resource_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Sustainable Energy Transitions</h1>\n",
    "<div>A project by <a href=\"http://www.ssgouridis.org\" target=\"_blank\" >Sgouris Sgouridis</a> and <a href=\"http://www.csaladen.es\" target=\"_blank\" >Dénes Csala</a> at <a  href=\"http://www.masdar.ac.ae\" target=\"_blank\">Masdar Institute of Science and Technology</a></div>\n",
    "<h2><br>Resource distribution notebook</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This workbook will guide you through the mining, processing, formatting and saving the renewable resource distribution input data for the <a target=\"_blank\" href=\"http://set.csaladen.es/set2.html\">SET 2.0</a> model.\n",
    "\n",
    "<p>This is document has been created using  <a target=\"_blank\" href=\"http://ipython.org/\">IPython Notebook</a> in the <a target=\"_blank\" href=\"https://store.continuum.io/cshop/anaconda/\">Anaconda</a> distribution and it can be edited and run in <i>active</i> mode by clicking download in top right corner of this page. The code is partitioned into sections, called <i>cells</i>. When you are using this workbook in <i>active</i> mode, double-click on a cell to edit it and then run using <code>Ctrl + Enter</code>. Hitting <code>Shift + Enter</code> runs the code and steps into the next cell, while <code>Alt + Enter</code> runs the code and adds a new, empty cell. If you are running this notebook on a presonal computer, you will need a machine with at least <i>1GB</i> of memory (<i>2GB</i> recommended) and a processor of <i>1GHz</i>.\n",
    "<h2>Data</h2>\n",
    "<p>The outputs of this work are the calibration values for $\\omega$, $\\theta$ and $j$.\n",
    "</p>\n",
    "<h2>Processing</h2>\n",
    "<p>The data processing has been done using the <a target=\"_blank\" href=\"https://store.continuum.io/cshop/anaconda/\">Anaconda</a> distribution of <a target=\"_blank\" href=\"https://www.python.org/\">Python 2.7</a> using the <i>IPython Notebook</i> editor. If you have not generated your own data, please download <a href=\"http://set.csaladen.es/data/results/index.html\" target=\"_blank\">all</a> results files, and run this <i>Iptyhon</i> notebook in the same folder. Then the data is loaded into a <a target=\"_blank\" href=\"http://pandas.pydata.org/\">pandas</a> dataframe, which represents the backbone of the data analysis. Numerical processing is done with <a target=\"_blank\" href=\"http://www.numpy.org/\">NumPy</a> and for plotting we use <a target=\"_blank\" href=\"http://matplotlib.org/\">matplotlib</a>. Please make sure you have all of these compoents set up in your <i>Python</i> installation.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Code</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt, pandas as pd, numpy as np, json, copy\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Country and region name converters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#country name converters\n",
    "\n",
    "#EIA->pop\n",
    "clist1={'North America':'Northern America',\n",
    "'United States':'United States of America',\n",
    "'Central & South America':'Latin America and the Caribbean',\n",
    "'Bahamas, The':'Bahamas',\n",
    "'Saint Vincent/Grenadines':'Saint Vincent and the Grenadines',\n",
    "'Venezuela':'Venezuela (Bolivarian Republic of)',\n",
    "'Macedonia':'The former Yugoslav Republic of Macedonia',\n",
    "'Moldova':'Republic of Moldova',\n",
    "'Russia':'Russian Federation',\n",
    "'Iran':'Iran (Islamic Republic of)',\n",
    "'Palestinian Territories':'State of Palestine',\n",
    "'Syria':'Syrian Arab Republic',\n",
    "'Yemen':'Yemen ',\n",
    "'Congo (Brazzaville)':'Congo',\n",
    "'Congo (Kinshasa)':'Democratic Republic of the Congo',\n",
    "'Cote dIvoire (IvoryCoast)':\"C\\xc3\\xb4te d'Ivoire\",\n",
    "'Gambia, The':'Gambia',\n",
    "'Libya':'Libyan Arab Jamahiriya',\n",
    "'Reunion':'R\\xc3\\xa9union',\n",
    "'Somalia':'Somalia ',\n",
    "'Sudan and South Sudan':'Sudan',\n",
    "'Tanzania':'United Republic of Tanzania',\n",
    "'Brunei':'Brunei Darussalam',\n",
    "'Burma (Myanmar)':'Myanmar',\n",
    "'Hong Kong':'China, Hong Kong Special Administrative Region',\n",
    "'Korea, North':\"Democratic People's Republic of Korea\",\n",
    "'Korea, South':'Republic of Korea',\n",
    "'Laos':\"Lao People's Democratic Republic\",\n",
    "'Macau':'China, Macao Special Administrative Region',\n",
    "'Timor-Leste (East Timor)':'Timor-Leste',\n",
    "'Virgin Islands,  U.S.':'United States Virgin Islands',\n",
    "'Vietnam':'Viet Nam'}\n",
    "\n",
    "#BP->pop\n",
    "clist2={u'                 European Union #':u'Europe',\n",
    "u'Rep. of Congo (Brazzaville)':u'Congo (Brazzaville)',\n",
    "'Republic of Ireland':'Ireland',\n",
    "'China Hong Kong SAR':'China, Hong Kong Special Administrative Region',\n",
    "u'Total Africa':u'Africa',\n",
    "u'Total North America':u'Northern America',\n",
    "u'Total S. & Cent. America':'Latin America and the Caribbean',\n",
    "u'Total World':u'World',\n",
    "u'Total World ':u'World',\n",
    "'South Korea':'Republic of Korea',\n",
    "u'Trinidad & Tobago':u'Trinidad and Tobago',\n",
    "u'US':u'United States of America'}\n",
    "\n",
    "#WD->pop\n",
    "clist3={u\"Cote d'Ivoire\":\"C\\xc3\\xb4te d'Ivoire\",\n",
    "u'Congo, Rep.':u'Congo (Brazzaville)',\n",
    "u'Caribbean small states':'Carribean',\n",
    "u'East Asia & Pacific (all income levels)':'Eastern Asia',\n",
    "u'Egypt, Arab Rep.':'Egypt',\n",
    "u'European Union':u'Europe',\n",
    "u'Hong Kong SAR, China':u'China, Hong Kong Special Administrative Region',\n",
    "u'Iran, Islamic Rep.':u'Iran (Islamic Republic of)',\n",
    "u'Kyrgyz Republic':u'Kyrgyzstan',\n",
    "u'Korea, Rep.':u'Republic of Korea',\n",
    "u'Latin America & Caribbean (all income levels)':'Latin America and the Caribbean',\n",
    "u'Macedonia, FYR':u'The former Yugoslav Republic of Macedonia',\n",
    "u'Korea, Dem. Rep.':u\"Democratic People's Republic of Korea\",\n",
    "u'South Asia':u'Southern Asia',\n",
    "u'Sub-Saharan Africa (all income levels)':u'Sub-Saharan Africa',\n",
    "u'Slovak Republic':u'Slovakia',\n",
    "u'Venezuela, RB':u'Venezuela (Bolivarian Republic of)',\n",
    "u'Yemen, Rep.':u'Yemen ',\n",
    "u'Congo, Dem. Rep.':u'Democratic Republic of the Congo'}\n",
    "\n",
    "#COMTRADE->pop\n",
    "clist4={u\"Bosnia Herzegovina\":\"Bosnia and Herzegovina\",\n",
    "u'Central African Rep.':u'Central African Republic',\n",
    "u'China, Hong Kong SAR':u'China, Hong Kong Special Administrative Region',\n",
    "u'China, Macao SAR':u'China, Macao Special Administrative Region',\n",
    "u'Czech Rep.':u'Czech Republic',\n",
    "u\"Dem. People's Rep. of Korea\":\"Democratic People's Republic of Korea\",\n",
    "u'Dem. Rep. of the Congo':\"Democratic Republic of the Congo\",\n",
    "u'Dominican Rep.':u'Dominican Republic',\n",
    "u'Fmr Arab Rep. of Yemen':u'Yemen ',\n",
    "u'Fmr Ethiopia':u'Ethiopia',\n",
    "u'Fmr Fed. Rep. of Germany':u'Germany',\n",
    "u'Fmr Panama, excl.Canal Zone':u'Panama',\n",
    "u'Fmr Rep. of Vietnam':u'Viet Nam',\n",
    "u\"Lao People's Dem. Rep.\":u\"Lao People's Democratic Republic\",\n",
    "u'Occ. Palestinian Terr.':u'State of Palestine',\n",
    "u'Rep. of Korea':u'Republic of Korea',\n",
    "u'Rep. of Moldova':u'Republic of Moldova',\n",
    "u'Serbia and Montenegro':u'Serbia',\n",
    "u'US Virgin Isds':u'United States Virgin Islands',\n",
    "u'Solomon Isds':u'Solomon Islands',\n",
    "u'United Rep. of Tanzania':u'United Republic of Tanzania',\n",
    "u'TFYR of Macedonia':u'The former Yugoslav Republic of Macedonia',\n",
    "u'USA':u'United States of America',\n",
    "u'USA (before 1981)':u'United States of America',\n",
    "}\n",
    "\n",
    "#Jacobson->pop\n",
    "clist5={u\"Korea, Democratic People's Republic of\":\"Democratic People's Republic of Korea\",\n",
    "u'All countries':u'World',\n",
    "u\"Cote d'Ivoire\":\"C\\xc3\\xb4te d'Ivoire\",\n",
    "u'Iran, Islamic Republic of':u'Iran (Islamic Republic of)',\n",
    "u'Macedonia, Former Yugoslav Republic of':u'The former Yugoslav Republic of Macedonia',\n",
    "u'Congo, Democratic Republic of':u\"Democratic Republic of the Congo\",\n",
    "u'Korea, Republic of':u'Republic of Korea',\n",
    "u'Tanzania, United Republic of':u'United Republic of Tanzania',\n",
    "u'Moldova, Republic of':u'Republic of Moldova',\n",
    "u'Hong Kong, China':u'China, Hong Kong Special Administrative Region',\n",
    "u'All countries.1':\"World\"\n",
    "}\n",
    "\n",
    "#NREL solar->pop\n",
    "clist6={u\"Antigua & Barbuda\":u'Antigua and Barbuda',\n",
    "u\"Bosnia & Herzegovina\":u\"Bosnia and Herzegovina\",\n",
    "u\"Brunei\":u'Brunei Darussalam',\n",
    "u\"Cote d'Ivoire\":\"C\\xc3\\xb4te d'Ivoire\",\n",
    "u\"Iran\":u'Iran (Islamic Republic of)',\n",
    "u\"Laos\":u\"Lao People's Democratic Republic\",\n",
    "u\"Libya\":'Libyan Arab Jamahiriya',\n",
    "u\"Moldova\":u'Republic of Moldova',\n",
    "u\"North Korea\":\"Democratic People's Republic of Korea\",\n",
    "u\"Reunion\":'R\\xc3\\xa9union',\n",
    "u'Sao Tome & Principe':u'Sao Tome and Principe',\n",
    "u'Solomon Is.':u'Solomon Islands',\n",
    "u'St. Lucia':u'Saint Lucia',\n",
    "u'St. Vincent & the Grenadines':u'Saint Vincent and the Grenadines',\n",
    "u'The Bahamas':u'Bahamas',\n",
    "u'The Gambia':u'Gambia',\n",
    "u'Virgin Is.':u'United States Virgin Islands',\n",
    "u'West Bank':u'State of Palestine'\n",
    "}\n",
    "\n",
    "#NREL wind->pop\n",
    "clist7={u\"Antigua & Barbuda\":u'Antigua and Barbuda',\n",
    "u\"Bosnia & Herzegovina\":u\"Bosnia and Herzegovina\",\n",
    "u'Occupied Palestinian Territory':u'State of Palestine',\n",
    "u'China Macao SAR':u'China, Macao Special Administrative Region',\n",
    "#\"C\\xc3\\xb4te d'Ivoire\":\"C\\xc3\\xb4te d'Ivoire\",\n",
    "u'East Timor':u'Timor-Leste',\n",
    "u'TFYR Macedonia':u'The former Yugoslav Republic of Macedonia',\n",
    "u'IAM-country Total':u'World'\n",
    "}\n",
    "\n",
    "#country entroids->pop\n",
    "clist8={u'Burma':'Myanmar',\n",
    "u\"Cote d'Ivoire\":\"C\\xc3\\xb4te d'Ivoire\",\n",
    "u'Republic of the Congo':u'Congo (Brazzaville)',\n",
    "u'Reunion':'R\\xc3\\xa9union'\n",
    "}\n",
    "\n",
    "def cnc(country):\n",
    "    if country in clist1: return clist1[country]\n",
    "    elif country in clist2: return clist2[country]\n",
    "    elif country in clist3: return clist3[country]\n",
    "    elif country in clist4: return clist4[country]\n",
    "    elif country in clist5: return clist5[country]\n",
    "    elif country in clist6: return clist6[country]\n",
    "    elif country in clist7: return clist7[country]\n",
    "    elif country in clist8: return clist8[country]\n",
    "    else: return country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consult the notebook entitled *pop.ipynb* for the details of mining the data from the UN statistics division online database. Due to being the reference database for country names cell, the cell below needs to be run first, before any other databases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#use local path\n",
    "#pop_path='https://dl.dropboxusercontent.com/u/531697/datarepo/netset/db/\n",
    "pop_path='E:/Dropbox/Public/datarepo/netset/db/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"21\" halign=\"left\">Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>1950.0</th>\n",
       "      <th>1951.0</th>\n",
       "      <th>1952.0</th>\n",
       "      <th>1953.0</th>\n",
       "      <th>1954.0</th>\n",
       "      <th>1955.0</th>\n",
       "      <th>1956.0</th>\n",
       "      <th>1957.0</th>\n",
       "      <th>1958.0</th>\n",
       "      <th>1959.0</th>\n",
       "      <th>...</th>\n",
       "      <th>2091.0</th>\n",
       "      <th>2092.0</th>\n",
       "      <th>2093.0</th>\n",
       "      <th>2094.0</th>\n",
       "      <th>2095.0</th>\n",
       "      <th>2096.0</th>\n",
       "      <th>2097.0</th>\n",
