOoqKjwJz/5yYG+ffuabfCRjttRD4jOAL5D1jHzkpZpkmhORKzruqlDN/1HL338mKHcAGeHOY/p9MgppQxLWYbFsTBnsFIexJ1S1ZTSie1TqqQ4Ni7OPp16cIjztOTnEEKIhhp7QDMzb2qLtpyIyspK7YILLhiwY8cOr1KK7rjjjm9/9rOf7X/jjTeSfvWrX/V179A+b968LYFA4KA7jE+dOjWzrKwsIRQKaRMnTtz/2GOPfQsA6enpp1566aV7Fi1a1OX666/fdd1118Ue4vzb3/62929+85ttWVlZUQAIBoPs9kLt27fPyMzMjAKAYRgoKSkJNWxvenq6mZmZGf7yyy+9HS5c4eAbpvlhPwxzBeQmoiIOETTSTZ8G09eS72P3LOtRpYwIK91UyhONO51qxZ1S5UZOqbpj4oiVrivWNTfEwT6l6mHWPE4vnBegxh6fIYQQ7dJf//rXLr169YouXrz4SwDYu3evXltbS9dff33/f/zjHxsHDRoU/vGPf5w1a9astHvvvXdX/L6PPvro9p49e1qmaWLEiBG5y5YtC5xxxhl1AOD3+9WKFSs2Nny/zZs3B773ve81ekrxuuuu+y4/P7/ojDPOqDr33HMrbrrppr3BYPCgQLdu3Trv1q1bfQUFBeHG6jiZNeW04MT4ZSLqC/t2DEK0OiIQkeXVNMvb0u9l98DpEcWGe3GDczrVucCh/pQqK2UoO8B5yA5zOjlj43S5QlUIcTIYMmRI3d1339132rRp6T/60Y8qJkyYUL106dJARkZGeNCgQWEAuOqqq/b+8Y9/7AH74rWY5557LvnZZ59NNU2Tdu/e7SkrK/O74eqKK67Y38jbHdHDDz+84+qrr9731ltvdXn55ZdTXnnllZTPP/98IwD8/e9/756Xl5fo9XrV7373uy09e/a0muHjt6rjGSezDUBRczdEiJONplkeaJZHt29x02KOdoUq2/Mc642zQxuz0qHsq1LBrGvMGtVPNZ1Z0+xeOdKZNR0gg5l0AB4JdUJ0PoMGDQqvXLly3Wuvvdb17rvvTl+4cGHlRRdddOBo+23YsME7e/bsnitWrFiflpZmTZo0KSsUCsX+/khKSmr0wcsDBw6s++STT4IXXHBBVWPbCwsLw4WFhbtvvfXW3SkpKaft3LlTB+rHXB3v5zwZNGXM1R9Q/yRxDcBpAMpaslFCdCatdYVqQ8xQzLrJSo8q1qOsdMVsmEppJrOhWOmWUoZi1pVSumJlsGLd6aHTmVmHHfAMYqWBWSfFun3a1Q52Guypzkw6Q9PBZDBIhz2GzpAxdEK0nvLyck+PHj3MG2+8cV9SUpJ67rnnUmbOnLlz+/bt3jVr1viKiorC8+bNSxk1atRBYWj//v16IBBQycnJ1tatW43Fixd3HT16dKOBKd4dd9yx8z//8z8zTjvttM2ZmZlmXV0dPfLII2n33HPPrr/85S9df/KTn1RomobVq1f7dV3n1NTUdtdDdThNuhVD3LwJYD4zf9JC7RFCtBIiaESWF5rlbcv7ddhhTo8y61FWmlJsRFnpFrNuKaVbrHRVH/IMZtad07A6lDLg9OIRc6wnj47Qi6c5tyvR7YskYDghT25ZIjq8FStWBO66664MTdNgGAbPmTNnSzAY5CeffLJ88uTJp7gD2qdPn747fr/hw4fXFRUV1WZnZxdmZmaGS0pKqpvyflOmTKnYuXOncfbZZ+cyM4gIU6dO3QMAf/7zn1N+9atf9fX7/cowDH7mmWf+HX+lYXtH9VeVHqYAURDAQGdxIzO3+sAyIqph5oQTqePFwsJRAB5rpiYJITqQBr14phPuosruzbMa9OIxK0PZvXg6nMB3SC8es07KvgedxqwR3DF3IDfoNejFg9FJLqJYfx/fV9ocFTXHsaE1lZWVlRcXF7e7ezaJxpWVlaUWFxdnNbbtsDGRiDwAZgEoBVAO+5RgDyL6AzP/hogGM/OqFmivEEK0qpOrF0+LMhumfaNhwzldq5uxXjz7lK3bi6ecCyiY2WCldBzci6c54/EO6cXT7bF40osnREs4Uh/cIwCCALKYuQoAnMffPExETwCYAKD/4XYmorkAzgewi5mLGmybDju4pTGzpHjRPjArABYBCs6LmBUBFgCLmJkARQDDXq+cdeyUY82egphZs7fb8/YyiBmavQ80ez0526EBRMzMRFAAmAgcN1VE7jzFlSE++pQQNx+3zp3XnP5tYiK7mQCBSHPKaXHlNdj/EIttgzslGUB/NJqmDEAZ9giMtuH04kVZ6aZi3XR69OxTtKxbzuO/rFgvnnu6Vul8cC9eLOjFevHAOoF499FbIUT7dqRw9QMA2XF3owUzVxLRNAB7AJx3lLqfBTAb9tPDY5xbOZwDoF1fCdBp2QHDDhZ2qFDEHAscbsBAffCIBYvYfHywsAMHU324YC1uvRMu3KABDaAG60mzp/a8vZ2ImfT67ZqzTnOWSQM055/yse0aUD8Sm1mn+jK65gQG5yWOEwNKESkGFNtT5vplKMCy/89COVNWRIqJWNll3XXO/+W4ZWe7Mw9Vvx0N1sEpWx9S7f1iYdVdrxoEWGcbcXyQhRNMnW2oX09xYZe4PpTCCazERHYorZ+PD6qxcMrutD68uutiy6DmOaXo9OL5oFm+FurCatnLb4U4CRwpXKn4YOViZouIdjd2x9oG5ZYQUVYjmx4DcAeAN46loScV+3uJ78WwiNmCHSAUAapBz0V8yHBDhXJDhbPtoFBBdpCw19WHB7a7BBjUMFTY+2tOjwe5oUN3wgPVhwp3mxYXNuzwAeha/bqDw4Vdt0YSMMQJIPsclP37Ocp4T3Fs2P5GVVxYZQUoECl1aFhFXICND68qLqByfKhV9aGUnfDZMMjCqR9xZeAGXDe0ErC9Tb8oIVrBkcLVOiK6gpkb9jxdDuC4nl9ERBcA2M7MZc30j6wmG/3NN6ZFVE4A6XY40bRDw4W7TouFDyA+XLhTAqA7LyGEaHNkj4bXwWz/vSThVYg2c6RwdROAvxLRT2E/7oYBnA4gAODHx/pGzlWHdwM4t4nlrwNwXRPa2STp1dUGgKwTrUcIIYQQ4kgOe3qHmbcz8xkAZsK+WvAbADOZeRgzH0+37imwB8CXEVE5gAwAK4mo12He/2lmHsrMQ9GWozuFEEIIEfP88893W7Fihd9dHjZsWO6SJUuCbdWet956K2ns2LEDj16y9TTl2YIfAPjgRN+ImVcD6OEuOwFrqFwtKIQQojOaM6covznru/HGNcc1ZOdYvf76691M06woKSkJnWhdpmmiI9081NViA5OJaD6ApQByiWgbEV3TUu8lhBBCiKO7/fbbe/fv379wxIgR2RMnTux/77339ly7dq1v1KhR2YWFhfklJSW5q1at8gPApk2bvMOHD8/JyckpGD58eM7mzZu9CxYsSFi4cGG3e+65JyMvL69g7dq1PgCYP39+91NPPTU/Kyur6N13300E7OB0/fXXZxQVFeXn5OQUzJo1KxWwe5rOOOOMnIkTJ/bPzc0t3Lhxo7d///6FU6ZM6ZednV14wQUX9H/99deThgwZktevX7+iRYsWBQFg0aJFwcGDB+fl5+cXDB48OK+srMzXVt/j0bRYXGTmS4+yPaul3lsIIYQQB1uyZEnw73//e/fVq1evi0ajdNpppxUMHjy49tprr+339NNPbzn11FPDH3zwQcK0adMyP/vss0033HBD5mWXXbb3P/7jP/b+7ne/S5k2bVrfhQsXfjV+/PgD559/fsXVV1+9363bNE1avXr1+pdeeqnrzJkz+0yYMGHT7373u9SuXbtaa9asWV9XV0enn3563sSJEysB4IsvvkhYtWrV2ry8vMjGjRu9W7du9b/00ktfl5SUbBk0aFD+Cy+8kLJ8+fINL774YrcHH3yw99ixY78qLi4Off755xs8Hg9ef/31pDvuuCPjvffe+6rtvtHD63h9cUIIIYQ4xOLFixPPO++8A4mJiQyAzznnnAOhUEhbtWpV4uTJk09xy0UiEQKAVatWJbzzzjtfAcC0adP23X///RmHq3vy5Mn7AWDEiBE1t99+uxcAFi5c2GXDhg3BN998szsAVFVV6evWrfN7vV4eNGhQTV5eXuyeZ+np6eFhw4bVAUBOTk7duHHjKjVNw5AhQ2ofeOCBPgCwb98+fcqUKf3Ly8v9RMTRaPSkfVyUhCshhBCiE2jsWcJKKSQlJZkbNmxYdyJ1+/1+BgDDMGBZFjnvR4888sg3kyZNqowv+9ZbbyUFg0EVv87r9cYap2larD5d12P13XnnnemjR4+uWrBgwVcbN270jhs3LvdE2tyS5GaQQgghRCcwZsyY6vfee69rbW0tVVRUaAsXLuwWDAZVRkZGZO7cud0BO2wtXbo0AACDBw+ueeaZZ7oDwFNPPZU8dOjQagBITEy0Kisrj5ofzjnnnIonnngiLRwOEwB88cUXvqbsdziVlZV6RkZGxGlP6vHW0xokXAkhhBCdwOjRo2snTJhQUVBQUPiDH/zglEGDBtV07drVmj9//td/+tOfUnNzcwuys7MLX3vttW4A8MQTT3zz/PPPp+bk5BTMnz8/Zc6cOVsBYOrUqfsef/zxXvn5+bEB7Y255ZZb9uTl5YVOPfXU/Ozs7MKf/exn/U7kVN6dd965c8aMGRlDhgzJsyzreKtpFdRYN+HJhohqmDnhBCsZBfvRO0IIIdrOejCXNkdFzXJsaEVlZWXlxcXFbXr7oYqKCq1r166qqqpKGz58eO6TTz65ZeTIkbVt2ab2qqysLLW4uDirsW0y5koIIYToJC6//PJ+mzdvDoTDYbrkkkv2SrBqGRKuhBBCiE7i73//+7/bug2dgYy5EkIIIYRoRhKuhBBCCCGakYQrIYQQQohmJOFKCCGEEKIZSbgSQgghhGhGcrWgEEII0Rb+uSa/Wes7vWh9s9Ynjpv0XAkhhBCdxIwZM3pmZ2cXZmdnF86cObMHAMyePTslJyenIDc3t+DCCy/sDwDffvut8f3vf/+UoqKi/KKiovx//OMfCQCwaNGi4ODBg/Py8/MLBg8enFdWVuYDgMcffzzl3HPPPWXUqFHZ/fr1K7rhhhsO+5DnzkB6roQQQohO4KOPPgq++OKLKStWrFjPzCgpKck/88wzax5++OHeS5cu3dC7d2/zu+++0wHg+uuv73vrrbd+9/3vf7968+bN3u9///vZX3/99dri4uLQ559/vsHj8eD1119PuuOOOzLee++9rwBg3bp1wbKysnWBQEANHDiwaPr06d8NHDgw2rafum1IuBJCCCE6gcWLFyf+4Ac/ONClSxcFAD/84Q/3L1u2LGHixIn7e/fubQJAz549LQD45JNPumzevDng7ltdXa3v379f27dvnz5lypT+5eXlfiLi+GcFjhw5sjIlJcUCgIEDB4a++uorn4QrIYQQQnRYjT1LmIhARIdsYGYsX758fWJi4kHbrr322szRo0dXLViw4KuNGzd6x40bl+tu83q9sbK6rvOJPKS5vZMxV0IIIUQnMG7cuOq33367W1VVlVZZWam9/fbb3YcNG1bz5ptvJu/cuVMHAPe04MiRIysfeuihHu6+n376aQAAKisr9YyMjAgAPPXUU6lt8TnaAwlXQgghRCcwcuTI2ssuu2zvkCFD8ktKSvJLS0t3n3vuuTW33XbbjlGjRuXl5uYW3HjjjX0B4Omnn966cuXKhJycnIJTTjmlcPbs2WkAcOedd+6cMWNGxpAhQ/Isy2rbD3QSo8a6CU82RFTDzAknWMkoAI81T4uEODEMWFF46iLwhusQCNcgIVqFpOgBdFP70d3ah2TsQSrtRhrtRpqxG2nGHqR6q5Bk6LCYwKzDYgMma1AwYDKB2YDJOizWYUGDUjosuPMeREFgOPuws411WDBgwtkfGhQbMKHDcut211H8fhoUOe9HOqz4ut19oEGR8/7ue8WW3W3OfkRgxG2DDos0KNKgQGCKW68RmDUozdnmroMGRQR2y1P81HlBg9KcMpq7zpnGL2sA3H3i58mZ10j+cXq81oO5tDkqapZjQysqKysrLy4u3tPW7RDNo6ysLLW4uDirsW0y5kqI42BBC0fgDYXgD9UhEKlGYrQSXawD6OYGI+xGGnYjTd+NNH0PUj17keLdh2T/PiT7a5DoA5DovEQ7RXa4U07YdKfxL+UETuUGTCegxi+7IVjFBVeOn7oh2A287jYPom6Ipgbr3IDthmd30aXP5AAAIABJREFUfzTYPz4wNxaCY+E7LvAifpsz7wZlcj7vQSE6PjCH4dv5vbb+HydEC5NwJTodBpTTaxSqQyBSg4RIFZLMA+hmub1Ge5HiBiNtN9I8Tjjy7UWK7wC6BSwYPgA+AF3b+vOItsPQwICmoAOw/yOOymyt8yVENAHA72H/v3mGmX/TYPutAK4FYALYDeCnzLyllZonOjAJV6LdsaBFIvDWhuGL1CIYbqTXiPcgVXOCkbEbaYbTa+Tbj+6BaiT5ACQ4LyFEB0REOoA/AjgHwDYA/ySiN5l5XVyxVQCGMnMtEU0D8FsAU1q/taKjkXAlWhUDyoRRF4bPHWsUqUKSWYGu5n50571I4b1IgROODOeUmncPUr370T2wD8l+C4YXgLetP4sQ4qQ2DMCXzPw1ABDRXwD8CEAsXDHzorjynwG4vFVbKDosCVfimFjQwlF4QiH4w26vURWSrAPopvYiRe1FCjnjjWJjjXYjze018lcjyQ/pNRJCtLx0AFvjlrcBOOMI5a8B8E6Ltkh0GhKuOpH4XqMQ/CHnCjWzAl2tfUhW+5DMe5Dq9hq5wcjYixT/PiT7DqCbPwqvO9ZICCHamkFEy+OWn2bmp535xm5g2ehwLyK6HMBQAKObuX2ik5Jw1Y7E9RqFahGMViMx4vQauYOw3bFGtBtpbjDy7kGq9BoJIToik5mHHmbbNgB945YzAHzbsBARjQdwN4DRzBxu/iaePDZu3Og9//zzszdv3rw2fv2UKVP63XHHHd+VlJSE2qpt8W699dY+f/vb37obhoH/9//+3/YrrrjiAAD8+c9/7vbss8+mLFy48CsAuOuuu3q98MILqd98880aAHjxxRe7PvPMM2kffPDBl8fyfs8//3y3goKCUHN+fglXrcTtNWp4hVoFulr70V05Y43c+xrpe5DqBiPvPiT796N7fK+RXKEmhBBH9k8A2UTUH8B2AJcAuCy+ABENBvAUgAnMvKu1G1g0pyi/Oetbc+Oa9cez30svvXTSXCH55Zdfel577bXkTZs2rdU0jb/55huPu23cuHHVv/zlL/u5y8uWLUtMTEy0tm/fbqSnp5uffPJJ4vDhw6uP9T1ff/31bqZpVki4agMWtEgUnroQ/OG4+xq5l++7wQh7kKrvQg99D1I9e5Dq2Ydk/16k+GuQ4GNo0mskhBCtgJlNIvo5gPdg34phLjOvJaKZAJYz85sAZsG+19wrRAQA3zDzBW3W6FZgmiYuuuiirDVr1gQHDBgQeuWVV8rPPvvs7IcffnjrWWedVfvUU08lP/LII72YmcaPH3/giSee2A4AwWBw8JVXXrlryZIlXbp27Wo9+OCD2+68886+3377rfehhx76ZurUqRUbN270XnbZZf3r6uo0APj973//zTnnnFOzZcsWz6RJkwZUV1frlmXRH/7why3jx4+vnjJlStYXX3yRQEQ8derUPffdd98uj8eD6upqvbKyUktLS7NOOeWU2IOf+/TpYyYlJVlr1qzxFRUVhb/77jvPxIkT93/wwQeJpaWlBz7//PPEX//619sBYOrUqZllZWUJoVBImzhx4v7HHnvsWwC48cYb0997771uuq7zmDFjKidPnrx/4cKF3T777LOkhx56qPdrr732FQDccMMNmfv27TP8fr965plntgwePPiYglenCVf70c3yIrI3BH/EvRt2fK+RM9ZI34UemtNr5N7XyO01kivUhBCiHWHmtwG83WDdvXHz41u9UW2svLzc/9RTT5Wfe+65NZMnT86aNWtWWtw2z4wZM9JXrFixPi0tzRw1alTO888/3620tPRAXV2dNnbs2Konnnhi+znnnHPKPffck/7RRx9tWrlypf/qq6/uP3Xq1Io+ffqYH3300aZgMMirV6/2XXrppQPWrFmzfu7cuclnn312xUMPPbTTNE1UVVVpS5cuDe7YscPjnqLcs2ePDgB+v1+lpKREzz///FMWL168ORAIHDROrqSkpHrx4sWJlmWhf//+4REjRtS88847XS+55JIDGzduDJx11lk1APDoo49u79mzp2WaJkaMGJG7bNmyQFZWVuTtt9/u/vXXX6/RNA179uzRU1NTrfHjxx84//zzK66++ur9ADB8+PCcp59+esupp54a/uCDDxKmTZuW+dlnn206lu+504SrZOzXAaS0dTuEEEKIttKrV6/IueeeWwMApaWlex9//PHYw5k//vjjhDPPPLOqT58+JgBMmTJl34cffphYWlp6wOPx8MUXX1wJAIWFhXU+n0/5fD4eNmxY3fbt270AEIlE6Jprrum3bt26gKZp2LJliw8AzjzzzJrrr78+KxqNahdffPH+ESNG1OXl5YW3bt3qu/LKK/tOnDix4sc//nElAFx++eVZs2bN2vrxxx8nXnjhhQPefvvtr+67776eCQkJ6q677to9YsSI6k8//TTBsiycccYZ1WeddVbNAw880OfTTz8N9u/fPxQMBhkAnnvuueRnn3021TRN2r17t6esrMw/ZMiQOp/Ppy655JJ+P/zhDyumTJlS0fD7qaio0FatWpU4efLkU9x1kUiksYsjjkiejSWEEEJ0Es7pz0aXj/SsYcMwWNPsyKBpGnw+HwOAruuwLIsA4MEHH+zZo0eP6Pr169etXr16XTQa1QDgvPPOq16yZMnG9PT0yFVXXdV/9uzZKWlpadaaNWvWjR07tmrOnDk9LrnkkiwA+PTTT7v88Ic/rJo1a9aOXr16RUpLSzMXLFjQdfLkyQcAYPTo0dXLly9PXLp0aeLIkSOru3fvrsLhMC1cuDBp2LBh1QCwYcMG7+zZs3t++OGHmzZt2rRu3LhxFaFQSPN4PPjXv/61ftKkSQdef/31bmPGjMlu+Dkty0JSUpK5YcOGde7r66+/Xtuw3NFIuBJCCCE6iR07dngXLlyYAAAvvvhi8ogRI2IDwM8666yaZcuWJe3YscMwTROvvPJK8pgxY5o8QLyiokLv3bt3VNd1zJkzJ8WyLADApk2bvOnp6dHbbrttz+WXX75n5cqVwR07dhiWZeGqq6468MADD2xfvXp1EAByc3PrnnjiiRQA+OMf/7jt448/7uL1enngwIFRABgyZEho9+7dnmXLliWOGDGiDgCKiorqnn322bTvfe971QCwf/9+PRAIqOTkZGvr1q3G4sWLuzrt0/bt26dPmTKl4sknn9y6fv36IAAkJiZalZWVGgAkJyerjIyMyNy5c7sDgFIKS5cuDRzr9yzhSgghhOgkBgwYEJo7d25KTk5Owf79+43p06fvdrf169cveu+9924fPXp0Tn5+fuGgQYNqL7/88gNNrfvmm2/eNX/+/JTi4uK8TZs2+QOBgAKA9957L6mgoKAwPz+/4I033uh+xx13fFdeXu4ZOXJkbl5eXsFPf/rT/jNnztwGAM8///y/58+fn5KTk1MwfPjwvJ///OffWZaFGTNm9ATsXrPi4uKa5ORk0+09O/PMM6u3bdvmGzt2bA0ADB8+vK6oqKg2Ozu7sLS0NKukpKQaAA4cOKBPmDAhOycnp2DUqFG5DzzwwFYAmDp16r7HH3+8V35+fsHatWt98+fP//pPf/pTam5ubkF2dnbha6+91u1Yv2c6UjfgyYKIapj5hK6yI8IoAI81U5OEEEIcn/XMKG2Oiprj2NCaysrKyouLi/e0dTtE8ygrK0stLi7Oamyb9FwJIYQQQjQjCVdCCCGEEM1IwpUQQgghRDOScCWEEEII0YwkXAkhhBBCNCMJV0IIIYQQzUjClRBCCNEJbNy40ZudnV3Y2LYpU6b0W7Fihb+129TcFi1aFBw6dGhuVlZWUf/+/QunTJnSr6qqSgOAl19+uUtRUVH+gAEDCvv371943XXXZQBAWVmZb9iwYbl5eXkFAwYMKLz00kv7nWg7Os2zBYUQQoiTSRGK8puzvjVYs/54933ppZe2NGdb2sLWrVuNqVOnnjJv3ryvx48fX6OUwnPPPdf9wIED2oYNG7y33XZb5ptvvvnl4MGDQ9FoFI888kgaANx0002Zv/jFL75zb5j6+eefH/Md2RuSnishhBCikzBNExdddFFWTk5OwYQJEwa4vTrDhg3LXbJkSRAAnnrqqeScnJyC7OzswmnTpqW7+waDwcHTpk1LLywszB8xYkTOokWLgsOGDcvNyMg49YUXXugK2L1jJSUluQUFBfkFBQX5CxYsSACALVu2eIYOHZqbl5dXkJ2dXfjuu+8mmqaJSZMmZWVnZxfm5OQU3H///T0atjUjI+NUpRT27Nmja5pW8s477yQCQElJSe6aNWt88eUfeeSRHj/5yU/2jh8/vgaw7+Z+9dVX7+/bt6/5X//1X71uu+22HYMHDw4BgMfjwa9+9avdALBr1y5Pv379Im49w4YNqzvR71nClRBCCNFJlJeX+2+44YbdmzZtWpeUlKRmzZqV1mC7Z8aMGemLFy/etG7durWrVq1KeP7557sBQF1dnTZ27NiqtWvXrk9ISLDuueee9I8++mjTK6+88uWvf/3rdADo06eP+dFHH21at27d+pdeeunrW265JRMA5s6dm3z22WdXbNiwYd369evXnnHGGbVLly4N7tixw7N58+a1mzZtWnfTTTftjW+LYRjo379/aOXKlf4FCxYkFhQU1C5evDixrq6Odu7c6S0qKgrHl1+3bl1g6NChtY197o0bNwbOOOOMRrfddNNN3/3gBz/IOeuss7Lvv//+Hnv27NGP/xu2SbgSQgghOolevXpFzj333BoAKC0t3fvpp58mxm//+OOPE84888yqPn36mB6PB1OmTNn34YcfJgKAx+Phiy++uBIACgsL60aOHFnl8/l42LBhddu3b/cCQCQSocsuuywrJyenYPLkyad89dVXfgA488wza+bPn59666239vn8888D3bt3V3l5eeGtW7f6rrzyyr6vvvpql+7du1sN2ztixIiq999/P+nDDz9Muv3223csXbo0acmSJQnFxcU1zfWd/PKXv9y7evXqtRdddNG+JUuWJJ1++ul5dXV1dCJ1tli4IqK5RLSLiNbErZtFRBuI6Asi+hsRHfPDEIUQQghxfIjoiMtHet6wYRisaXZs0DQN7oOTdV2HZVkEAA8++GDPHj16RNevX79u9erV66LRqAYA5513XvWSJUs2pqenR6666qr+s2fPTklLS7PWrFmzbuzYsVVz5szpcckll2Q1fM8xY8ZUf/zxx4krV65MmDx5ckVlZaX+/vvvJ40cObKqYdn8/Py65cuXBxtre05OTmjZsmWNbgOArKys6M0337z3/fff/8owDCxfvvyExl21ZM/VswAmNFi3AEARMw8CsAnAXS34/kIIIYSIs2PHDu/ChQsTAODFF19MHjFiRHX89rPOOqtm2bJlSTt27DBM08Qrr7ySPGbMmOrGaztURUWF3rt376iu65gzZ06KZdmdUZs2bfKmp6dHb7vttj2XX375npUrVwZ37NhhWJaFq6666sADDzywffXq1YeEnzFjxtSsXLkyUdM0DgaDXFhYWDtv3ry0sWPHHtKm6dOn73r55ZdTPvjgg9jDvOfMmZP8zTffGHfdddfORx99tPcXX3zhAwDLsjBjxoyeAPDqq692CYfDBADffPONceDAAT1+DNbxaLGrBZl5CRFlNVj3j7jFzwBc3FLvL4QQQoiDDRgwIDR37tyUG2+8sV///v3D06dP3x2/vV+/ftF77713++jRo3OYmc4+++wK9yq6prj55pt3TZo06ZTXX3+9+8iRI6sCgYACgPfeey/p8ccf72UYBgeDQeuFF174d3l5ueeaa67JUkoRAMycOXNbw/oCgQD36tUrMnTo0BoAGDVqVPWbb76Z3Nig8759+5rz5s37+vbbb8/Yu3evR9M0PvPMM6tLS0sPZGZm1j300ENbL7300gF1dXUaEWH8+PEVAPDuu+92mT59eqbP51MAcP/992/LzMw0j+V7bYiO1AV4opxw9RYzFzWy7e8AXmLmPzehnhpmTjhauSPXgVEAHjuROoQQQpyw9cwobY6KmuPY0JrKysrKi4uL97R1O0TzKCsrSy0uLs5qbFub3OeKiO4GYAJ44QhlrgNwnbMo9+MSQoiTF4NgQYPpTC1oUNChoDvLBhg6LCRg79GrE6J9a/XQQkRXAjgfwNl8hG4zZn4awNPOPs12VYAQQpyUKBZMFDSYcQHFgg4FDQrGIS/AAwUPGN7YFPCCnRfgBeA76EVxU4K/wdQHDd7YVIcf5EztZS90+KDBAx1eGPBChw4N9vGkKceUQwYiC9HRtGq4IqIJAO4EMJqZG73fhBBCHCcV12tiOiHFioUTd2qAYTg9KYaz7HHCiMd5+Zxg4osLKH5naq93A4oGf2xaH07sQKLBF/fyxl5Gg3Bir2t6OBFCnORa7A8yEc0HMAZAKhFtA3Af7KsDfQAWOJd/fsbMN7RUG4QQx6TxUzvaEcKJx+lB8aC+B8UT12via7T3xA4m/gY9KD5QrMfE3qbHhRQ9LqDosV4VL3R4nJfde6IB8LTVFyjEUSilFGma1nKDnUWrcAbhq8Ntb8mrBS9tZPX/ttT7CdHBKPhQjQTUoBvCSEMEvWGhB9weFPdUTsPTO/U9KfWndtxwQvA5vSZeaPDE5nV4YMA4plM7Qohjt2b37t0FaWlpFRKw2i+lFO3evbsrgDWHKyN/iQrRWghR+FGNJNSiO8JIQxQZUEgHoR90ZMKDfgigNxKQiiB0dAHQpa2bLYRoHqZpXrtz585ndu7cWQR5Qkp7pgCsMU3z2sMVkHAlxInQEUIA1eiCOiQjjJ4wkQ5GXxCyYKAvvOiLAPogEd0QANDdeQkhOpmSkpJdAC5o63aIlifhSoiDMTyoQRC16Io6pCKCXrCQAUZfaMiEgSz40AdBpCMRAfhhn6gTQgghAEi4Ep2DBR9qkIAadEcIqYiiDxTSwegHHf3gQV/4kY4AeiIRXiQCSDxqrUIIIUQjJFyJ9okQRQDVSEQtkhFCD5joA0YGgCzo6AsvMuFHHyQgFQnQZPySEEKI1iHhSpw84scvpSKMHrCQDoW+IPSDgUz4kAE/0pGIrjJ+SYgmYVYALDBbYFbO1J23l5Wy55XiuHlAKXudZblTOFN73p0qBWfZfpkmOfME07Sn9jzBNHdh0qS2/laEaFESrkRLsscvJaAGXRFCStz4pX7Q0Bce9IMX6Qiij4xfEieJg8NIw1DiBg8FZnbCR30wcUOIG0DqA4kbRPigEFL/OjiE1K9zlzVnXnNe7rzeYF6DUrozr0MpHcwn2/2/ohKuREcn4UocK8u5/5J9O4FURJAOCxkAMp3bCWTCjwwE0ROJMGT8UofX1DBSH0Lqe0jsqd1DEh9G7HkcFEbs8AEoRXHL1CCYaAcFkvogUr9smjqU0uICSX0QUUo7CcNIO8SKAEsDWwRWGtgigtLAykemPP5GdHgSroQ7fqnKGb8UQQ9EY6fj7MDkRV/4kYEEJCMBGroC6NrWze7Q7BAShlJhWFYYphlFNGrBNM0GvSLcIIhQLKC4YeRwp2fqQ8fhekfiX4cGEreHBHJn9GN3+PChQ1kaMWtgSwezTkrpYGWQUgYU66TYgFIGKTjr4CHFHrLYnrfYS4q9ZMEDi7xk2fPkzivyIrYMH5nkI4t8ZJGHLM1LFrywNK+9nbz2OvKQpXvJImebZpDSnW2aAaV7SGkGKcMDS9PoiL+JJOA3rfx9C9G6JFx1VDrqEEQ1khBCqnP/pT5gZEJDJnT0gw8Zzv2XusIPINl5ieNhj1sJQakILCuCaDQK0zQRiZiIRBTCYYVwmBEKAaEQoa6OEAppCIV01NUZCIU8CIW8CIe9CId9ME0vgIDz6oQOCh+WBlYasWV3K3F8+FA6KT5M+GCDFDxOEGksfDgBBI2FDy+Z5CVFXjLRMHz4yCQPlHak8OElS3cCCLnhw0OWbkDpRwkfQoh2TsJV+3Ho+KU+sNAHQD8QMmGgH/yx+y/5O/OBuQmYTSgVgmXZYcjuGTIRjVoIhxUiEYVQiBEKEUIhOGFIbxCGPAiHfQiHfbAsD4AE59UaH+Bo4cMZbMNWw/BhkFI6nF6PBuHDA4s9ZPeENAwfzoviekTQMHw4gUTzkomG4cNLluYhS/citi4WPjxQukFK9zi9IBI+hBDtmYSrtmXBjyokoA7dEUIaougN5Qz4tscv9ZXxSwAA5mjcKbIITNOMC0MWIhF2wpDbM6QhFNJQV6cjHPagrs5AOOxFKORDJOKHZRloxu+SwFEdKmKQMg2oqIeU6SXL8pFp+siyAhRVAS2qgmRyghbhIEU5QYsgiSJI0CKUpEWoixbWErWInqSFKYGieqIWMRK1iJ5AESNBi3oStYjXR6Yh4aNdY+el4qb2PMXWuesBguXs5y7Xl6FYWXfeLkfO+xy8juK2IbYtfmrPUyPbEJseOk/Ovu58/DqKK1+/TcPOY/rGhGiHJFw1Nw0R+J37L6Ug7Nx/qX78Uha8SEcAGQg645e6AejW1s1udkpF4nqGok4YiiISsYNQOGz3DNXVAeGwFusZqqvTEQrVnyaLRHwIhXxg9sAOEk0MRMwaOKqDIwYp00NW1IAK+8iq8ZNp+Tym5ddMDlJU2UEnqoJaFEkURqIWQaIWoUQtoiVpYUqkiJ6kRbRELaInahEjicJGUIsaQYp6E7So4SfTo1OsfSer+IN5Ywd1ALDQ8KB98PaDD/AHH9yBhgf6g6f1ZQ6eutvdA3L8AT7+QH/owb1+ff0h3T2oH7y2YTnNqVeLW+/+V4uri+Lm3Hktrg44dWgHlUVsWXPKU9zUbU2Hf64c80G/IwBQDDAYdR3+w4tOT8JVU7jjl7qgDimIoCcs9IGKnY7LdMYvZSARSe10/FL94On6MUPRqOWEIeWEIcSdItMQChlOD5EnbsyQD5GID8xeAN6D34Qtvb6HJ+oly3R6eJSfTNNHpgqQGQ1QNJygRTiBokg0IpzQJUJJWgQJFKEkLaIlaRFKsnt5jCQtoiVQxJOoRfQgRY2gFvUmUMTj1yyP8/7eRj7t8bAARAFYIFggmABqnXnl9DBwbF5zgoL70pwwUT8lZ7172NXiDtPuvO4s6c68Bs3psao/eAOAHhcMYgd6PjhSAO7BDXD/q3H9gQ8AyDkgumXtknYhFTfPAJjtrcqp7uBt9fPxB1kwx/YDO+XcutxyzCAGVNwbufseusyxfd02aE697nrFdn8NM8c+lPse7NRBDepx646fJ2d/dr8ntqcNy1L8e6j6bdSwXNyyvU9ceSA2Twe1oX5ft/1aY/U45TRnGQxn3m4Hxc2D7e8svl63LZr7ncaV1+I+DzntJLded13st4e4Xx+gO1PPffdBiA6ts4ar+vFL3VCHVETRC5bzOBT3/ks+ZCCI3ifh+CX70FEfhuzTZBYikSiiUYVQyEIkgganyXTnNFn8eCEPhWp1LRohDyzTgDI9pCwPWcpHFvnJRICiFCBTC2pRBOweHiRqEU6kiErUwhE76ESiSVpYSwhGtMREJ/BoEU8iRfQELeqxe3giHoNYh/0XbGP3sjJhBxjTCSp2gCGnh4VioUY5oUSBEG4QXjgutNT3F9TPa9BATM4BjgBFzoGIYi9i2GVi2wBSALu3V1QMthRYKbBVPw9LgS17CksBpuVMFciKApYCmQpkKZBlHTSvmQqaaYEsBa2Rl+5OlYLmHACbcnBrSG9knWic21vn/gTceVAj6wAwEcXtR3FlKS7AElP96Tp7GfX7sbMP19fviu/9i+XO+Peqb6ubz2Ah9hOuL9DgMwEE52fjvv1BdcTWOfnKaR+BYzHMCZyxXBb3Vs6HAdV/Vwr6NgjRwXWecLUYJvzYgb5IQE8kQm/F8Uv2vX7s8ULu4Gl3zJA7XiiuZ8gI17InXMOeUC08kVp4QjXkC9eQJ1xD3nCt5o3UUIBMDlJEBTWTE7UIkijsjt1BohbREsk5paVF9SQtrCdqEQpSlIJalBI9ES3oj1pBirKuMQNxvS8U632xp/WhhRlgBSgmZ94+crAisHOuiRWBlH0nInLOO1EtgGrYYcW+IRJIOUcV5cxbfGgosRTImSdnnmLzCpplL+tuKDEPE0qUPdWdcKLzsZ2SsQhkAaSIyIIzz85pNeeAYjG5HRuw6js57M4A5zuDAinldG4o+7aSsD8/KSaCBfd7dLrBSIPFzBZATBosON1HztGYCXAyFbnHsrhzdeQeF5kaHk3BbsMBEECsnNrYPgjGp4SDUkTsiBk7ohMYse4uik8AThvso7GTkhH3vk5AiD/6x9bB/l3E2sfOKTXnPePTBLF7mI+tix3Jieyy7NRHiNuv/jPEPi+BiJgVFJgYDMXs1G+XtJcVKWaw+52BySkHBdYBgOuXibm+HENBKbtOe506XBDu0P67rRsgRAvrPOFqNAwAvZtU1r7HUMjpGYqSaUYMMxw1zLDpjdaZvmid5YvUsi9aY/nD1ewPV6tAuAqBaDUHwlXkD1dxIFpNgXAlfKFqzW/WIEBRTtTCSKAIB7UIglqEEimMoBZBkKJI1MKUoIW1AJlwAgi7U8sAlAG2EkAWwO4/Ry0GmbAPCnZXjx1SFKCZ9mkAsgCqA7QadsKIBTKj0JweE910ek4sBY+p4FcHBxM7kLA9Rf1BQAFkOVnJdIIGM8gCQdnBgpxp7MXKLs92sCBmO5Qxg9g+pwalQLDvPml3wrjfgwKg7IBC9jpyQxpbiEs9AOxlZkWkmfa/qDWTmRSBlEGaFfvOSFdgzQRpDJAF1hiaZgGaAnQTSrdYaSZbusmWEWXTq2A1Hsy40bVCCCE6oU4TrkZ9/vtwyNe1zB+p4mC4Av5wJftCVZwQ3k/+cBUFQhUUCFXCF6nUgtEaza9FKEgR3U9RMsjSnOBiWIDXZOgWgyyGZgG6AnSnRwVKgUwGTPdUEZPGgIoA1j6A98TCht27oWI9HFAKYAXdCRx2j1BcuFCmogZyAAAFPklEQVQWiNgJHk6gUnZQIHIDg4J9fo3tbhZYDFggtsAWk6aZzLAIikFk2vuTssOEZgKmU6/OIGcdkQLrSrPLWLHQYWkmmwYDvoO+6GMJGRJIhBBCdECdJlyF37lZC4L8JoBKAPudno64F5zwQAqkW2AyAY1BmgUNbm+GU8YNIzpiIYQNBdVIr8bhEkQTksWJhI/G9uUjbBNCiA6GiCYA+D3s8YbPMPNvGmz3AZgHoATAXgBTmLm8tdspOp5OE64+BzwA5zatNB9mXgghRHtARDqAPwI4B8A2AP8kojeZeV1csWsA7GfmgUR0CYCHAExp/daKjkZuNyKEEKIjGgbgS2b+mpkjAP4C4EcNyvwIwHPO/KsAzibnsk8hTkSn6bkCUAVgQ1s3QgghOrl/t9L7pAPYGre8DcAZhyvDzCYRVQBIAbCnVVooOqz2Eq4CRFTTDPUYsMd7C9FeyW9YtHs0g+5E8/yOA0S0PG75aWZ+2n2bRso3HOfRlDJCHLN2Ea6YuVlOXxLRcmYe2hx1CdEW5DcsOoJW+h1vA9A3bjkDwLeHKbONiAwAXQHsa+F2iU5AxlwJIYToiP4JIJuI+hORF8AlAN5sUOZNAFc68xcD+IDdhzIJcQLaRc+VEEIIcSycMVQ/B/Ae7FsxzGXmtUQ0E8ByZn4TwP8CeJ6IvoTdY3VJ27VYdCTUmUI6EV0Xdz5eiHZHfsOiI5DfsejoOlW4EkIIIYRoaTLmSgghhBCiGbXrcEVEfYloERGtJ6K1RPRLZ30yES0gos3OtLuzPo+IlhJRmIimN6hrAhFtJKIviehXbfF5ROdzHL/hHxHRF0T0LyJaTkQj4+q60im/mYiuPNx7CtHcjuN3PIaIKpzf8b+I6N64uuTvYtHutevTgkTUG0BvZl5JREkAVgC4EMBVAPYx82+cP5zdmflOIuoBoJ9TZj8zP+zUowPYhLjHJAC4tMFjEoRodsfxG04EUMPMTESDALzMzHlElAxgOYChsO/TswJACTPvb4vPJTqX4/gdjwEwnZnPb1CP/F0sOoR23XPFzDuYeaUzXwVgPew77sY/0uA52H/Iwcy7mPmfAKINqmrKYxKEaHbH8RuujrtUPAH1Nzz8PoAFzLzPCVQLAExonU8hOrtj/R0fgfxdLDqEdh2u4hFRFoDBAJYB6MnMOwD7Dz2AHkfZvbHHJKQ3fyuFOLym/oaJ6MdEtAHA/wH4qbNafsPipHAMfxcPJ6IyInqHiAqddfI7Fh1ChwhXzqmS1wDczMyVx1NFI+va7/lS0e4cy2+Ymf/GzHmwewF+7VbRWNHmbaUQR3YMv+OVAPoxczGAPwB43a2ikbLyOxbtTrsPV0Tkgf2H+QVm/quz+jtnDIA7FmDXUappymMShGgRx/sbZuYlAE4holTIb1i0sWP5HTNzJTNXO/NvA/DI71h0JO06XBERwb7D7npmfjRuU/wjDa4E8MZRqmrKYxKEaHbH+hsmooHOPiCiIQC8APbCvgv1uUTU3bki61xnnRAt7jh+x73ifsfDYB+L9kL+LhYdRHu/WnAkgI8ArAagnNX/Cftc/8sAMgF8A2AyM+8jol6wr6jq4pSvBlDAzJVE9AMAv0P9YxIebNUPIzql4/gN3wngCtgXZdQBuJ2ZP3bq+qmzLwA8yMx/arUP8v/bs2MTAIEgiKLTrmBkkVYlnIFWIBPc4XsFbLQsH5Zf+7DHe5ItyZVnj48xxvnOcotZ3tJxBQAwm6XfggAAsxFXAABF4goAoEhcAQAUiSsAgCJxBQBQJK4AAIrEFQBA0Q01gcJlA2r8RgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93efadd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93efaf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93e99f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93de7ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93db6f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, stats=['eroi_adj', 'mix'], to_plot=True)\n",
+    "for scenario_fig in scenarios+scenarios_dlr:\n",
+    "    if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "    else: tot_name = 'Total (PWh/a)'\n",
+    "    EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                                   'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "    InteractiveShell.ast_node_interactivity = 'last'\n",
+    "    colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "    fig = plt.figure(figsize=(7,5)) # 7,5\n",
+    "    _ = ax = fig.add_subplot(1, 1, 1)\n",
+    "    _ = ax.plot(years, EROIs_mixes[[(scenario_fig, year, 'EROI adj') for year in years]].loc[tot_name], \\\n",
+    "            label='EROI adj', color='black', zorder=20, linewidth=3)\n",
+    "    _ = ax.set_ylabel('Quality-adjusted EROI')\n",
+    "    _ = ax.set_ylim([11, 21]) # 5, 10; 13, 18; 11.5, 17.5\n",
+    "    # ax.set_zorder(1)\n",
+    "    _ = ax2 = ax.twinx()\n",
+    "    _ = ax2.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[(scenario_fig, year, 'mix') for year in years]].iloc[:15], \\\n",
+    "                  labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "    _ = ax2.set_title(scenario_fig)\n",
+    "    _ = plt.ylabel('Share in the mix')\n",
+    "    _ = plt.xlabel(scenario_fig+' scenario')\n",
+    "    _ = xticks(years)\n",
+    "    _ = handles, labels = ax2.get_legend_handles_labels()\n",
+    "    _ = ax2.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.1,0.5)) \n",
+    "    _ = ax.legend(loc='center left', bbox_to_anchor=(1.1,0.97))\n",
+    "    _ = ax.set_zorder(ax2.get_zorder()+1)\n",
+    "    _ = ax.patch.set_visible(False)\n",
+    "    _ = fig.savefig('Evolution of EROI adj in '+scenario_fig+'.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7rVq1qgUAMjMzFXFxcV0Syec6uEQiQVxcXFdGRsagzwKVSmU5cOBAYXZ2ds6ZM2fyX3zxxUCLxYKzZ8+qdu/e7Xnt2rXs1NTUwszMzKGMPS4JVwaA/UTUAWHQ+RWAx9kXFwR6AUICre11cpB23oDgNrZnL4RkyGbrvvvtLIzfhmD1t7ks7eOfIyEoFx0Qaka/zm5xkTFrHGcKhAdfCYAGANsguGEBoRrLdet3/wuAh5hQ6cQXwsOuDcJk7AwEa2z/9rMB/MEqXy0EC9SndofMBXDB2v4+CA+nkgHaaYWQFLYNwA0IFn/7ROwB5RzGed+HEBPcDsGDMWA1I6CvBGUcho5B54weQ92HAABrfPAJq6XzpjDGaiHcr2tuRRCr1+t/Idw7NUTUYX1tGOJ6BgiW5FwIk/02CEq5Hp9XlLkPQtWXgSyYnNHhcQD/ZoyVWz2WNUyo+/8agA12lvsfEFE7hLCgHRCqPt1hVQDseQtC6MlwvFP9nwtfgfAcOQjBkt0NYeJs4yEIYT3NEDxXDzDG6gGAMXYYQqLuKQghMWUQ8gLseQJAlp2S8hEEa3k9hJyWN4Yhs42NEEKLsq3yfIjhhcEN59wzECb7Hw/yGRjeGL4Xwv8pA0Luxpv9D7B6HlZA+G2rIHghfgur8YiP/+Ob/mFCTz31VLNt38aNG8N9fHxm/PWvf/V9/vnn6wCAMWbVib8IYwwDbbdhsVjoueeeC4yKiopbsmRJVF1dnayyslJy6tQpTXJycotWq7V4enpaVqxYMSHDyWgYz1TOMCGiTwB8i43ywmMcx0JEfwBQxBh73dmycBwLCSUL3wIwbzgKxBjIcwHANxhjWc6WhTP60OcrEC+1Wsw54ww+/g9OZmZm6cyZMxuGPnL0UKlUCV1dXV+aU82bNy/61VdfrZg/f373s88+G1BWViY/evRoUVZWlvyee+6JKi8vvyYWC1WfzWYzgoODpx85ciRfKpWy1atXRxYUFHzB2LR161avI0eO6D766KMSuVzOAgICpp88eTLv/fffd29ubpb86U9/qgKAJ598MtDf39/48ssv165fvz509erVrU888USzfVuZmZn6mTNnho7er+J4uGfAgTDGFnFFwPWw5mfwB8EEhAn1/ueOB0UAABhj87kiMHlgjBkYY3FcERi/8PHftZHL5exPf/rTjYyMDPWVK1cU8fHxhmnTpnVt2bKlzxO1ZcsWv/j4+K74+HjDYO20traK9Xq9US6Xs/3792urqqpkAJCUlNRx4MAB946ODmpubhYdO3ZsQlad4soAh8PhcDgcDmfc0T9nYPPmzf0LtUCj0bBNmzbVvvLKKz4A8M4775QWFBQogoOD44OCguILCgoU77zzTunNrvPkk082ZWZmquPj42N37tzpGRYW1gMAixYt6lq3bl1TfHz8tNWrV0+dN2/eF9aRIaJxYWgaKTxMiMPhcDgcDofzBcZDmNB4JikpKeI73/lObUpKyhfKi/IwIQ6Hw+FwOBwOZwLz1a9+NbS7u1u0YsWKoVacdwmGW//YqRCRBULVBQ7HVVEyxm5Z+eZ9n+Pi8H7PmazcVt/nuAb//e9/S50tgyNxCWUAQDdjbELWduVMDoiof3nC4cL7Psdl4f2eM1kZQd8fT1gsFguJRCIeTz5MLBYLAbAMeeA4g2utHA6Hw+FwOJz+ZNXX1+usE1zOEFgsFqqvr9cBcLmKca7iGeBwOBwOh8PhjBEmk+nJmpqabTU1NfHgxuPhYAGQZTKZnnS2ILeKS1QTIqJO7jLmuDK324d53+e4MrzfcyYrvA9zXAmu6XE4HA6Hw+FwOJMUHibE4XA4HA6Hw/kCaWlpUyQSyTYAPEzI9ekLYZozZ05d/51cGeBwOBwOh8PhfAGJRLLN19c31tvbu5lXFHJtrMnNcTU1NdsA3Nd/P9f0OBwOh8PhcMYQIvoXEdUR0YCVZ0hgKxEVEtFVIpo91jICiPf29m7jioDrIxKJmLe3dysEL8+X94+xPBwOh8PhcDiTne0AVt5k/yoAkdbX0wD+PgYy9UfEFYGJg/V/OeC83+XDhDZv3kwAFjpbDs6kpuL111+vGOuLbt682RNAzFhfl8Ox4+rrr7/eMdYX3bx581QAPmN9XQ4HADoK/bt3HP1x+kjaYIx9TEShNzlkDYAdTCj5eJ6I3InIjzFWPZLrcjgD4dLKABGRWCx++rHHHntGqVSanS0PZ9LyTwBvDHGMhIgu233+B2PsH7d7QSIiX1/fX65cuXIh7/scJ/J13HyBHYf2ezseBrDWAe1wOMODwSxpkpRIM9x69hZslALYMMQZI+37AQDsjUyV1m1OUwZ+8YtfxDqyvZ/85Cc5jmxvMLZu3ep1+fJl9Y4dO8rH4nquiEsrAwCWmM3m/9u5c6clKiqqftasWbU6nc7obKE4nAEwMcYSHdjer2pqajbt3r27OyUlpUCr1fJ+zxlTxC3iYmXOkIqoo/s9hzO2GNGkzFfWqtPU/qxTpvg/PKlrgK9sGGeOtO8PtOovD9nhjAqunjPwAwAwmUyi7Oxsn//85z/Tjxw5ElpXV6dwtmAczmhBRAkAfggAra2tyt27d8c0NTXJnSwWZzLAYJRWSfM8P/Cs1f9HH67OUIudLRKH43AYzOJGcZHuqK54yptT3N0+dou1dEpr/4pNmgb4eo+RFJUAguw+BwKoGqNrjytee+01r6ioqLjo6Oi4tWvXhuXn58sWLlwYFRUVFbdw4cKogoICGQC8++67uhkzZsTExsbG3XHHHVEVFRWubvAeM1xWGSAiAvAegGzbNsYYFRcXe+3atWva3r17I8rKyjSusMIyh3MrMMbSATwGoW4wOjs7ZXv27ImpqalROlcyzoTFjA5FviJH/5a+23OvZ7S0Ucrj9TkTDyOalNnKHP3b+nb9B/qpiiJFODESmYiubcWz/q3wch9DafYB2GitKrQAQOtkzBe4fPmy4tVXX/U7c+ZMfl5eXvYbb7xR/swzzwQ/8sgjjfn5+dlf+9rXGjdt2hQEAMuXL+/IyMjIzcnJyX7ggQeaXn75ZV9ny+8quKzWZE2q2X7vvfe+29vbez0zM9O3vr5eY9tfVVWlq6qq0nl5eXXOmDGjJioqqkUkclndh8P5AoyxnTExMYEFBQW/tlgsZDAYJKmpqdErVqwoDA4OHvOETs7EhHqpXnVV1ahOV4eRiRwaL8zhjAsYLOJmcYnmsobkxfJQYuRpt5NZRJb0P1qej+uC1qERB0T0HwB3A9ATUSWAnwGQCldl/wfgIIBkAIUAugA84cjruwpHjhxxS0lJafbz8zMBgI+Pjzk9PV196NChIgDYtGlT089//vNAACgpKZGtXbs2sL6+Xtrb2ysKCgoyOFN2V8JllQEbISEhDEBrZGRka2VlpTo9Pd23srKyT3tvbGxUnzp1auqlS5cM06dPr5k2bVqjVCrl7gKOy5OUlHQhIiIi/+jRoxFGo1FsNBrFhw4dikpKSiqKjIxsdbZ8HNdF1CEq11zUGBX5ijBiNFZhERzO2GFCs6JAUaO5pPETd4qnfvkA1guxMeO35p8kGKCQOvryjLGHh9jPAHzT0dd1NRhjIKJhzdmeffbZ4G9/+9s1GzZsaE1NTdW+/PLL/qMt30RhQpnKAwMDO1NSUooefPDBrIiIiAb7+rgdHR3yzz77LOTtt9+ecf78ed/u7m4e68pxeYKDgztWr16dJ5fLTYCwyuCJEycirl275uVs2TguBoNFUi8p8NjjUen9tnewMk85lRiNy2dET0+PyxuyOE6AwSJuEhfpjulKpmybotOd1sWKO8VfCv0hWDohMVx92fxy4mgoApzhs3LlyrZ9+/Z51tTUiAGgtrZWnJCQ0Llt2zYPAHjjjTc8ExMTOwCgvb1dHBwcbASA7du382fgLTAhB1QvLy/D8uXLy9rb229kZGT45OXleRuNRjEAGAwGSXp6esDVq1f9IiMj62fPnl2n0+l6nS0zh3O7+Pr6dq9duzY3NTU1qrOzU8YYwyeffBJqMBjEiYmJdc6WjzPOsaBHXi4v0XyqmSJpk0Q6W5yhIKJ7pVLp95KTkyv8/f27nC0PxwWweQEua/zEHQN5AT5HBFOjSWwp/YXpV3MYRANV9Jm0jFUpUHsSExN7vve971XfddddMSKRiMXHx3f9/e9/L3/88cdD//KXv/h6eXmZduzYUQoAP/rRj6oefvjhqT4+Pr2JiYmd5eXlvLDGMKHRSrAloiAAOwD4Qkh0/Adj7C9E5AngfQChAEoBPMgYax6irU7GmHqgfZs3b5YC+Oxm5xsMBtHVq1f1169f9+3u7v6Clk9ELCQkpHn27Nk1Pj4+3cP8ehyOPf98/fXXb7rOwM368O2et3nz5iUAfm/73N7eLt2/f39ka2trXyJxfHx89aJFi6qEfHsOxw4TWpQ5ymrNRU2oqFd0u8nnX/8Z+9mg6ww4ut8T0TcgrOtBSqXS+MADD2RrNBrTrbbPmQRYcwHUaWqRokgRMhwvl4y6bzSLNA2/N/945hCHtjKGpTc74Hb7/ngiMzOzdObMmQ3OloPjODIzM/UzZ84M7b99NF3AJgDfY4zFAlgA4JtEFAfgBQAnGGORAE5YP48qcrncMnfu3LrHHnvs2l133VWi0+n6Jv2MMSotLfX86KOP4vbs2RNZWlqq5RWIOK6IVqs1rlu3Lk+v1/clEGdlZfmdOHEi2GKxOFM0zjiCeqha86kmf8qbUzRun7jFjkARcAZnALQCQHd3t/Tw4cNTzWYz13Q5n2NCsyJHkaPfqW/Tv6+fqixUhg1HEVCLWgtrSN80DEWAw5lwjFqYkLUEVrX1fTsR5UBYPW8NhAx6AHgLwGkAW0ZLDnvEYjGLj49vmjZtWlNRUZFbZmamb11dnda2v7q62q26utrN09Oza8aMGTXR0dHNvAIRx5VQKpXmNWvWFBw6dCi8qqpKBwAFBQXeBoNBcs8995RIJBKu6U5GGJi4VVyi+UwjVpQqQgD4OVuk24ExVkhED0OotEL19fWa06dPBy1dupSvLDqZsXkBrqhJUagIJUYet3K6p6Q664p5tmQ7e3r6aInI4YxnxmSmS0ShABIAXADgY6uVa/07ZZBzniaiy9blvB2qtBARIiIi2tavX5+/Zs2anODg4C+EKTU1NalOnz4dvnPnzvgrV6549/b2co2AM2aMtO/LZDLL6tWri0JDQ5ts28rLyz32798fYTAYeF+eTDAYpTekeZ7/9azT/0cfblUExiXD7feMscORkZHHbJ/z8/O9r169ypMFJyMmtChy7bwABcrwW016D5QVpH9svluxnT0dM1picjjjnVFPICYiDYBdAJ5jjLUNN3aZMfYPAP+wttE5WvL5+/t3+fv7Fzc1NcmvXLniU1RUpLdYLAQAnZ2d8gsXLgSnp6f7x8bG1s2aNatOpVKZR0sWDgdwTN8Xi8XsnnvuKTl9+rQpLy9vCgDU1NS47dmzJyolJaWA9+MJjhkdikJFheYzTYC4WxztbHGGw630+6VLl541GAxzy8vLPQDgs88+C/Hy8uoJCAgYtWcFZ5zAYBG3iEvVaWqmKFSEEaPbXAiMmaIVVzK292zyPoJV41ZJ5nDGglG1EhKRFIIi8A5j7CPr5loi8rPu9wMwLqqdeHp6GpYtW1a+YcOGq9OnT6+WSqV9k6Xe3l5JZmam/86dO2ecPHkyqKWlReZMWTmc4SASibBkyZKKWbNm9S1h39TUpN69e3dMa2srL5c3AaFeqldfUudOeXOKTHdSFyvuFrs5W6bRgIiwbNmyUlv+l8VioWPHjk1tb2+fkBXyOLD3ArTq39OHKwtGUvrW0jVbcSZja88L/lwR4HBGURkgwQXwJoAcxtgf7XbtA/C49f3jAPaOlgy3g0ajMS1atKjqscceuzp37twKlUrVV3bUbDaL8vLyprz33nvTDx48GFZdXe1KiXecSQgRYeHChdULFizoi6lua2tT7NmzJ6ahoYGXXZsgiNpFZW4n3Iq83/TWay5rYshME95gIZfLLStXriyyGW5sCcUmk4knFE8UhFyAIrcTbsVTtk1x053SxYo7xLeUD9AfIlPzXYqDuT/s+WvIZ7iDL0rF4WB0w4TuBPAYgGtElGHd9iKAVwAhdNpdAAAgAElEQVR8YC0RVw7gq6Mow20jl8stiYmJdQkJCfW5ubkeV69e9W1paVECwop4ZWVlnmVlZZ6+vr5ts2bNqgkNDW3n5Rs545WEhIR6hUJhOnPmTBhjjLq6umR79+6NSU5OLvDz8+O12l0RBrOkQVKs/USrktXIJqV109PT05CUlFR85MiRSABoaGjQnD59OmjZsmU8odiVMaFFUaio1lzS+A61LsCtIBEZqpZK9zc83LMvrBRht69UEIyOksmVeD3+9VhHtrc5a/OYr1vAGZjRrCb0CYDBZsc3rc87nhCLxWzatGlNcXFxTSUlJW4ZGRk+tbW1fa73mpoat8OHD7t5eHh0TZ8+vTYmJqZJLOaLG3PGH7Gxsc1yudx8/PjxqWazWdTb2ytJTU2NXr58eWFoaGi7s+XjDBMLuuVl8lLtp1pfcbt43C8SNtqEh4e3zZkzpzItLS0QEKpn6fX6rlmzZvH66K6ELRfgipopCkaSCzAwCnF78TLxga7VhpOhNfC7/fA5ghH/ESolcjgTBR5fOUyICOHh4W3h4eFt1dXVqvT0dJ+ysjJP2/7m5mbVxx9/HJaWlhYwbdq02unTpzfIZDJe3J0zrggPD29LTk7OP3LkSGRvb6/YZDKJjhw5Erl48eLimJiYFmfLx7kJJjSrrqtq1JfUoSKjyKEWOldn7ty5tQ0NDSrbmHz+/PlgvV7fHRgYyBOKxzsmtCiKFNWaixpfcYc4fDQu4SarzV5sOY2k3s+mtsJ9JOG9ZmxHOb6GYIcJxxmS559/3u/DDz/09PPz6/Xy8jIlJCR06XQ687///W9vo9FIoaGhhg8//LBEq9Va/vWvf3n85je/8ReJREyr1ZovX76c52z5XQFeZvA28PPz60pOTi556KGHrkVFRdWJxeK+SX9nZ6fs4sWLQW+//faMTz/91L+zs5MrXJxxRWBgYOd9992Xq1QqjYCQfHnq1KmpmZmZemfLxvky1E03tGe1BVO2TXHTntPGiowutUjYmGBNKC5zd3fvBoTFJI8dOza1ra2NJ8qPRwQvQInbCbeiKdumuOlOjjwXYDB8lQUZd5k/pTtNaREjVAQs2IpibITDwpY4Q/Pxxx+r9u/f73Ht2rXsAwcOFF29elUNABs2bGjOysrKycvLy46Oju7eunWrHgBeeeUVv6NHj+bn5eVlHz58uNC50rsOXBkYAR4eHr1Lly6t2LBhw7UZM2ZUyWQyk21fb2+v+OrVq347d+6cceLEieCmpiaerMkZN3h7e/esXbs2V6vVGmzbzp07F3LhwgUfZ8rFscLAxE3iIvcD7mVTtk8JUGWpIokRjz+8CTKZzLJy5cpCmUxmBoCenh6eUDzeMKFFkafI0b+jb9X/Rx+mzB9JRaChYKZIzaUr8T2F8jvMlyJ7oBxZUv3PUYBvYdKH5Y01p0+f1qxatapFo9EwDw8Py/Lly1sAIC0tTTlnzpzoqKiouF27dnldv35dAQCJiYkdGzZsCP3DH/6gN5lMN2+c0wdXBhyAWq023XnnndWPPfbYtfnz55er1eq+CkQWi4Xy8/O933///fgDBw6EV1VVqZwpK4djw93dvXft2rW5Hh4efQnEV65cCTxz5kwgY3yhYqfAYJRWSnO93vdq0L+vnyovl0/KxODbxcPDozcpKanI9rmxsVF98uTJYN6fnYjNC3DSzgvQPjpegM+xdM92O37Vs6NX+RX2abQRspF56L+LHPwULrFex0RjsHv36aefDnvttdfK8/Pzs7ds2VJlW1Dz3XffLf/lL39ZVVFRIZs1a9a0mpoabkQZBlwZcCAymcwye/bs+g0bNlxbvHhxsf0kCxBWgd27d2/srl27ooqKitz4A4rjbDQajWnt2rV5U6ZM6bBty87O9jl69Gio2czXJRszzGhX5Cpy9G/pezz3e8ZImiXezhbJVQkLC2tPTEystH0uKirSZ2Rk8N9zrDGhRZGvyPF616tF/x99mDJvNL0An0Nkblmi3ZXX3hasXIWjsRaIR3bNx5GDP4Dn6DiJu+++u+PIkSO6rq4uam1tFR0/ftwdALq6ukTBwcFGg8FA7733Xl/+5vXr1+VJSUmdf/7zn6s8PDxMxcXFE77MsiPg8eyjgFgsRlxcXHNsbGxzWVmZNj093bempqavekFdXZ326NGjWp1O1z1jxoya2NjYZrFYzDUDjlNQKBSW++67L//w4cPhlZWV7gBQXFzsdeDAAfGqVauKpVIp75ujBBmoTpWpalZnqMPJTHzC4SASExNrGxoaVKWlpZ4AcOHChSC9Xt8VFBTEE4pHEwaLuFVcpr6itijyHV8RaChEIkPNGtU7Dafa18qfw19Gfj+tRQ62c0XAhjNKgS5evLhr5cqVrXFxcdMCAgIMM2bM6NTpdOYXXnihat68ebEBAQG9sbGxXR0dHWIA+M53vhNYWloqZ4zRokWL2hYsWNA91jK7IlwZGEWICKGhoe2hoaHtNTU1yvT0dN+ysjJPm0egtbVVefbs2S9UIJLL5bwCEWfMkUqlLDk5uej48eOhxcXFXgBw48YN93379kXde++9hQqFgrsJHIioXVSmuaAxKwoUYQSa4mx5bgeR2FCt8ygal+MVEWHp0qVlH330kaK5uVllTSiOeOCBB7Ld3NwmZY34UcWEVkWxokp9Se0jaZOEOUMEmaSj5EH5duObHd+R/gYvjjykJwm52M0VgfHAz372s5o//vGPVe3t7aKFCxdG/+AHP6hdtGhR15YtW+r7H3v06NGigdrg3BweJjRG+Pr6dq9atarkoYceuhYTE/OFCkRdXV2yS5cuBb399tszzp4969/R0cGVNM6YIxaLsWLFitK4uLha27a6ujrNnj17onifdAAMZkmtJN9jl0eV907vEGWBMpzgeisVksjY4O2bVhAes98tMAnj1mtkTSgushV2MBgMkkOHDk01Go0u95uPS4RcgGJrLoBGd0IXK2mTeA59ouNRy+tzNkreoN90/kbkEEVgLvJxFFEOEI3jAB599NGQmJiYuBkzZsSmpKQ0L1q0iC+U6WD4A36McXd3712yZEnF/PnzqzIzM71zcnJ8DAaDBACMRqM4KyvLLzs72zc8PLxx9uzZNV5eXoah2uRwHAURYfHixZUKhcJ45cqVQEBYQ2P37t0xKSkp+e7u7r1DtcHphwXd8hJ5qfac1k/cIXbZCQaRucVDn1Pjqc8JV8RENXg88WuL2M1tXCfnubu79y5durT48OHDUYwxNDU1qU+dOhW8fPnyMr5i/G0ieAGq1ZfU3pI2yaisC3AreKpLrj5i/K/+fw1vGXbhgYgRNxiHIpzDVIi5sXS8sH///hJnyzDR4cqAk1CpVOaFCxfWJCYm1mZlZXllZWX5dnR0yAGhAlFhYaG+sLBQHxQU1JKQkFATEBDAY105Y8b8+fNrFQqF+dy5cyEA0NHRId+zZ09McnJy/pQpU3qcLZ9LYEST6rqqVn1ZHebai4RZOnWeheX6KVcjJJ5uKq+nn2+UhYbGOFuq4RIaGtqemJhYcenSpSBASCjW6/Vds2fP/lKIAWcQbLkA6WqzIk8RRozGwf+fmQN1mVfXdx7xX2/a33sSS0cenhSGUqQhGBKMayWXw3E0XBlwMlKplCUkJDTMnDmzIT8/3z0zM9Ovqampr/xoRUWFe0VFhbu3t3fHrFmzasLDw1tFIm6w4Iw+M2fObFAoFKbTp0+HWywW6u7ulu7bty9m5cqVBXxl18GhLrqhuazpUmYrpxIjp4RNOAZm0OrKi719r4RKFPB1u/+rZZrFi0dueXUCc+bMqWtoaFCVlJR4AcDFixeDvLy8ukNCQjqGOndSY0KrokRRpb6onuKsXICBsfREe57NSW45H7jEctZ8BXOCRtykPypwFX5QgC9Ux5l0cGVgnCASiRATE9MSHR3dUl5ersnIyPCtqqrS2fbX19drjh07FuHm5tZjrUDUJJFIxm28LmdiEB0d3SKXywuOHTsWYTKZREajUXzw4MGopUuXFk2dOrXN2fKNGxgs4mZxifacViqvkAc7W5yRwUwqTXWhj/9lf4nUMFU1f36R+0MPTSWZbJRrw48etoTiXbt2KW0JxSdOnJi6fv36bJ1OxxOK7WFg4jZxqfqK2qLIU4QSG19VrojMrXM8DxTf1ZgXMBfplI9o3xE36oUqXIceGvDFQTmTEq4MjDOICCEhIR0hISGFtbW1yvT0dJ/S0lJPxhgBQFtbm+KTTz4JTUtLC4iLi6udMWNGvUKhGJcVPTgTg9DQ0PbVq1fnHTx4MLK3t1diNptFx44dizAYDCVxcXHNzpbPqTD0yiplxdpPtV6SZslUZ4szMphFoWws9A24oJfKOmOkQUEFnk89pZfo9eNqMni7SKVStmrVqsJdu3bFGQwGicFgkBw+fDji/vvvz+XlcwGY0aYoVtwYf16AzxGJemsXe3xYO72xzm8acmSVCBp56VIdapENN7hD6QARORyXhCsD4xgfH5/ulStXlra2tt5IT0/3KSgo8DaZTCIA6O7ulqalpQVmZmb6RUdH1yckJNRptVpu4eKMCn5+fl1r1qzJPXDgQFRXV5eMMUZnzpwJNxgM5QkJCZMv9tqMNmWe8obmgiZI1CMaB/HTI0Mmbyn0DbiolStaokRqdbXH45vLFPHxkc6Wy9HodDrj0qVLiw4dOhTFGKOmpibViRMnQu65557SSZlQLHgBylTpKpMyVxk23rwA9kgknaXJ2ne7/BuNnhEo1DbBSz3iRtVowjXIMQUaB4g44Tlw330O7R/37tt32+sWJCQkxKSnp+fm5eXJVq9eHVlQUHDdkbJNNrgy4ALodDrj3XffXTl//vzqzMxM7+zs7L4KRCaTSXz9+nXfnJwcn7CwsMbZs2fX6vV6nuDJcTh6vd6wdu3a3NTU1Ki2tjYFAJw/fz64u7tbsnDhwurJMJkiA9Wq09WtqquqsImwSJhE2lnq639BolQ3REAs7tCuvDdXu2pVFE3gxKSQkJCOuXPnVly8eDEYAEpKSrzS0tK6EhMT65wt25hhRru8WF6puaiZImmThDpbnKFQKBrzHpDuFFGzuyoCOfoOaBUjbxQtyABDEMZ0YTSOY0hPT891tgwTCa4MuBBKpdK8YMGCmjlz5tRmZ2d7Xbt2zbe9vb2vAlFRUZG+qKhIHxAQ0JKQkFDDV9vkOBqdTmdct25dbmpqamRjY6MaADIzM/17enokd999d8VEnUOK2kSl2vNai7xIHkYgH2fLM1LE4p4bU/wuGzVuVaEAzIr4+FyPxx8PFqnVLu/lGA6zZ8+ub2hoUNsW2Lt8+XKQt7d3d0hISLuzZRtVGJjshixfd0QXLOp1jQpXOk3Zta9ZPtA1tIf1zsWlAAMUI0/wlaEdF9GLCLjkgn+TjZdeesnnnXfe0QPAY489Vv/Tn/60TqVSJXR1daU7W7aJAlcGXBCpVMpmzpzZMH369IaCggL3zMxMX9vEDBBWjr1x44a7Xq/vnDlzZnVERASvQMRxGCqVyrxmzZr8gwcPTq2pqXEDgLy8vCkGg0GyYsWKUrFYPDHirxlM0jppsfYTrVZaJw11tjiOQCTqrdX7ZLbrPEoiAECs15d4PvWUShYUNCmUABtEhKSkpLKWlhZFU1OTmjGG48ePh69fvz5noq6lQQaq1R3VGeSV8pEvyjUmMIuPR1bW17pSfa4bElsW40ykBeKRP8gk6MLH6MB0+DlASM4oc/bsWdW7777rlZaWlsMYw5w5c2KXLl06sZV2J8BniC6MSCRCdHR0y1e/+tXce++9Ny8gIKDVfn9DQ4P6xIkTEe++++60zMxMvclkmvhxHJwxQS6XW1JSUgqDg4P7EohLS0s9U1NTp/b29rr2uGJBl7xQnqPfqe/w/MgzSlondflJA5GpyWtKZl549B5vnUdJBMnlDe4bNxb7vvxymCwoyOU9HbeDVCplK1euLJLL5SYA6O3tlRw+fNj1+29/GIyKXEWO93ZvL3mlq1S6YoZQ73NZj7XvDT5rWNH0FXwc5RBFQAQDDqMR87ki4CqcPn1ak5yc3OLm5mbR6XSWe++9t/nUqVNaZ8s10ZhYg94khYgQHBzccd999xU+8MAD16dOndpARH3W2fb2dsW5c+dC3n777ekXLlzw7e7u5guqcEaMRCJhq1atKo6MjOxLIK6qqtLt3bs30iX7mBGNqnRVrve/vEXux9xjxR1i148lJnObh1du7tToPW6e+rxoElGv+q67cvx+9zudesECp68e62x0Op1x2bJlRbbxsrm5WXXixIkQxiaGc0vUISr3/MCzWXdKF0sWco1IADK3xfocyXu48UTke6aN1WuwL5ZBNHJDFsGIXajGUox8TQLOmDFR7sXxDlcGJhje3t49K1asKHvkkUeuxcXF1UgkErNtX09Pj/TKlSsBO3funHHmzJnA1tZWvrgKZ0SIRCIsXbq0fPr06dW2bQ0NDZrdu3dHt7e3u0T/EnWKKrWntYVT3pzioT2vjREZRSNPTnQ6lm439+KcqVF7ZXqfqzEksohlYWH5vr/6lcH94YdjSSp1if/NWBAcHNwxf/78Ctvn0tJSz8uXL7t2LLkFXeqL6jz92/ogaZPUZb6LSGSsm+O7t/T+uguxf7Q8X/Yk3nRUXoMZ21GOtQh1UHucMSIpKanj4MGD7u3t7aK2tjbRwYMHPZYsWcLDhByMa1gKOLeMm5ubcfHixTfmz59fc/XqVX12drZPd3e3FABMJpMoOzvbJycnZ0poaGjTzJkza/V6fQ+vtc25HYgIixYtqlIoFKZLly4FAUBra6ty9+7dMatXr8739PQ0OFvGgRC1i8rcTruJ5JXyCWQpZEa19kahj9/lILGkNxYARFptpec3vgF5VFSUs6Ubr8yaNau+vr5eVVRUpAeEhGK9Xt8dFhbmcpMOSb2kwP2gu4+4S+wiuQECEklX+Z1eH7Z+pbo47vv4Q/Gf8F1H5bFYsBXF2IgJVyp3rBlJKdDbZdGiRV2PPPJI4+zZs2MBIYH4zjvv7B5rOSY6XBmY4CgUCvO8efNqZ8+eXZedne157do1X1tZSMYYlZSUeJWUlHgBgFgstigUCpNcLjfJ5XKjQqEwKRQKk1KpNCmVSqNCoTCpVKq+l1wuN0+GcpKc4ZGYmFinUChMn3zySRhjDJ2dnbI9e/bEJCcn5/v6+o6fwduEVu15bZ3qmmoCTQ6YWamqL/QJuOgjlXYJ1lSJpM0tJaVGs2xZJPEb9aZYE4rLW1palLZiDCdPnnSthGITmt1OuzUpC5Qu16/liqb85ZoPTAm1tTFfx1tlb2Oj4xTXn6MA38K4VIyIaCWAvwAQA9jGGHul3/5gAG8BcLce8wJj7OCYC+pkXnrppdqXXnqp1n6brZJQdHR0L19jYORwZWCSIJFI2IwZMxrj4+Mbi4qKdBkZGb4NDQ1fWGjFbDaLOjs7ZZ2dnbLhtElEzKo4mKyKQ38FwqRUKo0qlcqkVCpNKpXKxKsaTWzi4+Ob5HK5+eTJk1MtFgsZDAZJampq9IoVKwqDg4M7nCocg1leKi9wO+EWKjKKXG7CNDCMyRXNhT4BF93l8jbbhMeknD27wP3RR8NECgX3BgwTiUTCVq5cWbRr167Ynp4eaW9vr+TQoUMR69evz5XJZON3lXcGi6xMlq87rgsVGUUutwq2RlNxPUXykTS8oS08BfurDuJex32H7yIHP8W4LKFKRGIAfwOwHEAlgEtEtI8xlm132I8BfMAY+zsRxQE4CPBQJ47j4crAJEMkEiEyMrI1MjKytbKyUp2RkeFTX1+vMRgMEsbYLVkPGWPU09Mj7enpkba2tg59AgCZTNanPNgrEXbKQ58CoVarTRKJhIcuuRiRkZGtcrk8/+jRoxFGo1FsNBrFhw4dilq6dGlRRETE8DqKgxF1iMrdD7nLpA3SCVNCUyprL/bxv6BQqpr6FBuJr2+R59NP66S+vuNyAjTecXNzMy5btqzowIED0YwxamlpUR4/fjxk1apVJePRuUI9VO1+yN0iq5G5YL9mFi/PnOx1vQe13i0Gr8U403gOd4Y4rPnHkYM/jE9FwMo8AIWMsWIAIKL3AKwBYK8MMABu1vc6AFVjKiFn0sCVgUlMYGBgZ2BgYDEgZOwbDAZxV1eXxPbq7u6W9vT0SLq7uyU9PT2Snp4eicFgkBoMBklPT4/EbDbfspm/t7dX0tvbK2lvH14orkQisfQPW+qvQFjDloxKpdIkl8st4/GhDUBCRJftPv+DMfYPp0kzygQHB3esXr067+DBg1EGg0FisVjo+PHjET09PSXx8fFNYyaIGW2a85oa9VX1hLGQiyXdFT5+lyxqbU1fNSBSKms9Hn20S5mQMN4swy7X74OCgjoXLFhQ/tlnn4UAQFlZmeelS5e65s2bVzvUuWMGQ68yS1ms/VQbSYxcr3IXmCHA50Lh/a2n9coeKOYgrSsL0wMc1vxa5GC70xWBofp+AIAKu8+VAOb3a+MlAEeJ6FsA1ACWjYagHA5XBjgAhJhZhUJhVigU5uEmfBqNRurq6pLaKQ82xUFqUyAMBoPEYDBIe3p6JEaj8ZYfWiaTSWQymYYduiQSiexDl4w274NSqRxIgTAplcqxCl0yMcYSx+JC4wVfX9/utWvX5qampkZ1dnbKGGM4e/ZsWE9PjyQxMbFuVC9uC504oQsR9YomhCIgEhuqvX3Su9zcyz+f8ItEXZqkpHK3NWsiSSwej5NCl+z3s2bNamhoaFAVFBR4A0BaWlqgXq/vDg8Pb3O2bOJWcYn7AXc3SavEBb0BAMjcFu5/unJd3Xl/i1FhikMmK0WY4yoeJSEXu52uCABD9/2BrFb9PeEPA9jOGPsDES0E8DYRxTPGxm/YGscl4coA57aRSqVMp9P16nS6YSXYmc1m6urqEnd3d0u6urqkNuXB5oGw8z5IbDG7t1pj2GKxUHd3t9RaOUk5nHNsyoNNgbD3QFgVB6O9AsFDl4aPp6enYd26dbn79++PbG1tVQLApUuXgnp6eiR33nln1Wh4cUSdogr3w+4Sad3ECAkiMjbop2Q16TwLIoj6ykFb5NHR+R7/8z+BYq12QnzP8cbdd99d0dzcrGpoaOhLKPbw8Mj28PBwTkKxGR2a85oqV/ZykchYHxtwqP6+qsyQDrOuLR5Z6hr4uQ195jCZi3wchav8PpXAF9Y8CMSXw4C+AWAlADDGPiMiBQA9gNE1pnAmHVwZ4IwZYrGYabVak1arNQHoGep4xhh6enrsQ5f6wpZsHgg774PEYDDcVuiS7dzhHi+RSMz2OQ8Gg2Hj3//+93AAv2SMuVwpwtFGq9Ua161bl5eamhphS1q/du2aX09PjyQpKancYZ4ZM9o0FzU1qgxVJGF8xordCkTmVg+v3GpP7+wIIqa3bRe5u5d7Pf20VBYaypWAUcQ+obi7u1tqzX2JeOCBB8Y8oVhaI813P+QeIOpxXS+XWNJdMdNvb1tyZV5UNfOvjkeWdyvch2WwGRZxKMI5TIXYZdZPugQgkojCANwA8BCAR/odUw5gKYDtRBQLQAGgHhyOg+HKAGfcQkRQKpVmpVJp9vLyGlboUm9vr6h/3oN9zoP1JbUpALcZuiTu6OgQd3R0yK2b7rC+Xr7VtiYLSqXSvGbNmoJDhw6FV1VV6QCgoKDA22AwSO65556SEXlbGCyyClm+7rguWGRw3cnS51g6dR5F5Xqfq+Eikblvwk9SabPb/fc3ahYvjnCmdJMJrVZrXL58eVFqamq0xWKh1tZW5bFjx0KTk5OLxyI3iYzU4HbcrUNR6tpVoWTylsL5+j2GJRWlsQWILJ2JzMAeKIcV+jkswlCKNARDgvEYKjcgjDETET0L4AiEsqH/YoxdJ6KXAVxmjO0D8D0A/ySi70AIIfo6c+KSvJcuXXJo+NXcuXPHZN2CrVu3el2+fFm9Y8eO8pG0c/XqVfm3vvWtoJKSEoVEImExMTHdb7zxRnlQUJDp1KlTqueffz6ooaFBSkRs3rx5Hdu2batoaWkRbdy4MbSqqkpmMpkoMDDQcObMmUJHfTdHMagyQET78eX4tT4YY/eNikQczgiQyWQWmUzWO9za4CaTiaxhS/Z5D19InLblPAzhQehmjHU68KtMOGQymWX16tVFR48eDS0tLfUEgPLyco/9+/eLk5OTi+Ry+S1bW0WdokrdYZ1IVueK1VT6wwwat4riKX5pIWKx8fOHLlGvav78IveHHppKMpmHEwWclAQEBHQuXLiw7NNPPw0FhD578eJF3/nz59eM2kUZzPIieYHupC6czKQf+oTxi1pzI2exej+be6NmWhpmFyzEZ2FGyBxniPRHBa7CDwq43Kra1jUDDvbb9lO799kA7hxruThfpquri1JSUiJ/85vfVDzyyCOtALB//35tTU2NBAA2bNgwdceOHcXLli3rtFgseOuttzxaWlpEW7ZsCUhKSmr7yU9+UgcAFy5ccJw3zIHc7IZ8dcyk4HCchEQiYVqt1qjVao3DOd5isdhCl6Q25eHGjRuf5eXlXRptWScCYrGY3XPPPSWnT5825eXlTQGAmpoatz179kSlpKQUqFQq87AaMqNDc0lTpUqfCCFBzKRS1xT6+F/yl0h7vmB5kwYFFXg+9ZReotePh4TIScuMGTMa6+vr1fn5+d4AcOXKlQC9Xt81depUhycUi7pEle4H3cXSelfPeWEWD8+83OU4KomtbYo6gaTcFTgaZYHYcWE8elThOvTQQD70wRxX5LXXXvPaunWrDxEhNja2e8+ePSX5+fmyxx9/PLSxsVHi5eVl2rFjR2lkZGTvu+++q3vllVf8jEajyMPDw/T+++8XBwUFmQZrOyoqKu7cuXN5np6eZk9Pz1m//OUvK5599tnGtWvXhn39619vWLt2bV/Y7z/+8Q/P2bNnd9gUAQBISUlpB4DnnnvO/8EHH2xctmxZJyCUcH/iibAsFVAAACAASURBVCeaAaCmpka6YsWKvnPmz58/fhbgtGPQm5IxdoYxdgbABQCNABoAXLDbzuFMOkQiEVQqlVmv1/cEBwd3REdHtyQlJX3CGNvqbNlcBZFIhCVLllTMmjWrL1muqalJvXv37pjW1tabW/eEkKA877e8SZ2ujnJtRYBZFMrG/JCIQ60BIWdjJNKevkRKkVpd7bV5c9mUH/4wUqLXc2/AOGDx4sUV3t7efQvnnTp1Krypqclxk1ALelTpqlz9Dr2/tF7q57B2nQLr9fW9lL/WeEAZ29QU9T4ezFmGEzEOVQR0qMV1uMF9eIUiOK7H5cuXFa+++qrfmTNn8vPy8rLfeOONcgB45plngh955JHG/Pz87K997WuNmzZtCgKA5cuXd2RkZOTm5ORkP/DAA00vv/yy783aT0xM7Dh+/LgmLS1NERgYaPjkk080AJCenq5esmTJFzz9WVlZytmzZ3cN1E52drYyMTFxwH3f/OY36771rW+Fzp8/P2rLli2+paWl49KDNeiNSUQSIvodhIz3twDsBFBBRL8jonH5ZTgcjmtARFi4cGH1ggUL+mI429raFHv27IlpaGgYcIJFXXTDY49HtUeqR7TIIFKPnbSORyZrKwoKO1YXFHYiSibr8OrbIRZ3aO+9N9f3t7/1UcTHO24BJgcjr5aXRr4aWXhHyh2TpsShLaFYqVQaAcCWUGwwGEY8wRU3i4v07+i7tee1McTIVRJgB8HSERR4qvT+1lNuwe3tYX/FszkP4X3HerbUaMQ1yDEFGoe2yxlXHDlyxC0lJaXZz8/PBAA+Pj5mQJisP/30000AsGnTpqa0tDQNAJSUlMjuuuuuyKioqLitW7f65ubm3lRRvOuuuzrOnDmjOXHihPbJJ5+sy8nJUZaUlEh1Op1Jp9M5ZGxbv359W2Fh4bUnnniiIS8vTzlnzpy4qqqqcZeve7NB5/cAPAGEMcbmMMYSAEwF4A4eQsThcBxAQkJC/d13311MRAwAurq6ZHv37o2prq5W9R1kRof6ojrPe4e3n6xG5riFiZyARNJZFhB8uiIk4vBUhbLF3mplVsTH5/q98orI7d57Y2iMFr+4FchEnV6feuUkfj2xfsHDC0L9U/0jpO3ScSfnaKLRaEwrVqwoFIlEDBAU2GPHjoXddk6nGW3aM9pC/Xv6qeIOsct7gEhkbIgIOVT71drPvPTd3b4/xi/y/h/+6lhFQIEWZAAIgrtD2+WMOxhjsD0bhsOzzz4bvHnz5rr8/Pzs1157rWwoRX358uXt58+f13766aeaFStWtHt5eZl27tzpsWDBgo7+x06bNq3nypUrqoHaiY2N7b58+fKA+wBBiXnmmWea9uzZUzJjxozOo0ePjjsl9mY/1GoAT9mXSmSMtQHYBCB5qIaJ6F9EVEdEWXbbZhHReSLKIKLLRDRvJMJzOBzXJzY2tnnFihWFYrHYAgirVKempkaXlpRqpJXSPO8d3tCkaaJd2WIqFvfc8A08VxIWdSBEpakL+sI+vb7E+4c/bPDavDlGpFYP+kBxFtImaWXYG2F5dybfKY3/UXysulTt7WyZnIm/v3/XwoULy2yfKyoq3M+fP3/TcIQvwcCkldJc77e8Japs1YSoDiWWdFfGBu9tXl95xUdtNOk24e9Fv8KPox16ERnacRG9iIDX0AdzXJ2VK1e27du3z7OmpkYMALW1tWIASEhI6Ny2bZsHALzxxhueiYmJHQDQ3t4uDg4ONgLA9u3bh+wjERERxubmZklJSYkiLi6ud+HChR1/+9vffL/yla98SRl46qmnGtPS0jTvvfeezrbtww8/dLt48aLy+9//ft0HH3zgdfLkyT6P9euvv+5ZXl4u2bdvn7a9vV0EAM3NzaKysjJ5WFiYc9YquQk3c1WwgUpYMcbMw9TUtgN4DcAOu22/A/BzxtghIkq2fr57+OJyOJyJSHh4eFtycnL+4cOHI41Go9hkMomOHD4SKYdc4gnPFmfLd7uIRP+fvfOOj6pK///73OmTzEx6rwSS0EEQEBEUyyLFvoqiYPe7Wd1dXXUt+11X9/vbpu6u7q6KXUQFF1SKiogoVZAiBEJ6D4RAQnqZZOae3x8JLCWNMEMSuO/XK68p995zn5mcufc85zzP52k5HBS6u9rul5cgxIkVR4XJVO43e3a1dfz4hI6O7zXcOB2pjoL4N+NtjjRHVG+b09cYMWJERXl5ufVoEvyuXbsig4ODGwcOHFjd1bHCKcocqx1OU4mpnycI/xejqSpnWPjn6tWFObFCCm5hcfESfjrIoyfR08B66hhOP8+n6L+cLSnQo4wdO7bp17/+dekll1ySrCiKHDZsWMPSpUsLXn311aJ58+bFvfTSS2FHE4gBnn766QO33nprQmhoaPPYsWPri4qKuszpGTVqVL3b3apbcemll9b+6U9/irziiitOqRfk6+srly1blvOLX/wi+je/+U20Xq+XgwcPbnz11VeLoqOjXQsWLMh77LHHoioqKgyKosgJEybU3XHHHVXbtm2zPvzwwzE6nU5KKcUdd9xRPmXKlHbzC3oT0dHyphDiM+ATKeWCk96/Hbi5O9KiQog4YKWUcljb669o1dJdLIS4FZglpTy5yEZ77dRLKduNEU5JSTEA33fVhoaGF3njlVdemd/ZDp314Z4el5KSchmt4XznDGUHy+QXy78Y2uRusgAIBFdxVcFFXFTR27adDkK4KgOC0g/5B6UPOq5q8NGNTT6TJuU7brppoDAY+lT+la5WVxa5LLIy+qPoWH29vjuJmXci2dvRRm/0e4CUlJTfAtedbruexO12i88++yzx0KFDvtBajPCGG25I77AmisRlzjRn29fZBwlV9LmY4Z5i9T2QMTpgNZcXFSS60DdNY1XFWi6P7vrI00DByWoOcTmebbfnVMvWYmAd0tO+35fYvXt3wciRI8t72w4Nz7F79+6gkSNHxp38fmcXpJ8Dnwgh7gZ20Fpz4ELAAlzfQzt+BXwlhHiB1hCliT1sR0ND41xCIg2lhuxhXw2LiHBH5LzP+4nVVJskkq/4Kq6BBv3lXF7W22Z2jVrnF5BdHBiyZ6CiqCfHgEtjfHx2wH33her8/PqOVKiKyzfbNz/u7Thj4NbAWCC0t03qD7TJ5OYuXbp0cENDg9HlculWrVo18MYbb0w3m80nJB8qdUqR3+d+ZsMRQ9/5v58xUvoFZGWMN33HhKLSwU2Yai5hQ/12LvTsgF3QwlJKuZw4j7aroaFxjA6dASnlfmC8EGIqMBQQwJdSym/O4Hw/Ax6WUi4VQtwMvAVc0d6OQoj7gfu7slND41zjfOv7olEccHztcJv2mxIBgghqvod7MhawYFA55VaADWyIaqDBMJOZJSdF2/QR1CabozA/JOzHeEXnOmXAp9hsJQH33IMpMbHPVJJVGpWKsK/CDsUuiI00HjF6NqSjB/THft+WUJy7fPnyZFVVxdGE4hkzZuQqigIqDT7bfYp9dvR3GdyTkS0hYTvyLnFtUYaVlifVY60Yy3aZwWBPh/C4eZcirqPvhdJpaJxDdHnBlVKuBdYefS2E8AN+LqX8fz043zzgl23P/wO82cl5XwdebzunVtlV47zhvOn7Kg3WH61Fvtt8E09ODrZjd93DPZnv8/6gAxzwBdjBjtBGGnU3cmOhDl3v2HwKssXH90BOSMT2aL3eeeqsr15fY58166DvFVcMEqIPDAYlqqXIkh/3XpwI/jY4TkjRZxIx+2u/Dw8Pb7j44osLNmzYEA9QUlLit2XLlvDJiZPX+33uF6pr0Hk2ibbXUesio9cfvqJ6pyGupmZAJX5lI0g1lRDtaXUflZfJYy697qhqaJzrdOgMCCGigf8FIoDPgA+BPwBz2573hAPAFOA7YCqQ3cN2NDQ0+isSaThoyHZ85QjTNeo6TKK0YFHv4q6sj/hoQB55fgD72BfURJP+Vm7NM2DooZ6jJ5CqxXo4OzTihxCDsaG90A+X5YILsv1uvz1eMZt7fTVAtIia4HXB++Peigu1lFq0WVYPM2zYsCOHDx/2ycjICAHYvXt3RMLuhLGBBHaZUNyfEMJVERf3dd20sj2GkIaGqDJC9g8lzVFBkOelEp8lm4c4xxwpDY2+SWcrAwuAdcBSYBqwBUgDhkspD3bVsBDiI1qVgoKEECXAM8B9wEtCCD3QxH+XhDU0NM4DRJModXztaDGVmLo1QDZgkHOYk7uUpXH72BcIkEee37u8O+h2bs+xYDnLRa+kNJqqc8Mit9pN5up2Byr6sLDcgPvvdxjCwno9PtxUZiqM+TCmOWxlWLziVjxoj7MGNpfCJyr803PN9mMmXzK5sHx/uVpeWx4GsIIVA4IJTg8nvKm3bfMEOl1TyYDYVXJGyT6ro7k5uIDYwuHsCa3DZvb4yR4hnd/R678fDY3zhc6cgQAp5e/bnn8lhCgDLpRStq+UcBJSyls72DTmNOzT0NA4F1BptO62FvpuPTUkqCt06PgpPy1YyUrXDnaEAuxnv+1t3k66gzuy7dhd3jH6RAyGuvzQyK1Gi7WiXV14YbGU+d9+e4Nl9OjenXl30xjwQ0Bh/Bvx/r55vh6sYlxbDl+Xw6s+sDYS1CRA0ZyBVic36MsgdV7tPOPrvB5QS62xhRZlMYsT7uf+DCtWd2/beCYYjTW5A2NW6WfmZ/pb3G57GkNyx7AjxonZ82pY80jnRc0ROF2EEO8DD0opq9tex9Kq3tip6pGGBnSRMyCE8Idj2XoHAasQwgdASnnEy7ZpaGicAxjKDFmOVY4wXUPHIUFdIRDMYlaJFWvLBjZEARzmsPUt3kqey9ysQAK9VsRFp2ssCYnY7va1lca3u4OiNPhOnVpkv/baQUKn67VkBkOlYX/Ukqj6yCWRsTpnz7/rEzl0AJZXw6sBsDMUCPJMu+cIkmbLXkuebZNtkJBCZ8To+ik/zVnAgmQXLqWKKvPHfBw/l7k5Sqc1PvsuVp/SjKTQbwxX5+WFGlTVvJmLsi5hw0AVnec/0HWk867mCPSQjcBWIcQjQCTwGPBrj55hmIf/N3s5q3ULekJ1dbXys5/9LHrDhg02k8kk/fz8XH/9619Lpk6dWl9UVKRPSUmJ2b17t9VoNMqoqCjnP//5z+KhQ4c677nnnuhNmzbZhRDSaDTKJUuW5CYnJ/e5YmNH6cwZcNAqKXp80tvOtkcJDPCWURoaGv0f4RQHHWscTlNR90KCusPlXF5mxepezepYiaSaatPbvJ08hzlZEUR4NBxDUZrLgsN21dn9Cjqa6VdNSUlZ/nffHaWz2XqngJRKs2OvIz/+jXhfxx5H5Jk3KFUoLIbFjfBaKBRE0Jo3pnESSo1S4L/S31dfrT/hfx9DTOM0phWuZGU8QAEFjtWsjpjGtAO9Y2lPkdLhn5M5xLFJuSKvIFYB/edMz5jFiiSJ4vlk+Klk8KnmCPQUKeV8IUQa8C1QDozuTki3RufMmTMnLjY21llQULBXp9Oxb98+Y2pqqkVVVa655pqBt912W8XKlSvzADZv3mw5cOCA4fvvv/c5ePCgISMjI02n05Gbm2uw2+1nOaT19OhMWjTuLNqhoaFxrqDSZN1jLfD93neQkMLjM+UXcVG5BYtrOcsHqKiinnrDe7yXfAu3ZA9gwBmr0AjhqggM2VvhF5A1UIj2NfcVP7+iwPvvNxjj4nrFCdDV6Q6Frwg/EvNBTIyhznCGSZbuZkgvhvfc8E4EVHgwtOgcxE2d71bfAz67fTp0cscy9kgppdajYW1b2BIeTnjDSEb2k2rasiU4bGfBSN1OLi7YnwjwDnem38073hmsjyOL1fR6on1/RghxB62iL3OBEcAXQoi7pJS7e9eyM+Oxxx4LX7JkSUB4eHhzYGCga/To0Q3PPfdc2Ysvvhj0zjvvBLe0tIi4uDjnkiVL8m02m/r222/7/+lPf4pQFEXabDb39u3bM49v7/bbb4+5+uqrq+fMmVN95ZVXJvj5+bn/85//FPz9738Pys/PN7788svHnPa0tDTTjz/+6PPZZ5/lHV30HTJkSPOQIUOaly9fbtPr9fLxxx8/fHT/iRMnNgL8/ve/Dw0NDW05ekxCQkLL2fiuzoQOl/naKg0ffX7xSdse9KZRGhoa/RP9IX120MIgp22zLdkbjsBRRjGq6hZuyTZgUAGcOHUf8mHiPvbZe9yocNf4B+3LSEj6zOEfmJV4SuVgQBgMlY5bbskJ/+MfY4xxcZ7WVO8cics3yzd72BPDCifNnBSSMD8h2VBnsPassZZ62JQFd+WCXcLwBHghESo8rwpzDmE4aMgKXhBMZ47AUaYzvSSa6Jqjr1eyMv4ABzyfbOtx1PqI6PX7J7q3uC/evz9ZgvwLj2d4zREYQi6bSEDXT+Oo+g43ApOklB9JKZ8E/gd4r5dtOiPWr19vXbFihf+ePXv2ff7557mpqanHKjrPmTOncu/evemZmZn7kpKSGl9++eUggD//+c/hq1evzsrMzNy3atWqnJPbnDx5cu369ettAAcPHjRmZWWZATZt2uQ7ZcqUuuP33bVrl3nIkCENev2p8+apqamWkSNHNrRn9x133HFkzZo1fsnJyUPuu+++qE2bNnWnmnuv0tmP75Hjnp+cIXa3F2zR0NDopwinKPP7wq8wcGngIF29znE2zplEUu3t3J5pxuwCcOFSlrBk4A52nFz5twvURrtfbnpC0memoJC9yUJRT73yC9FsnTAhPfz55318p0xpN4HYWyhNSmX4ivD0CTdNaBhz/5hBgVsCezhz31AJn2fArEIwW2BSIrybAA0mz1p87iFaRIVjlSM/4NOARKVJ6ZbDpEPHLdySZ8PWDNCWUDywgYa+UiTjFIRwVcTGr66eWvuja9Thw8kS3L/mxewn+It3VsDiKWAHMej7TOGQfouU8jop5aHjXv8AjOtFk86Y7777zvfqq6+u8vX1lf7+/uqVV155bGVtx44dljFjxiQlJiYOWbp0aWBaWpoZYOzYsXVz5syJe/HFF4NcrlO1Ja688sq6LVu2+O7YscOcmJjYGBQU1FJYWGjYsWOHz9SpU+tOOaAHJCQktOTk5Ox97rnnShRFYfr06UnLli2zeaJtb9FZzoDo4Hl7rzU0NM5HVJosaZZ822bbIKGKs141NpbYhju5M2MhCxPrqDOqqGIFKwY00lg0iUmHOz9atvjaSnJDwndE6/TNHc56GqKjswPuuy9IHxR09uKZJaqlxFIQ+16sDPkmJF5IcZoOzlGOlMHnR+AVP9gSDvSwnfMUiduUZ8p2fOMYINynX6DNF1/3LdyS8y7vJrtwKdVUmxazOH4e8/pcQrFO17Q/Nu4r3RUHs9zh9fUDVUTLnbxb9D5zvRO+E0ExewjHjOcVibxNdfUBHGdlzqNLhBCPSyn/KoR4uYNdfnFWDfIgUnZcSub++++PX7JkSc5FF13U+PLLLweuW7fOBvDhhx8WrV271mf58uWOUaNGDd21a1daWFjYMTWv+Pj4lurqav2KFSscl1xySe2RI0f0CxYs8Pfx8VH9/f1PiOsfNWpUU3p6utXtdnOyNsTw4cMbP/vssw6vpxaLRd588801N998c01oaGjLJ5984nfttdfW9vS78DadXY1kB8/be62hoXGeoT+szwn6IKjJvtE+uDccgaOEEea8m7sz/PE/lkC8hjUxq1kdLju4VAnhqoyK+/ZQePT3yTp9s097+yg+PqWBKSmFIU8+OUgfFHRWBtGiRdQGrw1OHzdnXOW4O8YNCF0TmnB6UqxShf3F8FIGJJVDYCjMHdzmCGicBqJB7A/4JOCQ32q/ZOEWxp62E0VU43SmFxx9XUihYxWr+lRStsFYk5eQsFI3s2SfCK+vj3aha5rFigPvM9c7MrlBHCCNIHzof6tSRUWZvPxySG+bcRxHFXl2dPDXb7n00kvrvvrqK0dDQ4Oorq5W1qxZc6zKdUNDgxITE9PidDrFokWLAo6+n5aWZpo6dWr9P/7xjwP+/v6uvLy8U367Y8aMqZs/f37IFVdcUXfppZfW/fvf/w4bP378KasCQ4cOdY4YMaL+kUceiVDVVj9hz549poULF/rNmjWrtrm5Wbz44ovHFNbWrVtn/fzzz303btxoLSgoMAC43W727NljiY2N7bNKQtD5ykCyECKV1lWAhLbntL3WlIQ0NM5TRLM4bP/GXm8uMJ/VcJnOCCCg5R7uyVjAgkGHOOQDsJnNEQ006K/hmuLjZ2H1hvqC6Pg1gXq9s331HZ2uzjZtWont6qsThaKclelb0yFTUfRH0c7wFeHxiut0i4OpLZBdAgub4c1wOBjtHSvPE1SarLutBT2pidERF3BB5QEOHNzO9jCAH/ghPJzwhtGM7vWEYou1LCM+cq1tel6u2cfl8mvGUHc531Rv5BLvJJI7KCMNO370+TjqU9i5M50VKwYDfaaytJRyRduj9/MDzrIU6JQpUxqmTZtWPWTIkKGRkZHOESNG1DscDjfAE088cWDcuHGDIyMjmwcPHtxQV1enA3j44YejCgoKTFJKMWnSpJoJEyY0ntzupEmT6jZs2GAfNmyY0+l0NldXV+smT57c7qz9woULC1JSUqJjY2OHWSwW1c/Pz/38888XK4rC8uXLc1NSUqL/8Y9/hJlMpmPSopmZmaYHHnggtrm5WQEYNWpU/RNPPHGovfb7CqKjZZi2ghUdIqUs9IpF7dtSL6Vsd/YuJSXFAHx/tmzR0GiHN1555ZX5ne3QWR/u6XEpKSmXAc+fbps9RuK0pLVpqvfiSkBnNNGkfMAHCcUUH0skTib5yE3cVKBHL31sJRnhUZvbTQ4G3OZhw7L9582LUXx8epiYexq4afTf7l844PUB/r65vu2qFnWMqxF+LIG3BSyMgrreSky9Eyn3drTRG/0eICUl5bfAdafbblfoKnW5/iv9A3R1Oo+vBKmovMu7g4oosgPo0at3cVd6JJG9VKFYSod/XkZ84PeWq/PyQo2qamnEXDWBLc5URp5mf+wmPlSQjo5o/LreuQ8hpYvVq/PYsuVoyFS1fOaZTot59bTv9xQhxFjgaSCW4yZ6pZQjetrm7t27C0aOHFnuAfN6THV1teJwONTa2lrloosuSnrttdcKJ02a1G7irkbX7N69O2jkyJFxJ7/fmbToWRvsa2ho9G30Ffocvy/9gnS1uj6tA27GrM5lbs5iFsfnkOMPkEFGwPssEL8IGbwmLKi43URIXVBQfsB991mN0dFelwrVV+sPRC2Nqo36OCpO13Q6xcGaqmFjKcw3w/IoaB7kPSvPM9zU2DbZDlnTrF5b7VJQuJmb817n9cE11JhcuJTFLB74AA+k++BzlisUS1dQ6K68AdZU/VW5BVE6KfW1+JaPYpeSR4J3HAEzVeyCfucIuN31LFpUTk5OX5c+/YDWQmN7gD6taX863H777bHZ2dkWp9MpZs+eXaE5At6hT87uaWho9A1Eszhs/9ZeZ87rOyFBXWHAIG/jtrxP+TRmD3uCAQop8v9nzaGrn/CLybHp9ccGXsJkKvebPbvaOn68d2Kjj6LSbE+zF8S/GW/12+0X1f0Daw7DqnJ41QbrIkH2jazFcwWJNOw3ZPmt9otWnIrX+/jRhOJ3eGewC5dSQ41pMYsH3Mmd2WcroVgIV2VE9Ma6QWqOe3J+SbIAUUFA6TD2+hwkvOfSvJ1hpJYfaGYgfSnWvmuamyt46y03hw71h9obh6WUy3vbCE+zYsWK/N624XxAcwY0NDRORdJs2WfJtW2yDRRuEdzb5pwuCgo3cEORUVEP71DThgAUNDX5PltQkPRETEx2kNHYYkpKyghMSUkQBkNQV+31FF297nD4yvDymIUxMYZaQzdmFqWEQwfg0xp4NRBSQ4B+9/33B4RTHHJ87WgyFZvOsGjb6RFJZNMMZuQvY1kCQBFF9i/5MnIGM/Z7+9wGY21edOya4KE1B+suKCsbDLCfiOLh7AmsJMA7oXF6GlhPHcPpX0nsdXUlvPaanfr601aR6iWeEUK8CXwDOI++KaX8pPdM0ugvaM6AhobGCeiO6HL9vvTz19fo+3RIUOdI1eGXl/VwuEz6tDy4eOnhw9EApc3NlmcLCpKeuu66L8c++KB3QoIkbp9cn/y4d+P0gRsDYwVdOVOqCwpK4MMmeD0MiiOB9pObNc4cicucac62r7cPFG7RK7KWoxldVUrpwR/4IQxgG9vCwglvuIALKr1zRul2+OdmB4fuiJtwsLQkoapqMEAWg/JG82NkAz7eUfVRcLKKCsbTv5Lay8pyePPNGFyuHqtI9QJ3AcmAgf+GCUlAcwY0uqRDZ0AIsYdOJETPJClFQ0Oj7yFaRLn9O3uNOcfs3ZAZr6PWh0VuLbM5WvMDbggOPmTT6VwLDh6MV4EjLpfpudWrZ/7pssuyhg4deorSRE9RnEpVyJqQ0rh34iJM5aYuQk7cTthbDO+osCAKKuM8ZYdGxyh1SpHfF34mQ4Wh1x3daUzbX0aZpZBCB8AXfBEXQkhTFFEe65MAQrirwqM31tispRFTi4rKQxoaBgLsZHT2BLbEt2D0zqSgoIWllHI5cV5p31tkZqazeHESUvatQhBdM1JKOby3jdDon3R2EZjZ9vjztsf32x7nAFoCh4bGuYKkxZxhzrFvsA8UbuG1kJmzgaJzlkbHf2M0GutOkD++MiDgiEWvr3+jtHSIy+1Wampq9I8++mjSs88+mzNu3LieV52USPN+c0Hs+7Fq6OrQeCFFJ8mRzlrYWgpv6OE/0eDsN3kY/R6VRp8dPoU+232SBKJPFM1UULiFW/LnM39wNdXHJxTv88XXIwnFBkNtflT82iCLaDBPy81rsbW0RAFsYmLWZNYPVNF5a8Dr5l2KuI7+M7EgpcrGjVmsXdvrjmIP2SKEGCKl3Nfbhmj0P7pUExJCXCylvPi4TU8IITYBz3nbOA0NDe+iq9Tl+n3h59e/Q4JaMVsqTTRsFQAAIABJREFUsiJjv4tTFPcpS/vCZCq/4fnniSsqMvz+978f2NjYqGtsbNT95je/SYqJiWkYNWpUzbhx42rGjh1bZzKZuiyqKFpEXeDmwOL4N+ODrcXW+I73rK+ANYfhVSt8HQVqX1ckOefQl+tz/L7wC9bVn45y09nBivVYQnELLUottcajCcU6dF030CFStfvlZYaE70j2cbUUXZ2bF2Ryu30A0knO9bIjoPIyecyl/6hdqaqTZctKSE3tc33kNJgEzBNC5NOaMyAA6dEojmHDPHuf2Lv3rNUtGDduXNILL7xQPHnyZG0yux26szzoI4SYJKXcCCCEmAicNe1cDQ0NL9BChX29vdqSZek/M3cdIl3+Qek5QSF7272RKw5HccjTT/vrfH19x4eH173wwguZTz75ZGJNTY1eSklhYaG1sLDQumzZsjCj0agmJyfXjR49umbChAk1ycnJjeK4iWRjubE4elF0Y8SyiHilpaPiYBWlsLwKXvWHbWFAf0lAPLdwUWVfZ6+wZFn69ApMBBFNM5iR/xmfJQAUU2z/gi+iZjGrpCftCeGuDo/aXOVjKx0c2NCQdUVh4QCdlHqAA4QXX8DOaC86AvAs2TzEWU3KPiNcrhoWLKiluLi/Xwun9bYB5wMtLS0YDL2SauRVuuMM3AO8LYRw0JpDUA3c7VWrNDQ0vIOkxZxlzrGvsycIt+j3g1Qh3NUR0RtrrL5l7ToChujo7OBHH40Tx129hw4d2vjyyy9nvPDCCzH79u2zHy0zD9Dc3KykpqbaU1NT7e+99x4Oh6Nl2NBhlWMcY3Luzbi3YXj+8HYSIaUKxcXwcSO8Fgy54dDPlFPOJSSqsciY5fjaEae0KP1igDeKUVWllJZuZWs4wA52hIYT3jCWsUdOpx29oa4gOn5tgF7fFBtXVZU+8cCBYw5rFY6y4ewJaMLivaTYR0jnd/SfVcampjLmzzdSVdXvE/bP1dpQmZmZxquvvnrQuHHj6rZv3+4bGhra/NVXX+VMnTo18ehMf2lpqX7s2LGD9+/fv8flcpGSkhL13Xff2QHmzZtX/vTTT59Q/feTTz6xP/fccxHNzc0iNjbWuWjRogKHw6E++uij4atWrfJzOp3K2LFj6z744INCRVEYN25c0rhx4+q2bt3qO3369Kpnn322rHe+De/RpTMgpdwBjBRC2GmtWNxnynBraGh0H12VLs/vCz+7vrr/hwQB6PSNxTHxa+x6Q2O7SiWWCy7I8L/nniQhTo0Rj4uLc/7rX//Krqmp0W3ZssW2bds2e2pqqr2srOwEVZXq6mrDps2bQjaxKeRlXmYQgxqnMKXmKq48chWx+xx85oS3IuFwf9AhP+cRTaLUb5Wf21hq7HfhHj/hJwfKKLMWUOAAWMWq2FBCG6OJ7kZCsVRtjoLM0IhtSQKpjio7lDmkouLY77wRc9UodhmPEOi9Vf15pPNiP3IEKisLmD8/FKfT0tumaHROUVGReeHChXkTJ04snD59+oAFCxZ0WCH8xRdfDC4sLDSlpaXtMxgMlJWVnRBvV1paqv/jH/8Yvn79+iy73a4+/fTTYX/4wx9CX3jhhdLHHnvs0AsvvFAKcN1118UvWrTIcdttt1UDVFVV6bZt25bp3U/ae3TpDAghQoE/AhFSyquFEEOAi6SUb3ndOg0NjTOnhSP2DfZKS+a5EBLUitWnNCMiZuNAIWR71zC3bcaMbPuMGV0OCO12u/uqq66quuqqq6oAigqKlI0fb2xJX58eubtud1gNNSfcSLLJtmSTbXmTN0MNkDwG6qZCzdVQcxE0nEmUt8YZIGmxpFlybBttiUKKfvlvaKtQnP86rydXUWV24VI+5uOB93N/ug2bq8MDhbsmPPL7I772A4OFlI2TSkrKomtrj4XptKCvv4jvnYXEeaeyMMB1pPNuP3IEiooyee+9BFS1V+XVhRDTgJcAHfCmlPLP7exzM/B7WiMzdkspbzurRvYBIiMjnRMnTmwEGD16dENBQUGHUrhr1661/8///M/ho4vBoaGhJyTjf/fddz65ubnmcePGJQO0tLSIMWPG1AF8+eWXtr/97W9hTU1NSlVVlX7IkCGNtEbDcOutt57WKl1/ozs/hHeBd4Cn215nAYsBzRnQ0OjLSFzmHHO2/Tv7AOESAb1tjmeQzUGhuwv8A7PaH+gL0eR/990HrGPGnNbMsK5BVx72RVj5xAUTo+6oucMXOOjCdXAdq9Ul/Nu4mY2R+6gJdLUm5QHQAmIL2LaA7Y8Q6QeuSVBzOdTMhJqB0HJmn1WjOyg1SoH/5/6++qr+v+LVllCc+zZvn5BQfBd3ZbWXUKzX1xdGx6/10xsa43SqWnlVfn6zv9MZd3S7imiZycqK3YyK8ZrRU8ng037kCOzcmc6KFb1urxBCB/wbuBIoAbYJIZYfrwYkhBgEPAlcLKWsFEJ0WsFZCBELDJJSrhFCWAC9lLLWe5/i7GA0Go+JOuh0OtnY2Kjo9XrpdreO8xsaGo5dl6WUCCE6k8Vn0qRJNSdXNm5oaBC//vWvY7du3bpv4MCBLY888khEU1PTsdwam82mntrauUN3koiCpJQf01bEQkrpAjwie6ahoeEddNW6/MDFgZWONY7BwiW8U1DoLCOEqyI6/psK/8Cs9hV5DIaq4Mceq7SOGTOg3e0nI1F98nxyhvzvkPyLp18cOPBfA5MNNQZfqCyDD9P1XLb/cmYor/KFazc1heWw6yPIuQsOJUDTyc1VgX4lBDwMcYNgxAAYeidEfwiOyu5dazVOBzd1vpt9s4I/CI7TV+n7tSTu8YQT3jSLWXlHX5dQYlvJyqgT95KqzVGYHjfo82i9odFhcrn2z8rJUfydzmOz/xLUu3inaDU/8Z4jMI4sVtM/FLKkdPHVV1l9wRFoYxyQI6XMk1I2A4uAa0/a5z7g31LKSgAp5SE6QAhxH7AEmN/2VhTwmcet7iNER0c7f/jhBx+ADz744FjY0BVXXFHz2muvBbe0tM7FnBwmdOmll9Zv377dd+/evSaA2tpaJTU11dTQ0KAAhIWFuaqrq5UVK1Z0GIp0LtKdlYF6IUQgbQXIhBATaFs20dDQ6GO4qLRttFVY0619WkHldDEYavOj478J0emb2016Vnx8SoOffNKiDwjoVuKu4lSqhvxuSHXg1sCBICWUlsDSOng1CNJDgVNCKhygzobq2W3Xv1wwfA72NWDfCPbKk66n+WDOB/N7EKIDOQrqL2sLKZoM9Vr5955j2Wup8t3mi9Kk9I+B6GkyghHVpZQe+J7vIwB+5MfQCCIaLuTCIwh3bVjk1nKbvWQwgN3pzP1JXl6UQcoTnP5neDZ7AfO8p+ozhFw2kYCuHzi6bnc9ixaVk5PTl/pLJFB83OsSYPxJ+yQCtMm564DfSylXddDez2l1MLYCSCmzu1pJOG3OohRoVzzxxBNlt9xyy4BFixYFXnLJJTVH33/44YcPZ2VlmZKTk4fq9Xo5b968w0899dTho9sjIiJc8+fPL5g9e/aA5uZmAfDMM8/sHzFiRPWcOXMODxkyZGhUVFTzyJEj63vjc/UWQsrOJbWFEBcA/wSGAXuBYOCnUsrd3jfvmA31Usp2E59SUlIMwPdnyxYNjXZ445VXXpnf2Q5CCCew57i3XpdSvt5Vw130/cuA5wGQuEy5phz7t/Z4xaWcEysBrUhpsxdlhEZuTRKi/UGHLjg4L+TJJyMUs9ncnRYthZbc0Q+NDjXUNDbCixXwRgSU2s/ESjewFSxfgv0bsO8AW/NxIUUnYwP3RVB7BVTPgJoh0Hwm5+9F7kTKvR1t9Ea/B3hWPPtb4LrTsrSfoaKykIUJeeT5AejQybnKjd9NSchTDYYGP4DwurqMS4uKEsVJK09vcXf6vbzlvRnweArYRyRm+r7GYnNzBW+95ebQIU8OjKvlM89c3tkOXfV9IcRPgZ9IKe9te30HME5K+dBx+6ykNdzwZlpn+jcAw6SUVe2cb6uUcrwQ4kcp5WghhB7YeSZ1Bnbv3l0wcuTI8p4er9H32L17d9DIkSPjTn6/O5NTacAUIInWm1sm2pK3hsbp4pJSjvVGw0qNUuD3pZ/VcMTQ7xRUOkdtDI3YfsDuV9DhoMaYmJgZ9NBDA4VO13XSqIor6j9ROQmvJiTBd1kwMw7qgz1hqQ6YCI0TofEPUFYH4muwrQL7OrBnwgmKJbWgWw1+q8HvcSAKnJOh5iqomQG1QedOKKbX+v25joLCT/lp/nzmD66iyuzGLT5VPrl4DPHp/lLfnHzkSOYFZWWn/DZWc2WGVx2BCIrZQ3i/cATq6kp47TU79fW9IaPcVd8vAY5XQosCDrSzzxYpZQuQL4TIBAYB29ppb50Q4inAIoS4EkgBVvTYeo3ziu44A99LKS+g1SkAQAixE7jAa1adBob9BrUlpKUCHT4odGtmUEPjXMBw0NBizjJnW9Os/afSZzdRlOayqPi1islU05ECkvSZPDnDb/bsbg16dPW6w8MfH97iSLNGw7258JZXiyL5grweaq6HGoDi1nwC+xqwbwD7YU4cSJWA6UMI/hCCFWA41F/aFlJ0KdSb2sI0Nc4vLFjUW7g59R3x5uhmqRqqXC7j34qL41eYTKuGVFef0vd3MyLnar70XihMEAdIIwgf+v7qY1lZDm++GYPL5YW6CtITzvo2YJAQIh7YD8wGTlYK+gy4FXhXCBFEa9hQHu3zBK11ofYADwBfAG96wE6N84AOnQEhRBitMW0WIcRo/rvkbQesZ8G2bhGwPEChrcKnVKRLNasNqkVtVH3UJrePu0W1qm7VR5WqRZWqRVVUs2qQRmmQBmmWOmlBwUoH4QcaGn2ZgE8DDLTOEp1TmEyV2VFx30YrOlf7zr0QLY5bbinwnTy5W46ALc2WNeLRETH6xuKDMLwFis96PkU0uH4GR34GR1RgJ5i/aAsp+gFsTcettqrAbvDZDT4vQbgPqOOOCykaDk7tgnV+oNM3Fl8Ut9PX0Bhe+O/9+wdKIK+pyf58U9Ol75wYb04R0YXj2RrrterCDspIw44ffV+XPzMzncWLk5DS49+FXWk6vDLiwyOtap89R0rpEkI8CHxF6+Li21LKNCHEc8B2KeXytm1XCSH20bpa+JiUsqKD9lTgjbY/DY3TorOVgZ8Ad9K6dPUi/3UGaoGnvGtWzxCq0OsadHZdg85Ouz+XU5FCqtIk61SL2ui2uptUH7VZtaout49bqtZWB0KapU41qgZplGapP+ZAaPl/GhoeRaoO/5yskPAfOw530unqAlNSKs2DB3ftBLlxDnh9QGH04sgB8O9s+FUSqL0+jlaAsdA0Fpp+B4caQXwDvqvA/h3Y94H1+GWAelC+Bce34HgaCIPmyVBzRauEaW04dKxBr9FPkdLHtj8jPOr7RCGk7iKjozq3vj77y6qqQQDvQsgF0PAQrXe6CgJKR5Aa4sTsndAdHyrYg4kQfL3SvqeQUmXjxizWrvVKmNQkc2Hml5EL43yVFo+sNkgpv6B1Bv/493533HMJPNL21ylCiItp9VBiaR3bibYmuqeupnFe0+GAVkr5HvCeEOJGKeXSs2jTWUVIoYgm4as0Kb76yu6P71WD2qha1AbVqjapPqrTbXW7VB9VbXMghGpRdapJNUijNEm9NKPDB4H3ysBraPRr1Lrw6M3lvrYDHToCwmQqD378cWkID2+34vDx6Kv1paN+OUrvU+Dyh0mH4Pu+Iid4ChaQM6F2ZutEy/6DoDsaUrQe7KWceN04CMaPIehjCBLAEGiYAjU/gZoroc6ihRT1c9T60IhtB+1+hcf6rLWlpXBJTU3Q9RC8GvwAHoXYEdA4FmvxCFKt1fh5Z8beTBW7gOjW8/ZZVNXJsmUlpKZ6PHdKh9r4UvCXJT/323Y0vPAUaeE+wFvAw8AOzp2cI42zRHdGv1FCCDutN6o3aM0VeEJKudqrlvVxlBbForQoFmq63vcoUieb2xyIRrfV3dy2CuF2+7ilalGFtEhFNal6aZSmtjAmK0o/WJLV0DgDdLqm/dHx31gNxvq4jvZRHI7ikKee8tfZbF3OTPpv9c8Y9tthCUrLt7lwTTw09P345uMIA/e9UHkvVKrAXjB93hZStAXs9ceFFEkgDaxpYH0FwsygXthWFXkGVI+Bpl5fCtHoNjpdY0l0/FqrwVh/LFcmsLEx64qCggE6KfWLIP9CGJwL5mYQP4UBDpaVHiDS4RWDjNTyA80MxLMSlZ7G5aphwYJaios9XmU9XFdbvD7qbetAY2VfD8msllJ+2dtGaPRPuuMM3C2lfEkI8RMgBLiL1orE57Uz0BOEWxh1dTqjrk7nZ+imEINUpEs1tToQqo/qVK1qs9vqdqtWVVWtqlAtqlDNql6apFHLg9Dob1ishzIjYtYPUBS1wx+EITo6O/jRR+PE0fryHSBcoj7x+cSDYV8FRME9RfBuv1dXUoAR4BwBh5+Ew04Q68D6JTi+A/se8Dl+CrAJlA1tScrPQlQwtEz6b0hRTYwWUtRn8fHdnx4evXmQEPLYfTmuqip94oEDx1YI/EH9DHImwuBa0B0G02GeGAcbs8Ds2RUhPQ2sp47hdKt2R6/R1FTG/PlGqqoiPduwVG+17c1cEPpJol7IrtXKeok2+XeAb4UQzwOfAM6j26WUOz11rmHDPFtpeu9euqxbkJmZaZw5c+ag7OzstK72PZmVK1faXnzxxdBvv/02p2cWnj90xxk4miswHXhHSrlbCNGhfraGZxGq0OsadXZd42nmQRhlvWpVG1SL6nT7uJtVH9WlWlW32+oWqkVFWqReNapGaZRGqZMmLbCg5wiX6K8a8b2IdAUEp+UGBu/rVNXHPHp0esC99yZ3dc0xHjIWjX5otM1cdkAHY9xwwOMzhH0BE8iroP4qqAcOlIPuC7CtbnMAijhR5eUwGD6FwE8h8OdAIjQeDSm6CupsbZXlNXoTtSEkYnup43gJXSldIw8dyh1aUXHK4GsYON+EnFta5b6BHb5wfzQsKPKYSQpOVlHBeLoMyetVKisLmD8/FKfToyvoZtFS9XHYf6pn+Wb12fDC43jxpNfHy5lKYOpZtKVf0NLSQhdzS+cd3XEGdgghVgPxwJNCCBvaDaRPI6RQhFP4KE7Fh8retua8wMg7vW1C/0EId1VkzLo6i095Z46AapsxI8s+Y0bnN2OJGro6NDPpz4MGCPlyPvw6CeR5M1kRBO65UDUXqgAywLiyLaRoE9hrW1VKjpEFliywvAGhRpBjoO4yqJkO1ROgsc9Of56j6HRN+6Pi15qNxrpjzquQsnFSSUlZdG1tu78PCfIHnt8JR3zhT22z4e8HwwX18KtuThl1gqCFpZRyOXFn3JY3KSzMZMGCBFTVo2Ieg42Hc9dHvR0apGuM9WS73kJKeRmAEGKAlPIE2VEhxDmRPOx2u5k9e3bs9u3bfUNDQ5tfeuml4rlz58bv27cvHWDPnj2m2bNnD0hLS0tfsmSJ/bHHHosOCAhwDR8+vOFoG4888khEaWmpoaioyBgQEOBavHhxwdy5c2NTU1OtOp2Ov/71r8WzZs2q7b1P2bt050d0DzAKyJNSNgghAmkNFdLQ0NA4LfT6+sLoAd/46/VNUR3uJEST/913H7COGdNpmI9wiqqhzwytDtyiC4CJR+CHfh8WdKYkQ3MylD8K5S5gA1hXgX0tOH5sDSk65ig1g/gebN+D7Y8Q6Q+ui48LKUporXyq4SWsvqUZEVGbBgrlv4NZnapWXllQ4Axoaorr6Lh/8WDGizw6GNSDsMsKX/q3bnk8FkY0wdT6MzDLzbsUcR19e2Vt5850Vqzw6Ky9QLY84b8h949Ba/vrdWQJp9Z/+g8wphds8ShFRUXmhQsX5k2cOLFw+vTpA3744QerzWZzb9682TJx4sTG+fPnB912220VDQ0N4sEHH4z7+uuvM4cOHeqcOXPmCc5QamqqdevWrRm+vr7ymWeeCQXIysra9+OPP5qnT58+KDc3d6/Vaj0v4yS64wxManscoUUHaWho9JRWqcRNiaKzfBaDoSr44YcbjXFxnc5oWYoseaN+MSrYWPVdI1wbDk2aUtdJ6IHLoOEyaAAOVoPyJdi+alMpyuPEIo2VrYXRAlZCwK+AeGia0lYVeTrUOrQVYQ+hNoaE79zv8M87YdBpcrkOXJ2X52N1ucI6OnI5s9J/wT/bBsEK8FEBXGiGbAu0CJidANv3QUxPckNU/kUec/tw7RIpXaxenceWLR51BBxKU9nXkQvUC80dq5n1VYQQycBQwCGEuOG4TXY4NwqxRkZGOidOnNgIMHr06IaCggLTnXfeWf7GG28EjRs3rnjZsmX+27ZtS9+1a5c5KirKOXz4cCfAnDlzKt58881jVeanTZtW5evrKwE2b97s+9BDDx1qa7MpIiKiec+ePebx48c39sZn7G264ww8dtxzMzCOVumqTuPQhBBvAzOBQ1LKYce9/xDwIK2JbJ9LKR8/XaM1NDT6E9IZHLazyC8gt/OZfqu1NOSppyz6gICOExZVXFH/icoe8FpUlJD3HoL3+93Nu7dwgDobqmdDNUAuGI5KmG4Ee9VJ94N8MOeD+V0I0YEcDfWXtVZFrr4EGrRCK6ePonMeiI5bazSaak8ofGd3OnN/kpcXZZCyQ+WrH7gw6zo+O6m/O1T4NBcmDoYaHRw2wPUJsOn0E4r/QDY/x6uVuc8It7ueRYvKycnxaIXlqZa8jJURHw6wKN6oVHxWSKJ1rOUHzDru/Vrgvl6xyMMYjcZjfVmn08nGxkZl3rx5lX/5y18iFi1aVDt8+PCGsLAwd15eHp1NWvv4+Byb0Ggt4aBxlC6v51LK4zsXQoho4K/daPtd4F/AguOOvQy4FhghpXQKIfq2XJmGhsYZIZSWw1Gx37rNlqpOZxt1wcH5IU8+Ga6YzR3OZOkadOXDHx/udOw9bIZ4CaV9O5Shj5MALb+Eil9ChRvYApYv20KKdoBv83EhRW4Q28F3O/g+DxF2cF8ENRVw03Yh6qWU+b34UfoFVp+DGeHRGxNOVs4Kq6vLuKyoKFHQ8YpZLgPyJ7FxgERpZ6Qz1Anv5MFPB7Uu3uz0hXui4YPuJxQ/Qjq/9axSjEdpbq7grbfcHDrksTh+Pe76V0M+L73XsbNfTyhIKZcBy4QQF0kpv+9te84WVqtVTpkypfqRRx6J+de//lUAMGrUqKaSkhJjWlqaaejQoc5FixYFdHT8pEmT6hYuXBhwzTXX1KampppKS0uNI0aM6Iv1I84KPZncKQGGdbWTlHK9ECLupLd/BvxZSuls2+dQD86voaHRDzAaa3Kj4r+J0OlaOlX6MCYmZgY99NBAodN1mL9q22fLGvHo0HB9w78Ow2PJ51OS8NlAB1wMjRdDI1BWC8pq8P0K7OvAnsWJ9U5qQPcV+APPAD8XQoRKKbUwonZRm4LDfiw+ZWVMSpl05EjmmLKyTgejhwjeP4pd4S0YO7lf31ADT5XA/7Xl4nwYDBc0wK/LuzTvTtJ5sQ87AnV1Jbz2mp36+kBPNRmpqynaEP22Ld5QNbDrvfsHZ8MR6I4U6Nlk7ty5R7788kv/G264oQZaHYR//vOfhTNnzhwYEBDgGj9+fF16enq795/HH3/80B133BGbmJg4RKfTMX/+/AKLxXLeLhd06QwIIf7JfytaKrQmE+/u4fkSgUuEEP+P1gp+j0opt3Vw3vuB+7trp4bGuUL/7/tStfvlZ4aEb08Wgs4G7dLnkksy/W69tePBkBtn/BvxhTGLbA6YVAfb++6g5RzCBuqNUHMjrWUVi1rzCexr2iRMyzmhUMoaTzgC/b/fn4qic5ZGxX2rN5lqTlwZk7J5XGlp0cCqqk4dgVp8y0ey21aHrRux38+WtSYUr2ybDX0iBkY0wpUdJxRfRzrv9GFHoKwshzffjMHlqRAeqc6z7cp8K3R5kk5IrRZPPyApKan5+BoDzz33XNnR5+vWrfO99dZby/X6/14ubrrpppqbbrrplJoEf/vb3w4c/9pqtcqlS5cWeMfq/kd3Lrjbj3vuAj6SUm46g/P5AxOAC4GP2+SwTvHGpJSvA68DCCHORB1BQ6Nf0b/7vlofFrm1zOYo7nyAIUSL4+abC3ynTOlwMKSv1peO+uUonU/BBhWuCwCnJgzdS8SAKwWOpMARFdgB5i/A/g8orIKVnjhH/+73p9JRQT1FVWsvKyqqDm1o6HRW2omxZgw7xEHC7d07owJ8UAjjLJBpAZeA2xJgWzrEnaoMNZUMPu3DjkBmZjqLFychPTNot4iWyk/CF9VO88ntu59Zo9tceeWVCYWFhaZ169Zl9bYt5wLdyRl4z4PnKwE+aRv8/yCEUIEg4LAHz6GhodELKDpnaXTcN0ajqa5zbWudri7wZz87Yh4ypMM8Av+t/hlD/3dgqK753kr4qF/H9J5rKMCF0HQhND0DP0PKvb1tU99COoNCdxX6B2afkoyrd7sPT8vPV+zNzR1L6wIudE1TWFeXTWLE6Z3brsJnOTBhCFTroNwA1yXA95lwXAjEaLJZjUcTcT2GlCobN2axdq3HBu0jjAdz1ka9Fx6oa4zxVJt9DSGECbgRiOO4sZ2U8rnessmbfP3117m9bcO5RIfOgBDiYynlzUKIPXBKfVoJHAH+0Za80l0+o1WF6DshRCJgBLqOadTQ0OjTmMwVWVFx38UqirtDNRQAYTKVBz/+uDSEh7d7UxYuUZ/4QuLBsFU1RkjQwcFzomiOxvmBojQfjIr7VjGZq08ZaFtbWgqvzssLMrndPp21oSJcN7Hk4FYmxPXMiuRmeC8XbkhsTSje7QN3xcCHhaBACCVsJg5dJxK/vYWqOlm2rITUVI9MACiozb8LWJf3TOC682FCYRmtSmE7AGcv26LRz+hsZeCXbY8zO9geBHxAawc8BSHER8ClQJAQooTWRLO3gbeFEHuBZmBeeyFCGhoa/QXp9g8Z8NLxAAAgAElEQVTMzA4K7frmrTgcxSFPPeWvs9l829tuPGwsGv3QCLP54L9a4MnzqpKwRv/HbD2cGRmzPq49hzigsTHryoKCATopO12NlyAf4p/5y7juDLX+r62F/y2BZ9tWIBYHwQUNmB/PZic2zPS9kDuXq4YFC2opLvaISpi/0nhwbdS7YpSp8wTtc4goKeW03jZCo3/S4YVJSlna9ljYwS6FQog5nRx/awebbu++eRoaGn0VIdw14dGbqnx8D3Z5szVERuYEP/54rDAYTh2ESNSQr0Myk/8c4hDqZAn9W+pP43xDOoNCUgv9gzLb1eiPra7OmLh/f5Kg02R6AP7CbzJf4ece6v+/K4MfrbC8LaH46Wh+n1xM5DV9rxBVU1MZ8+cbqaqK9ERzP7HmZH4W/tEAs+Lue06P99gshBgupdzT24Zo9D86CxOq5dTwoGNIKe1Syh1esUpDQ6NPo9M3FkfHr7EZDF3H4JpHj04PuPfeZNFONRjRLKqHPDOkMuj7rQJGB2tJwhr9CUVpLouK+w6TuerU+Hsp3SMPH84ZWl7ercH9R8xOf5I/ezC5VQEWFsJ4M6RbwSV4/u7x3LR1HwkJpyYU9xaVlQXMnx+K09mpBHF3MOCueyN0edk8++6+WzzNe0wC7hRC5NMaJiQAKaUc0btmafQHOlsZsAEIIZ4DDgLv09q55gC2s2KdhoZGn8PqczAzImZDghCdhzwAqu3qq7Pss2a1O8CxFFvyRv0yyWo8cr8KH2urARr9CrOlIisytv08GSFl46SSkoPRtbXdGpRuYFLmbXzkBZUbmwqf5WIYm0hLrYmKCj3XX5/Ali2ZWK29H6JbWJjJggUJqOoZS8nG6KsKN0S97Ygx1JyvxQiv9vYJhuFZ9am9nHndgilTpgxcunRpflBQkLs7+2dmZhpnzpw56Hi5Uk9y4403xs2cObP6rrvuqvRG+96iOz/An0gpxx/3+lUhxFa6V4VYQ0PjnEG2BIWk5ncUDnECQjT533XXAevYsacO8iWuyCWROQmvtBiETLbCwTBvWKuh4R1kc2DInoKAoIx21Xh0qlp5ZUGBM6CpKb47rWWQlHcZ33qv+NUFiWn8/gMT11+fiNsNe/b4MG9eLIsXF6D0Yg7xzp3prFjhgcGldN9v35H1Ssjn52XtACGEXUpZA9T2ti29wbp163J62wZP4XK5OL5mwtmkOz8ctxBijhBCJ4RQ2vIEuuWBaWhonBsI4aqIilt72D8os2s5Qr2+KvixxyqtY8eeogSkNCjlI385dP/Afy8WQg5JgIPd1FDX6GuoiJZCYnp/dvksoigth6Pj1xzpyBEwulwHZuXkKAFNTd1ycEsJKxnDjkg3+g6rb58RIexnI7HMmlXLM88UH3t/yZJA/vznYK+csyukdPHVV1mecASsorliTeSCA/NDVw4+Hx2BNj5se9xBa12oHcf9be/ooP7Ab3/729D/+7//CwG45557oidMmJAIsGzZMtu1114bDxAZGTm8tLRUn5mZaRwwYMDQ2bNnxw4cOHDoxRdfPKiurk4AbNiwwZqUlDRk1KhRyX/7299C2juXqqo88MADUYMGDRqamJg45I033vAHWLlypW3cuHFJ06ZNGxAfHz/0mmuuiVfV1hqLKSkpkQkJCUMTExOH3H///cfkgtetW+c7evTo5KioqOHvvPOOf1ftjx8/PnHWrFnxSUlJQwFeeeWVgOHDhw9OTk4ectttt8W6XC4vfcP/pTsuyG3AS21/EtjU9p6GhsZ5gN5Qlx8TvyZYp28O7GpfYbUeDHnqKbM+ICD85G2+Gb7ZI38dYdTXX2GG3bHesVbDG7jQNR4g4tAuRjWsZ7LuWy6z72ZksBu9OF+8AZP5SHZU7HfRis7VbgKuzenMm5afH2FQ1W4l6FbhKBvGXv8GfDqV4+0xZqrYjg8WWqv3Pv30IX780cqnn7b+jv/3f2Pw83OTknLEK+dvD7e7nkWLysnJOeMaB6NNpdlrI9+N8tM5u7wunctIKWe2PXZrJao/cdlll9W98MILocChXbt2WZubmxWn0ynWr1/vO2nSpFNWQoqKiswLFy7MmzhxYuH06dMHLFiwwD8lJeXIPffcE/f3v/+9aMaMGXUPPPBAuzU+FixY4Ldnzx5Lenp6WmlpqX7cuHGDr7rqqjqA9PR0y65du/Li4uJaxowZk/z111/7jho1qvGLL77wz8vL26soCuXl5ccc+rKyMsP27dszdu3aZb7++usH3nXXXZWdtZ+amurz448/piUnJzfv3LnTvGTJkoDt27dnmEwmefvtt8e89tprgQ8++GCFl75moHtFxwqAa71phIaGRl9ESl97cWZY5JZEIbpeRdQFBeWHPPlkmGKxnJgIqOKMfzMuP+bD3RIujtOShPs2TZiqC4mt2MEY51qmGtYz2T+HgQES5Tx14GRLQHBabmDwvg7zWsLq6jIuKypKFN1bbacRc9UodhmPENhpzYEeo9DMauqJ5r/qPIoCCxYUMmGChbQ0K6oKDz4YT3Oz4Fe/8upAA4Dm5greesvNoUNn1I8U1OY/BK4teCpgY98smqbhMSZNmtQwb948n8rKSsVkMskRI0bUbdiwwfr99/+fvfMOj6rK+/jnzJ2WTnoPAVLoEVFYsVBVUOwiuq5lLavo67vqq7xr2UVQ33WVta0odogFUSyIq6J0ULGAhpbeSCcJ6WXKvef9IwmLmDKBTBr38zzzzMy95577m+TemfM951e+8/nXv/518Nj2kZGRtilTpjQBTJgwoTEvL89SWVmp1NXVKRdeeGE9wE033VS5adMmv2OP3b59u89VV1112Gg0Eh0d7Zw8eXL9jh07PP38/LRx48Y1jGgNuh8zZkxjdna2ecaMGfUWi0W7+uqrh1544YU18+fPr2nr6+KLL65WFIWJEyc2V1ZWmrrqf/z48Q0jR460A3z55Zc++/bt80xKShoF0NzcbAgJCXH70kCXYkAIYQVuBsYAR2Y8pJQ3udEuHR2dPkVrDgnfVejnn+tSYK85Pj496L//O04oyq/cHYy1xpKke+KavbP/ywJrBt3M1UBGgqzHuzKLuKrvmezczHTLNs4JKiXcD/jNj+XJiDA4KqKGbnVYPQ63fx9IKRMPH06fWOZ6LnsHxsYz+aY5n1j3xcq8QD5n89taBd7ekvXrM5k+PYHMTA+khHvuicXhENx/v/sKgNbXF7J8uS8NDSc0ix9gaCzZGvWmcaylXBcCJwEWi0VGRUXZli1bFjRp0qT6pKSkpg0bNvjk5+dbJkyY0Hxse7PZfGShUlEU2dTUZJBS0k4iu9/QWckri8VydL84nU5hMpn45ZdfUj/99FPf9957z/+ll14K2blzZwaA1Wo90r6t38769/T01I5qL+bNm1e5bNmyoi6N7kFcmcV4CwgDzge2AlGcpIEqOjonAwaDvSxm+Fd1fv65rgQ1Ss+zzkoLvueexGOFgP8P/um/u9K7zjt7XLAuBPoWDeGsJKBkB2em/4OFabP54uAQqpt9qQs6lZ/jF7B81PvMH15KuB7D0YrFWpU5PH6dt9Xj8G9c3gCQ0jGppCS7O0JAQzguYl3Fz5zqPiFwPaksaEcItBEZ6WTbtnRGjWo8sm3hwqE8/ni7vtQnTFlZFs89F0JDwwldW3O90tOKhv0zaKylvG9iHXT6hClTptQvW7YsdNq0aXWzZs2qW7lyZfDo0aMbDS4GvwcFBane3t7q+vXrvQFWrFgR0F67qVOn1q1ZsybA6XRSXFxs/OGHH7zPPvvsho76rampMRw+fFiZP39+zfLlywtSU1M9O7PD1f5nz55d+9lnn/kXFRUZAcrKypSMjAyzSx/2BHAlZiBOSjlPCHGJlHKlEOJdYL27DdPR0el9zJbqrOjYTVEd+UX/CiEcfvPm5XlPm/arwZBwiob4Z2ILwv+9QsAifQavl1Ex2MoIPbSXcQ3bOVtsZrrPLiYG27CGA+0PbHWOQjr8g1Kzg0L2dTjIN2ha3bSCguqwhgaXswBJ0G7m9YPrme2+1JdJZPEmXYuTsDCVrVszmDEjgX37WgYxDz8cjd0uWLy4rMfsSU9PZfXqROTxB/eacdatCPuk/Bqfjv8fOi0IIc4C4qWUbwohggFvKWVuT/XfE6lAu8vUqVPrnn/++bAZM2Y0+Pr6ahaLRZ555pn13enj9ddfz7vllltiPTw8tBkzZtS21+a6666r/vbbb71HjRo1RgghFy9eXBgTE+Pcs2dPu31WV1crc+fOjbPZbALgscceK2i3YTf7nzhxYvPDDz9cNHPmzARN0zCZTPL5558/mJCQYO/OZ+4uorOlCwAhxA9SyklCiG3AHbTUHPhBSvmbTCHuQgjRIKVs17dysVhsAr7rLVt0dNrh1UVy0cudNejsGj7e4xaLxdOBp7rbZ/tIzc8/KyMk/GfXfnANhobABQsqrWPG/KromLnCXJD059Bmz6LLfGFPaM/YptMRdkz1hURV7ObUpq1MNW5hmt8BRgdpKL2VWeVGKdnX0U53XPcAi8Xih4FLu9tv1+d1VkYO3drs4VnZYSVco6qWz87NNfjauw6oP5pFPJK2hEXuG9AGUUwegXjhekByZaXCrFnx/PLLf/7W//u/RTzxROkJ2SKlxo4dGWzadEKfd5ixKm9H9OsBEcb6/rZiVcPv5czOGhzvtX+8CCEWAacBiVLKBCFEBPCBlPLM4+0zJSUlLykpyX3uYzq9TkpKSlBSUlLssdtdWRl4RQjhDzwMfAp4A3/tWfN0dHT6Dq0+POrbCm/fYpd+uIXFUhG8cKE0hYf/RwhItJCNwekj/++AFNq0BLD3TbLkQUwjHlW5DDv8I6fbNzHDvI1zAvKJ9aflO1nnBDFbqrOiYjdHKoqjw0G+h8Nx8IKcnECLqnZrkPcmN6YuYZEbioq1YqGGXXh2SwgABAaqbNqUwXnnxfPTTy3X0T/+EYndbmDp0uLjqkOgaTbWri1kz57jFgIC6bzD78es54M/TzQIunb41gG4DJgA7AaQUhYLIfQCsTou4Uo2oddaX24Dem01QEdHx/0oSnNR9LCNniZzQ6wr7Q2+voUhDz7op/j6HvmREXZRM+qxqJLgbfd6wFqX+tHpGAlaDX7lGSTUfMcZ6mame+zgrKBKgvwB/762b/Ahnf6B6VlBoZ0PXgOamjLPzcuLVaTsVjasDcxMu4k33ScEBA6+oo4Y2k2Z2CX+/hobN2Zy/vlx7NzZcl8/80w4drvg+eeLuiUInM4aVq5soLDwuF2hvIS98vPId5rO8cjX3YK6h11KKYVoyfYrhOi1VQmdgU+3Zu+EEJ+15bTV0dEZ2Fg9y9MjY7YONxg0lwY3psjIrOCFC4cKk+lIe49Cj5yku7UmS8WkoVCpz1B3Ew3hKCf40H7G1H/DmXIz072/Z3JwI16hgO5m5WaEcFZGxmxr9vCq6HTgObSmJnVKUdFIQfdmqVMYn3U+690bN/MceZzTScCwK/j6anz9dSZz5sSxY0eLS86yZWHY7YLlywtdEgTNzWW8/LKZ6uqI4zVjkqUwY0NUcoyPoXsuWDoAvC+EeBkYIoS4FbgJeLWPbdIZIHR3Kb9DP0odHZ2BgnQGBB3IDgzZn+jqEdZTTkkLuPXWRNGWo03ijPg4OC3uX+8ahXx8jNtMHUR0UrhL/17tA8yWmuyooZvDOi2mJ6WaVF6eNaaiotsz+weJzp/M90PdGr9xLancRc+sOrSkHc1i7twRbN7cklr21VdDsdsFb7xR0KkgqKrK4+WXQ7HZPDpu1DEKWvM/gr4++D/+3+kJB44TKeVSIcS5QC2QCPxNSvl1H5ulM0Dorhj42S1W6Ojo9ApCqNURMdvqPb3KXRUCms+cORm+F110ZObU0GioGPvwkFL/3VeFwn49zV876IW7+jPSOSQgIzM4LKXTQbSQsunMwsLSmLo6l0VzG4fxLx3PnhAbVvcV2BtPFskuZA7qDp6eks8/z+bii4fz9ddDAFi5MgS7XfDWWwf5dfbgFvLz00lOHoGmHVecULDSULQ16k3LKHOFLgSOEyGEAqyXUs4CdAGg021cKTo2F/hcSqnphcZ0dAYuRmNjfvTwDf5GY7NrvsVC2PxvvLHI8/TTjww4vNO9Msffl2oz1d0yUg8S/k/hrmxGVO/kdw69cFf/RghnVUTM9gZPr/JOhYCiaVXn5uXZApqbu10fowHPw0mkWGsYclyz5C4RSDHfEo3BDcG1Vqtk3bocLr98GJ9/3hKjsmpVMA6HgVWr8jAeddvv3p3KunXHuTIh5eVeqWmrwtfEm8XxCQmdFqSUqhCiUQjhJ6Ws6foIHZ1f48oNeDXwnBDiQ+BNKWWv55nV0dE5Mby8i1LDo79NEEK2M7XXDkZjdfA99zSahw1rSRqgYYt90z9j6Ft/9YFPT8w/eYAiQT1MwKE0RtZ9yxRtM9M9vuHMkFr8goCgvrZPp3NM5tqc6NhNoYrR3mkQttnpLJ6Tk+Pp5XR2uzCYHVP9ZL5XC4lut7BRj2Chlt3HkTmoW+ewSD75JId584axdm3LZ1mzJhC7XbBmTS5Go5Ovvsph587jEgJmnLXvhH1YeaVPqvsCq08+moG9QoivgSPFrKSU/91TJxj74os9+v/ad8cdJzyenDp1atyHH36YGxQUpLrSPj093Tx37tz4zMzM/a6e47bbbovauHGj38yZM2sWLVpUOnv27DiHw2F45plnDs6ePfs3NQ8mTZqUuHTp0oJzzjmnsb3++iOuZBP6gxDCF7gGeLM1Uv1NYJWUUq9ErKPTr5G24LDdB4cEZLv8JS48PUtDHnjAYgwMjAAw1hpLxy1sKvNNmx4Hh0+KDBXtFO7y3sXEEL1w10BEqn7+WRnBYT+PFF2kqfSx2XJm5+ZGmDSt66J7x6BisJ3HV9X7GXt8WX1cQeDgS2qPO3NQdzCZYM2aXK65RrJmTUtcxaefBjB3rso11+wgP/+43HriTZU5W6PeDAo31p/0VcmFELOB5wAFeE1K+UQH7a4EPgBOl1L+1EF3/259nFRs3bo1y93neOedd4LLy8t/8fDwkK+88op/XFxc80cffZTnjnM5HA6OytHRa7gU2CSlrAU+BN6j5YfwMmC3EOIuN9qmo6NzAgiDoyJ62IaqIQHZLs/kK0FBuWGPPupnDAz0Bxiyyyv1d/M+qfZNOzdpsAoBO6b6HIblreGK1Lt4PnMcew6ZsZsiKY6ezfqRj/Nw4recGelW/28dtyCEWh0Zs604JPznUV0JgbD6+rS52dmxxyMEJKh/4O2irUxz7yD9GXKZ1gtCoA2jEd57L49rrik/su2rr4J59NHpOJ3dclESSOc9Q75NSxv6r2Hh/a+IWK/T6ue/DJgDjAauEUKMbqedD/DfwPed9SelXNnewx229xYPP/xw6GOPPRYCcPPNN0f/7ne/SwBYu3atzyWXXDIMIDIyclxJSYkxPT3dPHz48DFXX3310Li4uDFnnnlmfH19vQDYvn27Z2Ji4uhTTjll5NNPPx3S3rk0TeO2226Lio+PH5OQkDD61Vdf9QeYMWNGXFNTk2HChAmjHnroobBFixZFbd682W/kyJGja2trDVdccUVs2zGLFy8+0veqVav8x40bNyo2Nnbsl19+6Q3Q2NgorrzyytiEhITRo0aNGr1u3TofgOeffz5wzpw5w2fMmBF39tlnJwD89a9/DR07duyohISE0ffcc89xZ+hyFVdiBi4G/giMAN4CJkkpDwkhPGkpTf0v95qoo6PTXUzm2pzoYRvDFMXh6eox5ri49KA//zlOKIoinKJxxAtKRuQn10dC6qALEnaiNL/Ozbl/54EwvXDX4MRkrsuJit0UYjTaojttKKVMqKpKP6209LiDcRfyZPZ7XOPeANhrSOXPPZQ5qDsoCrz99kFstjo++qjFbTAnZwhvvz2Ca6/NxmSSXXXhI2zlX0a+5ZjiUajXDvgPk4AsKWUOgBDiPeAS4MAx7R4FngTu66wzIUQ88HdahMURQSulHLD1oaZPn16/dOnSUODQL7/84mm32w02m01s27bN+6yzzvqNZ8rBgwetb7/9ds6UKVPyL7jgguHJycn+d9xxx+Gbb7459plnnjl44YUX1t92223tiunk5OQhe/fu9UhNTd1fUlJinDRp0qjzzjuvftOmTVmenp4T0tLSDgCEhoY6fvrpJ6/k5OSD27dv9ywpKTG1uRxVVFQcccN1Op1i7969qatXr/ZbsmRJxOzZszP+8Y9/hABkZGQc+Pnnn60XXHBBfHZ29j6A3bt3e+/Zs2d/aGio+tFHH/lmZWVZ9+zZkyqlZNasWXFffPGF95w5c37jktRTuLIycCXwjJRyvJTyKSnlIQApZSMteWx1dHS6xiiE+Omox5/ccxopffxyU4eO+HJYN4SA9DzrrNTge+9NFIqimCtNBafdtC8z8pPpYwebEJAgv2FKxlDybbfz8qhWIaDjPnrpuj8aqfkOyU4dOuKLYUajrXORJ6VjUklJ9okIgRe4M20p97tXCIwli7d7OHNQdygvz2LChDJOPbXsyLa8PD/eeisOu73TFYIp1oMZRcP/6TPFo9Dts5v9jK6u/Uig4Kj3hRyTvl0IMQGIllJ+5sL53gReApzAdCCZlgncActZZ53VuHfvXq+qqiqDxWKRp512Wv327ds9v/vuO58ZM2b8ZmAcGRlpmzJlShPAhAkTGvPy8iyVlZVKXV2dcuGFF9YD3HTTTZXtnWv79u0+V1111WGj0Uh0dLRz8uTJ9Tt27Oj0N3TkyJG2goICyw033BC9Zs0aX39//yNxC/PmzasCmDJlSkNhYaEZ4Ntvv/W+/vrrK1vta46IiLDv3bvXCnD22WfXhoaGqgBffvml77Zt23xHjx49esyYMaOzs7OtaWlp3V6x7A6uBBCXSCm3Hb1BCPEPKeX/Sik3uskuHZ3BhlNKeZp7T6E1hkb+UOrrd9D12UMhnH5XXpnjPX36KCRa0DbTvlFLlvgY1PVJbjS0TygmvGA+qw07OFtPYdh79MJ1/x+EUKvDo76p8fIp7fIeEFLWTz94sCqsoSHueM/3KRel3sUL7p2tD6CE79yUOcgV0tNTWb06ESkNXHRRIYoi+fHHluDqgwd9SU6O57rrsrBYtKMPU9Cangn+svCuIT+crPdbV9d+e//PI6ssQggD8Axwo4vn85BSbhRCCCllPvCIEGI7sMhVg/sbFotFRkVF2ZYtWxY0adKk+qSkpKYNGzb45OfnWyZMmNB8bHuz2Xzk76coimxqajJIKWkrj9MZUna5wPUbgoOD1X379h34+OOPfV988cWQ1atXB3zwwQd5AFarVQIYjUZUVRVdncPT0/PI/SOl5O677y65//77K7pt1HHiysrAue1sm9PThujo6Bw/BoO9dOiI9Y2+fgddXxI2GBoCFywo8p4+PUHYRe2oRytTxiy6aIRBXT+oAvsa8ai6i+czIymO3sHZeoGvQYrRVJ8bG/9vg5dPaZe1HIyqWn5hdnZzWEND5y5EnfAjp2Veyifuna03U8surHi7MXNQR0ipsX17Gu+9NwopW8YKQsAFFxRxxhnFR9oVFvqwYkU8TU1HxhOhSn1h2tB/Nd415IeTMvOYixQCR19/UUDxUe99gLHAFiFEHvA74FMhREcCo7lVQGQKIf5LCHEZ0K5//EBiypQp9cuWLQudNm1a3axZs+pWrlwZPHr06EaDK1WxgaCgINXb21tdv369N8CKFSvazfQ1derUujVr1gQ4nU6Ki4uNP/zwg/fZZ5/d0F7bNkpKSoyqqnLjjTdWP/bYY0V79+7tdCXhrLPOqn/77bcDAPbs2WMpKSkxjx8//jeiZs6cObVvvfVWUE1NjQEgNzfXVFRU5Nb0ux12LoRYANwBjBBC7Dlqlw/wjTuN0tHRcR2L9XBmVOzmGINBdXnAIMzmyuCFCzVTRMRQa5GSfcrd79ot5S9NcKedvY2KwbaSG3LuZNmIZjx0d6BBi9R8h+Smh4T/lChE1xNcHg7HwQtycgItqnrcAfE5DMs7ix3DJAb3zdYLHHxBLbG9GDDchqbZWLu2kD17fit2hIDzzy/BYJB8802LuC4p8WblygSuvy796pCcA2+FfZRgdDWN8cnLj0C8EGIYUERLGvfft+1srRdwJGWxEGILcF8n2YTuBjxpCTZ+FJgB3NCTBvdEKtDuMnXq1Lrnn38+bMaMGQ2+vr6axWKRZ555Zrd8519//fW8W265JdbDw0ObMWNGbXttrrvuuupvv/3We9SoUWOEEHLx4sWFMTExzs76zcvLM918882xmqYJgCVLlhR21n7hwoWHrrvuuqEJCQmjFUXh5ZdfzvPw8PjNcsHll19eu3//fuvprTV+PD09tXfeeSc3MjKyU3tOBNHRsoUQwg/wpyUg5S9H7aqTUh52l0Ed2NIgpWz3i3uxWGwCvutNe3R0juHVRXLRy5016OwaPt7jlhj+OtUvIPvW4NCUbs1OGnx9C0MefNBP8fH1CP+seV/803dFCpk5aGIDJMgfmJQ5jw9CCogZ0tf2DHJulJJ9He10x3UPsFgsfhi4VAi1Nizq2ypvnxKXKjv7NzVlnpeXF6tIedyZocoJKh5OTkA9Pm714eWfZHAvve9i43TWsHJlA4Uu+Phv3hzK1q1HxEpEqKnql0cc+cG+uJTzfQBTw+/lzM4auHLtCyEuAJ6lJbXoG1LKx4UQS4CfpJSfHtN2C52LgR4nJSUlLykpqddcVXTcT0pKSlBSUlLssds7W3aQUso8IcSdx+4QQgT0tiDoiPjR70uH3atKVS3NTqfFrjqtDtVpVZ1Oq1RVi1RVi9BUs1FVTYrUjGZNU6xSKlYQHiBcW2fS0emHjBj5MULIbgkBY2RkVsj998coqqVuzP3fZQf89NA4UAfNDF4ZIYXXsIrNzDhZ/ZRPGoymhvzo2I0BRlOzS0JgaE1N6pSiopZGQBoAACAASURBVJGifV9tl6jDu2I8e7zdLgSuIpV7+yBzUHNzGS+/bKa62rVg3+nTy1AUyaZN0QDFZQ7/qY9i2fgQmeFDcNssZj+gR8SOlPJz4PNjtv2tg7bTOutLCJEA3A8M5aixnZRyxgkbqjPo6UwMvAvMBXbREtRy9BeoBPpLuiphMjf4m+jUtes3SImmqeZGVTU3qaqlWXVaHU6n1aGqFlV1WlGdFqmqFoOqmhVNM5k0zWiWmmKV0uABBvf+EOjouIAQslti1pqUlBpw662J3tkyI+meR6ym+s2nuMu23qYJa/VDPH7oWe6Od6vrhk6/ICT8pxrfITnRrrgFIaU6vrw8a2xFxQkNrm2YayeyS5QS7t4c+aPJZlUfZA6qqsrj5ZdDsdk8XD1EIB33X2LYFhTKWQtXMRQgtRjPqY+SsPkhMiIDBp0gkHiSSSz9sebIB8By4FV6SKzonDx0KAaklHNbnwdVMGEbQmBQjHZPxWj3hO6lbpVSqK0iokl1Wmyq02pXnRbVqVpV1WmVqtMiVNUsNM1s1NRWISEVq5QGTxBmN30kHZ2O0Hxmz87wnXvx8Jh38n4Z9vo9I6HK5foD/RkN4XiX32fdzvLhDXjrqwEnCX7+OX64kABDSNl8ZmFhSUxdXeKJnM+J0jyVrfWZJLg3PWYAJewkstczB+Xnp5OcPAJNczlI0c/QXLY+8i1tsrVoJHOpMCnIe98hVkrILMXjnEdJ3PQQGUODcLjT9F7DQi6xWPEiAajpa3PawSmlfKmvjdAZmHQWQHxqZwdKKXf3vDkDAyGkYjTavI1GW7dzPGiawaGp5iZVtTarTovN6bTYVdWqOp1WrXU1QmiqxaCqJqOmmUxSUyyaVKy0CAm3RpPrDEKEsPnfeGOR78jThoxbsCLHN31Fp/f1QGI3EzLn8UFgDiN6351Cp9+jaFr1rLy8psDm5hOa0NIQzitZU/o9v4vtIdPax0wtP2HFh95ded69O5V167p1D03zyE3/LOLdWC+D48gv4N1zqDQbkXetZJgmIecQ1qmPkrjxQdJHhA5gQWCigBgkfvTLiVEhRFt2nHVCiDuAjwFb2/7+4tKt07/pbHD5z072SVoi1XW6icGgmQyGZpPR1NztpWZNU+yqamlUnZYWIaFanarT6mxdjZCqahGqala0ViGhScUiNcVTj484STEaa4LuvrshpNa/adwVf/RV7HmDYtBcTlDxdbzlXM9sPW2hTruYnc6SOTk5Hl5OZ/iJ9CNB3sW/ctdyqXuvNYGDz6lhGMed6rTbSOlk/focvv/e5e8FI2rjspDPi//kt6vdlZY7zuWwyYhc8AbDVQ3yK7BMe4zEjQ+SkRCOveeM7wUUyoiinkBG9LUpXXCsK/f9R+3rTy7dOv2YztyEpvemITpdYzCoZoOh0WwyNXbrOCmRmmZsc2tqVlWrXXVanU6nVVWdFk1VLWiqSWiayaBpJkXTFKPUjEYpFbOUBpOUwgrC0pJTTmcgIDw8SkPv+4uWuDqlPPKTv48dDEHCNsy1j/BIyZMsjNdQdHGr0y4+NlvO7NzcCJOmnfAM+1LuS3+RO93vv/8kuczsxcxBqtrAe+9VkJXl8jkjlNqCHdFveA8zVXdapO3W6VSZFbJveY3hThVReBjL9MdI/PpB0kdHDgBBYOAw4VQQQhyC0L42pysGqyu3Tu/iktuJEGIsMBr+s3wppUx2l1E6PYsQCEVxeiiK0wNz9wKt25ASTdOMNk0zN2uqya6pJruqmp2qanFqqllTNZOmOi1omklqqtmgaSahaUZF04yK1BRTq7AwSyksurBwL0pgYF7ULXc7Jtz3lMmzaOeArySsIRxruDLrZl4fVo/PCfl+6wxuQhsa0mbk5ycI1wpqdsr7zEtbyFPuFwJXkMp9vZg5yGYr57XXoKLCpSxMILXrfPakvxn6SaLiYtKCG86h2mQk+8bljHCoiOJqzDMeZ+TXD5A+Lvo/Liz9CkE9wRQSwQgMtFuYqj8ihDgdKJBSlra+vx64AsgHHulRN6F/j+3Z6/TCfT1at2DSpEmJS5cuLTjnnHO6N2Payr333hvx9ttvBwUEBDhVVRWPPPJIoZ+fn/bwww9H/vLLL2lt7RwOB2FhYUm7d+8+MHTo0IHrAncUXYoBIcQiYBotYuBzWqoP7wB0MXASIQQGRXFaFcVpPdE8Cq3ColnTzDZNNdlU1ezUVJNDVS2qpppVVTVrqmqWmmYWmmqiZcXCqGia0Sg1xaRJxYQ0WKQ0tAoLnTbMw0ekj0w6r3nMH2+IF1r9gA8S3sO47CtZMySThEHh4qTjJqSUCVVVaaeVlvbIdbKDM9Pn8777hcAosllN7wncqqpcXnkllOZml74brMJR/WH46poLvLK6/Xf9/RRqzApZf3iROJsTUVaDaebjJH75FzJOjeU3VVf7kGYCyCWaYSh9kMXpxHkZmAUghDgHeAK4CzgFeAW4su9M6984nU6Mxl8Pg2+//fayJUuWlO3evds6c+bMxIqKipRbbrnFnJ6ebk5MTLQDrF271jchIaFpsAgBcG1l4EogCfhZSvlHIUQo8Jp7zdIZzLQKi5aVih4RFiabpppsmma2qarJoalmh9oiKlRNNUtVNUtNNQtNM6FqJkWqJoMmFaPUFKMmFXOrsLAO9ExP3kmn7TmjuNEU/PhdA341oJKAkhtZYf+Mi/q7v+7JiZk6kihhdj9IYSil4/TS0rz4qqoeEQJpJOZMY0unrjA9gj+lfE8kyomvYrhEZmYaq1YlIF2b3R9rLsvaErUiPFBpcnEF4bdcOZlak5HMq/9FXLMDQ3kdpnP/TuKXC0k/fUSfCwInvmQxlAhMfVDToedQjpr9nw+8IqX8EPhQCPFLH9p1wtTW1houvvji4SUlJWZN08TChQuLb7311qq1a9f6/OUvf4lWVZWkpKTG5OTk/GMr+V577bUxKSkpXs3NzYaLLrqo6plnnikGiIyMHHfNNddUbN682fe222479Kc//amqvXOfeuqpzYqiUFpaapw7d+7h5OTkgMcff7wUYNWqVQHz5s0bVIHZroiBJimlJoRwCiF8gUO4EJAihHiDljoFh6SUY4/Zdx/wFBAspdSr250oUjoFOIWUDgFq62vV0PoQoBmk1AxSSkXTVIOUUml7aJoUgCaE0IRAEwINROt7IdueW7YZZMtrgyaEIkFIIQwSlNZnA0IYJSiyZZleQbg3A1KLsHB4KIrDA45rZfAIRwmLZk0z21XV5GhZtTA7VdWiaapZU1UTmmqWqmYyaKpJSM2kCIOzqYc+zvHiDB9zWsqU9ZtiLYcLA/vYlhPChrnu/3iw6DEeTtDjAvoRAidRFDGHRq7HlzOIwEACLZVT+84sKeunHzxYFdbQ0CMBviWEFU5kV6SK0b2fy0wdP2LulcxBUjrZujWLrVtdmvU2oNkfDtiWszhwS4/Mkl8ykbo1d5M57znim+wYDtdjPO8JEv99PxlTEuiL704NT7IYRiCWAbkScCyKEMIopXQCM4E/HbVvQGcg/Oijj3zDwsIcW7ZsyQKorKxUGhsbxW233Tbsq6++Sh8/frztsssui33qqaeC//a3vx06+tinn366KDQ0VHU6nUyZMiXx+++/95g8eXITgNVq1Xbt2pXe2bk3bdrkZTAYZHh4uPO66647fPvtt8c+/vjjpU1NTWLz5s1+y5cvL3DfJ+99XLlQfhJCDKGlkMUuWpLy/+DCcSuAFzjGnUgIEQ2cCxzslqX9CSk1oG0A7jwy+Ab1qEG4ZpBSU6SUrc9tg3FaB+EYpUTRNNG6TRg1TbQ+GxQpRUskrzQomqYYNU0xSKkoUiqKpimKlCZFSqNBSqNo+T8aoZdT0rmIBqomhFMTQtOEUFsfmtryvm27pgohNSE01WCQrW1k67Yjz62v0VraoAmB+uvnNlHTJmiQQgitRcCItteyVdgced0qaBA2RRptihTCmxZRo9AiajobmLpcpKfHEaJ+vE9I5pgP3p4g0Abs4FlDONdySeYfeTO2hiGD4Qd64ONFJVMo51pMXE4kPhz3DLE7MKpq+fm5ucLPbu+RDDw1+B4ax94hjXi51/VQ4GQd1YzohcxBqtrA6tUVZGa6dE8NMTSVbYxcyanW0h69By88hfq195Jx2TMkNNgwVDdinPMkievuI+OckSc4i9MdLGQzDC88ezFY2/2sArYKISqAJmA7gBAijv5ZD8FlTj311KaHHnooesGCBZGXXHJJzezZs+u/++47j6ioKNv48eNtADfeeGPlsmXLQmiZqD7CypUrA1asWBHkdDpFeXm5KSUlxdomBq6//vp2VwMAli9fHvr+++8Henl5qcnJyTkGg4GpU6c2NjY2GlJSUix79uzxOOWUUxqCg4P7flW0B+lSDEgp72h9uVwI8SXgK6Xc48Jx24QQse3segZYCKzthp0dEtLQoNWazYUGOHoArhk0TR41A46x5T3GloE3xpZB+K8G363PBkXTlNaBuKK0DsKNUrY4rktpNLQMEM2tD50uMIBikFJByq4b91MkHC1k1FaBo2lCqAjRJxkywhobbePq6iuD9++f0Bfn7ykOMCr7Stb4pjJ6IC/VD3wUmkmgiEtxcB1BjCII6JcrTRF1dTVnFBd7WFTVuyf6a8JafQq/mCoJ6pH+OuXvZHNeL8QJNDeX8dprCpWVLom4cz2z09aGrxruYXC65Xft3HE0rLuP9Ev+SUJdM0ptE8qFT5Lwyf+QOXMMx5fZwlVMFDAUiW+/TxPabaSUjwshNgLhwFdSHvmhNdASOzBgGT9+vG337t0HPvzwQ7+HHnoocsOGDbWXX355dVfHpaWlmV944YXQXbt2pQYHB6tXXHFFbHNz85HJMh8fH62jY9tiBo7dfumllx5OTk4OSE9P95g/f/6gchEC1wKIz2lvm5RyW3dPJoS4GCiSUqaIHkomMys/3wBE9UhnOjodIKBFKErZXpRDn4jCGXl5Fuhfs7XdoYohZTfzeuPHXD7ofqAHCJIASplJNdfhyXlEYhkYg6VpBQV+QI8M3B0YG8/km+Y8hoX1RH+dchlp/G8vuKZUVOTw2mvh2GxdrloaURteCVlX+ke/X9xu1/TRNH6+kIy5T5FQ04RSb0O5eCkJH91D5vnjqe/xEyqUEkUjgYM7176Ucmc72zL6wpaeJC8vzxQSEuK84447Dvv4+GgrV64MXLJkSWlRUZF53759lrFjx9qSk5MDzz777Lqjj6uqqlI8PDy0gIAAtaCgwLhlyxa/qVOn1nV0Hle4/vrrD19++eVxdXV1yrvvvpt3Qh+sH+KKm9DRBSyswCRa3IW6VXRMCOEJPASc52L7P/Ef37cB7femo9MdBvu1b8dUv5T7Cv/Ko3pcQG9jpo5TKGE+cA1hhBNOy4xin9MX172GcFzEuoqfOTXG7SdLJIcPesE9JS0tlfffT3QlUDjaWJO/I+p1vxhTba+JwLMSaVz/F9LnPElCVQPGRjuGS58m/v3/JuuiUzmhAdsRDFQSTmVrrQD9O6an6OFUoF2xa9cujwceeCDKYDBgNBrliy++mO/p6SmXL1+eN2/evBFtAcT33Xdf+dHHnXHGGU1jx45tjI+PHxMTE2ObOHHiCQvNiRMnNlutVm3cuHGNvr6+Ha4sDFSE7KbrRqvP/5NSymtcaBsLfCalHCuEGAds5D9RnlFAMTCpLT9uJ/00SCm9OthpAr5z+QPo6PQ8ryLly5016PQaPt7jhJhOSyD+gECC+jkXZF5PcvRhArv9t9A5DtoCfy+gkevx43eEY6Ana3zcKGFfh6d3x3Xf0uBh4NLu9ns0ErRbeC3nDW52f+agIZRxED+3BgxL6WTjxmy++cYFFySp3ez7c/rLIetcrh3Q0+zKxXr+EyRW1rcIP4sR+e5/kXX56dQed6eCOkIoIpw4DG4VlDWMlDM7NeU4r/3+REpKSl5SUpKe5GUQkZKSEpSUlBR77PbjuVkKgbFdtjoGKeVeIKTtvRAiDzhNzyakozP4ySA+Zx4feO0hSQ8OdjfeVDCFCq7FxGX9L/C3v/AIj2S8wc3uvx5N1PMjRrcKAaezjvfeqyI7u0sh4CnslZ9GrGqc6ZnbpzE6E4fRvPFB0s59gsTyWkw2J+KaF4hLXkD2/N91O/B1oNcK0NHpU1yJGfgXcHRAyilAigvHraKlWFmQEKIQWCSlfP34TdXR0Rlo1OBbfjvL697jmkHts9untAX+Xt4a+JtIEBDU12b1Z1ZwQ+oSFrl/MCxw8gmVxLlRkDU1lfLqqyaqqrp0dTrNUpT5dWRy1BDF1i8Cw5OGYtv8EOnn/p2EkmrMdifiDy8ywuEk5w9n0WWgKODEjyxiiBzgtQJ0dPoUl1KLHvXaCaySUn7T1UFduRFJKWNdOLeOjs4AxIGx8Tn+fPBB/i/OgTm4r+0ZZEgCKWEmNVyPJ+cSiXlgBP72BzYyI+2PrOidgeNjZHOBGzMHlZdn89prkdjtna46KGjN/wj6+uD/+H/X71JqjolqEQQz/4/EoirMThVx48sMtzvJvWkaHaWA1PAii1iCBkmtAB2dPsUVMfAB0OZTmS6ltLnRHh0dnQGMBO0rzsu4lneiKgnSf6R7CjO1TKCUq2gL/I0AIvrarIHGXsZmncdXvTMgvohUHnTjbPX+/Wl8+GGXFYVDlPrCbVFvWhPNlf1OCLSRGIF9y19Jm/k4iQcrsaga4tbXGG5Xyb19Jr9O42glm9hBVytAR6dP6VAMiJbA3KeA64A8WlyEQoQQ/5JSPiGEmCCl/Ll3zNTR0env5DAs70rWWH/mVF0EnCgCJ9EUcWFr4O8kwjHg29dmDWQOEp1/Oj8O7ZUMVvHk8LGbVgSkdPDVV7ns3NnFfSa1a3z2pSeHfpRgFLJPK0W7Qlwojq1/JX3G4yTklmPVJNzxJsMcTsRd51OJiYMMRQzGWgE6On1NZysD/wQ8gVgpZR2AEMIXWCqEeAmYDQxzv4k6Ojr9mTq8K+5kWc1bXK//SJ8I3lRyJuX8Xg/87WkO4192Cr8E27C2VyekZxlCGT8SjuKGlJZOZy3vvltDbm6ns+JW4aj+MHx1zQVeWQPKjz42+IggSMwqwyol/DmZ2MMmKhbdh/vTv+ronKR0JgYuAOKPqmaHlLJWCLEAqADmuNs4HR2d/osTpWkZd+Yt5Ml4OxY9YLW7tB/42y8COwcTDXgeTiLFUkWAp9tPZqKe7zHiR5fFvrpNY2MJr7xipaYmurNm482lWZuiVoYHKk0DUkxGB+Lc+lfSpz7K8KwyfCTwyOucZvbl4AN/orzLDnTcR87YnhWXw3u2bsGkSZMSly5dWnDOOec0dt26fV544YXA5557LkxKiZSSa6+9tmLJkiVlGzdu9Lrnnnui7Xa7wW63i0svvbTq6aefLn7++ecDFy1aFBUaGupwOBzijjvuKPuf//mfAZclszMxoMl2ihBIKVUhRHl7Fe90dHQGPxK0zUzPvJZ3wksJH1Azj32MJIgSZlHDdXgySw/8dTd2TPWT+V4tJDrA7SdryxyU4IYVnbKyLF5/PRqHw9JREwOa7ZGALXl/DdzmvoDl3kBQGzGS4h0f4Jh5E6P3Z+EJ8OAzxNjtiEX/xaG+NlFncOB0OjEa/zMMfv/9931ffPHFkK+//jojNjbW0djYKF566aVAgJtvvnnYqlWrss8444wmp9NJSkrKkaD9iy66qCo5OflgUVGRcezYsWOuuuqq6ujoaGcffKTjprNlzANCiOuP3SiE+APQq1XodHR0+gcHic7/HTvLZrIpsZRw3Ye9K8zUMpkMniGDYuooJ4JVjOIChmIefNWl+xMqBtt5fFW9n7G9k83qEbK5wA1CYM+eVJYvH9GZEAg0NBanxLxUO8CFQDOBpDIeM5GMDA1GbEkmY3wiDW0NHllG9EPPENaXRur0HrW1tYZp06bFJSYmjo6Pjx/z6quv+gOsXbvWZ9SoUaMTEhJGz5s3L7apqek3hRSvvfbamLFjx46Ki4sbc8899xxJthAZGTnuvvvuC584cWLiG2+84X/0MU8++WT4E088URgbG+sA8PT0lG2z/IcPHzbGxMQ4AIxGIxMnTmw+9pyRkZHOmJgYW1ZWlrln/xLup7MfozuBj4QQNwG7aKk1cDrgAVzWC7bp6Oj0ExrwrPwzz1W9zi3ur9Y6kBE4iTkS+DuE0wnTA397HwnqH3i7aCvTeqe+xVzS+FsPp7iU0sEXX+Tx44+drL5JeYlXWvrq8DVxFqEOVHHpaK0VEH1srYAgf9TNK8k49ybidx/AG+D/XiHS7kA8tZCSvjFXp7f46KOPfMPCwhxbtmzJAqisrFQaGxvFbbfdNuyrr75KHz9+vO2yyy6Lfeqpp4L/9re//WrF6Omnny4KDQ1VnU4nU6ZMSfz+++89Jk+e3ARgtVq1Xbt2pR97vszMTI8zzzyzXRejP/3pT2WjRo0aO3ny5Lrzzjuv5s4776z09PT8lffMgQMHzAUFBZbRo0cPuKybHa4MSCmLpJSTgSW0ZBM6CCyRUk6SUhb1kn06Ojp9iBOleTm3pQZS6asLgQ7wpoLzSSOZHGpwksdQljGKyYRj4DczVgMeVW2krCyLrVvTePNNta/NaY+/8ERWrxW6iyeHT3o4zaXDUc2KFYf48cf4jppYhLP2g7D38z6JWD1ygAoBDS/SGUM9IxiFqWWwfywBfmibVpA5aRx1bduWvknEnx8nQtN6z1id3ufUU09t2r59u++CBQsiv/zyS+/AwEA1JSXFGhUVZRs/frwN4MYbb6zcsWOHz7HHrly5MmD06NGjRo8ePTozM9N6tFvP9ddf31H9ig5ZunRpyXfffZc6a9as2vfffz9w2rRpR+75devW+Y8cOXL01VdfPfzZZ5/NDw0N7Zffi53R5ReIlHITsKkXbNHR0eknSJDfcGbGfFaHFROpxwUcjUIziUcCf4NJGOQVf6V0UltbRFZWE3v3enPwYARStgnDfpey8kUWpD7J//bONevLIX7o4cxB9fVFvPqqF7W1kR01GWkqz94StSIk1NgwMDP6tdQK8MbTtfSrfj5oG94ka/atjPj255aVtuffJtzuQCz7G0UG9yeL1ekDxo8fb9u9e/eBDz/80O+hhx6K3LBhQ+3ll1/eZWXqtLQ08wsvvBC6a9eu1ODgYPWKK66IbW5uPnKV+Pj4tCsj4+Limr755hvPiy++uK69/WPGjLGNGTOm/N577y0PDAw8pbS0VIH/xAwc7+fsD+i3kI6Ozq8oIqLgHLYVn82OxGIi/frann6AJJgiriGVf5NPI0b2M4JHGUnCIM3+09RUSnp6KmvW5PL3v6s8++xQPvtsJPn5UV0VuepL1jE37U5e7B0hYKSBnRgY0oOZg0pKMnnuuWBqa4e0t1sgHQ/4b0tLjV02ItTY8JvZ0H6PiXziKGA0I/AktDuH+nihff06WVNPp6Zt2/LVhN3yV6L1FYLBSV5ensnHx0e74447Dt99991lv/zyi+cpp5zSXFRUZN63b58FIDk5OfDss8/+1eC9qqpK8fDw0AICAtSCggLjli1bXPodW7hwYemDDz4YdfDgQSNAU1OTeOyxx0IA3nvvPT+t9ULbu3evVVEUGRQUNOBWADpiIC4t6ujouIFGPKru56mKF7mzQ9eEkwYLtZxKKfOBqwknlEigw5naAY/DUU1p6SEOHIB9+8Korw+DgRWouYtTMy9hbe8E0AqcfEQFo3ooYFhKyS+/pPHppx0KmSGGptKNkSs51Vo68Ir6KZQQQxP+nJDrlqcH8otXyL7oDoZv/I4hAG9+RIjdgVj5dw4q/W6dapDRw6lAu2LXrl0eDzzwQJTBYMBoNMoXX3wx39PTUy5fvjxv3rx5I1RVJSkpqfG+++77VcrZM844o2ns2LGN8fHxY2JiYmwTJ06sd+V88+fPryktLTXOnDkzUUqJEIJrr722AuDtt98O/Mtf/hJttVo1o9EoX3vttdyjMxENdEQ72UP7HUKIBimlVwc7TcB3vWuRjs6veBUpX+6sQafX8PEeJ8R0WqqEnxAqBttKbsi5k2UjmvEYcFkQeoS2wN+LaOQPRwJ/B5+/fxua1szhw8Wkp9vZu9efsrJuzdIexY1y0aJ9He10y3Xf0uBh4NK2tzkMyxtFapQdS+/8Oj9CGot6KGBY02z8+98F7N7dYUzO+Z6ZaZ+EvzfCalDdXzStJzFQSQSVBBOP6Ln7yWZHXPpfDPtyO0eywcyfQ8U7T5HfS4KghpFyZmcNjvfa70+kpKTkJSUlDbic+Todk5KSEpSUlBR77PbBI2t0dHS6hQT5A5My5/FBSAExJ19cgJk6plPEHzBxKVF4D+KKv1Jq1NcXkZNTz969XuTmRqBpvRNg62bKCSpOIiWs14TAnB4UAg5HNStXNlJU1K4QMOOsez107aE/+O4dWKsBglpCKCaCOETPu9JZzMhPl5Fz+X8z7LMtBACs/oIguwPx/jPkDaIJWx2dXkG/ZU5SNISjCY/aKvwba/CzG9CEgooRp0FBFQqqoXWbMKAZ2p4NaMKAphjQhEAaWp8VA5pBIBWBNLQ89HiU/kwZIYXXsIrNzOjZLCgDASON3EQ+SxmGTw+ng+xP2GzlFBRUsm+fibS0CGy2TivXDkTq8aoczx7venysXbfuAUaQy7oeyhxUX1/Iyy/7Ul8f0d7uEabDudui3giMMNYPpMJ0TQSSRxTDUdx7b5lM8PG/yL3qHuTHG1oEx8cbCLzsLsRHz5NrGlhrKDo6fYouBgYhTpSmOnxqDxPQVEyELZ+hWibxIpsR5kziPfKI9S4n2FtiCAT3BUAqODUjTtWEQzPi1Ew4NAVVNeGQbdvatre+p+21GbtsbSeNODUzdtrem3BIM3Zpxo4RpzTipO29CQcmHCioR94bcYrW7dKEo030iLZ9bQ8FVRhxoqC2CSKhoB55f4woanuIZqyOgRJF2oS1ibnY4QAAIABJREFU+iEeP/Qsd8dLDIPXDaY9DNi4ihxeIIZABt9KiNNZx6FDpRw4oLF/fwjV1cFA7xTc6gNsmGsnskv2WvE7X8r5gdAeyRxUWJjBihXDUH/r9iOQzj8P2Zn1z6D1iYYedK1xMw78yGYoURh7794yGuGDZ8m79n7k6i9aMnp9toWAi+5ArF1GrsVM//eD1tHpB+hiYAAhQdox19fgV1tBUHMhUc5chmlZxCmZxJuzGeGVR6xPPT4e0IMZLo4TFaNBxWiw0TuTdn2Iqb//4qgY7O/y++wFvDS8Ae+TazVA4GAO2bxGBOGDSARomp3q6iIyM23s2eNHcXEY0GvB3ybU+vGW0j7J41KHt/181tdnkNjurHqPY6SB74AAPE+oHyklP/2Uxueft3sd+hqay9dHvOX4nUfRQFmx0vAik1hCsPTNKpuiwDtPkW8yId/+tEX8rt+B/wW3IT57iRwPa/8VBEKI2cBztKTofU1K+cQx++8FbgGcQDlwk5Qyv9cN1Rn06GKgn6AhnE141FQzpLGUsOYCotVsRsgs4kwZJFjyiPUuINrHgdkHGHgp5XT6jF2cmnkla4LyGDZ4BsKuoXIOWawghGGDwB1ISo3GxhJyc2vZt8+TrKwIVLXX8sz7CFvFeEtZ5RyvTC72Sg8YZzkUDBjgld4y4QgjyBblhPSOEACVDyhnNLEn1IumNfPpp0WkpLR7H073yE1fF/FurJfBYTmh8/QW3awV4E4UBVb+nYNmE/KNDwkB2LSTIbNvZcQXr5Dt6dH/BIEQQgGWAecChcCPQohPpZQHjmr2M3CalLJRCLEAeBKY3/vW6gx2dDHQCzhRmuvxrjlMQFMJ4fZ8hqpZxBmyiDNmEu+ZR6xXGaE+7nbb0Tm5OERw0XW8pX7F+SdbqlDJRLJIZgij+36gckLY7ZUUFVVw4IDC/v1hNDX1UopTqYYoDaWTrUW1F3ulmy70yggJN9b3m+Jq5YT0nkf4w2Ry6QmKSbv9MCtX2igu/o3/vxG14aWQf5fc4rd7YFyrJvKJRcGHfhXLYDDAq0soMBvRlq9uSYu77Sf8Zt1E3PrXyPbxor9VI5gEZEkpcwCEEO8BlwBHxICUcvNR7XcCf+hVC3VOGnQxcAK0uu3U1eLbUE5wYxGRzhyGa9mMUDJIMOcyzDOH4b6twW2D3ldGp1OMQoifjnr/ipTSLVOqzVhqF7Oo5EkWxmsoJ1cg9yiyWYknp/eeu0yPoqoNVFSUkJrqZN++ICorg+iFCQIFrXmoqbp0qkde06Ve6Z4zPHPCvA2OnhAevXbdu4XzSePRExQCtbUFvPKKHw0NAcfuijFW52+PesMvxlTbYVrRfkNLrYBm/Om3VY8NBlj2N4osZuRzbxEO8N0v+M76I3FfvU6Wn0+vCoKurv1IoOCo94XA5E76uxn4ogft6zYvvji2R1eX77ij9+oWvPXWW0NGjx7dPHHixGaASZMmJS5durTgnHPOaewtG47ms88+8/nnP/8Zunnz5qy+OP+x6GKgA1rddmpr8GsoI9RWQLQjmxGi1T/fmsNwzwKi/exYfIHeCWDTGcg4pZSnufMEGsLxAfOybuG1YfX4DIxZxp4ilnzeRGFa/5qt7BIpndTUFJGd3ciePT4UFEQgpdsHhlbhqB5tLq84zzPbeYlXmu/p1uIwRchYN5zK7de92xhGHp+doKg8eDCd5OQRqOoxv7VS/ZPvrswXQ/6doIj+W9EZAAMVRFBFMHE9WSvAXRgM8OyDFJuMyKVvEgHww158ZtxI/NdvkBng12uCoKtrv72/ZbvuTEKIPwCnAVN7wrCTkU8++WSI0+msaRMDJ4LT6WQwFRyDk1QMOFGaG/CqqySwoZQwex6xag7DDRkkGLMZ4ZHDcO9Wt50A4DezOTo6/Y0UxmfN4wP/TBJOrriAMApZjpNLTtCfuzdpaiolP7+KffssZGRE4nC4ub6BlEMMzYdOtZRUX+iVIS72Sg+MM1cFQksFV5128KGcnwjByPGVsJJS4/vv01m//jf3o7ewVfw78t3mczzy+3cci6CWUIoJJw7RP9zDusNTCymxmNEef5kogN0H8J5+Awkb3yQzyB+1r+2jZSXg6HS/UUDxsY2EELOAh4CpUkpbL9nWr7j//vvD16xZExAeHm4PDAx0TpgwoXH+/PnVt99+e8zhw4eNVqtVe+211/InTJjQnJGRYb7hhhtiKysrjYGBgc7k5OS8vLw804YNG4bs3LnT5x//+Ef4hx9+mA2watUq/zvvvHNoXV2dsnz58rzZs2fXO51O7rzzzqhvvvnGx263i1tvvfXQ/fffX/HZZ5/5PProo+EhISGOAwcOeH7++eeZs2fPjp80aVL97t27vUeNGtV40003VSxZsiSysrLSuGLFipzp06c3bt682fPee++NaW5uNlitVm3FihW5SUlJ/e7/OKjEQFu2nTp86ssJbiwmwpHLMJnNCEMGCZZsRnjkEetbwxAPWtx2Bm3aPZ2Tg0oCSq4n2f45F/Z/N4OeJIASnqWJ6+j/hbMcjmpKSg5x4ADs3x9GfX0YtPg0uwOBdEQodSVTPAoaL/ZKM8/xygoNVJpCgeOtMnxyYaSBnRx/5iBNa+Ljj0vYt+83QuAMa0HG+si3YnwM9v48uG4iiDwi3V8rwN08djdlZhNy0Qstg+496XhNu56EjSvICA3sc0HwIxAvhBgGFAFXA78/uoEQYgLwMjBbSnmo903se7Zt2+a5bt06/7179x5wOBzilFNOGT1hwoTGW265Zegrr7ySP27cONumTZu8FixYELNz586M22+/Peb3v/995V133VX57LPPBi5YsCB6w4YN2bNmzaqeO3duzR//+Meqtr6dTqfYu3dv6urVq/2WLFkSMXv27Ixnn302yM/PT923b19qU1OTOP3000dedNFFtQB79uzx+vnnn/ePHDnSnp6ebi4oKLCuXr06Z+LEifnjx48f9c477wT+9NNPae++++6Qxx9/PHz69OnZSUlJzT/88EOayWTik08+8Vm4cGHU+vXrs/vuL9o+A14MTGOzZqW5IIs4z0KifG1Y9Ww7OicFk/jeuYuJoSdVXIAP5fydahYQh6GfuixoWjOVlcVkZNjZu9efsrJQ3DgLb0RtiDMdLpvpmWu/xDvN62xrfpjVoMa463yDnBPLHGS3V/LGG07Kyn4lUhW0pmeCvyy8a8gP/Tmtr4MhZBFDTG/WCnA3f7uTQ2YT8oFniAHYn4Xn1OtI3LSCjIgQnH1ll5TSKYT4L2A9LalF35BS7hdCLAF+klJ+CjwFeAMfCCEADkopL+4rm/uCLVu2eM+ZM6fa29tbAvLcc8+tbm5uNvz888/e8+bNO+IWarfbBcDPP//s9cUXX2QDLFiw4PDixYujOup73rx5VQBTpkxpuP/++80AGzZs8E1LS/P89NNP/QHq6uqUAwcOWM1m8/+zd+ZxUhXX4v/Wvbf37tk3GJYZZBMxRFFUHgoqbgnGRCWggFs0icvLoibG90sUt7wfz+h7MUbFGOOGxO3F+PLT+ERFNCJGSQbZhnWYgdmX7p7u6eUu9fuje4YBWWafAe6XT3Pvrapbt7rmdPc5VadOya985SvRiRMnJtvvLy4uTkybNi0GMH78+Ng555wTVhSFk08+ue3+++8fDtDc3KzOmzevtKKiwi2EkLquD8nfrSPeGPiAWQr7TrXZ2BwT/J1pGsfKTs8eWvgF9fyUcahDbEZPSpNIpJrt2yN88YWPiorhWFa/zVj4RLL5RFdd44XebfIS/+bsrzjr8hVxBMyQHAnc2YvIQaHQLpYuzSUW22fB9zC1tWrViKe9Y50tQ3VRu4WfrZRQiPPoMQI687Pv0uB0Im//D0ZLCeU78cxcxIT3nqV8ZNGgGgRvAm/ul3ZXp/PZA96oIYaUX15GYVkWgUDA2Lx588YD3NJl3G63BNA0DdM0Rfp54qGHHqq87LLLwp3L/uUvfwl4vd591ps4nc6OximK0lGfqqod9d1xxx3FM2fObH3nnXe2l5eXO88555whuZ7viDcGbGxsjmKchPkx1dzDWFxkD3ZzOkgkGqiqamL9eiebNw8jkeinAQlp5atttdNce8Jz/OXq171b80c6wvZapv7gPDbzyx4aAjt3lvPCC8dhWZ1+U6V1VaCs/OnCP08YsouEPWyjhAw8R3gI3i5w6zU0OjTkj/6dEsuCbZW422cISorRB7t9Ngdm1qxZkRtvvHF0W1tbja7rYsWKFVlXXXVVw4gRI5JPP/109nXXXddiWRZr1qzxnHHGGbGTTjop+tRTT2XffPPNzUuXLs055ZRTIgB+v98Mh8OH/Ryed955occffzx/zpw5rS6XS65bt85VUlLSY/kIh8PqiBEjkgBLly4dsu6BtjFgY2Mz9NCIcgOVPMgYfEPAb9kwwtTX17Fxo8X69QWEQvn0w5ojBSsxWgvVnOWpiH3DX+45z7ujKKAkhwMDtcHWsUkJFbzZg8hBUlp8/PEWVqzYR0a9Itn838Neilzg2z4UR9otXFQwCicBjqm1Rv+6kCaHhrzlPkpNC3buThkE7z5D+dhRtkFwOAYyFGg7M2fObLvwwgtDkyZNOqG4uDjxla98JZqZmWkuX758xw033DB6yZIlwwzDEN/61reazzjjjNjjjz9eefXVV5f8+te/LmpfQAywYMGC5htvvLHkiSeeKHz11VcP6rP/4x//uLGiosJ14oknHi+lFDk5Ofqbb77ZYx//O+64o/b6668vfeSRR4rOPPPM8OHvGBzEgaZghhpCiKiU0nfgPBzA6gFuko1NZ34nJUsPVeBQMtzT+4TgbFI+pUcPKnHms5NHKSELz6C1w7KSBIPVbN0aY926LKqrizhwKMBe4RJGeKKjsX62d4dxiX9zxhnuqkJNyJ5FsBkcruFKuf5gmf0h96l8fg58s7v1HhA/jVTgIZfutdM023jttTo2bdon7v7Jruqt7xY/OyJLTQye/B4IlTpyaKGQIpzHdiSpP/w32TfcRalppj7TxYUk3/0D5RNKSR7u3jQhJspzD1Wgp7I/lCgrK6uYMmVK42C3IxQKKZmZmVZra6tyxhlnTHjiiSd2zZgxY1D2BzjSKSsry5syZUrJ/un2zICNjc3gI9CZwzZ+xwgKB8FvWUqLaLSGiopWvvjCzfbtxZhmSV8/JFNJNJzkqmn5mm+r+IavPHeCsykXe5+SwUMlxsdY3TYEEokGfv97aGjoMARUrPgDue/uuiPnb0PJ5SZKgCqKCBCgGDuiFADXXkqLQ0Ne+38YYxiIPXU4Z13FhBV/YMsJYxlyYR+PdRYuXDh669atnkQiIebPn99kGwJ9j20M2NjYDCYm57CVP1DEqAE2AnQ9SFVVLRs3amzcOIxYrLe77e6DQBpFaqRmuqcq8g3fZufXfFsL89RYAVDQl88ZYJKotOAgggsdz6CHZ+wNFsup5cRu7qjb0lLB736XTyzWYUAUqJE9K0c84zre2TgUDIGUG1ABFrmMRhkCbnZDkIXfIOhwsP2qOzguqSNqG3GefTUT3vk95VMm7msQmBaRtgQtLVFiDSHMmhaSc+xeHTD+53/+Z+dgt+FoxzYGbGxsBgOL09jOs2QzYQCVFdOMUFGxh08+cbN9+0ik7DN3CQ2zbYyjpe4c787EJb7N3lmeimFuxTzyIp0JwmiEcBDHjYEHFQ9u3GTiIANB5z0LjiSXpn35KVuY203Z27ZtMy++OA7Z7solrbn+jVteKHptrFNYg/t7qlJLDi0UUYzDji7VFeZdRMipse2K2xmbSCIamnGctYix/3U37wwvJlrdgqchRHYsiZ9UiM92QnMGq9E2Nv2AbQzY2NgMLJPZxvME+GoPFmz2BMuKsXt3JWvWuNi0aSRS9snorVckmyc76xsv8G2Xl/g2Z53kqilQRDdHmQcHAyU9uu8kgQfw4MCNDzdZqGRwtLsuncNmlnTDEJDS5MMPt/H++x33uIUe/GPRq8FL/OWDN0YsiBBgN0Vk4Gc4/biZ3dGAlOhxnaZwG9HGMMnaIGrUiWf+PCLLljPFMFDCEdw3/YKLFi5ky4gRxAa7zTY2A4FtDNjY2AwMx7GTP+DkzAGIYGJZCWprK/n0U4X160dhmr00AKSVo8TqTnXvCV3s26Je7CvPHzW0Q3xG06P7UdyYuBF4cOMhgJMsBP0SDemIYBS7eKsbMmiaUV55pYHy8g4ZOsFZv33liD8U5qmxkv5o4uFahJtdFGCRQ4ntBrQvUmImDZpbY7Q2tZKoDSJqWnDXBckMRsniAAZT6XFY8+ez9aWXGKfrKPE42vPPM2HBAraMGoXtn25z1GMbAzY2Nv3LcKr4HRZf6+dRcyl1Ghoq+ewzi3/+cxS63uOZBwUrMUIL157p2dV2ia/cc753e2GmmhgGDOvDFvcGC4UgGmGcJHBj4cGBBw9ustHwQTcXxR4L+GlkLXk4u/jbF4/X89RTCk1NJZCSi7tyPth5d+4HA6+Aq9SQS5BC2w1ISqRuEozGCTVHSNQFsWpacNcG8Te1kiNl943dsWOJXHEFW/74R8Ylk6iJBOoLLzD+iivYUlpqGwQ2Rze2MWBjY9M/5FLNoySY349GgJQmLS1VfP55gs8/H0Uicdzhb/oyTozwBGdTw2zvdv2b/s2B0927C53CGt3Xze0m8fRi3TZcJPGg4MGJmwBushAM5ZmJoYdKjI8wuxw5qLl5J08+WUQiFSI0R2mreW/Es8oUV93AGQKCVjLYTRFZ+BhKxuiAYJi0RhO0tESI1YeQNS1otS0EGsLkmBbZ0LcbEY4ZQ/TKK9myfDnjEwnUZBL1xReZMH8+W447jmhfPuuI5O/r+zbIw6mTB3zfApsDYxsDNjY2fUsG9fwHYW7gOJS+j82PlBbhcBX//Gecv/99ONFoSXercAkj/FVXTe0lvnJ5iW9zziRXYz4D7ycvEYTQaMVJDDcmHlTcePGQgYMMjjHlrx+xWEYNU7o4or5ly2b++MfxSKmAlBf7tmx+uejlsW7FdPRzOwEM3OyiEMihBDEIoXYHENMiGksQDEaJNoQx0wq/ry5ETtIgAAQGsj0lJbQtXEj5smWMj8fRdB1l+XLGf/vbbB0/nshAtsXGZqDoN2NACPE0MAeol1JOTqc9CFwMJIHtwLVSymB/tcHGxmYA8dLE3TRxG2NR+yF8ZiSym3XrWlmzZhjhcLdG7RWsZKkjWH2xrzy+MLAue6q7ppCBUf71jsW6LpK4AQ9OPPhwk41CFhzbG0ANCLdSzrwuKNVSGqxcuZ1VqyZCasbouaI/Nc4LbOh/hVyjmlzCFDICjR7NcA1VLEkinqQ5lFq4q9e0oNa24KkLkdOWGHoubSNHElu0iPIXXmBCLIZmGCgvv8y4yy9n28SJtA52+441Fi9eXLhs2bI8gEWLFjXcdddd9Y8++mjuI488UiiE4Pjjj4+9/vrrO6urq7Vrr7129J49e5wADz/8cOX5558fff/997233nrrqHg8rrjdbuuZZ57ZOWXKlMQjjzyS+5e//CUrFosplZWVrosuuij4xBNP7B7cdzs49OfMwDPAo8BzndLeAe6UUhpCiCXAncAd/dgGGxub/sZFiNup4ReMxUVun9Ydi9WyYUOQ1avzaW4e0fUbpcxRYrVneyuCCwPrPBd6txa7lb7eRCyNIIJKECcxXB2hOF14yMRBJoIjfW+BI5uZlPNQFwwB04zwxz82sW3bBIDxjsYdK0c8kzdMi/Sff74gTAbVDCMLL8OB4f32rH5GSoyETlNrjGhjauGuUtOMuy5EVriNTI6wWa7iYuJXXUX5888zvq0Nh2GgvPIK4y69lG0nnEBosNt3rPDhhx96X3zxxdzPP/98k5SSqVOnHn/66adHf/WrXw1bvXr15mHDhhl1dXUqwPe+972Rt956a90FF1wQ2bp1q/OCCy4Yt2PHjg1TpkyJf/rpp5sdDgevv/564Kc//emIt99+ezvAxo0bvWVlZRs9Ho81duzYybfffnvd2LFj9cF91wNPvxkDUspVQoiS/dL+t9PlJ8Dl/fV8GxubfsZBhO9RxX9wHJ4+jGiSSDSweXMjq1fnUFdXRBfDJbqEETrZVVP3bf96MS+woXCYFukrH2sThRY0IriIp0f3tfTofhbql2KQ2wwVRrKL/+3CKHs8XseTT2q0tIwWSP0n2X/bviRvRX+tDdDxsItCVLIZjThyogFJiaWbtLTGCDe3kqgLQk0L7poggWCEbHmU7XA8bBjxq6+m/LnnmBCN4jBNxGuvMVbX7ZCjA8XKlSv9X/va14IZGRkWwNe//vWWNWvW+C6++OKWYcOGGQCFhYUmwN/+9reMrVu3etrvjUQiaktLi9Lc3KzOmzevtKKiwi2EkLqud7ivzpgxI5ybm2sCjB07Nr59+3aXbQwMLNcBLx0sUwjxXeC76Ut7bYPN0EXF6svqhrzsq8RZyE5+TQmZfeTPrOstbN1ay+rVWezePYwuRAJRsJJjHC17LvaVJxYG1mWd7K4tAjJ72IJYenQ/mh7dp1MozmwEeUBeD+u26QJ9Lvc+mlhL7mEjBzU27uB3vxtOMunOUmJ1/1v8vHWqu7rvFXSN3eQRpYARaAMQXreHSIk0LELpSD2x+iCypgVnbZBAUys5pkUu9PEM4BCmsJDENdew+bnnmNDaitOyEH/+MycKIS6VUv73YLfvaEdK+aU0IQRCiC9lSCn57LPPNvn9/n3yrr/++lEzZ85sfeedd7aXl5c7zznnnI4wwU6ns6Osqqr7GArHEoOiaAgh/g9gAMsOVkZK+STwZLq8vYrfpnsIdFSSaBho6DgwcGLgwkzHXbfwYOHDwofEh8CPxI/ADwRQCCDwo6bPVfxo+NHwoeHFiQ8ND04UlL5s+pCVfYUk32QHSxlBXh8YAYYRZufOalav9rFz5wgOGxlEylwlVnu2d2doQeALd9r1p7uRikxU6vASTv99/bjJSofi9Bz2bpt+o0/lPhU5yCDvMErrpk2beOWVCUipnOfdXv7nYctLPYrh7NWzOyMIkkkNReTgpRtubv2PaRJpS6Yi9aQX7jpqWvA3hMjWTXstS2fy80lecw3lzz7L+HAYl5QI4GUhxAIp5UEHNW16zznnnBO57rrrSu67775aKSVvvvlm9m9/+9uK7373u6X/9m//VldUVGTW1dWphYWF5owZM8JLliwpuO++++oAPv74Y8/06dNj4XBYHTFiRBJg6dKl9qDOARhwY0AIcTWphcXnygOZfDZHL91R0L2AH4kP0k4YCgFIK+eCDFQCqPg6FHRHh5LuQUPFAQxE5I+jH4HBbLbzewoZ2UuXBtOMUlW1m08+cbNly0ikPGR9bqEHT3bV1H87sIFv+zcUddv1R6EJFy0E0MnAj59ClCPbN9vmsFg8Tw1fPUTkICkNVqzYzscfH+/AjDxV+Oe6qzLW9cnO1KTcgCooQiOL0YjBVaqlRCYNGptaCVY1Yuysx1PZQF4sabu3dYfcXJLXXkv5s88yIRjEBajA1UKIl48ZXWYQQoHOmDGj7corr2w6+eSTj4fUAuLzzz8/etttt9WceeaZExVFkZMnT2577bXXKp588smq66+/ftT48eMnmaYpTjvttNbp06dX3nHHHbXXX3996SOPPFJ05plnhgf6PRwJiP6U4fSagb90iiZ0IfAwMFNK2dCNeqJSygNGGxACB7C69609hlGJ4SCBhokDHScmLgxcWHgw+1BB79MR9CHE7yQsPVSBQ8lwT+8TgrOBB7tbZzewOINtPEcuY3vhFmBZcaqrK1mzxsHGjSOxrIMOQihYiePSrj8LAutyTnbXdscHuQ0ndfiIk4GLDPLS4TmPCaTEkmBIiSklupRYUmJae1+WZWFZEtO0sCwLDAvTtMC0sEwLDBNpmGBaqaNhIoxUOoaJ0E2EkXophoWIJbl7wXflQRWE/pD7VD4/B755wMwfs4mHDzFzZRhhXnwxxM6dI0u1looPRz6dXay19tS9bC8OqsgjSj6j0QZnlklKrLhOQ2OYUGUjVkUd3spGCpIG7sFoz9FIKIT2hz8wNhjkU2COlPKAG5L1VPaHEmVlZRVTpkxpHOx22PQdZWVleVOmTCnZP70/Q4suB2YBeUKI3cDdpKIHuYB3hBAAn0gpv99fbbDZB5MMmhlJmEnonILCNLx8lVyy0h7SNjYpJFPYzrMEmML4ntUgderqdvHpp7Bu3WhM8yD1SJmrxGrO9u4MLwis81zo3TbcrZhdid5ioFGHhwgZCDLIxk0eon82OJMSCZhSYsj00ZIYUmKllWwjfZRmStmWVkrBloaFNE1k+3lamZamBWnlGmPvUTEshG4gDAvFMDu9LNT0uWZYKGbqWjMtNNNElaAA7S4uA/V5VgfoOV1jBuWHNATa2mp46imXaGkedkvmp5v/K/+tCYroxV4YghayqKWIPDyM7HE9PUBK9LYE9Q1hIrsaYGcd/j3N5BsmhRxlC3mHEpmZGNddx2cPP8zFBzMEbGyONPozmtAVB0j+fX89zyaNSoxcmhlDlCmYTMPJVAIcTw7O7m/RbnOMMY4dPIOb6T1Y4CilQVNTJZ9/brJ27UiSyQPW4RZ6cKqrpu7bgfXKPP+GokIteni3HYUm3DTjx+zk7lPcveahx5M0tURprQti7G7CEW7DaVgoupFSttMKdruSrbQr25ZEJfV9OfQWdNukGEElKw4ROai+fjtPPTUiYETCb414oe5fPFU9dXlL4KWSIhxkMgrRt7vgHghLkojGqa8LEq2oR+ysJ6O2hXxLdu8zcKwhELoQakIVWlJRNF0TTkNTnIZDdRsOxW05VY/lVL3Spflwqj7p1nzCpQWEWwsoLtWruDW/5tYCqkvzOZyqV3OqPpdL9Todqtt46KFThs56LhubXmL/sB2puAkyjCATiHMyglNxM5VMRpIF9g+ETTcZyS5+h+CCLu7Q2o4OKHUFAAAgAElEQVSUFsFgJf/4R4LPPhtBLPal+zu7/izKKMv5qquukEMvToziog4fCTJwEaAAR9cjmFiSZDxJU0uESG0Qo6oR555mMprC5MpUmNIuhSq1OYLw0sTn5OA6yG/aF19s5r//e8KZ7l1b3xz1wmi/oru6/QwHVeQTI59RqIzrbZMPhmURC8eor20hVlGPurOezIYQeZKBnXkYQExFqElFqAlFaLqmOA1Nceqa4uqssFtO1Sudmhe35set+oVL8ytuza+4NL/qVv2aS/NpLtXvcGpep0v1OR2q26kItb/WjtmhRW2OKmxjYCgjMMikiVG0cgI6p6ByKn6+Sg4BO9qDTR9QwB4eRWcuJV2+R0pJJLKbsrIon3wynGi0ZP8CeUpb7dneitDCwDrP+Yd2/Um5+3hpJQOFADl4yIPDGyWWRbwtpfRHa1qw9jTh3NNMZnMr2fIQi4xV4Qh7HVlBjzO3TSpOQyARIAQoyPZzKQQIC0tIaWFJU0hpCYlESlNIpLCkhUzlt+e1HxUpLUAKC0tBynSeVCB1Tvpakk6TUgHS+SjQfm1zWFTi/A2DggMYi1Lq/PWvO7VPV4/6TcGb276f+Xn33N4UmsmijiLycfe9Mm6YtIbaaKxuJlFRj7argeymVnKAbu2wPQBIgdKhsKuKpmuKKzXKrrhMh+qxXKrXdKge6Uop67g0n3CpPuHS/KpHCyguza+6VJ/Dpfm19Ei706l6XJri1MB2VbWxGUxsY2AooNFGPs0cRxtfxeRUnJxCBuPJQbP9PwcFKXUsS08fTSxLxzQNTNPCNE0Mw8QwJIZh0doaZdKkwW5x98iijgeJcH0XNmRqJxqtZv36MJ98UkgwuI9i5BZ68BRXdf28wAYxN+X6c+CoPwoNuGkhgEUGfnwUHc7dx7KIRRM0NbfSVtOCtTul9GcHowefBVOFI+xxZIacjqyorvrjUc0rw4pHCaou145EvX99dHtOfWTzqMO9ZRXV0oRqOhTN0oRqOYRqaUKzNKGaTuGwNMUpHUKz2l9ORZNa6lw6hCYdima5hKP9XLanOxUHncrgFI50mopDaGhCwyk0KUBqCBShIKSUqhBSQbZbDUIRoCDS1oOEVB4KEiGFEEippAwdkFKkDB8LAe0GkEBaEqQQUgrSebQbQ9LEkoa0pIUlDaS0pCkNLGkhpSl9zhzzcH3Yz1g8R/UBIwcZRpgXXgiP3LPOtark6USJI9TV0fwEPnZRhIsMRiHI6YuG6ibBYISmPc0kd9bhrGggN9xGFhDoi/oPhyocYbcWaA248tuy3MMNryNTuFSfbB9hT7nG+FSX5tfc7Qq74nE4Na/boXgcQggXqTV/NjY2Rxm2MTBwSLwEGU6QiSSYiuBUPJxMFsPIALyD3cAhiZQWUrYr5UaHUm5ZFqZppJVyq0Mx13WJrtNxTCYFyWTqqOsKyaSaTlPRdTV91NB1R8fRMNqnlrs6vfwBd9/dj53Qh/ho4l6a+CFjUbtgZMbjdWzc2Mzq1Xk0Nnb49qddf6q/4StPLMwoyz6g648ggpMG/MTJwE2AArSDr1sxLaLROM2NrURrWmB3I+7qFrLDbWTCl2O0K0KLeB2ZLYoWiCRUXyKqeqyw6lFbhNOzPdng3xDdkRdKbuvVaK6JqZjSVBJmsjfVHLEczhgClG08MngN/AHlXHmABcPRaDW/e9J5g3w/8njJX8arQh5ulkXioJJ8Emk3oJ4tnE+T1GlqjtCyuwljZx3uXQ3kRhP9OptraYor5NEyIxnugli2u9jM85Yo+b4xrnxviS/bU5ylKa4MOHaia9nY2HQd2xjoawQ6WTQxmggnonMqGqfi50Ry8JHNYTdWGsJIaXRSypNYloFpmlhW+0i5iWmmlPJk0sIw9irliQToukDXRVoZVzpe+yrlGsmkhmE40HVHOgzlIIxISakgdQWpq0IaGpapCctwYJoOYRlOYVpOYVpuYVjDtNahv5DMTZCfUcudjMV5GN/7ZLKJ8vIGPvkki+rqIqAQpMxX26rP8aSi/pzv217sEvts+KXjoBYvEQJoZJCDm1wOEMfcMAlH4rQ0honXtEBVE+7qZrKjcTKAfULxpRT+jCo0XzSuehKtitsMKm6tSdE8FcnmjM1tFQVtyaaj1Zd60OmCMTR4u3X+C+X8+gCGQG3tNt8fHve9kf9c/BxvxaEXCSs0kUU9wyjA1X3XHCmxEjqNja2Eqhoxd9bhqWokP6739S69wnCq7pDXkd2a4SpI5HhGWnneUjXfV+rO8472ZbmHZSlCO+jviyUtPWrGmkJGpK1BDybqkk2GIU1UoaAJDVUoQkVBFapIpalCQUERilCFIhQEqZz0dUeaoqRKKUIghCqEEKTOFSEUBdGepwiEEEIoqRkpkUoT6XRQBEIBFJEONWgz9Jj82OS+2XE+zfqb1h9234Ly8nLnnDlzxm3dunVD5/R58+aN/ulPf1o3derUeF+2qafceuutw//0pz9la5rGL37xiz1XXXVVEOCFF17IeuaZZ3JXrFixHeDOO+8sWrZsWV5lZeV6gBdffDHzqaeeyn/vvfe2ded5zz//fNakSZPiffX+bWOgpziIkE8L42jjJCSn4mIqGYwlG3WILVCU0sI0o+h6lHg8QSyWpLXVpLVVEgqpRCJqJ+Vc7VDI25Vzw0iNlks5oNFUBNJQkDEVS9eEZapIwyFM0yEs0ylMwyUMyyVMyyUMyy0M6RaG9Cq69AgDr0hKr6ILrzDwK0nhU5LCK3TFryQVr9CV9LXmVXTVJ3TVrRiaV+iqR+gOtzAcbsXUSIVp7MpupD54vp97o4c4iHALu/klx+E+xIZhuh5kx45aPv44QGVlMZDrFnrwFHd1+fzAemWuf8OwAq2tfWZAotCAh2Da3SeAj0LEvj7VukkoEqOlIUy8uhl2N+GrbiY7lmSfEUpFqFGXFmh2ujx72lR3slVxWy2KS2sSqnen3pKxLVY1LKk3299VNnu5mBCv7OfiJqWkrGzztLcfVf93xAtZmWriYD7o8bQbkIcMRiK6prRLiRFL0tAQJlzZgNxZh293EwW6SQFQ0Ju3IxAJp+oL+ZzZkUx3UTLXM0rme8doeb4Sd65ndCDgystQhPolA0O3jGjUikV3xeur6vXm5O5EvVkZrxUV8WplR3yPqyJe49mdqA8EjVZP+t4+NFD6BwFSSc1IWapQpCIUS0WRqlAsTWiWKhSZylOlimI5FFUqqFITiqUKVTqEJlWhyNS5mj5qlioUVKFKDVWqQkmXU3EoWnta+zkORZMKCppQpSZU1LT7Xvo+hBBtt3DKYHfVMc1LL720a7Db0M62bdscr732Ws6WLVs2KIoiKysrO7wKzjnnnMgPf/jDjoGGNWvW+P1+v7lnzx6tuLjY+Nvf/uY/44wzIt195uuvv55lGEbINgYGBgsfLRQT5vi0a880PJxELgWDvHujlDqGESGRaCMeTxCN6rS2WoTDEAqphEJOwmE34bCXtjY/Ugbotm+qtNTU6LihCiumCctwCMt0CNNwCMtyYprtCrlb0S1Pu0IudDyKLn1CJ610C5+i40sr4n5Fb1fIFa/QNU9KIde8iq66heHwCt3hEoamiI5QjvbCsu6iEuMaKvhPSgkcxAgwjFZ27armk0/cbNs2SsHyjHU0V38jq3zzgox1OV911RUAWQjCuKjDTxUZePBTgJZSgKRE6ibB1giVDSGSe5pR9jThrW4hN6GTCWRCSuF3qL6grriqYh53Mqw4rSbF6WxE9ezSg5k749XFFkF7waxN13iDTDr/fllW0vn2/9v+f3f8UvvxyE8OtDZA4mQX+STJYzQqh9xt2JIk2+I01IWIVKZi+AeqWygwLbq3A3YaRahtLtUf8jtz2rLcw/Vc72iZ7y115PlKPbmeUZk+Z7Yf2j9T0krIZGur0RZtMkLxf8Z311WF1tZUxKvFzli1VhGvdu+K13p2J+ozEjLpY7/ZtCMdCcLEVE1pqgzdfX1Dt/Afg92GYwbDMLj00ktL1q9f7x0zZkz8lVdeqTj33HPH/epXv6o666yz2pYuXZrz0EMPFUkpxezZs4OPP/74HgCv13vS1VdfXb9q1aqMzMxM84EHHth9xx13jKyurnYuWbKkcsGCBaHy8nLnlVdeWRqLxRSAX//615XnnXdedNeuXY7LLrtsTCQSUU3TFL/5zW92zZ49OzJv3rySdevW+YQQcsGCBY133313vcPhIBKJqOFwWMnPzzePO+44vb3tw4cPNwKBgLl+/XrX5MmTE3V1dY6LL7645b333vMvWrQo+Omnn/rvu+++PQALFiwYVVZW5ovH48rFF1/c8p//+Z/VADfddFPx22+/naWqqpw1a1Z47ty5LStWrMj65JNPAkuWLBn22muvbQf4/ve/P6q5uVlzu93WU089teukk07qsqFgGwMACgmyaaaECF/B4FQcnIKfE8lNuzoMzGiKabZhGFHi8RixWJJIxCASkQSDgnDYkVbwPbS2+kgkPHAgtyMpVWTcJYyYWxgJv5JsyXAkGrKUuJGrxqw8tY0CNSLy1Ta1UI2q+VrUkau0ObyK4fCKpOoWhsOjGJpH6A4t5Wd7pCwaMwErfTQR6WuBlU5vP5cITER6taVIp6dWVqZ+evausJTpV/u56LhWIJ0u0Bg6bkIKCS5jJ48zktwDuFBYVoyqqio+/dTJpo3F+Uo0cK5nR+jKYV9Upl1/itPuPs1kECKDXFzkSIlfN2kJtRFqqKFydxPKniZ8tUFyk0ZKDgVqTFM9TQnFGYkqrqYWj4NGoTkaUL27zXDW7kTdcGmHvbXpa3Q9OPrlx3av0O8ZNja7Zd/vaoVGsmmkiEJcB46YZVnEW+PU17bQtqsBZWcdmXVB8rsjq3sX5+a1ZbmLjTzvaJHnLXXm+0q9OZ6RmW7N7zWlpcWsuDNkRNsa9WCiJtFg/D26PVzR9GFkR2yPoyJe46lM1Prqky1+C6vDkLaxOdapqKhwL126tOL888+Pzp07t+TBBx/M75TnWLx4cfHnn3++KT8/3zjzzDPHP//881mLFi0KxmIx5eyzz259/PHH95x33nnH/fznPy/+8MMPt6xdu9Z97bXXli5YsCA0fPhw48MPP9zi9XrlF1984briiivGrF+/ftPTTz+dc+6554aWLFlSaxgGra2tyurVq701NTWOdpelxsZGFcDtdlu5ubn6nDlzjlu5cuVWj8ezjxk7derUyMqVK/2maVJaWpqYPn169K233sqcP39+sLy83HPWWWdFAR5++OE9hYWFpmEYTJ8+fcKaNWs8JSUlyTfffDN7x44d6xVFobGxUc3LyzNnz54dnDNnTujaa69tATjjjDPGP/nkk7tOPPHExHvvvee78cYbR33yySdbutrHx5Yx4CRMIS2MI97h2nMKmZSSjdL90Z7DIqWJaUZJJlPuOW1tSSIRi1BIEg6rhEIa4bCbUMhLNOrHNL2AVyANB2bMJcy4V9ETASWhZygJI0eJteWpbZE8d1tDvi+qFGlRtUCNavlq1JGntrnz1DZ3lhL3qEJ2JUybBSQQJBHopBRoA2ijPeQIHUpye4iRdiX5YMoynV4pxVlB7JcGAiWtTKf+KZ3+3/fYfq6mz9R0mvqlczpeX1r0K2WHQSAlWMj0Eax0npQg29PTu81KKbFk+n6ZypNSpl+dztsSiEHXcAUGF7CN3zOc4fvNBFhWgtraSj79VHVv/GfgFLVSzg+s1+eWbIgVuNpU3ChkECODBukhN2niCbWh1wXRq3fTtLuJRF2QHN0kV6B4hepujgtHpFXRmppVR1OjpjkaheKrMiLZ9XrzCAY7vozNsUMksvu6l29uWep6fpLmlO27IcfwU0kRXjIYCeS1FzctIuE2GmtaiKdj+Gc1hskDDhVZytIUZ9ijZbZmuApi2Z4RZq53tMj3lrrzfaVen7PAkYRkixHW65PNcneynrJ4LTtbN5g76v83UZmojVTF6wiZkSPGVcfGZihRVFSUPP/886MAixYtanrkkUc63PI++ugj3+mnn946fPhwA2DevHnNH3zwgX/RokVBh8MhL7/88jDACSecEHO5XJbL5ZLTpk2L7dmzxwmQTCbFd77zndEbN270KIrCrl27XACnn3569Hvf+16JruvK5Zdf3jJ9+vTYxIkTE1VVVa6rr7565MUXXxz61re+FQZYuHBhyYMPPlj10Ucf+b/5zW+OefPNN7fffffdhT6fz7rzzjsbpk+fHvn44499pmly2mmnRc4666zo/fffP/zjjz/2lpaWxr1erwR49tlnc5555pk8wzBEQ0ODo6yszH3yySfHXC6XNX/+/NFf//rXQ/PmzQvt3z+hUEj5xz/+4Z87d26H62QymezW2puj0RiwCNDECMJMIslUFKbh46tkk0vvoylYViLtnhNLu+cYtLZahEKCcFglGHQSDrvV1pDmiLXiEbruV/RkQElYWUqcXDUmc5U2CrSoVaBGjXw1GivwR82CzGg8T21z56gxr1/RnaRcegJAMq2wJxAYCAwUdBRMFCwUEqjEUQin1WGBipAKWAJhKWAKpCUQlkCYqaNiAqaFpRtYhoW0UoptShu2wAJpWSAlwpLp4IMSTCtdRiKkRJpmRxlhWqkpXssCK1WPkr5PWKkXlrX3KCWKuTe/vbzoqNtKtcmSmJaFIlNtElJimBJF7i2ryr33K1Y6T0qUVMx2+jtmu/fuf+nH2g+NxQy28gz5HNfJCJBSp6GhUvv8U33chnfFHOcGOT/jC+3kibVh/EgrgBp3EaxNkqwLYu2pRtvzBaIuhLAsJWApjmRMaEZYaPEmocXqVSXUqKn+ajOa02K0DrrtY2MTqKvY/OqbF7jPd285EbBwUkEBOnmMRmGCYRIKhtlR3UxiZz3OinpyglGy+bJ7p+FQPCGvIyuS6S6MZ3tGWDmeUcLtKsDlKlCEliGajFZrd6LeqohXi7/F9zgrgp+7Kmv/n3dPoiGQkMn+2NTKxsYmzf5ryjtfS3lwXzJN06SipH76FUXB5XJJAFVVMU1TADzwwAOFBQUF+muvvbbTsiw8Hs9UgIsuuiiyatWq8tdeey3zmmuuKf3BD35Qd8sttzStX79+45/+9KeMxx57rOCll17KeeWVVyo+/vjjjLfeemv7JZdc0nr11VePXLRo0ajt27e7ly1bthNg5syZkaVLlxZYliW+973vNWRnZ1uJREKsWLEiMG3atAjA5s2bnY8++mhheobDvOyyy0ri8bjicDj45z//uemNN97I+OMf/5j9+OOPF+w/4m+aJoFAwNi8efPGnvbxkW8M3I1FDZvSrj0BjicX18HDF34JKSWWFUXX29LuOTqRiCFaw5Y70mx5W5ukN9KEt7VR8bc2qpl6mFw1JvPUqChUoxSoUTVfbXPka1FytJiSr0VFbnYs6ci1EigYaYXdsgSmCaYlkKbAMgXSBAyQhkj5thgCpQ5ENaCDMEHVLUTSgKSBSBoIXUdJ6ChJAyVp4NBN1KSBljTQ9NS1wzBxmBYIFAMhdBCmBMNCGBJhWgLTSunuot1HxkqN3UuEIDXqnd4aCWEBHT41qSMyNT2Q+hS256XvTQ2bi1S5dHkhQXZEMO+UJjumHJASRHs7pBCioy7ASn/2JUJYe+8nNSWRbqQASwhkKqi6sGS6DoB0fVY6/Hrq2VLp/L5AKBIprL3vEwlKe3stgZK2UyCdbkmhWMj4oAQW/T/ozKeJyWn/ZylNWpp3jd64Kvwv61/jUs9aLszZopknmmZQRTQmkf/ThGNPFTkNIXw6jnBUKPGg0IxGoZoNKLJBFbKaWHabFS+GxGC8Kxubw3Lzyn9rvLfqP0fleOIRctiUyMEXNDB3N2Hu3MaeXQ3kRuKpNSsCkXSq3qDPmRMszsipVRyZSeHIsnBmYWlZalxxalXJBq0stkeriNdkVDav9tUn3/RbWPYaFhubIUBNTY1zxYoVvtmzZ0dffPHFnOnTp0feeuutLICzzjorescdd4ysqanR8vPzjVdeeSXnpptuqu9q3aFQSB0xYkRSVVUeffTRXNNMTW1v2bLFWVpamrztttsao9GosnbtWm9NTU3I5XJZ11xzTXD8+PGJ6667rhRgwoQJsccffzz3X//1X5t++9vf7p40adIJo0ePTowdO1YHOPnkk+MNDQ2ONWvW+J999tlKgMmTJ8eeeeaZ/Pvuu283QEtLi+rxeKycnByzqqpKW7lyZebMmTNbQ6GQEolElHnz5oVmzZoVGT9+/IkAfr/fDIfDCkBOTo41YsSI5NNPP5193XXXtViWxZo1azxnnHFGl3fKPvKNgcUosJ9vtJQGhhFR9USbOxmJ+WKhZCDWnMxsazAz2hrNzEidDLTWktFaJwKROnKIkqNGyVWiIk+LiFwlKgJqXNEl0gB0Cbobki6ELlH1VJqalCgGoFuwx0DdmUDVDVTdROiGIGHiShoI3QRTKJYEw0wNipsWwjLBMsEyENIgZSAYKZ8dqQMGwkpr8+igGAIMFJkEywChg2UKQVKSnjKQmCiWLiwzqVqOmJlwmNJ0f6nPhu6irCOVL/fxQHA/DqTM9UWaKk6peLt+Rtlz1gXWx5qmRBy1bnw1zXj+vUKREaG1taDodUKR9Sg0CCHrSbqSUh+GZJit9Nscady849/FRoVtuxNk7K5QCtp0Z9JUnAlddRsJxROPae5gPNOnJVSfswXTvSte692dqCsKx6rsYAQ2Nj2kK6FA+4MxY8bEn3766dybbrppdGlpaeL2229vaDcGRo8erd911117Zs6cOV5KKc4999zQwoULg12t+0c/+lH9ZZdddtzrr7+ePWPGjFaPx2MBvP3224FHHnmkSNM06fV6zWXLlu2sqKhwfOc73ymxrNTw5L333rsb4Pnnn995ww03jP7Nb35T6HK55C233FL3+uuvZy1evLhw8eLFdYqiMGXKlGhra6vaPjtx+umnR5YvX5539tlnRwHOOOOM2OTJk9vGjRt3wqhRoxJTp06NAASDQXXOnDljE4mEALj//vurABYsWNB84403ljzxxBOFr7766vbly5fvuOGGG0YvWbJkmGEY4lvf+lZzd4wBcagplqGCECIqpTxgxIRZK+9RM2ONv89srRFZkWoCkTolI9YgMpQ2qQhTxEAkJSIuIWki4gYiaQlhgkwp4lgGKSXcSCvh7Q70OoikEMIAYYCSTCnmig6qDooBWvpaMxGqAZoupSOJ6bBHlY4pfifvlksPVeBQMtzT++b9+eozxtSsuTGrtVypiqE0SWHUCUVpQHHVQUaTNLNtObTpZ66Rd8v1B8vsD7kHmHSP8q8JoZ0TVVy+FiuRkZT6kT+wZXMkEZJ3y3MPVaCnsj+UKCsrq5gyZUrjYLfDpu8oKyvLmzJlSsn+6Uf8F+gHHywWHig1wGGAJrvkG34gA+ggRtHQt5VsjlFe/+dzjmRqVqzDiSrlfGav4LU5utmEzETqIzH1wxe2sRmiCCEuBH5NKvjFU1LK/7tfvgt4DpgKNAHzpJQVA91Om6OfI94YAETsKIuzbGPTFZKpHxB7t87BIxXKdm/I2v1D2O4NXbs3jK08yGtvntIpQlf7ubLf+d409jnv/BLslZDUMRV3a998kb5uPyodZZV0fnscr71pSkecL6dtedrY9AQhhAr8FjgP2A38XQjxhpSy8yLQ7wAtUsqxQoj5wBJg3sC31uZo52gwBixg82A3wuaYpmGQnhsGUj8cKYUT9oaFpWMd9L57KOyrhO5N+3KoWGhXPDuHjd03lGxqHq5dKd1/DwY6KbZ05B04jQPcu1cp/XI5sV+a2Cfvy+Xaw9qKfcq3B6dtV4T3KsQifS06KcTtQXBT6UPLEOtpW+R+R6ub9yd7+NzeUoP9vW8zeIT7oI5pwDYp5Q4AIcQfgUto/05PcQmwOH3+KvCoEELII8G/2+aI4kgxBjxCiENt7KQBxkA15ijE7r/e8S2xWCzn0H3oEUJ81un6SSnlk12o25b9/sXuv94xVyBsuT8ysfuwF4jF4jv07ju/GKjqdL0bOG2/OjrKSCkNIUSI1D4Vth+/TZ9yRBgDUspDrgMQQnwmpTxloNpztGH3X+/prz60Zb9/sfuvd9hyf+Ri92Hv6IP+O9Bs3v4j/l0pY2PTa+xIIzY2NjY2NjY2A8tuYGSn6xFA9cHKCCE0IBNoHpDW2RxTHBEzAzY2NjY2NjY2RxF/B8YJIUqBPcB84Mr9yrwBXA2sBi4H3hvM9QKTmXz84Ut1nfUcet+C8vJy55w5c8Zt3bp1w/558+bNG/3Tn/60burUqfG+bNNA8/7773t/8pOfjGxsbHQIIeS0adMiTz31VFUgELBefvnljHvvvbe4ra1NkVJy3nnnhZ588sndZWVlrhtuuKEkHA6ryWRSnHbaaZHly5fv6k07jhZjoCs+qDYHx+6/3jNYfWj/7XqH3X+9w5b7Ixe7D3tHr/ovvQbgFuBtUvG+npZSbhBC3At8JqV8A/g98LwQYhupGYH5vW300cJLL73UK+V3KFBVVaUtWLDguOeee27H7Nmzo5Zl8eyzz2YHg0Fl8+bNzttuu23UG2+8se2kk06K67rOQw89lA9w8803j/rBD35Q17652qefftrrzRSPCjehLi5IszkIdv/1nsHqQ/tv1zvs/usdttwfudh92Dv6ov+klG9KKcdLKY+TUj6QTrsrbQggpYxLKedKKcdKKae1Rx46ljAMg0svvbRk/Pjxky688MIxra2tCsC0adMmrFq1yguwdOnSnPHjx08aN27cCTfeeGNx+71er/ekG2+8sfiEE044fvr06ePff/9977Rp0yaMGDHixGXLlmVCavZh6tSpEyZNmnT8pEmTjn/nnXd8ALt27XKccsopEyZOnDhp3LhxJ/z1r3/1G4bBZZddVjJu3LgTxo8fP+mee+4p2L+tI0aMONGyLBobG1VFUaa+9dZbfoCpU6dOWIQfdtgAAAkESURBVL9+vatz+Yceeqjg29/+dtPs2bOjAIqicO2117aMHDnS+OUvf1l022231Zx00klxAIfDwc9+9rMGgPr6esfo0aM7IrlNmzatyzsNH4yjwhiwsbGxsbGxsbE5uqioqHB///vfb9iyZcvGQCBgPfjgg/n75TsWL15cvHLlyi0bN27c8I9//MP3/PPPZwHEYjHl7LPPbt2wYcMmn89n/vznPy/+8MMPt7zyyivb7rvvvmKA4cOHGx9++OGWjRs3bnrppZd2/PjHPx4F8PTTT+ece+65oc2bN2/ctGnThtNOO61t9erV3pqaGsfWrVs3bNmyZePNN9/c1LktmqZRWloaX7t2rfudd97xT5o0qW3lypX+WCwmamtrnZMnT050Lr9x40bPKaec0nag911eXu457bTTDph38803133ta18bf9ZZZ4275557ChobG9We93AK2xiwsbGxsbGxsbEZchQVFSXPP//8KMCiRYuaPv74Y3/n/I8++sh3+umntw4fPtxwOBzMmzev+YMPPvADOBwOefnll4cBTjjhhNiMGTNaXS6XnDZtWmzPnj1OgGQyKa688sqS8ePHT5o7d+5x27dvdwOcfvrp0eXLl+fdeuutwz/99FNPdna2NXHixERVVZXr6quvHvnqq69mZGdnf2nTxenTp7e+++67gQ8++CDwk5/8pGb16tWBVatW+aZMmXKoUMnd4oc//GHTF198seHSSy9tXrVqVeDUU0+dGIvFerXvzZA0BoQQI4UQ7wshNgkhNgghfphOzxFCvCOE2Jo+ZqfTJwohVgshEkKI2/er60IhRLkQYpsQ4meD8X4Gmh703yVCiHVCiH8KIT4TQszoVNfV6fJbhRBXD9Z7Gmh60IezhBChdB/+UwhxV6e6uiyDtuz3Dlv2e8dgyX0Pn23Lfids2e8dgyn7NgdHCHHI60Otp9Y0TSpKSs1VFAWXyyUBVFXFNE0B8MADDxQWFBTomzZt2vjFF19s1HVdAbjooosiq1atKi8uLk5ec801pY8++mhufn6+uX79+o1nn31262OPPVYwf/78kv2fOWvWrMhHH33kX7t2rW/u3LmhcDisvvvuu4EZM2a07l/2+OOPj3322WfeA7V9/Pjx8TVr1hwwD6CkpET/0Y9+1PTuu+9u1zSNzz77rFfrBoakMUBqI4/bpJTHA6cDNwshJgE/A96VUo4D3k1fQ2phzQ+AX3WuROzd7vsiYBJwRbqeo53u9t+7wBQp5VeB64CnIPUlCNxNaiOUacDd7V+ExwDd7UOAD6WUX02/7oUeyaAt+73Dlv3eMVhy35Nn27K/L7bs947BlH2bg1BTU+NcsWKFD+DFF1/MmT59eqRz/llnnRVds2ZNoKamRjMMg1deeSVn1qxZkQPX9mVCoZA6bNgwXVVVHnvssVzTTA32b9myxVlcXKzfdtttjQsXLmxcu3att6amRjNNk2uuuSZ4//337/niiy++pKzPmjUrunbtWr+iKNLr9coTTjih7bnnnss/++yzv9Sm22+/vf7ll1/Ofe+993ztaY899lhOZWWlduedd9Y+/PDDw9atW+cCME2TxYsXFwK8+uqrGYlEQgBUVlZqwWBQ7byGoCcMyWhCUsoaUtvNI6VsFUJsIrUT3yXArHSxZ4GVwB1SynqgXgjx9f2q6sp230cdPei/zkLqY++mJhcA70gpmwGEEO8AFwLL+/ktDDrd7cNDVNUtGbRlv3fYst87Bkvue/JsW/b3xZb93jGYsn+kcLhQoP3BmDFj4k8//XTuTTfdNLq0tDRx++23N3TOHz16tH7XXXftmTlz5ngppTj33HND7VF2usKPfvSj+ssuu+y4119/PXvGjBmtHo/HAnj77bcDjzzySJGmadLr9ZrLli3bWVFR4fjOd75TYlmWALj33nt371+fx+ORRUVFyVNOOSUKcOaZZ0beeOONnAMt8h05cqTx3HPP7fjJT34yoqmpyaEoijz99NMjixYtCo4aNSq2ZMmSqiuuuGJMLBZThBDMnj07BPDXv/414/bbbx/lcrksgHvuuWf3qFGjerWbuBjEkLVdQghRAqwCJgOVUsqsTnktUsrsTteLgYiU8lfp68uBC6WU16evFwGnSSlvGbA3MMh0tf+EEN8C/h0oAL4upVydnnp3SynvT5f5BRBr799jha70oRBiFvAaqU1iqoHb02HieiyDtuz3Dlv2e8dgyX1Xn93pejG27O+DLfu9YzBlfyhRVlZWMWXKlMbBbodN31FWVpY3ZcqUkv3Th6qbEABCCD+pD9uPpJThnlRxgLShbf30Id3pPynln6SUE4FvAve1V3Ggon3byqFNN/pwLTBaSjkF+A3wensVByh72D60Zb932LLfOwZL7rv57INW0dNnHw3Yst87BlP2bWwGiyFrDAghHKQ+kMuklP+dTq4TQgxL5w8D6g9TTVe2+z4q6Wn/SSlXAccJIfI4hvsPuteHUspw+7S7lPJNwNHTPrRlv3fYst87Bkvuu/vsQ2D/7WzZ7xGDKfs2NoPJkDQGhBCC1M57m6SUD3fKat+am/Txz4epqmO7byGEk9TufW/0dXuHGt3tPyHE2PQ9CCFOBpxAE6mdEc8XQmSnF5Cdn0476ulBHxZ16sNppD5bTXRTBm3Z7x227PeOwZL7njz7ENiyb8t+txlM2R/CWO3+8TZHPum/pXWgvCG5ZkCkQpx9CHzB3ob/G7AGeBkYBVQCc6WUzUKIIuAzICNdPgJMklKGhRBfA/6Lvdt9PzCgb2YQ6EH/3QFcBehADPiJlPKjdF3Xpe8FeEBK+YcBeyODSA/68BbgRlIRKWLArVLKj9N1dVkGbdnvHbbs947BkvsePtuW/U7Yst87BlP2hyplZWVvFBUVTcrPzw8pijL0lEWbLmNZlmhoaMisra3dOGXKlG/snz8kjQEbGxsbGxsbG5vB4/PPPy/QNO0pUguph6QniU2XsYD1hmFcP3Xq1C+5CtrGgI2NjY2NjY2Njc0xim3p2djY2NjY2NjY2Byj2MbA/2+/DgQAAAAABPlbD3JZBAAAUzIAAABTMgAAAFMyAAAAUzIAAABTAfqy/6fiPrzbAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d959706d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, stats=['eroi_adj', 'mix'], to_plot=True)\n",
+    "# f, axarr = plt.subplots(2, sharex=True)\n",
+    "# axarr[0].plot(x, y)\n",
+    "# axarr[0].set_title('Sharing X axis')\n",
+    "# axarr[1].scatter(x, y)\n",
+    "if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "else: tot_name = 'Total (PWh/a)'\n",
+    "EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                               'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "InteractiveShell.ast_node_interactivity = 'last'\n",
+    "colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "fig = plt.figure(figsize=(10,4)) # 7,5\n",
+    "_ = ylims = [11, 21] # 5, 10; 13, 18\n",
+    "\n",
+    "ax1 = fig.add_subplot(1, 3, 1)\n",
+    "_ = ax1.plot(years, EROIs_mixes[[('BL', year, 'EROI adj') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI adj', color='black', zorder=20, linewidth=3)\n",
+    "_ = ax1.set_ylabel('Quality-adjusted EROI')\n",
+    "_ = ax1.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "_ = ax12 = ax1.twinx()\n",
+    "_ = ax12.stackplot(years, EROIs_mixes.fillna(0)[[('BL', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax1.set_title('BL (business as usual)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax12.get_legend_handles_labels()\n",
+    "_ = ax1.set_zorder(ax12.get_zorder()+1)\n",
+    "_ = ax1.patch.set_visible(False)\n",
+    "# ax12.get_yaxis().set_visible(False)\n",
+    "_ = ax12.tick_params(labelright=False)\n",
+    "\n",
+    "ax2 = fig.add_subplot(132)\n",
+    "_ = ax2.plot(years, EROIs_mixes[[('BM', year, 'EROI adj') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI adj', color='black', zorder=20, linewidth=3)\n",
+    "# ax2.set_ylabel('EROI')\n",
+    "_ = ax2.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax22 = ax2.twinx()\n",
+    "_ = ax22.stackplot(years, EROIs_mixes.fillna(0)[[('BM', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax2.set_title('BM (+2°C)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax22.get_legend_handles_labels()\n",
+    "_ = ax2.set_zorder(ax22.get_zorder()+1)\n",
+    "_ = ax2.patch.set_visible(False)\n",
+    "_ = ax2.tick_params(labelleft=False)\n",
+    "_ = ax22.tick_params(labelright=False)\n",
+    "# ax2.get_yaxis().set_visible(False)\n",
+    "# ax22.get_yaxis().set_visible(False)\n",
+    "\n",
+    "ax3 = fig.add_subplot(133)\n",
+    "_ = ax3.plot(years, EROIs_mixes[[('ADV', year, 'EROI adj') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI adj', color='black', zorder=20, linewidth=3)\n",
+    "# ax3.set_ylabel('EROI')\n",
+    "_ = ax3.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax32 = ax3.twinx()\n",
+    "_ = ax32.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[('ADV', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax3.set_title('ADV (100% renewable)') # ER (+2°C wo CCS nor nuclear)\n",
+    "_ = plt.ylabel('Share in the mix')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax32.get_legend_handles_labels()\n",
+    "_ = ax32.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.25,0.45)) # (1.1,0.5)\n",
+    "_ = ax3.legend(loc='center left', bbox_to_anchor=(1.25,1.03)) # (1.1,0.97)\n",
+    "_ = ax3.set_zorder(ax32.get_zorder()+1)\n",
+    "_ = ax3.patch.set_visible(False)\n",
+    "# ax3.get_yaxis().set_visible(False)\n",
+    "_ = ax3.tick_params(labelleft=False)\n",
+    "_ = plt.subplots_adjust(wspace=0.08, hspace=0.08)\n",
+    "\n",