       "      <th>2098.0</th>\n",
       "      <th>2099.0</th>\n",
       "      <th>2100.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>7451.0</td>\n",
       "      <td>7547.0</td>\n",
       "      <td>7651.0</td>\n",
       "      <td>7763.0</td>\n",
       "      <td>7882.0</td>\n",
       "      <td>8010.0</td>\n",
       "      <td>8146.0</td>\n",
       "      <td>8290.0</td>\n",
       "      <td>8443.0</td>\n",
       "      <td>8604.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61322.0</td>\n",
       "      <td>61112.0</td>\n",
       "      <td>60897.0</td>\n",
       "      <td>60676.0</td>\n",
       "      <td>60449.0</td>\n",
       "      <td>60218.0</td>\n",
       "      <td>59982.0</td>\n",
       "      <td>59741.0</td>\n",
       "      <td>59497.0</td>\n",
       "      <td>59249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>228827.0</td>\n",
       "      <td>233364.0</td>\n",
       "      <td>238148.0</td>\n",
       "      <td>243180.0</td>\n",
       "      <td>248460.0</td>\n",
       "      <td>253988.0</td>\n",
       "      <td>259762.0</td>\n",
       "      <td>265780.0</td>\n",
       "      <td>272039.0</td>\n",
       "      <td>278537.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3933881.0</td>\n",
       "      <td>3963991.0</td>\n",
       "      <td>3993557.0</td>\n",
       "      <td>4022570.0</td>\n",
       "      <td>4051019.0</td>\n",
       "      <td>4078898.0</td>\n",
       "      <td>4106198.0</td>\n",
       "      <td>4132914.0</td>\n",
       "      <td>4159041.0</td>\n",
       "      <td>4184577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>1214.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>1276.0</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>1523.0</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2329.0</td>\n",
       "      <td>2314.0</td>\n",
       "      <td>2299.0</td>\n",
       "      <td>2285.0</td>\n",
       "      <td>2272.0</td>\n",
       "      <td>2260.0</td>\n",
       "      <td>2248.0</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>2226.0</td>\n",
       "      <td>2217.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 151 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Value                                                    \\\n",
       "Year           1950.0    1951.0    1952.0    1953.0    1954.0    1955.0   \n",
       "Country                                                                   \n",
       "Afghanistan    7451.0    7547.0    7651.0    7763.0    7882.0    8010.0   \n",
       "Africa       228827.0  233364.0  238148.0  243180.0  248460.0  253988.0   \n",
       "Albania        1214.0    1243.0    1276.0    1311.0    1350.0    1390.0   \n",
       "\n",
       "                                                       ...                 \\\n",
       "Year           1956.0    1957.0    1958.0    1959.0    ...         2091.0   \n",
       "Country                                                ...                  \n",
       "Afghanistan    8146.0    8290.0    8443.0    8604.0    ...        61322.0   \n",
       "Africa       259762.0  265780.0  272039.0  278537.0    ...      3933881.0   \n",
       "Albania        1433.0    1477.0    1523.0    1570.0    ...         2329.0   \n",
       "\n",
       "                                                                               \\\n",
       "Year            2092.0     2093.0     2094.0     2095.0     2096.0     2097.0   \n",
       "Country                                                                         \n",
       "Afghanistan    61112.0    60897.0    60676.0    60449.0    60218.0    59982.0   \n",
       "Africa       3963991.0  3993557.0  4022570.0  4051019.0  4078898.0  4106198.0   \n",
       "Albania         2314.0     2299.0     2285.0     2272.0     2260.0     2248.0   \n",
       "\n",
       "                                              \n",
       "Year            2098.0     2099.0     2100.0  \n",
       "Country                                       \n",
       "Afghanistan    59741.0    59497.0    59249.0  \n",
       "Africa       4132914.0  4159041.0  4184577.0  \n",
       "Albania         2237.0     2226.0     2217.0  \n",
       "\n",
       "[3 rows x 151 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#population data\n",
    "pop=pd.read_csv(pop_path+'pop.csv').set_index(['Country','Year']).unstack(level=1)\n",
    "pop.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define database parsers and units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#initialize data and constants\n",
    "data={}\n",
    "countries={i for i in pop.index}\n",
    "years={int(i[1]) for i in pop.columns}\n",
    "dbs={'bp','eia'}\n",
    "datatypes={'prod','cons','emi','res'}\n",
    "allfuels=['oil','coal','gas','nuclear','biofuels','hydro','geo_other','solar','wind']\n",
    "fossils=['oil','coal','gas']+['nrg','nrg_sum']\n",
    "transp=1 #transparency\n",
    "#colorlist=np.array([[166,86,40,transp*255],[153,153,153,transp*255],[152,78,163,transp*255],\n",
    "#                    [228,26,28,transp*255],[247,129,191,transp*255],[55,126,184,transp*255],\n",
    "#                    [82,56,65,transp*255],[255,255,51,transp*255],[77,175,74,transp*255]])/255.0\n",
    "colorlist=np.array([[131,13,9,transp*255],[85,20,52,transp*255],[217,20,14,transp*255],\n",
    "                    [213,9,98,transp*255],[64,185,85,transp*255],[202,200,46,transp*255],\n",
    "                    [106,23,9,transp*255],[251,212,31,transp*255],[112,133,16,transp*255]])/255.0\n",
    "gcolors={allfuels[i]:colorlist[i] for i in range(len(allfuels))}\n",
    "\n",
    "def reset(what='all',datatype='all'):\n",
    "    global data\n",
    "    if what=='all':\n",
    "        #reset all values of database\n",
    "        fuels=allfuels+['nrg','nrg_sum']\n",
    "        data={i:{int(k[1]):{'energy':{j:{k:{l:np.NaN for l in dbs} for k in datatypes}\\\n",
    "                              for j in fuels},'population':long(pop.loc[i][k])*1000,\\\n",
    "                                              'consumer_efficiency':0.5,\\\n",
    "                                              'cumulative_emissions':0}\\\n",
    "                              for k in pop.columns}\\\n",
    "                              #we use population as the default database for country names\n",
    "                              for i in pop.index} \n",
    "    else:\n",
    "        countries=data.keys()\n",
    "        for i in countries:\n",
    "            for j in years:\n",
    "                if datatype=='all':\n",
    "                    data[i][j]['energy'][what]={k:{l:np.NaN for l in dbs} for k in datatypes}\n",
    "                else:\n",
    "                    data[i][j]['energy'][what][datatype]={l:np.NaN for l in dbs}\n",
    "\n",
    "reset()\n",
    "\n",
    "kbpd_to_TWh=365.25*0.001628200 #unit conversion from thousand barrels of oil per day to TWh per year\n",
    "Gboe_to_TWh=1628.2 #unit conversion from thousand million barrels of oil to TWh\n",
    "EJ_to_TWh=277.77 #unit conversion from exa Joule to TWh\n",
    "bcf_to_TWh=0.2931 #unit conversion from billion cubic feet of natural gas to TWh\n",
    "tcf_to_TWh=bcf_to_TWh*1000.0 #unit conversion from trillion cubic feet of natural gas to TWh\n",
    "qbtu_to_TWh=293.297222 #unit conversion from quadrillion British thermal units to TWh\n",
    "mtoe_to_TWh=11.63 #unit conversion million metric tons of oil equivalent to TWh\n",
    "kgge_to_gm3=1.49 #unit conversion from kilogram of natural gas to cubic meter, based on CH4\n",
    "mtlnge_to_TWh=14.45 #unit conversion million metric tons of gas (LNG) equivalent to TWh\n",
    "cm_to_cf=35.3 #unit conversion from million cubic meters to million cubic feet\n",
    "tcm_to_TWh=tcf_to_TWh*cm_to_cf #unit conversion from trillion cubic meters of natural gas to TWh\n",
    "kgge_to_TWh=kgge_to_gm3*tcf_to_TWh*cm_to_cf*1e-18 #unit conversion from kilogram of natural gas to TWh\n",
    "#mtge_to_TWh=kgge_to_gm3*tcf_to_TWh*cm_to_cf*1e-9 #unit conversion from kilogram of natural gas to TWh\n",
    "mtge_to_GJ=53.6\n",
    "mtge_to_TWh=mtge_to_GJ*1e-9*EJ_to_TWh\n",
    "t_to_st=1.10231 #unit conversion from metric ton to short ton\n",
    "tcoe_to_toe=0.7 #unit conversion from metric tons of coal equivalent to metric tons of oil equivalent\n",
    "mtcoe_to_TWh=tcoe_to_toe*mtoe_to_TWh #unit conversion million metric tons of coal equivalent to TWh\n",
    "#mtcoe_to_TWh=8.141\n",
    "mstcoe_to_TWh=mtcoe_to_TWh*t_to_st #unit conversion million metric short tons of coal equivalent to TWh\n",
    "c_to_co2=44.0/12 #unit conversion from C to CO2 mass\n",
    "\n",
    "carbon_budget=840*c_to_co2 #840 GtC as per http://www.ipcc.ch/report/ar5/wg1/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = 2 #set database path\n",
    "#online path = 0\n",
    "#backup path = 1\n",
    "#local path  = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read, process & convert renewable resource maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CSP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "solar=pd.read_excel(pop_path+'maps/nrelsolar.xlsx',sheetname='DNI')\n",
    "#http://en.openei.org/doe-opendata/dataset/19316a50-e55d-45e2-b692-efcebfe16c95/resource/3e72f32a-7de1-4e5d-a25a-76928769625f/download/solarresourceenergy.xlsx\n",
    "solar_res={}\n",
    "solar_cf={}\n",
    "for i in solar.T.iteritems():\n",
    "    country=cnc(i[1][0])\n",
    "    if i[0]<10: #record capacity factors\n",
    "        solar_cf[i[1][9]]=i[1][10]\n",
    "    if country in data:\n",
    "        if country not in solar_res:solar_res[country]={}\n",
    "        clas=i[1][1]\n",
    "        cap=i[1][5]/1000.0 #workbook doest not import commas correctly, fix\n",
    "        solar_res[country][clas]=cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x32cb2a90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Su3jxYr19mD17NrO2tmZHjhxhV65cYfPmzWOmpqZKv3uqttnQvq2srGQikYjN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\nDmHAt3MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x84e1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(solar_res['United States of America'].keys(),solar_res['United States of America'].values())\n",
    "plt.ylabel('avail energy [Twh/year]')\n",
    "plt.title('DNI-based solar thermal power\\nwith 6 hrs of storage\\n 31MW/km$^2$ plant power density')\n",
    "plt.xlabel('solar resource class (~ 1/cost)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "solar=pd.read_excel(pop_path+'maps/nrelsolar.xlsx',sheetname='Tilt=Lat')\n",
    "#http://en.openei.org/doe-opendata/dataset/19316a50-e55d-45e2-b692-efcebfe16c95/resource/3e72f32a-7de1-4e5d-a25a-76928769625f/download/solarresourceenergy.xlsx\n",
    "solar_res2={}\n",
    "for i in solar.T.iteritems():\n",
    "    country=cnc(i[1][0])\n",
    "    if country in data:\n",
    "        if country not in solar_res2:solar_res2[country]={}\n",
    "        clas=i[1][1]\n",
    "        cap=i[1][6]\n",
    "        solar_res2[country][clas]=cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x3305b0b8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G1Tl8+DAzMTFhd+/eZYypj6Ls7+/PeDwe4/P53L/Xr19nycnJjM/nsyNHjjBfX18mFAqZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x32dff710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(solar_res2['United States of America'].keys(),solar_res2['United States of America'].values())\n",
    "plt.ylabel('avail energy [Twh/year]')\n",
    "plt.title('GHI-based solar PV power\\nwith 10% efficiency\\n 1.5% productive land')\n",
    "plt.xlabel('solar resource class (~ 1/cost)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x33446be0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HcOfOnQpjefDL+Z+JoCIjRozAN998g+TkZKSmpmLYsGHSNh8fH/z5559Qq9Xw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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3342aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(solar_res2['United Arab Emirates'].keys(),solar_res2['United Arab Emirates'].values())\n",
    "plt.ylabel('avail energy [Twh/year]')\n",
    "plt.title('GHI-based solar PV power\\nwith 10% efficiency\\n 1.5% productive land')\n",
    "plt.xlabel('solar resource class (~ 1/cost)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "convert to capacities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def interpolate(d,years,gfit=2,depth=1,polyorder=1,override=False,ends=False):\n",
    "#d=helper\n",
    "#years=[2015]\n",
    "#gfit=1\n",
    "#depth=extrapolatedict[fuel]\n",
    "#polyorder=1\n",
    "#override=True\n",
    "#ends=True\n",
    "#if True:\n",
    "    #depth * length of interpolation substrings will be taken to the left and right\n",
    "    #for example for {1971:5,1972:6,1973:7,1974:5} interpolating it over 1969-1990\n",
    "    #for the section 1960-1970 (2 elements) the values from 1972,1973,1974 (3 elements) will be taken with depth 1.5\n",
    "    #for the section 1974-1990 (15 elements) all values  (4 elements) will be taken to extrapolate\n",
    "    #override to extend interpolation to edges, i.e. extrapolate\n",
    "    if (gfit>2): \n",
    "        print 'interpolate takes only 1 (polynomial) or 2 (exponential) as 3rd argument [default=2]'\n",
    "        return\n",
    "    mydict={}\n",
    "    if d!={}:\n",
    "        missing_points=[[]]\n",
    "        onbeginning=False\n",
    "        onend=False\n",
    "        for year in years:\n",
    "            if year not in d.keys():\n",
    "                missing_points[-1].append(year)\n",
    "            else:\n",
    "                missing_points.append([])\n",
    "        for m in missing_points:\n",
    "            if m:\n",
    "                fit=gfit\n",
    "\n",
    "                #if only one point, set min extrapolation depth to 2\n",
    "                if (len(m)==1): depth=max(depth,2)\n",
    "                \n",
    "                #check if it is ends of the interval, \n",
    "                if ((m[-1]<np.sort(d.keys())[0])|(m[0]>np.sort(d.keys())[-1])): \n",
    "                    #if not set to override then extrapolate mean only\n",
    "                    if not override: \n",
    "                        fit=0                    \n",
    "\n",
    "                if fit==0: #take average\n",
    "                    y = {k: d[k] for k in set(d.keys()).intersection(range(int(max(min(years),min(m)-int(3))),\\\n",
    "                                                                           int(min(max(years),max(m)+int(3))+1)))}\n",
    "                    #returned empty, on beginning\n",
    "                    if y=={}:\n",
    "                        if m[-1]<np.sort(d.keys())[0]:y={np.sort(d.keys())[0]:d[np.sort(d.keys())[0]]}\n",
    "                        elif m[0]>np.sort(d.keys())[-1]:y={np.sort(d.keys())[-1]:d[np.sort(d.keys())[-1]]}\n",
    "                    for i in range(len(m)):\n",
    "                        mydict[m[i]]=np.mean(y.values())\n",
    "                elif fit==1:\n",
    "                    #intersector\n",
    "                    y = {k: d[k] for k in set(d.keys()).intersection(range(int(max(min(years),\\\n",
    "                                min(m)-int(depth*len(m)))),int(min(max(years),max(m)+int(depth*len(m)))+1)))}\n",
    "                    #returned empty\n",
    "                    if y=={}:\n",
    "                        if m[-1]<np.sort(d.keys())[0]:y={np.sort(d.keys())[0]:d[np.sort(d.keys())[0]]}\n",
    "                        elif m[0]>np.sort(d.keys())[-1]:y={np.sort(d.keys())[-1]:d[np.sort(d.keys())[-1]]}\n",
    "                            \n",
    "                    w = np.polyfit(y.keys(),y.values(),polyorder) # obtaining regression parameters\n",
    "                    if (polyorder==1):\n",
    "                        intersector=w[0]*np.array(m)+w[1]\n",
    "                    else:\n",
    "                        intersector=w[0]*np.array(m)*np.array(m)+w[1]*np.array(m)+w[2]\n",
    "                    for i in range(len(m)):\n",
    "                        mydict[m[i]]=max(0,intersector[i])\n",
    "                else:\n",
    "                    #exponential intersector\n",
    "                    y = {k: d[k] for k in set(d.keys()).intersection(range(int(max(min(years),\\\n",
    "                                min(m)-int(depth*len(m)))),int(min(max(years),max(m)+int(depth*len(m)))+1)))}\n",
    "                    #returned empty\n",
    "                    if y=={}:\n",
    "                        if m[-1]<np.sort(d.keys())[0]:y={np.sort(d.keys())[0]:d[np.sort(d.keys())[0]]}\n",
    "                        elif m[0]>np.sort(d.keys())[-1]:y={np.sort(d.keys())[-1]:d[np.sort(d.keys())[-1]]}\n",
    "                    \n",
    "                    w = np.polyfit(y.keys(),np.log(y.values()),1) # obtaining log regression parameters (exp fitting)\n",
    "                    intersector=np.exp(w[1])*np.exp(w[0]*np.array(m))\n",
    "                    for i in range(len(m)):\n",
    "                        mydict[m[i]]=max(0,intersector[i])\n",
    "                    \n",
    "                #record ends adjustment beginning and end\n",
    "                if ends:\n",
    "                    if (m[-1]<np.sort(d.keys())[0]):\n",
    "                        onbeginning=True\n",
    "                        beginning=m[-1]\n",
    "                    if (m[0]>np.sort(d.keys())[-1]): \n",
    "                        onend=True\n",
    "                        end=m[0]\n",
    "        #finish ends adjustment\n",
    "        if ends:\n",
    "            if onbeginning:\n",
    "                #calculate adjustment scaler\n",
    "                if (mydict[beginning]==0): scaler=0\n",
    "                elif (beginning+1 in d): scaler=d[beginning+1]*1.0/mydict[beginning]\n",
    "                else: scaler=d[np.sort(d.keys())[0]]*1.0/mydict[beginning]\n",
    "                #readjust data\n",
    "                for year in mydict:\n",
    "                    if (year<=beginning):\n",
    "                        mydict[year]*=scaler\n",
    "            if onend:\n",
    "                #calculate adjustment scaler\n",
    "                if (mydict[end]==0): scaler=0\n",
    "                elif (end-1 in d): scaler=d[end-1]*1.0/mydict[end]\n",
    "                else: scaler=d[np.sort(d.keys())[-1]]*1.0/mydict[end]\n",
    "                #readjust data\n",
    "                for year in mydict:\n",
    "                    if (year>=end):\n",
    "                        mydict[year]*=scaler\n",
    "\n",
    "    #return interpolated points\n",
    "    return mydict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\py27\\lib\\site-packages\\numpy\\lib\\polynomial.py:594: RankWarning: Polyfit may be poorly conditioned\n",
      "  warnings.warn(msg, RankWarning)\n"
     ]
    }
   ],
   "source": [
    "#extrapolate solar classes\n",
    "helper=interpolate(solar_cf,range(0,15),1,1,2,True,True)\n",
    "solar_cf.update(helper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "solar_cap={}\n",
    "for i in solar_res:\n",
    "    if i not in solar_cap:solar_cap[i]={}\n",
    "    for j in solar_res[i]:\n",
    "        solar_cap[i][j]=solar_res[i][j]*solar_cf[j]/8760\n",
    "solar_cap2={}\n",
    "for i in solar_res2:\n",
    "    if i not in solar_cap2:solar_cap2[i]={}\n",
    "    for j in solar_res2[i]:\n",
    "        solar_cap2[i][j]=solar_res2[i][j]*solar_cf[j]/8760       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "wind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wind=pd.read_excel(pop_path+'maps/nrelwind.xlsx',sheetname='Onshore Power',skiprows=2)\n",
    "#http://en.openei.org/doe-opendata/dataset/c186913f-6684-4455-a2f2-f26e152a9b35/resource/4dc4a6fd-3a63-47df-bcbe-e9c83b83b38e/download/nrelcfddawindsc20130603.xlsx\n",
    "wind_cap={}\n",
    "for i in wind.T.iteritems():\n",
    "    country=i[1][0]\n",
    "    if country in data:\n",
    "        for k in range(1,10):\n",
    "            if country not in wind_cap:wind_cap[country]={}\n",
    "            clas=k\n",
    "            cap=i[1][k] + i[1][k+10]# + i[1][k+20] #last one too far 0-50,50-100,100-5000\n",
    "            wind_cap[country][clas]=cap/1000.0 #workbook reported values in GW, we use TW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x33929c50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ntm3b4oMPPkBpaan0+oOnuYqKihAeHg4HBwc4OTlh/PjxmD59eoWn4tauXQtPT0/Y29vj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33929048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(wind_cap['Argentina'].keys(),wind_cap['Denmark'].values())\n",
    "plt.ylabel('avail cap [TW]')\n",
    "plt.title('onshore wind resources\\nwith 90m hub height\\n 5MW/km$^2$ power density')\n",
    "plt.xlabel('wind resource class (~ 1/cost)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wind=pd.read_excel(pop_path+'maps/nrelwind.xlsx',sheetname='Onshore Energy',skiprows=2)\n",
    "#http://en.openei.org/doe-opendata/dataset/c186913f-6684-4455-a2f2-f26e152a9b35/resource/4dc4a6fd-3a63-47df-bcbe-e9c83b83b38e/download/nrelcfddawindsc20130603.xlsx\n",
    "wind_res={}\n",
    "for i in wind.T.iteritems():\n",
    "    country=i[1][0]\n",
    "    if country in data:\n",
    "        for k in range(1,10):\n",
    "            if country not in wind_res:wind_res[country]={}\n",
    "            clas=k\n",
    "            cap=i[1][k] + i[1][k+10]# + i[1][k+20] #last one too far 0-50,50-100,100-5000\n",
    "            wind_res[country][clas]=cap*1000.0 #workbook reported values in PWh, we use TWh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x33d2cc88>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vJ/DEHnrQNePx0Ovw8HB88sknKC4uxuHDhxEeHg6xWIxPP/0Uffv2hVgs5j7k\n67Nu2rRJ71dtZTIZ97udnd0TZdXnSfp7+Ft3QMN9u3LlSkyZMgW7du3Crl27MGfOHHz99dd47733\nHpuznq+vL0JCQrBq1SrExcVhy5Yt2L59O+/lScugAkKeyOHDh9GhQwe9r/n4+KBXr144ePAgUlJS\nmlxPREQE0tPTUVlZqXORXKlU4oMPPkBlZSUCAwN1Pvw2b96MYcOGNViXq6srVq9eDaVSiWHDhmHz\n5s0NPsSMLScnB4wx7ijkwIEDsLa2RufOnQE8OELbu3cvfH19uWWys7PRtWtXbjowMBBWVlb4+OOP\n4eXlBblcDqVSiTfffBObN29GcHAwV/gUCgWsra1RVFSEgQMHNit7586dIRaLcfDgQfj4+HDtBw4c\n4H5vyf6ABwXA19cXU6ZMQUxMDFauXNloARGLxToX6uuNGzcOU6ZMQevWreHq6trgqJgYHxUQ0qip\nU6di8ODBcHd3x+3bt7Fy5UpkZmZiy5YtjS6za9cuVFdXQyqVNrnuiIgIfPXVVygtLcWmTZu49v79\n++P8+fO4fv06PvjgA6791q1b2LNnD7799lu963NxccGePXsQERGBIUOGID09/YnH5W6OsrIyTJgw\nAZMmTUJRURHmzp2Lf/3rX7CxsQEATJo0CXPmzIGTkxP8/f3x448/YuvWrfj111+5dVhaWiIkJAQp\nKSmIiYkBALRu3Rpdu3bF+vXrkZCQwM1rZ2eHWbNmcfeqDBgwAHV1dTh58iSOHj2qc2rscWxtbfGv\nf/0L8fHxkMvl6NKlC1avXo0zZ85wR3st1V9RURFWrVqFV199FR06dMDly5exb98+BAQENLpMx44d\nkZ2djXPnzsHBwQEODg6wsLDA66+/jilTpmDevHl6x44nxkcFhDTq6tWrGD16NEpLS+Hg4AA/Pz9k\nZGTonFZ6lLW1Na8P7v79+8PS0hK1tbXo27cv1+7g4IAePXrg6NGjGDBgANe+detW+Pj46NyDAuje\n6NamTRvs2bMHAwYMwKuvvtpkoWtpr7/+OiQSCfr27Yv79+/jzTffxKeffsq9Pn/+fIhEIsTGxqK0\ntBQeHh7YsGEDwsLCdNajVCrx66+/6hyVhYeH4/jx4zptABAfH4927dph+fLlmDZtGmxsbODl5YWo\nqChuHr43An722WeoqanBO++8AwAYPnw4Jk6cqHNDX0v0Z2dnh4KCAowYMQKlpaVwdHTE4MGD8cUX\nXzS6zNQ62yV5AAAgAElEQVSpU3Hq1Cn4+/ujqqoKWVlZ6N+/P3fd7euvv27wdXHydNCIhOSZ8I9/\n/AP+/v5m+ZemUqmEp6cnVq5caeoofzvDhw9HXV0dfvrpJ1NH+VuiIxDyTAgODtb7uBTy93Tr1i38\n/vvvSE9PR2Zmpqnj/G09tQKSlJSEI0eOwMHBAV9++SUA4MKFC1i1ahXu378PkUiEMWPGcBcd09LS\nkJWVBZFIhKioqAZfDSR/L9OmTTN1hEaZ4nlRf3c9evRAeXk54uLiEBISYuo4f19P64aT06dPs/Pn\nz7OpU6dybfPmzWPHjh1jjDF25MgR9tFHHzHGGCsuLmbTp09ndXV17Nq1a2zixIk6N2k1pbE7bU3N\nHHNRJn4oE3/mmIsy8WNIpqf2LCxvb+8G30cXCATcM3Lu3r2L1q1bA3jw9NPg4GCIRCLI5XK4uLig\nsLCQVz9qtbplg7cQc8xFmfihTPyZYy7KxI8hmUx6DWT06NGYP38+1q1bBwD45JNPAADl5eU6d6bK\nZDKUl5ebJCMhhBD9TPo03l27diEqKgpJSUkYPXo0kpKSTBmHEELIE3iqX+MtLS3FwoULuYvoUVFR\nOo8Ar59OT08HAG4Mhvnz50OlUul9hIJardY59FKpVEbcAkII+etKTU3lflcoFFAoFE3O/1RPYbEH\nT//lpmUyGfLy8uDr64uTJ09yD7gLCAjA0qVLMXjwYJSXl6OkpAQeHh5616lvI69cuWK8jTCQRCJB\nZWWlqWPooEz8UCb+zDEXZeKnXbt2T/wH+FMrIImJicjLy0NlZSViYmKgUqkwbtw4rFmzBlqtFpaW\nltzY1q6urggKCkJsbCwsLCwwZswY+qokIYSYmb/kneh0BMIPZeKHMvFnjrkoEz/t2rV74mVoSFtC\nCCEGoUeZEPIMunSzClcrah8/YwtzsrOAkzX93UkeoAJCyDPo+p1axO0899T7XfhSJzhZP92xVoj5\noj8lCCGEGIQKCCGEEINQASGEEGIQKiCEEEIMQgWEEEKIQaiAEEIIMQgVEEIIIQahAkIIIcQgVEAI\nIYQYhAoIIYQQg1ABIYQQYhAqIIQQQgxCBYQQQohBntrTeJOSknDkyBE4ODhwY6IDwI4dO7Br1y4I\nhUL07NkTb731FgAgLS0NWVlZEIlEiIqKgr+//9OKSgghhIenVkCUSiUGDRqE5cuXc21qtRqHDx/G\nl19+CZFIhNu3bwMALl26hEOHDmHx4sUoKyvDJ598gqVLl9KwtoQQYkae2iksb29v2NnZ6bTt2rUL\nkZGREIlEAIBWrVoBAHJzcxEcHAyRSAS5XA4XFxcUFhY+raiEEEJ4MOmAUlevXkVeXh42btwIsViM\nUaNGoVOnTigvL4eXlxc3n0wmQ3l5uQmTEkIIeZRJC4hGo8Hdu3cxf/58FBYWYtGiRTqnuPhQq9VQ\nq9XctEqlgkQiaemozSYWi80uF2XixxwzCf7vdO/TJhKJmtwX5rivKBN/qamp3O8KhQIKhaLJ+U1a\nQJycnBAYGAgA8PDwgFAoRGVlJWQyGW7cuMHNV1ZWBplMpncd+jaysrLSeKENJJFIzC4XZeLHHDMx\nrdYk/Wo0mib3hTnuK8rEj0QigUqleqJlnurXeBljYIxx07169cKpU6cAAFeuXEFdXR0kEgkCAgJw\n8OBB1NXV4fr16ygpKYGHh8fTjEoIIeQxntoRSGJiIvLy8lBZWYmYmBioVCoolUqsWLECU6dOhaWl\nJSZOnAgAcHV1RVBQEGJjY2FhYYExY8bQN7AIIcTMPLUCMnnyZL3t77//vt72YcOGYdiwYcaMRAgh\npBnoTnRCCCEGoQJCCCHEIFRACCGEGIQKCCGEEINQASGEEGIQKiCEEEIMQgWEEEKIQaiAEEIIMQgV\nEEIIIQahAkIIIcQgJn0aLyHPgks3q3C1ovap9+tkZwEna/obj5ivRgvItWvXeK1AIBBALpe3WCBC\nzM31O7WI23nuqfe78KVOcLIWP/V+CeGr0QIyadIkXisQi8X47rvvWiwQIYSQZ0OjBcTKygrr1q17\n7Aqio6NbNBAhhJBnQ6MnWKOionitYPTo0S2VhRBCyDOk0QISHh4OANBqtcjMzMT9+/f1zhcWFmaU\nYIQQQszbY7/iIRQKsW7dOlhaWjaro6SkJLz33nuYNm1ag9e2bt2K4cOH486dO1xbWloaJk2ahNjY\nWBw/frxZfRNCCGl5vL4j+PzzzyM3N7dZHSmVSsyePbtBe1lZGU6cOAEnJyeu7dKlSzh06BAWL16M\nmTNnIjk5WWcsdUIIIabH6z6Q+/fvY9GiRfDy8oKjo6PO+OT145g/jre3N0pLSxu0p6SkYNSoUVi4\ncCHXlpubi+DgYIhEIsjlcri4uKCwsBCenp68+iKEEGJ8vApIhw4d0KFDhxbvPDc3F46OjnBzc9Np\nLy8vh5eXFzctk8lQXl7e4v0TQggxHK8C8sYbb7R4x7W1tUhLS0N8fHyz1qNWq6FWq7lplUoFiUTS\n3HgtTiwWm10uysSP4PZtk/QrEoka3RfmmAkwz/ePMvGXmprK/a5QKKBQKJqcn/ejTOrq6nDlyhXc\nfuQ/3K5duz5hxAdKSkpw/fp1TJ8+HYwxlJeXIy4uDgsWLIBMJsONGze4ecvKyiCTyfSuR99GVlZW\nGpTJmCQSidnlokz8MK3WJP1qNJpG94U5ZgLM8/2jTPxIJBKoVKonWoZXAcnPz8eiRYtw//593Lt3\nDzY2NqiuroajoyOWL1/OuzPGGHcx3M3NDatWreJemzBhAhYuXAh7e3sEBARg6dKlGDx4MMrLy1FS\nUgIPD48n2jBCCCHGxauApKSkYMiQIRg8eDCio6OxZs0abNq0CWIx/+f0JCYmIi8vD5WVlYiJiYFK\npYJSqeRef/jCvKurK4KCghAbGwsLCwuMGTNG53VCCCGmx6uAXLlyBS+//LJOW2RkJCZMmIAhQ4bw\n6mjy5MlNvv7okcywYcMwbNgwXusmhBDy9PG6D8TW1hb37t0DAEilUly6dAl37txBdXW1UcMRQggx\nX7yOQAIDA3H06FH07dsXSqUSCQkJEIlE6NOnj7HzEUIIMVO8CsjDD1YcMmQIvLy8cO/ePfj7+xsr\nFyGEEDP3RCMS3rhxA+Xl5fD29jZWHkIIIc8IXgXkxo0bSExMxIULFwAA3333HX777TccO3YM//rX\nv4yZjxBCiJnidRF95cqV6NGjB1JSUmBh8aDm+Pn54cSJE0YNRwghxHzxKiCFhYWIjIyEUPj/Z7e1\ntUVVVZXRghFCCDFvvAqIg4MDSkpKdNouXbqk8wh2Qgghfy+8roG8+uqrWLhwISIjI6HVarF//36k\npaUhMjLS2PkIIYSYKV4FJDw8HBKJBL/++iscHR2xd+9eDB8+HL179zZ2PkIIIWaKVwHRarXo1asX\nevXqZew8hBBCnhG8roGMHTsWa9aswblz54ydhxBCyDOC1xHIrFmzsG/fPnz22Wews7NDv3790L9/\nf7qITgghf2O8CkinTp3QqVMnjBo1CidOnMDevXsxdepUdOrUCf369UNwcDCsra2NnZUQQogZ4XUK\ni5tZKET79u3Rvn17tGrVCuXl5di/fz9iYmKwd+9eY2UkhBBihngdgdy5cweHDh3C3r17cfnyZQQF\nBWHixIno0qULgAc3Gs6fPx/9+/c3alhCCCHmg1cBiYmJgUKhwKBBg9CrVy9YWlrqvO7h4YGAgIAm\n15GUlIQjR47AwcEBX375JQBg/fr1OHz4MCwsLNCmTRuMHz8etra2AIC0tDRkZWVBJBIhKiqKnvxL\nCCFmpskCcuLECfj6+mLZsmWQSqVNrmjChAlNvq5UKjFo0CCdkQf9/PwwcuRICIVCbNiwAenp6Rg5\nciQuXbqEQ4cOYfHixSgrK8Mnn3yCpUuX0rC2hBBiRpq8BrJ161aMGzcOK1euxK+//ory8nKDO/L2\n9oadnZ1Om5+fH/d8LU9PT5SVlQEAcnNzERwcDJFIBLlcDhcXFxQWFhrcNyGEkJbX5BHI7NmzUVNT\ng5MnT+Lo0aPYvHkz7Ozs0KNHD/Ts2RNeXl46D1hsjqysLISEhAAAysvL4eXlxb0mk8maVbwIIYS0\nvMdeA7GyskJAQAB3jePPP//E0aNH8f333+Py5ctQKBR45ZVX4OnpaXCIzZs3QyQSoW/fvk+8rFqt\nhlqt5qZVKhUkEonBWYxFLBabXS7KxI/g9m2T9CsSiRrdF+aYCTDP948y8Zeamsr9rlAooFAompz/\niUYkBAA3Nze4ublh6NChqKqqwvHjx3Hv3r0nT/p/srOzcfToUcydO5drk8lkuHHjBjddVlYGmUym\nd3l9G1lZWWlwHmORSCRml4sy8cO0WpP0q9FoGt0X5pgJMM/3jzLxI5FIoFKpnmgZ3gXkypUruHDh\nAqqrq3Xaw8PDeXfGGANjjJs+duwYtmzZgoSEBJ1vdgUEBGDp0qUYPHgwysvLUVJSAg8PD979EEII\nMT5eBWTz5s346aef8Nxzz8HKykrnNb4FJDExEXl5eaisrERMTAxUKhXS0tJQV1eHefPmAXhwIX3M\nmDFwdXVFUFAQYmNjYWFhgTFjxtA3sAghxMzwKiDbt2/HggUL8Nxzzxnc0eTJkxu0KZXKRucfNmwY\nhg0bZnB/hBBCjIvXV6jEYjHat29v7CyEEEKeIY0WEK1Wy/0MHz4c//nPf3Dz5k2ddq2JLuQRQggx\nvUZPYY0YMaJBW0ZGRoO2H374oWUTEUIIeSY0WkAefuQIIYQQ8qhGC4izszMKCwvRqVOnFrvbnBBC\nyF9Hk9/CSkhIgEAggJeXF3x8fODr6wtPT09YWDzx/YeEEEL+YpqsBGvXrkVRURHy8/ORl5eHbdu2\n4f79++jcuTNXUPz8/J5WVkIIIWakyQIiEong5eUFLy8vDBkyBIwx/Pnnnzhy5Ai2b9+OtLQ0uohO\nCCF/U7xHJDx9+jROnz6NvLw8lJWVwcPDAz4+PsbORwghxEw1WUCSk5ORn5+PmpoaeHl5wdvbG0ql\nEh06dHha+QghhJipJgvInj174OTkhNDQUPj4+MDT0xNisfhpZSOEEGLGeF9E37p1K4qKitCmTRt4\ne3vDx8cHXbp0gb29/dPKSgghxIwYfBH9m2++wZ07d7Bx48anlZUQQogZeeKL6KdPn8aFCxfg4OCA\nPn36GDsfIYQQM/XYi+inT5/G5cuX4eTkBF9fX7z44ovw8fFB27Ztn1ZGQgghZqjJAqLVajFs2DD4\n+PjA0dGxWR0lJSXhyJEjcHBwwJdffgngwZHNkiVLUFpaCrlcjtjYWNja2gIA0tLSkJWVBZFIhKio\nKPj7+zerf0IIIS2ryYdcnTlzBn379m128QAeDB41e/Zsnbb09HR069YNiYmJUCgUSEtLAwBcunQJ\nhw4dwuLFizFz5kwkJyfrDIVLCCHE9JosIDdu3Gixjry9vWFnZ6fTlpubi9DQUABAWFgYcnJyuPbg\n4GCIRCLI5XK4uLigsLCwxbIQQghpPpM+ZreiogJSqRQAIJVKUVFRAQAoLy+Hk5MTN59MJkN5eblJ\nMhJCCNGvyWsgNTU1+PDDD5tcQUJCQouFEQgELbYuQgghxvXY+0CUSqXROpdKpbh16xb3r4ODA4AH\nRxwPnz4rKyuDTCbTuw61Wg21Ws1Nq1QqSCQSo2U2lFgsNrtclIkfwe3bJulXJBI1ui/MMRNgnu8f\nZeIvNTWV+12hUEChUDQ5f5MFxMLCAmFhYS0SDAAYYzoXw59//nlkZ2cjMjIS2dnZCAgIAAAEBARg\n6dKlGDx4MMrLy1FSUgIPDw+969S3kZWVlS2WuaVIJBKzy0WZ+GFarUn61Wg0je4Lc8wEmOf7R5n4\nkUgkUKlUT7RMkwWkJb/5lJiYiLy8PFRWViImJgYqlQqRkZFYvHgxsrKy4OzsjNjYWACAq6srgoKC\nEBsbCwsLC4wZM4ZObxFCiJlpsoAMGzasxTqaPHmy3vY5c+Y02ndL9k8IIaRlNfotrOPHj/P6AD9x\n4kSLBiKEEPJsaLSALFq0iNcKFi9e3GJhCCGEPDsaPYVVXV2NmJiYx66grq6uRQMRQgh5NjRaQB53\n/0c9urhNCCF/T40WEF9f36eZgxBCyDPGpI8yIYQQ8uyiAkIIIcQgVEAIIYQYhFcBycnJgUajMXYW\nQgghzxBeBSQ1NRVjx47F6tWrUVBQYOxMhBBCngFNPsqk3hdffIELFy5g3759+Oqrr2BlZYX+/fuj\nX79+kMvlxs5ICCHEDPEqIADg7u4Od3d3vP322zh58iS+++47pKamwtvbGwMGDEBISAiEQrqkQggh\nfxe8CwgAlJSUYN++fdi3bx8EAgGGDx8OJycn7Ny5E7///jumTZtmrJyEEELMDK8CsnPnTuzbtw9X\nr15FcHAwJk6cCC8vL+71wMBAjBkzxmghCSGEmB9eBeTYsWMYPHgwAgICYGlp2eB1KysrOvoghJC/\nGV4FZMaMGY+dx9/fv9lhCCGEPDt4FZDly5frX9jCAo6OjujVqxfc3d0NDrFt2zZkZWVBIBDAzc0N\n48ePR3V1NZYsWYLS0lLI5XLExsbC1tbW4D4IIYS0LF5fm7KxsUFOTg4YY5DJZGCMITc3F0KhEJcv\nX0Z8fDz27NljUIDy8nLs3LkTCxcuxJdffgmNRoP9+/cjPT0d3bp1Q2JiIhQKBdLS0gxaPyGEEOPg\nVUCuXr2KmTNn4v3338fIkSPx/vvvY9asWbh27RqmTJmCadOmNesDXqvVorq6GhqNBrW1tZDJZMjN\nzUVoaCgAICwsDDk5OQavnxBCSMvjdQqroKAAnp6eOm2dOnVCYWEhgAfXP8rKygwKIJPJMHjwYIwf\nPx5WVlbw8/ODn58fKioqIJVKAQBSqRQVFRUGrZ8QQohx8DoCcXd3x8aNG1FbWwsAqK2txQ8//MBd\n97h+/Trs7e0NCnD37l3k5uZixYoV+Pbbb1FTU4N9+/Y1mI8GriKEEPPC6whkwoQJWLp0KUaPHg17\ne3vcuXMHnTt3xqRJkwAAd+7cMfg+kJMnT0Iul3MFqHfv3jhz5gykUilu3brF/evg4KB3ebVaDbVa\nzU2rVCpIJBKDshiTWCw2u1yUiR/B7dsm6VckEjW6L8wxE2Ce7x9l4i81NZX7XaFQQKFQNDn/YwsI\nYwyMMSQkJODmzZu4efMmWrduDScnJ26ezp07GxzYyckJBQUFqK2thaWlJU6ePInOnTvD2toa2dnZ\niIyMRHZ2NgICAvQur28jKysrDc5jLBKJxOxyUSZ+mFZrkn41Gk2j+8IcMwHm+f5RJn4kEglUKtUT\nLfPYAiIQCDBt2jSkpKTAyclJp3C0BA8PD/Tp0wdxcXEQiURwd3fHgAEDUF1djcWLFyMrKwvOzs6I\njY1t0X4JIYQ0D69TWO7u7rh69Srat29vlBBvvPEG3njjDZ02e3t7zJkzxyj9EUIIaT5eBUShUGDB\nggUIDQ1tcAQSHh5ulGCEEELMG68CcubMGcjlcpw+fbrBa1RACCHk74lXAfnwww+NnYMQQsgzhvcI\nUJWVldi7dy+2bNkC4MEjSAy9eZAQQsizj1cBycvLw5QpU7Bv3z5s2rQJwIPBpVatWmXUcIQQQswX\nrwKydu1aTJkyBbNnz4ZIJALw4Ou3RUVFRg1HCCHEfPEqIKWlpejWrZtOm4WFBTQajVFCEUIIMX+8\nCoirqyuOHTum03by5Em4ubkZJRQhhBDzx+tbWKNGjcLChQvRo0cP1NbWYuXKlTh8+DCmT59u7HyE\nEELMFK8C4uXlhS+++AL79u2DtbU1nJycsGDBAjg6Oho7HyGEEDPFq4AAD8btGDp0qDGzEEIIeYbw\nKiB37tzBli1bcPHiRVRXV+u8lpCQYJRghBBCzBuvApKYmIi6ujoEBQVBLBYbOxMhhJBnAK8Ccvbs\nWSQnJ8PS0tLYeQghhDwjeH2N183NjR5bQgghRAevI5CuXbtiwYIFCAsLg1Qq1XmNnsZLCCF/T7wK\nSH5+PhwdHXHy5MkGr7VEAamqqsI333yD4uJiCAQCxMTEwMXFBUuWLEFpaSnkcjliY2Nha2vb7L4I\nIYS0DLN4nPuaNWvQo0cPfPDBB9BoNKipqcHmzZvRrVs3DB06FOnp6UhLS8Nbb71l1ByEEEL4M/nj\n3KuqqpCfnw+lUgkAEIlEsLW1RW5uLkJDQwEAYWFhyMnJaXZfhBBCWo7JH+d+/fp1SCQSrFixAnFx\ncfj2229RU1ODiooK7nqLVCpFRUVFs/sihBDScnidwqp/nHu3bt0QHR0NoOUe567VanH+/Hm8++67\n6Ny5M9auXYv09PQG8wkEAr3Lq9VqqNVqblqlUkEikTQ7V0sTi8Vml4sy8SO4fdsk/YpEokb3hTlm\nAszz/aNM/KWmpnK/KxQKKBSKJufnVUCM+Th3mUwGR0dHdO7cGQDQp08fpKenQyqV4tatW9y/Dg4O\nepfXt5GVlZXNztXSJBKJ2eWiTPwwrdYk/Wo0mkb3hTlmAszz/aNM/EgkEqhUqidaxuSPc5dKpXB0\ndMSVK1e49bq6uuL5559HdnY2ACA7OxsBAQHN7osQQkjLMYvHuUdHR2PZsmWoq6tDmzZtMH78eGi1\nWixevBhZWVlwdnZGbGxsi/RFCCGkZZjF49zd3d3x6aefNmifM2dOi6yfEEJIy6PHuRNCCDEI7/tA\nCCGEkIdRASGEEGIQKiCEEEIMQgWEEEKIQRq9iD537txG7/5+GA1pSwghf0+NFhAa54MQQkhTGi0g\nYWFhTzEGIYSQZ02jBWTv3r3o378/ACAzM7PRFdCRCiGE/D01WkAOHDjAFZB9+/Y1ugIqIIQQ8vfU\naAGZOXMm97uxRyQkhBDy7OH9KJN6jDEwxrhpoZC+CUwIIX9HvApIeXk5Vq9ejdOnT+Pu3bs6r/3w\nww9GCUYIIcS88Tp8WLlyJSwsLDB37lxYW1tj4cKFCAgIwHvvvWfsfIQQQswUrwJy9uxZxMTEwN3d\nHQKBAO7u7oiJicG2bduMnY8QQoiZ4lVAhEIhRCIRAMDOzg63b9+GlZUVysvLjRqOEEKI+eJ1DcTD\nwwNHjx5F79694e/vj8WLF0MsFnPjmLcErVaLmTNnQiaTIS4uDnfu3MGSJUtQWloKuVyO2NhY2Nra\ntlh/hBBCmofXEcj7778PX19fAEBUVBS6du2KDh06YNKkSS0WZPv27Wjfvj03nZ6ejm7duiExMREK\nhQJpaWkt1hchhJDm41VANBoN7O3tAQBisRivvfYa3n77bbRu3bpFQpSVleHo0aOIiIjg2nJzcxEa\nGgrgwWNVcnJyWqQvQgghLYPXKazx48dDoVAgJCQEvXv3hrW1dYuGSElJwahRo1BVVcW1VVRUQCqV\nAgCkUikqKipatE9CCCHNw6uArFixAocOHcLu3buRnJyMnj17om/fvujRowd3cd1QR44cgYODA9zd\n3aFWqxudr7FHy6vVap3lVCoVJBJJszIZg1gsNrtclIkfwe3bJulXJBI1ui/MMRNgnu8fZeIvNTWV\n+12hUEChUDQ5P68C0qpVKwwcOBADBw5EaWkpDhw4gO+//x5JSUlYvXp1swLn5+cjNzcXR48eRW1t\nLe7du4dly5ZBKpXi1q1b3L8ODg56l9e3kZWVlc3KZAwSicTsclEmfphWa5J+NRpNo/vCHDMB5vn+\nUSZ+JBIJVCrVEy3zxI8yqaiowK1bt1BZWQk7O7snXbyBkSNHYuTIkQCAvLw8bN26Fe+//z7Wr1+P\n7OxsREZGIjs7GwEBAc3uixBCSMvhVUAuXbqE/fv348CBA6itrUVQUBCmT58ODw8PowWLjIzE4sWL\nkZWVBWdnZ8TGxhqtL0IIIU+OVwGZM2cOAgMDMXbsWCgUCqM9QNHX15f7urC9vT3mzJljlH4IIYQ0\nH68CsmrVKlhYPPHZLkIIIX9hvKqChYUFbt26hcLCQlRWVuo8zp0GlCKEkL8nXgXkjz/+wLJly+Di\n4oLi4mJ06NABxcXF8Pb2pgJCCCF/U7wKyA8//IDx48cjKCgI0dHR+Pzzz5GVlYXi4mJj5yOEEGKm\neF0Nv3HjBoKCgnTaQkNDsXfvXqOEIoQQYv54FZBWrVrh1q1bAABnZ2ecPXsW165dg9ZENzMRQggx\nPV6nsCIiIpCfn48+ffrglVdeQUJCAgQCAQYPHmzsfIQQQswUrwISGRnJ/R4aGgqFQoHq6mq4uroa\nLRghhBDzZtDNHU5OTi2dgxBCyDPGOLeUE0II+cujAkIIIcQgVEAIIYQYhAoIIYQQg1ABIYQQYhAq\nIIQQQgxCBYQQQohBTD7IR1lZGZYvX46KigoIBAJERETg5Zdfxp07d7BkyRKUlpZCLpcjNjYWtra2\npo5LCCHk/5i8gIhEIowePRru7u6orq5GXFwc/P39kZWVhW7dumHo0KFIT09HWloa3nrrLVPHJYQQ\n8n9MfgpLKpXC3d0dAGBtbY327dujrKwMubm5CA0NBQCEhYUhJyfHhCkJIYQ8yuQF5GHXr1/HxYsX\n4eXlhYqKCkilUgAPikxFRYWJ0xFCCHmYyU9h1auursaiRYsQFRUFa2vrBq8LBAK9y6nVaqjVam5a\npVJBIpEYLaehxGKx2eWiTPwIbt82Sb8ikajRfWGOmQDzfP8oE3+pqanc7wqFAgqFosn5zaKAaDQa\nfPXVV+jfvz969eoF4MFRx61bt7h/HRwc9C6rbyMrKyuNnvlJSSQSs8tFmfhhJhr3RqPRNLovzDET\nYJ7vH2XiRyKRQKVSPdEyZnEKKykpCa6urnj55Ze5tueffx7Z2dkAgOzsbAQEBJgoHSGEEH1MfgSS\nn5+Pffv2wc3NDf/+978hEAgwYsQIREZGYvHixcjKyoKzszNiY2NNHZUQQshDTF5AvL298cMPP+h9\nbc6cOU85DSGEEL7M4hQWIYSQZw8VEEIIIQahAkIIIcQgVEAIIYQYhAoIIYQQg1ABIYQQYhAqIIQQ\nQv7P074AABM1SURBVAxCBYQQQohBqIAQQggxCBUQQgghBqECQgghxCBUQAghhBiECgghhBCDUAEh\nhBBiECoghBBCDGLy8UAe59ixY1i7di0YY1AqlYiMjDR1JEIIITDzIxCtVovVq1dj9uzZ+Oqrr3Dg\nwAFcvnzZ1LEIIYTAzAtIYWEhXFxc4OzsDAsLC4SEhCAnJ8fUsQghhMDMC0h5eTkcHR25aZlMhvLy\nchMmIoQQUs+sCwghhBDzZdYX0WUyGW7cuMFNl5eXQyaT6cyjVquhVqu5aZVKhXbt2j21jE9CIpGY\nOkIDlOnx2rUDcnw7mDqGDnPMVM/c3j+AMvGVmprK/a5QKKBQKJqc36yPQDw8PFBSUoLS0lLU1dXh\nwIEDCAgI0JlHoVBApVJxPw/vAHNijrkoEz+UiT9zzEWZ+ElNTdX5LH1c8QDM/AhEKBTi3Xffxbx5\n88AYQ3h4OFxdXU0dixBCCMy8gABA9+7dkZiYaOoYhBBCHiH66KOPPjJ1iJYml8tNHUEvc8xFmfih\nTPyZYy7KxM+TZhIwxpiRshBCCPkLM+uL6IQQQswXFRBCCCEGMfuL6HwlJSXhyJEjcHBwwJdffmnq\nOACAsrIyLF++HBUVFRAIBIiIiMDLL79s0kz379/Hhx9+iLq6Omg0GvTp0wdvvPGGSTPV02q1mDlz\nJmQyGeLi4kwdBwAwYcIE2NraQiAQQCQS4dNPPzV1JFRVVeGbb75BcXExBAIBYmJi4OnpabI8V65c\nwZIlSyAQCMAYw7Vr1zB8+HCT/7e+bds2ZGVlQSAQwM3NDePHj4eFhWk/8rZv346MjAwAMOnngb7P\nyzt37mDJkiUoLS2FXC5HbGwsbG1tm14R+4s4ffo0O3/+PJs6daqpo3Bu3rzJzp8/zxhj7N69e2zS\npEns0qVLpg3FGKuurmaMMabRaNisWbNYQUGBiRM9sHXrVpaYmMg+++wzU0fhTJgwgVVWVpo6ho7l\ny5ezzMxMxhhjdXV17O7duyZO9P9pNBo2duxYVlpaatIcZWVlbMKECez+/fuMMcYWLVrEsrOzTZrp\nzz//ZFOnTmW1tbVMo9GwTz75hJWUlJgki77Py++++46lp6czxhhLS0tj69evf+x6/jKnsLy9vWFn\nZ2fqGDqkUinc3d0BANbW1mjfvr1ZPMvLysoKwIOjEY1GY+I0D5SVleHo0aOIiIgwdRQdjDEwM/qe\nSVVVFfLz86FUKgEAIpHo8X8lPkUnT55EmzZt4OTkZOoo0Gq1qK6uhkajQU1NDVq3bm3SPJcvX4aH\nhwcsLS0hFArh4+OD33//3SRZ9H1e5ubmIjQ0FAAQFhbG68G1f5lTWObu+vXruHjxoklPNdTTarWY\nMWMGrl27hoEDB8LDw8PUkZCSkoJRo0ahqqrK1FF0CAQCzJs3D0Kh8P+1d+9BUZX/A8ffLAssdwRE\nvH1djTQRsNQIEZjxkpY6isqIljJ4SQcxFR1FpqEkKcOyvCCgpnhLNIfyUjqGmSaZKYqJAiIlKqMI\nC7vcF1h2f38wnskAL5guv3pe/8DunnOezzkw53Oe55x9PgwfPpwRI0YYNZ7i4mJsbW1JSEjg5s2b\n9OrVixkzZmBubm7UuO47c+YMQ4YMMXYYODo6MnbsWObNm4eFhQVeXl54eXkZNabu3buzd+9eqqqq\nMDMzIzMzkxdeeMGoMf1VeXk5Dg4OQNPFb3l5+SPX+df0QNozrVbL559/TmhoKAqFwtjhIJPJWL16\nNYmJiVy/fp3CwkKjxnN/LFapVLa7K/6VK1cSFxdHVFQUx44dIzc316jx6PV6bty4wahRo4iLi8PC\nwoIDBw4YNab7dDodGRkZDB482NihUF1dTUZGBgkJCWzatAmtVkt6erpRY