+    "_ = fig.savefig('Evolution of EROIs adj IEA-ADV.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### EROIs w/o GW adjustment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d95a68630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d95a686d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d959be550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93e6def0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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wDzQ7O4yxKiUlpe/xxx+vT01NHS8IAk9KSup59dVX9ffee2/EK6+8Eji4oB0Ann766bo1a9ZEBwQEmFNSUox6vZ5mer4Fxrnj6pkxxiIAfDq45uqS7U8DSAHw//g1AmCMPQjgwYGHEzjnN/UPu2nTplQALwFAXV2dMj09PaKjo+PiJznGGJ84cWL99OnTDWKxmIq8ETIa2dEndAsNUoO0R6aVyaQ10kDBJCidHdbNYMzaonHP1y+yH/VsNUbYNmCHyuWqqkvQjWdQi6cQBwHFHFg/FKdljBk5525Dca7hkJOTo01OTqbkcpTIycnxTU5OjrjavmEfuWKM3Yv+he63XCuxAgDO+RsA3hh4jnEoYwgODu658847i86fPx+Ym5sbZLfbGeec5eTkBGu1Wq+0tDRtcHBwz1BekxAy/JiFtYpaRc2yGhmXaWUe4iaxg/rwDT/GrC0aVa5ukS3dy9Tl57kWn0pdsKq6Hd9DKXZCAx+Mc3YwhAyXYU2uGGNLADwJYD7n3KnJi1gs5rNmzaqPiYlpO3bsWERLS4sbAHR0dCg++eST+ISEhIbZs2fXSiQSGsUiZCTgsAg9gkHSIDHKdDKJVC/1E/U4ug/f8GOCpVmjytMvtB33lHQq/R7AB3aXrKoejGrshRgzMN7ZoRAy3BxZiuGfANIA+DLGagD8Fv13B8oA/HugDMJZzvlDjorhRvj5+fXdcccdxRcuXPC/cOFCiM1mEwCgsLAwQK/Xe86bN08bHh7e7cwYCSFXYUOnuF3cJKmV9PfhM0gDmY1pnB2WozDB0qRR5VYvsB1Xe3SIgv8PW4zvYYPrvV4pOvFHGPBTxEIA1bshY5LDkivO+ZqrbP6Ho653MwRBQEpKSmN0dHT7sWPHIhoaGlQA0N3dLfvss8/GxcbGNs2dO7dGLpfTbYWEOEN/H74GSZOkU6qXCgN9+Lwx7H34hp8gWBpD1Tl1adYvVX4d1rBf4Q+tf8Mj/i5YVd2G5SjDewiHJ+KcHQwhzuRytVmcycvLy7x69erS3Nxc33PnzoVarVYRAJSVlfnV1taq58yZo4uJiel0dpyEjHp29Ig6RQZJvcQk08oU0lppoGARggAEOTu04SII5oZQdU79fOtJ96A2U8Tz+Lnhj3ja0wKpr7Nju4IGOuyDDFNpCpAQgJKrKzDGkJyc3BwZGdmRnp4eVltb6wkAPT090n//+9+x5eXlLfPmzatWKpU2Z8dKyGhxsQ9ftZTLtDJPcYvYn4FFOTsuZxAEc0OoZ05dqvWUKrytO+Y1PFT9czyv7IPC9RIXOdrxZzThUcQ6OxRCXAklV9fg4eFhWbFiRUVRUZHXmTNnwsxmsxgAqqqqfOrq6tSzZs3Sjx8/vo1a6BDyLXGYhW6hXtIg6ZXpZFKZXuYv9Aku1YfPGQTBbAj1yjbMs5xyC2vtjt+Fu6tSsFXUAc94Z8d2BQYrVqMM2xEJFSVWhFyOkqvrYIwhISGhLTw8vOvEiRMarVbrDQAmk0mcnp4eVV5e3p6WlqZXqVTUCJqQaxnsw1cz0IevURLA7KOjHMJQEESm+lDP7Ma51jNuUS0diZ/htoq5eMNsQJDrJVUAEAkt9kGBZLhmfCPItqRtQ/o93JS/qWgoz0e+O0quboCbm5t16dKlVWVlZa2nTp0K7+3tlQBATU2N565du1TTp0+vnjBhQguNYpExj8Mq9AoN4kZxl0wvE8t0Ml9Rt8gTgEu3jHEGQWSqC/XKappjOauMaWlPPIW5FbdiR7cWka43/Qf0TwG+hCY8RCNVI9kTTzwRtGfPHu+goCCzj4+PdfLkyT1qtdr2zjvv+FksFhYREWHas2dPlUqlsr/99ttezz33XLAgCFylUtkyMjJKnB3/SEHJ1bcQGxvbodFoCk6ePBlSVlbmBwAWi0V06tSpiIqKCu8FCxboPD09zc6Ok5BhY0O3qEPUIK2TWmQ6mVJaKw1kNuZqhSxdikjUVxvindUyx3pOHtPcNiEXyZXJONGajwmuWWSTwYo7UY63EAl3SqxGshMnTigPHDjglZeXV2ixWNikSZMSJk+e3LNu3bq2xx9/vBkAHn300eAtW7b4Pv30042bN28O+uKLL0ojIyMtzc3NImfHP5JQcvUtyeVy26JFi/QxMTFtX375ZXh3d7cMAAwGg8fu3bsTp0yZUjtlypRGQXC1u6QJuUmDffiaBvrw6UZuHz5nEIn6akN8LrTOsZyXxTa1JVUgWp+KA/WnMSfG2bFdUwyq8AnckUB3AY4G6enp7kuXLm13d3fnAPjixYvbASAzM1Pxm9/8JqSrq0tkNBpF8+fP7wCAlJSU7nXr1kXcfvvtbevWrWtzavAjDCVX31FERERXcHBw4enTp4OLiooCgP5G0OfPn7/YCNrPz48aQZORq78Pn0FaL+2V6WRyaY00QDAJAQBcq2+dixOJemtCfC60zbJmysY1tibWI6j2NhysPoRlEc6O7ZqUaMVWtOGHiHZ2KGToXKvj3IMPPhi5Z8+e8lmzZvVu2bLF5/jx4yoA+OCDD/RHjx51279/v3rSpEmJ2dnZBYGBgXSn/A2g5OomSKVSe1paWk1sbGxrenp6ZGdnpxwAmpub3fbu3ZuQnJxcN23atAaRSEQtdIjLYxbWIm4Vt0irpVymu9iHL8LZcY1UInFvdYhvZvtM8wXZ+MbWxDZ4Nd6Ld6vewwbXTVgYLFiHcryBaChGV9sgAqSlpXVv3LgxvKenp95isbAjR454btiwoamnp0cICwuzmEwm9uGHH3oHBQVZAKCgoEC2cOFC48KFC42HDx/2rKyslAYGBvY6+3WMBJRcDYGQkJCeu+66q/Crr74KysvLC+ScM7vdzrKyskIGRrGqgoKC6AeSuI7BPnwGiVGml0mkOqmfqFfkA8DH2aGNdCJxjz7EN7NrhiVbEm9oSeyGe+v/4eXyrXg4xgWrqv/XeFTgY6gxju4CHK3mz5/fs2TJko6EhITEkJAQ08SJE41qtdr21FNP1U2fPj0+JCTEHB8f39Pd3S0CgMceeyxUq9XKOOds7ty5nTNnzqT3sRvErjVM6EoYY0bOudvNnGPTpk2pAF4aopCuqaGhQZGenh7R2tqqHNw2UNLBMGvWrDpqBE2cor8PX6O0RmqV6WTuknpJILMz+nA1hMTiHl2wb0b3dEuOOL6lJcYEedef8WTDn/DLaAukrvu9dkMLXkM77hm2KcAiDqwfihMNxXvDcMrJydEmJyc3OzOGjo4OQa1W27u6uoRZs2aNe+2113Rz587tcWZMI1VOTo5vcnJyxNX2ue4v/AgVEBDQe8cddxRlZmYGZGVlhdjtdsY5R0FBQaBer/eaN2+eNiwsjBpBE8fhsLO+r/Xh8xZ3in0wBvrwOYNYYtSF+GUYp5nzRPGG5vE2iPv+jodLf47no/ugcM07AAFAgBn3ogLbEAM5jViOFffcc094WVmZwmQysbvvvruFEivHoOTKAUQiEaZPn94QExPTfuzYsYjGxkZ3AOjq6pIdPHhwXFxcXOPcuXNrZTIZNYImN88Oo6hT1Citk5qkOqlCWisNGGt9+JxBLOnWBvtl9KaY84XEuuY4O0TWD7Cu9GFsjXDJquqXSkIF9sETMTQFONYcOHCgytkxjAWUXDmQt7e3afXq1SU5OTm+GRkZGqvVKgBAaWmpf01NjWdqaqouKiqKGkGTG8fBmYU1i5vFrdJqKWQ6mZe4RezHwCKdHdpYIZZ0VwX7nzelmAqQUN8Syzj4QSwvfxBvBLtsVfVBKjThH+jGD+guQEIciZIrBxMEAZMnT26OiorqSE9PD6+rq1MD/Y2gDx8+HBsVFdUyb968aoVCQbe3kqvj4EK3oFcUKfqUBcoQ6sPnHBJpV2Wwf4ZliqmQJ9Y1xwicCycxt2wDdvi7bFX1QQJMeACVeAWxkNHPDiGORsnVMFGr1ZaVK1eWFxYWep89e1Yz2Ai6srLSp66uzmOgEXS7s+MkroOZmEFeJm93y3YLEHWJqBefk0ikXZVB/uetk01F9gm1zdEC5+IcJFesx3seLltV/VKTUIZ98EUETQESMlwouRpGjDEkJia2hoeHdx4/fjxMr9d7AUBfX5/k2LFj0eXl5W3z58/Xq1Qqq7NjJU5iRbtMJzO4Zbt5SholgQACnR3SWCWRdlYEB5y3J5tKbBNrm6JFnEvKEFN1H7ZLXbqq+iAPNOJd9GAVtawhZLi5bs2VUczd3d162223Vd5yyy0VcrncMri9urraa9euXUl5eXk+I6FEBhkidvRK6iQl6kNqnf9b/h6eX3iOH0isiBNIZR0V4ZojFUu8dprvqTkZMbmxcXwDDzQsw0FdHMoiT2OOa/dOFKEPj6AYTfDGKkQ4Oxzi+iZPnjweAEpKSqSxsbGJzo5nNKCRKyeKi4tr12g0XV9++WVoRUWFL9DfCPrkyZMXG0Gr1WpqBD0acVhFbSK9Mk9pV5QowpmNuf700qjGuVTWUREUcI5NNFWYJ9Y0Ros5l7bAu/5/8UrPTtwzMhaAp6AMe+EHDfUCHAkOrlw5pFO1t+3fX/RdnpeVlVU8lHEQSq6cTqFQ2G699VZdVVVV65dffhlhNBqlAFBfX++xe/fuxKlTp9ZMmjSpiRpBjwIcXDAKNYoiRY8yTxkqmIQoZ4dE+pOq4MDzosS+csuk6sYoMeeyLrg3/wp/aP0bHonlEJizo/xGnjBgJ0xYRlOA5PqeeeaZgJ07d/oCwPr165t+85vfNCqVysk9PT1Zzo5tNKHkykVERkZ2hYSEFJw6dSqkuLjYHwCsVqvw1VdfhVVUVHgvXLhQ6+PjY3J2nOTbYybWIC+Xtyqzlf7iTrHG2fEQAOB2mby9IjDgnDjJVGVJ1jdoJJzLeiFv/z2eqvojno61Qezr7Ci/kQi9+F9o8WfEQQyRs8Mhru3LL79UfvDBBz6ZmZlFnHNMnTo1/pZbbulydlyjESVXLkQqldoXLFhQHRsb23r8+PGISxpBu+/Zsydx0qRJdSkpKQaRiP4PdXlWdMiqZfXKLKWHtEEaDCDA2SERoD+paqsICjwvTuirsk6qbtRI7Ha5BWLjFmysehJ/juqDYiRMqXHMQhk+QiCC6C5AcmPS09Pdly1b1u7h4WEHgNtuu63t2LFjKmfHNRpRcuWCQkNDjXfddVfh2bNng/Lz8y82gr5w4UJIVVWVV1pampY6k7sgO/okDRK9MkcplWllYYwztbNDIoO4XSZvKw8KPCePN+msk/UNGondLrdC1Pce7il6GFsjOqEeCUkV4I16fAgrFiPO2aG4OsbYEgCvABABeItzvvmy/WEA3gXgOXDMU5zzz4Y90GFCN0oNH1rI46LEYjGfO3du3apVq4q8vLwu9n5qa2tTfvzxx/EnT54Mtlqtrr8WZLTrX5heoTqhKvN/y1/w/tg7Tl4lj2Cc0e+WS+B2uaKlNCLi89oFXv+yrdGf85teXx8vsnPRfqwoCkGtZQPei++EWuHsSL+RGD34BYrRiAAsBk0vfwPGmAjA3wEsBZAAYA1jLOGyw34FYDfnfDKAuwFsG94oh9fChQu7P/vsM8+uri6hs7NT+Oyzz7wWLFhA04IOQCNXLi4wMLD3Bz/4QVFGRkZgdnZ28EAjaJaXlxek0+m85s+frw0NDTU6O86xRjAK1YpiRY8yVxki9Akj406yMYXb5YqW8sDAc8pxfdW2KfoGX6ndruGA/QRSS+/Fu35aRI6U6TSOVJThXwhCAN0F+C1MB1DOOa8EAMbYhwC+D6DwkmM4/tvQXA2gblgjHGZz587tWbt2bcuUKVPigf4F7XPmzKFZEAdgjhomZIy9DWA5gEbOedLAth8AeAZAPIDpnPOMGzyXkXPudjPxbNq0KRXASzdzDmdraWmRHT16NKK5udn90u3jx49vnDNnTq1UKqVG0A7EzKxJViFrdsty8xN3jIDFzmMSt8mVzeWBAefc40y1nVMNhgip3a7gAM/GpPL1eM+zAEkjp/2LH2qxGxxpCHV2KEOoiAPrh+JE13tvYIzdAWAJ5/z+gcfrAczgnD98yTFBAL4A4AXADcAiznnmUMR2NTk5Odrk5ORmR52fDK+cnBzf5OTkiKvtc+TI1XYAWwHsuGRbPoD/B+B1B1531PLx8THp47+iAAAgAElEQVTdfvvtJdnZ2X6ZmZmhg42gi4uL/aurqz1TU1O1kZGRNMQ7lGzolFZL69yy3FRSgzQE1NPPRXGrQtlUERiYoYrpq7FP1Td4ymy2EAAoQ0zVvXhXdgazR06ZAjGM+BWq8SvEQUTLN65DzBi79EP6G5zzNwb+frVlE5ePJqwBsJ1z/iJjbBaA9xhjSZxz+qBKborDkivO+QnGWMRl24qA/jYw5LsRBAFTpkxpioqK6jh27Fi4wWDwAACj0Sj9/PPP46Kjo5tTU1NrqBH0TbDDJGmS6JQ5SomsUhbOOKOpGJfFrQplY3lAUIY6pq/OnqIzqGU2WzAA1CGo+n68ZT+EZZHOjvJbsGMhSrELofClKcAbYOWcp1xjXw3wtbVpobhy2u9HAJYAAOf8DGNMDsAXQONQB0rGFlpzNUJ5enqaV61aVZafn+/z1VdfaSwWiwgAKioqfGtra9Vz5szRx8XFUSPoG8VhE3WIdIp8hU1RpAgTrALdieXSuFXh1lgeEHjeM7qvnk/TGlQymy0IAEZcVfVBAajBR2CYQ0nVEDkPIJYxFgmgFv0L1tdedowewC0AtjPG4gHIATQNa5RkVHLZ5Iox9iCABwceumyczsQYw4QJE1oiIyM70tPTw6qrqy82gv7Pf/4TXVZW1jZ//ny9u7s7NYK+BqFHqJGXyLuVOcpgUa+IKqa7PG5RujVUBgRmeEaaDHyatt5dbrMFAkAX3Jt/iT+1/R0/iRkRVdUHSdGFZ1CHJxEH4apTWeQ74JxbGWMPAziM/jILb3POCxhjzwLI4JzvB/A4gDcZY4+hf8rwPk71CsgQcNmkZWDe/A2gf9Gik8Nxae7u7tbly5dXFhcXe54+fTrcZDKJAUCv13vt2rVLNXPmzOqEhIRWmo7txyysWVYha3LLdvMVt4lH00LhUYxblO6GioDADO+Ivgb7dG29Um6zBQBAL+Ttm/GUYcRUVf8vO76HUuyEBj6g3pIOMFCz6rPLtv3mkr8XApgz3HGR0c9lkyvy7Y0fP749LCys68svv9RUVlb6AIDZbBafOHEisry83DstLU2nVqstzo7TKWzoltZKa9yy3NylddJQ9K+rIC6PW9zc6yv8AzN8w/sa+fSqernCZosHADMk3a9iY/WT+HOMCfKRNZUWjGrshRgzaAqQkNHIYXehMMb+CeAMgHGMsRrG2I8YY6sZYzUAZgE4yBg77Kjrj1VKpdL2ve99T3vrrbeWKZVK8+D2uro69e7du5OysrJ87fYxciMMh0ncKC7z+LdHlf+b/gqvg17jBxIr4vK4yc29tjgyZn/HNK9D/AfaXOn8mpp4hc3mYYWobwfWF/mhSfR/eCXeBLnE2dHeMCk68ReUohqhmIEgZ4dDyHexZcsWnw0bNoTd7Hlyc3Nl8+fPjwkLC0uKiopKXLZsWVR1dbUYAI4dO6ZMSUkZFxERkRQZGZl41113hXd1dQnV1dXiBQsWxIwbNy4hOjo6cf78+TE3/4qGniPvFlxzjV37HHVN8l/R0dGdoaGhBSdPngwpLS292Aj67Nmz4ZWVlT4LFizQent7j75G0Bx2UadIpyhQWBSFijDBIoyc2+8JAG5yU9VW+gdm+of1NWO6tl6itFrjAcAOZvkUy8sfwJuhjQgYKQVAB9mwAqV4DxFQU9sa0u/8+fND+nM8bdq0oqE8nyP19PSwFStWxD733HPVa9eu7QCAAwcOqAwGgxgA1q1bF71jx47KRYsWGe12O959912v9vZ24cknnwxZuHBh569//etGAPjqq69csrsC1U8ZxWQymf2WW26pvu2220pUKlXf4PbGxkb3f/3rX4nnzp0LsNlGR8UG1svqlNnKIt8dvt2+H/hGuuW4xQkWQe7suMiN4iZ3VXVRZOwnxqleX7Dbq/JEadXV45VWq5oD9uOYVxKFyu7vY398IwJGVqNZDXTIRBP2Ix5quOQbARk7tm7d6hMXF5cwbty4hFWrVkUCQGlpqXTWrFlxcXFxCbNmzYorKyuTAsAHH3ygnjhx4vj4+PiE2bNnxw2OKl1LXFxcQnNzs8hut8PT03PS1q1bfQBg1apVkR9//PHXfm/feOMN7ylTpnQPJlYAsGLFiq5p06b1vfjii/533nlny6JFi4xAfwmiH/7wh20ajcZqMBgkGo3m4qzMjBkzXLLCPCVXY0BYWFj3XXfdVZiUlGQYXNRut9tZZmZm6J49e+IbGhpG5n/4FrTIS+VF3ru8G/23+werzqjiRT0ij29+InEd9j53D31RZOwnPZO9/s1ur8oXFuj1492sVk8O8CxMKpuAvJY0HB+nQ4SXs6P9VuRoxxaUQY9wTEGgs8MhJCMjQ/7CCy8EHT9+vLSkpKTw9ddf1wPAQw89FLZ27dqW0tLSwrvuuqtl48aNGgBYvHhxd3Z2dnFRUVHhHXfc0frss89e9+c4JSWl+8iRI+6ZmZny0NBQ08mTJ90BICsry23BggVfuzEtPz9fMWXKlJ6rnaewsFCRkpJy1X0/+clPGh955JGIGTNmxD355JOBWq3WJZcF0IL2MUIikfDU1NTamJiYtvT09Ij29nYFALS2tir37dsXP2HCBMOMGTPqxWKxa9+GbEO3tE5ao8xWuslqZBoAPs4OiXwX9l6VR7XWL+BCcLCpjc3Q1sPdYrm4uLsUsZX3Ybt8RFVVH8RgxWqUYTsiocLIi5+MWocPH/ZYsWJFW1BQkBUAAgICbEB/8nPo0KEKANi4cWPr7373u1AAqKqqkq5atSq0qalJYjabBY1Gc92lJKmpqd3Hjx9312q10vvvv7/xnXfe8auqqpKo1WqrWq0eksW+t99+e+fcuXPz9u3bp/7888/VU6dOTcjLyysIDg52qZJDNHI1xgQFBfXceeedRZMnT64VBIEDAOec5ebmBu3atSuhtrZW6ewYr8BhETeJyzz+41Hh/5a/3OtTr/EDiRUZcey9KrW2KCr2E0uy11G2Wltov0WvH+9usXgB/VXVl+GgbhxKo85gdrCzo/3WIqFFFlrwEeKhAk1LE5fCOQdj7IY/QD/88MNhmzZtaiwtLS3cunWrzmQyXTdnWLx4cdfZs2dVp06dcr/11lu7fHx8rO+//77XzJkzuy8/NjExse/ChQtXfb+Jj4/vzcjIuOZ7UUBAgO2hhx5q/fjjj6smTpxo/OKLL9yvdayzUHI1BolEIj5z5kzD7bffXujr63txqLazs1O+f//++PT09FCz2ezcnw0Ou9ApaN3Pupf6/cPP6rPHJ1ZRqohmdkajrSOSvUelriqKivvYMtHzmLBKW2i5Rae7mFS1wLt+LXZWhqBOcwjLwp0d7bemQBteQzkqEYFkBDg7HEKuZsmSJZ379+/3NhgMIgBoaGgQAcDkyZONb731lhcAvP76694pKSndANDV1SUKCwuzAMD27du/cZYgJibG0tbWJq6qqpInJCSYZ82a1f33v/89cN68eVckVw888EBLZmam+4cffqge3LZnzx6Pc+fOKX72s5817t692+fo0aMXm3Jv27bNW6/Xi/fv36/q6uoSAKCtrU3Q6XSyyMhI8+XndzZ6oxrDfH19+26//fbirKws/8zMzBCbzSYAQFFRUUB1dbXnvHnzdOHh4cPaCJr1snpFqaJdmaMMEhlFEcN5beIIdqOHp1bv65+tCTR1CDOr6iwqi+ViwcwuuDf/As+1bcOmkVVVfRCDFXehDG8iCu4YWWvCyJiTkpLS9/jjj9enpqaOFwSBJyUl9Xz00UfaV199VX/vvfdGvPLKK4E+Pj7WHTt2aAHg6aefrluzZk10QECAOSUlxajX62XfdI1JkyYZB2+USktL63ruuedCFi1adMX7iLu7O//kk0/KH330Uc2TTz6pEYvFPD4+vvfVV1/VazQa644dOyqfeOKJ0JaWFokgCHzmzJnd69evbz9//rzyscceCxOJRJxzztavX988f/78q67PciY2Eir9M8aMnHO3bz7y2jZt2pQK4KUhCmnUaW9vlx49ejS8oaHhawvCY2JimlNTU2vkcrnjbiu0oFWmlTW4Zbl5S1ok9Kl/VLAbPTyr+pMqc1ftjLo6Hw+z+WLh1suqqoucGel3FoMqfAJ3JMDP2aGMMEUcWD8UJxqK94bhlJOTo01OTm52dhxkaOTk5PgmJydHXG0fjVwRAP2NoFevXl2Wl5fnc+7cuYuNoMvLywcbQetiY2M7vuk8N8wOo6ROUu2W46aU6qUaBuY9ZOcmTmTvVntVVfv6ZYf5m7tEM7R1fWqz+eJIlRmS7m3YVP0UNo+8quqDlGjFVrThhxhZjaEJIcOGkityEWMMEydObImMjOxMT08Pq6mp8QSA3t5eyZEjR2LKy8tb582bV+3m5vbd7srgsIhbxTplrpLJS+XhzM5G5psruRKzdak9K2t8/XLD/cxdopnauh612XyxWKYVor6dWFf1KLZEdEI90gqA9mOwYB3K8QaioQB9GCCEXBMlV+QKKpXKsnz58ori4mKvM2fOhA02gtZqtd51dXUes2bN0sfHx7fdUCNoDi50C3pFoaJPWaDUCCbBJVsVkO+I2TrVXhW1vn65kb5mo2imrs7oaTJdTKpGeFX1/xqPCnwMNcZh5L4GQsiwoeSKXBVjDPHx8W3h4eGdJ06c0FRVVV1sBH38+PGo8vLy9rS0NL2Hh8dVG0GzPmaQl8nb3bLdAkTdopF39xe5Pmbr9PQqr/Xxy4vwMRvFM7V1HV6XJFUcsJ/AvLJ78a6/DhEjNyFxRzNeRSfuoSlAQsiNo+SKXJdSqbQtWbJEW15e3nby5Mnw3t5eCQDU1tZ67tq1SzVt2rSa5OTkZsYYYEW7TCerd8ty85I0SQIBqko92jBm61B7l9f7+F5Mqjq9TKaLhTL7q6pPLr8H73sWIWHc9c7l0gSYcR8q8HfEQA7fb34CIYT8FyVX5IbExMR0hIaG5p88eTK0rKzMDwCsVqvozJkz4WUFZfKVypXZmgaNN+PM09mxkqHHmK3d07vU4O2bH+Vt6RXP1NW1e/f1fa36+Iiuqn6pJFRgHzwRQ1OAhJDvhoqIkhsml8vtixYt0i9buqxQKVdeLArX3NkcsMOwY/FxfjzQjiHpcEBcBGO2di/fwuKouL1uMerz4qW6ipZllZUx3n19F0claxFcvQSH9CO2qvogFZqwG1XIQzRiqK0SIY7Q0dEhrF27Nlyj0STFxMQkpqSkjBssFqrX68XLly+P0mg0SdHR0Ynz58+Pyc3NldlsNtx3332a2NjYxLi4uISkpKT44uJiqbNfy/XQyBW5MRxcMArViiJFT0peimaSaVLfF/giOBOZgRwcVliFdKRrilDk/X18XxuM4D5nh0y+O8asbV4+JY3evkVRnuYeyQxdXbNvX9/XbkZogXf9I/hb3z+xNtJZcQ4JASY8gEq8gljIqGYVGUZJQzw6mo+iIT2fA6xbty4iPDzcpNVq80UiEQoLC6W5ubkKu92OlStXxqxdu7bl008/rQSA06dPK+rq6iRnzpxxMxgMkuLi4gKRSISKigqJh4eHS3+Sp+SKXBczsQZ5ubxVma0MEHeKwwa3SyHly7G8NglJbQdwIKIFLQoAaECD21t4K2EmZtYvxEKDGC7eCJp8DWPWVi/f4kZvn+IYtaW3dYaursmvt/dri7m74N78FDa3vYqNI7Oq+qUmoQz74IsImgIkY8MTTzwRtGfPHu+goCCzj4+PdfLkyT3PPvtsw4svvuj7zjvv+FksFhYREWHas2dPlUqlsr/99ttezz33XLAgCFylUtkyMjJKLj3fPffcE7Z06dKOdevWdSxevDja09PT9q9//Uv70ksv+VZVVUm3bNlSN3hsQUGBLCsry+3jjz+uFIn6awcnJCSYExISzPv371eJxWL+85//vGnw+NmzZ/cCwDPPPBMQEBBgGXxOdHT0VW+kciU0LUiuZEW7rFJW5P2Rd73/2/4BHic84sWd4qvW9YlARM9GbCyajdl1AvobQdthZ6dxOvhVvBqvhdb1GkGTKzBmbfHxyyuJHr/PI8IjS7pYV9GwvKIi2q+39+I0Xy/k7b/FMyVeaPPahp/EjujESo0G7IMWWYhFBLWtIWPDiRMnlAcOHPDKy8srPHjwYEVubu7F6vbr1q1ry8/PLyopKSkcN25c75YtW3wBYPPmzUFffPFFaUlJSeHnn39efvk5582b13XixAkVABgMBmlpaakcAE6dOuU+f/78r/UUzM7OlickJPSIxVeO6+Tm5iqSk5Ov2sZm/fr1rUeOHPEcP358wgMPPBB66tQpxU19I4YBJVeknx29knpJifqQWuv/lr+H52HPeEmjJOhGniqGmN+KW+vvx/2FAQi42Ai6BS2Kd/Fu/Kf4NMQM88h9Ix7FGLM0+/jnlkSP36cO98iWLNZVGVZUVET59/aGDB5jhqT7ZfxvkRfa3J7Fb8eN2HY1ACBCHx5FERrhg1WIcHY4hAyn9PR096VLl7a7u7tzLy8v++LFi9sH92VmZiqmTp06Li4uLuGjjz7yKSgokANASkpK97p16yJefPFFX6v1yvrRixcv7j579qx7ZmamPC4urtfX19ei0+kkmZmZbgsXLryiYfN3ER0dbSkvL89/9tlnawRBwLJly8Z98sknqqE4t6PQtOBYxmEVtYl0ynylXVGsCGc2dlO3zgcjuO/H+HHxCZzwP4mTIVZYBQ6ODGQElqHMazmWa2MROyS/bOTmMMHS7ONb2OrpUxqtspi6puvqDIE9PVGXHjMqqqpfKgVl2As/aGgKkIxN1+sl/OCDD0bu2bOnfNasWb1btmzxOX78uAoAPvjgA/3Ro0fd9u/fr540aVJidnZ2QWBg4MVes5GRkZaOjg7xgQMH1KmpqV2tra3iHTt2eLm5udm9vLy+ti5q0qRJfUVFRUqbzYbBKb5BEyZM6P3444+vOYqsUCj4nXfe2XnnnXd2BgQEWPbu3ev5/e9//4qG0K6CRq7GIMEoVLtluBX7bffr893lG60sUMYyGxuSOy8ECEhDWuOP8eOCUIRe/MHvQIdsJ3aO24u9Yb3opZ87J2GCpck3ILs0etw+b41HnuQWvbZuZXl5ZGBPT+jgMXYwy36sKApGnfU+vBvfCbXLD8FflycMOAgdziMWGlCpEDJmpaWldR8+fFjd09PDOjo6hCNHjlz8fejp6RHCwsIsJpOJffjhhxeXgRQUFMgWLlxofPnll+u8vLyslZWVV7xXTJ06tfv111/3X7RoUXdaWlr33//+98AZM2Zc8UE6MTHRNHHiRONPf/rTYLu9P+/Ky8uTvf/++54rVqzoMpvN7MUXX7xYV+748ePKgwcPup88eVKp1WolAGCz2ZCXl6cIDw83D/G3Z0jRyNUYwcysSV4ub1ZmK/3FHWKNo6/nBz/zj/Cj0jM445uO9FAzzCIAyEWuXyUq1UuxVJeIxE5Hx0H6CYKl0dsvv8PTuyza3WI2TtfX1wYZjV+7y48D9uOYX3Yfto/squqDROjF/0GHzYiFGCN3KpOQITJ//vyeJUuWdCQkJCSGhISYJk6caFSr1TYAeOqpp+qmT58eHxISYo6Pj+/p7u4WAcBjjz0WqtVqZZxzNnfu3M6ZM2f2Xn7euXPndn/55ZceSUlJJpPJZO7o6BDNmzfvqqNK77//vnbTpk2a8PDwJIVCYff09LT95S9/qRYEAfv376/YtGmT5uWXXw6UyWQ8NDTU9Le//a26pKRE9uMf/zjcbDYLADBp0iTjU0891ejI79XNYtcbJnQVjDEj59ztm4+8tk2bNqUCeGmIQhoZbOiUVctqlVlKtdQgdVr9oTa0SfZjf3gVqtSXbh+P8S3LsbzaHe62az2X3BxBMDf4+Od1qr0qot0s5urp9fUs2GgMu/SYy6qqj4ZSBByzUIaPEIggeDg7GHKFIg6sH4oTDcV7w3DKycnRJicnNzszho6ODkGtVtu7urqEWbNmjXvttdd0c+fOvepCcnJ9OTk5vsnJyRFX20cjV6ONHX2SJolema2UyqpkYYwzp49AeMHLsgEbyi/ggte/8e+wPvSJAaAYxT466NSLsVg/GZPbGGjN+1ARBLPBxz+vS+1VEe1mtfRNr66vDu7uvqLHYyliKzdgh/wrzBzZVdUHeaMeH8KKxYj75oPJsOKco7GxAocPN2PDBmdHM2bdc8894WVlZQqTycTuvvvuFkqsHIOSq9GAwybqEOmVeUqrolgRxqzM5d5YGBimYmpbHOK6PsWnmhKUeANAL3rF+7E/Kh/57SuxUu8JT5evX+LKBMFs8A3I7fbwrIxWWi3madX11aFXSapqEVz9I/yDH8aSqKudZ8QRowdPQI/fIw4iWkvqUji3o76+AgcOqGAwxAAYkb/jjLH3ADzMOe8YeBwO4G3O+S3OjezbOXDgQJWzYxgLKLkawQSjUCMvlncr85Qhol7RiKiSrYLKugZrqvKQ1/o5Pg83wigBgEpUem7DNtVCLKyegRktNIr17QgiU72vf67Rw7MqWmm1WFJqDDpNV1fE5ce1wLv+YWzt/RBrRkdSBXCkogz/QhACMN7ZwZBLcG5HbW059u/3QlPTaBgZPQngK8bYTwGEAHgCwOPODYm4KoclV4yxtwEsB9DIOU8a2OYNYBeACABaAHdyztscFcNoxMysSVYpa3bLcvMTt4tDv/kZrmkCJnREI7rgM3wWmo98XwAwwyz6HJ9HFKDA+/v4vs4Xvi59N4grEESmOr+AnF6VWhultFpqUmoMWk1X1xWJdidUTb/Ac+2joqr6ID/UYjc40mgK0KVwboVeX4kDB7zR0jJq/m04568zxgoAHAPQDGAy59zg5LCIi3LkyNV2AFsB7Lhk21MA/sM538wYe2rg8ZMOjGF0sKFTWiOtc8tyc5fWS0OB0dH/TAml7Q7coZuACa0HcTC8E50yAKhGtcdreC0xFam1qUhtFGiW5woiUV+tb0B2n4enPlputdak1NRrw66SVPVC3v4cftHwJ/wyxgbxqPi5gQTdeBo1+BVNAboUzq2oqirHp5/6o61t1CRVgxhj6wH8GsAGABMBfMYY+yHnPMe5kRFX5LDkinN+gjEWcdnm7wNIG/j7uwDSQcnV1XGYxI1ivVuOm0hWKQtnnI3aKY9xGNcVgYjCwzgcnIWsgMFG0Mdw7GIj6CAEUSNoACJRX41fYJZZpa6OklmttVNrDJURnZ1XTPGZIeneioerf4k/xZggv6nisC7EjoUoxS5o4EtTgC6DcwvKyyvw6aeB6Owczf8utwOYyzlvBPBPxtg+9L+PTXJuWMQVDfeaqwDOeT0AcM7rGWP+w3x918ZhF3WIdIoChUVRpAgTLMJoWKdwQ2SQ2VdiZc0ETGg7gAMRrWiVA4ABBrc38WbCLMyqW4AFDWOzETS3yeRtlb7+eXKle4NGZrXWTqltqIjs6Ii+/EgrRH3v456qR/C3yG6onH6n6JAJQA0+AsMcSqpcht1uQmlpJQ4eDEF396j/d+Gcr7rs8TnG2HRnxeNM06dPH/fCCy9Uz5s3j+40vAaXXdDOGHsQwIMDD102zqHAelitokTRpcxRBo+UhemOEolI40ZsLPwP/hN0DucC7bAzO+zsFE6FlKDEeyVWVoUh7IoidqMSs3WqPKprffzzgiSS3tjrJVX9VdVXlj+INzRN8B89SZUUXXgGdXgScRDoLgeXYLf3oahIi0OHQmA0jp6ftWtgjP2cc/48Y2zLNQ559DufPClpaL9/+flFQ3o+B7NYLJBIJM4OwyGGe71CA2MsCAAG/rxmhVXO+Ruc8xTOeQqAK7tFjnDMwlrkJfIinw99mvzf9Q9RnVWNF/WKqOAhAAkkfAmW1P0P/qfIH/4XPxk1o1nxDt5JOIiDIRZYRu0brUjUV+MbkF0aM26fMjDkXLwKnd1zamqqbi8tDbk8seKA7RjSSiKgNa7Gx/FN8Hd3VtxDzI7voRh1EPALjKPEygXY7b3IySnCCy/YsGfPeBiNLt04dwgNJiyZ1/gaMUpKSqRRUVGJd999d3hMTEzinDlzYru7u9n06dPHnThxQgkA9fX14pCQkAkAYLVa8eCDD4bGxcUlxMXFJfzxj3+8YrZp7969HpMmTRqfkJAQv3Tp0qiOjg4BAH72s58FJSUlxcfGxiauWbMmfLDdzfTp08c9/PDDIdOmTRv3hz/8IWAYX/6wGu4Rof0A7gWweeDPT4b5+s5lQ7e0VlqrzFYqZbUyDQAfZ4fkykIR2vtj/LjoOI4HnMKpEBtsjIPjPM4HlqHMcwVWaKMRbXR2nEODW+WK1kpf/xylwq05FACUFotuaq2BX62kAgf4BUwpX4/3PIuQMFrWVPULRjX2QowZNAXoEmw2I3Jy9PjiiwiYTKN+pOpynPMDA3++6+xYhoJer5e///77lbNnz9YtW7YsaseOHddslvziiy/66XQ6WUFBQaFEIkFDQ8PX2kjV19eL//SnPwWdOHGi1MPDw/70008H/v73vw944YUX6p944onGF154oR4AVq1aFfnhhx+q165d2wEA7e3tovPnz5c49pU6lyNLMfwT/YvXfRljNQB+i/6kajdj7EcA9AB+4KjruwwOs7hZrFPmKAV5hTyc2dnoeiN0MBFEWIiFDYlIbP8En0TUoc4dANrRLn8P742fhEmNS7CkVg65/ZvO5YoYs7Wr1Lp6H//8ELG4Lw4A3Mxm3TSDAVerqA4ApYit2oAdslFTVX2QDJ34Ewz4KZVWcAlWaxeysmpw5EgkzOYxl1RdjjGWAuBpAOG45L2Tcz7RaUF9ByEhIabZs2f3AsDkyZN7tFqt7FrHHj161OOhhx5qGpy6CwgI+FqrsvT0dLeKigr59OnTxwOAxWJhU6dO7QaAQ4cOqf76178G9vX1Ce3t7eKEhIReAB0AsGbNmlbHvDrX4ci7BddcY9eIqmb7nXDYRV0ivaJAYVYUjK2F6Y4SgADT/bi/5EqoN4UAACAASURBVDRO+x3H8VALLAIAZCPbvwIVnkuxVJeAhBHTCFok7q329i3sU3tVRjLG4wFAZTJVTjMYJIFG41WTqhZ416/He+ZDWDba1uXZsAKleA8RUFNi5XRWaycyMupw9GgkLJYxn1RdYif6C4fmARiRH+YAQCqVXrwpSCQS8d7eXkEsFnObrT9v6unpuTgFzzkHY+yaNxFxzjF37tzOy6u+9/T0sMcffzz8q6++KoyJibH89Kc/De7r67u4DEmlUo3Y79+NGtULxYcb62X1ilJFuzJHGSQyiiKcHc9oI0DAXMxtSkBC+yf4JFwHnRoAutAl3Y3dsfGIb1mO5dVucHPRRtDcIlc2V/r656gUylbN4FYPk6liel2d3L+396pV020QTNuwqfIxvBRng1h0tWNGLA10+BgyTAG9iTubxdKOr74yID09GjYbTcleqYlzvt/ZQTiCRqMxnTt3zm3BggU9O3fuvDhNuGjRos7XXnvN77bbbusanBa8dPQqLS3N+Pjjj4fl5+fLkpKSTF1dXUJVVZUkODjYCgCBgYHWjo4O4cCBA14rVqwYUwXDKbm6WRa0yLXyRmWW0lvSIgkCEOTskEY7b3hb7sN95RnI8P4P/qMZbARdhCIfHXQet+JW/SRMand2nIMYs7Z5eGoN/5+9845vuur++OcmaVabrqR7pXvSsiyPBURxoYAKguAAURQUx6M86E8fFAUnLhQFURyIuAEfZAjIBsse3XuPNG3TdKQZzbi/P1KgQulMmjT9vl+vvmjy/X7vPSlJvueee87neHplBXE4OvO2MKXUXacrSpbJhBKN5qrqv4vkI7L4TuxyK0KEYzkffDTiPdThGTBRXVvT1taA1NRaHDvGOFVd8xoh5CsA+wHoLj5JKd1qO5Msw0svvSSfNWtW2M8//yweP378pR2A559/vi4/P58XExMTz+Fw6MMPP1z33//+t+7icX9/f8MXX3xROnv27LC2tjYCAK+99lpVYmJi04MPPlgXFxcXHxgY2JaUlOQgubE9h1Bq/7JBhJBWSqlzf8ZYtGjReACrLGKQCa3cam6l8LxQwK3kBhEQppLJRjSjmbMd24MLUPCPpMwIRCinYmq5G9xsVmnK4bSWeXpl613dS8IIaa/MpdTkodUWjZHJRJ5are+1rtWC1/wffFi7Fk9FDJjBAwGBAdNQiA2QQgS+rc0Z0uh09Th2TIHU1HCYTAO50M6hr702xxIDWeLe0Iu5NgGIAZCFy9uClFL6aE/HSEtLK01KSqq3hn0MA09aWpokKSlJ2tkxJnLVUyj0HAWnTJguJPwCJjHdXnCFq+FBPFichjT3PdgTrIbaCQAKUeixFmtdb8bNFdfhugFsBE11AmFticQn3Z0vUF7OnaLUJNZoCsfIZO7uOt01ozUUoPtxc94MbA5pgrtjOVahKMXvECCJqQK0KTpdHQ4fVuLEiQhQKrG1OYOIJErpMFsbwTA4YJyrrqAwsVpYFcJsoVaQJQhmtbEc62bnQCQhqTECES27sCswC1kSANBBx96FXdJMZHrejbvLxBBbrRE0IQaFm0dxrackO5jNabvsPFBq8FKri5JlMrFbW1uXydpKuNfMwGb9AdzsWM6HAEp8DAUWgPn82BKtVo4DB5px5kw4KHWMPpMDywlCSBylNNvWhjDYP4xz1QlES2r4BfxG5wvOvmwVu9PKLQb7wxnOxpmYebERtLQFLVwAKEe56zqsi78BN1SOxdg6SzaC5jipSsReWSaRW1koIR10yyg1+KjVhcnV1V4ivb7LKKcRrLYvsLD4OXwcoQfXcT6TBAbMQgHWIwwuuKaWDoOVUatl2LevFefPhwNwWNHGAWAcgIcJISUw51wRmLcFB5UUA8PA4Dhf5P3FACWvlCd3Pu/s7lTv5AvgmvkwDPZNDGJapJBm7cbugAu44A0AeuhZ+7E/OBvZnvfgnlIf+Oi6G+famLRC59oyiU+aB4/f9E9ZBEr1fq2tRcnV1b7OBkO3EagCRJRMxk7XAkQ5VrQqAiXYBhfEMVWANkOlqsJff2mRnh4GDE2Fe0LIJACfAGAD+IpS+m4n59wH4HUAFEAapfSBaww3yVp2MjgeQ9u5MkHtJHOqcL7gzOeWc4MJCLO6dhD44JvuwT0VwzCsYTu2SxvRyAcAGWQuX+LL+BSkVN+IG2vY6LmyAWHp69w8ihSekuwQNtvwz2gUpboAlarkOpnMT9gDp0oHbvNifCRfi6ccq1pOCAU+QyMewTUrIBmsTEtLBfbs0SMrq1Npj6ECIYQNYA2AWwFUAjhNCPmj47YeISQSwMsAxlJKlYSQq9q7XIRSWmZtmxkch6HnXFHoOQ2cckGGgAryBSHEyCSmOzLhCG99Ck9l/4W//M7gjK8JJmKEkRzF0YBc5HrchbtKgxDURSNoSp24qhKxVyZcXCtCCcE/clUIpdrAlpbS0TKZv6AHZewUoAcwMW8mfgtWwtNxHCsCPeagEOsQDgHT1skmNDWV4c8/KfLypLY2xU5IBlBIKS0GAELIzwDuBtAxZ+pxAGsopUoAoJRes98tA0NvGDLOFa+Ip+coOHnCTGEQS8diVtVDCCc40TtxZ/UwDFP+gT+kdagTAkAd6oTf4tvY63BdzS24ReYEpw66JCaNs0tNmdgnXczjNV8VASCUakKamkpHyeVBvB5qAynhLp+BzW0Ol7AegyJsgzuimC1Am6BUlmLXLoLCwqGYH8ohhJzp8PhLSumX7b8HAKjocKwSwJgrro8CAELI3zBvHb5OKd1tLWNtTV5eHnfKlCmRBQUFWb29dseOHaIPP/zQ5+DBg4XWsM3RGDLOlftedycATJRqCBOEIM0TeCLnEA75piLV3wgjMcFETuKkXz7yPaZiamkEK7DYzbNQ6SnOlbLYV2/vEUpbQxsby0fI5SE8k6lHzoQRrLb1eLzoWayOdKiEdRfU43M04yFmC9AmKBTF2LnTCSUlUlubYkMMlNLR1zjWWZ7ZlcKOHACRMPfBDQRwlBCSQCntVISYEBICIJJSuo8QIgDAoZS29M10ICHBsguSzEzkWHK8nqDX63Gx9yDDZSxXNsXAMAhgg42bcXPN43g8yw9+qovPK6Hkb8TGmP2ua0c7i9PiWGzDPwQuCaWqiIaGnBl5eex/yWSxPJNJ2JP5ChFeEofs5iexLtZhHCsW2vAoclAHNzyEIZ3XM+BQCtTVFeHbb6vw2WdhKCkJ6v6iIUslgI5/n0AA1Z2cs41SqqeUlgDIAzrvGkAIeRzAZgBfdBjvfxa1eAAwGo2YPXt2SERERPzYsWMjz5w5w4+Li7vk5GVkZPDi4+NjAWDz5s2uoaGh8aNGjYrevHmz+8VzFi9e7H///feHjB07NnL69OmharWazJgxQxoVFRUXGxsbt337dpEtXps9wThXDEMSX/jqHsf8czdxE085EdalXlmHGxu9Xygqij/b0iICAJbJ1BytUOTOyM11Sq6piXUymXqkKq4Dt/lZfFIQicLQfEQ7jlBjAoqQhxZ8jVjwwSxXBwpKKWpqCvHVVzKsXRuO8vIAW5s0CDgNIJIQEkoI4QKYDeDK3oD/A3ATABBCJDBvExZfY7ynAIwF0AwAlNICANdMgLdXysvL+c8++2xtYWFhlpubm/HUqVNCkUhkTE1NFQDAF198IXnggQcUarWaPP3009I//vij8PTp03m1tbX/+Lynp6cL9+zZU7h9+/aSlStXegNAfn5+9o8//li8YMECaccG0EMRx1hJMzD0AharrcZdnN/kIc6TRrOM7Mm60Oz11dUheRqNKwAoDQbuRxUVUeO43MLf2tqMvui5ojgF6CHcmD8DmwMbIHachHUR6vA1VJjJbAEOKJSaIJMV4Y8/XCGXO4QIaxCnqWEg5qGUGgghTwPYA3M+1TeU0ixCyAoAZ9qbMO8BcBshJBuAEcALlFLFNYbUUUrbSHu3M0IIB1dvM9o9AQEBupSUFA0AjBgxQl1aWsqbN29e/fr16yXJyckV27Zt8zh9+nTOhQsX+IGBgbphw4bpAODBBx9UfPXVV5cKeiZNmtTo4uJCASA1NdXlmWeeqW0fU+vv79+WkZHBHzNmTBfFQo4N41wxDBGoictrKpZ4pzs5i2pC0EHHzI/Ha3tVKi3Yq1SKf62tDdS291k71tYWkQjoVwFlDwJN3c2ghLt8Nn7W7cXtjpPbx4IOC1GMVYgED4yq90BBqQmVlYXYvt0DdXWD3klnw6S5XVhY9rZkv0cST+4JfDQg81JKdwHYdcVzyzr8TgEsbv/pjsOEkP8CEBBCbgWwCMB2C5o7IHC53EsOIZvNphqNhvXwww8rV65c6f/zzz+3DBs2TO3r62ssLi4G6aJtrrOz88X+ihgMPYoHGmZbkMHBMalcXMtzpBE7m0LC90a0O1ZX4USp4gWDITXHZEq/HVBefL4OcHoIiJgChFVdYzFiAtF/gQU5PpCL9+L2YGu9kgFnOApQBDXWIhY8ZiE2IFBqQGlpHj77TIlvvolCXd2gdmi92K1Vb4n35zWFv8PaGfBjTBJPPpgV4l8CUAcgA8BCmJ22V2xqkYUQCoV0woQJTYsXLw6eN29ePQAMHz5cW1lZyc3KyuIBwM8//+x5revHjRun2rRpkycApKen82QyGTcxMVE7MNbbJ8wXJoNDwmLrqj3EeS3unvlhLNa1q/o4RmNdQn29MkahCGcBYgB0N1C8AXBfAoQo2j8jOwGPBED0NlCxEGi4uCopRmjpZOx0yUWs48gQuEGODdDgns4TexmsAKV6FBcXYedObyiVgzryyYKpLYVfUfK2ZL/LeIHj5IZRSk0A1rf/OBxz585t+PPPPz2mT5/eDJgdrk8//bRsypQpEZ6enoYxY8aocnJyBJ1d++KLL9bOmTMnJCoqKo7NZuOLL74oFQgEQzqcRQZDOI8Q0kopde7PGMvJ8vEAVlnIJAa7hBp5fGWxxDuDL3SRd1lF5WQ0yhPr6pqjGhrCyTUiuHKA/RQQtAX/FMW8EWj6FE7ZX+G90k/wXJfNmAcVbGjxFErwPiLBZRZeA4LJpEdhYRF27vRFc7N79xfYL64sbd1jrufq/+t5NFjM1nT1fZ2DB+gcS8xpiXtDL+YaC3ObnBCYF10Xewv2uGI2LS2tNCkpqd46FvaPZcuW+TQ1NbE/+eSTKysqGa5BWlqaJCkpSdrZMeYLlGHwQ4zNIteKKrF3hp+Tk6bLaAvXYJAl1dWpIpTKcNJNE1sfwLgZKN0CNDwDhMgALgAcAtyGgZMM6P0BYz160ULHbrkO+dgCbwQxQqADgsmkQ15eMXbtCoBKNYhFZakxiSsveUN8wGmqS34I4NB5eV8DeB7AWZiT3x2GW2+9NbysrIx3+PDhfFvb4igwzhXDoIXN1lZ6SHLV7h6FYaSLrT8A4BkM1SPkck1oU1MY6WUT23uB5olA1mNw8twKfXvOloYNvBgCbPYEvi4DEvrRCNqGuKMGP0CHO+E4ETh7xmTSIju7FH/+GQi1etA6snyib3xAlCFbIT7oH8BpcYgqxh7QRCn909ZGWIO//vqryNY2OBpdOleEkAx0Xmp6MRyaaBWrGBiuCTXwBQ3FEu80ocC5PrC7s/kGQ+XImhq9tLk5tK8zmkD0W/Bo4Q6siQQOaYGFUqCMZz56SgSMjgderAKWyQfNeoUNDZ5HGd5BJDiOEHqzc4xGNTIzy7BnTzA0mkEaqaI00qmhZKnnETwkSpeyCR3U25g9hRAysv3Xg4SQ9wFsBXBpMUUpPWcTwxjsmu7uBFMGxAoGhm4gxNgociuTib0zAzgcbbdRFoFeXz66psYY1NLSZ6cKMCesT8EO5xxcVDC+XQVkZwEv+ANf+Jp3B3QEeCMQ+N0T+KYEuM6eq2QorkcBtsAXfj3X72LoI0ZjKy5cKMdff0mh0w3KSJUTjKq7XPIq3hbv847iNgxFRf4Pr3jcsd0OBTBxAG1hGCR06VxRSssAgBASCiAe5jdSzsUu4wwM1obN0VR4SrK1bh7FoYTQbm9Ozm1tZaNrakiAStUvSYQ2OKlewrvVq7C4E0dOSIE1VcD9SuAxKZDXXkGTKQRS4oBnaoC3ZQDfvqpFPCHDzzDgVmYL0OoYDC04d64S+/eHoq1tUDpV/uzmihc8UjVPuJ0O5bOMg/I1WAJK6UUF97Ar732EkKHobDL0gO62BV0BfAWzp34B5u3AJELIWQDzKaXN1jeRYehB9XxhfbHEO00kEDb0qHeaS1tbyXUyGcevtbVTHavecBTj8qbh9wAFJN04IePUQFoO8JoP8JE/oCeAgQCr/IAd7sAXpcBN6v7a0284aMWLqMQKRILNaNtZFYOhGadPV+PgwVDo9YPOIWHDpJ0oLCl9R7zPfRRfxvQt/CebAYy84rnfAIyygS0Mdk5324KrAWQDmN2u8QFilmx9FcBnAOZa1zyGoQQhBqWre2mNp1dWEIej65HWj6tOV5Qsk/G81ep+bf8BQBNca+/HT5o/cWcvdIZ4FHi3BrivEZgvBS60l4UXCIBbYoH5cuDDakBk6nIY60BxA/LxK/zhg0GtnWT36PWNOHmyBocOhcNoHHTbrZ4stexp91NNSzxSpSJW26Cz35oQQmJg3rlxI4RM73DIFUCPeo0OJiZMmBCxZcuWEolE0qOKyLy8PO6UKVMiCwoKsqxhz7333iudMmVK0yOPPKLs/mz7oTvnaiyldF7HJ9rbBawghBT0dVJCyL8BPA5zJGw9pfTjvo7FMPjhcFrLPL2y9a7uJWGEwKPbCyilbmanSuil0fS7150JRL8B8wqfxOeRbeD1MSN9pBY4nQu86w28FQBoWYAJwHofYLc7sLYMmNLSX1t7jBeq8CsobmScKqvS1taA1NRaHDsWMdicKgKqT+ZXlrwlPiC4WVgSBMDP1jbZKdEw5x+7A5ja4fkWmO9jfSYBlpU+yQRy+jvG4cOHCy1hiz1gMBjA4dimyKi7LQKLd7UmhCTA/IZMBpAEYAohhFGCHnJQnUAozw0K/asmNGpniJtHSQQh3bwfKTV5aDSFk0pK5JOLiyO8NBr//lpRipCyBGQ2zsc3sX13rC7CAfBKLXA+C7i+w5Z5BQ+YGgU8EAIorFuZ5wQVXkcuZPDDjei2mpKhj+h09di3Lw8rV7ri8OEYGI2DpEwUcCG6+qfdTubUhL6vOxH0dVS7Y8VwDSil2yiljwCYQil9pMPPs5TSVFvb1xteeeUVnzfffNMbAObPnx/0r3/9KwoAtm3bJrr77rtDASAgIGCYTCbj5OXlccPCwuJnz54dEhERET927NhIlUpFAODo0aPC6OjouOHDh8d89NFH3p3NZTKZsHDhwsDIyMj4qKiouPXr13sAwI4dO0TJycnRkyZNCgsNDY2/6667Qk0mc2B/0aJFAeHh4fFRUVFxCxYsuPT9dfjwYZcRI0bEBAYGDvv22289uht/zJgxUVOnTg2Njo6OB4C1a9d6Dhs2LDYmJibugQceCDEYDFb6C1+muy+EvwkhywC8QTtIuRNCXgVwoo9zxgI4QSlVt491GMA0AO/1cTyGQQQhBoWbR3GtpyQ7mM3p4fYDpSaxRlOULJO5euh0FtHUaYOTaineqv4AL1ghuTumDThWAKwWA68GAap2h+onCbDfDfikDJjdbSPoXmLCROTjFwRBwlQBWg2drg6HDilx8mQEKJXY2pyeQ01x3LqS1z0Pse51yZayCAaR7fYBpfS4rW3oLzfddJPqgw8+8AFQe+HCBWFbWxtLp9ORI0eOuIwbN+6qyHp5eTl/06ZNxSkpKWV33nln2MaNGz0WLVrUMH/+fOmqVavKJ0+erFq4cGGni7iNGze6Z2RkCHJycrJkMhknOTk59rbbblMBQE5OjuDChQvFUqlUP2rUqJi//vrLZfjw4Zpdu3Z5FBcXZ7JYLNTX119aiMrlcqczZ87kXrhwgT9t2rSIRx55RNnV+Onp6c7nz5/PiomJaTt37hx/8+bNnmfOnMnl8Xj0oYceCl63bp346aefVljpzwyge+fqGZhVaQsJIRdgrhYcAeA8gMf6OGcmgLcIIWIAGgB3AjjTx7EYBgkcJ1WJ2CvLJHIrCyXkn+1krgmlRi+1ujBZJhO7tbVZLLp5FOPyp+F3/+4T1vsDC8BzCuCeZuDxYGBfuyZQrRNwfwTwUwOwrgLw6/8SygeV2AKCsYxTZTW0WjkOHGjGmTPhoHTQqJDziKFppktW9RviA75Sp6Z+b6EzDG7GjRunfvjhh52VSiWLx+PRxMRE1dGjR4XHjx8Xffrpp+VXnh8QEKBLSUnRAMCIESPUpaWlPIVCwW5paWFPnjxZBQCPPvqo4sCBA25XXnv06FHRfffd18DhcBAUFGQYM2aM6tixY0I3NzfTsGHDWsPDw/UAEB8fry4qKuJOnDhRxePxTLNnzw6ZPHly06xZsy4tQO+6665GNpuNUaNGaRUKhVN34ycmJrbGxMS0AcDu3btFmZmZwqSkpFgA0Gq1LG9vb6uHrrqTYmgGMJMQEg4gDuZtwv+jlPZZzZVSmkMIWQngLwAqAGkArnqhhJAFABb0xE4Ge8WkFTrXlkl80jx4/KaeJ5xTavBRqwuTq6u9RHq9xXKGmuBa9wB+VO/C5AGUIpDqgT1FwAYP4IVgoKH9vfyHJ3DEFVhZDjym7H6HvhO4aMHrqMb/IQosy2/hMwBQq2XYt68V58+Ho5t2SfaElKMsfdnzmOFR13OhnB5ImDAMDXg8Hg0MDNStWbNGkpycrEpKStLs27dPVFZWxhsxYsRV+nxcLvfSjhWbzaYajYZFKYW5rq1ruupbzOPxOo4Lg8FAnJyccOHChZw//vjD9eeff/b4/PPPvU+cOJEPAHz+ZVmbi+N2Nb5QKDR1OJ/MnDlTsWbNmqpujbYgPfpGp5QWUUq3U0r/oJQWEUKiCSF97gxOKf2aUjqSUnoDgAYAVyXHU0q/pJSOppSORifOF4P9Qlj6Ondxbm5Y9P9oQMiRaB6/qdM9+augVO+nUuXeVVjYcnNZWYxIr+9ZhKsbzAnrD+f4QO6+C5P7LdXQe1gAHlUCWZnA3Q2Xn2/kAAvDgFvDgVKnXgxowu3IhQxsvIxoxrGyAipVFX7/vQjvv++H8+cjYIX8U0vDgbF1qnNeTkbwmrqS0E+kC9zORnAIZdT3LQQhhEcIeYAQ8l9CyLKLP7a2q7ekpKSo1qxZ43PjjTe23HLLLS3fffedV1xcnJrF6tkCTyKRGF1cXIx79uxxAYANGzZ4dnbehAkTWjZv3uxpMBhQXV3NOXXqlMv48eNbrzVuU1MTq6GhgT1r1qymdevWVeTk5Ai7sqOn40+aNKl5x44dHlVVVRwAkMvl7Pz8fG6PXmw/6E7nKhHABwD8AfwPwKcA1gIYg6tVa3sMIcSbUlpLCAkGMB3A9X0di8FeoNSJqyoRe2XCxbUilJBeNHCltC1ApSq6TibzFxoMFt3aKkNw2WTsFGYhwQ5W775G4H8lwC8NwL9DAHm7Q3XAHRgmApZXAs/Vd7nm8UcFtoKDMcwWoFVoaanA7t0GZGf3W9pjoPBmqyqfdz+h/rf7CamAZbCD97nDsg1AE8yNmwdnL1GYnZLVq1f7Tpw4sdXV1dXE4/Ho2LFjVb0Z4+uvvy597LHHpAKBwDRx4sRO9S7nzJnTmJqa6hIbGxtPCKHLly+vDA4ONqSnp3c6ZmNjI3vKlCkROp2OAMCbb75Z0ZUNPR1/1KhR2ldeeaXq5ptvjjKZTHBycqKrV68uj4qKauvNa+4tpKvQGiHkJIDPARwHMAnAiwB+BPAqpbTPLT4IIUcBiAHoASymlO7v5vxWSqlzX+cDgOVk+XgAq/ozBkNnmDTOLjVlYp90MY/X3KtcFEKpNrClpWS0TBYgMBpdLWlVG5xUr+KNqvfwf3YqRdDAAp4NBH644m/2rxbg61Ig7p8ffBZ0eAEleJuJVFmFpqZy7NplQn6+1Nam9AQWTLrxgvLSt8X7RCmCyn5XzQ4wOXiAzrHEQJa4N/RirkxKaUJ/xkhLSytNSkqqt5RNDLYlLS1NkpSUJO3sWHe5TDxK6Yb23/MIIUsAvEQp7ZG42LWglI7vz/UMtofFapO7eRYqPcW5Uha7d9EmQqkmuLm5dHRNTRDPaPm2Gqm4Pv8e/M+/Dt526lgBgKcJ2FQO3N8ALJIC5e2NoE+IgFHxwMtVwH9rAQ7ghwrsAx9xTLTK4iiVpdi5k4Wion61Sxoo3Fha+RNup5UveRwLcWf3TGiXwWKkEkKGUUozbG0Ig/3TnXPFJ4SMwOV8AxWAxHaVdqYb+JCDmrjclhKxdzrbxbVail4m+BJKW6VNTeUj5fJgazhVzRDVPYgf1DswdRD1zpusAjKzzY2g1/uYhUe1LOC1IGCLB6atOYTfxgUwbWssTH19MXbu5KK0VGprU7qDgBpG8GQlb4gPcO90LgzBIEqsdzDGAZhHCCmBeVuQwKyrnWhbsxjske6cqxoAH13jMdMNfMhganUWVZdLfNK9uVxVr8u5CaWq8MbGiuFyuZRrMlncqTKBGL7HnIKF+CJCB/6gKZO/jMgErKs0R7Eel5pb5wBAugt2TJyMl/4tw5tv1qBDhQ1DH6CUoq6uGDt28FFRYfcNd4WkreEhUbr8dfGhAD+OihFatj132NoAhsFDd1IMNw6QHQx2CIvVVuMuzm/yEOdJWazeR5pYJlNzRGNjdZJcHupErVMOXo6gssnYKcjEMAdI5J2gBtIzEfksFyXfJsJgINDrCT74wB9//OGBr74qxfjxtm8EPdiglEIuL8L27c6orrZzrSdqinJSlL7qeZjeL8oMZRPaaSUWw8BBCHFtlyUauPZVDIOe7qoFX6SUvtf++0xK6W8djr1NKf2vtQ1kGGioictrKpZ4pzs5i2pCAPj2dgSWydQY1dBQk1hXF8ah1Cp5QnpwWpdhReVK/F8UBcsxErzdIccOvgFjpPVUPAAAIABJREFUvwzAmQXZePRRKTIyzMm6+fkC3HhjLBYsqMFbb8ng6WmLRtCDC0pNqK4uwvbtrpDLLaLsby24MLTc45Jb+ZZ4v3cEV2n3UbUhxo8w9xY8C/OOTcfvGwqA+f9iuIrutgVn43JbmpcB/Nbh2CQAjHPlMJhULq6VFRLvdF8nrrpPNyK2yaSMUShqE+rrw9hWcqoA4Dj+lX8P/udXCx9HSeg1YTry8QPCwYdZnmH0aC3OncvFW2954913A6DVsmAyAevW+eL7770xa1Y9XnhBjnYVYoYOUGpERUURtm/3QH29XW+nBXKayl/0+Fu3wO1sKI9YPg+Rof9QSqe0/zto5DkYbE9vGjdfGR1wjGjBEIfF1lWLvdPzwmO28vwCT8Q6cdUevR2DbTIphtXW5s3IyxMl1dVFsyntjSBmj2mGqO4e/F6aguNRtfARWWOOAccF9diLKmxBzCXH6iIcDvDaa7U4cyYbY8Zc3pJobWXhm2+8ER8/DLffHo7du11gYgJZoNSA0tI8fPZZI779Ngr19XaZf8eGSXO7sCD3bNC6morQVcHPuJ+K5JHB0/iZwbGZMGFCRMe+ft2Rl5fHjYyMjO/NHAsXLgyMiIiIX7hwYWB1dTUnMTExJjY2Nm737t0unZ2fnJwcfeTIkS5FRe2N7j7Q9Bq/d/aYYdBAjTy+sljincEXusiD+joKx2isS6ivb4hRKCJY6GG/wD5gAjH8iAcKHsNXgzRhvVMobkcetiAUzt000Y2P1yE1NR9r1oixapUvSkr4AACTCdi71x1797ojPl6Np56S45FHlOjQKmJIQKkexcXF2LHDG42NdhvN9GSpZc+6n2z6j0dqqAtLz8hqMCBh7VqLRiszFy3K6e8Yhw8fLrSELV3xww8/eNXV1V0QCAT0yy+/9IiIiNBu3bq11Bpz6fV6ODlZZb3fJd1FrpIIIc2EkBaYJRiaOzweNgD2MVgSYmwWuZXmSCN3tASH7Yvsq2PlZDTKR9bU5M/MyxPHKRTRLMBqLTYqEFg+AucVc7ApVgf+wH9CrIEASvyOMuxGDJzB69E1LBbwzDMKFBRk4bffCjB27D9VkbOyhFi0KBTBwcPw0ku+qKlx/LYnJlMb8vNz8fHHrdi0KRqNjb2OulobAqq/nl+Rvz9gQ6Ui/D2/18SHY1xY+p79nzMwWJhXXnnF58033/QGgPnz5wf961//igKAbdu2ie6+++5QAAgICBgmk8k4eXl53LCwsPjZs2eHRERExI8dOzZSpVIRADh69KgwOjo6bvjw4TEfffRRp+3NTCYTFi5cGBgZGRkfFRUVt379eg8AmDhxYoRGo2GNGDEidunSpb6vvfZa4MGDB91iYmLimpubWffee6/04jXLly+/NPZPP/3kMWzYsFipVJpwMcKlVqvJjBkzpFFRUXGxsbFx27dvFwHA6tWrxXfccUfYxIkTI8aPHx8FAK+++qpPQkJCbFRUVNzzzz9vdeHd7qoFHf8LegjAZmurPCQ5re4eRWGE1XcpBK7RWJNUW6uKUCrDiJW1dvTgtL6O1yvfwcuOk7AOAOOQhx0Ihhv65giw2cCMGc2YMaMZZ87w8eGHPvj9dzHaW0agrs4JK1cG4JNP/DBtWgOWLJFj5Mg+d1OwS0wmHfLyirFzZwBaW+0yAiQiurr5bucUr3geCRKzNYNId42hKwgh4wBEUkq/JYR4AXChlJbY2q6ectNNN6k++OADHwC1Fy5cELa1tbF0Oh05cuSIy7hx466qhiwvL+dv2rSpOCUlpezOO+8M27hxo8eiRYsa5s+fL121alX55MmTVQsXLgzsbK6NGze6Z2RkCHJycrJkMhknOTk59rbbblMdOHCgUCgUjsjNzc0GAB8fH/2ZM2ecN27cWH706FGhTCZzKigoyAKAjtuTBoOBZGRk5Pzyyy9uK1as8J80aVL+ypUrvQEgPz8/+/z58/w777wzsqioKBMAzp0755Kenp7l4+Nj3Lp1q2thYSE/PT09h1KKW265JeLPP/90ueOOO3rV9qc3MMKEDgs18AWK/MCQA5Vh0X8EeIgLogjL1Ke8Dq7BUD2murro3rw8n0ilMoJY+X1zAmMKglFufBtLox3GseKhGd+jCEcRDTcILDLm6NFa/PRTGYqL0/Hii1Xw8tJfOqbVsvDTTxKMGhWPG26IxJYtroM+L8tk0iIjIxcffqjHr7/GorXVoi2T+g81JnJrCn/3+7m0MfwdySqvPTFitmZAWrMwWB9CyGsA/g/m4i4AcAKwyXYW9Z5x48apMzIynJVKJYvH49HRo0erjh49Kjx+/Lho4sSJVzkaAQEBupSUFA0AjBgxQl1aWspTKBTslpYW9uTJk1UA8Oijjyo6m+vo0aOi++67r4HD4SAoKMgwZswY1bFjx7rMm4qJidFVVFTwHn744aDNmze7enh4XOoGM3PmTCUApKSktFZWVnIBIDU11WXu3LmKdvu0/v7+bRkZGXwAGD9+fLOPj48RAHbv3u165MgR17i4uLj4+Pi4oqIifm5uLr8vf8OewiRROhiEGBtFbmUysXdmAIej7deKmW8wVI6Qy9tCm5oGpNS4BS71c/C9ahvusesKr14zEgXYC3+IYR2NJX9/A1aurMHy5XJ8/bUH1qzxQceO8kePuuLoUVeEh2vx5JNyLFyogIvL4MnLMhrVyMgow969wdBo7C5SxSf6xlkumbIV4oN+wU7Ndi35wNAvpgEYAeAcAFBKqwkhg6qwhsfj0cDAQN2aNWskycnJqqSkJM2+fftEZWVlvBEjRlwV4eZyuZe+J9hsNtVoNCxKKdqbtHRJV32Lr4WXl5cxMzMz+/fff3ddu3at9y+//OL522+/lQIAvz2XlMPhwGg0XuwSc82xhELhpdUkpRTPPfec7IUXXhiwvo5M5MpBYHM0FV6+ZwvCY7a6+PifieVwtH1e1Qv0+opxFRUl0/PzAwfCsTInrN+f44U6t224R2rt+QYMJ6iwFgU4i0iIYf0IBp9P8dRTDcjMzMGuXXm45ZZGdPwSLCriY8mSEAQFJeLZZ/1RVmbfOWxGowpnz+bg/fcJtm2LhcaeokCUhnEaSr7y3lbYEv62aIPvtthgp2Z3W1vFYFXaqPluTgGAEGJH78eek5KSolqzZo3PjTfe2HLLLbe0fPfdd15xcXFqFqtn7oBEIjG6uLgY9+zZ4wIAGzZs6FTodsKECS2bN2/2NBgMqK6u5pw6dcpl/PjxrV2NLZPJOEajEfPmzWt88803qzIyMrqMdI0bN061adMmTwBIT0/nyWQybmJi4lVO4h133NH8/fffS5qamlgAUFJS4lRVVWXV4BITuRrUUD1fWF8s8U4TCYQNfa76u4iwra1sdE0NAlWqEEtY1xMqEVA+BTt4aRjuWBo/sSjCfnjBDwMfhWOxgDvuUOGOO1TIzubigw988MsvEqjV5m/PxkYOPv3UD59/7ovJk5VYskSOcePsR/ndYGjG2bNVOHAgFG1tdvW+4MDYOsU5v/wt8X5JHK+e0T0aWvxKCPkCgDsh5HEAjwJYb2Obes2ECRNaVq9e7Ttx4sRWV1dXE4/Ho2PHju1V7tHXX39d+thjj0kFAoFp4sSJzZ2dM2fOnMbU1FSX2NjYeEIIXb58eWVwcLChq3FLS0ud5s+fLzWZTAQAVqxYUdnV+S+++GLtnDlzQqKiouLYbDa++OKLUoFAcFU4a/r06c1ZWVn86667LgYwR7V++OGHkoCAgC7t6Q+kL6G7gYYQ0kop7dcqYTlZPh7AKguZZFMIMShd3UtrPL2ygjgcXae6IL3Bpa2t5DqZjO3X2hpsCft6gh6c1hVYVvkWljpWwjobGryDCrwA+0piVijY+PRTCdav90Z1Nfeq46NHq/DMM3I88EAjODZacxkMTTh1qhqHDoVBb18Vdb7slsr/eKSqn3Y7FcpnGe074mf/5OABOscSA1ni3tDL+W4FcBvMOo97KKV/9eb6tLS00qSkpAHbmmKwLmlpaZKkpCRpZ8eYyNUggsNpLfP0yta7upeEEdLHarMOiHS64mSZjOujVg/oCvwUriu4G9t8auBnt5pEfSIMJTgIDwTbmWMFAGKxEa+/LsfSpXL88IM7Pv3UB+fOXXbMz5xxwcMPu2DpUh0WLKjFU0/VD1iLHb2+ESdO1ODw4XAY7UelnAWT7kZBacnbkv1uY/hVnVZEMQwNCCFsmJ2pWwD0yqFiGJowzpXdQ3UCYW2JxCfdnS9Q9n+7jlLqZnaqBF4azYD2xFLBWTEPG5q3YIZjJayzoMNSlGIF7N9ZdHIC5s1rxLx5jTh8WIgPP/TBrl0eaE8QRWUlD8uWBeG99/wxa1Y9liyptVqLnbY2BVJT63DsWASMRrtJVHdnaeRPup1W/p/H3yFubJ3d2MVgOyilRkKImhDiRiltsrU9DPYP41zZKYQYFG4exbWekuxgNqet/1/wlJo8tNriMTKZi6dWa52qtWtgAjH8glmFj+KbMC0EVlNytwmBKMd+OCNqEDhWVzJhghoTJpSgpKQSH37ojU2bvNDUZNaVUanY+PprH3z7rQ9uuaURixfLceutKvQw6bVLdLp6HD2qQGpqBCi1i/cDATWM4lWXvCE+wJvkXBQMK+u4MQxKtAAyCCF/AbiUmE0pfdZ2JjHYK4xzZWdwnFQlYq8sk8itLJQQC7SUodQk1mqLkqurXT10ugEvE6+Cf8Vk7OSmYbhjRQAI9HgOhXgf0WAP8qrb0FA9PvusCu+8I8Pnn4vx+efeKC21fIsdrbYWhw834eTJcFDadcufAUJI2hQPu6bVvup5OMCPo3KsiCqDpdnZ/sPA0C2Mc2UXmLRC59oyiU+aB4/fZJn8J0qNEo2maEx1tYdbW9uA3zT04KjfxCvlb+BVxxECvYg3qrAXHCTBbvKDLIJIZMKLL9bhP/+pw5Ytrli92gd//31Z0uNii53XXgvE/Pm1+Pe/6+Dra+xiRDMaTQ0OHFDh7NkwUNppq4yBhZpiufUlyzwPs2a6ZIWwiX1EzxjsG0rpd7a2gWHwwDhXNoSw9HVuHkUKT0l2CJttsMy2EqUGb7W6aEx1tUSk19sksfoMRhVMxXafGvg5WrTKgPkowOeIAsd6/RRtDpsN3HdfM+67z9xi54MPzC122tout9h5990AfPyxH6ZNU+CFF2rRiQAh1Opq/PWXGhcuhAPwHeBXcRVcGJrvFeVUvSXe7xvq1DigW+MMgx9CSCSAdwDEAbik7k0pHdDcVYbBweDezhiUUOrEbSn2DTheHB79u8TLJz2GzTb0vx0KpXpflSr3rsLCllvKyqJFev2Ar8ZbIVTMws/F1+FMZA387Kw1ST/xhAzHUYf1iHVox+pKRo/W4uefy1BSko4XXqiGRHJlix0vjBwZjwkTIrB1q7nFjkpVia1bi/D++/64cCEC5rJ1mxHMaSxb67WjoCXibeGPvltiQ53sr8Ezw6DgWwCfAzAAuAnARgDf29QiK5OcnBx95MiRLoU8u2Lx4sX+3t7eiTExMXGRkZHxP/zwg9uOHTtEw4f/M01Er9dDLBYnldm7sHEvYCJXA4ZJ4+xSUyb2SRfzeM2WW+lQ2hagUhWPlsl8nQ0Gm0SKTCDGzZhR8DC+c7yEdcCE2cjHBkSAN4Q/L/7+Brz3ngzLl9fgm2+ubrFz5Igbjhxxg0TSjJEjJRg9WoEOrTMGGjZMmtuFhWVvS/Z7JPHkAyaKy+DQCCil+wkhhFJaBuB1QshRAK/1ecSdCZZNLZicmWPR8XqJwWAA5wqdvCeeeEK+YsUK+blz5/g333xzdH19fdpjjz3GzcvL40ZHR7cBwLZt21yjoqI0ISEh+k4HHoQwkSsrw2K1yT0k2bnh0f8j/sHHYni8Zi+LDEypLqi5OXdafr52QkVFjLPBYJPWG9Xwq7gOp+tn4dcYLQRXi1MOZlxRh4Ooxk+IGdKOVUcEgsstdnbsyMPNN/+zxU59vSv27g3BRx8l4s8//dHYOKArUS92a9Vb4v15TeHvsHYG/BiTxJMzVX8MlkJLCGEBKCCEPE0ImQbADnIIe05zczPrxhtvjIiOjo6LjIyMX79+vQcAbNu2TRQbGxsXFRUVN3PmTKlGo7kq2vzggw8GJyQkxEZERMQ///zz/hefDwgIGLZkyRK/UaNGRX/zzTfXjAqPHDlSy2azUVNTw5kyZUrDxo0bL7XN+emnnzxnzpzZYOnXa0uYG4ZVoCYut6VE7J3OdnGtlsKCZd2EUm1wc3PpqJqaQL4NtYH04KjfwtKKFVjmWArrZiimIhe/IBwCOJbDaClYLOD66zPR0JCN6OhIpKZ6IytLAr3evGDTajk4edIPp0/7IiqqASkpcgQHa6xiCkxtKfyKkrcl+13GC8oDrDEHw+CEEDIJwCcA2AC+opS+e43zZgD4DcB1lNIz1xjuOQBCAM8CeAPARAAPW9xoK7J161ZXX19f/aFDhwoBQKFQsNVqNVm4cGHo3r178xITE3XTpk2Tvv/++17Lli2r7XjtRx99VOXj42M0GAxISUmJPnnypGDMmDEaAODz+aazZ8/mdTX3gQMHnFksFvXz8zPMmTOn4YknnpC+9dZbNRqNhhw8eNBt3bp1FdZ75QMP41xZFFOrs6i6XOKT7s3lqiyaMEsoVUubmspGyuXBPBsLLp7FyIKp2O4jg//g03bqDiEU+A0q3OlglYCWpL6+GDt2cFFWJgUAeHvrcM89FbjttmqcPCnBuXPeaGkxO6UmE0Furhi5uWL4+6swZowcw4Y1WkIvy5WlrXvM9Vz9fz2PBovZGsd7LzL0i3ZV9TUAbgVQCeA0IeQPSmn2FeeJYHaYTnY1HqX0dPuvKgCPWN5i6zNy5EjN0qVLg5588smAu+++u2nSpEmq48ePCwIDA3WJiYk6AJg3b55izZo13gD+4Vx99913nhs2bJAYDAZSV1fnlJaWxr/oXM2dO1d5rTnXrVvn8+uvv4qdnZ2NGzduLGaxWJgwYYJarVaz0tLSeOnp6YLhw4e3enl5dV95PIiwiXNFCHkewGMwdxfPAPAIpfTqaqNBAovVVuMuzm/yEOdJWSzLtu8glKrCGxsrhsvlUq7JZNMbfiuEikfxTdOvmOWYekATkYv/QQqRBfTFHA1KKerqirF9uwCVlZ3nDAqFRtx0kxw33CBHeroHTp3yhkx2ucVOdbULfv/dBfv36zBqVC2Sk+shEPSyxQ41JnHlJcvFBzl3u+RJAVhmm53BEUkGUEgpLQYAQsjPAO4GkH3FeW8AeA/Akq4GI4REAXgBQAg63DsppRMtaLNVSUxM1J07dy57y5YtbkuXLg3Yt29f8/Tp0xu7uy43N5f72Wef+Zw9ezbHy8vLeO+990q1Wu2lFZJIJLrm5/hiztWVz99zzz0NGzdu9MzLyxPMmjXLobYEARs4V4SQAJhXCXGUUg0h5FcAswFsGGhb+gc1cXlNxRLvdCdnUU0ILFxqzjKZmiMaG6uSamtDnWzsVFHAuBkzCuZhQ6gazo7nePDRiI1QYiYcSzrCElBqQk1NEXbscEF1dc+isWw2MGKEEiNGKFFSIsTx4z4oLPRAe6d7NDfzcPBgEP7+2x8JCfVISamFRNJlix0+0Tc+IMqQrRAf9A/gtAy4GC7DoCQAQMetpkoAYzqeQAgZASCIUrqDENKlcwXztuE6AOsBDMooS2lpqZO3t7dh0aJFDSKRyPTdd9+JV6xYUVNVVcXNzMzkJSQk6DZu3CgeP358S8frlEolWyAQmDw9PY0VFRWcQ4cOuU2YMKHlWvP0hLlz5zZMnz49oqWlhf3jjz+W9uuF2SG22hbkABAQQvQw72FX28iOPmBSubhWVki8032duGqLf8mzTKamKKWyOrG2NoxDqc23pmTwrbwLf7DP4DrHdDzGIB+7EAhPDGjzaruHUhOqq4uwfbsr5PK+RypDQ9UIDS1BQ0Mljh/3Rnq6F3Q6s5RFWxsb58754Px5H4SFKZGSIkdYWOvlBHlKI5waSl/xPGJ6SJQeyibUJkUbDHYNhxDSMUfqS0rpl+2/d5YLeqmCtT05fRWAeT2cy0Ap/bxPVtoJZ8+eFbz88suBLBYLHA6Hrl27tkwoFNJ169aVzpw5M9xoNCIpKUm9ZMmSuo7XXX/99ZqEhAR1ZGRkfHBwsG7UqFGq/toyatQoLZ/PNw0bNkzt6uo6ME3iBxBC6cBXSxNC/g3gLQAaAHsppQ92c34rpdS5P3MuJ8vHw/xB6hMstq7aQ5zb4u5ZEMZimSxeAcU2mRqjFYqaYfX14WxKba71YQBb8y5eKn8NyyNNYDteVSkXzViLWswHEwXpCKVGVFQUYvt2T9TXW37LTadj4fRpMc6c8UZjI/+q497eatbokWV3jXc+967vIUk0V2EXbXIYLEoOHqBzLDFQV/cGQsj1AF6nlN7e/vhlAKCUvtP+2A1AEcw5VIB596EBwF0dk9oJIRer2p6FOQ/pdwC6i8cppT3e0kpLSytNSkqq7+n5DPZNWlqaJCkpSdrZMVtsC3rAvO8dCqARwG+EkIcopZuuOG8BgAXtD20UYaMGHl9ZIvFO5wtdaoOsMQPbZFLE1dfXxSkUEWxK7SI6dB7DC6dgh1c1AhwzSTgRhdgLH/gwjtUlKDWgtLQI27dLoFRa7/+dxzNh3Lg6pKTUITvbDSdPeqOi4rLgbG2t0LRrd2zqMUR8dyNq/z0JdT5ug3MLhsHmnAYQSQgJBVAFc/rJAxcPUkqbAFxy3gkhhwAs6aRa8CzMEa+LkbAXOhyjABiFdoarsIXTcguAEkppHQAQQrYCSAHwD+eqPbT7Zfs5rVcOYlWIsVnkWlEl9s7wc3LSWCV5m2M01sfX1ytiFYoIFuwjgboVwobHsb7xJzzgmE4HB634AFX4N2zSFsguoVSPoqIi7Nzpg8bGgXOmWSwgIaGJnRAnH1FzQik8cSTxRKY2qM1gvoHVNsPpnT8QsOpP+E2/Doolk1E7QopBW/TCMPBQSg2EkKcB7IFZiuEbSmkWIWQFgDOU0j96OA6TMsDQa2zhXJUD+BchRAjztuDNAK6lKzKgsNnaKg9JTqu7R1EYYVknidzJaKxNqKtrjGloiCAdVk22hALGrZheMBcbQ9Vw9uz+ikFIFIqxD54IYhwrAIDJ1IaCgmLs2uWL5uYBj5h6stSyp91PNS3xSJWKItv8MB51VQ1QfrwbXt8ehrdCZf5u0urB+jEVXj+mwuuGGDQ9Nwm1d49Gs8MpqzFYBUrpLgC7rnhu2TXOvbGz5wkh1wGooJTWtD+eC+BeAGUwbzs6XKUbQ/8ZcOeKUnqSELIZwDmYezSdR3uEyjZQA1/QUCzxThMKnOsDrTWLk9FYk1Rb2xKpVIYTO1L1rYFP5d3Yxj6FMXaxJWlx2NBiOcqwFI65xdlbTCYdcnOLsWtXAFpbB/T/nIDqk/mVJW+JDwhuFpYEAfDreDzAE4b3H4BsxQzUfH0Inmv+gk9uNS713TySC7cjuXCL8IH2yVsgXzARChc+bNZih2HI8AXMOy4ghNwA4F0AzwAYDvO9a4btTGOwV2ySy0QpfQ396cdkAQgxNorcymRi78wADkdrtWgG12CQDa+tbQ1vbAwnFpZr6A8GsDUr8X/ly7DCMRPWASAEZdgPEcIZxwomkwZZWaXYvTsIavWAVqE6kzbFI67na1/1PBzkzVF3+1kTcEGfvg2KRbdCsesCRKv+hPfBbLhfrL0plIP/nx8Q8sbvCJg7HnWL70RdiAQO05OMwe5gd4hOzYK5InELgC2EkAs2tIvBjhlyCu1sjqbCU5KtdfMoDiXEelIHfIOhcoRcrgttarKoUrslSENi4RTskFQiyDGdDhbasATFeAfRYHVajj10MBrVyMgow969wdBoBtCpoqY4bl3J656HWPe6ZEtZpPd5hSwCTBmBlikj0JJZAd4HO+H960lING3mnqiNanBW74Hf2n3wnToCDUsmQ54SBau02GGwGEZwobC1Eb2ETQjhUEoNMKexLOhwbMjdQxl6hmNGLDrB0ytTHyjdVx0WtT3I3bMokhBqlQ+FQK+vGFdRUTI9Pz/Q3hyrVggbHsL3RcORFlGJIMfUDPJDBTLQhJWIGdKOldGowtmzOXj/fYJt22Kh0fRLyqSn8Iih6SFRWk6J9OOmrJC14TNF2aEs0v//h4Qg6DY8gYry1UhfNg2Vfu64JDpqMIL8fgbiscsRl/wqojcdg7uBqS+0J1ohRD6CUIBE6JBgHwU8veAnAIcJIdtgzhM+CgCEkAgATbY0zNokJydHHzlyRNifMT777DNxZGRkfERERHx4eHj8smXLfABg//79zomJiTExMTFxYWFh8YsXL/YHgNWrV4s9PDySYmJi4sLDw+M//PBDu8hN7i1DxusWe2U7AfDv9sQ+ItTry0fLZKZAlUpqrTn6CgVMv2Na/lxsDG2Fi2MmrBMY8AQK8SmiwB46i4arMBiacfZsFQ4cCEVb24BFqqQcZenLnscMj7qeC+VYMSIsEcG4fAbkr9wD+aa/4fHpHvicL8Mlx/F0MVzmfA6X//4K3YKbUPvUbaj3cIbDCRTaPSzUQYR6SCCEKwJBBm8hCaX0LULIfphzBPfSy+KQLJhzr/pOcYJlPythmTkWHa+XGAwGcDiX3Ypff/3Vde3atd5//fVXvlQq1avVavL555+LAWD+/PmhP/30U9H111+vMRgMSEtLu6R7N3XqVOXGjRvLq6qqOAkJCfH33XdfY1BQkMEGL6nPDBnnylo4t7WVXieTsfxbW4NtbUtnyOFddRf+YDlswjoAiFGNvWBh5BBuX2MwNOHUqWocPBgOg2FAnCoOjK13OBeWvy3eJ0ng1UkHYs6LOHGARyZA+cgEKA9mQ/jRLvj8mQZPY7sbVaEA79XNCFq5Hf6zr0f9ksmojfZHly12GPqFCU6ogDvUkEACAbzgQH0fKaUnOnku3xa29IcocmY0AAAgAElEQVTm5mbWXXfdFSaTybgmk4m8+OKL1Y8//rhy27ZtopdeeinookL7xo0bywQCwT+KRR588MHgtLQ0Z61Wy5o6dapy1apV1QAQEBAw7P77768/ePCg68KFC2sXLFhwqYnze++95/fuu+9WSqVSPQAIhUL6n//8px4AGhoaOMHBwXoA4HA4GDVq1FVSKwEBAYbg4GBdYWEhl3Guhggina74OpmM66tWS21tS2cYwNa+jxdKX8GbUQ6bsA4YMRf5WI9IcIfoe1mvV+LECTkOHw6H0bJNw6+FN1tV9Zz7CdVz7idCBayBceS64qY4qG+KQ0mRHJUf7oL3D3/Dq1kDNgCodGB/dQg+3xyGz23DoFx8J+Q3J6CVkXKwCBoIUAkxKDwRAA5CbG0QQ9ds3brV1dfXV3/o0KFCAFAoFGy1Wk0WLlwYunfv3rzExETdtGnTpO+//77XsmXLajte+9FHH1X5+PgYDQYDUlJSok+ePCkYM2aMBgD4fL7p7NmzeVfOV1BQIBg7dqy6M1sWLFggj42NTRgzZkzLbbfd1vTUU08phELhPxy67OxsbkVFBS8uLk7X2Rj2zNC8IfUVSqmbTlecLJPxvTQau1XlTUNi4VRsl1Qg2HEjOe6QYwcMGAub39xtQlubAn//XY+//w6H0ehh7elYMOnGCcpL3xbvdxkrqAiw9nx9IdwH+rWPoOrd2ZB9vg/iz/fBp6wePAAwUWB3Ojx2p8MjIRDqp2+DfN4NUPKcGCmHXsFCPUSogxgCuCEQBFYRWWawDiNHjtQsXbo06Mknnwy4++67myZNmqQ6fvy4IDAwUJeYmKgDgHnz5inWrFnjDXOrn0t89913nhs2bJAYDAZSV1fnlJaWxr/oXM2dO1fZyXRd8sEHH8geeeSRhh07drj++uuv4t9++0186tSpPADYvn27R0xMjAuXyzV9/PHHZT4+PoMui5JxrnoCpdRdpysaU13tLNZq7SpJvSNqCJRPYF3D95jrmArrZkyYjnz8gHDwYfMejAOOTleHI0cacPx4BCi1emKwG0srX+h2Rvl/HseCPdnaQVFd6iqA6f+mom7JZNT9dhJuq/fA+3gBLrXYyayE8IlvELpsMwLnMy12usMEJ1TBDSpIIIYQ3rAT8WOG3pOYmKg7d+5c9pYtW9yWLl0asG/fvubp06c3dnddbm4u97PPPvM5e/ZsjpeXl/Hee++VarXaSzsiIpGo07zGiIgIzd9//y286667Wjo7Hh8fr4uPj69bvHhxnVgsHl5TU8MGLudc9fV12gOOul1kGSg1eWo0BXcUF9feWVwcIdZq/bq/aOChgOl/uDvHG7XC7zHXbp2/fuOCeuxFFbYgZsg5Vmp1NXbuLMTKlWKkpkaDUra1piKghpG86oKd/pvKGsPf9Vkp2Rfjydb2q2LIFrBZwOzr0ZT6OgpOrkD2fWNQz+VcjlRdbLEj/TcSH1qL4LQyc5SLAVoIUIgA5GMY1BiGIAQjtt2xYhjElJaWOolEItOiRYsannvuOfmFCxeEw4cP11ZVVXEzM/+fvfMOj6pM2/j9nnOmZiZlJoWQkEJIIxEEXNoS6djA1VU2KrI2LKirKMV1P1eRhVU+xcK6ICzrKip8WFZECdIjoAhKiUAgiaSQhCQkmcn0csr7/TGTEBFpKTNJzu+6cl0ZzjnveWYSMvc8z/M+91EVAKxevdqYk5PzMzFkNptZjUYjGQwGsbKyksvPzw+7lPvNmzev9i9/+Uv8qVOnOABwuVxk4cKF0QDwf//3f2GS5NNkR44cUbMsSyMjI7vNhxw5c3U+KBUjXa6Tw06fjgjzeoM67V2H6Orf4XOyD8O7c3mM4joU4VMkI6SHfWpuaqrA1q0UhYVJHX0rDeHN0/UFtfON+XGxnD2of+8vl6EpcK17AhXVJlS/vglR7+76ucXOh98g6sNvEDU6A5Yne6LFDoNG6FCPSKgQij5gZFPz7siBAwc0zz77bDzDMOA4ji5btqxCq9XSt99+u3zq1KkpzQ3tc+bMqW993YgRI1zZ2dnO1NTUrISEBM+QIUPsl3K/3NxcS21tLTd+/Ph0SikIIZg2bVoDAHzwwQfGP//5z33UarXEcRxdtWpVWeudhl0dcnZXafBCCHFQSts0p2dNVlYOgNcveBKlQrTTeXJoTU1kqNcb1LNYBLDuJZhd/hf8vTs3rAMamLEGFtyCpECH0mlQSlFfX4pNm1QoL+8wSyb/zaQ0RWP5Xw1f0zv1R5NZQrvv71IrXF6QVTt9FjtFNWctdppJjYFr5kSceXBst7XYkcDhNMJhQyQM0CKmE+99HBl0enss1B7vDZ1JQUFB+cCBAxsCHYdM+1BQUBA5cODApPMd6z4ysS1QyvdyOE4OramJ0fF80PeVHEH2yZuw0ditG9YBYBSK8CUSEIYOb9gOCigVUFlZiry8MNTVdWh5VwnBdovuRNUi4/bofkpz0G7O6Cg0StA/XYfGxyad32KnpA6apz9A4oL/Iu6eHNQ/fRPOJBjRpbaCnwcP1KiEASKMiIUCHSzcZWR6Lj1bXFHq7W23l/6mpqZXiCAEvVBxQmOeieWm1bin+/ZVAYAKVqxCPe7uIZ6AkuRBSUkZNm2KgcXSocMW4znLqXkR33geCjuQrCKdM7ohmGltsXPEb7Hz8TkWO282W+wMhmn2jV3MYofADB3qEAklwuRyn4xMZ9EzxRWlnnibrew3NTW9NaIY9KKKAtIXmFI0DR8m26Hv3sJqMEqwBb1hRPd+noDPoubIkUps2dIHLleH/R6ykFwTtCcr/m7cHj5YXRuUw26Dgav6wPPeI6hcMg2nl36FyFX5iK5pghIAeBHkv9/D+N/vYRyaAtufJuHMHSPQxHXYtoIrhoJDNcJgQyTCEYJYoIdkfmVkgogeJa4Ipe4+Vmv5NbW18eouIKoA4AyiTt+Kz+i3+G33zjIoYMebqMHMHjA3h+fN+P77OuTnJ4PnO+znamCcNU+E77M8HbE3Sc94u8TvezAQqYe4YCrq/nor6t7fg4h/bEHM4VYWO/tPQj99OfR/+Qieh8fhzOOT0BCmDajFjgdqVCECAiLlcp+MTDDQY8TVyKoqPtbhEFVdRFQJYN1vYFb5M1jcvRvWASATJ7EdUYjt5sLK7T6DPXuasHdvX0hSh2QTCCg/XF1VttC4XTtOWx4Pnx+azBWg4ID7x8B8/xiYdxxDyOubEH2uxc5zH6PPy1+g951+i5202E6y2CFogg61MEKJcPQB0wMyvTIyXYgeI66SrFYFgC6xq6QQmSdvRJ6hAkldQgheMSxceAmVmNt1TV0vCbu9Ctu3e3D4cF+gY2YF6Yin4YGwg/V/NexKMLKu7v16BoBxWXCMy0LZT3WoWrLRZ7Fjc/stdtxg/7UTMf/O71CLHQoOpxEKKyIRBh16Awhv1zvIyMi0G907I9LFcEHddD/+fTILhSkVSOrefRJ9UYZSeLq1sDKZyrFmTQWWLInH4cMpANp5chKVspV1P30a+39llpSXjG9Ebc40sq4u8QGiq9IvBvzy+1Fd9RZ+fCkXpxIj0eJ51myxM+llZFz9LDJWbIfBw7fpZ85DhVLE4gSyYcEAxCEJmX5hJSMTMN5///3wAwcOqJsfDx06NH3Xrl0BGzT85Zdf6seOHRtUmzV6TOYqmKGAtBE3Fd+JtUndvmGdgQd/QRleRDqY9hYbQQClEmprS7FxoxbV1UkdcQs14ZtydUdrFhh3xiYorEH1B6WnEKqB9OebUT93st9i5yvE7P0J+ubjRyoR8sg7SH7hU8Q/MBpnnrhUix0CK0JQAyM4hCMeLHrcmIyexLJl2e3ac/noo0ePt+d6v8b69evDBUGwDBkyxN3WtQRBQHcaHtqMnLkKMPWIPH0tdtVMwZcZdujVF7+iCxOPUyiEDX9DRrcTVpTyKC09gX/+04SVK/uhurqdswuU9lWYyv8VveEnW8rf9e/2+jwzQWGVy0IBpsVi50UUf/ciCqeeY7FTZ4Hi7xez2GFRAwOOIw3VuBp6pCEdRqSAle14ZNqfuXPnxiYnJ2eNHDkydcqUKcnPP/98zLFjx1Q5OTmpWVlZmUOGDEk/dOiQGgCKi4uVI0aMSEtLS+s/YsSItJKSEuXWrVtDtm3bFv7cc8/FZ2Rk9D927JgKANauXRtx1VVXZSYlJWV/9dVXOsAnnB5++OH47OzszLS0tP6vvPJKJODLNA0bNixtypQpyenp6VlFRUXK5OTkrNzc3MTU1NSsm2++OXn9+vX6wYMHZyQmJmbv3LlTCwA7d+7UDho0KCMzM7P/oEGDMgoKCoL2/0j3k4tdBBGM5w3MKp2H/03v9g3rBDyexEm8ijSw3UzQS5ILJ05UYNOm3rDb271HTgHRfrOuqHKRcXtUurIxqb3Xl2k/hvWD66NWFjv/2YVo03ksdq7NQNPD16HgjsmoYqIQAxViIW88kOkEdu3apf3iiy8ijhw5UsjzPLn66qv7Dxo0yDljxozElStXVlx11VWeHTt2hMycOTPhu+++K37kkUcS7rrrrsY//elPjW+88YZx5syZfbZt23ZywoQJTZMnT7bcd9995ua1BUEgR44cOb5u3bqwBQsW9L7++uuL33jjjciwsDDx6NGjx10uF/nNb36TMWXKFCsA/PjjjyGHDh06lpGR4S0qKlJWVlaq161bVzpkyJCKAQMGZH744YfGH3744cSaNWvCFy1aFDt27NiTAwcOdO/fv/+EQqHA+vXr9fPmzYvfvHnzycC9or+OLK4CQCEyT96EjYZyJHfv8QoAEI1qbAGHgehezfmCYMXhw6exbVsiPJ52f27xnOXU3IhvPA+FHkhSM/Kwz65EnAHCq9NQ87epqP2X32KnuJXFzq4TCN91AqPnr4fr0TtR99AfYNJquqXFjkyQkZ+fr7vhhhuadDodBUAnTpzY5Ha7mUOHDummTp3a0pLi9XoJABw6dChk06ZNJwFg5syZphdffPFXx3xMnTrVDAAjR450zJ07VwkA27ZtCz1x4oR2w4YNEQBgs9nYwsJCtVKppAMGDHBkZGS07K6Ni4vzDB061AUAaWlprnHjxlkZhsHgwYOdCxcu7A0AJpOJzc3NTS4vL1cTQijP80FbAZHFVSfigrrpT/hH478xo3v3VQEAgYAHUILlSAOH4Bu1eKV4vY3Yu7cBu3f3RTuP9eAgOidpT55aFLk94mpVnTzss4uj0aDmiTtQ+Pij0G88gPTXVyM2fz/CWix2KqB56mUkLViG+HtuQf1T9+JMQmyXt9iRCWLO5yUsSRL0er1w4sSJwrasrVarKQBwHAdRFIn/fmTJkiWnbrvtNmvrc7/88ku9Vqv92Ww4pVLZEhzDMC3rsSzbst4zzzwTN3r0aNvWrVtPFhUVKceNGxe0Lh7dq0QTpFBAysMNJ6JxRt0jhJUBNdiLevwLmd1GWDmdNdi0qQQvvxyB/Px0iKKivZaOYe3VLxm3FVtTXuI2xq3JuFpV15kmujLthwAlyhCDE8hCIwaiF5KRyYQhfso4OHa8i58K1uPo9N+hXq06O3TUbAX3xmrE9puEAbc9gaS9h39pJC0j0x6MGTPGvnnz5jCn00ksFguzbdu2cK1WK8XHx3vfeeedCMAntvbu3asBgEGDBjlWrVoVAQArVqwwXHPNNXYA0Ol0otVqvah+mDhxomX58uVRHo+HAMCPP/6oupTrfg2r1crGx8d7/fFEXuk6nYGcuepgGmGouQXrpT3I6V5lsfMj4Q4U4130g6qb/G5ZLKewdauIY8eS23NZFpJ7tKa8fJFxe+hwTXVce64t04kQ2KHFaRjBIALxYHHB35Or0uBZ/TJOLZmH6qXvI3LVJ4iubfBb7Agg/90K43+3wjhsAGx/uht1d9wIC9s9Pp7IBAGjR492Xn/99Zb+/ftnxcXFeQYMGOAICwsT165dW/rggw8mLl68OFYQBHLrrbeaRowY4Vq+fPmpe+65J+nNN9/sZTQahdWrV5cDwLRp00wzZ85Mevvtt2M++eSTX+15euqppxrKy8tVV111VSallBgMBj4vL++Ke6SeeeaZ2hkzZiQvXbq0V05OjvXiVwQOcr40YbBBCHFQSts2v4eQHACvt09EF0cE41mKJ8rm4NXuP2EdAEJRj8/hwZhuYL1BKUVDQyk2bVKirKxPey5tYJw1j4V/b50b8U2invF2792h3RUWddDDhEjooEccyJVXAHgeeO9zRLz1AWIKin455DghFp6H/oAzj09DQ5g+oBY77clxZNDp7bFQu7w3dCIFBQXlAwcObAhkDBaLhQkLC5NsNhszYsSI9Lfffrti1KhRzkDG1FUpKCiIHDhwYNL5jnV6doEQkg5gXat/6gvgeUrpG50dS0dxAumlN2FjeClSekK2imIyivAR+kLj+wTeZaFURFXVSWzcGIa6unYr3zKQvMPVVeULjDu147VlsiVN10OAElUIhwuRiIIaMQDapXSrUAAzbod5xu0wb/8OIa+/i5ivdiOi2WLnVA1Uz72JPi//C73vmoyGOffjTGpiJ1nsyHRL7r777sSSkhKNx+Mhd9xxR6MsrDqGgGauCCEsgGoAwyilFRc4r0tkrlxQNz2JNxv+hYd6xmBHLRrxEey4CYmBDqVNSJIHJ0+WYdOmaJjNhvZaNpRxn3kw9KDpWcPuPvLk9C6HA1pUwwiCCMSD67w+qJIKKF/7D6I+/BJRNsfPexZZBpj0W5ifvg91E0bA0VkxtTNy5kqmWxBUmatzGA/g5IWEVVeAAtJmXFeci3WJVoT1DGE1FkX4HInQwxjoUK4YUXTg6NFT2LKlD5zOdskyElBhkKqmbIFxp+qmkJIEdJCXoEwHwKAeoWiAEVqEog9IYKyZUhPhXT4f1S/PRs2ytTCuWIeYitO+gaKiBGzajYhNuxExIB2Ox+5C3T23oEmllEc5yMgEE4EWV3cAWBvgGNpEIww1t+IzcTeu7QklQECNJrwLE3IRtFtgLwrPN+GHH2qRn58Er7ddZkhpibfxntDD9X817Oody9lT22NNmQ5HhALViIADRkRCgygAUYEOqpkwPaRnH0L9vAdQ/9FXCFv6PmK+KzhrsfNjEUIefgF9n18K/oHbcGbWPaiPMlyCxY6MjEyHEzBxRQhRArgZwLO/cvwhAA/5HwZaBP4CEYznLTxeOhtL0kRwPWM/zzAUIw/xMHRRvzOPpx67d5vw3Xcp7TOjikpZyvqyvxq+ZqbqjiUxpAtn8XoOTmhQDSMoDIgDh6CfJ8aywJ03wXLnTbDsPQzNa+8i5vPtMPCCz0KqrhGKv69E3GvvIfb269A4937UDUg/aygtIyPT+QRStNwA4CCltO58BymlKwGsBHx19c4M7GIUIa30RuSFlyKlZ0zOVsKKZTiDBwJTJmkzdns1duxw4fDhvqC0zZkJNeGb7tAdrXnRZ5zc/eeWdXUYNECPehihRRjiQdBlM4sjrobr4zdQXlmLqjfeQ9S7nyHaZPFb7HjAfLABUR9sQNSYobDM+iPqpoyFjen+e5VlZIKOQIqrO9HFSoJuqCxP4s36lXi4Z/RVAcAA/IQtiEEMut5zNpvLsXkzQVFROzTcUylVYSr/H8Mu3K3/MYklVDZNDl4kKFCNcNgRCSM0iAYQ1AMHL5c+vSAseQY1f3sCtf/6GIZ/rkFMScXZpvv8/QjL34+w1ETZYkdGJhAEZLcgIUQLoBJAX0qp5RLOD+huQQpIWzGx5A/4KMGC8J4xPZmDA6/gNGZ1sU/5lEqoqytFXp4GlZVtHs6phGC9VXfi9ELj9qh+SrNc9gteXNCgCgZQGNEbHHSBDqgzkSTgy3zo33gPMTv3I+zc44YwCM0WO316BdxiR94t2Mz3R9u3+vGb7OPtup7MBbnQbsGAJIwppU5KqfFShFWgaYShZjy2n74OW9J7jLBKRSlKwXcpYUWpgPLyIixb1ogVK/q1VVglcE0Vb0Vt/MnW7+/a/4v9JEMWVkEIg0aE4gT6ohyDoEAmUhGDtJ4mrACAYYCbx8G2473zW+yYLOBefw+xKRMx4PYnZYudnsz8+fNjUlNTs1JTU7MWLFgQDQBvvfWWMS0trX96enr/W265JRkATp8+zV133XUp2dnZmdnZ2ZlbtmwJAYCdO3dqBw0alJGZmdl/0KBBGQUFBSoAWLp0qXHSpEkpOTk5qYmJidmPPPJI1x8o3QaCrlE8WBDBeJbh0dKn8HrPaVhn4cYLqMBfu9BOQEly48SJcmze3AtWa5vi5iA6bgwpqVxk3G7MVtV37dld3RNfuS8MdkTCAC1iAHkTwbkMSD9rsfPmakSu+gQxdY1QAD6LnU+3wPjpFp/FzhPTUZd7g2yx01PYvXu3ds2aNcYDBw4cp5RiyJAhmcOHD3e8+uqrsXv37j0RGxsr1NXVsQDw8MMP93n66afrrrvuOntJSYnyuuuuSy0tLT02cOBA9/79+08oFAqsX79eP2/evPjNmzefBIDCwkJtQUFBoUajkfr165c9Z86cun79+vGBfdaBQRZX56EYqaU3Ii/sJPr1jIZ1AEhEBbZDj5QuIqwEwYaCgips25YIt7tNO/9iWVvl7IhvXY+F7U9WM+2xi1CmHXFDgypEQIQRvaFAu9oRdWeiDBAXzkLdXx/FmdWfI/ytDxHzYyuLnX0/Qj9tLvTPvgbPw7moe+wuNHYjix2Z85Cfn6+78cYbm0JDQyUAuOmmm8z79u0LmTJlijk2NlYAgJiYGBEAvvnmm9CSkpKWDKfdbmfNZjNjMpnY3Nzc5PLycjUhhPI8T5rPGTVqlNVoNIoA0K9fP/fJkydVsriSgRsq61N4ve5tzOw65bC2wsCLOSjFS0gHA3LxCwKM19uIffvqsWtXXwjCFYtfFpJ7nLasYpFxe+hv1KflN+xggsAMPepghAphiAfTBTdTBBEqJeiDU2F+cKrPYue1/yBm856fW+z8zxtIePlfiLtrMhpm3ydb7HRXztdjTQgBIeQXByil+OGHH47rdLqfHZsxY0bC6NGjbVu3bj1ZVFSkHDduXMsHcqVS2XIuy7I/E149DXmTLgAK0K2YUNQLtYoeJaxiUYkjsGAxMoJeWLlctdi8uQQvvxyOHTsyIAhX5GMYyThOzzfsLDKnvIwtce+n/0Z9Wvb5CzwUHKoRieNIRw0GIQL9kIEIJIPxlbNk2ofxw+HYuAKlx/Nw5OFc1Om0Z4eO2hxgV6xDTOaNuOqmh9F3+3e/NJKW6dqMGzfOnpeXF26z2Rir1crk5eVFDB061LFhwwZDbW0tCwDNZcFRo0ZZFy9e3OIw8e2332oAwGq1svHx8V4AWLFiRbfahdue9PjMlQkRtVPxMb8D47tGOaw9IBDwCH7CP5AGNsgFttVaiW3beBw5kgyg15UswUDy/FZdWbEocntIjuZUm3cQyrQLHqhRBQMEGBELBeIAyD+bTiI1Ed6356Nq8Wyc/ucan8XOqZqzFjt5uxCRt8tnsfP4NNTdewvMClnmdnlGjRrlvOuuuxoHDx6cCQDTp0+vnzRpkmP27Nk1OTk5GQzD0OzsbOenn35avnLlysoZM2YkpKWl9RdFkQwbNsw2cuTIU88880ztjBkzkpcuXdorJyfHGujnFKwE1Lj5UumIUQwiGO8KPFw6C2+k8lD2nHZOI05jCxgMvjKh0ilQStHYWIZNmxQoLb3ikl0446p7JOwH8zMRexLDWY+8OyrQEDRBh1oYoUQ4+shZqeBBFIF1mxD2jw9+brHTTK9IeB+4DWee/CMa2sFiRx7FINMtCGbj5oBQgn5lN2BT6En060nNyyL+iGL8C6lQBunPnVIR1dWlyMvTo6bmiix2CCh/jaq6fIFxp/r6kJN9AMS0c5Qylw4FhxqEwYJIhCEEvQHIw1eDEJYF7poMy12TfRY7S/6DmA07zlrs1DZAuWgF4pe8i96yxY6MzMUJzjfZDsIDpfVpvFa3DI/1nL4qAAhHHb6EgN8iOHc/SpIXpaWlyMuLgtl8RT8bHfE03Bd6uOE5w9fx0ZyzZ/18gwsvVKiEATyM6AUlegPoHeigZC6dEVfD9cmbKD9Vg6o33kP0u58hymz9ucXOh18gasxvYJl1D+omjwleix1CyPUA3gTAAlhFKX35nONPA5gBQABQD+B+SmlFpwcq0+3oMeLqAaziP8OtnBmGnvTGK+H3KMaHSIE6CEswouhEYWEFNm+Og8NxBVlEKg5Q1pW9aMznbg45kciQ7mVx0mUgsCAEtTCCQwT6gIHst9gNSIiF8NqfcXrhk6g512KHUmDnfoTt3I+wtCSfxc6DU4PLYocQwgL4J4CJAKoAfE8I2UApLWx12iEA11BKnYSQmQD+F0Bu50cr093oMT1XhOCK7W+6JDo04L9wYWIQzgUSBAsOHKjBjh1J8HrVl3u5hvDmu/U/1r1gzI+N42y/sPqQ6QRYnPaX+0IRgt4gQb7bVKbNSBLwxU7o31iNmPy2Wex0Ss8VIWQEgPmU0uv8j58FAErpS79y/iAAb1FKf9sesZ0PueeqeyH3XPUsKK5DMT5BEnRBlsnxeOrx7bcmfPNNX4iXO6yTSumKhvLnDLtwp/5oEktoRMcEKfMr8FChChHwIFIu9/VEGAb43XjYfjceth+LoHrlHcR8shlGt8e347jZYuetD9Hrd+NhmnMf6oYNhCuAIcfB52HbTBWAYRc4/wEAmzo0IpkegyyuuhMamLEGFtwSZFPWHY5q7NzpwsGDfUFp1OVcqiKC9TZd4emFxh0xyYqmK2pyl7lCCKzQogaR4BCOeLBIDnRIMsHBgHR43l+MU6/OQ/U/3v+lxc4nm2H8ZDOMwwf6LHb+cH2HWexwhJAfWj1eSSld6f/+fNnU85ZqCCF3A7gGwOh2jk+mhxKkbYgyl80oFKEGatyCpECH0oLZXIF168rx6qtxOHCgHyi9xN83SpM5c/m/ojf8ZE9ZFPJhr/9mJF0akSQAACAASURBVCua5ExVZ8CiBhE4jlRU4WrokY50GJEC1jcDSUamNTFGn8VOxQ4cWfEiSq9Kg6P18e8KoL9rDvqlTEL2SysRZbG1+3uOQCm9ptXXylbHqoCftUXEAzh97gKEkAkA/gfAzZTSbr0DsqioSJmampp17r/n5uYmHjhw4LJbNDqKp59+undycnJWampq1urVq1t2GH/wwQfhEyZMaOnpfPbZZ3slJCRkNz9es2ZN2Lhx4y7b0eH9998Pb+/nL2euujoqWLEK9bg7SLJVlEo4c6YUeXkanDp1WebHCoj2KSFFVQsjd0RmKhuSOihCmZ8jQIkqRMCNSERDhVgA8tR6mctCpQR96A8wP/QHmLft9VvsfIMIyW+xU3Eaqr+8joSXViJu8hio1m4kfSillRdetc18DyCVEJIMoBrAHQDuan2Cv89qBYDrKaVnOjieX5C9LLtdd3AfffTo8Su5bt26dUGzQ/Knn35SfPrpp4bi4uJjDMPQU6dOtWzGGjdunP3JJ59seV/Zt2+fTqfTidXV1VxcXJzwzTff6EaMGGG/3HuuX78+XBAEy5AhQ9zt9TzkzFVXZjBKUA0WdwfB7ixKBVRUFGH58ka8/XY/nLr0SehxnLXy9chNJdaUv6s+7f1RRqayIbh6xbobBHaEoBgJ+AkDISAbSYhDBlQwBDo0ma7PhBFw5K1E6fGNOPLQH35psbN2I/oDKCOEPNeRcVBKBQCPA9gM4DiAjyilxwghCwghN/tPewWADsDHhJDDhJANHRlTMCAIAn7/+98npaWl9b/++uv72mw2ZujQoem7du3SAsCKFSsMaWlp/VNTU7NmzpzZ8ndcq9UOmjlzZlxWVlbmyJEj03bu3KkdOnRoenx8/FUffvhhGODLjA0ZMiS9f//+mf3798/cunVrCABUVFQorrnmmvSMjIz+qampWV999ZVOEATcdtttSampqVlpaWn9X3zxxWgAUCgUsNvtrNVqZRQKBVJSUlqMn3v37i3o9Xrx6NGjKgCoq6tTTJkyxbxjxw4dAOzfv1+Xk5NjB4Bp06YlZGdnZ/br1y/rqaeeaukPffTRR+NSUlKy0tLS+j/00EPxW7duDdm2bVv4c889F5+RkdH/2LFjqmPHjqlycnJSs7KyMocMGZJ+6NChy85qyZmrrogCdryJGsxE4MdKSJIbJSXlyMvrBav1krNnLCTXRO3Jipcit0VcraoLvh2N3Q0WddDDhEjooEccCNICHZJM9yYtCd4VL6Lq5dk4vXztzy124Js7dayjY6CU5gHIO+ffnm/1/YSOjiHYKC8vV69YsaJ80qRJjqlTpya98sorUa2OKebPnx934MCB41FRUUJOTk7a+++/Hz59+vQml8vFjB071rZ8+fLqiRMnpjz33HNxu3fvLj548KD6vvvuS542bZqld+/ewu7du4u1Wi09cuSI6s477+x79OjR4++8845h/PjxlsWLF9cKggCbzcbs3btXW1NToygpKTkGAA0NDSwAqNVqyWg08pMnT07Jz88v0Wg0P+uTGzJkiD0/P18niiKSk5M9I0eOdGzatCnsjjvuaCoqKtJce+21DgB47bXXqmNiYkRBEDBy5Mj0ffv2aZKSkrx5eXkRpaWlRxmGQUNDAxsZGSlOmDChafLkyZb77rvPDAAjRoxIW7lyZcVVV13l2bFjR8jMmTMTvvvuu+LLeZ1lcdXVyMRJbEcUYgMsrETRjh9/rMTWrQlwuS555180a6+eFf6d/YnwfUkhDN+TJuR3NmfLfUZEQY0YyNPqZQJARCikvzyM+nkPoH7dJoT97yrofyyGA0C3zxIFI7169fJOmjTJAQDTp09vXLp0aYs58549e0KGDx9u6927twAAubm5pq+//lo3ffr0JoVCQW+//XYrAGRlZblUKpWkUqno0KFDXdXV1UoA8Hq95IEHHkgsLCzUMAyDiooKFQAMHz7c8fDDDyfxPM/cfvvt5pEjR7oyMjI8lZWVqnvuuafPlClTLLfeeqsVAO6+++6kV155pXLPnj26W265pW9eXt7JF154ISYkJER69tln60eOHGn/9ttvQ0RRxLBhw+zXXnutY+HChb2//fZbbXJyslur1VIAeO+99wzvvvtupCAIpL6+XlFQUKAePHiwS6VSSXfccUfiTTfdZMnNzbWc+/pYLBbm0KFDuqlTp7ZUhLxe72WPmpHLgl0FFi4sRjEKkYJYhAYsDp43Y8+eE1i8WIENGzLhcl10/hgDyTNWU1b0Tfyq03V9X4171rAnPYTh5Qbp9scBLYrRByUYAL6l3KeGMdCBychwHDBtCiwFn2MrgCGU0rZ6FMpcAYSQX318obmXHMdRxj+Kn2EYqFQqCgAsy0IURQIAixYtiomOjuaPHz9eeOTIkUKe5xkAuOGGG+y7du0qiouL8957773Jb731ljEqKko8evRo4dixY23Lli2LvuOOO5IA4Ntvvw296aabbK+88kpNr169vNOnT0/YunVr2NSpU5sAYPTo0fYffvhBt3fvXt2oUaPsERERksfjIdu2bdMPHTrUDgAnTpxQvvXWWzFff/11cXFxceG4ceMsbrebUSgUOHz48PHbbrutaf369eFjxoz5RZJCFEXo9XrhxIkThc1fpaWll51llcVVV6AvyvAT3JgXwFKO212HLVuK8dJLodi+PQP8xcWRgXHW/E/E10WmvoulHfHvpY/UVMlzkdobBmcQjuNIwSkMggYZSEMUUsFBNqqWCVoopU2BjqGnUlNTo9y2bVsIAKxZs8YwcuTIlgbwa6+91rFv3z59TU0NJwgCPv74Y8OYMWMuuUHcYrGwsbGxPMuyWLZsmVEUffq5uLhYGRcXx8+ePbvh7rvvbjh48KC2pqaGE0UR9957b9PChQurjxw5ogWA9PR01/Lly40A8M9//rNqz549oUqlkvbr148HgMGDB7vr6+sV+/bt040cOdIFANnZ2a5333036re//a0dAMxmM6vRaCSDwSBWVlZy+fn5Yf74GJPJxObm5lrefvvtyuPHj2sBQKfTiVarlQEAg8EgxcfHe995550IAJAkCXv37r3sv6eyuApmGHjwHE6gBElIQmBGEdhslfjss5NYvDgae/emgdILTqshoPxwdWXxtrj3KhtT/jd2YeTO9DDWI7/Rtx8ilKhANI6jPxpwNaLRF5kIQwKI/P9ZRkbmwvTt29f9zjvvGNPS0vqbzWZuzpw59c3HEhMT+eeff7569OjRaZmZmVkDBgxw3n333ZcshGfNmnVm7dq1xoEDB2YUFxerNRqNBACbN2/W9+/fPyszM7P/559/HjFv3ry68vJyxahRo9IzMjL633///ckLFiyoAoD333+/bO3atca0tLT+I0aMyHj88cfrRFHE/PnzYwBf1mzgwIEOg8EgNGfPhg8fbq+qqlKNHTvWAQAjRoxwZWdnO1NTU7OmT5+eNGTIEDsANDU1sddff31qWlpa/5ycnPSFCxdWAsC0adNMS5cu7ZWZmdn/2LFjqrVr15b+5z//iUxPT++fmpqa9emnn1624bxsfxOsxOMUtkGL9ABNWW9sLMNXX7H46aeESzk9lHHXzwg92PAXw+4EI3vxUqHMJUJghRIN0MGDUKihRww4aAMdloxMG+gU+5tgRLa/6V7I9jddCQIeT+IkXkUa2E7ORFAq4fTpk8jL0+H06YtO4yagwiBVTfl8Q75iiq44EcBlTV+X+QUeKFAHLZwIBYdQGPzjEQLXYycjIyMjc9nI4iqYiEY1toDDQHTuLjpKeZSWliIvLxIm00V3IWqJt/GPoQX1zxu+7h3L2S97Gq4MAEACizpoYIUeQCjCoEU0CC4pUygjIyMjE7zI4ioYIBBwP0rwNtLAoWMcuM6HJLlQWFiBr77qDYfjIjOqqJSlrC/7q+FrcruuMIklVN6BdjkwMEGFRughIhQhCEE0WHkaukz3hFJIogQ7L8Lp4eFxeeF1uCHa3aBNDpweIw9hkenmyOIq0ESgFptAMQztaoNwQQTBioMHT2PHjkR4PBf8M6cmfFOu7mjNAuPO2ASFNfCT4LsCBHYoUY8QuP19UlFQwADIE9BluiaUQhQlOPxiye30gHe4IdrcoFYniNUFzuaE0uqC2uaC1uVFCHzl7POWtMfc3rnxy8h0NgERV4SQcACrAGTD51J+P6V0byBiCSASclGM99APqk76OXg8Ddi7twF79qRAFC8gqijtpzCV/8WwW/qjviCZJfSyd0r0ILz+PikHQsFCDyPUMMBnqSEjE5RQCkGU4PAKcHl4uF1e8HY3RJsL1OYCsTjBWZ1Q2lzQ2N3QurzQ4gJiSUZG5ucEKnP1JoCvKKW3E0KUQA/b/RSKenwOD8Z0Um+V03kaO3c6ceBAX1D6q7sPlRCsv9MVnV5o3B6VpjRdtKG9ByKBRT00sEAPqVWflGzfIxNQKIUgSHDwwtnMkt0Nye7LLDFWJziry5dZsrsQ4uahBRDm/5KRkWlnOl1cEUJCAVwL4F4AoJR6AXg7O44AQTEZRfgIfaGBssPv1tRUga1bKQoLky50WgLXVDEv4hvvjLCDySpyoYxWD4PADDUaoYOAUGigQwxY2UZGpuOhFHyzWHJ74XV64HV4INpcgNUFxuqEwuqEwurLLIV4eGggiyWZi1BUVKScPHlyarOfX2tyc3MT582bVzdkyBB3IGJrL3bu3KmdO3dun4aGBgUhhA4dOtS+atWqSr1eL3300UehCxYsiHM6nQylFBMnTrSsXLmyqqCgQPXggw8mWa1W1uv1kmHDhtnXrl1b0ZY4ApG56gugHsB/CCEDARwA8CSl1BGAWDoPLRrxEey4qYOzVZRS1NeXIi9PhYqKxF87jYPouCHkp1OLjNuNV6nO/Op5PQiHv0/KhVCoEIpIKBABBGh4q0y3glLwggiHV4DDzcPj8kCweyDanCAWJ4jNBc7qgsrmyyzpPALUAML9XzLdlGxkt2uv7VEcPX6l165bt65NYiIYqKys5KZNm5ayevXq0gkTJjgkScJ7770X0dTUxJw4cUI5e/bshA0bNvw0aNAgN8/zWLJkSRQAPPbYYwlPPPFEXfPA1P3797d58HUgxBUHYDCAP1FK9xFC3gTwZwB/bX0SIeQhAA+1uqbrMhZF+ByJ0HegxxulAiorS5GXF4a6ul9tPI9lbVVPRex1Ph62P0nDCJ3XRB9c8OBQBy3s/j6pCGgQCaDLDCOUCSwShccvllweHh6nB4LdDdHqBGwuMFYXOKsTaqsLaocbIV5ZLMkECYIg4Pe//33S0aNHtX379nV//PHH5Xq9Xho6dGj6q6++Wnnttdc6V6xYYViyZEkvSimZMGFC0/Lly6sBQKvVDrrnnnvO7Nq1KzQsLExctGhR1TPPPNPn9OnTysWLF5+aNm2apaioSHnXXXclu1wuBgDefPPNUxMnTnRUVFQobrvttr52u50VRZH84x//qJgwYYI9Nzc36ccffwwhhNBp06Y1vPDCC2dax5qUlHTVqVOnjphMJjY6OvrqjRs3Ft1www32IUOGpL/33nvl2dnZnubzlyxZEv2HP/yhccKECQ7AN839vvvuMwPAE088ET979uyaQYMGuQFAoVDgz3/+cz0AnDlzRpGYmNhSQRs6dKirra9zIERLFYAqSuk+/+NP4BNXP4NSuhLASsA3hbfzwmtH1GjCuzAhFxcZc9AGJMmDkpIybNoUA4vlvN6DLCT3WE1ZxcLIHaHD1NXxHRZLcOLrk1LDAj0oQqFHCKJB0NNeB5kL4BdLdq8At9sLT3PPkr8Mx1qc4GxOqGxuqO0u6HkRKgCy+bhMl6O8vFy9YsWK8kmTJjmmTp2a9Morr0QtWLCgrtVxxfz58+MOHDhwPCoqSsjJyUl7//33w6dPn97kcrmYsWPH2pYvX149ceLElOeeey5u9+7dxQcPHlTfd999ydOmTbP07t1b2L17d7FWq6VHjhxR3XnnnX2PHj16/J133jGMHz/esnjx4lpBEGCz2Zi9e/dqa2pqFM1lyoaGhp+NIuI4DsnJye6DBw+qS0pKVP3793fm5+frxowZ46itrVW2FlYAUFhYqPnjH//YeL7nXVRUpJk3b17d+Y499thjdTfeeGPaoEGDHOPHj7c89thjjZGRkW0yFu90cUUprSWEVBJC0imlRQDGAyjs7Dg6nGEoRh7iYUDfDllfFO04cqQSW7b0gct13lKjkXGe/lP4PuvTEXuT9Iy34wReMEHQBDUa5D6pno0kwS1IcHh5uFz+zJLD19xNrS6wVic4mwsqqwtauxshgiyWZHoIvXr18k6aNMkBANOnT29cunRpNIAW0bFnz56Q4cOH23r37i0AQG5urunrr7/WTZ8+vUmhUNDbb7/dCgBZWVkulUolqVQqOnToUFd1dbUSALxeL3nggQcSCwsLNQzDoKKiQgUAw4cPdzz88MNJPM8zt99+u3nkyJGujIwMT2Vlpeqee+7pM2XKFMutt95qPTfekSNH2rZv364vKytTzZ07t+bf//531K5du+wDBw5st6TLk08+2fi73/3Oun79+tAvvvgi/N13340qLCws1Gg0V+wPGKhy258AfOjfKVgK4L4AxdH+KGHDP1GHGThvFqnN8LwZ339fh/z8ZPD8L8p6DCTvSHVl2aLI7bprNafiAPTukDiCAyeUONPSJ6WHEUq59NIdkSS4eF8Zzu32wuPwleF8mSUnWKsLCqsTKv/oAJ0oQQ1AHei4ZWSCDULIBR9fyG+Y4zjKMD5XNoZh0GyczLIsRFEkALBo0aKY6Oho/tNPPy2TJAkajWYIANxwww32Xbt2FX366adh9957b/ITTzxR9/jjjzcePXq08LPPPgtdtmxZ9Lp16wwff/xxeet7jhkzxr5s2bKouro65WuvvVb9+uuv99q+fbt+1KhRtnPjy8zMdP3www/a85lNp6Wlufft26cdMWLEeUt+SUlJ/KxZsxpnzZrVmJqamvXDDz9ocnJynL/6YlyEgIgrSulhANcE4t4dygD8hC2IQQza3xLG7a7D7t0WfPddX0jSL5qswxlX3UNhB8zPROxJMLDu7pil4sHhDLSwQQ8GoYiAGpEgSAp0YDKXTyux5HL5dsMJdhckq78M598Np7K5oXW4ESJK0ABoc5OpjExPp6amRrlt27aQCRMmONasWWMYOXKkvfXxa6+91vHMM8/0qamp4aKiooSPP/7Y8Oijj575tfXOxWKxsPHx8V6WZfHWW28ZRdFXXSsuLlYmJyd7Z8+e3eBwOJiDBw9qa2pqLCqVSrr33nub0tLSPPfff/8vRgCNGTPGMWPGjOQ+ffp4tFotzcrKcq5evTrqs88+Kzn33Dlz5pwZNmxY5s0332wZN26cAwCWLVtmmDx5svXZZ5+tnTp1asq4cePsAwYM8IiiiL/97W8x8+fPr/vkk09Cp0yZYlOpVPTUqVNcU1MT27oH60ro2o3iwQIHB17BaczCRX35Lhu7vQrbtnlQUNAX55S2CKgwRHW6bIFxp+qGkJ8Szj3ehaFgUA8NmqADRRh00CIGDOICHZjM+RElOAURDg8Pt5uH1+H2ZZasLhD/nCWFf8aSxuGBThZLMjKBoW/fvu533nnH+OijjyYmJyd75syZU9/6eGJiIv/8889Xjx49Oo1SSsaPH285Xybo15g1a9aZ2267LWX9+vURo0aNsmk0GgkANm/erF+6dGkvjuOoVqsVP/zww7Ly8nLFAw88kCRJEgGABQsWVJ27nkajob169fJec801DgDIycmxb9iwwXC+pvM+ffoIq1evLp07d258Y2OjgmEYOnz4cPv06dObEhISXIsXL6688847+7pcLoYQggkTJlgA4KuvvgqdM2dOgkqlkgDgxRdfrEpISBAu53U9F3KhFGCwQAhxUErbtJOLEOQAeL2dQjpLKkqxHQb0aedSlMlUjq++Iigp+cWYBB3xNNwberj+OcOu+BjOoW/X+wYCgiao0AgdeH+fVDQ4+Y03UFAKKlE4Bb/Viau5DHd2ejdrdUFha5VZkqj8QU3mkjn+wgt0enss1B7vDZ1JQUFB+cCBAxsCHYdM+1BQUBA5cODApPMdk/8gXiks3HgBFfhrO+4EpFRCbW0pNm7Uoro66ZyD4gBlXdkLxnzulpATiQzBr05aD3JcLX1SeigQiki5T6pjaRZLvAC7x9ezxDvcEJp94Wy+3XAKm88XLsThgZZShEAeTSEjIyNzRcji6kpIRAW2Q4+UdhJWlPIoKzuJvLxINDb+rF9LQ3jzXfojtS8ad/aO42zt38vVsQjgcAYa2KAHQSjCoEE0COShpW2AUkiiBIcgwukR4Hb5xgYIdrcvs2R1grW4oLQ6oba7oXG6oaOQxZKMjIxMZyGLq8uBgRdzUIqXkA4G5OIXXARJcuHEiQps2tQbdnurcQpUSlM0Vjxn2EXv0h9JYgntClPCKRg0QI0m6CEiFDqEIAZMt96t2O5IFB5egNXpgcPmgtfihNRoAzHZoWi0QWNxQOf0IIQCevi+ZGRkZGSCDFlcXSq9UIXtUKF/O9jXCIIVhw+fxrZtifB4WtZTEcH6e93x6r8Zd8SkKMzBbZxMYIUK9QgBj1CooUcUOEQBiAp0aMGKJMHtFWB1euG0ueBpckAy2cA02qAw2RHS5IDe5YUWkF9HGZluiiRJEmEYJvibnWUuiL8JX/q147K4uhgEAh7BT/gH0sCCadNaXm8j9u5twO7dfSE2GyRTmsw1VfzZsFu4N/RwkpJIwWhJ44YCZxACB0KhgB6RUCEcQGigAwsWRAku79mME9/kgNRoA2uyQdloh6bJjlCPAA3k2UsyMj2Zo/X19f2joqIsssDqukiSROrr68MAHP21c2RxdSGMOI3NYDCkjdkqp7MGX39tx/ffp4BSIwAoINonhxRXLTRuN/ZXNSS1R7jthAgWZ6CFFXoAoQiHBlEgSAh0YIFClODw8LA5PXBZfRkn2ujLOKnM/oyTV5BHC8jI+CACAREIYUQCRmAIIxLCiAxhJYawopLVnNeepCcgCMKM2traVbW1tdlAGz+sywQSCcBRQRBm/NoJsrg6PyKmoxirkAplG14ji+UUtm4VcexYS4mvN2utnBPxrXtm2PdJakZse4mxrTBohBom6CAiFCHQIQYMYgHEBjq0zkAUYfcIsDk8cFmd8JrtgMnuE04mO7QWB0J5UW4GlwkmWsSLQMCIDGEEQhjJL14EhnASQ1jKEk5kGQVlCCdxjIKyREE5RimxjAIco6IcoyQso6QcowTHqIiCUYFjlMT/PeEYFeFYFeEYFcsxSsZ/jFUwKoZjlCzLKFj/MY4lCpZlFBzHKDn43lcu9HfT0kkvVNAxZMiQMwBuDnQcMh2PLK7OJRx12AABObiy8hylFA0Npdi0SYmysgQAYCG5JmhPVvzduD18sLq2T7vGezkQ2KBEPXTw+PukosHBCMAYsJg6CEpBRQk2Dw+bwwO31QneL5zYBhuUZjt0TQ6EihJ0AHSBjlcmaKAAEc/JvDSLF9EvXESWcBJDOMoyCpElHGUZjrJE6RMxjBKc/8svXoiCUVOfOFESjlH7xYuyRbwoGBVhGSXLMUrmrHhRshyj5DhGyfrFi4JjlCwuLl5kZGQCjPwf9CwSfo9ifIgUqKG47KspFVFVdRIbN4ahri4FAKJZe/WT4d85ngzflxjC8J2dpfJAgTpo4UQoOITCABUM6AY7zPyjCGxuHnaH2yecTHbQRhs4kx1qkx1aqxNhooRQyH1hwQb1Z178pSKGJ2BE4i8ZNZeOfileFBLHKMAShXRWvKioLwvTWry0yrr4xAujYNSEY5QMyyhbsi7+TAznEzAqliUc58+8yOJFRkamzch/QABAhwb8Fy5MvILeKkny4OTJMuTlxaCpKY2B5LlWU1G00LhD91tNZWfZtUhgUQdNS59UGLSI7op9UpRCEiRY3V6fcLL4Mk6k0Q7O3+OkszgRSinCAIQFOt4gpLV48fW7gBFIi2hhRUJYiSWcxDCcxBKF5Bcv1F868osXFfVlXhTwlYtUpFX5iHCsmnCMgvE99okXf4moWbgwvqyLimUZJcsSjvOXkjgACv+XjIyMTLekp4sriutQjE+QBN1lTjwXRQeOHj2FLVv6wOnMiGBctTMjvi+aF/FNQhjr6VjjZAYmqNDonycVghBEgw3+PilKIfIirB4edrsLHosTgj/jpPBnnHQ2J0IpetbEdgLGxTFKl4JVe5Ss1qNidYJWESpoFeE0RGlAiNLI6BQGVqc0KjSKMIWCVbEsUfiFjMpfPlJwLFFwLKNgIYsXGRkZmYDSc8WVBmasgQW3XOaUdZ5vwg8/1CI/P4l4Pf2GqqvK/ha3UzNRW9oHQK92j5PADiXqEQJ3yzwpBQwADO1+rzZAKQRehMXthdPuhrvJAcFkBzHZoGi0Q222Q2d3QU+BCPi+uhsCSxROjlG5Fazao+Z0XjWnE7WKCMkvkohOaWR1ykg2RBGh1CkNKo0iXKNVhGoYwsk7DWVkZGS6ET1TXI1CMb5AH4Rfxpu8x1OP3btN+O67FL3kNM4IO1D+P/G7+xhZV1o7Rub190nZoff3SalhRIAbrikF7xVgdXvhsLnhsTggmuxAow0Kkw0akwM6hxt6oOs3x19OFilEaVCFKA0qDReqVXEhKqD9e7xEKnkFKng8Eu92Sx7eI/ECIQQEhBCAMIQBAYjvMQEhhBDAf5wQ33cgDBjie36E8Z9DATBMq3OAlmsZ/7kEAOP7VxACMIDvRKDlfAK03FZGRkZGBj1NXKlgxUrU44+4dEFkt1djxw4XDh9KuFpZY3kxOr/6Zl1RIto+QVsCi3poYIEeUqs+qU7dTdhst+I6OzVcNNnBmGw+u5UmB/QOD3ToWsLpZ1kkFRfCazi9oFGES1pFONUpjUSnMDJ+gaRo7ywSpZSKkDy8JLg91Ot1iR6vU3ILdtEpWAWHxkcN8AAAC4hJREFU2CTYqFmwUbNgJSbeShr4JqZRsHAm3sqZBavCzNuUZsGqsggOtQhRCUCJLrARgQGhAKEsYSgDhgKgLGEoAaGEEMqCoQwhlPjOAQEjMYRQBgwYQiQCApYwYMBIBIQyhIDxP2Z8xyjxr+E/r2UtxncfNN+bIQQsGDCEkQgIYQlLffciYMD47k/Ofu+PgbJg4Y+Jsj4d6o+bIc33YgkLAlAGDDjCwvc8WeJ7TuRn6zFgCEMY6o+fMGAoIS3PE4xfBvtjBWk+r/n5oOV74ruGNMft/3eQ5rXQ6hwQQnxr+rQv43s9CQHxC3JCzq7VLMwZ+MS2T1H7BTllCGGI3+/Ld71fkPu/byXm4dPZrcQ+CNN8NgCGgECgokPeZSLT3ek54moxeNwPBpFIuaTzzeZybN5MtMVHdHfrf7TNT8p3x3L2KzdOJjBDjUboICAUGugQAxYxAGKueM2LIFF4vDwsLi+cVhe8/owTabBBabZBa/bZrYQgSK1WCBg3xyidHKNyqzgtr2L1vFYRKmgU4dApDTREaWB0CqM/ixSh1CoMGq0iTHOlWSRKqeiVeAdPBbdb8vIuyc07RLdgE52CRbCLTYINZsFKzbyNmAQLaeQtbCNv4UyClTPzVqVJsCotgl1tEx0q6pvE3qOmsUugBKBEoq0cIeQZ1DK/JISOl38xZLo3PUdczYMCFyuvUSqhrq4UeRvVmXWHxOcNXzNTU45FsIRebsbG4e+TciEUKoQiEor27TWSztqtuGwueM0OiCYbmEY7lCYbtE0OhLp5aABEt9c9rxB/FknpVrAaj4oL4dWcXtBy4ZJW6csihSgMrF4ZyYUoDYoQZYRKq4hoziJdkkARqcQLVHB5JN5rkbyNLreVt4su0SY4hCbRTpsEGzXzNvgFEfEJIgtr4q0Ks2BTmgWrskmwa9ySRwHIA0NlZGRkZNpGzxFXF4JSARUVJ7WbPlfd7srn/2bcEZmQaI2/xKt5cP4+qVCw0CMCGkSiDW/QogSnV4DN6YHD6mzxqWNMdqhMNmjMDoR1tt1KqyySR8VpvSpWx2sUYaKvzGagWkUEo1dGsiFKo0J3NoukVnEhapwni9SqdObxUK/HLXl5p+jma0Sn2+o8ZbaINqmJt1GTYCONfBMafWKIM/kyRQoTb1U0CTa1VXSoeCrIu+NkZGRkZIKGni2uJMmNoqKyjJ0f0rkkT/XH0MN9uDDKXugKsKiHGhboQREKPUIQDYJLFWLNPnVWv08db7aD+jNOquaMEy9CC0Db9if4Cy4xi2RktUqDUqc0qLSKcLVWEaZtziJRSiWBim4v5T0eife6JI/XIbp4m+iULIJdPCXYBbOjzGkSDjf5y2aMSfCJokbeqmgSrKomwa6yiU6VBKnHlc5kZGRkZLo/PVNcCYJNe/RA+e+++yd5QZ3XKz288fwzrgiaoEbDpfRJ+e1W7B4BdqcbTosTQovBr73F4DdUaAefusvPIkWoVVwox7Iq6pUE3i15RJfkoXbRSa2ik1gFO2kSbDgp2NDIW8RG23FqEiy0kbeKZsHCN/JWt0WwqyyCXe2QXEqgw8SfjIyMjIxMl6dniSuvtzH9x69OPXno5ZD7tfszVaFi6+fvhBJnWvqk9DBC6Rtm2exT5+bR6LDB7c84odEGxmTzDb+0OKEXJehx6bu6mrNIHgWrcavYEF6taJVFUhhIiNLAKLlQyrJaolCEMiyrBcuFMC6JpzbRKVoEu2QWbDDxFlorWBgTb2VNvIVtdFVxJt6iMAlWlUWwqSyCQ+WlfM/6WcvIyMjIyASIHvOGu+S/v/eOt+7lByprByGkpU/KRvUgXg2UXg6c0wvB4gAxNcJrKseZRhusZgdCLA6ESvT8u8/8WSSXklE1KJVaj5rVCf/f3v2GyHXVYRz/PjM7s7uJsaRIm1JrFSuEFPzXUhH7oiKmUYQoKLZvjFQIFAqKVCK+EFEKFcQ/iC8MVqmiSFGjAf8GFKJUJNvQqjVpG7VqmmLa7GY3m91kd3YeX9wbOiy7ze44O5OZeT5v5t6zZ849d/ndu7+999x7Rke2NCrVsUaluqmp6nhT1U2mOiZVN8vVcbkyVmmoWp1szOjM4rQmG0VS9OLi9PjUwgv1qbm/j55tzI7ONM6PNmlWevDrioiIiDYNTXJ15+QBXOX04QVemK4wOjXN+OS/2DYzzxYbqqrNSSPzrlQvWCOLTdUWliq1s03VJxub6m5Uam5U6ixVRqsLlXqloVq1UanXpptzI8VTZzP1s41zm85eeG5sdmlutNf7GxEREb0xNMnV3ufQKKpf1IguqFqZV2VpQdW5xZFaY57m2Exjbvxic34Lzcu3FREREbGaniRXkp4FzgFLQMP2rRu9zUehBt6OF8GLG725iIjoMUm7gK8BVeBbth9c9vNR4LvALcAZ4MO2n+12P2Pw9HI8zzttv7kbiVVERAwXSVXgG8B7gB3A3ZJ2LKv2MWDK9k3AV4AvdreXMagyWDoiIgbRbcAJ2/+wvQD8ENi9rM5u4OFy+UfAuzIJeXRCr8ZcGfiNJAPftL2/C9s8BxzvwnYiImJ1/+zSdq4H/tOyfhJ422p1bDckTVNMUP9iV3oYA6tXydU7bJ+SdA1wSNJx24dbK0jaC+wtV8clne/AdkeARgfaieiVxHD0PX1O++hMHI9LmmhZ39/yz/pKV6CWzxi9ljoR69aT5Mr2qfLztKQDFJdvDy+rsx/o6BUtSRMZ4xX9LDEcg6BLcXwSuKFl/dXAqVXqnJQ0AlwFTG5wv2IIdH3MlaTNkrZcWgZ2An/tdj8iImKgHQHeIOl1kurAXcDBZXUOAnvK5Q8Cv7WdK1fxf+vFlatrgQPlmMER4Ae2f9WDfkRExIAqx1DdB/ya4lUM37b9pKTPAxO2DwIPAd+TdILiitVdvetxDBINU5IuaW+XBs9HbIjEcAyCxHEMuqFKriIiIiI2Wt5zFREREdFBfZ1cSbpB0u8kHZP0pKSPl+VXSzok6Znyc2tZvl3SHyVdlHT/srZ2SXpK0glJn+7F/sTwaSOGd0v6s6THJU1Iur2lrT1l/Wck7VltmxGd1kYc3yFpuozjxyV9tqWtnIuj7/X1bUFJ1wHX2T5aPoH4GPB+4KPApO0Hy4Nzq+195Xu1bizrTNn+UtlOFXgaeDfFo7lHgLtt/63rOxVDpY0YfgVw3rYlvRF4xPZ2SVcDE8CtFO/peQy4xfZUL/YrhksbcXwHcL/t9y1rJ+fiGAh9feXK9vO2j5bL54BjFG/cbZ3S4GGKgxzbp20fAZbP3LyWaRIiOq6NGJ5teVR8My+98PBO4JDtyTKhOgTs6s5exLBbbxy/jJyLYyD0dXLVStJrgbcAfwKutf08FAc9cM1lvr7SNAnXd76XEatbawxL+oCk48DPgXvK4sRwXBHWcS5+u6QnJP1S0s1lWeI4BsJAJFflrZIfA5+wPdNOEyuU9e/90ug764lh2wdsb6e4CvCFS02sVLWzvYx4eeuI46PAjbbfBHwd+OmlJlaomziOvtP3yZWkGsXB/H3bPymL/1uOAbg0FuD0ZZpZyzQJERui3Rgu5+N8vaRXkRiOHltPHNuesT1bLv8CqCWOY5D0dXKl4jXvDwHHbH+55UetUxrsAX52mabWMk1CRMetN4Yl3VR+B0lvBerAGYq3UO+UtLV8ImtnWRax4dqI420tcXwbxd+iM+RcHAOi358WvB34PfAXoFkWf4biXv8jwGuAfwMfsj0paRvFE1WvLOvPAjtsz0h6L/BVXpom4YGu7kwMpTZieB/wEYqHMuaBT9n+Q9nWPeV3AR6w/Z2u7UgMtTbi+D7gXqBBEceftP1o2VbOxdH3+jq5ioiIiLjS9PVtwYiIiIgrTZKriIiIiA5KchURERHRQUmuIiIiIjooyVVEREREByW5ioiIiOigJFcRERERHZTkKiIiIqKD/gdUtWByfut71AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93ab7ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, stats=['EROI w/o GW', 'mix'], to_plot=True, extend_wo_GW = True)\n",
+    "for scenario_fig in scenarios+scenarios_dlr:\n",
+    "    if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "    else: tot_name = 'Total (PWh/a)'\n",
+    "    EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                                   'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "    InteractiveShell.ast_node_interactivity = 'last'\n",
+    "    colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "    fig = plt.figure(figsize=(7,5)) # 7,5\n",
+    "    _ = ax = fig.add_subplot(1, 1, 1)\n",
+    "    _ = ax.plot(years, EROIs_mixes[[(scenario_fig, year, 'EROI w/o GW') for year in years]].loc[tot_name], \\\n",
+    "            label='EROI', color='black', zorder=20, linewidth=3)\n",
+    "    _ = ax.set_ylabel('EROI')\n",
+    "    _ = ax.set_ylim([5, 13]) # 5, 10; 13, 18; 11.5, 17.5\n",
+    "    # ax.set_zorder(1)\n",
+    "    _ = ax2 = ax.twinx()\n",
+    "    _ = ax2.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[(scenario_fig, year, 'mix') for year in years]].iloc[:15], \\\n",
+    "                  labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "    _ = ax2.set_title(scenario_fig)\n",
+    "    _ = plt.ylabel('Share in the mix')\n",
+    "    _ = plt.xlabel(scenario_fig+' scenario')\n",
+    "    _ = xticks(years)\n",
+    "    _ = handles, labels = ax2.get_legend_handles_labels()\n",
+    "    _ = ax2.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.1,0.5)) \n",
+    "    _ = ax.legend(loc='center left', bbox_to_anchor=(1.1,0.97))\n",
+    "    _ = ax.set_zorder(ax2.get_zorder()+1)\n",
+    "    _ = ax.patch.set_visible(False)\n",
+    "    _ = fig.savefig('Evolution of EROI wo GW in '+scenario_fig+'.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d95a1d0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, stats=['EROI w/o GW', 'mix'], to_plot=True, extend_wo_GW = True)\n",
+    "# f, axarr = plt.subplots(2, sharex=True)\n",
+    "# axarr[0].plot(x, y)\n",
+    "# axarr[0].set_title('Sharing X axis')\n",
+    "# axarr[1].scatter(x, y)\n",
+    "if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "else: tot_name = 'Total (PWh/a)'\n",
+    "EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                               'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "InteractiveShell.ast_node_interactivity = 'last'\n",
+    "colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "fig = plt.figure(figsize=(10,4)) # 7,5\n",
+    "_ = ylims = [5, 13] # 5, 10; 13, 18\n",
+    "\n",
+    "ax1 = fig.add_subplot(1, 3, 1)\n",
+    "_ = ax1.plot(years, EROIs_mixes[[('BL', year, 'EROI w/o GW') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI w/o GW', color='black', zorder=20, linewidth=3)\n",
+    "_ = ax1.set_ylabel('Quality-adjusted EROI')\n",
+    "_ = ax1.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "_ = ax12 = ax1.twinx()\n",
+    "_ = ax12.stackplot(years, EROIs_mixes.fillna(0)[[('BL', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax1.set_title('BL (business as usual)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax12.get_legend_handles_labels()\n",
+    "_ = ax1.set_zorder(ax12.get_zorder()+1)\n",
+    "_ = ax1.patch.set_visible(False)\n",
+    "# ax12.get_yaxis().set_visible(False)\n",
+    "_ = ax12.tick_params(labelright=False)\n",
+    "\n",
+    "ax2 = fig.add_subplot(132)\n",
+    "_ = ax2.plot(years, EROIs_mixes[[('BM', year, 'EROI w/o GW') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI w/o GW', color='black', zorder=20, linewidth=3)\n",
+    "# ax2.set_ylabel('EROI')\n",
+    "_ = ax2.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax22 = ax2.twinx()\n",
+    "_ = ax22.stackplot(years, EROIs_mixes.fillna(0)[[('BM', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax2.set_title('BM (+2°C)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax22.get_legend_handles_labels()\n",
+    "_ = ax2.set_zorder(ax22.get_zorder()+1)\n",
+    "_ = ax2.patch.set_visible(False)\n",
+    "_ = ax2.tick_params(labelleft=False)\n",
+    "_ = ax22.tick_params(labelright=False)\n",
+    "# ax2.get_yaxis().set_visible(False)\n",
+    "# ax22.get_yaxis().set_visible(False)\n",
+    "\n",
+    "ax3 = fig.add_subplot(133)\n",
+    "_ = ax3.plot(years, EROIs_mixes[[('ADV', year, 'EROI w/o GW') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI w/o GW', color='black', zorder=20, linewidth=3)\n",
+    "# ax3.set_ylabel('EROI')\n",
+    "_ = ax3.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax32 = ax3.twinx()\n",
+    "_ = ax32.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[('ADV', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax3.set_title('ADV (100% renewable)') # ER (+2°C wo CCS nor nuclear)\n",
+    "_ = plt.ylabel('Share in the mix')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax32.get_legend_handles_labels()\n",
+    "_ = ax32.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.25,0.45)) # (1.1,0.5)\n",
+    "_ = ax3.legend(loc='center left', bbox_to_anchor=(1.25,1.03)) # (1.1,0.97)\n",
+    "_ = ax3.set_zorder(ax32.get_zorder()+1)\n",
+    "_ = ax3.patch.set_visible(False)\n",
+    "# ax3.get_yaxis().set_visible(False)\n",
+    "_ = ax3.tick_params(labelleft=False)\n",
+    "_ = plt.subplots_adjust(wspace=0.08, hspace=0.08)\n",
+    "\n",
+    "_ = fig.savefig('Evolution of EROIs wo GW IEA-ADV.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Quality-adjusted EROIs w/o GW adjustment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d93e13668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d94073d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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vab6yWCySiooK74qKCu+8vDwAgI+Pj8G5u1GtVpt4TS/GGGOu9re//c1/zZo1wUSE5ORkw+uvv156zz33xNTW1sr8/f0t2dnZxfHx8aatW7eqV6xYEWo2myW+vr6Wv//972cjIyN5Jv41cOWsQwLwPoBCIcQqp1M7AdwDYIX95w5XxeBqSqXSGhcXp4mLi9MAgNVqpcrKSmVZWZmqoqJCVV1drWpubpa3va6hoUHZ0NCgPHnyZKC9HrPz7MagoCC9VCrt6cdhjDE2gOXk5LivXLky9LvvvjsRGhpqqayslM6ZMyd27ty5tb///e9r33rrLf8lS5ZE7t2798y0adN0s2fPPiGRSLBq1aqA5cuXh2zcuPFibz9Df+TKFq3xAOYDyCeio/Zjz6AlwfoHES0CUALgLhfG0KOkUqkICwvTh4WF6QFUCSGg0WjcnFu9NBqNsu11BoNBXlJS4ltSUuL7448/QiqV2vz9/ZuCg4N1jtmNSqXS2guPxBhjbIDYs2eP96xZs+pDQ0MtABAcHGw9cuSI5+eff34GAJYsWVL30ksvRQDAuXPn3G6//faI6upquclkkkRGRhp7M/b+zJWzDr8B0FF/2C9ddd++hIjg4+Nj8vHxqUtNTa0DgObmZklZWZmqvLxcVVlZqaqpqfFs291otVolVVVVXlVVVV75+fkAALVafUl3o4+PD3c3MsYY6zIhBIioS7MeH3744ag//OEPFVlZWZrdu3d7LV++PMzV8Q1UA35l+L7G3d3dNmjQoMZBgwY1AoDVakVVVZXSeXajXq93a3udRqNRajQa5alTpwLt9ZgDAwObHLMbg4OD9TKZjKcNM8YYa9f06dMb77zzzrhnnnmmMiQkxFpZWSkdPnx403vvvef70EMP1a1fv95v5MiROgDQarXSqKgoMwBs2rTJv3cj79840eplUqkUoaGhhtDQUAOAagDQaDRy59mNDQ0NHkJcmkM1NzfLL1y44HPhwgUfoGVND39//yb77EZdeHi4zsPDg7sbGWOMAQBGjhzZ/Nhjj5VPmDAhSSKRiLS0NP0777xTcs8998S8/fbbIY7B8ADw7LPPls2ZM2dwcHCwaeTIkU0lJSWKXg6/36K2X+B9ERE1CSE8r6eOpUuXTgCwuptC6lFGo1FSXl7u6Wj1qqmp8bRYLFccLe/l5dXs3N3o5+dn5O5GxlgvK1y3bt387qioO74belJubm5xenp6TW/HwbpHbm5uQHp6esyVynGLVj+gUChsMTEx2piYGC0A2Gw2VFdXXzK7samp6bLuRq1W667Vat3PnDkTAABubm4Wx+zGsLAwXUhIiF4ul/f9TJsxxhjrpzjR6ockEgmCg4MNwcHBrd2NWq1WXlpa6umY3VhfX+8hhLik+cpkMslKS0t9SktLfX766SdIJBLh5+d3yRZCKpWK10lhjDHGugknWgOEl5eXOSkpqSEpKakBAEwmk6S8vNzDMbuxurpaZTabL+lutNlsVFNT41lTU+N5/PjxYABQqVTGwMDA1sTL39+/mbcQYowxxq4NJ1oDlJubmy06OloXHR2tA1q6G2tra90d3Y1VVVUqnU532eBGnU6n0Ol0inPnzvkDgFwutzq6G0NDQ3WhoaF6Nzc33kKIMcYY6wJOtG4QEokEgYGBzYGBgc2OwZg6nU7mPLuxrq7Ow2azXdLdaDabpWVlZeqysjL1kSNHQETC19dX77x3o5eXl7l3nooxxhjr2zjRuoGpVCpLYmJiQ2JiYgMAmM1mqqio8HBe08tkMl3yZ0QIQXV1dZ51dXWeJ06cAAB4enqaHN2NYWFhusDAQAN3NzLGGGOcaDEncrlcREZGNkVGRjYBqBRCoK6uTuFo9aqurlY1Nja6t72uqanJrampya+4uNgPAGQymTUgIOCSLYQUCgV3NzLGGLvhcKLFOkRE8Pf3N/r7+xuHDh1aCwB6vV7qvHdjbW2tZ9vuRovFIq2oqPCuqKjwzs3NdWxFdMnsRrVazd2NjLEb2rq0dcndWd/SY0sLu7M+1j040WJXxcPDwxofH6+Jj4/XAIDFYqHKysrW7saqqiqV0Whs292I+vp6j/r6eo+ioqIgez0mpzW9moKCgvRS6RXXYGWMMXYdnnjiidDt27f7hYaGmvz9/S3Dhw/Xq9Vq64cffhhoNpspJibGuH379nNeXl62Dz74wPfVV18Nk0gkwsvLy5qTk1PU2/H3R5xosesik8lEeHh4U3h4eGt3Y0NDg5vzIHuNRqNse51er3c7f/683/nz5x3djTZ/f3/n7kadu7s7dzcyxlg3OXDggMeuXbt88/Pzj5vNZho2bFjK8OHD9VlZWfWPPfZYDQAsW7YsbM2aNQHPPvts1YoVK0K//PLLk7Gxseaamhr+l/A14kSLdSsigq+vr8nX17cuLS2tDgAMBoO0rKzM07GmV21trafVar1ktLzFYpFUVlZ6VVZWeuXl5QEA1Gq1wXnvRrVabeIthBhj7Np8/fXXqhkzZjSoVCoBQEybNq0BAA4fPqx8/vnnw7VarbSpqUk6adIkDQCMHDlSl5WVFXPHHXfUZ2Vl1fdq8P0YJ1rM5ZRKpXXw4MGNgwcPbgQAq9WKqqoqj9LS0tbZjQaDQd72Oo1Go9RoNMpTp04F2usxBwQEtM5uDA4ONkilUt5CiDHGuqCjvY3vv//+2O3bt58eO3asYc2aNf779+/3AoCtW7eW7Nu3z3Pnzp3qYcOGpR49erQgJCTE2qNBDwCcaLEeJ5VKERoaqg8NDdUDqBJCoLGx0e3ixYut3Y0NDQ2XdTcaDAb5hQsXfC9cuOBrr8fm7++vd271UiqV/JcAY4y1Y/LkybolS5ZE6/X6crPZTHv37vVZsGBBtV6vl0RFRZmNRiNt27bNLzQ01AwABQUFiszMzKbMzMymPXv2+Jw9e9YtJCTE0NvP0d9wosV6HRFBrVab1Gp1XWpqah0AGI1GSVlZmWdZWZmqqqpKVVNTo7JYLJd0N1qtVklVVZWqqqpKdezYMQCAt7d3s/PsRl9fXyN3NzLGGDBp0iT99OnTNSkpKanh4eHGoUOHNqnVauvTTz9dNmrUqOTw8HBTcnKyXqfTSQHgkUceiSguLlYIISgjI6NxzJgxnGRdA+qoKbEvIaImIYTn9dSxdOnSCQBWd1NIrIfZbDZUVVUpnWc36vV6tytdp1AoLE6zG3UhISF6mUzW9//QMzZwFa5bt25+d1TUHd8NPSk3N7fYsTNHb9FoNBK1Wm3TarWSsWPHJr777rvnMzIy9L0ZU3+Vm5sbkJ6eHnOlctyixfoFiUSCkJAQg73ZuhoAGhsb5c6zG+vr6z3a/sPBaDTKLl686HPx4kWfw4cPQyKRCD8/vyan7sYmT09PS288E2OM9bR58+ZFnzp1Smk0Gmn27Nm1nGS5HidarN/y9vY2e3t71ycnJ9cDgMlkkpSXl3uWlZV5VlZWqmpqalRms/mSKck2m41qampUNTU1quPHjwMAvLy8moOCgnTBwcFN4eHhOn9//2bubmSMDUS7du0619sx3Gg40WIDhpubmy06OlobHR2tBVq6G2tqatzLyspatxDS6XSKttdptVp3rVbrfubMmQB7PZbAwMCmoKAgXVhYmC40NLRJLpdzdyNjjLGrxokWG7AkEgmCgoKag4KCmgHUAIBWq5U5dzfW1dV5CCEuab4ymUyy0tJSdWlpqfrIkSMgIuHn59e6hVBERIROpVJxdyNjjLEr4kSL3VC8vLwsSUlJDUlJSQ0AYDabyd7dqKqqqlJVV1d7mkymtlsIUW1trWdtba1nYWFhMACoVCpjYGBg65peAQEBzRKJpL1bMsYYu4FxosVuaHK5XERFRemioqJ0QMuCfrW1te72xVQ9q6qqVFqt1r3tdTqdTqHT6RTnzp3zt9djDQgIaN27MTQ0tMnNzY23EGKMsRscJ1qMOSEiBAQENAcEBLR2NzY1NclKS0s9KyoqVJWVlaq6ujpPm812SXej2WyWlpeXq8vLy9VHjx4FEQlfX1+D85pe3t7e5l55KMYY68Tw4cOTjhw5cqKoqMht5syZ8adOnSro7ZgGEk60GLsCT09PS0JCgiYhIUEDABaLhSoqKjwca3pVV1erjEbjZd2NdXV1HnV1dR4nTpwIAgAPDw+TfXajLiwsTBcUFGTg7kbGblyf3nprcnfWd8vOnYXXct2RI0dOdGcc7FKcaDF2lWQymYiIiGiKiIhoAlAphEB9fb3CeZB9Y2PjZd2Ner3erbi42K+4uNjPXo8tICBA55jdGBYW1qRQKLi7kTHmMi+++GLwli1bAgBg/vz51c8//3yVh4fHcL1ef6S3YxuoONFi7DoREfz8/Ix+fn7GIUOG1AKAwWCQlpaWqsrLy1VVVVWq2tpaD6vVeknzlcVikVRUVHhXVFR45+XlAQB8fHwMzrMb1Wq1qRceiTE2AB08eNBj69at/ocPHy4UQmDEiBHJv/zlL7W9HddAx4kWYy6gVCqtcXFxmri4OA0AWK1WqqysVDqv6dXc3Cxve11DQ4OyoaFBefLkyUB7PWbHFkLh4eG6oKAgvVQqbXsZY4xd0ddff626+eabG7y9vW0AcMstt9R/9dVXXr0d10DHiRZjPUAqlYqwsDB9WFiYHkCVEAIajcbNudVLo9Eo215nMBjkJSUlviUlJb4//vgjiEgoFAqL88vd3d2iUCgsSqXS4u7ubnF3d7d4eHhYlEqlRalUWhQKhY1Xumc3IiKaDuBtAFIA7wkhVrQ5HwXgIwA+9jJPCyE+6/FAe0h/2Nt4IOJEi7FeQETw8fEx+fj41KWmptYBQHNzs6SsrExVXl6usm8h5Nm2u1EIQc3NzfL2WsM6uZdzcmZVKBRmhUJhtSdlZnd3d6sjKVMqlRYPDw+LQqGwcnLG+jMikgJYC2AagIsAfiSinUKI407FngPwDyHEO0SUAuAzADE9HmwPyczM1N17770xf/nLXyqEEPjss898N23adPaVV17p7dAGNE60GOsj3N3dbYMGDWocNGhQIwBYrVZUVVUpnWc36vV6t6ut91qTMzc3N2vbVjPn1jNHC5qj9UyhUFh5FiXrQ0YBOC2EOAsARLQNwG0AnBMtAcDb/l4NoKxHI+xhGRkZ+rlz59bedNNNyUDLYPjx48cbejuugY4TLcb6KKlUitDQUENoaKgBQDXQspK9Xq+XGQyG1ldzc/MlL6PReMmrbatYVwghyHF9V68hIri5uVna69p0vJxbzuyJGidnzFXCAVxw+nwRwOg2ZV4E8CUR/R6AJ4CpPRNai2tdjuF6vPjii5UvvvhipfMxx4zDxMREE6+h1f040WKsH5HL5UKtVpvVanWXFz81m81kMBhker1e1tzcfFmCZjQaW3+aTCaZ0WiUWSyWa0nOcLXJGYDLkrP2xpw5d2tycsacyIgox+nzBiHEBvv79vq+2w5SmgNgkxDiTSIaC+B/iChNCMHLrLBuw4kWYwOcXC4XcrncfDUr01sslqtuObuW5Axo2cTbZDLJtNquzzJ3Ss46HHPWdlIAJ2cDkkUIMbKDcxcBRDp9jsDlXYOLAEwHACHEd0TkDiAAQFV3B8puXJxoMcYuI5PJhLe391UnZ/akTGowGOSO9+21nDlaz8xm8zWtVXGNyZnVkaC1N+asva5NXkqjX/sRQDwRxQIoBTAbwNw2ZUoA/BLAJiJKBuAOezc9Y92FEy3GWLeQyWTCy8vL7OXlZQbQ3JVr7MmZ9Gpazq4jOZOaTCapTqdTdPUauVxu7WzMWdtuTaVSaZVKpTyHvg8QQliI6GEAe9CydMMHQogCIloOIEcIsRPAYwA2EtEjaOlWXCh4DQTWzTjRYoz1GntyZvHy8rJ09Rqr1QpHUuY87swVyZnZbJaazeZrSs7c3NxaW8wcrWbOMzadEjROzlzEvibWZ22OPe/0/jiA8T0dF7uxcKLFGOtXpFIpVCqVRaVSXVVy1tzcLHMed9Zey1lzc3PrhACTyXRdyRmALidnMpnM2sGYs9ZWs7atZzKZjJMzxvoBTrQYYwOeVCqFp6enxdPTs8vJmc1mg1OXptRgMMibm5ul9iRNbjQapW1bz0wm0zX9nWqxWKQWi0Xa1NTU5WtkMpnN0VrmaD270pjLo9xtAAAgAElEQVQzuVzOyRm7bmvWrPHPycnxzM7OLrmeevLy8hS///3vI8+dO+cuk8lEUlKSYf369SWRkZGWr776yuOJJ56IrKmpkRORGDVqlO6999670NDQIFmwYEFMWVmZm8VioYiICOP+/ftPd9ezuQInWowx1g6JRHJNyZkjGXO0nrVtOWs7IeBql8NwsFgsEovF4tbU1NTlRWylUqmtvaU0OlrrzMPDg5MzF/rxxx+Tu7O+X/ziFz2+Lte10uv1NGvWrPhXX331wty5czUAsGvXLq+KigoZAGRlZQ3Ozs4+O3Xq1CabzYaPPvrIt6GhQfLUU0+FZ2ZmNv75z3+uAoDvv//+sq3L+hpOtBhjrJtIJBJ4eHhYPTw8rP7+/sauXOOcnHU0KaDtmDOTySS7ljHbVqtVotfr3a5mh4G2yVnbSQGOhWeVSqXZMSHAzc2N16Hqo/72t7/5r1mzJpiIkJycbPjXv/517uTJk2733HNPTG1trczf39+SnZ1dHB8fb9q6dat6xYoVoWazWeLr62v5+9//fjYyMrLDf3gkJCSkHDp0qMjPz8/q5+c37OWXX77w8MMP195+++2xCxcurLn99ttbpwlv2LDB76abbtI5kiwAmDVrlhYA/vjHP4bdfffdtVOnTm0CWv6/+t3vflcPABUVFfJf/epXrdeMHj26z69sz4kWY4z1IufkDECXkzOj0Si90oSANq1nUiHEVW9gea3JmdMWTpeMOZNIJDYimgPgWyHEdXU9sauTk5PjvnLlytDvvvvuRGhoqKWyslIKAA8++GDU3Llza3//+9/XvvXWW/5LliyJ3Lt375lp06bpZs+efUIikWDVqlUBy5cvD9m4cePFjuofOXKkbu/evarBgwcbIyIijN98843q4Ycfrj1y5IjnRx99dN657LFjx5Q33XSTvr16jh8/rlywYEFte+ceeuihqoULFw5655139JMnT25csmRJbUxMTJeXoekNnGgxxlg/I5FIoFQqrUql0urn59el5My+cr/U3qXZutZZc3OztM2YM7nRaJQ6ErRrTc4MBoPEYDDIAbTt2gkFsBUtq7JzotWD9uzZ4z1r1qz60NBQCwAEBwdbAeDIkSOen3/++RkAWLJkSd1LL70UAQDnzp1zu/322yOqq6vlJpNJEhkZ2emftQkTJuj279+vKi4udrvvvvuqPvzww8Bz587J1Wq1Ra1Wd0sr5x133NGYkZGR/7//+7/qL774Qj1ixIiU/Pz8grCwsC538fc0TrQYY+wGQERwd3e3uru7W7t6jT05k7RdTsPRgta21czRrWmz2bqSnLXbYsFcRwgBIupyn/PDDz8c9Yc//KEiKytLs3v3bq/ly5eHdVZ+2rRp2g0bNgRdvHjR+Nprr5Xu3LnTd/Pmzb5jxozRtS2bmprafODAAVV79SQnJxtycnI85s2b19De+eDgYOuDDz5Y9+CDD9ZNmTIl7ssvv1QtXLiw3bJ9ASdajDHG2mVPzmzu7u4mX19fU1euEULAZDJJ2k4IcPzUarXa4uLiowCKXRs9a2v69OmNd955Z9wzzzxTGRISYq2srJQGBwdbhw8f3vTee+/5PvTQQ3Xr16/3GzlypA4AtFqtNCoqygwAmzZt8r9S/XFxceb6+nqZ2WymlJQU09ixY3Vr164NWbly5WUtl4sXL65dvXp1yLZt29SzZ8/WAMD27du9o6KizI8//njV6NGjk2+99VZNZmZmEwCsW7fOb+bMmY1Hjx5VTpkypcnLy8tWX18vOX/+vCI2NrZLfzZ7i8sSLSL6AMBMAFVCiDT7sWEA3kXLNgcWAEuFED+4KgbGGGM9i4igUChsCoXC5OPj094XYOG6devm93hgDCNHjmx+7LHHyidMmJAkkUhEWlqa/p///GfxO++8U3LPPffEvP322yGOwfAA8Oyzz5bNmTNncHBwsGnkyJFNJSUlV1wbbtiwYU1Wa0uj6eTJk7Wvvvpq+NSpUy/bK0ulUokdO3acXrZsWeRTTz0VKZPJRHJysuGdd94piYyMtGRnZ5994oknImpra+USiUSMGTNGN3/+/IYff/zR45FHHomSSqVCCEHz58+vmTRpUrtjvfoKctVuA0Q0EYAOQLZTovUlgNVCiM+J6GYATwohJnehriYhhOf1xLN06dIJAFZfTx2MMcauW7clWt3x3dCTcnNzi9PT02t6Ow7WPXJzcwPS09NjrlTOZdvZCyEOAKhrexiAt/29GpfvpM4YY4wxNmD09BitPwLYQ0Qr0ZLkjevh+zPGGGOM9RiXtWh1YAmAR4QQkQAeAfB+RwWJ6H4iyiGiHPCgfcYYY4z1Qx0mMES0Cy1dfe0SQtx6Dfe7B8Af7O//H4D3Oql/A4AN9li6vgEYY4wxxlgf0VlL0UoX3K8MwCQAXwPIBHDKBfdgjDHGGOsTOky0hBD7AYCI3AHEoaV164wQorkrFRPRxwAmAwggoosAXgCwGMDbRCQD0Azg/uuKnjHGGGOsD+twjBYRyYjodQAXAXwEYDOAC0T0OhHJr1SxEGKOECJUCCEXQkQIId4XQnwjhBghhEgXQowWQhzuvkdhjDHGWF+m0Wgkc+fOjY6MjEyLi4tLHTlyZOK+ffs8AaCkpEQ2c+bMQZGRkWmDBw9OnTRpUlxeXp7CarVi4cKFkfHx8akJCQkpaWlpySdOnOjy3pu9rbOuwzcAeAGIFUJoAYCIvNHSpbgSP4+1YowxxtjVSkNyt9Z3DIXdWp8LZGVlxURHRxuLi4uPSaVSHD9+3C0vL09ps9lw6623xs2dO7d29+7dZwHg0KFDyrKyMvl3333nWVFRIT9x4kSBVCrFmTNn5N7e3t2yd2JP6GzW4UwAix1JFgAIIRrRMnPwZlcHxhhjjLHu9cQTT4TGxsamjhs3Ln7WrFmxzz//fDAAvPnmmwFpaWnJiYmJKf/1X/81WKvVSgDggw8+8I2Pj09NTExMGTlyZGLb+ubNmxe1ZcsWNQBMmzZt8F133RUDAKtXrw5YtmzZJXsjFhQUKI4cOeL59ttvl0qlUgBASkqKafbs2Zrdu3d7yWQy8eSTT1Y7yo8bN84wffp0XXl5uTw4ONjsuGbw4MHmwMDALu/Z2ds6S7SEaGfZeCGEFZ3MRmSMMcZY33PgwAGPXbt2+ebn5x//9NNPz+Tl5bWuqp+VlVV/7NixwqKiouOJiYmGNWvWBADAihUrQr/88suTRUVFx7/44ovTbeucOHGi9sCBA14AUFFR4Xby5El3APj2229VkyZNumQz6aNHj7qnpKToZbLLO9Py8vKU6enp7W6lM3/+/Lq9e/f6JCUlpSxevDji22+/VV7XL6KHdZZoHSeiBW0PEtE8ACdcFxJjjDHGutvXX3+tmjFjRoNKpRK+vr62adOmNTjOHT58WDlixIjEhISElH/+85/+BQUF7gAwcuRIXVZWVsybb74ZYLFYLqtz2rRpuv/85z+qw4cPuyckJBgCAgLM58+flx8+fNgzMzNTd9kF12Dw4MHm06dPH1u+fPlFiUSCm2++OXHHjh1e3VF3T+hsjNZDAD4honsBHEZLK9YvACgB/LoHYmOMMcZYN+lsb+P7778/dvv27afHjh1rWLNmjf/+/fu9AGDr1q0l+/bt89y5c6d62LBhqUePHi0ICQlp7baLjY01azQa2a5du9QTJkzQ1tXVybKzs309PT1tvr6+l4yjGjZsWHNhYaGH1WqFoxvQYciQIYZ//etfvh3Fp1Qqxd1339149913NwYHB5s/+eQTn9tuu+2yzar7og5btIQQpUKI0QCWAygGUAJguRBilBCitIfiY4wxxlg3mDx5sm7Pnj1qvV5PGo1GsnfvXh/HOb1eL4mKijIbjUbatm2bn+N4QUGBIjMzs+mtt94q8/X1tZw9e/ay2X4jRozQrV+/Pmjq1Km6yZMn69auXRsyevToy1qzUlNTjUOHDm169NFHw2y2lhwsPz9fsXnzZp9Zs2ZpTSYTvfnmmwGO8vv37/f49NNPVd98841HcXGxHACsVivy8/OV0dHRpm7+9bjMFbe2EULsA7DP8ZmIfAA8JIT4b1cGxhhjjLHuM2nSJP306dM1KSkpqeHh4cahQ4c2qdVqKwA8/fTTZaNGjUoODw83JScn63U6nRQAHnnkkYji4mKFEIIyMjIax4wZY2hbb0ZGhu7gwYPeaWlpRqPRaNJoNNKJEye229q0efPm4qVLl0ZGR0enKZVKm4+Pj/WNN964IJFIsHPnzjNLly6NfOutt0IUCoWIiIgw/vWvf71QVFSkeOCBB6JNJpMEAIYNG9b09NNPV7nyd9WdqKOmRCKKBPBnAGEA/gVgK4C/AFgAYKsQoseWdyCiJiGE55VLdmzp0qUTAKzuppAYY4xdm8J169bN746KuuO7oSfl5uYWp6en1/RmDBqNRqJWq21arVYyduzYxHffffd8RkZGu4PQWedyc3MD0tPTY65UrrMWrWwA+wH8E8B0AP8BUABgiBCiojuCZIwxxljPmTdvXvSpU6eURqORZs+eXctJlut1lmj5CSFetL/fQ0SVAH4hhDC6PizGGGOs7yCi/wHwsBBCY/8cDeADIcQvezeyq7Nr165zvR3DjabTMVpE5AuA7B8rAHgQkScACCHqXBwbY4wx1ld8A+B7InoUQDiAJwA81rshsf6gs0RLjZZlHcjp2E/2nwLAIFcFxRhjjPUlQoj1RFQA4CsANQCG8zAa1hUdJlpCiJgejIMxxhjrs4hoPlomiC0AMBTAZ0T0OyFEbu9Gxvq6DtfRsq8A73g/vs25h10ZFGOMMdbH3AEgQwjxsRDiTwAeBPBRL8fE+oHOtuB51On9X9ucu9cFsTDGGGN9khDidiFEldPnHwCM6sWQes2oUaMSDxw44NHbcfQXnY3Rog7et/eZMcYYG3CI6EkhxOtEtKaDIsuuufK0tORrvrY9x44Vdmt9LmY2myGXy3s7DJfrrEVLdPC+vc+MMcbYQORIXg538Oo3ioqK3AYNGpQ6e/bs6Li4uNTx48fH63Q6cm6hKi8vl4WHhw8BAIvFgvvvvz8iISEhJSEhIeW///u/g9rW+cknn3gPGzYsKSUlJXnGjBmDNBqNBAAef/zx0LS0tOT4+PjUOXPmRDu23Bk1alTiww8/HP6LX/wi8eWXXw7uwcfvNZ21aCURUR5aWq8G29/D/plnHDLGGBvwhBC77D8HxHiskpIS982bN58dN27c+ZtvvnlQdnZ2hxs5v/nmm4Hnz59XFBQUHJfL5aisrLxkJ+jy8nLZK6+8EnrgwIGT3t7etmeffTbkL3/5S/DKlSvLn3jiiaqVK1eWA8Dtt98eu23bNvXcuXM1ANDQ0CD98ccfi1z7pH1HZ4lW9zZpMsYYY/0UEY0E8CyAaDh9dwohhvZaUNcgPDzcOG7cOAMADB8+XF9cXKzoqOy+ffu8H3zwwWpH915wcLDV+fzXX3/teebMGfdRo0YlAYDZbKYRI0boAODzzz/3WrVqVUhzc7OkoaFBlpKSYgCgAYA5c+bcUOtwdra8w/meDIQxxhjrw7agZZHSfAC2Xo7lmrm5ubUO/ZFKpcJgMEhkMpmwWltyKL1e3zoGWwgBIupwqJAQAhkZGY1tV5vX6/X02GOPRX///ffH4+LizI8++mhYc3Nz61AlLy+vfvv7uxadjdFijDHGWItqIcROIcQ5IcR5x6u3g+oOkZGRxh9++METALZs2dLalTh16tTGd999N9BsNgPAZV2HkydPbsrJyVEdO3ZMAQBarVaSl5en0Ov1EgAICQmxaDQaya5duzrsnrwRcKLFGGOMXdkLRPQeEc0hot84Xr0dVHd4+umnK99///3A4cOHJ9XU1LT2dD3yyCPVERERpqSkpNTExMSU999/38/5urCwMMv69euLZ8+ePSghISFlxIgRSfn5+e4BAQHWrKys6pSUlNQZM2bEpaenN/X8U/UdJETfn0BIRE1CCM/rqWPp0qUTAKzuppAYY4xdm8J169bN746KuuO74SrutRlAEoAC/Nx1KIQQXV5XMjc3tzg9Pb3GFfGxnpebmxuQnp4ec6VyHY7RIqJ8dLKMQ38bAMgYY4xdh3QhxJDeDoL1P53NOpxp//mQ/ef/2H9mAdC7LCLGGGOs7/kPEaUIIY73diCsf7nirEMiGi+EcN7r8Gki+hbAclcHxxhjjPURGQDuIaJzAIxoWVNScO8Ou5LOWrQcPIkoQwjxDQAQ0TgAPdInzhhjjF0rIpoO4G0AUgDvCSFWtFPmbgAvomWoTK4QYm4H1U13VZxsYOtKorUIwAdEpEbLH0QNeFNpxhhjV0vAhh5ag4qIpADWApgG4CKAH4lop3PXHxHFA/gTgPFCiHoiumyLGYeBspQD63lXTLSEEIcBpBORN1pmKWpcHxZjzGUEBND6Alq++IT9k+2S48J+Rctxsn9RovV6AUEte8w7l/35Hj/Xidb3AiCQcxyO2pzLCXsZx3FquRuE4/0ldYiWbhxHjQRyfkrnewgSRG2OOZen1rsARIJEm2Mt5W0Ep5haPthIXHLs57qp9ckEiETrc5FTjM7lWs/Zy5IjHtjsMaH1WnKqk5x+247rqc31LfcRl11PTvdtLU+CJE7/lSSOuluPo7U82Y8DAhKn+CT2eiX2PycSABK8g54wCsBpIcTZlqBpG4DbADiPsVoMYK0Qoh4AhBBVPRIZu6FcMdEiomAArwAIE0LMIKIUAGOFEO+7PLpu5FbiZrZ52k63HnD+OnDmdNz+F8PPxzu/5pIzTl8SHZVt+cv98vrb386743jaLd/6l3nbe7dzjAS1Vw+1d00nz0VOX6Bt46HLrvm5LHUQJzkfuuSLpJ36L3veS+NvW1d796RLYnUu20E9Ts9MTsec731Z+db6rnBf+zP9fO7S+h1fchKn38vPX3gC1PplevkXIdnrc47jkkUIGetHZESU4/R5gxBig/19OIALTucuAhjd5voEALCPO5YCeFEI8YWrgu1tRUVFbjNnzow/depUwdVeu3v3bq8333wz+Kuvvjp95dLMWVe6DjcB+BAtezwBwEkAfwfQrxIt30995QDiejsOxhhj3cYihBjZwTlq51jbf2LJAMQDmAwgAsBBIkoTQjS0WyFRNIB4IcReIlICkAkhtNcWOpCW1r17Ch87hsLurK8rzGYzHHshsvZ1ZWX4ACHEP2BvkBZCWABYO7+EMcYY61UXAUQ6fY4AUNZOmR1CCLMQ4hyAIrQkXpchosUAtgNY71Tfv7o14h5gtVoxe/bs6Li4uNTx48fH5+TkuKekpLQmfPn5+YrU1NRkANi+fbt3bGxs6ogRIxK3b9/u4yjz6KOPhs2ZMyd6/Pjx8b/5zW9i9Xo93XnnnTEJCQkpycnJKbt27fLqjWfrq7qSaDURkT/s/xIgojGw78DNGGOM9VE/AognolgicgMwG8DONmX+BWAKABBRAFq6Es92UN9DAMYDaAQAIcQpAB0Onu+rSkpK3JctW1Z1+vTpArVabf3hhx88vLy8rIcOHVICwPr16wPmzp1bq9fr6eGHH47ZuXPn6R9//LGoqqrqkmarvLw8jz179pzetWvXuddeey0IAE6ePHl869atZ++///4Y582pb3RdSbQeRcsfzsH2fuxsAMtcGhVjjDF2Hey9Lw8D2AOgEMA/hBAFRLSciG61F9sDoJaIjgP4CsATQojaDqo0CiFMjg9EJEMnu6f0VeHh4cZx48YZAGD48OH64uJixcKFC2s2btwYYLFYsGPHDt9FixbVHj161D0iIsI4ZMgQo0QiQVZW1iW/l+nTpzeoVCoBAIcOHVItWLCg1l5nc1hYmCk/P9+955+ub+rKGK0CAJMAJKKlz7sIvBk1Y4yxPk4I8RmAz9oce97pvUBLY8KjXahuPxE9A0BJRNMALAWwqxvD7RFubm6tyaFUKhUGg0Fyzz331L/22mth27Zt0w4ZMkQfEhJiPXv2LIg6bpTy9PRsXaajP+yZ3Ju6kjB9J4SwCCEKhBDHhBBmAN+5OjDGGGOsD3kaQDWAfAAPoCWBe65XI+omHh4eYtKkSZpHH300auHChTUAMGzYsOaLFy+6FRQUKABg27Ztfh1dn5GRodu8ebMfAOTl5SnKy8vdhg4d2twz0fd9nW0qHYKW6bFKIhqOn2dweAPw6IHYGGOMsT5BCGEDsNH+GnAWLFhQ9/nnn/v+5je/aQRakq+//vWv52fOnBnn5+dnGT16tK6wsFDZ3rVPPvlk1fz586MTEhJSpFIp1q9fX6xUKrmZy446avIjonsALAQwEi2DCh2JlhbAJiHEJz0RoD2WJiHEdW378xK9NAHA6m4KiTHG2LUpfEG8ML87KuqO74aruNd4tGzVE42WRgrHXoeDulpHbm5ucXp6eo1rIrw+zz//fLBGo5G+/fbbbWdmsg7k5uYGpKenx1ypXGebSn8E4CMiukMI8c/uDI4xxhjrZ94H8AiAwxhgSxxNmzZt8Pnz5xX79+8/2duxDERdGQwfYd9+R4uWJtObADwthPjSpZExxhhjfYdGCPF5bwfhCv/+97/P9HYMA1lXBsPfK4RoBPArtKwZ8jsAl+2AzhhjjA00RHQTEd0E4CsieoOIxjqO2Y8z1qmutGg5xmbdDOBDIUQudTbnkzHGGBs43mzz2XnLHwEgswdjYf1QVxKtw0T0JYBYAH8iIi+gdY95xhhjbMASQjhWjh8khLhk1Xgi6vJAeHbj6krX4SK0rB/yCyGEHoAbWroPGWOMsRvF9naO/b8ej4L1O11JtDIAqAAMJaKJAFIB+HR+CWOMMdb/EVESEd0BQE1Ev3F6LQQw4LaZmTRpUlxNTY20q+WLiorc4uPjU10Vzx133BHz4Ycf+rqq/p7Qla7DJ5zeuwMYhZbprZ32SxPRBwBmAqgSQqQ5Hf89WvafsgD4VAjx5NUGzRhjjPWQRLR8l/kAmOV0XAtg8fVUnAYkX8/1bR1r2dPxuuzfv/90d8TSF1gsFshkXUlzXOuKLVpCiFlOr2kA0gBUdqHuTQCmOx8goikAbgMwVAiRCmDl1YfMGGOM9QwhxA4hxO8AzBRC/M7ptUwIcai347sazz33XPDLL78cBACLFi2KHDNmTAIA7Nixw+u2226LBYDw8PAh5eXlsqKiIrdBgwalzp49OzouLi51/Pjx8TqdjgDg4MGDHomJiSnDhg1LWrVqVVB797LZbHjggQci4uPjUxMSElI2btzoCwC7d+/2GjVqVOL06dMHxcbGpt56662xNlvLsO+lS5eGDx48ODUhISHl/vvvj3DUtX//ftXw4cOTIiIihjhatzqrf/To0QmzZs2KTUxMTAWAdevW+Q0ZMiQ5KSkpZe7cudEWi8VFv+H2Xcvm0BfRkmx1SghxAEBdm8NLAKwQQhjtZaqu4f6MMcZYjxJC9Ps9fqdMmaL79ttvVQBw9OhRj6amJqnRaKQDBw6oMjIytG3Ll5SUuC9btqzq9OnTBWq12pqdne0LAIsWLYpZtWpVydGjR090dK/s7Gyf/Px8ZWFhYcH//d//nXz++ecjzp8/LweAwsJC5dq1ay+cPn26oKSkRPHvf/9bVVlZKf3ss898T506VXDy5Mnjr7zySrmjrsrKSnlOTs6JHTt2nHrhhRfCr1R/Xl6e5xtvvFF65syZgp9++sl9+/btfjk5OSdOnDhxXCKRiHfffde/e3+znbtiokVEfyWiNfbX3wAcBJB7jfdLADCBiL4nov1E9ItrrIcxxhhjVyEjI0Ofn5/vWV9fL1EoFGLkyJG6gwcPenz33XdemZmZurblw8PDjePGjTMAwPDhw/XFxcWK2tpaqVarld5yyy06ALj33ntr27vXwYMHve6+++46mUyGyMhIy+jRo3XffPONBwAMGTKkafDgwWapVIrU1FT9mTNn3Pz8/KwKhcI2e/bs6I8++shHpVK1rm5w6623NkilUowYMaK5trZWfqX6hw4d2pSUlGQCgC+++MLr2LFjHunp6clJSUkp33zzjffZs2cV3f277UxXOi9znN5bAHwshPj2Ou7nC2AMgF8A+Id9yuxlGy4S0f0A7r+KOBljjDHWAYVCISIiIoxr164NGDVqlC49Pd2wd+9er/PnzyuGDx/e3La8m5tb63ezVCoVBoNBIoRAV5bS7GgfZUccTvXCYrGQXC7H0aNHC3fu3Om9bds233feeSfoP//5z0kAcHd3by3vqLez+j08PGxO5emuu+6qXbt2bekVg3aRrozR+sjpteU6kiygpdvxE9HiB7SsxxXQwX03CCFGCiFGoiXBY4wxxnoFESmIaC4RPUNEzztevR3X1Ro3bpxu7dq1wZMnT9ZOnTpV+9FHHwWmpKToJZKujSQKCAiwqlQq6549e1QAsGnTJr/2yk2aNEm7fft2P4vFgrKyMtkPP/ygmjBhQlNH9Wo0GkldXZ30t7/9rebdd9+9UFhY6NFZHF2tf/r06Y27d+/2LS0tlQFAZWWl9OTJk25dethu0mFLERH9QwhxNxHlo2X1W2cCLeOv3hJC7LiK+/0LLbMVvyaiBLSsydUndzJnjDHGnOwAoEHLrHtjL8dyzSZNmqRds2ZNSGZmZpO3t7dNoVCI8ePHX9Zt2Jn333+/+L777otRKpW2zMzMxvbKzJ8/v+HQoUOq5OTkVCISL7300sWoqChLXl5eu3U2NDRIZ86cGWc0GgkAXn755QudxdDV+keMGNH83HPPlf7yl79MsNlskMvlYs2aNSUJCQmmq3nm60EdNb8RUagQopyIoju4NgDAFiFEUgfXfwxgsr1cJYAXAPwPgA8ADANgAvC4EGLfFYMkahJCeF6pXGdeopcmAFh9PXUwxhi7boUviBfmd0dF3fHdcBX3Oua8VNG1yM3NLU5PT+fGhQEiNzc3ID09PeZK5Tps0RJClNt/nu+gyHkiyurk+jkdnJp3paAYY4yxPuYQEQ0RQu4vCCIAACAASURBVOT3diCsf+ms61CLy7sMWwkhvIUQh10SFWOMMda3ZABYSETn0NJ1SACEEGJo74bF+rrOWrS8AICIlgOoQEu3HwHIAuDVI9ExxhhjfcOM3g6A9U9dWTbhv4QQo50+v0NE3wN43UUxMcYYY30CEXkLIRrRsuUOY1etK4mW1T4WaxtauhLnALC6NCrGGGOsb9iKlr0OD6PlO9B5ESkBYFBvBMX6j64kWnMBvG1/CQDf2o8xxhhjA5oQYqb9Z2xvx8L6p64sWFoshLhNCBEghAgUQtwuhCjugdgYY4wx1oMmTZoUV1NTI+1q+aKiIrf4+PjUq7nHAw88EBEXF5f6wAMPRJSVlcmGDh2alJycnPLFF1+o2is/atSoxAMHDnS6gGlfdsUWLSJyB7AIQCoAd8dxIcS9LoyLMcYYG9DS1q1L7s76ji1dWni9dezfv/90d8TSmS1btgRWV1cfVSqVYsOGDb5xcXHNn3zySbEr7mU2myGXy11RdZd1Zc39/wEQAuC/AOwHEAEeFMgYY4z1K88991zwyy+/HAQAixYtihwzZkwCAOzYscPrtttuiwWA8PDwIeXl5bKioiK3QYMGpc6ePTs6Li4udfz48fE6nY4A4ODBgx6JiYkpw4YNS1q1alVQe/ey2Wx44IEHIuLj41MTEhJSNm7c6AsAmZmZcQaDQTJ8+PDkZ599NuSFF16I+Oqrr9RJSUkpjY2NkjvuuCPGcc1LL73UWvfHH3/sO2TIkOSYmJg0R8uXXq+nO++8MyYhISElOTk5ZdeuXV4AsGbNGv8ZM2YMyszMjJswYUICAPz5z38OTktLS05ISEh55JFHwlz3W75cV8ZoxQkh7iKi24QQHxHRVgB7XB0YY4wx1pcQUQaAeCHEh0QUCEAlhDjX23F11ZQpU3QrV64MBlB19OhRD5PJJDEajXTgwAFVRkbGZQ0oJSUl7ps3bz47bty48zfffPOg7Oxs36VLl9YtWrQoZvXq1SW33HKL7oEHHoho717Z2dk++fn5ysLCwoLy8nLZqFGjkn/1q1/p9u3bd9rDw2P4iRMnjgNAcHCwOScnxzM7O7vk4MGDHuXl5fJTp04VAIBzF6bFYqH8/PzCv//97+rly5eHTZ8+/eRrr70WBAAnT548fuTIEfebb745/syZM8cA4KefflLl5eX9f/buPL6K6u4f+Oc7c/dskBC2hJAgCdkkQihKSgQRrbRiWxURMSpKsWAf64Jaqz8Xqq/W4tJSBfFpqWCVWsXi8rT6gEpBofgINrKDSxCQfcl+l5nz/f0xMzeXGCBIQkjyfbfzmjsz55577n2lnQ/nnJnZ0KNHD/O1115L/Oyzz3yffvrpJmbG6NGj+//zn/+MHzNmzEk9eujbak6PVsReHyGiQgBJADJbrUVCCCHEGYaIHgRwD4B77V1uAH9puxadvOHDh9etW7cu7vDhw5rX6+UhQ4bUrFixIrBq1aqEUaNGfSN0pKWlhUpKSuoBYNCgQXUVFRXegwcP6tXV1foPfvCDGgC48cYbDzb1WStWrEi46qqrDrlcLvTp08c499xzaz744IPjzrPKzc0N7dixw3v99df3efXVVxO7du0avcPBuHHjDgNASUlJ7c6dOz0AsHLlyvjrrrvuoN2+YO/evcPr1q3zAUBpaWlVjx49TAB4++23E5cvX56Yn5+fX1BQkP/555/7Nm/e7PtmC1pHc3q0niOirgDuB/AGgHgA/69VWyWEEEKcWX4MYBCAtQDAzF8TUbu6ebfX6+X09PTQM888023o0KE1RUVF9UuXLk3Yvn27d9CgQcHG5T0eT/TpMLquc319vcbMIKLGRb/hWM9RPp7U1FRz/fr1G//+978nzp49u/vLL7+c/Morr1QAgM/nYwBwuVwwTZNO9BmBQEDFtuW2227bfdddd7XJcyabc9XhH5n5MDMvZ+Z+zNydmeeejsYJIYQQZ4gwW2d2BgAiOi0Ps25pJSUlNc8880yPkSNHVo8ePbp6/vz5qfn5+XWa1pwBLqBbt25mfHy8+c4778QDwPPPP5/cVLkRI0ZUv/rqq8mGYeDrr792ffTRR/GlpaW1x6t79+7dLtM0ccMNNxx55JFHdq1bt+64PWDDhw+v+ctf/pIMAJ9++ql39+7dnoEDB34jMI4ZM6bqhRde6FZZWakBwJdffunetWtXczqaWsRJfRARveXcU0QIIYToRP5GRHMBdCGinwC4EcB/t3GbTtqIESOqZ82a1XPUqFG1iYmJyuv18ne/+92Tmqv0pz/9qWLy5MmZfr9fjRo1qqqpMmVlZUdWrlwZn5eXV0BE/PDDD+/MyMgwjldvRUWF+6abbspUShEAzJgxY+fxyt999937ysrK+ubk5OTruo65c+dW+P3+b3RzXX755VUbNmzwfec738kFrN6uF1988cu0tLTjtqel0Ml07xHRJ8w8qBXbc6zPrWXmU/rXw8P0cCmAp1qoSUIIIb6dTQ/yg2UtUVFLnBtO8vMuAnAxrLvDv8PMS07m/eXl5RVFRUVtMnwlWl55eXm3oqKizBOVO9mus0++XXOEEEKI9omIdFjBajSAkwpXQpxwUJaILiUiDZCblAohhOh8mNkEUEdESW3dFtH+NKdH62oAvyeiRQD+zMynfOdZIYQQop0JAlhHREsARCd1M/Otbdck0R6cMGgx87VElAhgAoA/ExED+DOAhcwsd4gXQgjRGfyPvQhxUpo1R4uZq+weLT+A22DdT+QuIprFzH9ozQYKIYQQbY2Z57d1G0T71JyHSl8GYBKAs2A993AoM+8jogCATQAkaAkhhOjQiCgbwK8B5AOI3lWcmfu1WaNEu9CcO5RdCeApZh7IzDOZeR8AMHMdrPuICCGEEB3dnwHMAWAAuADAAlidDx3W0KFDByxfvvy4Nw09njvuuKN39+7dB+bm5uZnZ2cXvPjii0lvvfVWwjnnnJMbWy4SiSAlJaVo+/bt7lNv9ZmnOUOHu5l5eewOInqMme9h5ndbqV1CCCHEmcTPzO8SETHzdgAPEdEKAA9+6xr/pzCvxVoHAD9Y36YXqxmGAZfr6Fjx05/+dO+MGTP2rl271nfhhRcOOHDgQPnkyZM9W7Zs8QwYMCAMAK+//npiTk5Ofd++fSNNVtzONadH66Im9o1p6YYIIYQQZ7CgfaujbUT0MyL6MYDubd2ok1FVVaWNHDmy/4ABA/Kzs7ML/vu//7srALz++usJeXl5+Tk5Ofnjxo3LrK+v/8bDDCdOnJhRWFiY179//4Lbb7+9t7M/LS3t7OnTp/cqLi4eMG/evK7H+uzBgwcHdV3Hnj17XJdeeumhBQsWRB/ds3DhwuRx48Ydaunve6Y4ZtAioqlEtA5ALhF9GrN8CeDT09dEIYQQ4uQR0SVEtIWIPiOiXxyn3JVExEQ05DjV3QYgAOBWAMUAygBc37Itbl2vvfZaYs+ePSNbtmzZuG3btg2XX355VV1dHd18881ZL7/88udbt27daBgGZs6cmdr4vU8++eSu9evXb9q8efOGDz/8MGH16tV+55jP51Nr1qzZMmXKlMPH+uz33nsvTtM07tWrl1FWVnbotddeSwaA+vp6ev/995PKysqO+d727ng9Wi8BGAvgdXvtLMXMfO1paJsQQgjxrdh3c38G1ghMPoAJRJTfRLkEWOFp9fHqY+b/Y+YaZt7JzJOY+XJm/ndrtL21DB48uH7FihWJU6dOTXv77bfjU1JSzPLycl96enpo4MCBIQC44YYbDn7wwQcJjd87f/785Pz8/Lz8/Pz8bdu2+crLy6MXBFx33XXHDEnPPvtsj9zc3Py77rorfcGCBV9omoYRI0bU1dXVaeXl5d5XX3016ZxzzqlNTU01W+dbt73jzdFiZq4golsaHyCiZGbusN18Qggh2r2hAD5j5i8AgIj+CuCHADY2KvcrAL8FMP14lRFRDoC7APRFzLmTmUe1YJtb1cCBA0Nr167duGjRoqT77rsvbenSpVWXX375kRO9b/PmzZ6nn366x5o1azalpqaaV1xxRWYwGIx21CQkJKhjvdeZo9V4/49+9KNDCxYsSN6yZYt//PjxHTpPnKhHCwDWAPjYXq+J2RZCCCHOVGkAdsRs77T3RRHRIAB9mPmtZtT3CoC1AO6HFbicpd2oqKhwJyQkqGnTph267bbb9v7nP/8JnHPOOcFdu3Z51q9f7wWABQsWpJSWlh51M/LDhw/rfr9fJScnmzt27HAtW7bslB9FdN111x169dVXU1auXJkwYcKEE4a99uyYPVrMfKm9zjp9zRFCCCGazUVEsf/wf46Zn7Nff2NCNwB2XtgT258CcEMzP8tg5jnfqpVniDVr1vjvvffedE3T4HK5ePbs2dsDgQA/++yzFePGjTvLNE0UFRXVTZ8+fX/s+4YNG1ZfWFhYl52dXZCRkREqLi6uOdW2FBcXB30+nzr77LPrEhMTj9kj1hEQMzd9gGjw8d7IzGtbpUVNt6WWmeNOpY6H6eFSWP+jEkII0XY2PcgPlrVERcc7NxDRMAAPMfP37O17AYCZf21vJwH4HIATGnoCOATgMmb+OKYe5+q4WwHsA/B3ACHn+MlMoykvL68oKio60Nzy4sxWXl7eraioKPNE5Y43R+uJ4xxjAO1mXFoIIUSn838AsokoC8AuAFcDuMY5yMyVALo520S0DMD02JBlWwPrnOf0kMUOFzIAuTO8OK7jDR1ecDobIoQQQrQUZjaI6GcA3gGgA5jHzBuIaAaAj5n5jWbWI9NnxClp1kOliagQ33y+04LWapQQQghxqpj5HwD+0WjfA8coO7Kp/UT0HQA7mHmPvX0dgCsAbIc1NNmhr5gTp+6Ed4YnogdhPTj6D7Ce7/RbAJe1cruEEEKIM8FcAGEAIKLzAfwG1nMOKwE8d5z3CQGg+Q+VvhDAHmaeBKAIgLdVWyWEEEKcGfSYXqvxsK5sXMTM/w9A/zZsl2gnmjN0WM/MiogMIkqEddWFTP4T7RAzGi7vbvw6Zh83dfzostRQF32jDm7qfdbnU0PZhvc1+jw66lJgu2zDPmZNZ5AOJhdAOjPpALkAuAFqzj+ehBDNpxORi5kNWJ0OU2KONWv6jejcmvNH8jERdQHw37CuvqgB8FGrtqoVdEneGjEM3yYAIOfcR8A3zp/UeNt5HXPuo6POl877CGCAYvZFPyfm1i3f2McggBptA059sXURqMnPdNpCMfVH63DaYb22v3NMO6w6CTF1x76PjsoRBDA1/Cb2e5so4+wh53PBmrXB5Lwvtm5q9D67vRR9PzW8j5yMQk7d9nutF85vqTntaWgfyPkFOipmKGY9wko3mDWTWTcU6yYr3WTWFSvNVKwrVi6TWWeldMWsMyudlb1mdkEpDcw6Wcc0sl8Ts0bMmmavdWZNA5MWE/50gFwS/kQHshDAv4joAIB6ACsAgIj6wxo+7LCGDh064PHHH99x/vnn133bOp5++umU3//+9z2ZGcyMiRMnHpgxY8bed999N+7222/vEw6HtXA4TD/60Y8OP/nkk1/PmjUr5cEHH0zv0aNHJBKJ0LRp0/beeeed7fqWGCcMWsw8zX75LBG9DSCRmdvdQ6VTe/7HDSCvrdshRGsigkZkeqGZZ8zw/nHCn2LWDFa6OonwZy3NC38uBmkS/sSpYOZHiehdAL0A/C833HxSA/Bfp1T5F4Ute07qt35Ti9Z3kgzDgMvVECv+9re/Jc6ePbv7kiVLtmZmZkbq6upozpw5KQBw0003ZS1cuPDzYcOG1RuGgdhnJ44dO/bwggULvtq1a5ersLCw4KqrrjrSp08fow2+Uos4YdCyJ/99Yx8zL2+dJgkhOpL2Ff40k+0QeOLwp5Oy1s0NfzqzpknPX/vT1MOjmXlrW7TlVFRVVWmXXXZZv927d3uUUnT33Xd//ZOf/OTw66+/nvCLX/yij3Nn+AULFmz3+/1H3c184sSJGeXl5XHBYFAbO3bs4aeeeuprAEhLSzt7woQJB95///3Em2++ed+UKVOiD5j+7W9/2+s3v/nNzszMzAgABAIBdnqnDh065MrIyIgAgMvlQnFxcbBxe9PS0oyMjIzQZ5995unQQQtH35zNB+tBnWsgNywVQrRT7ST8mYp14yTCH+z9J9vzJ8O+ncRrr72W2LNnz8iyZcs+A4CDBw/qdXV1dPPNN2f97//+75aBAweGfvzjH2fOnDkz9YEHHtgX+94nn3xyV48ePUzDMFBSUjJg9erV/nPPPbceAHw+n1qzZs2Wxp+3bds2/3e/+90mhx2nTJmyNy8vr/Dcc8+tvvjiiytvueWWg4FA4Khwt3HjRs+OHTu8+fn5oabqaC+aM3Q4NnabiPrAusWDEKKlMIc15qAGhDWlIjpzxKWU4VLKdCul3Eopt2nCxQwGoIichZxtJoICiBv2k72f2NrvlNXsGX1OGQ2AFi0HaLDKaGwNjxBbZXQ07NNBchJuSWd4+DMZmsHqpMMfM+uIDX8Af93W36uzGjx4cP19993XZ+rUqWk//OEPKy+55JKaVatW+dPT00MDBw4MAcANN9xw8JlnnukO68K3qPnz5yc///zz3QzDoP3797vLy8t9TtC67rrrDjfxccf1+OOP7540adKht956K/Fvf/tbyiuvvJLy0UcfbQGAN998s2tubm68x+NRv/vd77b36NHDbIGv32a+zRUTOwEUtnRDhGhXmCMaECLmkK5UWGc2XEpFXMymyzSVWyn2mCa7lSKPacJjmprHNHWPUrrXNF1u03R7TNPrVsrrVspLgAfW0q4woBSRUoBiIpOt16YiUnbgU2xts71PKYDtbWYi5RxTZF1u6RyzgyQzEdvvoZj9sPc7r51jZF36YW9bAdLatvc7oVM54dPap8WEUSdogq33OwGTYG1r9rYOK3RqHTV0xoS/tm6KaAEDBw4MrV27duOiRYuS7rvvvrSlS5dWXX755UdO9L7Nmzd7nn766R5r1qzZlJqaal5xxRWZwWAw+jefkJDQ5EOh+/fvX//hhx8GLrvssuqmjhcUFIQKCgr233HHHftTUlLO2bNnjw40zNH6tt/zTNOcOVp/QMOlbRqAcwCUt2ajhGhxzAYBQY05pDMbMeHIdCtlukyTPVY4gts0yaOU5jFNzWuauts03V7TdLuV8riV8rhN06cBblhLfFt/tbZEgKYzazoQc1eLzse+p4eyg6NyAmhM8ORGx5zgGQ2ZTgC1yyF2nxMw2Qma1n5wbOi09lu9mQ3vd8Jk7LHYMBobMMm+HtnZF13D7um0rznWY8Jow7YTPO3rksWZp6Kiwt29e3dj2rRphxISEtT8+fNTZsyYsWfXrl2e9evXewsLC0MLFixIKS0tPSoYHT58WPf7/So5OdncsWOHa9myZUkjRoxoMjzFuvvuu/f88pe/TD/nnHO2ZWRkGPX19fTEE0+k3n///fv++te/Jl111VWVmqZh3bp1Pl3XuVu3bh0y0Tfr9g4xrw0AC5n5w1ZqjxAWZtMORmGNOaxbvUURl1IqOpxmhSN4TJPsRbd7jVwe03S7TdPtUcrrUsqnM7tghaJOHYxE6yDrTjA6mHW9EwdOIBo6nXBpHhU8rTB6whO0aB1r1qzx33vvvemapsHlcvHs2bO3BwIBfvbZZyvGjRt3ljMZfvr06ftj3zds2LD6wsLCuuzs7IKMjIxQcXFxTXM+b/z48ZV79uxxXXjhhQOYGUSEiRMnHgCAv/zlLym/+MUv+vh8PuVyufiPf/zjl7FXLHYkxCf4PwUiCqDh7rdbmPm0T0ojolpmjjuVOl4qKCgF8FQLNUk0xqwICGnMIbvXKKLbc4zsYMRupZQ9jGYFI6vXSPdYw2gut1Jej9Vz5NOZ3W39lYQQrWITmMtaoqKWODecTuXl5RVFRUXt+p5QokF5eXm3oqKizBOVO2Z8JCI3gJkAygBUwOoa7k5Ef2Dm3xDRIGb+pIXaK043OxiR02Nk9RoZLmbDHkpTHqXYbZqwe4206Dwj03R5rN4it9s0PW6lfC5mDwC/vQghhBACxx86fAJAAEAmM1cDgP0InseJaA6ASwBkHevNRDQPwKUA9jFzYaNj02GFuFRmlnTfHMxsB6OQHYxir0xTbqVMtzX5Gl5rnpEVjJTSYiZfW0Nppul1MXtIgpEQQgjRqo4XtL4PIDvmLrhg5ioimgrgAIAxJ6j7eQBPw3rKeZR9e4iLAHSYKwqOyR5Gc+YZuazhNDP2kn178jW8Rw+ludym6fJYE6/dHqV8LqU8ZN3HzHfCzxVCCCHEGeF4QUtxExO4mNkkov1N3Sm3UbnlRJTZxKGnANwN4PWTaehpwRyJuZdROPZeRi6lnKG06ARst31VWnSeUcNQmnPJvhfWIoQQQohO6HhBayMRXcfMjXukrgXwrZ6nRESXAdjFzOWn+wrg4t27I4poq91r5HLmGdmX63vdSnljLtkXQgghhDhlxwtatwB4jYhuhPXIHQbwHVhzen58sh9kX714H4CLm1l+CoApzWhnsww4fNgNIOdU6xFCCCGEaK5j3s2YmXcx87kAZsC66vArADOYeSgz7/oWn3UWrMnz5URUASAdwFoi6nmMz3+OmYcw8xBY9+8SQgghRBt74YUXuqxZsyY6X3jo0KEDli9fHmir9rz11lsJF1xwQf8Tl2wbzXnW4XsA3jvVD2LmdQC6O9t22BoiVx0KIYTojGbPLsxryfqmTVv/rab1nKzFixd3MQyjsri4OHiqdRmGgY56o1JHqz2fi4gWAlgFYAAR7SSim1rrs4QQQghxYnfddVevrKysgpKSkuyxY8dmPfDAAz02bNjgLS0tzS4oKMgrLi4e8Mknn/gAYOvWrZ5hw4bl5OTk5A8bNixn27ZtniVLlsQtXbq0y/3335+em5ubv2HDBi8ALFy4sOvZZ5+dl5mZWfj222/HA1aIuvnmm9MLCwvzcnJy8mfOnNkNsHqgzj333JyxY8dmDRgwoGDLli2erKysgvHjx/fNzs4uuOyyy7IWL16cMHjw4Ny+ffsWvv/++wEAeP/99wODBg3KzcvLyx80aFBueXl5u7jYrNViJDNPOMHxzNb6bCGEEEIcbfny5YE333yz67p16zZGIhE655xz8gcNGlQ3efLkvs8999z2s88+O/Tee+/FTZ06NePf//731p/+9KcZ11xzzcH/+q//Ovi73/0uZerUqX2WLl36+ejRo49ceumllZMmTTrs1G0YBq1bt27Tyy+/nDRjxozel1xyydbf/e533ZKSksz169dvqq+vp+985zu5Y8eOrQKATz/9NO6TTz7ZkJubG96yZYtnx44dvpdffvmL4uLi7QMHDsx78cUXUz7++OPNL730UpdHH3201wUXXPB5UVFR8KOPPtrsdruxePHihLvvvjv9nXfe+bztftHm6dj9dUIIIYQAACxbtix+zJgxR+Lj4xkAX3TRRUeCwaD2ySefxI8bN+4sp1w4HCYA+OSTT+L++c9/fg4AU6dOPfTwww+nH6vucePGHQaAkpKS2rvuussDAEuXLk3cvHlz4I033ugKANXV1frGjRt9Ho+HBw4cWJubmxt23p+WlhYaOnRoPQDk5OTUjxo1qkrTNAwePLjukUce6Q0Ahw4d0sePH59VUVHhIyKORCLt4gHmErSEEEKITqCpZxsrpZCQkGBs3rx546nU7fP5GABcLhdM0yT78+iJJ5746oorrqiKLfvWW28lBAIBFbvP4/FEG6dpWrQ+Xdej9d1zzz1pI0aMqF6yZMnnW7Zs8YwaNWrAqbT5dGm1OVpCCCGEOHOMHDmy5p133kmqq6ujyspKbenSpV0CgYBKT08Pz5s3rytgBa9Vq1b5AWDQoEG1f/zjH7sCwNy5c5OHDBlSAwDx8fFmVVXVCfPDRRddVDlnzpzUUChEAPDpp596m/O+Y6mqqtLT09PDdnu6fdt6TjcJWkIIIUQnMGLEiLpLLrmkMj8/v+D73//+WQMHDqxNSkoyFy5c+MWf//znbgMGDMjPzs4uWLRoURcAmDNnzlcvvPBCt5ycnPyFCxemzJ49ewcATJw48dCsWbN65uXlRSfDN+X2228/kJubGzz77LPzsrOzC37yk5/0PZXhvnvuuWfPQw89lD548OBc0zS/bTWnHTXVlXimIaJaZo47xUpKYT3+RwghRNvZBOaylqioRc4Np1F5eXlFUVFRm97SqLKyUktKSlLV1dXasGHDBjz77LPbhw8fXteWbWqvysvLuxUVFWWeqJzM0RJCCCE6iWuvvbbvtm3b/KFQiK6++uqDErJanwQtIYQQopN48803v2zrNnQ2MkdLCCGEEKKVSNASQgghhGglMnQohBBtgAHFIJNBzCBTQYtuK2isoCkFzbRfs4KmTOjK2TahswGXUtBgv45u26+dBfbCEbjZgIsMuBCBG/Y2InBTzDY522F4ELNNIXhhQo/dJntbi8BNYXgoAjeZ0LUI3FoYHjLg0pxt+3318oBb0ZlI0BJCnBYKZAJQdqBwFidIKAYpO1w4oSJ2zSZ0ZUJnZzHgctaI3Y7AHQ0Wdrggex8bcME++cPZ54QD530x20740I61bYcQLQK3FhNIdAMuzQ40sdu6vU834dJgjSh0xlEFf1s3QIjTSYKWEGcoE1o4AncwCF+wHv5wDeIjdQiYdniI7cVg+6Qf7cGwT/gc03MRDRJ2T0XsdrQnImZbi912woT9Hq1xuDDg0p3tmFChOYuCrgHQ7UUIIToNCVpCtAIGlAFXMAxPMAhfqBZx4WokmJVIMo6gizqIFD6AbrQfqXQA3fR96O46iBT3QaR4DyLFewRd/GF4PQA8ABLb+vsIIVrB/63Pa9H6vlO4qUXrEy1CgpYQTVCgSATu+hC8oToEwjWID1cjwTyMruZhdOUD6MYHkUIH0E3bj1TXfqS69yPVfQjJvkNI9lYjwcfQAgACbf1dhBDC8dBDD/V48cUXfH3mnQAAIABJREFUuwFAWVnZ/gceeGDf008/nTJr1qweRIS8vLz6xYsXf/n111+7Jk2a1HfXrl0eAHjyySe/uvjii2vff//9wB133JERDAY1n8+nnn/++S+LiopCs2bNSnnrrbe61NfXa1999ZV3zJgxR5599tmdbfttzwwStESHwwCb0Bv3JkWqkGjavUk4gG5shyTNCUkH0M1zGF29h9HVH4LPDcDd1t9FCCFayooVKwIvvfRSypo1azYxM4qLi/POO++82scff7zXqlWrNvfq1cvYu3evDgA333xznzvuuGPv9773vZpt27Z5vve972V/8cUXG4qKioIfffTRZrfbjcWLFyfcfffd6e+8887nALBx48ZAeXn5Rr/fr/r37184ffr0vf3794+07bduexK0xBlHgQy7NylYD3+4FnGRKiQalUgyDyHZCUpkhyT9ALq5DqCb9yBSPIeQ7K9ColdB90Mm3QohRNSyZcviv//97x9JTExUAPCDH/zg8OrVq+PGjh17uFevXgYA9OjRwwSADz/8MHHbtm3R/w+tqanRDx8+rB06dEgfP358VkVFhY+IOPbZhcOHD69KSUkxAaB///7Bzz//3CtBS4KWaGF2b1LImcRdh0CkGgmRSiQZh9GVY4PSAXRz7UN3/QC6ue2g5D2Mrv4g/G4ACfYihBCiBTT1bGMiAhF94wAz4+OPP94UHx9/1LHJkydnjBgxonrJkiWfb9myxTNq1KgBzjGPxxMtq+s6n8oDpDsSCVriKApkGHAFQ/CG6uEP1iLOqEJipAqJ5iEkc8wkbi1mErfHHnbzHUEXv4LuA+Br6+8ihBCiwahRo2puvPHGzF/96ld7mBn/+Mc/uj7zzDMVU6ZMyfrlL3+5t2fPnubevXv1Hj16mMOHD6967LHHuv/qV7/aCwArV670l5SU1FdVVenp6elhAJg7d263tv1G7YMErQ7GsHqT6oPwhexbAhh2b5J5CMk4iBS2h9y0A+imH0C36JVuh5Dsq0OcF0C8vQghhOgghg8fXnfNNdccHDx4cB5gTYa/+OKLa++8887dpaWluZqmcWFhYd2iRYsqnnvuuR2TJ0/OyMnJyTdNk84999zqkpKSr+655549kydPzpo1a1bP0tLSqrb+Tu0BNdWVeKYholpmjjvFSkoBPNUyLWodDKgI3PVheJy5SeFqJBhH0MWMCUrYj1Q9ZhK3x56b5DuCLj4TLrlPkRDiTLaJGWUtUVGLnBtOo/Ly8oqioiK5MX4HUV5e3q2oqCjzROWkR6sFmdCOmpvk3BLAvtJN2bcDcIKSfgDd3PuR6jmEZO9hdPXXIMELIM5ehBBCCNHOSdCy2TeYrLdvCRCuRZwzidu50o0PIoX2I5WcK90OIsWzH6neQ0j2HkEXXwQeLwAvgKS2/j5CCNHZEdElAH4P64kEf2Tm3zQ6fgeAyQAMAPsB3MjM2097Q0WH1mmC1i/xaCQOtVvsO3G79iM1ekuAw+jqr0aCl6FJb5IQQnQARKQDeAbARQB2Avg/InqDmTfGFPsEwBBmriOiqQB+C2D86W+t6Mg6TdD6NX7pBjDghAWFEEJ0BEMBfMbMXwAAEf0VwA8BRIMWM78fU/7fAK49rS0UnUJnfHK8EEKIji8NwI6Y7Z32vmO5CcA/W7VFolPqND1aQgghOhwXEX0cs/0cMz9nv27qZplNXmZPRNcCGAJgRAu3Twjp0RJCCNFuGcw8JGZ5LubYTgB9YrbTAXzduAIiGg3gPgCXMXOodZvbtrZs2eLJzs4uaLx//PjxfdesWXPG3GT6jjvu6J2VlVWQnZ1dsGDBgi7O/r/85S9dRo8efZazfe+99/bMyMgodLZfeumlpFGjRvU/2c974YUXurTm95ceLSGEEB3R/wHIJqIsALsAXA3gmtgCRDQIwFwAlzDzvtPdwMLZhXktWd/6aes3fZv3vfzyy2fMlZafffaZe9GiRclbt27doGkaf/XVV27n2KhRo2p+/vOf93W2V69eHR8fH2/u2rXLlZaWZnz44Yfxw4YNqznZz1y8eHEXwzAqi4uLgy31PWJJj5YQQogOh5kNAD8D8A6ATQD+xswbiGgGEV1mF5sJ6ykYrxDRf4jojTZq7mljGAYuv/zyzJycnPxLLrmkX3V1tTZ06NABy5cvDwDA3Llzk3NycvKzs7MLpk6dGp3TFggEBk2dOjWtoKAgr6SkJOf9998PDB06dEB6evrZL774YhJg9ZgVFxcPyM/Pz8vPz89bsmRJHABs377dPWTIkAG5ubn52dnZBW+//Xa8YRi44oorMrOzswtycnLyH3744e4A4Ha7UVNTo1dVVWlutxtnnXVW9KHUvXv3NhISEsz169d7AWDv3r3usWPHHn7vvffiAeCjjz6KLy0trQGAiRMnZhQWFub179+/4Pbbb+/t1DFt2rS0s846qyAnJyd/ypQp6UuWLIlbunRpl/vvvz89Nzc3f8OGDd4NGzZ4S0tLswsKCvKKi4sHfPLJJ6fU2yU9WkIIITokZv4HgH802vdAzOvRp71RbayiosI3d+7ciosvvrh23LhxmTNnzkyNOeZ+6KGH0tasWbMpNTXVKC0tzXnhhRe6lJWVHamvr9cuuOCC6jlz5uy66KKLzrr//vvTVqxYsXXt2rW+SZMmZU2cOLGyd+/exooVK7YGAgFet26dd8KECf3Wr1+/ad68eckXXnhh5WOPPbbHMAxUV1drq1atCuzevdu9bdu2DQBw4MABHQB8Pp9KSUmJXHrppWctW7Zsm9/vP2peXXFxcc2yZcviTdNEVlZWqKSkpPaf//xn0tVXX31ky5Yt/vPPP78WAJ588sldPXr0MA3DQElJyYDVq1f7MzMzw//4xz+6fvHFF+s1TcOBAwf0bt26maNHjz5y6aWXVk6aNOkwAAwbNiznueee23722WeH3nvvvbipU6dm/Pvf/976bX9z6dESQgghOomePXuGL7744loAKCsrO7hy5croc20/+OCDuPPOO6+6d+/ehtvtxvjx4w/961//igcAt9vNV155ZRUAFBQU1A8fPrza6/Xy0KFD63ft2uUBgHA4TNdcc01mTk5O/rhx4876/PPPfQBw3nnn1S5cuLDbHXfc0fujjz7yd+3aVeXm5oZ27Njhvf766/u8+uqriV27djUB4Nprr82cOXPmjvPPP7/6Rz/6UT/TNHH//ff3+PWvf50KACUlJTUrV66MW7ZsWfy5555bc/7559euWbMmbuXKlYGsrKxgIBBgAJg/f36y3bOWv23bNl95ebkvOTnZ9Hq96uqrr+47f/78LvHx8arx71NZWal98skn8ePGjTsrNzc3f9q0aX337dvnblzuZEjQEkIIIToJIjrm9vGefexyuVjTrMigaRq8Xi8DgK7rME2TAODRRx/t0b1798imTZs2rlu3bmMkEtEAYMyYMTXLly/fkpaWFr7hhhuynn766ZTU1FRz/fr1Gy+44ILq2bNnd7/66qszAWDlypWJP/jBD6pnzpy5u2fPnuGysrKMJUuWJI0bN+4IAIwYMaLm448/jl+1alX88OHDa7p27apCoRAtXbo0YejQoTUAsHnzZs/TTz/d41//+tfWrVu3bhw1alRlMBjU3G43/vOf/2y64oorjixevLjLyJEjsxt/T9M0kZCQYGzevHmjs3zxxRcbTuEnl6AlhBBCdBa7d+/2LF26NA4AXnrppeSSkpLo5PHzzz+/dvXq1Qm7d+92GYaBV155JXnkyJHNnlxeWVmp9+rVK6LrOmbPnp1imiYAYOvWrZ60tLTInXfeeeDaa689sHbt2sDu3btdpmnihhtuOPLII4/sWrduXQAABgwYUD9nzpwUAHjmmWd2fvDBB4kej4f79+8fAYDBgwcH9+/f7169enV8SUlJPQAUFhbWP//886nf/e53awDg8OHDut/vV8nJyeaOHTtcy5YtS7Lbpx06dEgfP3585bPPPrtj06ZNAQCIj483q6qqNABITk5W6enp4Xnz5nUFAKUUVq1a5T+V31yClhBCCNFJ9OvXLzhv3ryUnJyc/MOHD7umT5++3znWt2/fyAMPPLBrxIgROXl5eQUDBw6su/baa480t+7bbrtt38KFC1OKiopyt27d6vP7/QoA3nnnnYT8/PyCvLy8/Ndff73r3XffvbeiosI9fPjwAbm5ufk33nhj1owZM3YCwAsvvPDlwoULU3JycvKHDRuW+7Of/WyvaZp46KGHegBWb1pRUVFtcnKy4fSqnXfeeTU7d+70XnDBBbUAMGzYsPrCwsK67OzsgrKysszi4uIaADhy5Ih+ySWXZOfk5OSXlpYOeOSRR3YAwMSJEw/NmjWrZ15eXv6GDRu8Cxcu/OLPf/5ztwEDBuRnZ2cXLFq0qEtT37e56HhdhWcKIqpl5lN6BiERSgE81UJNEkII8e1sYkZZS1TUEueG06m8vLyiqKjoQFu3Q7SM8vLybkVFRZknKic9WkIIIYQQrUSClhBCCCFEK5GgJYQQQgjRSiRoCSGEEEK0EglaQgghhBCtRIKWEEIIIUQrkaAlhBBCdAJbtmzxZGdnFzR1bPz48X3XrFlzSg9PPhO8//77gSFDhgzIzMwszMrKKhg/fnzf6upqDQD+9re/JRYWFub169evICsrq2DKlCnpAFBeXu4dOnTogNzc3Px+/foVTJgwoW9LtkkeKi2EEOLECBHoiMCFMFww4UYEHpjwwoAXJvxQ8EMhAEYcGHEA4sCIByEBQDw0JIDQBXvb+qucKQpRmNeS9a3H+k3f9r0vv/zy9pZsS1vYsWOHa+LEiWctWLDgi9GjR9cqpTB//vyuR44c0TZv3uy58847M954443PBg0aFIxEInjiiSdSAeCWW27JuPXWW/c6N2f96KOPTulO8I1J0BJCiPbKCj9huBCBCwbcMOCxg48PJnx2+ImDssMPId4OQVbwAeKhIwGEROhIgI44uBAPF+LgRhxcCMANP9zQ4QbgBhA4xVYbp/y9xbdmGAYuv/zyzPXr1wf69esXfOWVVyoSEhLU0KFDBzz++OM7zj///Lq5c+cmP/HEEz2ZmUaPHn1kzpw5uwAgEAgMuv766/ctX748MSkpyXz00Ud33nPPPX2+/vprz2OPPfbVxIkTK7ds2eK55pprsurr6zUA+P3vf//VRRddVLt9+3b3FVdc0a+mpkY3TZP+8Ic/bB89enTN+PHjMz/99NM4IuKJEyceePDBB/fFtjUzM/Psr776at2hQ4f07t27n/M///M/W8aMGVNTXFw8YP78+RWFhYUhp/wTTzzR/aqrrjo4evToWsC6i/ykSZMOA8Ctt96afuedd+4eNGhQEADcbjd+8Ytf7AeAffv2ufv27Rt26hk6dGh9S/7mErSEEKLlMDREoCECFyJwIwI3zG+En0A0ADHiAcTbAcjp9WlYu5AADfHR8ONCAB7EwQU/PNCi4UeIZqmoqPDNnTu34uKLL64dN25c5syZM1NnzJixN+a4+6GHHkpbs2bNptTUVKO0tDTnhRde6FJWVnakvr5eu+CCC6rnzJmz66KLLjrr/vvvT1uxYsXWtWvX+iZNmpQ1ceLEyt69exsrVqzYGggEeN26dd4JEyb0W79+/aZ58+YlX3jhhZWPPfbYHsMwUF1dra1atSqwe/du97Zt2zYAwIEDB/TYtrpcLmRlZQXXrl3r27Ztmzc/P79u2bJl8SNHjqzds2ePJzZkAcDGjRv911133cGmvveWLVv8d999d5O9qbfccsve73//+zmDBg2qvfDCCytvueWWg926dTNP/de2v0dLVSSEEGcoK/zoCEOHYYcfIzrk5YWCHyb8YASgokNeVm8P7KBD9qLHBB8dCXAhHm4E4EYc3PDCDQ0eAJ62/cpCNK1nz57hiy++uBYAysrKDs6aNas70DCc+8EHH8Sdd9551b179zYAYPz48Yf+9a9/xZeVlR1xu9185ZVXVgFAQUFBvdfrVV6vl4cOHVq/a9cuDwCEw2G66aab+m7cuNGvaRq2b9/uBYDzzjuv9uabb86MRCLalVdeebikpKQ+Nzc3tGPHDu/111/fZ+zYsZU//vGPqxq3t6SkpPrdd99N+PLLL7133XXX7j/96U+py5cvrykqKqptqd/k5z//+cEf/vCHVYsXL0588803uzz//POpGzdu3Oj3+1vkGYWtFrSIaB6ASwHsY+ZCe99MAGMBhAF8DmASMzf7gZVCiA6jIfy47PDjssOPr1H4iYNCAIgZ8gISo70+Wkz40REPHXFwR4e+AvDADzcg4UcIACCi424f7/nHLpeLNc26hk7TNDgPddZ1HaZpEgA8+uijPbp37x5ZtGjRl0op+P3+YgAYM2ZMzfLly7csWrQo6YYbbsi69dZb9/7sZz87uH79+o1///vfE2fPnt395ZdfTn7llVcqYj9z5MiRNbNnz07du3ev58knn9z11FNP9Xz33XcThg8fXt24fXl5efUff/xxoKkHYefk5ARXr14dGDZsWJPDgpmZmZHbbrvt4G233XYwOzu74OOPP/aXlpbWHfPHOAmt2aP1PICnASyI2bcEwL3MbBDRYwDuBXBPK7ZBCNF8yg4/zrCXFYCsXh9r2KthwjPseT+IDn0lwOr9SYwJP3FNzPmJgwc+CT9CtIXdu3d7li5dGjd69Ojal156KbmkpKQm9vj5559fe8899/TZvXu3KzU11XjllVeSp02btu9Y9TVWWVmpp6enh3Vdx9NPP51imtYI3NatWz1ZWVnhO++880Btba22du3awO7duyu9Xq+64YYbjuTk5IRuvPHGrMb1jRw5snby5MlZffr0CQUCAS4oKKhbsGBB6t///vdtjctOnz5937nnnpt32WWXVY4aNaoWAGbPnp186aWXVt177717xo0bd9aoUaNqBg4cGDJNE7/61a96PPTQQ3tfffXVxLFjx1Z7vV7+6quvXEeOHNFj52ydqlYLWsy8nIgyG+3735jNfwO4srU+X4gORsGDWvhQDw8M+2qvhvDTMOR1dPhxlsRo74/LDkBuxEOPDnkF4IEPLgBeexFCdED9+vULzps3L2XatGl9s7KyQtOnT98fe7xv376RBx54YNeIESNymJkuvPDCyqZ6iI7ltttu23fFFVectXjx4q7Dhw+v9vv9CgDeeeedhFmzZvV0uVwcCATMF1988cuKigr3TTfdlKmUIgCYMWPGzsb1+f1+7tmzZ3jIkCG1AFBaWlrzxhtvJDc1Yb1Pnz7GggULvrjrrrvSDx486NY0jc8777yasrKyIxkZGfWPPfbYjgkTJvSrr6/XiAijR4+uBIC33347cfr06Rler1cBwMMPP7wzIyOjxS7aoON1E55y5VbQessZOmx07E0ALzPzX5pRTy0zx51aW1AK4KlTqUOIFkUw4EUNElCLrgihOwz0gol0EDKgIRMepMGHdATQHfHQ5b53okPYxEBZS1TUEueG06m8vLyiqKjoQFu3Q7SM8vLybkVFRZknKtcmk+GJ6D5Yl/i+eJwyUwBMsTdl0r5oH3QE4UcNElGPZITQAwbSwOgDQgZcyIAHGfCjF+LRFX4AXexFCCFEB3TaAwwRXQ9rkvyFfJzuNGZ+DsBz9nta7OoCIU6SNWQXQB26oB4piKAXTKSBkQHdXnxIgx+9EQ8/fADa/d2VhRBCtIzTGrSI6BJYk99HMHOLzOYX4qTFDtl1QRjdEUYvMPpEw5MHfeBFGuLRA3HQ7WneQgghxElqzds7LAQwEkA3ItoJ4EFYVxl6ASyxLyn9NzP/tLXaIDoRa8iuFgmoQwpC6A4T6VBIh4a+0fDkRy/EIRkByJCdEOL0U0op0jSt9SZHi9PCnsCvmlO2Na86nNDE7j+11ueJDkfBjTrEoRZJCCIFYfSGid4A+oLQB270hQdpCDQasktp43YLIcSxrN+/f39+ampqpYSt9kspRfv3708CsL455WWSuTh9Gobs6tAFQXRHJDpk1wcu9IUL6fAjHQH0sG8/ad2oQAgh2j3DMCbv2bPnj3v27CkE5CridkwBWG8YxuTmFJagJU7N0UN2YXRHBOng6JBdH3vIrrcM2QlxxmI2wByBUhEwm1DKgFImTNNalFIwDAXTtNaGwTAMRiTCMAwgEgEiEYpZEyIRDeEwwTB0hMOESMSFSESDYVRh6tS2/sZtori4eB+Ay9q6HeL0kqAlGmO4UXvUkJ1zlV1faNEhu94IIE2G7IQ4ZcwRMBtQKgKlTDAbME0DSik76DhrbhRyAMMAwmHY21bAsUKNFXQiEd1+7bJf6zAMHZGIG4bhQiTigmm6YJ0LXAD8p+Eby2PXRKciQaszcIbs4lGHrggi1b4xZh8Q+kBDJtwyZCc6Hev2MgaUMqJBh9npwTFhmoYdcpxw0zjkcEwvztE9OUf35ugxa5cddqygY4Uct72cjpBz2hDY1MAGgQ0NbGrESgebPjK+8eBgIToyCVrtlXNjzATUR4fs0sBIB6EvXOgDNzIQQC/EIUWG7MQZhpmjw1VWyLGGqo4esmI75JjRgHP84SotpjdHtxctJuS47W03DMPdKOSc4ZgJMDWwSWDDCS06lKETKx1KuUiZLmutXKTYDVN5SLGbTOUmkz1Q8JLBXjLZSwa8mskeGPCSST7NgI8M+MkgLxnwkkFeMjU/GeTTIpqPDHjJ1H1kkMdaaz4yNA+ZupcM3Uum5iHT5SVDd5NyecnUAThL40c67QMeO/0/oRBtRILWmePYQ3YZ0NAHLmTC28SQnRAnh1nFhBwzOmTlBBxrPo4zZGX15EQiCqYJRCINQ1ZHD1UhpidHiwaco4esXDEhR0ebhhxmAgy7x8WMCS6mTtbaRUo5wcUdDS6mcpFiD5nsIZO9ZMJLBntgwquZ7IUBn2aQHWjICS52MIGXTM0OLuSzw0xMcNF9ZOheMjQ3KSe4uDxk6h5SOhqG9+RZlEK0IxK0WtOJhuz6woN0eNEHceiOOLhkyK7TssJPGKYZhFIRGEYEkUgEkYiBcFghFDIRDgOhEB/Vi2PNz3F6crSYeTlOb441D6dhPo4Tcjz20mJfwA4upj1MZOpQpgY27R6XiBNc3KSUy90QXNx2cHHHBBevFWTgBBefFVbgIwMeMskOJuTTDPLbrz1WcImGFi8ZmtfqcdE8pHQ7tGhuMl2NgosQQrQa+T+ZkxU7ZJeMMHoggt72s+ycIbs+CKA3AkhBHGTIrmOyeoKCUCoM0wzbwchAOGwiEjERCjFCIYX6eiAUItTXE4JBDfX1LgSDOoJBt714EA77EIm4gaZ7KQls6FBhF6mwC8pwkTI1sIoNLtbwkFJumIabVMTqbTHYSyY8ZCoPmfB6rZ4WqzfFZK/Vq2L1slCEfJr12gkuVlAxNOu4ofvsbaunxRoqsoeOXC5iDe1mGE4IIU4fCVrWkF0d4lATHbLrCRPpYPSBZj8I2Id0+NAL8YiTIbt2yRoec3qLQjAMA5GIYYcihXBYIRhkOxTBDkWaHYpc0WAUCvkQCnlgmm4AcfYCAkdcUCGXFXLCXjIML5mGnyJmQIuYcRRR8VrIjNciRqIWQqIWRJIWoiR/UEuMC2tJWlBP1EJ6khZ0JWohd7wWdsdrYU+8FvZ4SDk9L4G2/AmFEEKcvM4TtO5BBEFssp9l50Yf+JCOOPRAHFwNJ0xxBrAmSofs3qIQDMMZSjMRDpv2UJpCMAgEg0B9PSEU0u1g5EJ9vY5QyI1g0ItQyINQyAtWLg3scUGxPceGrDk0EfJrhumnCMVrYRVPYU7UQpyohcx4LWwmaUEzSQ9FumjBcJIvpCcEQnWJWsiVqIXcCVrIk6CFPQGKeHRi6c0R7Y0CYNprBbLXgAmCCYDt/RxznEHgJl7Dfs12eYYGBkD2PtgLQ8Ou0/5NhWhDnSdo/QZuAHlt3YwOiVnFDKNZvUWGEYmGonDYRDBozS+yhtKs3qL6ev2oYbRQUNdD9eSKBMkDFXGTafjIMPyaYfgpYsZpYRVHERWnRdjqFQohUQtSkhaiRC2EJKuXCEl6CIneIOJ9YTNBC5vxWtjwU8SjEbyQicTim5ywYa0bAoeKBo6G4GEFi9ig0bAPTYQOZ1/jwOGUoUb7CBoYBC263VBGi1nH/kdrtE+3S+oxx5xFj66tO5PL3cmFaGWdJ2iJBtYdoK2J16ZpwDDCTQyjOcGIEAyS3UukafW15A4H4QrVkidUB3eoTvOaQfaRYQYoouK0sIrXwipBC3M8hZCkhZCohyhJC2pdtJCWoIU0e3jMlaiF3IlayB3nibjjfWGvl0z5e2xbsYHDjAkcTq+F2UTvhhNIAMT0aDQcbxw62C4LxAYPDU6ocI45gaOp/xwdNjT73VpM6ED0CNnhwgoWTYWOhtsQHNUjyWx/B0AxoMBQ7GzbxxhgMJitfXCOsXOXLkApjpax+mqdNQPMYMXR/bHbiDmuWIEUw7S37Q886jUxg5WyG2tvmyp6HMygRsfoqP0q5rj92rTKkl0v2e/VnG37vdaiQAxodj0NZWPKMUNXjOA997bcH60QZzo5sbUHSoWhVBCmGYFphhGJRGAYJsJhA6GQsideM4JBQjDIrlAtPOF65QnWkCdcB2+whr2hGs0briFfsJb8HEKCFuYEa4jM7hkKUZIVgvQkPehO0kKueC0cDUPxFPbEa2GvrrMGPzrYrRVPmXXjSyuIxK6V3dPh9JSY0RDirLWYENLw2gkZiEYIDVD2pX0AwGQvVpghRnSbQGAGiO0Qw3ZfibLKOEGFVEOMYSbr5HvUyR/Wa7ZOwmwnDnZO8hx7cgdYKXu74cTesLZOvhw9QTecuGGfpKMnfNM6YVM0NBy9TTEnbigFjRlk2p9jWtGI7W1ll4VimE5ZxdDtfVqjIKCxtRAf3dujn64/pE5C5riKTkWCVkuzLtMPwTRDMM0IDCMcnXgdDts9RiF2h+tNb7iOvaFa5QvXsC9UA1+wmn3hGgoEq8ipt8T7AAAJTElEQVQfrtHiglXkj9RQAgW1JCsIUZIWtHuFQnqSFvQ7PUNxWsSdoIW8AWuIjI5qU/sdMIsNMCYAw16bdmhpGN6J7Uk5OsQAmt05QGDnH/jKDil2+GAFcHRSCoGUfUwBZDJIkTOWBM0uR1ZDrBBgKsAwrRO9oUCmCTIUYJogw15MBTJMaIaCZlplNFNFtzVTQXf2mQouQ0FX9mvFHfpkzw0LAYCy/4CVvW3lRoqWYcDKilYx55hV1smdAIGI2KmHQCbIypqIZk5iFxFAUHYCZSJ77SRSIsUxebZhnM8ZOrRaS9CYwSDSuOGrkdNehlUxWynaKUfWPKaGMk5dYETbCpBm/0R2fXZdThYk0pwfkhu2iRmwijLBqRvQYLebyKrLSuYUHctkK6DHtteqk522UuyYJ0DQrKFR+18Gzj8ICBpbFVh1EjQygN0n/RciRDsmQcu6K3UIphmGUk5vkaFFQhG3ETS94XrDG65Tvkit8oerORCqYX+4GvHBSg6Eqyk+eESLC1dr8aFKigtXuxKNGi1BC+mJdu9QgtUr5EvUQp4Eq2fI69PM40+abv1rzBg4KrjEBpjY8OL0yDADJpPTwWAFF2W/brTAWdvjUHzUBBi2XhuweiZMAkyGZjKgyOpdMAHNtNZOOLGCihNWFDTz6MDiMRT0aFhR0A0TmlLQTYbLNKHzyc1FYVjZyrA6gshk+3exR1Oc17EjLjEjOGBFVkeMiulosTp8yPptyOr5MYmc34tNENm/FzX8ZkSmxmSCovucE6YzBsew0gSsNRFZn2Gfdckuz85x679EdlsBuwzbp9OYukBOkrBO5E7SaNgfPWOTfYbXyCmnQFa8tUJBzHusEzbZ/2UoKDAxMymrFjAUKWYwuGENJmarrFWGoZiJYS0A7O2G99rlyC5nHXde25/plImtz2lDNEWJluT7eVu3QIjTqNMErQf/cVMoBPe6pPARLT5cpSWEKvX4SJU7UdW5ErWQK4FCrgQt7E3QQonxWtjjIj65HgSr16ghwDT0wpggGAyEGahnwDSsgOKEFqXYXgNQDfvYdAILN6xNa00mjnpNhp0AogHG2kfKWTN0OzVoJkO3e1iiPSlGQ0+K21B2SLEXpb4RVJSdBQzYozlWU+xAZo0CNWwTnCkfsSHNDmqEmO/u9CyxAimrF4kQGz5iA4gRfe0cIzLBZII0BYYJ0kyATN0Obdbvotu9UpoCacpem2B7HzST4DLY1CPKcJkwrb+DmD6KZjnZ8kIIITqkThO0rto1j+J06IYVVkzFMA1G2LAnhdYwUMmAwdaJ21D2ydkOKoaVHDSDoZtWGc20Xuv2SVw3YYUVZQ0fWUNIVg+MG9GpIg0BK2bNMWun1yOmt4iiPSAKYEXEdpBAbPgwrPKxocMJG7CeuUIwAdO06tbs76fZ+zQFkLJ6TnQFIgOsm1bXjqbsgGIwuxSxZrDh4ujk4ROkim8bOiSsCCGEaOc6TdCa8jUQb4cOM6Y7JmabTLvHJ9rz07BfMwEynB4PQIdVl6YA3d6vW70j7FLRGQqxTiY1NFGWj3/4tGrrzxdCCCHaiU4TtD4EPADntlyNkjaEEOJMRkSXAPg9rCtH/8jMv2l03AtgAYBiAAcBjGfmitPdTtGxyc3qhBBCdDhEpAN4BsAYAPkAJhBRfqNiNwE4zMz9ATwF4LHT20rRGUjQEkII0RENBfAZM3/BzGEAfwXww0Zlfghgvv36VQAXEpFcaSpaVKcZOgRQDWBzWzdCCCE6uS9P0+ekAdgRs70TwLnHKsPMBhFVAkgBcOC0tFB0Cu0laPmJqLYF6nHBmgMvRHslf8Oi3aOH6B60zN+xn4g+jtl+jpmfcz6mifKNJ9c2p4wQp6RdBC1mbpEhTiL6mJmHtERdQrQF+RsWHcFp+jveCaBPzHY6gK+PUWYnEbkAJAE41MrtEp2MzNESQgjREf0fgGwiyiIiz/9v715C4yrDMI7/H6RuvECK9IJoBV0EBcULgtBFN9YiggoKdWOkC0EoWKRS6cKFUnAhIrhuRcRNwVtARQIKKoo0DVWpUduVm9JCUxojIoqPi/OFDiFtMoc5k5wzz2+T4cuZN3PgmS8v5/YBu4HJJdtMAhPl9RPAF15cDjxiQFpxRCsiIqIf5ZqrvcDnVI93OGL7pKRXgGnbk8Bh4F1Jp6mOZO1eu08cXaVRat4lPdtz/j6idZLh6ILkOEbJSDVaEREREcOUa7QiIiIiGtLqRkvSTZK+lDQr6aSk58v4RklTkk6Vn2NlfFzSd5L+lrR/Sa1dkn6VdFrSS2uxPzF6amT4UUk/SjohaVrS9p5aE2X7U5ImLvc3IwatRo53SLpYcnxC0ss9tTIXR6e0+tShpK3AVtszkq4DjgOPAc8Ac7ZfK1/UMdsHJG0CtpVtLth+vdS5CvgNeJDqdt9jwFO2fx76TsVIqZHha4E/bVvSncBR2+OSNgLTwH1UzwE6Dtxr+8Ja7FeMlho53gHst/3IkjqZi6NzWn1Ey/YZ2zPl9R/ALNWTfnuXVXiH6guP7XO2jwH/LCm1mqUaIgauRoYXem4/v4ZLD1d8CJiyPVeaqylg13D2IkZdvzm+gszF0TmtbrR6SboFuBv4Hths+wxUEwCwaYW3L7dUw42D/5QRl7faDEt6XNIvwCfAnjKcDMe60Mdc/ICkHyR9JumOMpYcR+d0otEqp1PeB/bZnq9TYpmx9p5TjdbpJ8O2P7Q9TnV04NXFEsttOthPGXFlfeR4Bthm+y7gLeCjxRLLbJscR6u1vtGStIHqi/2e7Q/K8NlyzcDitQPnViizmqUaIhpRN8O2vwJulXQDyXCssX5ybHve9kJ5/SmwITmOrmp1oyVJVE/2nbX9Rs+vepdVmAA+XqHUapZqiBi4fjMs6bbyHiTdA1wNnKd6+vVOSWPlzq6dZSyicTVyvKUnx/dT/S86T+bi6KC233W4Hfga+An4rwwfpLo24ChwM/A78KTtOUlbqO7Mur5svwDcbnte0sPAm1xaquHQUHcmRlKNDB8Anqa6oeMv4EXb35Rae8p7AQ7ZfntoOxIjrUaO9wLPAf9S5fgF29+WWpmLo1Na3WhFRERErGetPnUYERERsZ6l0YqIiIhoSBqtiIiIiIak0YqIiIhoSBqtiIiIiIak0YqIiIhoSBqtiIiIiIak0YqIiIhoyP/WsOYZ47WoPwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d94073ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d94075ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d940b09b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, stats=['EROI w/o GW adj', 'mix'], to_plot=True, extend_wo_GW = True)\n",
+    "for scenario_fig in scenarios+scenarios_dlr:\n",
+    "    if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "    else: tot_name = 'Total (PWh/a)'\n",
+    "    EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                                   'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "    InteractiveShell.ast_node_interactivity = 'last'\n",
+    "    colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "    fig = plt.figure(figsize=(7,5)) # 7,5\n",
+    "    _ = ax = fig.add_subplot(1, 1, 1)\n",
+    "    _ = ax.plot(years, EROIs_mixes[[(scenario_fig, year, 'EROI w/o GW adj') for year in years]].loc[tot_name], \\\n",
+    "            label='EROI w/o GW adj', color='black', zorder=20, linewidth=3)\n",
+    "    _ = ax.set_ylabel('Quality-adjusted EROI')\n",
+    "    _ = ax.set_ylim([11, 21]) # 5, 10; 13, 18; 11.5, 17.5\n",
+    "    # ax.set_zorder(1)\n",
+    "    _ = ax2 = ax.twinx()\n",
+    "    _ = ax2.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[(scenario_fig, year, 'mix') for year in years]].iloc[:15], \\\n",
+    "                  labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "    _ = ax2.set_title(scenario_fig)\n",
+    "    _ = plt.ylabel('Share in the mix')\n",
+    "    _ = plt.xlabel(scenario_fig+' scenario')\n",
+    "    _ = xticks(years)\n",
+    "    _ = handles, labels = ax2.get_legend_handles_labels()\n",
+    "    _ = ax2.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.1,0.5)) \n",
+    "    _ = ax.legend(loc='center left', bbox_to_anchor=(1.1,0.97))\n",
+    "    _ = ax.set_zorder(ax2.get_zorder()+1)\n",
+    "    _ = ax.patch.set_visible(False)\n",
+    "    _ = fig.savefig('Evolution of EROI adj wo GW in '+scenario_fig+'.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3d95a71ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, stats=['EROI w/o GW adj', 'mix'], to_plot=True, extend_wo_GW = True)\n",
+    "# f, axarr = plt.subplots(2, sharex=True)\n",
+    "# axarr[0].plot(x, y)\n",
+    "# axarr[0].set_title('Sharing X axis')\n",
+    "# axarr[1].scatter(x, y)\n",
+    "if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "else: tot_name = 'Total (PWh/a)'\n",
+    "EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                               'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "InteractiveShell.ast_node_interactivity = 'last'\n",
+    "colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "fig = plt.figure(figsize=(10,4)) # 7,5\n",
+    "_ = ylims = [11, 21] # 5, 10; 13, 18\n",
+    "\n",
+    "ax1 = fig.add_subplot(1, 3, 1)\n",
+    "_ = ax1.plot(years, EROIs_mixes[[('BL', year, 'EROI w/o GW adj') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI w/o GW adj', color='black', zorder=20, linewidth=3)\n",
+    "_ = ax1.set_ylabel('Quality-adjusted EROI')\n",
+    "_ = ax1.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "_ = ax12 = ax1.twinx()\n",
+    "_ = ax12.stackplot(years, EROIs_mixes.fillna(0)[[('BL', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax1.set_title('BL (business as usual)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax12.get_legend_handles_labels()\n",
+    "_ = ax1.set_zorder(ax12.get_zorder()+1)\n",
+    "_ = ax1.patch.set_visible(False)\n",
+    "# ax12.get_yaxis().set_visible(False)\n",
+    "_ = ax12.tick_params(labelright=False)\n",
+    "\n",
+    "ax2 = fig.add_subplot(132)\n",
+    "_ = ax2.plot(years, EROIs_mixes[[('BM', year, 'EROI w/o GW adj') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI w/o GW adj', color='black', zorder=20, linewidth=3)\n",
+    "# ax2.set_ylabel('EROI')\n",
+    "_ = ax2.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax22 = ax2.twinx()\n",
+    "_ = ax22.stackplot(years, EROIs_mixes.fillna(0)[[('BM', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax2.set_title('BM (+2°C)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax22.get_legend_handles_labels()\n",
+    "_ = ax2.set_zorder(ax22.get_zorder()+1)\n",
+    "_ = ax2.patch.set_visible(False)\n",
+    "_ = ax2.tick_params(labelleft=False)\n",
+    "_ = ax22.tick_params(labelright=False)\n",
+    "# ax2.get_yaxis().set_visible(False)\n",
+    "# ax22.get_yaxis().set_visible(False)\n",
+    "\n",
+    "ax3 = fig.add_subplot(133)\n",
+    "_ = ax3.plot(years, EROIs_mixes[[('ADV', year, 'EROI w/o GW adj') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI w/o GW adj', color='black', zorder=20, linewidth=3)\n",
+    "# ax3.set_ylabel('EROI')\n",
+    "_ = ax3.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax32 = ax3.twinx()\n",
+    "_ = ax32.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[('ADV', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax3.set_title('ADV (100% renewable)') # ER (+2°C wo CCS nor nuclear)\n",
+    "_ = plt.ylabel('Share in the mix')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax32.get_legend_handles_labels()\n",
+    "_ = ax32.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.25,0.45)) # (1.1,0.5)\n",
+    "_ = ax3.legend(loc='center left', bbox_to_anchor=(1.25,1.03)) # (1.1,0.97)\n",
+    "_ = ax3.set_zorder(ax32.get_zorder()+1)\n",
+    "_ = ax3.patch.set_visible(False)\n",
+    "# ax3.get_yaxis().set_visible(False)\n",
+    "_ = ax3.tick_params(labelleft=False)\n",
+    "_ = plt.subplots_adjust(wspace=0.08, hspace=0.08)\n",
+    "\n",
+    "_ = fig.savefig('Evolution of EROIs adj wo GW IEA-ADV.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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+++ b/Evolution of EROI/Evolution of EROIs.ipynb	
@@ -0,0 +1,2100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "\n",
+       "<i>The raw code for this IPython notebook is by default hidden for easier reading.\n",
+       "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.</i>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "\n",
+    "<i>The raw code for this IPython notebook is by default hidden for easier reading.\n",
+    "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.</i>''')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evolution of EROIs Until 2050\n",
+    "\n",
+    "## _by Adrien Fabre_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll examine one key aspect of the sustainability of a 100% renewable energy mix: its technical possibility. It may sounds ridiculous, but the fact that renewable technologies deliver more energy than they consume has never been checked properly. First, I'll explain you the problem that could arise; second, I'll go into the data to see if we're screwed or not.\n",
+    "\n",
+    "_I do not cite references in the text, but provide them at the end. The [publication](https://www.sciencedirect.com/science/article/pii/S0921800918320718) and its [presentation](https://drive.google.com/open?id=1NaHAZZbjRtzTKs7V0HWrNVXppZLtz9-N) are on-line._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## I. The EROI of a Technology Is Not Intrinsic\n",
+    "\n",
+    "_I try to be pedagogical here, for people not familiar with Input-Output analysis. If this is still too complicated to understand, you can read only the bold contents of part I, and jump to the results in part II._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### I.1. A Simple Model With A Unique Energy Technology\n",
+    "\n",
+    "The coefficient $a_{i,j}$ of technology matrix A represents the quantity of input $i$ required to produce one unit of output $j$.\n",
+    "\n",
+    "$$\\overset{A=\\left(a_{\\text{in, out}}\\right)=\\begin{pmatrix}a_{1,1} & \\cdots & a_{1,n}\\\\\n",
+    "\\vdots & \\ddots & \\vdots\\\\\n",
+    "a_{n,1} & \\ldots & a_{n,n}\n",
+    "\\end{pmatrix}\\leftarrow\\text{ inputs}}{\\underset{\\text{outputs}}{\\downarrow}}$$\n",
+    "\n",
+    "Below is an illustrative technology matrix with three inputs (and the same three outputs): an energy technology, materials, and energy. Let $m_e$ be the quantity of materials required to produce one unit of energy.\n",
+    "\n",
+    "$$A=\\begin{pmatrix}0 & 0 & 1\\\\\n",
+    "m_{e} & m_{m} & 0\\\\\n",
+    "E_{e} & E_{m} & 0\n",
+    "\\end{pmatrix}\\begin{array}{c}\n",
+    "\\text{energy technology}\\\\\n",
+    "\\text{materials}\\\\\n",
+    "\\text{energy}\n",
+    "\\end{array}$$\n",
+    "\n",
+    "One can check that $m_e$ corresponds to the definition above, and one can find the meaning of other coefficients (_hint: the 1 on the top-right corner means that one unit of energy technology is required to produce one unit of energy_)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$$\\left[\\begin{matrix}0 & 0 & 1\\\\m_{e} & 0.2 & 0\\\\0.1 & 0.5 & 0\\end{matrix}\\right]$$"
+      ],
+      "text/plain": [
+       "⎡ 0    0   1⎤\n",
+       "⎢           ⎥\n",
+       "⎢mₑ   0.2  0⎥\n",
+       "⎢           ⎥\n",
+       "⎣0.1  0.5  0⎦"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "import sympy as sym\n",
+    "from pylab import *\n",
+    "sym.init_printing()\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "m_e, e_e, m_m, e_m = sym.symbols('m_e e_e m_m e_m') # i_j: units of i required for 1 unit of j\n",
+    "unit_energy_output = sym.Matrix([0, 0, 1])\n",
+    "unit_tech_output = sym.Matrix([1, 0, 0])\n",
+    "# A = sym.Matrix([[0, 0, 1], [m_e, m_m, 0], [e_e, e_m, 0]]) # energy technology, material, energy\n",
+    "A = sym.Matrix([[0, 0, 1], [m_e, 0.2, 0], [0.1, 0.5, 0]]) # energy technology, material, energy\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can compute the system-wide EROI of the economy represented by the technology matrix above.\n",
+    "\n",
+    "The system-wide **EROI, or Energy Returned On Invested, is the ratio between the energy delivered by the system, and the energy required to build, operate, maintain and dismantle it.** In other words, it is the inverse of the amount of energy required to produce one unit of energy, when the series of all embodied inputs are taken into account."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "After some algebra (<a href=\"javascript:code_toggle()\">toggle code</a> below to see it), we find the formula for the EROI:"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML('''After some algebra (<a href=\"javascript:code_toggle()\">toggle code</a> below to see it), we find the formula for the EROI:''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$$\\frac{- 0.5 m_{e} + 0.72}{0.5 m_{e} + 0.08}$$"
+      ],
+      "text/plain": [
+       "-0.5⋅mₑ + 0.72\n",
+       "──────────────\n",
+       "0.5⋅mₑ + 0.08 "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L = sym.eye(3)-A\n",
+    "Linv = L.inv()\n",
+    "EROI = sym.simplify(1/unit_energy_output.transpose().dot((Linv-sym.eye(3)).dot(unit_energy_output)))\n",
+    "EROI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unsurprisingly, one can see in the Figure below that the EROI decreases with the material intensity of the energy technology, because extracting and processing material requires energy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yfoXCOmaOdj/Q76jdD/zh6EWu7/C/DdxtjDkBOAnYlKsdBUuCk1r+/KUzae8d4Ik8NbUwWb9CYB0zR7sf6HfU7gf+cPQiZwFfRGLA2cAtAMaYuDEmZ89B1eHqSS1/znHTCZeW5K1YZ7J+hcA6Zo52P9DvqN0P/OHoRS7v8OcDB4Efi8haEblZRHLWTUxH/+RawYyEApy9aDr3btifl05RJutXCKxj5mj3A/2O2v3AH45e5PK5JAicAlxtjHlcRL4NXAN8NnUhEbkSuBKgYU4DXfEuegd66R5wOiqpjdTSn+inK94FQE24hoRJDGd48kq7rXUb8UScWFmMgARo7XM+jIiGopQFyoY7Ha4orSBSGqG5p5lT5wW5d2Mfj2/fR8O0OAZDOBgmGorS2ttKwiQIBULEymK09bUxODRIsCRIdbiajv4O4ok4AQlQE6mhK95F32AfglBXXuekY7AXgLryOvZ27iWeiE8qTckXQ5NJEzhNt0ZDUZp7mieVpp3tOyeVpqkcp0zTlDzOuTpOmaapuad5+Djn6jhlmqZ9XfuGHbWce6lp6ujvoERK1J17qWlq7W0lMZRQc+6lixiTm7tbEZkJrDLGNLrjZwHXGGNeN9Y6K1asMKtXr57S/na272Ru1dxJrdPWE+fU6//GlWfP598uPGFK+02XqfjlG+uYOdr9QL+jdj/Q5Sgia4wxK9JZNmdFOsaYfcBOETnenXQesDFX+6uJ1Ex6neryEGfMn5aXcvyp+OUb65g52v1Av6N2P/CHoxe5rqVzNXCbiDwDnAx8OVc7Sj76TJYLls5k28FuthzozLLRSKbql0+sY+Zo9wP9jtr9wB+OXuQ04Btj1hljVhhjlhlj3myMyVmLQ32DU+vU5PwlThv5uW4yeap++cQ6Zo52P9DvqN0P/OHoRdF8aSvIlNabWRXmpLnVOS/WmapfPrGOmaPdD/Q7avcDfzh6UTQBv668bsrrXrC0nmd2tbOnrTeLRiPJxC9fWMfM0e4H+h21+4E/HL0omoCfSZlasuvDe3N4l++HMj/rmDna/UC/o3Y/8IejF0UT8JP1WafCgulRFs6I5rQcPxO/fGEdM0e7H+h31O4H/nD0omgCfqZcsLSeJ7YforU7XmgVi8ViyQlFE/AzLVO7YOlMEkOGv23KzV2+H8r8rGPmaPcD/Y7a/cAfjl4UTcDvHcjsEevE2VU0VIVzVqyTqV8+sI6Zo90P9Dtq9wN/OHpRNAE/2QbFVBERzl86k0deOEh3f/bbus7ULx9Yx8zR7gf6HbX7gT8cvSiagJ8Nzl9aT//gEA9tzl/XhxaLxZIviibg10ZqM97G6Y3TqCnPTdeH2fDLNdYxc7T7gX5H7X7gD0cviibg9yf6M95GMFDCeYvr+ftzB4gPZrfrw2z45RrrmDna/UC/o3Y/8IejF0UT8LP1IcQFS2fS2TfIY9tasrK9JH74UMM6Zo52P9DvqN0P/OHoRdEE/Gxx1qI6ykOBvHV9aLFYLPmiaAJ+TTg77VOHSwO88oQZ/OXZvfTEs1dbJ1t+ucQ6Zo52P9DvqN0P/OHoRdEE/IRJZG1b73/5PNp6BvjVEzuzts1s+uUK65g52v1Av6N2P/CHoxdFE/Cz2anwqcfWcPq8adz8yDYGEtl5eeuHTo+tY+Zo9wP9jtr9wB+OXhRNwM82HzpnAXva+7hz3Z5Cq1gsFktWKJqAn+zFPVuce/x0TphZyU0PbWVoKPOO3rPtlwusY+Zo9wP9jtr9wB+OXhRNwM82IsKHzl3ACwe6+PtzBwqtY7FYLBkzbsAXkWdF5BmP37Nux+RqaOtry/o2X3fiLObURLjxwS0Yk9ldfi78so11zBztfqDfUbsf+MPRi+AE81+fFwulBAMlXHn2fK774wae3N7K6fOmFVrJYrFYpsy4d/jGmJeMMS+5y50INAGBlOlqiJXFcrLdt586l2kVIW56aGtG28mVXzaxjpmj3Q/0O2r3A384ejFRkU5MRH4D3A+8H/gA8DcR+a2IqEpxQAI52W4kFOB9Zzby9+cO8Ny+qVfFypVfNrGOmaPdD/Q7avcDfzh6MdFL2/8BNgILjTFvNca8BVgAPAt8N9dyk6G1rzVn275sZSMVoQDff2jblLeRS79sYR0zR7sf6HfU7gf+cPRiooD/cmPM540xw18fGYcvACtzq6aHqvJS3nH6Mdz59B52HuoptI7FYrFMiYkCvuTFIgtEQ9Gcbv+Ks+ZRInDzI1O7y8+1Xzawjpmj3Q/0O2r3A384ejFRwP+HiFwnIiMCv4h8FliVO63JUxYoy+n2Z1VFeMvy2fx69U5auibfFnau/bKBdcwc7X6g31G7H/jD0YuJAv7VOLVztojI7SLyOxHZCpzkzlNDS29226/34sqzF9A/OMStj26f9Lr58MsU65g52v1Av6N2P/CHoxcTVcvsMMa8HTgf+AnwU+B8Y8zFxhh/fnmQAQtnRDl/ST23PvZSTjo6t1gsllySVtMKxpitxpi7jDF3GmO2isjxIvLDXMtNhorSirzs56pzFtDeO8Avn9gxqfXy5ZcJ1jFztPuBfkftfuAPRy8mqoe/TETuFZH1InK9iNSLyO049fI35kcxPSKlkbzsZ/kxNZwxfxo3P/LipPq9zZdfJljHzNHuB/odtfuBPxy9mOgO/4fAL4C3AQeBp4BtOPXy/zvHbpOiuac5b/v60LkL2dfRxx/X7U57nXz6TRXrmDna/UC/o3Y/8IejFxMF/DJjzE+MMc8bY74NDAHXGGP68uCmlrMX1bFkVixrTSdbLBZLPpgo4IdFZLmInCIipwBdwLKUcTVEgvl7xBIRrjp3AVsPdnPfpv1prZNPv6liHTNHux/od9TuB/5w9GKi1jL3Ad8cY9wAr8qF1FTI94cQFzXN5OvTItz44FbOX1LPqE8VjsAPH2pYx8zR7gf6HbX7gT8cvZioWua5xphXjvFLK9iLSEBE1orIn7Kj7E2+y9SCgRKufuUint7Zxm9X75pweT+U+VnHzNHuB/odtfuBPxy9mKiWzmdSht8+at6X09zHx4FNk1ebHIb8l6VffOocXjZvGl/880YOdIz/WqMQfpPFOmaOdj/Q76jdD/zh6MVEZfiXpgxfO2rehRNtXETmAK8Dbp6k16QJB8O53sURlJQIN7xtGfHBIa7744Zxly2E32Sxjpmj3Q/0O2r3A384ejGZxtNGF1Kn07Dat4DP4NTuySmFKlObV1fBJ19zHHdv2Mdfn9075nJ+KPOzjpmj3Q/0O2r3A384ejHRS1szxrDX+AhE5PXAAWPMGhE5d5zlrgSuBGiY00BXvIvegV66B7oBqI3U0p/opyveBUBNuIaESdDR73RGkuw9fuOBjTTEGoiVxQhIYLi96mgoSlmgbLjti4rSCiKlkeEyuEgwQjQUpbmnGYMhHAwTDUVp7W0lYRKEAiFiZTHa+toYHBokWBKkOlxNR38H8UScgAS49PQZ/H7tDv79D88wp66H+bUznXQM9gJQV17HlpYtVEeqJ5WmZL+Z+UrTpoObmF4xnYAEqInU0BXvom+wD0GoK687Ik1TOU6ZpunZ/c8yq3LWlI5TPtK0t3MvFaGKvJ17U0nTttZtVIWrVJ17qWnqjndTHalWd+6lpqmtt42KUIWacy9dZLzOuUUkAXTj3M1HgGRj8AKEjTGl46z7FeA9wCAQBmLAHcaYd4+1zooVK8zq1asnlYAk29u201jdOKV1s8GGPe288bv/4K3LZ/P1t590xPxC+6WDdcwc7X6g31G7H+hyFJE1xpgV6Sw7US2dgDEmZoypNMYE3eHk+JjB3l33WmPMHGNMI867gL+PF+wzJRQI5WrTabG0oYqrzpnPb9fs4pEXDh4xv9B+6WAdM0e7H+h31O4H/nD0Iq3G0/yAhk6Fr37VIuZPr+DaO56lJz6yNU0NfhNhHTNHux/od9TuB/5w9CIvAd8Y86Ax5vW53Mdky7JyQbg0wFfftoxdrb18457NI+Zp8JsI65g52v1Av6N2P/CHoxdFc4c/OKSjffrTGqdx2cpj+fGjL/LUjsMdHWvxGw/rmDna/UC/o3Y/8IejF0UT8IMlE1U4yh+fufAEZsXC/NvvnqF/MAHo8hsL65g52v1Av6N2P/CHoxdFE/CT1ZQ0EC0L8qW3nMgLB7q48YGtgC6/sbCOmaPdD/Q7avcDfzh6UTQBP1k/VQuvPGEGbz65gRsf3MLz+zrV+XlhHTNHux/od9TuB/5w9KJoAn48ES+0whFc94alVIZL+cztz9A70F9onQnRmIej0e6o3Q/0O2r3A384elE0AT8ggUIrHMG0ihCff+NSnt7Zxu/XtE68QoHRmIej0e6o3Q/0O2r3A384elE0Ab8mUlNoBU/esGwW550wg1se3s+mvbofA7XmYSraHbX7gX5H7X7gD0cviibgJ9uc0IaI8JW3nkg0HOCKnzw5YTPKhURrHqai3VG7H+h31O4H/nD0omgCft+g3kA6IxbmyxfPpa13gCtuXX3EV7ha0JyHSbQ7avcD/Y7a/cAfjl4UTcCXtFprLhzH1ZfznXcsZ8Oedj7xq3UkFHZ+rj0PQb+jdj/Q76jdD/zh6EXRBPy68rpCK4xLXXkd5y2u57OvX8K9G/dzw19z3gnYpNGeh6DfUbsf6HfU7gf+cPSiaAK+9jK1pN/7Xj6P9648lh8+8iK3Pf5Sga1Goj0PQb+jdj/Q76jdD/zh6IU/vw/2INmRgFZS/T77+iXsONTDdX/cwJyacs45bnoBzQ6jPQ9Bv6N2P9DvqN0P/OHoRdHc4fuJYKCE77zzFI6rr+Qjtz3F8/s6C61ksViOAoom4GsvUxvtFy0L8qPLV1AeCvD+nzzJgc7Cv/XXnoeg31G7H+h31O4H/nD0omgCfu+A7kcsL79ZVRFuee9pHOqO88+3rqY3niiA2WG05yHod9TuB/odtfuBPxy9KJqAn+z8Vytj+Z04p4pvX3oyz+xu51O/WcdQAatras9D0O+o3Q/0O2r3A384elE0Ad/PnL90Jv9x0WL+un4fX73nuULrWCyWIqVoaunURmoLrTAuE/ld8Yp5bG/p5vsPbaMiFOTqVy1EJL8fd2jPQ9DvqN0P9Dtq9wN/OHpRNHf4/QndzQ9P5CcifP4NS3nrKbP55n2b+fydG/JevKM9D0G/o3Y/0O+o3Q/84ehF0QR87R9CpOMXDJTwjYtP4p/Pmsetj73Ex3+9jvjgUB7sHLTnIeh31O4H+h21+4E/HL0omiKdYqGkRPiP1y2hLlrGV/76HG09cW5696lUlNlDZbFYMqNo7vBrwrrbp56s3wfPWcDXLl7GP7Y0886bH+dQd+572NGeh6DfUbsf6HfU7gf+cPSiaAJ+whS2DvtETMXvn1bM5fvvWcFzezt4+02Psrstt3V/tech6HfU7gf6HbX7gT8cvSiagK+9U+Gp+r1mST0/ff/pHOjs5+LvPcqWA7lrhkF7HoJ+R+1+oN9Rux/4w9GLogn4xczL5tfy6ytXMjhkuPimx1i7Q3//uBaLRR9FE/Crw9WFVhiXTP2WNMS4/aozqYqU8s4fPs5Dmw9myeww2vMQ9Dtq9wP9jtr9wB+OXhRNwD8aOKa2nN9etZJ5dRVc8ZMn+fmqlzBGX89ZFotFJ0UT8Nv62gqtMC7Z8ptRGeZXHzyDly+s4z//sJ6P/OIp2nsHsrJt7XkI+h21+4F+R+1+4A9HL4om4B9NxMKl/Pjy07j2tSdw74b9XPTtR3jKlutbLJYJKJqAHyuLFVphXLLtV1IifPCcBfz2qpWIwNtveozvPbg1o+YYtOch6HfU7gf6HbX7gT8cvSiagB+QQKEVxiVXfsuPqeHPHzuLC5fO5Kt3P8d7f/wEBzun1s6H9jwE/Y7a/UC/o3Y/8IejF0UT8Fv7dBdp5NKvKlLKd9+5nC+/5USeePEQr/32I/zfC82T3o72PAT9jtr9QL+jdj/wh6MXRRPwj3ZEhHe+7Bju/OgrqCkv5T0/epyv3f0cA4n8Nb5msVh0UzQBPxqKFlphXPLld/zMSu786Cu49LS53PjgVi75/mPsaOlJa13teQj6HbX7gX5H7X7gD0cvchbwRWSuiDwgIptEZIOIfDxX+wIoC5TlcvMZk0/H2XOvAAAeUElEQVS/SCjAV966jO+8Yzkv7O/iNf/9EP9z/wv0DYzf/of2PAT9jtr9QL+jdj/wh6MXubzDHwQ+bYxZDJwBfEREluRqZy29LbnadFYohN8bTmrg3k+dzasX1/PN+zZz4bceHvcLXe15CPodtfuBfkftfuAPRy9yFvCNMXuNMU+5w53AJmB2rvZn8WZWVYT/fdcp/PT9p1Miwnt/9ARX/WwNe3Lc8qbFYtFHXnrVEJFGYDnwuMe8K4ErARrmNNAV76J3oHe4V/jaSC39if7hHmZqwjUkTGK4tbpkmxYtPc4VN1YWIyCB4bfo0VCUskDZ8BW5orSCSGmE5h6nFkskGCEaitLc04zBEA6GiYaitPa2kjAJQoEQsbIYbX1tDA4NEiwJUh2upqO/g3giTkAC1ERq6Ip30TfYhyDUldc56Rh0gmpdeR29A71sb9s+qTQlv+bLRppOaYzyw/fN45ePH+Bnj+7noc0HeO8r6nnbimoipSGqw9V09neyvW37pNI02eOUaZoO9RwCyOlxyiRNiaHE8HHWdO6lpqlvoG/YMR/n3mTTZIxhX9c+dedeapqMMexs36nm3EsXyXVbLCISBR4CvmSMuWO8ZVesWGFWr149pf10xbtUv0jR5LertYcv3LWRezfuZ8H0Cr74pibOXFinynEstDtq9wP9jtr9QJejiKwxxqxIZ9mc1tIRkVLgduC2iYJ9piSvxlrR5DenppwfXLaCH19+GgMJwztvfpyrf7mWTfv3FlptQjTloxfa/UC/o3Y/8IejFzkr0hERAW4BNhljvpmr/VimzitPmMHKBbXc9NBWbnxwK3evN7z7jDgfOmcBM2LhQutZLJYsk8s7/JcD7wFeJSLr3N9FudpZJBjJ1aazgla/cGmAT7z6OO7/1Dlc2FTLTx97ibO+9gBf/NPGKTfRkEu05mMS7X6g31G7H/jD0Yucl+FPhkzK8Lvj3VSEKrJslD20+4HjeLADvvP3Lfx+7S5CwRIuW9nIlWfPpy6qo96x9nzU7gf6HbX7gS5HNWX4+UR7mZp2P3AcG+sq+K9/Oon7P30uFzXN4uZHtnHWVx/gK3/dxKHueKEV1eejdj/Q76jdD/zh6EXRBHyDnicVL7T7wUjHeXUVfPOSk7nvU+dwwdJ6fvDwNl7x1b/ztbufo7WAgV97Pmr3A/2O2v3AH45eFE3ADwd1v2TU7gfejgumR/nWpcu575Nnc97ier730FbOvOHvXHvHszy/r1OFoya0+4F+R+1+4A9HL/Ly4VU+0FIndiy0+8H4jgtnVPKddyzn6lct5Ef/9yJ3PLWLXz6xgzMX1PLeMxt59eJ6AiVSUEcNaPcD/Y7a/cAfjl4UzR1+a6/u9qm1+0F6jsfVV3LD25ax6trz+LcLT2B7czcf/Nkazvn6A/zg4a2092Snf91MHAuJdj/Q76jdD/zh6EXRBPyEGb8lyEKj3Q8m51hTEeJD5y7g4c+8kpvefQqzqyN8+S/PccZX7ufff/8sm/fnprhHez5q9wP9jtr9wB+OXhRNkU4oECq0wrho94OpOQYDJVzYNIsLm2axcU8Htz66ndvX7OIXj+/gjPnTuPjUuby2aSYVZdk51bTno3Y/0O+o3Q/84ehF0dTD19S2hRfa/SB7jq3dcX755A5+9cROdhzqIVIa4LVNM3nrKXNYuaA2o7J+7fmo3Q/0O2r3A12Ok6mHXzR3+G19bWoOgBfa/SB7jjUVIT587kI+dM4C1rzUyu1P7eZPz+zhjrW7mVUV5s3LZ/O2U2azcEZlwRxzhXY/0O+o3Q/84ehF0QT8waHBQiuMi3Y/yL6jiLCicRorGqfxuTcs4W+b9nPHU7v5wcPb+N6DW1k2p4q3Lp/NG0+ezbSK9B6Rteejdj/Q76jdD/zh6EXRBPxgie6kaPeD3DqGSwO8flkDr1/WwMHOfu58eg+3r9nF5+/ayBf/vImV82u5oGkmFyypH7fhNu35qN0P9Dtq9wN/OHphy/DzhHY/KIzjpr0d3Pn0Hu5Zv49tzd2IwCnH1HDh0plcsHQmx9SWF9xxMmj3A/2O2v1Al+NRWYbf0d+h5gB4od0PCuO4eFaMxbNifOaC49lyoIu71+/j7g37+NJfNvGlv2xi8awYFy6dyYVNMzmuPqo+H7X7gX5H7X7gD0cviibgxxOFb9hrPLT7QWEdRYRF9ZUsqq/k6vMWsfNQD/ds2Mc9G/bxrfs3899/20xjbTmnzgvzhhODnDG/lnBpoGC+Y2GPc+Zo9wN/OHpRNAE/IPr++VPR7ge6HOdOK+cDZ83nA2fN50BnH3/beIB7N+7jrnXN3L76SULBEl42bxrnHDedc4+fzoLpUZw+dwqLpjwcC+2O2v3AH45e2DL8PKHdD/zh2NzdzsbdcR7afJCHNh9kywGnk+eGqjDnHD+dc46bzpkL64iFSwvi54c81O6o3Q90OU6mDF9VwF+2bJl59NFH6e3tpbvb7b29tpb+/n66utze22tqSCQSdHS4vbdXO723v7D7BaZXTCcWixEIBGhtdXukj0YpKyujpcXtkb6igkgkQnOz2yN9JEI0GqW5uRljDOFwmGg0SmtrK4lEglAoRCwWo62tjcHBQYLBINXV1XR0dBCPxwkEAtTU1NDV1UVfXx8iQl1dHV1dXfT2uj3S19Wx/eB2ohKdVJra2pwe6fOVpi17t1BdWp12mqZynDJN08aXNjK9fPpwmjZt38tjWw7w5I5O1uzto6O9nRKGWNxQzcolc1laF2JxfYSq8nBe0tTc20xoIKTq3Budph0tO6igQtW5l5qmeGmcCirUnXupaeqmm9BAKKfHKd00zZs3z58BP5M7/O1t22msbsyuUBbR7gf+dxxIDLFuZxsPPX+QVdtaeHpXGwMJQ4lA0+wqXjZvGmfMr2VF4zSqIrl5AvB7HmpAux/ocjwqa+kIhS+/HQ/tfuB/x9JACac1TuO0xmkA9MYTrN3RyqptLax68RC3PvoSP3zkRURgyawYZ8yv5bTGGpYfU0N9ljpt93seakC7H/jD0YuiucPX1MekF9r9oPgd+wYSrN3RxuMvtrBqWwtrd7TRPzgEwOzqCCcfU83yudWccmwNSxtilAUn/2Ku2PMwH2j3A12OR+Udfle8S80B8EK7HxS/Y7g0wMoFtaxcUAtA/2CCjXs6eGpHG2t3tLJ2Rxt/fmYvAKFACUtnx1g+t4ZTjq3mpDnVzKmJTFgTqNjzMB9o9wN/OHpRNAG/d7C30Arjot0Pjj7HsmCA5cc4RTowD4D9HX3Dwf+pHa3c9vhL/OgfLwJQXV5KU0MVTbOraJod48TZVRwzrXzEReBoy8NcoN0P/OHoRdEEfIslG9THwsPt+4PzInjT3g6e2dXO+t3trN/Tzi3/t42BhFMUWhkOuheBGE2zq6iu7GdO5RDBQNH0LWQpIoom4NeV1xVaYVy0+4F19KI0UMKyOdUsm1M9PK1/MMEL+7t4drd7Edjdzq2PvUTcfR9QFtzGovooJ8yMccLMSk6YGeP4mZVMryzLq/tYaD/O2v3AH45eFE3A7x3oVfMhhBfa/cA6pktZMOAW61QNTxtIDPHC/i6e2L6b3a2G5/Z18tDmg/xuza7hZeqiIY5PuQAsnBFl4Yxo3j8S05CH46HdD/zh6EXRBPzugW6mM73QGmOi3Q+sYyaUBkpY0hCjvPzQiPrZLV39PL+vk037Onl+XwfP7evk56teGq4dBDCjsoxF9VEWTncuAAtmRFk0o5K6aCgnzUVozcMk2v3AH45eFE3At1g0Uhst48yFZZy58HARQGLIsONQD1sOdKX8Ovndml10xw93jl0VKWXhjCjz6ipG/BprK4iE/NmWi6WwFE3Ar43UFlphXLT7gXXMBun4BUpkOHi/Zkn98HRjDPs6+thyoIsX9nex5aBzMXh4VNEQwKyqsBP86yqY714Ejq0tZ+608glbES2GPCw0fnD0omgCfn+in0om30dqvtDuB9YxG2TiJyLMqoowqyrCWYtGFhd09Q+yvbmb7S3dvHiwmxebu3mxpZu/PLuXtp6BEcvWx8o4Zlo5x0yrcP7WRoaH66Khos7DfOEHRy+KJuB3xbtUvznX7gfWMRvkyi9aFjziRXGS1u44L7Z0s/NQDztaethxyPk9urWZO9b2kfoxfXkowIxYkHm1MWbXRJhdXc6cmgizayLMqY5QFy2jpKSwzQZoP8bgD0cviibgWyxHKzUVIWoqQpxyTM0R8/oGEuxu63UuAi09vNTSwwsHmznY1c/anW1HPB2EgiXMro4wuzpCQ3WYWVVH/q0os2HDrxTNkasJH3mya0K7H1jHbKDNL1waYMH0KAumH65C2N7XTlXYeVLo6h9kd2svu1p72N3Wy67W3uHxB57v5GBn/xHbjIWDNFRHmFUVZlZ1hFmxMPVVYWbGwsysClNfGSYWCU65hpG2PPTCD45eFE3AT5jExAsVEO1+YB2zgXY/GOkYLQty/MxKjp/pXR4dHxxif0cfe9p62dvex572Xva29bG3vZc9bX2s29lG66inBIBwaQkzY2Hq3d/MqjAzKsuYEXP/usNRj6cFv+WhnyiagN/R38G0yLRCa4yJdj+wjtlAux9MzjEULGHuNKf2z1j0DSQ40NHPvo4+9nX0caCjj33tzvD+DueisG9D3/CXyKmUhwJMT14AKsNMryyjtLSHRXXd1FWGqIuWURctozYamlLrpbnCD8fZi5wGfBG5EPg2EABuNsbckMv9WSyW/BMuDXBMbTnH1I59UTDG0N47wIHOfg529nOgs48DHf0c6HR/HX1s2tfBw5v76ewfBA4csY1YOEhdpXMBmB4toy4aojZaxrSKEHXRENMqnAtDbUWIqkipij6OtZGzgC8iAeB/gdcAu4AnReROY8zGXOyvOlw98UIFRLsfWMdsoN0PCuMoIlSXh6guD3Fc/fjVGfd2tDA4GOFgVz/Nnf00d8Vp7uo//OuMs2lvBwe7+unsG/TcRrBEqKlwgn+tezGYVl5KTUWIaRUhaspH/q0uL53w+4VU/HCcvcjlHf7pwBZjzDYAEfkV8CYgJwHfYrEUB5FQgOrY+MVISfoHE7R2D9DS3U9LV5xD3c7F4VB3cjhOS3c/e9raOdQdp733yPcNSSpCAfeiVErNqL/V5SFq3PGq8lICgT6GqsuJhYO+ahk1lwF/NrAzZXwX8LJc7aytr031VVe7H1jHbKDdD/Q7TsavLBhgZlWAmVXpdVE5mBiirXeAVveC0NoT51D3AK09cVq64rT1xJ35PXF2t/UOj4/XMWBlWZCq8lKqIqVUu3+rIiH37+FfLBJ0/oad8coCXCxyGfC9CtCOyDYRuRK4EqBhTgNd8S56B3rpHnB7b4/U0p/opyvu9t4eriFhEnT0u723uyfGrg7n0/NYWYyABGjtc3ukD0UpC5TR0uv2SF9aQaQ0QnOP2yN9MEI0FKW5pxmDIRwMEw1Fae1tJWEShAIhYmUx2vraGBwaJFgSpDpcTUd/B/FEnIAEqInU0BXvom+wD0GoK69z0uF2klBXXkdLT8twmtNNU1tfW17TtL9rP0DaaZrKcco0TcnjnKvjlGmaOvo72N62XdW5NzpNh3oPDZ+LWs691DR1x7vZ17Uvp8dpRlWA0rJO6qcl01Tjpqn8iDSVBcIMJcJsP3SA9r5B+uIBdre1MZgI0d47QHc/9MVLaOnuoa23h12tQ3T2DdHeO0DiyPfUIygPlRANB5hbU85Nlx0/5TSlS876tBWRlcDnjTEXuOPXAhhjvjLWOpn0aXuo95Dqt+ba/cA6ZgPtfqDfUbsfpOdojKEnnqCjb4D23gE6egfdv+54yvTSgHDD25ZNyUVLn7ZPAotEZB6wG7gUeGeudhYQPVW2vNDuB9YxG2j3A/2O2v0gPUcRoaIsSEVZkFlVkTxYTUzOCpCMMYPAR4F7gE3Ab4wxG3K1v+Qjp1a0+4F1zAba/UC/o3Y/8IejFzmth2+M+Qvwl1zuw2KxWCzp4Z/6RBOgvbsx7X5gHbOBdj/Q76jdD/zh6EXRBPyygI4OosdCux9Yx2yg3Q/0O2r3A384elE0AT9ZTUwr2v3AOmYD7X6g31G7H/jD0YuiCfgWi8ViGZ+iCfgVpRWFVhgX7X5gHbOBdj/Q76jdD/zh6EXRBPxIqY56rmOh3Q+sYzbQ7gf6HbX7gT8cvcjZl7ZTQUQOAi9NcfU6oDmLOtlGux9Yx2yg3Q/0O2r3A12Oxxpjpk+8mLKAnwkisjrdz4sLgXY/sI7ZQLsf6HfU7gf+cPSiaIp0LBaLxTI+NuBbLBbLUUIxBfwfFFpgArT7gXXMBtr9QL+jdj/wh+MRFE0ZvsVisVjGp5ju8C0Wi8UyDr4L+CJyoYg8LyJbROQaj/llIvJrd/7jItKozO9TIrJRRJ4RkftF5Nh8+qXjmLLcxSJiRCSvtRHS8RORf3LzcYOI/CKffuk4isgxIvKAiKx1j/VFefb7kYgcEJH1Y8wXEfkf1/8ZETlFmd+7XK9nRORRETkpn37pOKYsd5qIJETk4ny5TRljjG9+QADYCswHQsDTwJJRy3wYuMkdvhT4tTK/VwLl7vCH8umXrqO7XCXwMLAKWKHJD1gErAVq3PEZ2vIQp4z3Q+7wEmB7nh3PBk4B1o8x/yLgrzhdkZ4BPK7M78yU4/vafPul45hyLvwdpxn4i/PtONmf3+7wTwe2GGO2GWPiwK+AN41a5k3Are7w74DzRMSrf92C+BljHjDG9Lijq4A5eXJL29Hli8DXgL58ypGe3z8D/2uMaQUwxhxQ6GiAmDtcBezJox/GmIeBQ+Ms8ibgp8ZhFVAtIrPyYzexnzHm0eTxpTD/J+nkIcDVwO1Avs/BKeG3gD8b2Jkyvsud5rmMcXrdagdq82KXnl8qV+DcZeWTCR1FZDkw1xjzp3yKuaSTh8cBx4nIP0RklYhcmDc7h3QcPw+8W0R24dz9XZ0ftbSZ7LlaSArxfzIhIjIbeAtwU6Fd0iWnPV7lAK879dHVjNJZJlekvW8ReTewAjgnp0Yeu/aYNuwoIiXAfwOX50toFOnkYRCnWOdcnDu/R0SkyRjTlmO3JOk4vgP4iTHmv0RkJfAz13Eo93ppUcj/k7QRkVfiBPxXFNrFg28B/2aMSeSvECEz/BbwdwFzU8bncOSjcnKZXSISxHmcnuixLFuk44eIvBr4D+AcY0x/ntySTORYCTQBD7on8UzgThF5ozFmtQK/5DKrjDEDwIsi8jzOBeDJPPgl9z+R4xXAhQDGmMdEJIzT/oqWR/+0ztVCIiLLgJuB1xpjNDZAvwL4lft/UgdcJCKDxpg/FFZrHAr9EmGSL1GCwDZgHodfli0dtcxHGPnS9jfK/JbjvPBbpDUPRy3/IPl9aZtOHl4I3OoO1+EUTdQqc/wrcLk7vBgnmEqej3UjY78UfR0jX9o+UYBzcTy/Y4AtwJn59krXcdRyP8EHL219dYdvjBkUkY8C9+C8Hf+RMWaDiHwBWG2MuRO4BefxeQvOnf2lyvy+DkSB37p3BjuMMW9U5lgw0vS7BzhfRDYCCeBfTR7vANN0/DTwQxH5JE5RyeXGjQz5QER+iVPkVee+R/gcUOr634TzXuEinKDaA7wvX25p+l2H8+7tRvf/ZNDkubGyNBx9h/3S1mKxWI4S/FZLx2KxWCxTxAZ8i8ViOUqwAd9isViOEmzAt1gslqMEG/AtFovlKMEG/ALitkT5s5TxoIgcFJFxmzQQkZOn0vqiiDSIyO8mWKbRq3XAdNZ1l/v3yXqlsc0VIvI/7vC5InJmlrY73W1Rda2InDVq3idEpDxlvCsb+9SEe6zfmcH6D2arJVUR2S4iddnYlmVsbMAvLN1Ak4hE3PHXALvTWO9knDrUaSMiQWPMHmPMlJpwncS6WQ/4xpjVxpiPuaPn4rSkmA3OA54zxiw3xjwyat4ngHKPddTgfkmeCY3AlAO+xX/YgF94/orz1SM47a/8MjlDRE532wJf6/49XkRCwBeAS0RknYhcIiIVbtvdT7rLvsld/3IR+a2I3AXcm3r37g4/IiJPub9xg+iodS8XkTtE5G4ReUFEvuZOvwGIuF63udPeLSJPuNO+LyIBd3qXiHxJRJ52G0Crd6e/XUTWu9MfdqedKyJ/Eqdvg6uAT7rbO0tEXhSRUne5mHunWDrK/Vhx+h5I9kFwjIicjNMa6EXutiIpy38MaAAeEJEHUqZ7+U4XkdvdvH9SRF7ukXcBEfm6O/8ZEflgSroeFJHfichzInKbuF8ZicipIvKQiKwRkXvEbcnSXf7LIvIQ8HERWeD6PCkiX0g+iYjIz5LngTt+m4iM/sDvBuAsN/2fHMvTXf8zIvKsm/4bUrbxdvf4bhb3KWms88Od9w53O+tF5KtHnGgM9xmx3v19ImX6Z918uk9Efiki/+Km/6mUZRaJyBqv7VrwV9MKxfYDuoBlOM04h4F1OHewf3Lnx4CgO/xq4HZ3+HLguynb+TLwbne4GtgMVLjL7QKmufMacT8Tx7l7DbvDi3C+EB2xzCjX1HUvx2laoMr1fgmndU2ArpR1FgN3AaXu+I3AZe6wAd7gDn8N+E93+FlgdjIt7t/UPPk88C8p+/gx8GZ3+Ergvzzc7wLe6w6/H/iDVz6OWmc7UJcyPpbvL4BXuMPHAJs8tnVlyvJlwGqcZhnOxWnNdQ7OzddjOI2ElQKPAtPddS7B+ZoXnKYubkzZ9p+Ad7jDVyXzH6dRvmQ6q4AXcc+llHWH83UCz9e6Psl+HKaluPyXO3wR8Lfxzg+ci+gOYDpO8xR/Tzl223GayTjVPQcqcL5I34DTHMkKnP+PCE57Ty8kzwPgAeDklP+Fqwv9v63156umFYoRY8wz7p3rO3A+d0+lCrhVRBbhBJxSvDkfeKOI/Is7HsYJPgD3GWO8Go8rBb7r3ukmcJocngz3G2PaAcRp4uBYRja3C06RyanAk+6Na4TDjYfFcYIVwBqc4iyAfwA/EZHfAHek4XEz8BngDzjNA/yzxzIrgbe6wz/DCdiTZSzfVwNL5HBriTERqTTGdKasez6wTA73iFSFc5GN47RhswtARNbhXFjbcBqwu8/dbgDYm7K9X49K25vd4V8A3wAwxjwkIv8rIjNw0n67cZoLH4+xPF8N/Ni4/TiMOp+Sx2iN657E6/yoBR40xhx0p9+G08lIamNjrwB+b4zpdpe5AzgL54L4R2NMrzv9rpR1bgbeJyKfwrk4nj5BOo9abMDXwZ04/6jnMrLt/i8CDxhj3uJeFB4cY30B3maMeX7ERJGX4bwn8OKTwH7gJJx/psl2dJLaymcC73NJcBo5u9Zj3oBxb8lS1zfGXOV6vw5Y516QxsQY8w9xipvOAQLGmHG7o0uulsYyafni5N3KZCAaA8G567xnxESRc/HORwE2GGNWjrG9sY7paH4GvAunPan3p7H8WJ4XMnaeJf1HnwNjpSsdh8lMB6cDks/hPDGsMTpb1lSBLcPXwY+ALxhjnh01vYrDL3EvT5neifNYm+Qe4OqU8t/laeyzCthrnPbZ34NzF5kNBlLK0O8HLnbvMhGRaTJBH74issAY87gx5jqgmZFN+MKRaQf4Kc67jx+PsdlHOdyI3ruA/0sjHV778eJe4KPJkTEuUPcAH5LD7xqOE5GKcbb5PDBdnHb0EZFSEVk6xrKrgLe5w6MbCvwJzstnjDEbPNb1Oo+8PO8F3i9urSURmTaO+3g8DpwjInXivMt5B/DQqGUeBt4sIuXuvt8CPIJzzN4gImERiXL4vRfGmD7X/XuMfQ5YsAFfBcaYXcaYb3vM+hrwFRH5ByMD8gM4xQjrROQSnCeBUuAZcV6sfjGN3d4IvFdEVuEU56R71zgRP3A9bjPGbAT+E+eF8TPAfcBE3eh9PflSD+ef/+lR8+8C3uKmPVmV8jaghpQX3qP4GM4j/zM4F7ePp5mOv0rKS9txtr3Cfcm5EaccfTQ3AxuBp9x0fZ9xnq6N023ixcBXReRpnLLrsV6qfwL4lIg8gZO37Snb2Q9sYuwg+Aww6L6I/eRYnsaYu3GeQle7xU7/Msb2xsUYsxe4Fuf8fRp4yhjzx1HLPIVzoXoC5wJxszFmrTHmSdfhaZxipNWpacU5BwzOxckyBra1TIvvccuc32SMeU+hXfKNe9fda4wxInIpzgvcN6XMexY4JVme7mdEJGqM6XLT9TBwpXuBwH1/VWWM+WxBJZVjy/AtvkZEvoNTi2TSH6IVCafivHwXnJe974fhXtV+BHyzGIK9yw9EZAlOpYRbU4L974EFwKsKKecH7B2+xWKxHCXYMnyLxWI5SrAB32KxWI4SbMC3WCyWowQb8C0Wi+UowQZ8i8ViOUqwAd9isViOEv4/HypUtXBHnYUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f9286d0da58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array(range(31))/20\n",
+    "fig, ax = subplots()\n",
+    "\n",
+    "ax.plot(x, [EROI.subs(m_e, M) for M in x], label=\"EROI\")\n",
+    "ax.set_xlabel('Material intensity of the energy technology')\n",
+    "ax.set_ylabel('EROI')\n",
+    "# ax.set_title('EROIs for different energy mixes')\n",
+    "ax.legend(loc=1)\n",
+    "ax.grid(color='g', alpha=0.5, linestyle='dotted', linewidth=0.5)\n",
+    "ax.axhline(y=1, color='black', alpha=0.5, linestyle='dotted', linewidth=0.5)\n",
+    "fig.savefig('EROIs for different material intensity 2.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For an intensity above 0.6, it is below 1. **An EROI below 1 means that the energy technology is not worth developing, because it consumes energy rather than providing it. Such a system is not sustainable** (and not realistic): for it to happen the society should have accumulated energy in the past from an energy source no more accessible, and would waste this energy in that absurd technology.\n",
+    "\n",
+    "For even higher intensities, the EROI falls below 0, which means that the energy (recursively) required to produce one unit of energy is infinite. Here, free energy coming from the past wouldn't suffice to build the energy technology: one would also need to have free materials (i.e. materials requiring no energy to access them). No need to say that such a world is physically impossible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### I.2. A Simple Model With A Mix of Two Technologies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let us consider two energy technologies, with the same energy intensity, but different materials intensities.\n",
+    "\n",
+    "Even if this example is purely illustrative, let us call them PV (for solar photovoltaic) and gas (for gas power-plant electricity). The numbers are completely made up, but they respect the fact that PV is more material intensive than gas. Here is our new technology matrix, where inputs (and outputs) are (in that order): PV, gas, materials, energy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANwAAABkCAMAAADaHa6rAAAAPFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo1xBWAAAAE3RSTlMAMquZdlQiEEAw7USJZs3d77tsrvmlqQAAAAlwSFlzAAAOxAAADsQBlSsOGwAABblJREFUeAHtnOm2oyAQhHGfmbhlfP93HRajNBRIJ8M5mqt/YrBs+GxELUlEseilFF+0TIZJiGKpark0X8QmBkVULgqu/SaunaXBcH099MWuOlrLKz+qPbgdw42yi3ZTcj7zyoNtP9wA4YZZ7dePh3sbQV55YiOQDMI99cDZLB3awy/LK/frSy6BcIuGeyyJ42deeTKKL0Rw3VIp4WOpfT0oySsHFT76Z1vIgf6wYyG4dulVyCIRLq8cwPWinGSn6p9gGynCcDpzhWEkcvSlNYnOJPdrbBsxqvNmkFex+ILg8vYzZnS/+bI36tGgPxzwEJwwI0TDG1ByyX06ORqo0+2pL1hg81YE4Z66Nw+HR2aNkle+NXVf6Se5vvaAvdRfg3B5r8rM6H6bxajGhGF+a7QUQg1G3Zx8c5lX7tMtsmcVCc2DmRNdLy8jyWyZ5R7cY3nUdZVw54vhvHinKtCnXEqLrginT7kvhetSL1HieplrpmVMu+m9IFxKf1w118vcDWeOwJ05Rk84lfTO3KnSwWhMIHN5XVZmdAYOlWK4vC4rMzpt8NG3xnoSgnDMJ6688jhNQ25WurIvrUdsCJfXZWVG58BJre0fQLi8Lisz+n+GY/pTeeUbG7ZiabdMyFxel5UZfYPDVuwbcCc0ZQNWLBsubz9Ljd6N1lIIz4rt9eZp1h/7+3x3QPm1/N6Sr1fOacpCKzaaub/otXFel5UZfTvu0IqNwsFLQd6rMjP6BgetWD7cOU1ZaMV6cPXRHUpml5Vp+a6pw1asA1eV8zJWw7oL7JZbTzjTCrZiHTja4OvAYSu2i70NuQxcuhW7Z+8qcAwr9npwe4sZa1fJHANpl349XFfHhpz9QFxurZUexE+bb3m5JMEGf/05Z3VLapcWU1n1aoHHRRZSeUi1lTvyrqqez/RpBVuY5Hpp5hy7dDBTvM2zqxX7terIZbFtib5U26cj79Tjc788tu3JK06g4H4Ezn3SWqdD7M/wNIwrdyxRKlazYlTJPv+20mP0fDjJyY3jBfIErwIC59qlpjsGZ3K7chnTdjBeVbw+XfmsJjmJkj813g30qsD7JHDQLn0Qw9oOAOQxOFc+6sfKcmGfdW4gu01k3YbDvlRwyiaSR+CQXDZlsp6cScuCXwKBgN6Gg3Zp/3qs9XZG8ggckqu5xsGh2KtwLcCBkJrC+WZsF56OimbIRuH86DJxodEKNdaUoXqx2oZD+a7DPy5A8ggckotKA+OmhUphIChWcL/+rKasOVPJlNcpeMrJybj6qBN5BA7J6zfYYL2QTfz9Y904A7vUAOB9gTwG58sbxdYmzC2k9fuB6Pbtm90twdVR/vpsk3or7lVZCmJwnrzQY0nNhvMCeS1bCwjcbsZ2iznXmuhgts2QfcmFbYl6VTrydpJ3rVWpL+WeNlqwBYqq5M0geVew26XrKNbOsZ+8uHJqiXo1O/LR3Lm+AbcH8uogBRSObLr+lxvuqjm8M3dn7oRH4O6WJ0xKUpPuzCUdphOKflLmHNv0KBufycehE0UVu3kN1Z9YL82c73ZyXFbZFJZ8VnfObAtF1uI3Ex8FAuc+KDFdVqZcjH3Jf5iTGG4zMZksJXDA7Yw9fX4q53tDBgPUi/kIHHA7Y3Cfyt+FA/UewyFbKQL3sbwchrpi+82vnxkn/LuCnTnkdkbgPpY/5ZuQlv+WB9V7nDnkdkbhtHlE/raAKZdterJtBtTMY7iP+1nU/ULRZZsq9lueQCDAZ3dLZJtGUvGpvNQG2xtvH82AQsxgQCaLCBxwO2NwH8pnDVex3/IIUG8CHLg6xuA+lBu7dwq/jMAtfvci7puyLJdVNoZlyiq6YWYbzun/rkC6pTVD1piyPJdVMOVt1ZfmxXgoR4Hy25SVB4ZmLnCkrlp8w92ZO+ER+AHdUr8pe/fx6oQpU9Nb9CL/bUj9i2ddvzHD7JRculH6n0nrWvwDSoBVzC/DoV0AAAAASUVORK5CYII=\n",
+      "text/latex": [
+       "$$\\left[\\begin{matrix}0 & 0 & 0 & p\\\\0 & 0 & 0 & - p + 1\\\\0.7 & 0.1 & 0.2 & 0\\\\0.1 & 0.1 & 0.5 & 0\\end{matrix}\\right]$$"
+      ],
+      "text/plain": [
+       "⎡ 0    0    0     p   ⎤\n",
+       "⎢                     ⎥\n",
+       "⎢ 0    0    0   -p + 1⎥\n",
+       "⎢                     ⎥\n",
+       "⎢0.7  0.1  0.2    0   ⎥\n",
+       "⎢                     ⎥\n",
+       "⎣0.1  0.1  0.5    0   ⎦"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = sym.symbols('p') # proportion of PV in energy mix\n",
+    "# m_pv (resp. m_oil) units of materials required for 1 kWh from PV (resp. oil)\n",
+    "m_pv, m_oil = 0.7, 0.1 # \n",
+    "m_pv, m_oil, m_e, e_pv, e_oil, e_e = sym.symbols('m_pv m_oil m_e e_pv e_oil e_e') \n",
+    "unit_energy_output = sym.Matrix([0, 0, 0, 1])\n",
+    "energy_mix = [p, 1-p] # proportion of [PV, oil]\n",
+    "A = sym.Matrix([[0, 0, 0, p], [0, 0, 0, 1-p], [0.7, 0.1, 0.2, 0], [0.1, 0.1, 0.5, 0]]) # order of ligns/columns: PV, oil, materials, energy\n",
+    "# A = sym.Matrix([[0, 0, 0, pv], [0, 0, 0, 1-pv], [m_pv, m_oil, m_e, 0], [e_pv, e_oil, e_e, 0]]) # order of ligns/columns: PV, oil, materials, energy\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you might have guessed, _p_ represents the share of PV in the energy (or electricity) mix.\n",
+    "\n",
+    "Using simple algebra, one obtains the formula for the EROI:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$$\\frac{- 0.3 p + 0.67}{0.3 p + 0.13}$$"
+      ],
+      "text/plain": [
+       "-0.3⋅p + 0.67\n",
+       "─────────────\n",
+       " 0.3⋅p + 0.13"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L = sym.eye(4)-A # sym.Matrix(3,3,[1, -m, -1, 0, 0.8, -1, -e, 0, 1])\n",
+    "Linv = L.inv()\n",
+    "EROI2 = sym.simplify(1/unit_energy_output.transpose().dot((Linv-sym.eye(4)).dot(unit_energy_output)))\n",
+    "EROI2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This corresponds to the system-wide EROI. But now that we have two technologies, we can compute the EROI of each of them.\n",
+    "\n",
+    "The EROI of a technology is the ratio between the energy delivered by one unit of this technology (over its lifetime), and the energy required to build, operate, maintain and dismantle it.\n",
+    "\n",
+    "In our example, the EROIs of PV and gas are, in that order:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$$- 0.697674418604651 p + 1.55813953488372$$"
+      ],
+      "text/plain": [
+       "-0.697674418604651⋅p + 1.55813953488372"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$$- 2.30769230769231 p + 5.15384615384615$$"
+      ],
+      "text/plain": [
+       "-2.30769230769231⋅p + 5.15384615384615"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EROIpv = sym.simplify(1/unit_energy_output.transpose().dot((Linv-sym.eye(4)).dot(sym.Matrix([1, 0, 0, 0]))))\n",
+    "EROIoil = sym.simplify(1/unit_energy_output.transpose().dot((Linv-sym.eye(4)).dot(sym.Matrix([0, 1, 0, 0]))))\n",
+    "\n",
+    "EROIpv\n",
+    "EROIoil"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that they depend on the energy mix: **the EROI of a technology is not an intrinsic property**.\n",
+    "\n",
+    "Indeed, **it depends on the whole economic system**, or more precisely, of all technologies used in their value chain. (The value chain what I called above the recursive or embodied inputs; its analysis is known as _structural path analysis_ in the literature).\n",
+    "\n",
+    "Here, **the higher the share of PV in the mix, the lower the EROI of both technologies**: this comes from the higher material intensity of PV.\n",
+    "\n",
+    "Put graphically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f9288028f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array(range(21))/20\n",
+    "fig, ax = subplots()\n",
+    "\n",
+    "ax.plot(x, [EROI2.subs(p, PV) for PV in x], label=\"System-wide EROI\")\n",
+    "ax.plot(x, [EROIpv.subs(p, PV) for PV in x], label=\"EROI of PV\", linestyle='dashed') # , linestyle='dashed'\n",
+    "ax.plot(x, [EROIoil.subs(p, PV) for PV in x], label=\"EROI of gas\", linestyle='dotted') # , linestyle='dotted'\n",
+    "ax.set_xlabel('Share of PV in the energy mix')\n",
+    "ax.set_ylabel('EROI')\n",
+    "# ax.set_title('EROIs for different energy mixes')\n",
+    "ax.legend(loc=1)\n",
+    "ax.grid(color='g', alpha=0.5, linestyle='dotted', linewidth=0.5)\n",
+    "fig.savefig('EROIs for different energy mixes - gas 2.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One can see that for highest penetration of PV, the EROI falls below unity. In other words, a renewable energy mix with 100% PV is not sustainable in this example. \n",
+    "\n",
+    "Even more worrying, if one computes the EROI of PV in an energy mix relying mostly on gas, one would find a high-enough EROI for PV (meaning, above 1).\n",
+    "\n",
+    "Hence, one cannot conclude that a technology is sufficiently efficient (or sustainable) just by computing its EROI in the current energy mix.\n",
+    "\n",
+    "Yet, EROIs computations are _always_ done from actual data of our economy, and could falsely represent the efficiencies of energy technologies in another energy mix, say, a 100% renewable one.\n",
+    "\n",
+    "In the next sections, I'll go into the data and compute for the first time EROIs of different electricity technologies under an energy transition towards renewables.\n",
+    "\n",
+    "Let's hope that we'll find EROIs above one for at least one renewable technology!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## II. Estimation of Current and Future EROIs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "# One needs to install my fork of pymrio, available at https://github.com/bixiou/pymrio\n",
+    "#   To do this, clone my repository from github, go on the console on the pymrio folder, and\n",
+    "#   type 'python3 setup.py sdist', then 'python3 setup.py install'\n",
+    "import pymrio\n",
+    "from pymrio.core.mriosystem import IOSystem as IOS\n",
+    "from pymrio.tools.iomath import div0\n",
+    "from pymrio.tools.iofunctions import *\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "# import scipy.io\n",
+    "import scipy.sparse as sp \n",
+    "from scipy.sparse import linalg as spla\n",
+    "import operator\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pylab import *\n",
+    "import pickle\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "# Unfortunately, the database THEMIS is not open (you can ask to NTNU or thomas.gibon@list.lu) if they can provide it to you\n",
+    "path_themis = '/media/adrien/dd/adrien/DD/Économie/Données/Themis/'\n",
+    "path_io = '/media/adrien/dd/adrien/DD/Économie/Données/'\n",
+    "# path_io = '/media/sf_U_DRIVE/Données/'\n",
+    "years = [2010, 2030, 2050]\n",
+    "scenarios = ['BL', 'BM']\n",
+    "scenarios_dlr = ['REF', 'ER', 'ADV'] \n",
+    "# themis = pymrio.themis_parser(path_themis)\n",
+    "themis = pickle.load(open(path_io+'Themis/themis.pkl', 'rb'))\n",
+    "# themis, EROIs_mixes, dlr, EROIs_Mixes = pickle.load(open(path_io+'Themis/EROIs.pkl', 'rb'))\n",
+    "# EROIs_mixes, dlr, EROIs_Mixes = pickle.load(open(path_io+'Themis/EROIs.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compute EROIs, I use input-output tables (i.e. technology matrices) representing the global economy. \n",
+    "\n",
+    "The original database is [Exiobase](https://www.exiobase.eu/), and the model I used is [THEMIS](https://sci-hub.tw/https%3A//pubs.acs.org/doi/abs/10.1021/acs.est.5b01558), which is precisely what we need: input-output tables (IOT) of 2010, and prospective IOT for 2030 and 2050, under two [scenarios](http://gen.lib.rus.ec/book/index.php?md5=4D5AF94A02A48B799CF77641509C1C6B) of the International Energy Agency: BaseLine (with a high share of fossils) and Blue Map (with a high share of renewables).\n",
+    "\n",
+    "I won't give much more details, let's go straight to the results. Let's see current EROIs first:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EROIs of electric technos in 2010:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">BL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2010</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>biomass w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass&amp;Waste</th>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ocean</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geothermal</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar CSP</th>\n",
+       "      <td>21.6</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar PV</th>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind offshore</th>\n",
+       "      <td>9.4</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind onshore</th>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>13.2</td>\n",
+       "      <td>0.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>13.9</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>0.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>12.2</td>\n",
+       "      <td>19.76</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario         BL       \n",
+       "year           2010       \n",
+       "stat           EROI    mix\n",
+       "biomass w CCS     –   0.00\n",
+       "biomass&Waste  11.4   0.01\n",
+       "ocean           5.5   0.00\n",
+       "geothermal      5.4   0.00\n",
+       "solar CSP      21.6   0.00\n",
+       "solar PV        9.3   0.00\n",
+       "wind offshore   9.4   0.00\n",
+       "wind onshore    9.5   0.01\n",
+       "hydro          13.2   0.16\n",
+       "nuclear        10.5   0.14\n",
+       "gas w CCS         –   0.00\n",
+       "coal w CCS        –   0.00\n",
+       "oil             8.4   0.06\n",
+       "gas            13.9   0.21\n",
+       "coal           12.9   0.42\n",
+       "Total (PWh/a)  12.2  19.76"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('EROIs of electric technos in 2010:')\n",
+    "results(themis, scenarios='BL', year=2010, stats=['eroi', 'mix'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One can notice that PV and wind panels have a lower EROI than electricity from fossil fuels. \n",
+    "\n",
+    "The system-wide EROI for all electricity is given at the bottom line: it is 12.1.\n",
+    "    \n",
+    "Some EROIs are missing, because in 2010, the technology didn't exist yet on an industrial scale."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's see how EROIs would evolve in the energy transition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EROIs of electric technos in 2050 in the Blue Map scenario (with mostly renewables):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">BM</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>biomass w CCS</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass&amp;Waste</th>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ocean</th>\n",
+       "      <td>5.8</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geothermal</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar CSP</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar PV</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind offshore</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind onshore</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>13.1</td>\n",
+       "      <td>0.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas w CCS</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal w CCS</th>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>19.6</td>\n",
+       "      <td>0.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>40.22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario         BM       \n",
+       "year           2050       \n",
+       "stat           EROI    mix\n",
+       "biomass w CCS   4.0   0.01\n",
+       "biomass&Waste   5.2   0.05\n",
+       "ocean           5.8   0.00\n",
+       "geothermal      5.4   0.02\n",
+       "solar CSP       7.9   0.06\n",
+       "solar PV        6.0   0.06\n",
+       "wind offshore   6.2   0.04\n",
+       "wind onshore    7.3   0.08\n",
+       "hydro          13.1   0.14\n",
+       "nuclear         7.4   0.24\n",
+       "gas w CCS       9.1   0.05\n",
+       "coal w CCS      7.1   0.12\n",
+       "oil             7.3   0.01\n",
+       "gas            19.6   0.11\n",
+       "coal           12.4   0.01\n",
+       "Total (PWh/a)   8.0  40.22"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('EROIs of electric technos in 2050 in the Blue Map scenario (with mostly renewables):')\n",
+    "results(themis, scenarios='BM', year=2050, stats=['eroi', 'mix'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The EROIs that matters (those of renewable) decrease, as anticipated in the previous section.\n",
+    "\n",
+    "The result that global EROI decreases until 8 cast doubts on the energetic efficiency of renewable electricity. However, the EROI of each technology is expected to remain well above 1, which suggest that renewables are truly sustainable.\n",
+    "\n",
+    "Phew! We can be reassured!\n",
+    "\n",
+    "Uh, wait a minute! Look at this electricity mix in the Blue Map scenario: there is only 24% of solar and wind, and there remains 30% of fossils. Sure, more than half of includes Carbon Capture and Storage (CCS), but we are still far from a 100% renewable energy mix. Would these optimistic results hold in a more ambitious scenario?\n",
+    "\n",
+    "Let's have a look at the Greenpeace's [Energy [R]evolution](https://www.greenpeace.org/archive-international/Global/international/publications/climate/2015/Energy-Revolution-2015-Full.pdf) scenario, which proposes a 100% energy mix as soon as 2050, without CCS nor nuclear. I'll skip the details once again, but I have changed the electricity mix of THEMIS' BM 2050 to incorporate this scenario. Here are the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">Baseline ('BL')</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Blue Map ('BM', +2°C)</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Advanced ER ('ADV', 100% renewable)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2010</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>biomass w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass&amp;Waste</th>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ocean</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geothermal</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar CSP</th>\n",
+       "      <td>21.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar PV</th>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind offshore</th>\n",
+       "      <td>9.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind onshore</th>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>13.2</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>0.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>13.9</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>12.2</td>\n",
+       "      <td>19.76</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>34.29</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>45.97</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>28.01</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>40.22</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>36.74</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>64.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario      Baseline ('BL')                                   \\\n",
+       "year                     2010         2030         2050          \n",
+       "stat                     EROI    mix  EROI    mix  EROI    mix   \n",
+       "biomass w CCS               –   0.00     –   0.00     –   0.00   \n",
+       "biomass&Waste            11.4   0.01   6.3   0.02   5.9   0.03   \n",
+       "ocean                     5.5   0.00   2.4   0.00   2.9   0.00   \n",
+       "geothermal                5.4   0.00   5.2   0.01   5.1   0.01   \n",
+       "solar CSP                21.6   0.00   8.9   0.00   9.1   0.01   \n",
+       "solar PV                  9.3   0.00   7.4   0.01   7.2   0.01   \n",
+       "wind offshore             9.4   0.00  11.0   0.01  10.5   0.01   \n",
+       "wind onshore              9.5   0.01   9.3   0.04   8.1   0.04   \n",
+       "hydro                    13.2   0.16  11.9   0.14  11.9   0.12   \n",
+       "nuclear                  10.5   0.14   7.3   0.11   7.0   0.10   \n",
+       "gas w CCS                   –   0.00     –   0.00   7.5   0.00   \n",
+       "coal w CCS                  –   0.00     –   0.00   6.2   0.00   \n",
+       "oil                       8.4   0.06   9.8   0.02   9.9   0.01   \n",
+       "gas                      13.9   0.21  15.0   0.21  14.9   0.23   \n",
+       "coal                     12.9   0.42  11.5   0.45  11.5   0.45   \n",
+       "Total (PWh/a)            12.2  19.76  10.9  34.29  10.7  45.97   \n",
+       "\n",
+       "scenario      Blue Map ('BM', +2°C)                      \\\n",
+       "year                           2030         2050          \n",
+       "stat                           EROI    mix  EROI    mix   \n",
+       "biomass w CCS                   4.6   0.00   4.0   0.01   \n",
+       "biomass&Waste                   5.5   0.06   5.2   0.05   \n",
+       "ocean                           3.7   0.00   5.8   0.00   \n",
+       "geothermal                      5.2   0.01   5.4   0.02   \n",
+       "solar CSP                       8.2   0.02   7.9   0.06   \n",
+       "solar PV                        6.4   0.02   6.0   0.06   \n",
+       "wind offshore                   7.7   0.03   6.3   0.04   \n",
+       "wind onshore                    7.1   0.08   7.3   0.08   \n",
+       "hydro                          12.8   0.18  13.1   0.14   \n",
+       "nuclear                         7.3   0.19   7.4   0.24   \n",
+       "gas w CCS                       7.9   0.01   9.1   0.05   \n",
+       "coal w CCS                      7.1   0.05   7.1   0.12   \n",
+       "oil                             9.5   0.03   7.3   0.01   \n",
+       "gas                            17.3   0.14  19.7   0.11   \n",
+       "coal                           11.6   0.18  12.4   0.01   \n",
+       "Total (PWh/a)                   9.1  28.01   8.0  40.22   \n",
+       "\n",
+       "scenario      Advanced ER ('ADV', 100% renewable)                      \n",
+       "year                                         2030         2050         \n",
+       "stat                                         EROI    mix  EROI    mix  \n",
+       "biomass w CCS                                   –   0.00     –   0.00  \n",
+       "biomass&Waste                                 5.2   0.05   4.6   0.05  \n",
+       "ocean                                         4.8   0.01   4.9   0.03  \n",
+       "geothermal                                    3.8   0.03   3.9   0.07  \n",
+       "solar CSP                                     9.3   0.07   7.8   0.22  \n",
+       "solar PV                                      5.4   0.14   4.7   0.21  \n",
+       "wind offshore                                 6.5   0.04   6.4   0.10  \n",
+       "wind onshore                                  7.2   0.17   5.8   0.24  \n",
+       "hydro                                        11.0   0.13  10.9   0.08  \n",
+       "nuclear                                       8.3   0.02     –   0.00  \n",
+       "gas w CCS                                       –   0.00     –   0.00  \n",
+       "coal w CCS                                      –   0.00     –   0.00  \n",
+       "oil                                          10.0   0.01     –   0.00  \n",
+       "gas                                          16.5   0.18     –   0.00  \n",
+       "coal                                         10.4   0.16  11.5   0.00  \n",
+       "Total (PWh/a)                                 8.1  36.74   5.8  64.04  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results(themis, longnames=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd346e3def0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EROIs_mixes = results(themis, scenarios = scenarios+scenarios_dlr, to_plot=True)\n",
+    "# f, axarr = plt.subplots(2, sharex=True)\n",
+    "# axarr[0].plot(x, y)\n",
+    "# axarr[0].set_title('Sharing X axis')\n",
+    "# axarr[1].scatter(x, y)\n",
+    "if 'total' in EROIs_mixes.index: tot_name = 'total'\n",
+    "else: tot_name = 'Total (PWh/a)'\n",
+    "EROIs_mixes = EROIs_mixes.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                               'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', tot_name]]\n",
+    "InteractiveShell.ast_node_interactivity = 'last'\n",
+    "colors = ['lime', 'green', 'pink', 'olive', 'gold', 'orange', 'teal', 'aqua', 'b', 'r', 'magenta', 'silver', 'brown', 'purple', 'dimgrey']\n",
+    "fig = plt.figure(figsize=(10,4)) # 7,5\n",
+    "_ = ylims = [5, 13] # 5, 10; 13, 18\n",
+    "\n",
+    "ax1 = fig.add_subplot(1, 3, 1)\n",
+    "_ = ax1.plot(years, EROIs_mixes[[('BL', year, 'EROI') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI', color='black', zorder=20, linewidth=3)\n",
+    "_ = ax1.set_ylabel('EROI')\n",
+    "_ = ax1.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "_ = ax12 = ax1.twinx()\n",
+    "_ = ax12.stackplot(years, EROIs_mixes.fillna(0)[[('BL', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax1.set_title('BL (business as usual)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax12.get_legend_handles_labels()\n",
+    "_ = ax1.set_zorder(ax12.get_zorder()+1)\n",
+    "_ = ax1.patch.set_visible(False)\n",
+    "# ax12.get_yaxis().set_visible(False)\n",
+    "_ = ax12.tick_params(labelright=False)\n",
+    "\n",
+    "ax2 = fig.add_subplot(132)\n",
+    "_ = ax2.plot(years, EROIs_mixes[[('BM', year, 'EROI') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI', color='black', zorder=20, linewidth=3)\n",
+    "# ax2.set_ylabel('EROI')\n",
+    "_ = ax2.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax22 = ax2.twinx()\n",
+    "_ = ax22.stackplot(years, EROIs_mixes.fillna(0)[[('BM', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax2.set_title('BM (+2°C)')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax22.get_legend_handles_labels()\n",
+    "_ = ax2.set_zorder(ax22.get_zorder()+1)\n",
+    "_ = ax2.patch.set_visible(False)\n",
+    "_ = ax2.tick_params(labelleft=False)\n",
+    "_ = ax22.tick_params(labelright=False)\n",
+    "# ax2.get_yaxis().set_visible(False)\n",
+    "# ax22.get_yaxis().set_visible(False)\n",
+    "\n",
+    "ax3 = fig.add_subplot(133)\n",
+    "_ = ax3.plot(years, EROIs_mixes[[('ADV', year, 'EROI') for year in years]].loc[tot_name], \\\n",
+    "        label='EROI', color='black', zorder=20, linewidth=3)\n",
+    "# ax3.set_ylabel('EROI')\n",
+    "_ = ax3.set_ylim(ylims)\n",
+    "# ax.set_zorder(1)\n",
+    "ax32 = ax3.twinx()\n",
+    "_ = ax32.stackplot(years, EROIs_mixes.replace('–',0).fillna(0)[[('ADV', year, 'mix') for year in years]].iloc[:15], \\\n",
+    "              labels=EROIs_mixes.index[:15], colors=colors, alpha = 0.8, zorder=1)\n",
+    "_ = ax3.set_title('ADV (100% renewable)') # ER (+2°C wo CCS nor nuclear)\n",
+    "_ = plt.ylabel('Share in the mix')\n",
+    "_ = xticks(years)\n",
+    "handles, labels = ax32.get_legend_handles_labels()\n",
+    "_ = ax32.legend(handles[::-1], labels[::-1], loc='center left', bbox_to_anchor=(1.25,0.45)) # (1.1,0.5)\n",
+    "_ = ax3.legend(loc='center left', bbox_to_anchor=(1.25,1.03)) # (1.1,0.97)\n",
+    "_ = ax3.set_zorder(ax32.get_zorder()+1)\n",
+    "_ = ax3.patch.set_visible(False)\n",
+    "# ax3.get_yaxis().set_visible(False)\n",
+    "_ = ax3.tick_params(labelleft=False)\n",
+    "_ = plt.subplots_adjust(wspace=0.08, hspace=0.08)\n",
+    "\n",
+    "# _ = fig.savefig('Evolution of EROIs IEA-ADV - factor 2-60.png', dpi=200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, the system-wide EROI of the power sector decreases even more in a 100% renewable scenario, from 12.1 in 2010 to 5.8 in 2050. **This is quite worrying for the energy transition, because it means that a 100% renewable electricity system would be twice as less efficient as the current one**. \n",
+    "\n",
+    "**Adrien Fabre**, 2018  \n",
+    "[personal website](https://sites.google.com/view/adrien-fabre)  \n",
+    "adrien.fabre@psemail.eu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "### This work\n",
+    "* The [working paper](https://drive.google.com/file/d/13r89ST7UfQ9IykRvn550yoLBWAwOsCD9/view)\n",
+    "* The [presentation](https://drive.google.com/open?id=1NaHAZZbjRtzTKs7V0HWrNVXppZLtz9-N)\n",
+    "\n",
+    "### Theory\n",
+    "* Input-Output Analysis: [wikipedia](https://en.wikipedia.org/wiki/Input%E2%80%93output_model), [Miller & Blair (2009)](http://gen.lib.rus.ec/book/index.php?md5=8803A190575D7587AA8112F76BB82070), [Eurostat](http://ec.europa.eu/eurostat/documents/3859598/5902113/KS-RA-07-013-EN.PDF/b0b3d71e-3930-4442-94be-70b36cea9b39?version=1.0)\n",
+    "* EROI: [King et al. (2010)](http://careyking.com/wp-content/uploads/2013/10/ASME-ES90414_EROIMethodology-Wind_2010_King.pdf), [Murhpy et al. (2011)](http://science-and-energy.org/wp-content/uploads/2016/03/Murphy-et-al-2011-Order-from-Chaos-EROI-Protocol.pdf)\n",
+    "* EROI is not intrinsic: [King (2014)](https://sci-hub.tw/https%3A//doi.org/10.1016/j.energy.2014.05.032)\n",
+    "\n",
+    "### Estimations of EROI\n",
+    "* [Dale (2010)](https://ir.canterbury.ac.nz/bitstream/handle/10092/5156/Dale2011GlobalEnergyModelling-ABiophysicalApproach.pdf)\n",
+    "* [Weißbach et al. (2013)](https://sci-hub.tw/https%3A//www.sciencedirect.com/science/article/pii/S0360544213000492)\n",
+    "* [Hall et al. (2014)](https://sci-hub.tw/https%3A//www.sciencedirect.com/science/article/pii/S0301421513003856)\n",
+    "\n",
+    "### Link between EROI and affluence\n",
+    "Some authors argue that the value of EROI is of primary concern ([Hall et al., 2009](http://dieoff.com/_Energy/WhatIsTheMinumEROI_energies-02-00025.pdf); [Lambert & Lambert, 2011](https://mahb.stanford.edu/wp-content/uploads/2014/03/sustainability_LambertLambert_2011.pdf); [Lambert et al., 2014](https://www.sciencedirect.com/science/article/pii/S0301421513006447); [Fizaine & Court, 2016](https://sci-hub.tw/https%3A//www.sciencedirect.com/science/article/pii/S0301421516302087)), as they draw a link between the system-wide EROI and affluence of a society. Here is how [Hall (2011)](https://www.mdpi.com/2071-1050/3/10/1773) summarizes the argument:\n",
+    "> Think of a society dependent upon one resource: its domestic oil. If the EROI for this oil was 1.1:1 then one could pump the oil out of the ground and look at it. If it were 1.2:1 you could also refine it and look at it, 1.3:1 also distribute it to where you want to use it but all you could do is look at it. Hall et al. 2008 examined the EROI required to actually run a truck and found that if the energy included was enough to build and maintain the truck and the roads and bridges required to use it (i.e., depreciation), one would need at least a 3:1. Now if you wanted to put something in the truck, say some grain, and deliver it that would require an EROI of, say, 5:1 to grow the grain. If you wanted to include depreciation on the oil field worker, the refinery worker, the truck driver and the farmer you would need an EROI of say 7 or 8:1 to support the families. If the children were to be educated you would need perhaps 9 or 10:1, have health care 12:1, have arts in their life maybe 14:1 and so on. [Hall (2011)](https://www.mdpi.com/2071-1050/3/10/1773)\n",
+    "\n",
+    "The reasoning of Hall relies on the observation that all sectors of the economy require energy, and that the more efficient is the energy production (i.e. the higher is the EROI), the more energy is available to the rest of the economy. In strict logic, Hall's argument relies on two questionable assumptions: that factors of production (and especially the labor force) are used at their full capacity, and that technical and organizational progress will not be sufficient to sustain current level of prosperity with significantly less labor (or other factors of production in limited supply). If one rejects these assumptions, one can imagine a sustained level of prosperity with a lower system-wide EROI, provided that a higher share of factors of production be devoted to the energy sector: for example, unemployed people could be mobilized to sustain the energy surplus available to the rest of society. In parallel to a shift in the labor force, [Raugei (2019)](https://www.nature.com/articles/s41560-019-0327-0) explains that an increased efficiency of energy use may also counteract the decrease in energy services implied by a declining EROI. That being said, given that current system-wide EROI is already declining due to the decline in fossil fuels quality ([Dale 2011](http://www.sciencedirect.com/science/article/pii/S0301421511006185), [Poisson, 2013](https://www.mdpi.com/1996-1073/6/11/5940), [Court & Fizaine, 2017](http://www.sciencedirect.com/science/article/pii/S0921800915303815)) and that technical progress is incremental, the aforementioned analyses should not be neglected. Under the current system of production, which will persist in the short term, EROI should not decrease too much for prosperous standards of living to be sustained.  \n",
+    "\n",
+    "\n",
+    "### Data\n",
+    "* [Exiobase](https://www.exiobase.eu/)\n",
+    "* [THEMIS](https://sci-hub.tw/https%3A//pubs.acs.org/doi/abs/10.1021/acs.est.5b01558) Thank you a lot Thomas Gibon for providing me the code and helping me!\n",
+    "* [IEA scenarios](http://gen.lib.rus.ec/book/index.php?md5=4D5AF94A02A48B799CF77641509C1C6B)\n",
+    "* [Energy [R]evolution](https://www.greenpeace.org/archive-international/Global/international/publications/climate/2015/Energy-Revolution-2015-Full.pdf) (commanded and funded by Greenpeace, realised by a team of the DLR using their model [REMix](https://www.dlr.de/tt/en/desktopdefault.aspx/tabid-2885/4422_read-12423/))\n",
+    "* [Cecilia 2050](https://cecilia2050.eu/publications/168)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix\n",
+    "\n",
+    "For the sake of curiosity, here are the EROIs in all available input-output tables from THEMIS and Greenpeace:\n",
+    "\n",
+    "EROIs and mix for Greenpeace scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>scenario</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">Reference DLR ('REF')</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Energy [R]evolution ('ER', +2°C, no CCS nor nuclear)</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Advanced ER ('ADV', 100% renewable)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2010</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2030</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2050</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stat</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>biomass w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass&amp;Waste</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ocean</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geothermal</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar CSP</th>\n",
+       "      <td>35.5</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar PV</th>\n",
+       "      <td>13.7</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind offshore</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wind onshore</th>\n",
+       "      <td>9.7</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>12.2</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>0.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>12.2</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal w CCS</th>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>14.9</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>–</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>11.8</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total (PWh/a)</th>\n",
+       "      <td>12.1</td>\n",
+       "      <td>22.60</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>36.26</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>50.11</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>33.60</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>49.20</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>36.74</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>64.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "scenario      Reference DLR ('REF')                                   \\\n",
+       "year                           2010         2030         2050          \n",
+       "stat                           EROI    mix  EROI    mix  EROI    mix   \n",
+       "biomass w CCS                     –   0.00     –   0.00     –   0.00   \n",
+       "biomass&Waste                   8.5   0.02   6.4   0.03   5.0   0.03   \n",
+       "ocean                           4.7   0.00   2.0   0.00   2.5   0.00   \n",
+       "geothermal                      5.6   0.00   3.8   0.01   2.5   0.01   \n",
+       "solar CSP                      35.5   0.00   9.3   0.00   8.0   0.01   \n",
+       "solar PV                       13.7   0.00   7.0   0.02   5.3   0.02   \n",
+       "wind offshore                   9.1   0.00   8.6   0.01   7.8   0.01   \n",
+       "wind onshore                    9.7   0.02   9.1   0.05   7.2   0.05   \n",
+       "hydro                          12.2   0.16  11.4   0.14  11.2   0.13   \n",
+       "nuclear                        12.2   0.11   7.3   0.10   7.1   0.08   \n",
+       "gas w CCS                         –   0.00     –   0.00     –   0.00   \n",
+       "coal w CCS                        –   0.00     –   0.00     –   0.00   \n",
+       "oil                             8.4   0.05  11.1   0.02  11.4   0.01   \n",
+       "gas                            14.9   0.23  15.3   0.23  15.6   0.25   \n",
+       "coal                           11.8   0.40  11.3   0.40  11.3   0.39   \n",
+       "Total (PWh/a)                  12.1  22.60  10.7  36.26  10.1  50.11   \n",
+       "\n",
+       "scenario      Energy [R]evolution ('ER', +2°C, no CCS nor nuclear)         \\\n",
+       "year                                                          2030          \n",
+       "stat                                                          EROI    mix   \n",
+       "biomass w CCS                                                  –     0.00   \n",
+       "biomass&Waste                                                5.3     0.06   \n",
+       "ocean                                                        4.3     0.01   \n",
+       "geothermal                                                   3.6     0.03   \n",
+       "solar CSP                                                    8.5     0.05   \n",
+       "solar PV                                                     5.6     0.11   \n",
+       "wind offshore                                                5.6     0.03   \n",
+       "wind onshore                                                 7.2     0.15   \n",
+       "hydro                                                       11.0     0.14   \n",
+       "nuclear                                                      8.3     0.02   \n",
+       "gas w CCS                                                      –     0.00   \n",
+       "coal w CCS                                                     –     0.00   \n",
+       "oil                                                         10.0     0.01   \n",
+       "gas                                                         16.6     0.21   \n",
+       "coal                                                        10.7     0.19   \n",
+       "Total (PWh/a)                                                8.4    33.60   \n",
+       "\n",
+       "scenario                   Advanced ER ('ADV', 100% renewable)               \\\n",
+       "year           2050                                       2030         2050   \n",
+       "stat           EROI    mix                                EROI    mix  EROI   \n",
+       "biomass w CCS     –   0.00                                   –   0.00     –   \n",
+       "biomass&Waste   4.7   0.06                                 5.2   0.05   4.6   \n",
+       "ocean           4.5   0.03                                 4.8   0.01   4.9   \n",
+       "geothermal      3.7   0.07                                 3.8   0.03   3.9   \n",
+       "solar CSP       7.7   0.17                                 9.3   0.07   7.8   \n",
+       "solar PV        4.4   0.20                                 5.4   0.14   4.7   \n",
+       "wind offshore   5.9   0.08                                 6.5   0.04   6.4   \n",
+       "wind onshore    6.0   0.22                                 7.2   0.17   5.8   \n",
+       "hydro          11.1   0.10                                11.0   0.13  10.9   \n",
+       "nuclear           –   0.00                                 8.3   0.02     –   \n",
+       "gas w CCS         –   0.00                                   –   0.00     –   \n",
+       "coal w CCS        –   0.00                                   –   0.00     –   \n",
+       "oil             9.2   0.00                                10.0   0.01     –   \n",
+       "gas            17.2   0.06                                16.5   0.18     –   \n",
+       "coal           10.8   0.01                                10.4   0.16  11.5   \n",
+       "Total (PWh/a)   5.9  49.20                                 8.1  36.74   5.8   \n",
+       "\n",
+       "scenario              \n",
+       "year                  \n",
+       "stat             mix  \n",
+       "biomass w CCS   0.00  \n",
+       "biomass&Waste   0.05  \n",
+       "ocean           0.03  \n",
+       "geothermal      0.07  \n",
+       "solar CSP       0.22  \n",
+       "solar PV        0.21  \n",
+       "wind offshore   0.10  \n",
+       "wind onshore    0.24  \n",
+       "hydro           0.08  \n",
+       "nuclear         0.00  \n",
+       "gas w CCS       0.00  \n",
+       "coal w CCS      0.00  \n",
+       "oil             0.00  \n",
+       "gas             0.00  \n",
+       "coal            0.00  \n",
+       "Total (PWh/a)  64.04  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results(themis, scenarios=['REF', 'ER', 'ADV'], longnames=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are similar results for an older project of future IO tables, [Cecilia 2050](https://cecilia2050.eu/publications/168) :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cecilia = pymrio.cecilia_parser('/var/www/FutureIOT/') # <--- PATH to modify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>step</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">-1: original</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">0: balanced</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1: growth</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2a: new mix</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2b: new techno</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">3: 2° scenario</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>var</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "      <th>EROI</th>\n",
+       "      <th>mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>wind</th>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>18.3</td>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hydro</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coal</th>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nuclear</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "step    -1: original       0: balanced       1: growth       2a: new mix  \\\n",
+       "var             EROI   mix        EROI   mix      EROI   mix        EROI   \n",
+       "wind             1.1  0.00         0.4  0.00       1.0  0.00        18.2   \n",
+       "hydro            5.1  0.14         3.5  0.13      12.9  0.19        12.7   \n",
+       "coal             8.1  0.46         5.7  0.46      13.8  0.48        11.0   \n",
+       "gas              9.1  0.20         6.6  0.19      19.7  0.17        24.9   \n",
+       "nuclear         11.6  0.20         9.1  0.22      24.4  0.16        15.7   \n",
+       "total            7.9  1.00         5.5  1.00      14.3  1.00        14.0   \n",
+       "\n",
+       "step          2b: new techno       3: 2° scenario        \n",
+       "var       mix           EROI   mix           EROI   mix  \n",
+       "wind     0.04           19.1  0.04           18.3  0.04  \n",
+       "hydro    0.27           13.4  0.27           12.0  0.25  \n",
+       "coal     0.20           11.8  0.20           11.0  0.21  \n",
+       "gas      0.19           26.6  0.19           24.9  0.19  \n",
+       "nuclear  0.30           17.7  0.30           17.5  0.30  \n",
+       "total    1.00           15.2  1.00           14.4  1.00  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EROIs_cecilia = pd.DataFrame()\n",
+    "EROIs_mixes_cecilia = pd.DataFrame()\n",
+    "steps = [-1, 0, 1, '2a', '2b', 3]\n",
+    "for step in steps:\n",
+    "    eroi_s = cecilia[step].erois()\n",
+    "    EROIs_cecilia[step] = eroi_s\n",
+    "    eroi_s = eroi_s.rename(index={'Power sector': 'total'})\n",
+    "    cecilia[step].elec_supply = dict()\n",
+    "    cecilia[step].elec_mix = dict()\n",
+    "    cecilia[step].elec_mix['total'] = 1\n",
+    "    cecilia[step].elec_supply['total'] = cecilia[step].impacts('Total Energy supply', secs = cecilia[step].energy_sectors('electricities')).sum()\n",
+    "    for sec in cecilia[step].energy_sectors('electricities'):\n",
+    "        cecilia[step].elec_supply[sec] = cecilia[step].impacts('Total Energy supply', secs = sec).sum()\n",
+    "        cecilia[step].elec_mix[sec[15:]] = round(cecilia[step].elec_supply[sec]/cecilia[step].elec_supply['total'],2)\n",
+    "    cecilia[step].elec_mix = pd.DataFrame(pd.Series(cecilia[step].elec_mix), columns=['mix'])\n",
+    "    EROIs_mixes_cecilia[[(step, 'EROI'), (step, 'mix')]] = pd.DataFrame(eroi_s).join(cecilia[step].elec_mix)\n",
+    "EROIs_cecilia = pd.DataFrame(EROIs_cecilia).rename(columns = {-1:'-1: original', 0:'0: balance', 1:'1: BAU', '2a':'2a: new mix', '2b':'2b: techno', 3:'3: 2°'})\n",
+    "# EROIs_cecilia\n",
+    "EROIs_mixes_cecilia = pd.DataFrame(EROIs_mixes_cecilia, columns = pd.MultiIndex.from_product([steps, ['EROI', 'mix']], names=['step', 'var']))\n",
+    "EROIs_mixes_cecilia.rename(columns = {-1:'-1: original', 0:'0: balanced', 1:'1: growth', '2a':'2a: new mix', '2b':'2b: new techno', 3:'3: 2° scenario'})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO!: pb EROI_wo_GW ER/ADV and EROIs REF\n",
+    "# TODO: calculer EROIs en partant du foreground\n",
+    "# TODO: construire scénario carbon neutral:\n",
+    "#       - pour changer mix énergétique, reprendre code 'cleaner' dans eroi_exio.ipynb\n",
+    "#       - se baser sur greenpeace, comparer avec Luderer, et compléter avec WWF pour non élec\n",
+    "#       - comme scénarios: greenpeace (>appendix), ecosys (mix: appendix), Jackobson-Delucci, Luderer (GIEC, short), Garcia (energy, transport), \n",
+    "# other:DDPP (country case studies and overall change in CO2 intensity, mix and electric cars), Denholm (grid), Chatzi (grid), Child (out but contains references)\n",
+    "# TODO: compléter rapport greenpeace par évaluation des émissions d'autres polluants (cf. Table 4 de Child et al 2018)\n",
+    "# TODO: - équilibrer les tables: sums cols != sums rows (--> GRAS) => pas possible, car on n'a pas de vecteur de demande ou de production (ARDA_file.xls absent)\n",
+    "#       - pythoniser, rendre fonctionnel et modulaire le code matlab \n",
+    "#       - mettre à jour les données (Exiobase 2 ou 3, ecoinvent 3)\n",
+    "#       (- agréger secteurs/régions pour gagner en rapidité)\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "\n",
+    "import pymrio\n",
+    "from pymrio.core.mriosystem import IOSystem\n",
+    "from pymrio.tools.iometadata import MRIOMetaData\n",
+    "from pymrio.tools.iofunctions import *\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scipy.io\n",
+    "import scipy.sparse as sp \n",
+    "from scipy.sparse import linalg as spla\n",
+    "import pickle\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "from openpyxl import load_workbook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_themis = '/media/adrien/dd/adrien/DD/Économie/Données/Themis/'\n",
+    "path_io = '/media/adrien/dd/adrien/DD/Économie/Données/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "themis = pymrio.themis_parser(path_themis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/media/adrien/dd/adrien/DD/Économie/Données/Themis/themis.pkl'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-12fba90d4230>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mthemis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_io\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'Themis/themis.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/media/adrien/dd/adrien/DD/Économie/Données/Themis/themis.pkl'"
+     ]
+    }
+   ],
+   "source": [
+    "themis = pickle.load(open(path_io+'Themis/themis.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "themis = dict()\n",
+    "for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']:\n",
+    "    themis[s] = pickle.load(open(path_io+'Themis/themis_'+s+'.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "erois_prices = pickle.load(open(path_io+'Themis/erois_prices.pkl', 'rb'))\n",
+    "erois_prices_wo_GW = pickle.load(open(path_io+'Themis/erois_prices_wo_GW.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def themis_parser(exio_files, year = None, scenario = None, themis = None, themis_caracs=None, labels=None, dlr_files=None, combo=True, compute_all=False):\n",
+    "    \"\"\" THEMIS parser (by adrien fabre aka. bixiou on github)\n",
+    "\n",
+    "    The THEMIS model is not open. You can ask NTNU for it, they might accept.\n",
+    "\n",
+    "    Two options for this parser: 1. either provide the year (2010, 2030, or 2050) and scenario ('BL' or 'BM'),\n",
+    "                                 2. either provide none of them, and all combinations will be loaded in a dict\n",
+    "    exio_files must give the path to the root folder of THEMIS\n",
+    "    if dlr_files is provided, the 3 Greenpeace=DLR scenarios are added (in case 2.); dlr_files = True is equivalent to dlr_files = exio_files\n",
+    "    if combo = True and dlr_files is provided, create 3 'combo' scenarios (improperly named): \n",
+    "        2010 : ADV 2050 mix on 2010 techno (i.e. 2010 transformation matrix A); 2030: ER 2050 mix on 2010 techno; 2050: BL 2010 mix on 2050 techno\n",
+    "    compute_all precalculates EROIs, prices, value-added, employments\n",
+    "    The folder should include a file called 'Supplementary info & mixes.xlsx' which provides the IEA scenarios of energy demand.\n",
+    "        You can ask this file to adrien.fabre@psemail.eu\n",
+    "    themis_parser runs in ~1h if combo=True and compute_all=True, ~1 min if they are false\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    def load_themis(matrix='A', year=2010, scenario='BL'):\n",
+    "        # matrix is A, Sb, Sf or S; year is 2010, 2030 or 2050; scenario is BL or BM\n",
+    "        if matrix=='S': \n",
+    "            Sb = load_themis('Sb', year, scenario)\n",
+    "            Sf = load_themis('Sf', year, scenario)       \n",
+    "            nb_stressors_b = Sb.shape[0] - Sf.shape[0] # check .shape for sparse\n",
+    "            nb_sectors_f = Sf.shape[1]\n",
+    "            res = sp.hstack([sp.vstack([Sf, sp.csc_matrix((nb_stressors_b, nb_sectors_f))]), Sb])\n",
+    "        else:\n",
+    "            if matrix=='A': data = 'A'\n",
+    "            elif matrix=='Sb': data = 'S'\n",
+    "            elif matrix=='Sf': data = 'S_f'\n",
+    "            else: data = matrix\n",
+    "\n",
+    "            year = '_' + str(year)\n",
+    "            if data=='S_f': matrix_name = data + '_' + scenario\n",
+    "            else: matrix_name = data + year + '_' + scenario\n",
+    "\n",
+    "            if data=='S_f': res = themis[matrix_name][:,:,2]\n",
+    "            else: res = themis[matrix_name]\n",
+    "        return(res)  \n",
+    "    \n",
+    "    def secondary_energy_demand():\n",
+    "        TWh2TJ = 3.60 * 1e3\n",
+    "        secondary_energy_demand = np.array([0]*A.shape[0])\n",
+    "        for name in energy_demand.index: \n",
+    "            if name in list(labels['sectors']):\n",
+    "                secondary_energy_demand[np.where(list(map(lambda s: s == name, labels['idx_sectors'])))[0]] = \\\n",
+    "                    energy_demand[[(reg, year) for reg in labels['regions']]].loc[name] * TWh2TJ\n",
+    "        return(secondary_energy_demand)\n",
+    "    \n",
+    "    if themis is None or themis_caracs is None or labels is None: \n",
+    "        themis = scipy.io.loadmat(exio_files + 'Data/THEMIS2.mat')\n",
+    "        themis_caracs = scipy.io.loadmat(exio_files + 'Data/Characterization_endpoint2.mat')\n",
+    "        label = pd.read_excel(exio_files + 'Data/THEMIS2_labels.xls', header=0)\n",
+    "        idx_name = label['Name']\n",
+    "        idx_region = label['Region']\n",
+    "        idx_region.loc[np.where(idx_region=='AME')] = 'Africa and Middle East'\n",
+    "        idx_region.loc[np.where(idx_region=='CN')] = 'China'\n",
+    "        idx_region.loc[np.where(idx_region=='EIT')] = 'Economies in transition'\n",
+    "        idx_region.loc[np.where(idx_region=='IN')] = 'India'\n",
+    "        idx_region.loc[np.where(idx_region=='LA')] = 'Latin America'\n",
+    "        idx_region.loc[np.where(idx_region=='PAC')] = 'OECD Pacific'\n",
+    "        idx_region.loc[np.where(idx_region=='US')] = 'OECD North America'\n",
+    "        idx_region.loc[np.where(idx_region=='RER')] = 'OECD Europe'\n",
+    "        idx_region.loc[np.where(idx_region=='AS')] = 'Rest of developing Asia'\n",
+    "        label2 = pd.read_excel(exio_files + 'Data/THEMIS2_labels.xls', header=0, sheet_name=2)\n",
+    "        idx_impacts = label2['FullName']\n",
+    "        label3 = pd.read_excel(exio_files + 'Data/THEMIS2_labels.xls', header=0, sheet_name=3)\n",
+    "        idx_caracs = label3['Abbreviation THEMIS']\n",
+    "        labels = {'regions': idx_region.unique(), 'idx_regions': idx_region, 'impacts': idx_impacts.unique(), 'idx_impacts': idx_impacts, \n",
+    "                  'sectors': idx_name.unique(), 'idx_sectors': idx_name, 'caracs': idx_caracs.unique(), 'idx_caracs': idx_caracs, 'name': 'labels'}\n",
+    "    if year is None and scenario is None: \n",
+    "        if dlr_files is not None: \n",
+    "            if combo: scenarios = ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'] \n",
+    "            else: scenarios = ['BL', 'BM', 'REF', 'ER', 'ADV']\n",
+    "            if type(dlr_files)!=str: dlr_files = exio_files\n",
+    "        else: scenarios = ['BL', 'BM']\n",
+    "        all_themis = dict()\n",
+    "        for s in scenarios:\n",
+    "            all_themis[s] = dict()\n",
+    "            for y in [2010, 2030, 2050]:\n",
+    "                if s == 'BL' or s == 'BM': \n",
+    "                    all_themis[s][y] = themis_parser(exio_files, y, s, themis, themis_caracs, labels)\n",
+    "                    all_themis[s][y].scenario = s\n",
+    "                else:\n",
+    "                    if s == 'combo':\n",
+    "                        if y in [2010, 2030]: \n",
+    "                            yr, not_yr, sc = 2010, 2050, 'ADV'\n",
+    "                            if y == 2030: sc = 'ER'\n",
+    "                            all_themis[s][y] = all_themis['BL'][yr].copy(new_name='THEMIS')\n",
+    "                            all_themis[s][y].dlr_elec = all_themis[sc][2050].dlr_elec\n",
+    "                            all_themis[s][y].dlr_capacity = all_themis[sc][2050].dlr_capacity\n",
+    "                            global_mix = all_themis[sc][not_yr].mix(scenario = sc, path_dlr = dlr_files)[y]\n",
+    "                        else: \n",
+    "                            yr, not_yr, sc = 2050, 2010, 'BL'\n",
+    "                            all_themis[s][y] = all_themis['BL'][yr].copy(new_name='THEMIS')\n",
+    "                            global_mix = all_themis['BL'][2010].mix_matrix(global_mix = False)\n",
+    "                        all_themis[s][y].aggregate_mix(mix = global_mix)\n",
+    "                        all_themis[s][y].change_mix(global_mix = global_mix, year = not_yr, only_exiobase = False)\n",
+    "                        \n",
+    "                    elif s == 'REF': all_themis[s][y] = all_themis['BL'][y].copy(new_name='THEMIS') # TODO: include attribute scenario\n",
+    "                    else: all_themis[s][y] = all_themis['BM'][y].copy(new_name='THEMIS') \n",
+    "                    all_themis[s][y].scenario = s\n",
+    "                    if s != 'combo':\n",
+    "                        global_mix = all_themis[s][2010].mix(scenario = s, path_dlr = dlr_files)[y]\n",
+    "                        all_themis[s][y].dlr_elec = all_themis[s][2010].dlr_elec\n",
+    "                        all_themis[s][y].dlr_capacity = all_themis[s][2010].dlr_capacity\n",
+    "                        all_themis[s][y].adjust_capacity = all_themis[s][2010].adjust_capacity\n",
+    "                        all_themis[s][y].adjustment_capacity = all_themis[s][2010].adjustment_capacity\n",
+    "                        if s in ['REF', 'ER', 'ADV']: \n",
+    "                            all_themis[s][y].wo_GW_adj = all_themis[s][y].copy(new_name='THEMIS')\n",
+    "                            all_themis[s][y].wo_GW_adj.change_mix(global_mix = global_mix, year = y, only_exiobase = False, adjust_GW = False)\n",
+    "                        all_themis[s][y].change_mix(global_mix = global_mix, year = y, only_exiobase = False, adjust_GW=True)\n",
+    "        if compute_all:\n",
+    "            for s in scenarios:\n",
+    "                for y in [2010, 2030, 2050]:                  \n",
+    "                    if s=='combo' and y==2050: all_themis[s][y].world_mix = all_themis[s][y].agg_mix\n",
+    "                    else: all_themis[s][y].world_mix = all_themis[s][y].aggregate_mix(recompute=True)\n",
+    "                    if s in ['REF', 'ER', 'ADV']: \n",
+    "                        all_themis[s][y].wo_GW_adj.eroi_adj = all_themis[s][y].wo_GW_adj.erois(factor_elec = 2.6, \\\n",
+    "                                                                                               recompute=True).rename(index={'Power sector': 'total'})\n",
+    "                        all_themis[s][y].wo_GW_adj.eroi = all_themis[s][y].wo_GW_adj.erois(recompute=True).rename(index={'Power sector': 'total'})\n",
+    "                        all_themis[s][y].wo_GW_adj.energy_prices()\n",
+    "                        all_themis[s][y].wo_GW_adj.employ_direct = all_themis[s][y].wo_GW_adj.employments()\n",
+    "                        all_themis[s][y].wo_GW_adj.employments(indirect = False, recompute = True)\n",
+    "                    all_themis[s][y].eroi_adj = all_themis[s][y].erois(factor_elec = 2.6, recompute=True).rename(index={'Power sector': 'total'})\n",
+    "                    all_themis[s][y].eroi = all_themis[s][y].erois(recompute=True).rename(index={'Power sector': 'total'})\n",
+    "                    all_themis[s][y].energy_prices()\n",
+    "                    all_themis[s][y].employ_direct = all_themis[s][y].employments()\n",
+    "                    all_themis[s][y].employments(indirect = False, recompute = True) # pb with employments combo 2050\n",
+    "\n",
+    "        return(all_themis)\n",
+    "    elif year is None or scenario is None: print('scenario and year must be both given or None')\n",
+    "    else:\n",
+    "        A = load_themis('A', year, scenario)\n",
+    "        S = load_themis('S', year, scenario)\n",
+    "        if scenario=='BL': skip, skipfoot = 6, 67 # TODO: make a separate function the extraction of IEA scenarios\n",
+    "        elif scenario=='BM': skip, skipfoot = 52, 21\n",
+    "        energy_demand = pd.read_excel(exio_files+'Supplementary info & mixes.xlsx', \\\n",
+    "                                         header=[0,1], index_col=0, skiprows=list(range(skip)), skipfooter=skipfoot, sheet_name=11) #TODO: select good columns>?\n",
+    "        energy_demand.index = ['Electricity by ' + name[0].lower() + name[1:] for name in list(energy_demand.index)]\n",
+    "        if scenario=='BL': skip, skipfoot = 27, 46\n",
+    "        if scenario=='BM': skip, skipfoot = 73, 0\n",
+    "        capacity = pd.read_excel(exio_files+'Supplementary info & mixes.xlsx', \\\n",
+    "                                         header=[0,1], index_col=0, skiprows=list(range(skip)), skipfooter=skipfoot, sheet_name=11)\n",
+    "        capacity.index = ['Electricity by ' + name[0].lower() + name[1:] for name in list(capacity.index)] # in GW\n",
+    "#         C = themis_caracs['C_H_CED_22'] # midpoint characterization\n",
+    "#         C_large = themis_caracs['C_H_CED_large'] # midpoint characterization taken by Thomas Gibon: should be preferred to C\n",
+    "#         C_index = themis_caracs['EP_H_CED_22']\n",
+    "#         G = themis_caracs['G_H_CED_22'] # endpoint characterization\n",
+    "#         G_index = themis_caracs['IMP_H_CED_22'] # cf. THEMIS2_labels.xls/C_IMP for more details\n",
+    "#         mid2end = themis_caracs['mid2end']\n",
+    "        meta_rec = MRIOMetaData(system='pxp', name='THEMIS', version=scenario)\n",
+    "        core_data = dict()\n",
+    "        extensions = {'labels':labels, 'impact': {'S': S, 'name': 'impact'}, \n",
+    "                      'energy':{'demand': energy_demand, 'secondary_demand': secondary_energy_demand(), 'capacity': capacity, 'name': 'energy'}} \n",
+    "\n",
+    "        return IOSystem(A=A, name='THEMIS', version=scenario, year=year, meta=meta_rec, **dict(core_data, **extensions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def results(themis, stats=['eroi', 'world_mix'], scenarios=['BL', 'BM', 'ADV'], to_plot = False, not_all_2010=True, rounded=True, fillNA=True, longnames=False,\n",
+    "            dlr_sectors=False, longtotal=True, year = None, extend_wo_GW = False): # TODO: re-order index, mix_real, specific region\n",
+    "    '''\n",
+    "    Returns a panda dataframe with results from stats and scenarios specified.\n",
+    "    stats can include: 'eroi', 'eroi_adj', 'eroi_wo_GW', 'eroi_wo_GW_adj', 'world_mix', 'employ', 'employ_direct', 'energy_price'\n",
+    "    scenarios can include: BL, BM, REF, ER, ADV, combo\n",
+    "    not_all_2010 keeps only one scenario for year 2010\n",
+    "    fillNA fills NaN with '–' instead of 0, rounded rounds the figures to max 2 digit, longnames details the scenario name, dlr_sectors removes CCS from sectors,\n",
+    "        longtotal puts 'Total (PWh/a)' instead of 'total' for the last index: all these parameters should be set to False to plot figures\n",
+    "    to_plot = True overrides all previous parameters and set them to False, use this if the results are used to be plotted\n",
+    "    if year is in [2010, 2030, 2050], only the result for the given year are returned\n",
+    "    extend_wo_GW copy/pastes (normal) EROI to the eroi_wo_GW column for scenarios not concerned by the adjustment (BL, BM, combo)\n",
+    "    '''\n",
+    "    if to_plot: not_all_2010, rounded, fillNA, longnames, dlr_sectors, longtotal = False, False, False, False, False, False\n",
+    "    if type(scenarios)==str: scenarios = [scenarios]\n",
+    "    if type(stats)==str: stats = [stats]\n",
+    "    names_stats = {'eroi': 'EROI', 'world_mix': 'mix', 'eroi_adj': 'EROI adj', 'eroi_wo_GW': 'EROI w/o GW', 'eroi_wo_GW_adj': 'EROI w/o GW adj',\\\n",
+    "                   'employ': 'k Employ', 'employ_direct': 'k Employ direct', 'energy_price': 'price'}\n",
+    "    stats_names = {'EROI': 'eroi', 'mix': 'world_mix', 'EROI adj': 'eroi_adj', 'EROI w/o GW': 'eroi_wo_GW', 'EROI w/o GW adj': 'eroi_wo_GW_adj',\\\n",
+    "                   'k Employ': 'employ', 'k Employ direct': 'employ_direct', 'price': 'energy_price'}\n",
+    "    stats = [stats_names[s] if s in stats_names.keys() else s for s in stats]\n",
+    "    if dlr_sectors: secs = list(np.array(themis[scenarios[0]][2050].energy_sectors('elecs_names'))\\\n",
+    "                                                      [['CCS' not in s for s in themis['BM'][2010].energy_sectors('elecs_names')]])\n",
+    "    else: secs = themis[scenarios[0]][2050].energy_sectors('elecs_names')\n",
+    "    res = pd.DataFrame(index = secs+['total'])\n",
+    "    years = [2010, 2030, 2050]\n",
+    "    for y in years:\n",
+    "        for s in scenarios:\n",
+    "            for stat in stats:\n",
+    "                if y in themis[s].keys():\n",
+    "                    if stat in ['eroi_wo_GW', 'eroi_wo_GW_adj', 'eroi_GW', 'eroi_GW_adj', 'eroi_TWh', 'eroi_TWh_adj']:\n",
+    "                        if s in ['REF', 'ER', 'ADV']:\n",
+    "                            res[(s, y, 'eroi_wo_GW'+'_adj'*(stat[-4:]=='_adj'))] = getattr(themis[s][y].wo_GW_adj, \\\n",
+    "                                            'eroi'+'_adj'*(stat[-4:]=='_adj')).rename(index={'Power sector': 'total', 'total': 'total'}) #loc[secs].fillna(0).\n",
+    "                        else: \n",
+    "                            if extend_wo_GW: res[(s, y, 'eroi_wo_GW'+'_adj'*(stat[-4:]=='_adj'))] = getattr(themis[s][y], \\\n",
+    "                                            'eroi'+'_adj'*(stat[-4:]=='_adj')).rename(index={'Power sector': 'total', 'total': 'total'}) #loc[secs].fillna(0).\n",
+    "                    else:\n",
+    "                        res[(s, y, stat)] = getattr(themis[s][y], stat).rename(index={'Power sector': 'total', 'total': 'total'}) #loc[secs].fillna(0).\n",
+    "                        if stat=='energy_price': \n",
+    "                            res[(s, y, stat)].at['total'] = res[(s, y, stat)].loc[secs].fillna(0).dot(themis[s][y].world_mix.loc[secs].fillna(0))\n",
+    "                        elif stat in ['employ', 'employ_direct']: res[(s, y, stat)].at['total'] = res[(s, y, stat)].loc[secs].sum()\n",
+    "                        \n",
+    "    stats = np.unique([c[2] for c in res.columns])\n",
+    "    res = pd.DataFrame(res, columns=pd.MultiIndex.from_product([scenarios, years, stats], names=['scenario', 'year', 'stat']))\n",
+    "    \n",
+    "    res = res.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\\\n",
+    "                                   'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', 'total']]\n",
+    "    if rounded and 'world_mix' in stats: \n",
+    "        res[[(s, y, 'world_mix') for y in years for s in scenarios]] = round(res[[(s, y, 'world_mix') for y in years for s in scenarios]], 2)\n",
+    "    if rounded and 'employ' in stats: \n",
+    "        res[[(s, y, 'employ') for y in years for s in scenarios]] = round(res[[(s, y, 'employ') for y in years for s in scenarios]]) # TODO: int or millions\n",
+    "    if rounded and 'employ_direct' in stats: \n",
+    "        res[[(s, y, 'employ_direct') for y in years for s in scenarios]] = round(res[[(s, y, 'employ_direct') for y in years for s in scenarios]])\n",
+    "    if rounded and 'energy_price' in stats: \n",
+    "        res[[(s, y, 'energy_price') for y in years for s in scenarios]] = round(res[[(s, y, 'energy_price') for y in years for s in scenarios]], 1)\n",
+    "    if longtotal: res = res.rename(index={'total': 'Total (PWh/a)'})\n",
+    "    if fillNA: res = res.fillna('–')\n",
+    "    long = {'BL': \"Baseline ('BL')\", 'BM': \"Blue Map ('BM', +2°C)\", 'REF': \"Reference DLR ('REF')\", \\\n",
+    "                'ER': \"Energy [R]evolution ('ER', +2°C, no CCS nor nuclear)\", 'ADV': \"Advanced ER ('ADV', 100% renewable)\", 'combo': 'Combination of scenarios'}\n",
+    "    new_col_names = [long[c] for c in res.columns.levels[0]]\n",
+    "    if not_all_2010: # TODO: rearrange columns so that same scenarios are gathered\n",
+    "        scenarios2010 = ['BL']*('BL' in scenarios) + ['REF']*('REF' in scenarios) + ['combo']*('combo' in scenarios)\n",
+    "        res = res[[(sc, 2010, st) for sc in scenarios2010 for st in stats]+[(s, y, st) for s in scenarios for y in [2030, 2050] for st in stats]]\n",
+    "    if type(year)==int: res = res[[(s, year, st) for s in scenarios for st in stats]]\n",
+    "    if longnames: res.columns.set_levels([long[c] for c in res.columns.levels[0]], level=0, inplace=True)\n",
+    "    res.columns.set_levels([names_stats[s] for s in res.columns.levels[2]], level=2, inplace=True)\n",
+    "    return(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def export_all_excel(path_themis, themis):\n",
+    "    book = load_workbook(path_themis + 'all_results.xlsx')\n",
+    "    writer = pd.ExcelWriter(path_themis + 'all_results.xlsx', engine = 'openpyxl')\n",
+    "    writer.book = book\n",
+    "    writer.sheets = dict((ws.title, ws) for ws in book.worksheets)\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'ADV'], stats=['eroi', 'mix'])\n",
+    "    res.to_excel(writer, 'Main Results BL-BM-ADV EROI,mix')\n",
+    "    res = results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi', 'mix'])\n",
+    "    res.to_excel(writer, 'Greenpeace EROI,mix')\n",
+    "    res = results(themis, scenarios=['BL', 'BM'], stats=['eroi_adj', 'mix'])\n",
+    "    res.to_excel(writer, 'IEA EROI adj,mix')\n",
+    "    res = results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi_adj', 'mix'])\n",
+    "    res.to_excel(writer, 'Greenpeace EROI adj,mix')\n",
+    "    res = results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi_wo_GW', 'mix'])\n",
+    "    res.to_excel(writer, 'Greenpeace wo GW EROI,mix')\n",
+    "    res = results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi_wo_GW_adj', 'mix'])\n",
+    "    res.to_excel(writer, 'Greenpeace wo GW EROI adj,mix')\n",
+    "\n",
+    "    for sc in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']:\n",
+    "        res = results(themis, scenarios=[sc], stats=['eroi', 'eroi_adj', 'eroi_wo_GW', 'eroi_wo_GW_adj', 'world_mix', 'employ', 'employ_direct', 'energy_price'])\n",
+    "        res.to_excel(writer, sc) # to_latex exists also\n",
+    "\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['employ', 'employ_direct'])\n",
+    "    res.to_excel(writer, 'Employments')\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['energy_price'])\n",
+    "    res.to_excel(writer, 'prices')\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi'])\n",
+    "    res.to_excel(writer, 'EROI')\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi_adj'])\n",
+    "    res.to_excel(writer, 'EROI adj')\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi_wo_GW'])\n",
+    "    res.to_excel(writer, 'EROI wo GW')\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi_wo_GW_adj'])\n",
+    "    res.to_excel(writer, 'EROI wo GW adj')\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi', 'eroi_adj', 'eroi_wo_GW', 'eroi_wo_GW_adj'])\n",
+    "    res.to_excel(writer, 'EROIs')\n",
+    "    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['mix'])\n",
+    "    res.to_excel(writer, 'mix')\n",
+    "    writer.save()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def erois_and_prices(self, secs = None, var='Total Energy supply', source='secondary', netting_fuel = True, factor_elec = 1, recompute=False):\n",
+    "    '''\n",
+    "    Returns the series of regional EROIs and prices of the list of sectors secs for each region, considering the energy from source with notion var.\n",
+    "    '''\n",
+    "    if secs is None: \n",
+    "        if self.scenario in ['REF', 'ER', 'ADV', 'combo']: secs = list(np.array(self.energy_sectors('electricities'))\\\n",
+    "                                                                    [['CCS' not in s for s in self.energy_sectors('electricities')]])\n",
+    "        else: secs = self.energy_sectors('electricities')\n",
+    "    if recompute or not hasattr(self, 'eroi_price'):\n",
+    "        res = pd.DataFrame(index = pd.MultiIndex.from_product([list(self.regions)+['World'], secs+['total']], names=['region', 'sector']), \\\n",
+    "                           columns = ['eroi', 'price'])\n",
+    "        for reg in self.regions:\n",
+    "            for i, sec in enumerate(secs):\n",
+    "                res['eroi'][(reg, sec)] = self.ger(secs = sec, regs = reg, var = var, source = source, netting_fuel = netting_fuel, factor_elec = factor_elec)\n",
+    "                res['price'][(reg, sec)] = self.price_energy(secs = sec, regs = reg, digits=5, indirect = True)\n",
+    "            res['eroi'][(reg, 'total')]=self.ger(secs=secs, regs=reg, var = var, source = source, netting_fuel = netting_fuel, factor_elec = factor_elec)\n",
+    "            res['price'][(reg, 'total')] = self.price_energy(secs = secs, regs = reg, digits=5, indirect = True)\n",
+    "        for i, sec in enumerate(secs):\n",
+    "            res['eroi'][('World', sec)]=self.ger(secs=sec, regs=self.regions, var=var, source=source, netting_fuel =netting_fuel, factor_elec = factor_elec)\n",
+    "            res['price'][('World', sec)] = self.price_energy(secs = sec, regs = self.regions, digits=5, indirect = True)        \n",
+    "        res['eroi'][('World', 'total')] = self.ger(secs=secs, regs=self.regions, var=var, source=source, netting_fuel=netting_fuel, factor_elec=factor_elec)\n",
+    "        res['price'][('World', 'total')] = self.price_energy(secs = secs, regs = self.regions, digits=5, indirect = True)\n",
+    "        self.eroi_price = res.copy()\n",
+    "    return(self.eroi_price)\n",
+    "IOS.erois_and_prices = erois_and_prices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/pandas/core/indexing.py:194: 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",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BL\n",
+      "BM\n",
+      "REF\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/scipy/sparse/compressed.py:742: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
+      "  SparseEfficiencyWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ER\n",
+      "ADV\n",
+      "combo\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/pymrio-0.3.6-py3.5.egg/pymrio/tools/iofunctions.py:496: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  return(round(factor_elec * supply / er, 1)) # TODO!: code the option non_unitary_themis=False for biofuels and cie\n",
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/pymrio-0.3.6-py3.5.egg/pymrio/tools/iofunctions.py:496: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  return(round(factor_elec * supply / er, 1)) # TODO!: code the option non_unitary_themis=False for biofuels and cie\n",
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/pymrio-0.3.6-py3.5.egg/pymrio/tools/iofunctions.py:375: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  return(round(self.value_added(secs, regs, prod = prod, indirect = indirect) / ((self.energy_supply @ prod) / TWh2TJ), digits))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "duration:  54.79802465041478\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time.time() # 55 min (38 before GW and adj)\n",
+    "themis = themis_parser(path_themis, dlr_files = True, compute_all = True)\n",
+    "print('duration: ', (time.time()-start)/60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/pymrio-0.3.6-py3.5.egg/pymrio/tools/iofunctions.py:496: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  return(round(factor_elec * supply / er, 1)) # TODO!: code the option non_unitary_themis=False for biofuels and cie\n",
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/pymrio-0.3.6-py3.5.egg/pymrio/tools/iofunctions.py:375: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  return(round(self.value_added(secs, regs, prod = prod, indirect = indirect) / ((self.energy_supply @ prod) / TWh2TJ), digits))\n",
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/pymrio-0.3.6-py3.5.egg/pymrio/tools/iofunctions.py:496: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return(round(factor_elec * supply / er, 1)) # TODO!: code the option non_unitary_themis=False for biofuels and cie\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "duration:  75.64047181606293\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time.time() # 75 min\n",
+    "for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']: \n",
+    "    for y in [2010, 2030, 2050]: _ = themis[s][y].erois_and_prices(recompute = True)\n",
+    "erois_prices = pd.concat([themis[s][y].eroi_price for y in [2010, 2030, 2050] for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']], \\\n",
+    "                         keys=[(s,y) for y in [2010, 2030, 2050] for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']], names=['scenario', 'year'])\n",
+    "print('duration: ', (time.time() - start)/60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']:\n",
+    "    pickle.dump(themis[s], open(path_io + 'Themis/themis_'+s+'.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(themis, open(path_io + 'Themis/themis_GW.pkl', 'wb'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(erois_prices, open(path_io + 'Themis/erois_prices_GW.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "export_all_excel(path_themis, themis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['employ', 'employ_direct'])\n",
+    "# results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi', 'mix'])\n",
+    "# themis['ER'][2050].wo_GW_adj.eroi\n",
+    "# results(themis, scenarios=['ER'], year=2050, stats=['eroi_wo_GW', 'eroi_wo_GW_adj'])\n",
+    "# results(themis, scenarios=['BM', 'ADV'], year=2050, stats=['eroi', 'eroi_adj', 'world_mix', 'employ', 'employ_direct', 'energy_price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5424284924033836"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([85.04637284])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "17.73060840008799"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.42386596515522706"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.18442262]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1.62636141])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.013058898678254827"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# N = 2111, p = 87/EROI + 17 (R^2=0.50); log(p+1) = -0.18*log(EROI+1) + 1.62 (R^2=0.42)\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "EROI_for_prediction = np.array([i/100 for i in range(1,10000)], ndmin=2, dtype=float).transpose()\n",
+    "inv_EROI_for_prediction = 1/EROI_for_prediction\n",
+    "log_EROI_for_prediction = np.log10(EROI_for_prediction)\n",
+    "\n",
+    "erois = np.array(erois_prices['eroi'])\n",
+    "prices = np.array(erois_prices['price'])\n",
+    "erois_cleaned = erois[np.logical_not(np.logical_or(pd.isnull(erois), pd.isnull(prices)))]\n",
+    "prices_cleaned = prices[np.logical_not(np.logical_or(pd.isnull(erois), pd.isnull(prices)))]\n",
+    "EROI = np.array(erois_cleaned[np.logical_not(np.isposinf(np.array(erois_cleaned, dtype=float)))], ndmin=2, dtype=float).transpose()\n",
+    "EROI_inv = div0(1, EROI)\n",
+    "p = prices_cleaned[np.logical_not(np.isposinf(np.array(erois_cleaned, dtype=float)))]\n",
+    "reg_inv = LinearRegression().fit(EROI_inv, p)\n",
+    "reg_inv.score(EROI_inv, p) # BL2010: R^2=0.53, p = 71/EROI + 20\n",
+    "reg_inv.coef_ # 71\n",
+    "reg_inv.intercept_ # 20 \n",
+    "p_predicted = reg_inv.predict(inv_EROI_for_prediction)\n",
+    "\n",
+    "log_EROI = np.log10(EROI+1)\n",
+    "log_p = np.array(np.log10(np.array(p+1, dtype=float)), ndmin=2, dtype=float).transpose()\n",
+    "# log_EROI_cleaned = np.array(log_EROI, ndmin=2, dtype=float).transpose()\n",
+    "# log_p_cleaned = log_p[np.logical_not(np.isposinf(log_EROI))]\n",
+    "reg_log = LinearRegression().fit(log_EROI, log_p)\n",
+    "reg_log.score(log_EROI, log_p) # BL2010: R^2 = 0.54, log(p) = -0.44*log(EROI) + 1.87\n",
+    "reg_log.coef_ # -0.44\n",
+    "reg_log.intercept_ # 1.87\n",
+    "log_p_predicted = reg_log.predict(log_EROI_for_prediction)\n",
+    "\n",
+    "reg = LinearRegression().fit(EROI, p)\n",