+ratSvjx48nNjaWVatW\noVQqkcna7ynYxMTkkcuIHsgz1tjYyJo1awgICODVV181djgPsLKyol+/fly6dMmoU8Tk5uaSkZFB\nZmYm9fX11NbWEh8fz/z5840W0333hz3s7Ozw9vYmPz+fl156yWjxODo64uTkJF25+vj4tJsEcunS\nJXr16oWdnZ2xQyErKwsXFxdsbGwAeO2117h27Rp+fn5GjWvo0KHS8GNKSsoD5SqMzcHBAY1GI/20\nt7d/5DrtN/21QXu7eoWmpx26detm9CdS7quoqJCGierr68nKyjL67MVvvfUWiYmJxMfHs2jRIjw8\nPNpF8qirq0Or1QJNvcjLly/TvbtxZ8B1cHDAycmJO3fuAE0nyvYyP1x6enq7GL4CcHZ25vr169TX\n12MwGMjKyqJr167GDouKigoAVCoV586dM2pC+/v5cuDAgZw8eRKAkydPNpu4tiX/mh7IunXryM7O\nprKykrCwMCZPnixlemPJzc3l9OnT/O9//2PZsmWYmJgwdepUXn75ZaPFpNFo2LhxI3q9HoPBgK+v\nLwMGDDBaPO1ZeXk5n376KSYmJjQ2NuLv70///v2NHRYzZsxgw4YN6HQ6OnXqxLx584wdEnV1dWRl\nZTF37lxjhwI0zeTt4+NDZGQkpqamKJVKo9+/AlizZg1VVVWYmpoye/Zsoz0A0dL5MjAwkC+++IKf\nfvqJjh07EhER8cjtiKlMBEEQhDb5Vw1hCYIgCM+PSCCCIAhCm4gEIgiCILSJSCCCIAhCm4gEIgiC\nILSJSCCCIAhCm4gEIgiCILSJSCDCEwkJCaG4uLhN68bExHDixIl/OKL//xISEti3b99zbTM6OpqC\ngoLn2uazVl5eTkREBDqdztih/GeIBCI8kZ07d+Li4mLsMISncOHCBaysrKRSA2119epVYmJiCA0N\nbXXqmby8PKKjo5+qndaEh4dz5coV6bW9vT0eHh4cP378mbQnNCcSiNAu6fV6Y4fQLmJ4FtLS0vD3\n93/q7VhYWDBs2DCmT5/e6jIXL17klVdeeeq2Hpefnx9paWnPrb3/un/NXFhC2508eZLffvtNKiO7\nYMECevbsKc2FExYWxvLly+nRowfBwcGsX7+eTp06kZCQgIWFBSUlJeTk5NCtWzcWLlwo9VAuX75M\ncnIyGo0Gf3//h050uX//fm7fvo2ZmRkXLlwgJCSEoUOHcvDgQX788Udqamrw9PTknXfewdramoaG\nBpKSkrh06RJ6vZ7OnTuzfPly7OzsUKvVbNmyhdzcXGxtbRk3bpxUqCohIQEnJyeCg4MByM7OZsOG\nDSQmJgJNV7UjR44kPT2dO3fusGvXLtRqNcnJyeTm5mIwGBgyZAgzZ84E4MSJExw+fJjy8nLc3NyY\nM2dOq8WUcnNz+eqrrygsLMTS0pLg4GCpgM991dXVbNiwgfz8fPR6Pb1792bOnDk4OjpKf6vU1FQq\nKiqws7MjODgYPz8/ioqKSEpKoqCgALlcjoeHB4sWLWoWg06n48qVK8yZM+fx/jkews3NDTc3N7Ky\nslpdJjMzk7CwMABu377Njh07+PPPP5HL5YwePZrAwEB0Oh27d+/m119/xcTEBB8fH6ZNm4ZcLqey\nspKNGzdy7do1TExM6N69OzExMcTHx6NSqYiLi0MmkzFp0iTGjRvHiy++SHFxMSqVql0Utfq3EwlE\nwN3dnR07dgCgVqtpbGwkLy8PgHv37lFXV0ePHj1aXPfMmTO89957KJVK4uPjSUlJYeHChVRWVrJm\nzRrCw8MZNGgQR48eJS0tjYCAgFbjyMjIYPHixbz77rs0NDRw9OhRMjIy+PDDD7G1tSU5OZkvv/yS\nhQsXcurUKWpra0lKSkIul1NQUCAVVVq7di09evRg8+bNFBYWEhsbi6urK/369Xus43HmzBmioqKw\ntbUF4JNPPsHT05MFCxYgk8n4448/ADh//jwHDx4kMjISV1dXDhw4wLp161i5cmWzbapUKlatWsXc\nuXPx8fGhpqaG0tLSZssZDAaGDRvGkiVLaGxsJDExka1bt7J06VLq6upITk4mLi4OV1dXNBoNVVVV\nAOzbt4/+/fuzYsUKdDqdFOPfFRUVIZPJpIT0LGk0GioqKlAqlWi1WmJjYxk3bhzLly9Hp9NJdWhS\nU1PJz8+XanOvXr2ab775hsmTJ3P48GGcnZ1ZtmwZgPR/OX/+fHJycggLC8PDw0NqUyaT4erqys2b\nN0UCeQ7EEJaAi4sLlpaWFBQUkJOTQ//+/XF0dOTOnTvk5OQ8tP6Ft7c3vXr1QiaT4e/vL92YzczM\npHv37nh7eyOTyRgzZoxU7aw1vXv3lqaQNjMzIy0tjSlTptChQwfkcjlBQUGcPXsWvV6PqakplZWV\n3L17FxMTE3r27IlCoaC0tJS8vDzefvtt5HI5SqWSYcOGcerUqcc+Hm+++SaOjo6YmZmRn5+PRqNh\n2rRpmJubI5fL6dOnDwDHjx8nMDCQLl26IJPJCAwMpKCgAJVK1Wyb6enpeHp64uvri0wmw8bGpsWk\nbGNjg7e3N2ZmZigUCiZMmEBOTo70uUwm49atW9TX1+Pg4CBN5W5qakpJSQllZWUPxPh31dXVWFpa\nPvBeTU0NmzdvJiwsjIiICFJTU6Wr+KSkpBb353FcvHhRmr34woULODg4MGbMGORyOQqFQqqEmZ6e\nTlBQELa2ttja2hIUFMTPP/8MgFwuR61WU1xcjEwme6xaLAqFgurq6jbFLDwZ0QMRgKZeyJUrVygq\nKsLd3R1ra2uys7PJy8vD3d291fX+mhQsLCyk+hllZWXNiuU8qnjO3z9XqVR89tlnD1RGk8vllJeX\nExAQQGlpKWvXrqWmpgZ/f3+mTp2KWq3GxsZGqvsO0LFjR27cuPHog9BCHKWlpTg7O7dYOa6kpITt\n27ezc+fOB94vKytrdvWrUqlwdXV9ZNv19fVs376d33//nerqagwGA1qtFoPBgIWFBRERERw6dIjE\nxET69OlDSEgIXbp0Yfr06ezdu5eoqChsbGwYO3Zsi+UMrK2tqa2tfeC9+4WgZs2axb179/jhhx+I\njo7GxsaGkSNHtvlKPjMzU7rXUlpaSqdOnVpcTq1WP9BGx44dUavVAIwbN46vv/6ajz76CIDhw4cT\nGBj40Ha1Wm2zet/CsyESiABA3759uXDhAiUlJUycOBErKyvS09O5fv06b7zxxhNvr0OHDpw/f/6B\n91oasvmrv5fQdHZ2JiwsjN69e7e4fFBQEEFBQahUKj7++GO6dOmCl5cXVVVVaLVaqXywSqWSKgta\nWFhQV1cnbeP+iaq1OJycnFCpVOj1+mZJxNnZmYkTJz5WUSBnZ2fy8/MfudyhQ4e4e/cuq1atws7O\njoKCAiIjIzEYDJiYmEi1vRsaGkhJSWHTpk3ExMRgb28v1eLIzc1l5cqVuLu7Nztpu7q6YjAYUKvV\n0jHx9fWVPu/SpQuhoaGEhoY+MtaHaWxsJDs7m/DwcKDpOP7yyy8tLuvo6IhKpZJ6UyUlJVJsCoWC\nkJAQQkJCKCwsJCYmBjc3Nzw8PFosuarX6ykqKmp1yFX4Z4khLAFo6oFcvXqV+vp6HB0d6du3L5cu\nXaKyspKePXs+8fYGDBhAYWEh586dQ6/Xc+TIETQazRNtY8SIEaSkpEhDKBUVFWRkZABNj5DeunUL\nvV6PQqHA1NQUmUyGk5MTvXv3Zs+ePTQ0NHDz5k1OnDgh3XtRKpVkZmZSVVWFRqPhyJEjD43Bzc2N\nDh06sGfPHurq6mhoaODatWtSfN9++600ll9TU8PZs2db3I6fnx9ZWVnSEFxVVVWL38PQarWYm5tj\naWlJVVUV+/fvlz4rLy8nIyODuro6TE1NUSgUUlI7e/YsZWVlQFMvQyaTtXiClcvleHp6kp2d/dD9\nfhwGg4GGhgZ0Ot0Dv0NTElMqlVISHzhwoHS8dTodWq1WSqi+vr7SgwEVFRWkpqZKf6+LFy9SVFQE\nIO3v/X22t7fn3r17D8SUn5+Pi4uLuP/xnIgeiABA586dUSgU9O3bFwBLS0s6deqEvb19iyeiR7G1\ntWXx4sVs27aNxMRE/P39n7iW+P0ywLGxsajVauzt7fH19WXQoEFoNBq2bNlCWVkZCoUCX19fabhk\n4cKFbN68mblz52JjY0NwcLB0ozUgIICsrCzCw8NxcXFh6NChHD58WGrz7/sqk8mIjIxk27ZtzJs3\nDxMTE/z8/OjTpw/e3t7U1dWxdu1aVCoVVlZWeHl54ePj02xfnJ2diYqKYteuXSQmJmJtbc2UKVOa\nfRdjzJgxrF+/nlmzZuHo6MjYsWOlpGkwGPjuu+/YuHEj0JQMZ8+eDTSdOLdv305tbS329vbMmDGj\n1e/rjBgxgmPHjj11+dmcnBxiYmKk19OmTcPd3Z0PPvig2eO7CoWC6OhokpOT2b9/P+bm5owePRo3\nNzcmTZqEVqtl6dKlAAwePJiJEycCcPfuXbZu3UplZSXW1taMGjVKGlKdMGEC27ZtY/fu3UyaNImx\nY8dy+vRpXn/99afaL+HxiYqEgvAf9P777zNz5syn/jJhaxYvXsySJUueax3yiooKVqxYwerVq5HL\nxbXx8yASiCAI/yidTsf333/P+PHjjR2K8IyJBCIIgiC0ibiJLgiCILSJSCCCIAhCm4gEIgiCILSJ\nSCCCIAhCm4gEIgiCILSJSCCCIAhCm/wf4x9iGNcwkoMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33b56470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(wind_res['Argentina'].keys(),wind_res['Denmark'].values())\n",
    "plt.ylabel('avail energy [TWh/year]')\n",
    "plt.title('onshore wind resources\\nwith 90m hub height\\n 5MW/km$^2$ power density')\n",
    "plt.xlabel('wind resource class (~ 1/cost)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wind=pd.read_excel(pop_path+'maps/nrelwind.xlsx',sheetname='Offshore Energy',skiprows=3)\n",
    "#http://en.openei.org/doe-opendata/dataset/c186913f-6684-4455-a2f2-f26e152a9b35/resource/4dc4a6fd-3a63-47df-bcbe-e9c83b83b38e/download/nrelcfddawindsc20130603.xlsx\n",
    "wind_res2={}\n",
    "country=''\n",
    "for i in wind.T.iteritems():\n",
    "    if str(i[1][0])!='nan':\n",
    "        country=str(i[1][0])\n",
    "    water=str(i[1][1])\n",
    "    if water in {'shallow','transitional'}: #skip deep for now\n",
    "        if country in data:\n",
    "            for k in range(2,11):\n",
    "                if country not in wind_res2:wind_res2[country]={}\n",
    "                clas=k-1\n",
    "                cap=i[1][k] + i[1][k+10]# + i[1][k+20] #last one too far 0-50,50-100,100-5000\n",
    "                if clas not in wind_res2[country]:wind_res2[country][clas]=0\n",
    "                wind_res2[country][clas]+=cap*1000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x33dccac8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KT7OtXLkSzs7OsLKywuDBg6WjvbVr12L69OnSWCcKhaLSo1EyPCwg9Lf5+PhAoVBIg/8c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\nIgiCIEyKaCCCIAjCpPwHCaTzrXdsQxEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33dc7198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(wind_res2['Denmark'].keys(),wind_res2['Denmark'].values())\n",
    "plt.ylabel('avail energy [TWh/year]')\n",
    "plt.title('offshore wind resources\\nwith 90m hub height\\n 5MW/km$^2$ power density')\n",
    "plt.xlabel('wind resource class (~ 1/cost)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wind=pd.read_excel(pop_path+'maps/nrelwind.xlsx',sheetname='Offshore Power',skiprows=3)\n",
    "#http://en.openei.org/doe-opendata/dataset/c186913f-6684-4455-a2f2-f26e152a9b35/resource/4dc4a6fd-3a63-47df-bcbe-e9c83b83b38e/download/nrelcfddawindsc20130603.xlsx\n",
    "wind_cap2={}\n",
    "country=''\n",
    "for i in wind.T.iteritems():\n",
    "    if str(i[1][0])!='nan':\n",
    "        country=str(i[1][0])\n",
    "    water=str(i[1][1])\n",
    "    if water in {'shallow','transitional'}: #skip deep for now\n",
    "        if country in data:\n",
    "            for k in range(2,11):\n",
    "                if country not in wind_cap2:wind_cap2[country]={}\n",
    "                clas=k-1\n",
    "                cap=i[1][k] + i[1][k+10]# + i[1][k+20] #last one too far 0-50,50-100,100-5000\n",
    "                if clas not in wind_cap2[country]:wind_cap2[country][clas]=0\n",
    "                wind_cap2[country][clas]+=cap/1000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x34386c18>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HN3iTn4eHB3r27Im9e/eisrISoaGhaNOmDYAbb6g357dq1QpRUVHScikpKQgP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\niWQiCIIg2O1/d9uIThp3r/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3305b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(wind_cap2['Denmark'].keys(),wind_cap2['Denmark'].values())\n",
    "plt.ylabel('avail cap [TW]')\n",
    "plt.title('offshore wind resources\\nwith 90m hub height\\n 5MW/km$^2$ power density')\n",
    "plt.xlabel('wind resource class (~ 1/cost)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save resource data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res={}\n",
    "for i in solar_res:\n",
    "    if i not in res: res[i]={\"wind\":{\"cap\":{},\"res\":{}},\"pv\":{\"cap\":{},\"res\":{}},\"csp\":{\"cap\":{},\"res\":{}}}\n",
    "    res[i][\"csp\"][\"res\"]=solar_res[i]\n",
    "    res[i][\"csp\"][\"cap\"]=solar_cap[i]\n",
    "    res[i][\"pv\"][\"res\"]=solar_res2[i]\n",
    "    res[i][\"pv\"][\"cap\"]=solar_cap2[i]\n",
    "for i in wind_res: #onshore\n",
    "    if i not in res: res[i]={\"wind\":{\"cap\":{},\"res\":{}},\"pv\":{\"cap\":{},\"res\":{}},\"csp\":{\"cap\":{},\"res\":{}}}    \n",
    "    res[i][\"wind\"][\"res\"]=wind_res[i]\n",
    "    res[i][\"wind\"][\"cap\"]=wind_cap[i]\n",
    "for i in wind_res2: #offshore\n",
    "    if i not in res: res[i]={\"wind\":{\"cap\":{},\"res\":{}},\"pv\":{\"cap\":{},\"res\":{}},\"csp\":{\"cap\":{},\"res\":{}}}    \n",
    "    for j in wind_res2[i]:\n",
    "        if j not in res[i][\"wind\"][\"res\"]:res[i][\"wind\"][\"res\"][j]=0\n",
    "        res[i][\"wind\"][\"res\"][j]+=wind_res2[i][j]\n",
    "    for j in wind_cap2[i]:\n",
    "        if j not in res[i][\"wind\"][\"cap\"]:res[i][\"wind\"][\"cap\"][j]=0\n",
    "        res[i][\"wind\"][\"cap\"][j]+=wind_cap2[i][j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x3459f8d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MTPz0009Se+/evXHt2jXcvXsX06ZNk9rz8vIQGRmJb7/9ttLlNW3aFJGRkQgI\nCMDgwYNx4MCBpx6HXBvZ2dmYOHEiJk+ejLS0NMyfPx8ffPABjI2NAQCTJ0/GvHnzYGdnB09PT+zZ\nsweHDh3CsWPHpGUYGBjAx8cHW7duRXBwMADA2toa7du3x/bt2xESEiLNa2pqijlz5kj3wgQGBqK8\nvByXLl1CbGys2umzmpiYmOCDDz7A3Llz4eDggDZt2mDz5s24cuWKdBRYW+tLS0vDxo0bMWjQIDRv\n3hx//fUXfvvtN3Tt2rXKPq6uroiIiMDVq1dhaWkJS0tL6OvrY+jQoZgyZQpCQ0OxYMECjbeXag+L\nCWnk9u3bePfdd5GZmQlLS0t07NgRx48fVzv19CSFQqHRm3jv3r1hYGCA0tJS+Pr6Su2Wlpbo3Lkz\nYmNjERgYKLUfOnQI7dq1U7vHBVC/qa5JkyaIjIxEYGAgBg0aVG3Rq21Dhw6Fubk5fH19UVZWhuHD\nh2PJkiXS65999hn09PQwdepUZGZmws3NDTt27EDfvn3VluPn54djx46pHa35+/vj4sWLam0AMHfu\nXDg5OWHNmjWYMWMGjI2N0bp1a4waNUqaR9ObDr/44guUlJRg5MiRAIBhw4Zh0qRJajcP1sb6TE1N\nkZKSghEjRiAzMxO2trYYOHAgli1bVmWf6dOn4/Lly/D09ERRURFOnDiB3r17S9fpvvnmmwpfQaf6\nwZEWqdF5/fXX4enp2SA/gfr5+aFVq1bYsGGDrqP84wwbNgzl5eXYu3evrqP8I/HIhBodb2/vSh/Z\nQv9MeXl5OHPmDA4cOIDw8HBdx/nHqtdiEhcXhy1btkAIAT8/vwqPpYiKisLBgwcBPDxF8v7770t3\n6dbUl/45ZsyYoesIVdLF86v+6Tp37oycnBzMnDkTPj4+uo7zz1VfN7QolUoxadIkcffuXVFWViZm\nzJghbt68qTbPlStXRGFhoRBCiNjYWDFnzhyN+1alqrt8dYmZNMNMmmuIuZhJM3+XTPX2bK7U1FQ0\nbdoU9vb20NfXh4+PD2JiYtTmad26NUxMTAAArVq1Qk5OjsZ9qxIfH1+7G1ILmEkzzKS5hpiLmTTz\nd8lUb8UkJydHuisWeHhj06NiUZnjx4+jU6dOz9SXiIjqV4N8avDly5cRERGBt956S9dRiIhIA/X2\n1eDk5GTs2bMHn376KYCHj8MAUOFC+o0bN7B8+XLMmTMHjo6OT9UXeHh49vghWlBQUO1vDBHR39zu\n3bulnz02UAoNAAAgAElEQVQ8PODh4VHt/PX2bS43NzdkZGQgMzMT1tbWiI6OxkcffaQ2T1ZWFpYv\nX45JkyZJhUTTvo9UttG3bt2q/Q3Sgrm5OQoKCnQdQw0zaaYhZgKAe+V6uH3vQb2u085UH3aKqk9u\nNMR9xUyacXJyeuoP4vVWTORyOcaMGYPQ0FAIIeDv7w9nZ2eEhYVBJpMhMDAQP/30E+7fv4/NmzdD\nCAE9PT0sWbKkyr5E9NDd+6WY+evVmmesRUtfbgE7Rf0+94warn/EHfA8MqkZM2mmIWYCgJQ8JWb8\nN6Ve17n05RZoa1t1MWmI+4qZNOPk5PTUfRrkBXgiImpcWEyIiEhrLCZERKQ1FhMiItIaiwkREWmN\nxYSIiLTGYkJERFpjMSEiIq2xmBARkdZYTIiISGssJkREpDUWEyIi0hqLCRERaY3FhIiItMZiQkRE\nWmMxISIirbGYEBGR1lhMiIhIaywmRESkNRYTIiLSGosJERFpjcWEiIi0xmJCRERaYzEhIiKtsZgQ\nEZHWWEyIiEhrLCZERKQ1FhMiItIaiwkREWmNxYSIiLTGYkJERFpjMSEiIq2xmBARkdZYTIiISGss\nJkREpDUWEyIi0hqLCRERaY3FhIiItMZiQkREWmMxISIirbGYEBGR1vR1HYCoMbmZW4Tb90rrfb12\npvqwU/CzHzVcLCZET+Hu/VLM/PVqva936cstYKcwrPf1EmmKH3WIiEhrLCZERKQ1FhMiItIaiwkR\nEWmtXi/Ax8XFYcuWLRBCwM/PD0OGDFF7/datW1i7di2uXbuGESNGYODAgdJrEydOhImJCWQyGfT0\n9LBkyZL6jE5ERNWot2KiUqmwefNmzJ8/H9bW1pg9eza6deuGZs2aSfOYmZnhvffew9mzZyv0l8lk\nWLBgAczMzOorMhERaajeTnOlpqaiadOmsLe3h76+Pnx8fBATE6M2j4WFBVq0aAE9Pb0K/YUQEELU\nV1wiInoK9XZkkpOTA1tbW2naxsYGqampGveXyWQIDQ2FXC5HQEAAAgMD6yImERE9g0Zz0+LixYth\nbW2N/Px8LF68GM7Ozmjbtm2F+eLj4xEfHy9NBwUFwdzcvD6j1sjQ0JCZNNAQM8ny83WyXj09vWr3\nhS5y1ZSpIf7+mElzu3fvln728PCAh4dHtfPXWzGxsbFBVlaWNJ2TkwMbGxuN+1tbWwN4eCrMy8sL\nqamplRaTyja6oKDgGVPXDXNzc2bSQEPMJFQqnaxXqVRWuy90kaumTA3x98dMmjE3N0dQUNBT9am3\nayZubm7IyMhAZmYmysvLER0dja5du1Y5/+PXR0pKSlBcXAwAKC4uxh9//IHmzZvXeWYiItJMvR2Z\nyOVyjBkzBqGhoRBCwN/fH87OzggLC4NMJkNgYCDy8vIwe/ZsPHjwADKZDEeOHMFXX32F/Px8LFu2\nDDKZDEqlEr169YKnp2d9RSciohrU6zWTTp06YdWqVWpt/fr1k362srLCunXrKvRTKBRYtmxZnecj\nIqJnwzvgiYhIaywmRESkNRYTIiLSGosJERFpjcWEiIi0xmJCRERaYzEhIiKtsZgQEZHWWEyIiEhr\nVd4Br9LwwXEymQwymazWAhERUeNTZTEZMWKERgswNDTE999/X2uBiIio8amymBgaGmLFihXVdhZC\n4JNPPqn1UERE1LhUWUwGDRoEe3v7GhcwcODAWg1ERESNT5UX4DUdGGXo0KG1FoaIiBqnah9Bv2TJ\nErRr1w7t2rWDm5sb9PT06isXERE1ItUWkzZt2iA+Ph779++HSqVCq1atpOLSunVrGBoa1ldOIiJq\nwKotJq+//jqAh18TvnbtGpKSkpCYmIijR4+iqKgILVq0wOLFi+slKBERNVwajbQol8vRsmVLNG3a\nFI6OjnB0dERkZCTS09PrOh8RETUC1RaT/Px8JCQkICEhAYmJiSgoKEDr1q3Rtm1bzJ49Gy4uLvUU\nk4iIGrJqi8nYsWPRrFkzvPLKK3jllVfg6OhYX7mIiKgRqbaYDBs2DImJidi5cyecnZ3Rtm1btGvX\nDm3atIFCoaivjERE1MBpfAH++vXrSExMRFhYGNauXQtra2u0bdsWo0aNqo+cRETUgGn01GC5XI4W\nLVrAz88Pfn5+6NWrF7Kzs/HLL7/UdT4iImoENLoAn5iYiMTERKSnp8PGxgbt2rXDsGHD4O7uXl85\niYioAavxAryjoyPatWuHAQMGwN3dXaPndRER0T9LtcXkww8/hK+vb31lISKiRqraayYbN26srxxE\nRNSIVVtMhBD1lYOIiBqxak9zqVQqXL58udoFtG/fvlYDERFR41NtMSkrK8P69eurPEKRyWRYs2ZN\nnQQjIqLGo9piolAoWCyIiKhGGt20SEREVB1egCciIq1VW0y2bdtWXzmIiKgRq7KYLFy4UKMFLFq0\nqLayEBFRI1XlBfiUlBScOHGixlNdaWlptR6KiIgalyqLSatWrXDy5MkaF9C6detaDURERI1PlcVE\n09NcRERE/GowERFpjcWEiIi0xmJCRERaYzEhIiKtVftsrsddvnwZUVFRyM3NhbW1NXx8fNChQ4e6\nzEZERI2ERkcmhw4dwsqVK2FmZoYuXbrA3NwcX3/9NQ4dOlTX+YiIqBHQ6Mjk8OHDmD9/Pp577jmp\nrXfv3ggNDcWgQYPqLBwRETUOGl8zcXR0VJtu0qRJrYchIqLGSaNi8q9//Qvr16/H7du3UVpailu3\nbuHbb79FUFAQVCqV9I+IiP6ZNDrNtXHjRgBAdHS0WntUVBQ2bNggTe/atava5cTFxWHLli0QQsDP\nzw9DhgxRe/3WrVtYu3Ytrl27hhEjRmDgwIEa9yUiIt3RqJjUxmiLKpUKmzdvxvz582FtbY3Zs2ej\nW7duaNasmTSPmZkZ3nvvPZw9e/ap+xIRke5oVEzs7e21XlFqaiqaNm0qLcvHxwcxMTFqBcHCwgIW\nFhY4f/78U/clIiLd0fg+k3PnziEhIQH5+flq7ZMmTdKof05ODmxtbaVpGxsbpKam1nlfIiKqexoV\nkz179iAsLAze3t44ffo0AgMDER0djZ49e9Z1vqcWHx+P+Ph4aTooKAjm5uY6TFSRoaEhM2mgIWaS\nPfFhqr7o6elVuy90kaumTA3x98dMmtu9e7f0s4eHBzw8PKqdX6NicuLECcydOxfPPfccIiIiMGrU\nKPj6+mLv3r0aB7OxsUFWVpY0nZOTAxsbm1rvW9lGFxQUaJyzPpibmzOTBhpiJqGjby0qlcpq94Uu\nctWUqSH+/phJM+bm5ggKCnqqPhp9NbiwsFC6YVFfXx/l5eVwc3NDQkKCxityc3NDRkYGMjMzUV5e\njujoaHTt2rXK+R8f4fFp+xIRUf3S6MjE0dER6enpaN68OZo3b46jR4/CzMwMZmZmGq9ILpdjzJgx\nCA0NhRAC/v7+cHZ2RlhYGGQyGQIDA5GXl4fZs2fjwYMHkMlkOHLkCL766isoFIpK+xIRUcOgUTEZ\nNmyYdBj21ltvYdWqVSguLsaYMWOeamWdOnXCqlWr1Nr69esn/WxlZYV169Zp3JeIiBoGjYpJly5d\npJ/d3NywevXqOgtERESNj0bXTCIjI3Hjxg21tuvXr+PkyZN1EoqIiBoXjYrJrl271O7zAAA7Ozvs\n3LmzTkIREVHjolExefDgAUxMTNTaTExMUFhYWCehiIiocdGomDg7O+P06dNqbWfPnuU3qoiICICG\nF+DfeustLFmyBKdOnYKjoyMyMjJw6dIlzJ49u67zERFRI6BRMWnbti2WL1+OqKgoZGVlwc3NDaNG\njYKdnV1d5yMiokZA4wc92tnZcQwRIiKqlMbD9hIREVWFxYSIiLTGYkJERFpjMSEiIq1VeQF+/vz5\nkMlkNS4gJCSkVgMREVHjU2Ux8ff3r88cRETUiFVZTPr27VuPMYiIqDGrspicPHkSvXv3BgCEh4dX\nuQAewRARUZXFJDo6Wiomv/32W5ULYDEhIqIqi8njz91asGBBvYQhIqLGSePHqTwihIAQQpqWy/nt\nYiKifzqNiklOTg42b96MxMTECmOY7Nq1q06CERFR46HRYcWGDRugr6+P+fPnQ6FQYOnSpejatSvG\njh1b1/mIiKgR0KiYJCcnIzg4GC4uLpDJZHBxcUFwcDAOHz5c1/mIiKgR0KiYyOVy6OnpAQBMTU2R\nn58PIyMj5OTk1Gk4IiJqHDS6ZuLm5obY2Fh4eXnB09MTX331FQwNDdGyZcu6zkdERI2ARsXkww8/\nlL7BNWrUKBw6dAgPHjzAgAED6jQcERE1DhoVE6VSCQsLCwCAoaEh3njjjToNRUREjYtGxWTChAnw\n8PCAj48PvLy8oFAo6joXERE1IhpdgF+7di26dOmCsLAwjBs3DitXrsS5c+egVCrrOh8RETUCGh2Z\nWFhY4KWXXsJLL72EzMxMREdHY+fOnVi3bh02b95c1xmJiKiBe+pnody7dw95eXkoKCiAqalpXWQi\nIqJGRqMjk5s3byIqKgrR0dEoLS1Fz5498fHHH8PNza2u8xERUSOgUTGZN28eunfvjnHjxsHDw4MP\ndyQiIjUaFZONGzdCX/+pHzBMRET/EBpVCH19feTl5SE1NRUFBQVqj6Dn4FhERKRRMTl79ixWr16N\npk2bIj09Hc2bN0d6ejratm3LYkJERJoVk127dmHChAno2bMnRo8ejS+//BInTpxAenp6Xecjokbq\nZm4Rbt8rrff12pnqw07B67r1TaNikpWVhZ49e6q19enTB+PGjcPIkSPrJBgRNW5375di5q9X6329\nS19uATuFYb2v959Oo/JtYWGBvLw8AIC9vT2Sk5Nx584dqFSqOg1HRESNg0ZHJgEBAUhKSkKPHj0w\nYMAAhISEQCaTYeDAgXWdj4iIGgGNismQIUOkn/v06QMPDw8UFxfD2dm5zoIREVHj8Uw3j9jZ2dV2\nDiIiasT4lQciItIaiwkREWmNxYSIiLTGYkJERFrj0xupweId1ESNB4sJNVi8g5qo8ajXYhIXF4ct\nW7ZACAE/Pz+1+1ce+c9//oO4uDgYGRlhwoQJcHV1BQBMnDgRJiYmkMlk0NPTw5IlS+ozOhERVaPe\niolKpcLmzZsxf/58WFtbY/bs2ejWrRuaNWsmzRMbG4s7d+7g66+/RkpKCjZt2oTPPvsMACCTybBg\nwQKYmZnVV2QiItJQvZ0YTk1NRdOmTWFvbw99fX34+PggJiZGbZ6YmBj06dMHANCqVSsUFRVJzwQT\nQqiNo0JERA1HvR2Z5OTkwNbWVpq2sbFBampqjfPk5OTAysoKMpkMoaGhkMvlCAgIQGBgYH1FJyKi\nGjSaC/CLFy+GtbU18vPzsXjxYjg7O6Nt27YV5ouPj0d8fLw0HRQUBHNz8/qMWiNDQ0Nm0oAsP18n\n69XT06tyXzTETIBucjXETED1uRri33lDzAQAu3fvln728PCAh4dHtfPXWzGxsbFBVlaWNJ2TkwMb\nG5sK82RnZ0vT2dnZ0jzW1tYAHj4O38vLC6mpqZUWk8o2uqCgoNa2ozaYm5szkwaEjoY4UCqVVe6L\nhpgJ0E2uhpgJqD5XQ/w7b6iZgoKCnqpPvV0zcXNzQ0ZGBjIzM1FeXo7o6Gh07dpVbZ6uXbsiMjIS\nAJCcnAxTU1NYWVmhpKQExcXFAIDi4mL88ccfaN68eX1FJyKiGtTbkYlcLseYMWMQGhoKIQT8/f3h\n7OyMsLAwyGQyBAYGokuXLoiNjcWHH34IhUKB4OBgAMC9e/ewbNkyyGQyKJVK9OrVC56envUVnYiI\nalCv10w6deqEVatWqbX169dPbXrMmDEV+jk4OGDZsmV1mo2IiJ4dnxlBRERaYzEhIiKtsZgQEZHW\nWEyIiEhrLCZERKQ1FhMiItJao3mcCtUtDkRFRNpgMSEAHIiKiLTDj4RERKQ1FhMiItIaiwkREWmN\nxYSIiLTGYkJERFpjMSEiIq2xmBARkdZYTIiISGssJkREpDUWEyIi0hqLCRERaY3FhIiItMZiQkRE\nWmMxISIirbGYEBGR1lhMiIhIaywmRESkNRYTIiLSGosJERFpjcWEiIi0xmJCRERaYzEhIiKtsZgQ\nEZHWWEyIiEhrLCZERKQ1FhMiItIaiwkREWmNxYSIiLTGYkJERFpjMSEiIq2xmBARkdZYTIiISGv6\nug7wT3Qztwi375XW+3rtTPVhp+DnByKqfSwmOnD3film/nq13te79OUWsFMY1vt6iejvjx9TiYhI\naywmRESkNRYTIiLSGosJERFprV4vwMfFxWHLli0QQsDPzw9DhgypMM9//vMfxMXFwcjICBMnToSL\ni4vGfYmISDfq7chEpVJh8+bN+PTTT7F8+XJER0fjr7/+UpsnNjYWd+7cwddff41x48Zh48aNGvcl\nIiLdqbdikpqaiqZNm8Le3h76+vrw8fFBTEyM2jwxMTHo06cPAKBVq1YoKipCXl6eRn2JiEh36q2Y\n5OTkwNbWVpq2sbFBTk6ORvNo0peIiHSHF+CJiEhr9XYB3sbGBllZWdJ0Tk4ObGxsKsyTnZ0tTWdn\nZ8PGxgbl5eU19n0kPj4e8fHx0nRQUBCcnJxqazNqhZMTEOPeXNcx1DCTZhpiJqBh5mqImQDA3Nxc\n1xEqaIiZdu/eLf3s4eEBDw+PauevtyMTNzc3ZGRkIDMzE+Xl5YiOjkbXrl3V5unatSsiIyMBAMnJ\nyTA1NYWVlZVGfR/x8PBAUFCQ9O/xHdJQMJNmmElzDTEXM2mmoWZ6/H20pkIC1OORiVwux5gxYxAa\nGgohBPz9/eHs7IywsDDIZDIEBgaiS5cuiI2NxYcffgiFQoHg4OBq+xIRUcNQr/eZdOrUCatWrVJr\n69evn9r0mDFjNO5LREQNg97ChQsX6jpEXXNwcNB1hAqYSTPMpLmGmIuZNPN3yCQTQog6ykJERP8Q\n/GowERFpjcWEiIi09rcdaXHdunW4cOECLC0t8e9//1vXcZCdnY01a9bg3r17kMlkCAgIwCuvvKLr\nWCgrK8OCBQtQXl4OpVKJHj164F//+peuY0GlUmH27NmwsbHBzJkzdR0HADBx4kSYmJhAJpNBT08P\nS5Ys0XUkFBUVYf369UhPT4dMJkNwcDBatWqlszy3bt3CypUrIZPJIITAnTt3MGzYMJ3/rR8+fBgn\nTpyATCbDc889hwkTJkBfX/dvf0eOHMHx48cBQGfvCZW9V96/fx8rV65EZmYmHBwcMHXqVJiYmFS/\nIPE3lZiYKK5duyamT5+u6yhCCCFyc3PFtWvXhBBCPHjwQEyePFncvHlTt6H+v+LiYiGEEEqlUsyZ\nM0ekpKToOJEQhw4dEqtWrRJffPGFrqNIJk6cKAoKCnQdQ82aNWtEeHi4EEKI8vJyUVhYqONE/0ep\nVIpx48aJzMxMnebIzs4WEydOFGVlZUIIIVasWCEiIiJ0mkkIIf78808xffp0UVpaKpRKpVi8eLHI\nyMio9xyVvVd+//334sCBA0IIIfbv3y+2b99e43L+tqe52rZtC1NTU13HkFhZWUmP01coFGjWrFmD\neb6YkZERgIdHKUqlUsdpHh7FxcbGIiAgQNdR1AghIBrQ91WKioqQlJQEPz8/AICenl7Nnx7r0aVL\nl9CkSRPY2dnpOgpUKhWKi4uhVCpRUlICa2trXUfCX3/9BTc3NxgYGEAul6Ndu3Y4c+ZMveeo7L3y\n3Llz0kN3+/btq9GDdXV/nPcPdPfuXdy4cUOnpyMep1KpMGvWLNy5cwcvvfQS3NzcdJpn69ateOed\nd1BUVKTTHE+SyWQIDQ2FXC5HQEAAAgMDdZrn7t27MDc3x9q1a3Hjxg20aNECo0ePhqGhoU5zPXLq\n1Cn4+PjoOgZsbGwwcOBATJgwAUZGRujYsSM6duyo61ho3rw5du7cifv378PAwACxsbFo2bKlrmMB\nAO7duwcrKysADz8I37t3r8Y+f9sjk4aquLgYK1aswKhRo6BQKHQdB8DDJwx8+eWXWLduHVJSUnDz\n5k2dZXl07tbFxaXBHQksXrwYS5cuxezZs/G///0PSUlJOs2jUqlw7do1vPTSS1i6dCmMjIxw4MAB\nnWZ6pLy8HOfOnUPPnj11HQWFhYU4d+4c1q5di2+//RbFxcWIiorSdSw0a9YMr776KkJDQ7FkyRK4\nuLhALm+Yb8kymazGeXhkUo+USiWWL1+O3r17o1u3brqOU4GJiQk8PDwQFxens8fVJCUl4dy5c4iN\njUVpaSkePHiANWvWYNKkSTrJ87hHp0YsLCzg5eWF1NRUtG3bVmd5bGxsYGtrK32a7dGjR4MpJnFx\ncWjRogUsLCx0HQWXLl2Cg4MDzMzMAADdu3fHlStX4Ovrq+NkgJ+fn3Sa8scff1QbakOXrKyskJeX\nJ/3X0tKyxj4NswzWkob2yXbdunVwdnbW+TdbHpefny+dTiotLcWlS5d0+pTlN998E+vWrcOaNWsw\nZcoUtG/fvkEUkpKSEhQXFwN4eHT5xx9/oHlz3T4R18rKCra2trh16xaAh2+aDeWZdVFRUQ3iFBcA\n2NnZISUlBaWlpRBC4NKlS2jWrJmuYwF4+P8fAGRlZeHs2bM6K3BPvle+8MILiIiIAABERERU+WDd\nx/1tj0xWrVqFhIQEFBQUIDg4GEFBQdInAF1ISkrCb7/9hueeew6ffPIJZDIZRowYgU6dOuksEwDk\n5eXhm2++gUqlghAC3t7e6NKli04zNUT37t3DsmXLIJPJoFQq0atXL3h6euo6FkaPHo3Vq1ejvLwc\nTZo0wYQJE3QdCSUlJbh06RLGjx+v6ygAHj6xvEePHpg5cyb09PTg4uKi8+tdjyxfvhz379+Hnp4e\n3n//fZ18gaKy98ohQ4bgq6++wokTJ2Bvb4+pU6fWuBw+ToWIiLT2tz7NRURE9YPFhIiItMZiQkRE\nWmMxISIirbGYEBGR1lhMiIhIaywmRESkNRYTemYjR47E3bt3n6lvSEgIwsPDazlR47d27Vrs2rWr\nXtc5b948XL9+vV7XWdfu3buHqVOnory8XNdR/jFYTOiZbdu2DQ4ODrqOQVo4f/48TExMpOERnlV8\nfDxCQkIwatSoKh9/k5ycjHnz5mm1nqpMnDgRly9flqYtLS3Rvn17HDt2rE7WRxWxmFCDp1KpdB2h\nQWSoC2FhYejVq5fWyzEyMoK/vz/eeeedKue5cOECOnfurPW6NOXr64uwsLB6W98/3d/22Vz0bCIi\nInDmzBlpqNzJkyfD1dVVejZPcHAwZs2aheeffx7Dhg3D119/jSZNmmDt2rUwMjJCZmYmEhMT4ezs\njI8++kg6cvnjjz/w3XffIS8vD7169ar2AZx79uxBeno6DAwMcP78eYwcORJ+fn44ePAgjh8/jqKi\nInTo0AFjx46FqakpysrKsH79esTFxUGlUqFp06aYNWsWLCwskJubi40bNyIpKQnm5uYYPHiwNOjW\n2rVrYWtri2HDhgEAEhISsHr1aqxbtw7Aw0+7L774IqKionDr1i18//33yM3NxXfffYekpCQIIeDj\n44P33nsPABAeHo5Dhw7h3r17cHNzw7hx46ocGCopKQk7duzAzZs3YWxsjGHDhkmDET1SWFiI1atX\nIzU1FSqVCq1bt8a4ceNgY2Mj/a727t2L/Px8WFhYYNiwYfD19UVGRgbWr1+P69evQ19fH+3bt8eU\nKVMqZCgvL8fly5cxbtw4zf44quHm5gY3NzdcunSpynliY2MRHBwMAEhPT8fWrVtx9epV6Ovr45VX\nXsGQIUNQXl6O7du34/fff4dMJkOPHj3w9ttvQ19fHwUFBfjmm29w5coVyGQyNG/eHCEhIVizZg2y\nsrKwdOlSyOVyvPHGGxg8eDBatWqFu3fvIisrq0EM0PV3x2JCatzd3bF161YAQG5uLpRKJZKTkwEA\nd+7cQUlJCZ5//vlK+546dQqffvopXFxcsGbNGvz444/46KOPUFBQgOXLl2PixIno2rUrfvnlF4SF\nhaF3795V5jh37hymTZuGDz/8EGVlZfjll19w7tw5LFq0CObm5vjuu++wadMmfPTRR4iMjMSDBw+w\nfv166Ovr4/r169IAUStXrsTzzz+PDRs24ObNmwgNDYWjoyM8PDw02h+nTp3C7NmzYW5uDgD44osv\n0KFDB0yePBlyuRxpaWkAgJiYGBw8eBAzZ86Eo6MjDhw4gFWrVmHx4sUVlpmVlYUlS5Zg/Pjx6NGj\nB4qKipCdnV1hPiEE/P39MX36dCiVSqxbtw6bN2/Gxx9/jJKSEnz33XdYunQpHB0dkZeXh/v37wMA\ndu3aBU9PTyxcuBDl5eVSxidlZGRALpdLxaku5eXlIT8/Hy4uLiguLkZoaCgGDx6MWbNmoby8XBpD\nZ+/evUhNTZXGIv/yyy+xb98+BAUF4dChQ7Czs8Mnn3wCANLf5aRJk5CYmIjg4GC0b99eWqdcLoej\noyNu3LjBYlIPeJqL1Dg4OMDY2BjXr19HYmIiPD09YWNjg1u3biExMbHa8Tu8vLzQokULyOVy9OrV\nS7qoGxsbi+bNm8PLywtyuRwDBgyQRnGrSuvWraXHXhsYGCAsLAzDhw+HtbU19PX1MXToUJw+fRoq\nlQp6enooKCjA7du3IZPJ4OrqCoVCgezsbCQnJ+Ott96Cvr4+XFxc4O/vj8jISI33R//+/WFjYwMD\nAwOkpqYiLy8Pb7/9NgwNDaGvr482bdoAAI4dO4YhQ4bAyckJcrkcQ4YMwfXr15GVlVVhmVFRUejQ\noQO8vb0hl8thZmZWaYE2MzODl5cXDAwMoFAo8NprryExMVF6XS6X488//0RpaSmsrKykx8/r6ekh\nMzMTOTk5ahmfVFhYCGNjY7W2oqIibNiwAcHBwZg6dSr27t0rfbpfv359pdujiQsXLkhPWT5//jys\nrKwwYMAA6OvrQ6FQSKN7RkVFYejQoTA3N4e5uTmGDh2KkydPAgD09fWRm5uLu3fvQi6XazSWjEKh\nQAvYfc0AAAX8SURBVGFh4TNlpqfDIxOqwN3dHZcvX0ZGRgbc3d1hamqKhIQEJCcnw93dvcp+jxcI\nIyMjafyPnJycCoP+1DQI0JOvZ2Vl4d///rfaiG/6+vq4d+8eevfujezsbKxcuRJFRUXo1asXRowY\ngdzcXJiZmUlj3AOAvb09rl27VvNOqCRHdnY27OzsKh0NLzMzE1u2bMG2bdvU2nNycip8Ks7KyoKj\no2ON6y4tLcWWLVtw8eJFFBYWQgiB4uJiCCFgZGSEqVOn4ueff8a6devQpk0bjBw5Ek5OTnjnnXew\nc+dOzJ49G2ZmZhg4cGClwy+YmpriwYMHam2PBrUaM2YM7ty5g6NHj2LevHkwMzPDiy+++Myf8GNj\nY6VrM9nZ2WjSpEml8+Xm5qqtw97eHrm5uQCAwYMHY/fu3fjss88AAAEBARgyZEi16y0uLq4wvjnV\nDRYTqqBdu3Y4f/48MjMz8frrr8PExARRUVFISUnByy+//NTLs7a2RkxMjFpbZad1HvfkMKF2dnYI\nDg5G69atK51/6NChGDp0KLKysvD555/DyckJHTt2xP3791FcXCwNkZyVlSWNmGhkZISSkhJpGY/e\ntKrKYWtri6ysLKhUqgoFxc7ODq+//rpGgxvZ2dkhNTW1xvl+/vln3L59G0uWLIGFhQWuX7+OmTNn\nQggBmUwmjWVeVlaGH3/8Ed9++y1CQkJgaWkpjSWSlJSExYsXw93dvcIbuKOjI4QQyM3NlfaJt7e3\n9LqTkxNGjRqFUaNG1Zi1OkqlEgkJCZg4cSKAh/sxOjq60nltbGyQlZUlHWVlZmZK2RQKBUaOHImR\nI0fi5s2bCAkJgZubG9q3b1/psLIqlQoZGRlVnpal2sXTXFSBu7s74uPjUVpaChsbG7Rr1w5xcXEo\nKCiAq6vrUy+vS5cuuHnzJs6ePQuVSoUjR44gLy/vqZYRGBiIH3/8UTrNkp+fj3PnzgF4+LXUP//8\nEyqVCgqFAnp6epDL5bC1tUXr1q3xww8/oKysDDdu3EB4eLh0rcbFxQWxsbG4f/8+8vLycOTIkWoz\nuLm5wdraGj/88ANKSkpQVlaGK1euSPn2798vnfsvKirC6dOnK12Or68vLl26JJ2mu3//fqX3eRQX\nF8PQ0BDGxsa4f/8+/l979xMKTRzHcfxtbRqWpqSVm4MSxYXEhFKk2BJ74ODiTykOaiU5+FdOTlLa\nIkNx25xwcHZxkIvi4o7SYh3sZmqfw2bKszyPp+nxHJ7P67TTHna+s/X7zMzv9+sbi8Xc756enjg7\nOyOVSpGbm4thGG7AnZ6eEo/HgczTh8/n+3Cw9fv91NTUcHl5+cu6vyKdTvP6+orjOO8+QybQysvL\n3UCvq6tzr7fjOCSTSTdcLctyFxUkEgn29/fd/+v8/Jzb21sAt963mk3T5O7u7t05XV9fEwwGNV/y\nTfRkIlnKysowDIOqqioA8vPzKS0txTTNDwel3ykqKiISiWDbNtFolJaWlj/unf7W6nh5eZmHhwdM\n08SyLOrr63l8fGRzc5N4PI5hGFiW5b5SmZycZGNjg7GxMQoLC+nv73cnaVtbW7m4uGBiYoJgMEhb\nWxsHBwfub/5cq8/nY2ZmBtu2GR8fJycnh+bmZiorK2loaCCVSrG6usr9/T0FBQXU1tbS2NiYVUtJ\nSQmzs7Ps7u4SjUYJBAIMDAxk7fXo7u5mbW2NkZERiouLCYVCboCm02kODw9ZX18HMsE4OjoKZAbR\nnZ0dXl5eME2ToaGhT/cDtbe3c3x87LnF7tXVFUtLS+7x4OAg1dXVLCwsZC0JNgyDubk5tre3icVi\n5OXl0dXVRUVFBeFwmGQyyfT0NABNTU309fUBcHNzw9bWFs/PzwQCATo7O93Xrr29vdi2zd7eHuFw\nmFAoxMnJCR0dHZ7qkq9Tp0WR/9z8/DzDw8OeNy5+JhKJMDU19a191xOJBIuLi6ysrOD36575OyhM\nROSvcRyHo6Mjenp6/vWpyF+mMBEREc80AS8iIp4pTERExDOFiYiIeKYwERERzxQmIiLimcJEREQ8\n+wGyY9MH5hVUvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x342fa5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(res['Norway']['wind']['cap'].keys(),res['Norway']['wind']['cap'].values())\n",