+    "reg.score(EROI, p) # 0.014"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7faa23759c88>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7faa23759c18>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7faa23759f60>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0, 30)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0, 80)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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pi/ZQpZe4aQXje0/j9d2vc/9N99MjoMdVfV9cuB8TYkNYfbBG4sAsYFYX5jx4gB3pFwDqDAorFIobC5dSIaWUB01+835SyolSykIpZYGU8g4pZXfT58Xmnqw9Zn/1u6WBCCk5/t2PVzXegh+OsXB7BqcLSlm4PcOiWfP+tpNU6Y2B5yq9RHv513joPPjt2t9SX0DaFd6e3p+5wyKJCPBi7rBIy0OlLpzlwSsUCoWZVlehau2KMOd9HwzuDsDFzduvamx7d8j29AssTcoiv6jcZv/hbMGc2OfZcHIDK4+tvKrvNDNvfG+2PjfCJcMOzvPgFQqFwkyrMu72Jfnp+cW46zSUeLblRGAXemW63lPVGc7cIWuP5DLtZjt3h4Qu7hOIDY7libVPUFh27StFZ8aH8eqkvgztHsirk/paXDJKnkChUIDrPvcWgb3rYc3Bs7wyqS+FpZX4nhyK79aNICUI0ah88Hnje3M0t4jtJh82YCkgyiooYdGODKSENm4aMs6Vobk0l/zyx3h2w7N8OOHDWsd15sdvCmbGh9n42c1uqspqA+46DZ8/nAAYM3LS84s5eOZSk89BoVC0TFqVcR8X08kSPARj6uDWtHM8els3DnfpzYjzS+HkSZLdAhyMnKsG/tPZ8SxNyrKpDAWj4R8VHWwxlMYAaChtdZP56OBHTIuexpioMQ7jmf34gOWzuYyrvTzBypRsvk7JtqRaXos5KBSKlkGrcsvMjA+js5+txEDGhRJmfZDIK8WBAJz++oer1mCZGR/GZ7PjHbJPzAVE5l6rAL7V0/ES4Tzy7SMUVRTZD8Xy/Wdstj/Zc5oXVh1uFreJjSCaqWdspZNesa6mWioUitZLqzLuAI8Nj7LZjgz0prLawAm/UM57+1KxcbODkWtqDRZr37zAnQd6LSC7KJvfb/y9zXHJmYUUllbZ7CurMvB5UhYzFje9qqO1INrnDydwz4BQm8bgzuavUCiuT1qVWwYcBbV6BvuwPf08VdUG9ob3Y9SRfbiH+fL5wwnNpsFidmlY+9G9NuznzT1vclePu7irx10Adb4xmN8omnpu1oJogOU+KJ+7QnFjUa9wWFMycOBAuX///qsexz5Yat6+a/c3hL/4LKSnQ1RU/QM1IRXVFcR/EE9OcQ6H/+8wwW2DawKcVQaEAAToDcbj3XUalj3ieixAoVDcuDRGOKzVGXezwayoMqDVCF6eEFPjG09Lg169YPFiePjhJphxwzh6/ihxi+K4Lfw2fpj1AxqhsXkQAby46jBnCksZ2TuIt6f3dzrOgh+OsXRvFlJKZsWHq5W2QnGD01yqkC0Ka6GtaoPkRevgZI8eEBwMW7b8InPr06EPb41+i/Un1/OvpH8BtiqOG1PzOJZXzJUKPasPnmXIgs0OujDm7Jqi8mqKK/Qs3J6hhMQUCkWDaXXGPSEywCZAaAAWbjtp3BAChg83Gvdr+EZizdyBc/lVz1/xh01/YP9Z27cU+yyV7EvlPL/qsI3xXn0wx2HMz/cqYTCFQtEwWp1xjwv3I8TXw2bfqQslNRujR0NuLvz0k8O516J6UwjBh7/6kOC2wUxZPoWC0pqgam1ZKu9vz7Cs4MP8vRzHxLiiH/76FsuDQFWiKhSKumh12TIAl8ts0wvzL5fVbIwda/xcuxZuusmy21n1ZnMFMwO8Avhq6lcM+WgIs76exfczv0er0Vp858v3n+GiVYqkBF5YdZitaeeY2D+UvadtDXZkoLdNIVReUTnrUvOuybUoFIrWSatbuQOUVupr3+7UCfr3hx9+sDnmWjeXvrnzzfxr7L9Yf3I9f93+V8v+eeN7kzJ/NHOHRdq4lySw4Wg+L32byquT+jIowo/gdm2YOyySS3YPs03H8lWjbIVCUSetcuXet3N7DmZfttm2Ydw4eO01uHQJfI1tXs2FTdeyufScuDnsyd7Dy9te5uaQm7mzx52W380b35uwAG9eXH0Yg1V4oKraQGFpJcvn3mLZl1dUzumCms5PpZV6dBqB3iBVo2yFQuGUVpcKaWbif3Zy5GwRMSHtWP34ENtf7toFQ4bA8uUwtaaHyC/RXLq0qpQhHw7hxMUT7J69m5iOMTbzSMsr5k+rD2OSi7fkvwNMXbjbxvCb0QqYPiiMEF9P1ShbobgBuCHy3OvCYjTD2hM3sAdMnAgffdRs3+cq2UXZDFo8CHetO4vHbuC3n590UG5cmZKNAO4ZEArA5Pd2Ox1LYFSlVH52heLGoTHGvVW6ZcxYqzf2DPaxCZjuGj6KgG++gaoqcHP7RecZ2i6Ub2Z8w7CPhjF37Qz01S+CdLf4y8158Gbe3XLC6ThaDXi76/Dx0JGWV6yMu0KhqBWXjLsQ4jRQDOiBainlQCGEP/AlEAGcBu6VUl6zvDz7HqKj+wTZBBmTBoxg/OoVsG0bjBx5raZVKwNDBvLppE+ZumIqPu7/JrDyGdx0Wqf+cmf7BEbpgqLyaorKq3l+1WE+3HWKmJB2FJRU2sgTKxQKRUNW7iOklBestucBm6WUC4QQ80zbf2jS2dXBW5vSbLa3p5+3CZiuDOzDcHcPDixYyK0twLgDTOkzhVduf4UXfnyB+KgoFox8zenqOy7cj7nDIi3pjzoNDO4WaKNlD3Di3BVOnLsCODbJNruoisuqSM0tIrpTO3w83fDzcqewtNLyqXz2CsX1ydW4ZSYAw00/fwJs5Roa95Lyapvt8ioDc4dF4uPpRlJGAZvTL/Bj5M0k7NnE00v388+ZDXJXNRt/HPJHsouyeW//e2zJ6UFc+O8cjlmalGUx7AB+Xu4OjUqcsfZILjPjw2z0d8wRFetzBVj2awSsmHuLMvAKxXWGq3nuEtgghEgWQswx7QuSUuYCmD47OjtRCDFHCLFfCLH//PnzVz9jE2OcVHum5hbx2Igofsoxpkn+0PNWAksvU7zxl9GacYYQgn+P+zdT+0zluY3P8cnBTxyOeXdLus32+SuVfLQzg1cn9SWqY1sC27o7HdvcJNuc019bqNx6v0EaxcwUCsX1hasr91ullGeFEB2BjUKIn139AinlImARGLNlGjFHp7w9vT+nL5TY5LubjdvwHh1YffAsWyIHUqZrw4zTiU31tU2CVqPls0mfcbHsIrO/mY2/pz9397zb8vuyKoPDOennS9h7qoDXJvezcbcEeLs7+NzNOf3WK/e6OFNYWv9BCoWiVdHgVEghxEvAFeARYLiUMlcI0QnYKqXsWde5zZEK6azfKcD9S5LYnn6Bf33zD4adSiHjUDoDejRNB6KmanhdXFHM7Z/ezuH8w6yevpqxUWMt41u7ZazRmMpa7Rtg2/vOzT73ZUmZZF8qt+zv3sEbnVbDsbxiy7627lqOvDy20dehUCial2aR/BVCeAshfMw/A6OBI8A3wAOmwx4A1jRsuo3HWjSrtn6nXfy9EMDKmDvwLb9C/rKvGjy2M8yG93RB6VXL8fq08WH9r9fTu0NvJn4xkQ0nNwDG6tW5wyLRaewb5BndKNYNsGd9kMibG9KY9YFt2z6z1PDOeXcwrHsgHm4ahnUPZOOzw+ng08ZmzCuVeka9ubXR16FQKFoerrhlgoBVQgjz8UullOuEEPuA5UKI2UAWMLWOMZoMVwTAkjMLWbH/DBLYERFLflt/Bu/8Hni8znEXbjvJjz+fQ2+Q6OwbgZj4fG+mw/bVrN79Pf3ZdN8m7vj0DiZ8MYFvZ3zLyMiRzBvfm3nje/PUFwdYffCs5XiNMAZErRtgW2vMOAuMfjo73mZ77+mLDsectFbWVCgUrZ56V+5Sygwp5U2mP9FSyldM+wuklHdIKbubPh0tRjPgigBYYkYB1aa6fanR8vPICfhu3QTnzjkdMzmzkBmL9rDxaD5603nVBsn8NUccVvDCrt20/bYr2L8dBHgFsOn+TXT3787dy+5mU8Ymy7FvT+/PxNgQfL3cmBgbwoq5t1gaYJdUVFtk67VaDTmXylySAB4U4e+wr1ugN+AoLaxQKFonrU4VMiEyANNbBAhRaxGQu06DVhhXuKv73QHV1aS88m+nYyZmFFCld4w9GKR0eHjMHBRW53Z9mN883tyQxoxFe3jB1Ekq0CuQzfdvprt/d+5aehdrfq7xcr09vT8H54/mvsERFv/6xtQ8Vh88awmYVlcb+GJvFrM+SGRpUpbl4XH/kiR6/Wkt9y9Jsoz35MgetPOoeWnr3sGbjc8Ob1KXk0Kh+GVpdfIDG1PzLKtrvUGyMTXPpkm2ObD4+cMJfJ2SzRd7s1hV7cuszr3x/+xDlk6fzczBETZjJkQG4KYVVFoZeIExaGn/8DC7YBobULV+86jUS5YmZfHFvjN0aOvOxNjObHlgC+OXjmfy8sl8OOFD7r/pfsDRHWWwUxQzAEiorDIwf80RDFIiZU3a4/b0C9y/JIknR/bg3vf3WO4hwB29gyzXZM261DzVv1WhaKW0upW7MwNkvRo2BxaNHZs8LWqLnw64k8jCs+x+f5nDmHHhfiybM5jRfYLQml4KtBrB/Luinfqw543vzdbnRjTK8JnfKszOHInxIZVXVMHC7Rks3nqOzfdvZnjEcB5Y/QDvJL4D2D4UyqsMNg8iMCpFagVoNAKDlBgkDmmQe09fJDGjwMawQ809te8UVVvnKIVC0fJpdcbdmQGqzQ+fEBmA1nSF63rcynkvXybsWs1TXxxwGDcu3I+buvhaDKKUksLSyiafv/mtYkZ8GO46x9u/LjWPtu5t+X7m90zqNYmn1j/F/C3zie/qb3E12aPBKAE8bVAYL0+IsRxnf+igCH/8vBwLoMz31JylExHgxdxhkWrVrlC0YlqdW8aZWyQ5s9BpI464cD8GhPmx73QhlTo3lt00hsf3LOe1nQdhen+Hsa9VQ4+4cD/iwv2YPCCU19Yes2mrZza0bXRtmBT2Bj9lVfPX7X/l1KVTfPSb10nJvMLG1Dyb4i0pjPn+bjoNkweE8vnDCRYX1TubjrP39EV6BfkQHxlA6tnLaAQWnfhBEX42RtycpaNQKFo3142eu7NGHNbKkQDBRRfYufAhvhw8iVm7nOe9/xINPeyLopYmZfHhrlOcOHcFieSy7ksuu/0PTxnD3Z3f5MtHRnP/kiT2nr6In6cbuUUVlrFmxYfxyqS+Dtdk9tfrNAKEQK83PsAeHBxBam5Rs6hKNvReNlVxmEJxvXHD6blbExfuR1peMW9vOm5RQNxg55/PaxfIt72HMSX5BygogADHlbl5Vd0YkjMLbZpuuDqO9WrZ/oEkEPhWT8dNBnPB7W2+zn6ISe+/w6pHJwPw/KrDLE3Kshx/JOeyJeZgxtptpTdIpg3qQmdfT4rLqiyVsPaqkldLQxuSW1flmj+VgVcoGk+r87nXhtko7ki/wMLtGby+Po0jZ4scjnsvYQptKsrgP/+56u+0zgk358ovTcri86QsZixOdCnn3J61R3Kd7vfWDyeo8hUMophvcx/ix1M/AjB5QCjuVo74wzmXHapVEyID0GmMGflajWDygFASIgMcgtOLtp9s1Jyd0dCG5M4C5QqFovFcN8bdmVHUGySDIvyI6uBt2Xe8QwTZQ0fBv/4FV644nLM0KYv7liTZrIadYZ8T/traYzYZLK4YNGeYxc+c4WGIJrjiDdq6BTDqs1G8sfsNBoT5smzOYIZ2D7T40p1+t1VtQFpeMbM+SCSzwFYwLLOg1OHB0Fjsaw3s4xf2hVwqU0ehaFquG7dMdKd2TvXOT5y7Qsr80TYCY6ETX4bBg+H99+HZZy3H2nd3gtrdFPYryyM5l222Jc47KtXHzPgwtqSdY+PRfMs+AYQHeJFXVM6wiH68++tD/GbNb3hu43NsSN/N2M4vMS6mE/tOX3QaDE7MKKBab1SI1OsNrD2Sa5EEFkB7Lzcul1YhqVvGoCGYs4JqEzWzd9lcbf2AQqGw5box7j6ebjZNKMwUllYBRqNZY6jDjK33FiyARx6Bdu0Ax9X/l/uyajXuY6ODbZQbS53I9M776hAbnx3e4GuZe1s3th0/T2W1cUw3reDNe2NtDOSKqSt44ruX+E/yX9makUJw9fP08O9BZIe2PHpbN8DYizUhMoCEyABj/rteIjTC4UHw+zG9ePm71CbPEnIWv1ialMWi7SctcsRV1Qbe33aSsioZuc7jAAAgAElEQVQ9eZfKyCsq52iuoztNoVA0jOvGuCdEBtDGTUO5nZHV2SWGmzM4bv/tPHpPGAlvvgl/+QuAQ7ejn7Ivs+CHY05XkdYrzY4+bWzSGc2kn2+cGFdcuB/LHjFW2EqMfnV7IymEwKd6Ch0rtVxwf51st6cou/gYJ84PJzLQm4/3nLasjB8cHEG1yWVk/rRfVfcM9mn2LCH7YDGYusBYvaVATTWtveCZQqFwnesmFRKMhts+b9y6GMfeHbAnZSF+2zbDyZMQZCzBn/Ppfgdj8+qkvpb2dbW5GWYsTrSstK3p7OfJY8Oj6sxCaWz65QurDvN5UhbV4hwX3N6gQnsU7+qRRGgfp7RCh0Eaq1bbe7lzsaSmIOum0PaseXyIy9/TFCz44Rgf7znt8PCtDQ83DT//dVzzTkqhaCU0i557ayIu3I/lc2+ptcrSPoPj+2mPQ3k5/O1vlmMeva0b9jLqa4/kWgz4G+vTHDJhzCvt0X2CHOaUU1jG86sOM/KtbU6DtM6ExKyFv+ringGhaDUCnexIUOXfaVc1jRLtZtJ5HIPutFGSQKuhsMS20rakUt9kWTGuYA4+u2rYwVa5sj6NfYVC4ch1tXKvD7MhNfuWP384gbgFz8PixXDwIMTEAI6dkF6d1JfUs5f53Mo4z4oP454BoQ4rbnv9dXvMTbzN57y75QRvbkjDTu4FAbRxqz8/fGlSFi+sPmyR/i3THKKs7T+p0BdzZ8TT9PCexvJ9OTbjawQu5Z43FcNf38Jpq8wcnQacvORYCPX1oGuHtoyL6UTPYB+H4Cs47z6lUFyvNGblfkMZd3DiArlwAXr2JD+8B3N+8w86tvdk7m3dSMsrtmnfZ18sNLpPENvTzzst0lnwwzE+35tJcbne4fvt2+Sl5RUzf80R9AbpEAzWCri9dxDlVfo6K0jN7qisi6VMjO3MQ7cF8Mi3j/BN2jfEdkzg8tlHoNr4VmFWitQKeGZ0Tx4bEXXV97Q+zC0PzQzrHoi/tzubjuVzpcLxHlnj00ZLSaUegzTeuxmDwliZku1ycZRCcT1wQ1eouopDBkdgIElzniN+wR8JD/2Gb/oMZ8vP+Xz56C02xnTygFC+2n+GKr3ETSsI9GlTaxekUdHBfLzntNPvN6+gzW3yvk7JxiAlGpOio95gVHTUYExNN6dE7ki/wOoD2UQF+TgEWM3uKGtWT1vNZz99xhNrn6DC47eEikfQF4/E3F5ECGEjImZ+6KXnF3PwzKUmTUe0f2idvVRmY+zrotjK+BsknCuucKn7lEJxo3NdGPer1YN5N3IYHsHdeWHLh2yNHEiRR1sHo2GWBTZ/D8DXKdm15pVX1OJf1mkEUkqHNnlaAVMHGmUB/LzcKSytZENqHoesBML2ni5k7+lCvtp/hmVzBgNY5A6iQ9pTWFppuQfL9p5h45Ge/HXwev6+50lOVLyDh9s2/Ksex00GU22QvLj6MFkFJYyKDnbQeF+4PYOjuUVNkrFin4VkKaiywlkaqz0aoKNPm2si7qZQtHZcNu5CCC2wH8iRUt4lhOgKfAH4AynAfVLKptfIrYeGapg4Y2y/UF4Y8xirP32GP23+gD/e/ZSD0bAuggKjYR/WvQOBPm0cVtLGblFg7/ESwMsTYixGGIzG2Wyo7Mfx83LnULZt6iAYm3y8v+0kW9POOei6u2sFD93a1UozBjw1L+Iv1lPotoTcNo/RvnoG7aonYpA6Fm7PYJ1VAxRrtqdf4KkvDlBQUnlVwmLm86zvn3VK5Kg+QYzo2dGSa++kKZal0vWeAaFOYx0KhcKWhqzcnwSOAe1M268B/5RSfiGEWAjMBt5r4vnVi3UGTEWVga9Tshv8H95ofCbxdV4K967/lP5PzybKagz7ylWtyX0CRmM6eUCozXhx4X7MGRppE5QFeHRYpGPD7VqqOGvmBf/afJw8K+VHgM2mRt72VOoly/efsdnn6aZDXzEWT/1ALrov5JLbx5RotxJQ+VvayJ6cvVRW670xB4d3pF8gq6DEIrFsP+fkzEJeXHWYM4WljOwdxNt2ksq2RWRGrGMagE2u/V+/TeXI2SJiQtrxp7ujHb5PGXWFom5cCqgKIUKBT4BXgGeAu4HzQLCUsloIMRh4SUo5pq5xmiOgap9jrtXA8kdvadB/frOxGhzqw4Apo42NtFNTwd+YjnffkiSn0gZgXI3/bkxNYNLa8KXlFfPhrlMgJQ8NcTTsDbrGRXtsVukaUzMOZ6vczr4e5Fwqt2yP6hNERZWexIwCQnw98fE9wNqcV9FzER/9eMaHPU3iyXLHgezQCPjbxL68/F2qQ/bK1IW7bTJyJsaGOBh4V6+1ruItheJGpDkDqm8Dvwd8TNsBwCUpZbVpOxvoXMuk5gBzAMLCmlYvHIwruNjQ9pbCJb0B3t92kkX3u3YfkjMLuXfhbvQmv/e3r/6LPhNHwsMPw8qVIISDz9h65e6mrWnS7cxFtOmZ2+r8blfcC2Z//8qUbL5KzrZosc+/K5ojZy9zobiCH9POoddLtFpBdEh78i6Xo5fGtMO5t3WzG38ES3aN5fXEv3D8ygp+vLSXdj73UVV8GwItAL5ebgzv0cEmrVNKLLo09gJl9i8RW4+fd+n+298P6wf1sqQsHlUdoRSKRlGvcRdC3AWck1ImCyGGm3c7OdTpK4CUchGwCIwr90bOs04q7JKm84vqX4WaefDDJMvqVy/h/1INbHvtNaOg2DvvwFNPOfiMewb7OF1dOpO5rc1oNzRWYN29qbYq2fe3nWTzz+fYdCwfnVbDtLjQWle/s2/tw+xbvyQl9w88ue5Jdma9hXubNfhVPYKHIYbfj+nFzPgwgtt5sGhHBlIa8+5rEyiz7u4EMLxHB5f/DswkZhRQZfV3KTEGdsMCvJkZH6aaeSgUDcCVlfutwK+EEOMBD4w+97cBXyGEzrR6DwVqr9xpZqbdHGYTeJx2s2tvCPcvSbJJtQPIvFjKxM43s3rCBHjuOaN6ZHw8M+PDLD7htLxiQnw9HQxsQ9r0NfRBYP0wqS03/UcrP3y13kBnX0+brlT2Pm6AAZ0GsP3B7XyZ+iWPff8M+eXzSAi+myG9jHr388b3ZpSpT21dOjQr5t5Sp8/dFRIiA3DTaRxkHNYeySWroEQ181AoGkCDiphMK/ffmbJlVgArrQKqP0kp/1vX+c1ZxFSb8aqLXn9aW2tJ/NgQdxa+9QgYDLB3L8kVbSwrbfMK1V0rWDZnsMMK2hVXi9NqWSfHO9Ot6dPJh18nRNikPr675QRvrE+zvD7pNIIvHzXOzV6wa2JsCKculFClN+Cm1TDtZmOws7SqlH/s+gevbF+AQUqGBP+aVfe/jr+nP9cKZ/pAr07qy6LtJ22qXCMCvNj63IhrNi+F4pfkWhcx/QH4QgjxN+AAsOQqxrpqnGVj1MegCP9ai2nWna1k7V/eZdz/uxcmTWLfSx84PAgq9ZKVdtk5rrbpM+udm1fkzkjOLOTtTccdVrJHc4t5ftVhGxkBsypmZZUBjUbw8oQYyzzspYzt5RHMbz0z48PwKJ1GUFk4l3SfsT33Izq/+RV/Hv5Hnoh/Ai83r3qv62oxF2TZP6ytV+6gmnkoFPVxXcoPNKSoadSbW2uV5tVpBOvDL9Bt7gMk3zKWyUMecyjAcdaQuiHzrM3v7ixDxhnWMgK1XbczqV17hnYP5LPZ8TY6MJXiFGWe/+OSTKKDVyf+dvtLPNT/IXSaX6b2TfncFTcqN7wqJNiqLLrSMm7js8OZOyzSaYTYICXretwCf/87cbvX8dz2T21+LwQkZRTU2ZKvLkXD19Yeo7zK6OaprLJtjbcyJbtew27m0JlLlqbYj42Icpov/+qkvgztHsjE2BCnY0R3MpYvWK+I3WVX/MvnE1yxgKJiXx797lH6vNuHTw5+QrWh2uk4TUFt92ze+N5sfW6EMuwKhQtcF/ID1jQkUGnGHDRcmZLNifxikjMLkRhdHgmRASR3nUPaF9t4LHEFRR7evB8/BTCmBp44X2JZFdu7hZytzAH+tPow6eeuUGVlvA1AcVmVZdvZw8YZemlsdrH1+HmWPWJc+TtbwVu7rQZ1DXAojCquqLbcC6hpQrLvdCFtiCGo8h+UafZSUv4VD655kJe3v8wLQ1/gvn734aZ1c3G29ZOcWcjk93Zbtu1lm6+WBT8cY3lyNp5uGh4b0b3RtQcKRUvnujPu5oyVyiqDgzhWXVj7yq2NI8CsJUlUjv4/2laW8setH1PcxpulsbaNJJy15LN/0CzcdpJNR/Nr9bG/v6Mm7e+eAaEs3ZvlIGFQG9Y55/WlWJozf6wDtSv2n+EeU9rkvPG9LZWoU0yGViDwMsQTLoezcHoJf9n2F2Z/M5u/bv8rzw95ngdiH8Bd69q9ros5n+yz2bZOhbxa7KWca3soKxTXA9edWyYu3I/5d0Ube4ZKycvfpbrc5MHsDgAs7g2LgdZoeebOZ9jc7Wb+tuG/TP9pvc251g0wliZlcd+SJIrLqnDXaYxNMzSCzcdqN+xgfBN4cbVRWjgu3I9Hh0ba/H5ibAgebsbx3LXCoYXgO5uO88yXB20eKCtTsp26OOLC/ZgSF2p5Q9AbpI1byHzMBDs3zriYTtzd8272PbKP72d+T0fvjsz5bg5R/4rin3v+SXFFcV23uF4ullY57LMPCDcW+6bmTTm2QtHSuO5W7gCFpZUYpGyQayY5s5Dpi/ZYJH2/MKU4Wueu6zzc8ft+NZqnZrNg7b/xrixnycAJAJw8d4VZHyTy4OAIK9GuC/QO9qF/uB8CbJp91IZBwvw1R+gZ7MO88b0JC/C2KZ7ybqOz5LuD0Tf/47F88ooqqNRLMi+WIsDyQPkqOZtqvfNV/OQBobUqW5p5e3p/gtt5OAQyhRCM7z6ecVHj2HByA6/ufJVnNjzDX7b9hbkD5/JE/BOE+Dj379dFVAdvhwC3WWysLlwJtto3NXd1bIWiNXLdZsu4kkNujX3v1D6dfPjhyWGAYw7962sOMejF33LbkR18NXEOz/W4GykEWgFd/L1s8rHBuMp+6VcxvPRtqk1aY/cO3mQUlDoIgNnr1VhfkzN3i32+vrtW8OTIHpy9VMayvVmWvPxBEX7c1rOjRVLYbMybSmFxb85e3tj9BiuPrUQrtMzqN4vfDf4d0R2jGzTOqDe3cuJ8CW10GubfHV2v28Te3VKXn94+O6qxGjgKxbVENeswYc4hb4jRspcsOJpbzMi3tjGyV0c+3nOaymoD+05fZO+pAlYfPMvCcb/jH1LHlNWLqB5whj/f8SiijbvT1WGVXlJYWsmyRxJsKk0TMwp4c0Oaw1y0GuGwiq4rUGyfr58QGWBJjVy+/wwGU+B27+lC9p02Bout2/jVVvHaUJ38QZ0HsXzqck5ePMnbiW+z5MASPj74MWO6jeHxQY8zLmocWo223nE2Pju83mOssXe3rEvNq9W4n7frJ9sYDRyFojVw3fnczZjTAgGXmit3DfR22Hfi3BUWbs+goqrGqG46Zlzd6zVafnfn0/w3YQrTU9ay8cfX+WKqMRA5d1ikTTq8tbhYiK+nRe/F7PIxt94TGHPrY0LaMfuTfTz1xQHLGH5e7sYOSjga/09nxzOseyAebhqGdQ/k09nxFsMc7m9beCStPq2DsPY0NKXUfM67W05wqdiff4//N2eePsNfR/yVw+cOc/eyu4n6dxSv73qdglLn39lY7Aua6ipwste8aYwGjkLRGrgu3TJmGiLOVZesr1le102nITLQm6O5tkHDqT9tZMGGd7kcGs6u1z8gURfAueIKBFiaeYDzLBazETa7SpIyCmxW4RNjQxjUNYA/rT5sETizjgnUd91ajbBJubTueCQAD52G8moDnX092DnvDstx1o27a+u36pBVVMu9rtJXsfrn1by77122ZW7DQ+fBjJgZPHbzY8SFxDm9hobSkAKnp744wNbj5xneo4NyyShaBcotY0dDct6jO7Wr1bjPGRqJj6ebxYjZa5ev6DeKM75B/Hf1Aob/ejxrxz3Jxl5DLL72xIwCzl4qczoXe7mCxTtsXTqbjuXz3U+5Nrrt1XpZ57VYX7e0K4S6OcIPDzct29MvIIEyUwwg+1I5QxZsthj4hMgAyzXqJQ5uIvsH5z0DQmu9125aN6ZGT2Vq9FQO5x/mv/v+y2c/fcZHBz/i5pCbeXjAw0yPmU67Nu1oLOb0TVdQBl1xI3DdumWgJufd3KKtLpVGH083S1qgAEb3CWJo90BendSXeeN7W1Ij48L9WDH3Fnw9bZ+LiWH9uPPBd0gPDOO/axbw0saFiIpK5q85wpsb0lix/4zFVSNEjbG0r8a0dxN08fPCYPd2Vd+1WF+3PeeKKzie7zxd0brBx68XJ9r8zn77/W0nLdW1VdUGBLh0r/sG9eW9u94j55kc3hn7DmXVZTz63aN0erMTD65+kB2ZO2js22RyZiHPrzrMC6sOu5z+qlBcr1zXbhlovErj/LuibVQXnR1/7/u70RuMbhud1pQuqa/i99s+4ZF9qznWIYJn7nqGYx0jHc5/dVJfegb72Kx+zd+ZlFHATzmXGd6jA/cNjrAcI4A7egfxqEPzjdqvOz2/2EYobGJsCN8eOuu0g1OorwcDI/xZn5pHmRO1zNML7gSMbg3rMc0dmpxJAdeHlJJ9Z/exJGUJy44so7iymB4BPXgo9iEeiH2A4LauCYTZa/G46zSWil2ForXTGLfMdW/coWEG3uz/tm8lZ129as54iQlpb5NS+PK3qRzKvgzAHSf28o8N/8GnpIh3bp3BwoQp6K0yRYZ2D+RCcQXH8mpW0TpT4ZWz77yadEVrf7SPp5vFl26NVsDdN4U4KEaa8dRpOPa3cbWKkHm41S1b7Mr8SypLWHF0BUsOLGFn1k60QsvIyJH8ut+vmdhrIm3d29Z6rr3ksbN0UoWitaJ87k5oSFDV7HZ5d8sJp63kVqZks2L/GUuAUisgpnN7/Lzc6RnsQ2pukWWsrT3imRLVl6e/+TfP7fiM0emJvDDmMY4EG41NcVmVjWEHY5WodRaLdTPo+ppu1IZ9oPH+JUkOhh2MQdba0gKFgD/dbcxVr62is7aYRkPuv7e7Nw/GPsiDsQ+SdiGNTw59wtLDS7lv1X1ohQeDgkfz4ohHGRU5ykHPJiEyADetsKzc63Nd1XcfG9MfQKFoSVz3xt06uFjpYrWqfUclPy93Zn2QSEWVwUY+QC/hUPZlDmUfZlCEH9VWvg69QZKl8eKJX/2e9d0H89Lm9/nm06fZPHwyl56fzwtbs51+d20+a3MbPXOh1Y70C+w9VUBBSaWNAbJeJW9MzbPpXrT5WH6t8sYB3u7IWtTKpKzRYbHvJwvGwE1txrQxQm4APQN78uodr9LHew6/+2Y5JdqtJJ3dwp1LvyHQK5Bp0dOY1XcW8aHxaITGps+sAItOjjOs3z7M12JtwOv7vULRGrjujbufl7tlpWqQtsqLtWFfBGU2UHU5sJKznATwpKR3sA8/MJQdXfvzu12f8+ttX6M/vI29N89gZcwdGKxcNRJjYHd4jw6k5RXz9qbjFtmBWR8kOjQLMbtQzAbI3ofv62m7uq3NsHdo6875KzXFPW5aga+nG+293Dlx7opl/9ojuXw2Ox4wCqUFtfNgeM+OrD6QTdbFUj7bc5rEjAKKy6pIzS1iXEynelsP1pfCuD41Hw9DNB6GaPyr5tA1NJ2g4P0sObCEd/e9S2i7UCb3nkxf/zGUXunG5AGhNvduZnyYg1vI/u1j7ZFcG+Nt//sPd51Sxl3R6rjufe7vbjnB6+trqkCt28+5inWw1SBr6QTuBDet4N6BXWwkAKLzT/K3TQvpn32MtMAw/nHbA2zuNsihCYg1o/oEsflYvlN3ipmh3QNJiAywyU0fEO7HvtN1Z42M7hNEWZXeZjVubtxh719/dVJfByNnX/pvjzlwbO9zT84s5E+rD9vUDDiTDahtDkUVRaz5eQ0rj61kbfo6Kg0VaKU/3obBeFbfShtDNAItc4dF8uGuUxbNoGVzBpOWV2wz5txhNamuztoS1nbtyZmFLr0pKBRXS2N87tqXXnqpmabjyKJFi16aM2fONfs+MFZzfp2SbWMYw/y9GNTV9b6gIb6eDO4WSBd/L/p2bs9+F9Ps+nZuz9zhUaw5mGPRjznf1p+v+o0iMySSwSdTeDDlewZn/sRp/xDOtuvodByfNjoulVUhpUSnFfTt3J7BkQH8bOWzn3tbN2K7+LLmYA5SStx0Gv45rT/+Xm6cOH/Faa9YAfxjyk34ebmz+edzNmP1DW1Ppd7ApdJK2rbR8eQdPWxcP6sO5KDVCP679QSX6ngbunClgmdG92RQV39CfD0t589YnGijJ28+9sFbu9rew9D2dPTxoFJvYO5t3SxzaKNrw03BNzE9ZjpelXdx4GQ7DKKMEs1Orug2UKxbR7U4S8b5Msor/AEtegk6raCjj4fN9R7KvsSuExdYczCHwd0CGRMdzP8SMymtrGmeXqk3cI+pGM1yDYv2cPDMZQ7nXGb1wRxu6RZouUaFoin5y1/+kvvSSy8tasg59bplhBAewHagjen4r6SUfxZCdAW+APyBFOA+KWVl7SP9MsSF+/HyhBjmrzliyURJiAxocAaKdVAzr6i81qwSa6bdHGZx8axMyear5Gz0egNuOi2zXn+W3/xvKEO2f8NTu5by1ed/ILFLDP8ZPI2dEbE2K/lpN4c5rH6TMwspqdRzrqjc0uAacNDU2Ziah4dJ4sD6Adc72Ie/Teprc13WAUT7QGjPYB/AMUA6NjrYQSjNmjY6De9uOWFznxMzCqiqdnzY1CYbYN8f196VM7xHOIu3j8Cn6jakKKOYZEq1uyjRbiFNvw7h4YGnYQCe+kGUVk9k7RHb+ZoD5OaYQFpeMReu2P5TtlePTMwosKn8bUg8QaG4Frjic68AbpdSXhFCuAE7hRBrgWeAf0opvxBCLARmA+8141wbjbk5hXWp/L3v77Gsps16LK5mSLw9vT+Dugaw9kguFVV69tq5PrzctdyfEG4Zw2xAzWJhZkO37YUx8MIYvtz2BNn/eIeZ25fzv+V/4mCn7qy980GODRrO2H6hNuNAjYGtqDLKC1hjbaztXSa9g30YEO7n1IVgb0BrC4Ta7+8e5MPcYZEs33/GQYtdqxEcPHOJ/ZmFNpkyCZEBuOk0NgqZXu71C4rZX5P5c9743paHmp+XO/PXeOJdNQSNvoIptxTx/t7llGiSKNXu5l9H3yHKdwCXdTF46uNxk11w12owGKQlJvD2puM23xnVsa3Dv4eGZucoFNeaeo27NDrlzVE1N9MfCdwOzDTt/wR4iRZq3MHW6N27cLeNzO729AtM/M9ODppy1OvLkEjOLKSwtJKnRvYgLa/YwbiXVur5eM9pRkUH2xhRZ92e/LzceXlTBpX97mRxn9FM+3kLv937NX/84AUq1oezf/x0Zm27jX2XJSG+nrx5byyJGQWWzJ1qg7Tov9unS6aeLbKZV1mV3uVm3rUFQp1lEgH8bkwvtqadI7+onMGRAfh4upFzqYwvTPEGe8mFh26JYPXBHADyiioordTbGOvaqE0B0jqN1fxXK2Qbwr2H8OPsCew5eQE/3xzSi7by7fFvSXf7hEtun9DRM4z48NH4awcyK3Y8ceF+DhlBD9m5isx/l65m5ygUvwQuZcsIIbRAMhAFvAucBC5JKc1dkrOBzrWcOweYAxAWdu0yDupyu1hngJj5yWTYzXy4M8Opcbd3S0weEGojxmXG2Wu6syIpMKYaSqBK50bHZ35L4NA3Obn4f1z8+xvc+v5r9Hd7m9V9RvDZgPFMea+UVyb1RasRVJusmN4geXvTccvDxlmREdS4PVwRzqpNNtl6v/k6zA8ajTBWhs6/O5qNqXmsO5KHlMb91g+IpUlZtQZhVx/MsTHu9n+PsV18bdxA9q4cZw+lmodqd2A4Lw1/ieyibL47/h3fHf+O708upbTqAz485satYbcyptsY5tzRj6OZfozv27nWh7y9LpBC0ZJoULaMEMIXWAXMBz6SUkaZ9ncBfpBS1rksvNbNOmornLl34W6H1bZPGy3FFTUBtKiObdn0zG0O47696Tg7TaJbWgHTBoXxdUo2FVUGEDViPW46Db2CfPg5v5hBEf48ObKHZU4aUWOYzZglDKbEhVJaUc13P52l2gC9z2XwQPJ3TDy6FY/qSo4EdePUXVOpuHc687bm2IzjrhW083Sz8Rd39vXATaux+KftpQNiQ9vj4+lGdKd2NhkjrmCtHGl9HQPD/Rzu7+g+NbIJE/6z01LJa49WI1huymaydz89PKQrH+85bbzXwIRaGm3U9mCvbX9FdQW7zuxi/Yn1rD+5nkP5hwAI8g5iVLdRjOk2hlGRowhqG+TSfVEomppmr1CVUl4SQmwFEgBfIYTOtHoPBeqPMF4j6iuc+cO43g4+97ExnWxWvPav4taGxtzsws20cs++WMre0xctRjwxo4CNqXkWN8/29AvsySigWm+sQLUXAoMa6YFlSVk2bwHHOkYyb9wT/H34b5hwdCtTD2/i7iUL4NM3iRk0gjeC4tnRtT8VOncq9dIhEPjYiO42K0/7KlRrV5R1Aw9XDHxCZICxV61VYNEgnb8ZbTyaz/b083z+cAJtdLZ6de5WvmuDQfJ1SrbFv2/tflq0I8PylqMV0D3Ix+m8nK2o63rgt9G14faut3N719t5bdRr5BbnsuHkBtafXM+6E+v430//AyC6QzQjIkYwousIbgu/jQAv5WNXtFxcyZbpAFSZDLsnMBJ4DdgCTMGYMfMAsKY5J9oQ6iuciQv3Y/mjg52u4moLqNoXMgkB8++K5rM9py3669vTL/BzXhFPjezJz3bKi+bMCmcuHMBi3GrjsqcPn8bdzWdxdzOyKo9nz+4h5LuVfHBlA0XuXmzqHs/3vYawI2IAlTpj8ZK/t7vDdQzv0aHWTD2c9cUAACAASURBVB9n0gd1ERfuR3Sndg6r8G4d23LRbuVuPXb3IB+blX2lXqLVgN5gPO6LvVlEh7QnITLAxv0kpfG+mw282d/vCg2plO3k04kHYh/ggdgHMEgDKbkpbM7YzI+nf+TDgx/yn33/QSC4Kfgmo7GPGMGw8GG092jv8nwUiuamXreMEKIfxoCpFqPXYbmU8mUhRCQ1qZAHgF9LKStqH+naFjG5kurYkAYPyZmFTHt/j8XQaIBnx/Rk8Y4MLpU65nnbpx6aiQjwwstd69DwozHcE9OBC9+s486fdzImfQ++5Vcshn5TVDzdZ03i6amDHM4z+9wj/L0sK3egwSt3cCwy0mngy0dv4bM9p9l0LJ8A7zbkFpWbUkCNYwNMWbgb6396we3a2OS9m4vN0vKKLWms9o1H6mtaYk1j+uo6o1Jfyb6cffx46ke2nN7C7jO7qdBXGCUQOsUxImIEQ8OHckuXW/D3dL2WQqGoC6UK2QAa0lTZzNKkLKOhMUjcTUbwsz2nXcp5N2Ou2LRv+GFmVnwYPx7LJ7eozuckYHzA6EwuDZ2+mlszDzH+552MTk/Er7wYdDoYOhTuvNP4p2dPh0pYc2ZNY3zu1mN8uS+Lju08mHtbN+N1mFwgOq2G23p0oKNPG0oqqjl45hJjo4MdagXmDovkg52nah6eAp41dX8yP6g3pOY5vCU8N8b2mLrmf7Xqms4ory4nMTvRYuyTspOoMhgf9tEdorm1y60MCRvCkLAhRPhGIOqoRFYoakMZ9wYw/PUtNlkXEQFebH1uRL3nOTMQT31xwBIAdYZGQGSgNw8NibQUCM1YtKdGXVIDnX29eGtarCWQON3q9w1Fa9Dzemgp9+Qegu++gyNHAMjx78S5QUPo/5spMGIEdOjQLAbPmfyuRiNs0k+1GoHBIBHC2Olq3vjeNQ9PJ7LH4PiWYF65A8xYXLMqf+iWCIu2zbXWhCmrKmPf2X3szNrJzqyd7Dqzi6IKY0pqiE+I0dB3MRr7fkH9XGoYrlAoyd8GMDY62GblXldTZWucBevent6f+wZHMPm93U7PGWnXYCMxo4Bqk7yvBhjcLZCnRvawGTeqQ1sHSWBX0Wu0/L0ogHv+/neS5/6eJf/bgv+2TQzP2E/8j9/BuhUAlPbqw+F2PTjQpS8fd+3LwsdHNomGfHFZlU1cQYKNYcdqW4NRLA1qis3Mevn2mA21WbTMfE9fWHXYklZaWW2w/L3uSL9AVkFJo99IGoOnmyfDwocxLHwYAHqDntTzqRZjvzNrJ8tTlwPQ1r0tA0MGEt853vgnNJ4Qn5Bmn+P1hpJnds4Nu3KHhvncXcE+zdCMvS/bkrlRZcBATX642R/tTAGyMaz8v1scxtIa9Iwuy+a9kMucWfkdHQ7tx6O6EgOCwq7duXjTQH7wDudb7wgy/EJwd9Py4GDjSrg+101d7hN7tBpjZNTe/22dlWS9qq+L51cdZmlSltPfmQt469OSv5ZkXc5iV9Yudp3ZRVJOEofyDllcOZ19OhMfGm8x+HEhcXU2KbnRcUXc7npArdwbSEOaKrvC29P7E9zOg9UHczhXXGHxqdtnoZgLgd7edJxdJy44NAWprM2/0wDaeegsGSLW6DVawseNgPG9OTdzLncu3E5M5lHizx5l1OVTdF67hicrSngSuOjZjuTOvTiwvRcER/FlUDcKvdo77bpknWpYn195YmwI9w2OcPpmYJ3+KKVRYiAswLvO/7CTB4TylamJisaUdWPGUiDWgrRfwtqHEdY3jBl9ZwBGv/2B3APszdlLUk4SSTlJfH3sawA0QkNMxxgGhQxiUOdBxIXEEd0hmja6Nr/kJbQY6pNvvpG5oY17czBvfG9GRQczY3GixbA6a2YRF+7HUyN7sO/0RYeUTXed5qpX7kXl1RapY60AhKBDW3cmxna2PNDiwv34aO4wEjOi8fNy567Vh5GjDXQryCYu55jpz8+MOrHXMm52uw4cDepGVU4CTBkFcXEQFGSTaijqeBsUGPPTa6vuTIgMsKQ7mqnvP6xZCsD8sEjLK7YEiT/ec7rWlNiWgofOg8FdBjO4y2DLvvMl5y3Gfm/OXlYeW8kHBz4AwE3jRkzHGAZ0GkBcpzgGdBpAv6B+eLrdeIqU9lIR9gJvNzI3tFumubCu3NQIuDXK0aduxplvOzmzkNfWHnOo8rwaRvUJoqNPm1o1UOyDoNa0K79CdP5JYvJO0jf/BH3zT9L1Yk7NAZ06cTmqF19X+nIsIIyMjhGcCOzCJa2Hw1g6rWDawC51arHYZzJdzat2cwSM68PeB2wtO2HddL0hc5NSklGYQUpuCim5KSTnJpOSm0JBmfFtTyu09O7Q22LsB3QaQGxw7A3h0rkRfO4qW6YertU/gqbKqTbHBGK7+OLdRseK/WfQm9QLA73dyb5U3uAx3XUaErr681POZRttGfs8fmdMjA2he5CP0Rj5aeHgQUhJgQMHIDUVQ+pRNOVlluPPdwjhYPtQ0gPDSA8Iw9C9B7s0flx0967XB94UKZq/BPY+4LnDIvl4z+matxqM8Zf5d0XX2oTdVaSUnCk6Y2Pwk88mk19ibMUoEET5R9EvqB99/3975x4dR3Xn+c/t1lvWo/WwZVmW5De2bDD4BYQwZgMZIAzvkEASwoQJyZxkN5zM7obDzhKGmbDMzCaHPTOzAbMwEzI8DNghMIfwSgCbhx/I2NiyY8uSJVnWw5bUerakbnXf/aO6SlXV1S+pJVvy/ZzT6u7qUtWtrq7f/dXv/u73N3cNF867kAvnXcgizyJcwhVn64pzCWXcYzDdAy9T4THat+mk7ZIsVy4rYdPiYqPmqj7FX9gmYSUyD4BgEE6c0FIv6+rg0CF6937GnKbjpAXHdXu6s/NpKlpAzuqVrPyT9bB8ufZYuhSyx0MLyRTXNv9Pst97Ks/Vt57ebQkTVBfn0NLjs3yXbgGXLy0xxlvcAn4czulPBe0D7YbB//z053ze+Tn13fXI8H1Zbnoua+at4cK5FxoGf828NRRmFaZk/4rUowZUY5CKgRd9so45DS8a5rRH83s7yRgWe5xal1mIF593C62YtxM76rv48HgXaW4XSGnEui9cUMCgP8iwf4wFhdn0j45R2+yN3Ua3WzPQS5fCzTcDUAjg90NDA8c/+oztL/6Bhd2nWOxtY8n+T+Ct7dZtLFgA1dWwaBEh8rmpO42Wgnm0Fc5jz7GOmPufaGeQ7P/Ewh4DvramzOK56+Mv162e7zjekgrm583nK3lf4SvLv2IsG/IPcfjMYT7v1Iz956c/55Ujr7Bl33hxn4X5Cw0vv2ZuDStLVnJByQXkZuSmrG2K6eO8Me6THXixev59vHukkzULCixVkMzEMhpO0r/mdRI1+OuqPNxzWbWjfK4Q44bkoRtqONTWhwBO9vgMLRwdPVvHbP/NsgSnekfY0+Tlhd0t3LWpMiJeHre9GRmwciVLV67kS9dcx67GbpYvLiajygODg1Bfrz2OHoXGRmhqgp07WX/yJBtC4x2X3OKCigpYtEjrAKqqtPcLFkBFBZ+d8OMPBAkhLNlHsdqWjOYMOIf29OPXC4PfvLac7iG/sc41NWWOMXen2rJTRW5GLhsWbGDDgg3Gsh+9sI93jx1lWUU3l1/gMwz/Ww1vMRYaM9arLqxmVekqVpWsYlXpKlaWrmRlyUqlpXOOc94Yd/1CTDbmbs7dNhOScKC1jwOtB42JMuaLN5rRMBt9l9CUIO2pkMl4knXt/RHLhICf3bzGYkjM6NoyFy4oMAx9IpEdCTy3u4Vt+1ojc/YTbG9ElsycOXDxxdrDfhyBAAd319Gw5xBrg16qB85oYZ+mJnj3XWhrs6TV/AXwjbRMOvOK6MwvZe7+JbzTn8bp3CKeKSwl55ubWXnxcpg3T+twiC8yZ8bcweuOwoqyPMd5CeawX7TMoGjLp2MQ+P4XP+O3B9qBfPbX51OdW86/f/2/AxAIBmjwNnD4zGHL4/eNv2c0OC6LsSBvgWb0S1exsmQlq0pXcUHJBZTklCiZhXOA88a4Q2QpuXiYDZe9nJ0ZsxStUbDihhpHo6Frv2t53Nr0e3Mxi2Q9SfsdCWgeu9fnjxrD1QdRr/n5+wl/F2Zild2L1t4JGaz0dNZcsZY1V6yN0pAAdHRAayucOgWtrfQdPk7geBOrBruR+/dyz+kOMnQv9OW/G/9fj4fh4lLmzSnizdK59OYXwbx5dP/fz6lftZhlFy7lwFgWH/UJNi2bx7oqj2Noz+vzO85LiBf2u/vp3exp6mFhYTZlhdmGw/HYG0fYsrORkISsdJcxgSzVSQB26Wfz+3R3OheUXMAFJRdw68pbjeXBUJATvScMY3+k6wiHzxzmqX1P4QuMS3l4sjwsL14e8VhWtEyFeKaR88q4J4sldzsk+fKqeXT2jxAIhiyqjrphh/EQh9fnj6hkVNvs5eVPTxrryvAft0vw0A01htHTOwW328Wp3uGYsW7zlPzD7f2WWqDxaOgaivl5NHniWGX3nPab6rj2eEPSYeFC7RGmLPzQ9/vNpz4mr7+X+UPdlPZ2UTLkpXTIy+ZCSdfxZoq6vJQ2t1Az2k+6z6pDfxGwBkFvdh4dRcX82J3LNzPn0JOdjzcnnwu6FtFzIJ8vdYzRlZlHT04+3ux8+jNzLWE/eyrkq/taqT+jfff1Z4aoPzPEzvou/n1Xk+V3NRKIlFKINaidTDaYXfp58/LSmOsDuF1ulhYtZWnRUm5ccaOxPCRDnOw7Sd2ZOo51H+NY9zGOdh/lvab3+PXnv7ZsoyK/QjP2RVbDv8iziDSXMkep5Lz+NuNdDHbDZR5ENafq/dsnTRYpgcjybhq6powZ3YP3+rQiG/rs1Sc/aODtw508v7uF53e3xMxW0e9IklVGLC/IiplO6RLwpZXzqGvvZ9g/xpXLSsdTIW1l96LpwejHnczdyESxn891VR7+/buXG2G1d03jCK8X59Cy2mdkq1y+tITaI60UDfZS5vNS4x5hrK2d0iEvJUO9FA4PUDTcT2VvB2vbj+Hx9ZOxS7sjuN3WjqA7jdGnChkuKWbMU0x3H5Rm5tKXmctw1hwuzZrDqsxc+rLm0J81h75M7fn4yQCEtfideGJHY0RdXvOx20NGsX4T+t1bvHKLieASLqoKq6gqrOL6ZddbPhvyD3G857jF6B/rPsaLdS/SO9JrrJfmSmOxZzFLi5ayxLOExZ7FLPEsYUnREhYVLjovJ2hNlvPWuEe7GMxEqyOqr6uvH23ADKzG1NxZuF0ChDB0zu0e7zuHOy3vE5mGH6+mp9mDTnMJouXY6BEoe4emT6769SdNlpmuANv2teIfC7HdFI/XSSauPVGinU/9O/HkZHCgdTwVVs9i0dukZ6+0p2fRVVpOb2E2x+fFuLORkjn+YTzD/RT5+vAM91My3M9VpW5aj7WQP9RH8egA1X3DVHd1kz86SMHIENljsaWcR9Iy6AsbfAoLOEmW1glk5tKfmctnh/+DdV9ZC/n51I+4ONgfIqvYw+uHuvH4BIOZOQTc6WzZ0QAQM5feyaDf/M8fcqitn9Xl+bz6wysS/fqjkpuRy0VlF3FR2UW2r0/SPdw9bvS7jnKs5xgNPQ3saN7BoN96F1WeV24Ye7vxL84uVjF+B85b426Pn0YriB3PYJrX0Q350Y4Bx2yYh26o4bZLKpBoeiiAo8e7q7Hb0QveurfFaPtEYrBmD9pe+WllWZ6hQqnfXCwuyWXbvlaOdgxQ19bHcyZxrid2NHK4vZ++4QBtvcPGgOJoINIzj9VJpop4qa5OA+p6p+yUvRKr0DgAQjCcnYMvK4eThWW4gIx0F5mXVPDCnhZjdjISSyeaMRYgf3SQ/JEhCkYGKRgZJH90iPzwc8HIIKVjPqpcfpZljDHacIplXS3hdYZwIeF1bVvLwg8As7886k5nIDOHoYwctmVkM5iZw2BGNnmflMGyBZCfD3l548+5uTBnDv/jrQaCvUGqMrLo7M3iG/8wwHP3fwkyMnh+dwu/ePcoQyNj/GlN2aQ8fe3rE5TklFCSU8LlCy+3fCalpMvXRYO3gYaeBhq9jdprbwNvHX+L9kHruc7PzB839p4lVBdWU11Yrd1NFFSdt3H+82YSkx37pCZIbmKTXVHSXmPVJcAlNA1zXdrXFa6TaleAdEqFvOPJjy0CWACr5udZYrLJTsQyz5wFa+57ultMWD/ezpXLSugbDiQ0HyBVTMUkNXOYp6V7iDfrOijMTqd3OMC1NWVG51DfOcD+k72sXVhITmYar9S2EgxqAmr2MFxhdhqbV8w1On+Azv4ReocDSL1DEAIZ/p0gYUQfsJWSUhFg73/ZxHPvHOTl9w6TM+Ijz+9jzugweX4fxXKUtMFB5vh95I1qn+WO+sgLjLAkK0SWbxD6+2Ek8dnNIXcaA2mZ+NKz8GVk4UvPIseTz5JFZVrHEO4cLM/665wcbWKa/mx/nZ2tZS4l4Xn7Aj5OeE/Q4A0b/p4G4/WJ3hP4g9Y6wiU5JeMGv6DK+lxYRX5mfsL7PluoGapJcvUvPrAUc76oooDfJnArapf2vXJZCS09PkvxDxg38FpWzHjaowDu3FTJgsJsY4apvuzRW9YAmiH+698cNLzpdLdgla1e6ReXlfDrezcZ72PF3O2Dep6cDH762qGUGfRoJFMKb7JEG0NJ5HuJJ2Mc7XMnJyHDLfjq+oXUlBfw8GuH4tbHzXALHr5xNYfa+ti696ShdS+ADdUei8aQPvYSTTZalzXQw00P3VDjnBIbCMDAgGboh4Y4crydv31xD9mBEXL8I+QERliWK/iLi+fy2sf1dHf2GMtzAiPkB0fZUJIBQ0PaY3BQex6NX0EsApcruuE3vT/YE6BxKMj8siI2rlrg2GGEMjNod/loll6agt00Bc7QPHqapuF2mn1tNA22WtI5Qcvu0Q19dUH4ubCayoJKFuYvPCdSO6dkhqoQYiHwLFoSQgjYIqX8P0KIImArUA00AXdIKVOndDUNfOcLiywXZl1bX9xZmLXN3gjNdvukIDM3XDif3Mw0Tg+M8t7R04SCmif/Sm0r168uM9K0JZqh8I2O8fjXL9ZS7+6/0mJcjnYMWOLG9oyMWJOmnD471NbHC7tbEspxnyiBoJw2qV2nVNfaZq9RpSnNLbh4YSGjYyG+tqHSyFGPFpNOJMvHHg4CrRBJeWG2UXzkjic+jjpDGLTvyOvzs6Aw21LURAI3X1zBJZWeiLoD5lDXhCZHpadDUZH2AP5wOpNd1eMy1fPzM/n7B68GYHB3C39j68BuXlvOBqfQzNhYpMEfHrY+fL7k3vf00HWmD0//IJeP+cneN0roNT+uUDBi9y5gQfhxecSnEBJwOheaC6CpSNBckkaTx0dz4WGO5R/i7Tlj+NKsJysz5KIikM3C0BwWhvKoEAUsdBWyMK2YivRiFmbNpSjTg8jOhsxM7ZGRMf4wv4/1WZJ3MPFIJOY+BvyVlHKfECIPqBVCvAPcA/xeSvmYEOIB4AHgJylr2TRw16ZK3jt62hi8lJK4hmj7vtaEtx+S8Or+NqNYtvm0BcZCjoU9Xt3fRll+luUiNmemAEaWjtfnNzqjWBkp0T677ZIKtu9rNQZ443mYiXDlshJLZ5fuFmdVand7eKAXNCOqe8EHWg9SXZwTM4snkSwf+zwDgTVVdF2Vhy8sLYnpAJi/ozTXeChHoM1XiFZ3INnJUbEwD3pLoHvIz91P7+bZezcZHWZCMfe0NCgo0B4p5Hanspj3XxHZIYyMaI/RUcdn1+goZSMjlI2Ossm+Tvco8tQw3WMDNNFLi2uAVvcQJ9N9nMwYoTWrlw9yujiVGySo665JYBiy+6GiHxb2hZ8dXheOWG2AI+npzoZ/AsQ17lLKdqA9/HpACHEErWO8CdgcXu1XwPvMMOMO8P0/WcLO+jMJZ3JMxPyZi3a4hHaCY4l9vVnXETPt0cnjjJWREu0z+0DnI6/Xxa2gZOYbYSkCGJ/i/05dh8WQXRaemKXvb7qJdb6aun24XQI3znMD9O9NL0AyMBzgvmc/pbN/hILsdFp6fFxbU8ajt6yJqWD57L2bjElLG6uLuHb1fLbubSEzzUVBTgZz87TCG+uqPDxy02pLHdnp6hj138JfvbSfpm4f/qBkR32XxcCfTTldx7KY6enaIz91MXMBlIQf0WIgwVCQzqFOTvad5KS3iVZvs/bc38rJgVb+MNhO2/BpQrZ8tAyZTmVmMeWuQspd+Swgn3I5h/JQLgvGcigPZFLuzyTbL7UOx+/XHqOjcPx48seSTMxdCFEN7ABWAy1SykLTZ14pZcTVK4S4D7gPoLKycl1zc3PSjZxqzKEPiK1FUtvsjbjNvmbVPE73j9A3HGAkEGT5vLyonlpRTjr3fnExT3xwnIGRyNtK0LzfH129PGp6pVl7XQD/9U81RcHJxJbBWa72k8buqAb/G5sq+Vl4jEDHXng8PD541srcmYuRO/3S52S6uXHtAm6Loi+vF+2OJYWckGKmQ7ue/KCB3//xtDF4mqy20FRwwf/8nUVKISvdxR//9rppbUM0Ul0WcyoZC43RMdjBz97cySv7PyfoOsMY3Sya56dgzgBtA22cGjjFyFjkwLYny0N5XjnleeUsyF9A+ZxyHr360alThRRCzAG2AfdLKfsTHWCQUm4BtoA2oJpM46YLcypjvBir0232aCDI4fZ+AkFJulvwo6uXc+3q+fzuUDsCa0z+jvUL+cFVSxkYDjgKfgF8dLyLjxu7GQv3IC6bcazvHLDMcq3vHLAcR6xjjIVTuqB94M6M08m0e1jAlE9eisW6qvEqTZ6cDLbsaLB0PkOjQbbvazVSU+14fX5CcRygl2pbkzI2+jiAWbbA7yB0BlpHnmojH6vz2FhdZPm9bqwuStl+J0uqy2JOJWmuNCryKzjSXEZuKN/Ih/WM5PD+f74K0FI+e0d6aRtoM4y9/fWRxiO0D0SO6yTUhkRWEkKkoxn256SUukZrpxBivpSyXQgxHzg9oRacQyQ6k7Klx5oVs7fJa2SdBIKSJz9oYMvd6w1j6eRxPHD9SnY1dlvUF3WCEkueol1Y7N0j1glOO46dSZkRsN9+m0M3A8MBntrZSFBqGR5OBlE/Pr3IyJt1HUlNXqpt9nL307sZ8gfJyXDx63sn7+2bO7YVZXnc8eQnxsClBPyBEI+/e8yxWpYRmnHo3HSy05MrfOFU29YlBJ6cjPFJZmEJ5rGQTOldTzwHxh5CetaUjaVIHsdwUhghBJ5sD55sDzVza6JuIxgKkvbT5KckJZItI4CngSNSyl+YPnoN+DbwWPj5t0nv/Rwj0ZmU9hOWm+lmODAeYunst95qRfM4Xv3hFVz3+A4j3TEa+iCdfvHbFQh7hwP8/O2jUxb6MBtH88QfcPYszcebTIihttnLbb/82Hjv84e47Zcfs+0vtbyHZLYTbd1djd2YQ5ECzan66HgXe5t6HEMjeue2u7HbMdy2obqIbz29O+GJZXp+u45LwCM3rTZEyPTOHFJf3DsRB0YZ9NRhdnYmGk5yu9wT2nci3cEXgG8BB4UQ+8PLHkQz6i8JIe4FWoCvTqgF5xCJzqS0n7DK4lxLrPprGxIfePq7W9YYMWG3W1AzP5/PW/uMkIdLwJ0btYFLJ49Pj45NV+gjmRCWef1E0O9M7Gzb12pkvcTrwOK1yyIB4XaxsiyPg6f64sou/+CqpfzgqqXGXVhlUQ4SKM7NMLKeoslY2PH6/BZRtq9vHNcGMrcNKY2yiqkaWJ0OKQiFlbMVTkokW+ZDomfwfCm1zTn7JGqMrqkpi8iMmKhW/MM3rjYGTQGL3vsjN622bM988d++roLV5QXGpBW3S3DgZC8P/uZg1AHCVDEVYmDRDI2AhPeVSLtuvaQCEX4GLPVu48ku2y/Ubz2927LtRCp8Xbq4mMz0cQOrh7fszoV+PKmMuU+HFITi3OC81ZaZDE7e4WS04u0eZrSLL9qFuaIsj237Wnn505O8Hc7Zf+XTk7wwhTNDp8IDXFflYdtfXh4RcwfNe09kX7HaZf/Ob72kgqMdA6yYl8fc/CyWlOTy+LvHqJmfn5CM8a7Gbmrm58et8GUPE8UysHbnYirO30Ty4BUzD2XcJ0AqvFanbRztGDC8/2iFNpwuzHVVHm0ykmkQdqpnhk6VB7iuykPdI9dGLI+2r2QMp/07f+KDBpP6Zh/vhF/trO/i+1cudsxZ1/ep6wgJoaWuSnC8a4vWiSsDq5hqlHGfAMl6rXbNk9pmL6d6h0lzuwzJ34HhAP/41lFAMy7vHz2dsOiWXgTEjNsFbeFCH+B8e68X/J6bn8X3JyDwNZ0GymlfyRpO+3k73R9dPKuuvd+i2wNa1tNLta2MBUPGoLaUWqrr969c7HjnNl1a9gqFHWXcJ0AyXqtdZ7yle0gr7jEWwiVgdbjItl2j5O3Dnbx/7Ay3r6uIGz83FwERwJLSXFq8w7ywp4WXa1stKXW6kJQ1z76P9/7YydbvXT7lhieZakHxSNZw6udt275Wo1h4NOpO9fHYG0eM+PpjbxyJOi8BtFKLToU0zKmUIpzuqFBMB8q4TxCzbov5vR270X6zrsMwSHqR7SPth/jOFxZF1EL1j4V4YXcL2/e1srAwmxPdPqOIgtlI2j3SxaVzaDgzZKTRQTifeyxkTG23MxaKr6szWaIVmH7igwZO94/wtQ3jWSN//7sjtPT4IoqCmJlo3N9cx1bnymUlZKW7+azFy5lBPz0+rfPb1+LlJ9et5NX9p2JuM5ou0boqDw/dUGN874/8Rx0ryvJmjPd+/4uf8VZdJ7mZbn58zYopkyCYylm5Z3PG79lEGfcJkmgqoF1YSq8AZM5V9wcl/aNjPHrLGqMW6lh4urxEq6Wp19zc39rHFY/93iiPt7O+i0dvWcNDN9QY+ibPfHTCMFxpboFAUyoUJn15e/pTmit6tkqqsHd0W/e2UNfWh57d9bExJQAAE51JREFUeaD1IC3dQzz1YaOhZa97y9GEs5KN++vevr17+/C4do7s/d6eJk2+IJb8AEBmevTORZ/lqt9hbN/XOiOMjVnaejgQNDrmVBv4Kauxm8S2Z2MHoIz7BHEanBsJBCPCDXdtqmTPiW7eP3aGCxcUkJedzkM31LB1b4tFs0VgrYW6PZz9EgzJCLnYU7a6p1v3tnC0cwD/WIhPGroteuBfXb+Q1eUFlnqvuqd7z2XV2mxXIfjOFxZN+Y/a3tHNzc+K0K15s64jokiJXUjNqVZqoujevt1z1223U86vXfM+PyuNuzZWkpedbtH+AW1Sl10PyJOTgUtome1ut4uXPz2Z8pmnU8H7x85ELEsk1TNZpnJcIpFtT2XncjZRxn2CmEMCQozXPN1Z38WeE92GJOrzu1sM72dHfRc767vITNdi30c6xosq3Gqayq8PCNaEjfKJM4OWQtYLCq2FreflZxkTcaSUlhqoeh68fyzE3qYeS/EGwIj/28MFU+HJ2LVrVpTl8d4fOw3PHbQ7G7Pnri/Tufvp3cYsUfPAM2Dox9S19VHfOUBb7zBZ6W6+c8X4YKfZ2zdXUDLLJFwbnoV7ZtAPUkbUu/3XP98YM4NGV/80F88IhrTtbF5eyrtHOmfEAOvm5aURstROqZ6TJVVptY+9cYTn9rRojtLGSh64fmVC2zZ3AKOBEN9+ZjfFuZkU5qQboUKzQ7GiLM8oj6mPh0W7Xs6m2Jky7hPEbCTeruuweKD6BfH41y+OCEXocXCvz88L340eUqht9lrqry4rzY0ac19RlmcpBCJcgjvWL+Q206xW3Zh4fX4jzfJf3js+XlPVZGim0pOxzwfY+r3LI2Lu19SUOcbcH3vjSMT0/7cPd/L+0dPa9xpFj14PJ5iLWFy6uJh/+kM9/rEQHf0jjhWLklELtYd79O/7d4fajeVSSkryMlM6PyCa8XAauE52MFt3UKY65p6KtFr7gLc5nBdv2/a7ucHRIIOjPpp7tFDhnhPdllnIbheG8/HKpyd5+MbVjoXIzW2KFV6cKpRxnwS6h+3JybBUSALNwNed6qPZa83IMBdzME/lt2u07GrsNn5sI4EQOZlpbP3e+KQkXdd9275WXv3MmuMeDEoE44O80YyJJyfDCEeE5LjmyXSm762r8vDU3esjlr30/cg6Om/WdThuI5EiI+bQVUZ4Vmi0Ts/cjkQnFNkFxlxCO8/XrZ7P3qYey2xUvdOd7F1RNOPhNHANRCzT68JeW1NGR/8I7x87w+blpZZCHJMthJ0ok02rdfpt6OG8eNvWO5d7f7WXXl8g4nN7eMp8VxkISqMDt18v9jY9v6eFl2pbyU538YOrliXVUbpyCksSXjmMMu4pQI+r229h9UFQnZvXlrNsXl6Ed+jkJXtyMiwx4QOtfdzx5Cf8bViOoLbZy9e2fGLIApvRS/bVlBdw16bKqJ6LWePEFX4P567+iJOccKKYQ1eBsRD7mr1Gx+ZyTb5alNn7TKTsXSo6S7vx0I3Z1r0tluVOpQDNtV3N36n5rjNZnO4MnMIVUxHyc/ptmMN58VhX5eHr6xc6/r7s4Smz557uFhEduP5bsrepf2TMeJ3o4HRts5cnPmggLb+kKuGDCaOMe4rQLwan0nk6Q/5ghHdo9pJHAiEeeb2Oh/6sxjC0ZoIhyV//5qBRxcfJsOtI4K9fPWjE0aPdjpo1TqJVaDpXYsL6Le2zu5rx+cdVOG9eW84bB9s18TUXVJfMsRQ+//KqeXzvT5awI1xxCyEsSpyBoORox0DSx/n87hb+5vU6RsdC5Ge5eeC6VQARqY6JeKUTMXhOcrK1zV4Ot/db1tPj5GYvPtbdjtNAajyipbnaHReIFGVLxe9L/23YY+46iXy/+vqv7j9FZprbEnPfuKg4ZszdqQM3Cwx2D44yMGotzhNvcFp3/OwqsImSVCWmybJ+/Xr56aefTtv+zgaPvXGELTsbHcvozc3LID87A6Q0Bvn0SkHmiy0jzcXDf1bDw6/XRahAJst/M1Vpsv8gYeamgDnN+jXHx81iYHYZ319/0kRH/6hle19cVhIxIzXe/h+0FY0GLezmdkUKvsViMmMc9pj7v7x3nJ+/fdT4/X151Ty2hMNelzzyNj0OYQc7N68tT9pz/9bTuy2dxxeXlXDp4mKjLW4BP/7yCoCIZdGkNlLFuZAN4zQJ7tFb1sT8jZjPZfuv7me0vT6p6tnKc08xD1y/0tA8f3pno+ViOj3g5/SA5pE/+JuDRqbHV9cv5Lnd47fS5gHX7ftaOdY5wL6WXiPFUccsG+tEWrjwsr3yzyufnuQ7X1hEXXt/TB2bcxmnoiLmC9bpzkNfx6kKVrJZIE6hDtDOx1hI8tBvDyU8WWkyYxx2lUp7SE3PJAKtCli8sNbaioKkDHssAbUVZXmO4b3pDvmdCxIQ+jlKJuZuPpdMwAtXxn0K0I3IpYuL+dqTHxPN+dYzPR6+cTUZbmF47/YBVxiPvY0LXcH3wuJWevrfmYFRvD4/Xl+AxSW5hjaNnhWj4w9K4yI330KbjeFM9eh1YoVCJnKh2bHn7NsJhRIXbkvlGEe0kFpts5e87HRuXlvOrsZuKotyuKTSQ117P8W5GXzc0MXAyBjVJbkJZdXoukR1bdo4Rkaai+9fudhwGPT/c2rLdIf8zpUxpGR13c3n8j//ou1osvtTYZkpRjeSsWqm3rWpktsuqTA0T26NoSUT7cKz33rec1m1caG1dA/F9NguqiiwZJLoudmpvI2d6Z2FE4+9cYSnPjxBMCTJz3Jz18Yq/t+HJwiFJBnp8b83s3DbVSvm8vO3/0j3UIDi3HS23L1h2mZpmmei2nEKHTiFpKYrxDJRZvrvTwgxdQWyFRPD7EFWFufyzEcnON0/Yhk5F0R6mrFiyvHUB0cDIYtnvnTunJhtnGvLJImW2jVRYhmXmXrR1TZ7+bdPmpBSkpXu4l//fBPrqjxUFuca5818PPbjtBrIPssdWfdQgNt/+TGv/GVqhNzMabWjgcjzGWsA1WnQzykkdS5lVTkx2VTLmYgy7tOIWV7gzqfGB/zyMtMsNThjKUlG86TNt57ahBnThzIyVn/1qnmGXMKKsjx2hjNJnHKz36nr4J/+UM8F8/K4pqYsaUMcLeZ5Lgx0TRSnYwIss4H1mLvTcUaL2etIUifkVt85YIzNSGBgWBsH0jucCxcUONaGBeexCHtIakO1hweuWzljzt35QiIFsp8BbgBOSylXh5cVAVuBaqAJuENK6Z26Zs4u1lV5jNmp5nCNfsHEUpKM5kmb43P2ENB3rlhMS/cQW3Y2IqUmcqXrtz+/u4XH3z3GPZdVW4pT6DH4d+o62B+efbu/tY/9rX1kpVvDPskMDJk9vHNhoGuiOB1TtONxWh4vZi9IjZBbbbOX1w5YQy517f0RHc6Vy0r4/FQfm5eXWtL+nM7tXZsqLb+ng6f6ItZRnH0S8dz/Dfhn4FnTsgeA30spHxNCPBB+/5PUN2/2ot8mOtXgjKYkGW9AyB4Csl+gehaPU2hAV5e0Z5b80x/qI/ZjD/sAEfH/RKojnSsDXRMh2jE5HY/Tcerrm4ulPPDKAY6fGaIohTH3XY3dEUqX162eH9HhbFpczLOmVNB4HXZedjowLqcxkzrm84VECmTvEEJU2xbfBGwOv/4V8D7KuE8IuyE3G2OzcbYb5ng41XS1xx3tdwhO8dWN1UURt+xCWCM95v/T8/YDQUm6Wxh1XPX9PvlBA51hHZkVZXncdklFRO79TMFJniBaCqbTcvs5euevNqe8jfpENb0k4H1fHJ9fMZmOdSZ3zOcLCWXLhI37f5jCMr1SykLT514pZdwr83zMlkmEVFYnSna/5qyHaJMq7n56N3uaeoyYuz3sY/6/B39zkOdNOft3bark0VvWOMolpLs1ffmZFm+faUQbtJ7sYPZ0DYafrevjXOKczJYRQtwH3AdQWXl+nph4OHnZ07VfIO6F86zDzE2nsA9E6qHr73c1dkfIJehiZ+q2fmqJliky2QyS6chAcZI1sKtcFudm0D3kP6+NvxMTNe6dQoj5Usp2IcR84HS0FaWUW4AtoHnuE9yfYoqYaMcS7f9uvaSCl2tbI3TqL11cTJpbRHjuoZCMuK1XnppCJ1ro0CnX3mn853xmosb9NeDbwGPh59+mrEWKGY05E8ged95632URMXf7erE8NcX5h9OYFESXf5iKSlEzlURSIV9AGzwtEUK0Aj9FM+ovCSHuBVqAr05lIxUzi1hhgC0O2u1mEhnkVZw/RAsdRkslnYpKUTOVRLJl7ozy0ZdS3BaFIqqnpji71DZ7E5LHmAqcQoBmo69i7s4obRnFOYeKuZ9b2GWpM9JcvPBdld0Ui2RKNCbCOZkto1Aky9nKHlI4s6ux21LGMV5207neOSeTwqmva6+uFe9/9Nm/aeHi6mNBZ4kNJ80hu6bUtn2tpBXMS/qLVMZdoVDE5NLFxaQ7SFKb0Y3Q8c4B9jRpSiTTMSCeqKHWjWbN/Py4Ok16AZS1Cwst0h8CTbrD/D/m7eryHebZv9p35pzy66Tkap753dI9xDMfncAflLiy80qT/W6UcVcoFDFZV+Xhhfsuixpzd6ompjOVA+LRhOdiKXDurO8yitw43YGYKyY1dfssBXEk4DeJxD35QQNvh9U89Y7M7RJ894pFuF2CkO37CEk4cLKX2mYv66o8bN/Xaqh1BsZCjjVxAwkUf4+GMu4KhSIusSYs2cM2ZqZyQDyaMmc8BU4htILwTncgdgML1opnIampakarbRoMSZ768ASLinM4fmbI8pkkXKDn2Bke/rMaXv70pLFdt9vlWBNX99wngjLuCoViUtjDNm6XYHV5vlFceir3m4gypz0D674vLrYooJqxG9ib1pbTPeQ3/t+FpqoZq7ZxMCRZXDonwrjr6DUTxsJlMwVw+7oKHrh+ZcTM72tqyti2r5V/fGIg6arlyrgrFIpJES9sM5X7TUSZU1+eyCCvXgbPXHS8ttlrqW9grnfgcgmqinJoPDOEbu4zwrVrN6+YyzMfNjI8FqKjb5hgeAWnmgm3hWdyR6sN/L9u7WwhSVQqpEKhmFVMhaCZfZtO72N1bk6fJ9POiaRCKuOuUCgU5zgTMe6uqWqMQqFQKM4eyrgrFArFLEQZd4VCoZiFKOOuUCgUsxBl3BUKhWIWooy7QqFQzEKUcVcoFIpZiDLuCoVCMQtRxl2hUChmIcq4KxQKxSxkUsZdCHGtEOKoEOK4EOKBVDVKoVAoFJNjwsZdCOEG/gW4DlgF3CmEWJWqhikUCoVi4kzGc98IHJdSNkop/cCLwE2paZZCoVAoJsNk9NwXACdN71uBTfaVhBD3AfeF344KIQ5NYp/nOiVAV9y1Ziaz+dhAHd9MZ7Yf34pk/2Eyxl04LIvQD5ZSbgG2AAghPk1WtnImMZuPbzYfG6jjm+mcD8eX7P9MJizTCiw0va8A2iaxPYVCoVCkiMkY973AMiHEIiFEBvB14LXUNEuhUCgUk2HCYRkp5ZgQ4ofAW4AbeEZKWRfn37ZMdH8zhNl8fLP52EAd30xHHZ+NaS2zp1AoFIrpQc1QVSgUilmIMu4KhUIxC5kW4z7bZQqEEE1CiINCiP0TSVk61xBCPCOEOG2ekyCEKBJCvCOEqA8/e85mGydDlON7WAhxKnwO9wshrj+bbZwoQoiFQoj3hBBHhBB1QogfhZfPivMX4/hmy/nLEkLsEUIcCB/f34SXLxJC7A6fv63hJJbY25rqmHtYpuAYcA1a+uRe4E4p5eEp3fE0IoRoAtZLKWfFJAohxJXAIPCslHJ1eNk/AD1SysfCHbRHSvmTs9nOiRLl+B4GBqWU//tstm2yCCHmA/OllPuEEHlALXAzcA+z4PzFOL47mB3nTwC5UspBIUQ68CHwI+DHwHYp5YtCiCeAA1LKX8ba1nR47kqmYIYhpdwB9NgW3wT8Kvz6V2gX1IwkyvHNCqSU7VLKfeHXA8ARtNnks+L8xTi+WYHUGAy/TQ8/JPCfgFfCyxM6f9Nh3J1kCmbNyQgjgbeFELVhuYXZyDwpZTtoFxgw9yy3Zyr4oRDi83DYZkaGLcwIIaqBi4HdzMLzZzs+mCXnTwjhFkLsB04D7wANQK+Uciy8SkI2dDqMe0IyBTOcL0gpL0FTyPxB+LZfMbP4JbAEWAu0Az8/u82ZHEKIOcA24H4pZf/Zbk+qcTi+WXP+pJRBKeVatFn/G4GVTqvF2850GPdZL1MgpWwLP58GfoN2QmYbneF4px73PH2W25NSpJSd4YsqBDzFDD6H4VjtNuA5KeX28OJZc/6cjm82nT8dKWUv8D5wKVAohNAnnSZkQ6fDuM9qmQIhRG54YAchRC7wZWA2Kl++Bnw7/PrbwG/PYltSjm74wtzCDD2H4QG5p4EjUspfmD6aFecv2vHNovNXKoQoDL/OBq5GG1d4D7g9vFpC529aZqiG05IeZ1ym4GdTvtNpQgixGM1bB03O4fmZfnxCiBeAzWgyqp3AT4FXgZeASqAF+KqUckYOSkY5vs1ot/QSaAK+p8eoZxJCiCuAncBBIBRe/CBaXHrGn78Yx3cns+P8XYg2YOpGc75fklI+ErYzLwJFwGfAN6WUozG3peQHFAqFYvahZqgqFArFLEQZd4VCoZiFKOOuUCgUsxBl3BUKhWIWooy7QqFQzEKUcVcoFIpZiDLuCoVCMQv5/zWIe7pDXfJKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faa27101048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = plt.subplot(111)\n",
+    "plt.scatter(x=EROI, y=p, marker = '.')\n",
+    "plt.plot(EROI_for_prediction, p_predicted, color='r')\n",
+    "plt.plot(EROI_for_prediction, np.power(log_p_predicted, 10)-1, color='g')\n",
+    "ax.set_xlim(0, 30)\n",
+    "ax.set_ylim(0, 80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/adrien/anaconda3/lib/python3.5/site-packages/pymrio-0.3.6-py3.5.egg/pymrio/tools/iofunctions.py:463: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  if self.name != 'Cecilia' and var != 'Total Energy supply':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "wind onshore      9.4\n",
+       "wind offshore     9.3\n",
+       "solar PV          9.2\n",
+       "coal             12.6\n",
+       "oil               8.2\n",
+       "gas              13.7\n",
+       "nuclear          10.4\n",
+       "hydro            13.1\n",
+       "coal w CCS        NaN\n",
+       "gas w CCS         NaN\n",
+       "biomass&Waste    11.3\n",
+       "biomass w CCS     NaN\n",
+       "ocean             5.5\n",
+       "geothermal        5.3\n",
+       "solar CSP        21.5\n",
+       "Power sector     12.1\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "themis['BL'][2010].erois(recompute=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def embodied_impact(self, secs=None, regs=None, var='Total Energy supply', source='secondary', group_by='region', sort=False, production = None):\n",
+    "    '''\n",
+    "    Returns a vector of impact of type var embodied in the production of (sec, reg) in secs x regs, excluding their own production, and including only impacts\n",
+    "    from sectors in energy_sectors(source). Results are grouped by group_by (default: region) and can be sorted in decreasing order (default: unsorted).\n",
+    "    '''\n",
+    "    secs, regs = self.prepare_secs_regs(secs, regs) # TODO: source = None\n",
+    "    if production is None: production = self.production(secs, regs)\n",
+    "    if var=='Total Energy supply' and self.name != 'Cecilia':\n",
+    "        impacts = self.secondary_energy_supply * (self.embodied_prod(secs, prod=production) - production)\n",
+    "        impacts = pd.Series(impacts, index = pd.MultiIndex.from_arrays([self.labels.idx_regions, self.labels.idx_sectors], names=['region', 'sector']))\n",
+    "    else:\n",
+    "        share_demand = div0(self.embodied_prod(secs, regs, production)-production, self.x)\n",
+    "        impacts = self.impacts(var)*share_demand        \n",
+    "    impacts = impacts[self.index_secs_regs(self.energy_sectors(source))]\n",
+    "    if sort: return(sorted_series(impacts.groupby(group_by).sum()))\n",
+    "    else: return(impacts.groupby(group_by).sum())    \n",
+    "    \n",
+    "IOS.embodied_impact = embodied_impact"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def energy_required(self, secs, regs=None, var='Total Energy supply', source='secondary', netting_fuel = True):\n",
+    "    '''\n",
+    "    Returns the energy required to produce one unit of energy in sectors secs in regs, considering the energy from source with notion var, and \n",
+    "    decomposed according to the sources in partition_sources.\n",
+    "    \n",
+    "    The formula is: \n",
+    "    (energy embodied in production (excluding supplied) - fuels as direct inputs for electricity from hydrocarbon (if netting_fuel is True)) / energy supplied\n",
+    "    '''\n",
+    "    if len(secs)==1: secs = secs[0]\n",
+    "    sec_string = type(secs)==str or type(secs)==np.str_\n",
+    "    if netting_fuel and ((sec_string and secs in self.energy_sectors('elec_hydrocarbon')) \\\n",
+    "                         or secs==self.energy_sectors('elec_hydrocarbon') or secs==self.energy_sectors('electricities')): input_fuel=True\n",
+    "    else: input_fuel = 0 # We want to include fuels that are used for transportation, not transformed into electricity (this is not secondary anymore)\n",
+    "    secs, regs = self.prepare_secs_regs(secs, regs)\n",
+    "    if self.name != 'Cecilia' and var != 'Total Energy supply': \n",
+    "        print('GER not implemented for var of type ' + var + \", doing it for 'Total Energy supply' instead\")\n",
+    "        var = 'Total Energy supply'\n",
+    "    prod = self.production(secs, regs)\n",
+    "    embodied = self.embodied_impact(secs, regs, var, source, production = prod).sum()\n",
+    "    if input_fuel:\n",
+    "        if self.name == 'Cecilia': input_fuel = self.inputs(secs, var_impacts=[var], \\\n",
+    "            source=inter_secs(self.energy_sectors('secondary_fuels'), self.energy_sectors(source)), order_recursion=2)[2][0][1]\n",
+    "        elif secs==self.energy_sectors('electricities'): \n",
+    "            prod_fuel = self.production(self.energy_sectors('elec_hydrocarbon'), regs)\n",
+    "            input_fuel = ((self.secondary_fuel_supply * self.A.dot(prod_fuel))[self.index_secs_regs(self.energy_sectors(source))]).sum()\n",
+    "        else: input_fuel = ((self.secondary_fuel_supply * self.A.dot(prod))[self.index_secs_regs(self.energy_sectors(source))]).sum()\n",
+    "    return(embodied - input_fuel)\n",
+    "\n",
+    "IOS.energy_required = energy_required"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eroi_tot = dict()\n",
+    "for s in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']: \n",
+    "    eroi_tot[s] = dict()\n",
+    "    for y in [2010, 2030, 2050]: eroi_tot[s][y] = themis[s][y].ger(themis[s][y].energy_sectors('electricities'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ADV': {2010: 11.9, 2030: 8.1, 2050: 5.8},\n",
+       " 'BL': {2010: 12.1, 2030: 10.8, 2050: 10.6},\n",
+       " 'BM': {2010: 12.1, 2030: 9.0, 2050: 8.0},\n",
+       " 'ER': {2010: 11.9, 2030: 8.3, 2050: 5.9},\n",
+       " 'REF': {2010: 11.9, 2030: 10.6, 2050: 10.0},\n",
+       " 'combo': {2010: 7.2, 2030: 7.5, 2050: 11.5}}"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eroi_tot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = results(themis, scenarios = scenarios+scenarios_dlr, not_all_2010=False, rounded=False)\n",
+    "erois_as_mean = dict()\n",
+    "for s in scenarios+scenarios_dlr:\n",
+    "    erois_as_mean[s] = dict()\n",
+    "    for y in years:\n",
+    "        erois_as_mean[s][y] = round(1/sum((res[(s, y, 'mix')]/res[(s, y, 'EROI')].replace('–', float('Inf')))[:-1]), 1)\n",
+    "erois_as_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# average price of PV\n",
+    "(themis['BL'][2010].eroi_price.loc[[(r, 'Electricity by solar PV') for r in themis['BL'][2010].regions]]['price']*themis['BL'][2010].secondary_energy_demand[themis['BL'][2010].index_secs_regs('Electricity by solar PV')]).sum()/themis['BL'][2010].secondary_energy_demand[themis['BL'][2010].index_secs_regs('Electricity by solar PV')].sum()\n",
+    "(themis['BL'][2050].eroi_price.loc[[(r, 'Electricity by solar PV') for r in themis['BL'][2010].regions]]['price']*themis['BL'][2050].secondary_energy_demand[themis['BL'][2010].index_secs_regs('Electricity by solar PV')]).sum()/themis['BL'][2050].secondary_energy_demand[themis['BL'][2010].index_secs_regs('Electricity by solar PV')].sum()\n",
+    "(themis['ADV'][2050].eroi_price.loc[[(r, 'Electricity by solar PV') for r in themis['BL'][2010].regions]]['price']*themis['BL'][2050].secondary_energy_demand[themis['BL'][2010].index_secs_regs('Electricity by solar PV')]).sum()/themis['ADV'][2050].secondary_energy_demand[themis['BL'][2010].index_secs_regs('Electricity by solar PV')].sum()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Evolution of EROI/erois_prices.pkl b/Evolution of EROI/erois_prices.pkl
new file mode 100644
index 00000000..06100626
Binary files /dev/null and b/Evolution of EROI/erois_prices.pkl differ
diff --git a/Evolution of EROI/erois_prices_wo_GW.pkl b/Evolution of EROI/erois_prices_wo_GW.pkl
new file mode 100644
index 00000000..530af2ec
Binary files /dev/null and b/Evolution of EROI/erois_prices_wo_GW.pkl differ
diff --git a/impacts_by_sector.ipynb b/impacts_by_sector.ipynb
new file mode 100644
index 00000000..dbfe4a8d
--- /dev/null
+++ b/impacts_by_sector.ipynb
@@ -0,0 +1,1597 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymrio\n",
+    "from pymrio.core.mriosystem import IOSystem as IOS\n",
+    "from pymrio.core.mriosystem import concate_extension\n",
+    "from pymrio.tools.iomath import div0\n",
+    "from pymrio.tools.iofunctions import *\n",
+    "from pymrio.tools.ioparser import *\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "# import scipy.io\n",
+    "import scipy.sparse as sp \n",
+    "from scipy.sparse import linalg as spla\n",
+    "import operator\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pylab import *\n",
+    "import pickle\n",
+    "from openpyxl import load_workbook\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import statsmodels.api as sm\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_io = '/mnt/dd/adrien/DD/Économie/Données/' # where the Exiobase data is\n",
+    "path_dl = '/home/adrien/Downloads/' # where the conso by age is, and where the impacts by age are stored"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data from already executed notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# exio3 = parse_exiobase3(path_io+'Exiobase 3.4/exiobase3.4_iot_2011_pxp.zip')\\n\n",
+    "# exio3.calc_all()\n",
+    "# exio3.x = exio3.x.squeeze()\n",
+    "# exio3.impact = exio3.satellite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exio3 = pickle.load(open(path_io+'Exiobase 3.4/exio3.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "EOFError",
+     "evalue": "Ran out of input",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mEOFError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-3ecbc17a3305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimpacts_FR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_io\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'Exiobase 3.4/impacts_FR.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mimpacts_age\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_io\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'Exiobase 3.4/impacts_age.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mEOFError\u001b[0m: Ran out of input"
+     ]
+    }
+   ],
+   "source": [
+    "impacts_FR = pickle.load(open(path_io + 'Exiobase 3.4/impacts_FR.pkl', 'rb'))\n",
+    "impacts_age = pickle.load(open(path_io + 'Exiobase 3.4/impacts_age.pkl', 'rb'))\n",
+    "impacts_age_pc = pickle.load(open(path_io + 'Exiobase 3.4/impacts_age_pc.pkl', 'rb'))\n",
+    "impacts_intensity_age = pickle.load(open(path_io + 'Exiobase 3.4/impacts_intensity_age.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregation of sectors into broad categories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def consumption_sectors(self, notion): \n",
+    "    '''\n",
+    "    Returns the list of sectors (or names) corresponding to notion, which can be: food, drug, cloth, housing, furniture, health, transport, communication, leisure, education, catering, diverse\n",
+    "    '''\n",
+    "    if notion is None or notion == 'total': sectors = self.sectors\n",
+    "    elif notion not in ['food', 'tobacco', 'cloth', 'housing', 'furniture', 'health', 'transport', 'communication', 'leisure', 'education', 'catering', 'diverse']:\n",
+    "        print('Error: notion unknown')\n",
+    "    elif notion=='food': sectors = ['Paddy rice',  'Wheat',  'Cereal grains nec',  'Vegetables, fruit, nuts',  'Oil seeds',  'Sugar cane, sugar beet',  'Plant-based fibers',  'Crops nec',  'Cattle',  'Pigs',  'Poultry',  'Meat animals nec',  'Animal products nec',  'Raw milk', 'Fish and other fishing products; services incidental of fishing (05)', \n",
+    "'Products of meat cattle', 'Products of meat pigs', 'Products of meat poultry', 'Meat products nec', 'products of Vegetable oils and fats', 'Dairy products', 'Processed rice', 'Sugar', 'Food products nec', 'Beverages', 'Fish products']\n",
+    "    elif notion=='tobacco': sectors = ['Tobacco products (16)']\n",
+    "    elif notion=='cloth': sectors = ['Textiles (17)', 'Wearing apparel; furs (18)', 'Leather and leather products (19)']\n",
+    "    elif notion=='housing': sectors = ['Electricity by coal', 'Electricity by gas', 'Electricity by nuclear', 'Electricity by hydro', 'Electricity by wind', 'Electricity by petroleum and other oil derivatives', 'Electricity by biomass and waste', 'Electricity by solar photovoltaic', 'Electricity by solar thermal', 'Electricity by tide, wave, ocean', 'Electricity by Geothermal', 'Electricity nec', 'Steam and hot water supply services', 'Collected and purified water, distribution services of water (41)', 'Construction work (45)', 'Real estate services (70)']\n",
+    "    elif notion=='furniture': sectors = ['Paper and paper products', 'Printed matter and recorded media (22)', 'Furniture; other manufactured goods n.e.c. (36)'] \n",
+    "    elif notion=='health': sectors = ['Health and social work services (85)'] \n",
+    "    elif notion=='transport': sectors = ['Motor Gasoline', 'Gas/Diesel Oil', 'Heavy Fuel Oil', 'Refinery Gas', 'Liquefied Petroleum Gases (LPG)', 'Kerosene', 'Motor vehicles, trailers and semi-trailers (34)', 'Other transport equipment (35)', 'Sale, maintenance, repair of motor vehicles, motor vehicles parts, motorcycles, motor cycles parts and accessoiries', 'Retail trade services of motor fuel', 'Railway transportation services', 'Other land transportation services', 'Transportation services via pipelines', 'Sea and coastal water transportation services', 'Inland water transportation services', 'Air transport services (62)', 'Supporting and auxiliary transport services; travel agency services (63)'] \n",
+    "    elif notion=='communication': sectors = ['Office machinery and computers (30)', 'Electrical machinery and apparatus n.e.c. (31)', 'Radio, television and communication equipment and apparatus (32)', 'Post and telecommunication services (64)'] \n",
+    "    elif notion=='leisure': sectors = ['Recreational, cultural and sporting services (92)'] \n",
+    "    elif notion=='education': sectors = ['Education services (80)'] \n",
+    "    elif notion=='catering': sectors = ['Hotel and restaurant services (55)'] \n",
+    "    elif notion=='diverse': sectors = ['Fabricated metal products, except machinery and equipment (28)', 'Machinery and equipment n.e.c. (29)', 'Wholesale trade and commission trade services, except of motor vehicles and motorcycles (51)', 'Retail  trade services, except of motor vehicles and motorcycles; repair services of personal and household goods (52)', \n",
+    "'Wool, silk-worm cocoons',  'Manure (conventional treatment)',  'Manure (biogas treatment)',  'Products of forestry, logging and related services (02)', 'Anthracite',  'Coking Coal',  'Other Bituminous Coal',  'Sub-Bituminous Coal',  'Patent Fuel',  'Lignite/Brown Coal',  'BKB/Peat Briquettes',  'Peat', 'Natural Gas Liquids', 'Other Hydrocarbons', 'Uranium and thorium ores (12)', 'Iron ores', 'Copper ores and concentrates', 'Nickel ores and concentrates', 'Aluminium ores and concentrates', 'Precious metal ores and concentrates', 'Lead, zinc and tin ores and concentrates', 'Other non-ferrous metal ores and concentrates', 'Stone', 'Sand and clay', 'Chemical and fertilizer minerals, salt and other mining and quarrying products n.e.c.', 'Wood and products of wood and cork (except furniture); articles of straw and plaiting materials (20)', 'Wood material for treatment, Re-processing of secondary wood material into new wood material', 'Pulp', 'Secondary paper for treatment, Re-processing of secondary paper into new pulp', 'Crude petroleum and services related to crude oil extraction, excluding surveying', 'Natural gas and services related to natural gas extraction, excluding surveying', 'Coke Oven Coke', 'Gas Coke', 'Coal Tar', 'Aviation Gasoline', 'Gasoline Type Jet Fuel', 'Kerosene Type Jet Fuel', 'Refinery Feedstocks', 'Ethane', 'Naphtha', 'White Spirit & SBP', 'Lubricants', 'Bitumen', 'Paraffin Waxes', 'Petroleum Coke', 'Non-specified Petroleum Products', 'Nuclear fuel', 'Plastics, basic', 'Secondary plastic for treatment, Re-processing of secondary plastic into new plastic', 'N-fertiliser', 'P- and other fertiliser', 'Chemicals nec', 'Charcoal', 'Additives/Blending Components', 'Biogasoline', 'Biodiesels', 'Other Liquid Biofuels', 'Rubber and plastic products (25)', 'Glass and glass products', 'Secondary glass for treatment, Re-processing of secondary glass into new glass', 'Ceramic goods', 'Bricks, tiles and construction products, in baked clay', 'Cement, lime and plaster', 'Ash for treatment, Re-processing of ash into clinker', 'Other non-metallic mineral products', 'Basic iron and steel and of ferro-alloys and first products thereof', 'Secondary steel for treatment, Re-processing of secondary steel into new steel', 'Precious metals', 'Secondary preciuos metals for treatment, Re-processing of secondary preciuos metals into new preciuos metals', 'Aluminium and aluminium products', 'Secondary aluminium for treatment, Re-processing of secondary aluminium into new aluminium', 'Lead, zinc and tin and products thereof', 'Secondary lead for treatment, Re-processing of secondary lead into new lead', 'Copper products', 'Secondary copper for treatment, Re-processing of secondary copper into new copper', 'Other non-ferrous metal products', 'Secondary other non-ferrous metals for treatment, Re-processing of secondary other non-ferrous metals into new other non-ferrous metals', 'Foundry work services', 'Medical, precision and optical instruments, watches and clocks (33)', 'Secondary raw materials', 'Bottles for treatment, Recycling of bottles by direct reuse', 'Transmission services of electricity', 'Distribution and trade services of electricity', 'Coke oven gas', 'Blast Furnace Gas', 'Oxygen Steel Furnace Gas', 'Gas Works Gas', 'Biogas', 'Distribution services of gaseous fuels through mains', 'Secondary construction material for treatment, Re-processing of secondary construction material into aggregates', 'Financial intermediation services, except insurance and pension funding services (65)', 'Insurance and pension funding services, except compulsory social security services (66)', 'Services auxiliary to financial intermediation (67)', 'Renting services of machinery and equipment without operator and of personal and household goods (71)', 'Computer and related services (72)', 'Research and development services (73)', 'Other business services (74)', 'Public administration and defence services; compulsory social security services (75)', 'Food waste for treatment: incineration', 'Paper waste for treatment: incineration', 'Plastic waste for treatment: incineration', 'Intert/metal waste for treatment: incineration', 'Textiles waste for treatment: incineration', 'Wood waste for treatment: incineration', 'Oil/hazardous waste for treatment: incineration', 'Food waste for treatment: biogasification and land application', 'Paper waste for treatment: biogasification and land application', 'Sewage sludge for treatment: biogasification and land application', 'Food waste for treatment: composting and land application', 'Paper and wood waste for treatment: composting and land application', 'Food waste for treatment: waste water treatment', 'Other waste for treatment: waste water treatment', 'Food waste for treatment: landfill', 'Paper for treatment: landfill', 'Plastic waste for treatment: landfill', 'Inert/metal/hazardous waste for treatment: landfill', 'Textiles waste for treatment: landfill', 'Wood waste for treatment: landfill', 'Membership organisation services n.e.c. (91)', 'Other services (93)', 'Private households with employed persons (95)', 'Extra-territorial organizations and bodies'] \n",
+    "    elif notion=='undecided': sectors = ['Beverages', 'Paper and paper products', 'Printed matter and recorded media (22)']\n",
+    "    return(sectors)\n",
+    "\n",
+    "IOS.consumption_sectors = consumption_sectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sectors = ['food', 'tobacco', 'cloth', 'housing', 'furniture', 'health', 'transport', 'communication', 'leisure', 'education', 'catering', 'diverse']\n",
+    "imp = exio3.impact.F.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregation of impacts under broad categories (e.g. all CO2 emissions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pd.set_option('display.max_rows', 2000)\n",
+    "# exio3.impact.unit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impacts = ['CO2 (kg)', 'CH4 (kg)', 'NOx (kg)', 'SOx (kg)', 'N2O (kg)', 'NH3 (kg)', 'CO (kg)', 'PFC (kg CO2eq)', 'HFC (kg CO2eq)', 'SF6 (kg)',\n",
+    "         'PM10 (kg)', 'PM2.5 (kg)', 'Pb (kg)', 'Cd (kg)', 'Gravel and sand (kg)',\n",
+    "         'Arsenic (kg)', 'NMVOC (kg)', 'TSP (kg)', 'N (kg)', 'P (kg)', 'Energy (TJ)', 'Wood (kt)', 'Copper (kt)', 'Metal Ores (kt)', 'Non-Metallic Minerals (kt)', \n",
+    "         'Crops (kt)', 'Cropland (km2)', 'Forest area (km2)', 'Permanent pastures (km2)', 'Land use (km2)', 'Water Consumption Blue (Mm3)', \n",
+    "         'Water Consumption Green (Mm3)', 'Water Withdrawal Blue (Mm3)', 'Wages (incl. social contributions) (M€)', \n",
+    "         'Operating surplus (investment, rents, profit) (M€)', 'Value added (wages, surplus and taxes) (M€)', 'Employment (k pers)', 'Employment hours (M.hr)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agg_impacts = exio3.impact.F\n",
+    "exio3.impact.F = agg_impacts.append([ # TODO: énergie, déchets, minerais, bois TODO: Global Warming Potential (CO2eq)\n",
+    "#     pd.Series(agg_impacts.iloc[['CO2 - combustion - air' in i for i in imp],:].sum(axis = 0), name = 'CO2 - combustion - air (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['CO2' in i for i in imp],:].sum(axis = 0), name = 'CO2 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['CH4' in i for i in imp],:].sum(axis = 0), name = 'CH4 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['NOx' in i for i in imp],:].sum(axis = 0), name = 'NOx (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['SOx' in i for i in imp],:].sum(axis = 0), name = 'SOx (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['N2O' in i for i in imp],:].sum(axis = 0), name = 'N2O (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['NH3' in i for i in imp],:].sum(axis = 0), name = 'NH3 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['CO -' in i for i in imp],:].sum(axis = 0), name = 'CO (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['PFC' in i for i in imp],:].sum(axis = 0), name = 'PFC (kg CO2eq)'),\n",
+    "    pd.Series(agg_impacts.iloc[['HFC' in i for i in imp],:].sum(axis = 0), name = 'HFC (kg CO2eq)'),\n",
+    "    pd.Series(agg_impacts.iloc[['SF6' in i for i in imp],:].sum(axis = 0), name = 'SF6 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['PM10' in i for i in imp],:].sum(axis = 0), name = 'PM10 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['PM2.5' in i for i in imp],:].sum(axis = 0), name = 'PM2.5 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Pb' in i for i in imp],:].sum(axis = 0), name = 'Pb (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Cd' in i for i in imp],:].sum(axis = 0), name = 'Cd (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['sand' in i for i in imp],:].sum(axis = 0), name = 'Gravel and sand (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['As -' in i for i in imp],:].sum(axis = 0), name = 'Arsenic (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['NMVOC' in i for i in imp],:].sum(axis = 0), name = 'NMVOC (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['TSP' in i for i in imp],:].sum(axis = 0), name = 'TSP (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['N -' in i for i in imp],:].sum(axis = 0), name = 'N (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['P - a' in i or 'P - w' in i for i in imp],:].sum(axis = 0), name = 'P (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Energy Carrier Supply' in i for i in imp],:].sum(axis = 0), name = 'Energy (TJ)'),\n",
+    "    pd.Series(agg_impacts.iloc[['s wood -' in i for i in imp],:].sum(axis = 0), name = 'Wood (kt)'), # from Wood to Crops, includes both used and unused extraction\n",
+    "    pd.Series(agg_impacts.iloc[['Ores - Copper' in i for i in imp],:].sum(axis = 0), name = 'Copper (kt)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Metal Ores' in i for i in imp],:].sum(axis = 0), name = 'Metal Ores (kt)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Non-Metallic Minerals' in i for i in imp],:].sum(axis = 0), name = 'Non-Metallic Minerals (kt)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Primary Crops' in i for i in imp],:].sum(axis = 0), name = 'Crops (kt)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Cropland' in i for i in imp],:].sum(axis = 0), name = 'Cropland (km2)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Forest area' in i for i in imp],:].sum(axis = 0), name = 'Forest area (km2)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Permanent pasture' in i for i in imp],:].sum(axis = 0), name = 'Permanent pastures (km2)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Cropland' in i or 'land Use' in i or 'pasture' in i for i in imp],:].sum(axis = 0), name = 'Land use (km2)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Water Consumption Blue' in i for i in imp],:].sum(axis = 0), name = 'Water Consumption Blue (Mm3)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Water Consumption Green' in i for i in imp],:].sum(axis = 0), name = 'Water Consumption Green (Mm3)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Water Withdrawal' in i for i in imp],:].sum(axis = 0), name = 'Water Withdrawal Blue (Mm3)'),\n",
+    "    pd.Series(agg_impacts.iloc[['wages' in i for i in imp],:].sum(0), name='Wages (incl. social contributions) (M€)'),\n",
+    "    pd.Series(agg_impacts.iloc[['surplus' in i for i in imp],:].sum(0), name='Operating surplus (investment, rents, profit) (M€)'),\n",
+    "    pd.Series(agg_impacts.iloc[['axes' in i or 'wages' in i or 'surplus' in i for i in imp],:].sum(0), name='Value added (wages, surplus and taxes) (M€)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Employment:' in i and 'Vulnerable' not in i for i in imp],:].sum(0), name='Employment (k pers)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Employment hours:' in i and 'Vulnerable' not in i for i in imp],:].sum(0), name = 'Employment hours (M.hr)')])\n",
+    "del(agg_impacts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Computation of French footprints by sector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impacts_FR = pd.DataFrame(index = sectors, columns = impacts + ['GWP 100 (kg CO2eq)'])\n",
+    "for impact in impacts: # , 'GWP 100 (kg CO2eq)', 'GWP 500 (kg CO2eq)', 'GWP 20 with feedback (kg CO2eq)', 'GWP 100 with feedback (kg CO2eq)'\n",
+    "    for secs in sectors:\n",
+    "        impacts_FR.at[secs,impact] = exio3.embodied_impact(regs = 'FR', secs = exio3.consumption_sectors(secs), var = impact).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for secs in sectors: # Source: https://en.wikipedia.org/wiki/Global_warming_potential (21/03/2020). Note that CF4=PFC.\n",
+    "    # I assume that the period is 100 years (for consistence with PFC and HFC), that HFC=HFC-134a, and I don't account for PFTBA nor CFC (absent from data).\n",
+    "    impacts_FR.at[secs, 'GWP 100 (kg CO2eq)'] =    (impacts_FR.at[secs, 'CO2 (kg)'] + \n",
+    "                                                 28*impacts_FR.at[secs, 'CH4 (kg)'] + \n",
+    "                                                268*impacts_FR.at[secs, 'N2O (kg)'] +\n",
+    "                                                    impacts_FR.at[secs, 'HFC (kg CO2eq)'] + \n",
+    "                                                    impacts_FR.at[secs, 'PFC (kg CO2eq)'] + \n",
+    "                                              22200*impacts_FR.at[secs, 'SF6 (kg)'])\n",
+    "#     impacts_FR.at[secs, 'GWP 20 (kg CO2eq)'] = (impacts_FR.at[secs, 'CO2 (kg)'] + 84*impacts_FR.at[secs, 'CH4 (kg)'] + 264*impacts_FR.at[secs, 'N2O (kg)']\n",
+    "#         + impacts_FR.at[secs, 'HFC (kg CO2eq)'] + 6900*impacts_FR.at[secs, 'CFC (kg)'] +  impacts_FR.at[secs, 'PFC (kg CO2eq)'] + 15100*impacts_FR.at[secs, 'SF6 (kg)'])\n",
+    "#     impacts_FR.at[secs, 'GWP 500 (kg CO2eq)'] = (impacts_FR.at[secs, 'CO2 (kg)'] + 7.6*impacts_FR.at[secs, 'CH4 (kg)'] + 153*impacts_FR.at[secs, 'N2O (kg)']\n",
+    "#         + impacts_FR.at[secs, 'HFC (kg CO2eq)'] + 1620*impacts_FR.at[secs, 'CFC (kg)'] +  impacts_FR.at[secs, 'PFC (kg CO2eq)'] + 32600*impacts_FR.at[secs, 'SF6 (kg)'])\n",
+    "#     impacts_FR.at[secs, 'GWP 20 with feedback (kg CO2eq)'] =(impacts_FR.at[secs, 'CO2 (kg)']+86*impacts_FR.at[secs, 'CH4 (kg)']+268*impacts_FR.at[secs, 'N2O (kg)']\n",
+    "#         + impacts_FR.at[secs, 'HFC (kg CO2eq)'] + 7020*impacts_FR.at[secs, 'CFC (kg)'] + impacts_FR.at[secs, 'PFC (kg CO2eq)'] + 16300*impacts_FR.at[secs, 'SF6 (kg)'])\n",
+    "#     impacts_FR.at[secs, 'GWP 100 with feedback (kg CO2eq)']=(impacts_FR.at[secs, 'CO2 (kg)']+34*impacts_FR.at[secs, 'CH4 (kg)']+298*impacts_FR.at[secs, 'N2O (kg)']\n",
+    "#         + impacts_FR.at[secs, 'HFC (kg CO2eq)'] + 5350*impacts_FR.at[secs, 'CFC (kg)'] + impacts_FR.at[secs, 'PFC (kg CO2eq)'] + 22800*impacts_FR.at[secs, 'SF6 (kg)'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impacts_FR = impacts_FR.append(impacts_FR.sum().rename('Total'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CO2 (kg)</th>\n",
+       "      <th>CH4 (kg)</th>\n",
+       "      <th>NOx (kg)</th>\n",
+       "      <th>SOx (kg)</th>\n",
+       "      <th>N2O (kg)</th>\n",
+       "      <th>NH3 (kg)</th>\n",
+       "      <th>CO (kg)</th>\n",
+       "      <th>PFC (kg CO2eq)</th>\n",
+       "      <th>HFC (kg CO2eq)</th>\n",
+       "      <th>SF6 (kg)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Land use (km2)</th>\n",
+       "      <th>Water Consumption Blue (Mm3)</th>\n",
+       "      <th>Water Consumption Green (Mm3)</th>\n",
+       "      <th>Water Withdrawal Blue (Mm3)</th>\n",
+       "      <th>Wages (incl. social contributions) (M€)</th>\n",
+       "      <th>Operating surplus (investment, rents, profit) (M€)</th>\n",
+       "      <th>Value added (wages, surplus and taxes) (M€)</th>\n",
+       "      <th>Employment (k pers)</th>\n",
+       "      <th>Employment hours (M.hr)</th>\n",
+       "      <th>GWP 100 (kg CO2eq)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>food</th>\n",
+       "      <td>4.6038e+10</td>\n",
+       "      <td>1.82645e+09</td>\n",
+       "      <td>2.48764e+08</td>\n",
+       "      <td>1.0571e+08</td>\n",
+       "      <td>8.43762e+07</td>\n",
+       "      <td>1.0842e+09</td>\n",
+       "      <td>4.80603e+08</td>\n",
+       "      <td>1.87943e+08</td>\n",
+       "      <td>3.34055e+09</td>\n",
+       "      <td>15696.7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>326981</td>\n",
+       "      <td>5113.18</td>\n",
+       "      <td>78940.1</td>\n",
+       "      <td>1233.98</td>\n",
+       "      <td>78086</td>\n",
+       "      <td>65100.6</td>\n",
+       "      <td>153888</td>\n",
+       "      <td>6525.88</td>\n",
+       "      <td>13281.7</td>\n",
+       "      <td>1.23668e+11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tobacco</th>\n",
+       "      <td>2.0873e+08</td>\n",
+       "      <td>1.27062e+06</td>\n",
+       "      <td>957459</td>\n",
+       "      <td>438072</td>\n",
+       "      <td>36410.3</td>\n",
+       "      <td>319388</td>\n",
+       "      <td>1.39263e+06</td>\n",
+       "      <td>544247</td>\n",
+       "      <td>1.10959e+07</td>\n",
+       "      <td>53.0975</td>\n",
+       "      <td>...</td>\n",
+       "      <td>196.008</td>\n",
+       "      <td>9.88068</td>\n",
+       "      <td>216.771</td>\n",
+       "      <td>2.47331</td>\n",
+       "      <td>512.407</td>\n",
+       "      <td>252.329</td>\n",
+       "      <td>835.528</td>\n",
+       "      <td>49.9677</td>\n",
+       "      <td>107.875</td>\n",
+       "      <td>2.66884e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cloth</th>\n",
+       "      <td>5.62381e+09</td>\n",
+       "      <td>3.78534e+07</td>\n",
+       "      <td>1.8268e+07</td>\n",
+       "      <td>1.34145e+07</td>\n",
+       "      <td>777788</td>\n",
+       "      <td>8.7317e+06</td>\n",
+       "      <td>3.07197e+07</td>\n",
+       "      <td>1.53066e+07</td>\n",
+       "      <td>1.55019e+08</td>\n",
+       "      <td>877.119</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5771.16</td>\n",
+       "      <td>137.842</td>\n",
+       "      <td>1050.49</td>\n",
+       "      <td>148.961</td>\n",
+       "      <td>7937.93</td>\n",
+       "      <td>5007.85</td>\n",
+       "      <td>14268.8</td>\n",
+       "      <td>540.64</td>\n",
+       "      <td>1086.36</td>\n",
+       "      <td>7.08195e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>housing</th>\n",
+       "      <td>8.90251e+10</td>\n",
+       "      <td>4.69189e+08</td>\n",
+       "      <td>2.74467e+08</td>\n",
+       "      <td>2.53277e+08</td>\n",
+       "      <td>5.93657e+06</td>\n",
+       "      <td>6.87397e+07</td>\n",
+       "      <td>8.55665e+08</td>\n",
+       "      <td>5.19373e+08</td>\n",
+       "      <td>5.22836e+09</td>\n",
+       "      <td>30657.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>49291.4</td>\n",
+       "      <td>886.702</td>\n",
+       "      <td>6124.28</td>\n",
+       "      <td>1384.99</td>\n",
+       "      <td>146534</td>\n",
+       "      <td>101506</td>\n",
+       "      <td>272023</td>\n",
+       "      <td>6177.8</td>\n",
+       "      <td>11831.9</td>\n",
+       "      <td>1.10182e+11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>furniture</th>\n",
+       "      <td>1.48434e+10</td>\n",
+       "      <td>8.66913e+07</td>\n",
+       "      <td>5.40087e+07</td>\n",
+       "      <td>5.67801e+07</td>\n",
+       "      <td>1.39992e+06</td>\n",
+       "      <td>1.62805e+07</td>\n",
+       "      <td>1.31991e+08</td>\n",
+       "      <td>1.01606e+08</td>\n",
+       "      <td>5.1127e+08</td>\n",
+       "      <td>3155.16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12219.2</td>\n",
+       "      <td>205.557</td>\n",
+       "      <td>1443.26</td>\n",
+       "      <td>325.103</td>\n",
+       "      <td>26593.5</td>\n",
+       "      <td>16948.9</td>\n",
+       "      <td>47635.2</td>\n",
+       "      <td>1287.79</td>\n",
+       "      <td>2487.53</td>\n",
+       "      <td>1.83288e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>health</th>\n",
+       "      <td>2.17589e+10</td>\n",
+       "      <td>1.8216e+08</td>\n",
+       "      <td>6.95835e+07</td>\n",
+       "      <td>5.98221e+07</td>\n",
+       "      <td>4.56454e+06</td>\n",
+       "      <td>6.25076e+07</td>\n",
+       "      <td>1.35321e+08</td>\n",
+       "      <td>7.70453e+07</td>\n",
+       "      <td>7.14571e+08</td>\n",
+       "      <td>4007.91</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26012.3</td>\n",
+       "      <td>486.894</td>\n",
+       "      <td>4497.09</td>\n",
+       "      <td>426.939</td>\n",
+       "      <td>44892.1</td>\n",
+       "      <td>32616.8</td>\n",
+       "      <td>84868.4</td>\n",
+       "      <td>2042.93</td>\n",
+       "      <td>3921.6</td>\n",
+       "      <td>2.89633e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>transport</th>\n",
+       "      <td>1.03982e+11</td>\n",
+       "      <td>1.14701e+09</td>\n",
+       "      <td>6.88906e+08</td>\n",
+       "      <td>4.71762e+08</td>\n",
+       "      <td>5.92737e+06</td>\n",
+       "      <td>6.76336e+07</td>\n",
+       "      <td>1.02633e+09</td>\n",
+       "      <td>5.45198e+08</td>\n",
+       "      <td>1.46835e+09</td>\n",
+       "      <td>15919.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>42892.6</td>\n",
+       "      <td>731.791</td>\n",
+       "      <td>5319.86</td>\n",
+       "      <td>1541.01</td>\n",
+       "      <td>119773</td>\n",
+       "      <td>82626.8</td>\n",
+       "      <td>223262</td>\n",
+       "      <td>6262.17</td>\n",
+       "      <td>12552.1</td>\n",
+       "      <td>1.40054e+11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>communication</th>\n",
+       "      <td>1.85497e+10</td>\n",
+       "      <td>8.75936e+07</td>\n",
+       "      <td>6.57192e+07</td>\n",
+       "      <td>6.94359e+07</td>\n",
+       "      <td>1.36241e+06</td>\n",
+       "      <td>1.71339e+07</td>\n",
+       "      <td>2.8382e+08</td>\n",
+       "      <td>2.36586e+08</td>\n",
+       "      <td>5.55905e+08</td>\n",
+       "      <td>5096.85</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10554.1</td>\n",
+       "      <td>210.278</td>\n",
+       "      <td>1481.37</td>\n",
+       "      <td>386.104</td>\n",
+       "      <td>41196.4</td>\n",
+       "      <td>29043.6</td>\n",
+       "      <td>76850.9</td>\n",
+       "      <td>1627.26</td>\n",
+       "      <td>3134.92</td>\n",
+       "      <td>2.22731e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>leisure</th>\n",
+       "      <td>1.47323e+10</td>\n",
+       "      <td>1.04767e+08</td>\n",
+       "      <td>4.54913e+07</td>\n",
+       "      <td>3.00335e+07</td>\n",
+       "      <td>2.18117e+06</td>\n",
+       "      <td>2.72336e+07</td>\n",
+       "      <td>7.2962e+07</td>\n",
+       "      <td>3.50481e+07</td>\n",
+       "      <td>3.33401e+08</td>\n",
+       "      <td>1992.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12609.2</td>\n",
+       "      <td>199.019</td>\n",
+       "      <td>1876.28</td>\n",
+       "      <td>198.347</td>\n",
+       "      <td>27485.2</td>\n",
+       "      <td>19979.2</td>\n",
+       "      <td>51979.2</td>\n",
+       "      <td>1111.9</td>\n",
+       "      <td>2083.78</td>\n",
+       "      <td>1.8663e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education</th>\n",
+       "      <td>8.62347e+09</td>\n",
+       "      <td>7.74654e+07</td>\n",
+       "      <td>2.90968e+07</td>\n",
+       "      <td>1.64142e+07</td>\n",
+       "      <td>2.06024e+06</td>\n",
+       "      <td>2.66596e+07</td>\n",
+       "      <td>4.07529e+07</td>\n",
+       "      <td>1.75068e+07</td>\n",
+       "      <td>1.93642e+08</td>\n",
+       "      <td>1129.41</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10181.7</td>\n",
+       "      <td>151.077</td>\n",
+       "      <td>1660.36</td>\n",
+       "      <td>106.749</td>\n",
+       "      <td>18894.3</td>\n",
+       "      <td>13094</td>\n",
+       "      <td>34869.7</td>\n",
+       "      <td>766.5</td>\n",
+       "      <td>1431.4</td>\n",
+       "      <td>1.15809e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>catering</th>\n",
+       "      <td>6.3725e+09</td>\n",
+       "      <td>1.18438e+08</td>\n",
+       "      <td>2.76231e+07</td>\n",
+       "      <td>1.24122e+07</td>\n",
+       "      <td>4.90299e+06</td>\n",
+       "      <td>6.07395e+07</td>\n",
+       "      <td>3.79539e+07</td>\n",
+       "      <td>1.38067e+07</td>\n",
+       "      <td>1.9741e+08</td>\n",
+       "      <td>1002.63</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24145.7</td>\n",
+       "      <td>324.638</td>\n",
+       "      <td>4323.45</td>\n",
+       "      <td>102.572</td>\n",
+       "      <td>12515.4</td>\n",
+       "      <td>10082.9</td>\n",
+       "      <td>24701.2</td>\n",
+       "      <td>779.755</td>\n",
+       "      <td>1540.29</td>\n",
+       "      <td>1.12362e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>diverse</th>\n",
+       "      <td>1.88063e+11</td>\n",
+       "      <td>1.34904e+09</td>\n",
+       "      <td>6.48154e+08</td>\n",
+       "      <td>5.17793e+08</td>\n",
+       "      <td>2.12463e+07</td>\n",
+       "      <td>2.27739e+08</td>\n",
+       "      <td>2.09406e+09</td>\n",
+       "      <td>1.57319e+09</td>\n",
+       "      <td>8.74549e+09</td>\n",
+       "      <td>50173.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>167465</td>\n",
+       "      <td>3048.7</td>\n",
+       "      <td>22956.4</td>\n",
+       "      <td>3024.55</td>\n",
+       "      <td>406744</td>\n",
+       "      <td>283170</td>\n",
+       "      <td>755573</td>\n",
+       "      <td>18484.7</td>\n",
+       "      <td>35451.4</td>\n",
+       "      <td>2.42963e+11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>5.17821e+11</td>\n",
+       "      <td>5.48793e+09</td>\n",
+       "      <td>2.17104e+09</td>\n",
+       "      <td>1.60729e+09</td>\n",
+       "      <td>1.34772e+08</td>\n",
+       "      <td>1.66792e+09</td>\n",
+       "      <td>5.19157e+09</td>\n",
+       "      <td>3.32315e+09</td>\n",
+       "      <td>2.14551e+10</td>\n",
+       "      <td>129761</td>\n",
+       "      <td>...</td>\n",
+       "      <td>688319</td>\n",
+       "      <td>11505.6</td>\n",
+       "      <td>129890</td>\n",
+       "      <td>8881.78</td>\n",
+       "      <td>931164</td>\n",
+       "      <td>659429</td>\n",
+       "      <td>1.74075e+06</td>\n",
+       "      <td>45657.3</td>\n",
+       "      <td>88910.8</td>\n",
+       "      <td>7.35261e+11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  CO2 (kg)     CH4 (kg)     NOx (kg)     SOx (kg)  \\\n",
+       "food            4.6038e+10  1.82645e+09  2.48764e+08   1.0571e+08   \n",
+       "tobacco         2.0873e+08  1.27062e+06       957459       438072   \n",
+       "cloth          5.62381e+09  3.78534e+07   1.8268e+07  1.34145e+07   \n",
+       "housing        8.90251e+10  4.69189e+08  2.74467e+08  2.53277e+08   \n",
+       "furniture      1.48434e+10  8.66913e+07  5.40087e+07  5.67801e+07   \n",
+       "health         2.17589e+10   1.8216e+08  6.95835e+07  5.98221e+07   \n",
+       "transport      1.03982e+11  1.14701e+09  6.88906e+08  4.71762e+08   \n",
+       "communication  1.85497e+10  8.75936e+07  6.57192e+07  6.94359e+07   \n",
+       "leisure        1.47323e+10  1.04767e+08  4.54913e+07  3.00335e+07   \n",
+       "education      8.62347e+09  7.74654e+07  2.90968e+07  1.64142e+07   \n",
+       "catering        6.3725e+09  1.18438e+08  2.76231e+07  1.24122e+07   \n",
+       "diverse        1.88063e+11  1.34904e+09  6.48154e+08  5.17793e+08   \n",
+       "Total          5.17821e+11  5.48793e+09  2.17104e+09  1.60729e+09   \n",
+       "\n",
+       "                  N2O (kg)     NH3 (kg)      CO (kg) PFC (kg CO2eq)  \\\n",
+       "food           8.43762e+07   1.0842e+09  4.80603e+08    1.87943e+08   \n",
+       "tobacco            36410.3       319388  1.39263e+06         544247   \n",
+       "cloth               777788   8.7317e+06  3.07197e+07    1.53066e+07   \n",
+       "housing        5.93657e+06  6.87397e+07  8.55665e+08    5.19373e+08   \n",
+       "furniture      1.39992e+06  1.62805e+07  1.31991e+08    1.01606e+08   \n",
+       "health         4.56454e+06  6.25076e+07  1.35321e+08    7.70453e+07   \n",
+       "transport      5.92737e+06  6.76336e+07  1.02633e+09    5.45198e+08   \n",
+       "communication  1.36241e+06  1.71339e+07   2.8382e+08    2.36586e+08   \n",
+       "leisure        2.18117e+06  2.72336e+07   7.2962e+07    3.50481e+07   \n",
+       "education      2.06024e+06  2.66596e+07  4.07529e+07    1.75068e+07   \n",
+       "catering       4.90299e+06  6.07395e+07  3.79539e+07    1.38067e+07   \n",
+       "diverse        2.12463e+07  2.27739e+08  2.09406e+09    1.57319e+09   \n",
+       "Total          1.34772e+08  1.66792e+09  5.19157e+09    3.32315e+09   \n",
+       "\n",
+       "              HFC (kg CO2eq) SF6 (kg)  ... Land use (km2)  \\\n",
+       "food             3.34055e+09  15696.7  ...         326981   \n",
+       "tobacco          1.10959e+07  53.0975  ...        196.008   \n",
+       "cloth            1.55019e+08  877.119  ...        5771.16   \n",
+       "housing          5.22836e+09  30657.3  ...        49291.4   \n",
+       "furniture         5.1127e+08  3155.16  ...        12219.2   \n",
+       "health           7.14571e+08  4007.91  ...        26012.3   \n",
+       "transport        1.46835e+09  15919.6  ...        42892.6   \n",
+       "communication    5.55905e+08  5096.85  ...        10554.1   \n",
+       "leisure          3.33401e+08   1992.3  ...        12609.2   \n",
+       "education        1.93642e+08  1129.41  ...        10181.7   \n",
+       "catering          1.9741e+08  1002.63  ...        24145.7   \n",
+       "diverse          8.74549e+09  50173.3  ...         167465   \n",
+       "Total            2.14551e+10   129761  ...         688319   \n",
+       "\n",
+       "              Water Consumption Blue (Mm3) Water Consumption Green (Mm3)  \\\n",
+       "food                               5113.18                       78940.1   \n",
+       "tobacco                            9.88068                       216.771   \n",
+       "cloth                              137.842                       1050.49   \n",
+       "housing                            886.702                       6124.28   \n",
+       "furniture                          205.557                       1443.26   \n",
+       "health                             486.894                       4497.09   \n",
+       "transport                          731.791                       5319.86   \n",
+       "communication                      210.278                       1481.37   \n",
+       "leisure                            199.019                       1876.28   \n",
+       "education                          151.077                       1660.36   \n",
+       "catering                           324.638                       4323.45   \n",
+       "diverse                             3048.7                       22956.4   \n",
+       "Total                              11505.6                        129890   \n",
+       "\n",
+       "              Water Withdrawal Blue (Mm3)  \\\n",
+       "food                              1233.98   \n",
+       "tobacco                           2.47331   \n",
+       "cloth                             148.961   \n",
+       "housing                           1384.99   \n",
+       "furniture                         325.103   \n",
+       "health                            426.939   \n",
+       "transport                         1541.01   \n",
+       "communication                     386.104   \n",
+       "leisure                           198.347   \n",
+       "education                         106.749   \n",
+       "catering                          102.572   \n",
+       "diverse                           3024.55   \n",
+       "Total                             8881.78   \n",
+       "\n",
+       "              Wages (incl. social contributions) (M€)  \\\n",
+       "food                                            78086   \n",
+       "tobacco                                       512.407   \n",
+       "cloth                                         7937.93   \n",
+       "housing                                        146534   \n",
+       "furniture                                     26593.5   \n",
+       "health                                        44892.1   \n",
+       "transport                                      119773   \n",
+       "communication                                 41196.4   \n",
+       "leisure                                       27485.2   \n",
+       "education                                     18894.3   \n",
+       "catering                                      12515.4   \n",
+       "diverse                                        406744   \n",
+       "Total                                          931164   \n",
+       "\n",
+       "              Operating surplus (investment, rents, profit) (M€)  \\\n",
+       "food                                                     65100.6   \n",
+       "tobacco                                                  252.329   \n",
+       "cloth                                                    5007.85   \n",
+       "housing                                                   101506   \n",
+       "furniture                                                16948.9   \n",
+       "health                                                   32616.8   \n",
+       "transport                                                82626.8   \n",
+       "communication                                            29043.6   \n",
+       "leisure                                                  19979.2   \n",
+       "education                                                  13094   \n",
+       "catering                                                 10082.9   \n",
+       "diverse                                                   283170   \n",
+       "Total                                                     659429   \n",
+       "\n",
+       "              Value added (wages, surplus and taxes) (M€) Employment (k pers)  \\\n",
+       "food                                               153888             6525.88   \n",
+       "tobacco                                           835.528             49.9677   \n",
+       "cloth                                             14268.8              540.64   \n",
+       "housing                                            272023              6177.8   \n",
+       "furniture                                         47635.2             1287.79   \n",
+       "health                                            84868.4             2042.93   \n",
+       "transport                                          223262             6262.17   \n",
+       "communication                                     76850.9             1627.26   \n",
+       "leisure                                           51979.2              1111.9   \n",
+       "education                                         34869.7               766.5   \n",
+       "catering                                          24701.2             779.755   \n",
+       "diverse                                            755573             18484.7   \n",
+       "Total                                         1.74075e+06             45657.3   \n",
+       "\n",
+       "              Employment hours (M.hr) GWP 100 (kg CO2eq)  \n",
+       "food                          13281.7        1.23668e+11  \n",
+       "tobacco                       107.875        2.66884e+08  \n",
+       "cloth                         1086.36        7.08195e+09  \n",
+       "housing                       11831.9        1.10182e+11  \n",
+       "furniture                     2487.53        1.83288e+10  \n",
+       "health                         3921.6        2.89633e+10  \n",
+       "transport                     12552.1        1.40054e+11  \n",
+       "communication                 3134.92        2.22731e+10  \n",
+       "leisure                       2083.78         1.8663e+10  \n",
+       "education                      1431.4        1.15809e+10  \n",
+       "catering                      1540.29        1.12362e+10  \n",
+       "diverse                       35451.4        2.42963e+11  \n",
+       "Total                         88910.8        7.35261e+11  \n",
+       "\n",
+       "[13 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "impacts_FR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Computation of impact by sector and age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conso_age = pd.DataFrame(index = sectors, columns = range(101))\n",
+    "for sec in sectors: conso_age.at[sec,:] = pd.read_excel(path_dl + 'conso_age_FR_2011.xlsx', header = None, sheet_name = sec).iloc[:,0]\n",
+    "conso_tot_age = conso_age.sum(0)\n",
+    "conso_age = conso_age.div(conso_age.sum(1), axis=0) # at i,j: share of conso of sector i by age j (so that sum of each row = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impacts_age = pd.DataFrame(index = sectors, columns = pd.MultiIndex.from_product([impacts + ['GWP 100 (kg CO2eq)'], list(range(101))], names=('impact', 'age')))\n",
+    "for sec in sectors:\n",
+    "    for i in impacts + ['GWP 100 (kg CO2eq)']:\n",
+    "        impacts_age.loc[sec, [(i, a) for a in range(101)]] = np.array(conso_age.loc[sec] * impacts_FR.at[sec, i])\n",
+    "impacts_age = impacts_age.append(impacts_age.sum().rename('Total'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conso_tot_age.sum() # /!\\ Conso totale me paraît faible TODO\n",
+    "# impacts_age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pop_by_age_FR11 = pd.read_excel('pop_FR_by_age_2011.xls', header = None).iloc[:,0] # Pb: at 100 it is people >= 100, not = 100 .iloc[:,0]\n",
+    "impacts_age_pc = impacts_age.div(pop_by_age_FR11, level = 1, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>impact</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">CO2 (kg)</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">GWP 100 (kg CO2eq)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>91</th>\n",
+       "      <th>92</th>\n",
+       "      <th>93</th>\n",
+       "      <th>94</th>\n",
+       "      <th>95</th>\n",
+       "      <th>96</th>\n",
+       "      <th>97</th>\n",
+       "      <th>98</th>\n",
+       "      <th>99</th>\n",
+       "      <th>100</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>food</th>\n",
+       "      <td>291.069</td>\n",
+       "      <td>288.401</td>\n",
+       "      <td>289.051</td>\n",
+       "      <td>294.421</td>\n",
+       "      <td>309.579</td>\n",
+       "      <td>321.119</td>\n",
+       "      <td>339.932</td>\n",
+       "      <td>364.446</td>\n",
+       "      <td>390.774</td>\n",
+       "      <td>425.053</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1456.91</td>\n",
+       "      <td>1521.68</td>\n",
+       "      <td>1651.3</td>\n",
+       "      <td>1408.27</td>\n",
+       "      <td>1538.32</td>\n",
+       "      <td>2195.32</td>\n",
+       "      <td>1886.3</td>\n",
+       "      <td>1757.68</td>\n",
+       "      <td>1731</td>\n",
+       "      <td>1688.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tobacco</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.40158</td>\n",
+       "      <td>1.12534</td>\n",
+       "      <td>1.12305</td>\n",
+       "      <td>0.910557</td>\n",
+       "      <td>1.09874</td>\n",
+       "      <td>1.56799</td>\n",
+       "      <td>1.34728</td>\n",
+       "      <td>1.25541</td>\n",
+       "      <td>1.23635</td>\n",
+       "      <td>1.20627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cloth</th>\n",
+       "      <td>45.8478</td>\n",
+       "      <td>47.5394</td>\n",
+       "      <td>47.3445</td>\n",
+       "      <td>47.9375</td>\n",
+       "      <td>49.8275</td>\n",
+       "      <td>50.7743</td>\n",
+       "      <td>52.6244</td>\n",
+       "      <td>55.0688</td>\n",
+       "      <td>57.689</td>\n",
+       "      <td>61.2424</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20.2863</td>\n",
+       "      <td>19.4105</td>\n",
+       "      <td>18.9845</td>\n",
+       "      <td>14.3875</td>\n",
+       "      <td>13.686</td>\n",
+       "      <td>19.5311</td>\n",
+       "      <td>16.7819</td>\n",
+       "      <td>15.6376</td>\n",
+       "      <td>15.4002</td>\n",
+       "      <td>15.0255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>housing</th>\n",
+       "      <td>524.174</td>\n",
+       "      <td>507.993</td>\n",
+       "      <td>506.633</td>\n",
+       "      <td>513.668</td>\n",
+       "      <td>535.454</td>\n",
+       "      <td>549.324</td>\n",
+       "      <td>572.508</td>\n",
+       "      <td>606.265</td>\n",
+       "      <td>643.044</td>\n",
+       "      <td>688.691</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1916.77</td>\n",
+       "      <td>2016.36</td>\n",
+       "      <td>2194.37</td>\n",
+       "      <td>1875.63</td>\n",
+       "      <td>2046.51</td>\n",
+       "      <td>2920.55</td>\n",
+       "      <td>2509.45</td>\n",
+       "      <td>2338.34</td>\n",
+       "      <td>2302.84</td>\n",
+       "      <td>2246.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>furniture</th>\n",
+       "      <td>96.8201</td>\n",
+       "      <td>96.7365</td>\n",
+       "      <td>97.4483</td>\n",
+       "      <td>99.7265</td>\n",
+       "      <td>104.388</td>\n",
+       "      <td>106.621</td>\n",
+       "      <td>110.368</td>\n",
+       "      <td>115.554</td>\n",
+       "      <td>120.492</td>\n",
+       "      <td>127.589</td>\n",
+       "      <td>...</td>\n",
+       "      <td>263.824</td>\n",
+       "      <td>278.06</td>\n",
+       "      <td>303.157</td>\n",
+       "      <td>259.512</td>\n",
+       "      <td>283.581</td>\n",
+       "      <td>404.695</td>\n",
+       "      <td>347.729</td>\n",
+       "      <td>324.018</td>\n",
+       "      <td>319.1</td>\n",
+       "      <td>311.336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>health</th>\n",
+       "      <td>3.15671e-13</td>\n",
+       "      <td>1.48303</td>\n",
+       "      <td>18.8029</td>\n",
+       "      <td>34.6759</td>\n",
+       "      <td>49.7498</td>\n",
+       "      <td>60.3963</td>\n",
+       "      <td>68.8487</td>\n",
+       "      <td>74.7159</td>\n",
+       "      <td>74.5122</td>\n",
+       "      <td>77.7845</td>\n",
+       "      <td>...</td>\n",
+       "      <td>585.727</td>\n",
+       "      <td>612.31</td>\n",
+       "      <td>664.017</td>\n",
+       "      <td>565.733</td>\n",
+       "      <td>616.782</td>\n",
+       "      <td>880.201</td>\n",
+       "      <td>756.301</td>\n",
+       "      <td>704.731</td>\n",
+       "      <td>694.033</td>\n",
+       "      <td>677.147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>transport</th>\n",
+       "      <td>699.292</td>\n",
+       "      <td>718.187</td>\n",
+       "      <td>715.195</td>\n",
+       "      <td>724.106</td>\n",
+       "      <td>757.93</td>\n",
+       "      <td>778.837</td>\n",
+       "      <td>815.141</td>\n",
+       "      <td>861.814</td>\n",
+       "      <td>918.936</td>\n",
+       "      <td>988.415</td>\n",
+       "      <td>...</td>\n",
+       "      <td>361.276</td>\n",
+       "      <td>348.356</td>\n",
+       "      <td>342.628</td>\n",
+       "      <td>260.864</td>\n",
+       "      <td>249.625</td>\n",
+       "      <td>356.237</td>\n",
+       "      <td>306.092</td>\n",
+       "      <td>285.22</td>\n",
+       "      <td>280.89</td>\n",
+       "      <td>274.056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>communication</th>\n",
+       "      <td>129.356</td>\n",
+       "      <td>129.087</td>\n",
+       "      <td>125.238</td>\n",
+       "      <td>123.639</td>\n",
+       "      <td>126.188</td>\n",
+       "      <td>127.189</td>\n",
+       "      <td>131.769</td>\n",
+       "      <td>138.944</td>\n",
+       "      <td>147.959</td>\n",
+       "      <td>160.254</td>\n",
+       "      <td>...</td>\n",
+       "      <td>193.029</td>\n",
+       "      <td>201.268</td>\n",
+       "      <td>216.806</td>\n",
+       "      <td>183.381</td>\n",
+       "      <td>197.935</td>\n",
+       "      <td>282.471</td>\n",
+       "      <td>242.709</td>\n",
+       "      <td>226.16</td>\n",
+       "      <td>222.727</td>\n",
+       "      <td>217.307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>leisure</th>\n",
+       "      <td>78.0152</td>\n",
+       "      <td>83.63</td>\n",
+       "      <td>87.4734</td>\n",
+       "      <td>92.5638</td>\n",
+       "      <td>100.831</td>\n",
+       "      <td>107.009</td>\n",
+       "      <td>114.921</td>\n",
+       "      <td>124.21</td>\n",
+       "      <td>133.163</td>\n",
+       "      <td>144.613</td>\n",
+       "      <td>...</td>\n",
+       "      <td>134.721</td>\n",
+       "      <td>140.303</td>\n",
+       "      <td>151.129</td>\n",
+       "      <td>127.866</td>\n",
+       "      <td>138.049</td>\n",
+       "      <td>197.007</td>\n",
+       "      <td>169.276</td>\n",
+       "      <td>157.734</td>\n",
+       "      <td>155.339</td>\n",
+       "      <td>151.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.6653</td>\n",
+       "      <td>68.4776</td>\n",
+       "      <td>102.489</td>\n",
+       "      <td>134.503</td>\n",
+       "      <td>167.056</td>\n",
+       "      <td>200.935</td>\n",
+       "      <td>233.497</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>catering</th>\n",
+       "      <td>29.1846</td>\n",
+       "      <td>35.8694</td>\n",
+       "      <td>40.1806</td>\n",
+       "      <td>44.9382</td>\n",
+       "      <td>50.9783</td>\n",
+       "      <td>55.6412</td>\n",
+       "      <td>60.8766</td>\n",
+       "      <td>66.4777</td>\n",
+       "      <td>72.0051</td>\n",
+       "      <td>78.6062</td>\n",
+       "      <td>...</td>\n",
+       "      <td>49.1062</td>\n",
+       "      <td>51.0987</td>\n",
+       "      <td>55.2235</td>\n",
+       "      <td>46.9384</td>\n",
+       "      <td>51.6129</td>\n",
+       "      <td>73.6562</td>\n",
+       "      <td>63.2881</td>\n",
+       "      <td>58.9727</td>\n",
+       "      <td>58.0774</td>\n",
+       "      <td>56.6644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>diverse</th>\n",
+       "      <td>1901.95</td>\n",
+       "      <td>1773.29</td>\n",
+       "      <td>1648.42</td>\n",
+       "      <td>1556.85</td>\n",
+       "      <td>1519.13</td>\n",
+       "      <td>1461.83</td>\n",
+       "      <td>1443.03</td>\n",
+       "      <td>1455.98</td>\n",
+       "      <td>1472.64</td>\n",
+       "      <td>1532.86</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3567.05</td>\n",
+       "      <td>3783.04</td>\n",
+       "      <td>4152.25</td>\n",
+       "      <td>3578.58</td>\n",
+       "      <td>3937.3</td>\n",
+       "      <td>5618.87</td>\n",
+       "      <td>4827.94</td>\n",
+       "      <td>4498.74</td>\n",
+       "      <td>4430.45</td>\n",
+       "      <td>4322.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>3795.71</td>\n",
+       "      <td>3682.22</td>\n",
+       "      <td>3575.79</td>\n",
+       "      <td>3562.19</td>\n",
+       "      <td>3672.53</td>\n",
+       "      <td>3721.23</td>\n",
+       "      <td>3844.52</td>\n",
+       "      <td>4030.53</td>\n",
+       "      <td>4232.15</td>\n",
+       "      <td>4518.61</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8550.1</td>\n",
+       "      <td>8973.02</td>\n",
+       "      <td>9751</td>\n",
+       "      <td>8322.08</td>\n",
+       "      <td>9074.51</td>\n",
+       "      <td>12950.1</td>\n",
+       "      <td>11127.2</td>\n",
+       "      <td>10368.5</td>\n",
+       "      <td>10211.1</td>\n",
+       "      <td>9962.64</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13 rows × 3939 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "impact            CO2 (kg)                                               \\\n",
+       "age                    0        1        2        3        4        5     \n",
+       "food               291.069  288.401  289.051  294.421  309.579  321.119   \n",
+       "tobacco                  0        0        0        0        0        0   \n",
+       "cloth              45.8478  47.5394  47.3445  47.9375  49.8275  50.7743   \n",
+       "housing            524.174  507.993  506.633  513.668  535.454  549.324   \n",
+       "furniture          96.8201  96.7365  97.4483  99.7265  104.388  106.621   \n",
+       "health         3.15671e-13  1.48303  18.8029  34.6759  49.7498  60.3963   \n",
+       "transport          699.292  718.187  715.195  724.106   757.93  778.837   \n",
+       "communication      129.356  129.087  125.238  123.639  126.188  127.189   \n",
+       "leisure            78.0152    83.63  87.4734  92.5638  100.831  107.009   \n",
+       "education                0        0        0  29.6653  68.4776  102.489   \n",
+       "catering           29.1846  35.8694  40.1806  44.9382  50.9783  55.6412   \n",
+       "diverse            1901.95  1773.29  1648.42  1556.85  1519.13  1461.83   \n",
+       "Total              3795.71  3682.22  3575.79  3562.19  3672.53  3721.23   \n",
+       "\n",
+       "impact                                             ... GWP 100 (kg CO2eq)  \\\n",
+       "age                6        7        8        9    ...                91    \n",
+       "food           339.932  364.446  390.774  425.053  ...            1456.91   \n",
+       "tobacco              0        0        0        0  ...            1.40158   \n",
+       "cloth          52.6244  55.0688   57.689  61.2424  ...            20.2863   \n",
+       "housing        572.508  606.265  643.044  688.691  ...            1916.77   \n",
+       "furniture      110.368  115.554  120.492  127.589  ...            263.824   \n",
+       "health         68.8487  74.7159  74.5122  77.7845  ...            585.727   \n",
+       "transport      815.141  861.814  918.936  988.415  ...            361.276   \n",
+       "communication  131.769  138.944  147.959  160.254  ...            193.029   \n",
+       "leisure        114.921   124.21  133.163  144.613  ...            134.721   \n",
+       "education      134.503  167.056  200.935  233.497  ...                  0   \n",
+       "catering       60.8766  66.4777  72.0051  78.6062  ...            49.1062   \n",
+       "diverse        1443.03  1455.98  1472.64  1532.86  ...            3567.05   \n",
+       "Total          3844.52  4030.53  4232.15  4518.61  ...             8550.1   \n",
+       "\n",
+       "impact                                                                         \\\n",
+       "age                92       93        94       95       96       97       98    \n",
+       "food           1521.68   1651.3   1408.27  1538.32  2195.32   1886.3  1757.68   \n",
+       "tobacco        1.12534  1.12305  0.910557  1.09874  1.56799  1.34728  1.25541   \n",
+       "cloth          19.4105  18.9845   14.3875   13.686  19.5311  16.7819  15.6376   \n",
+       "housing        2016.36  2194.37   1875.63  2046.51  2920.55  2509.45  2338.34   \n",
+       "furniture       278.06  303.157   259.512  283.581  404.695  347.729  324.018   \n",
+       "health          612.31  664.017   565.733  616.782  880.201  756.301  704.731   \n",
+       "transport      348.356  342.628   260.864  249.625  356.237  306.092   285.22   \n",
+       "communication  201.268  216.806   183.381  197.935  282.471  242.709   226.16   \n",
+       "leisure        140.303  151.129   127.866  138.049  197.007  169.276  157.734   \n",
+       "education            0        0         0        0        0        0        0   \n",
+       "catering       51.0987  55.2235   46.9384  51.6129  73.6562  63.2881  58.9727   \n",
+       "diverse        3783.04  4152.25   3578.58   3937.3  5618.87  4827.94  4498.74   \n",
+       "Total          8973.02     9751   8322.08  9074.51  12950.1  11127.2  10368.5   \n",
+       "\n",
+       "impact                           \n",
+       "age                99       100  \n",
+       "food              1731  1688.88  \n",
+       "tobacco        1.23635  1.20627  \n",
+       "cloth          15.4002  15.0255  \n",
+       "housing        2302.84  2246.81  \n",
+       "furniture        319.1  311.336  \n",
+       "health         694.033  677.147  \n",
+       "transport       280.89  274.056  \n",
+       "communication  222.727  217.307  \n",
+       "leisure        155.339   151.56  \n",
+       "education            0        0  \n",
+       "catering       58.0774  56.6644  \n",
+       "diverse        4430.45  4322.65  \n",
+       "Total          10211.1  9962.64  \n",
+       "\n",
+       "[13 rows x 3939 columns]"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "impacts_age_pc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impacts_intensity_age = impacts_age.div(conso_tot_age*10e9, level = 1, axis = 1).loc['Total',:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# impacts_intensity_age"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Export"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with pd.ExcelWriter(path_dl + 'impacts_age_FR_2011.xlsx') as writer:  \n",
+    "    for i in impacts + ['GWP 100 (kg CO2eq)']: \n",
+    "        if len(i) < 32: name = i\n",
+    "        elif i == 'Wages (incl. social contributions) (M€)': name = 'Wages (M€)'\n",
+    "        elif i == 'Operating surplus (investment, rents, profit) (M€)': name = 'Operating surplus (M€)'\n",
+    "        elif i == 'Value added (wages, surplus and taxes) (M€)': name = 'Value added (M€)'\n",
+    "        else: print(i)\n",
+    "        impacts_age[i].to_excel(writer, sheet_name = name) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with pd.ExcelWriter(path_dl + 'impacts_age_pc_FR_2011.xlsx') as writer:  \n",
+    "    for i in impacts + ['GWP 100 (kg CO2eq)']: \n",
+    "        if len(i) < 32: name = i\n",
+    "        elif i == 'Wages (incl. social contributions) (M€)': name = 'Wages (M€)'\n",
+    "        elif i == 'Operating surplus (investment, rents, profit) (M€)': name = 'Operating surplus (M€)'\n",
+    "        elif i == 'Value added (wages, surplus and taxes) (M€)': name = 'Value added (M€)'\n",
+    "        else: print(i)\n",
+    "        impacts_age_pc[i].to_excel(writer, sheet_name = name) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with pd.ExcelWriter(path_dl + 'impacts_age_FR_2011.xlsx') as writer:  \n",
+    "    for i in impacts + ['GWP 100 (kg CO2eq)']: \n",
+    "        if len(i) < 32: name = i\n",
+    "        elif i == 'Wages (incl. social contributions) (M€)': name = 'Wages (M€)'\n",
+    "        elif i == 'Operating surplus (investment, rents, profit) (M€)': name = 'Operating surplus (M€)'\n",
+    "        elif i == 'Value added (wages, surplus and taxes) (M€)': name = 'Value added (M€)'\n",
+    "        else: print(i)\n",
+    "        impacts_intensity_age[i].to_excel(writer, sheet_name = name) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(impacts_intensity_age, open(path_io + 'Exiobase 3.4/impacts_FR.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(impacts_age_pc, open(path_io + 'Exiobase 3.4/impacts_FR.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(impacts_age, open(path_io + 'Exiobase 3.4/agg_impacts_age.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(impacts_FR, open(path_io + 'Exiobase 3.4/impacts_FR.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(exio3, open(path_io + 'Exiobase 3.4/exio3.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregation ex ante: biases the results (e.g. underestimate CO2 emissions by 20%)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# exio3 = parse_exiobase3(path_io+'Exiobase 3.4/exiobase3.4_iot_2011_pxp.zip')\n",
+    "# exio3.calc_all()\n",
+    "# exio3.x = exio3.x.squeeze()\n",
+    "# exio3.impact = exio3.satellite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# agg_exio3 = pickle.load(open(path_io+'Exiobase 3.4/agg_exio3.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# agg_region = [1 if x=='FR' else 0 for x in exio3.regions]\n",
+    "# agg_sector = []\n",
+    "# for s in exio3.sectors:\n",
+    "#     for i, sec in enumerate(sectors):  \n",
+    "#         if s in exio3.consumption_sectors(sec):\n",
+    "#             agg_sector = agg_sector + [i]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pymrio.core.mriosystem.IOSystem at 0x7faac51515d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# agg_exio3 = exio3.aggregate(region_agg = agg_region, sector_agg = agg_sector, region_names = ['RoW', 'FR'], sector_names = sectors, inplace = False) \n",
+    "# agg_exio3.calc_all()\n",
+    "# agg_exio3.x = agg_exio3.x.iloc[:,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# agg_impacts = agg_exio3.impact.F\n",
+    "# agg_exio3.impact.F = agg_impacts.append([ \n",
+    "# #     pd.Series(agg_impacts.iloc[['CO2 - combustion - air' in i for i in imp],:].sum(axis = 0), name = 'CO2 - combustion - air (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['CO2' in i for i in imp],:].sum(axis = 0), name = 'CO2 (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['CH4' in i for i in imp],:].sum(axis = 0), name = 'CH4 (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['NOx' in i for i in imp],:].sum(axis = 0), name = 'NOx (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['SOx' in i for i in imp],:].sum(axis = 0), name = 'SOx (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['N2O' in i for i in imp],:].sum(axis = 0), name = 'N2O (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['NH3' in i for i in imp],:].sum(axis = 0), name = 'NH3 (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['CO -' in i for i in imp],:].sum(axis = 0), name = 'CO (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['PM10' in i for i in imp],:].sum(axis = 0), name = 'PM10 (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['PM2.5' in i for i in imp],:].sum(axis = 0), name = 'PM2.5 (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Pb' in i for i in imp],:].sum(axis = 0), name = 'Pb (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Cd' in i for i in imp],:].sum(axis = 0), name = 'Cd (kg)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Cropland' in i for i in imp],:].sum(axis = 0), name = 'Cropland (km2)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Forest area' in i for i in imp],:].sum(axis = 0), name = 'Forest area (km2)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Permanent pasture' in i for i in imp],:].sum(axis = 0), name = 'Permanent pastures (km2)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Cropland' in i or 'land Use' in i or 'pasture' in i for i in imp],:].sum(axis = 0), name = 'Land use (km2)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Water Consumption Blue' in i for i in imp],:].sum(axis = 0), name = 'Water Consumption Blue (Mm3)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Water Consumption Green' in i for i in imp],:].sum(axis = 0), name = 'Water Consumption Green (Mm3)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Water Withdrawal' in i for i in imp],:].sum(axis = 0), name = 'Water Withdrawal Blue (Mm3)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['wages' in i for i in imp],:].sum(0), name='Wages (incl. social contributions) (M€)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['surplus' in i for i in imp],:].sum(0), name='Operating surplus (investment, rents, profit) (M€)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['axes' in i or 'wages' in i or 'surplus' in i for i in imp],:].sum(0), name='Value added (wages, surplus and taxes) (M€)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Employment:' in i and 'Vulnerable' not in i for i in imp],:].sum(0), name='Employment (k pers)'),\n",
+    "#     pd.Series(agg_impacts.iloc[['Employment hours:' in i and 'Vulnerable' not in i for i in imp],:].sum(0), name = 'Employment hours (M.hr)')])\n",
+    "# del(agg_impacts)\n",
+    "# agg_exio3.impact.F = agg_exio3.impact.F.drop(agg_exio3.impact.F.index[range(len(imp))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pd.set_option('display.max_rows', 25)\n",
+    "# agg_exio3.impact.F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# agg_impacts_FR = pd.DataFrame(index = sectors, columns = impacts)\n",
+    "# for impact in impacts:\n",
+    "#     for secs in sectors:\n",
+    "#         agg_impacts_FR.at[secs, impact] = agg_exio3.embodied_impact(regs = 'FR', secs = secs, var = impact).sum()\n",
+    "# agg_impacts_FR = agg_impacts_FR.append(agg_impacts_FR.sum().rename('Total'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# agg_impacts_FR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pickle.dump(agg_exio3, open(path_io + 'Exiobase 3.4/agg_exio3.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pickle.dump(agg_impacts_FR, open(path_io + 'Exiobase 3.4/impacts_FR.pkl', 'wb'))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/impacts_of_imports.ipynb b/impacts_of_imports.ipynb
new file mode 100644
index 00000000..3a33b22f
--- /dev/null
+++ b/impacts_of_imports.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymrio # /!\\ Clone and install pymrio from https://github.com/bixiou/pymrio\n",
+    "from pymrio.core.mriosystem import IOSystem as IOS\n",
+    "from pymrio.core.mriosystem import concate_extension\n",
+    "from pymrio.tools.iomath import div0\n",
+    "from pymrio.tools.iofunctions import *\n",
+    "from pymrio.tools.ioparser import *\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_io = '/mnt/dd/adrien/DD/Économie/Données/' # where the Exiobase data is"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exio3 = pickle.load(open(path_io+'Exiobase 3.4/exio3.pkl', 'rb')) # cf. below to generate the data (from exiobase3.4_iot_2011_pxp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EU27 = ['AT', 'BE', 'BG', 'CY', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GR', 'HR', 'HU', 'IT', 'LT', 'LU', 'LV', 'MT', 'NL', 'PL',\n",
+    "       'PT', 'RO', 'SE', 'SI', 'SK', 'IE']\n",
+    "\n",
+    "steel = ['Basic iron and steel and of ferro-alloys and first products thereof', 'Secondary steel for treatment, Re-processing of secondary steel into new steel'] \n",
+    "aluminum = ['Aluminium and aluminium products',  'Secondary aluminium for treatment, Re-processing of secondary aluminium into new aluminium']\n",
+    "cement = ['Cement, lime and plaster']\n",
+    "power = ['Electricity by coal',  'Electricity by gas',  'Electricity by nuclear',  'Electricity by hydro',  'Electricity by wind',\n",
+    " 'Electricity by petroleum and other oil derivatives',  'Electricity by biomass and waste',  'Electricity by solar photovoltaic',\n",
+    " 'Electricity by solar thermal',  'Electricity by tide, wave, ocean',  'Electricity by Geothermal',  'Electricity nec']\n",
+    "chemicals = ['Chemical and fertilizer minerals, salt and other mining and quarrying products n.e.c.', 'Chemicals nec']\n",
+    "# 'Precious metals',\n",
+    "#  'Secondary preciuos metals for treatment, Re-processing of secondary preciuos metals into new preciuos metals',\n",
+    "#  'Aluminium and aluminium products',\n",
+    "#  'Secondary aluminium for treatment, Re-processing of secondary aluminium into new aluminium',\n",
+    "#  'Lead, zinc and tin and products thereof',\n",
+    "#  'Secondary lead for treatment, Re-processing of secondary lead into new lead',\n",
+    "#  'Copper products',\n",
+    "#  'Secondary copper for treatment, Re-processing of secondary copper into new copper',\n",
+    "#  'Other non-ferrous metal products',\n",
+    "#  'Secondary other non-ferrous metals for treatment, Re-processing of secondary other non-ferrous metals into new other non-ferrous metals',\n",
+    "#  'Foundry work services',\n",
+    "#  'Fabricated metal products, except machinery and equipment (28)',\n",
+    "#  'Machinery and equipment n.e.c. (29)',\n",
+    "#  'Office machinery and computers (30)',\n",
+    "#  'Electrical machinery and apparatus n.e.c. (31)',\n",
+    "#  'Radio, television and communication equipment and apparatus (32)',\n",
+    "#  'Medical, precision and optical instruments, watches and clocks (33)',\n",
+    "#  'Motor vehicles, trailers and semi-trailers (34)',\n",
+    "#  'Other transport equipment (35)'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55.77436023656755"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exio3.embodied_impact_imports('CO2 (kg)', steel, EU27) / 1e9 # in MtCO2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exio3.embodied_impact_imports('CO2 (kg)', power, EU27) / 1e9 # in MtCO2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.75427192302997"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exio3.embodied_impact_imports('CO2 (kg)', cement, EU27) / 1e9 # in MtCO2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "141.1210982452209"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exio3.embodied_impact_imports('CO2 (kg)', chemicals, EU27) / 1e9 # in MtCO2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.633704888723567"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exio3.embodied_impact_imports('CO2 (kg)', aluminum, EU27) / 1e9 # in MtCO2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1527.6481685135705"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exio3.embodied_impact_imports('CO2 (kg)', exio3.sectors, EU27) / 1e9 # in MtCO2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: change directly the parser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exio3 = parse_exiobase3(path_io+'Exiobase 3.4/exiobase3.4_iot_2011_pxp.zip')\n",
+    "exio3.calc_all()\n",
+    "exio3.x = exio3.x.squeeze()\n",
+    "exio3.impact = exio3.satellite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sectors = ['food', 'tobacco', 'cloth', 'housing', 'furniture', 'health', 'transport', 'communication', 'leisure', 'education', 'catering', 'diverse']\n",
+    "imp = exio3.impact.F.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impacts = ['CO2 (kg)', 'CH4 (kg)', 'NOx (kg)', 'SOx (kg)', 'N2O (kg)', 'NH3 (kg)', 'CO (kg)', 'PFC (kg CO2eq)', 'HFC (kg CO2eq)', 'SF6 (kg)',\n",
+    "         'PM10 (kg)', 'PM2.5 (kg)', 'Pb (kg)', 'Cd (kg)', 'Gravel and sand (kg)',\n",
+    "         'Arsenic (kg)', 'NMVOC (kg)', 'TSP (kg)', 'N (kg)', 'P (kg)', 'Energy (TJ)', 'Wood (kt)', 'Copper (kt)', 'Metal Ores (kt)', 'Non-Metallic Minerals (kt)', \n",
+    "         'Crops (kt)', 'Cropland (km2)', 'Forest area (km2)', 'Permanent pastures (km2)', 'Land use (km2)', 'Water Consumption Blue (Mm3)', \n",
+    "         'Water Consumption Green (Mm3)', 'Water Withdrawal Blue (Mm3)', 'Wages (incl. social contributions) (M€)', \n",
+    "         'Operating surplus (investment, rents, profit) (M€)', 'Value added (wages, surplus and taxes) (M€)', 'Employment (k pers)', 'Employment hours (M.hr)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agg_impacts = exio3.impact.F\n",
+    "exio3.impact.F = agg_impacts.append([ # TODO: énergie, déchets, minerais, bois TODO: Global Warming Potential (CO2eq)\n",
+    "#     pd.Series(agg_impacts.iloc[['CO2 - combustion - air' in i for i in imp],:].sum(axis = 0), name = 'CO2 - combustion - air (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['CO2' in i for i in imp],:].sum(axis = 0), name = 'CO2 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['CH4' in i for i in imp],:].sum(axis = 0), name = 'CH4 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['NOx' in i for i in imp],:].sum(axis = 0), name = 'NOx (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['SOx' in i for i in imp],:].sum(axis = 0), name = 'SOx (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['N2O' in i for i in imp],:].sum(axis = 0), name = 'N2O (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['NH3' in i for i in imp],:].sum(axis = 0), name = 'NH3 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['CO -' in i for i in imp],:].sum(axis = 0), name = 'CO (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['PFC' in i for i in imp],:].sum(axis = 0), name = 'PFC (kg CO2eq)'),\n",
+    "    pd.Series(agg_impacts.iloc[['HFC' in i for i in imp],:].sum(axis = 0), name = 'HFC (kg CO2eq)'),\n",
+    "    pd.Series(agg_impacts.iloc[['SF6' in i for i in imp],:].sum(axis = 0), name = 'SF6 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['PM10' in i for i in imp],:].sum(axis = 0), name = 'PM10 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['PM2.5' in i for i in imp],:].sum(axis = 0), name = 'PM2.5 (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Pb' in i for i in imp],:].sum(axis = 0), name = 'Pb (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Cd' in i for i in imp],:].sum(axis = 0), name = 'Cd (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['sand' in i for i in imp],:].sum(axis = 0), name = 'Gravel and sand (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['As -' in i for i in imp],:].sum(axis = 0), name = 'Arsenic (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['NMVOC' in i for i in imp],:].sum(axis = 0), name = 'NMVOC (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['TSP' in i for i in imp],:].sum(axis = 0), name = 'TSP (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['N -' in i for i in imp],:].sum(axis = 0), name = 'N (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['P - a' in i or 'P - w' in i for i in imp],:].sum(axis = 0), name = 'P (kg)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Energy Carrier Supply' in i for i in imp],:].sum(axis = 0), name = 'Energy (TJ)'),\n",
+    "    pd.Series(agg_impacts.iloc[['s wood -' in i for i in imp],:].sum(axis = 0), name = 'Wood (kt)'), # from Wood to Crops, includes both used and unused extraction\n",
+    "    pd.Series(agg_impacts.iloc[['Ores - Copper' in i for i in imp],:].sum(axis = 0), name = 'Copper (kt)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Metal Ores' in i for i in imp],:].sum(axis = 0), name = 'Metal Ores (kt)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Non-Metallic Minerals' in i for i in imp],:].sum(axis = 0), name = 'Non-Metallic Minerals (kt)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Primary Crops' in i for i in imp],:].sum(axis = 0), name = 'Crops (kt)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Cropland' in i for i in imp],:].sum(axis = 0), name = 'Cropland (km2)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Forest area' in i for i in imp],:].sum(axis = 0), name = 'Forest area (km2)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Permanent pasture' in i for i in imp],:].sum(axis = 0), name = 'Permanent pastures (km2)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Cropland' in i or 'land Use' in i or 'pasture' in i for i in imp],:].sum(axis = 0), name = 'Land use (km2)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Water Consumption Blue' in i for i in imp],:].sum(axis = 0), name = 'Water Consumption Blue (Mm3)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Water Consumption Green' in i for i in imp],:].sum(axis = 0), name = 'Water Consumption Green (Mm3)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Water Withdrawal' in i for i in imp],:].sum(axis = 0), name = 'Water Withdrawal Blue (Mm3)'),\n",
+    "    pd.Series(agg_impacts.iloc[['wages' in i for i in imp],:].sum(0), name='Wages (incl. social contributions) (M€)'),\n",
+    "    pd.Series(agg_impacts.iloc[['surplus' in i for i in imp],:].sum(0), name='Operating surplus (investment, rents, profit) (M€)'),\n",
+    "    pd.Series(agg_impacts.iloc[['axes' in i or 'wages' in i or 'surplus' in i for i in imp],:].sum(0), name='Value added (wages, surplus and taxes) (M€)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Employment:' in i and 'Vulnerable' not in i for i in imp],:].sum(0), name='Employment (k pers)'),\n",
+    "    pd.Series(agg_impacts.iloc[['Employment hours:' in i and 'Vulnerable' not in i for i in imp],:].sum(0), name = 'Employment hours (M.hr)')])\n",
+    "del(agg_impacts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(exio3, open(path_io + 'Exiobase 3.4/exio3.pkl', 'wb'))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pop_FR_by_age_2011.xls b/pop_FR_by_age_2011.xls
new file mode 100644
index 00000000..8c93834b
Binary files /dev/null and b/pop_FR_by_age_2011.xls differ
diff --git a/pymrio/__init__.py b/pymrio/__init__.py
index 014e1779..20bf39bc 100644
--- a/pymrio/__init__.py
+++ b/pymrio/__init__.py
@@ -71,3 +71,5 @@
 from pymrio.tools.iomath import calc_M
 from pymrio.tools.iomath import calc_e
 from pymrio.tools.iomath import calc_accounts
+
+from pymrio.tools.iofunctions import *
\ No newline at end of file
diff --git a/pymrio/core/mriosystem.py b/pymrio/core/mriosystem.py
index dcc0cb18..f60902d6 100644
--- a/pymrio/core/mriosystem.py
+++ b/pymrio/core/mriosystem.py
@@ -1425,7 +1425,8 @@ def __init__(self, Z=None, Y=None, A=None, x=None, L=None,
                 description=description,
                 name=name,
                 system=system,
-                version=version
+                version=version,
+                year=year
             )
 
         if not getattr(self.meta, 'name', None):
@@ -1795,7 +1796,7 @@ def aggregate(self, region_agg=None, sector_agg=None,
         # work.
 
         if not inplace:
-            self = self.copy()
+            self = self.copy(new_name=self.name) # DONE: to avoid "_copy" at the end of the name so that methods like 'production' work well
 
         try:
             self.reset_to_flows()
@@ -1940,7 +1941,7 @@ def aggregate(self, region_agg=None, sector_agg=None,
             self.x = pd.DataFrame(
                 data=conc.dot(self.x),
                 index=mi_reg_sec,
-                columns=self.x.columns,
+                # columns=self.x.columns,
             )
             self.meta._add_modify('Aggregate industry output x')
         else:
@@ -1954,64 +1955,67 @@ def aggregate(self, region_agg=None, sector_agg=None,
                 index=self.population.index,
             )
 
+        first = True
         for extension in self.get_extensions(data=True):
-            self.meta._add_modify('Aggregate extensions...')
-            extension.reset_to_flows()
-            st_redo_unit = False
-            for ik_name, ik_df in zip(
-                    extension.get_DataFrame(data=False, with_unit=False),
-                    extension.get_DataFrame(data=True, with_unit=False)):
-
-                # Without unit - this is reset aftwards if necessary
-                if ik_df.index.names == ['region',
-                                         'sector'] == ik_df.columns.names:
-                    # Full disaggregated extensions - aggregate both axis
-                    # (this is the case if the extions shows the flows from
-                    # pda to cba)
-                    extension.__dict__[ik_name] = pd.DataFrame(
-                        data=conc.dot(ik_df).dot(conc.T))
-
-                    # next step must be done afterwards due to unknown reasons
-                    extension.__dict__[ik_name].columns = mi_reg_sec
-                    extension.__dict__[ik_name].index = mi_reg_sec
-                    st_redo_unit = True
-                elif (ik_df.index.names == ['region', 'sector'] and
-                      ik_df.columns.names == ['region', 'category']):
-
-                    # Full disaggregated finald demand satellite account.
-                    # Thats not implemented yet - but aggregation is in place
-                    extension.__dict__[ik_name] = pd.DataFrame(
-                        data=conc.dot(ik_df).dot(conc_y.T))
-                    # next step must be done afterwards due to unknown reasons
-                    extension.__dict__[ik_name].columns = mi_reg_Ycat
-                    extension.__dict__[ik_name].index = mi_reg_sec
-
-                elif ik_df.columns.names == ['region', 'category']:
-                    # Satellite account connected to final demand (e.g. F_Y)
-                    extension.__dict__[ik_name] = pd.DataFrame(
-                        data=ik_df.dot(conc_y.T))
-                    # next step must be done afterwards due to unknown reasons
-                    extension.__dict__[ik_name].columns = mi_reg_Ycat
-                    extension.__dict__[ik_name].index = ik_df.index
+            if first:
+                first = False
+                self.meta._add_modify('Aggregate extensions...')
+                extension.reset_to_flows()
+                st_redo_unit = False
+                for ik_name, ik_df in zip(
+                        extension.get_DataFrame(data=False, with_unit=False),
+                        extension.get_DataFrame(data=True, with_unit=False)):
+
+                    # Without unit - this is reset aftwards if necessary
+                    if ik_df.index.names == ['region',
+                                             'sector'] == ik_df.columns.names:
+                        # Full disaggregated extensions - aggregate both axis
+                        # (this is the case if the extions shows the flows from
+                        # pda to cba)
+                        extension.__dict__[ik_name] = pd.DataFrame(
+                            data=conc.dot(ik_df).dot(conc.T))
+
+                        # next step must be done afterwards due to unknown reasons
+                        extension.__dict__[ik_name].columns = mi_reg_sec
+                        extension.__dict__[ik_name].index = mi_reg_sec
+                        st_redo_unit = True
+                    elif (ik_df.index.names == ['region', 'sector'] and
+                          ik_df.columns.names == ['region', 'category']):
+
+                        # Full disaggregated finald demand satellite account.
+                        # Thats not implemented yet - but aggregation is in place
+                        extension.__dict__[ik_name] = pd.DataFrame(
+                            data=conc.dot(ik_df).dot(conc_y.T))
+                        # next step must be done afterwards due to unknown reasons
+                        extension.__dict__[ik_name].columns = mi_reg_Ycat
+                        extension.__dict__[ik_name].index = mi_reg_sec
+
+                    elif ik_df.columns.names == ['region', 'category']:
+                        # Satellite account connected to final demand (e.g. F_Y)
+                        extension.__dict__[ik_name] = pd.DataFrame(
+                            data=ik_df.dot(conc_y.T))
+                        # next step must be done afterwards due to unknown reasons
+                        extension.__dict__[ik_name].columns = mi_reg_Ycat
+                        extension.__dict__[ik_name].index = ik_df.index
 