    "plt.ylabel('avail cap [TW]')\n",
    "plt.title('total wind resources\\nwith 90m hub height\\n 5MW/km$^2$ power density')\n",
    "plt.xlabel('wind resource class (~ 1/cost)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#save data\n",
    "file('data/resources/res.json','w').write(json.dumps(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Potentials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate remaining potentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#biomass\n",
    "#http://www.sciencedirect.com/science/article/pii/S0961953414005340\n",
    "#Table 5, they all come on one tier\n",
    "#nuclear\n",
    "#the chance for developing nuclear is essentially zero, e.g. infinite cost\n",
    "#hydro\n",
    "#http://www.intpow.com/index.php?id=487&download=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create EROEI - resource class functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#load savedata if exists\n",
    "predata=json.loads(file('data/resources/res.json','r').read())\n",
    "res={}\n",
    "for c in predata:\n",
    "    res[c]={}\n",
    "    for f in predata[c]:\n",
    "        res[c][f]={}\n",
    "        for r in predata[c][f]:\n",
    "            res[c][f][r]={}\n",
    "            for year in predata[c][f][r]:\n",
    "                res[c][f][r][int(year)]=predata[c][f][r][year]\n",
    "predata={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 0.14894710891899998,\n",
       " 2: 39.526918731184,\n",
       " 3: 212.81736247049523,\n",
       " 4: 343.88919981628334,\n",
       " 5: 432.757817556891,\n",
       " 6: 3079.03440882861,\n",
       " 7: 767.4486675578074}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "res['Afghanistan']['csp']['res']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "resource normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#pv classes = 4-14\n",
    "#wind classes = 1-9\n",
    "#csp classes = 1-10\n",
    "\n",
    "def histo(x,y,b): #one elment histogram rebin interpolator (for wind 9->10 classes)\n",
    "    if b>len(x):\n",
    "        if b==10 and len(x)==9:\n",
    "            yy=np.histogram(x,weights=y,bins=b)[0]\n",
    "            #return yy #turn off\n",
    "            toadd=((yy[3]+yy[5])/2.0)*0.7 #multipl. to adjust for recurrency .88 approx.\n",
    "            shares=np.array(yy)/np.sum(yy) #redistribute based on resource size\n",
    "            for s in range(10):\n",
    "                if s not in [4]:\n",
    "                    shares[s]*=(abs(1.0/(4-s))) #scale based on dist from number\n",
    "            shares=shares/np.sum(shares)\n",
    "            for s in range(10):\n",
    "                if s not in [4]:\n",
    "                    yy[s]-=toadd*shares[s]\n",
    "                    yy[4]+=toadd*shares[s]\n",
    "            return yy\n",
    "        else: print \"error, this is a custom function...\"\n",
    "    else:\n",
    "        return np.histogram(x,weights=y,bins=b)[0]\n",
    "\n",
    "lim={\"pv\":[4,14],\"wind\":[1,9],\"csp\":[1,10]}\n",
    "\n",
    "newres={}\n",
    "for c in res:\n",
    "    newres[c]={}\n",
    "    for r in res[c]:\n",
    "        newres[c][r]={}\n",
    "        for f in res[c][r]:\n",
    "            newres[c][r][f]={}\n",
    "            helper=dict(res[c][r][f])\n",
    "            x=[]\n",
    "            y=[]\n",
    "            for k in range(0,15):\n",
    "                if k<lim[r][0] or k>lim[r][1]: #pop items outside class limits - these are small errors\n",
    "                    if k in helper:helper.pop(k);\n",
    "                elif k not in helper:helper[k]=0 #else insert it if is not there already\n",
    "            for i in range(len(sorted(helper.keys()))):\n",
    "                x.append(float(sorted(helper.keys())[i]))\n",
    "                y.append(float(helper[sorted(helper.keys())[i]]))\n",
    "            #plt.figure()\n",
    "            #plt.bar(x,y,alpha=0.6)\n",
    "            yy=histo(x,y,10)        \n",
    "            xx=range(1,11)\n",
    "            #plt.bar(xx,yy,alpha=0.6,color='red')\n",
    "            for i in xx:\n",
    "                newres[c][r][f][i]=yy[i-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res=newres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#save data\n",
    "file('data/resources/res_exp.json','w').write(json.dumps(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#load res savedata if exists\n",
    "predata=json.loads(file('data/resources/res_exp.json','r').read())\n",
    "res={}\n",
    "for c in predata:\n",
    "    res[c]={}\n",
    "    for f in predata[c]:\n",
    "        res[c][f]={}\n",
    "        for r in predata[c][f]:\n",
    "            res[c][f][r]={}\n",
    "            for year in predata[c][f][r]:\n",
    "                res[c][f][r][int(year)]=predata[c][f][r][year]\n",
    "predata={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load energy savedata (from output of calculator.ipynb)\n",
    "predata=json.loads(file('data/historical/savedata.json','r').read())\n",
    "data={}\n",
    "for c in predata:\n",
    "    data[c]={}\n",
    "    for year in predata[c]:\n",
    "        data[c][int(year)]=predata[c][year]\n",
    "predata={}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "exponential distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EAJCfn297DKBpGr6+vowYMcKx6YQQFZI6ewp9zhQsjz2N1qKd2XGEAddsBtHR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\nAP4f889fhFdQI1sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x89eacf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#class-based distribution function\n",
    "pwr=3\n",
    "s=sum([i**pwr for i in range(10)])*1.0\n",
    "classconverter={i+1:i**pwr/s for i in range(10)}\n",
    "plt.plot(classconverter.keys(),np.array(classconverter.values())*100)#,color='#dd1c77',lw=2,alpha=0.9, align='center')\n",
    "plt.xlabel('Resource Quality [decile]')\n",
    "plt.ylabel('Percentage allocated [%]')\n",
    "plt.xlim(0.5,10.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.           2.77777778  22.22222222  75.        ] [ 0.          0.16666667  1.33333333  4.5       ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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wqQvGGDIyMiooIkKqh2qXLDiOo3uTpFiUWAlxHt2GIoQQUiJKFoQQQkpEyYIQ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbec9c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l=4\n",
    "k=np.array([i**3 for i in range(l)])\n",
    "plt.plot(np.arange(l)/3.0,k*100.0/sum(k),'magenta',lw=2,label=u'$f(x)=x^3/\\sum_{classes}{x^3}$')\n",
    "print k*100.0/sum(k),k*6.0/sum(k)\n",
    "plt.ylabel(\"allocated share [%]\")\n",
    "plt.xlabel(\"resource quality distribution [1 - best, 0 - worst]\\nexample with 3 classes\")\n",
    "plt.title(\"Function for distributing in-country\\n generation into resource classes\")\n",
    "plt.legend(loc=2)\n",
    "plt.savefig('res3exp.png',bbox_inches = 'tight', pad_inches = 0.1, dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.           0.22675737   1.81405896   6.12244898  14.51247166\n",
      "  28.3446712   48.97959184] [ 0.          0.01133787  0.09070295  0.30612245  0.72562358  1.41723356\n",
      "  2.44897959]\n"
     ]
    },
    {
     "data": {
      "image/png": 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oUbbvimqXLDiOq/HPJknJKLGSmsTjWw+ILotgamhC+tj0MicLegxFCCHVFH+X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\n7OA6Mck9twAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbeca06a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l=7\n",
    "k=np.array([i**3 for i in range(l)])\n",
    "plt.plot(np.arange(l)/6.0,k*100.0/sum(k),'magenta',lw=2,label=u'$f(x)=x^3/\\sum_{classes}{x^3}$')\n",
    "print k*100.0/sum(k),k*5.0/sum(k)\n",
    "plt.ylabel(\"allocated share [%]\")\n",
    "plt.xlabel(\"resource quality distribution [1 - best, 0 - worst]\\nexample with 6 classes\")\n",
    "plt.title(\"Function for distributing in-country\\n generation into resource classes\")\n",
    "plt.legend(loc=2)\n",
    "plt.savefig('res6exp.png',bbox_inches = 'tight', pad_inches = 0.1, dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def classconvert(a,b,pwr=3):#a classed resource in 10 classes, return exponential distribution, with fill control\n",
    "    classvector=[]\n",
    "    for i in range(1,11):\n",
    "        if a[i]>0:\n",
    "            if not np.isnan(a[i]):\n",
    "                classvector.append(i)\n",
    "    s=sum([i**pwr for i in classvector])*1.0\n",
    "    z={}\n",
    "    for i in range(1,11):\n",
    "        if i in classvector:\n",
    "            z[i]=i**pwr/s\n",
    "        else: z[i]=0\n",
    "    w={}\n",
    "    tofill=b\n",
    "    for i in range(10,0,-1):\n",
    "        if a[i]>0:\n",
    "            if tofill*z[i]>a[i]: #used to be tofill>a[i]:\n",
    "                w[i]=a[i]/b\n",
    "                tofill-=a[i]\n",
    "                z.pop(i);\n",
    "            else: break\n",
    "    q=sum(w.values())\n",
    "    for i in range(1,11):\n",
    "        if i not in w:\n",
    "            w[i]=max(0,z[i]/sum(z.values())*(1.0-q))\n",
    "    return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR Democratic Republic of the Congo\n",
      "pv weighted average class of resources 7.12773500916\n",
      "pv weighted average class of generation 6.23294322676\n",
      "pv normalized resource utilization 0.87446337704\n"
     ]
    },
    {
     "data": {
      "image/png": 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KQsOGDbl//z4KhUJOfjk/j/8Bzs3S0hJ7e3tOnjxpMD/3NGT3jn/44Qe+++47WrZsSa1a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      "text/plain": [
       "<matplotlib.figure.Figure at 0x893d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "csp weighted average class of resources 5.01079508516\n",
      "csp weighted average class of generation 8.20048718482\n",
      "csp normalized resource utilization 1.63656406727\n"
     ]
    },
    {
     "data": {
      "image/png": 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I/Pz88PHxYejQofL2O3bsINBiGzXiF9KpUycuX76c57EKb7afJ6rFzFkllHhX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\nQgghhA2TRC2EEELYsP8PbkFDuHbftY8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20024208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wind weighted average class of resources 3.24172097171\n",
      "wind weighted average class of generation 6.27677889088\n",
      "wind normalized resource utilization 1.9362489695\n"
     ]
    },
    {
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MGfzwww/cuHGD0NBQvvvuO77//vtC9508eTIHDhxg0aJFXL9+ndWrV7Njx44823l4eNCu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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ea8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pv classes = 4-14\n",
    "#wind classes = 1-9\n",
    "#csp classes = 1-10\n",
    "\n",
    "groei={}#resources\n",
    "groei3={}#built\n",
    "sd={\"pv\":[],\"wind\":[],\"csp\":[]}\n",
    "nru={\"pv\":[],\"wind\":[],\"csp\":[]}\n",
    "sd2={\"pv\":[],\"wind\":[],\"csp\":[]}\n",
    "rr={\"pv\":\"solar\",\"wind\":\"wind\",\"csp\":\"csp\"}\n",
    "for c in res:\n",
    "    croei={}\n",
    "    #if True:\n",
    "    try:\n",
    "        for r in res[c]:\n",
    "            if r not in groei: groei[r]={}\n",
    "            if r not in groei3: groei3[r]={}\n",
    "            \n",
    "            #calculate exponential dsitribution in-country\n",
    "            dynamicres={} #record initial sum of res - necessary for exhaustion mechanism\n",
    "            for cl in range(10,0,-1): #start from top, step down\n",
    "                if not np.isnan(res[c][r]['res'][cl]):\n",
    "                    if cl not in groei[r]: groei[r][cl]=0\n",
    "                    if cl not in croei: croei[cl]=0\n",
    "                    v=res[c][r]['res'][cl]\n",
    "                    groei[r][cl]=np.nansum([groei[r][cl],v])\n",
    "                    croei[cl]=np.nansum([croei[cl],v])\n",
    "                                    \n",
    "                    #exponential distribution across resource classes for existing production\n",
    "                    #s=res[c][r]['res'][cl]*1.0/sum(res[c][r]['res'].values()) #old, even distribution\n",
    "                    if c in data:\n",
    "                        for year in data[c]:\n",
    "                            if rr[r] in data[c][year]['energy']:\n",
    "                                if year<2015:\n",
    "                                    if cl not in groei3[r]: groei3[r][cl]=0\n",
    "                                    s=classconvert(res[c][r]['res'],data[c][year]['energy'][rr[r]]['cons']['navg3'])[cl]\n",
    "                                    v=s*data[c][year]['energy'][rr[r]]['cons']['navg3']\n",
    "                                    if not np.isnan(v):\n",
    "                                        if v>0.01:\n",
    "                                            sd2[r].append({\"country\":c,\"class\":cl,\"value\":v,\"year\":year})\n",
    "                                            if year==2014:\n",
    "                                                groei3[r][cl]=np.nansum([groei3[r][cl],v])\n",
    "                                    \n",
    "\n",
    "                    sd[r].append({\"country\":c,\"class\":cl,\"value\":res[c][r]['res'][cl],\"year\":2014})\n",
    "        \n",
    "    except: print 'ERROR',c\n",
    "            \n",
    "aroei={}\n",
    "titles=['Photovoltaic','Solar Thermal','Wind']\n",
    "t2=['\\n10% overall efficiency\\ncovering 1.5% of productive land in each country',\n",
    "    '\\ncalculated with 6 hours of storage\\n31MW/km$^2$ power density',\n",
    "    '\\nonshore + offshore, calculated for 90m hub height\\n 5MW/km$^2$ power density']\n",
    "t2=['','','']\n",
    "t3=['Global Horizontal Irradiance [kWh / sqm / day]','Direct Normal Irradiance [kWh / sqm / day]','NREL Wind Power Class']\n",
    "t4=[[0.5,10.5],[0.5,10.5],[0.5,10.5]]\n",
    "t5=[[0,70000],[0,250000],[0,100000]]\n",
    "t6=[[0,70],[0,10],[0,300]]\n",
    "t7=[[0,50000],[0,50000],[0,2000]]\n",
    "locs=[2,2,1]\n",
    "cvtr={\"pv\":0.5,\"wind\":1,\"csp\":0.64}\n",
    "cvtr2={\"pv\":0.25,\"wind\":0,\"csp\":1.87}\n",
    "for j,r in enumerate(groei):\n",
    "#if True:\n",
    "    x=[]\n",
    "    y=[]\n",
    "    for i in range(len(sorted(groei[r].keys()))):\n",
    "        if float(groei[r][sorted(groei[r].keys())[i]])>0.1:\n",
    "            x.append(float(sorted(groei[r].keys())[i]))\n",
    "            y.append(float(groei[r][sorted(groei[r].keys())[i]]))\n",
    "    aroei[r]=np.average(x,weights=y)\n",
    "    fig=plt.figure(figsize=(7,5))\n",
    "    plt.bar(x,y,color='#dd1c77',lw=2,alpha=0.7, align='center')\n",
    "    plt.title('Global '+titles[j]+' Energy Resource'+t2[j]+' Utilization',fontsize=14,y=1.05)\n",
    "    plt.ylabel('Available energy [TWh / year]')\n",
    "    plt.xlabel('Normalized Resource Quality Class [1-worst to 10-best]')\n",
    "    plt.ylim(t5[j])\n",
    "    plt.tick_params(axis='y', colors='#dd1c77')\n",
    "    z=np.average(x,weights=y)\n",
    "    print r,'weighted average class of resources',z\n",
    "    z2=z*1.0\n",
    "    plt.axvline(z,color='#dd1c77',lw=2,ls='--')\n",
    "    #new_tick_locations = [2,8]+[np.round(z,1)]\n",
    "    new_tick_locations = [np.round(z,1)]\n",
    "\n",
    "\n",
    "    x=[]\n",
    "    y=[]\n",
    "    for i in range(len(sorted(groei3[r].keys()))):\n",
    "        x.append(float(sorted(groei[r].keys())[i]))\n",
    "        y.append(float(groei3[r][sorted(groei3[r].keys())[i]]))\n",
    "    ax=plt.twinx()\n",
    "    plt.plot([-10],[-10],color='#dd1c77',lw=2,label='Total Energy Resource')\n",
    "    plt.plot([-10],[-10],color='royalBlue',lw=2,label='Generation in 2014')\n",
    "    ax.bar(x,y,color='royalBlue',lw=2,alpha=0.7, align='center')\n",
    "    plt.plot([-10],[-10],color='grey',lw=2,ls='',label=' ')\n",
    "    plt.plot([-10],[-10],color='grey',lw=2,ls='--',label='weighted average')\n",
    "    ax.tick_params(axis='y', colors='royalBlue')\n",
    "    z=np.average(x,weights=y)\n",
    "    print r,'weighted average class of generation',z\n",
    "    print r,'normalized resource utilization',z/z2\n",
    "    plt.axvline(z,color='royalBlue',lw=2,ls='--')\n",
    "    plt.ylim(t6[j])\n",
    "    plt.xlim(t4[j])\n",
    "    plt.legend(loc=locs[j],framealpha=0)\n",
    "\n",
    "    ax2 = fig.add_axes((0.16,-0.01,0.705,0.0))\n",
    "    ax2.yaxis.set_visible(False)\n",
    "    ax2.set_xlabel('Normalized Resource Utilization')\n",
    "    ax2.set_xlim(1,10)\n",
    "\n",
    "    new_tick_locations += [np.round(z,1)]\n",
    "    new_ticks = np.round(np.array(new_tick_locations)/new_tick_locations[0],2)\n",
    "    \n",
    "    nru[r]=new_tick_locations\n",
    "    \n",
    "    ax2.set_xticks(new_tick_locations)\n",
    "    ax2.set_xticklabels(new_ticks)\n",
    "\n",
    "#wind class http://rredc.nrel.gov/wind/pubs/atlas/appendix_A.html\n",
    "#NREL Renewable Resource Data Center\n",
    "    plt.savefig(str(r)+'3.png',bbox_inches = 'tight', pad_inches = 0.1, dpi=150)\n",
    "    plt.savefig('plots/'+str(r)+'.png',bbox_inches = 'tight', pad_inches = 0.1, dpi=150)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "save for viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#save\n",
    "file(pop_path+'../universal/res.json','w').write(json.dumps({\"res\":sd,\"inst\":sd2})) \n",
    "import zipfile\n",
    "try:\n",
    "    import zlib\n",
    "    compression = zipfile.ZIP_DEFLATED\n",
    "except:\n",
    "    compression = zipfile.ZIP_STORED\n",
    "zf = zipfile.ZipFile(pop_path+'../universal/res.zip', mode='w')\n",
    "zf.write(pop_path+'../universal/res.json','res.json',compress_type=compression)\n",
    "zf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "save for later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file('data/resources/res_redist.json','w').write(json.dumps(newres))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "shares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'csp': [5.0, 8.1999999999999993],\n",
       " 'pv': [7.0999999999999996, 6.2000000000000002],\n",
       " 'wind': [3.2000000000000002, 6.2999999999999998]}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalized resource utilization\n",
    "nru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MSbf7yi5v3LhRErO0tJTp6emxn376iTEm/7mpLHTHvQFJTEzE2LFjERQUhIEDB0pef/To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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc2d34630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#shares\n",
    "w=[0.827]\n",
    "p=[0.157]\n",
    "c=[0.016]\n",
    "#Shares\n",
    "W=[0.827]\n",
    "P=[0.157]\n",
    "C=[0.016]\n",
    "#thetas - normalized resource utilization\n",
    "wt=[1.09] #wind\n",
    "wp=[0.68] #pv\n",
    "wc=[1.18] #csp\n",
    "wt=[nru['wind'][1]/nru['wind'][0]] #wind\n",
    "wp=[nru['pv'][1]/nru['pv'][0]] #pv\n",
    "wc=[nru['csp'][1]/nru['csp'][0]] #csp\n",
    "#js\n",
    "jt=[1] #wind\n",
    "jp=[1.77] #pv\n",
    "jc=[1.59] #csp\n",
    "javg=(nru['wind'][0]+nru['csp'][0]+nru['pv'][0])/3.0\n",
    "jt=[nru['wind'][0]/javg] #wind\n",
    "jp=[nru['pv'][0]/javg] #pv\n",
    "jc=[nru['csp'][0]/javg] #csp\n",
    "\n",
    "tgt=1.57 #target share wind\n",
    "tgp=1 #target share pv\n",
    "tgc=1.48 #target share csp\n",
    "s=0.35\n",
    "z=0.5\n",
    "f=2\n",
    "x=[2014]\n",
    "for i in range(2015,2101):\n",
    "    k=i-2014\n",
    "    x.append(i)\n",
    "    wt.append(wt[0]+(tgt-wt[0])*(i-2014)/(2101-2015))\n",
    "    wp.append(wp[0]+(tgp-wp[0])*(i-2014)/(2101-2015))\n",
    "    wc.append(wc[0]+(tgc-wc[0])*(i-2014)/(2101-2015))\n",
    "    w.append(w[k-1]+((1.0/3)*(1/wt[k-1])**z*jt[0]**s)**f)\n",
    "    p.append(p[k-1]+((1.0/3)*(1/wp[k-1])**z*jp[0]**s)**f)\n",
    "    c.append(c[k-1]+((1.0/3)*(1/wc[k-1])**z*jc[0]**s)**f)\n",
    "    jt.append(jt[0])\n",
    "    jp.append(jp[0])\n",
    "    jc.append(jc[0])\n",
    "    W.append(W[k-1]+w[k]/(w[k]+p[k]+c[k]))\n",
    "    P.append(P[k-1]+p[k]/(w[k]+p[k]+c[k]))\n",
    "    C.append(C[k-1]+c[k]/(w[k]+p[k]+c[k]))\n",
    "    \n",
    "fig,ax=plt.subplots(1,5,figsize=(17,4))\n",
    "ax[0].plot(x,wt)\n",
    "ax[0].plot(x,wp)\n",
    "ax[0].plot(x,wc)\n",
    "ax[0].set_title('Utilization')\n",
    "ax[1].plot(x,w,label='wind')\n",
    "ax[1].plot(x,p,label='pv')\n",
    "ax[1].plot(x,c,label='csp')\n",
    "ax[1].legend(loc=2)\n",
    "ax[1].set_title('New additions')\n",
    "ax[3].plot(x,W)\n",
    "ax[3].plot(x,P)\n",
    "ax[3].plot(x,C)\n",
    "ax[3].set_title('Cumulative added')\n",
    "ax[2].plot(x,[w[k]/(w[k]+p[k]+c[k]) for k in range(len(w))])\n",
    "ax[2].plot(x,[p[k]/(w[k]+p[k]+c[k]) for k in range(len(w))])\n",
    "ax[2].plot(x,[c[k]/(w[k]+p[k]+c[k]) for k in range(len(w))])\n",
    "ax[2].set_title('Share in new additions')\n",
    "ax[4].plot(x,[W[k]/W[k] for k in range(len(w))])\n",
    "ax[4].plot(x,[P[k]/W[k] for k in range(len(w))])\n",
    "ax[4].plot(x,[C[k]/W[k] for k in range(len(w))])\n",
    "ax[4].set_title('Share in cu. relative to wind')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2100 omega wind 0.257054639053 omega pv 0.519887654827 omega csp 0.22305770612\n"
     ]
    }
   ],
   "source": [
    "for year in [86]:\n",
    "    print x[year],'omega wind',W[year]*1.0/(W[year]+P[year]+C[year]),\n",
    "    print 'omega pv',P[year]*1.0/(W[year]+P[year]+C[year]),\n",
    "    print 'omega csp',C[year]*1.0/(W[year]+P[year]+C[year])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 omega/wind wind 1.0 omega/wind pv 0.579075359648 omega/wind csp 0.188456527352\n",
      "2040 omega/wind wind 1.0 omega/wind pv 1.26459829228 omega/wind csp 0.494114852701\n",
      "2060 omega/wind wind 1.0 omega/wind pv 1.63175468273 omega/wind csp 0.666491573744\n",
      "2080 omega/wind wind 1.0 omega/wind pv 1.8649120938 omega/wind csp 0.782938078709\n",
      "2100 omega/wind wind 1.0 omega/wind pv 2.02247917696 omega/wind csp 0.867744332264\n"
     ]
    }
   ],
   "source": [
    "for year in [6,26,46,66,86]:\n",
    "    print x[year],'omega/wind wind',W[year]*1.0/(W[year]),\n",
    "    print 'omega/wind pv',P[year]*1.0/(W[year]),\n",
    "    print 'omega/wind csp',C[year]*1.0/(W[year])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pv : theta 0.87323943662 xk 1 rtau 13.7419354839 avg class 7.1\n",
      "csp : theta 1.64 xk 1.48 rtau 7.31707317073 avg class 5.0\n",
      "wind : theta 1.96875 xk 1.57 rtau 11.1746031746 avg class 3.2\n"
     ]
    }
   ],
   "source": [
    "eroei={\"pv\":12,\"wind\":22,\"csp\":12}\n",
    "xk={\"pv\":tgp,\"wind\":tgt,\"csp\":tgc}\n",
    "rtau={}\n",
    "for r in eroei:\n",
    "    rtau[r]=eroei[r]*1.0/(nru[r][1]/nru[r][0])\n",
    "    print r,': theta',nru[r][1]/nru[r][0],'xk',xk[r],'rtau',rtau[r],'avg class',nru[r][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><br><p>We would like to express our gratitude to all of the developers of the libraries used and especially to the affiliates of <i>EIA, BP and World Bank</i> - and for this particular workbook <i>NREL</i>, for their great database and openly accesible data. The data manipulation algorithms are open sourced and freely reproducible under an <a href=\"http://opensource.org/licenses/MIT\" target=\"_blank\">MIT license</a>.</p>\n",
    "<br>\n",
    "<p><a href=\"http://www.csaladen.es\" target=\"_blank\">Dénes Csala</a>&nbsp;&nbsp;|&nbsp;&nbsp;2016</p>"
   ]
  }
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