-                else:
-                    # Standard case - aggregated columns, keep stressor rows
-                    extension.__dict__[ik_name] = pd.DataFrame(
-                        data=ik_df.dot(conc.T))
-                    # next step must be done afterwards due to unknown reasons
-                    extension.__dict__[ik_name].columns = mi_reg_sec
-                    extension.__dict__[ik_name].index = ik_df.index
-
-                if st_redo_unit:
-                    try:
-                        _value = extension.unit.iloc[0].tolist()[0]
-                        extension.unit = pd.DataFrame(
-                            index=mi_reg_sec,
-                            columns=extension.unit.columns,
-                            data=_value)
-                    except AttributeError:
-                        # could fail if no unit available
-                        extension.unit = None
+                    else:
+                        # Standard case - aggregated columns, keep stressor rows
+                        extension.__dict__[ik_name] = pd.DataFrame(
+                            data=ik_df.dot(conc.T))
+                        # next step must be done afterwards due to unknown reasons
+                        extension.__dict__[ik_name].columns = mi_reg_sec
+                        extension.__dict__[ik_name].index = ik_df.index
+
+                    if st_redo_unit:
+                        try:
+                            _value = extension.unit.iloc[0].tolist()[0]
+                            extension.unit = pd.DataFrame(
+                                index=mi_reg_sec,
+                                columns=extension.unit.columns,
+                                data=_value)
+                        except AttributeError:
+                            # could fail if no unit available
+                            extension.unit = None
         self.calc_extensions()
         return self
 
diff --git a/pymrio/mrio_models/exio20/misc/WB_data.xls b/pymrio/mrio_models/exio20/misc/WB_data.xls
new file mode 100644
index 00000000..98af4755
Binary files /dev/null and b/pymrio/mrio_models/exio20/misc/WB_data.xls differ
diff --git a/pymrio/tools/iofunctions.py b/pymrio/tools/iofunctions.py
new file mode 100644
index 00000000..ea7172da
--- /dev/null
+++ b/pymrio/tools/iofunctions.py
@@ -0,0 +1,1124 @@
+"""
+Diverse methods and functions to manipulate IOSystems, such as production of some sectors, their embodied impacts, inputs or EROI. 
+Mostly developed for Exiobase 1 and 2, may not be compatible with other databases.
+by adrien fabre (aka. bixiou on github), feel free to ask: adrien.fabre@psemail.eu
+"""
+
+from pymrio.core.mriosystem import IOSystem as IOS
+from pymrio.tools.iomath import div0
+from pymrio.tools.iomath import sorted_series
+from pymrio.tools.iomath import approx_solution
+from pymrio.tools.iomath import mult_cols
+from pymrio.tools.iomath import mult_rows
+from pymrio.tools.iomath import inter_secs
+from pymrio.tools.iomath import gras
+from pymrio.tools.ioparser import themis_parser
+import pandas as pd
+import numpy as np
+import scipy.sparse as sp 
+from scipy.sparse import linalg as spla
+from openpyxl import load_workbook
+
+# TODO: manage Themis, Cecilia, and futures
+
+def energy_sectors(self, notion): # TODO: other sectors, difference from elecs_names to add after elec and to display
+    '''
+    Returns the list of energy sectors (or names) corresponding to notion, which can be: secondary, secondary_fuels, elec_hydrocarbon, electricities,elecs_names
+    '''
+    if notion is None: sectors = self.sectors
+    elif notion not in ['secondary', 'secondary_fuels', 'elec_hydrocarbon', 'electricities', 'elecs_names', 'secondary_heats', 'renewable', 'renew_names']:
+        print('Error: notion unknown')
+    elif notion=='elec_hydrocarbon': sectors = ['Electricity by coal', 'Electricity by gas']
+    elif notion=='secondary_fuels': sectors = ['Motor Gasoline', 'Gas/Diesel Oil', 'Heavy Fuel Oil', 'Liquefied Petroleum Gases (LPG)', \
+         'Naphtha', 'Non-specified Petroleum Products', 'Kerosene', 'Kerosene Type Jet Fuel', 'Petroleum Coke', 'Aviation Gasoline', 'Gasoline Type Jet Fuel']
+    elif notion=='electricities': sectors = ['Electricity by ' + s for s in self.energy_sectors('elecs_names')]
+    elif notion=='renewable': sectors = ['Electricity by ' + s for s in self.energy_sectors('renew_names')]
+    elif notion=='secondary_heats': sectors = ['Steam and hot water supply services'] + self.energy_sectors('secondary_fuels')
+        
+    if self.name.lower()=='cecilia' or (self.name.lower()=='exiobase' and self.meta.version=='1'): 
+        if notion=='secondary_fuels': sectors = ['Motor spirit (gasoline)', 'Gas oils', 'Fuel oils n.e.c.', 'Kerosene, including kerosene type jet fuel', \
+                                                'Petroleum gases and other gaseous hydrocarbons, except natural gas']
+        elif notion=='secondary': sectors = ['Electricity by hydro', 'Electricity nec, including biomass and waste', 'Steam and hot water supply services',
+ 'Electricity by nuclear', 'Electricity by coal', 'Electricity by gas', 'Motor spirit (gasoline)', 'Gas oils', 'Fuel oils n.e.c.', 'Electricity by wind', 
+ 'Kerosene, including kerosene type jet fuel', 'Petroleum gases and other gaseous hydrocarbons, except natural gas']
+#  ['Coal and lignite; peat (10)', 'Natural gas and services related to natural gas extraction, excluding surveying', 'Nuclear fuel', 'Motor spirit (gasoline)',
+#         'Gas oils', 'Fuel oils n.e.c.', 'Kerosene, including kerosene type jet fuel', 'Petroleum gases and other gaseous hydrocarbons, except natural gas']
+        elif notion=='elecs_names': 
+            sectors = ['wind', 'hydro', 'coal', 'gas', 'nuclear']
+
+    elif self.name.lower()=='exiobase' and self.meta.version[0]=='2':
+        if notion=='elec_hydrocarbon': sectors = sectors + ['Electricity by petroleum and other oil derivatives']
+        elif notion=='secondary_fuels': sectors = ['Aviation Gasoline', 'Gas/Diesel Oil', 'Gasoline Type Jet Fuel', 'Heavy Fuel Oil', 'Kerosene', \
+     'Kerosene Type Jet Fuel', 'Liquefied Petroleum Gases (LPG)', 'Motor Gasoline', 'Naphtha', 'Non-specified Petroleum Products', 'Petroleum Coke'] #TODO:check
+        elif notion=='secondary': sectors =['Steam and hot water supply services', 'Electricity by petroleum and other oil derivatives',
+ 'Electricity by hydro', 'Electricity by wind', 'Electricity by biomass and waste', 'Electricity by tide, wave, ocean',
+ 'Electricity by Geothermal', 'Electricity by solar photovoltaic', 'Electricity by solar thermal', 'Electricity by coal', 'Electricity by gas',
+ 'Electricity by nuclear', 'Electricity nec', 'Motor Gasoline', 'Gas/Diesel Oil', 'Heavy Fuel Oil', 'Kerosene Type Jet Fuel',
+ 'Liquefied Petroleum Gases (LPG)', 'Naphtha', 'Non-specified Petroleum Products', 'Kerosene', 'Petroleum Coke', 'Aviation Gasoline', 'Gasoline Type Jet Fuel']
+#             ['Other Bituminous Coal', 'Coking Coal', 'Lignite/Brown Coal', 'Sub-Bituminous Coal', 'Anthracite', \
+#  'Natural gas and services related to natural gas extraction, excluding surveying', 'Nuclear fuel', 'Biogas', 'Biodiesels', 'Charcoal', 'Ethane', \
+#  'Gas Coke', 'Gas Works Gas', 'Other Hydrocarbons', 'Other Liquid Biofuels', 'Motor Gasoline', 'Gas/Diesel Oil', 'Heavy Fuel Oil', 'Kerosene Type Jet Fuel', \
+#  'Liquefied Petroleum Gases (LPG)', 'Naphtha', 'Non-specified Petroleum Products', 'Kerosene','Petroleum Coke', 'Aviation Gasoline', 'Gasoline Type Jet Fuel']
+        elif notion=='elecs_names': sectors = ['wind', 'solar photovoltaic', 'coal', 'petroleum and other oil derivatives', 'gas', 'nuclear', 'hydro']
+
+    elif self.name.lower()=='themis':
+        if notion=='elec_hydrocarbon': sectors = sectors + ['Electricity by oil']
+        elif notion=='secondary': sectors = ['Steam and hot water supply services', 'Electricity by hydro', 'Electricity by wind onshore',\
+                 'Electricity by wind offshore', 'Electricity by biomass&Waste', 'Electricity by ocean', 'Electricity by geothermal', \
+                 'Electricity by solar PV', 'Electricity by solar CSP', 'Electricity by coal', 'Electricity by gas', 'Electricity by oil', \
+                 'Electricity by nuclear', 'Motor Gasoline', 'Gas/Diesel Oil', 'Heavy Fuel Oil', 'Kerosene Type Jet Fuel', 'Liquefied Petroleum Gases (LPG)', \
+                'Naphtha', 'Non-specified Petroleum Products', 'Kerosene', 'Petroleum Coke', 'Aviation Gasoline', 'Gasoline Type Jet Fuel']
+        elif notion=='elecs_names': sectors = ['wind onshore', 'wind offshore', 'solar PV', 'coal', 'oil', 'gas', 'nuclear', 'hydro', 'coal w CCS', \
+                                               'gas w CCS', 'biomass&Waste', 'biomass w CCS', 'ocean', 'geothermal', 'solar CSP']
+        elif notion=='renew_names': sectors = ['wind onshore', 'wind offshore', 'solar PV', 'hydro', 'biomass&Waste', 'biomass w CCS', \
+                                             'ocean', 'geothermal', 'solar CSP']
+    else: print('Function not yet implemented for this IOSystem')
+    return(sectors)
+
+@property
+def regions(self): 
+    '''
+    Returns the list of all regions in the IOSystem
+    '''
+    if self.name == 'THEMIS' or self.name == 'Cecilia': return(self.labels.regions)
+    else: return(list(self.get_regions())) 
+
+@property
+def sectors(self): 
+    '''
+    Returns the list of all sectors in the IOSystem
+    '''
+    if self.name == 'THEMIS' or self.name == 'Cecilia': return(self.labels.sectors)
+    else: return(list(self.get_sectors()))
+
+def prepare_secs_regs(self, secs, regs):
+    '''
+    Prepare the values by default of secs and regs for many methods: all sectors and all regions of the IOSystem.
+    '''
+    if secs is None: secs = self.sectors
+    elif type(secs)==str: secs=[secs]
+    if regs is None: regs = self.regions # regions bugs with impacts ... [reg, secs]
+    elif type(regs)==str: regs=[regs]
+    return((secs, regs))
+
+def index_secs(self, secs, vec_sectors=None):
+    '''
+    Returns an array of the indexes of secs in vec_sectors.
+    
+    If secs is a string, returns the indexes of secs in vec_sectors; if secs is a list or array, returns the indexes of any of its elements.
+    By default, vec_sectors is the sectors of the IOSystem.
+    '''
+    if vec_sectors is None: vec_sectors = self.sectors
+    if type(secs)==str: return(np.array([i for i,x in enumerate(vec_sectors) if x == secs]))
+    else: return(np.array([i for i,x in enumerate(vec_sectors) if x in secs]))
+    
+def index_regs(self, regs, vec_regions=None):
+    '''
+    Returns an array of the indexes of secs in vec_regions.
+    
+    If regs is a string, returns the indexes of regs in vec_regions; if regs is a list or array, returns the indexes of any of its elements.
+    By default, vec_regions is the regions of the IOSystem.
+    '''
+    if vec_regions is None: vec_regions = self.regions
+    if type(regs)==str: return(np.array([i for i,x in enumerate(vec_regions) if x == regs]))
+    else: return(np.array([i for i,x in enumerate(vec_regions) if x in regs]))
+
+def regs_or_no(self, regs, yes=True):
+    if yes: res = regs
+    else: res = self, not_regs(regs)
+    if type(res)==str: res=[res]
+    return(res)
+
+def not_regs(self, regs): return(list(filter(lambda r: r not in regs, self.regions)))
+
+@property
+def nb_regions(self): return(len(self.regions))
+
+@property
+def nb_sectors(self): return(len(self.sectors))
+
+def find(self, string, vec='impacts', unit = False): # TODO: allow insensitivity to case
+    '''
+    Find vec=impacts/sectors containing string.
+    '''
+    if vec=='impacts' or vec=='Impacts' or vec=='impact': 
+        if self.name == 'THEMIS': array = np.array(self.labels.idx_impacts)
+        else: array = np.array(self.impact.unit.iloc[:,0].index) # TODO: check to replace by impact.S.index
+    elif vec=='sectors' or vec=='sector': array = np.array(self.sectors)
+    if unit: return([self.impact.unit.iloc[i,:] for i,x in enumerate(array) if string in x]) 
+    else: return([x for i,x in enumerate(array) if string in x])
+
+def is_in(self, secs = None, regs = None): # TODO: trim spaces for themis' foreground
+    '''
+    Returns a list of booleans, where an element i is True iff it corresponds to a reg in regs and a sec in secs in the double index regions x sectors.
+    
+    By default, secs is set to all sectors and regs to all regions.
+    '''
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if self.name=='THEMIS': return(np.array([r in regs for r in self.labels.idx_regions])*np.array([s in secs for s in self.labels.idx_sectors]))
+    else: return([r in regs and s in secs for r in self.regions for s in self.sectors])
+
+def index_secs_regs(self, secs = None, regs = None):
+    '''
+    Returns an array of the indexes of any element (reg, sec) of regs x secs in the double index of the IOSystem.
+    
+    By default, secs is set to all sectors and regs to all regions.
+    '''
+    return(list(np.where(self.is_in(secs, regs))[0]))
+
+def final_demand(self, secs = None, regs = None, only_positive = True):
+    '''
+    Returns the vector of final demand for (sec, reg) in secs x regs, computed from Y.
+    
+    If only_positive is True, doesn't take into account 'Changes in inventories' and 'Changes in valuables', which can be negative. (Works only for Exiobase)
+    '''
+    if only_positive and self.name=='EXIOBASE':
+        pos_y = ['Final consumption expenditure by government', 'Gross fixed capital formation', 'Export', \
+                'Final consumption expenditure by non-profit organisations serving households (NPISH)', 'Final consumption expenditure by households']
+        return(self.Y[[(reg,dom) for reg in self.regions for dom in pos_y]].sum(axis='columns')*self.is_in(secs, regs))
+    else: return(self.Y[[(reg,dom) for reg in self.regions for dom in io.Y.columns.levels[1]]].sum(axis='columns')*self.is_in(secs, regs))
+
+@property
+def secondary_energy_demand(self): # TODO: precalculate at the instantiation / TODO!: rename in 'electricity_demand' because this is what it is
+    '''
+    Returns the vector of secondary energy demand.
+    '''
+    if self.name=='THEMIS': return(self.energy.secondary_demand)
+    else: return(self.final_demand(self.energy_sectors('secondary')))
+    
+@property
+def VA(self): # TODO: exiobase
+    '''
+    Returns the vector of value added, by summing Operating surplus (Consumption of fixed capital, Rents on land, Royalties on resources, 
+    Remainin net operating surplus), Compensation of Employees (wages & salaries, employers social contributions) and Fixed capital formation, unit: M€
+    '''
+    if self.name=='THEMIS':
+        if not hasattr(self, 'VA_filled'): self.VA_filled = np.array(self.impact.S.tocsc()[list(range(1618,1624))+[1630]].sum(axis=0)).flatten()
+        return(self.VA_filled)
+    else: print('"Value added" not yet implemented for database other than THEMIS')
+
+def production(self, secs=None, regs=None, non_unitary_themis = True): # TODO: change name 'non_unitary_themis' to 'secondary_energy_sector'?
+    '''
+    Returns the vector of production of (sec, reg) in secs x regs, computed from x. /!\ In the case of energy, "production" is the demand from non-energy sectors.
+    /!\ Beware, for THEMIS, production is unitary, unless non_unitary_themis = True, 
+    In this case it is inferred using energy_demand/energy_supply for energy sectors, but is 0 for non-energy sectors.
+    Setting non_unitary_themis to True provides better estimates for global EROIs but cannot cover foreground sectors, while setting it to False provides the 
+    same estimates as True when the sector is unique (e.g. one sector in one region) and covers foreground, but is imprecise for several sectors or regions
+    '''    
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if self.name!='Cecilia' and self.name!='exio34_ntnu':
+        if not hasattr(self, 'secondary_energy_supply'): self.secondary_energy_supply = self.energy_supply * self.is_in(self.energy_sectors('secondary'))
+        if not hasattr(self, 'secondary_fuel_supply'): self.secondary_fuel_supply = self.energy_supply * self.is_in(self.energy_sectors('secondary_fuels'))
+    if self.name=='THEMIS': 
+        if non_unitary_themis: return(self.is_in(secs, regs) * div0(self.secondary_energy_demand, self.energy_supply))
+        else: return(self.is_in(secs, regs))
+    else: return(self.x*self.is_in(secs, regs)) 
+    
+def impacts(self, var='Total Energy supply', regs=None, secs=None, join_sort=False): # var = 'Value Added'
+    '''
+    Returns the vector of impact of type var related directly to (sec, reg) in secs x regs, computed from F.
+    
+    If join_sort is True, sorts the results by decreasing order of impact, grouped by sector.
+    By default, secs is set to all sectors and regs to all regions.
+    /!\ will be 0 for THEMIS & Total Energy supply if non_unitary_themis=True in production because production will be 0 (it doesn't matter for EROIs 
+    computation because production is then computed from another way, and is not 0)
+    '''
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if self.name=='Cecilia' or (self.name=='EXIOBASE' and self.meta.version=='1'):
+        if var=='supply' or var=='Total Energy supply': var = 'Gross Energy Supply - '
+        elif var=='use': var = 'Gross Energy Use - '
+        impacts = self.materials.F.loc[[s.startswith(var) for s in self.materials.F.index]][[(r,s) for r in regs for s in secs]].sum()
+    else: 
+        if var=='Total Energy supply': 
+            y = self.production(secs, regs)
+            impacts = (self.energy_supply * y)[self.index_secs_regs(secs, regs)]
+        else: impacts = self.impact.F.loc[var].loc[regs,secs]
+    if join_sort: return(sorted_series(impacts.groupby('sector').sum()))
+    else: return(impacts)   
+
+@property
+def energy_supply(self):
+    '''
+    Returns the vector of energy supply per unit of product (in TJ).
+    '''
+    if self.name=='Cecilia' or (self.name=='EXIOBASE' and self.meta.version=='1'): 
+        return(self.materials.S.loc[[s.startswith('Gross Energy Supply - ') for s in self.materials.S.index]].sum())
+    elif self.name=='EXIOBASE' and self.meta.version[0]=='2': return(self.impact.S.loc['Total Energy supply'])
+    elif self.name=='THEMIS': 
+        if not hasattr(self, 'supply_filled'): 
+            self.supply_filled = ['Energy Carrier Supply' in sector for sector in self.labels.idx_impacts] * self.impact.S # act as .dot()
+            self.supply_filled[self.index_secs_regs('Electricity by solar CSP')] = np.ones(self.regions.size) * \
+                max(self.supply_filled[self.index_secs_regs('Electricity by solar CSP')]) # to have credible figures for solar CSP
+                # without this, supply[CSP] = [0,0,0,10,80,5,0,0] which is weird, I set everything to 80 # except wind, no such discrepancy in other technos
+        return(self.supply_filled)
+    else: return('Property not yet implemented for this IOSystem.')
+    
+@property
+def employment_low(self): # TODO: exiobase
+    '''
+    Returns the vector of Employment: low skilled, unit: 1000 persons
+    '''
+    if self.name=='THEMIS':
+        if not hasattr(self, 'empl_low'): self.empl_low = self.impact.S.tocsc()[1624].toarray().flatten()
+        return(self.empl_low)
+    else: print('"Employment" not yet implemented for database other than THEMIS')
+    
+@property
+def employment_medium(self): # TODO: exiobase
+    '''
+    Returns the vector of Employment: medium skilled, unit: 1000 persons
+    '''
+    if self.name=='THEMIS':
+        if not hasattr(self, 'empl_medium'): self.empl_medium = self.impact.S.tocsc()[1625].toarray().flatten()
+        return(self.empl_medium)
+    else: print('"Employment" not yet implemented for database other than THEMIS')
+            
+@property
+def employment_high(self): # TODO: exiobase
+    '''
+    Returns the vector of Employment: high skilled, unit: 1000 persons
+    '''
+    if self.name=='THEMIS':
+        if not hasattr(self, 'empl_high'): self.empl_high = self.impact.S.tocsc()[1626].toarray().flatten()
+        return(self.empl_high)
+    else: print('"Employment" not yet implemented for database other than THEMIS')
+                    
+@property
+def employment_all(self): # TODO: exiobase
+    '''
+    Returns the vector of Employment (all skills combined), unit: 1000 persons
+    '''
+    if self.name=='THEMIS':
+        if not hasattr(self, 'empl_all'): self.empl_all = np.array(self.impact.S.tocsc()[1624:1627].sum(axis=0)).flatten()
+        return(self.empl_all)
+    else: print('"Employment" not yet implemented for database other than THEMIS')
+             
+def embodied_prod(self, secs=None, regs=None, prod = None):
+    '''
+    Returns the vector of embodied production for (sec, reg) in secs x regs, i.e. all the production required to produce their production, including them.
+    
+    When the Leontief inverse is not known, computes an approximate solution from the technology matrix A.
+    If the production is pre-calculated, it can be passed as an argument.
+    /!\ Beware, for THEMIS, production is inferred using energy_demand/energy_supply for energy sectors, but is unitary for non-energy sectors.
+    '''
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if prod is None: prod = self.production(secs, regs)
+    if self.L is None: return(spla.cgs(sp.eye(self.A.shape[0])-self.A, prod, approx_solution(self.A,prod).transpose().toarray()[0], tol=1e-7)[0])
+    else: return(np.dot(self.L, prod))
+
+def embodied_impact(self, secs=None, regs=None, var='Total Energy supply', source='secondary', group_by='region', sort=False, production = None):
+    '''
+    Returns a vector of impact of type var embodied in the production of (sec, reg) in secs x regs, excluding their own production, and including only impacts
+    from sectors in energy_sectors(source) if self.name!='exio34_ntnu'. Results are grouped by group_by (default: region) and can be sorted in decreasing order (default: unsorted).
+    '''
+    secs, regs = self.prepare_secs_regs(secs, regs) # TODO: source = None
+    if production is None: production = self.production(secs, regs)
+    if var=='Total Energy supply' and self.name != 'Cecilia':
+        impacts = self.secondary_energy_supply * (self.embodied_prod(secs, prod=production) - production)
+        impacts = pd.Series(impacts, index = pd.MultiIndex.from_arrays([self.labels.idx_regions, self.labels.idx_sectors], names=['region', 'sector']))
+    else:
+        share_demand = div0(self.embodied_prod(secs, regs, production)-production, self.x)
+        impacts = self.impacts(var)*share_demand        
+    if self.name!='exio34_ntnu': impacts = impacts[self.index_secs_regs(self.energy_sectors(source))] # /!\ hack: c'est pck sur THEMIS je m'intéresse à energy mais pas sur Exio 3 
+    if sort: return(sorted_series(impacts.groupby(group_by).sum()))
+    else: return(impacts.groupby(group_by).sum())  
+    
+def employment(self, secs = None, regs = None, skill='all', prod = None, indirect = True): # TODO: exiobase
+    '''
+    For THEMIS: Returns the employment (in k persons) of the embodied production of sectors secs in regions regs.
+    skill can be 'low', 'medium', 'high' or 'all'.
+    '''
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if prod is None: prod = self.production(secs, regs)
+    if indirect: x = self.embodied_prod(secs, prod=prod)
+    else: x = prod
+    if skill=='low': return((self.employment_low * x).sum())
+    elif skill=='medium': return((self.employment_medium * x).sum())
+    elif skill=='high': return((self.employment_high * x).sum())
+    elif skill=='all': return((self.employment_all * x).sum())
+
+def employments(self, secs = None, skill='all', recompute = True, indirect = True):
+    '''
+    Returns the serie of employments for the given skills at the global level of the list of sectors secs.
+    '''
+    if secs is None: secs = self.energy_sectors('electricities')
+    if recompute or not hasattr(self, 'employ'):
+        employments = pd.Series()
+        for i, sec in enumerate(secs): 
+            empl_sec = self.employment(secs = sec, skill=skill, indirect = indirect)
+            employments.at[secs[i][15:]] = empl_sec
+        self.employ = employments
+    return(self.employ)
+
+def value_added(self, secs = None, regs = None, prod = None, indirect = True): # TODO: exiobase
+    '''
+    For THEMIS: Returns the value added (in M€) of the embodied production of sectors secs in regions regs.
+    '''
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if prod is None: prod = self.production(secs, regs)
+    if indirect: return((self.VA * self.embodied_prod(secs, prod=prod)).sum())
+    else: return((self.VA * self.production(secs, prod=prod)).sum())
+
+def price_energy(self, secs = None, regs = None, digits=0, indirect = True): # TODO: exiobase; while let the choice of indirect? indirect=False makes no sense
+    '''
+    For THEMIS: Returns the price of energy (in M€/TWh = €/MWh) of sectors secs in regions regs, computed using the value added of the embodied production.
+    '''
+    TWh2TJ = 3.6e3
+    if secs is None: secs = self.energy_sectors('electricities')
+    prod = self.production(secs, regs)
+    return(round(self.value_added(secs, regs, prod = prod, indirect = indirect) / ((self.energy_supply @ prod) / TWh2TJ), digits))
+
+def energy_prices(self, secs = None, recompute = False, indirect = True):
+    '''
+    Returns the serie of energy prices at the global level of the list of sectors secs.
+    '''
+    if secs is None: secs = self.energy_sectors('electricities')
+    if recompute or not hasattr(self, 'energy_price'):
+        prices = pd.Series()
+        for i, sec in enumerate(secs): 
+            price_sec = self.price_energy(secs = sec, indirect = indirect)
+            prices.at[secs[i][15:]] = price_sec
+        self.energy_price = prices
+    return(self.energy_price)
+
+def sorted_array(self, array, index=None, group_by=None):
+    '''
+    Returns the sorted panda series of the array, grouped by group_by if it is not None, and indexed by index (the default index is that of regions x sectors)
+    '''
+    if index is None: index = self.x.index
+    if group_by is None: return(sorted_series(pd.Series(array, index=index)))
+    else: return(sorted_series(pd.Series(array, index=index).groupby(group_by).sum()))
+    
+# Traverse value chain backwards from regs-secs. group_by: None, sector, region / nb_main: number or 'all
+def inputs(self, secs=None, regs=None, var_impacts=[], source='all', order_recursion=4, nb_main=5, group_by='sector'): 
+    '''
+    Returns the Structural Path Analysis, i.e. the inputs recursively embodied in the production of secs in regs.
+    
+    var_impacts specifies the impacts of inputs to be displayed (e.g.: 'Total Energy supply', 'global warming (GWP100)', 'Employment' or 'Employment hour')
+    source allows to restricts the inputs to certain sectors
+    order_recursion gives the number of steps for the recursive inputs
+    nb_main gives the number of inputs that are shown for each step
+    group_by allows to aggregate the inputs by region or sector (by default)
+    
+    Returns a tuple of ([impacts], value of inputs, sums[impacts/values][step] of all impacts and values by step (not only the first nb_main which are shown))
+    For THEMIS, returns only the second elements, embodied inputs, whose values have no clear interpretation because their units vary and can be physical.
+    '''
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if nb_main=='all': nb_main==self.nb_sectors()*self.nb_regions()
+    if source=='all': source = self.sectors
+    elif source=='secondary': source = self.energy_sectors('secondary')
+    if type(var_impacts)==str: var_impacts = [var_impacts]
+    nb_var = len(var_impacts)
+    demand = [None for i in range(order_recursion)]
+    impacts = [[None for i in range(order_recursion)] for j in range(nb_var)]
+    sums = [[] for i in range(nb_var+1)]
+    if self.name=='THEMIS':
+        multi_index = pd.MultiIndex.from_arrays([self.labels.idx_regions, self.labels.idx_sectors], names=['region', 'sector']) # TODO: set as .x.index
+        demand[0] = pd.Series(self.is_in(secs, regs), index = multi_index)
+        for i in range(0, order_recursion):
+            if i+1<order_recursion: demand[i+1] = pd.Series(self.A.dot(demand[i]), index = multi_index)
+        demand = list(map(lambda j: self.sorted_array(j * self.is_in(source, self.regions), index=multi_index, group_by=group_by)[0:nb_main], demand))
+        return(demand) # TODO: stop showing recursive inputs as soon as they are 0.
+    else:
+    #     demand[0] = final_demand(secs, regs)
+        demand[0] = self.production(secs, regs)
+        for i in range(0, order_recursion):
+            if nb_var>0: share_demand_i = div0(demand[i], self.x)
+            for l in range(0, nb_var): 
+                impacts_l_i = self.impacts(var_impacts[l])*share_demand_i
+                impacts[l][i] = impacts_l_i[self.index_secs_regs(source, self.regions)]
+                sums[l].append(impacts[l][i].sum())
+            if i+1<order_recursion: demand[i+1] = np.dot(self.A, demand[i])
+            sums[nb_var].append(demand[i].sum())
+        for k in range(0, nb_var): impacts[k] = list(map(lambda j: self.sorted_array(j, group_by=group_by)[0:nb_main],impacts[k]))
+        demand = list(map(lambda j: self.sorted_array(j, group_by=group_by)[0:nb_main], demand))
+        return((impacts, demand, sums))
+
+def outputs(self, secs, out_sectors=None, nb_main=5):
+    '''
+    Returns a tuple: (the final demand, the list of sectors taking secs as inputs (i.e. the outputs), sorted decreasingly by use of secs)
+    
+    The first nb_main values are shown, out_sectors allows to restrict the outputs to certain sectors.
+    '''
+    if out_sectors is None: out_sectors = self.sectors
+    if self.name=='THEMIS': 
+        multi_index = pd.MultiIndex.from_arrays([self.labels.idx_regions, self.labels.idx_sectors], names=['region', 'sector'])
+        outputs_Z = sorted_series(pd.Series(np.array(self.A[self.index_secs_regs(secs, self.regions),:].sum(axis=0))[0],\
+                   index=multi_index).groupby('sector').sum()[out_sectors])[0:nb_main]
+    else: 
+        production = self.x.iloc[self.index_secs_regs(secs, self.regions)].sum()
+        outputs_Z = sorted_series(self.Z.iloc[self.index_secs_regs(secs, self.regions)]\
+                                    .sum().groupby('sector').sum()[out_sectors]/production)[0:nb_main]
+    if not hasattr(self, 'Y') or self.Y is None: return(outputs_Z)
+    else:
+        if not hasattr(self, 'y'): self.y = np.sum(self.Y, axis=1)
+        if out_sectors is None: out_sectors = self.sectors
+        return((self.y.iloc[self.index_secs_regs(secs, self.regions)].sum()/production, outputs_Z))
+
+def energy_required(self, secs, regs=None, var='Total Energy supply', source='secondary', netting_fuel = True):
+    '''
+    Returns the energy required to produce one unit of energy in sectors secs in regs, considering the energy from source with notion var, and 
+    decomposed according to the sources in partition_sources.
+    
+    The formula is: 
+    (energy embodied in production (excluding supplied) - fuels as inputs for electricity from hydrocarbon (if netting_fuel is True))
+    
+    if source = 'all', result is disaggregated between electricities and secondary_heats, and it also returns direct energy (i.e. direct energy input net of fuel transformed)
+    '''
+    if len(secs)==1: secs = secs[0]
+    sec_string = type(secs)==str or type(secs)==np.str_
+    if netting_fuel and ((sec_string and secs in self.energy_sectors('elec_hydrocarbon')) or secs==self.energy_sectors('elec_hydrocarbon') \
+                          or secs==self.energy_sectors('electricities')) or secs==list(np.array(self.energy_sectors('electricities'))\
+                          [['CCS' not in s for s in self.energy_sectors('electricities')]]): input_fuel=True
+    else: input_fuel = 0 # We want to include fuels that are used for transportation, not transformed into electricity (this is not secondary anymore)
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if self.name != 'Cecilia' and var != 'Total Energy supply': 
+        print('GER not implemented for var of type ' + var + ", doing it for 'Total Energy supply' instead")
+        var = 'Total Energy supply'
+    prod = self.production(secs, regs)
+    if source == 'all': source = ['electricities', 'secondary_heats']        
+    else: source = [source]
+    embodieds, input_fuels, des, embodied_input_fuels = [], [], [], []
+    embodied_thermal_plant = self.embodied_prod(secs, prod = prod) * self.is_in(self.energy_sectors('elec_hydrocarbon'))
+    embodied_input_fuel = (self.secondary_fuel_supply * self.A.dot(embodied_thermal_plant)).sum()
+    for s in source:
+        embodieds.append(self.embodied_impact(secs, regs, var, s, production = prod).sum())
+        if len(source) != 1: des.append(((self.secondary_energy_supply * self.A.dot(prod))[self.index_secs_regs(self.energy_sectors(s))]).sum())
+        if netting_fuel and (s == 'secondary_heats' or s == 'secondary'): embodied_input_fuels.append(embodied_input_fuel)
+        else: embodied_input_fuels.append(0)
+        if input_fuel and len(source) != 1:
+            if self.name == 'Cecilia': input_fuel = self.inputs(secs, var_impacts=[var], \
+                source=inter_secs(self.energy_sectors('secondary_fuels'), self.energy_sectors(s)), order_recursion=2)[2][0][1]
+            elif secs==self.energy_sectors('electricities') or secs==list(np.array(self.energy_sectors('electricities'))\
+                              [['CCS' not in s for s in self.energy_sectors('electricities')]]): 
+                prod_fuel = self.production(self.energy_sectors('elec_hydrocarbon'), regs)
+                input_fuels.append(((self.secondary_fuel_supply * self.A.dot(prod_fuel))[self.index_secs_regs(self.energy_sectors(s))]).sum())
+            else: input_fuels.append(((self.secondary_fuel_supply * self.A.dot(prod))[self.index_secs_regs(self.energy_sectors(s))]).sum())
+        else: input_fuels.append(0)
+    if len(source) == 1: return(embodieds[0] - embodied_input_fuels[0])
+    else: return((np.array(embodieds) - np.array(embodied_input_fuels), np.array(des) - np.array(input_fuels)))
+
+def ger(self, secs, regs=None, var='Total Energy supply', source='secondary', netting_fuel = True, factor_elec = 1, return_separate = False):
+    '''
+    Returns the Gross Energy Ratio (defined in Brandt & Dale, 2011) of sectors secs in regs, considering the energy from source with notion var.
+    
+    The formula is: 
+    energy supplied / (energy embodied in production (excluding supplied) - fuels as direct inputs for electricity from hydrocarbon (if netting_fuel is True))
+    
+    return_separate = True allows to return each component of the result separately, together with direct energy use.
+    '''
+    secs, regs = self.prepare_secs_regs(secs, regs)
+    if return_separate: er, de = self.energy_required(secs, regs, var, 'all', netting_fuel) @ np.array([factor_elec, 1])
+    else: er = factor_elec*self.energy_required(secs, regs, var, 'electricities', netting_fuel) + self.energy_required(secs, regs, var, 'secondary_heats', netting_fuel)
+#     if ((type(secs)==str and secs in self.energy_sectors('electricities')) or secs==self.energy_sectors('electricities')): 
+#         return(round(factor_elec * self.impacts(var, regs, secs).sum() / er, 1))
+#     else: return(round(self.impacts(var, regs, secs).sum() / er, 1))
+    if var=='Total Energy supply' and source=='secondary': supply = self.secondary_energy_demand[self.index_secs_regs(secs, regs)].sum()
+    else: supply = self.impacts(var, regs, secs).sum() # TODO!: Why impacts doesn't work in all cases?!
+    if return_separate: return((factor_elec * supply, er, de))
+    else: return(round(factor_elec * supply / er, 1)) # TODO!: code the option non_unitary_themis=False for biofuels and cie
+
+def erois(self, secs = None, var='Total Energy supply', source='secondary', netting_fuel = True, factor_elec = 1, recompute=False):
+    '''
+    Returns the serie of EROIs (Gross Energy Ratio) of the list of sectors secs, considering the energy from source with notion var.
+    '''
+    if secs is None: 
+        if self.scenario in ['REF', 'ER', 'ADV', 'combo']: secs = list(np.array(self.energy_sectors('electricities'))\
+                                                                    [['CCS' not in s for s in self.energy_sectors('electricities')]])
+        else: secs = self.energy_sectors('electricities')
+    if recompute or not hasattr(self, 'eroi') or not hasattr(self, 'direct_energy'):
+        gers, des, ers = pd.Series(), pd.Series(), pd.Series()
+#         des = pd.Series()
+        for i, sec in enumerate(secs): 
+            supply, er, de = self.ger(secs=sec, regs=self.regions, var=var, source=source, netting_fuel=netting_fuel, factor_elec=factor_elec, return_separate = True)
+            eroi_sec = round(supply / er, 1)
+            gers.at[secs[i][15:]] = eroi_sec
+            ers.at[secs[i][15:]] = er
+            des.at[secs[i][15:]] = de
+#             gers.set_value(secs[i][15:], eroi_sec)
+#         gers.set_value('Power sector', self.ger([s for s in secs], self.regions, var, source, netting_fuel, factor_elec))
+        supply, er, de = self.ger(secs, self.regions, var, source, netting_fuel, factor_elec, return_separate = True)
+        gers.at['Power sector'] = round(supply / er, 1)
+        ers.at['Power sector'] = er
+        des.at['Power sector'] = de
+    # we could also have used that eroi_total = 1/sum(share_mix_s / EROI_s, s in sectors)
+        self.eroi = gers.copy()
+        self.direct_energy = des.copy()
+        self.share_direct_energy = des.copy() / ers.copy()
+    return(self.eroi)
+
+def erois_and_prices(self, secs = None, var='Total Energy supply', source='secondary', netting_fuel = True, factor_elec = 1, recompute=False):
+    '''
+    Returns the series of regional EROIs and prices of the list of sectors secs for each region, considering the energy from source with notion var.
+    '''
+    if secs is None: 
+        if self.scenario in ['REF', 'ER', 'ADV', 'combo']: secs = list(np.array(self.energy_sectors('electricities'))\
+                                                                    [['CCS' not in s for s in self.energy_sectors('electricities')]])
+        else: secs = self.energy_sectors('electricities')
+    if recompute or not hasattr(self, 'eroi_price'):
+        res = pd.DataFrame(index = pd.MultiIndex.from_product([list(self.regions)+['World'], secs+['total']], names=['region', 'sector']), \
+                           columns = ['eroi', 'price'])
+        for reg in self.regions:
+            for i, sec in enumerate(secs):
+                res['eroi'][(reg, sec)] =self.ger(secs=sec,regs=reg,var=var,source=source,netting_fuel=netting_fuel,factor_elec=factor_elec)
+                res['price'][(reg, sec)] = self.price_energy(secs = sec, regs = reg, digits=5, indirect = True)
+            res['eroi'][(reg, 'total')]=self.ger(secs=secs,regs=reg,var=var,source=source,netting_fuel=netting_fuel,factor_elec=factor_elec)
+            res['price'][(reg, 'total')] = self.price_energy(secs = secs, regs = reg, digits=5, indirect = True)
+        for i, sec in enumerate(secs):
+            res['eroi'][('World', sec)]=self.ger(secs=sec, regs=self.regions, var=var, source=source, netting_fuel=netting_fuel, factor_elec=factor_elec)
+            res['price'][('World', sec)] = self.price_energy(secs = sec, regs = self.regions, digits=5, indirect = True)        
+        res['eroi'][('World', 'total')]=self.ger(secs=secs,regs=self.regions,var=var,source=source,netting_fuel=netting_fuel,factor_elec=factor_elec)
+        res['price'][('World', 'total')] = self.price_energy(secs = secs, regs = self.regions, digits=5, indirect = True)
+        self.eroi_price = res.copy()
+    return(self.eroi_price)
+
+def err(self, secs, regs=None, var='Total Energy supply', partition_sources=['secondary_heats', 'electricities'], netting_fuel = True, decimals=2):
+    '''
+    Returns energy required to produce one unit of energy in sectors secs in regs, decomposed according to the sources in partition_sources.
+    '''
+    ratios = {}
+    for source in partition_sources:
+#         ratio[source] = round(1/self.ger(secs = secs, regs = regs, var = var, source = source, netting_fuel = netting_fuel), decimals)
+        ratios[source] = round(self.energy_required(secs=secs, regs=regs, var=var, source=source, netting_fuel=netting_fuel)\
+                           /self.impacts(var, regs, secs).sum(), decimals) # impacts = supply
+    return(ratios) # TODO: put this and errs in the install, check them before
+    
+def errs(self, secs=None, var='Total Energy supply', partition_sources=['secondary_heats', 'electricities'], netting_fuel = True, recompute = False):
+    '''
+    Returns the series of energy required of the list of sectors secs, decomposed according to the sources in partition_sources.
+    '''
+    if secs is None: secs = self.energy_sectors('electricities')
+    if recompute or not hasattr(self, 'errs'):
+        errs = pd.DataFrame(columns = partition_sources)
+        for i, sec in enumerate(secs): 
+            err_sec = self.err(sec, regs = self.regions, var = var, partition_sources = partition_sources, netting_fuel = netting_fuel)
+            errs = errs.append(pd.Series(er_sec).rename(secs[i][15:]))
+        errs = errs.append(pd.Series(self.err([s for s in secs], regs = self.regions, var = var, partition_sources = partition_sources, \
+                                           netting_fuel = netting_fuel)).rename('Power sector'))
+        self.errs = errs.copy()
+    return(self.errs) # TODO: add column 'total'
+       
+def regional_mix(global_mix, nb_regions = 9, nb_sectors = None):
+    '''
+    Returns an array of regional mixes (i.e. stacked shares of sec in each reg), computed from an array of global mix (i.e. shares of sec x reg in global total)
+    '''
+    if nb_sectors is None: nb_sectors = int(max(global_mix.shape)/nb_regions)
+    return(div0(global_mix, np.kron(np.eye(nb_regions),np.ones((nb_sectors, nb_sectors))).dot(global_mix)))
+
+def mix(self, scenario = None, path_dlr = '/media/adrien/dd/adrien/DD/Économie/Données/Themis/', recompute = False): # TODO: change name to mix_dlr
+    '''
+    Returns an array of global mix (i.e. shares of sec x reg in global total) and stores it as an attribute
+    for DLR (= Greenpeace) scenario in ['REF', 'ER', 'ADV'] for year in [2010, 2030, 2050]
+    ''' 
+    if scenario is None: scenario = self.scenario
+    if not hasattr(self, 'dlr_elec') or self.dlr_elec=={}:
+        self.dlr_elec = dict()
+        for reg in ['World', 'Africa', 'China', 'Eurasia', 'India', 'Latin America', 'Middle East', \
+                        'OECD Europe', 'OECD North America', 'OECD Asia Oceania', 'O-Asia']:
+            data = pd.read_excel(path_dlr+'Greenpeace_scenarios.xlsx', header=[1], index_col=0, skiprows=[0], skipfooter=144-53, \
+                                 sheet_name=scenario+' '+reg, usecols=[1,2,6,10])
+            self.dlr_elec[reg] = pd.DataFrame(columns = [2012, 2030, 2050])\
+                .append(data.loc[['    - Lignite', '    - Hard coal (& non-renewable waste)']].iloc[[1,3]].sum(axis=0).rename('coal'))\
+                .append(data.loc['    - Gas'].iloc[1].rename('gas')).append(data.loc[['    - Oil', '    - Diesel']].iloc[[1,2]].sum(axis=0).rename('oil'))\
+                .append(data.loc['  - Nuclear'].iloc[0].rename('nuclear')).append(data.loc['    - Biomass (& renewable waste)'].iloc[1].rename('biomass&Waste'))\
+                .append(data.loc['  - Hydro'].rename('hydro')).append(data.loc['of which wind offshore'].rename('wind offshore'))\
+                .append((data.loc['  - Wind']-data.loc['of which wind offshore']).rename('wind onshore'))\
+                .append(data.loc['  - PV'].rename('solar PV')).append(data.loc['    - Geothermal'].iloc[1].rename('geothermal'))\
+                .append(data.loc['  - Solar thermal power plants'].rename('solar CSP')).append(data.loc['  - Ocean energy'].rename('ocean'))\
+                .rename(columns = {2012: 2010})
+        self.dlr_elec['Africa and Middle East'] = self.dlr_elec['Africa'] + self.dlr_elec['Middle East']
+        self.dlr_elec['OECD Pacific'] = self.dlr_elec.pop('OECD Asia Oceania')
+        self.dlr_elec['Rest of developing Asia'] = self.dlr_elec.pop('O-Asia')
+        self.dlr_elec['Economies in transition'] = self.dlr_elec.pop('Eurasia') # TODO: put this and below in a dataframe format
+        
+    if not hasattr(self, 'dlr_capacity'):
+        self.dlr_capacity = dict()
+        for reg in ['World', 'Africa', 'China', 'Eurasia', 'India', 'Latin America', 'Middle East', \
+                        'OECD Europe', 'OECD North America', 'OECD Asia Oceania', 'O-Asia']:
+            data = pd.read_excel(path_dlr+'Greenpeace_scenarios.xlsx', header=[1], index_col=0, skiprows=[0], skipfooter=144-23, \
+                                 sheet_name=scenario+' '+reg, usecols=[12,13,17,21])
+            self.dlr_capacity[reg] = pd.DataFrame(columns = [2012, 2030, 2050])\
+                .append(data.loc[['    - Lignite', '    - Hard coal (& non-renewable waste)']].sum(axis=0).rename('coal'))\
+                .append(data.loc['    - Gas (w/o H2)'].rename('gas')).append(data.loc[['    - Oil', '    - Diesel']].sum(axis=0).rename('oil'))\
+                .append(data.loc['  - Nuclear'].rename('nuclear')).append(data.loc['    - Biomass (& renewable waste)'].rename('biomass&Waste'))\
+                .append(data.loc['    - Hydro'].rename('hydro')).append(data.loc['of which wind offshore'].rename('wind offshore'))\
+                .append((data.loc['    - Wind']-data.loc['of which wind offshore']).rename('wind onshore'))\
+                .append(data.loc['    - PV'].rename('solar PV')).append(data.loc['    - Geothermal'].rename('geothermal'))\
+                .append(data.loc['    - Solar thermal power plants'].rename('solar CSP')).append(data.loc['    - Ocean energy'].rename('ocean'))\
+                .rename(columns = {2012: 2010})
+        self.dlr_capacity['Africa and Middle East'] = self.dlr_capacity['Africa'] + self.dlr_capacity['Middle East']
+        self.dlr_capacity['OECD Pacific'] = self.dlr_capacity.pop('OECD Asia Oceania')
+        self.dlr_capacity['Rest of developing Asia'] = self.dlr_capacity.pop('O-Asia')
+        self.dlr_capacity['Economies in transition'] = self.dlr_capacity.pop('Eurasia')
+        
+        self.adjustment_capacity = dict()
+        self.adjust_capacity = dict()
+        for y in [2010, 2030, 2050]:
+            self.adjustment_capacity[y] = pd.Series(index = pd.MultiIndex.from_product([self.regions, self.dlr_elec['World'].index], names=['region', 'sector']))
+            self.adjust_capacity[y] = [0] * len(self.labels.idx_sectors)
+            for reg in self.regions: # TODO: which energy.capacity are 0?
+                for sec in self.dlr_elec['World'].index:
+                    self.adjust_capacity[y][self.index_secs_regs('Electricity by ' + sec, reg)[0]] = div0(div0(self.dlr_capacity[reg][y].loc[sec], \
+                        self.dlr_elec[reg][y].loc[sec]), div0(self.energy.capacity[(reg, y)].loc['Electricity by ' + sec], \
+                        self.energy.demand[(reg, y)].loc['Electricity by ' + sec]), replace_by=1)
+                    self.adjustment_capacity[y][(reg, sec)] = div0(div0(self.dlr_capacity[reg][y].loc[sec], self.dlr_elec[reg][y].loc[sec]), \
+                        div0(self.energy.capacity[(reg, y)].loc['Electricity by ' + sec], self.energy.demand[(reg, y)].loc['Electricity by ' + sec]), replace_by=1)
+
+    if recompute or not hasattr(self, 'mixes'):
+        mix = dict()
+        for year in [2010, 2030, 2050]:
+            mix[year] = []
+            dlr_sectors = self.dlr_elec['World'].index
+            for i in self.index_secs_regs(['Electricity by ' + s for s in self.energy_sectors('elecs_names')], self.regions):
+                sec, reg = self.labels.idx_sectors[i][15:], self.labels.idx_regions[i]
+                if sec in dlr_sectors: mix[year] = mix[year] + [self.dlr_elec[reg].loc[sec, year]]
+                else: mix[year] = mix[year] + [0]
+            mix[year] = div0(mix[year], np.array(mix[year]).sum())
+        self.mixes = mix
+    return(self.mixes)
+        
+def change_mix(self, global_mix = None, inplace = True, method = 'regional', path_dlr = None, scenario = None, year = None, only_exiobase = True,adjust_GW=True): 
+    '''
+    (for THEMIS) Returns matrix A with electricity inputs replaced according to the new mix global_mix
+    global_mix gives the share of each techno-region in the global mix, so that its sum is the number of regions
+    As arguments, either global_mix and year must be passed, or path_dlr, scenario and year
+    only_exiobase = True doesn't modify the matrix for outputs other than background ones (in practice, True or False doesn't change EROI results)
+    method = ['gras', 'regional', 'global']: GRAS is preferable but requires Z (and y) (absent from THEMIS), 'regional' (default) assumes the same mix for each
+    sector of a given region, 'global' the same mix for each sector globally
+    '''
+    if year is None: print('year must be provided, otherwise I cannot infer total demand')
+    if type(global_mix)==dict and scenario is not None and year is not None: global_mix = global_mix[scenario][year]
+    elif global_mix is None and path_dlr is not None:
+        if scenario is not None: self.scenario = scenario
+        global_mix = self.mix(path_dlr = path_dlr)[year]
+        
+    if hasattr(self, 'dlr_elec'): dlr_sectors = self.dlr_elec['World'].index
+    else: dlr_sectors = self.energy_sectors('elecs_names')
+    TWh2TJ = 3.6e3
+    self.secondary_energy_demand[np.where(self.secondary_energy_demand!=0)[0]] = 0
+    for reg in self.regions: # TODO: integrate this change in secondary_energy_demand more properly
+        if hasattr(self, 'dlr_elec'):
+            for sec in dlr_sectors: self.secondary_energy_demand[self.index_secs_regs('Electricity by ' + sec, reg)] = self.dlr_elec[reg][year][sec] * TWh2TJ
+        else:
+            total_demand = self.secondary_energy_demand[self.index_secs_regs(dlr_sectors)].sum()
+            self.secondary_energy_demand[self.index_secs_regs(self.energy_sectors('electricities'))] = global_mix * TWh2TJ
+                        
+    if inplace: A = self.A
+    else: A = self.A.copy()
+    elec_idx = self.index_secs_regs(self.energy_sectors('electricities'))
+    if not hasattr(self, 'energy_supply_original'):
+        self.energy_supply_original = self.energy_supply.copy()
+        self.supply_filled = self.energy_supply.copy() # fill unitary energy supplied of zero energy sectors such as geothermal Africa
+        for i in np.array(elec_idx)[np.where(self.energy_supply_original[elec_idx]==0)[0]]: # fills (reg, sec) with 0 supply with mean value of other regs 
+            sec_i = self.labels.idx_sectors[i]  
+            self.supply_filled[i] = self.energy_supply_original.dot(self.is_in(sec_i))/len(np.where(self.energy_supply_original*self.is_in(sec_i)!=0)[0])
+
+    if only_exiobase: idx0 = 42219
+    else: idx0 = 0
+    elecs_by_sec = mult_rows(A[elec_idx,idx0:], self.energy_supply[elec_idx]) # unit of elec needed by each unitary sec
+    
+    if method=='global':
+    # global_mix . elecs_by_sec: matrix of elec need by sec and type of elec -> /unitary_supply (<=> *unit_per_supply): convert back to arbitrary units
+        A[elec_idx,idx0:] = mult_rows(global_mix.reshape(-1,1).dot(elecs_by_sec.sum(axis=0)), div0(1, self.energy_supply[elec_idx]))
+    elif method=='regional' or method=='region':
+        agg_matrix = sp.kron(sp.eye(self.nb_regions),np.ones((len(self.energy_sectors('electricities')), len(self.energy_sectors('electricities')))))
+        A[elec_idx,idx0:] = mult_rows(agg_matrix.dot(elecs_by_sec), div0(regional_mix(global_mix, self.nb_regions), self.energy_supply[elec_idx]))
+    elif method=='gras' or method=='GRAS': # GRAS method on submatrix of elecs, never tested
+        if inplace: A = self.Z
+        else: A = self.Z.copy()    
+        elecs_by_sec = mult_rows(A[elec_idx,idx0:], self.energy_supply[elec_idx]) # unit of elec needed by each sec
+#         print('starting GRAS')
+        new_elecs_by_sec = grasp(elecs_by_sec, new_row_sums = elecs_by_row.sum()*global_mix)
+#         print('GRAS complete')
+        A[elec_idx,idx0:] = mult_rows(new_elecs_by_row, div0(1, self.energy_supply[elec_idx]))
+    else: print('method unknown')
+    
+    if adjust_GW and self.scenario in ['REF', 'ER', 'ADV'] and hasattr(self, 'adjust_capacity'): 
+        renewable_idx = self.index_secs_regs(self.energy_sectors('renewable'))
+        A[:, renewable_idx] = mult_cols(A[:, renewable_idx], np.array(self.adjust_capacity[year])[renewable_idx])
+    return(A)
+
+def mix_matrix(self, secs = None, method='demand', global_mix = True, digits = 2): # TODO: store as attribute
+    '''
+    Returns the share of energy supplied by each sec in secs (among the energy supplied by all secs), according to the IOT.
+    The result is at the global level if global_mix = True, at regional level if not.
+    The energy supplied can be given by the secondary_energy_demand attribute or by the method impacts (there is a difference when energy_supply=0)
+    '''
+    PWh2TJ = 3.6e6
+    if secs is None: secs = self.energy_sectors('electricities')
+    mix = {}
+    if not global_mix:
+        mix['total'] = self.secondary_energy_demand[self.index_secs_regs(secs)].sum()
+        if method=='impacts': print('Regional mix not coded for "impacts" method')   
+        return(self.secondary_energy_demand[self.index_secs_regs(secs)]/mix['total'])
+    else:
+        if method=='impacts' or method=='impact':
+            if global_mix:
+                mix['total'] = self.impacts(secs = secs).sum()
+                for sec in secs: mix[sec[15:]] = self.impacts(secs = sec).sum()/mix['total']
+        elif method=='demand':
+            mix['total'] = self.secondary_energy_demand[self.index_secs_regs(secs)].sum()
+            for sec in secs: mix[sec[15:]] = self.secondary_energy_demand[self.index_secs_regs(sec)].sum()/mix['total']
+        mix['total'] /= PWh2TJ
+        return(round(pd.Series(mix), digits))
+
+def aggregate_mix(self, mix = None, secs = None, path_dlr = '/media/adrien/dd/adrien/DD/Économie/Données/Themis/', recompute=False):
+    '''
+    Returns the aggregate global mix from an array of global mix. Thus, the sum of the original array is 1 and the sum of the output is the number of regions.
+    '''
+    if not hasattr(self, 'agg_mix') or recompute:
+        if secs is None: secs = self.energy_sectors('electricities')
+        elec_idx = self.index_secs_regs(secs)
+        if mix is None and self.scenario not in ['REF', 'ER', 'ADV', 'combo']: global_mix = self.mix_matrix(method='demand', global_mix = True, digits = 5)
+        else:
+            if mix is None: mix = self.mix(path_dlr = path_dlr)[self.meta.year]
+            global_mix = {}
+            for sec in self.labels.idx_sectors[elec_idx].unique():
+                global_mix[sec[15:]] = round(mix[np.where(self.labels.idx_sectors[elec_idx]==sec)[0]].sum(), 5)
+#             if hasattr(self, 'dlr_elec'): global_mix.append(pd.Series({'total': self.dlr_elec['World'][self.meta.year].sum()/1000}))
+            if hasattr(self, 'dlr_elec'): global_mix['total'] = self.dlr_elec['World'][self.meta.year].sum()/1000            
+        self.agg_mix = pd.Series(global_mix)
+    return(self.agg_mix)
+
+def results(themis, stats=['eroi', 'world_mix'], scenarios=['BL', 'BM', 'ADV'], to_plot = False, not_all_2010=True, rounded=True, fillNA=True, longnames=False,
+            dlr_sectors=False, longtotal=True, year = None, extend_wo_GW = False, regional = False): # TODO: re-order index, mix_real, specific region
+    '''
+    Returns a panda dataframe with results from stats and scenarios specified.
+    stats can include: 'eroi', 'eroi_adj', 'eroi_wo_GW', 'eroi_wo_GW_adj', 'world_mix', 'employ', 'employ_direct', 'energy_price', 'share_direct_energy'
+    scenarios can include: BL, BM, REF, ER, ADV, combo
+    not_all_2010 keeps only one scenario for year 2010
+    fillNA fills NaN with '–' instead of 0, rounded rounds the figures to max 2 digit, longnames details the scenario name, dlr_sectors removes CCS from sectors,
+        longtotal puts 'Total (PWh/a)' instead of 'total' for the last index: all these parameters should be set to False to plot figures
+    to_plot = True overrides all previous parameters and set them to False, use this if the results are used to be plotted
+    if year is in [2010, 2030, 2050], only the result for the given year are returned
+    extend_wo_GW copy/pastes (normal) EROI to the eroi_wo_GW column for scenarios not concerned by the adjustment (BL, BM, combo)
+    regional = True overrides other options and provide estimates disaggregated by region rather than sectors
+    '''
+    if to_plot: not_all_2010, rounded, fillNA, longnames, dlr_sectors, longtotal = False, False, False, False, False, False
+    if type(scenarios)==str: scenarios = [scenarios]
+    if type(stats)==str: stats = [stats]
+    names_stats = {'eroi': 'EROI', 'world_mix': 'mix', 'eroi_adj': 'EROI adj', 'eroi_wo_GW': 'EROI w/o GW', 'eroi_wo_GW_adj': 'EROI w/o GW adj',\
+                   'employ': 'k Employ', 'employ_direct': 'k Employ direct', 'energy_price': 'price', 'share_direct_energy': 'Share of direct energy'}
+    stats_names = {'EROI': 'eroi', 'mix': 'world_mix', 'EROI adj': 'eroi_adj', 'EROI w/o GW': 'eroi_wo_GW', 'EROI w/o GW adj': 'eroi_wo_GW_adj',\
+                   'k Employ': 'employ', 'k Employ direct': 'employ_direct', 'price': 'energy_price', 'Share of direct energy': 'share_direct_energy'}
+    if dlr_sectors: secs = list(np.array(themis[scenarios[0]][2050].energy_sectors('elecs_names'))\
+                                                      [['CCS' not in s for s in themis['BM'][2010].energy_sectors('elecs_names')]])
+    else: secs = themis[scenarios[0]][2050].energy_sectors('elecs_names')
+    years = [2010, 2030, 2050]
+    
+    if regional:
+        PWh2TJ = 3.6e6
+        stats = ['eroi', 'world_mix']
+        res = pd.DataFrame(index = list(themis[scenarios[0]][2050].regions)+['total'])
+        for s in scenarios:
+            secs = themis[s][2010].energy_sectors('electricities')
+            for y in years:
+                if y in themis[s].keys():
+                    if not hasattr(themis[s][y], 'mix_by_region') or not hasattr(themis[s][y], 'eroi_by_region'): 
+                        mix_r = {}
+                        eroi_r = {}
+                        mix_r['total'] = themis[s][y].secondary_energy_demand[themis[s][y].index_secs_regs()].sum()
+                        for r in themis[s][y].regions:
+                            mix_r[r] = themis[s][y].secondary_energy_demand[themis[s][y].index_secs_regs(regs = r)].sum()/mix_r['total']
+                            eroi_r[r] = themis[s][y].eroi_price['eroi'][(r, 'total')]
+                        mix_r['total'] /= PWh2TJ
+                        if hasattr(themis[s][y], 'eroi'): eroi_r['total'] = themis[s][y].eroi[-1]           
+                        themis[s][y].mix_by_region = pd.Series(mix_r)
+                        themis[s][y].eroi_by_region = pd.Series(eroi_r)
+                    res[(s, y, 'world_mix')] = themis[s][y].mix_by_region
+                    res[(s, y, 'eroi')] = themis[s][y].eroi_by_region
+        stats = np.unique([c[2] for c in res.columns])
+        res = pd.DataFrame(res, columns=pd.MultiIndex.from_product([scenarios, years, stats], names=['scenario', 'year', 'stat']))
+        
+    else: 
+        stats = [stats_names[s] if s in stats_names.keys() else s for s in stats]
+        res = pd.DataFrame(index = secs+['total'])
+        for y in years:
+            for s in scenarios:
+                for stat in stats:
+                    if y in themis[s].keys():
+                        if stat in ['eroi_wo_GW', 'eroi_wo_GW_adj', 'eroi_GW', 'eroi_GW_adj', 'eroi_TWh', 'eroi_TWh_adj']:
+                            if s in ['REF', 'ER', 'ADV']:
+                                res[(s, y, 'eroi_wo_GW'+'_adj'*(stat[-4:]=='_adj'))] = getattr(themis[s][y].wo_GW_adj, \
+                                                'eroi'+'_adj'*(stat[-4:]=='_adj')).rename(index={'Power sector': 'total', 'total': 'total'}) #loc[secs].fillna(0).
+                            else: 
+                                if extend_wo_GW: res[(s, y, 'eroi_wo_GW'+'_adj'*(stat[-4:]=='_adj'))] = getattr(themis[s][y], \
+                                                'eroi'+'_adj'*(stat[-4:]=='_adj')).rename(index={'Power sector': 'total', 'total': 'total'}) #loc[secs].fillna(0).
+                        else:
+                            res[(s, y, stat)] = getattr(themis[s][y], stat).rename(index={'Power sector': 'total', 'total': 'total'}) #loc[secs].fillna(0).
+                            if stat=='energy_price': 
+                                res[(s, y, stat)].at['total'] = res[(s, y, stat)].loc[secs].fillna(0).dot(themis[s][y].world_mix.loc[secs].fillna(0))
+                            elif stat in ['employ', 'employ_direct']: res[(s, y, stat)].at['total'] = res[(s, y, stat)].loc[secs].sum()
+
+        stats = np.unique([c[2] for c in res.columns])
+        res = pd.DataFrame(res, columns=pd.MultiIndex.from_product([scenarios, years, stats], names=['scenario', 'year', 'stat']))
+
+        res = res.loc[['biomass w CCS', 'biomass&Waste', 'ocean', 'geothermal','solar CSP','solar PV','wind offshore',\
+                                       'wind onshore','hydro','nuclear', 'gas w CCS', 'coal w CCS', 'oil', 'gas', 'coal', 'total']]
+    if rounded and 'world_mix' in stats: 
+        res[[(s, y, 'world_mix') for y in years for s in scenarios]] = round(res[[(s, y, 'world_mix') for y in years for s in scenarios]], 2)
+    if rounded and 'employ' in stats: 
+        res[[(s, y, 'employ') for y in years for s in scenarios]] = round(res[[(s, y, 'employ') for y in years for s in scenarios]]) # TODO: int or millions
+    if rounded and 'employ_direct' in stats: 
+        res[[(s, y, 'employ_direct') for y in years for s in scenarios]] = round(res[[(s, y, 'employ_direct') for y in years for s in scenarios]])
+    if rounded and 'energy_price' in stats: 
+        res[[(s, y, 'energy_price') for y in years for s in scenarios]] = round(res[[(s, y, 'energy_price') for y in years for s in scenarios]], 1)
+    if rounded and 'share_direct_energy' in stats: 
+        res[[(s, y, 'share_direct_energy') for y in years for s in scenarios]] = round(res[[(s, y, 'share_direct_energy') for y in years for s in scenarios]], 2)
+    if rounded and 'eroi' in stats: 
+        for y in years: 
+            for s in scenarios: res[(s, y, 'eroi')] = res[(s, y, 'eroi')].map('{:,.1f}'.format)
+    if rounded and 'eroi_adj' in stats: 
+        for y in years: 
+            for s in scenarios: res[(s, y, 'eroi_adj')] = res[(s, y, 'eroi_adj')].map('{:,.1f}'.format)
+    if longtotal: res = res.rename(index={'total': 'Total (PWh/a)'})
+    if fillNA: res = res.fillna('–').replace('nan', '–')
+    long = {'BL': "Baseline ('BL')", 'BM': "Blue Map ('BM', +2°C)", 'REF': "Reference DLR ('REF')", \
+                'ER': "Energy [R]evolution ('ER', +2°C, no CCS nor nuclear)", 'ADV': "Advanced ER ('ADV', 100% renewable)", 'combo': 'Combination of scenarios'}
+    new_col_names = [long[c] for c in res.columns.levels[0]]
+    if not_all_2010: # TODO: rearrange columns so that same scenarios are gathered
+        scenarios2010 = ['BL']*('BL' in scenarios) + ['REF']*('REF' in scenarios) + ['combo']*('combo' in scenarios)
+        res = res[[(sc, 2010, st) for sc in scenarios2010 for st in stats]+[(s, y, st) for s in scenarios for y in [2030, 2050] for st in stats]]
+    if type(year)==int: res = res[[(s, year, st) for s in scenarios for st in stats]]
+    if longnames: res.columns.set_levels([long[c] for c in res.columns.levels[0]], level=0, inplace=True)
+    res.columns.set_levels([names_stats[s] for s in res.columns.levels[2]], level=2, inplace=True)
+
+    if regional: return((res, themis))
+    else: return(res)
+    
+def export_all_excel(path_themis, themis):
+    book = load_workbook(path_themis + 'all_results.xlsx')
+    writer = pd.ExcelWriter(path_themis + 'all_results.xlsx', engine = 'openpyxl')
+    writer.book = book
+    writer.sheets = dict((ws.title, ws) for ws in book.worksheets)
+    res = results(themis, scenarios=['BL', 'BM', 'ADV'], stats=['eroi', 'mix'])
+    res.to_excel(writer, 'Main Results BL-BM-ADV EROI,mix')
+    res = results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi', 'mix'])
+    res.to_excel(writer, 'Greenpeace EROI,mix')
+    res = results(themis, scenarios=['BL', 'BM'], stats=['eroi_adj', 'mix'])
+    res.to_excel(writer, 'IEA EROI adj,mix')
+    res = results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi_adj', 'mix'])
+    res.to_excel(writer, 'Greenpeace EROI adj,mix')
+    res = results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi_wo_GW', 'mix'])
+    res.to_excel(writer, 'Greenpeace wo GW EROI,mix')
+    res = results(themis, scenarios=['REF', 'ER', 'ADV'], stats=['eroi_wo_GW_adj', 'mix'])
+    res.to_excel(writer, 'Greenpeace wo GW EROI adj,mix')
+
+    for sc in ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo']:
+        res = results(themis, scenarios=[sc], stats=['eroi', 'eroi_adj', 'eroi_wo_GW', 'eroi_wo_GW_adj', 'world_mix', 'employ', 'employ_direct', 'energy_price'])
+        res.to_excel(writer, sc) # to_latex exists also
+
+    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['employ', 'employ_direct'])
+    res.to_excel(writer, 'Employments')
+    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['energy_price'])
+    res.to_excel(writer, 'prices')
+    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi'])
+    res.to_excel(writer, 'EROI')
+    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi_adj'])
+    res.to_excel(writer, 'EROI adj')
+    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi_wo_GW'])
+    res.to_excel(writer, 'EROI wo GW')
+    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi_wo_GW_adj'])
+    res.to_excel(writer, 'EROI wo GW adj')
+    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['eroi', 'eroi_adj', 'eroi_wo_GW', 'eroi_wo_GW_adj'])
+    res.to_excel(writer, 'EROIs')
+    res = results(themis, scenarios=['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'], stats=['mix'])
+    res.to_excel(writer, 'mix')
+    res = results(themis, regional = True)[0]
+    res.to_excel(writer, 'By region')
+    res = results(themis, stats = 'share_direct_energy')
+    res.to_excel(writer, 'Share of direct energy')
+    writer.save()
+
+def consumption_sectors(self, notion): # TODO: other sectors, difference from elecs_names to add after elec and to display
+    '''
+    Returns the list of sectors (or names) corresponding to notion, which can be: food, drug, cloth, housing, furniture, health, transport, communication, leisure, education, catering, diverse
+    '''
+    if notion is None or notion == 'total': sectors = self.sectors
+    elif notion not in ['food', 'tobacco', 'cloth', 'housing', 'furniture', 'health', 'transport', 'communication', 'leisure', 'education', 'catering', 'diverse']:
+        print('Error: notion unknown')
+    elif notion=='food': sectors = ['Paddy rice',  'Wheat',  'Cereal grains nec',  'Vegetables, fruit, nuts',  'Oil seeds',  'Sugar cane, sugar beet',  'Plant-based fibers',  'Crops nec',  'Cattle',  'Pigs',  'Poultry',  'Meat animals nec',  'Animal products nec',  'Raw milk', 'Fish and other fishing products; services incidental of fishing (05)', 
+'Products of meat cattle', 'Products of meat pigs', 'Products of meat poultry', 'Meat products nec', 'products of Vegetable oils and fats', 'Dairy products', 'Processed rice', 'Sugar', 'Food products nec', 'Beverages', 'Fish products']
+    elif notion=='tobacco': sectors = ['Tobacco products (16)']
+    elif notion=='cloth': sectors = ['Textiles (17)', 'Wearing apparel; furs (18)', 'Leather and leather products (19)']
+    elif notion=='housing': sectors = ['Electricity by coal', 'Electricity by gas', 'Electricity by nuclear', 'Electricity by hydro', 'Electricity by wind', 'Electricity by petroleum and other oil derivatives', 'Electricity by biomass and waste', 'Electricity by solar photovoltaic', 'Electricity by solar thermal', 'Electricity by tide, wave, ocean', 'Electricity by Geothermal', 'Electricity nec', 'Steam and hot water supply services', 'Collected and purified water, distribution services of water (41)', 'Construction work (45)', 'Real estate services (70)']
+    elif notion=='furniture': sectors = ['Paper and paper products', 'Printed matter and recorded media (22)', 'Furniture; other manufactured goods n.e.c. (36)'] 
+    elif notion=='health': sectors = ['Health and social work services (85)'] 
+    elif notion=='transport': sectors = ['Motor Gasoline', 'Gas/Diesel Oil', 'Heavy Fuel Oil', 'Refinery Gas', 'Liquefied Petroleum Gases (LPG)', 'Kerosene', 'Motor vehicles, trailers and semi-trailers (34)', 'Other transport equipment (35)', 'Sale, maintenance, repair of motor vehicles, motor vehicles parts, motorcycles, motor cycles parts and accessoiries', 'Retail trade services of motor fuel', 'Railway transportation services', 'Other land transportation services', 'Transportation services via pipelines', 'Sea and coastal water transportation services', 'Inland water transportation services', 'Air transport services (62)', 'Supporting and auxiliary transport services; travel agency services (63)'] 
+    elif notion=='communication': sectors = ['Office machinery and computers (30)', 'Electrical machinery and apparatus n.e.c. (31)', 'Radio, television and communication equipment and apparatus (32)', 'Post and telecommunication services (64)'] 
+    elif notion=='leisure': sectors = ['Recreational, cultural and sporting services (92)'] 
+    elif notion=='education': sectors = ['Education services (80)'] 
+    elif notion=='catering': sectors = ['Hotel and restaurant services (55)'] 
+    elif notion=='diverse': sectors = ['Fabricated metal products, except machinery and equipment (28)', 'Machinery and equipment n.e.c. (29)', 'Wholesale trade and commission trade services, except of motor vehicles and motorcycles (51)', 'Retail  trade services, except of motor vehicles and motorcycles; repair services of personal and household goods (52)', 
+'Wool, silk-worm cocoons',  'Manure (conventional treatment)',  'Manure (biogas treatment)',  'Products of forestry, logging and related services (02)', 'Anthracite',  'Coking Coal',  'Other Bituminous Coal',  'Sub-Bituminous Coal',  'Patent Fuel',  'Lignite/Brown Coal',  'BKB/Peat Briquettes',  'Peat', 'Natural Gas Liquids', 'Other Hydrocarbons', 'Uranium and thorium ores (12)', 'Iron ores', 'Copper ores and concentrates', 'Nickel ores and concentrates', 'Aluminium ores and concentrates', 'Precious metal ores and concentrates', 'Lead, zinc and tin ores and concentrates', 'Other non-ferrous metal ores and concentrates', 'Stone', 'Sand and clay', 'Chemical and fertilizer minerals, salt and other mining and quarrying products n.e.c.', 'Wood and products of wood and cork (except furniture); articles of straw and plaiting materials (20)', 'Wood material for treatment, Re-processing of secondary wood material into new wood material', 'Pulp', 'Secondary paper for treatment, Re-processing of secondary paper into new pulp', 'Crude petroleum and services related to crude oil extraction, excluding surveying', 'Natural gas and services related to natural gas extraction, excluding surveying', 'Coke Oven Coke', 'Gas Coke', 'Coal Tar', 'Aviation Gasoline', 'Gasoline Type Jet Fuel', 'Kerosene Type Jet Fuel', 'Refinery Feedstocks', 'Ethane', 'Naphtha', 'White Spirit & SBP', 'Lubricants', 'Bitumen', 'Paraffin Waxes', 'Petroleum Coke', 'Non-specified Petroleum Products', 'Nuclear fuel', 'Plastics, basic', 'Secondary plastic for treatment, Re-processing of secondary plastic into new plastic', 'N-fertiliser', 'P- and other fertiliser', 'Chemicals nec', 'Charcoal', 'Additives/Blending Components', 'Biogasoline', 'Biodiesels', 'Other Liquid Biofuels', 'Rubber and plastic products (25)', 'Glass and glass products', 'Secondary glass for treatment, Re-processing of secondary glass into new glass', 'Ceramic goods', 'Bricks, tiles and construction products, in baked clay', 'Cement, lime and plaster', 'Ash for treatment, Re-processing of ash into clinker', 'Other non-metallic mineral products', 'Basic iron and steel and of ferro-alloys and first products thereof', 'Secondary steel for treatment, Re-processing of secondary steel into new steel', 'Precious metals', 'Secondary preciuos metals for treatment, Re-processing of secondary preciuos metals into new preciuos metals', 'Aluminium and aluminium products', 'Secondary aluminium for treatment, Re-processing of secondary aluminium into new aluminium', 'Lead, zinc and tin and products thereof', 'Secondary lead for treatment, Re-processing of secondary lead into new lead', 'Copper products', 'Secondary copper for treatment, Re-processing of secondary copper into new copper', 'Other non-ferrous metal products', 'Secondary other non-ferrous metals for treatment, Re-processing of secondary other non-ferrous metals into new other non-ferrous metals', 'Foundry work services', 'Medical, precision and optical instruments, watches and clocks (33)', 'Secondary raw materials', 'Bottles for treatment, Recycling of bottles by direct reuse', 'Transmission services of electricity', 'Distribution and trade services of electricity', 'Coke oven gas', 'Blast Furnace Gas', 'Oxygen Steel Furnace Gas', 'Gas Works Gas', 'Biogas', 'Distribution services of gaseous fuels through mains', 'Secondary construction material for treatment, Re-processing of secondary construction material into aggregates', 'Financial intermediation services, except insurance and pension funding services (65)', 'Insurance and pension funding services, except compulsory social security services (66)', 'Services auxiliary to financial intermediation (67)', 'Renting services of machinery and equipment without operator and of personal and household goods (71)', 'Computer and related services (72)', 'Research and development services (73)', 'Other business services (74)', 'Public administration and defence services; compulsory social security services (75)', 'Food waste for treatment: incineration', 'Paper waste for treatment: incineration', 'Plastic waste for treatment: incineration', 'Intert/metal waste for treatment: incineration', 'Textiles waste for treatment: incineration', 'Wood waste for treatment: incineration', 'Oil/hazardous waste for treatment: incineration', 'Food waste for treatment: biogasification and land application', 'Paper waste for treatment: biogasification and land application', 'Sewage sludge for treatment: biogasification and land application', 'Food waste for treatment: composting and land application', 'Paper and wood waste for treatment: composting and land application', 'Food waste for treatment: waste water treatment', 'Other waste for treatment: waste water treatment', 'Food waste for treatment: landfill', 'Paper for treatment: landfill', 'Plastic waste for treatment: landfill', 'Inert/metal/hazardous waste for treatment: landfill', 'Textiles waste for treatment: landfill', 'Wood waste for treatment: landfill', 'Membership organisation services n.e.c. (91)', 'Other services (93)', 'Private households with employed persons (95)', 'Extra-territorial organizations and bodies'] 
+    elif notion=='undecided': sectors = ['Beverages', 'Paper and paper products', 'Printed matter and recorded media (22)']
+    return(sectors)
+
+def embodied_conso(self, regs, secs): return(np.dot(self.L, final_demand(self, secs, regs)))
+
+def embodied_import(self, var, secs, regs_imp, regs_exp=None, add=True, join=False, round_bn=False): 
+    # TODO: /!\ this function hasn't been checked/tested
+    '''
+    sums across all regions embodied hours/employment/gain (=var) imported from sectors secs in regs_exp to regs_imp,
+    where var embodied in imports from reg to regs_imp in sector secs is equal to the product of:
+    . share of production in sec in reg exported to regs_imp
+    . (hours of, if var='hours') employment in sec in reg
+    . (if var=='gain') hourly wage in reg_ref - hourly wage in reg
+    join=False returns the results per country
+    add=True sums all hours/gain/employment across sectors
+    '''
+    # taking share_export if function of embodied_conso instead of embodied_value_added amounts to assume that the 
+    #    ratio of value_added per production is constant within a sector-region among the different processes
+    #    Indeed, if say all Korean imports of embodied Chinese labor and only them consist in small transformation
+    #    of an higly valued imported product (say from Vietnam), then our calculations of Korean imports of Chinese labor 
+    #    will be biased upward, and imports of Chinese labor by other countries biased downard. 
+    #    Still, this assumption seems reasonable. And we cannot relax it simply (if so, we could not use L any more,
+    #    we would have to compute A, A^2, A^3,... and the value added at each step in the global value chain).
+    if join:
+        if regs_imp is None: print('regs_imp should not be None for join=True')
+        if type(regs_exp)==str: nb_regs_exp = 1
+        else: nb_regs_exp = len(regs_exp)
+        if regs_exp is None: regs_exp = self.not_regs(regs_imp)
+        share_export = div0(self.embodied_conso(regs_imp)[self.index_secs_regs(secs, regs_exp)], self.x.loc[regs_exp,secs])
+        if var!='gain': res = self.impacts(var, regs_exp, secs)*share_export
+        else: 
+            hourly_wage_ref = np.tile(hourly_wage(regs_imp, secs), nb_regs_exp)
+            res = (hourly_wage_ref - hourly_wage(regs_exp, secs))*impact('Employment hour', regs_exp, secs)*share_export
+    else:
+        if regs_exp is None: 
+            regs_ref = regs_imp
+            yes = True
+        elif regs_imp is None:
+            regs_ref = regs_exp
+            yes = False
+        else: print("regs_exp or regs_imp should be None for join=False")
+        share_export = np.array([div0(self.embodied_conso(self.regs_or_no(reg, yes))[self.index_secs_regs(self.regs_or_no(reg, not yes), \
+                                secs)], self.x.loc[self.regs_or_no(reg, not yes),secs]) for reg in regs_ref])
+        if  var!='unknown':
+            res = np.array([self.impacts(var, self.regs_or_no(reg, not yes), secs) for reg in regs_ref])*share_export
+        else:          
+            hourly_wage_ref = np.array([np.tile(hourly_wage(reg, secs), len(regions)-1) for reg in regs_ref])
+            hourly_wage_not_ref = np.array([hourly_wage(self.regs_or_no(reg, not yes), secs) for reg in regs_ref])
+            hours = np.array([impact('Employment hour', self.regs_or_no(reg, not yes), secs) for reg in regs_ref])
+#              # /!\ Employment hours are computed indirectly because data is aberrant
+#             hours = 1600*np.array([impact('Employment', regs_or_no(reg, not yes), secs) for reg in regs_ref])
+            if regs_imp is None:
+                share_export = np.array([np.concatenate([div0(self.embodied_conso(r)[self.index_secs_regs([reg], secs)], \
+                    self.x.loc[[reg],secs]) for r in self.not_regs(reg)]) for reg in regs_ref])
+                hours = np.array(list(map(lambda r: np.tile(r,(len(regions)-1)), hours))) 
+                hourly_wage_not_ref = np.array([np.concatenate([hourly_wage(r, secs, indirect=True, \
+                    positive=True) for r in self.not_regs(reg)]) for reg in regs_ref]) # TODO: better assessment of hourly wages
+            res = (hourly_wage_ref - hourly_wage_not_ref)*hours*share_export
+    if not add:
+        if round_bn:
+            return(round(res*pow(10,-9)))
+        else:
+            return(res)
+    else:
+        if join: 
+            r = res.sum()
+            if Var=='Employment hour': res = print(round(r), 'hours')
+            elif Var=='Employment': print(round(r*pow(10,-3),1), 'M persons')
+            else: print(round(r*pow(10,-9),1), 'G€')
+        else: r = [res[i].sum() for i in range(len(res))]
+        if round_bn:
+            return(round(r*pow(10,-9)))
+        else:
+            return(r)
+
+def imports(self, secs, regs_imp, regs_exp=None):
+    '''
+    Returns value of imports by regions in regs_imp from sectors in secs from regions in regs_exp, decomposed by secs, regs_exp.
+    '''
+
+    if regs_imp is None: print('regs_imp should not be None for join=True')
+    if regs_exp is None: regs_exp = self.not_regs(regs_imp)
+    imps = (self.Z.loc[[(r, s) for r in regs_exp for s in secs], [(r, s) for r in regs_imp for s in self.sectors]].sum(1)
+        + self.Y.loc[[(r, s) for r in regs_exp for s in secs], [r in regs_imp for r in self.Y.columns.get_level_values('region')]].sum(1))
+    return(imps)
+    
+def impact_imports(self, var, secs, regs_imp, regs_exp=None):  
+    '''
+    Returns impact of type var of goods imported by regions in regs_imp from sectors in secs from regions in regs_exp.
+    '''
+    if regs_exp is None: regs_exp = self.not_regs(regs_imp)
+    imps = self.imports(secs, regs_imp, regs_exp)
+    share_export = div0(imps, self.x.loc[regs_exp,secs])
+    impact = self.impacts(var, regs_exp, secs) * share_export
+    return(impact.sum())
+           
+def disseminate(self, vec, regs, secs):
+    '''
+    Returns a vector of the same size as self.x where the coefficients of vec are 'disseminated' 
+    at locations of regs, secs (other coefficients are 0).
+    '''
+    temp = self.x.copy()
+    temp.at[[(r, s) for r in regs for s in secs]] = vec
+    temp = temp*self.is_in(secs = secs, regs = regs)    
+    return(temp)
+
+def embodied_impact_imports(self, var, secs, regs_imp, regs_exp=None):  
+    '''
+    Returns embodied impact of type var of goods imported by regions in regs_imp from sectors in secs from regions in regs_exp.
+    '''
+    if regs_exp is None: regs_exp = self.not_regs(regs_imp)
+    imps = self.imports(secs, regs_imp, regs_exp)
+    prod = self.disseminate(imps, regs_exp, secs)
+    impact = self.embodied_impact(var = var, production = prod)
+    return(impact.sum() + self.impact_imports(var, secs, regs_imp, regs_exp))
+
+IOS.imports = imports
+IOS.impact_imports = impact_imports
+IOS.disseminate = disseminate
+IOS.embodied_impact_imports = embodied_impact_imports
+IOS.not_regs = not_regs
+IOS.regs_or_no = regs_or_no
+IOS.embodied_conso = embodied_conso
+IOS.embodied_import = embodied_import
+IOS.aggregate_mix = aggregate_mix
+IOS.mix_matrix = mix_matrix
+IOS.change_mix = change_mix
+IOS.mix = mix
+IOS.regional_mix = regional_mix
+IOS.prepare_secs_regs = prepare_secs_regs
+IOS.index_secs = index_secs
+IOS.index_regs = index_regs
+IOS.nb_regions = nb_regions
+IOS.nb_sectors = nb_sectors
+IOS.find = find
+IOS.is_in = is_in
+IOS.final_demand = final_demand
+IOS.index_secs_regs = index_secs_regs
+IOS.production = production
+IOS.impacts = impacts
+IOS.embodied_prod = embodied_prod
+IOS.embodied_impact = embodied_impact
+IOS.sorted_array = sorted_array
+IOS.inputs = inputs
+IOS.outputs = outputs
+IOS.ger = ger
+IOS.err = err
+IOS.errs = errs
+IOS.erois = erois
+IOS.erois_and_prices = erois_and_prices
+IOS.energy_sectors = energy_sectors
+IOS.regions = regions
+IOS.sectors = sectors
+IOS.secondary_energy_demand = secondary_energy_demand
+IOS.energy_supply = energy_supply
+IOS.energy_required = energy_required
+IOS.VA = VA
+IOS.value_added = value_added
+IOS.price_energy = price_energy
+IOS.energy_prices = energy_prices
+IOS.employment_high = employment_high
+IOS.employment_medium = employment_medium
+IOS.employment_low = employment_low
+IOS.employment_all   = employment_all
+IOS.employments = employments
+IOS.employment = employment
+IOS.results = results
+IOS.consumption_sectors = consumption_sectors
+# IOS. = 
+# IOS. = 
+# other: internal_energy, composition_impact, change IOT for efficiency or gdp, calc_Z, etc., not_regs, regs_or_no, gdp, import, embodied_import
\ No newline at end of file
diff --git a/pymrio/tools/iomath.py b/pymrio/tools/iomath.py
index b3bc7662..054d6f4b 100644
--- a/pymrio/tools/iomath.py
+++ b/pymrio/tools/iomath.py
@@ -10,7 +10,9 @@
 
 import pandas as pd
 import numpy as np
+import scipy.sparse as sp
 import warnings
+import operator
 
 import pymrio.tools.ioutil as ioutil
 
@@ -388,3 +390,224 @@ def calc_accounts(S, L, Y, nr_sectors):
                          columns=S.columns)
 
     return (D_cba, D_pba, D_imp, D_exp)
+
+def sorted_series(series): 
+    '''
+    Returns the sorted panda series, grouped by group_by if it is not None, and indexed by index (the default index is that of regions x sectors)
+    '''
+    return(sorted(series.items(), reverse=True, key=operator.itemgetter(1)))
+
+def approx_solution(A, y, n=10):
+    '''
+    Returns the approximate solution x of the sparse matrix equation: (1-A).x=y, by computing the series of A^k.y, with k<n
+    '''
+    s, x = sp.csc_matrix(y).transpose(), sp.csc_matrix(y).transpose()
+    for i in range(n):
+        x = A.dot(x)
+        s += x
+    return(s)
+
+def div0(a, b, replace_by=0):
+    '''
+    Returns the Hadamard division of the arrays (or matrices) of same shape a and b, where elements of the results are 0 in locations where those of b are 0.
+    '''
+    if sp.issparse(b): 
+        b = np.array(b.toarray(), dtype='float')
+        sparse = True
+    else: sparse = False
+    if type(a)==int or type(a)==float: a = a*np.ones_like(b)
+    if sp.issparse(a): a = a.toarray()
+    a = np.array(a, dtype='float')
+#     b = np.array(b, dtype='float')
+    div = np.divide(a, b, out=np.array(replace_by*np.ones_like(a)), where=b!=0)
+    if sparse: return(sp.csc_matrix(div)) # TODO: a true sparse division, not through dense matrix
+    else: return(div)
+
+def mult_rows(mat, vec):
+    '''
+    Multiply (each element of) the k-th row of matrix by k-th element of vector for all k, returns the resulting matrix.
+    '''
+    if type(vec)==list: vec = np.array(vec)
+    if sp.issparse(mat): #         return(matrix.toarray() * sp.diags([vector]).dot(np.ones(matrix.shape))) 
+        nb_r, nb_c = mat.shape
+        if sp.issparse(vec): vec = vec.toarray()
+#         return(mat.multiply(sp.csc_matrix((np.tile(vec.data,nb_c).flatten(), np.tile(np.arange(nb_r),nb_c), np.arange(0,nb_c*np.max(vec.shape)+1,nb_r)), \
+        return(mat.multiply(sp.csc_matrix((np.tile(vec,nb_c).flatten(), np.tile(np.arange(nb_r),nb_c), np.arange(0,nb_c*np.max(vec.shape)+1,nb_r)), \
+                                          shape=mat.shape)))
+    else: return(np.array(mat) * np.diag(vec).dot(np.ones(mat.shape))) 
+
+def mult_cols(mat, vec): 
+    '''
+    Multiply (each element of) the k-th column of matrix by k-th element of vector for all k, returns the resulting matrix.
+    '''
+    if type(vec)==list: vec = np.array(vec)
+    if sp.issparse(mat): #         return(matrix.toarray() * (np.ones(matrix.shape)).dot(sp.diags([vector])))
+        nb_r, nb_c = mat.shape
+        if sp.issparse(vec): vec = vec.toarray()
+#         return(mat.multiply(sp.csc_matrix((np.repeat(vec.data, nb_r).flatten(),np.tile(np.arange(nb_r),nb_c),np.arange(0,nb_r*np.max(vec.shape)+1,nb_r)),\
+        return(mat.multiply(sp.csc_matrix((np.repeat(vec, nb_r).flatten(),np.tile(np.arange(nb_r),nb_c),np.arange(0,nb_r*np.max(vec.shape)+1,nb_r)),\
+                                          shape=mat.shape)))
+    else: return(np.array(mat) * (np.ones(mat.shape)).dot(np.diag(vec)))
+        
+def inter_secs(secs1, secs2): 
+    '''
+    Returns the intersection of lists secs1 and secs2.
+    '''
+    return(list(np.array(secs1)[np.where([sec in secs2 for sec in secs1])[0]]))
+    
+def gras(A, new_row_sums = None, new_col_sums = None, max_iter=1e2, criterion='relative', tol=1e0): 
+    '''
+    GRAS algorithm, which balances a matrix A in order to respect new_row_sums and new_col_sums (see Junius & Oosterhaven, 2003).
+    FOR DENSE MATRIX
+    criterion can be: 'absolute', 'relative' (default), or 'convergence': in the latter, it stops when improvement falls below tol.
+    
+    Algorithm directly transcripted from matlab: results are the same as in matlab; I haven't checked that the code is valid but it seems.
+    Original program of Bertus Talsma, adapted by Dirk Stelder in Gauss, transferred to Matlab by Maaike Bouwmeester.
+    Matlab code can be found in /util/gras.m at https://cecilia2050.eu/system/files/cecilia_scenario_tool_version1.zip
+    '''
+    if sp.issparse(A): print('Your matrix is sparse: use grasp() instead.')
+    if new_row_sums is None: u = A.sum(axis=1)
+    else: u = np.array(new_row_sums)
+    if new_col_sums is None: v = A.sum(axis=0)
+    else: v = np.array(new_col_sums)
+    nb_rows, nb_cols = A.shape
+    sm = 0.00000000000000001
+    converged = False
+    P, N = np.clip(A, 0, None), np.clip(-np.array(A), 0, None)
+    r, s = np.ones(nb_rows), np.ones(nb_cols)
+#     nnn, nn = np.ones(nb_rows), np.ones(nb_cols)
+#     ppp, pp = np.ones(nb_rows), np.ones(nb_cols)
+#     r_inv = div0(1, r)
+    iterator = 1
+    while iterator < max_iter:
+        s_inv, r_inv = div0(1, s), div0(1, r)
+        pp = mult_rows(P, r).sum(axis=0) # pp[col] = (P[:,col] * r).sum()
+        nn = mult_rows(N, r_inv).sum(axis=0) # nn[col] = (N[:,col] * r_inv).sum()
+        s = (v + (v**2 + 4*pp*nn)**0.5) / (2*pp + sm)
+        row_new = (np.diag(r).dot(P).dot(np.diag(s)) - np.diag(r_inv).dot(N).dot(np.diag(s_inv))).dot(np.ones(nb_cols))
+        eps1_abs = np.ones(nb_rows).dot(np.abs(row_new - u))
+        rel = np.abs(row_new / (u + sm) - 1)
+        rel[np.where(u==0)] = 0
+        eps1_rel = np.max(rel)
+        ppp = mult_cols(P, s).sum(axis=1)
+        nnn = mult_cols(N, s_inv).sum(axis=1)
+        r = (u + (u**2 + 4*ppp*nnn)**0.5) / (2*ppp + sm)
+        ri = div0(1, r)
+        col_new = np.ones(nb_rows).dot((np.diag(r).dot(P).dot(np.diag(s)) - np.diag(r_inv).dot(N).dot(np.diag(s_inv))))
+        eps2_abs = np.ones(nb_cols).dot(np.abs(col_new - v))
+        rel = np.abs(col_new / (v + sm) - 1)
+        rel[np.where(v==0)] = 0
+        eps2_rel = np.max(rel)
+        if criterion == 'relative': tol_current = (eps1_rel + eps2_rel)/2
+        else: tol_current = (eps1_abs + eps2_abs)/2
+        if iterator > 1: improvement = (tol_previous - tol_current)/tol_previous
+        if (criterion in ['relative', 'absolute'] and tol_current < tol) or (criterion=='convergence' and (iterator > 1 and improvement < tol)):
+            iterator = max_iter
+            converged = True
+#             print('converged')
+        else: 
+            iterator += 1
+            tol_previous = tol_current
+            if iterator>1: print(time()-time_i)
+            time_i = time()
+            print(iterator, tol_previous)
+    if converged:
+#         return(P-N) # why not simply this?
+#         P = np.diag(r).dot(A).dot(np.diag(s))
+#         N = np.diag(div0(1, r)).dot(A).dot(np.diag(div0(1, s)))
+#         N[np.where(P>=0)] = 0
+#         X = np.clip(P, 0, None) + N 
+#         return(X)
+        return(np.diag(r).dot(P).dot(np.diag(s))-np.diag(div0(1, r)).dot(N).dot(np.diag(div0(1, s))))
+    else: print('did not converged')
+        
+def grasp(A, new_row_sums = None, new_col_sums = None, max_iter=1e2, criterion='convergence', tol=1e-5): 
+    '''
+    GRAS algorithm, which balances a matrix A in order to respect new_row_sums and new_col_sums (see Junius & Oosterhaven, 2003).
+    FOR SPARE MATRIX
+    criterion can be: 'absolute', 'relative' (default), or 'convergence': in the latter, it stops when improvement falls below tol.
+    
+    Algorithm directly transcripted from matlab: results are the same as in matlab; I haven't checked that the code is valid but it seems.
+    Original program of Bertus Talsma, adapted by Dirk Stelder in Gauss, transferred to Matlab by Maaike Bouwmeester.
+    Matlab code can be found in /util/gras.m at https://cecilia2050.eu/system/files/cecilia_scenario_tool_version1.zip
+    '''
+    if not sp.issparse(A): print('Your matrix is not sparse, you should use gras instead.')
+    if new_row_sums is None: u = sp.csc_matrix(A.sum(axis=1))
+    else: u = sp.csc_matrix(new_row_sums)
+    if new_col_sums is None: v = sp.csc_matrix(A.sum(axis=0))
+    else: v = sp.csc_matrix(new_col_sums)
+    if u.shape[0]<u.shape[1]: u = u.transpose()
+    if v.shape[0]>v.shape[1]: v = v.transpose()
+    nb_rows, nb_cols = A.shape
+    sm = 0.00000000000000001
+    converged = False
+    P, N = A.multiply(A > 0), -A.multiply(A < 0)
+    r, s = sp.csc_matrix(np.ones(nb_rows)).transpose(), sp.csc_matrix(np.ones(nb_cols)) # r,u,ppp,nnn are columns / s,v,pp,nn are rows
+    improvements_saturated, iterator = 0, 1
+    while iterator < max_iter:
+        s_inv, r_inv = div0(1, s), div0(1, r)
+        pp = sp.csc_matrix(mult_rows(P, r).sum(axis=0)) # pp[col] = (P[:,col] * r).sum()
+        nn = sp.csc_matrix(mult_rows(N, r_inv).sum(axis=0)) # nn[col] = (N[:,col] * r_inv).sum()
+        s = (v + (v.power(2) + 4*pp.multiply(nn)).power(0.5)) / (2*pp + sp.csc_matrix(sm*np.ones(nb_cols)))
+        s_inv = sp.csc_matrix(div0(1, s))
+        row_new = (mult_rows(sp.eye(nb_rows),r).dot(P).dot(mult_cols(sp.eye(nb_cols),s)) - \
+                   mult_rows(sp.eye(nb_rows),r_inv).dot(N).dot(mult_cols(sp.eye(nb_cols), s_inv))).dot(sp.csc_matrix(np.ones(nb_cols)).transpose())
+        eps1_abs = np.abs(row_new - u).sum()
+        rel = np.abs(row_new / (u + sp.csc_matrix(sm*np.ones(nb_rows)).transpose()) - 1)
+        rel[np.where(u.toarray()==0)] = 0
+        eps1_rel = np.max(rel)
+        ppp = sp.csc_matrix(mult_cols(P, s).dot(np.ones(nb_cols))).transpose() # mult_cols(P, s).sum(axis=1)
+        nnn = sp.csc_matrix(mult_cols(N, s_inv).dot(np.ones(nb_cols))).transpose() # mult_cols(N, s_inv).sum(axis=1)
+        r = (u + (u.power(2) + 4*ppp.multiply(nnn)).power(0.5)) / (2*ppp + sp.csc_matrix(sm*np.ones(nb_rows)).transpose())
+        r_inv = sp.csc_matrix(div0(1, r))
+        col_new = sp.csc_matrix(np.ones(nb_rows)).dot((mult_rows(sp.eye(nb_rows),r).dot(P).dot(mult_cols(sp.eye(nb_cols),s)) - \
+                                                       mult_rows(sp.eye(nb_rows),r_inv).dot(N).dot(mult_cols(sp.eye(nb_cols),s_inv)))) # mult_rows(...)=diag(s)
+        eps2_abs = np.abs(col_new - v).sum()
+        rel = np.abs(col_new / (v + sp.csc_matrix(sm*np.ones(nb_cols))) - 1)
+        rel[np.where(v.toarray()==0)] = 0
+        eps2_rel = np.max(rel)
+        if criterion == 'relative': tol_current = (eps1_rel + eps2_rel)/2
+        else: tol_current = (eps1_abs + eps2_abs)/2
+        if iterator > 1: improvement = (tol_previous - tol_current)/tol_previous
+        else: improvement = tol + 1
+        if (criterion in ['relative', 'absolute'] and tol_current < tol) or (criterion=='convergence' and iterator > 1 and improvements_saturated > 5):
+            iterator = max_iter
+            converged = True
+#             print('converged')
+        else: 
+            if improvement < tol: improvements_saturated += 1
+            iterator += 1
+            tol_previous = tol_current
+            if iterator>2: print(time.time()-time_i)
+            time_i = time.time()
+            if iterator%7==0: print(iterator, tol_previous, improvement)
+    if converged:
+#         return(P-N) # why not simply this?
+#         P = np.diag(r).dot(A).dot(np.diag(s))
+#         N = np.diag(div0(1, r)).dot(A).dot(np.diag(div0(1, s)))
+#         N[np.where(P>=0)] = 0
+#         X = np.clip(P, 0, None) + N 
+        X = mult_rows(sp.eye(nb_rows),r).dot(P).dot(mult_cols(sp.eye(nb_cols),s))-\
+                mult_rows(sp.eye(nb_rows),div0(1, r)).dot(N).dot(mult_cols(sp.eye(nb_cols),div0(1, s)))
+        return(X)
+    else: print('did not converged')
+
+def global_objects(): 
+    '''
+    Returns the list of global objects.
+    '''
+    return(np.array(list(globals().keys()))[[s[0]!='_' for s in list(globals().keys())]])
+
+def max_gap(a, b): 
+    '''
+    Returns the maximum (non infinite) spread/gap between two vectors.
+    '''
+    return(np.max(div0(np.abs(a-b), a)))
+
+def max_diff(a, b): 
+    '''
+    Returns the maximum absolute difference between two vectors.
+    '''
+    return(np.max(np.abs(a-b)))
+    
+def bip(): os.system('play --no-show-progress --null --channels 1 synth 0.2 sine 500')
diff --git a/pymrio/tools/iometadata.py b/pymrio/tools/iometadata.py
index 5a86d2d6..563a073d 100644
--- a/pymrio/tools/iometadata.py
+++ b/pymrio/tools/iometadata.py
@@ -21,8 +21,9 @@ def __init__(self,
                  name=None,
                  system=None,
                  version=None,
+                 year=None,
                  path_in_arc='',
-                 logger_function=logging.info):
+                 logger_function=logging.info): #   
 
         """ Organzises the MRIO meta data
 
@@ -130,6 +131,8 @@ def __init__(self,
                 self.change_meta('system', system)
             if version:
                 self.change_meta('version', version)
+            if year:
+                self.change_meta('year', year)
 
         else:
             if not description:
@@ -139,6 +142,7 @@ def __init__(self,
                 ('name', name),
                 ('system', system),
                 ('version', version),
+                ('year', year),
                 ('history', []),
                 ])
             self.logger("Start recording metadata")
@@ -158,11 +162,13 @@ def __str__(self):
                 "MRIO Name: {mrio}\n"
                 "System: {system}\n"
                 "Version: {ver}\n"
+                "Year: {year}\n"
                 "File: {metafile}\n"
                 "History:\n{hist}".format(desc=self.description,
                                           mrio=self.name,
                                           system=self.system,
                                           ver=self.version,
+                                          year=self.year,
                                           metafile=self._metadata_file,
                                           hist=hist))
 
@@ -235,6 +241,10 @@ def system(self):
     def version(self):
         return self._content['version']
 
+    @property
+    def year(self):
+        return self._content['year']
+
     def change_meta(self, para, new_value, log=True):
         """ Changes the meta data
 
diff --git a/pymrio/tools/ioparser.py b/pymrio/tools/ioparser.py
index e8b31171..20854919 100644
--- a/pymrio/tools/ioparser.py
+++ b/pymrio/tools/ioparser.py
@@ -12,6 +12,8 @@
 
 import pandas as pd
 import numpy as np
+import scipy.io
+import scipy.sparse as sp
 import zipfile
 from collections import namedtuple
 
@@ -25,6 +27,8 @@
 # Constants and global variables
 from pymrio.core.constants import PYMRIO_PATH
 
+from pymrio.tools.iomath import div0
+
 
 # Exceptions
 class ParserError(Exception):
@@ -654,7 +658,7 @@ def parse_exiobase2(path, charact=True, popvector='exio2'):
     if popvector is 'exio2':
         logging.debug('Read population vector')
         io.population = pd.read_csv(os.path.join(PYMRIO_PATH['exio20'],
-                                                 './misc/population.txt'),
+                                                 'misc/population.txt'),
                                     index_col=0, sep='\t').astype(float)
     else:
         io.population = popvector
@@ -1890,3 +1894,246 @@ def parse_eora26(path, year=None, price='bp', country_names='eora'):
         meta=meta_rec)
 
     return eora
+
+def themis_parser(exio_files, year = None, scenario = None, themis = None, themis_caracs=None, labels=None, dlr_files=None, combo=True, compute_all=False):
+    """ THEMIS parser (by adrien fabre aka. bixiou on github)
+
+    The THEMIS model is not open. You can ask NTNU for it, they might accept.
+
+    Two options for this parser: 1. either provide the year (2010, 2030, or 2050) and scenario ('BL' or 'BM'),
+                                 2. either provide none of them, and all combinations will be loaded in a dict
+    exio_files must give the path to the root folder of THEMIS
+    if dlr_files is provided, the 3 Greenpeace=DLR scenarios are added (in case 2.); dlr_files = True is equivalent to dlr_files = exio_files
+    if combo = True and dlr_files is provided, create 3 'combo' scenarios (improperly named): 
+        2010 : ADV 2050 mix on 2010 techno (i.e. 2010 transformation matrix A); 2030: ER 2050 mix on 2010 techno; 2050: BL 2010 mix on 2050 techno
+    compute_all precalculates EROIs, prices, value-added, employments
+    The folder should include a file called 'Supplementary info & mixes.xlsx' which provides the IEA scenarios of energy demand.
+        You can ask this file to adrien.fabre@psemail.eu
+    themis_parser runs in ~1h if combo=True and compute_all=True, ~1 min if they are false
+    """
+    
+    def load_themis(matrix='A', year=2010, scenario='BL'):
+        # matrix is A, Sb, Sf or S; year is 2010, 2030 or 2050; scenario is BL or BM
+        if matrix=='S': 
+            Sb = load_themis('Sb', year, scenario)
+            Sf = load_themis('Sf', year, scenario)       
+            nb_stressors_b = Sb.shape[0] - Sf.shape[0] # check .shape for sparse
+            nb_sectors_f = Sf.shape[1]
+            res = sp.hstack([sp.vstack([Sf, sp.csc_matrix((nb_stressors_b, nb_sectors_f))]), Sb])
+        else:
+            if matrix=='A': data = 'A'
+            elif matrix=='Sb': data = 'S'
+            elif matrix=='Sf': data = 'S_f'
+            else: data = matrix
+
+            year = '_' + str(year)
+            if data=='S_f': matrix_name = data + '_' + scenario
+            else: matrix_name = data + year + '_' + scenario
+
+            if data=='S_f': res = themis[matrix_name][:,:,2]
+            else: res = themis[matrix_name]
+        return(res)  
+    
+    def secondary_energy_demand():
+        TWh2TJ = 3.60 * 1e3
+        secondary_energy_demand = np.array([0]*A.shape[0])
+        for name in energy_demand.index: 
+            if name in list(labels['sectors']):
+                secondary_energy_demand[np.where(list(map(lambda s: s == name, labels['idx_sectors'])))[0]] = \
+                    energy_demand[[(reg, year) for reg in labels['regions']]].loc[name] * TWh2TJ
+        return(secondary_energy_demand)
+    
+    if themis is None or themis_caracs is None or labels is None: 
+        themis = scipy.io.loadmat(exio_files + 'Data/THEMIS2.mat')
+        themis_caracs = scipy.io.loadmat(exio_files + 'Data/Characterization_endpoint2.mat')
+        label = pd.read_excel(exio_files + 'Data/THEMIS2_labels.xls', header=0)
+        idx_name = label['Name']
+        idx_region = label['Region']
+        idx_region.loc[np.where(idx_region=='AME')] = 'Africa and Middle East'
+        idx_region.loc[np.where(idx_region=='CN')] = 'China'
+        idx_region.loc[np.where(idx_region=='EIT')] = 'Economies in transition'
+        idx_region.loc[np.where(idx_region=='IN')] = 'India'
+        idx_region.loc[np.where(idx_region=='LA')] = 'Latin America'
+        idx_region.loc[np.where(idx_region=='PAC')] = 'OECD Pacific'
+        idx_region.loc[np.where(idx_region=='US')] = 'OECD North America'
+        idx_region.loc[np.where(idx_region=='RER')] = 'OECD Europe'
+        idx_region.loc[np.where(idx_region=='AS')] = 'Rest of developing Asia'
+        label2 = pd.read_excel(exio_files + 'Data/THEMIS2_labels.xls', header=0, sheet_name=2)
+        idx_impacts = label2['FullName']
+        label3 = pd.read_excel(exio_files + 'Data/THEMIS2_labels.xls', header=0, sheet_name=3)
+        idx_caracs = label3['Abbreviation THEMIS']
+        labels = {'regions': idx_region.unique(), 'idx_regions': idx_region, 'impacts': idx_impacts.unique(), 'idx_impacts': idx_impacts, 
+                  'sectors': idx_name.unique(), 'idx_sectors': idx_name, 'caracs': idx_caracs.unique(), 'idx_caracs': idx_caracs, 'name': 'labels'}
+    if year is None and scenario is None: 
+        if dlr_files is not None: 
+            if combo: scenarios = ['BL', 'BM', 'REF', 'ER', 'ADV', 'combo'] 
+            else: scenarios = ['BL', 'BM', 'REF', 'ER', 'ADV']
+            if type(dlr_files)!=str: dlr_files = exio_files
+        else: scenarios = ['BL', 'BM']
+        all_themis = dict()
+        for s in scenarios:
+            all_themis[s] = dict()
+            for y in [2010, 2030, 2050]:
+                if s == 'BL' or s == 'BM': 
+                    all_themis[s][y] = themis_parser(exio_files, y, s, themis, themis_caracs, labels)
+                    all_themis[s][y].scenario = s
+                else:
+                    if s == 'combo':
+                        if y in [2010, 2030]: 
+                            yr, not_yr, sc = 2010, 2050, 'ADV'
+                            if y == 2030: sc = 'ER'
+                            all_themis[s][y] = all_themis['BL'][yr].copy(new_name='THEMIS')
+                            all_themis[s][y].dlr_elec = all_themis[sc][2050].dlr_elec
+                            all_themis[s][y].dlr_capacity = all_themis[sc][2050].dlr_capacity
+                            global_mix = all_themis[sc][not_yr].mix(scenario = sc, path_dlr = dlr_files)[y]
+                        else: 
+                            yr, not_yr, sc = 2050, 2010, 'BL'
+                            all_themis[s][y] = all_themis['BL'][yr].copy(new_name='THEMIS')
+                            global_mix = all_themis['BL'][2010].mix_matrix(global_mix = False)
+                        all_themis[s][y].aggregate_mix(mix = global_mix)
+                        all_themis[s][y].change_mix(global_mix = global_mix, year = not_yr, only_exiobase = False)
+                        
+                    elif s == 'REF': all_themis[s][y] = all_themis['BL'][y].copy(new_name='THEMIS') # TODO: include attribute scenario
+                    else: all_themis[s][y] = all_themis['BM'][y].copy(new_name='THEMIS') 
+                    all_themis[s][y].scenario = s
+                    if s != 'combo':
+                        global_mix = all_themis[s][2010].mix(scenario = s, path_dlr = dlr_files)[y]
+                        all_themis[s][y].dlr_elec = all_themis[s][2010].dlr_elec
+                        all_themis[s][y].dlr_capacity = all_themis[s][2010].dlr_capacity
+                        all_themis[s][y].adjust_capacity = all_themis[s][2010].adjust_capacity
+                        all_themis[s][y].adjustment_capacity = all_themis[s][2010].adjustment_capacity
+                        if s in ['REF', 'ER', 'ADV']: 
+                            all_themis[s][y].wo_GW_adj = all_themis[s][y].copy(new_name='THEMIS')
+                            all_themis[s][y].wo_GW_adj.change_mix(global_mix = global_mix, year = y, only_exiobase = False, adjust_GW = False)
+                        all_themis[s][y].change_mix(global_mix = global_mix, year = y, only_exiobase = False, adjust_GW=True)
+        if compute_all:
+            for s in scenarios:
+                for y in [2010, 2030, 2050]:                  
+                    if s=='combo' and y==2050: all_themis[s][y].world_mix = all_themis[s][y].agg_mix
+                    else: all_themis[s][y].world_mix = all_themis[s][y].aggregate_mix(recompute=True)
+                    if s in ['REF', 'ER', 'ADV']: 
+                        all_themis[s][y].wo_GW_adj.eroi_adj = all_themis[s][y].wo_GW_adj.erois(factor_elec = 2.6, \
+                                                                                               recompute=True).rename(index={'Power sector': 'total'})
+                        all_themis[s][y].wo_GW_adj.eroi = all_themis[s][y].wo_GW_adj.erois(recompute=True).rename(index={'Power sector': 'total'})
+                        all_themis[s][y].wo_GW_adj.energy_prices()
+                        all_themis[s][y].wo_GW_adj.employ_direct = all_themis[s][y].wo_GW_adj.employments()
+                        all_themis[s][y].wo_GW_adj.employments(indirect = False, recompute = True)
+                    all_themis[s][y].eroi_adj = all_themis[s][y].erois(factor_elec = 2.6, recompute=True).rename(index={'Power sector': 'total'})
+                    all_themis[s][y].eroi = all_themis[s][y].erois(recompute=True).rename(index={'Power sector': 'total'})
+                    all_themis[s][y].energy_prices()
+                    all_themis[s][y].employ_direct = all_themis[s][y].employments()
+                    all_themis[s][y].employments(indirect = False, recompute = True) # pb with employments combo 2050
+
+        return(all_themis)
+    elif year is None or scenario is None: print('scenario and year must be both given or None')
+    else:
+        A = load_themis('A', year, scenario)
+        S = load_themis('S', year, scenario)
+        if scenario=='BL': skip, skipfoot = 6, 67 # TODO: make a separate function the extraction of IEA scenarios
+        elif scenario=='BM': skip, skipfoot = 52, 21
+        energy_demand = pd.read_excel(exio_files+'Supplementary info & mixes.xlsx', \
+                                         header=[0,1], index_col=0, skiprows=list(range(skip)), skipfooter=skipfoot, sheet_name=11) #TODO: select good columns>?
+        energy_demand.index = ['Electricity by ' + name[0].lower() + name[1:] for name in list(energy_demand.index)]
+        if scenario=='BL': skip, skipfoot = 27, 46
+        if scenario=='BM': skip, skipfoot = 73, 0
+        capacity = pd.read_excel(exio_files+'Supplementary info & mixes.xlsx', \
+                                         header=[0,1], index_col=0, skiprows=list(range(skip)), skipfooter=skipfoot, sheet_name=11)
+        capacity.index = ['Electricity by ' + name[0].lower() + name[1:] for name in list(capacity.index)] # in GW
+#         C = themis_caracs['C_H_CED_22'] # midpoint characterization
+#         C_large = themis_caracs['C_H_CED_large'] # midpoint characterization taken by Thomas Gibon: should be preferred to C
+#         C_index = themis_caracs['EP_H_CED_22']
+#         G = themis_caracs['G_H_CED_22'] # endpoint characterization
+#         G_index = themis_caracs['IMP_H_CED_22'] # cf. THEMIS2_labels.xls/C_IMP for more details
+#         mid2end = themis_caracs['mid2end']
+        meta_rec = MRIOMetaData(system='pxp', name='THEMIS', version=scenario)
+        core_data = dict()
+        extensions = {'labels':labels, 'impact': {'S': S, 'name': 'impact'}, 
+                      'energy':{'demand': energy_demand, 'secondary_demand': secondary_energy_demand(), 'capacity': capacity, 'name': 'energy'}} 
+
+        return IOSystem(A=A, name='THEMIS', version=scenario, year=year, meta=meta_rec, **dict(core_data, **extensions))
+
+def cecilia_parser(path, step = None, system='pxp'):
+    """ Cecilia 2050 parser (by adrien fabre aka. bixiou on github)
+
+    The Cecilia 2050 model is open and can be found at https://cecilia2050.eu/publications/168
+
+    Two options for this parser: either provide the step (-1: original aggregated S & U tables, 
+                                                           0: preprocessed tables, after balancing with the GRAS algorithm
+                                                           1: (BAU) efficiency gains (less inputs per output (cf. EffVector), and more output (cf. GDPVector))
+                                                           2a: new energy mix 
+                                                           2b: (techno) technical change
+                                                           3: curbing growth to respect the 2 degree scenario)
+                                 either provide none of them, and all combinations will be loaded in a dict
+    path must give the path to the root folder of Cecilia 2050, and files of both .zip should be placed in that folder.
+    For further information, ask adrien.fabre@psemail.eu
+    """
+    
+    def IOT_from_SUT(S, U, kind='p'): # Computes Z table
+        S = np.array(S, dtype='float')
+        if kind=='pxp': return(np.transpose(U.dot(div0(S,np.diag(np.sum(S, axis=1)).dot(np.ones(S.shape)))))) #sum(S).ones=sum of rows of S
+        elif kind=='ixi': return(div0(S,(np.ones(S.shape)).dot(np.diag(np.sum(S, axis=0)))).dot(U)) # ones.sum(S) = sum of columns of S
+
+    def load_matrix(matrix='U', step=0, system='pxp'):
+        if type(step) == str: step_num = int(step[0])
+        else: 
+            step_num = step
+            step = str(step)
+        if step_num<=0: step = 'preprocess/'
+        if step_num==-1: # TODO: other matrices
+            if matrix=='U':
+                M = np.loadtxt(path + 'preprocess/mrUseAggregated.txt', delimiter='\t', skiprows=2, usecols=list(range(3,519)))
+            elif matrix=='V':
+                M = np.loadtxt(path + 'preprocess/mrSupplyAggregated.txt', delimiter='\t', skiprows=2, usecols=list(range(3,519))) 
+            elif matrix=='Y': 
+                M = np.loadtxt(path + 'preprocess/mrFinalDemandAggregated.txt', delimiter='\t', skiprows=2, usecols=list(range(3,31)))
+        elif step_num==0: M = np.loadtxt(path + step + matrix + '.txt', delimiter='\t')
+        else: M = np.loadtxt(path + 'step' + step + '/' + matrix + 'end.txt', delimiter='\t')                
+        return(M)
+    
+    def load_extensions():
+        sectors = pd.read_excel(path + 'supply_use_tables_bau_2050.xlsx', 2, skiprows=2).iloc[0:129,2]
+        regions = ['EU', 'HI', 'BX', 'WW'] # EU, High Income, Fast developing countries, RoW
+        index = pd.MultiIndex.from_product([regions, sectors], names=['region', 'sector'])
+        F_init = np.loadtxt(path + 'preprocess/mrMaterialsAggregated.txt', delimiter='\t', skiprows=2, usecols=tuple(range(2,518)))
+        index_F = np.loadtxt(path + 'preprocess/mrMaterialsAggregated.txt', dtype='str', delimiter='\t', skiprows=2, usecols=0)
+        F_init = pd.DataFrame(F_init, index = index_F, columns = index)
+        return({'labels': {'regions': regions, 'sectors': sectors, 'index': index, 'name': 'labels'}, 'materials': {'F_init': F_init, 'index': index_F, 'name': 'impact'}})
+
+    def load_system(step=0, system='pxp', x_init = None):
+        S = load_matrix('V', step, system)
+        U = load_matrix('U', step, system)
+        y = load_matrix('Y', step, system)
+        Z = IOT_from_SUT(S, U, system)
+        x = y.sum(axis=1) + Z.sum(axis=1)
+        A = Z.dot(np.diag(div0(1,x)))
+        L = np.linalg.inv(np.eye(A.shape[0])-A)
+        index = extensions['labels']['index']
+        index_y = pd.MultiIndex.from_product([extensions['labels']['regions'], ['Final consumption expenditure by households', \
+            'Final consumption expenditure by non-profit organisations serving households (NPISH)', 'Final consumption expenditure by government', \
+            'Gross fixed capital formation', 'Changes in inventories', 'Changes in valuables', 'Export']], names=['region', 'sector'])
+        if step==-1: x_init = x
+        elif x_init is None: 
+            S_init = load_matrix('V', -1, system)
+            U_init = load_matrix('U', -1, system)
+            y_init = load_matrix('Y', -1, system)
+            Z_init = IOT_from_SUT(S_init, U_init, system)
+            x_init = y_init.sum(axis=1) + Z_init.sum(axis=1)            
+        extensions['materials'].update({'S': pd.DataFrame(div0(extensions['materials']['F_init'], x_init), index = extensions['materials']['index'], columns = index)})
+        extensions['materials'].update({'F': pd.DataFrame(extensions['materials']['S'] * x, index = extensions['materials']['index'], columns = index)})
+        Z = pd.DataFrame(Z, index = index, columns = index)
+        Y = pd.DataFrame(y, index = index, columns = index_y)
+        x = pd.Series(x, index = index)
+
+        meta_rec = MRIOMetaData(system=system, name='Cecilia', version=step)
+        if step==0 or step==-1: year = 2000
+        else: year = 2050
+        core_data = dict()
+        return IOSystem(A=A, Z=Z, Y=Y, x=x, L=L, name='Cecilia', version=step, year=year, meta=meta_rec, **dict(core_data, **extensions))
+    x_init = None
+    extensions = load_extensions()
+    if step is None:
+        cecilias = dict()
+        for step in [-1, 0, 1, '2a', '2b', 3]: cecilias[step] = load_system(step, system, x_init)
+        if step==-1: x_init = cecilias[-1].x
+        return(cecilias)
+    else: return(load_system(step, system, x_init))
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